diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 5273d8b96..b64cfeea2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -36,7 +36,10 @@ working-directory: [ "libs/langgraph", "libs/sdk-py", - "libs/cli" + "libs/cli", + "libs/checkpoint", + "libs/checkpoint-sqlite", + "libs/checkpoint-postgres" ] uses: ./.github/workflows/_lint.yml with: @@ -50,7 +53,10 @@ matrix: working-directory: [ "libs/langgraph", - "libs/cli" + "libs/cli", + "libs/checkpoint", + "libs/checkpoint-sqlite", + "libs/checkpoint-postgres" ] uses: ./.github/workflows/_test.yml with: diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index b57aea1e8..460574b36 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -6,7 +6,7 @@ on: working-directory: required: true type: string - default: 'libs/langgraph' + default: "libs/langgraph" env: PYTHON_VERSION: "3.11" @@ -104,7 +104,7 @@ jobs: REGEX="^$SHORT_PKG_NAME==\\d+\\.\\d+\\.\\d+((a|b|rc)\\d+)?\$" fi echo $REGEX - PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1) + PREV_TAG=$(git tag --sort=-creatordate | grep -P $REGEX | head -1 || echo "") echo $PREV_TAG if [ "$TAG" == "$PREV_TAG" ]; then echo "No new version to release" @@ -137,8 +137,7 @@ jobs: - build - release-notes permissions: write-all - uses: - ./.github/workflows/_test_release.yml + uses: ./.github/workflows/_test_release.yml with: working-directory: ${{ inputs.working-directory }} secrets: inherit @@ -198,9 +197,15 @@ jobs: "$PKG_NAME==$VERSION" \ ) - # Replace all dashes in the package name with underscores, - # since that's how Python imports packages with dashes in the name. - IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)" + if [[ "$PKG_NAME" == *checkpoint* ]]; then + # since checkpoint packages are namespace packages, import them with . convention + # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite + IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)" + else + # Replace all dashes in the package name with underscores, + # since that's how Python imports packages with dashes in the name. + IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)" + fi poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))" diff --git a/.github/workflows/size.yml b/.github/workflows/size.yml new file mode 100644 index 000000000..4d3497508 --- /dev/null +++ b/.github/workflows/size.yml @@ -0,0 +1,28 @@ +name: Check File Size + +on: + push: + branches: + - main + pull_request: + branches: + - main + workflow_dispatch: + +jobs: + file-size-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@v44 + - name: Filter by size + run: | + large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M) + if [ -n "$large_added_files" ]; then + echo "Large files added: $large_added_files" + echo "# Large files added:" >> $GITHUB_STEP_SUMMARY + echo "$large_added_files" >> $GITHUB_STEP_SUMMARY + exit 1 + fi diff --git a/README.md b/README.md index 10cd6932b..87d19961f 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,6 @@ ![Version](https://img.shields.io/pypi/v/langgraph) [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph) [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues) -[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.com/channels/1038097195422978059/1170024642245832774) [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/) ⚡ Building language agents as graphs ⚡ @@ -11,9 +10,6 @@ > [!NOTE] > Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)). -> [!TIP] -> Looking to deploy your LangGraph application? [Join the waitlist](https://www.langchain.com/langgraph-cloud-beta) for [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/), our managed service for deploying and hosting LangGraph applications. - ## Overview [LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. @@ -62,7 +58,7 @@ from typing import Annotated, Literal, TypedDict from langchain_core.messages import HumanMessage from langchain_anthropic import ChatAnthropic from langchain_core.tools import tool -from langgraph.checkpoint import MemorySaver +from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import END, StateGraph, MessagesState from langgraph.prebuilt import ToolNode diff --git a/docs/_scripts/copy_notebooks.py b/docs/_scripts/copy_notebooks.py index b0f06cb67..2440185f9 100644 --- a/docs/_scripts/copy_notebooks.py +++ b/docs/_scripts/copy_notebooks.py @@ -43,6 +43,7 @@ _MANUAL = { "tool-calling.ipynb", "tool-calling-errors.ipynb", "pass-config-to-tools.ipynb", + "many-tools.ipynb", "dynamic-returning-direct.ipynb", "managing-agent-steps.ipynb", "respond-in-format.ipynb", @@ -58,6 +59,7 @@ _MANUAL = { "human_in_the_loop/time-travel.ipynb", "human_in_the_loop/edit-graph-state.ipynb", "human_in_the_loop/wait-user-input.ipynb", + "human_in_the_loop/review-tool-calls.ipynb", "node-retries.ipynb", ], "tutorials": [ diff --git a/docs/docs/cloud/concepts/api.md b/docs/docs/cloud/concepts/api.md index 042d80c2c..ec0b1ddf6 100644 --- a/docs/docs/cloud/concepts/api.md +++ b/docs/docs/cloud/concepts/api.md @@ -12,6 +12,10 @@ An assistant is a configured instance of a [`CompiledGraph`][compiledgraph]. It The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the API reference for more details. +#### Configuring Assistants + +You can save custom assistants from the same graph to set different default prompts, models, and other configurations without changing a line of code in your graph. This allows you the ability to quickly test out different configurations without having to rewrite your graph every time, and also give users the flexibility to select different configurations when using your LangGraph application. See this how-to for information on how to configure a deployed graph. + ### Threads A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run. diff --git a/docs/docs/cloud/deployment/graph_rebuild.md b/docs/docs/cloud/deployment/graph_rebuild.md new file mode 100644 index 000000000..c7853b30a --- /dev/null +++ b/docs/docs/cloud/deployment/graph_rebuild.md @@ -0,0 +1,146 @@ +# Rebuild Graph at Runtime + +You might need to rebuild your graph with a different configuration for a new run. For example, you might need to use a different graph state or graph structure depending on the config. This guide shows how you can do this. + +!!! note "Note" + In most cases, customizing behavior based on the config should be handled by a single graph where each node can read a config and change its behavior based on it + +## Prerequisites + +Make sure to check out [this how-to guide](./setup.md) on setting up your app for deployment first. + +## Define graphs + +Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following: + +``` +my-app/ +|-- requirements.txt +|-- .env +|-- openai_agent.py # code for your graph +``` + +where the graph is defined in `openai_agent.py`. + +### No rebuild + +In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of `openai_agent.py`, which looks like the following: + +```python +from langchain_openai import ChatOpenAI +from langgraph.graph import END, MessageGraph + +model = ChatOpenAI(temperature=0) + +graph_workflow = MessageGraph() + +graph_workflow.add_node("agent", model) +graph_workflow.add_edge("agent", END) +graph_workflow.set_entry_point("agent") + +agent = graph_workflow.compile() +``` + +To make the server aware of your graph, you need to specify a path to the variable that contains the `CompiledStateGraph` instance in your LangGraph API configuration (`langgraph.json`), e.g.: + +``` +{ + "dependencies": ["."], + "graphs": { + "openai_agent": "./openai_agent.py:agent", + }, + "env": "./.env" +} +``` + +### Rebuild + +To make your graph rebuild on each new run with custom configuration, you need to rewrite `openai_agent.py` to instead provide a _function_ that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify `openai_agent.py` as follows: + +```python +from typing import Annotated, TypedDict +from langchain_openai import ChatOpenAI +from langgraph.graph import END, MessageGraph +from langgraph.graph.state import StateGraph +from langgraph.graph.message import add_messages +from langgraph.prebuilt import ToolNode +from langchain_core.tools import tool +from langchain_core.messages import BaseMessage +from langchain_core.runnables import RunnableConfig + + +class State(TypedDict): + messages: Annotated[list[BaseMessage], add_messages] + + +model = ChatOpenAI(temperature=0) + +def make_default_graph(): + """Make a simple LLM agent""" + graph_workflow = StateGraph(State) + def call_model(state): + return {"messages": [model.invoke(state["messages"])]} + + graph_workflow.add_node("agent", call_model) + graph_workflow.add_edge("agent", END) + graph_workflow.set_entry_point("agent") + + agent = graph_workflow.compile() + return agent + + +def make_alternative_graph(): + """Make a tool-calling agent""" + + @tool + def add(a: float, b: float): + """Adds two numbers.""" + return a + b + + tool_node = ToolNode([add]) + model_with_tools = model.bind_tools([add]) + def call_model(state): + return {"messages": [model_with_tools.invoke(state["messages"])]} + + def should_continue(state: State): + if state["messages"][-1].tool_calls: + return "tools" + else: + return END + + graph_workflow = StateGraph(State) + + graph_workflow.add_node("agent", call_model) + graph_workflow.add_node("tools", tool_node) + graph_workflow.add_edge("tools", "agent") + graph_workflow.set_entry_point("agent") + graph_workflow.add_conditional_edges("agent", should_continue) + + agent = graph_workflow.compile() + return agent + + +# this is the graph making function that will decide which graph to +# build based on the provided config +def make_graph(config: RunnableConfig): + user_id = config.get("configurable", {}).get("user_id") + # route to different graph state / structure based on the user ID + if user_id == "1": + return make_default_graph() + else: + return make_alternative_graph() +``` + +Finally, you need to specify the path to your graph-making function (`make_graph`) in `langgraph.json`: + +``` +{ + "dependencies": ["."], + "graphs": { + "openai_agent": "./openai_agent.py:make_graph", + }, + "env": "./.env" +} +``` + +See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file) \ No newline at end of file diff --git a/docs/docs/cloud/deployment/setup.md b/docs/docs/cloud/deployment/setup.md index 0ab7900d0..170e19394 100644 --- a/docs/docs/cloud/deployment/setup.md +++ b/docs/docs/cloud/deployment/setup.md @@ -1,16 +1,31 @@ # How to Set Up a LangGraph Application for Deployment -A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies. If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud. +A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies. + +This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment. + +!!! tip "Setup with pyproject.toml" + If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud. + +!!! tip "Setup with a Monorepo" + If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so. + The final repo structure will look something like this: ```bash my-app/ -|-- requirements.txt # package dependencies -|-- .env # environment variables -|-- openai_agent.py # code for an agent -|-- anthropic_agent.py # code for another agent -|-- langgraph.json # configuration file for LangGraph +├── my_agent # all project code lies within here +│ ├── utils # utilities for your graph +│ │ ├── __init__.py +│ │ ├── tools.py # tools for your graph +│ │ ├── nodes.py # node functions for you graph +│ │ └── state.py # state definition of your graph +│   ├── requirements.txt # package dependencies +│   ├── __init__.py +│   └── agent.py # code for constructing your graph +├── .env # environment variables +└── langgraph.json # configuration file for LangGraph ``` After each step, an example file directory is provided to demonstrate how code can be organized. @@ -21,13 +36,11 @@ Dependencies can optionally be specified in one of the following files: `pyproje The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range: ``` -langgraph>=0.1.7 -langchain-core>=0.2.7 -orjson>=3.10.1 -langsmith>=0.1.50 -httpx>=0.27.0 -langchain-core>=0.2.8 +langgraph>=0.2.0,<0.3.0 +langchain-core>=0.2.27,<0.3.0 langsmith>=0.1.63 +orjson>=3.10.1 +httpx>=0.27.0 tenacity>=8.3.0 uvicorn>=0.29.0 sse-starlette>=2.1.0 @@ -35,18 +48,24 @@ uvloop>=0.19.0 httptools>=0.6.1 jsonschema-rs>=0.18.0 croniter>=1.0.1 +structlog>=24.4.0 ``` Example `requirements.txt` file: ``` langgraph +langchain_anthropic +tavily-python +langchain_community langchain_openai + ``` Example file directory: -``` +```bash my-app/ -|-- requirements.txt # Python packages required for your graph +├── my_agent # all project code lies within here +│   └── requirements.txt # package dependencies ``` ## Specify Environment Variables @@ -61,42 +80,66 @@ OPENAI_API_KEY=key ``` Example file directory: -``` + +```bash my-app/ -|-- requirements.txt -|-- .env # file with environment variables +├── my_agent # all project code lies within here +│   └── requirements.txt # package dependencies +└── .env # environment variables ``` ## Define Graphs Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][compiledgraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file). -Example `openai_agent.py` file: +Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example) to see their implementation): + + ```python -from langchain_openai import ChatOpenAI -from langgraph.graph import END, MessageGraph +# my_agent/agent.py +from typing import TypedDict, Literal -model = ChatOpenAI(temperature=0) +from langgraph.graph import StateGraph, END +from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes +from my_agent.utils.state import AgentState # import state -graph_workflow = MessageGraph() +# Define the config +class GraphConfig(TypedDict): + model_name: Literal["anthropic", "openai"] -graph_workflow.add_node("agent", model) -graph_workflow.add_edge("agent", END) -graph_workflow.set_entry_point("agent") +workflow = StateGraph(AgentState, config_schema=GraphConfig) +workflow.add_node("agent", call_model) +workflow.add_node("action", tool_node) +workflow.set_entry_point("agent") +workflow.add_conditional_edges( + "agent", + should_continue, + { + "continue": "action", + "end": END, + }, +) +workflow.add_edge("action", "agent") -agent = graph_workflow.compile() +graph = workflow.compile() ``` !!! warning "Assign `CompiledGraph` to Variable" - The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module. + The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)). Example file directory: -``` +```bash my-app/ -|-- requirements.txt -|-- .env -|-- openai_agent.py # code for your graph -|-- anthropic_agent.py # code for your graph +├── my_agent # all project code lies within here +│ ├── utils # utilities for your graph +│ │ ├── __init__.py +│ │ ├── tools.py # tools for your graph +│ │ ├── nodes.py # node functions for you graph +│ │ └── state.py # state definition of your graph +│   ├── requirements.txt # package dependencies +│   ├── __init__.py +│   └── agent.py # code for constructing your graph +└── .env # environment variables ``` ## Create LangGraph API Config @@ -106,14 +149,11 @@ Create a [LangGraph API configuration file](../reference/cli.md#configuration-fi Example `langgraph.json` file: ```json { - "dependencies": [ - "." - ], - "graphs": { - "openai_agent": "./openai_agent.py:agent", - "anthropic_agent": "./anthropic_agent.py:agent" - }, - "env": "./.env" + "dependencies": ["./my_agent"], + "graphs": { + "agent": "./my_agent/agent.py:graph" + }, + "env": ".env" } ``` @@ -126,17 +166,19 @@ Example file directory: ```bash my-app/ -|-- requirements.txt -|-- .env -|-- openai_agent.py -|-- anthropic_agent.py -|-- langgraph.json # configuration file for LangGraph +├── my_agent # all project code lies within here +│ ├── utils # utilities for your graph +│ │ ├── __init__.py +│ │ ├── tools.py # tools for your graph +│ │ ├── nodes.py # node functions for you graph +│ │ └── state.py # state definition of your graph +│   ├── requirements.txt # package dependencies +│   ├── __init__.py +│   └── agent.py # code for constructing your graph +├── .env # environment variables +└── langgraph.json # configuration file for LangGraph ``` -## Upload to GitHub - -To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository. - ## Next -After you setup your repo, it's time to [deploy your app](./cloud.md). +After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md). diff --git a/docs/docs/cloud/deployment/setup_pyproject.md b/docs/docs/cloud/deployment/setup_pyproject.md index 90d8245c0..767171f05 100644 --- a/docs/docs/cloud/deployment/setup_pyproject.md +++ b/docs/docs/cloud/deployment/setup_pyproject.md @@ -1,16 +1,29 @@ # How to Set Up a LangGraph Application for Deployment -A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies. If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md). +A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies. + +This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment. + +!!! tip "Setup with requirements.txt" + If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md). + +!!! tip "Setup with a Monorepo" + If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so. The final repo structure will look something like this: ```bash my-app/ ├── my_agent # all project code lies within here +│ ├── utils # utilities for your graph +│ │ ├── __init__.py +│ │ ├── tools.py # tools for your graph +│ │ ├── nodes.py # node functions for you graph +│ │ └── state.py # state definition of your graph │   ├── __init__.py -│   └── agent.py # code for your graph -│-- .env # environment variables -│-- langgraph.json # configuration file for LangGraph +│   └── agent.py # code for constructing your graph +├── .env # environment variables +├── langgraph.json # configuration file for LangGraph └── pyproject.toml # dependencies for your project ``` @@ -22,13 +35,11 @@ Dependencies can optionally be specified in one of the following files: `pyproje The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range: ``` -langgraph>=0.1.7 -langchain-core>=0.2.7 -orjson>=3.10.1 -langsmith>=0.1.50 -httpx>=0.27.0 -langchain-core>=0.2.8 +langgraph>=0.2.0,<0.3.0 +langchain-core>=0.2.27,<0.3.0 langsmith>=0.1.63 +orjson>=3.10.1 +httpx>=0.27.0 tenacity>=8.3.0 uvicorn>=0.29.0 sse-starlette>=2.1.0 @@ -36,6 +47,7 @@ uvloop>=0.19.0 httptools>=0.6.1 jsonschema-rs>=0.18.0 croniter>=1.0.1 +structlog>=24.4.0 ``` Example `pyproject.toml` file: @@ -51,7 +63,7 @@ readme = "README.md" [tool.poetry.dependencies] python = ">=3.9.0,<3.13" -langgraph = "^0.1.7" +langgraph = "^0.2.0" langchain-fireworks = "^0.1.3" @@ -64,9 +76,6 @@ Example file directory: ```bash my-app/ -├── my_agent -│   ├── __init__.py -│   └── agent.py └── pyproject.toml # Python packages required for your graph ``` @@ -86,10 +95,7 @@ Example file directory: ```bash my-app/ -├── my_agent -│   ├── __init__.py -│   └── agent.py -|-- .env # file with environment variables +├── .env # file with environment variables └── pyproject.toml ``` @@ -97,26 +103,35 @@ my-app/ Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][compiledgraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file). -Example `agent.py` file: +Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation): ```python # my_agent/agent.py -from langchain_fireworks import ChatFireworks -from langgraph.graph import END, StateGraph, add_messages -from typing_extensions import TypedDict, Annotated +from typing import TypedDict, Literal -model = ChatFireworks(model="accounts/fireworks/models/firefunction-v2", temperature=0) +from langgraph.graph import StateGraph, END +from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes +from my_agent.utils.state import AgentState # import state -class State(TypedDict): - messages: Annotated[list, add_messages] +# Define the config +class GraphConfig(TypedDict): + model_name: Literal["anthropic", "openai"] -graph_workflow = StateGraph(State) +workflow = StateGraph(AgentState, config_schema=GraphConfig) +workflow.add_node("agent", call_model) +workflow.add_node("action", tool_node) +workflow.set_entry_point("agent") +workflow.add_conditional_edges( + "agent", + should_continue, + { + "continue": "action", + "end": END, + }, +) +workflow.add_edge("action", "agent") -graph_workflow.add_node("agent", model) -graph_workflow.add_edge("agent", END) -graph_workflow.set_entry_point("agent") - -agent = graph_workflow.compile() +graph = workflow.compile() ``` !!! warning "Assign `CompiledGraph` to Variable" @@ -126,10 +141,15 @@ Example file directory: ```bash my-app/ -├── my_agent +├── my_agent # all project code lies within here +│ ├── utils # utilities for your graph +│ │ ├── __init__.py +│ │ ├── tools.py # tools for your graph +│ │ ├── nodes.py # node functions for you graph +│ │ └── state.py # state definition of your graph │   ├── __init__.py -│   └── agent.py # code for your graph -|-- .env +│   └── agent.py # code for constructing your graph +├── .env └── pyproject.toml ``` @@ -143,9 +163,9 @@ Example `langgraph.json` file: { "dependencies": ["."], "graphs": { - "my_fantastic_agent": "./my_agent/agent.py:agent" + "agent": "./my_agent/agent.py:graph" }, - "env": "./.env" + "env": ".env" } ``` @@ -158,18 +178,19 @@ Example file directory: ```bash my-app/ -├── my_agent +├── my_agent # all project code lies within here +│ ├── utils # utilities for your graph +│ │ ├── __init__.py +│ │ ├── tools.py # tools for your graph +│ │ ├── nodes.py # node functions for you graph +│ │ └── state.py # state definition of your graph │   ├── __init__.py -│   └── agent.py # code for your graph -│-- .env -│-- langgraph.json # configuration file for LangGraph -└── pyproject.toml +│   └── agent.py # code for constructing your graph +├── .env # environment variables +├── langgraph.json # configuration file for LangGraph +└── pyproject.toml # dependencies for your project ``` -## Upload to GitHub - -To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository. - ## Next -After you setup your repo, it's time to [deploy your app](./cloud.md). +After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md). diff --git a/docs/docs/cloud/how-tos/img/studio_forks.gif b/docs/docs/cloud/how-tos/img/studio_forks.gif deleted file mode 100644 index a9e285ca5..000000000 Binary files a/docs/docs/cloud/how-tos/img/studio_forks.gif and /dev/null differ diff --git a/docs/docs/cloud/how-tos/img/studio_forks.mp4 b/docs/docs/cloud/how-tos/img/studio_forks.mp4 new file mode 100644 index 000000000..e53f6a08a Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_forks.mp4 differ diff --git a/docs/docs/cloud/how-tos/img/studio_forks_poster.png b/docs/docs/cloud/how-tos/img/studio_forks_poster.png new file mode 100644 index 000000000..14825c281 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_forks_poster.png differ diff --git a/docs/docs/cloud/how-tos/img/studio_input.gif b/docs/docs/cloud/how-tos/img/studio_input.gif deleted file mode 100644 index 1ab783ac1..000000000 Binary files a/docs/docs/cloud/how-tos/img/studio_input.gif and /dev/null differ diff --git a/docs/docs/cloud/how-tos/img/studio_input.mp4 b/docs/docs/cloud/how-tos/img/studio_input.mp4 new file mode 100644 index 000000000..0e82f2244 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_input.mp4 differ diff --git a/docs/docs/cloud/how-tos/img/studio_input_poster.png b/docs/docs/cloud/how-tos/img/studio_input_poster.png new file mode 100644 index 000000000..6c23c4523 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_input_poster.png differ diff --git a/docs/docs/cloud/how-tos/img/studio_threads.gif b/docs/docs/cloud/how-tos/img/studio_threads.gif deleted file mode 100644 index a4a69310e..000000000 Binary files a/docs/docs/cloud/how-tos/img/studio_threads.gif and /dev/null differ diff --git a/docs/docs/cloud/how-tos/img/studio_threads.mp4 b/docs/docs/cloud/how-tos/img/studio_threads.mp4 new file mode 100644 index 000000000..8875e3f43 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_threads.mp4 differ diff --git a/docs/docs/cloud/how-tos/img/studio_threads_poster.png b/docs/docs/cloud/how-tos/img/studio_threads_poster.png new file mode 100644 index 000000000..5f2222789 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_threads_poster.png differ diff --git a/docs/docs/cloud/how-tos/img/studio_usage.gif b/docs/docs/cloud/how-tos/img/studio_usage.gif deleted file mode 100644 index 474f92c01..000000000 Binary files a/docs/docs/cloud/how-tos/img/studio_usage.gif and /dev/null differ diff --git a/docs/docs/cloud/how-tos/img/studio_usage.mp4 b/docs/docs/cloud/how-tos/img/studio_usage.mp4 new file mode 100644 index 000000000..0f8aa13a1 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_usage.mp4 differ diff --git a/docs/docs/cloud/how-tos/img/studio_usage_poster.png b/docs/docs/cloud/how-tos/img/studio_usage_poster.png new file mode 100644 index 000000000..83c5027b9 Binary files /dev/null and b/docs/docs/cloud/how-tos/img/studio_usage_poster.png differ diff --git a/docs/docs/cloud/how-tos/invoke_studio.md b/docs/docs/cloud/how-tos/invoke_studio.md index 2a33162b7..37d57e711 100644 --- a/docs/docs/cloud/how-tos/invoke_studio.md +++ b/docs/docs/cloud/how-tos/invoke_studio.md @@ -1,13 +1,15 @@ # Invoke Assistant -The LangGraph Studio lets you test different configurations and inputs to your graph. The UI allows you to see exactly how your +The LangGraph Studio lets you test different configurations and inputs to your graph. It also provides a nice visualization of your graph during execution so it is easy to see which nodes are being run and what the outputs of each individual node are. 1. The LangGraph Studio UI displays a visualization of the selected assistant. - 1. In the top-right dropdown menu of the left-hand pane, select an assistant. + 1. In the top-left dropdown menu of the left-hand pane, select an assistant. 1. In the bottom of the left-hand pane, edit the `Input` and `Configure` the assistant. 1. Select `Submit` to invoke the selected assistant. 1. View output of the invocation in the right-hand pane. -The following GIF shows these exact steps being carried out: +The following video shows these exact steps being carried out: -![Using LangGraph Studio](./img/studio_input.gif) \ No newline at end of file + diff --git a/docs/docs/cloud/how-tos/test_deployment.md b/docs/docs/cloud/how-tos/test_deployment.md index a3e5046fc..3a540b229 100644 --- a/docs/docs/cloud/how-tos/test_deployment.md +++ b/docs/docs/cloud/how-tos/test_deployment.md @@ -9,6 +9,8 @@ Starting from the LangSmi 1. In the top-right corner, select `Open LangGraph Studio`. 1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md). -The following GIF shows these exact steps being carried out: +The following video shows these exact steps being carried out: -![Using LangGraph Studio](./img/studio_usage.gif) \ No newline at end of file + diff --git a/docs/docs/cloud/how-tos/threads_studio.md b/docs/docs/cloud/how-tos/threads_studio.md index 7cc04f201..41f6a5b2c 100644 --- a/docs/docs/cloud/how-tos/threads_studio.md +++ b/docs/docs/cloud/how-tos/threads_studio.md @@ -6,14 +6,18 @@ 1. View the state of the thread (i.e. the output) in the right-hand pane. 1. To create a new thread, select `+ New Thread`. -The following GIF shows these exact steps being carried out: +The following video shows these exact steps being carried out: -![Using LangGraph Studio](./img/studio_threads.gif) + ## Edit Thread State The LangGraph Studio UI contains features for editing thread state. Explore these features in the right-hand pane. Select the `Edit` icon, modify the desired state, and then select `Fork` to invoke the assistant with the updated state. -The following GIF shows how to edit a thread in the studio: +The following video shows how to edit a thread in the studio: -![Using LangGraph Studio](./img/studio_forks.gif) \ No newline at end of file + diff --git a/docs/docs/cloud/index.md b/docs/docs/cloud/index.md index b23908dcb..61ff1ca72 100644 --- a/docs/docs/cloud/index.md +++ b/docs/docs/cloud/index.md @@ -6,13 +6,14 @@ - We are actively contributing improvements back to LangGraph informed by our work on LangGraph Cloud. - You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project. -!!! danger "Important" - LangGraph Cloud is a closed source, paid product in an invite-only stage. We are currently focused on providing high bandwidth support to make our select early customers successful. If you are interested in applying for access, please fill out [this form](https://www.langchain.com/langgraph-cloud-beta). - !!! warning "Under Construction" LangGraph Cloud documentation is under construction. Contents may change until general availability. -![GIF](./how-tos/img/studio_input.gif) + + + ## Overview diff --git a/docs/docs/cloud/reference/cli.md b/docs/docs/cloud/reference/cli.md index 5e77ad113..ee481ea20 100644 --- a/docs/docs/cloud/reference/cli.md +++ b/docs/docs/cloud/reference/cli.md @@ -13,7 +13,7 @@ The LangGraph CLI requires a JSON configuration file with the following keys: | Key | Description | | --- | ----------- | | `dependencies` | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name. | -| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph is defined. Example: `./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.graph.CompiledGraph`. | +| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: | | `env` | Path to `.env` file or a mapping from environment variable to its value. | | `python_version` | `3.11` or `3.12`. Defaults to `3.11`. | | `pip_config_file`| Path to `pip` config file. | @@ -49,7 +49,7 @@ Example: "." ], "graphs": { - "my_graph_id": "./your_package/your_file.py:variable" + "my_graph_id": "./your_package/your_file.py:make_graph" }, "env": { "OPENAI_API_KEY": "secret-key" diff --git a/docs/docs/concepts/agentic_concepts.md b/docs/docs/concepts/agentic_concepts.md index aca45192f..bd3c3c1f6 100644 --- a/docs/docs/concepts/agentic_concepts.md +++ b/docs/docs/concepts/agentic_concepts.md @@ -56,6 +56,25 @@ This is a pretty advanced interaction pattern. In this interaction pattern, the See [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for how to do this in LangGraph. +## Review Tool Calls + +This is a specific type of human-in-the-loop interaction but it's worth calling out because it is so common. A lot of agent decisions are made via tool calling, so having a clear UX for reviewing tool calls is handy. + +A tool call consists of: +- The name of the tool to call +- Arguments to pass to the tool + +Note that these tool calls can obviously be used for actually calling functions, but they can also be used for other purposes, like to route the agent in a specific direction. +You will want to review the tool call for both of these use cases. + +When reviewing tool calls, there are few actions you may want to take. + +1. Approve the tool call (and let the agent continue on its way) +2. Manually change the tool call, either the tool name or the tool arguments (and let the agent continue on its way after that) +3. Leave feedback on the tool call. This differs from (2) in that you are not changing the tool call directly, but rather leaving natural language feedback suggesting the LLM call it differently (or call a different tool). You could do this by either adding a `ToolMessage` and having the feedback be the result of the tool call, or by adding a `ToolMessage` (that simulates an error) and then a `HumanMessage` (with the feedback). + +See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for how to do this in LangGraph. + ## Map-Reduce A common pattern in agents is to generate a list of objects, do some work on each of those objects, and then combine the results. This is very similar to the common [map-reduce](https://en.wikipedia.org/wiki/MapReduce) operation. This can be tricky for a few reasons. First, it can be tough to define a structured graph ahead of time because the length of the list of objects may be unknown. Second, in order to do this map-reduce you need multiple versions of the state to exist... but the graph shares a common shared state, so how can this be? diff --git a/docs/docs/concepts/index.md b/docs/docs/concepts/index.md index 69d373016..ea6140c8d 100644 --- a/docs/docs/concepts/index.md +++ b/docs/docs/concepts/index.md @@ -20,7 +20,7 @@ Low Level Concepts - [State](low_level.md#state) - [Schema](low_level.md#schema) - [Reducers](low_level.md#reducers) - - [MessageState](low_level.md#messagestate) + - [MessageState](low_level.md#working-with-messages-in-graph-state) - [Nodes](low_level.md#nodes) - [`START` node](low_level.md#start-node) - [`END` node](low_level.md#end-node) diff --git a/docs/docs/concepts/low_level.md b/docs/docs/concepts/low_level.md index 8f0763f78..bac5bbdd5 100644 --- a/docs/docs/concepts/low_level.md +++ b/docs/docs/concepts/low_level.md @@ -49,9 +49,14 @@ The main documented way to specify the schema of a graph is by using `TypedDict` By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [notebook here](../how-tos/input_output_schema.ipynb) for how to use. By default, all nodes in the graph will share the same state. This means that they will read and write to the same state channels. It is possible to have nodes write to private state channels inside the graph for internal node communication - see [this notebook](../how-tos/pass_private_state.ipynb) for how to do that. + ### Reducers -Reducers are key to understanding how updates from nodes are applied to the `State`. Each key in the `State` has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. Let's take a look at a few examples to understand them better. +Reducers are key to understanding how updates from nodes are applied to the `State`. Each key in the `State` has its own independent reducer function. If no reducer function is explicitly specified then it is assumed that all updates to that key should override it. There are a few different types of reducers, starting with the default type of reducer: + +#### Default Reducer + +These two examples show how to use the default reducer: **Example A:** @@ -78,22 +83,48 @@ class State(TypedDict): In this example, we've used the `Annotated` type to specify a reducer function (`operator.add`) for the second key (`bar`). Note that the first key remains unchanged. Let's assume the input to the graph is `{"foo": 1, "bar": ["hi"]}`. Let's then assume the first `Node` returns `{"foo": 2}`. This is treated as an update to the state. Notice that the `Node` does not need to return the whole `State` schema - just an update. After applying this update, the `State` would then be `{"foo": 2, "bar": ["hi"]}`. If the second node returns `{"bar": ["bye"]}` then the `State` would then be `{"foo": 2, "bar": ["hi", "bye"]}`. Notice here that the `bar` key is updated by adding the two lists together. -### MessageState +#### Context Reducer -`MessageState` is one of the few opinionated components in LangGraph. `MessageState` is a special state designed to make it easy to use a list of messages as a key in your state. Specifically, `MessageState` is defined as: +You can use `Context` channels to define shared resources (such as database connections) that are managed outside of your graph's nodes and excluded from checkpointing. The context manager provided to the Context channel is entered before the first step of the graph execution and exited after the last step, allowing you to set up and clean up resources for the duration of the graph invocation. Read this [how to](https://langchain-ai.github.io/langgraph/how-tos/state-context-key) to see an example of using the `Context` channel in your graph. + +### Working with Messages in Graph State + +#### Why use messages? + +Most modern LLM providers have a chat model interface that accepts a list of messages as input. LangChain's [`ChatModel`](https://python.langchain.com/v0.2/docs/concepts/#chat-models) in particular accepts a list of `Message` objects as inputs. These messages come in a variety of forms such as `HumanMessage` (user input) or `AIMessage` (LLM response). To read more about what message objects are, please refer to [this](https://python.langchain.com/v0.2/docs/concepts/#messages) conceptual guide. + +#### Using Messages in your Graph + +In many cases, it is helpful to store prior conversation history as a list of messages in your graph state. To do so, we can add a key (channel) to the graph state that stores a list of `Message` objects and annotate it with a reducer function (see `messages` key in the example below). The reducer function is vital to telling the graph how to update the list of `Message` objects in the state with each state update (for example, when a node sends an update). If you don't specify a reducer, every state update will overwrite the list of messages with the most recently provided value. If you wanted to simply append messages to the existing list, you could use `operator.add` as a reducer. + +However, you might also want to manually update messages in your graph state (e.g. human-in-the-loop). If you were to use `operator.add`, the manual state updates you send to the graph would be appended to the existing list of messages, instead of updating existing messages. To avoid that, you need a reducer that can keep track of message IDs and overwrite existing messages, if updated. To achieve this, you can use the prebuilt `add_messages` function. For brand new messages, it will simply append to existing list, but it will also handle the updates for existing messages correctly. + +#### Serialization + +In addition to keeping track of message IDs, the `add_messages` function will also try to deserialize messages into LangChain `Message` objects whenever a state update is received on the `messages` channel. See more information on LangChain serialization/deserialization [here](https://python.langchain.com/v0.2/docs/how_to/serialization/). This allows sending graph inputs / state updates in the following format: + +```python +# this is supported +{"messages": [HumanMessage(content="message")]} + +# and this is also supported +{"messages": [{"type": "human", "content": "message"}]} +``` + +Since the state updates are always deserialized into LangChain `Messages` when using `add_messages`, you should use dot notation to access message attributes, like `state["messages"][-1].content`. Below is an example of a graph that uses `add_messages` as it's reducer function. ```python from langchain_core.messages import AnyMessage from langgraph.graph.message import add_messages from typing import Annotated, TypedDict -class MessagesState(TypedDict): +class GraphState(TypedDict): messages: Annotated[list[AnyMessage], add_messages] ``` -What this is doing is creating a `TypedDict` with a single key: `messages`. This is a list of `Message` objects, with `add_messages` as a reducer. `add_messages` basically adds messages to the existing list (it also does some nice extra things, like convert from OpenAI message format to the standard LangChain message format, handle updates based on message IDs, etc). +#### MessagesState -We often see a list of messages being a key component of state, so this prebuilt state is intended to make it easy to use messages. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like: +Since having a list of messages in your state is so common, there exists a prebuilt state called `MessagesState` which makes it easy to use messages. `MessagesState` is defined with a single `messages` key which is a list of `AnyMessage` objects and uses the `add_messages` reducer. Typically, there is more state to track than just messages, so we see people subclass this state and add more fields, like: ```python from langgraph.graph import MessagesState diff --git a/docs/docs/how-tos/index.md b/docs/docs/how-tos/index.md index b1783e492..09d5f2e8f 100644 --- a/docs/docs/how-tos/index.md +++ b/docs/docs/how-tos/index.md @@ -25,7 +25,7 @@ LangGraph makes it easy to persist state across graph runs. The guide below show - [How to manage conversation history](memory/manage-conversation-history.ipynb) - [How to delete messages](memory/delete-messages.ipynb) - [How to add summary conversation memory](memory/add-summary-conversation-history.ipynb) -- [How to create a custom checkpointer using Postgres](persistence_postgres.ipynb) +- [How to use Postgres checkpointer for persistence](persistence_postgres.ipynb) - [How to create a custom checkpointer using MongoDB](persistence_mongodb.ipynb) - [How to create a custom checkpointer using Redis](persistence_redis.ipynb) @@ -38,6 +38,7 @@ These guides cover common examples of that. - [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb) - [How to wait for user input](human_in_the_loop/wait-user-input.ipynb) - [How to view and update past graph state](human_in_the_loop/time-travel.ipynb) +- [Review tool calls](human_in_the_loop/review-tool-calls.ipynb) ## Streaming @@ -60,6 +61,14 @@ These guides show how to use different streaming modes. - [How to handle tool calling errors](tool-calling-errors.ipynb) - [How to pass graph state to tools](pass-run-time-values-to-tools.ipynb) - [How to pass config to tools](pass-config-to-tools.ipynb) +- [How to handle large numbers of tools](many-tools.ipynb) + +## State Management + +- [Use Pydantic model as state](state-model.ipynb) +- [Use a context object in state](state-context-key.ipynb) +- [Have a separate input and output schema](input_output_schema.ipynb) +- [Pass private state between nodes inside the graph](pass_private_state.ipynb) ## Other diff --git a/docs/docs/reference/checkpoints.md b/docs/docs/reference/checkpoints.md index eb8735dd6..a7031613d 100644 --- a/docs/docs/reference/checkpoints.md +++ b/docs/docs/reference/checkpoints.md @@ -7,6 +7,8 @@ You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow w - Resilience for long-running, error-prone agents - Time travel retry and branch from a previous checkpoint +Key checkpointer interfaces and primitives are defined in [`langgraph_checkpoint`](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint) library. + ### Checkpoint ::: langgraph.checkpoint.base.Checkpoint @@ -21,7 +23,7 @@ You can [compile][langgraph.graph.MessageGraph.compile] any LangGraph workflow w ### SerializerProtocol -::: langgraph.checkpoint.SerializerProtocol +::: langgraph.checkpoint.base.SerializerProtocol ## Implementations @@ -33,9 +35,20 @@ LangGraph also natively provides the following checkpoint implementations. ### AsyncSqliteSaver -::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver +::: langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver ### SqliteSaver ::: langgraph.checkpoint.sqlite.SqliteSaver + +### AsyncPostgresSaver + +::: langgraph.checkpoint.postgres.aio.AsyncPostgresSaver + +### PostgresSaver + +::: langgraph.checkpoint.postgres.PostgresSaver +handler: python + + handler: python diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 083661975..dfe321c89 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -134,7 +134,7 @@ nav: - Manage conversation history: how-tos/memory/manage-conversation-history.ipynb - Delete messages: how-tos/memory/delete-messages.ipynb - Add summary of the conversation history: how-tos/memory/add-summary-conversation-history.ipynb - - Create custom checkpointer using Postgres: how-tos/persistence_postgres.ipynb + - Use Postgres checkpointer for persistence: how-tos/persistence_postgres.ipynb - Create custom checkpointer using MongoDB: how-tos/persistence_mongodb.ipynb - Create custom checkpointer using Redis: how-tos/persistence_redis.ipynb - Human-in-the-loop: @@ -142,6 +142,7 @@ nav: - Wait for user input: how-tos/human_in_the_loop/wait-user-input.ipynb - View and update past graph state: how-tos/human_in_the_loop/time-travel.ipynb - Edit graph state: how-tos/human_in_the_loop/edit-graph-state.ipynb + - Review tool calls: how-tos/human_in_the_loop/review-tool-calls.ipynb - Streaming: - Stream full state: how-tos/stream-values.ipynb - Stream state updates: how-tos/stream-updates.ipynb @@ -157,6 +158,7 @@ nav: - Handle tool calling errors: how-tos/tool-calling-errors.ipynb - Pass graph state to tools: how-tos/pass-run-time-values-to-tools.ipynb - Pass config to tools: how-tos/pass-config-to-tools.ipynb + - Handle many tools: how-tos/many-tools.ipynb - State Management: - Use Pydantic model as state: how-tos/state-model.ipynb - Use a context object in state: how-tos/state-context-key.ipynb @@ -189,10 +191,12 @@ nav: - Quick Start: "cloud/quick_start.md" - How-to Guides: - "cloud/how-tos/index.md" - - Deployment: + - Setup: - Setup App: "cloud/deployment/setup.md" - Setup App (pyproject.toml): "cloud/deployment/setup_pyproject.md" + - Rebuild Graph at Runtime: "cloud/deployment/graph_rebuild.md" - Test App Locally: "cloud/deployment/test_locally.md" + - Deployment: - Deploy to Cloud: "cloud/deployment/cloud.md" - Self-Host: "cloud/deployment/self_hosted.md" - Streaming: diff --git a/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb b/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb index 867afabaf..efe9227f3 100644 --- a/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb +++ b/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb @@ -1,340 +1,366 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# Human-in-the-loop\n", - "\n", - "In this example we will build a ReAct Agent that has a human in the loop. We will use the human to approve specific actions.\n", - "\n", - "This examples builds off the base chat executor. It is highly recommended you learn about that executor before going through this notebook. You can find documentation for that example [here](./base.ipynb).\n", - "\n", - "Any modifications of that example are called below with **MODIFICATION**, so if you are looking for the differences you can just search for that." - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [], - "source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_community langchain_openai tavily-python"] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "OpenAI API Key: ········\n", - "Tavily API Key: ········\n" - ] - } - ], - "source": ["import getpass\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nos.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LangSmith API Key: ········\n" - ] - } - ], - "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"] - }, - { - "cell_type": "markdown", - "id": "21ac643b-cb06-4724-a80c-2862ba4773f1", - "metadata": {}, - "source": [ - "## Set up the tools\n", - "\n", - "We will first define the tools we want to use.\n", - "For this simple example, we will use a built-in search tool via Tavily.\n", - "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", - "metadata": {}, - "outputs": [], - "source": ["from langchain_community.tools.tavily_search import TavilySearchResults\n\ntools = [TavilySearchResults(max_results=1)]"] - }, - { - "cell_type": "markdown", - "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58", - "metadata": {}, - "source": [ - "We can now wrap these tools in a simple ToolExecutor.\n", - "This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.\n", - "A ToolInvocation is any class with `tool` and `tool_input` attribute.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", - "metadata": {}, - "outputs": [], - "source": ["from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)"] - }, - { - "cell_type": "markdown", - "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5", - "metadata": {}, - "source": [ - "## Set up the model\n", - "\n", - "Now we need to load the chat model we want to use.\n", - "Importantly, this should satisfy two criteria:\n", - "\n", - "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n", - "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n", - "\n", - "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", - "metadata": {}, - "outputs": [], - "source": ["from langchain_openai import ChatOpenAI\n\n# We will set streaming=True so that we can stream tokens\n# See the streaming section for more information on this.\nmodel = ChatOpenAI(temperature=0, streaming=True)"] - }, - { - "cell_type": "markdown", - "id": "a77995c0-bae2-4cee-a036-8688a90f05b9", - "metadata": {}, - "source": [ - "\n", - "After we've done this, we should make sure the model knows that it has these tools available to call.\n", - "We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", - "metadata": {}, - "outputs": [], - "source": ["model = model.bind_tools(tools)"] - }, - { - "cell_type": "markdown", - "id": "8e8b9211-93d0-4ad5-aa7a-9c09099c53ff", - "metadata": {}, - "source": [ - "## Define the agent state\n", - "\n", - "The main type of graph in `langgraph` is the [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph).\n", - "This graph is parameterized by a state object that it passes around to each node.\n", - "Each node then returns operations to update that state.\n", - "These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n", - "Whether to set or add is denoted by annotating the state object you construct the graph with.\n", - "\n", - "For this example, the state we will track will just be a list of messages.\n", - "We want each node to just add messages to that list.\n", - "Therefore, we will use a `TypedDict` with one key (`messages`) and annotate it so that the `messages` attribute is always added to.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ea793afa-2eab-4901-910d-6eed90cd6564", - "metadata": {}, - "outputs": [], - "source": ["import operator\nfrom typing import Annotated, Sequence, TypedDict\n\nfrom langchain_core.messages import BaseMessage\n\n\nclass AgentState(TypedDict):\n messages: Annotated[Sequence[BaseMessage], operator.add]"] - }, - { - "cell_type": "markdown", - "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4", - "metadata": {}, - "source": [ - "## Define the nodes\n", - "\n", - "We now need to define a few different nodes in our graph.\n", - "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", - "There are two main nodes we need for this:\n", - "\n", - "1. The agent: responsible for deciding what (if any) actions to take.\n", - "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n", - "\n", - "We will also need to define some edges.\n", - "Some of these edges may be conditional.\n", - "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n", - "The path that is taken is not known until that node is run (the LLM decides).\n", - "\n", - "1. Conditional Edge: after the agent is called, we should either:\n", - " a. If the agent said to take an action, then the function to invoke tools should be called\n", - " b. If the agent said that it was finished, then it should finish\n", - "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n", - "\n", - "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b547109f-f9e8-4e77-a7e7-ed2bae7a72ab", - "metadata": {}, - "outputs": [], - "source": ["from langchain_core.messages import ToolMessage\n\nfrom langgraph.prebuilt import ToolInvocation\n\n\n# Define the function that determines whether to continue or not\ndef should_continue(state):\n messages = state[\"messages\"]\n last_message = messages[-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"end\"\n # Otherwise if there is, we continue\n else:\n return \"continue\"\n\n\n# Define the function that calls the model\ndef call_model(state):\n messages = state[\"messages\"]\n response = model.invoke(messages)\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}"] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "73fd6432-42e8-472a-89ca-bb5ddbbcc35a", - "metadata": {}, - "outputs": [], - "source": ["# Define the function to execute tools\ndef call_tool(state):\n messages = state[\"messages\"]\n # Based on the continue condition\n # we know the last message involves a function call\n last_message = messages[-1]\n # We construct an ToolInvocation for each tool call\n tool_invocations = []\n for tool_call in last_message.tool_calls:\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n tool_invocations.append(action)\n\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n # We call the tool_executor and get back a response\n responses = tool_executor.batch(tool_invocations, return_exceptions=True)\n # We use the response to create tool messages\n tool_messages = [\n ToolMessage(\n content=str(response),\n name=tc[\"name\"],\n tool_call_id=tc[\"id\"],\n )\n for tc, response in zip(last_message.tool_calls, responses)\n ]\n\n # We return a list, because this will get added to the existing list\n return {\"messages\": tool_messages}"] - }, - { - "cell_type": "markdown", - "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b", - "metadata": {}, - "source": [ - "## Define the graph\n", - "\n", - "We can now put it all together and define the graph!\n", - "\n", - "**MODIFICATION**\n", - "\n", - "We modify the graph to **interrupt** before calling the tools. This lets the user give approval to continue. Note that this is a simple example and we could modify it to change the tool input, use some other channel besides input, etc." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "813ae66c-3b58-4283-a02a-36da72a2ab90", - "metadata": {}, - "outputs": [], - "source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n # Finally we pass in a mapping.\n # The keys are strings, and the values are other nodes.\n # END is a special node marking that the graph should finish.\n # What will happen is we will call `should_continue`, and then the output of that\n # will be matched against the keys in this mapping.\n # Based on which one it matches, that node will then be called.\n {\n # If `tools`, then we call the tool node.\n \"continue\": \"action\",\n # Otherwise we finish.\n \"end\": END,\n },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")\n\n# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=MemorySaver(), interrupt_before=[\"action\"])"] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "a0212d00", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# Human-in-the-loop\n", + "\n", + "In this example we will build a ReAct Agent that has a human in the loop. We will use the human to approve specific actions.\n", + "\n", + "This examples builds off the base chat executor. It is highly recommended you learn about that executor before going through this notebook. You can find documentation for that example [here](./base.ipynb).\n", + "\n", + "Any modifications of that example are called below with **MODIFICATION**, so if you are looking for the differences you can just search for that." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": ["from IPython.display import Image, display\n\ntry:\n display(Image(app.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] - }, - { - "cell_type": "markdown", - "id": "547c3931-3dae-4281-ad4e-4b51305594d4", - "metadata": {}, - "source": [ - "## Use it!\n", - "\n", - "We can now use it!\n", - "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f544977e-31f7-41f0-88c4-ec9c27b8cecb", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Output from node 'agent':\n", - "---\n", - "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_9yzjV53mMUwOgnoSDTWnZnsm', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-e0f2fbb3-994f-4742-ad5a-e273e595686a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_9yzjV53mMUwOgnoSDTWnZnsm'}])]}\n", - "\n", - "---\n", - "\n", - "Output from node 'action':\n", - "---\n", - "{'messages': [ToolMessage(content='[{\\'url\\': \\'https://www.weatherapi.com/\\', \\'content\\': \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1714809084, \\'localtime\\': \\'2024-05-04 0:51\\'}, \\'current\\': {\\'last_updated_epoch\\': 1714808700, \\'last_updated\\': \\'2024-05-04 00:45\\', \\'temp_c\\': 12.8, \\'temp_f\\': 55.0, \\'is_day\\': 0, \\'condition\\': {\\'text\\': \\'Overcast\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/night/122.png\\', \\'code\\': 1009}, \\'wind_mph\\': 11.9, \\'wind_kph\\': 19.1, \\'wind_degree\\': 240, \\'wind_dir\\': \\'WSW\\', \\'pressure_mb\\': 1013.0, \\'pressure_in\\': 29.9, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 96, \\'cloud\\': 100, \\'feelslike_c\\': 11.4, \\'feelslike_f\\': 52.4, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 1.0, \\'gust_mph\\': 14.9, \\'gust_kph\\': 23.9}}\"}]', name='tavily_search_results_json', tool_call_id='call_9yzjV53mMUwOgnoSDTWnZnsm')]}\n", - "\n", - "---\n", - "\n", - "Output from node 'agent':\n", - "---\n", - "{'messages': [AIMessage(content='The current weather in San Francisco is as follows:\\n- Temperature: 55.0°F (12.8°C)\\n- Condition: Overcast\\n- Wind: 11.9 mph from WSW\\n- Humidity: 96%\\n- Visibility: 9.0 miles\\n\\nFor more details, you can visit [Weather API](https://www.weatherapi.com/).', response_metadata={'finish_reason': 'stop'}, id='run-65726e33-9cf2-4adf-975b-859dc37eef67-0')]}\n", - "\n", - "---\n", - "\n" - ] + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_community langchain_openai tavily-python" + ] + }, + { + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OpenAI API Key: ········\n", + "Tavily API Key: ········\n" + ] + } + ], + "source": [ + "import getpass\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nos.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LangSmith API Key: ········\n" + ] + } + ], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")" + ] + }, + { + "cell_type": "markdown", + "id": "21ac643b-cb06-4724-a80c-2862ba4773f1", + "metadata": {}, + "source": [ + "## Set up the tools\n", + "\n", + "We will first define the tools we want to use.\n", + "For this simple example, we will use a built-in search tool via Tavily.\n", + "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.tools.tavily_search import TavilySearchResults\n\ntools = [TavilySearchResults(max_results=1)]" + ] + }, + { + "cell_type": "markdown", + "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58", + "metadata": {}, + "source": [ + "We can now wrap these tools in a simple ToolExecutor.\n", + "This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.\n", + "A ToolInvocation is any class with `tool` and `tool_input` attribute.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)" + ] + }, + { + "cell_type": "markdown", + "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5", + "metadata": {}, + "source": [ + "## Set up the model\n", + "\n", + "Now we need to load the chat model we want to use.\n", + "Importantly, this should satisfy two criteria:\n", + "\n", + "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n", + "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n", + "\n", + "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n\n# We will set streaming=True so that we can stream tokens\n# See the streaming section for more information on this.\nmodel = ChatOpenAI(temperature=0, streaming=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a77995c0-bae2-4cee-a036-8688a90f05b9", + "metadata": {}, + "source": [ + "\n", + "After we've done this, we should make sure the model knows that it has these tools available to call.\n", + "We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", + "metadata": {}, + "outputs": [], + "source": [ + "model = model.bind_tools(tools)" + ] + }, + { + "cell_type": "markdown", + "id": "8e8b9211-93d0-4ad5-aa7a-9c09099c53ff", + "metadata": {}, + "source": [ + "## Define the agent state\n", + "\n", + "The main type of graph in `langgraph` is the [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph).\n", + "This graph is parameterized by a state object that it passes around to each node.\n", + "Each node then returns operations to update that state.\n", + "These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n", + "Whether to set or add is denoted by annotating the state object you construct the graph with.\n", + "\n", + "For this example, the state we will track will just be a list of messages.\n", + "We want each node to just add messages to that list.\n", + "Therefore, we will use a `TypedDict` with one key (`messages`) and annotate it so that the `messages` attribute is always added to.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea793afa-2eab-4901-910d-6eed90cd6564", + "metadata": {}, + "outputs": [], + "source": [ + "import operator\nfrom typing import Annotated, Sequence, TypedDict\n\nfrom langchain_core.messages import BaseMessage\n\n\nclass AgentState(TypedDict):\n messages: Annotated[Sequence[BaseMessage], operator.add]" + ] + }, + { + "cell_type": "markdown", + "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4", + "metadata": {}, + "source": [ + "## Define the nodes\n", + "\n", + "We now need to define a few different nodes in our graph.\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", + "There are two main nodes we need for this:\n", + "\n", + "1. The agent: responsible for deciding what (if any) actions to take.\n", + "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n", + "\n", + "We will also need to define some edges.\n", + "Some of these edges may be conditional.\n", + "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n", + "The path that is taken is not known until that node is run (the LLM decides).\n", + "\n", + "1. Conditional Edge: after the agent is called, we should either:\n", + " a. If the agent said to take an action, then the function to invoke tools should be called\n", + " b. If the agent said that it was finished, then it should finish\n", + "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n", + "\n", + "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b547109f-f9e8-4e77-a7e7-ed2bae7a72ab", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import ToolMessage\n\nfrom langgraph.prebuilt import ToolInvocation\n\n\n# Define the function that determines whether to continue or not\ndef should_continue(state):\n messages = state[\"messages\"]\n last_message = messages[-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"end\"\n # Otherwise if there is, we continue\n else:\n return \"continue\"\n\n\n# Define the function that calls the model\ndef call_model(state):\n messages = state[\"messages\"]\n response = model.invoke(messages)\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "73fd6432-42e8-472a-89ca-bb5ddbbcc35a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the function to execute tools\ndef call_tool(state):\n messages = state[\"messages\"]\n # Based on the continue condition\n # we know the last message involves a function call\n last_message = messages[-1]\n # We construct an ToolInvocation for each tool call\n tool_invocations = []\n for tool_call in last_message.tool_calls:\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n tool_invocations.append(action)\n\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n # We call the tool_executor and get back a response\n responses = tool_executor.batch(tool_invocations, return_exceptions=True)\n # We use the response to create tool messages\n tool_messages = [\n ToolMessage(\n content=str(response),\n name=tc[\"name\"],\n tool_call_id=tc[\"id\"],\n )\n for tc, response in zip(last_message.tool_calls, responses)\n ]\n\n # We return a list, because this will get added to the existing list\n return {\"messages\": tool_messages}" + ] + }, + { + "cell_type": "markdown", + "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b", + "metadata": {}, + "source": [ + "## Define the graph\n", + "\n", + "We can now put it all together and define the graph!\n", + "\n", + "**MODIFICATION**\n", + "\n", + "We modify the graph to **interrupt** before calling the tools. This lets the user give approval to continue. Note that this is a simple example and we could modify it to change the tool input, use some other channel besides input, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "813ae66c-3b58-4283-a02a-36da72a2ab90", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n # Finally we pass in a mapping.\n # The keys are strings, and the values are other nodes.\n # END is a special node marking that the graph should finish.\n # What will happen is we will call `should_continue`, and then the output of that\n # will be matched against the keys in this mapping.\n # Based on which one it matches, that node will then be called.\n {\n # If `tools`, then we call the tool node.\n \"continue\": \"action\",\n # Otherwise we finish.\n \"end\": END,\n },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")\n\n# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=MemorySaver(), interrupt_before=[\"action\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a0212d00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n\ntry:\n display(Image(app.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass" + ] + }, + { + "cell_type": "markdown", + "id": "547c3931-3dae-4281-ad4e-4b51305594d4", + "metadata": {}, + "source": [ + "## Use it!\n", + "\n", + "We can now use it!\n", + "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f544977e-31f7-41f0-88c4-ec9c27b8cecb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output from node 'agent':\n", + "---\n", + "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_9yzjV53mMUwOgnoSDTWnZnsm', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-e0f2fbb3-994f-4742-ad5a-e273e595686a-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_9yzjV53mMUwOgnoSDTWnZnsm'}])]}\n", + "\n", + "---\n", + "\n", + "Output from node 'action':\n", + "---\n", + "{'messages': [ToolMessage(content='[{\\'url\\': \\'https://www.weatherapi.com/\\', \\'content\\': \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1714809084, \\'localtime\\': \\'2024-05-04 0:51\\'}, \\'current\\': {\\'last_updated_epoch\\': 1714808700, \\'last_updated\\': \\'2024-05-04 00:45\\', \\'temp_c\\': 12.8, \\'temp_f\\': 55.0, \\'is_day\\': 0, \\'condition\\': {\\'text\\': \\'Overcast\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/night/122.png\\', \\'code\\': 1009}, \\'wind_mph\\': 11.9, \\'wind_kph\\': 19.1, \\'wind_degree\\': 240, \\'wind_dir\\': \\'WSW\\', \\'pressure_mb\\': 1013.0, \\'pressure_in\\': 29.9, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 96, \\'cloud\\': 100, \\'feelslike_c\\': 11.4, \\'feelslike_f\\': 52.4, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 1.0, \\'gust_mph\\': 14.9, \\'gust_kph\\': 23.9}}\"}]', name='tavily_search_results_json', tool_call_id='call_9yzjV53mMUwOgnoSDTWnZnsm')]}\n", + "\n", + "---\n", + "\n", + "Output from node 'agent':\n", + "---\n", + "{'messages': [AIMessage(content='The current weather in San Francisco is as follows:\\n- Temperature: 55.0°F (12.8°C)\\n- Condition: Overcast\\n- Wind: 11.9 mph from WSW\\n- Humidity: 96%\\n- Visibility: 9.0 miles\\n\\nFor more details, you can visit [Weather API](https://www.weatherapi.com/).', response_metadata={'finish_reason': 'stop'}, id='run-65726e33-9cf2-4adf-975b-859dc37eef67-0')]}\n", + "\n", + "---\n", + "\n" + ] + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n\ninputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\nconfig = {\"configurable\": {\"thread_id\": \"thread-1\"}}\nwhile True:\n for output in app.stream(inputs, config):\n # stream() yields dictionaries with output keyed by node name\n for key, value in output.items():\n print(f\"Output from node '{key}':\")\n print(\"---\")\n print(value)\n print(\"\\n---\\n\")\n snapshot = app.get_state(config)\n # If \"next\" is present, it means we've interrupted mid-execution\n if not snapshot.next:\n break\n inputs = None\n response = input(\n \"Do you approve the next step? Type y if you do, anything else to stop: \"\n )\n if response != \"y\":\n break" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" } - ], - "source": ["from langchain_core.messages import HumanMessage\n\ninputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\nconfig = {\"configurable\": {\"thread_id\": \"thread-1\"}}\nwhile True:\n for output in app.stream(inputs, config):\n # stream() yields dictionaries with output keyed by node name\n for key, value in output.items():\n print(f\"Output from node '{key}':\")\n print(\"---\")\n print(value)\n print(\"\\n---\\n\")\n snapshot = app.get_state(config)\n # If \"next\" is present, it means we've interrupted mid-execution\n if not snapshot.next:\n break\n inputs = None\n response = input(\n \"Do you approve the next step? Type y if you do, anything else to stop: \"\n )\n if response != \"y\":\n break"] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png b/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png index a0cefb06b..d888e305f 100644 Binary files a/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png and b/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png differ diff --git a/examples/chatbots/customer-support.ipynb b/examples/chatbots/customer-support.ipynb index 374d3e538..b37f4e396 100644 --- a/examples/chatbots/customer-support.ipynb +++ b/examples/chatbots/customer-support.ipynb @@ -32,7 +32,10 @@ "scrolled": true }, "outputs": [], - "source": ["%%capture --no-stderr\n%pip install -U langgraph langchain-community langchain-openai scikit-learn"] + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-community langchain-openai scikit-learn" + ] }, { "cell_type": "markdown", @@ -48,7 +51,15 @@ "id": "3d1ef253-6b0c-4481-868c-e1fe84f2c8ff", "metadata": {}, "outputs": [], - "source": ["import requests\n\nurl = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\nresponse = requests.get(url)\n\nwith open(\"Chinook.db\", \"wb\") as file:\n file.write(response.content)"] + "source": [ + "import requests\n", + "\n", + "url = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\n", + "response = requests.get(url)\n", + "\n", + "with open(\"Chinook.db\", \"wb\") as file:\n", + " file.write(response.content)" + ] }, { "cell_type": "code", @@ -77,7 +88,12 @@ "output_type": "execute_result" } ], - "source": ["from langchain_community.utilities import SQLDatabase\n\ndb = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\ndb.get_usable_table_names()"] + "source": [ + "from langchain_community.utilities import SQLDatabase\n", + "\n", + "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n", + "db.get_usable_table_names()" + ] }, { "cell_type": "markdown", @@ -96,7 +112,11 @@ "id": "d9ea4e80-30e6-4d46-b480-35f0be2fb055", "metadata": {}, "outputs": [], - "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4o\")"] + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4o\")" + ] }, { "cell_type": "markdown", @@ -118,7 +138,9 @@ "id": "ea958e9f-ab1f-49b5-bd85-16332055297c", "metadata": {}, "outputs": [], - "source": ["from langchain_core.messages import HumanMessage, SystemMessage"] + "source": [ + "from langchain_core.messages import HumanMessage, SystemMessage" + ] }, { "cell_type": "markdown", @@ -137,7 +159,12 @@ "id": "975b039a", "metadata": {}, "outputs": [], - "source": ["# This tool is given to the agent to look up information about a customer\ndef get_customer_info(customer_id: int):\n \"\"\"Look up customer info given their ID. ALWAYS make sure you have the customer ID before invoking this.\"\"\"\n return db.run(f\"SELECT * FROM Customer WHERE CustomerID = {customer_id};\")"] + "source": [ + "# This tool is given to the agent to look up information about a customer\n", + "def get_customer_info(customer_id: int):\n", + " \"\"\"Look up customer info given their ID. ALWAYS make sure you have the customer ID before invoking this.\"\"\"\n", + " return db.run(f\"SELECT * FROM Customer WHERE CustomerID = {customer_id};\")" + ] }, { "cell_type": "code", @@ -145,7 +172,20 @@ "id": "1d5fa446", "metadata": {}, "outputs": [], - "source": ["customer_prompt = \"\"\"Your job is to help a user update their profile.\n\nYou only have certain tools you can use. These tools require specific input. If you don't know the required input, then ask the user for it.\n\nIf you are unable to help the user, you can \"\"\"\n\n\ndef get_customer_messages(messages):\n return [SystemMessage(content=customer_prompt)] + messages\n\n\ncustomer_chain = get_customer_messages | model.bind_tools([get_customer_info])"] + "source": [ + "customer_prompt = \"\"\"Your job is to help a user update their profile.\n", + "\n", + "You only have certain tools you can use. These tools require specific input. If you don't know the required input, then ask the user for it.\n", + "\n", + "If you are unable to help the user, you can \"\"\"\n", + "\n", + "\n", + "def get_customer_messages(messages):\n", + " return [SystemMessage(content=customer_prompt)] + messages\n", + "\n", + "\n", + "customer_chain = get_customer_messages | model.bind_tools([get_customer_info])" + ] }, { "cell_type": "markdown", @@ -166,7 +206,19 @@ "id": "a8604a3b-b484-4b2b-a914-4236cb98c524", "metadata": {}, "outputs": [], - "source": ["from langchain_community.vectorstores import SKLearnVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\nartists = db._execute(\"select * from Artist\")\nsongs = db._execute(\"select * from Track\")\nartist_retriever = SKLearnVectorStore.from_texts(\n [a[\"Name\"] for a in artists], OpenAIEmbeddings(), metadatas=artists\n).as_retriever()\nsong_retriever = SKLearnVectorStore.from_texts(\n [a[\"Name\"] for a in songs], OpenAIEmbeddings(), metadatas=songs\n).as_retriever()"] + "source": [ + "from langchain_community.vectorstores import SKLearnVectorStore\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "artists = db._execute(\"select * from Artist\")\n", + "songs = db._execute(\"select * from Track\")\n", + "artist_retriever = SKLearnVectorStore.from_texts(\n", + " [a[\"Name\"] for a in artists], OpenAIEmbeddings(), metadatas=artists\n", + ").as_retriever()\n", + "song_retriever = SKLearnVectorStore.from_texts(\n", + " [a[\"Name\"] for a in songs], OpenAIEmbeddings(), metadatas=songs\n", + ").as_retriever()" + ] }, { "cell_type": "markdown", @@ -182,7 +234,16 @@ "id": "0a2a2b74", "metadata": {}, "outputs": [], - "source": ["def get_albums_by_artist(artist):\n \"\"\"Get albums by an artist (or similar artists).\"\"\"\n docs = artist_retriever.get_relevant_documents(artist)\n artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n return db.run(\n f\"SELECT Title, Name FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Album.ArtistId in ({artist_ids});\",\n include_columns=True,\n )"] + "source": [ + "def get_albums_by_artist(artist):\n", + " \"\"\"Get albums by an artist (or similar artists).\"\"\"\n", + " docs = artist_retriever.get_relevant_documents(artist)\n", + " artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n", + " return db.run(\n", + " f\"SELECT Title, Name FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Album.ArtistId in ({artist_ids});\",\n", + " include_columns=True,\n", + " )" + ] }, { "cell_type": "markdown", @@ -198,7 +259,16 @@ "id": "da533f50", "metadata": {}, "outputs": [], - "source": ["def get_tracks_by_artist(artist):\n \"\"\"Get songs by an artist (or similar artists).\"\"\"\n docs = artist_retriever.invoke(artist)\n artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n return db.run(\n f\"SELECT Track.Name as SongName, Artist.Name as ArtistName FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId LEFT JOIN Track ON Track.AlbumId = Album.AlbumId WHERE Album.ArtistId in ({artist_ids});\",\n include_columns=True,\n )"] + "source": [ + "def get_tracks_by_artist(artist):\n", + " \"\"\"Get songs by an artist (or similar artists).\"\"\"\n", + " docs = artist_retriever.invoke(artist)\n", + " artist_ids = \", \".join([str(d.metadata[\"ArtistId\"]) for d in docs])\n", + " return db.run(\n", + " f\"SELECT Track.Name as SongName, Artist.Name as ArtistName FROM Album LEFT JOIN Artist ON Album.ArtistId = Artist.ArtistId LEFT JOIN Track ON Track.AlbumId = Album.AlbumId WHERE Album.ArtistId in ({artist_ids});\",\n", + " include_columns=True,\n", + " )" + ] }, { "cell_type": "markdown", @@ -214,7 +284,11 @@ "id": "b3c07010", "metadata": {}, "outputs": [], - "source": ["def check_for_songs(song_title):\n \"\"\"Check if a song exists by its name.\"\"\"\n return song_retriever.invoke(song_title)"] + "source": [ + "def check_for_songs(song_title):\n", + " \"\"\"Check if a song exists by its name.\"\"\"\n", + " return song_retriever.invoke(song_title)" + ] }, { "cell_type": "markdown", @@ -230,7 +304,23 @@ "id": "72a14d5c", "metadata": {}, "outputs": [], - "source": ["song_system_message = \"\"\"Your job is to help a customer find any songs they are looking for. \n\nYou only have certain tools you can use. If a customer asks you to look something up that you don't know how, politely tell them what you can help with.\n\nWhen looking up artists and songs, sometimes the artist/song will not be found. In that case, the tools will return information \\\non similar songs and artists. This is intentional, it is not the tool messing up.\"\"\"\n\n\ndef get_song_messages(messages):\n return [SystemMessage(content=song_system_message)] + messages\n\n\nsong_recc_chain = get_song_messages | model.bind_tools(\n [get_albums_by_artist, get_tracks_by_artist, check_for_songs]\n)"] + "source": [ + "song_system_message = \"\"\"Your job is to help a customer find any songs they are looking for. \n", + "\n", + "You only have certain tools you can use. If a customer asks you to look something up that you don't know how, politely tell them what you can help with.\n", + "\n", + "When looking up artists and songs, sometimes the artist/song will not be found. In that case, the tools will return information \\\n", + "on similar songs and artists. This is intentional, it is not the tool messing up.\"\"\"\n", + "\n", + "\n", + "def get_song_messages(messages):\n", + " return [SystemMessage(content=song_system_message)] + messages\n", + "\n", + "\n", + "song_recc_chain = get_song_messages | model.bind_tools(\n", + " [get_albums_by_artist, get_tracks_by_artist, check_for_songs]\n", + ")" + ] }, { "cell_type": "code", @@ -249,7 +339,10 @@ "output_type": "execute_result" } ], - "source": ["msgs = [HumanMessage(content=\"hi! can you help me find songs by amy whinehouse?\")]\nsong_recc_chain.invoke(msgs)"] + "source": [ + "msgs = [HumanMessage(content=\"hi! can you help me find songs by amy whinehouse?\")]\n", + "song_recc_chain.invoke(msgs)" + ] }, { "cell_type": "markdown", @@ -267,7 +360,32 @@ "id": "73e74268", "metadata": {}, "outputs": [], - "source": ["from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n\nclass Router(BaseModel):\n \"\"\"Call this if you are able to route the user to the appropriate representative.\"\"\"\n\n choice: str = Field(description=\"should be one of: music, customer\")\n\n\nsystem_message = \"\"\"Your job is to help as a customer service representative for a music store.\n\nYou should interact politely with customers to try to figure out how you can help. You can help in a few ways:\n\n- Updating user information: if a customer wants to update the information in the user database. Call the router with `customer`\n- Recommending music: if a customer wants to find some music or information about music. Call the router with `music`\n\nIf the user is asking or wants to ask about updating or accessing their information, send them to that route.\nIf the user is asking or wants to ask about music, send them to that route.\nOtherwise, respond.\"\"\"\n\n\ndef get_messages(messages):\n return [SystemMessage(content=system_message)] + messages"] + "source": [ + "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n", + "from langchain_core.pydantic_v1 import BaseModel, Field\n", + "\n", + "\n", + "class Router(BaseModel):\n", + " \"\"\"Call this if you are able to route the user to the appropriate representative.\"\"\"\n", + "\n", + " choice: str = Field(description=\"should be one of: music, customer\")\n", + "\n", + "\n", + "system_message = \"\"\"Your job is to help as a customer service representative for a music store.\n", + "\n", + "You should interact politely with customers to try to figure out how you can help. You can help in a few ways:\n", + "\n", + "- Updating user information: if a customer wants to update the information in the user database. Call the router with `customer`\n", + "- Recommending music: if a customer wants to find some music or information about music. Call the router with `music`\n", + "\n", + "If the user is asking or wants to ask about updating or accessing their information, send them to that route.\n", + "If the user is asking or wants to ask about music, send them to that route.\n", + "Otherwise, respond.\"\"\"\n", + "\n", + "\n", + "def get_messages(messages):\n", + " return [SystemMessage(content=system_message)] + messages" + ] }, { "cell_type": "code", @@ -275,7 +393,9 @@ "id": "ddf27314", "metadata": {}, "outputs": [], - "source": ["chain = get_messages | model.bind_tools([Router])"] + "source": [ + "chain = get_messages | model.bind_tools([Router])" + ] }, { "cell_type": "code", @@ -294,7 +414,10 @@ "output_type": "execute_result" } ], - "source": ["msgs = [HumanMessage(content=\"hi! can you help me find a good song?\")]\nchain.invoke(msgs)"] + "source": [ + "msgs = [HumanMessage(content=\"hi! can you help me find a good song?\")]\n", + "chain.invoke(msgs)" + ] }, { "cell_type": "code", @@ -313,7 +436,10 @@ "output_type": "execute_result" } ], - "source": ["msgs = [HumanMessage(content=\"hi! what's the email you have for me?\")]\nchain.invoke(msgs)"] + "source": [ + "msgs = [HumanMessage(content=\"hi! what's the email you have for me?\")]\n", + "chain.invoke(msgs)" + ] }, { "cell_type": "code", @@ -321,7 +447,15 @@ "id": "bd6ddd8b-7500-46a7-811d-3bcb937bda51", "metadata": {}, "outputs": [], - "source": ["from langchain_core.messages import AIMessage\n\n\ndef add_name(message, name):\n _dict = message.dict()\n _dict[\"name\"] = name\n return AIMessage(**_dict)"] + "source": [ + "from langchain_core.messages import AIMessage\n", + "\n", + "\n", + "def add_name(message, name):\n", + " _dict = message.dict()\n", + " _dict[\"name\"] = name\n", + " return AIMessage(**_dict)" + ] }, { "cell_type": "code", @@ -329,7 +463,45 @@ "id": "27494de5-8345-4c23-bc0e-81e0dd5d47d8", "metadata": {}, "outputs": [], - "source": ["import json\n\nfrom langgraph.graph import END, START\n\n\ndef _get_last_ai_message(messages):\n for m in messages[::-1]:\n if isinstance(m, AIMessage):\n return m\n return None\n\n\ndef _is_tool_call(msg):\n return hasattr(msg, \"additional_kwargs\") and \"tool_calls\" in msg.additional_kwargs\n\n\ndef _route(messages):\n last_message = messages[-1]\n if isinstance(last_message, AIMessage):\n if not last_message.tool_calls:\n return END\n else:\n if last_message.name == \"general\":\n if len(last_message.tool_calls) > 1:\n raise ValueError(\"Too many tools\")\n return last_message.tool_calls[0][\"args\"][\"choice\"]\n else:\n return \"tools\"\n last_m = _get_last_ai_message(messages)\n if last_m is None:\n return \"general\"\n if last_m.name == \"music\":\n return \"music\"\n elif last_m.name == \"customer\":\n return \"customer\"\n else:\n return \"general\""] + "source": [ + "import json\n", + "\n", + "from langgraph.graph import END, START\n", + "\n", + "\n", + "def _get_last_ai_message(messages):\n", + " for m in messages[::-1]:\n", + " if isinstance(m, AIMessage):\n", + " return m\n", + " return None\n", + "\n", + "\n", + "def _is_tool_call(msg):\n", + " return hasattr(msg, \"additional_kwargs\") and \"tool_calls\" in msg.additional_kwargs\n", + "\n", + "\n", + "def _route(messages):\n", + " last_message = messages[-1]\n", + " if isinstance(last_message, AIMessage):\n", + " if not last_message.tool_calls:\n", + " return END\n", + " else:\n", + " if last_message.name == \"general\":\n", + " if len(last_message.tool_calls) > 1:\n", + " raise ValueError(\"Too many tools\")\n", + " return last_message.tool_calls[0][\"args\"][\"choice\"]\n", + " else:\n", + " return \"tools\"\n", + " last_m = _get_last_ai_message(messages)\n", + " if last_m is None:\n", + " return \"general\"\n", + " if last_m.name == \"music\":\n", + " return \"music\"\n", + " elif last_m.name == \"customer\":\n", + " return \"customer\"\n", + " else:\n", + " return \"general\"" + ] }, { "cell_type": "code", @@ -337,7 +509,12 @@ "id": "8aec704a-46fe-4fb3-bdee-11c3bbffc370", "metadata": {}, "outputs": [], - "source": ["from langgraph.prebuilt import ToolNode\n\ntools = [get_albums_by_artist, get_tracks_by_artist, check_for_songs, get_customer_info]\ntool_node = ToolNode(tools)"] + "source": [ + "from langgraph.prebuilt import ToolNode\n", + "\n", + "tools = [get_albums_by_artist, get_tracks_by_artist, check_for_songs, get_customer_info]\n", + "tool_node = ToolNode(tools)" + ] }, { "cell_type": "code", @@ -345,7 +522,16 @@ "id": "4d5b75c6-73e0-4922-a765-a15be63f869e", "metadata": {}, "outputs": [], - "source": ["def _filter_out_routes(messages):\n ms = []\n for m in messages:\n if _is_tool_call(m):\n if m.name == \"general\":\n continue\n ms.append(m)\n return ms"] + "source": [ + "def _filter_out_routes(messages):\n", + " ms = []\n", + " for m in messages:\n", + " if _is_tool_call(m):\n", + " if m.name == \"general\":\n", + " continue\n", + " ms.append(m)\n", + " return ms" + ] }, { "cell_type": "code", @@ -353,7 +539,13 @@ "id": "fd4dbf98-dbb3-411a-bad6-2bb334072aaf", "metadata": {}, "outputs": [], - "source": ["from functools import partial\n\ngeneral_node = _filter_out_routes | chain | partial(add_name, name=\"general\")\nmusic_node = _filter_out_routes | song_recc_chain | partial(add_name, name=\"music\")\ncustomer_node = _filter_out_routes | customer_chain | partial(add_name, name=\"customer\")"] + "source": [ + "from functools import partial\n", + "\n", + "general_node = _filter_out_routes | chain | partial(add_name, name=\"general\")\n", + "music_node = _filter_out_routes | song_recc_chain | partial(add_name, name=\"music\")\n", + "customer_node = _filter_out_routes | customer_chain | partial(add_name, name=\"customer\")" + ] }, { "cell_type": "code", @@ -361,7 +553,33 @@ "id": "dcade924", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nfrom langgraph.graph import MessageGraph\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\ngraph = MessageGraph()\nnodes = {\n \"general\": \"general\",\n \"music\": \"music\",\n END: END,\n \"tools\": \"tools\",\n \"customer\": \"customer\",\n}\n# Define a new graph\nworkflow = MessageGraph()\nworkflow.add_node(\"general\", general_node)\nworkflow.add_node(\"music\", music_node)\nworkflow.add_node(\"customer\", customer_node)\nworkflow.add_node(\"tools\", tool_node)\nworkflow.add_conditional_edges(\"general\", _route, nodes)\nworkflow.add_conditional_edges(\"tools\", _route, nodes)\nworkflow.add_conditional_edges(\"music\", _route, nodes)\nworkflow.add_conditional_edges(\"customer\", _route, nodes)\nworkflow.add_conditional_edges(START, _route, nodes)\ngraph = workflow.compile()"] + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "from langgraph.graph import MessageGraph\n", + "\n", + "memory = MemorySaver()\n", + "graph = MessageGraph()\n", + "nodes = {\n", + " \"general\": \"general\",\n", + " \"music\": \"music\",\n", + " END: END,\n", + " \"tools\": \"tools\",\n", + " \"customer\": \"customer\",\n", + "}\n", + "# Define a new graph\n", + "workflow = MessageGraph()\n", + "workflow.add_node(\"general\", general_node)\n", + "workflow.add_node(\"music\", music_node)\n", + "workflow.add_node(\"customer\", customer_node)\n", + "workflow.add_node(\"tools\", tool_node)\n", + "workflow.add_conditional_edges(\"general\", _route, nodes)\n", + "workflow.add_conditional_edges(\"tools\", _route, nodes)\n", + "workflow.add_conditional_edges(\"music\", _route, nodes)\n", + "workflow.add_conditional_edges(\"customer\", _route, nodes)\n", + "workflow.add_conditional_edges(START, _route, nodes)\n", + "graph = workflow.compile()" + ] }, { "cell_type": "code", @@ -370,7 +588,7 @@ "metadata": {}, "outputs": [ { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "User (q/Q to quit): what music do you have?\n" @@ -395,7 +613,7 @@ ] }, { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "User (q/Q to quit): how about shakira?\n" @@ -446,7 +664,7 @@ ] }, { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "User (q/Q to quit): hm cool\n" @@ -483,7 +701,7 @@ ] }, { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "User (q/Q to quit): q\n" @@ -497,7 +715,27 @@ ] } ], - "source": ["import uuid\n\nfrom langchain_core.messages import HumanMessage\n\nfrom langgraph.graph.graph import START\n\nhistory = []\nwhile True:\n user = input(\"User (q/Q to quit): \")\n if user in {\"q\", \"Q\"}:\n print(\"AI: Byebye\")\n break\n history.append(HumanMessage(content=user))\n async for output in graph.astream(history):\n for key, value in output.items():\n print(f\"Output from node '{key}':\")\n print(\"---\")\n print(value)\n print(\"\\n---\\n\")"] + "source": [ + "import uuid\n", + "\n", + "from langchain_core.messages import HumanMessage\n", + "\n", + "from langgraph.graph.graph import START\n", + "\n", + "history = []\n", + "while True:\n", + " user = input(\"User (q/Q to quit): \")\n", + " if user in {\"q\", \"Q\"}:\n", + " print(\"AI: Byebye\")\n", + " break\n", + " history.append(HumanMessage(content=user))\n", + " async for output in graph.astream(history):\n", + " for key, value in output.items():\n", + " print(f\"Output from node '{key}':\")\n", + " print(\"---\")\n", + " print(value)\n", + " print(\"\\n---\\n\")" + ] } ], "metadata": { diff --git a/examples/chatbots/imgs/prompt-generator.png b/examples/chatbots/imgs/prompt-generator.png index 6bcc7083f..a4c096a41 100644 Binary files a/examples/chatbots/imgs/prompt-generator.png and b/examples/chatbots/imgs/prompt-generator.png differ diff --git a/examples/chatbots/information-gather-prompting.ipynb b/examples/chatbots/information-gather-prompting.ipynb index ddc4fb309..079478dc7 100644 --- a/examples/chatbots/information-gather-prompting.ipynb +++ b/examples/chatbots/information-gather-prompting.ipynb @@ -176,10 +176,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import START, MessageGraph\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "workflow = MessageGraph()\n", "workflow.add_node(\"info\", chain)\n", "workflow.add_node(\"prompt\", prompt_gen_chain)\n", diff --git a/examples/cloud_examples/img/webhook_results.png b/examples/cloud_examples/img/webhook_results.png index c0fea310a..81b9fe703 100644 Binary files a/examples/cloud_examples/img/webhook_results.png and b/examples/cloud_examples/img/webhook_results.png differ diff --git a/examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb b/examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb index 20011991b..ed97f4fb7 100644 --- a/examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb +++ b/examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb @@ -1,1063 +1,1063 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704", - "metadata": {}, - "source": [ - "# How to convert LangGraph calls to LangGraph Cloud calls" - ] - }, - { - "cell_type": "markdown", - "id": "2e9edff6-38a4-45b8-a612-fb594a226879", - "metadata": {}, - "source": [ - "So you're used to interacting with your graph locally, but now you've deployed it with LangGraph cloud. How do you change all the places in your codebase where you call LangGraph directly to call LangGraph Cloud? This notebook contains side-by-side comparisons so you can easily transition from calling LangGraph to calling LangGraph Cloud." - ] - }, - { - "cell_type": "markdown", - "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "id": "323db423-b644-40bd-9c2d-976a53f602f7", - "metadata": {}, - "source": [ - "We'll be using a simple ReAct agent for this how-to guide. You will also need to set up a project with `agent.py` and `langgraph.json` files. See [quick start](https://langchain-ai.github.io/langgraph/cloud/quick_start/#develop) for setting this up." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph langchain-openai" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdin", - "output_type": "stream", - "text": [ - "OPENAI_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ef5a3ec6-0cd0-4541-ab1b-d63ede22720e", - "metadata": {}, - "outputs": [], - "source": [ - "# this is all that's needed for the agent.py\n", - "from typing import Literal\n", - "from langchain_community.tools.tavily_search import TavilySearchResults\n", - "from langchain_core.runnables import ConfigurableField\n", - "from langchain_core.tools import tool\n", - "from langchain_openai import ChatOpenAI\n", - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "\n", - "@tool\n", - "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", - " \"\"\"Use this to get weather information.\"\"\"\n", - " if city == \"nyc\":\n", - " return \"It might be cloudy in nyc\"\n", - " elif city == \"sf\":\n", - " return \"It's always sunny in sf\"\n", - " else:\n", - " raise AssertionError(\"Unknown city\")\n", - "\n", - "\n", - "tools = [get_weather]\n", - "\n", - "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n", - "graph = create_react_agent(model, tools)" - ] - }, - { - "cell_type": "markdown", - "id": "eb9e138e-cb0e-480a-a32a-d63019720262", - "metadata": {}, - "source": [ - "Now we'll set up the langgraph client. The client assumes the LangGraph Cloud server is running on `localhost:8123`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3ab06b39-7bd1-4611-a37e-9b94e25643d2", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph_sdk import get_client\n", - "\n", - "client = get_client()" - ] - }, - { - "cell_type": "markdown", - "id": "ee4e8d83-e68d-40b8-a128-5c85e0aafc85", - "metadata": {}, - "source": [ - "## Invoking the graph" - ] - }, - { - "cell_type": "markdown", - "id": "ed935900-1ecc-4f39-9dc9-70a92f179d00", - "metadata": {}, - "source": [ - "Below examples show how to mirror `.invoke() / .ainvoke()` methods of LangGraph's `CompiledGraph` runnable, i.e. create a blocking graph execution" - ] - }, - { - "cell_type": "markdown", - "id": "a3e5c63f-d33b-4d27-b90c-205ee30f1197", - "metadata": {}, - "source": [ - "### With LangGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a11bd693-662e-42ad-aa8f-99531f0d091e", - "metadata": {}, - "outputs": [], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", - "invoke_output = await graph.ainvoke(inputs)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9f649fcc-81f0-4c94-9ee9-b2db31bfc5d4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what's the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_GOKlsBY2XKm7pZnmAzJweYDU)\n", - " Call ID: call_GOKlsBY2XKm7pZnmAzJweYDU\n", - " Args:\n", - " city: sf\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] - } - ], - "source": [ - "for m in invoke_output[\"messages\"]:\n", - " m.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "ec78ae0f-c474-472e-8273-658bb56f1476", - "metadata": {}, - "source": [ - "### With LangGraph Cloud" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "72bd6ac6-ace3-43b4-99b8-559b3d2a614f", - "metadata": {}, - "outputs": [], - "source": [ - "# NOTE: We're not specifying the thread here -- this allows us to create a thread just for this run\n", - "wait_output = await client.runs.wait(None, \"agent\", input=inputs)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0d81c783-2c7b-421a-a0f9-752e22039472", - "metadata": {}, - "outputs": [], - "source": [ - "# we'll use this for pretty message formatting\n", - "from langchain_core.messages import convert_to_messages" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "da0f4d54-662c-42b0-ba35-c52f30a2fb1e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what's the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_pQJsT9uLG3nVppN8Dt2OhnFx)\n", - " Call ID: call_pQJsT9uLG3nVppN8Dt2OhnFx\n", - " Args:\n", - " city: sf\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] - } - ], - "source": [ - "for m in convert_to_messages(wait_output[\"messages\"]):\n", - " m.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "129853ea-83e2-4adf-b5e4-60f70c0ccb73", - "metadata": {}, - "source": [ - "## Streaming" - ] - }, - { - "cell_type": "markdown", - "id": "1468248f-f50b-43f3-b566-48ae4a1b643b", - "metadata": {}, - "source": [ - "Below examples show how to mirror `.stream() / .astream()` methods for streaming partial graph execution results. \n", - "Note: LangGraph's `stream_mode=values/updates/debug` behave nearly identically in LangGraph Cloud (with the exception of additional streamed chunks with `metadata` / `end` events types)" - ] - }, - { - "cell_type": "markdown", - "id": "3ed2aae5-d137-4a5b-868b-ed4d551aefaa", - "metadata": {}, - "source": [ - "### With LangGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what's the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_302y9671bqMkMcpLZOWLNAnq)\n", - " Call ID: call_302y9671bqMkMcpLZOWLNAnq\n", - " Args:\n", - " city: sf\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", - "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n", - " chunk[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "5da024a7-1d7e-4212-9251-aaadaba6acbd", - "metadata": {}, - "source": [ - "### With LangGraph Cloud" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a3c02bf7-af0c-47b9-8339-1e303571220e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what's the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_NYVNSiBeF0oTAYnaDrlEAG7a)\n", - " Call ID: call_NYVNSiBeF0oTAYnaDrlEAG7a\n", - " Args:\n", - " city: sf\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", - "async for chunk in client.runs.stream(\n", - " None, \"agent\", input=inputs, stream_mode=\"values\"\n", - "):\n", - " if chunk.event == \"values\":\n", - " messages = convert_to_messages(chunk.data[\"messages\"])\n", - " messages[-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "d693e1b8-bb65-439f-bbbe-b6a12cc26f1a", - "metadata": {}, - "source": [ - "## Persistence" - ] - }, - { - "cell_type": "markdown", - "id": "bcd1a770-8f4a-4c5e-9e54-9851a6acb985", - "metadata": {}, - "source": [ - "In LangGraph, you need to provide a `checkpointer` object when compiling your graph to persist state across interactions with your graph (i.e. threads). In LangGraph Cloud, you don't need to create a checkpointer -- the server already implements one for you. You can also directly manage the threads from a client." - ] - }, - { - "cell_type": "markdown", - "id": "3afcc9e4-e650-497c-be05-1d6a6ad06af3", - "metadata": {}, - "source": [ - "### With LangGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ac145136-410c-41fe-a936-00c8f6d9116f", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.checkpoint.memory import MemorySaver" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5fc8fb7d-989d-42a4-88eb-f037cf64f8d3", - "metadata": {}, - "outputs": [], - "source": [ - "checkpointer = MemorySaver()\n", - "graph_with_memory = create_react_agent(model, tools, checkpointer=checkpointer)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a93f76d3-3d97-435f-8718-bacd56002872", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in NYC might be cloudy.\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in nyc\")]}\n", - "invoke_output = await graph_with_memory.ainvoke(\n", - " inputs, config={\"configurable\": {\"thread_id\": \"1\"}}\n", - ")\n", - "invoke_output[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ba3a2d61-ecdd-4a6e-b275-e7cb5f465def", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable things NYC is known for include:\n", - "\n", - "1. **Statue of Liberty**: A symbol of freedom and democracy.\n", - "2. **Times Square**: Famous for its bright lights, Broadway theaters, and bustling atmosphere.\n", - "3. **Central Park**: A large urban park offering a green oasis in the middle of the city.\n", - "4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.\n", - "5. **Broadway**: Renowned for its world-class theater productions.\n", - "6. **Wall Street**: The financial hub of the United States.\n", - "7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.\n", - "8. **Diverse Cuisine**: A melting pot of culinary experiences from around the world.\n", - "9. **Cultural Diversity**: A rich tapestry of cultures, languages, and traditions.\n", - "10. **Fashion**: A global fashion capital, home to numerous designers and fashion events.\n", - "\n", - "These are just a few highlights, but NYC offers countless other attractions and experiences.\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", - "invoke_output = await graph_with_memory.ainvoke(\n", - " inputs, config={\"configurable\": {\"thread_id\": \"1\"}}\n", - ")\n", - "invoke_output[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "990a9557-894f-4b1d-a9dd-8d089cf6e06b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", - "invoke_output = await graph_with_memory.ainvoke(\n", - " inputs, config={\"configurable\": {\"thread_id\": \"2\"}}\n", - ")\n", - "invoke_output[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "98a998ac-1ff2-4eb7-8ff6-5a513c098807", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'v': 1,\n", - " 'ts': '2024-06-22T02:31:49.722569+00:00',\n", - " 'id': '1ef303f9-4149-6b56-8001-a80d1e3c9dc6',\n", - " 'channel_values': {'messages': [HumanMessage(content=\"what's it known for?\", id='ea0d1672-05e9-4d77-9dff-b33bd5c824e7'),\n", - " AIMessage(content='Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?', response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 57, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-f0381dc0-d891-4203-8f77-3155ba17998c-0', usage_metadata={'input_tokens': 57, 'output_tokens': 28, 'total_tokens': 85})],\n", - " 'agent': 'agent'},\n", - " 'channel_versions': {'__start__': 2,\n", - " 'messages': 3,\n", - " 'start:agent': 3,\n", - " 'agent': 3},\n", - " 'versions_seen': {'__start__': {'__start__': 1},\n", - " 'agent': {'start:agent': 2},\n", - " 'tools': {}},\n", - " 'pending_sends': []}" + "cell_type": "markdown", + "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704", + "metadata": {}, + "source": [ + "# How to convert LangGraph calls to LangGraph Cloud calls" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get the state of the thread\n", - "checkpointer.get({\"configurable\": {\"thread_id\": \"2\"}})" - ] - }, - { - "cell_type": "markdown", - "id": "bd07ea78-12e9-475a-84e9-0d34f99c6024", - "metadata": {}, - "source": [ - "### With LangGraph Cloud\n", - "\n", - "Let's now reproduce the same using LangGraph Cloud. Note that instead of using a checkpointer we just create a new thread on the backend and pass the ID to the API" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "90b312d3-4b51-4953-8c78-8263a90b397a", - "metadata": {}, - "outputs": [], - "source": [ - "thread = await client.threads.create()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3e523086-29ab-4b21-b762-21136d32e6fa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in NYC might be cloudy.\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in nyc\")]}\n", - "wait_output = await client.runs.wait(thread[\"thread_id\"], \"agent\", input=inputs)\n", - "convert_to_messages(wait_output[\"messages\"])[-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f430c8ec-782c-4003-9e30-0736cbdd37ce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable features include:\n", - "\n", - "1. **Statue of Liberty**: A symbol of freedom and democracy.\n", - "2. **Times Square**: Known for its bright lights, Broadway theaters, and bustling atmosphere.\n", - "3. **Central Park**: A large urban park offering a natural retreat in the middle of the city.\n", - "4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.\n", - "5. **Broadway**: Famous for its world-class theater productions.\n", - "6. **Wall Street**: The financial hub of the United States.\n", - "7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.\n", - "8. **Diverse Cuisine**: A melting pot of culinary experiences from around the world.\n", - "9. **Cultural Diversity**: A rich tapestry of cultures, languages, and traditions.\n", - "10. **Fashion**: A global fashion capital, home to New York Fashion Week.\n", - "\n", - "These are just a few highlights of what makes NYC a unique and vibrant city.\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", - "wait_output = await client.runs.wait(thread[\"thread_id\"], \"agent\", input=inputs)\n", - "convert_to_messages(wait_output[\"messages\"])[-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "cb34efef-805d-455c-be3e-e2234d97b7cf", - "metadata": {}, - "outputs": [], - "source": [ - "thread = await client.threads.create()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "7628e108-338b-4eaf-9d57-defc2c7e2b46", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", - "wait_output = await client.runs.wait(thread[\"thread_id\"], \"agent\", input=inputs)\n", - "convert_to_messages(wait_output[\"messages\"])[-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "22a3f9e6-a550-4074-95eb-be3866b77718", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'values': {'messages': [{'content': \"what's it known for?\",\n", - " 'additional_kwargs': {},\n", - " 'response_metadata': {},\n", - " 'type': 'human',\n", - " 'name': None,\n", - " 'id': 'b62078f1-7c44-4a0e-b7b0-05e475ae3188',\n", - " 'example': False},\n", - " {'content': 'Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?',\n", - " 'additional_kwargs': {},\n", - " 'response_metadata': {'finish_reason': 'stop'},\n", - " 'type': 'ai',\n", - " 'name': None,\n", - " 'id': 'run-502c6cf3-d584-4e31-98a6-5e59f1d2a72f',\n", - " 'example': False,\n", - " 'tool_calls': [],\n", - " 'invalid_tool_calls': [],\n", - " 'usage_metadata': None}]},\n", - " 'next': [],\n", - " 'config': {'configurable': {'thread_id': 'fcff410c-9adb-416f-a7ce-09b230afcac9',\n", - " 'thread_ts': '1ef303f9-7d8e-6d0a-8001-2b4ce14235da'}},\n", - " 'metadata': {'step': 1,\n", - " 'run_id': '1ef303f9-73e6-6c6b-b407-39938d3dfd7e',\n", - " 'source': 'loop',\n", - " 'writes': {'agent': {'messages': [{'id': 'run-502c6cf3-d584-4e31-98a6-5e59f1d2a72f',\n", - " 'name': None,\n", - " 'type': 'ai',\n", - " 'content': 'Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?',\n", - " 'example': False,\n", - " 'tool_calls': [],\n", - " 'usage_metadata': None,\n", - " 'additional_kwargs': {},\n", - " 'response_metadata': {'finish_reason': 'stop'},\n", - " 'invalid_tool_calls': []}]}},\n", - " 'user_id': '',\n", - " 'graph_id': 'agent',\n", - " 'thread_id': 'fcff410c-9adb-416f-a7ce-09b230afcac9',\n", - " 'created_by': 'system',\n", - " 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'},\n", - " 'created_at': '2024-06-22T02:31:56.042330+00:00',\n", - " 'parent_config': {'configurable': {'thread_id': 'fcff410c-9adb-416f-a7ce-09b230afcac9',\n", - " 'thread_ts': '1ef303f9-7400-6e2c-8000-e8d5075bfa2a'}}}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get the state of the thread\n", - "await client.threads.get_state(thread[\"thread_id\"])" - ] - }, - { - "cell_type": "markdown", - "id": "6b0d8ec5-316f-48e5-bebf-8d66c8dbd450", - "metadata": {}, - "source": [ - "## Breakpoints\n", - "\n", - "### With LangGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b3722e75-b9f2-4a55-aae3-f6829a7a929e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what's the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_cYp3BijeW2JNQ9RqJRdkrbMu)\n", - " Call ID: call_cYp3BijeW2JNQ9RqJRdkrbMu\n", - " Args:\n", - " city: sf\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", - "async for chunk in graph_with_memory.astream(\n", - " inputs,\n", - " stream_mode=\"values\",\n", - " interrupt_before=[\"tools\"],\n", - " config={\"configurable\": {\"thread_id\": \"3\"}},\n", - "):\n", - " chunk[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "6a58a513-7adc-4523-9145-6777f20521e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is sunny!\n" - ] - } - ], - "source": [ - "async for chunk in graph_with_memory.astream(\n", - " None,\n", - " stream_mode=\"values\",\n", - " interrupt_before=[\"tools\"],\n", - " config={\"configurable\": {\"thread_id\": \"3\"}},\n", - "):\n", - " chunk[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "a84f11dd-e6db-4fc7-bde6-67db5bc01d0f", - "metadata": {}, - "source": [ - "### With LangGraph Cloud" - ] - }, - { - "cell_type": "markdown", - "id": "addd1bf8-d0da-40c2-9913-e145deed6b6d", - "metadata": {}, - "source": [ - "Similar to the persistence example, we need to create a thread so we can persist state and continue from the breakpoint." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "96591cf8-98fc-4fa0-a03a-29e18e672126", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what's the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_MVQEJtPYAj1nJ7J6YaCeLX8a)\n", - " Call ID: call_MVQEJtPYAj1nJ7J6YaCeLX8a\n", - " Args:\n", - " city: sf\n" - ] - } - ], - "source": [ - "thread = await client.threads.create()\n", - "\n", - "async for chunk in client.runs.stream(\n", - " thread[\"thread_id\"],\n", - " \"agent\",\n", - " input=inputs,\n", - " stream_mode=\"values\",\n", - " interrupt_before=[\"tools\"],\n", - "):\n", - " if chunk.event == \"values\":\n", - " messages = convert_to_messages(chunk.data[\"messages\"])\n", - " messages[-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "bb7f74bd-7ce1-4cd3-9203-2a8aed7b8620", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] - } - ], - "source": [ - "async for chunk in client.runs.stream(\n", - " thread[\"thread_id\"],\n", - " \"agent\",\n", - " input=None,\n", - " stream_mode=\"values\",\n", - " interrupt_before=[\"tools\"],\n", - "):\n", - " if chunk.event == \"values\":\n", - " messages = convert_to_messages(chunk.data[\"messages\"])\n", - " messages[-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "6af293ee-7866-4326-ba17-e3ffbb0c96c7", - "metadata": {}, - "source": [ - "## Steaming events" - ] - }, - { - "cell_type": "markdown", - "id": "cb4072c9-775e-4bf5-8a1a-fb822e6de9d7", - "metadata": {}, - "source": [ - "For streaming events, in LangGraph you need to use `.astream_events` method on the `CompiledGraph`. In LangGraph Cloud this is done via passing `stream_mode=\"events\"`" - ] - }, - { - "cell_type": "markdown", - "id": "4a158573-240a-44a3-b0ef-0cf9334042f2", - "metadata": {}, - "source": [ - "### With LangGraph" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "94b815e4-1dd2-4999-9e73-6e29836d9160", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/vadymbarda/.virtualenvs/langgraph-example-dev/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n", - " warn_beta(\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "Invalid Tool Calls:\n", - " get_weather (call_dsr61w9qcahi8CC7LV2S29O3)\n", - " Call ID: call_dsr61w9qcahi8CC7LV2S29O3\n", - " Args:\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "Tool Calls:\n", - " (None)\n", - " Call ID: None\n", - " Args:\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "Invalid Tool Calls:\n", - " None (None)\n", - " Call ID: None\n", - " Args:\n", - " city\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "Invalid Tool Calls:\n", - " None (None)\n", - " Call ID: None\n", - " Args:\n", - " \":\"\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "Invalid Tool Calls:\n", - " None (None)\n", - " Call ID: None\n", - " Args:\n", - " sf\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "Invalid Tool Calls:\n", - " None (None)\n", - " Call ID: None\n", - " Args:\n", - " \"}\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - "The\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " weather\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " in\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " San\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " Francisco\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " is\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " currently\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " sunny\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - ".\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " Enjoy\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " the\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - " sunshine\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", - "\n", - "!\n", - "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n" - ] - } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", - "async for chunk in graph.astream_events(inputs, version=\"v2\"):\n", - " if chunk[\"event\"] == \"on_chat_model_stream\":\n", - " chunk[\"data\"][\"chunk\"].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "08996d90-a3ff-4655-9763-1dd4971344d4", - "metadata": {}, - "source": [ - "### With LangGraph Clound" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "547bfcd2-01fe-4e7c-8734-0d02beb2c36e", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "2e9edff6-38a4-45b8-a612-fb594a226879", + "metadata": {}, + "source": [ + "So you're used to interacting with your graph locally, but now you've deployed it with LangGraph cloud. How do you change all the places in your codebase where you call LangGraph directly to call LangGraph Cloud? This notebook contains side-by-side comparisons so you can easily transition from calling LangGraph to calling LangGraph Cloud." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'function': {'arguments': '', 'name': 'get_weather'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'get_weather', 'args': '', 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': 'get_weather', 'args': '', 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'index': 0}]}\n", - "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [{'name': '', 'args': {}, 'id': None}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '{\"', 'id': None, 'index': 0}]}\n", - 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"{'content': '!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", - "{'content': '', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop'}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n" - ] + "cell_type": "markdown", + "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "id": "323db423-b644-40bd-9c2d-976a53f602f7", + "metadata": {}, + "source": [ + "We'll be using a simple ReAct agent for this how-to guide. You will also need to set up a project with `agent.py` and `langgraph.json` files. See [quick start](https://langchain-ai.github.io/langgraph/cloud/quick_start/#develop) for setting this up." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OPENAI_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef5a3ec6-0cd0-4541-ab1b-d63ede22720e", + "metadata": {}, + "outputs": [], + "source": [ + "# this is all that's needed for the agent.py\n", + "from typing import Literal\n", + "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_core.runnables import ConfigurableField\n", + "from langchain_core.tools import tool\n", + "from langchain_openai import ChatOpenAI\n", + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "\n", + "@tool\n", + "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", + " \"\"\"Use this to get weather information.\"\"\"\n", + " if city == \"nyc\":\n", + " return \"It might be cloudy in nyc\"\n", + " elif city == \"sf\":\n", + " return \"It's always sunny in sf\"\n", + " else:\n", + " raise AssertionError(\"Unknown city\")\n", + "\n", + "\n", + "tools = [get_weather]\n", + "\n", + "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n", + "graph = create_react_agent(model, tools)" + ] + }, + { + "cell_type": "markdown", + "id": "eb9e138e-cb0e-480a-a32a-d63019720262", + "metadata": {}, + "source": [ + "Now we'll set up the langgraph client. The client assumes the LangGraph Cloud server is running on `localhost:8123`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3ab06b39-7bd1-4611-a37e-9b94e25643d2", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph_sdk import get_client\n", + "\n", + "client = get_client()" + ] + }, + { + "cell_type": "markdown", + "id": "ee4e8d83-e68d-40b8-a128-5c85e0aafc85", + "metadata": {}, + "source": [ + "## Invoking the graph" + ] + }, + { + "cell_type": "markdown", + "id": "ed935900-1ecc-4f39-9dc9-70a92f179d00", + "metadata": {}, + "source": [ + "Below examples show how to mirror `.invoke() / .ainvoke()` methods of LangGraph's `CompiledGraph` runnable, i.e. create a blocking graph execution" + ] + }, + { + "cell_type": "markdown", + "id": "a3e5c63f-d33b-4d27-b90c-205ee30f1197", + "metadata": {}, + "source": [ + "### With LangGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a11bd693-662e-42ad-aa8f-99531f0d091e", + "metadata": {}, + "outputs": [], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", + "invoke_output = await graph.ainvoke(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9f649fcc-81f0-4c94-9ee9-b2db31bfc5d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what's the weather in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_GOKlsBY2XKm7pZnmAzJweYDU)\n", + " Call ID: call_GOKlsBY2XKm7pZnmAzJweYDU\n", + " Args:\n", + " city: sf\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is currently sunny.\n" + ] + } + ], + "source": [ + "for m in invoke_output[\"messages\"]:\n", + " m.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "ec78ae0f-c474-472e-8273-658bb56f1476", + "metadata": {}, + "source": [ + "### With LangGraph Cloud" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "72bd6ac6-ace3-43b4-99b8-559b3d2a614f", + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: We're not specifying the thread here -- this allows us to create a thread just for this run\n", + "wait_output = await client.runs.wait(None, \"agent\", input=inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0d81c783-2c7b-421a-a0f9-752e22039472", + "metadata": {}, + "outputs": [], + "source": [ + "# we'll use this for pretty message formatting\n", + "from langchain_core.messages import convert_to_messages" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "da0f4d54-662c-42b0-ba35-c52f30a2fb1e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what's the weather in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_pQJsT9uLG3nVppN8Dt2OhnFx)\n", + " Call ID: call_pQJsT9uLG3nVppN8Dt2OhnFx\n", + " Args:\n", + " city: sf\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is currently sunny.\n" + ] + } + ], + "source": [ + "for m in convert_to_messages(wait_output[\"messages\"]):\n", + " m.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "129853ea-83e2-4adf-b5e4-60f70c0ccb73", + "metadata": {}, + "source": [ + "## Streaming" + ] + }, + { + "cell_type": "markdown", + "id": "1468248f-f50b-43f3-b566-48ae4a1b643b", + "metadata": {}, + "source": [ + "Below examples show how to mirror `.stream() / .astream()` methods for streaming partial graph execution results. \n", + "Note: LangGraph's `stream_mode=values/updates/debug` behave nearly identically in LangGraph Cloud (with the exception of additional streamed chunks with `metadata` / `end` events types)" + ] + }, + { + "cell_type": "markdown", + "id": "3ed2aae5-d137-4a5b-868b-ed4d551aefaa", + "metadata": {}, + "source": [ + "### With LangGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what's the weather in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_302y9671bqMkMcpLZOWLNAnq)\n", + " Call ID: call_302y9671bqMkMcpLZOWLNAnq\n", + " Args:\n", + " city: sf\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is currently sunny.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", + "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n", + " chunk[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "5da024a7-1d7e-4212-9251-aaadaba6acbd", + "metadata": {}, + "source": [ + "### With LangGraph Cloud" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a3c02bf7-af0c-47b9-8339-1e303571220e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what's the weather in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_NYVNSiBeF0oTAYnaDrlEAG7a)\n", + " Call ID: call_NYVNSiBeF0oTAYnaDrlEAG7a\n", + " Args:\n", + " city: sf\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is currently sunny.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", + "async for chunk in client.runs.stream(\n", + " None, \"agent\", input=inputs, stream_mode=\"values\"\n", + "):\n", + " if chunk.event == \"values\":\n", + " messages = convert_to_messages(chunk.data[\"messages\"])\n", + " messages[-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "d693e1b8-bb65-439f-bbbe-b6a12cc26f1a", + "metadata": {}, + "source": [ + "## Persistence" + ] + }, + { + "cell_type": "markdown", + "id": "bcd1a770-8f4a-4c5e-9e54-9851a6acb985", + "metadata": {}, + "source": [ + "In LangGraph, you need to provide a `checkpointer` object when compiling your graph to persist state across interactions with your graph (i.e. threads). In LangGraph Cloud, you don't need to create a checkpointer -- the server already implements one for you. You can also directly manage the threads from a client." + ] + }, + { + "cell_type": "markdown", + "id": "3afcc9e4-e650-497c-be05-1d6a6ad06af3", + "metadata": {}, + "source": [ + "### With LangGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ac145136-410c-41fe-a936-00c8f6d9116f", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5fc8fb7d-989d-42a4-88eb-f037cf64f8d3", + "metadata": {}, + "outputs": [], + "source": [ + "checkpointer = MemorySaver()\n", + "graph_with_memory = create_react_agent(model, tools, checkpointer=checkpointer)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a93f76d3-3d97-435f-8718-bacd56002872", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in NYC might be cloudy.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in nyc\")]}\n", + "invoke_output = await graph_with_memory.ainvoke(\n", + " inputs, config={\"configurable\": {\"thread_id\": \"1\"}}\n", + ")\n", + "invoke_output[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ba3a2d61-ecdd-4a6e-b275-e7cb5f465def", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable things NYC is known for include:\n", + "\n", + "1. **Statue of Liberty**: A symbol of freedom and democracy.\n", + "2. **Times Square**: Famous for its bright lights, Broadway theaters, and bustling atmosphere.\n", + "3. **Central Park**: A large urban park offering a green oasis in the middle of the city.\n", + "4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.\n", + "5. **Broadway**: Renowned for its world-class theater productions.\n", + "6. **Wall Street**: The financial hub of the United States.\n", + "7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.\n", + "8. **Diverse Cuisine**: A melting pot of culinary experiences from around the world.\n", + "9. **Cultural Diversity**: A rich tapestry of cultures, languages, and traditions.\n", + "10. **Fashion**: A global fashion capital, home to numerous designers and fashion events.\n", + "\n", + "These are just a few highlights, but NYC offers countless other attractions and experiences.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", + "invoke_output = await graph_with_memory.ainvoke(\n", + " inputs, config={\"configurable\": {\"thread_id\": \"1\"}}\n", + ")\n", + "invoke_output[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "990a9557-894f-4b1d-a9dd-8d089cf6e06b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", + "invoke_output = await graph_with_memory.ainvoke(\n", + " inputs, config={\"configurable\": {\"thread_id\": \"2\"}}\n", + ")\n", + "invoke_output[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "98a998ac-1ff2-4eb7-8ff6-5a513c098807", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'v': 1,\n", + " 'ts': '2024-06-22T02:31:49.722569+00:00',\n", + " 'id': '1ef303f9-4149-6b56-8001-a80d1e3c9dc6',\n", + " 'channel_values': {'messages': [HumanMessage(content=\"what's it known for?\", id='ea0d1672-05e9-4d77-9dff-b33bd5c824e7'),\n", + " AIMessage(content='Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?', response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 57, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-f0381dc0-d891-4203-8f77-3155ba17998c-0', usage_metadata={'input_tokens': 57, 'output_tokens': 28, 'total_tokens': 85})],\n", + " 'agent': 'agent'},\n", + " 'channel_versions': {'__start__': 2,\n", + " 'messages': 3,\n", + " 'start:agent': 3,\n", + " 'agent': 3},\n", + " 'versions_seen': {'__start__': {'__start__': 1},\n", + " 'agent': {'start:agent': 2},\n", + " 'tools': {}},\n", + " 'pending_sends': []}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the state of the thread\n", + "checkpointer.get({\"configurable\": {\"thread_id\": \"2\"}})" + ] + }, + { + "cell_type": "markdown", + "id": "bd07ea78-12e9-475a-84e9-0d34f99c6024", + "metadata": {}, + "source": [ + "### With LangGraph Cloud\n", + "\n", + "Let's now reproduce the same using LangGraph Cloud. Note that instead of using a checkpointer we just create a new thread on the backend and pass the ID to the API" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "90b312d3-4b51-4953-8c78-8263a90b397a", + "metadata": {}, + "outputs": [], + "source": [ + "thread = await client.threads.create()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3e523086-29ab-4b21-b762-21136d32e6fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in NYC might be cloudy.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in nyc\")]}\n", + "wait_output = await client.runs.wait(thread[\"thread_id\"], \"agent\", input=inputs)\n", + "convert_to_messages(wait_output[\"messages\"])[-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f430c8ec-782c-4003-9e30-0736cbdd37ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable features include:\n", + "\n", + "1. **Statue of Liberty**: A symbol of freedom and democracy.\n", + "2. **Times Square**: Known for its bright lights, Broadway theaters, and bustling atmosphere.\n", + "3. **Central Park**: A large urban park offering a natural retreat in the middle of the city.\n", + "4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.\n", + "5. **Broadway**: Famous for its world-class theater productions.\n", + "6. **Wall Street**: The financial hub of the United States.\n", + "7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.\n", + "8. **Diverse Cuisine**: A melting pot of culinary experiences from around the world.\n", + "9. **Cultural Diversity**: A rich tapestry of cultures, languages, and traditions.\n", + "10. **Fashion**: A global fashion capital, home to New York Fashion Week.\n", + "\n", + "These are just a few highlights of what makes NYC a unique and vibrant city.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", + "wait_output = await client.runs.wait(thread[\"thread_id\"], \"agent\", input=inputs)\n", + "convert_to_messages(wait_output[\"messages\"])[-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cb34efef-805d-455c-be3e-e2234d97b7cf", + "metadata": {}, + "outputs": [], + "source": [ + "thread = await client.threads.create()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7628e108-338b-4eaf-9d57-defc2c7e2b46", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's it known for?\")]}\n", + "wait_output = await client.runs.wait(thread[\"thread_id\"], \"agent\", input=inputs)\n", + "convert_to_messages(wait_output[\"messages\"])[-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "22a3f9e6-a550-4074-95eb-be3866b77718", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'values': {'messages': [{'content': \"what's it known for?\",\n", + " 'additional_kwargs': {},\n", + " 'response_metadata': {},\n", + " 'type': 'human',\n", + " 'name': None,\n", + " 'id': 'b62078f1-7c44-4a0e-b7b0-05e475ae3188',\n", + " 'example': False},\n", + " {'content': 'Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?',\n", + " 'additional_kwargs': {},\n", + " 'response_metadata': {'finish_reason': 'stop'},\n", + " 'type': 'ai',\n", + " 'name': None,\n", + " 'id': 'run-502c6cf3-d584-4e31-98a6-5e59f1d2a72f',\n", + " 'example': False,\n", + " 'tool_calls': [],\n", + " 'invalid_tool_calls': [],\n", + " 'usage_metadata': None}]},\n", + " 'next': [],\n", + " 'config': {'configurable': {'thread_id': 'fcff410c-9adb-416f-a7ce-09b230afcac9',\n", + " 'thread_ts': '1ef303f9-7d8e-6d0a-8001-2b4ce14235da'}},\n", + " 'metadata': {'step': 1,\n", + " 'run_id': '1ef303f9-73e6-6c6b-b407-39938d3dfd7e',\n", + " 'source': 'loop',\n", + " 'writes': {'agent': {'messages': [{'id': 'run-502c6cf3-d584-4e31-98a6-5e59f1d2a72f',\n", + " 'name': None,\n", + " 'type': 'ai',\n", + " 'content': 'Could you please specify what \"it\" refers to? Are you asking about a specific city, person, object, or something else?',\n", + " 'example': False,\n", + " 'tool_calls': [],\n", + " 'usage_metadata': None,\n", + " 'additional_kwargs': {},\n", + " 'response_metadata': {'finish_reason': 'stop'},\n", + " 'invalid_tool_calls': []}]}},\n", + " 'user_id': '',\n", + " 'graph_id': 'agent',\n", + " 'thread_id': 'fcff410c-9adb-416f-a7ce-09b230afcac9',\n", + " 'created_by': 'system',\n", + " 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'},\n", + " 'created_at': '2024-06-22T02:31:56.042330+00:00',\n", + " 'parent_config': {'configurable': {'thread_id': 'fcff410c-9adb-416f-a7ce-09b230afcac9',\n", + " 'thread_ts': '1ef303f9-7400-6e2c-8000-e8d5075bfa2a'}}}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the state of the thread\n", + "await client.threads.get_state(thread[\"thread_id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "6b0d8ec5-316f-48e5-bebf-8d66c8dbd450", + "metadata": {}, + "source": [ + "## Breakpoints\n", + "\n", + "### With LangGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b3722e75-b9f2-4a55-aae3-f6829a7a929e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what's the weather in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_cYp3BijeW2JNQ9RqJRdkrbMu)\n", + " Call ID: call_cYp3BijeW2JNQ9RqJRdkrbMu\n", + " Args:\n", + " city: sf\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", + "async for chunk in graph_with_memory.astream(\n", + " inputs,\n", + " stream_mode=\"values\",\n", + " interrupt_before=[\"tools\"],\n", + " config={\"configurable\": {\"thread_id\": \"3\"}},\n", + "):\n", + " chunk[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6a58a513-7adc-4523-9145-6777f20521e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is sunny!\n" + ] + } + ], + "source": [ + "async for chunk in graph_with_memory.astream(\n", + " None,\n", + " stream_mode=\"values\",\n", + " interrupt_before=[\"tools\"],\n", + " config={\"configurable\": {\"thread_id\": \"3\"}},\n", + "):\n", + " chunk[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "a84f11dd-e6db-4fc7-bde6-67db5bc01d0f", + "metadata": {}, + "source": [ + "### With LangGraph Cloud" + ] + }, + { + "cell_type": "markdown", + "id": "addd1bf8-d0da-40c2-9913-e145deed6b6d", + "metadata": {}, + "source": [ + "Similar to the persistence example, we need to create a thread so we can persist state and continue from the breakpoint." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "96591cf8-98fc-4fa0-a03a-29e18e672126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what's the weather in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_MVQEJtPYAj1nJ7J6YaCeLX8a)\n", + " Call ID: call_MVQEJtPYAj1nJ7J6YaCeLX8a\n", + " Args:\n", + " city: sf\n" + ] + } + ], + "source": [ + "thread = await client.threads.create()\n", + "\n", + "async for chunk in client.runs.stream(\n", + " thread[\"thread_id\"],\n", + " \"agent\",\n", + " input=inputs,\n", + " stream_mode=\"values\",\n", + " interrupt_before=[\"tools\"],\n", + "):\n", + " if chunk.event == \"values\":\n", + " messages = convert_to_messages(chunk.data[\"messages\"])\n", + " messages[-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bb7f74bd-7ce1-4cd3-9203-2a8aed7b8620", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is currently sunny.\n" + ] + } + ], + "source": [ + "async for chunk in client.runs.stream(\n", + " thread[\"thread_id\"],\n", + " \"agent\",\n", + " input=None,\n", + " stream_mode=\"values\",\n", + " interrupt_before=[\"tools\"],\n", + "):\n", + " if chunk.event == \"values\":\n", + " messages = convert_to_messages(chunk.data[\"messages\"])\n", + " messages[-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "6af293ee-7866-4326-ba17-e3ffbb0c96c7", + "metadata": {}, + "source": [ + "## Steaming events" + ] + }, + { + "cell_type": "markdown", + "id": "cb4072c9-775e-4bf5-8a1a-fb822e6de9d7", + "metadata": {}, + "source": [ + "For streaming events, in LangGraph you need to use `.astream_events` method on the `CompiledGraph`. In LangGraph Cloud this is done via passing `stream_mode=\"events\"`" + ] + }, + { + "cell_type": "markdown", + "id": "4a158573-240a-44a3-b0ef-0cf9334042f2", + "metadata": {}, + "source": [ + "### With LangGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "94b815e4-1dd2-4999-9e73-6e29836d9160", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vadymbarda/.virtualenvs/langgraph-example-dev/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n", + " warn_beta(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "Invalid Tool Calls:\n", + " get_weather (call_dsr61w9qcahi8CC7LV2S29O3)\n", + " Call ID: call_dsr61w9qcahi8CC7LV2S29O3\n", + " Args:\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "Tool Calls:\n", + " (None)\n", + " Call ID: None\n", + " Args:\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "Invalid Tool Calls:\n", + " None (None)\n", + " Call ID: None\n", + " Args:\n", + " city\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "Invalid Tool Calls:\n", + " None (None)\n", + " Call ID: None\n", + " Args:\n", + " \":\"\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "Invalid Tool Calls:\n", + " None (None)\n", + " Call ID: None\n", + " Args:\n", + " sf\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "Invalid Tool Calls:\n", + " None (None)\n", + " Call ID: None\n", + " Args:\n", + " \"}\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + "The\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " weather\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " in\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " San\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " Francisco\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " is\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " currently\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " sunny\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + ".\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " Enjoy\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " the\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + " sunshine\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n", + "\n", + "!\n", + "============================\u001b[1m Aimessagechunk Message \u001b[0m============================\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", + "async for chunk in graph.astream_events(inputs, version=\"v2\"):\n", + " if chunk[\"event\"] == \"on_chat_model_stream\":\n", + " chunk[\"data\"][\"chunk\"].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "08996d90-a3ff-4655-9763-1dd4971344d4", + "metadata": {}, + "source": [ + "### With LangGraph Cloud" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "547bfcd2-01fe-4e7c-8734-0d02beb2c36e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'function': {'arguments': '', 'name': 'get_weather'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'get_weather', 'args': '', 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': 'get_weather', 'args': '', 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'index': 0}]}\n", + "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [{'name': '', 'args': {}, 'id': None}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '{\"', 'id': None, 'index': 0}]}\n", + "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'city', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'city', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'city', 'id': None, 'index': 0}]}\n", + "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '\":\"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '\":\"', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '\":\"', 'id': None, 'index': 0}]}\n", + "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'sf', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'sf', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'sf', 'id': None, 'index': 0}]}\n", + "{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '\"}', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '\"}', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '\"}', 'id': None, 'index': 0}]}\n", + "{'content': '', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'tool_calls'}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': '', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': 'The', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' weather', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' in', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' San', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' Francisco', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' is', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' currently', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' sunny', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': '.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' Enjoy', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' the', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': ' sunshine', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': '!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n", + "{'content': '', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop'}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", + "async for chunk in client.runs.stream(\n", + " None, \"agent\", input=inputs, stream_mode=\"events\"\n", + "):\n", + " if chunk.event == \"events\" and chunk.data[\"event\"] == \"on_chat_model_stream\":\n", + " print(chunk.data[\"data\"][\"chunk\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" } - ], - "source": [ - "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n", - "async for chunk in client.runs.stream(\n", - " None, \"agent\", input=inputs, stream_mode=\"events\"\n", - "):\n", - " if chunk.event == \"events\" and chunk.data[\"event\"] == \"on_chat_model_stream\":\n", - " print(chunk.data[\"data\"][\"chunk\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/code_assistant/langgraph_code_assistant_mistral.ipynb b/examples/code_assistant/langgraph_code_assistant_mistral.ipynb index fe947f11b..8d873d68c 100644 --- a/examples/code_assistant/langgraph_code_assistant_mistral.ipynb +++ b/examples/code_assistant/langgraph_code_assistant_mistral.ipynb @@ -154,7 +154,7 @@ "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\ngraph = builder.compile(checkpointer=memory)"] + "source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = MemorySaver()\ngraph = builder.compile(checkpointer=memory)"] }, { "cell_type": "code", diff --git a/examples/create-react-agent-hitl.ipynb b/examples/create-react-agent-hitl.ipynb index 0d9e46cc1..39392f205 100644 --- a/examples/create-react-agent-hitl.ipynb +++ b/examples/create-react-agent-hitl.ipynb @@ -1,247 +1,247 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4", - "metadata": {}, - "source": [ - "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n", - "\n", - "This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n", - "\n", - "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work." - ] - }, - { - "cell_type": "markdown", - "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a213e11a-5c62-4ddb-a707-490d91add383", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph langchain-openai" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "23a1885c-04ab-4750-aefa-105891fddf3e", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "OPENAI_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")\n", - "\n", - "# Recommended\n", - "_set_env(\"LANGCHAIN_API_KEY\")\n", - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", - "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\"" - ] - }, - { - "cell_type": "markdown", - "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82", - "metadata": {}, - "source": [ - "## Code" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7a154152-973e-4b5d-aa13-48c617744a4c", - "metadata": {}, - "outputs": [], - "source": [ - "# First we initialize the model we want to use.\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", - "\n", - "\n", - "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n", - "\n", - "from typing import Literal\n", - "\n", - "from langchain_core.tools import tool\n", - "\n", - "\n", - "@tool\n", - "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", - " \"\"\"Use this to get weather information.\"\"\"\n", - " if city == \"nyc\":\n", - " return \"It might be cloudy in nyc\"\n", - " elif city == \"sf\":\n", - " return \"It's always sunny in sf\"\n", - " else:\n", - " raise AssertionError(\"Unknown city\")\n", - "\n", - "\n", - "tools = [get_weather]\n", - "\n", - "# We need a checkpointer to enable human-in-the-loop patterns\n", - "from langgraph.checkpoint import MemorySaver\n", - "\n", - "memory = MemorySaver()\n", - "\n", - "# Define the graph\n", - "\n", - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "graph = create_react_agent(\n", - " model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "00407425-506d-4ffd-9c86-987921d8c844", - "metadata": {}, - "source": [ - "## Usage\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28", - "metadata": {}, - "outputs": [], - "source": [ - "def print_stream(stream):\n", - " for s in stream:\n", - " message = s[\"messages\"][-1]\n", - " if isinstance(message, tuple):\n", - " print(message)\n", - " else:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4", + "metadata": {}, + "source": [ + "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n", + "\n", + "This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n", + "\n", + "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "What's the weather in SF?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_0OMmuTLec9t8kxMVkllZCSxo)\n", - " Call ID: call_0OMmuTLec9t8kxMVkllZCSxo\n", - " Args:\n", - " city: sf\n" - ] - } - ], - "source": [ - "config = {\"configurable\": {\"thread_id\": \"42\"}}\n", - "inputs = {\"messages\": [(\"user\", \"What's the weather in SF?\")]}\n", - "\n", - "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247", + "metadata": {}, + "source": [ + "## Setup" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Next step: ('tools',)\n" - ] - } - ], - "source": [ - "snapshot = graph.get_state(config)\n", - "print(\"Next step: \", snapshot.next)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "83148e08-63e8-49e5-a08b-02dc907bed1d", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "id": "a213e11a-5c62-4ddb-a707-490d91add383", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-openai" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] + "cell_type": "code", + "execution_count": 2, + "id": "23a1885c-04ab-4750-aefa-105891fddf3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OPENAI_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")\n", + "\n", + "# Recommended\n", + "_set_env(\"LANGCHAIN_API_KEY\")\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\"" + ] + }, + { + "cell_type": "markdown", + "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82", + "metadata": {}, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a154152-973e-4b5d-aa13-48c617744a4c", + "metadata": {}, + "outputs": [], + "source": [ + "# First we initialize the model we want to use.\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", + "\n", + "\n", + "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n", + "\n", + "from typing import Literal\n", + "\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", + " \"\"\"Use this to get weather information.\"\"\"\n", + " if city == \"nyc\":\n", + " return \"It might be cloudy in nyc\"\n", + " elif city == \"sf\":\n", + " return \"It's always sunny in sf\"\n", + " else:\n", + " raise AssertionError(\"Unknown city\")\n", + "\n", + "\n", + "tools = [get_weather]\n", + "\n", + "# We need a checkpointer to enable human-in-the-loop patterns\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "memory = MemorySaver()\n", + "\n", + "# Define the graph\n", + "\n", + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "graph = create_react_agent(\n", + " model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "00407425-506d-4ffd-9c86-987921d8c844", + "metadata": {}, + "source": [ + "## Usage\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28", + "metadata": {}, + "outputs": [], + "source": [ + "def print_stream(stream):\n", + " for s in stream:\n", + " message = s[\"messages\"][-1]\n", + " if isinstance(message, tuple):\n", + " print(message)\n", + " else:\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "What's the weather in SF?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_0OMmuTLec9t8kxMVkllZCSxo)\n", + " Call ID: call_0OMmuTLec9t8kxMVkllZCSxo\n", + " Args:\n", + " city: sf\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"42\"}}\n", + "inputs = {\"messages\": [(\"user\", \"What's the weather in SF?\")]}\n", + "\n", + "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Next step: ('tools',)\n" + ] + } + ], + "source": [ + "snapshot = graph.get_state(config)\n", + "print(\"Next step: \", snapshot.next)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "83148e08-63e8-49e5-a08b-02dc907bed1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It's always sunny in sf\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in San Francisco is currently sunny.\n" + ] + } + ], + "source": [ + "print_stream(graph.stream(None, config, stream_mode=\"values\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f6f8965-b016-4e25-be63-31c00fc0a6de", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" } - ], - "source": [ - "print_stream(graph.stream(None, config, stream_mode=\"values\"))" - ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f6f8965-b016-4e25-be63-31c00fc0a6de", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/create-react-agent-memory.ipynb b/examples/create-react-agent-memory.ipynb index 826d5e701..2611bda69 100644 --- a/examples/create-react-agent-memory.ipynb +++ b/examples/create-react-agent-memory.ipynb @@ -1,255 +1,255 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4", - "metadata": {}, - "source": [ - "# How to add memory to the prebuilt ReAct agent\n", - "\n", - "This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n", - "\n", - "All we need to do to enable memory is pass in a checkpointer to `create_react_agents`" - ] - }, - { - "cell_type": "markdown", - "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a213e11a-5c62-4ddb-a707-490d91add383", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph langchain-openai" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "23a1885c-04ab-4750-aefa-105891fddf3e", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "OPENAI_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")\n", - "\n", - "# Recommended\n", - "_set_env(\"LANGCHAIN_API_KEY\")\n", - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", - "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\"" - ] - }, - { - "cell_type": "markdown", - "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82", - "metadata": {}, - "source": [ - "## Code" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7a154152-973e-4b5d-aa13-48c617744a4c", - "metadata": {}, - "outputs": [], - "source": [ - "# First we initialize the model we want to use.\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", - "\n", - "\n", - "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n", - "\n", - "from typing import Literal\n", - "\n", - "from langchain_core.tools import tool\n", - "\n", - "\n", - "@tool\n", - "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", - " \"\"\"Use this to get weather information.\"\"\"\n", - " if city == \"nyc\":\n", - " return \"It might be cloudy in nyc\"\n", - " elif city == \"sf\":\n", - " return \"It's always sunny in sf\"\n", - " else:\n", - " raise AssertionError(\"Unknown city\")\n", - "\n", - "\n", - "tools = [get_weather]\n", - "\n", - "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n", - "# to retain the chat context between interactions\n", - "from langgraph.checkpoint import MemorySaver\n", - "\n", - "memory = MemorySaver()\n", - "\n", - "# Define the graph\n", - "\n", - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "graph = create_react_agent(model, tools=tools, checkpointer=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "00407425-506d-4ffd-9c86-987921d8c844", - "metadata": {}, - "source": [ - "## Usage\n", - "\n", - "Let's interact with it multiple times to show that it can remember" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28", - "metadata": {}, - "outputs": [], - "source": [ - "def print_stream(stream):\n", - " for s in stream:\n", - " message = s[\"messages\"][-1]\n", - " if isinstance(message, tuple):\n", - " print(message)\n", - " else:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4", + "metadata": {}, + "source": [ + "# How to add memory to the prebuilt ReAct agent\n", + "\n", + "This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n", + "\n", + "All we need to do to enable memory is pass in a checkpointer to `create_react_agents`" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "What's the weather in NYC?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n", - " Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n", - " Args:\n", - " city: nyc\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It might be cloudy in nyc\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in NYC might be cloudy.\n" - ] - } - ], - "source": [ - "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n", - "\n", - "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))" - ] - }, - { - "cell_type": "markdown", - "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c", - "metadata": {}, - "source": [ - "Notice that when we pass the same the same thread ID, the chat history is preserved" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "187479f9-32fa-4611-9487-cf816ba2e147", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247", + "metadata": {}, + "source": [ + "## Setup" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "What's it known for?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "New York City (NYC) is known for many things, including:\n", - "\n", - "1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n", - "2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n", - "3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n", - "4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n", - "5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n", - "6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n", - "7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n", - "8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n", - "9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n", - "10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n" - ] + "cell_type": "code", + "execution_count": 1, + "id": "a213e11a-5c62-4ddb-a707-490d91add383", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "23a1885c-04ab-4750-aefa-105891fddf3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OPENAI_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")\n", + "\n", + "# Recommended\n", + "_set_env(\"LANGCHAIN_API_KEY\")\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\"" + ] + }, + { + "cell_type": "markdown", + "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82", + "metadata": {}, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a154152-973e-4b5d-aa13-48c617744a4c", + "metadata": {}, + "outputs": [], + "source": [ + "# First we initialize the model we want to use.\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", + "\n", + "\n", + "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n", + "\n", + "from typing import Literal\n", + "\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", + " \"\"\"Use this to get weather information.\"\"\"\n", + " if city == \"nyc\":\n", + " return \"It might be cloudy in nyc\"\n", + " elif city == \"sf\":\n", + " return \"It's always sunny in sf\"\n", + " else:\n", + " raise AssertionError(\"Unknown city\")\n", + "\n", + "\n", + "tools = [get_weather]\n", + "\n", + "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n", + "# to retain the chat context between interactions\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "memory = MemorySaver()\n", + "\n", + "# Define the graph\n", + "\n", + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "graph = create_react_agent(model, tools=tools, checkpointer=memory)" + ] + }, + { + "cell_type": "markdown", + "id": "00407425-506d-4ffd-9c86-987921d8c844", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "Let's interact with it multiple times to show that it can remember" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28", + "metadata": {}, + "outputs": [], + "source": [ + "def print_stream(stream):\n", + " for s in stream:\n", + " message = s[\"messages\"][-1]\n", + " if isinstance(message, tuple):\n", + " print(message)\n", + " else:\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "What's the weather in NYC?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n", + " Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n", + " Args:\n", + " city: nyc\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: get_weather\n", + "\n", + "It might be cloudy in nyc\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The weather in NYC might be cloudy.\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", + "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n", + "\n", + "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))" + ] + }, + { + "cell_type": "markdown", + "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c", + "metadata": {}, + "source": [ + "Notice that when we pass the same the same thread ID, the chat history is preserved" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "187479f9-32fa-4611-9487-cf816ba2e147", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "What's it known for?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "New York City (NYC) is known for many things, including:\n", + "\n", + "1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n", + "2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n", + "3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n", + "4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n", + "5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n", + "6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n", + "7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n", + "8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n", + "9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n", + "10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n" + ] + } + ], + "source": [ + "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n", + "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" } - ], - "source": [ - "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n", - "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))" - ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/customer-support/customer-support.ipynb b/examples/customer-support/customer-support.ipynb index 3715ce4f0..cf451188f 100644 --- a/examples/customer-support/customer-support.ipynb +++ b/examples/customer-support/customer-support.ipynb @@ -32,7 +32,10 @@ "id": "afc570bf-e129-415b-8f2d-8bbce08131ab", "metadata": {}, "outputs": [], - "source": ["%%capture --no-stderr\n% pip install -U langgraph langchain-community langchain-anthropic tavily-python pandas"] + "source": [ + "%%capture --no-stderr\n", + "% pip install -U langgraph langchain-community langchain-anthropic tavily-python pandas" + ] }, { "cell_type": "code", @@ -40,7 +43,24 @@ "id": "358e5666-b7c5-4e46-90a1-7ea273d86ee3", "metadata": {}, "outputs": [], - "source": ["import getpass\nimport os\n\n\ndef _set_env(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"ANTHROPIC_API_KEY\")\n_set_env(\"TAVILY_API_KEY\")\n\n# Recommended\n_set_env(\"LANGCHAIN_API_KEY\")\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Customer Support Bot Tutorial\""] + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")\n", + "_set_env(\"TAVILY_API_KEY\")\n", + "\n", + "# Recommended\n", + "_set_env(\"LANGCHAIN_API_KEY\")\n", + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "os.environ[\"LANGCHAIN_PROJECT\"] = \"Customer Support Bot Tutorial\"" + ] }, { "cell_type": "markdown", @@ -58,7 +78,68 @@ "id": "71638c2a-5038-439e-907a-de2bb548db34", "metadata": {}, "outputs": [], - "source": ["import os\nimport shutil\nimport sqlite3\n\nimport pandas as pd\nimport requests\n\ndb_url = \"https://storage.googleapis.com/benchmarks-artifacts/travel-db/travel2.sqlite\"\nlocal_file = \"travel2.sqlite\"\n# The backup lets us restart for each tutorial section\nbackup_file = \"travel2.backup.sqlite\"\noverwrite = False\nif overwrite or not os.path.exists(local_file):\n response = requests.get(db_url)\n response.raise_for_status() # Ensure the request was successful\n with open(local_file, \"wb\") as f:\n f.write(response.content)\n # Backup - we will use this to \"reset\" our DB in each section\n shutil.copy(local_file, backup_file)\n# Convert the flights to present time for our tutorial\nconn = sqlite3.connect(local_file)\ncursor = conn.cursor()\n\ntables = pd.read_sql(\n \"SELECT name FROM sqlite_master WHERE type='table';\", conn\n).name.tolist()\ntdf = {}\nfor t in tables:\n tdf[t] = pd.read_sql(f\"SELECT * from {t}\", conn)\n\nexample_time = pd.to_datetime(\n tdf[\"flights\"][\"actual_departure\"].replace(\"\\\\N\", pd.NaT)\n).max()\ncurrent_time = pd.to_datetime(\"now\").tz_localize(example_time.tz)\ntime_diff = current_time - example_time\n\ntdf[\"bookings\"][\"book_date\"] = (\n pd.to_datetime(tdf[\"bookings\"][\"book_date\"].replace(\"\\\\N\", pd.NaT), utc=True)\n + time_diff\n)\n\ndatetime_columns = [\n \"scheduled_departure\",\n \"scheduled_arrival\",\n \"actual_departure\",\n \"actual_arrival\",\n]\nfor column in datetime_columns:\n tdf[\"flights\"][column] = (\n pd.to_datetime(tdf[\"flights\"][column].replace(\"\\\\N\", pd.NaT)) + time_diff\n )\n\nfor table_name, df in tdf.items():\n df.to_sql(table_name, conn, if_exists=\"replace\", index=False)\ndel df\ndel tdf\nconn.commit()\nconn.close()\n\ndb = local_file # We'll be using this local file as our DB in this tutorial"] + "source": [ + "import os\n", + "import shutil\n", + "import sqlite3\n", + "\n", + "import pandas as pd\n", + "import requests\n", + "\n", + "db_url = \"https://storage.googleapis.com/benchmarks-artifacts/travel-db/travel2.sqlite\"\n", + "local_file = \"travel2.sqlite\"\n", + "# The backup lets us restart for each tutorial section\n", + "backup_file = \"travel2.backup.sqlite\"\n", + "overwrite = False\n", + "if overwrite or not os.path.exists(local_file):\n", + " response = requests.get(db_url)\n", + " response.raise_for_status() # Ensure the request was successful\n", + " with open(local_file, \"wb\") as f:\n", + " f.write(response.content)\n", + " # Backup - we will use this to \"reset\" our DB in each section\n", + " shutil.copy(local_file, backup_file)\n", + "# Convert the flights to present time for our tutorial\n", + "conn = sqlite3.connect(local_file)\n", + "cursor = conn.cursor()\n", + "\n", + "tables = pd.read_sql(\n", + " \"SELECT name FROM sqlite_master WHERE type='table';\", conn\n", + ").name.tolist()\n", + "tdf = {}\n", + "for t in tables:\n", + " tdf[t] = pd.read_sql(f\"SELECT * from {t}\", conn)\n", + "\n", + "example_time = pd.to_datetime(\n", + " tdf[\"flights\"][\"actual_departure\"].replace(\"\\\\N\", pd.NaT)\n", + ").max()\n", + "current_time = pd.to_datetime(\"now\").tz_localize(example_time.tz)\n", + "time_diff = current_time - example_time\n", + "\n", + "tdf[\"bookings\"][\"book_date\"] = (\n", + " pd.to_datetime(tdf[\"bookings\"][\"book_date\"].replace(\"\\\\N\", pd.NaT), utc=True)\n", + " + time_diff\n", + ")\n", + "\n", + "datetime_columns = [\n", + " \"scheduled_departure\",\n", + " \"scheduled_arrival\",\n", + " \"actual_departure\",\n", + " \"actual_arrival\",\n", + "]\n", + "for column in datetime_columns:\n", + " tdf[\"flights\"][column] = (\n", + " pd.to_datetime(tdf[\"flights\"][column].replace(\"\\\\N\", pd.NaT)) + time_diff\n", + " )\n", + "\n", + "for table_name, df in tdf.items():\n", + " df.to_sql(table_name, conn, if_exists=\"replace\", index=False)\n", + "del df\n", + "del tdf\n", + "conn.commit()\n", + "conn.close()\n", + "\n", + "db = local_file # We'll be using this local file as our DB in this tutorial" + ] }, { "cell_type": "markdown", @@ -81,7 +162,59 @@ "id": "654e2f81", "metadata": {}, "outputs": [], - "source": ["import re\n\nimport numpy as np\nimport openai\nfrom langchain_core.tools import tool\n\nresponse = requests.get(\n \"https://storage.googleapis.com/benchmarks-artifacts/travel-db/swiss_faq.md\"\n)\nresponse.raise_for_status()\nfaq_text = response.text\n\ndocs = [{\"page_content\": txt} for txt in re.split(r\"(?=\\n##)\", faq_text)]\n\n\nclass VectorStoreRetriever:\n def __init__(self, docs: list, vectors: list, oai_client):\n self._arr = np.array(vectors)\n self._docs = docs\n self._client = oai_client\n\n @classmethod\n def from_docs(cls, docs, oai_client):\n embeddings = oai_client.embeddings.create(\n model=\"text-embedding-3-small\", input=[doc[\"page_content\"] for doc in docs]\n )\n vectors = [emb.embedding for emb in embeddings.data]\n return cls(docs, vectors, oai_client)\n\n def query(self, query: str, k: int = 5) -> list[dict]:\n embed = self._client.embeddings.create(\n model=\"text-embedding-3-small\", input=[query]\n )\n # \"@\" is just a matrix multiplication in python\n scores = np.array(embed.data[0].embedding) @ self._arr.T\n top_k_idx = np.argpartition(scores, -k)[-k:]\n top_k_idx_sorted = top_k_idx[np.argsort(-scores[top_k_idx])]\n return [\n {**self._docs[idx], \"similarity\": scores[idx]} for idx in top_k_idx_sorted\n ]\n\n\nretriever = VectorStoreRetriever.from_docs(docs, openai.Client())\n\n\n@tool\ndef lookup_policy(query: str) -> str:\n \"\"\"Consult the company policies to check whether certain options are permitted.\n Use this before making any flight changes performing other 'write' events.\"\"\"\n docs = retriever.query(query, k=2)\n return \"\\n\\n\".join([doc[\"page_content\"] for doc in docs])"] + "source": [ + "import re\n", + "\n", + "import numpy as np\n", + "import openai\n", + "from langchain_core.tools import tool\n", + "\n", + "response = requests.get(\n", + " \"https://storage.googleapis.com/benchmarks-artifacts/travel-db/swiss_faq.md\"\n", + ")\n", + "response.raise_for_status()\n", + "faq_text = response.text\n", + "\n", + "docs = [{\"page_content\": txt} for txt in re.split(r\"(?=\\n##)\", faq_text)]\n", + "\n", + "\n", + "class VectorStoreRetriever:\n", + " def __init__(self, docs: list, vectors: list, oai_client):\n", + " self._arr = np.array(vectors)\n", + " self._docs = docs\n", + " self._client = oai_client\n", + "\n", + " @classmethod\n", + " def from_docs(cls, docs, oai_client):\n", + " embeddings = oai_client.embeddings.create(\n", + " model=\"text-embedding-3-small\", input=[doc[\"page_content\"] for doc in docs]\n", + " )\n", + " vectors = [emb.embedding for emb in embeddings.data]\n", + " return cls(docs, vectors, oai_client)\n", + "\n", + " def query(self, query: str, k: int = 5) -> list[dict]:\n", + " embed = self._client.embeddings.create(\n", + " model=\"text-embedding-3-small\", input=[query]\n", + " )\n", + " # \"@\" is just a matrix multiplication in python\n", + " scores = np.array(embed.data[0].embedding) @ self._arr.T\n", + " top_k_idx = np.argpartition(scores, -k)[-k:]\n", + " top_k_idx_sorted = top_k_idx[np.argsort(-scores[top_k_idx])]\n", + " return [\n", + " {**self._docs[idx], \"similarity\": scores[idx]} for idx in top_k_idx_sorted\n", + " ]\n", + "\n", + "\n", + "retriever = VectorStoreRetriever.from_docs(docs, openai.Client())\n", + "\n", + "\n", + "@tool\n", + "def lookup_policy(query: str) -> str:\n", + " \"\"\"Consult the company policies to check whether certain options are permitted.\n", + " Use this before making any flight changes performing other 'write' events.\"\"\"\n", + " docs = retriever.query(query, k=2)\n", + " return \"\\n\\n\".join([doc[\"page_content\"] for doc in docs])" + ] }, { "cell_type": "markdown", @@ -101,7 +234,205 @@ "id": "043b4341", "metadata": {}, "outputs": [], - "source": ["import sqlite3\nfrom datetime import date, datetime\nfrom typing import Optional\n\nimport pytz\nfrom langchain_core.runnables import ensure_config\n\n\n@tool\ndef fetch_user_flight_information() -> list[dict]:\n \"\"\"Fetch all tickets for the user along with corresponding flight information and seat assignments.\n\n Returns:\n A list of dictionaries where each dictionary contains the ticket details,\n associated flight details, and the seat assignments for each ticket belonging to the user.\n \"\"\"\n config = ensure_config() # Fetch from the context\n configuration = config.get(\"configurable\", {})\n passenger_id = configuration.get(\"passenger_id\", None)\n if not passenger_id:\n raise ValueError(\"No passenger ID configured.\")\n\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n query = \"\"\"\n SELECT \n t.ticket_no, t.book_ref,\n f.flight_id, f.flight_no, f.departure_airport, f.arrival_airport, f.scheduled_departure, f.scheduled_arrival,\n bp.seat_no, tf.fare_conditions\n FROM \n tickets t\n JOIN ticket_flights tf ON t.ticket_no = tf.ticket_no\n JOIN flights f ON tf.flight_id = f.flight_id\n JOIN boarding_passes bp ON bp.ticket_no = t.ticket_no AND bp.flight_id = f.flight_id\n WHERE \n t.passenger_id = ?\n \"\"\"\n cursor.execute(query, (passenger_id,))\n rows = cursor.fetchall()\n column_names = [column[0] for column in cursor.description]\n results = [dict(zip(column_names, row)) for row in rows]\n\n cursor.close()\n conn.close()\n\n return results\n\n\n@tool\ndef search_flights(\n departure_airport: Optional[str] = None,\n arrival_airport: Optional[str] = None,\n start_time: Optional[date | datetime] = None,\n end_time: Optional[date | datetime] = None,\n limit: int = 20,\n) -> list[dict]:\n \"\"\"Search for flights based on departure airport, arrival airport, and departure time range.\"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n query = \"SELECT * FROM flights WHERE 1 = 1\"\n params = []\n\n if departure_airport:\n query += \" AND departure_airport = ?\"\n params.append(departure_airport)\n\n if arrival_airport:\n query += \" AND arrival_airport = ?\"\n params.append(arrival_airport)\n\n if start_time:\n query += \" AND scheduled_departure >= ?\"\n params.append(start_time)\n\n if end_time:\n query += \" AND scheduled_departure <= ?\"\n params.append(end_time)\n query += \" LIMIT ?\"\n params.append(limit)\n cursor.execute(query, params)\n rows = cursor.fetchall()\n column_names = [column[0] for column in cursor.description]\n results = [dict(zip(column_names, row)) for row in rows]\n\n cursor.close()\n conn.close()\n\n return results\n\n\n@tool\ndef update_ticket_to_new_flight(ticket_no: str, new_flight_id: int) -> str:\n \"\"\"Update the user's ticket to a new valid flight.\"\"\"\n config = ensure_config()\n configuration = config.get(\"configurable\", {})\n passenger_id = configuration.get(\"passenger_id\", None)\n if not passenger_id:\n raise ValueError(\"No passenger ID configured.\")\n\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\n \"SELECT departure_airport, arrival_airport, scheduled_departure FROM flights WHERE flight_id = ?\",\n (new_flight_id,),\n )\n new_flight = cursor.fetchone()\n if not new_flight:\n cursor.close()\n conn.close()\n return \"Invalid new flight ID provided.\"\n column_names = [column[0] for column in cursor.description]\n new_flight_dict = dict(zip(column_names, new_flight))\n timezone = pytz.timezone(\"Etc/GMT-3\")\n current_time = datetime.now(tz=timezone)\n departure_time = datetime.strptime(\n new_flight_dict[\"scheduled_departure\"], \"%Y-%m-%d %H:%M:%S.%f%z\"\n )\n time_until = (departure_time - current_time).total_seconds()\n if time_until < (3 * 3600):\n return f\"Not permitted to reschedule to a flight that is less than 3 hours from the current time. Selected flight is at {departure_time}.\"\n\n cursor.execute(\n \"SELECT flight_id FROM ticket_flights WHERE ticket_no = ?\", (ticket_no,)\n )\n current_flight = cursor.fetchone()\n if not current_flight:\n cursor.close()\n conn.close()\n return \"No existing ticket found for the given ticket number.\"\n\n # Check the signed-in user actually has this ticket\n cursor.execute(\n \"SELECT * FROM tickets WHERE ticket_no = ? AND passenger_id = ?\",\n (ticket_no, passenger_id),\n )\n current_ticket = cursor.fetchone()\n if not current_ticket:\n cursor.close()\n conn.close()\n return f\"Current signed-in passenger with ID {passenger_id} not the owner of ticket {ticket_no}\"\n\n # In a real application, you'd likely add additional checks here to enforce business logic,\n # like \"does the new departure airport match the current ticket\", etc.\n # While it's best to try to be *proactive* in 'type-hinting' policies to the LLM\n # it's inevitably going to get things wrong, so you **also** need to ensure your\n # API enforces valid behavior\n cursor.execute(\n \"UPDATE ticket_flights SET flight_id = ? WHERE ticket_no = ?\",\n (new_flight_id, ticket_no),\n )\n conn.commit()\n\n cursor.close()\n conn.close()\n return \"Ticket successfully updated to new flight.\"\n\n\n@tool\ndef cancel_ticket(ticket_no: str) -> str:\n \"\"\"Cancel the user's ticket and remove it from the database.\"\"\"\n config = ensure_config()\n configuration = config.get(\"configurable\", {})\n passenger_id = configuration.get(\"passenger_id\", None)\n if not passenger_id:\n raise ValueError(\"No passenger ID configured.\")\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\n \"SELECT flight_id FROM ticket_flights WHERE ticket_no = ?\", (ticket_no,)\n )\n existing_ticket = cursor.fetchone()\n if not existing_ticket:\n cursor.close()\n conn.close()\n return \"No existing ticket found for the given ticket number.\"\n\n # Check the signed-in user actually has this ticket\n cursor.execute(\n \"SELECT flight_id FROM tickets WHERE ticket_no = ? AND passenger_id = ?\",\n (ticket_no, passenger_id),\n )\n current_ticket = cursor.fetchone()\n if not current_ticket:\n cursor.close()\n conn.close()\n return f\"Current signed-in passenger with ID {passenger_id} not the owner of ticket {ticket_no}\"\n\n cursor.execute(\"DELETE FROM ticket_flights WHERE ticket_no = ?\", (ticket_no,))\n conn.commit()\n\n cursor.close()\n conn.close()\n return \"Ticket successfully cancelled.\""] + "source": [ + "import sqlite3\n", + "from datetime import date, datetime\n", + "from typing import Optional\n", + "\n", + "import pytz\n", + "from langchain_core.runnables import ensure_config\n", + "\n", + "\n", + "@tool\n", + "def fetch_user_flight_information() -> list[dict]:\n", + " \"\"\"Fetch all tickets for the user along with corresponding flight information and seat assignments.\n", + "\n", + " Returns:\n", + " A list of dictionaries where each dictionary contains the ticket details,\n", + " associated flight details, and the seat assignments for each ticket belonging to the user.\n", + " \"\"\"\n", + " config = ensure_config() # Fetch from the context\n", + " configuration = config.get(\"configurable\", {})\n", + " passenger_id = configuration.get(\"passenger_id\", None)\n", + " if not passenger_id:\n", + " raise ValueError(\"No passenger ID configured.\")\n", + "\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " query = \"\"\"\n", + " SELECT \n", + " t.ticket_no, t.book_ref,\n", + " f.flight_id, f.flight_no, f.departure_airport, f.arrival_airport, f.scheduled_departure, f.scheduled_arrival,\n", + " bp.seat_no, tf.fare_conditions\n", + " FROM \n", + " tickets t\n", + " JOIN ticket_flights tf ON t.ticket_no = tf.ticket_no\n", + " JOIN flights f ON tf.flight_id = f.flight_id\n", + " JOIN boarding_passes bp ON bp.ticket_no = t.ticket_no AND bp.flight_id = f.flight_id\n", + " WHERE \n", + " t.passenger_id = ?\n", + " \"\"\"\n", + " cursor.execute(query, (passenger_id,))\n", + " rows = cursor.fetchall()\n", + " column_names = [column[0] for column in cursor.description]\n", + " results = [dict(zip(column_names, row)) for row in rows]\n", + "\n", + " cursor.close()\n", + " conn.close()\n", + "\n", + " return results\n", + "\n", + "\n", + "@tool\n", + "def search_flights(\n", + " departure_airport: Optional[str] = None,\n", + " arrival_airport: Optional[str] = None,\n", + " start_time: Optional[date | datetime] = None,\n", + " end_time: Optional[date | datetime] = None,\n", + " limit: int = 20,\n", + ") -> list[dict]:\n", + " \"\"\"Search for flights based on departure airport, arrival airport, and departure time range.\"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " query = \"SELECT * FROM flights WHERE 1 = 1\"\n", + " params = []\n", + "\n", + " if departure_airport:\n", + " query += \" AND departure_airport = ?\"\n", + " params.append(departure_airport)\n", + "\n", + " if arrival_airport:\n", + " query += \" AND arrival_airport = ?\"\n", + " params.append(arrival_airport)\n", + "\n", + " if start_time:\n", + " query += \" AND scheduled_departure >= ?\"\n", + " params.append(start_time)\n", + "\n", + " if end_time:\n", + " query += \" AND scheduled_departure <= ?\"\n", + " params.append(end_time)\n", + " query += \" LIMIT ?\"\n", + " params.append(limit)\n", + " cursor.execute(query, params)\n", + " rows = cursor.fetchall()\n", + " column_names = [column[0] for column in cursor.description]\n", + " results = [dict(zip(column_names, row)) for row in rows]\n", + "\n", + " cursor.close()\n", + " conn.close()\n", + "\n", + " return results\n", + "\n", + "\n", + "@tool\n", + "def update_ticket_to_new_flight(ticket_no: str, new_flight_id: int) -> str:\n", + " \"\"\"Update the user's ticket to a new valid flight.\"\"\"\n", + " config = ensure_config()\n", + " configuration = config.get(\"configurable\", {})\n", + " passenger_id = configuration.get(\"passenger_id\", None)\n", + " if not passenger_id:\n", + " raise ValueError(\"No passenger ID configured.\")\n", + "\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\n", + " \"SELECT departure_airport, arrival_airport, scheduled_departure FROM flights WHERE flight_id = ?\",\n", + " (new_flight_id,),\n", + " )\n", + " new_flight = cursor.fetchone()\n", + " if not new_flight:\n", + " cursor.close()\n", + " conn.close()\n", + " return \"Invalid new flight ID provided.\"\n", + " column_names = [column[0] for column in cursor.description]\n", + " new_flight_dict = dict(zip(column_names, new_flight))\n", + " timezone = pytz.timezone(\"Etc/GMT-3\")\n", + " current_time = datetime.now(tz=timezone)\n", + " departure_time = datetime.strptime(\n", + " new_flight_dict[\"scheduled_departure\"], \"%Y-%m-%d %H:%M:%S.%f%z\"\n", + " )\n", + " time_until = (departure_time - current_time).total_seconds()\n", + " if time_until < (3 * 3600):\n", + " return f\"Not permitted to reschedule to a flight that is less than 3 hours from the current time. Selected flight is at {departure_time}.\"\n", + "\n", + " cursor.execute(\n", + " \"SELECT flight_id FROM ticket_flights WHERE ticket_no = ?\", (ticket_no,)\n", + " )\n", + " current_flight = cursor.fetchone()\n", + " if not current_flight:\n", + " cursor.close()\n", + " conn.close()\n", + " return \"No existing ticket found for the given ticket number.\"\n", + "\n", + " # Check the signed-in user actually has this ticket\n", + " cursor.execute(\n", + " \"SELECT * FROM tickets WHERE ticket_no = ? AND passenger_id = ?\",\n", + " (ticket_no, passenger_id),\n", + " )\n", + " current_ticket = cursor.fetchone()\n", + " if not current_ticket:\n", + " cursor.close()\n", + " conn.close()\n", + " return f\"Current signed-in passenger with ID {passenger_id} not the owner of ticket {ticket_no}\"\n", + "\n", + " # In a real application, you'd likely add additional checks here to enforce business logic,\n", + " # like \"does the new departure airport match the current ticket\", etc.\n", + " # While it's best to try to be *proactive* in 'type-hinting' policies to the LLM\n", + " # it's inevitably going to get things wrong, so you **also** need to ensure your\n", + " # API enforces valid behavior\n", + " cursor.execute(\n", + " \"UPDATE ticket_flights SET flight_id = ? WHERE ticket_no = ?\",\n", + " (new_flight_id, ticket_no),\n", + " )\n", + " conn.commit()\n", + "\n", + " cursor.close()\n", + " conn.close()\n", + " return \"Ticket successfully updated to new flight.\"\n", + "\n", + "\n", + "@tool\n", + "def cancel_ticket(ticket_no: str) -> str:\n", + " \"\"\"Cancel the user's ticket and remove it from the database.\"\"\"\n", + " config = ensure_config()\n", + " configuration = config.get(\"configurable\", {})\n", + " passenger_id = configuration.get(\"passenger_id\", None)\n", + " if not passenger_id:\n", + " raise ValueError(\"No passenger ID configured.\")\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\n", + " \"SELECT flight_id FROM ticket_flights WHERE ticket_no = ?\", (ticket_no,)\n", + " )\n", + " existing_ticket = cursor.fetchone()\n", + " if not existing_ticket:\n", + " cursor.close()\n", + " conn.close()\n", + " return \"No existing ticket found for the given ticket number.\"\n", + "\n", + " # Check the signed-in user actually has this ticket\n", + " cursor.execute(\n", + " \"SELECT flight_id FROM tickets WHERE ticket_no = ? AND passenger_id = ?\",\n", + " (ticket_no, passenger_id),\n", + " )\n", + " current_ticket = cursor.fetchone()\n", + " if not current_ticket:\n", + " cursor.close()\n", + " conn.close()\n", + " return f\"Current signed-in passenger with ID {passenger_id} not the owner of ticket {ticket_no}\"\n", + "\n", + " cursor.execute(\"DELETE FROM ticket_flights WHERE ticket_no = ?\", (ticket_no,))\n", + " conn.commit()\n", + "\n", + " cursor.close()\n", + " conn.close()\n", + " return \"Ticket successfully cancelled.\"" + ] }, { "cell_type": "markdown", @@ -119,7 +450,145 @@ "id": "f3edabaf-7a23-4f9f-9c57-97b799bc21df", "metadata": {}, "outputs": [], - "source": ["from datetime import date, datetime\nfrom typing import Optional, Union\n\n\n@tool\ndef search_car_rentals(\n location: Optional[str] = None,\n name: Optional[str] = None,\n price_tier: Optional[str] = None,\n start_date: Optional[Union[datetime, date]] = None,\n end_date: Optional[Union[datetime, date]] = None,\n) -> list[dict]:\n \"\"\"\n Search for car rentals based on location, name, price tier, start date, and end date.\n\n Args:\n location (Optional[str]): The location of the car rental. Defaults to None.\n name (Optional[str]): The name of the car rental company. Defaults to None.\n price_tier (Optional[str]): The price tier of the car rental. Defaults to None.\n start_date (Optional[Union[datetime, date]]): The start date of the car rental. Defaults to None.\n end_date (Optional[Union[datetime, date]]): The end date of the car rental. Defaults to None.\n\n Returns:\n list[dict]: A list of car rental dictionaries matching the search criteria.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n query = \"SELECT * FROM car_rentals WHERE 1=1\"\n params = []\n\n if location:\n query += \" AND location LIKE ?\"\n params.append(f\"%{location}%\")\n if name:\n query += \" AND name LIKE ?\"\n params.append(f\"%{name}%\")\n # For our tutorial, we will let you match on any dates and price tier.\n # (since our toy dataset doesn't have much data)\n cursor.execute(query, params)\n results = cursor.fetchall()\n\n conn.close()\n\n return [\n dict(zip([column[0] for column in cursor.description], row)) for row in results\n ]\n\n\n@tool\ndef book_car_rental(rental_id: int) -> str:\n \"\"\"\n Book a car rental by its ID.\n\n Args:\n rental_id (int): The ID of the car rental to book.\n\n Returns:\n str: A message indicating whether the car rental was successfully booked or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\"UPDATE car_rentals SET booked = 1 WHERE id = ?\", (rental_id,))\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Car rental {rental_id} successfully booked.\"\n else:\n conn.close()\n return f\"No car rental found with ID {rental_id}.\"\n\n\n@tool\ndef update_car_rental(\n rental_id: int,\n start_date: Optional[Union[datetime, date]] = None,\n end_date: Optional[Union[datetime, date]] = None,\n) -> str:\n \"\"\"\n Update a car rental's start and end dates by its ID.\n\n Args:\n rental_id (int): The ID of the car rental to update.\n start_date (Optional[Union[datetime, date]]): The new start date of the car rental. Defaults to None.\n end_date (Optional[Union[datetime, date]]): The new end date of the car rental. Defaults to None.\n\n Returns:\n str: A message indicating whether the car rental was successfully updated or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n if start_date:\n cursor.execute(\n \"UPDATE car_rentals SET start_date = ? WHERE id = ?\",\n (start_date, rental_id),\n )\n if end_date:\n cursor.execute(\n \"UPDATE car_rentals SET end_date = ? WHERE id = ?\", (end_date, rental_id)\n )\n\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Car rental {rental_id} successfully updated.\"\n else:\n conn.close()\n return f\"No car rental found with ID {rental_id}.\"\n\n\n@tool\ndef cancel_car_rental(rental_id: int) -> str:\n \"\"\"\n Cancel a car rental by its ID.\n\n Args:\n rental_id (int): The ID of the car rental to cancel.\n\n Returns:\n str: A message indicating whether the car rental was successfully cancelled or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\"UPDATE car_rentals SET booked = 0 WHERE id = ?\", (rental_id,))\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Car rental {rental_id} successfully cancelled.\"\n else:\n conn.close()\n return f\"No car rental found with ID {rental_id}.\""] + "source": [ + "from datetime import date, datetime\n", + "from typing import Optional, Union\n", + "\n", + "\n", + "@tool\n", + "def search_car_rentals(\n", + " location: Optional[str] = None,\n", + " name: Optional[str] = None,\n", + " price_tier: Optional[str] = None,\n", + " start_date: Optional[Union[datetime, date]] = None,\n", + " end_date: Optional[Union[datetime, date]] = None,\n", + ") -> list[dict]:\n", + " \"\"\"\n", + " Search for car rentals based on location, name, price tier, start date, and end date.\n", + "\n", + " Args:\n", + " location (Optional[str]): The location of the car rental. Defaults to None.\n", + " name (Optional[str]): The name of the car rental company. Defaults to None.\n", + " price_tier (Optional[str]): The price tier of the car rental. Defaults to None.\n", + " start_date (Optional[Union[datetime, date]]): The start date of the car rental. Defaults to None.\n", + " end_date (Optional[Union[datetime, date]]): The end date of the car rental. Defaults to None.\n", + "\n", + " Returns:\n", + " list[dict]: A list of car rental dictionaries matching the search criteria.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " query = \"SELECT * FROM car_rentals WHERE 1=1\"\n", + " params = []\n", + "\n", + " if location:\n", + " query += \" AND location LIKE ?\"\n", + " params.append(f\"%{location}%\")\n", + " if name:\n", + " query += \" AND name LIKE ?\"\n", + " params.append(f\"%{name}%\")\n", + " # For our tutorial, we will let you match on any dates and price tier.\n", + " # (since our toy dataset doesn't have much data)\n", + " cursor.execute(query, params)\n", + " results = cursor.fetchall()\n", + "\n", + " conn.close()\n", + "\n", + " return [\n", + " dict(zip([column[0] for column in cursor.description], row)) for row in results\n", + " ]\n", + "\n", + "\n", + "@tool\n", + "def book_car_rental(rental_id: int) -> str:\n", + " \"\"\"\n", + " Book a car rental by its ID.\n", + "\n", + " Args:\n", + " rental_id (int): The ID of the car rental to book.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the car rental was successfully booked or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\"UPDATE car_rentals SET booked = 1 WHERE id = ?\", (rental_id,))\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Car rental {rental_id} successfully booked.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No car rental found with ID {rental_id}.\"\n", + "\n", + "\n", + "@tool\n", + "def update_car_rental(\n", + " rental_id: int,\n", + " start_date: Optional[Union[datetime, date]] = None,\n", + " end_date: Optional[Union[datetime, date]] = None,\n", + ") -> str:\n", + " \"\"\"\n", + " Update a car rental's start and end dates by its ID.\n", + "\n", + " Args:\n", + " rental_id (int): The ID of the car rental to update.\n", + " start_date (Optional[Union[datetime, date]]): The new start date of the car rental. Defaults to None.\n", + " end_date (Optional[Union[datetime, date]]): The new end date of the car rental. Defaults to None.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the car rental was successfully updated or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " if start_date:\n", + " cursor.execute(\n", + " \"UPDATE car_rentals SET start_date = ? WHERE id = ?\",\n", + " (start_date, rental_id),\n", + " )\n", + " if end_date:\n", + " cursor.execute(\n", + " \"UPDATE car_rentals SET end_date = ? WHERE id = ?\", (end_date, rental_id)\n", + " )\n", + "\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Car rental {rental_id} successfully updated.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No car rental found with ID {rental_id}.\"\n", + "\n", + "\n", + "@tool\n", + "def cancel_car_rental(rental_id: int) -> str:\n", + " \"\"\"\n", + " Cancel a car rental by its ID.\n", + "\n", + " Args:\n", + " rental_id (int): The ID of the car rental to cancel.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the car rental was successfully cancelled or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\"UPDATE car_rentals SET booked = 0 WHERE id = ?\", (rental_id,))\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Car rental {rental_id} successfully cancelled.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No car rental found with ID {rental_id}.\"" + ] }, { "cell_type": "markdown", @@ -137,7 +606,140 @@ "id": "a8e4ab3c-0086-4257-855b-97cc4037513f", "metadata": {}, "outputs": [], - "source": ["@tool\ndef search_hotels(\n location: Optional[str] = None,\n name: Optional[str] = None,\n price_tier: Optional[str] = None,\n checkin_date: Optional[Union[datetime, date]] = None,\n checkout_date: Optional[Union[datetime, date]] = None,\n) -> list[dict]:\n \"\"\"\n Search for hotels based on location, name, price tier, check-in date, and check-out date.\n\n Args:\n location (Optional[str]): The location of the hotel. Defaults to None.\n name (Optional[str]): The name of the hotel. Defaults to None.\n price_tier (Optional[str]): The price tier of the hotel. Defaults to None. Examples: Midscale, Upper Midscale, Upscale, Luxury\n checkin_date (Optional[Union[datetime, date]]): The check-in date of the hotel. Defaults to None.\n checkout_date (Optional[Union[datetime, date]]): The check-out date of the hotel. Defaults to None.\n\n Returns:\n list[dict]: A list of hotel dictionaries matching the search criteria.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n query = \"SELECT * FROM hotels WHERE 1=1\"\n params = []\n\n if location:\n query += \" AND location LIKE ?\"\n params.append(f\"%{location}%\")\n if name:\n query += \" AND name LIKE ?\"\n params.append(f\"%{name}%\")\n # For the sake of this tutorial, we will let you match on any dates and price tier.\n cursor.execute(query, params)\n results = cursor.fetchall()\n\n conn.close()\n\n return [\n dict(zip([column[0] for column in cursor.description], row)) for row in results\n ]\n\n\n@tool\ndef book_hotel(hotel_id: int) -> str:\n \"\"\"\n Book a hotel by its ID.\n\n Args:\n hotel_id (int): The ID of the hotel to book.\n\n Returns:\n str: A message indicating whether the hotel was successfully booked or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\"UPDATE hotels SET booked = 1 WHERE id = ?\", (hotel_id,))\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Hotel {hotel_id} successfully booked.\"\n else:\n conn.close()\n return f\"No hotel found with ID {hotel_id}.\"\n\n\n@tool\ndef update_hotel(\n hotel_id: int,\n checkin_date: Optional[Union[datetime, date]] = None,\n checkout_date: Optional[Union[datetime, date]] = None,\n) -> str:\n \"\"\"\n Update a hotel's check-in and check-out dates by its ID.\n\n Args:\n hotel_id (int): The ID of the hotel to update.\n checkin_date (Optional[Union[datetime, date]]): The new check-in date of the hotel. Defaults to None.\n checkout_date (Optional[Union[datetime, date]]): The new check-out date of the hotel. Defaults to None.\n\n Returns:\n str: A message indicating whether the hotel was successfully updated or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n if checkin_date:\n cursor.execute(\n \"UPDATE hotels SET checkin_date = ? WHERE id = ?\", (checkin_date, hotel_id)\n )\n if checkout_date:\n cursor.execute(\n \"UPDATE hotels SET checkout_date = ? WHERE id = ?\",\n (checkout_date, hotel_id),\n )\n\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Hotel {hotel_id} successfully updated.\"\n else:\n conn.close()\n return f\"No hotel found with ID {hotel_id}.\"\n\n\n@tool\ndef cancel_hotel(hotel_id: int) -> str:\n \"\"\"\n Cancel a hotel by its ID.\n\n Args:\n hotel_id (int): The ID of the hotel to cancel.\n\n Returns:\n str: A message indicating whether the hotel was successfully cancelled or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\"UPDATE hotels SET booked = 0 WHERE id = ?\", (hotel_id,))\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Hotel {hotel_id} successfully cancelled.\"\n else:\n conn.close()\n return f\"No hotel found with ID {hotel_id}.\""] + "source": [ + "@tool\n", + "def search_hotels(\n", + " location: Optional[str] = None,\n", + " name: Optional[str] = None,\n", + " price_tier: Optional[str] = None,\n", + " checkin_date: Optional[Union[datetime, date]] = None,\n", + " checkout_date: Optional[Union[datetime, date]] = None,\n", + ") -> list[dict]:\n", + " \"\"\"\n", + " Search for hotels based on location, name, price tier, check-in date, and check-out date.\n", + "\n", + " Args:\n", + " location (Optional[str]): The location of the hotel. Defaults to None.\n", + " name (Optional[str]): The name of the hotel. Defaults to None.\n", + " price_tier (Optional[str]): The price tier of the hotel. Defaults to None. Examples: Midscale, Upper Midscale, Upscale, Luxury\n", + " checkin_date (Optional[Union[datetime, date]]): The check-in date of the hotel. Defaults to None.\n", + " checkout_date (Optional[Union[datetime, date]]): The check-out date of the hotel. Defaults to None.\n", + "\n", + " Returns:\n", + " list[dict]: A list of hotel dictionaries matching the search criteria.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " query = \"SELECT * FROM hotels WHERE 1=1\"\n", + " params = []\n", + "\n", + " if location:\n", + " query += \" AND location LIKE ?\"\n", + " params.append(f\"%{location}%\")\n", + " if name:\n", + " query += \" AND name LIKE ?\"\n", + " params.append(f\"%{name}%\")\n", + " # For the sake of this tutorial, we will let you match on any dates and price tier.\n", + " cursor.execute(query, params)\n", + " results = cursor.fetchall()\n", + "\n", + " conn.close()\n", + "\n", + " return [\n", + " dict(zip([column[0] for column in cursor.description], row)) for row in results\n", + " ]\n", + "\n", + "\n", + "@tool\n", + "def book_hotel(hotel_id: int) -> str:\n", + " \"\"\"\n", + " Book a hotel by its ID.\n", + "\n", + " Args:\n", + " hotel_id (int): The ID of the hotel to book.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the hotel was successfully booked or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\"UPDATE hotels SET booked = 1 WHERE id = ?\", (hotel_id,))\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Hotel {hotel_id} successfully booked.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No hotel found with ID {hotel_id}.\"\n", + "\n", + "\n", + "@tool\n", + "def update_hotel(\n", + " hotel_id: int,\n", + " checkin_date: Optional[Union[datetime, date]] = None,\n", + " checkout_date: Optional[Union[datetime, date]] = None,\n", + ") -> str:\n", + " \"\"\"\n", + " Update a hotel's check-in and check-out dates by its ID.\n", + "\n", + " Args:\n", + " hotel_id (int): The ID of the hotel to update.\n", + " checkin_date (Optional[Union[datetime, date]]): The new check-in date of the hotel. Defaults to None.\n", + " checkout_date (Optional[Union[datetime, date]]): The new check-out date of the hotel. Defaults to None.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the hotel was successfully updated or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " if checkin_date:\n", + " cursor.execute(\n", + " \"UPDATE hotels SET checkin_date = ? WHERE id = ?\", (checkin_date, hotel_id)\n", + " )\n", + " if checkout_date:\n", + " cursor.execute(\n", + " \"UPDATE hotels SET checkout_date = ? WHERE id = ?\",\n", + " (checkout_date, hotel_id),\n", + " )\n", + "\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Hotel {hotel_id} successfully updated.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No hotel found with ID {hotel_id}.\"\n", + "\n", + "\n", + "@tool\n", + "def cancel_hotel(hotel_id: int) -> str:\n", + " \"\"\"\n", + " Cancel a hotel by its ID.\n", + "\n", + " Args:\n", + " hotel_id (int): The ID of the hotel to cancel.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the hotel was successfully cancelled or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\"UPDATE hotels SET booked = 0 WHERE id = ?\", (hotel_id,))\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Hotel {hotel_id} successfully cancelled.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No hotel found with ID {hotel_id}.\"" + ] }, { "cell_type": "markdown", @@ -155,7 +757,134 @@ "id": "2260eccb-8ae2-4a41-a1ba-f78ee3df3010", "metadata": {}, "outputs": [], - "source": ["@tool\ndef search_trip_recommendations(\n location: Optional[str] = None,\n name: Optional[str] = None,\n keywords: Optional[str] = None,\n) -> list[dict]:\n \"\"\"\n Search for trip recommendations based on location, name, and keywords.\n\n Args:\n location (Optional[str]): The location of the trip recommendation. Defaults to None.\n name (Optional[str]): The name of the trip recommendation. Defaults to None.\n keywords (Optional[str]): The keywords associated with the trip recommendation. Defaults to None.\n\n Returns:\n list[dict]: A list of trip recommendation dictionaries matching the search criteria.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n query = \"SELECT * FROM trip_recommendations WHERE 1=1\"\n params = []\n\n if location:\n query += \" AND location LIKE ?\"\n params.append(f\"%{location}%\")\n if name:\n query += \" AND name LIKE ?\"\n params.append(f\"%{name}%\")\n if keywords:\n keyword_list = keywords.split(\",\")\n keyword_conditions = \" OR \".join([\"keywords LIKE ?\" for _ in keyword_list])\n query += f\" AND ({keyword_conditions})\"\n params.extend([f\"%{keyword.strip()}%\" for keyword in keyword_list])\n\n cursor.execute(query, params)\n results = cursor.fetchall()\n\n conn.close()\n\n return [\n dict(zip([column[0] for column in cursor.description], row)) for row in results\n ]\n\n\n@tool\ndef book_excursion(recommendation_id: int) -> str:\n \"\"\"\n Book a excursion by its recommendation ID.\n\n Args:\n recommendation_id (int): The ID of the trip recommendation to book.\n\n Returns:\n str: A message indicating whether the trip recommendation was successfully booked or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\n \"UPDATE trip_recommendations SET booked = 1 WHERE id = ?\", (recommendation_id,)\n )\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Trip recommendation {recommendation_id} successfully booked.\"\n else:\n conn.close()\n return f\"No trip recommendation found with ID {recommendation_id}.\"\n\n\n@tool\ndef update_excursion(recommendation_id: int, details: str) -> str:\n \"\"\"\n Update a trip recommendation's details by its ID.\n\n Args:\n recommendation_id (int): The ID of the trip recommendation to update.\n details (str): The new details of the trip recommendation.\n\n Returns:\n str: A message indicating whether the trip recommendation was successfully updated or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\n \"UPDATE trip_recommendations SET details = ? WHERE id = ?\",\n (details, recommendation_id),\n )\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Trip recommendation {recommendation_id} successfully updated.\"\n else:\n conn.close()\n return f\"No trip recommendation found with ID {recommendation_id}.\"\n\n\n@tool\ndef cancel_excursion(recommendation_id: int) -> str:\n \"\"\"\n Cancel a trip recommendation by its ID.\n\n Args:\n recommendation_id (int): The ID of the trip recommendation to cancel.\n\n Returns:\n str: A message indicating whether the trip recommendation was successfully cancelled or not.\n \"\"\"\n conn = sqlite3.connect(db)\n cursor = conn.cursor()\n\n cursor.execute(\n \"UPDATE trip_recommendations SET booked = 0 WHERE id = ?\", (recommendation_id,)\n )\n conn.commit()\n\n if cursor.rowcount > 0:\n conn.close()\n return f\"Trip recommendation {recommendation_id} successfully cancelled.\"\n else:\n conn.close()\n return f\"No trip recommendation found with ID {recommendation_id}.\""] + "source": [ + "@tool\n", + "def search_trip_recommendations(\n", + " location: Optional[str] = None,\n", + " name: Optional[str] = None,\n", + " keywords: Optional[str] = None,\n", + ") -> list[dict]:\n", + " \"\"\"\n", + " Search for trip recommendations based on location, name, and keywords.\n", + "\n", + " Args:\n", + " location (Optional[str]): The location of the trip recommendation. Defaults to None.\n", + " name (Optional[str]): The name of the trip recommendation. Defaults to None.\n", + " keywords (Optional[str]): The keywords associated with the trip recommendation. Defaults to None.\n", + "\n", + " Returns:\n", + " list[dict]: A list of trip recommendation dictionaries matching the search criteria.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " query = \"SELECT * FROM trip_recommendations WHERE 1=1\"\n", + " params = []\n", + "\n", + " if location:\n", + " query += \" AND location LIKE ?\"\n", + " params.append(f\"%{location}%\")\n", + " if name:\n", + " query += \" AND name LIKE ?\"\n", + " params.append(f\"%{name}%\")\n", + " if keywords:\n", + " keyword_list = keywords.split(\",\")\n", + " keyword_conditions = \" OR \".join([\"keywords LIKE ?\" for _ in keyword_list])\n", + " query += f\" AND ({keyword_conditions})\"\n", + " params.extend([f\"%{keyword.strip()}%\" for keyword in keyword_list])\n", + "\n", + " cursor.execute(query, params)\n", + " results = cursor.fetchall()\n", + "\n", + " conn.close()\n", + "\n", + " return [\n", + " dict(zip([column[0] for column in cursor.description], row)) for row in results\n", + " ]\n", + "\n", + "\n", + "@tool\n", + "def book_excursion(recommendation_id: int) -> str:\n", + " \"\"\"\n", + " Book a excursion by its recommendation ID.\n", + "\n", + " Args:\n", + " recommendation_id (int): The ID of the trip recommendation to book.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the trip recommendation was successfully booked or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\n", + " \"UPDATE trip_recommendations SET booked = 1 WHERE id = ?\", (recommendation_id,)\n", + " )\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Trip recommendation {recommendation_id} successfully booked.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No trip recommendation found with ID {recommendation_id}.\"\n", + "\n", + "\n", + "@tool\n", + "def update_excursion(recommendation_id: int, details: str) -> str:\n", + " \"\"\"\n", + " Update a trip recommendation's details by its ID.\n", + "\n", + " Args:\n", + " recommendation_id (int): The ID of the trip recommendation to update.\n", + " details (str): The new details of the trip recommendation.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the trip recommendation was successfully updated or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\n", + " \"UPDATE trip_recommendations SET details = ? WHERE id = ?\",\n", + " (details, recommendation_id),\n", + " )\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Trip recommendation {recommendation_id} successfully updated.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No trip recommendation found with ID {recommendation_id}.\"\n", + "\n", + "\n", + "@tool\n", + "def cancel_excursion(recommendation_id: int) -> str:\n", + " \"\"\"\n", + " Cancel a trip recommendation by its ID.\n", + "\n", + " Args:\n", + " recommendation_id (int): The ID of the trip recommendation to cancel.\n", + "\n", + " Returns:\n", + " str: A message indicating whether the trip recommendation was successfully cancelled or not.\n", + " \"\"\"\n", + " conn = sqlite3.connect(db)\n", + " cursor = conn.cursor()\n", + "\n", + " cursor.execute(\n", + " \"UPDATE trip_recommendations SET booked = 0 WHERE id = ?\", (recommendation_id,)\n", + " )\n", + " conn.commit()\n", + "\n", + " if cursor.rowcount > 0:\n", + " conn.close()\n", + " return f\"Trip recommendation {recommendation_id} successfully cancelled.\"\n", + " else:\n", + " conn.close()\n", + " return f\"No trip recommendation found with ID {recommendation_id}.\"" + ] }, { "cell_type": "markdown", @@ -173,7 +902,48 @@ "id": "663f001e", "metadata": {}, "outputs": [], - "source": ["from langchain_core.messages import ToolMessage\nfrom langchain_core.runnables import RunnableLambda\n\nfrom langgraph.prebuilt import ToolNode\n\n\ndef handle_tool_error(state) -> dict:\n error = state.get(\"error\")\n tool_calls = state[\"messages\"][-1].tool_calls\n return {\n \"messages\": [\n ToolMessage(\n content=f\"Error: {repr(error)}\\n please fix your mistakes.\",\n tool_call_id=tc[\"id\"],\n )\n for tc in tool_calls\n ]\n }\n\n\ndef create_tool_node_with_fallback(tools: list) -> dict:\n return ToolNode(tools).with_fallbacks(\n [RunnableLambda(handle_tool_error)], exception_key=\"error\"\n )\n\n\ndef _print_event(event: dict, _printed: set, max_length=1500):\n current_state = event.get(\"dialog_state\")\n if current_state:\n print(\"Currently in: \", current_state[-1])\n message = event.get(\"messages\")\n if message:\n if isinstance(message, list):\n message = message[-1]\n if message.id not in _printed:\n msg_repr = message.pretty_repr(html=True)\n if len(msg_repr) > max_length:\n msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n print(msg_repr)\n _printed.add(message.id)"] + "source": [ + "from langchain_core.messages import ToolMessage\n", + "from langchain_core.runnables import RunnableLambda\n", + "\n", + "from langgraph.prebuilt import ToolNode\n", + "\n", + "\n", + "def handle_tool_error(state) -> dict:\n", + " error = state.get(\"error\")\n", + " tool_calls = state[\"messages\"][-1].tool_calls\n", + " return {\n", + " \"messages\": [\n", + " ToolMessage(\n", + " content=f\"Error: {repr(error)}\\n please fix your mistakes.\",\n", + " tool_call_id=tc[\"id\"],\n", + " )\n", + " for tc in tool_calls\n", + " ]\n", + " }\n", + "\n", + "\n", + "def create_tool_node_with_fallback(tools: list) -> dict:\n", + " return ToolNode(tools).with_fallbacks(\n", + " [RunnableLambda(handle_tool_error)], exception_key=\"error\"\n", + " )\n", + "\n", + "\n", + "def _print_event(event: dict, _printed: set, max_length=1500):\n", + " current_state = event.get(\"dialog_state\")\n", + " if current_state:\n", + " print(\"Currently in: \", current_state[-1])\n", + " message = event.get(\"messages\")\n", + " if message:\n", + " if isinstance(message, list):\n", + " message = message[-1]\n", + " if message.id not in _printed:\n", + " msg_repr = message.pretty_repr(html=True)\n", + " if len(msg_repr) > max_length:\n", + " msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n", + " print(msg_repr)\n", + " _printed.add(message.id)" + ] }, { "cell_type": "markdown", @@ -203,7 +973,17 @@ "id": "a3216948", "metadata": {}, "outputs": [], - "source": ["from typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass State(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]"] + "source": [ + "from typing import Annotated\n", + "\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph.message import AnyMessage, add_messages\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list[AnyMessage], add_messages]" + ] }, { "cell_type": "markdown", @@ -230,7 +1010,83 @@ ] } ], - "source": ["from langchain_anthropic import ChatAnthropic\nfrom langchain_community.tools.tavily_search import TavilySearchResults\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.runnables import Runnable, RunnableConfig\n\n\nclass Assistant:\n def __init__(self, runnable: Runnable):\n self.runnable = runnable\n\n def __call__(self, state: State, config: RunnableConfig):\n while True:\n configuration = config.get(\"configurable\", {})\n passenger_id = configuration.get(\"passenger_id\", None)\n state = {**state, \"user_info\": passenger_id}\n result = self.runnable.invoke(state)\n # If the LLM happens to return an empty response, we will re-prompt it\n # for an actual response.\n if not result.tool_calls and (\n not result.content\n or isinstance(result.content, list)\n and not result.content[0].get(\"text\")\n ):\n messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n state = {**state, \"messages\": messages}\n else:\n break\n return {\"messages\": result}\n\n\n# Haiku is faster and cheaper, but less accurate\n# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\nllm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n# You could swap LLMs, though you will likely want to update the prompts when\n# doing so!\n# from langchain_openai import ChatOpenAI\n\n# llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n\nprimary_assistant_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a helpful customer support assistant for Swiss Airlines. \"\n \" Use the provided tools to search for flights, company policies, and other information to assist the user's queries. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \" If a search comes up empty, expand your search before giving up.\"\n \"\\n\\nCurrent user:\\n\\n{user_info}\\n\"\n \"\\nCurrent time: {time}.\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\npart_1_tools = [\n TavilySearchResults(max_results=1),\n fetch_user_flight_information,\n search_flights,\n lookup_policy,\n update_ticket_to_new_flight,\n cancel_ticket,\n search_car_rentals,\n book_car_rental,\n update_car_rental,\n cancel_car_rental,\n search_hotels,\n book_hotel,\n update_hotel,\n cancel_hotel,\n search_trip_recommendations,\n book_excursion,\n update_excursion,\n cancel_excursion,\n]\npart_1_assistant_runnable = primary_assistant_prompt | llm.bind_tools(part_1_tools)"] + "source": [ + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import Runnable, RunnableConfig\n", + "\n", + "\n", + "class Assistant:\n", + " def __init__(self, runnable: Runnable):\n", + " self.runnable = runnable\n", + "\n", + " def __call__(self, state: State, config: RunnableConfig):\n", + " while True:\n", + " configuration = config.get(\"configurable\", {})\n", + " passenger_id = configuration.get(\"passenger_id\", None)\n", + " state = {**state, \"user_info\": passenger_id}\n", + " result = self.runnable.invoke(state)\n", + " # If the LLM happens to return an empty response, we will re-prompt it\n", + " # for an actual response.\n", + " if not result.tool_calls and (\n", + " not result.content\n", + " or isinstance(result.content, list)\n", + " and not result.content[0].get(\"text\")\n", + " ):\n", + " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", + " state = {**state, \"messages\": messages}\n", + " else:\n", + " break\n", + " return {\"messages\": result}\n", + "\n", + "\n", + "# Haiku is faster and cheaper, but less accurate\n", + "# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n", + "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n", + "# You could swap LLMs, though you will likely want to update the prompts when\n", + "# doing so!\n", + "# from langchain_openai import ChatOpenAI\n", + "\n", + "# llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n", + "\n", + "primary_assistant_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful customer support assistant for Swiss Airlines. \"\n", + " \" Use the provided tools to search for flights, company policies, and other information to assist the user's queries. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \" If a search comes up empty, expand your search before giving up.\"\n", + " \"\\n\\nCurrent user:\\n\\n{user_info}\\n\"\n", + " \"\\nCurrent time: {time}.\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "part_1_tools = [\n", + " TavilySearchResults(max_results=1),\n", + " fetch_user_flight_information,\n", + " search_flights,\n", + " lookup_policy,\n", + " update_ticket_to_new_flight,\n", + " cancel_ticket,\n", + " search_car_rentals,\n", + " book_car_rental,\n", + " update_car_rental,\n", + " cancel_car_rental,\n", + " search_hotels,\n", + " book_hotel,\n", + " update_hotel,\n", + " cancel_hotel,\n", + " search_trip_recommendations,\n", + " book_excursion,\n", + " update_excursion,\n", + " cancel_excursion,\n", + "]\n", + "part_1_assistant_runnable = primary_assistant_prompt | llm.bind_tools(part_1_tools)" + ] }, { "cell_type": "markdown", @@ -248,7 +1104,30 @@ "id": "36064ee6", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\nfrom langgraph.prebuilt import tools_condition\n\nbuilder = StateGraph(State)\n\n\n# Define nodes: these do the work\nbuilder.add_node(\"assistant\", Assistant(part_1_assistant_runnable))\nbuilder.add_node(\"tools\", create_tool_node_with_fallback(part_1_tools))\n# Define edges: these determine how the control flow moves\nbuilder.add_edge(START, \"assistant\")\nbuilder.add_conditional_edges(\n \"assistant\",\n tools_condition,\n)\nbuilder.add_edge(\"tools\", \"assistant\")\n\n# The checkpointer lets the graph persist its state\n# this is a complete memory for the entire graph.\nmemory = SqliteSaver.from_conn_string(\":memory:\")\npart_1_graph = builder.compile(checkpointer=memory)"] + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import END, StateGraph, START\n", + "from langgraph.prebuilt import tools_condition\n", + "\n", + "builder = StateGraph(State)\n", + "\n", + "\n", + "# Define nodes: these do the work\n", + "builder.add_node(\"assistant\", Assistant(part_1_assistant_runnable))\n", + "builder.add_node(\"tools\", create_tool_node_with_fallback(part_1_tools))\n", + "# Define edges: these determine how the control flow moves\n", + "builder.add_edge(START, \"assistant\")\n", + "builder.add_conditional_edges(\n", + " \"assistant\",\n", + " tools_condition,\n", + ")\n", + "builder.add_edge(\"tools\", \"assistant\")\n", + "\n", + "# The checkpointer lets the graph persist its state\n", + "# this is a complete memory for the entire graph.\n", + "memory = MemorySaver()\n", + "part_1_graph = builder.compile(checkpointer=memory)" + ] }, { "cell_type": "code", @@ -267,7 +1146,15 @@ "output_type": "display_data" } ], - "source": ["from IPython.display import Image, display\n\ntry:\n display(Image(part_1_graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(part_1_graph.get_graph(xray=True).draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "markdown", @@ -834,7 +1721,51 @@ ] } ], - "source": ["import shutil\nimport uuid\n\n# Let's create an example conversation a user might have with the assistant\ntutorial_questions = [\n \"Hi there, what time is my flight?\",\n \"Am i allowed to update my flight to something sooner? I want to leave later today.\",\n \"Update my flight to sometime next week then\",\n \"The next available option is great\",\n \"what about lodging and transportation?\",\n \"Yeah i think i'd like an affordable hotel for my week-long stay (7 days). And I'll want to rent a car.\",\n \"OK could you place a reservation for your recommended hotel? It sounds nice.\",\n \"yes go ahead and book anything that's moderate expense and has availability.\",\n \"Now for a car, what are my options?\",\n \"Awesome let's just get the cheapest option. Go ahead and book for 7 days\",\n \"Cool so now what recommendations do you have on excursions?\",\n \"Are they available while I'm there?\",\n \"interesting - i like the museums, what options are there? \",\n \"OK great pick one and book it for my second day there.\",\n]\n\n# Update with the backup file so we can restart from the original place in each section\nshutil.copy(backup_file, db)\nthread_id = str(uuid.uuid4())\n\nconfig = {\n \"configurable\": {\n # The passenger_id is used in our flight tools to\n # fetch the user's flight information\n \"passenger_id\": \"3442 587242\",\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\n\n_printed = set()\nfor question in tutorial_questions:\n events = part_1_graph.stream(\n {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n )\n for event in events:\n _print_event(event, _printed)"] + "source": [ + "import shutil\n", + "import uuid\n", + "\n", + "# Let's create an example conversation a user might have with the assistant\n", + "tutorial_questions = [\n", + " \"Hi there, what time is my flight?\",\n", + " \"Am i allowed to update my flight to something sooner? I want to leave later today.\",\n", + " \"Update my flight to sometime next week then\",\n", + " \"The next available option is great\",\n", + " \"what about lodging and transportation?\",\n", + " \"Yeah i think i'd like an affordable hotel for my week-long stay (7 days). And I'll want to rent a car.\",\n", + " \"OK could you place a reservation for your recommended hotel? It sounds nice.\",\n", + " \"yes go ahead and book anything that's moderate expense and has availability.\",\n", + " \"Now for a car, what are my options?\",\n", + " \"Awesome let's just get the cheapest option. Go ahead and book for 7 days\",\n", + " \"Cool so now what recommendations do you have on excursions?\",\n", + " \"Are they available while I'm there?\",\n", + " \"interesting - i like the museums, what options are there? \",\n", + " \"OK great pick one and book it for my second day there.\",\n", + "]\n", + "\n", + "# Update with the backup file so we can restart from the original place in each section\n", + "shutil.copy(backup_file, db)\n", + "thread_id = str(uuid.uuid4())\n", + "\n", + "config = {\n", + " \"configurable\": {\n", + " # The passenger_id is used in our flight tools to\n", + " # fetch the user's flight information\n", + " \"passenger_id\": \"3442 587242\",\n", + " # Checkpoints are accessed by thread_id\n", + " \"thread_id\": thread_id,\n", + " }\n", + "}\n", + "\n", + "\n", + "_printed = set()\n", + "for question in tutorial_questions:\n", + " events = part_1_graph.stream(\n", + " {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n", + " )\n", + " for event in events:\n", + " _print_event(event, _printed)" + ] }, { "cell_type": "markdown", @@ -887,7 +1818,90 @@ "id": "c5098273-e1f6-46bf-b63b-172bbd3d9104", "metadata": {}, "outputs": [], - "source": ["from typing import Annotated\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_community.tools.tavily_search import TavilySearchResults\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.runnables import Runnable, RunnableConfig\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass State(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]\n user_info: str\n\n\nclass Assistant:\n def __init__(self, runnable: Runnable):\n self.runnable = runnable\n\n def __call__(self, state: State, config: RunnableConfig):\n while True:\n result = self.runnable.invoke(state)\n # If the LLM happens to return an empty response, we will re-prompt it\n # for an actual response.\n if not result.tool_calls and (\n not result.content\n or isinstance(result.content, list)\n and not result.content[0].get(\"text\")\n ):\n messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n state = {**state, \"messages\": messages}\n else:\n break\n return {\"messages\": result}\n\n\n# Haiku is faster and cheaper, but less accurate\n# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\nllm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n# You could also use OpenAI or another model, though you will likely have\n# to adapt the prompts\n# from langchain_openai import ChatOpenAI\n\n# llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n\nassistant_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a helpful customer support assistant for Swiss Airlines. \"\n \" Use the provided tools to search for flights, company policies, and other information to assist the user's queries. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \" If a search comes up empty, expand your search before giving up.\"\n \"\\n\\nCurrent user:\\n\\n{user_info}\\n\"\n \"\\nCurrent time: {time}.\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\npart_2_tools = [\n TavilySearchResults(max_results=1),\n fetch_user_flight_information,\n search_flights,\n lookup_policy,\n update_ticket_to_new_flight,\n cancel_ticket,\n search_car_rentals,\n book_car_rental,\n update_car_rental,\n cancel_car_rental,\n search_hotels,\n book_hotel,\n update_hotel,\n cancel_hotel,\n search_trip_recommendations,\n book_excursion,\n update_excursion,\n cancel_excursion,\n]\npart_2_assistant_runnable = assistant_prompt | llm.bind_tools(part_2_tools)"] + "source": [ + "from typing import Annotated\n", + "\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import Runnable, RunnableConfig\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph.message import AnyMessage, add_messages\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list[AnyMessage], add_messages]\n", + " user_info: str\n", + "\n", + "\n", + "class Assistant:\n", + " def __init__(self, runnable: Runnable):\n", + " self.runnable = runnable\n", + "\n", + " def __call__(self, state: State, config: RunnableConfig):\n", + " while True:\n", + " result = self.runnable.invoke(state)\n", + " # If the LLM happens to return an empty response, we will re-prompt it\n", + " # for an actual response.\n", + " if not result.tool_calls and (\n", + " not result.content\n", + " or isinstance(result.content, list)\n", + " and not result.content[0].get(\"text\")\n", + " ):\n", + " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", + " state = {**state, \"messages\": messages}\n", + " else:\n", + " break\n", + " return {\"messages\": result}\n", + "\n", + "\n", + "# Haiku is faster and cheaper, but less accurate\n", + "# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n", + "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n", + "# You could also use OpenAI or another model, though you will likely have\n", + "# to adapt the prompts\n", + "# from langchain_openai import ChatOpenAI\n", + "\n", + "# llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n", + "\n", + "assistant_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful customer support assistant for Swiss Airlines. \"\n", + " \" Use the provided tools to search for flights, company policies, and other information to assist the user's queries. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \" If a search comes up empty, expand your search before giving up.\"\n", + " \"\\n\\nCurrent user:\\n\\n{user_info}\\n\"\n", + " \"\\nCurrent time: {time}.\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "part_2_tools = [\n", + " TavilySearchResults(max_results=1),\n", + " fetch_user_flight_information,\n", + " search_flights,\n", + " lookup_policy,\n", + " update_ticket_to_new_flight,\n", + " cancel_ticket,\n", + " search_car_rentals,\n", + " book_car_rental,\n", + " update_car_rental,\n", + " cancel_car_rental,\n", + " search_hotels,\n", + " book_hotel,\n", + " update_hotel,\n", + " cancel_hotel,\n", + " search_trip_recommendations,\n", + " book_excursion,\n", + " update_excursion,\n", + " cancel_excursion,\n", + "]\n", + "part_2_assistant_runnable = assistant_prompt | llm.bind_tools(part_2_tools)" + ] }, { "cell_type": "markdown", @@ -908,7 +1922,40 @@ "id": "910002ce-2431-4280-854a-a273c517611b", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import StateGraph\nfrom langgraph.prebuilt import tools_condition\n\nbuilder = StateGraph(State)\n\n\ndef user_info(state: State):\n return {\"user_info\": fetch_user_flight_information.invoke({})}\n\n\n# NEW: The fetch_user_info node runs first, meaning our assistant can see the user's flight information without\n# having to take an action\nbuilder.add_node(\"fetch_user_info\", user_info)\nbuilder.add_edge(START, \"fetch_user_info\")\nbuilder.add_node(\"assistant\", Assistant(part_2_assistant_runnable))\nbuilder.add_node(\"tools\", create_tool_node_with_fallback(part_2_tools))\nbuilder.add_edge(\"fetch_user_info\", \"assistant\")\nbuilder.add_conditional_edges(\n \"assistant\",\n tools_condition,\n)\nbuilder.add_edge(\"tools\", \"assistant\")\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\npart_2_graph = builder.compile(\n checkpointer=memory,\n # NEW: The graph will always halt before executing the \"tools\" node.\n # The user can approve or reject (or even alter the request) before\n # the assistant continues\n interrupt_before=[\"tools\"],\n)"] + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import StateGraph\n", + "from langgraph.prebuilt import tools_condition\n", + "\n", + "builder = StateGraph(State)\n", + "\n", + "\n", + "def user_info(state: State):\n", + " return {\"user_info\": fetch_user_flight_information.invoke({})}\n", + "\n", + "\n", + "# NEW: The fetch_user_info node runs first, meaning our assistant can see the user's flight information without\n", + "# having to take an action\n", + "builder.add_node(\"fetch_user_info\", user_info)\n", + "builder.add_edge(START, \"fetch_user_info\")\n", + "builder.add_node(\"assistant\", Assistant(part_2_assistant_runnable))\n", + "builder.add_node(\"tools\", create_tool_node_with_fallback(part_2_tools))\n", + "builder.add_edge(\"fetch_user_info\", \"assistant\")\n", + "builder.add_conditional_edges(\n", + " \"assistant\",\n", + " tools_condition,\n", + ")\n", + "builder.add_edge(\"tools\", \"assistant\")\n", + "\n", + "memory = MemorySaver()\n", + "part_2_graph = builder.compile(\n", + " checkpointer=memory,\n", + " # NEW: The graph will always halt before executing the \"tools\" node.\n", + " # The user can approve or reject (or even alter the request) before\n", + " # the assistant continues\n", + " interrupt_before=[\"tools\"],\n", + ")" + ] }, { "cell_type": "code", @@ -927,7 +1974,15 @@ "output_type": "display_data" } ], - "source": ["from IPython.display import Image, display\n\ntry:\n display(Image(part_2_graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(part_2_graph.get_graph(xray=True).draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "markdown", @@ -1238,7 +2293,64 @@ ] } ], - "source": ["import shutil\nimport uuid\n\n# Update with the backup file so we can restart from the original place in each section\nshutil.copy(backup_file, db)\nthread_id = str(uuid.uuid4())\n\nconfig = {\n \"configurable\": {\n # The passenger_id is used in our flight tools to\n # fetch the user's flight information\n \"passenger_id\": \"3442 587242\",\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\n\n_printed = set()\n# We can reuse the tutorial questions from part 1 to see how it does.\nfor question in tutorial_questions:\n events = part_2_graph.stream(\n {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n )\n for event in events:\n _print_event(event, _printed)\n snapshot = part_2_graph.get_state(config)\n while snapshot.next:\n # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n user_input = input(\n \"Do you approve of the above actions? Type 'y' to continue;\"\n \" otherwise, explain your requested changed.\\n\\n\"\n )\n if user_input.strip() == \"y\":\n # Just continue\n result = part_2_graph.invoke(\n None,\n config,\n )\n else:\n # Satisfy the tool invocation by\n # providing instructions on the requested changes / change of mind\n result = part_2_graph.invoke(\n {\n \"messages\": [\n ToolMessage(\n tool_call_id=event[\"messages\"][-1].tool_calls[0][\"id\"],\n content=f\"API call denied by user. Reasoning: '{user_input}'. Continue assisting, accounting for the user's input.\",\n )\n ]\n },\n config,\n )\n snapshot = part_2_graph.get_state(config)"] + "source": [ + "import shutil\n", + "import uuid\n", + "\n", + "# Update with the backup file so we can restart from the original place in each section\n", + "shutil.copy(backup_file, db)\n", + "thread_id = str(uuid.uuid4())\n", + "\n", + "config = {\n", + " \"configurable\": {\n", + " # The passenger_id is used in our flight tools to\n", + " # fetch the user's flight information\n", + " \"passenger_id\": \"3442 587242\",\n", + " # Checkpoints are accessed by thread_id\n", + " \"thread_id\": thread_id,\n", + " }\n", + "}\n", + "\n", + "\n", + "_printed = set()\n", + "# We can reuse the tutorial questions from part 1 to see how it does.\n", + "for question in tutorial_questions:\n", + " events = part_2_graph.stream(\n", + " {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n", + " )\n", + " for event in events:\n", + " _print_event(event, _printed)\n", + " snapshot = part_2_graph.get_state(config)\n", + " while snapshot.next:\n", + " # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n", + " # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n", + " # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n", + " user_input = input(\n", + " \"Do you approve of the above actions? Type 'y' to continue;\"\n", + " \" otherwise, explain your requested changed.\\n\\n\"\n", + " )\n", + " if user_input.strip() == \"y\":\n", + " # Just continue\n", + " result = part_2_graph.invoke(\n", + " None,\n", + " config,\n", + " )\n", + " else:\n", + " # Satisfy the tool invocation by\n", + " # providing instructions on the requested changes / change of mind\n", + " result = part_2_graph.invoke(\n", + " {\n", + " \"messages\": [\n", + " ToolMessage(\n", + " tool_call_id=event[\"messages\"][-1].tool_calls[0][\"id\"],\n", + " content=f\"API call denied by user. Reasoning: '{user_input}'. Continue assisting, accounting for the user's input.\",\n", + " )\n", + " ]\n", + " },\n", + " config,\n", + " )\n", + " snapshot = part_2_graph.get_state(config)" + ] }, { "cell_type": "markdown", @@ -1283,7 +2395,102 @@ "id": "20f99193-9195-42ae-8df1-0cf1489a164c", "metadata": {}, "outputs": [], - "source": ["from typing import Annotated\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_community.tools.tavily_search import TavilySearchResults\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.runnables import Runnable, RunnableConfig\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass State(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]\n user_info: str\n\n\nclass Assistant:\n def __init__(self, runnable: Runnable):\n self.runnable = runnable\n\n def __call__(self, state: State, config: RunnableConfig):\n while True:\n result = self.runnable.invoke(state)\n # If the LLM happens to return an empty response, we will re-prompt it\n # for an actual response.\n if not result.tool_calls and (\n not result.content\n or isinstance(result.content, list)\n and not result.content[0].get(\"text\")\n ):\n messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n state = {**state, \"messages\": messages}\n messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n state = {**state, \"messages\": messages}\n else:\n break\n return {\"messages\": result}\n\n\n# Haiku is faster and cheaper, but less accurate\n# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\nllm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n# You can update the LLMs, though you may need to update the prompts\n# from langchain_openai import ChatOpenAI\n\n# llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n\nassistant_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a helpful customer support assistant for Swiss Airlines. \"\n \" Use the provided tools to search for flights, company policies, and other information to assist the user's queries. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \" If a search comes up empty, expand your search before giving up.\"\n \"\\n\\nCurrent user:\\n\\n{user_info}\\n\"\n \"\\nCurrent time: {time}.\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\n\n# \"Read\"-only tools (such as retrievers) don't need a user confirmation to use\npart_3_safe_tools = [\n TavilySearchResults(max_results=1),\n fetch_user_flight_information,\n search_flights,\n lookup_policy,\n search_car_rentals,\n search_hotels,\n search_trip_recommendations,\n]\n\n# These tools all change the user's reservations.\n# The user has the right to control what decisions are made\npart_3_sensitive_tools = [\n update_ticket_to_new_flight,\n cancel_ticket,\n book_car_rental,\n update_car_rental,\n cancel_car_rental,\n book_hotel,\n update_hotel,\n cancel_hotel,\n book_excursion,\n update_excursion,\n cancel_excursion,\n]\nsensitive_tool_names = {t.name for t in part_3_sensitive_tools}\n# Our LLM doesn't have to know which nodes it has to route to. In its 'mind', it's just invoking functions.\npart_3_assistant_runnable = assistant_prompt | llm.bind_tools(\n part_3_safe_tools + part_3_sensitive_tools\n)"] + "source": [ + "from typing import Annotated\n", + "\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import Runnable, RunnableConfig\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph.message import AnyMessage, add_messages\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list[AnyMessage], add_messages]\n", + " user_info: str\n", + "\n", + "\n", + "class Assistant:\n", + " def __init__(self, runnable: Runnable):\n", + " self.runnable = runnable\n", + "\n", + " def __call__(self, state: State, config: RunnableConfig):\n", + " while True:\n", + " result = self.runnable.invoke(state)\n", + " # If the LLM happens to return an empty response, we will re-prompt it\n", + " # for an actual response.\n", + " if not result.tool_calls and (\n", + " not result.content\n", + " or isinstance(result.content, list)\n", + " and not result.content[0].get(\"text\")\n", + " ):\n", + " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", + " state = {**state, \"messages\": messages}\n", + " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", + " state = {**state, \"messages\": messages}\n", + " else:\n", + " break\n", + " return {\"messages\": result}\n", + "\n", + "\n", + "# Haiku is faster and cheaper, but less accurate\n", + "# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n", + "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n", + "# You can update the LLMs, though you may need to update the prompts\n", + "# from langchain_openai import ChatOpenAI\n", + "\n", + "# llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n", + "\n", + "assistant_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful customer support assistant for Swiss Airlines. \"\n", + " \" Use the provided tools to search for flights, company policies, and other information to assist the user's queries. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \" If a search comes up empty, expand your search before giving up.\"\n", + " \"\\n\\nCurrent user:\\n\\n{user_info}\\n\"\n", + " \"\\nCurrent time: {time}.\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "\n", + "# \"Read\"-only tools (such as retrievers) don't need a user confirmation to use\n", + "part_3_safe_tools = [\n", + " TavilySearchResults(max_results=1),\n", + " fetch_user_flight_information,\n", + " search_flights,\n", + " lookup_policy,\n", + " search_car_rentals,\n", + " search_hotels,\n", + " search_trip_recommendations,\n", + "]\n", + "\n", + "# These tools all change the user's reservations.\n", + "# The user has the right to control what decisions are made\n", + "part_3_sensitive_tools = [\n", + " update_ticket_to_new_flight,\n", + " cancel_ticket,\n", + " book_car_rental,\n", + " update_car_rental,\n", + " cancel_car_rental,\n", + " book_hotel,\n", + " update_hotel,\n", + " cancel_hotel,\n", + " book_excursion,\n", + " update_excursion,\n", + " cancel_excursion,\n", + "]\n", + "sensitive_tool_names = {t.name for t in part_3_sensitive_tools}\n", + "# Our LLM doesn't have to know which nodes it has to route to. In its 'mind', it's just invoking functions.\n", + "part_3_assistant_runnable = assistant_prompt | llm.bind_tools(\n", + " part_3_safe_tools + part_3_sensitive_tools\n", + ")" + ] }, { "cell_type": "markdown", @@ -1301,7 +2508,63 @@ "id": "928b756f-2934-4b1b-95d1-0c4f974b978f", "metadata": {}, "outputs": [], - "source": ["from typing import Literal\n\nfrom langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import StateGraph\nfrom langgraph.prebuilt import tools_condition\n\nbuilder = StateGraph(State)\n\n\ndef user_info(state: State):\n return {\"user_info\": fetch_user_flight_information.invoke({})}\n\n\n# NEW: The fetch_user_info node runs first, meaning our assistant can see the user's flight information without\n# having to take an action\nbuilder.add_node(\"fetch_user_info\", user_info)\nbuilder.add_edge(START, \"fetch_user_info\")\nbuilder.add_node(\"assistant\", Assistant(part_3_assistant_runnable))\nbuilder.add_node(\"safe_tools\", create_tool_node_with_fallback(part_3_safe_tools))\nbuilder.add_node(\n \"sensitive_tools\", create_tool_node_with_fallback(part_3_sensitive_tools)\n)\n# Define logic\nbuilder.add_edge(\"fetch_user_info\", \"assistant\")\n\n\ndef route_tools(state: State) -> Literal[\"safe_tools\", \"sensitive_tools\", \"__end__\"]:\n next_node = tools_condition(state)\n # If no tools are invoked, return to the user\n if next_node == END:\n return END\n ai_message = state[\"messages\"][-1]\n # This assumes single tool calls. To handle parallel tool calling, you'd want to\n # use an ANY condition\n first_tool_call = ai_message.tool_calls[0]\n if first_tool_call[\"name\"] in sensitive_tool_names:\n return \"sensitive_tools\"\n return \"safe_tools\"\n\n\nbuilder.add_conditional_edges(\n \"assistant\",\n route_tools,\n)\nbuilder.add_edge(\"safe_tools\", \"assistant\")\nbuilder.add_edge(\"sensitive_tools\", \"assistant\")\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")\npart_3_graph = builder.compile(\n checkpointer=memory,\n # NEW: The graph will always halt before executing the \"tools\" node.\n # The user can approve or reject (or even alter the request) before\n # the assistant continues\n interrupt_before=[\"sensitive_tools\"],\n)"] + "source": [ + "from typing import Literal\n", + "\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import StateGraph\n", + "from langgraph.prebuilt import tools_condition\n", + "\n", + "builder = StateGraph(State)\n", + "\n", + "\n", + "def user_info(state: State):\n", + " return {\"user_info\": fetch_user_flight_information.invoke({})}\n", + "\n", + "\n", + "# NEW: The fetch_user_info node runs first, meaning our assistant can see the user's flight information without\n", + "# having to take an action\n", + "builder.add_node(\"fetch_user_info\", user_info)\n", + "builder.add_edge(START, \"fetch_user_info\")\n", + "builder.add_node(\"assistant\", Assistant(part_3_assistant_runnable))\n", + "builder.add_node(\"safe_tools\", create_tool_node_with_fallback(part_3_safe_tools))\n", + "builder.add_node(\n", + " \"sensitive_tools\", create_tool_node_with_fallback(part_3_sensitive_tools)\n", + ")\n", + "# Define logic\n", + "builder.add_edge(\"fetch_user_info\", \"assistant\")\n", + "\n", + "\n", + "def route_tools(state: State) -> Literal[\"safe_tools\", \"sensitive_tools\", \"__end__\"]:\n", + " next_node = tools_condition(state)\n", + " # If no tools are invoked, return to the user\n", + " if next_node == END:\n", + " return END\n", + " ai_message = state[\"messages\"][-1]\n", + " # This assumes single tool calls. To handle parallel tool calling, you'd want to\n", + " # use an ANY condition\n", + " first_tool_call = ai_message.tool_calls[0]\n", + " if first_tool_call[\"name\"] in sensitive_tool_names:\n", + " return \"sensitive_tools\"\n", + " return \"safe_tools\"\n", + "\n", + "\n", + "builder.add_conditional_edges(\n", + " \"assistant\",\n", + " route_tools,\n", + ")\n", + "builder.add_edge(\"safe_tools\", \"assistant\")\n", + "builder.add_edge(\"sensitive_tools\", \"assistant\")\n", + "\n", + "memory = MemorySaver()\n", + "part_3_graph = builder.compile(\n", + " checkpointer=memory,\n", + " # NEW: The graph will always halt before executing the \"tools\" node.\n", + " # The user can approve or reject (or even alter the request) before\n", + " # the assistant continues\n", + " interrupt_before=[\"sensitive_tools\"],\n", + ")" + ] }, { "cell_type": "code", @@ -1320,7 +2583,15 @@ "output_type": "display_data" } ], - "source": ["from IPython.display import Image, display\n\ntry:\n display(Image(part_3_graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(part_3_graph.get_graph(xray=True).draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "markdown", @@ -1626,7 +2897,81 @@ ] } ], - "source": ["import shutil\nimport uuid\n\n# Update with the backup file so we can restart from the original place in each section\nshutil.copy(backup_file, db)\nthread_id = str(uuid.uuid4())\n\nconfig = {\n \"configurable\": {\n # The passenger_id is used in our flight tools to\n # fetch the user's flight information\n \"passenger_id\": \"3442 587242\",\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\ntutorial_questions = [\n \"Hi there, what time is my flight?\",\n \"Am i allowed to update my flight to something sooner? I want to leave later today.\",\n \"Update my flight to sometime next week then\",\n \"The next available option is great\",\n \"what about lodging and transportation?\",\n \"Yeah i think i'd like an affordable hotel for my week-long stay (7 days). And I'll want to rent a car.\",\n \"OK could you place a reservation for your recommended hotel? It sounds nice.\",\n \"yes go ahead and book anything that's moderate expense and has availability.\",\n \"Now for a car, what are my options?\",\n \"Awesome let's just get the cheapest option. Go ahead and book for 7 days\",\n \"Cool so now what recommendations do you have on excursions?\",\n \"Are they available while I'm there?\",\n \"interesting - i like the museums, what options are there? \",\n \"OK great pick one and book it for my second day there.\",\n]\n\n\n_printed = set()\n# We can reuse the tutorial questions from part 1 to see how it does.\nfor question in tutorial_questions:\n events = part_3_graph.stream(\n {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n )\n for event in events:\n _print_event(event, _printed)\n snapshot = part_3_graph.get_state(config)\n while snapshot.next:\n # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n user_input = input(\n \"Do you approve of the above actions? Type 'y' to continue;\"\n \" otherwise, explain your requested changed.\\n\\n\"\n )\n if user_input.strip() == \"y\":\n # Just continue\n result = part_3_graph.invoke(\n None,\n config,\n )\n else:\n # Satisfy the tool invocation by\n # providing instructions on the requested changes / change of mind\n result = part_3_graph.invoke(\n {\n \"messages\": [\n ToolMessage(\n tool_call_id=event[\"messages\"][-1].tool_calls[0][\"id\"],\n content=f\"API call denied by user. Reasoning: '{user_input}'. Continue assisting, accounting for the user's input.\",\n )\n ]\n },\n config,\n )\n snapshot = part_3_graph.get_state(config)"] + "source": [ + "import shutil\n", + "import uuid\n", + "\n", + "# Update with the backup file so we can restart from the original place in each section\n", + "shutil.copy(backup_file, db)\n", + "thread_id = str(uuid.uuid4())\n", + "\n", + "config = {\n", + " \"configurable\": {\n", + " # The passenger_id is used in our flight tools to\n", + " # fetch the user's flight information\n", + " \"passenger_id\": \"3442 587242\",\n", + " # Checkpoints are accessed by thread_id\n", + " \"thread_id\": thread_id,\n", + " }\n", + "}\n", + "\n", + "tutorial_questions = [\n", + " \"Hi there, what time is my flight?\",\n", + " \"Am i allowed to update my flight to something sooner? I want to leave later today.\",\n", + " \"Update my flight to sometime next week then\",\n", + " \"The next available option is great\",\n", + " \"what about lodging and transportation?\",\n", + " \"Yeah i think i'd like an affordable hotel for my week-long stay (7 days). And I'll want to rent a car.\",\n", + " \"OK could you place a reservation for your recommended hotel? It sounds nice.\",\n", + " \"yes go ahead and book anything that's moderate expense and has availability.\",\n", + " \"Now for a car, what are my options?\",\n", + " \"Awesome let's just get the cheapest option. Go ahead and book for 7 days\",\n", + " \"Cool so now what recommendations do you have on excursions?\",\n", + " \"Are they available while I'm there?\",\n", + " \"interesting - i like the museums, what options are there? \",\n", + " \"OK great pick one and book it for my second day there.\",\n", + "]\n", + "\n", + "\n", + "_printed = set()\n", + "# We can reuse the tutorial questions from part 1 to see how it does.\n", + "for question in tutorial_questions:\n", + " events = part_3_graph.stream(\n", + " {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n", + " )\n", + " for event in events:\n", + " _print_event(event, _printed)\n", + " snapshot = part_3_graph.get_state(config)\n", + " while snapshot.next:\n", + " # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n", + " # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n", + " # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n", + " user_input = input(\n", + " \"Do you approve of the above actions? Type 'y' to continue;\"\n", + " \" otherwise, explain your requested changed.\\n\\n\"\n", + " )\n", + " if user_input.strip() == \"y\":\n", + " # Just continue\n", + " result = part_3_graph.invoke(\n", + " None,\n", + " config,\n", + " )\n", + " else:\n", + " # Satisfy the tool invocation by\n", + " # providing instructions on the requested changes / change of mind\n", + " result = part_3_graph.invoke(\n", + " {\n", + " \"messages\": [\n", + " ToolMessage(\n", + " tool_call_id=event[\"messages\"][-1].tool_calls[0][\"id\"],\n", + " content=f\"API call denied by user. Reasoning: '{user_input}'. Continue assisting, accounting for the user's input.\",\n", + " )\n", + " ]\n", + " },\n", + " config,\n", + " )\n", + " snapshot = part_3_graph.get_state(config)" + ] }, { "cell_type": "markdown", @@ -1672,7 +3017,39 @@ "id": "2997e1f9-3a4b-4794-b71f-992da3a644fa", "metadata": {}, "outputs": [], - "source": ["from typing import Annotated, Literal, Optional\n\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\ndef update_dialog_stack(left: list[str], right: Optional[str]) -> list[str]:\n \"\"\"Push or pop the state.\"\"\"\n if right is None:\n return left\n if right == \"pop\":\n return left[:-1]\n return left + [right]\n\n\nclass State(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]\n user_info: str\n dialog_state: Annotated[\n list[\n Literal[\n \"assistant\",\n \"update_flight\",\n \"book_car_rental\",\n \"book_hotel\",\n \"book_excursion\",\n ]\n ],\n update_dialog_stack,\n ]"] + "source": [ + "from typing import Annotated, Literal, Optional\n", + "\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph.message import AnyMessage, add_messages\n", + "\n", + "\n", + "def update_dialog_stack(left: list[str], right: Optional[str]) -> list[str]:\n", + " \"\"\"Push or pop the state.\"\"\"\n", + " if right is None:\n", + " return left\n", + " if right == \"pop\":\n", + " return left[:-1]\n", + " return left + [right]\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list[AnyMessage], add_messages]\n", + " user_info: str\n", + " dialog_state: Annotated[\n", + " list[\n", + " Literal[\n", + " \"assistant\",\n", + " \"update_flight\",\n", + " \"book_car_rental\",\n", + " \"book_hotel\",\n", + " \"book_excursion\",\n", + " ]\n", + " ],\n", + " update_dialog_stack,\n", + " ]" + ] }, { "cell_type": "markdown", @@ -1702,7 +3079,301 @@ "id": "1ef67c85-b999-406c-a745-09fdc0dfa0b3", "metadata": {}, "outputs": [], - "source": ["from langchain_anthropic import ChatAnthropic\nfrom langchain_community.tools.tavily_search import TavilySearchResults\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_core.runnables import Runnable, RunnableConfig\n\n\nclass Assistant:\n def __init__(self, runnable: Runnable):\n self.runnable = runnable\n\n def __call__(self, state: State, config: RunnableConfig):\n while True:\n result = self.runnable.invoke(state)\n\n if not result.tool_calls and (\n not result.content\n or isinstance(result.content, list)\n and not result.content[0].get(\"text\")\n ):\n messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n state = {**state, \"messages\": messages}\n messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n state = {**state, \"messages\": messages}\n else:\n break\n return {\"messages\": result}\n\n\nclass CompleteOrEscalate(BaseModel):\n \"\"\"A tool to mark the current task as completed and/or to escalate control of the dialog to the main assistant,\n who can re-route the dialog based on the user's needs.\"\"\"\n\n cancel: bool = True\n reason: str\n\n class Config:\n schema_extra = {\n \"example\": {\n \"cancel\": True,\n \"reason\": \"User changed their mind about the current task.\",\n },\n \"example 2\": {\n \"cancel\": True,\n \"reason\": \"I have fully completed the task.\",\n },\n \"example 3\": {\n \"cancel\": False,\n \"reason\": \"I need to search the user's emails or calendar for more information.\",\n },\n }\n\n\n# Flight booking assistant\n\nflight_booking_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a specialized assistant for handling flight updates. \"\n \" The primary assistant delegates work to you whenever the user needs help updating their bookings. \"\n \"Confirm the updated flight details with the customer and inform them of any additional fees. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n \"\\n\\nCurrent user flight information:\\n\\n{user_info}\\n\"\n \"\\nCurrent time: {time}.\"\n \"\\n\\nIf the user needs help, and none of your tools are appropriate for it, then\"\n ' \"CompleteOrEscalate\" the dialog to the host assistant. Do not waste the user\\'s time. Do not make up invalid tools or functions.',\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\nupdate_flight_safe_tools = [search_flights]\nupdate_flight_sensitive_tools = [update_ticket_to_new_flight, cancel_ticket]\nupdate_flight_tools = update_flight_safe_tools + update_flight_sensitive_tools\nupdate_flight_runnable = flight_booking_prompt | llm.bind_tools(\n update_flight_tools + [CompleteOrEscalate]\n)\n\n# Hotel Booking Assistant\nbook_hotel_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a specialized assistant for handling hotel bookings. \"\n \"The primary assistant delegates work to you whenever the user needs help booking a hotel. \"\n \"Search for available hotels based on the user's preferences and confirm the booking details with the customer. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n \"\\nCurrent time: {time}.\"\n '\\n\\nIf the user needs help, and none of your tools are appropriate for it, then \"CompleteOrEscalate\" the dialog to the host assistant.'\n \" Do not waste the user's time. Do not make up invalid tools or functions.\"\n \"\\n\\nSome examples for which you should CompleteOrEscalate:\\n\"\n \" - 'what's the weather like this time of year?'\\n\"\n \" - 'nevermind i think I'll book separately'\\n\"\n \" - 'i need to figure out transportation while i'm there'\\n\"\n \" - 'Oh wait i haven't booked my flight yet i'll do that first'\\n\"\n \" - 'Hotel booking confirmed'\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\nbook_hotel_safe_tools = [search_hotels]\nbook_hotel_sensitive_tools = [book_hotel, update_hotel, cancel_hotel]\nbook_hotel_tools = book_hotel_safe_tools + book_hotel_sensitive_tools\nbook_hotel_runnable = book_hotel_prompt | llm.bind_tools(\n book_hotel_tools + [CompleteOrEscalate]\n)\n\n# Car Rental Assistant\nbook_car_rental_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a specialized assistant for handling car rental bookings. \"\n \"The primary assistant delegates work to you whenever the user needs help booking a car rental. \"\n \"Search for available car rentals based on the user's preferences and confirm the booking details with the customer. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n \"\\nCurrent time: {time}.\"\n \"\\n\\nIf the user needs help, and none of your tools are appropriate for it, then \"\n '\"CompleteOrEscalate\" the dialog to the host assistant. Do not waste the user\\'s time. Do not make up invalid tools or functions.'\n \"\\n\\nSome examples for which you should CompleteOrEscalate:\\n\"\n \" - 'what's the weather like this time of year?'\\n\"\n \" - 'What flights are available?'\\n\"\n \" - 'nevermind i think I'll book separately'\\n\"\n \" - 'Oh wait i haven't booked my flight yet i'll do that first'\\n\"\n \" - 'Car rental booking confirmed'\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\nbook_car_rental_safe_tools = [search_car_rentals]\nbook_car_rental_sensitive_tools = [\n book_car_rental,\n update_car_rental,\n cancel_car_rental,\n]\nbook_car_rental_tools = book_car_rental_safe_tools + book_car_rental_sensitive_tools\nbook_car_rental_runnable = book_car_rental_prompt | llm.bind_tools(\n book_car_rental_tools + [CompleteOrEscalate]\n)\n\n# Excursion Assistant\n\nbook_excursion_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a specialized assistant for handling trip recommendations. \"\n \"The primary assistant delegates work to you whenever the user needs help booking a recommended trip. \"\n \"Search for available trip recommendations based on the user's preferences and confirm the booking details with the customer. \"\n \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n \"\\nCurrent time: {time}.\"\n '\\n\\nIf the user needs help, and none of your tools are appropriate for it, then \"CompleteOrEscalate\" the dialog to the host assistant. Do not waste the user\\'s time. Do not make up invalid tools or functions.'\n \"\\n\\nSome examples for which you should CompleteOrEscalate:\\n\"\n \" - 'nevermind i think I'll book separately'\\n\"\n \" - 'i need to figure out transportation while i'm there'\\n\"\n \" - 'Oh wait i haven't booked my flight yet i'll do that first'\\n\"\n \" - 'Excursion booking confirmed!'\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\n\nbook_excursion_safe_tools = [search_trip_recommendations]\nbook_excursion_sensitive_tools = [book_excursion, update_excursion, cancel_excursion]\nbook_excursion_tools = book_excursion_safe_tools + book_excursion_sensitive_tools\nbook_excursion_runnable = book_excursion_prompt | llm.bind_tools(\n book_excursion_tools + [CompleteOrEscalate]\n)\n\n\n# Primary Assistant\nclass ToFlightBookingAssistant(BaseModel):\n \"\"\"Transfers work to a specialized assistant to handle flight updates and cancellations.\"\"\"\n\n request: str = Field(\n description=\"Any necessary followup questions the update flight assistant should clarify before proceeding.\"\n )\n\n\nclass ToBookCarRental(BaseModel):\n \"\"\"Transfers work to a specialized assistant to handle car rental bookings.\"\"\"\n\n location: str = Field(\n description=\"The location where the user wants to rent a car.\"\n )\n start_date: str = Field(description=\"The start date of the car rental.\")\n end_date: str = Field(description=\"The end date of the car rental.\")\n request: str = Field(\n description=\"Any additional information or requests from the user regarding the car rental.\"\n )\n\n class Config:\n schema_extra = {\n \"example\": {\n \"location\": \"Basel\",\n \"start_date\": \"2023-07-01\",\n \"end_date\": \"2023-07-05\",\n \"request\": \"I need a compact car with automatic transmission.\",\n }\n }\n\n\nclass ToHotelBookingAssistant(BaseModel):\n \"\"\"Transfer work to a specialized assistant to handle hotel bookings.\"\"\"\n\n location: str = Field(\n description=\"The location where the user wants to book a hotel.\"\n )\n checkin_date: str = Field(description=\"The check-in date for the hotel.\")\n checkout_date: str = Field(description=\"The check-out date for the hotel.\")\n request: str = Field(\n description=\"Any additional information or requests from the user regarding the hotel booking.\"\n )\n\n class Config:\n schema_extra = {\n \"example\": {\n \"location\": \"Zurich\",\n \"checkin_date\": \"2023-08-15\",\n \"checkout_date\": \"2023-08-20\",\n \"request\": \"I prefer a hotel near the city center with a room that has a view.\",\n }\n }\n\n\nclass ToBookExcursion(BaseModel):\n \"\"\"Transfers work to a specialized assistant to handle trip recommendation and other excursion bookings.\"\"\"\n\n location: str = Field(\n description=\"The location where the user wants to book a recommended trip.\"\n )\n request: str = Field(\n description=\"Any additional information or requests from the user regarding the trip recommendation.\"\n )\n\n class Config:\n schema_extra = {\n \"example\": {\n \"location\": \"Lucerne\",\n \"request\": \"The user is interested in outdoor activities and scenic views.\",\n }\n }\n\n\n# The top-level assistant performs general Q&A and delegates specialized tasks to other assistants.\n# The task delegation is a simple form of semantic routing / does simple intent detection\n# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\nllm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n\nprimary_assistant_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a helpful customer support assistant for Swiss Airlines. \"\n \"Your primary role is to search for flight information and company policies to answer customer queries. \"\n \"If a customer requests to update or cancel a flight, book a car rental, book a hotel, or get trip recommendations, \"\n \"delegate the task to the appropriate specialized assistant by invoking the corresponding tool. You are not able to make these types of changes yourself.\"\n \" Only the specialized assistants are given permission to do this for the user.\"\n \"The user is not aware of the different specialized assistants, so do not mention them; just quietly delegate through function calls. \"\n \"Provide detailed information to the customer, and always double-check the database before concluding that information is unavailable. \"\n \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n \" If a search comes up empty, expand your search before giving up.\"\n \"\\n\\nCurrent user flight information:\\n\\n{user_info}\\n\"\n \"\\nCurrent time: {time}.\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n).partial(time=datetime.now())\nprimary_assistant_tools = [\n TavilySearchResults(max_results=1),\n search_flights,\n lookup_policy,\n]\nassistant_runnable = primary_assistant_prompt | llm.bind_tools(\n primary_assistant_tools\n + [\n ToFlightBookingAssistant,\n ToBookCarRental,\n ToHotelBookingAssistant,\n ToBookExcursion,\n ]\n)"] + "source": [ + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.pydantic_v1 import BaseModel, Field\n", + "from langchain_core.runnables import Runnable, RunnableConfig\n", + "\n", + "\n", + "class Assistant:\n", + " def __init__(self, runnable: Runnable):\n", + " self.runnable = runnable\n", + "\n", + " def __call__(self, state: State, config: RunnableConfig):\n", + " while True:\n", + " result = self.runnable.invoke(state)\n", + "\n", + " if not result.tool_calls and (\n", + " not result.content\n", + " or isinstance(result.content, list)\n", + " and not result.content[0].get(\"text\")\n", + " ):\n", + " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", + " state = {**state, \"messages\": messages}\n", + " messages = state[\"messages\"] + [(\"user\", \"Respond with a real output.\")]\n", + " state = {**state, \"messages\": messages}\n", + " else:\n", + " break\n", + " return {\"messages\": result}\n", + "\n", + "\n", + "class CompleteOrEscalate(BaseModel):\n", + " \"\"\"A tool to mark the current task as completed and/or to escalate control of the dialog to the main assistant,\n", + " who can re-route the dialog based on the user's needs.\"\"\"\n", + "\n", + " cancel: bool = True\n", + " reason: str\n", + "\n", + " class Config:\n", + " schema_extra = {\n", + " \"example\": {\n", + " \"cancel\": True,\n", + " \"reason\": \"User changed their mind about the current task.\",\n", + " },\n", + " \"example 2\": {\n", + " \"cancel\": True,\n", + " \"reason\": \"I have fully completed the task.\",\n", + " },\n", + " \"example 3\": {\n", + " \"cancel\": False,\n", + " \"reason\": \"I need to search the user's emails or calendar for more information.\",\n", + " },\n", + " }\n", + "\n", + "\n", + "# Flight booking assistant\n", + "\n", + "flight_booking_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a specialized assistant for handling flight updates. \"\n", + " \" The primary assistant delegates work to you whenever the user needs help updating their bookings. \"\n", + " \"Confirm the updated flight details with the customer and inform them of any additional fees. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n", + " \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n", + " \"\\n\\nCurrent user flight information:\\n\\n{user_info}\\n\"\n", + " \"\\nCurrent time: {time}.\"\n", + " \"\\n\\nIf the user needs help, and none of your tools are appropriate for it, then\"\n", + " ' \"CompleteOrEscalate\" the dialog to the host assistant. Do not waste the user\\'s time. Do not make up invalid tools or functions.',\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "update_flight_safe_tools = [search_flights]\n", + "update_flight_sensitive_tools = [update_ticket_to_new_flight, cancel_ticket]\n", + "update_flight_tools = update_flight_safe_tools + update_flight_sensitive_tools\n", + "update_flight_runnable = flight_booking_prompt | llm.bind_tools(\n", + " update_flight_tools + [CompleteOrEscalate]\n", + ")\n", + "\n", + "# Hotel Booking Assistant\n", + "book_hotel_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a specialized assistant for handling hotel bookings. \"\n", + " \"The primary assistant delegates work to you whenever the user needs help booking a hotel. \"\n", + " \"Search for available hotels based on the user's preferences and confirm the booking details with the customer. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n", + " \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n", + " \"\\nCurrent time: {time}.\"\n", + " '\\n\\nIf the user needs help, and none of your tools are appropriate for it, then \"CompleteOrEscalate\" the dialog to the host assistant.'\n", + " \" Do not waste the user's time. Do not make up invalid tools or functions.\"\n", + " \"\\n\\nSome examples for which you should CompleteOrEscalate:\\n\"\n", + " \" - 'what's the weather like this time of year?'\\n\"\n", + " \" - 'nevermind i think I'll book separately'\\n\"\n", + " \" - 'i need to figure out transportation while i'm there'\\n\"\n", + " \" - 'Oh wait i haven't booked my flight yet i'll do that first'\\n\"\n", + " \" - 'Hotel booking confirmed'\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "book_hotel_safe_tools = [search_hotels]\n", + "book_hotel_sensitive_tools = [book_hotel, update_hotel, cancel_hotel]\n", + "book_hotel_tools = book_hotel_safe_tools + book_hotel_sensitive_tools\n", + "book_hotel_runnable = book_hotel_prompt | llm.bind_tools(\n", + " book_hotel_tools + [CompleteOrEscalate]\n", + ")\n", + "\n", + "# Car Rental Assistant\n", + "book_car_rental_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a specialized assistant for handling car rental bookings. \"\n", + " \"The primary assistant delegates work to you whenever the user needs help booking a car rental. \"\n", + " \"Search for available car rentals based on the user's preferences and confirm the booking details with the customer. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n", + " \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n", + " \"\\nCurrent time: {time}.\"\n", + " \"\\n\\nIf the user needs help, and none of your tools are appropriate for it, then \"\n", + " '\"CompleteOrEscalate\" the dialog to the host assistant. Do not waste the user\\'s time. Do not make up invalid tools or functions.'\n", + " \"\\n\\nSome examples for which you should CompleteOrEscalate:\\n\"\n", + " \" - 'what's the weather like this time of year?'\\n\"\n", + " \" - 'What flights are available?'\\n\"\n", + " \" - 'nevermind i think I'll book separately'\\n\"\n", + " \" - 'Oh wait i haven't booked my flight yet i'll do that first'\\n\"\n", + " \" - 'Car rental booking confirmed'\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "book_car_rental_safe_tools = [search_car_rentals]\n", + "book_car_rental_sensitive_tools = [\n", + " book_car_rental,\n", + " update_car_rental,\n", + " cancel_car_rental,\n", + "]\n", + "book_car_rental_tools = book_car_rental_safe_tools + book_car_rental_sensitive_tools\n", + "book_car_rental_runnable = book_car_rental_prompt | llm.bind_tools(\n", + " book_car_rental_tools + [CompleteOrEscalate]\n", + ")\n", + "\n", + "# Excursion Assistant\n", + "\n", + "book_excursion_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a specialized assistant for handling trip recommendations. \"\n", + " \"The primary assistant delegates work to you whenever the user needs help booking a recommended trip. \"\n", + " \"Search for available trip recommendations based on the user's preferences and confirm the booking details with the customer. \"\n", + " \"If you need more information or the customer changes their mind, escalate the task back to the main assistant.\"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \" Remember that a booking isn't completed until after the relevant tool has successfully been used.\"\n", + " \"\\nCurrent time: {time}.\"\n", + " '\\n\\nIf the user needs help, and none of your tools are appropriate for it, then \"CompleteOrEscalate\" the dialog to the host assistant. Do not waste the user\\'s time. Do not make up invalid tools or functions.'\n", + " \"\\n\\nSome examples for which you should CompleteOrEscalate:\\n\"\n", + " \" - 'nevermind i think I'll book separately'\\n\"\n", + " \" - 'i need to figure out transportation while i'm there'\\n\"\n", + " \" - 'Oh wait i haven't booked my flight yet i'll do that first'\\n\"\n", + " \" - 'Excursion booking confirmed!'\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "\n", + "book_excursion_safe_tools = [search_trip_recommendations]\n", + "book_excursion_sensitive_tools = [book_excursion, update_excursion, cancel_excursion]\n", + "book_excursion_tools = book_excursion_safe_tools + book_excursion_sensitive_tools\n", + "book_excursion_runnable = book_excursion_prompt | llm.bind_tools(\n", + " book_excursion_tools + [CompleteOrEscalate]\n", + ")\n", + "\n", + "\n", + "# Primary Assistant\n", + "class ToFlightBookingAssistant(BaseModel):\n", + " \"\"\"Transfers work to a specialized assistant to handle flight updates and cancellations.\"\"\"\n", + "\n", + " request: str = Field(\n", + " description=\"Any necessary followup questions the update flight assistant should clarify before proceeding.\"\n", + " )\n", + "\n", + "\n", + "class ToBookCarRental(BaseModel):\n", + " \"\"\"Transfers work to a specialized assistant to handle car rental bookings.\"\"\"\n", + "\n", + " location: str = Field(\n", + " description=\"The location where the user wants to rent a car.\"\n", + " )\n", + " start_date: str = Field(description=\"The start date of the car rental.\")\n", + " end_date: str = Field(description=\"The end date of the car rental.\")\n", + " request: str = Field(\n", + " description=\"Any additional information or requests from the user regarding the car rental.\"\n", + " )\n", + "\n", + " class Config:\n", + " schema_extra = {\n", + " \"example\": {\n", + " \"location\": \"Basel\",\n", + " \"start_date\": \"2023-07-01\",\n", + " \"end_date\": \"2023-07-05\",\n", + " \"request\": \"I need a compact car with automatic transmission.\",\n", + " }\n", + " }\n", + "\n", + "\n", + "class ToHotelBookingAssistant(BaseModel):\n", + " \"\"\"Transfer work to a specialized assistant to handle hotel bookings.\"\"\"\n", + "\n", + " location: str = Field(\n", + " description=\"The location where the user wants to book a hotel.\"\n", + " )\n", + " checkin_date: str = Field(description=\"The check-in date for the hotel.\")\n", + " checkout_date: str = Field(description=\"The check-out date for the hotel.\")\n", + " request: str = Field(\n", + " description=\"Any additional information or requests from the user regarding the hotel booking.\"\n", + " )\n", + "\n", + " class Config:\n", + " schema_extra = {\n", + " \"example\": {\n", + " \"location\": \"Zurich\",\n", + " \"checkin_date\": \"2023-08-15\",\n", + " \"checkout_date\": \"2023-08-20\",\n", + " \"request\": \"I prefer a hotel near the city center with a room that has a view.\",\n", + " }\n", + " }\n", + "\n", + "\n", + "class ToBookExcursion(BaseModel):\n", + " \"\"\"Transfers work to a specialized assistant to handle trip recommendation and other excursion bookings.\"\"\"\n", + "\n", + " location: str = Field(\n", + " description=\"The location where the user wants to book a recommended trip.\"\n", + " )\n", + " request: str = Field(\n", + " description=\"Any additional information or requests from the user regarding the trip recommendation.\"\n", + " )\n", + "\n", + " class Config:\n", + " schema_extra = {\n", + " \"example\": {\n", + " \"location\": \"Lucerne\",\n", + " \"request\": \"The user is interested in outdoor activities and scenic views.\",\n", + " }\n", + " }\n", + "\n", + "\n", + "# The top-level assistant performs general Q&A and delegates specialized tasks to other assistants.\n", + "# The task delegation is a simple form of semantic routing / does simple intent detection\n", + "# llm = ChatAnthropic(model=\"claude-3-haiku-20240307\")\n", + "llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=1)\n", + "\n", + "primary_assistant_prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You are a helpful customer support assistant for Swiss Airlines. \"\n", + " \"Your primary role is to search for flight information and company policies to answer customer queries. \"\n", + " \"If a customer requests to update or cancel a flight, book a car rental, book a hotel, or get trip recommendations, \"\n", + " \"delegate the task to the appropriate specialized assistant by invoking the corresponding tool. You are not able to make these types of changes yourself.\"\n", + " \" Only the specialized assistants are given permission to do this for the user.\"\n", + " \"The user is not aware of the different specialized assistants, so do not mention them; just quietly delegate through function calls. \"\n", + " \"Provide detailed information to the customer, and always double-check the database before concluding that information is unavailable. \"\n", + " \" When searching, be persistent. Expand your query bounds if the first search returns no results. \"\n", + " \" If a search comes up empty, expand your search before giving up.\"\n", + " \"\\n\\nCurrent user flight information:\\n\\n{user_info}\\n\"\n", + " \"\\nCurrent time: {time}.\",\n", + " ),\n", + " (\"placeholder\", \"{messages}\"),\n", + " ]\n", + ").partial(time=datetime.now())\n", + "primary_assistant_tools = [\n", + " TavilySearchResults(max_results=1),\n", + " search_flights,\n", + " lookup_policy,\n", + "]\n", + "assistant_runnable = primary_assistant_prompt | llm.bind_tools(\n", + " primary_assistant_tools\n", + " + [\n", + " ToFlightBookingAssistant,\n", + " ToBookCarRental,\n", + " ToHotelBookingAssistant,\n", + " ToBookExcursion,\n", + " ]\n", + ")" + ] }, { "cell_type": "markdown", @@ -1725,7 +3396,31 @@ "id": "fb812818-99c9-4bf3-b1e5-a394c7b9058d", "metadata": {}, "outputs": [], - "source": ["from typing import Callable\n\nfrom langchain_core.messages import ToolMessage\n\n\ndef create_entry_node(assistant_name: str, new_dialog_state: str) -> Callable:\n def entry_node(state: State) -> dict:\n tool_call_id = state[\"messages\"][-1].tool_calls[0][\"id\"]\n return {\n \"messages\": [\n ToolMessage(\n content=f\"The assistant is now the {assistant_name}. Reflect on the above conversation between the host assistant and the user.\"\n f\" The user's intent is unsatisfied. Use the provided tools to assist the user. Remember, you are {assistant_name},\"\n \" and the booking, update, other other action is not complete until after you have successfully invoked the appropriate tool.\"\n \" If the user changes their mind or needs help for other tasks, call the CompleteOrEscalate function to let the primary host assistant take control.\"\n \" Do not mention who you are - just act as the proxy for the assistant.\",\n tool_call_id=tool_call_id,\n )\n ],\n \"dialog_state\": new_dialog_state,\n }\n\n return entry_node"] + "source": [ + "from typing import Callable\n", + "\n", + "from langchain_core.messages import ToolMessage\n", + "\n", + "\n", + "def create_entry_node(assistant_name: str, new_dialog_state: str) -> Callable:\n", + " def entry_node(state: State) -> dict:\n", + " tool_call_id = state[\"messages\"][-1].tool_calls[0][\"id\"]\n", + " return {\n", + " \"messages\": [\n", + " ToolMessage(\n", + " content=f\"The assistant is now the {assistant_name}. Reflect on the above conversation between the host assistant and the user.\"\n", + " f\" The user's intent is unsatisfied. Use the provided tools to assist the user. Remember, you are {assistant_name},\"\n", + " \" and the booking, update, other other action is not complete until after you have successfully invoked the appropriate tool.\"\n", + " \" If the user changes their mind or needs help for other tasks, call the CompleteOrEscalate function to let the primary host assistant take control.\"\n", + " \" Do not mention who you are - just act as the proxy for the assistant.\",\n", + " tool_call_id=tool_call_id,\n", + " )\n", + " ],\n", + " \"dialog_state\": new_dialog_state,\n", + " }\n", + "\n", + " return entry_node" + ] }, { "cell_type": "markdown", @@ -1743,7 +3438,23 @@ "id": "b7c1140c-cd4e-4d69-bddd-7baa1eb4540e", "metadata": {}, "outputs": [], - "source": ["from typing import Literal\n\nfrom langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import StateGraph\nfrom langgraph.prebuilt import tools_condition\n\nbuilder = StateGraph(State)\n\n\ndef user_info(state: State):\n return {\"user_info\": fetch_user_flight_information.invoke({})}\n\n\nbuilder.add_node(\"fetch_user_info\", user_info)\nbuilder.add_edge(START, \"fetch_user_info\")"] + "source": [ + "from typing import Literal\n", + "\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import StateGraph\n", + "from langgraph.prebuilt import tools_condition\n", + "\n", + "builder = StateGraph(State)\n", + "\n", + "\n", + "def user_info(state: State):\n", + " return {\"user_info\": fetch_user_flight_information.invoke({})}\n", + "\n", + "\n", + "builder.add_node(\"fetch_user_info\", user_info)\n", + "builder.add_edge(START, \"fetch_user_info\")" + ] }, { "cell_type": "markdown", @@ -1769,7 +3480,75 @@ "id": "54297dc5-80b2-4bc6-8087-803caf1e0cf7", "metadata": {}, "outputs": [], - "source": ["# Flight booking assistant\nbuilder.add_node(\n \"enter_update_flight\",\n create_entry_node(\"Flight Updates & Booking Assistant\", \"update_flight\"),\n)\nbuilder.add_node(\"update_flight\", Assistant(update_flight_runnable))\nbuilder.add_edge(\"enter_update_flight\", \"update_flight\")\nbuilder.add_node(\n \"update_flight_sensitive_tools\",\n create_tool_node_with_fallback(update_flight_sensitive_tools),\n)\nbuilder.add_node(\n \"update_flight_safe_tools\",\n create_tool_node_with_fallback(update_flight_safe_tools),\n)\n\n\ndef route_update_flight(\n state: State,\n) -> Literal[\n \"update_flight_sensitive_tools\",\n \"update_flight_safe_tools\",\n \"leave_skill\",\n \"__end__\",\n]:\n route = tools_condition(state)\n if route == END:\n return END\n tool_calls = state[\"messages\"][-1].tool_calls\n did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n if did_cancel:\n return \"leave_skill\"\n safe_toolnames = [t.name for t in update_flight_safe_tools]\n if all(tc[\"name\"] in safe_toolnames for tc in tool_calls):\n return \"update_flight_safe_tools\"\n return \"update_flight_sensitive_tools\"\n\n\nbuilder.add_edge(\"update_flight_sensitive_tools\", \"update_flight\")\nbuilder.add_edge(\"update_flight_safe_tools\", \"update_flight\")\nbuilder.add_conditional_edges(\"update_flight\", route_update_flight)\n\n\n# This node will be shared for exiting all specialized assistants\ndef pop_dialog_state(state: State) -> dict:\n \"\"\"Pop the dialog stack and return to the main assistant.\n\n This lets the full graph explicitly track the dialog flow and delegate control\n to specific sub-graphs.\n \"\"\"\n messages = []\n if state[\"messages\"][-1].tool_calls:\n # Note: Doesn't currently handle the edge case where the llm performs parallel tool calls\n messages.append(\n ToolMessage(\n content=\"Resuming dialog with the host assistant. Please reflect on the past conversation and assist the user as needed.\",\n tool_call_id=state[\"messages\"][-1].tool_calls[0][\"id\"],\n )\n )\n return {\n \"dialog_state\": \"pop\",\n \"messages\": messages,\n }\n\n\nbuilder.add_node(\"leave_skill\", pop_dialog_state)\nbuilder.add_edge(\"leave_skill\", \"primary_assistant\")"] + "source": [ + "# Flight booking assistant\n", + "builder.add_node(\n", + " \"enter_update_flight\",\n", + " create_entry_node(\"Flight Updates & Booking Assistant\", \"update_flight\"),\n", + ")\n", + "builder.add_node(\"update_flight\", Assistant(update_flight_runnable))\n", + "builder.add_edge(\"enter_update_flight\", \"update_flight\")\n", + "builder.add_node(\n", + " \"update_flight_sensitive_tools\",\n", + " create_tool_node_with_fallback(update_flight_sensitive_tools),\n", + ")\n", + "builder.add_node(\n", + " \"update_flight_safe_tools\",\n", + " create_tool_node_with_fallback(update_flight_safe_tools),\n", + ")\n", + "\n", + "\n", + "def route_update_flight(\n", + " state: State,\n", + ") -> Literal[\n", + " \"update_flight_sensitive_tools\",\n", + " \"update_flight_safe_tools\",\n", + " \"leave_skill\",\n", + " \"__end__\",\n", + "]:\n", + " route = tools_condition(state)\n", + " if route == END:\n", + " return END\n", + " tool_calls = state[\"messages\"][-1].tool_calls\n", + " did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n", + " if did_cancel:\n", + " return \"leave_skill\"\n", + " safe_toolnames = [t.name for t in update_flight_safe_tools]\n", + " if all(tc[\"name\"] in safe_toolnames for tc in tool_calls):\n", + " return \"update_flight_safe_tools\"\n", + " return \"update_flight_sensitive_tools\"\n", + "\n", + "\n", + "builder.add_edge(\"update_flight_sensitive_tools\", \"update_flight\")\n", + "builder.add_edge(\"update_flight_safe_tools\", \"update_flight\")\n", + "builder.add_conditional_edges(\"update_flight\", route_update_flight)\n", + "\n", + "\n", + "# This node will be shared for exiting all specialized assistants\n", + "def pop_dialog_state(state: State) -> dict:\n", + " \"\"\"Pop the dialog stack and return to the main assistant.\n", + "\n", + " This lets the full graph explicitly track the dialog flow and delegate control\n", + " to specific sub-graphs.\n", + " \"\"\"\n", + " messages = []\n", + " if state[\"messages\"][-1].tool_calls:\n", + " # Note: Doesn't currently handle the edge case where the llm performs parallel tool calls\n", + " messages.append(\n", + " ToolMessage(\n", + " content=\"Resuming dialog with the host assistant. Please reflect on the past conversation and assist the user as needed.\",\n", + " tool_call_id=state[\"messages\"][-1].tool_calls[0][\"id\"],\n", + " )\n", + " )\n", + " return {\n", + " \"dialog_state\": \"pop\",\n", + " \"messages\": messages,\n", + " }\n", + "\n", + "\n", + "builder.add_node(\"leave_skill\", pop_dialog_state)\n", + "builder.add_edge(\"leave_skill\", \"primary_assistant\")" + ] }, { "cell_type": "markdown", @@ -1785,7 +3564,50 @@ "id": "e68b93f5-0f72-4e94-8e8b-b501ec82edcf", "metadata": {}, "outputs": [], - "source": ["# Car rental assistant\n\nbuilder.add_node(\n \"enter_book_car_rental\",\n create_entry_node(\"Car Rental Assistant\", \"book_car_rental\"),\n)\nbuilder.add_node(\"book_car_rental\", Assistant(book_car_rental_runnable))\nbuilder.add_edge(\"enter_book_car_rental\", \"book_car_rental\")\nbuilder.add_node(\n \"book_car_rental_safe_tools\",\n create_tool_node_with_fallback(book_car_rental_safe_tools),\n)\nbuilder.add_node(\n \"book_car_rental_sensitive_tools\",\n create_tool_node_with_fallback(book_car_rental_sensitive_tools),\n)\n\n\ndef route_book_car_rental(\n state: State,\n) -> Literal[\n \"book_car_rental_safe_tools\",\n \"book_car_rental_sensitive_tools\",\n \"leave_skill\",\n \"__end__\",\n]:\n route = tools_condition(state)\n if route == END:\n return END\n tool_calls = state[\"messages\"][-1].tool_calls\n did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n if did_cancel:\n return \"leave_skill\"\n safe_toolnames = [t.name for t in book_car_rental_safe_tools]\n if all(tc[\"name\"] in safe_toolnames for tc in tool_calls):\n return \"book_car_rental_safe_tools\"\n return \"book_car_rental_sensitive_tools\"\n\n\nbuilder.add_edge(\"book_car_rental_sensitive_tools\", \"book_car_rental\")\nbuilder.add_edge(\"book_car_rental_safe_tools\", \"book_car_rental\")\nbuilder.add_conditional_edges(\"book_car_rental\", route_book_car_rental)"] + "source": [ + "# Car rental assistant\n", + "\n", + "builder.add_node(\n", + " \"enter_book_car_rental\",\n", + " create_entry_node(\"Car Rental Assistant\", \"book_car_rental\"),\n", + ")\n", + "builder.add_node(\"book_car_rental\", Assistant(book_car_rental_runnable))\n", + "builder.add_edge(\"enter_book_car_rental\", \"book_car_rental\")\n", + "builder.add_node(\n", + " \"book_car_rental_safe_tools\",\n", + " create_tool_node_with_fallback(book_car_rental_safe_tools),\n", + ")\n", + "builder.add_node(\n", + " \"book_car_rental_sensitive_tools\",\n", + " create_tool_node_with_fallback(book_car_rental_sensitive_tools),\n", + ")\n", + "\n", + "\n", + "def route_book_car_rental(\n", + " state: State,\n", + ") -> Literal[\n", + " \"book_car_rental_safe_tools\",\n", + " \"book_car_rental_sensitive_tools\",\n", + " \"leave_skill\",\n", + " \"__end__\",\n", + "]:\n", + " route = tools_condition(state)\n", + " if route == END:\n", + " return END\n", + " tool_calls = state[\"messages\"][-1].tool_calls\n", + " did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n", + " if did_cancel:\n", + " return \"leave_skill\"\n", + " safe_toolnames = [t.name for t in book_car_rental_safe_tools]\n", + " if all(tc[\"name\"] in safe_toolnames for tc in tool_calls):\n", + " return \"book_car_rental_safe_tools\"\n", + " return \"book_car_rental_sensitive_tools\"\n", + "\n", + "\n", + "builder.add_edge(\"book_car_rental_sensitive_tools\", \"book_car_rental\")\n", + "builder.add_edge(\"book_car_rental_safe_tools\", \"book_car_rental\")\n", + "builder.add_conditional_edges(\"book_car_rental\", route_book_car_rental)" + ] }, { "cell_type": "markdown", @@ -1801,7 +3623,45 @@ "id": "ec40edb9-d415-4f43-8f9f-c82a239c607f", "metadata": {}, "outputs": [], - "source": ["# Hotel booking assistant\nbuilder.add_node(\n \"enter_book_hotel\", create_entry_node(\"Hotel Booking Assistant\", \"book_hotel\")\n)\nbuilder.add_node(\"book_hotel\", Assistant(book_hotel_runnable))\nbuilder.add_edge(\"enter_book_hotel\", \"book_hotel\")\nbuilder.add_node(\n \"book_hotel_safe_tools\",\n create_tool_node_with_fallback(book_hotel_safe_tools),\n)\nbuilder.add_node(\n \"book_hotel_sensitive_tools\",\n create_tool_node_with_fallback(book_hotel_sensitive_tools),\n)\n\n\ndef route_book_hotel(\n state: State,\n) -> Literal[\n \"leave_skill\", \"book_hotel_safe_tools\", \"book_hotel_sensitive_tools\", \"__end__\"\n]:\n route = tools_condition(state)\n if route == END:\n return END\n tool_calls = state[\"messages\"][-1].tool_calls\n did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n if did_cancel:\n return \"leave_skill\"\n tool_names = [t.name for t in book_hotel_safe_tools]\n if all(tc[\"name\"] in tool_names for tc in tool_calls):\n return \"book_hotel_safe_tools\"\n return \"book_hotel_sensitive_tools\"\n\n\nbuilder.add_edge(\"book_hotel_sensitive_tools\", \"book_hotel\")\nbuilder.add_edge(\"book_hotel_safe_tools\", \"book_hotel\")\nbuilder.add_conditional_edges(\"book_hotel\", route_book_hotel)"] + "source": [ + "# Hotel booking assistant\n", + "builder.add_node(\n", + " \"enter_book_hotel\", create_entry_node(\"Hotel Booking Assistant\", \"book_hotel\")\n", + ")\n", + "builder.add_node(\"book_hotel\", Assistant(book_hotel_runnable))\n", + "builder.add_edge(\"enter_book_hotel\", \"book_hotel\")\n", + "builder.add_node(\n", + " \"book_hotel_safe_tools\",\n", + " create_tool_node_with_fallback(book_hotel_safe_tools),\n", + ")\n", + "builder.add_node(\n", + " \"book_hotel_sensitive_tools\",\n", + " create_tool_node_with_fallback(book_hotel_sensitive_tools),\n", + ")\n", + "\n", + "\n", + "def route_book_hotel(\n", + " state: State,\n", + ") -> Literal[\n", + " \"leave_skill\", \"book_hotel_safe_tools\", \"book_hotel_sensitive_tools\", \"__end__\"\n", + "]:\n", + " route = tools_condition(state)\n", + " if route == END:\n", + " return END\n", + " tool_calls = state[\"messages\"][-1].tool_calls\n", + " did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n", + " if did_cancel:\n", + " return \"leave_skill\"\n", + " tool_names = [t.name for t in book_hotel_safe_tools]\n", + " if all(tc[\"name\"] in tool_names for tc in tool_calls):\n", + " return \"book_hotel_safe_tools\"\n", + " return \"book_hotel_sensitive_tools\"\n", + "\n", + "\n", + "builder.add_edge(\"book_hotel_sensitive_tools\", \"book_hotel\")\n", + "builder.add_edge(\"book_hotel_safe_tools\", \"book_hotel\")\n", + "builder.add_conditional_edges(\"book_hotel\", route_book_hotel)" + ] }, { "cell_type": "markdown", @@ -1817,7 +3677,49 @@ "id": "2ce9cf21-f708-4033-bca6-5f5d110b5662", "metadata": {}, "outputs": [], - "source": ["# Excursion assistant\nbuilder.add_node(\n \"enter_book_excursion\",\n create_entry_node(\"Trip Recommendation Assistant\", \"book_excursion\"),\n)\nbuilder.add_node(\"book_excursion\", Assistant(book_excursion_runnable))\nbuilder.add_edge(\"enter_book_excursion\", \"book_excursion\")\nbuilder.add_node(\n \"book_excursion_safe_tools\",\n create_tool_node_with_fallback(book_excursion_safe_tools),\n)\nbuilder.add_node(\n \"book_excursion_sensitive_tools\",\n create_tool_node_with_fallback(book_excursion_sensitive_tools),\n)\n\n\ndef route_book_excursion(\n state: State,\n) -> Literal[\n \"book_excursion_safe_tools\",\n \"book_excursion_sensitive_tools\",\n \"leave_skill\",\n \"__end__\",\n]:\n route = tools_condition(state)\n if route == END:\n return END\n tool_calls = state[\"messages\"][-1].tool_calls\n did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n if did_cancel:\n return \"leave_skill\"\n tool_names = [t.name for t in book_excursion_safe_tools]\n if all(tc[\"name\"] in tool_names for tc in tool_calls):\n return \"book_excursion_safe_tools\"\n return \"book_excursion_sensitive_tools\"\n\n\nbuilder.add_edge(\"book_excursion_sensitive_tools\", \"book_excursion\")\nbuilder.add_edge(\"book_excursion_safe_tools\", \"book_excursion\")\nbuilder.add_conditional_edges(\"book_excursion\", route_book_excursion)"] + "source": [ + "# Excursion assistant\n", + "builder.add_node(\n", + " \"enter_book_excursion\",\n", + " create_entry_node(\"Trip Recommendation Assistant\", \"book_excursion\"),\n", + ")\n", + "builder.add_node(\"book_excursion\", Assistant(book_excursion_runnable))\n", + "builder.add_edge(\"enter_book_excursion\", \"book_excursion\")\n", + "builder.add_node(\n", + " \"book_excursion_safe_tools\",\n", + " create_tool_node_with_fallback(book_excursion_safe_tools),\n", + ")\n", + "builder.add_node(\n", + " \"book_excursion_sensitive_tools\",\n", + " create_tool_node_with_fallback(book_excursion_sensitive_tools),\n", + ")\n", + "\n", + "\n", + "def route_book_excursion(\n", + " state: State,\n", + ") -> Literal[\n", + " \"book_excursion_safe_tools\",\n", + " \"book_excursion_sensitive_tools\",\n", + " \"leave_skill\",\n", + " \"__end__\",\n", + "]:\n", + " route = tools_condition(state)\n", + " if route == END:\n", + " return END\n", + " tool_calls = state[\"messages\"][-1].tool_calls\n", + " did_cancel = any(tc[\"name\"] == CompleteOrEscalate.__name__ for tc in tool_calls)\n", + " if did_cancel:\n", + " return \"leave_skill\"\n", + " tool_names = [t.name for t in book_excursion_safe_tools]\n", + " if all(tc[\"name\"] in tool_names for tc in tool_calls):\n", + " return \"book_excursion_safe_tools\"\n", + " return \"book_excursion_sensitive_tools\"\n", + "\n", + "\n", + "builder.add_edge(\"book_excursion_sensitive_tools\", \"book_excursion\")\n", + "builder.add_edge(\"book_excursion_safe_tools\", \"book_excursion\")\n", + "builder.add_conditional_edges(\"book_excursion\", route_book_excursion)" + ] }, { "cell_type": "markdown", @@ -1833,7 +3735,90 @@ "id": "acb19faf-66c8-4fd8-89ec-4d97d510ce4d", "metadata": {}, "outputs": [], - "source": ["# Primary assistant\nbuilder.add_node(\"primary_assistant\", Assistant(assistant_runnable))\nbuilder.add_node(\n \"primary_assistant_tools\", create_tool_node_with_fallback(primary_assistant_tools)\n)\n\n\ndef route_primary_assistant(\n state: State,\n) -> Literal[\n \"primary_assistant_tools\",\n \"enter_update_flight\",\n \"enter_book_hotel\",\n \"enter_book_excursion\",\n \"__end__\",\n]:\n route = tools_condition(state)\n if route == END:\n return END\n tool_calls = state[\"messages\"][-1].tool_calls\n if tool_calls:\n if tool_calls[0][\"name\"] == ToFlightBookingAssistant.__name__:\n return \"enter_update_flight\"\n elif tool_calls[0][\"name\"] == ToBookCarRental.__name__:\n return \"enter_book_car_rental\"\n elif tool_calls[0][\"name\"] == ToHotelBookingAssistant.__name__:\n return \"enter_book_hotel\"\n elif tool_calls[0][\"name\"] == ToBookExcursion.__name__:\n return \"enter_book_excursion\"\n return \"primary_assistant_tools\"\n raise ValueError(\"Invalid route\")\n\n\n# The assistant can route to one of the delegated assistants,\n# directly use a tool, or directly respond to the user\nbuilder.add_conditional_edges(\n \"primary_assistant\",\n route_primary_assistant,\n {\n \"enter_update_flight\": \"enter_update_flight\",\n \"enter_book_car_rental\": \"enter_book_car_rental\",\n \"enter_book_hotel\": \"enter_book_hotel\",\n \"enter_book_excursion\": \"enter_book_excursion\",\n \"primary_assistant_tools\": \"primary_assistant_tools\",\n END: END,\n },\n)\nbuilder.add_edge(\"primary_assistant_tools\", \"primary_assistant\")\n\n\n# Each delegated workflow can directly respond to the user\n# When the user responds, we want to return to the currently active workflow\ndef route_to_workflow(\n state: State,\n) -> Literal[\n \"primary_assistant\",\n \"update_flight\",\n \"book_car_rental\",\n \"book_hotel\",\n \"book_excursion\",\n]:\n \"\"\"If we are in a delegated state, route directly to the appropriate assistant.\"\"\"\n dialog_state = state.get(\"dialog_state\")\n if not dialog_state:\n return \"primary_assistant\"\n return dialog_state[-1]\n\n\nbuilder.add_conditional_edges(\"fetch_user_info\", route_to_workflow)\n\n# Compile graph\nmemory = SqliteSaver.from_conn_string(\":memory:\")\npart_4_graph = builder.compile(\n checkpointer=memory,\n # Let the user approve or deny the use of sensitive tools\n interrupt_before=[\n \"update_flight_sensitive_tools\",\n \"book_car_rental_sensitive_tools\",\n \"book_hotel_sensitive_tools\",\n \"book_excursion_sensitive_tools\",\n ],\n)"] + "source": [ + "# Primary assistant\n", + "builder.add_node(\"primary_assistant\", Assistant(assistant_runnable))\n", + "builder.add_node(\n", + " \"primary_assistant_tools\", create_tool_node_with_fallback(primary_assistant_tools)\n", + ")\n", + "\n", + "\n", + "def route_primary_assistant(\n", + " state: State,\n", + ") -> Literal[\n", + " \"primary_assistant_tools\",\n", + " \"enter_update_flight\",\n", + " \"enter_book_hotel\",\n", + " \"enter_book_excursion\",\n", + " \"__end__\",\n", + "]:\n", + " route = tools_condition(state)\n", + " if route == END:\n", + " return END\n", + " tool_calls = state[\"messages\"][-1].tool_calls\n", + " if tool_calls:\n", + " if tool_calls[0][\"name\"] == ToFlightBookingAssistant.__name__:\n", + " return \"enter_update_flight\"\n", + " elif tool_calls[0][\"name\"] == ToBookCarRental.__name__:\n", + " return \"enter_book_car_rental\"\n", + " elif tool_calls[0][\"name\"] == ToHotelBookingAssistant.__name__:\n", + " return \"enter_book_hotel\"\n", + " elif tool_calls[0][\"name\"] == ToBookExcursion.__name__:\n", + " return \"enter_book_excursion\"\n", + " return \"primary_assistant_tools\"\n", + " raise ValueError(\"Invalid route\")\n", + "\n", + "\n", + "# The assistant can route to one of the delegated assistants,\n", + "# directly use a tool, or directly respond to the user\n", + "builder.add_conditional_edges(\n", + " \"primary_assistant\",\n", + " route_primary_assistant,\n", + " {\n", + " \"enter_update_flight\": \"enter_update_flight\",\n", + " \"enter_book_car_rental\": \"enter_book_car_rental\",\n", + " \"enter_book_hotel\": \"enter_book_hotel\",\n", + " \"enter_book_excursion\": \"enter_book_excursion\",\n", + " \"primary_assistant_tools\": \"primary_assistant_tools\",\n", + " END: END,\n", + " },\n", + ")\n", + "builder.add_edge(\"primary_assistant_tools\", \"primary_assistant\")\n", + "\n", + "\n", + "# Each delegated workflow can directly respond to the user\n", + "# When the user responds, we want to return to the currently active workflow\n", + "def route_to_workflow(\n", + " state: State,\n", + ") -> Literal[\n", + " \"primary_assistant\",\n", + " \"update_flight\",\n", + " \"book_car_rental\",\n", + " \"book_hotel\",\n", + " \"book_excursion\",\n", + "]:\n", + " \"\"\"If we are in a delegated state, route directly to the appropriate assistant.\"\"\"\n", + " dialog_state = state.get(\"dialog_state\")\n", + " if not dialog_state:\n", + " return \"primary_assistant\"\n", + " return dialog_state[-1]\n", + "\n", + "\n", + "builder.add_conditional_edges(\"fetch_user_info\", route_to_workflow)\n", + "\n", + "# Compile graph\n", + "memory = MemorySaver()\n", + "part_4_graph = builder.compile(\n", + " checkpointer=memory,\n", + " # Let the user approve or deny the use of sensitive tools\n", + " interrupt_before=[\n", + " \"update_flight_sensitive_tools\",\n", + " \"book_car_rental_sensitive_tools\",\n", + " \"book_hotel_sensitive_tools\",\n", + " \"book_excursion_sensitive_tools\",\n", + " ],\n", + ")" + ] }, { "cell_type": "code", @@ -1852,7 +3837,15 @@ "output_type": "display_data" } ], - "source": ["from IPython.display import Image, display\n\ntry:\n display(Image(part_4_graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(part_4_graph.get_graph(xray=True).draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "markdown", @@ -2326,7 +4319,63 @@ ] } ], - "source": ["import shutil\nimport uuid\n\n# Update with the backup file so we can restart from the original place in each section\nshutil.copy(backup_file, db)\nthread_id = str(uuid.uuid4())\n\nconfig = {\n \"configurable\": {\n # The passenger_id is used in our flight tools to\n # fetch the user's flight information\n \"passenger_id\": \"3442 587242\",\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\n_printed = set()\n# We can reuse the tutorial questions from part 1 to see how it does.\nfor question in tutorial_questions:\n events = part_4_graph.stream(\n {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n )\n for event in events:\n _print_event(event, _printed)\n snapshot = part_4_graph.get_state(config)\n while snapshot.next:\n # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n user_input = input(\n \"Do you approve of the above actions? Type 'y' to continue;\"\n \" otherwise, explain your requested changed.\\n\\n\"\n )\n if user_input.strip() == \"y\":\n # Just continue\n result = part_4_graph.invoke(\n None,\n config,\n )\n else:\n # Satisfy the tool invocation by\n # providing instructions on the requested changes / change of mind\n result = part_4_graph.invoke(\n {\n \"messages\": [\n ToolMessage(\n tool_call_id=event[\"messages\"][-1].tool_calls[0][\"id\"],\n content=f\"API call denied by user. Reasoning: '{user_input}'. Continue assisting, accounting for the user's input.\",\n )\n ]\n },\n config,\n )\n snapshot = part_4_graph.get_state(config)"] + "source": [ + "import shutil\n", + "import uuid\n", + "\n", + "# Update with the backup file so we can restart from the original place in each section\n", + "shutil.copy(backup_file, db)\n", + "thread_id = str(uuid.uuid4())\n", + "\n", + "config = {\n", + " \"configurable\": {\n", + " # The passenger_id is used in our flight tools to\n", + " # fetch the user's flight information\n", + " \"passenger_id\": \"3442 587242\",\n", + " # Checkpoints are accessed by thread_id\n", + " \"thread_id\": thread_id,\n", + " }\n", + "}\n", + "\n", + "_printed = set()\n", + "# We can reuse the tutorial questions from part 1 to see how it does.\n", + "for question in tutorial_questions:\n", + " events = part_4_graph.stream(\n", + " {\"messages\": (\"user\", question)}, config, stream_mode=\"values\"\n", + " )\n", + " for event in events:\n", + " _print_event(event, _printed)\n", + " snapshot = part_4_graph.get_state(config)\n", + " while snapshot.next:\n", + " # We have an interrupt! The agent is trying to use a tool, and the user can approve or deny it\n", + " # Note: This code is all outside of your graph. Typically, you would stream the output to a UI.\n", + " # Then, you would have the frontend trigger a new run via an API call when the user has provided input.\n", + " user_input = input(\n", + " \"Do you approve of the above actions? Type 'y' to continue;\"\n", + " \" otherwise, explain your requested changed.\\n\\n\"\n", + " )\n", + " if user_input.strip() == \"y\":\n", + " # Just continue\n", + " result = part_4_graph.invoke(\n", + " None,\n", + " config,\n", + " )\n", + " else:\n", + " # Satisfy the tool invocation by\n", + " # providing instructions on the requested changes / change of mind\n", + " result = part_4_graph.invoke(\n", + " {\n", + " \"messages\": [\n", + " ToolMessage(\n", + " tool_call_id=event[\"messages\"][-1].tool_calls[0][\"id\"],\n", + " content=f\"API call denied by user. Reasoning: '{user_input}'. Continue assisting, accounting for the user's input.\",\n", + " )\n", + " ]\n", + " },\n", + " config,\n", + " )\n", + " snapshot = part_4_graph.get_state(config)" + ] }, { "cell_type": "markdown", diff --git a/examples/customer-support/img/customer-support-bot-4.png b/examples/customer-support/img/customer-support-bot-4.png index 021d8996c..8b9da2176 100644 Binary files a/examples/customer-support/img/customer-support-bot-4.png and b/examples/customer-support/img/customer-support-bot-4.png differ diff --git a/examples/customer-support/img/part-1-diagram.png b/examples/customer-support/img/part-1-diagram.png index b7d5e105e..9f8bddf3e 100644 Binary files a/examples/customer-support/img/part-1-diagram.png and b/examples/customer-support/img/part-1-diagram.png differ diff --git a/examples/customer-support/img/part-2-diagram.png b/examples/customer-support/img/part-2-diagram.png index 1659030bc..62eeeed6a 100644 Binary files a/examples/customer-support/img/part-2-diagram.png and b/examples/customer-support/img/part-2-diagram.png differ diff --git a/examples/customer-support/img/part-3-diagram.png b/examples/customer-support/img/part-3-diagram.png index 7b9dbc067..7a8a1e946 100644 Binary files a/examples/customer-support/img/part-3-diagram.png and b/examples/customer-support/img/part-3-diagram.png differ diff --git a/examples/customer-support/img/part-4-diagram.png b/examples/customer-support/img/part-4-diagram.png index d5d2e2e89..874f5bd9c 100644 Binary files a/examples/customer-support/img/part-4-diagram.png and b/examples/customer-support/img/part-4-diagram.png differ diff --git a/examples/human-in-the-loop.ipynb b/examples/human-in-the-loop.ipynb index 0dd453a4c..8dbc0b5d5 100644 --- a/examples/human-in-the-loop.ipynb +++ b/examples/human-in-the-loop.ipynb @@ -39,7 +39,10 @@ "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", "metadata": {}, "outputs": [], - "source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_openai"] + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_openai" + ] }, { "cell_type": "markdown", @@ -55,7 +58,18 @@ "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", "metadata": {}, "outputs": [], - "source": ["import getpass\nimport os\n\n\ndef _set_env(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"OPENAI_API_KEY\")"] + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")" + ] }, { "cell_type": "markdown", @@ -71,7 +85,10 @@ "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", "metadata": {}, "outputs": [], - "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")"] + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] }, { "cell_type": "markdown", @@ -89,7 +106,22 @@ "id": "6098e5cb", "metadata": {}, "outputs": [], - "source": ["from typing import Annotated\n\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph.message import add_messages\n\n# `add_messages`` essentially does this\n# (with more robust handling)\n# def add_messages(left: list, right: list):\n# return left + right\n\n\nclass State(TypedDict):\n messages: Annotated[list, add_messages]"] + "source": [ + "from typing import Annotated\n", + "\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph.message import add_messages\n", + "\n", + "# `add_messages`` essentially does this\n", + "# (with more robust handling)\n", + "# def add_messages(left: list, right: list):\n", + "# return left + right\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list, add_messages]" + ] }, { "cell_type": "markdown", @@ -109,7 +141,22 @@ "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", "metadata": {}, "outputs": [], - "source": ["from langchain_core.tools import tool\n\n\n@tool\ndef search(query: str):\n \"\"\"Call to surf the web.\"\"\"\n # This is a placeholder for the actual implementation\n # Don't let the LLM know this though 😊\n return [\n \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n ]\n\n\ntools = [search]"] + "source": [ + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " # Don't let the LLM know this though 😊\n", + " return [\n", + " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", + " ]\n", + "\n", + "\n", + "tools = [search]" + ] }, { "cell_type": "markdown", @@ -127,7 +174,11 @@ "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", "metadata": {}, "outputs": [], - "source": ["from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)"] + "source": [ + "from langgraph.prebuilt import ToolExecutor\n", + "\n", + "tool_executor = ToolExecutor(tools)" + ] }, { "cell_type": "markdown", @@ -148,7 +199,11 @@ "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", "metadata": {}, "outputs": [], - "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0)"] + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "model = ChatOpenAI(temperature=0)" + ] }, { "cell_type": "markdown", @@ -166,7 +221,9 @@ "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", "metadata": {}, "outputs": [], - "source": ["model = model.bind_tools(tools)"] + "source": [ + "model = model.bind_tools(tools)" + ] }, { "cell_type": "markdown", @@ -201,7 +258,53 @@ "id": "3b541bb9-900c-40d0-964d-7b5dfee30667", "metadata": {}, "outputs": [], - "source": ["from langchain_core.messages import ToolMessage\n\nfrom langgraph.prebuilt import ToolInvocation\n\n\n# Define the function that determines whether to continue or not\ndef should_continue(state):\n messages = state[\"messages\"]\n last_message = messages[-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"end\"\n # Otherwise if there is, we continue\n else:\n return \"continue\"\n\n\n# Define the function that calls the model\ndef call_model(state):\n messages = state[\"messages\"]\n response = model.invoke(messages)\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}\n\n\n# Define the function to execute tools\ndef call_tool(state):\n messages = state[\"messages\"]\n # Based on the continue condition\n # we know the last message involves a function call\n last_message = messages[-1]\n # We construct an ToolInvocation from the function_call\n tool_call = last_message.tool_calls[0]\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n # We call the tool_executor and get back a response\n response = tool_executor.invoke(action)\n # We use the response to create a ToolMessage\n tool_message = ToolMessage(\n content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n )\n # We return a list, because this will get added to the existing list\n return {\"messages\": [tool_message]}"] + "source": [ + "from langchain_core.messages import ToolMessage\n", + "\n", + "from langgraph.prebuilt import ToolInvocation\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "def should_continue(state):\n", + " messages = state[\"messages\"]\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"end\"\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return \"continue\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " response = model.invoke(messages)\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [response]}\n", + "\n", + "\n", + "# Define the function to execute tools\n", + "def call_tool(state):\n", + " messages = state[\"messages\"]\n", + " # Based on the continue condition\n", + " # we know the last message involves a function call\n", + " last_message = messages[-1]\n", + " # We construct an ToolInvocation from the function_call\n", + " tool_call = last_message.tool_calls[0]\n", + " action = ToolInvocation(\n", + " tool=tool_call[\"name\"],\n", + " tool_input=tool_call[\"args\"],\n", + " )\n", + " # We call the tool_executor and get back a response\n", + " response = tool_executor.invoke(action)\n", + " # We use the response to create a ToolMessage\n", + " tool_message = ToolMessage(\n", + " content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n", + " )\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [tool_message]}" + ] }, { "cell_type": "markdown", @@ -219,7 +322,45 @@ "id": "812b4e70-4956-4415-8880-db48b3dcbad2", "metadata": {}, "outputs": [], - "source": ["from langgraph.graph import END, StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(State)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n # Finally we pass in a mapping.\n # The keys are strings, and the values are other nodes.\n # END is a special node marking that the graph should finish.\n # What will happen is we will call `should_continue`, and then the output of that\n # will be matched against the keys in this mapping.\n # Based on which one it matches, that node will then be called.\n {\n # If `tools`, then we call the tool node.\n \"continue\": \"action\",\n # Otherwise we finish.\n \"end\": END,\n },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")"] + "source": [ + "from langgraph.graph import END, StateGraph, START\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(State)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", call_tool)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + " # Finally we pass in a mapping.\n", + " # The keys are strings, and the values are other nodes.\n", + " # END is a special node marking that the graph should finish.\n", + " # What will happen is we will call `should_continue`, and then the output of that\n", + " # will be matched against the keys in this mapping.\n", + " # Based on which one it matches, that node will then be called.\n", + " {\n", + " # If `tools`, then we call the tool node.\n", + " \"continue\": \"action\",\n", + " # Otherwise we finish.\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")" + ] }, { "cell_type": "markdown", @@ -237,7 +378,11 @@ "id": "6845ed6a-d155-4105-9160-28849877248b", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"] + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "memory = MemorySaver()" + ] }, { "cell_type": "markdown", @@ -255,7 +400,12 @@ "id": "79d29875-8aa8-434c-9f20-1c58346a6249", "metadata": {}, "outputs": [], - "source": ["# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"] + "source": [ + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])" + ] }, { "cell_type": "markdown", @@ -282,7 +432,11 @@ "output_type": "display_data" } ], - "source": ["from IPython.display import Image, display\n\ndisplay(Image(app.get_graph().draw_mermaid_png()))"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "display(Image(app.get_graph().draw_mermaid_png()))" + ] }, { "cell_type": "markdown", @@ -313,7 +467,14 @@ ] } ], - "source": ["from langchain_core.messages import HumanMessage\n\nthread = {\"configurable\": {\"thread_id\": \"2\"}}\ninputs = [HumanMessage(content=\"hi! I'm bob\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "thread = {\"configurable\": {\"thread_id\": \"2\"}}\n", + "inputs = [HumanMessage(content=\"hi! I'm bob\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "code", @@ -334,7 +495,11 @@ ] } ], - "source": ["inputs = [HumanMessage(content=\"What did I tell you my name was?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "inputs = [HumanMessage(content=\"What did I tell you my name was?\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "code", @@ -358,7 +523,11 @@ ] } ], - "source": ["inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "markdown", @@ -392,7 +561,10 @@ ] } ], - "source": ["for event in app.stream(None, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "for event in app.stream(None, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "markdown", @@ -427,7 +599,43 @@ "id": "5454f436-d56e-4499-9381-06192aca1b56", "metadata": {}, "outputs": [], - "source": ["import json\nfrom typing import Optional\n\nfrom langchain_core.messages import AIMessage\n\n\n# Helper function to construct message asking for verification\ndef generate_verification_message(message: AIMessage) -> None:\n \"\"\"Generate \"verification message\" from message with tool calls.\"\"\"\n serialized_tool_calls = json.dumps(\n message.tool_calls,\n indent=2,\n )\n return AIMessage(\n content=(\n \"I plan to invoke the following tools, do you approve?\\n\\n\"\n \"Type 'y' if you do, anything else to stop.\\n\\n\"\n f\"{serialized_tool_calls}\"\n ),\n id=message.id,\n )\n\n\n# Helper function to stream output from the graph\ndef stream_app_catch_tool_calls(inputs, thread) -> Optional[AIMessage]:\n \"\"\"Stream app, catching tool calls.\"\"\"\n tool_call_message = None\n for event in app.stream(inputs, thread, stream_mode=\"values\"):\n message = event[\"messages\"][-1]\n if isinstance(message, AIMessage) and message.tool_calls:\n tool_call_message = message\n else:\n message.pretty_print()\n\n return tool_call_message"] + "source": [ + "import json\n", + "from typing import Optional\n", + "\n", + "from langchain_core.messages import AIMessage\n", + "\n", + "\n", + "# Helper function to construct message asking for verification\n", + "def generate_verification_message(message: AIMessage) -> None:\n", + " \"\"\"Generate \"verification message\" from message with tool calls.\"\"\"\n", + " serialized_tool_calls = json.dumps(\n", + " message.tool_calls,\n", + " indent=2,\n", + " )\n", + " return AIMessage(\n", + " content=(\n", + " \"I plan to invoke the following tools, do you approve?\\n\\n\"\n", + " \"Type 'y' if you do, anything else to stop.\\n\\n\"\n", + " f\"{serialized_tool_calls}\"\n", + " ),\n", + " id=message.id,\n", + " )\n", + "\n", + "\n", + "# Helper function to stream output from the graph\n", + "def stream_app_catch_tool_calls(inputs, thread) -> Optional[AIMessage]:\n", + " \"\"\"Stream app, catching tool calls.\"\"\"\n", + " tool_call_message = None\n", + " for event in app.stream(inputs, thread, stream_mode=\"values\"):\n", + " message = event[\"messages\"][-1]\n", + " if isinstance(message, AIMessage) and message.tool_calls:\n", + " tool_call_message = message\n", + " else:\n", + " message.pretty_print()\n", + "\n", + " return tool_call_message" + ] }, { "cell_type": "code", @@ -514,7 +722,43 @@ ] } ], - "source": ["import uuid\n\nthread = {\"configurable\": {\"thread_id\": \"3\"}}\n\ntool_call_message = stream_app_catch_tool_calls(\n {\"messages\": [HumanMessage(\"what's the weather in sf now?\")]},\n thread,\n)\n\nwhile tool_call_message:\n verification_message = generate_verification_message(tool_call_message)\n verification_message.pretty_print()\n input_message = HumanMessage(input())\n if input_message.content == \"exit\":\n break\n input_message.pretty_print()\n\n # First we update the state with the verification message and the input message.\n # note that `generate_verification_message` sets the message ID to be the same\n # as the ID from the original tool call message. Updating the state with this\n # message will overwrite the previous tool call.\n snapshot = app.get_state(thread)\n snapshot.values[\"messages\"] += [verification_message, input_message]\n\n if input_message.content == \"y\":\n tool_call_message.id = str(uuid.uuid4())\n # If verified, we append the tool call message to the state\n # and resume execution.\n snapshot.values[\"messages\"] += [tool_call_message]\n app.update_state(thread, snapshot.values, as_node=\"agent\")\n else:\n # Otherwise, resume execution from the input message.\n app.update_state(thread, snapshot.values, as_node=\"__start__\")\n\n tool_call_message = stream_app_catch_tool_calls(None, thread)"] + "source": [ + "import uuid\n", + "\n", + "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n", + "\n", + "tool_call_message = stream_app_catch_tool_calls(\n", + " {\"messages\": [HumanMessage(\"what's the weather in sf now?\")]},\n", + " thread,\n", + ")\n", + "\n", + "while tool_call_message:\n", + " verification_message = generate_verification_message(tool_call_message)\n", + " verification_message.pretty_print()\n", + " input_message = HumanMessage(input())\n", + " if input_message.content == \"exit\":\n", + " break\n", + " input_message.pretty_print()\n", + "\n", + " # First we update the state with the verification message and the input message.\n", + " # note that `generate_verification_message` sets the message ID to be the same\n", + " # as the ID from the original tool call message. Updating the state with this\n", + " # message will overwrite the previous tool call.\n", + " snapshot = app.get_state(thread)\n", + " snapshot.values[\"messages\"] += [verification_message, input_message]\n", + "\n", + " if input_message.content == \"y\":\n", + " tool_call_message.id = str(uuid.uuid4())\n", + " # If verified, we append the tool call message to the state\n", + " # and resume execution.\n", + " snapshot.values[\"messages\"] += [tool_call_message]\n", + " app.update_state(thread, snapshot.values, as_node=\"agent\")\n", + " else:\n", + " # Otherwise, resume execution from the input message.\n", + " app.update_state(thread, snapshot.values, as_node=\"__start__\")\n", + "\n", + " tool_call_message = stream_app_catch_tool_calls(None, thread)" + ] }, { "cell_type": "markdown", @@ -535,7 +779,34 @@ "id": "03232f16-d6fe-46d0-afa0-a6f0d0bf16de", "metadata": {}, "outputs": [], - "source": ["class State(TypedDict):\n messages: Annotated[list, add_messages]\n tool_call_message: Optional[AIMessage]\n\n\ndef call_model(state):\n messages = state[\"messages\"]\n if messages[-1].content == \"y\":\n return {\n \"messages\": [state[\"tool_call_message\"]],\n \"tool_call_message\": None,\n }\n else:\n response = model.invoke(messages)\n if response.tool_calls:\n verification_message = generate_verification_message(response)\n response.id = str(uuid.uuid4())\n return {\n \"messages\": [verification_message],\n \"tool_call_message\": response,\n }\n else:\n return {\n \"messages\": [response],\n \"tool_call_message\": None,\n }"] + "source": [ + "class State(TypedDict):\n", + " messages: Annotated[list, add_messages]\n", + " tool_call_message: Optional[AIMessage]\n", + "\n", + "\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " if messages[-1].content == \"y\":\n", + " return {\n", + " \"messages\": [state[\"tool_call_message\"]],\n", + " \"tool_call_message\": None,\n", + " }\n", + " else:\n", + " response = model.invoke(messages)\n", + " if response.tool_calls:\n", + " verification_message = generate_verification_message(response)\n", + " response.id = str(uuid.uuid4())\n", + " return {\n", + " \"messages\": [verification_message],\n", + " \"tool_call_message\": response,\n", + " }\n", + " else:\n", + " return {\n", + " \"messages\": [response],\n", + " \"tool_call_message\": None,\n", + " }" + ] }, { "cell_type": "markdown", @@ -551,7 +822,27 @@ "id": "502dc688-c926-407e-8759-8c9e39eb4257", "metadata": {}, "outputs": [], - "source": ["workflow = StateGraph(State)\n\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\nworkflow.add_edge(START, \"agent\")\n\nworkflow.add_conditional_edges(\n \"agent\",\n should_continue,\n {\n \"continue\": \"action\",\n \"end\": END,\n },\n)\n\nworkflow.add_edge(\"action\", \"agent\")\n\napp = workflow.compile(checkpointer=memory)"] + "source": [ + "workflow = StateGraph(State)\n", + "\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", call_tool)\n", + "\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "workflow.add_conditional_edges(\n", + " \"agent\",\n", + " should_continue,\n", + " {\n", + " \"continue\": \"action\",\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "workflow.add_edge(\"action\", \"agent\")\n", + "\n", + "app = workflow.compile(checkpointer=memory)" + ] }, { "cell_type": "code", @@ -584,7 +875,13 @@ ] } ], - "source": ["thread = {\"configurable\": {\"thread_id\": \"4\"}}\n\ninputs = [HumanMessage(content=\"what's the weather in sf?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "thread = {\"configurable\": {\"thread_id\": \"4\"}}\n", + "\n", + "inputs = [HumanMessage(content=\"what's the weather in sf?\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "code", @@ -617,7 +914,11 @@ ] } ], - "source": ["inputs = [HumanMessage(content=\"can you specify sf in CA?\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "inputs = [HumanMessage(content=\"can you specify sf in CA?\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] }, { "cell_type": "code", @@ -648,7 +949,11 @@ ] } ], - "source": ["inputs = [HumanMessage(content=\"y\")]\nfor event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()"] + "source": [ + "inputs = [HumanMessage(content=\"y\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] } ], "metadata": { diff --git a/examples/human_in_the_loop/breakpoints.ipynb b/examples/human_in_the_loop/breakpoints.ipynb index 32d3732e2..d528306d0 100644 --- a/examples/human_in_the_loop/breakpoints.ipynb +++ b/examples/human_in_the_loop/breakpoints.ipynb @@ -1,478 +1,478 @@ { - "cells": [ - { - "attachments": { - "b5aa6d4c-8dfd-490d-a53c-69c1368cd5b5.png": { - "image/png": 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wbWV0YT4Kr5C7eQAAQABJREFUeAHsnQecpVdd/s+9d3rbXpNNB0I2EJJAFEVCEeWjoqKIH3sXFeyKnT9YsYK9F2wIigUQBQQlJgSkhBISSC+bTbK9TZ975/98f+c9d9+ZzG4SCDuzs8+Z3Pu+7+nnOS/s+d7fKY15uWRnBayAFbACVsAKWAErYAWsgBWwAlbgBAo0T+BvbytgBayAFbACVsAKWAErYAWsgBWwAqGAwdEvghWwAlbAClgBK2AFrIAVsAJWwAqcVAGD40nlcaAVsAJWwApYAStgBayAFbACVsAKGBz9DlgBK2AFrIAVsAJWwApYAStgBazASRUwOJ5UHgdaAStgBayAFbACVsAKWAErYAWsgMHR74AVsAJWwApYAStgBayAFbACVsAKnFQBg+NJ5XGgFbACVsAKWAErYAWsgBWwAlbAChgc/Q5YAStgBayAFbACVsAKWAErYAWswEkVMDieVB4HWgErYAWsgBWwAlbAClgBK2AFrIDB0e+AFbACVsAKWAErYAWsgBWwAlbACpxUAYPjSeVxoBWwAlbAClgBK2AFrIAVsAJWwAoYHP0OWAErYAWsgBWwAlbAClgBK2AFrMBJFTA4nlQeB1oBK2AFrIAVsAJWwApYAStgBayAwdHvgBWwAlbAClgBK2AFrIAVsAJWwAqcVAGD40nlcaAVsAJWwApYAStgBayAFbACVsAKGBz9DlgBK2AFrIAVsAJWwApYAStgBazASRUwOJ5UHgdaAStgBayAFbACVsAKWAErYAWsgMHR74AVsAJWwApYAStwyhX4xCc+kXbu3Jle8YpXpLm5uVNevgu0AlbACliBR6eAwfHR6eXYVsAKWAErYAWswGOgwC233JKOHTuWXve616W77777McjRWVgBK2AFrMBnUwGD42dTXedtBayAFbACVsAKLKnA/Px81398fLx77xsrYAWsgBVYmQoYHFdmv7hWVsAKWAErYAVWtQLtdrvbPiyPdlbAClgBK7CyFTA4ruz+ce2sgBWwAlbgMVQAWLnrrru8pu4x1PTTzapucZyamvp0s3E6K2AFrIAVOEUKGBxPkdAuxgpYAStgBZZXAaZDvvCFL0xXX311es5znpMMK8vbH3Vw7OvrW97KuHQrYAWsgBV4WAUMjg8rkSNYAStgBT4zBWZnZz+zDJz6MVHgAx/4QProRz8aebEZy+233/6Y5OtMPj0F6v+7GB4e/vQycSorYAWsgBU4ZQoYHE+Z1C7ICliBM1EBYOWiiy5KX/M1X5O8AcjyvgFve9vbFlRgdHR0wbMfTq0CMzMz3QIHBga6976xAlbACliBlamAwXFl9otrZQWswApXAIvVs5/97HTFFVekG2644YS1ffWrXx1h//d//5euvfbaE8ZzwGdXgaNHj6Y3vOENCwpZs2bNgmc/nFoF6uA4ODh4agt3aVbAClgBK/CoFTA4PmrJnMAKWAErkNLf/d3fpTvuuCPt378/XX/99SeU5JOf/GQ3rL+/v3vvm1OrwGJrI6Xb4nhq+2BxaQbHxYr42QpYASuwshUwOK7s/nHtrIAVWKEKrFu3rluzAwcOdO8X39SPGTjvvPMWB/v5FCnw13/91wtKGhkZSc2m/wlcIMopfqiD49DQ0Cku3cVZAStgBazAo1XA/2o+WsUc3wpYASsgBZ7ylKd0dTjR2sX6OXVEPvvss7tpfHPqFHjLW96SbrzxxgUFbtiwYcGzH069AvX/fXiq6qnX3yVaAStgBR6tAgbHR6uY41sBK2AFpECj0XhYHerWxnPPPTf19PQ8bBpHeGwVOHToUPrJn/zJh2S6du3ah/itdo8PfehD6Yd/+IfTYuvridrd6XTSnXfemVgf+tlw09PT3Wz9v42uFL6xAlbACqxYBTyKWbFd44pZASuwkhWonwF4orVyBw8e7Dbhwgsv7N775tQp8Mu//MupAPy2bdvS/ffff+oKf4Qlvfvd705vfvObY80s0zepJ+/Ll37pl6bLL7/8EeZy8mgTExPpq77qqyLSP//zP4fF/MlPfvJJE/3mb/5m+r3f+72EdfZ973tfeqzPWjxy5EiUz7RhOytgBayAFVj5ChgcV34fuYZWwAqsQAXq6xpPNAX1tttu69Z8586d3Xtu7rrrrsT0vM2bNz8i6+WCxJ/lh8nJybRnz57U29ub2Hn0Mz1j7x//8R/TO97xjqj1T/zET8TxJJ/lJkT211xzzYKdVP/oj/4ofcVXfEWEtVqtJaswNzeX3vrWt6bXve51iZ1zzz///PT85z8/vehFL0r1da1LJf7gBz+Y/vAP/zB22aVf2XWXdCf70QBtfuzHfmxBdjfffHMCJv/0T/80YO+Vr3xl9EM9EiD3pje9Kd10002xQRPlXXDBBenLv/zL0zOf+cyHWLfpz7r7xCc+kU4Gjg8++GBAI2nYAAotHve4x9WzSFgk+d8BFsmxsbGo46OxHBag97ThBbL6wQpYASuwYhXwVNUV2zWumBWwAitZgb1793art3Xr1u59/QYAKO5JT3pSuQ0ouPrqq9NVV12VXvrSl3b9l7phYP4Xf/EXadeuXUsFL/BjrSVlzs/PL/DnGBDKefvb377Af/EDZ05y3uTFF18c8PH0pz89XXLJJQno/fd///fF0R/y/Au/8AuJKbm//uu/3g1jWiRgBDjyefGLX5zqlthuxMf4Bsvi937v93Zz/cVf/MWAwOKxFOAAMt/wDd+QfvAHfzB9+MMfDmACBkn7jGc8I/3xH/9xwFLJo1zRG8vmV3/1V6f/+q//inT0wx/8wR+k5zznOemnfuqn0uHDh0v07pV+WQyN3cDqBuvgF3/xF3ePfGEn32/5lm9JX/u1X5ve+MY3xtpN2vrRj340/cu//Ev6tm/7tnTZZZel3/3d3031NYTr169fkPXu3bsXPC9++JM/+ZOuF0Bah1/es9/+7d9OvNNXXnlletaznhXH0hCH94cfHh6JK+DojXEeiVqOYwWsgBVYfgUMjsvfB66BFbACp6EC99xzT7fWSw18sVz953/+ZzcOAFYcA/zi6nBZ/OpXrE2vetWr0kte8pKuNxag3/qt30r1oz4YyFMG1rFf+qVf6sbFMgUMYUX77u/+7vQ///M/3bD6zetf//qwjnHe5GLHAP/lL395WupIi3pcrGQ4pjdijWJN3c/93M/VowRU/f7v//4Cv8f6YXZ2Nn3f931fd4oq8PaN3/iNC3ZRXepoFIARvZZyaAAc0h7aVneAPVB5Ivf3f//36Zu+6ZseAo+vec1rFiT5nu/5nvTa1742oAw4LFM4AcOv/MqvTD//8z8fVswT9WHJjLr+xm/8RiI/pqjisAjSh8Xdfvvt5fYhV96vP/uzP+v6/+zP/mxXu3379gX88/4V8OtG1A3vDzBMHg/nylTVE031frj0DrcCVsAKWIFTq4DB8dTq7dKsgBVYJQowRbC4//iP/4iB+s/8zM+kH/qhH0pf93VfF5am+k6e9emsWI2Ku/TSS8vtQ66sd3vXu94V/uRVLDkAGqCIhQtAZQOYuoWIKY44phd+x3d8R9yXL86fXOwAufoGMkxJBLwAJaZs4oAELHhM/zyRO+ecc7pB6PPN3/zN3ef6DVa0xVbRevhnev/qV786LIbkA3xhAWUzo/qGRounqt5www1hLVxc9ud93uelL/qiL+p6/+3f/m2qAx+6AFGLHesU6+3HIvid3/md0V/E5QcDrJnF8eMAlskXvvCFXUgEwn7gB34g1hgS78///M9L9O4VyyPxyO8Nb3hDQF0JxMKLBbDs+vut3/qtXRg9ESCTtt4eoPu5z31uZInVm3WS9feaqb/UGwsnsIvjRwra+nCuQG0B5IeL73ArYAWsgBVYXgUMjsurv0u3AlbgNFSAjUyYylgcMMHAmSvWxPe+970BbSUcEKtDS33gDSicyN16660LrDoFtorFC2jBGvXTP/3TC+KRH7AANC62CgET9d0ssYb92q/9WrcKTDdluiVrEbFUPvWpT+2GcYMlq9RjQYAeigUJf4CmlA1EYZErgIA1irZ9NhxW3rq1DNjauHFjFFUAigegvOjIc906zDMOYGSqLSBeB6Hf+Z3fSR/72MciDtBW2hke+mK67r/9278ltKwDPXELiDMtuLgnPvGJYREtz+XK2tIf/dEfjXetbqUu4f/v//2/6LstW7YkrN6f+7mfG5DMjw1ojuNdK3qQHxZpHH2w1HTV66+/Pt7jiKQvysCh3dd//dd332t+8CAuWmDZZG0lVs3i+N/HI7GMEr+8FyWtr1bAClgBK7AyFTA4rsx+ca2sgBVYwQpgYXk0jg1WTuQe//jHLxkEnDFNtThAbKkpsVikllp/iOXzRHCGJRIHPNTbgtWxbiUjznve8x4uXYflrExJ7XpWN0BscQWmABg2gAFYsH4VtxS0lLBP90q7AK3isABz3iaWMqzCdUshcA8UM/0Tx2Yxix1hbBCEA6Tr1uF//dd/Df+lphoD8sAcjvWJdasvU4Jx9fZ/yZd8yUM2s4lIta/6GkO8aee3f/u312Icv73ooosWWCc5x7I41tYWx48Idcc6TI7rKI52nHfeefHI9OPy3rCZDUC9ffv2EjV+TFj8HmLprcN5N3J1U475+Ew3X1qcr5+tgBWwAlbgs6OAwfGzo6tztQJWYBUrcKJdIAELLIgve9nLElMci1s8wC7+XLF8LeUAEyxUxRULUnku17rls/hxrUPBX/7lX8aGLyW8QAvwVBxTDrEc1R11w8K42LFZDFNkF7s6OJawv/qrv0o7duyIRzZRKa6+uVDx+0yuS1lYWev5hCc8IX3+539+tK1AWykHcH7/+98fj0xVrTvqWtecoyhY61dcgaj6lOUSVp/ait+P//iPl6AuzNd35V08bbYb+SQ3WINP5tgNt7h6WWhRHNbPAnb0Jz9ClD7k/a1bWetQSJ8u/t8AFtb6FGzKwNqJdf7hHGtS7ayAFbACVmDlK2BwXPl95BpaASuwwhRgo5W6YxomUxcZXLO5CaDwvOc9rxulbilkyipTGYtj583FjgE3FsO6q58bWfcv90DrUlP+mGbJOrW61bPAQX2gD4jUp9MCElic6nFKWfgtXitZLIwlDlcsTuzQWlz9OIf6xj4l/NO9snvoySysJ8qX+gE8rB1dXP+Pf/zj3Y1lSvr6DwBlfd4DDzxQgrvXT33qU917bjh2pQ5a1Lc+bfZEU38XZLLooQ6Gi4LC+scU0uLqO/pSjzL9mH5kKi3wyHTlMrWU94j3uAAtYFdAmemwi4/x4N3HuruUw2pbb2s9TtGcDXfsrIAVsAJWYOUrYHBc+X3kGloBK7DCFKgfhI5limmYiwfymzZt6tZ6YGCge89NOYide6Zxcr4gAMLZeUAoB78vdsRbyspX4mEZXLxTKLuEFutXmXJI/DKFtdk8/k8AawGL5QfrGyBJmcWx1o22FveKV7xiQXixYpbwL/zCL1ywUQv+9WNLTrY5S8njkVyximIpq1tY6+kAne/6ru8KEMLyWncANcdysLnQYoc1ks2AyoZEwF19c5oC8ktZTjkS49577+1midWN/IqjziU9fovPWCzx6lemc9bhk82RlnJMy8VSyEY5ONKU6bgl/vd///eX2/Qrv/IrcaxGfVdYNksqU22JWP9BgX5jt1wc4Me7+4IXvKAL3hzdUf8xhHbzQ0u9/ZFYX+WHDt57OytgBayAFVj5CvSs/Cq6hlbACliBlaVAfRBch8h6LesguTgOYIElsFhcGLzzWezYNKWsoaNMdjQFdhY7rG3ErTumGgJUxWF1YqBOmWU3Tw6oL1NROeORtXFLOeKwPpEPU3ELeHLsAoBEOYvPZlx8DAf5YsECPrFsYVUlzbp165Yq8hH5YfUD7oqljEQ/8iM/koAXpsdihV18XiNnHLJOE4elDD1LP4Rn7Yt8mdqJhQ7rXGk3UUo/1N+FkhSLLuc+0lbqWJ9yTL/wQ0L9R4ClrLolr3KlHRynUt4TLIpA3NOe9rQAcuDzuuuuW7BpE/2NZbhu4SY/6kU9ylTlevvpS8LrjrL5AaLAef2Hj3o8tGUaK2dG8s6UMyqZTs17wyY99TW9mzdvDu15x9mwafEPH/W8fW8FrIAVsALLr0Bz+avgGlgBK2AFTi8F6iBYn4pZb8XatWu7j/WBOZ5AJaC2GPa6CXTD4Pyf/umfFmysUjaqKRu2EB9IeulLXxpJyxRE1uYBFnVo4p6zAHGAExY0wOrLvuzLwu9EXwz2y6Y2nLe3GESwfDEtl+NGyA+Hxatu4aznXbem1oGqHueR3DO9EgCvQyPTLbGyYgFjU5x6+0ueWOSKK9Nl61NF2cimvrYPMKSv6tDIhjdYMeuOabh1Sxth1K3eRvqqHA1ST7vUOsl6eLln46L6O0PeWAeBdHb1ra93pT7s8luPX/LhSvz6Gk78WOMKeC/lTuRf4gKbrCEFGnG8M/Xpq0x15T1hU53i6j9U1PuxhPtqBayAFbACK0sBg+PK6g/XxgpYgdNAAawnDMix5HDe4VKuvp5vqemMgBZHLDCNEOsPjvw4a49z8NgBFIsR6wwpgymHWHRwT3/60+PKF/EKyHLPmjXWWtanypbIWKzIh3yLxYs0wNZiRxsB1fpaTeIwhRFAAIKKYzdOgAFQ4QzFYmkq4fUrZ09eddVVUQc2rvl03bXXXhsWt5IeaGPn2ZM5puLWLYRLTZFES0DsV3/1V6OO9fwAY9b+MT1z8fRjrGVAMdN7F8MagPbyl788sbtpOc+T4yuKY1rvI3GslWUK6uIdXkta+pZNjqjDO9/5znT55ZeXoIdcOaKE/qIveFc5OoQdduvTUuuJaBPvZf29Jpx3Fusi03gX747K+0ZdT+Re8pKXdIPY9dbOClgBK2AFVrYCDf3SOr+yq+jaWQErYAVOTwWAJKaFvvjFLw5L02PZCsCPTVYe7fQ+0vF/+3WrJfViLd9dd90VG7kAN0tZ6+r1Jx+mznKkAkc81C2s9XhL3QNwAMrDlbFU2uL3xje+MTYhKjC3GGhKvMVXwAuIw3H8CFNdb7nlli4gs/6vgC9WTSyU1JVplUtpvXPnzphuecUVV8QPAaU8po6yKQyAhqV2KYf1DQB+5StfueBoi6XiLuWHJRsQ5j2gnPo5ikvFfyz8eHeYiss0Y96T+pTsE+V/zz33JM6txAp95ZVXLoj2D//wD6ExVtz6Jj4LIvnBClgBK2AFVoQCBscV0Q2uhBWwAqtRAc47ZDplHUZWYzuXo01sLsPaOMBxMQQ/XH2wyHJExYte9KIA5To4co7h4h1tT5ZfAUemCb/pTW86WVSHWQErYAWsgBU4rRXoOa1r78pbAStgBVawAmygwhTAZz7zmSu4lqdn1ZgqerKpmCdrVX2d5eJ4noSzWBE/WwErYAWsgBXIChgc/SZYAStgBT6LCrChid3KVqA+lfTRninIukKmjC61jnVlt9q1swJWwApYASvw6BTw5jiPTi/HtgJWwApYgVWmAOsDi+M8zUfjys6k7BpqZwWsgBWwAlZgNStgcFzNveu2WQErYAWswMMqwBpJLIe4R3KmYj3D7du3dx+X2qW1G+gbK2AFrIAVsAKnuQIGx9O8A119K2AFrIAV+MwVuPDCCyMTwPEjH/nII86QnUKLe/Ob31xufbUCVsAKWAErsOoUMDiuui51g6yAFbACVuDRKvD85z+/m4QjIh6pYwOk4v7mb/4mcYSHnRWwAlbACliB1aiAwXE19qrbZAWsgBWwAo9Kgec973nd+K9//evjXMuux0luOA6kvs7xQx/60EliO8gKWAErYAWswOmrgMHx9O0719wKWAErYAUeIwXOOeecdNlll3Vze6SWw0ajkV74whd2001OTnbvfWMFrIAVsAJWYDUp4OM4VlNvui1WwApYASvwaSvw2te+Nr3qVa9Kz372s9Pw8PAjzudlL3tZHMfR39+frrrqqkeczhGtgBWwAlbACpxOCjR02PH86VRh19UKWAErYAWsgBWwAlbAClgBK2AFTq0Cnqp6avV2aVbAClgBK2AFrIAVsAJWwApYgdNOAYPjaddlrrAVsAJWwApYAStgBayAFbACVuDUKmBwPLV6uzQrYAWsgBWwAlbAClgBK2AFrMBpp4DB8bTrMlfYClgBK2AFrIAVsAJWwApYAStwahUwOJ5avV2aFbACVsAKWAErYAWsgBWwAlbgtFPA4HjadZkrbAWsgBWwAlbAClgBK2AFrIAVOLUKGBxPrd4uzQpYAStgBayAFbACVsAKWAErcNopYHA87brMFbYCVsAKWAErYAWsgBWwAlbACpxaBQyOp1Zvl2YFrIAVsAJWwApYAStgBayAFTjtFDA4nnZd5gpbAStgBayAFbACVsAKWAErYAVOrQIGx1Ort0uzAlbAClgBK2AFrIAVsAJWwAqcdgoYHE+7LnOFrYAVsAJWwApYAStgBayAFbACp1YBg+Op1dulWQErYAWsgBWwAlbAClgBK2AFTjsFDI6nXZe5wlbAClgBK2AFrIAVsAJWwApYgVOrQM+pLc6lWQErYAWsgBV45ArMz88/8sifQcxGo5Eoi6udFbACVsAKWAEr8FAFbHF8qCb2sQJWwApYgWVW4FQBY2lmKa9ci7+vVsAKWAErYAWsQFagoX8kT83PuVbcClgBK2AFrMAJFDjRP0X48+l02qnd7ujaSXPtudSea6fOfCfNd/RPGHGUL8bCkk/3X7bKgNhUYKPRjDjNZiu1WvnT5NpsxvNS1sZiiaTaS4WfoDn2tgJWwApYASuw6hQwOK66LnWDrIAVsAIrUwGgTvgmyDv+e2UBvXqNI5S4Aj2AcXJyMh05eiQdOngwHT16NB3YfyAdPnw4HRsfT9NTU2ludjZyzPkLHvUHYBZ4bAgM+3p6Ul9fX+rr708jIyNp7dq1ac2aNWlMn7Vcx0bTwOBQVCMDYqBofuZ7iSmsBsmQx19WwApYAStwhijgNY5nSEe7mVbACliB5VIggC4sfnkdYakHcBeAp+vs3FwaFwgeOnQw7duzNz24d2/av4/P/nTgQAbFifFjaXJqOk0LFDvtdlgaC9ABcQXkKI8Pcdr6xL0KnVc5uJ5WM/X29qVeQeTgwEBaJ4hct3592qDPpk2b0vZt29KWrVvTpi2b0+joWOoRdDYFn3y6KyArkCxti4z9ZQWsgBWwAlZgFStgi+Mq7lw3zQpYASuwnAoAVYsdcIf/lCyFe/bsSfffvzs9cP8D6YEH9HnwwbAqHpE1cWJiIk3I0ohFcWZmJgCwLfALOFQeTDXt0SdPPxXO6T+mnFJigVGuAY7y49oWnHKN8KpuTGHt7e2Nz4AskkPDw2lsdDStXbcubRVAnnX22em8c89N5+jD84BAs7Sh3rYCrXU/31sBK2AFrIAVWE0KGBxXU2+6LVbACliBFahAAchjx46lXbt2pTtvvz3dd999aZc+ewWPBzQFlamnwOKc4A7XkpUPMGQNYlwrix/PTHdtymqI6+7whgWwgtJSXrE28lw+XZgsfoJL/LBOUnZb/h3qoLyGZJEckzVyqyyPZ529I1144YXpwosuSueed17auHGTpr72Rh0Wg6QhMmTxlxWwAlbACqwyBQyOq6xD3RwrYAWswHIqAKAtBinAjHWKH//4x9N1116bPvzBD6Z77r03pqbG+sNq7WGfLH9MC+UDODI1tDjyFBom4uMCDilLz6yaxOTId/gDkSqz+0yCylEXXFwreOwAi/IPqyQWSYHjXAWSrJ/kuSFg3bxpY9q589L01Kc9LV2yc2fadtZ2WSfHYu1klX237YbHooivVsAKWAErsFoUMDiulp50O6yAFbACK0CBALeqHsAYn3GtTfzgBz6QXv93f59uuvnmsCKyUU1PBYqxfhBrodKRnumjWBbLmsKAQ/z0KeBIEcQNoJS/HiJ9XLnXB6thvT6koT51sA2rZFXPjnZoZafWmM4aO7jmaa3xrDjk3SNQHR4ZTjt27Eifc9XnpCs/56q0Q9bI0bGxmO5KGYZGVLCzAlbACliB1aaAwXG19ajbYwWsgBU4hQoUeCuAxpXPrCx1sU5R00/v1fTU337Na9Jtt94a4Mf6QcCxHImxuLrAHesVAxyxKAKMtU8ps5uOMvWARTLK576CRp7JhzyLizjyJw7WRjbNwY84HYCxgsd5WR3x1zY8YXUknOmssTGP/AY0lfX8889PVz/72emZX/AF6exzzsl1rupayqMcOytgBayAFbACp7sCBsfTvQddfytgBazAMisAXOEAq1ltZPPJT30qvf9970s33XRT2rd/fzp86FBAI5vQrNuwQTuVjubpqJVVUXQG6YVFD5ADs5ga2gLABH24gEg9U9bJQIzwqI2sh7lW+ZsyqrvIj6+SF1fK1ZfaIFhUWLEyElaskuX8SKaxMn0VgASAOdpjp6auXv2sZ6XLr7wybVAbqS+fejnx4C8rYAWsgBWwAqepAj6O4zTtOFfbClgBK7CcCgBUdYclbvfu+9J7r70uXS9o/NjHPpbu3707zlokjPhbtmzJG90IAAMEa3AVeSmOfs0MMAQYy9RUQLHAYrmSX/Ev9+VKXvONhfUjrO7Kc0kTNkGlaWjTHSyQ5F3ihJURv6hvO7VY/yiwZf0jx4jsvv/+OF+SXWJvv+12WSCfFRvpDAwO1ov0vRWwAlbACliB01oBg+Np3X2uvBWwAlZgeRSog9UhWRRvkZXxf6+5Jl2rzx133ZUOaqfUaZ25ONPOu6RibcRCB4wVhCsTOAMGBWoy90VjYn1jgcXqWsqLuFWTix+PgCiWwXq4HsKKWY9XJe1CYYHDqJMMhE3qp3Qd5Rd1rQCVvFuCyvacPgBkZS2NMnU/OT0d6zc5VuQ+HTFy9dXPTFdc+dSwPpbyF9StVMRXK2AFrIAVsAKniQIGx9Oko1xNK2AFrMBKUKCAFnWZ11rA8fGJdNONN6Z3vOMd6T3v/u+0/9DBNMNOpAIuoHFOMNhSXABrVh/OYiQPprUCeyU/IBKwWgBXdWikwMqRpsQr9+RX/IhW7gHCEqdKHhfCiz9xGkqvRPGJ5yod+eIifpRRQaMsjeHPl/x5Aoz37N2b3v3Od6ZDBw4Qki6/4sq0Tms62SmW8nClbvHgLytgBayAFbACp4kCBsfTpKNcTStgBazAcitQwAeTodAvHdh/IL3v+uvT29761vRRTU1l6ubYmjWJ8xqPHj0aG8mQZl4WOQAMC+S0LHNDQ0MLoLHbLsCtcgF1uge1yn2AXYmgax08S90KlJXnSFvLl+QlrMSNUlXH4rA44gqMBjTSDvkzXbX4E4c6RL0Ekmyiwz2b6tyoo0ewxN5/3+707Oc+N23dtq17bMdSdSKpnRWwAlbACliBlayAwXEl947rZgWsgBVYYQoAPXxmZqbT/7z73ektb35zuvXWW9hXJg2NjuiIjb50pAaNVD+sdgLHqempNDU5lWaGZwK4ihWuAJwyDlAMUFM6rJS4AmfgXOyCip8+JVy3XSteqV/x6+aNh1zkpSt1qt8XmOPKbq8lvO4fQKl0xZF3Q8AYV+75qM5zymNa19vvuCO96U1vSoePHE4v+PKvSOece26UubhOJT9frYAVsAJWwAqsZAUMjiu5d1w3K2AFrMAKUAB4Ko77/fv2pf9429vSf7/rXek2wVGjpzeNDg/Hxjex46jgiiuuAFhHMDYra+Pk1GSa0RU4K+BGPDbCkUfAV728kgewVSBOkbLVkDRy+JNXNzx881fx44orsFngLaBW/uWZK34lPs98eM6WyPxMeSUvBaZ5TUVVSOwIyzNhAOReTV195zv/Kx0+fCS96Gu+Jp173nlpcNGmOaXsqKC/rIAVsAJWwAqsUAUMjiu0Y1wtK2AFrMBKUKAAVKnLAR2v8T///e707295c7pPu6bON5ppTMdr9OlMQ4ArQAyrHPcAlT44pm+yDnBycjJN6NOrYyyArwJNTGdtKk15jkT66kJblQ/+YXnkpuZXIK6enrLLc6lHTpb9636lHMJxJR339XzYFKcxD6SqrtVfjq+2UDN2cwVoK+BsyyL5oHZbfe97r0ttTeX9khe8IO180qVpeGiYZHZWwApYAStgBU4bBQyOp01XuaJWwApYgVOrQAEmYBCQ2rPnwXS9jtt421vemu6++57U09+X1oyOpb6BgagY8eM8RAEh1sPiSj7A3dTUVJoYH0/9Ak1cAbZe3ccawgq4IrAKD0Cs+Zf8uC52BfjKtcQt8Yp/AK4861bPxXEJK/ECBBVf1ah49fiaSHgxO6CROLntgHLUUPphcfzf//3f2DiIab6XPeXyOM+ylFmuJSdfrYAVsAJWwAqsNAUMjiutR1wfK2AFrMAKUaBAHdWZkpXwxhs/kd6p3VM/efPNOu+wlUZGRtOANrohHlAI/HAGYnHwFOBUhyI2x5mYmEiD1QY5AW4VANanrxbAK3kRr+RT6rX4ufiThrACfvgvzq/EKVBcyin+pC/3XMOa2MzUWMppFTiOhmbravyjig7Sp+7QZVzAfJ3gsU/W1qHBobTz0p2y1A5021WP73srYAWsgBWwAitNAYPjSusR18cKWAErsMwKFGiiGtxPaV3iDR/6cHrLv/5r+vCHP5yaAp+1OmKiX5ZGYG9xfDiqBaxV6ckn73qT1xhOyup49MiRWNcIULV1xmOPwLNX6wRjKqjSBlAqGZDGfZmKGv6qE7ubRrYV4BWYw6+kKdZC6lcAkbDi6vf4lXaUa0nDc4MpqJklS/KoA7WIHNFB91gZm9opSE3J0AxIU778O2rnrJ6v0VmXrAHleJInadrq0OBwLlsZLa6TktlZAStgBayAFVgRChgcV0Q3uBJWwAqsNgUKfGSsWEQcp0VjsaBlmLrn7rvT29/+9nSDoFEUl8bWrk0D2uAFi1u0jIhytJkPG90E2FXQF/6KmVEyxZEcHNfRK5DCAWg9FUACouy2CkCVD7DIfeSt+4BHQVcdsiiTabIlTakL+S/2K+lKflyL474eXvcv91yJE2Cqqx6iTtgYWfuYUo+OIkmplzAcV9WXD3Vkjef1OsaEUtHw8iuuiPTErlWFlCvf0baaZiu/wq6hFbACVsAKfLoKGBw/XeWczgpYAStwEgUKfEQUEUIXTiqWeEhSKGJxWOGZxf4l8YnSKD5JSvISPa7Fs57nUn6KDBhxJuN/v+vd6cMf+lCaac+lkbGx2BUU4AEOG4KGdgEk3Qe8Cf660zirwgPIqJX+K2sdjx09FhbGjoCqrbKwPgKNbCiD5RFAjI/yj7J0pU4FJAEymoHVr624uFyOitHzYggsfRJ5UGfFoVzyIKyEl75a/BwFVGWUOlDniFelb7X4ZzVP2wWoetSOxY61nMBjWG9Vh2HtSHveeecFjD/EsknlcIv7aPFzjnX8m/CSFt/F8Rc/H0/58HdV3ll9RVd77KyAFbACVmD1K2BwXP197BZaASuwDAoAHxlAGmluVuv6jhxIjc6s1rf1Bvxky5RG4PPVoFvxg4C44jQY7wJM9ul+V+P2eO4CT1Ue9q6w+DGYL3lVKUk33xHUaPMWppLyHJvZkIY48qM2xX9cVsF3X/N/6X+veU/af+BAgM2Q1ibWLYKRjDQqi7wAKcotoBfh1IU4+iuwwVTN8cmJ1CM9EpvrAH5KB1RidWwxbVV+tI9nruUZ0Cztppy20hEWutAG4qq8ApmUjR8u4ik+OoUVEH/qSxrqIFfi0qal7olD3jji1PsJ7QFD8uro01R4jz5qRGgbG+YoLekPHToUVtzBvp70/Oddnc4/9+zYNIh2UCeupZ+i3/TcbOYpsYSrcpFn6cNoQyTN7aPFUX/Fi/rKGkp6XKlz3Osr1FG8ki/+Xcc7yn+E62ZSZ3H29A+n4bH16r++iFZ06qbxjRWwAlbACqw6BQyOq65L3SArYAVWigIMpjuy0h3YfXu6/YP/ndqTh9OWrZvS6MhAWLoyvRSUArYEAx0G53IwDPCg9XKNFhDJwF1e8o7hu264D/BsAms8KCT/R4A+wBQRIzAnJFaAQOWnBLL16ZtccVy1rnFmLt185770b2++Lu16cG9MKx3irEZNL6VdBcDIpcATcIIFD7cgjsonLMMFKXJZMzMzaVwWTWBLGaaOYDGmrCpuSx/KIA3gWKAuLJHkAJQRXuVb6kB8PkAb7S6QKc/wL8CUW0mxGRapH2G5jrn+pR1ccSXveKg0LSqWfMsz+fKJPJWgR+UXF+sg9UCZhw4fTtdpp9rG9KH0rCvOTzu2rJEeWSGKyPXJuZa8Iyc95N8c8M15F//SlVHFWJtJ26WFopaY3bzkEeVQOe51iRyrr3kVEnVQWHumnfbuPxJW6E3nXJx2XPq5aXTjWVSS1HZWwApYASuwyhUwOK7yDnbzrIAVWF4F5jtzaWL/veneG96Tju3bnY6dsz2dtXVjwFCc+acBuobmMbBvQAy4GLTryoC83EdA/iJW2aula+WTZ4tjIJQGWJlvk3Ee9HdkacIG1dQ0yl7BTKQhjv6EXsGq5BnwE/4p7Tk0na7/6H3p1jvuTb0Dg2lkzbAMgwNda2YXlBS/1IFpq0EhVLMGEwW4chpgkAjZscsqax2xMIa38mhRB0FV+NEe3QOMOM56BDSBsg5AWWUWoEZc6qBrqUsGLyXEj3ClD1c954f8jbWz1DWu8i5HhNTjRZdQR+XRbS9x9YnyKn/uCzwSFmtCaZ/uteIx2hvwKHh+/wdvSGvnHki9F65Po4O5XYAeH16Lpr7IIwCcXqNoBfLHe4MOHZ6VP2GUzX32w+DJu6H0cwCybntyeNYPeM556SYawlM4/ZAxr5et3e6kI4cn0p13PyAr6mzqb+lczgsvVhSBo50VsAJWwAqcEQoYHM+IbnYjrYAVWC4FAIeRNWvSjgvPTbdPjafbdx1Ju/dNpa0b1+goht6Aglw3gY1uYiDPTc3FQH+RXwlmgN8EGBnnV1MKgypLhAggMRFmK98MCpBEPdsCHYcnZtON9x5MH7htf1gYR0ZHE1NUCxSRUwaVPOWyWOsKkJL74pV9pAVgM/7kUikPC+W4jucIS6LiBPCQHujRh+eAwgoYO8SRpjF9VX4lPCBJYcVlHav2VfkSRnoceeKoe3GEkDeuhJN/eea+PKNBhKiO3BdHPXCRVxU/6qJ8KTuUJ01VDrGZtnxofC5d84n9aWq6ky7ZrmNO+uoKklvEJGu5XKd8X/8ucRRe3fKTAa6bIj/GqxIBkKnqRb0DhPHUfaiiK+mmZ2fTkaMTae/Bo2nN8EA656ztadO27anZOxhZ+MsKWAErYAXODAUMjmdGP7uVVsAKLJMCGntrs5eO1q71pHXrh9NUZyrt3nM4HTo2mTasHUmDgscyqseqhCWR57hoQN919fviSeaKElNPdVM9wgEBAmGAVNw8/M+Jco7VtyIU0CEW8Qi59YGj6YO37kuHj82mYZ3VGGcuskZPsAMEkSampOoawCH4KvDIM3kQN5dSlat01DPqWA9QMGkndMYheZOONYFhkdMVgCMca12sGSRf+Ue9FZ80dcgrcIdfFKNwBOE+AI57fSI99SS8chEOSMqPMkv6xfFLmlJuSc+VdKSPT1XXiC+/UhLp6F8cgIZ1eEYWvd0HJtINt6vt2hzo3A2Dx8snDpH5LgkpQg/xiKg4CghTtG6K1iWOgohW6pBTAvPyFLji+N2h5B/+ij0raDx4ZEJnUE6lsbGhtG3Lep3f2a91pZqWXKWLxP6yAlbACliBVa+AwXHVd7EbaAWswLIqoHF5W+scZ2bnAn7Wjg4KuubTrgcOpoOa+rdu7bDO8evTEB2YKOsANcAHNACQcAXpjj/lET7AkOEJKgAk9BV+EVNgWB7zWjXFUZ4x3hdggDIZwHIyjE+HJ+bSTfceTrsPTsqipIPqR0diKin58AGMIo3KY9onAFksjawnpB5RE6rSrX/URs8ZApk+WQdL8pwVLE1qp1HgbUhHfRTII2+skZTDNNUCj9Qh2qL4pC8QV674EV6vA/clvH5P7XgOa2Z1H+mqMrivT2PF2okrZXAf8XWNeqEB9dYzAByOusgPAMYvctCzKhQ6otusOmbXoUltPiR4bM+k9QMcS6LITFNVVP0XfRDQF3wad5E9VkviUA8+9HGAYMTL9SIi006JF6+K7iVTPET6yD0/U9LUtKDx8Ljymk9bN61J527dEPrxLs9pDWy8B0S3swJWwApYgTNCAYPjGdHNbqQVsALLpQAw2NE6x3LkhLZ6SaOa7gcw7t5zKB25/2AaG+5PI0P9ARMxjldlgY5imQoYETzkET9jfpAPl8GB+/ypngEH0UHATJAHoSXVcdggh5xnDp+V5evuA7Nplyxfs6KONZqe2tfX34Ut8ux+RBwBjYLFjqAvPlU4ZRUXEBOkkms7MtCbentaaUKWrJlZ4CrXk3azWQ4OqOxXvctRFgGktIMPpFOVQ95oVC8DmIs8FFbCw4N85VcPL2lL/BJvgT+eKoMySV/y4Bp+hMnxTDpcaT1loReO3VW5K3nE2knqzrpKXSOW4k/JOr378Izehfl0wdpm6hVh0jt8l54r1kB8lDiHQP0RI3wjT+gxp63qlKN08yHPnBdJyb0qQ4mmpgSNR6eUTyOdJWjcumGtfkDokWW0nfpmFY/4VduonZ0VsAJWwAqsfgUMjqu/j91CK2AFllmBMsDGCsQxFOxbs16bzcwInu6870Dac/BYbFrTK0oIK5EG7mywGoN+fQXyASqLHWHEq4K6MQCEMmWx8gyvyDHfVSihvHNUsp6eb6Y9x+bTsbmmpqcOxnmN+IdTQSDZvOpPmgBh2qIP99GuEhaVysnq37R/3fBgety2tWlKbf+/2/dUUyXVQkET1kQ2ywGkwvX3CzL1z5QaGGVWUBgNVhlAGFbPgDhEUDpS0uSSx2KrYIE78l8chh95FbiknLB8ql047gv4ERb3tLUKq1slw7P+pfgRs4of3VLSAo5qB+G8K8em2mmXpq7Oar1jT1t6NCScAiO6EqrkyDnyq/o556c4WBEVynOORabVQ8BlfuQ7gyLhuqcIpaWMjsqemZvVOsu+dMH2Denszes01bpXO+3OqD9kNdb6S8qwswJWwApYgTNLAYPjmdXfbq0VsALLoYBG4x0RYQzK9QVgADCb1o2lB/cfS7fs2p/2HBlPLQ3UC5xQTYyMjP4BhRj7y6MAQVisiESmgpJw5VoBVuGF+qYnJV6VItNFFbHRHNR0zb7UNzQSO6gyJTPgikSAj/LFysgVf9rB+YJzWBzxV13IKkBZz1EvtRMoDD8VOiA4vmT7+jQ22Jt27R9PuwTNdUd64LFY/dAjpnUCV+TNR/kx7ZM4bYXTFu4DvnjWp0LPuFdwuDoUFgAkXXH44bhS37gSXt0HkJF/lSbqSHzK1pVPyVc33XShi/IgHxxtAkVL3NAmAlSWNEXLozK+Tmiq6OzkET1nfeMt0DsQZzEqK86vFOZKC5VF1ZUvFkTidS2J8qbYltJ19IsFNcitFCBW9eGSP0onaJzTVNTB3ma67MItact61uH2aSqxajyvMza1G6t6v5tHPT+aYGcFrIAVsAKrVwGD4+rtW7fMCliBFaMAcAOIUCF9iUDYWITB/OhAv6aFpnTvUe142qs1cBqYNxvaiIZ1dAEDAqSAkOpRYTp3Q34AQt0xhM8OCMC4xAkM+LJ+LcpWAsI40y+bNnN88mFa6JgOoh/qG0j9OnajrzrYXZSSYbGiiwBGQFCfsDQKGtnMZa561m4qKiNvbhNAJH9chqS4SWu1pvNJZ61NR648P/37R+9O9x0Yz5bLKi7xWe9YXB+WRx5Ul9Jm2gXEkm+AGFZB7uUPKFNPwvAjTYAlbahcgba6lZD61kGynqabl+LUz46M/FVWSUe8yKcqm/AQXXEonU8BtlKXMAQSjzp34+im2ZeOtYbTsdnxNCt4jDrP66iOKhYgmPQcTgnJgrJ4MzqCPOoRfjpSI7KvwohPGwBPKkR9sCSz9hZobGua6vaxfr0Lfalf04qxJuOOv4e5jQHwEeIvK2AFrIAVOBMUMDieCb3sNloBK7CsCjCUj2mAqgXgALTlaYF6EsT1iPBagrYkyw7r/zhzr6GrAiNcfCkIyAfKk0MABFCYTWDkGGDAsRzgybysRjBBU4AZ8AZNZX6LNECEolINlaDyhCJD8uhtaP2hzlTs0YcyAIqGoDAseRX4AGRlaiqwUUAy/IlTQQhpciVUEPdytGNgoE/ta+gcwGa6YseG1KezJd/+8bu1k+vBAJc4WoTIyme6Bo9NbZjDER3hqFc0Qu1T3oE1WAF1X468CMhUnAxPGSRJSzr8ejQFNrTJOYY/YQUk8aZNBQjLtaTnWgCSe+JyLY6aFksvwFWHRcqN56o96EIbSB1p5B/p9TWsMzTbalqjPZuaeifAYIqJGIrHhkM4sppnSmv1EOHV1FRiY/HmGp3ORS9EM+an8r7oSfXvk++MNr2ZVgE9Ws8YZ0dSl/yikVoJ4zu0Y8MlnsjZzgpYAStgBVa/AgbH1d/HbqEVsAIrQIEFg2zWkjFtkHG+BvdASR+7oGjjmKHB/jz1UaPxgKgYlWNV0wBd8ZQq4jdkecTqyG6kuFZDA32AU4N+4AeLU3dIr7SBHEqPVSlnmS11THsUJqaeSYGEIK6nsjSWXUwLFAE6sRkOIKn7WOMIqOnDc4AizyoVS1bGCz3Ir0CXntKYdgod0tEk5CfjanrS9jWyjJ6b3iVwvvHe/WHd6lFtCSePGU1bxfHMWZLsSgo5UV5MGQ2KyvWhrlEXxY826hrWSOpAPD6Vm5NllOcCe0zLRbdSV/z54IejnbgSHlfVqUK1iItfKaGcFxl1olzaw7XKh2fSdnUiXJ/iuKPNtHd0cEjG6Nk0p/6O90D9pMCIyo8DOOJm0BOkahdffkBoYZ2OolmLetyayS6pOICQHW7RMvpP9eudUXv1bmopo1wGQ+6oKW9OfOIXBxA2so9Qf1kBK2AFrMDqV8DguPr72C20AlZgmRVgoJ6H3KoIo22N/oEE/IBH4KIlC09Llr7ePmGcADDOdAxrUgaDsBBWVqOAGuKQldJhPox7XdkFk7znBA/4t5Q38SkX6xTD//gvyld6/bVmVBc9twSRlAZEsG6xOJ4Bp4DE+r3SgCABHfhXz0AlH1wBrY4a2ic4WzM0IHDsS3MkVL36VOATt4wpk/nYIOhGbRY0NTMbUMhOpIBbgUfgb0DTVltsmCOgirIVhzqTnudwihd1kl85YoO1kLjQQlegLUCv8i/QVq4RT2WgK/fRX7pSn/KsoFx23OQ4XT/FpcTIpyqjhBXN0AtXoFSRo9744kfZ1J9pxAN9zTTXipTxflAyPyxQSlgEVRj9R57tDtbU48Be4DB4EZ2i3HIVJ5b+4j2lPG2E02zS/zlOxJd/g5cV5WTZrO5C86ysguysgBWwAlZgVStgcFzV3evGWQErsCIUwKpTVURGI42981N3wC3PgEWmpwr8Ws2e1KvBewYUxdcWqwACAMDU1kASBvL66xM44l3Ahmmg3DfbAKVgQs/AJH+AQMTlyshflqWmCK41K9BTcJQHAAKNgFkFGQGOtfvwVzyu+lJGNKmCDEEI6x0jTHUs4QgwJIvqprHBNIzFUWkANyxhfarfE7esifV063RUyYfv2ZMOHJvOQKU4itSFR/Ltl1W0V5+AsihdX6FH9VDVhSdajYtaUtdSJ12pAw4gLdNK63nGpj7kSxriUZfKhR9heo6P7rHIduuh54hPmTUXOSmMdgSIU9cqD3TmPkBS/lhu0ZV/qLGINrBKa9OaKE91UTY5ZVwpWmWqg5vqzONvnPzxk0W620dRZu4vkvKu0OdRvn6cYBov+6ZGtqqQokdZ5IpfPFNyvMy6sbMCVsAKWIEzQgGD4xnRzW6kFbACy6mADENdfgqEESgBbYzCAQVG5WyUg8UIB5/EtFMBJK7B2RwarTdIoxQBJIqEVZKppliCABH8gQmsexybwAAfGAAoyJs4QhHYM/wUU7vQCBoFjg2seHIxHZWEQBDxuZfLUJFhJ2dMTgSo3LgQN2+4grWRVAEyCsf2SD5jsjZuHNExH7KsKsMANqabtnV2oXgoPWHzaOy2Oi2Y/Mjde9PE1EzkA/BQn7A8Km8cbcXySBmlrAA9xY16KQ6QQ3gBMZ7JiyuuaBP5I5zCQtscHPkqg6gn16IF6ZsCOeJTdkyDre6JV1yUJX/8qEfXVX7RHxUskk9xRetSHoWgTw/HtVTTkVkDO6+dTjFCA3q4tp7zdGVVLYor/acfB5SOtytbH6WRpjJTJerIlGcd/CErte5b2rSJHzA6+YcD5RRtxCJNAuoZt7pTF3S1pHw7K2AFrIAVWN0KGBxXd/+6dVbACqwQBY5zQ4zWo1YZ4HQbg3xdBYKxUY6siAzXm5oSGBuiBPQJh/TMFFTC2ESnT8DAfUfAQP74BYgBbiKH2CyFgFK4rmyEA1hhiUwyLMbGKFRAVql5AWqs6VM8+XRBSbeKJ2ATMRQYAoQCJgjTB3icIw+BEKBWpqrOQxd6VsXSjg1jadvaYdW7qc1XAQ/qqHx0xfLIpj6btePql12yPW0eHUgfvHNPuv/geJohPR8VNKNKd8bHoz4DrP1jam9VX9pFXXgOKNN91Fd1iHrVwnQbm9tQPh/qT5qwAhIWETI0kgdtxwGJlBNrQJUvZfHcBc4qHnGLPuhfwAt/XKkj+ZJzgcSYNqq24lc0pO2SKM3r/MSWdsppYbFVnvo1IPKlfBzQSD3mZXEErwNolZOMjdI4/ygR7wv5qz/bOkoFayQ/NLAcsqnnaLiyJnuaouCshfIDuaUWRcW9UsS9v6yAFbACVuDMUMDgeGb0s1tpBazAMioQa9Bq5bPWEOtYjM7DX6N0OQbrHMMRm77omamKPRrRsykMcDA9OZXmdSg890wTDGhh0A/AxJA+fwNkiqEZroIIoEV/uABVxQVEsF52ZpSXNkOJ/IAJTVckZheiIhWQIFcDovJcsIH0tBH4iaM5mPZYpS3phjT19qIta9NGTVWlbsCiyCeDGFrIBewo043DfelZF2xKGwWR1wseb33wkM40ZPqs4oXVNaVx7bgK0A3MD+pw+mrdo/LAj3qgTXHAIM/Fh7pxH+1WXOoIiOEfbS8J8ede19KeADbiKT79E3lyj3ZVGRGX++oTMFflw2UpF1ZRBYS1Udew2ip/yp4jH4FerzQAHjuDACL1z0AMlMZ05MoaSSv4xx2rqJIGGMZ6SL13rLcVMsYaU5C3W2flEZDPu5kzV5uVOFch8okH5Uua6Gt+TLCzAlbACliBM0YBg+MZ09VuqBWwAsulQByZEIRRqwHgVz2CNHlqqnwYlGtA32KQHzGwenFEhwBAO48KlfRRiAb0s7I+AgY9GuiH5Uz+gA1bukQc5R9QqbyY0poH+2x6Iv6aFehNyXonKx/Df+xRAVLVNeBH94BMqScQQ4HBfJiiqBSuAITC2eWVNY44IAzOA5y2rR9N525ao6mo/cpTli5Nw2WvF+oZYKU4TJPE8tjWDqJDfSldtm0srRtopQ+M9KeP7jqkdY8TGQyZoqm0k1NTsQsrdRzUcR1M2QTAynRb6h0QSWXkClACPrgF0AQ4ESdCIjDusMwFfFdp8CROWCFVFnUv0BcJ9IUfcE4c7qM00lPpWj48B9yRn+JH2cQpTvel/pyl2NSU4qZAvylRWfMomtSMUqbs5v4NqyaWRbqlKot+79HLgiWzKc07StOYF4BKQzqc906dEdGB/8iPOuqTUTq/heVtjWuWL6dRUdVjqbWvVsAKWAErsEoVMDiu0o51s6yAFVgZCjDwD4sj14IlsWYxD7fLoJsrViNgi2gM0PPRE5oCOjerqYWyD2nwz1RWBvVML2RNG4P+GORHBtxmixHrHgu4sL4xpoUCE6IKvud1HEVHh71H/SDuXoAAAEAASURBVAQtYdWK8Ax8uQwqIrihTnIBXLSDDFRX6oCb1z3TTLHATSvfApvFHjWgtXmX7NiUtsraCORi1+yNhletVz4BSAGUGVTZ1XVwoJHO2zCi3Vibab1I8mP3HUz3aurqlM4aZMpuByACuFQu10FZHjmHMnSjkvpQVUqJK0CEP1f8daWuOMJxcSUOropHGmKhAyGURxj3AF/VCgLkAyzn8EgHeOo5tCv5Raz8VfIKcCzxdMU/fgyo7tlhlv7vYHWc1FEi0hR4pE5c2XmVCpEGl/PLbSM8pi5HXvJTnFjzqh8k4t3kR4xIVrWEB+KSEfcK532MZxZPKoxGw6i62FkBK2AFrMAZooDB8QzpaDfTCliB5VWAsfZDXOUZFrxqCB6GPN0ztbApSxKgKARMbWBJgAWAsD4x1q8Bkaxl1F+ZikgZ5Bdr2oAqRvYa+DPEB0x7m70CToGWNqSZ16eAV8ANieUKfHThQbAHuEQuWNAW0wKVlgNsZgHHeNJXLjatHx1MV5y/OY0Ny9pIu2RqzCCle+UVkIQWHU2tZHMWrGhqG9ZL7Z2azl47lNZqXd+God500wNH0x37j6a9hyfTDFbT6tB7LJXsDDowMKgjO/rC+kjbqHe46hrWw6otwBWOumCti+rqnmukKmmJpHusuWHVi8ecb4lXphfzXOIgU4AhV8pCOD61fLP9N4OeomVduCn14ZaP0mN1bAj2m+MzqUfTeZuxs2zWkPJ5J/jhgLWLFJGLEWzmZiqPjPLEaWl/orA2a82kqF8fakuanDD6J3zyF6HFjw12VFI+J7IWx7dWwApYASuwuhUwOK7u/nXrrIAVWGYFYrCttYaFRhhyi0A0CNcAXQN9oCBsVrrvAIEVMBAWMKJrUwe+t7E6asDeicG/Bv6Agkw+DP4jE8FAxCdDFcFOrIoh61e2yLHxTUP5M3UScJzX2Y0xDVPlFYBSIAQj8tQVp/tcv8iSbLNTWapMwCpTIIEx8mDqLJbCsHbpOXZ3lWVs67qxdOGmsTSojWxoV5m+GbCjsoAieKXsikr5EUfXWf1R+phg+SkyU27QkR7D/a10k9q25+hEmlH5saGPMhifUFt0BUb7lRbrJsxEfah7QJHKj8LIVfcF7KgX/uW5XBUtgBEAjTwWp9NzF7QJ0yfyKVfVAR3xK+kjbz1TJvf4d/ugSsclnOLQG0whRR/6e14/IswLmpusdZQuvBK8G5zpybEusemRnmFB9GjEDqryp41AX7Q1co0fEzRzVXHpaZw05DvqRc1ymzJX6rkbj4D8/uZYEdVfVsAKWAErsIoVMDiu4s5106yAFVh+BRiAy75XDbirIbYG3+xmWmCCFYlBaPGV4aXd0YROzFEM1AUJHD3R1OYws7ISBjwq31aLfIMFFA3IAyjLMF6AIDboEbQSgpWIIGBuTlMdw4IpEGEn1OwUXgAL0Kkc1rPIEdjgI8f0xtiNk6mKVXkAysz0TFgwaVfJgTWNTz5nYxoV8LGejtxIg0URgAnYjUV5yjfASBYy4AdQ0oc1gG1ZMWfnctqz1jTSGm0Qc96awfTxB46k2/cdS4fGp0IXLQDUukdtJiOYHFCaIa177JNVDgillfQFVshoh+7DAT/lvoqTA6pgpW0RTl2ruNGnVZoMYVVe8qPd4Udeih/QWN0vKIfsqzzCGslj8avKK+Ghpeod7UAjTVGe0zmXTW04NN/Qp+p74LaptuPQuBE/WJCanKk/8Mj7xRtRASQ/PMhlayU/Xih25UfH0+PdekX3x5fyQMtcVmTgLytgBayAFVj1ChgcV30Xu4FWwAqsCAWyySZXRfcM7IFHGCHYLY/Hj0OHArAihZVRQ3fWqQGPwjOBEVNMBVha94g9KsMiw3twoIAkoKAyBApCsAwNCmxPaW0jH1kGc/4ZHEiLVQsXEKHyA4BULr5RvfDL93GOofxBB6xgTFFlfWNxtKtXFrCzNoymnWetF/SwY0tea8lmLljGsJoCM9Q6rgIaLG9MeSX/jtrbkhWzDTQrb9bxkWePPhdro6CNgz3p/LWD6WMPHkl3H5jQzquzmr6ap8oCNuQzNDCQBrQrba/KCsyRJviHNlSWNnHRZ8FV8XimPugBGJMn6UKXSEoqssjXeKiei14lpOQfHa44Ub7SdS2N5E3a6lOAM/JRmaSLTYfUb2GNnZxJvVge+4BsvQVEUV3lE3GVjcrIpcOBaoGy0Adw1B8zfAFb+jZeTQCZD+8Z0FnVhrgLHPUMDbJeC8L8YAWsgBWwAqtaAYPjqu5eN84KWIHlVqBAhobn3QE9EMBwHCBhfM4gnTWKPZp2yNpEwIAdQoGrpg7h47ndljVPabAcKmUM8oGFGPXLK6ahKr/YCEeA0Fa8AAClpRhlrPV+jTQ3MaWpqrJYCTJiGisQoeBwQBLxq8cAJfnJM/sADMq3QCPWS+JixQPYmKYabdMzkLJxdCRdqk1xtq8bUfmKyfRZnTXZYkoleVEnrIvgi4LJK7BGQN2RxRUQareZpjsX0Nzu6RU0an2fNsDp0bTXfl3HZHXbrJ1X75QF8hP7x9O9h6bSpAASkGUKK3UCaLE+9gsiOfuyrbJjAxv1A0BInQE4QKq71pEWyz8AT7e5bnjiXfmTtuaKf/S5wkooacMp/4jDQ0lbruFVpcCvihvpqmfyoZ6s/eyZkyVWPwC0dGRJS5qSL23ujZ13FVPP+NE3BZSzzvRNqKyr3hf1RZrXDxL0paZED2oa9ZzybegTrVYcNOL9zLWj7aqHHvJax6ihv6yAFbACVuAMUMDgeAZ0sptoBazA8ikQg3RG3Jh1GHEz/NZVCBGD7yAS/OM/rD1Ao3YGZdfMOJW9SpZT5DTKhV1SWxroi65iQI9FL6BRIAYQzOlICzFD6sW6p/xjGqjWNs5NTms3Va1LBJQEBESiekwL5QpQYRsMsOOqT9SbOlPPoFU8s8PaCDROTU8HpOCrYsPCd8GWNekyTVMdABQBUtUFyKGuBRgbPRmaS3mBsT0CHmBGebdEKPOsneyV5REYnJUVsieDJJbHHoEjayfXDE2mddp59c6R8XTXwcm0dxwLKLuvctB9hidAkjMf2XmVdtF+pucGPFbP0VbdKzDaHe3XfUAl/pUjbeih5/o1NK35lfhxJU3No1gbQ/+af7kl325+8iReHLeiOs9pyjJTjntHBOuaBkx9Y+dcwJ6+JG0AuH5uUKGgIlZHfmDgTy9X1gFQFIgGOOpdmm0IGuMHi1Je1qFbJ/U/esWrIOvkiepe4vtqBayAFbACq0cBg+Pq6Uu3xApYgRWqQAzUGWlDDVyr4TaDeSCLq8biAQlY4ZjKCTQCAoBaJOMMPm1y0mrmHTOrVDF9M6CASJGPnoBB1kIKDkRrykLWKUEB0NhmfaMsjgFTFTRh82PjFRVIJtlRIdU1LJBRZz0qBMsTLuotSMFyOSNoxNqFAySAnc06t/ESTVE9b+NoQEqzV9ZF4Fawi2ULcAEeY1psQG/kHoXQ4kASoJg6yurIGYbzvX0CQU1ZnZFFcUYAKQDsBQS1jrF3cCCtGR5I28f601ljE+muQxPpnsNT6cCELI5aezmrNIDkwMBMtj4qDVZd2kwbAtIoS/UvVseAIrU9rlW7dDnu0AWdFrkCe13vE8RDW+CRlp8sn1JCiUcaYG9G1uOeCe07qx1nw5ILMJJXrTy0BNpZFxtrRqU1jlejtyev/2xONwX/M6pC3tgogJY8cMowK1A9lsrINyzm2dvfVsAKWAErcAYoYHA8AzrZTbQCVmD5FABIYgherTeLC+c4MtdPjudAsRJesVueHpgH59xjiWTKIcP4GM0rHRa4ho6eaKmMPIUVi5JgVPGAlwAgQca8LENk39YUTg5+BwwID0ubrgEayh9Hmnim3sWvgoiyEU5E1Bd5YG1kKmjsrqqkTF/lCJFzN2on1c3r0pCsgW09MwU3oDGmpwKNHL0hQFZ7AEkgSpG6WgkpqUzoBPT0yMrI2s7GnKyMxNcH62VMY1VevSqnT9M0+2WpXavr2aP96abhyfRJbZ7zoAByUhsMAbic/zin+o4MDaeh4eFYL9mj9rVV9rw2H4qdXaVPAKT8AdewKBZdqiu9EPohBlpxrbkSXvVWhKg1pfcWxidPHFd0KM/1fOUXQCo/6kYftwXDwGNTaz17hgcD6uH6gGAWNsrRh0xZjmmp6K1+iHwUxhpX1ow2tFst4DjHZjfVexh1UFnFRQ2pF5bzqhUlnxLHVytgBayAFVjdChgcV3f/unVWwAosswIMroVpAQOMybEOxrf8G5pKGENzfQFQsa5RI3/OJAQG5zWNEEthBpSZDHJKLpYRHOhGHy5Y8igFqx9jffLplzVpXhZKrIsBAZx5KGtjbLZTgVGRJsBID7kuGYK4p6aRIVc50sY8Vu4FLqy1m5zSjqYqFwc0AqVnbVybrrxgSzp306jWZQoSVb8AaKgGMMKKqo9sqrKUAYEKl3/+KIxGUHiAi1qmcplK2VJb+lTuXL/W9gGr0/2yvAl4AMJZWRBZ9zjQn/qGh9LQ5FTatGY6XbplLN1yYDzdsm883acprOOC5wnVcUZ1n5iaTMPaOGdQZz8y7RXrLLgFjLJDKboHfulaX/9IW6N63ERdQ6l8r+eAbwWppTlelZ7oC+KHR+1LaeswFn2gtLisie55llZxpiNQOKkpvGpTz9BA6BpWwChPjzIuNucF19IvYFv65x8NcplzWkc6KV3B9fwXGJ/rGFH0fsVLkeMXZox3T9Voc8xHDvK3FbACVsAKnAEKGBzPgE52E62AFVheBaqxfwaxblU0KMc6FM98A1gCgbDEcS9oDMDLliYsbExjbWg7zIwzbFiSQYN4gBUAAJgCHzyTJwN/pq0yRXV+ptoUR/FFpwLByppGHfBT+XUXtarIgfqUaapYFwGQaQEbVjx2eKUONJANa55yzqZ08dZ1aUDTQdWgAELgkfyjjZp2yzTV2AhI4YAj7S5WyFwHUIYWAHDKWvnPt/IREA1NXW3pBPsO6x7n+tKsIHl2djq1ppWPQBSAHNAxIEMCqhHVb9PoYHqcdne9VZvn3Lr/WLpHO7COTwk4sdjJ0japOGyewyfqoTxUZFhRURHHlTYHuOERcJbryCOONABnOPpGNwUiy5W+oV24yE/XiBc+il/pHbkQt8ongrmnXOlfprl2BNCz2mG1NTQToMgKVbRlnSxTm9uanstGQz2a5kvVsmU6lzPXAfjVVr1X6Nbb007TihO6sxYyhK8qplryI0XXKRLrJfEq7emG+cYKWAErYAVWpQIGx1XZrW6UFbACK0cBLGxVbRhh654D1xluY8HJYKLht8AsxuUBFjkBVq5wwAKDeCyMgi/tJxP35IF1rKN1j7F+UNM0meIJkMzpqIZGAJniaH3gnOBijnWC5KVP7CpaoETPAB7lY+mENimCWoQVsJQv9gto1PPMzIysdwJHlc8mNuTJFNWd2gznyvM2ydo3FNADxMQRD5pWqsxkYRQw4tfUuY5xDwTKvwJH/CiZcqkWqoQyuqcMrI6seWz3qk2Coh5NPY0zLrXmsVewNKt6MRW1R/Vq9WrzmIE+QeRAGhmdSVvXjqRLtq3R5jkT6UbOgHzwcDo6MSmrqeBxcjJNV/A4MDISU4MDzFWvsO5J06iTaleAL6AuKim1dC3TfAv84ReWSjTlnjhKv8BVfmhPnHBcqz4gDY7vSIt/zQ8NWvpRYE6QzLmOPZpuy4vF9F7eDerOu8E6zthdVbnwjqg2+T0BKtVGfpSgFKys1L+hRZDRDuCQwuNliC6sXlqF42dnBayAFbACZ4wCBsczpqvdUCtgBZZHAUEV0MewX+AXdwEFWPs0hK+sfExPbbP+rq31ehqQdxiVY6GDOSJOhgHakHcmBRABQFkSlSmgEDdKpiF9NegHFAWEAsbOlD6CrthBUzCRaUBxVRd9hTRCO2oZH8ruAhChqghTUQEZpqgeFWgBaVg2NeM24u7YsCY9d+fZmqI6FmvnRF/RvrA2aoMedooNQGTqqmAFWO3usspuqwEwAEkGR4qlPmgW32pvR3HYCZWD7sm3rWMpyIu0cwLHhqyNefMcWeFUXntWayx7ZrX+Ubuv9s+lNSMDadPYUDpbR4TcsXEk3br3qCyQx9JhbRzEBjrjmno7os8gx3f09SsPAamsvVg7AcaYviq90IF6FYisarhAzwX6KUKxEJMOC2r0AdoHmamtandMS676o/xwEBoQp/hHYaqPNOCHB34k6J2Q9XCUTXN4j1irKDjkSJOopfKVP1nwvs2H1ToDYvzIoDxYORnHs9AuxYs6VleKw4MpqnWrIwhK3eysgBWwAlbgzFDA4Hhm9LNbaQWswDIqEIPrasTdHWhnGqgG9nlQDvwBYp15AZdG71iWsgUoW4hYq4brgRYY3csBT5FOYbOamtiRxYlppdmCJAshUxUFFh1dw+qkdDG1VeACqBQYicwAkwpOus9VOVFdwS15TAisWNsYx2NQR0XeumY4Pf0J29KTzt6gNYMD2apYYFBQ1yOIAxKZYgqMMfU2dgJVGOAX8AcwqvyAaYhZ8EO5FcYIhOQFvHYKkOqqPDuCR67NlnadVd5tWRqxQjKNFesjz1yzhVJHcsgKuW5kMJ2zYSRdtHVclkdZH/cdTXfrA0DukwWyX5ZIpq4Oylo5qCm3LXZhVT2Ro8OUWcEZ8FiHwdCI6gKFcl0LpeLl3pKn7qNNaF/FIyy3M18XWxlLWvyJF3HJU1rFulIBLzvmzk32S0f1PZbcAEcgkB8oWHuqtIqPhvMC+uh7lc/71RZg0on8qBBay5+jPOr1yg1QyVGBqEHkqQR2VsAKWAErcIYoYHA8QzrazbQCVmC5FGCQHqNtDeJ11UC9DMLDusQzo3GFsV0M1/jIt1i1QDNgiVxws4JAgAXACmuesihr17iCF3lNokCBTXGmmU6aIYL1cZRIPbi2KF9AhAMsKAOoENbkZ8IDODLEsq6xbIgT9VP4qA6hv0hrGp923uY0xC6vgjhAMKaoChTZrId71jRGWVyBRMoB+BROO2iTvvIHi6tqE1a7oB3qKS/p09SutPOCa6a8NuZkTVM6dp2d05Tdxmy+p0w27WlqCmtH6y6bM1oDqTWRWN6ASM15TWsFmWuH+9N5Wv94kayO779jT7pVELn3qKatTk7IsjqrtmoDHQEkm+j0yQIZ9UUnIJLqAF+AFnrKH+DPFmIFykWfEUf3AX6Kwz0/CtDCuNcVR7i+8oPihdNzyYN8I7QKI4+2+rUpMIz1mscmU2tYR5P0CiDRmB8SFN5ReNWzAYqU25K+s1gko0xZG6UL53sqUfwBpFGXUp9cG33TJ/l9e0hQN45vrIAVsAJWYDUqYHBcjb3qNlkBK7CCFGDQLqdRtjAu1wuPTBIxNg+AVGjFlxEnLESZnfSsBBrIdwRN7JQaVkmN3lvaFAb/PIAHqOCxbJ1kOmmsVwQGBI9skILFkrJimiS5CmjaAqAewSQghAu4kT+ZFdCh1qTB0nh0fFxQNSmPHHegr5Wecu6m9MWX7pDVUesalU9YFjVtlPoBWtOqwjEdGzEp4J1VHcibXT77dP4gFr01oyNpTBbAAVkCsRQCNfBztJuLiBGOYZ5ktB3rmSrQkmfAowCSdZa0h7MdsYRiXeSYj149cwRHC4CU5XVOMNiaZYorU3eZ1jmXRlXXS1SPCzatSfcdHk8f23UwfWr3gXSfdmM9Nj0ZGwAdm5jQ1NW+gEh2bmU9ZYCv2pI1l3UX3eiM3CFRc+4Jj2M+5AOalfti9Qswr6eBzKpntA+nPIqLfuGBePogTVt93NZ05EY/7VIpepnQCsePBqw1JW7Optp4CQs3PzToCmDyUgZIRrwM7eRACHpn1XmHlD1+9XmrerazAlbACliB1a2AwXF1969bZwWswApQoFgDAwL4ihsG3tl6E5Y2eWYLZAYQBuZhoYzxvuIBH8d5QqGkFTwJtBj4Y13KLmcOJ+DdABoFEqyh7K7RA2aq2AEoyrhUK6YzFkhRvABNXacEY8DTlKAxproqfa+gcOfZG9MzHrdN0DWWoU8Fx7EiAsg9stzd8eDBdPO9e9Od9+9Ph45OxJmPAR4qE8AclIVy84Z16cJzt6Urn3hBuvRxO9IWrZWkbbmWQZBqP89CFxEl99S708a6KBjSotB5WRupV7bCZktsR/nP6dzHJuCo+rDWsck5kDN9AdJYbtlABijGEsturBcJILevH0tXCoY/ft/+dMNde9OtDxxKE2yeow9Xdo7tE0QOYoWU1RIIpn969Im8VBbrD9E4o5tu6D80l38bfSkTb32IQ1juOd3UXN2/rKcsoFo2yVGhke884DimW9YzNvMRKWRV4lMQ9exod6aONJFyURI/KACZ3fLpIF42gvWR1EoXtY9K0sY4TkXttrMCVsAKWIEzRwH/v/6Z09duqRWwAsuiABBzHMwyPVQVKWNxRuwCpbDgYMWRP5YfoC1wo0rPWB5LUrOhqZeKxF9T8TsM6lmmRtKYzik/PQiZBESyLsGVShpQo0E/DkwIG6MybesT/xgEIShXxQFuYhqr4gKNR48dS5MCR9Y44ljjt1PrGZ918Vnp4m3rBIstrR3sTWvGRtMuHXnxvlvuTNfddHe6Y/c+baQzpaM7qjWWVfrIRF+sGyTtdR++Of3nNR9KT774wvSNL/iC9PTLHh/tobyYrqr6hlyaoko1sTA2dXyEFuwFEItkUo/ghx1muQ9A0kZDLa17bHcEdrNMh2V9JWseAWlN99XxJABUbBiEBRLAVL5jw4NJJ2KmyTv3pPsPHEnj4xOhn2qSGlr/2NI6QtZq9h85Kiupjv0Q/DKNtU9ACcgDzrQLR52BUkCRnWxpC7qieW4InRYti/jxpXD6q+4CGmn4Eo6+BppbsqbOtwfUzyov966KyBbqHll/G1r/CtBizkVN+HVWcUNf1SGO71D48WmqqhqgHo3IBUcNSn0ri+YSVbKXFbACVsAKrEIFDI6rsFPdJCtgBVaQAlpfVs271HVxvTIwBFYyQGdALwhggD+vNWriE0buARhAX5x9WA3a46LwOQb+/MmD+GyKQhhnLjbnGpqmicUxw1Hswql8ohpkK3AIq1XlNyeS6NE9awYBnTmBClNeJwSMrGsE4qhfrzZfuWDLhvT5j9+WHr91TRoWMDYETccEYu+74fb0gVt2pY/deX/ac2Qi2r5mZDidtUXnJLLjqTbJoZ3UYlJTR4+OM/1V5ypqKutt9yjNgcMxtbRPMHbJBTs0lZWpptJEHBacgj5KOw9Md/iorYJDziIErrDeBiBp7SOWttiFVTAUO7BiddRRHgBnC0DqkcURWJTlEY24R4/9R8bTdZ/and73yXvTPt2r5JiaOqKptWtHh9Kw2jGjsvYd0XpPTd2dFBgDir3y7xc0Mj22t7JE5l1kVRflwifWPxYA5Eq/yR+t6049EP0EdEZ/KJwY+BfLI3AMfNL3JMeyzA66PTqWg01yeGf48QGwjPeEd0x/WGmxwPLjRExnVVru2VWV+qBh/DxAuN5fXZQKH/WdKkB68qVf7KyAFbACVuDMUcDgeOb0tVtqBazAMijAQJ3hdR6ya9SdR+FVTRiRyw9vASID96Y+gFWGxlLhDA3FYiWbW6Qj5zjqQ+mBpOxUkvJkHd7s4SnBhCxdXYAA/EA2HIN/fesZ01NM85Qv8FimQLK5DFbGKW2IAzRiieoXHF24eU163s4d6Ynb16WxwX5Zz5qCrcl0/S2707U335MmdabgurGRdNkTzk3bNq5LG9eNpc2a/jksSx5AFRv6KM2E4h3U9NWDh46m+/YdTHfd+2C69/696YM33pae+Lhz047tm9KYdmsFnjJ0oYP+VO84O1J5SDA9SwkBTlh2gVLBMvAo82NA1rwsj8Al6y0DGCuY7gjymKraFkgCjqwDpZxP3Xp/eu+n7ouptudsXpc4ZmTzmpG0fpSjPLTTqjSYmJ5Jd+09nHbtOyKr5LG0b3w6jcsqe0R1A8oASKa+xhWQlB+gV/qJPqAvcLGuMN9UfVOFKX7EUIP5ixeF+MAz74zSk2cAoO7bau/8jCyP0oW1pgAnf2ohheT4spSWaac5HwAzWyx5DwNGlZ7ZwLicgx7QOiqdfUnD+3C8VhHdX1bAClgBK7CKFTA4ruLOddOsgBVYGQoAhfwx2I+hODd8sNhAQXIM/oVm3Ok+W4ry2sfAQwFeHrUz6M+De9LoXtkAI2FdIztBlkxGAiVZlNgwBSAiV8BCZWCfpAw+5Nmd1Kg8YrojgKEPB8dPycrIhjhYl3Cs5duuDXCuumBzeuq5G6o1jdr8RvncsfdQes+Nd4cV8YqLz0nPuOxx6dKLzkqb1ssiOSRr49CApnJqsxytgWyxcY4AUjXQNNF5bZ4zlx48eCR9/FP3pPd/5JZ08533yl/TSdU+FRr1jOmUUe0MiBw7IhNhQA+WR/kqN6ZlMgVTAEXbFN4K663iyk9EpzMgFQdw1D3hAFYTS53aNq+1f7jDUwJkWTyfdMH2dOXjz05POW9r2rpuOA2xfpPFo3JA9RHtYnrnnkPpk/ft02d/nAd55Jg0kwVzEuvltDYEUrls2MN6UDbUKdNY6UN6IjpQnVh6BS+aCbzTH/HcBTT6hvchO0KpDXFjWjPxtBMRzwHnCp1vyALJdGXl1bUuSpt426RDT1O7z3IcR82VY2HwijrqGmVi/Q6XryWs8vTFClgBK2AFVrkCBsdV3sFunhWwAsurABCWUUMD/mrcHVe+NBCPwbcG/DJ3aXQOGKi+wI9CytRU7DrAToEMUkWeAIcyiOmQ3CiPTgU240ePpI6sYgFQgkdAkLoEAnBR9AKNAYzIJJiJ6akzM2majyyNRMWx8ctF29amZz5ua3rqeZtULgCjesiyNqfpkUDhcy6/KH3uky9M52/bqJ1Sh7VpDVMmsXwlWejm0pRAGWtcsyWrmMArH8GhZ+V99tYNaYfSPe/zL0uHNX21VyCHhXJO8BtnEiqPsJSRmUpm8qeWN8a5lQBwExgijDZKz8Ar5RuaSrsG01nRpy1AFkwGuMUaSNUv1jzK6tjpDfB+xlMuSk8RMK4b6k+bZGVkV1baGGsJdc9UUXajHdJOsDuH+tLF2iDo0LGJdPeeI+m2Bw6kT2pH1vsPHksHjk2nKUHpnHTUaSBRJu1nWitXjhDhnv6jPvmHArVJbYj+1TX3WW5XflvUPvqadnJFDV25w4ujRman5sLi3CNQ7Uik+XltlBP9zysiSNS8X5UYeQd8qmym0GKpjenMvI85awH58XJ4L3lUrXJfYIW0swJWwApYgTNGAYPjGdPVbqgVsALLoUC2lMWwXoNxBuH8l68MxHUnR3j+lLsY0CscoOiRJygEUAAtsauoIIl1jDLfBTFkjpB1S3MMsdbNahrovNYQdli/p0Csa3woMGBEeVI3dlsFYMIiJcBkTeMMoCkAyXVLmm7ZSk/YvjZdrd1Td561LvUFnAptZdkEZthM5srHj6WnCRTHBIwcW0F9qROuJWAM2IhveQhM2L+FtHyETjGllt1R+zS9c1N/vyAWJVRGBUXHn5WpwI8puDGlNy/UFPhgUVMp8g9dpTX5AosBVtIy1gaqrQ2B67wAEhCMNYRoUCyRuu4QxOIfU3ZphNZFNjSdFb85wWZD8QFVGgFotZpzaf1YI41oevD5m8fSVRduSbsPHI3dWO/cczjdd3A8HZ6cThNxHIj6Bosw0KdPAUmsuXH+pfy6awxVV1VSOmBRLd/IUgGbwsoZliE1cYmotabqUK07VV78RXx6QNN1BY1MQ51j05zIO0MganNsB/CYd1nFB09skxnUqQG9lvtV6boWyIjpLytgBayAFVjlChgcV3kHu3lWwAosrwLAzJxAKVuOqAsDeIb5+sRoP0MhEMHAPFBSYMEGJIzTA/Z0BRLZ8IRRO3lhXIPfACusicBGPvRd+Qkc06ziyT+mpwoES/lRutLqv/CjWMpgemIApyxqEZcAlUO+l5+zIX2BNsK5cNNoQCRl5p1FsZo1Up/Asp8zGJmKKphgkxnyYP1kE9CSVRLIaKhOAVwCGXhPCyqljeBVDWFznZZ2P22KapraQEd2OVVStaWuKg90iY1/SAf8tNAPKBYpURbKcRubuUClhOujdX9YJIGkhnZcZUdTdg/F8tiSHwAJPLdVT83pTA3VKYQVLMYaPrQE9Fg/KMtgX/Qbeagw1o+qrUxb7Zljd9hZHdXRSiPanAZL5RO0FvSgjiTZfehYunPvkXSTjvXgnMhD4zpnMuqs6qkOQLoap2LVbmnZ1PRR+hagV0CUr2+1J2uhSoeVUo/RHq5ALtfoR/1Y0Avc4qd6svazR/pieKQcsgQpQUIyJQ4/HLCRUlsWyRyF3IiOslF6PMdXBC3hfzyG76yAFbACVmAVKmBwXIWd6iZZASuwshSI4T9jbyiIjwbrAToEyFUXjdI1lNdYngF/AZ+wCimJuAUzESP5SEM+WMzYBAarJGP5+TKNUplwKkXspiloxCoW5ziSr2CBMrou4KKySCqgC5jyZ13ek89en56uNY0XrNfUUyU6ok1gjgmYjs1obaKy0yU+gB1HVGhOa9SRaah5ExaAiCmZ8lZ66jrQ1xNWyV4BZb/WOo5oyufoyEAaHR7WcR7Duh9SfHZqBUyxmAko2Y01gKrKN+hGkAXY0Aa0EwCRPyVhZc3ayU9xAO3QXG1sBbWqrVr/J2yW3oJX4msWJxA+rmmnh7V+kSmzR7Xb6/jEtCyxAivB9ZQsuRNTebMgNqHBBXhifawskFjuJHqAM30CkPf0yBq5cU3aoLWeh7UL6+5Dk+mAtATaqR9WQSA0rKINTeNVvqyNRDOO+MAFxFVx59WvtJV4dGdMCeZeRc9XFkcSR5v1HRpiqaU+QK/yicyJj6URwFR6PPN0VemhJ94H/qgHLu5IG/fEsLMCVsAKWIEzRQGD45nS026nFbACy6gAA+0y9GbMruE8g29Ap9SKmxiH5xumC7ZkPSMeA3cOdecOVok1fwIgIVBAJsdjzCk+EMLup7LvBQwwbTMsWrIkxYY7Cs8gkGtDcYTHdE2II4pWPvgJGEYGWuniLWt0n9In7j+UjogSx7Xm8KimXB6anNH5jhk4iK9Kqb6FDoFFfWQhVdGa+qrpmarLrK60pVfmtD4B46A2oBmUpXJUMDWi3VnHBI9rBI0jAsh+We3wXzc2lDauHU0b1o1qg53BWDcZ6wK1fq/JDq1YYlXv0DPfQDvRlAyuAjIBYgAlQKd6obtW/gkA22n/oSPpAW3sc+Dw0XRYO7xOaEopx4MclqUQeGT3VHaJRdtZrXMc1/OEznKc1j0OTgXKsLr2aU5xD3VQ3nEMihofMCufXkXsV/iAYHBwqJXWasMarK1HlVfoR2Zy9Ak5AIPtGZ0myYMskrE2VHnQznIvtSMewIc1lHZH//IeqI+agsSmpsCKqZWv0F5pAeQOOsiTvAFLfmDAxcZFvGDx4wZRiEcMKpEdaSgDd9w3h/nbClgBK2AFVrcCBsfV3b9unRWwAitEAdbjMdCOwTbMwZhcF8bsmdkUrlF5S+v8YkomEUgj0Am4i6G/LEcxZq/AKAb/YAOgqEMXADjSYTsSqGFxA3g45qPAYCRXjIgLaOge6xyV4T4+FRgAgocFiHcdmkj7J7SLqHYbnVWdBgSEmsgpAExpUPC2ZiCDEzNpsZyF9UyNmhD4HAG0ZLU7pg1bACVAJOqiugFztLUFnAgmBwSLwwLIPlkbewVZa7R2csuGsXTB9g3pnK3r0/q1HOkxkIa0lnBIYUPDQ6lX6yFbrA+MTWbQIjsAB2CKdYyqC9a8KdVlekpHjGizGs5e3Lf/aLpj14Pp1nseSLv2HBQ8HkuzqjOqKLnE0FUf2jEpoGTtJwpD78DTnKaEYrFk2ueQ+o3zLEcFw0MSpkfP8/pg/JsUZE5qd5wjk8pZlthpmT+Zijs0OIBg6ajyLjCGhlgPcXlnWE371T3vQNGWuOzOiqOeGYqBctLQ5/qojZ05jt7QWk7Fjx1VqanK73QEwtIEffIRJrohsRzTnVvMk636Sj1UAWTkjiRRV9KieeiUk/rbClgBK2AFVrkCBsdV3sFunhWwAsuvgMbuGodr8M5oG6LB4QcY6pF1gfGnZyxoeVqh4iiQQT/rA2P6qp4DKjWwBzIjrq4zghKsYcqRYX4+gkOQMytoDCjMo/3CBgEhVAEUYFoiIMI9kFkcYDKj+rznjv35XEA1Ap4ZE9A9fqw/PXHjWBrRjqJshNMnq2F8dM/h81OySt72wEGd67gr3fng/jQ+qTWAsrSNyZoIGPYKqCjwmKaCHjo6rh1cZVlT/mcrz3PWjMWOrGwkc3hiJt101wPpI7fdF20dFCTu2DiSdmjt4Dmb16ZtOmNxrayRPVpbOSCYpIwegWwBGmALXbgeOTae7tt7UOdEHki33btHwLhXMCu4kjUPIyTgt2m4N23Zsi5t1frEdcP96qL5tOvg0XTNTfemB3VG46QspkNq33qd6Tim6bXNRo/ymEr7dZTIAR3DsffIRFqvnVgv01Ell21fnzYMakWkphfPzGmnU+BTO8vepvWOnzw0kw7Nqm+Fj43e/jQsi2Xor7rMsN5RfUcb+gWiHdZaql+iL9UO2jKncPqKPpoHIHkXpJ9UhTBDWzZPbctCzLrO/A6pkeq/YhVuKj1HoYTTJeAS87BqguWRqc78aMF7xifez+rVjTQK0+8RUe+6d87w/7P3nk26Xddh5up+O+e+OQMX6SISAAHmHEBxzKTx2JLGlm25VOPPU/ML5kdMTdVUzQdPjS2P04xsl2RRIkWRYAZIAEQg8sXNOXTO3fM8a5/Tt0GBKn6xLtS9d9/3PefsvNc+BeznXWuvXb+rBKoEqgSqBLajBCo4bsdZrWOqEqgSeF9JwH1zMqPam83As3GuvBMgRT4BEk1Q7r0DJpJoWJ2bTnSCAYaG7J3jHlNNYUuAWEW7aNXqsQRHtU4CS/GQqbkkdfIROiAMG7ylgWziW42X/RNahBLbVEvnveahhwd74gN7hmLfSD/aQIBHrd/oUAwMDaUG0OM3znIMxXdfPhU/f/MsGja8je7eE5978IG4565jaMDmY3bqRizMzdG+e/swtcWL6vXpxXj79IU4d+Fy/Oz1M/GBu4/Elz78YOzmzEjB7/TFa/HqmStx6upMvHZmPu/X15aBoEgt3wT7IifGRjgvcgiQxYSVvmraq8npdUxRpzE7neFMRS00O929OXb0bnGUsxnvObA3Dk4MxSD7LtOcFPB0D+bpqzcTGF86fQVT1048+MA9aD13o2GlUTR2PYBTbx+w2j8QPX1DMQ3kvnHybLx98lT85K3LeFKdj4/hhfbBg8BtZygW0SouAtDC4L7hxXjhyly8PVP2NLaydz57kaHOdlY4DsU9k706HEL+/c4H85caZObcq89ZljlEmPlquSdU50hdpHc73/mCMe3AYL5bCM12IGz+8R6sYCpL+S6Ek46U3KfJO+fcW7b8lWpoMN/FApKIIV9g89VQJVAlUCVQJbATJFDBcSfMch1jlUCVwG2VgFrCDC7GXeQbmovA1y76jU7tEKt2HbFowpnplHF/WqY3MNDF+Xz4OikLfNIKhBKhFtP43F9HI5m/NGeTejpNsKTOFiZbcMkG2q8sKniUMNyzEQfRyB0exUyUvYej7D0cHR/lMxaTe3YBIj3xg1dOxb//q5/F20Derr174/f+0dfic5/9VKwtXosffuvP4tUXX41LF26gIQVsAK8BQHRsYiTuPnFXfPR3vhKn2Uf57e/8IH722ilgrDf+4Ouf5kzIXXHnkb3x4PGDcen6VLwKyH3/l2fQHt7McyZXAR+d7Ixitjo2OpIQq3fSZcDr+o0bMT2N+Wmz168fjeU+NJSfuv9w3L1/InYBwBNqQYFgjwHxLMpeNJff/8XJ+M8/fi3OXJ2O+07cF7/91d+K5elL8dLPfx6vvfJOzNzkUEZCD3tLB6lj//7d8ciHHo8vfumf4jF1Pf7o3/xRPPuzF+LizZm4+YG74gsPH48juyfiJh5VR+YXYgyt603A9urCdNxAK9gCYDsfviMpeeFwY5m9k4A7HwFSzaJgn5pi5lKAzJeA+fItWyWtI/ix/3QNOW8MKGtNa/3NgB8V/BEia8fclbzdG2o1ieGds/30sAp4WsBzMS3Hv3wNHbPl25CvbvtQr1UCVQJVAlUC214Cnf+VsO1HWQdYJVAlUCVwmySgF8upS6di6sKbsTgHxGhCSF/K+nsjbgATZ65zWDwJeSQFTlbcY+Ziv2gKzQzssXQXtsoinvumDp/VfgmW3qejGzR9a+xNXMY8clmNEiDQ7mkUJNODKETQAgvF/lpoTRS9ChyHhnrj3omB2Dc2GCOc1TgBCAmMew/sixkcvTwNNP7xd1+IV09diocffTT+8e//fjz1xc9E9+pcPP3n34wzb52MscndQF5fXLtyHaibZt/gHBrIuZjHXNV9kw8+/GA8+ZGPYL66EM+9/Fr0swfw7ruPxdGjB9nfOBp7d+Ekh/b7kc9NHNfcmJ5LxztLQOIyILYicCPcRcxcp9Fq3pyiDfb6KTs1eXdi3vrRe/fnOYvum9y3dyImJidjdPdkjExMRP/oaDzz+rn4D9/9WSxEf3z5K1+Lr3/lt2Jt9mK8/NzzgOpKTE7uhaK6yximZ2J2ZiZucGbjLJpNgfreE/fGhz7ySSCvE2+dOh9nLl1FzdkTB/fuigO7xtjDijaX/qslvo4G8hqfFtra+XCuvPcHhdQSCnHMUM4FsGcQIFNzaDz3vhBezdPDNfcpahKMaXECoyrEfGl4R6zNZ0PGCYyAJhCqpta9m0Mc+XF4fDD2jQ8nVJvVejQzHgCyh3YfjJH9d8XI+B7q4H3kU0OVQJVAlUCVwPaWQAXH7T2/dXRVAlUCt1kC62w2u3nxnZgGHJfmBUf2ICYG0DEW64LjWcER2GGFXiAQOGghwKxpwuoN/wSGXOznuIRJowscuHRfZ3/hBtC4qjMa99YBAS2QqG3U66qhjcuHX/kqsKiWCk0n7Qmlx9nXeA9HcoxhnjqGh9OJ3ezh27cnFtnn953n34z/8JfPxklMSp948vH45//sn8VXvvL34sq5U/Ef/9W/jBef/UV84vNPxee/+o144NFHYm5uKs6fOVfYBc3q9NR8XDx9Dq1hbzyB5u6hxz6MiekUpp+nElLvO3EcDeYuTCvxwIqGcC8wcxlQO8PZiMuMb409f23QyY4a1SX2Hi7ycT+g+x+HBgfjcY4VeerhO2Lv7vEYRjs5tmtXjO3ZHZOMo29sPH55+nL8P9/8YSxs9MdXv/6N+MZXn4ruldn4t//X/x3XMV397Je/Hl/46m/H4WNHqHsmzp+7gOzLnsNr7J88+8479GUlPvvUU/HgQx9wuuJNxvDW2Yt5PuOJY/txAITpKQCm050rHMdx+sZCzkU6DCKunRdlr+bUuWjjvKpZbiGtnSfflXLmo2nukeX4E8sCqV19yIM67ItfahTbnx90omM/UpvNdRVzVdtYR6aDgOMRIH3fJA6IMN3Ntqi7Dy1rfz8ecRMc76ng2L549VolUCVQJbADJMCSo4YqgSqBKoEqgf9WEii6QRfszeKdhlz8s05/d/CZSKFHLaUf712w56I900seK8h4N/mJAW19PBrjeY9qK93rlmnGcaOmUzBQsySY/E2hTU94pNJRtEwjAEMv4OOexhG0cx32+L148mL81x+9FG+fuxLHj98Zf/BP/0l84YtPoVZdjO9965vx/W9/n7zj8dXf/cfxyae+Gp/6ra/H/Y89GV2YXS4hkzXG0t3DMRc4qvnJd38Y3//zP40nHnsk/sW/+J8A00Px85dOxknqHkFbObZ7d4zv2RNHjh2MA4DkCI5oNFNtneF43MUooDTS15V7EWVsNY297OcbxlHNJMd97J3EnBWz1hE0jWPsvxzbvTdGd+2OBXj628+8EmevzcXXvv71+N3f+QfIcS7+/D//u3gJ09Ujd9wdn//Kf59j+PgXvhwPP/Gh4LALRQ14uS804jrms88+/YP46fe+GceOHYt/8k/+UXz+C5+NeX4s+KsX3o7nTl2OdTIO0v6uieF0pKNSsIQCbd6rgcx51OSU+YIEN/c0anbrnLTzI/QJjb5nnivZrXY655hyaA+1StUUVWhc56BKvcuSpWmS+psH36ce3ifrSA1mQc1sh6R833y7LOpHTagV/c1vUdNOvVQJVAlUCVQJbAsJbP4va1uMpg6iSqBKoErgfSiB1OgkCdC59tqApI+CRwaAQI1RQp/729DGra15hiALdFbvOjixfGZ3Ne8dq3jPTBQmPWJhw7x+LEt9LRi4wBdCWuAoDW75bghA6GjBxPwCxQDtDkI4fWie+pujMMbROr55+Wb8l6efj1+ePBeHDh2M3//934uv//Y/iN3A3bM/+l789Ac/xGnMKg5kOgDWWnp/nce76Rz7/JYgLk1LF2lXoB3qR3O3NBuv/Yx9hC/8ZXz8Yx+NP/jnfxhrvSPxg2dfjdnZJfYTjsQQmsERPoPA6wAa2kE0iR7PcWj3aHzsviPx1cePx9eeuCs+98gdccfeyczTQ74+ALIPgOygtRwcGwUYd8Uo2sZRzG3n6cDzOPR5+tnX4utoGn/n9/7HmES7+uyPvh9Pf/vHOR99APPaKppcTH/nMYOdnVuIZeSzyBg4ZYNjNjZiqI85mJ+JnwK/Vy+diQcffBiZ/NP47Oc+FVcxv/0333kepzszADcgy5mUo4DvEKCLkJE5QnD+qdMzJo2TzTI08+B8qEHVBLlkLz8QZLbmdci6BElfCQr7I4WQzpvRwJ4ASST/9EKrg6I0XSbCe+c7901mHgu3fWvqs04LZwO2UEOVQJVAlUCVwE6RQHWOs1Nmuo6zSqBK4LZJQBDLBTzrbdfcqeQhQic2poFqSY96A9XDSQHNXJ6zPreQZcgMVJYFu7cs2i1GlDf6SMn9jWiZXPjryTXLCAAEocPPrw0NA9CrAhzkdZ+cdfXhidQD7nsArz6Oo1DruMG+ve8993q8+NbZ2I9n0q+wF/DLX/4aefrixrXL8drLL8fFM+fpw0qcxFzzj/7P/yMeefyJuHD+QvzgO3/F2YjLDMKzHVWMoe3CW+kQmkLh8fVfPBsf/fw/jM98+tNx+tQ78eoLP4k/+86P4re/9jmc12DOOzAASOMgBvjWccyh/bviGx+5Jw7tmcCUdTDNQt3r+Mjdh+Pffe+luIzX1pQFwurgNbWP8n2eAQl0dnP/wguvxZ9+79k4cf/9aBu/wZ7KY/HKc9+L1158Oa5dm+dcxa54gT2O3/zP/zHuuu/leOmFX8Rfffsvcz+g87WMjPvoS/9AF15e2bt4+Xy88/rzcfSu++LRRx+P3/vd343Z6en40Y9+Et9+4a2Y+PhDHNUxkB5g+4E3akDma7Grvwvz0CHOftyIt6cWYo7jOwS5Xw3+GKDnVc+vdH+rjnI8f3GNF8r9jcKnc+3e1m4+HaBdCLUmzVjXeV4hvtdjQEwjrxpx0/3BIfdV5pMRFnx3H9JBTlPfu1N+taf1uUqgSqBKoEpgO0mgguN2ms06liqBKoH3nQRYX2dIjU/bO2HPBFbdZeHNPjTvXKTDduukq0VMaBAWzQ4sJPixiLfMxgbnD7rwNz3zlgrds5ZHcUAK7b454a/AQdubrPK9v5osk+w3PMZewpcuz6b3TR262Cf3u+nd86XTl+JnehhdWIzPfP4z8T/8/d+OPXv25ThuXL8UF8+ejXk0iz24fr16fTr+v//wX+Lbf/Fd9h4uxc0bNxk4+xJz8F2BA1COdgAc8bLah9bu0rmTOJy5iEnqifjc5z4fly+ci6d/+ov44qefTC1dN3v3PNdwAc3byEBPfP6xO+OBOw/FOF5eB9kD2QsMCt+79+/FHLYn/t+nX4xrM/NAKmaxHbWOHEWBFlLt6ezsfLz4yttx9tKN+J//lz+M++47kdrJ82fOxumTZwCsdcB5LU6duRj/+l/+qzTTnUVreu0aY8jDDosYtQrtYCqLfxxAbjkunno9FnGcsx+PsB//xCfRUM7ES6+8Es+++k48dteBGL9jb7aTjo2o4u6xgbh/11AcxoT19MxyXMLB0Sz7VLcyW0IfkGfId0FjWaBeQ9UMzr3vEJ90uCNA0jF/UDB47icx1OmPAJYCOtlbK1Rbn0ekrPmrBlVkjcx5nuXYvqxZi9XztpLB95OsbetNar1UCVQJVAlUCWxXCZQVyXYdXR1XlUCVQJXAbZZAs6TfXF270DYkSLrQzycW9y7eec6F+5aluIv7VuukdihX9VnGW8qruaSRHr2x4qgGAkvTQ2HRuv0IBX7aetri73kVCEgY06MpR1a4303XMwKq5zR6LISg+tzrZzkeYzruOn40Pv3Jj+P99N4ED/u7MHMTb6PTaCtL31Yxt73K0RhvvXMqzl68GAsrC1LHZvOa2BqGhnrYtxixghOhxbkbyGMlTpw4EQ889AhmoT3xy9ffSe3k4vwKUMo5h0DO+HBffAAQm8TL6zhObnYdOhS7Dx+JPUePsC/xaHzyww/HQbypOvaF5fWYR1OXgSY7aE5P4rjmCs6J7r3/wfgEgDc2NpZ5Z2en0RLOoCkGNimwvIYjmwsX4/U3T8b5S5cAV47kyCEUeEqNHuA4MoLH0f5uHCE5fvu4ERPjk/HkEx+JLz/1Ocxi0V6+TZvTQDXmv6uYFVvNiT0jcQ/mtgeB9V3sxewH0EXEIhlEzjy0ISGv+TEgj+Wgjs0fCWhPB0gFICnBHHhftIjUiBzS8ZKaSebRNCGTEqmhzP2QarmJ73JQaVJdxti2v/V6axa3xtb7KoEqgSqBKoHtKIGqcdyOs1rHVCVQJfC+lEALAUkKLMilArbG5dWLIFSW6CWnsJPA4OqcKDGTtT4Lftf/eLpsFv6s8dNssavLYymKiWoBAoBRgAQkDAkTefc3fAl0fHrRNo1zvqF7G2EPeZQ2cJ6COa0odQEvoh0g8skPPhoPPXS/nEFbOPVhDGt4ki3eO6mKpqwSq0oCmd4VTLVfpqOtQ8s5MspZilhvZr9JGBoaxkPpw3gwfQcvq+fjYc58vISH0xtoCsdxeHP3wd2xexeObviM79kbw5MTmKIOpmyGRleiBwB+7IE74xqmnzNoOy/dnIt9h9XAFmA6d+Fq9A+Nxeef+DhHbexKRzP2Sc+imm2WHtI/BqEl8a0xmOJ4ACwvPPaxl3NktBcYxKsp43X+BLHllbWYmJiML33pqfjhj5+Nc5xHeROPqhtojJcASSW6Z3iAsfZzBAmfJcxIkX8KZrMHtPErwR8HMtAYeuC8da79qD1Uhgy01OAk5DsnRLJXUqFn94V74twXmxFlXCYZLNaOrxQo40oYbaqwRA1VAlUCVQJVAttfAuX/NNt/nHWEVQJVAlUCt0UCucAuK/T8btb32Zdcx29ZdSds5QK/6WrjrARdXwJkeruUGvNg9gKDLONzPV9gAWAAUlY1OQQOBI/URFHdbwSN5LM72Wf6oXZxHJABLdg/V458SJDgawRz0DuO7osH7r83dnGsxeLSYmo1deTTm05rBihftKXp/LWMvhmYl6SWvMpII0OdGAO6htE6DrCPsqdXc9Py2+axO+6IRx9/krMZl9A0zsdbpy+gJZyKg5zr+OS9h2N4bCSGJ8ZjiM/AMOcODmKu2s95j6gvR8Yn4hOPPxh3Ht4XN+aW49SVKRzMCI6raDUXqG8xDhw8FB968smE4kJZeGPFgY0fNaiKvIQWp3xqqIk7wRoFYUwAvuMcWzLAGAY5tqSDFtgSHovSwTT07nvviyNHDsUgsnNu3Mu4BNwNYnrbx97L/oHeGACGvS96Ttt5d/CHBOen1R4Lj6ldbkBQDWd3H3y1AABAAElEQVTOH1fnQlPVLl80Q75bwKzvB05+lpgztb1ZRnBMDaPjyrcgta05gFKYbwXR1MWd72sNVQJVAlUCVQI7RwKb/zvcOUOuI60SqBKoEvjblYDw4QI8l9yShGvzWyvycpcL/mJmKlTk0j0dl5hTUEDrpbkiaZoiagbqcRvpSIcqPctwDWcqG63GkTwJEFw1bSyqo+xBuadM8/RuYTSRnumn+evdmFCOAHLqttbohGxh/Mcfuzu+9qVPxH33Hs/yehtVy7Wyssg5jwdwmHMoRnFko0Z1QE3lu1q51bJKMT2LHt4/FAf3DsYw8DXmURsT+4Ctviy1b+/eNFntGxyO6zem4yfPvxHvnLmU4PjEiaMxCjAOY2LqMSE9HBGiUx/3Y3bYz9gPRD7x2P1xx+G9cRGT1JdPX48V9kauYbI6hfmsGtRjd94Zdx6/CxEp39K3SY7q2M3xHx5R0csHvz1bxtD0v7lsAPK7Rjq0MRL7mzHsOnAEDeRQ0e6hidVz6W68uH78Q4/Ghx68K534zDNn1n8QLesw0NgPcKenUxzdbH0/topuq8lxHkPCnDjP+UNB0580eXbeeRcKJK/znpQ9jb4HOU7yJkDqKZZ+pCmr70zzlyaqvnRNnfZB0fj6li+ftyQaX0OVQJVAlUCVwLaWQPk5d1sPsQ6uSqBKoErg9kpA882yYG/64aO3uQrPpHwWAjkQgYiiBfKXvQIFHAbvgt9F/WaeUj6VPnjTXHNvI+aVQoDmka0GsjFULO3ZJuWtMwP3PJT75rtFAUHH4z8ePronri5dieucWTGFI5Xd5BNBPnDPsVgdnYzuyXEcyCyTN73CAI5LMTq+O+5+8EQcOLIvlt44hcMYjvTAa+rNxfVY0I1qO3BIZACifODO4XjkxC7gqYMJ5zoOZe6NUc5+1GuoQdAZG5tIwNObqGay/WjpDu4eS++oAxzT0Qsg6vQm4doWFCN/av10lnP00N4YHx2OyzdmYm5hIRZx6iMNT3Ce456DB1O7mt3SbJMuHgUk737wnnjtF69gBrqOGWkX499gj2TRFDqKVlZ7R7visYd2xYl7xukrZ0jGcBy68xGOIRnBbFWzXepM7d9afOrDj8Tq+cG4cvZ8XABk9al65+5h9kXisRZNo551l6FpTY5brSJZNkO711GAzB8ESBEauyTwZipTwrbpQGi3i6QuyTdDm4kfI/ghotRDUaDf985XAgPpzcFRS9aTJtS0g31tSQPM/auhSqBKoEqgSmDnSMB1SQ1VAlUCVQJVAv+NJJDLdL7KErtZaPtfXjeO+Zjwxg3XbjcwEpkwaEEX/k2edRbsLNvTrJAL2cE3wC4d5lDWPW7d0JLQmDDQgEMu+JvFv+XeK9jErwaPaxARH71zb+5zPD89Hxfx9mndmigO9PdwpAR7EnvRekJgwnHubwRcOz0D8cAHPhgnHnkghrHhHCPf3YcG496DnLc4zj5Ajp0YxIz18GQnPvP4ZHzli0fRXE6UvZR9o3HPI5/hfMbR7FILuSMjwzjiOR6vv4OZ6s3puPfI7njg+IHUMKZ5KvsaOxwXkvLMnpdRKadugOyJh++Jh+87infVOY4QOR9TN2eBqrU4eOBAHNx/4BZ0KXfCoTvujUc++GQcvWM3MN8Te8d6495DA3Hn3t7YM9QdQ/R/lHGcODIQ33jqSPzWF47GxK4B9lGuxeF7n4hDx+5PD64raGKFxlUcAi3MXIl9g12xHwc41ziX8uSlKY7E2Ij79owBzf1oG6Four2E7BeAdMN7zY3xAmQLkT7nvkUgNQs4R3zUPErQvk9phsp7knPEuGVAHf8Ij2po9dCbtJ3jb4Tgu2mFXDMmn9VUUs4+/LrO2aEaqgSqBKoEqgS2nQSqxnHbTWkdUJVAlcD7SQK59GaF7VI7F+p2rlnQc5P/NvvLo8aCHWDH/Gp7XOGXvXLFmUvuK9NkVe1QXotZplSp5inNEwEVgavVSLn4b9b8pSkf7ANBrZZ522vJgBdR4QUI3IXDlgnMKE9fn4ur7BFMQCF+dXkt+oGiwdX56Fmf40iNfqB1MNY7mNJS9vCdD8Ynn/pSzF69EOfeeJM9kd2xD7C66yhnNTKu/r5OHNg3xPEXEzi3GYiLF+djuWcw7nr483H8/g8nELb989pDP3vQQH73Jy/HTcDxyeN3xdF9k9GH85wBtI09vX2MQbkUmsnRCTj52Imjh/fH8SP74+lnX46fvHImHjp+BM3ocIxz7uMoeyFTe1aKpjyGh8fjsY9+Oq6cPx1/9q//NWNcjl3A41FMavXLqmOikbG+uOuOUby+7gYOOXvx5FT0TByPxz/1O5jPcjQJcl1Fw7qKB9bVhasR0+eiZ3ku5/T8jTnOl5yNPTgfGsekVy+qnjGpKfACJsdLaFbtu+NouqUYfm1w7lcpk15vs2DRWuc5jSuYqvZTtPmp2Hcoj+vYfAcEUd4fvtzzqGdff9fI1488/qllTpnmt1rIIoPfpG+/ttM1oUqgSqBKoErg75QEKjj+nZqu2tkqgSqBv2sSEMg67FlzLa/GsKy7hbVmcU5E65REDVEiI3m73ByovSXp60AJ31nGvXu3AAAnLqktYsGPAxbBoQCB+ElJ6rsFj+X5r5k/SgxAR4ECO2iLEYtopW7OAzzUceLgRLwD6FyaWYirM4s4oVmkLTVpaBc5cqJ7hbMe0Vit9eBIB4Bc4/iN4eHRePSjX6Sr6/H0n/zbuHLqbTRwG7GL/XyjOMEZ4zq+eyB6cRpz/tI8ZqzA3UOfjw9+6h/G5K4DgEyzK5L2rW/m5rV447VX45W3zsSB8aG4c/8uvLACjUODmIQCjY7DQP4cI9ccE19q5gY5YPGBuw/Ho/cdi7fOXuFzmXbGYmT3bKwvTsfy4hwmrQAkE9XK6NhdD8QXvvF7MTd1Nd74+Y/JN4XJbQeNYV+M6Ahnoh9PrmgZp5fx8gq0TZ6IRx/7Wtzz4EfArB68qQraOA1avBbds1eib3kaueHZlX2a565OhSx3z+6RhD21jX2Yqc4AjbNL7MFkzp19P39TcIxtnjyGg7mkwjL/3OsgpxsT5s4G2ljmNH94SMGU98EfAhAV7wBfvJNqvT3nUftWXz+lWLLbn3Kfz8rXMjVUCVQJVAlUCewYCVRw3DFTXQdaJVAlcLskkPxH47nATxNVoJHnrcvuvGevYonj2xvyujbvxgOoKQVoXM0XuEkzzHSkgrYJDeA6msAEAWCgmJSW2tpxW77VLlJZApVpAq1w1R7vYB41jtMLwCgduP/gZLx47mZcnJ6L1zCv3MvewkW8m/brwRQo6e0sAY5AEV5I1zbc69iNA5qlmNhzJJ787DfwTroRP/7Wf4qpyxfZI7gQ3UtAR2c15jbYZ3h9JboHdsWREx+Pxz/5D+L4vY/TtcZzKP0SGpfmp+L8mbfihz/8EXsTFzheg/2Hh/fgORXwBPbUsqXpJvk3RyypN0/uHe3CUc6D7Mv80icejf/93/xFPPfG2Th6YBd7HK9G79BpNJbsTNx9GE3nIOUQPWX6cW5z590Px1f+8R/G9yeH4s1fPBsrszc5h5L+z6/GMrN4g7F045Bn4uCJePDD34iHnvit6OsbQD4A4+pCbCxdi878pegGOiW0BeT14smLcYZ9mgc5g/LBA+P0nzM4GUMv1wW0utOAoxC4NTifW01T2zT3dLY/DnhV8+i+yfKu0E+pMOsS9OTDfJOQjM8lvXhlFSB5J5C9x664/1MNOd+b7+qmbI2F0z3Go4YqgSqBKoEqgZ0jgQqOO2eu60irBKoEboMEUuuHpicZJpViAk2Bmvx2cd7aBZoCsFjGxXyCIcofF/QW8VmnNagX2RsHaGSanjMxhwRI1rkKD2Vv23sv6gtQFEEkcAAe5mzho6TgaIdjHGaAnxXgYhTAOTwxEG9ylMXzeCV95PCuGBuf5fzDIY6Q6I8uoKeHMfR2r+N9FM+qG5OxyrNHUIyO7YvP/Hd/GA88/un45Qvfi7deeQ6AvIQDmKXoHZuMw2j1Hnjsc3H8nsdieHQiwYWBAjV6aF2OxdmpmLrwerz68+/Ht37wHPWvxzH2BO6ZxIvq8BBmqoPsYcT7KvLJEVO2Dd4Z51XA2ruXfZF33xG7J4bjmdfPxUPH9sa+idHUVnZtrOD3ZQNvrofQYAKP6QQGb6oDw+zV/BTOch6K11/6frzx4g/iwqk3Yg6IXuv0x+TBI3HvYx+PBx75ROw7cCctoa1dXIiVpZuxPncxumYuRc8cR4Bg6iqMT83OxXeefztOX7wRHz06zhEeelLFgQ/Q2Md1Hs3x1DyeTpH7rZFkte/55bxtzjTvxjIeY3txEtQjUJLgWY7+oOCRHI6vMKTaRJ79S3hkuAmVvHNCo3mb1to+tDE+i5I4/C11vWevamSVQJVAlUCVwHaUQAXH7TirdUxVAlUC7xsJFFBzuQ0IsBpvF+R2sNyTlrDDFVgxLlnSVT8g5iEQancysLpPsGThrinjqmaIFNMhTsEAygoSgmcSQmnD1t8rtBosW9mqcbQ7VB/XZxdjEU3mKGcLfuLEkTSjfObtK/GtX56Pv8f5ju7JU0NlfzUZ7QATnZWr0embjbWBqVhanoj14V0xMDARew/cBWzuj0c/9BW0kZhwMpZugFOt3uDwWF676PPqBvsnMbldWZqL2esX4ub5t+Mvv/uD+OO/+H70oRv9+594II5ztEaf0Mr+xg7eSJVJytmObA0ORDly1UOs/T3MXsc/+O3Pxv/2R38ef/zDV2NueT0+9cF1+lTaXd59NQYnDqKFnADoBtKzq7IZGp7E4c/n4o57nsCsdR6wZrDMTR9msgNDI4xxGK3vbKzO34zl2auxOnstNhZm8HSL5pHxCpOXL92IP/7uC/HmuUvx8MHRePwYWlMc+gh67mMVydzfON84xtk6lF93b99a6Hf0ahT1rNuHbA2KROc86USJvF163yXed0KzVPAeTvT4D8CZzMtqOzE/pqYi06aO8ib6PrVOnKz7V+RN3hqqBKoEqgSqBLavBCo4bt+5rSOrEqgSeJ9IwEW6S+xcZ29dbLuoN5GQ8FNuc1FfvkhPgDQTi3xW7+59FBY2z280nWC1aunUSJa2SnxT/bvMHBMUs1Tz1cBHQinls79cpxbcu7eYZwzuGR2MD96xN6bmluKFM1dSS/c5tJI9kitBjWffwDLmo2ggOVi+iz2D63PXYxGN29rQWPQM7cIMdAgAG4puj/HwqI0cPNqv9ZVYALbWMW/dWF6I5YUpTEKvx8sv/zL+8ofPxXeffSVu3piKLz9xV9zPER8TaAkHR0ZT49nDWY/CqwJox9qMKi/JjuKO8AikDXMkx2MP3RNf/cyl+Pd/9pP4Tz94mX2bC/HRh+6IhzlWZGUG8Bu7ED0ju6LXz8BodIBCjwbpwix4lLjgA5rDVmp5lzg/cwqPqeeAxqlYX5hmHAuxDjB6PAqEn+a2589fiX//rZ/Hj158Iw4P98Sj7BvdNdQHNAKfOApSI6oW0D2OM5gIMxx59zcKLTx6rqOa6tQyUkFBUcaOnNPhjV3Ot0PQRmb+ICHA84sGusb8ywbzPfOZPrQvaCvd37BPv1HHa6YqgSqBKoEqgb9TEqjg+HdqumpnqwSqBP7OSoCFfALd5gDKc/voGl5PnRnIqBdLtWSu24tmh4V8aohItKIMHE6POSiswPp/GVABCwDJ1mNmq4mCGje1Um1J4fFXg9W28Gja7OJSnOKswb0TIzHIGYvH9k3Ep9VkvnYhnnnzPBrJhbjv3I24hzMSjx/eF/tJFwzdc6h2TxNWP2vAHWo54nEHw17D7g7P3WgKbQRwWUPDtYGJ5vTUdFy4eDVOcsbhGyfPxMkzF+JNjt/o4+iLzz9yLD5y4ljs3j0Ro5y9OAg89uKN1HMPs9+KBdJSq7s1iJPq1RhZwq5nOu7evSu++PHHgd21+MufvhLff+GNeOfC1bjrl2fi+KHdcfzovjhycH+MTwK7gziv8YxIATXh0ZkR4leRN6ALbLqXcX1pCVhsnzmHEc3dtZsz1Hs93gQa3zx1KZ5/7VQcGe+PJw5PxiHMZXvoey/7Qv04/57feJM9jrNoKA05F17z6dd/tfPcXleoZ5WP50I6XXpK7eGjqeoG8ToS4hZ58X7ljfIpgkug5L1LW1RjbVyKtRdclGVqs3ksWlKSaqgSqBKoEqgS2BESqOC4I6a5DrJKoErgdkngFh6W5X/5tjcuwVmJ+4/FvSDnGt79iSUPpoKs2oWHDCz2LVMgqClHXmGsw77CRaLUKvnJSvluNVGWFB7/piB0bHW0QnWYTK7FW5en48HDu2NybDDGMEe9FzNRwXCA4yNOX5uJv3phJl4Cio7t3RUHcJqj99KRwYEYQps2gPZxECgawJw0zVrdPwciYTkZK9jCLrAfbxmzzNnFxZgHUqemZuPi5RvxzsXrcf7KTc5/7Is79ozEB+7aHx/AI+r+fXtibNdkjACO/ZiH9lC3Z1m24ZZs25jEHURcUoRKvbXq1OfYHUfi61/ojj1jQ/HD51+PU7T552dfzuc7gMfDeyfxAOtRH4x7dAi464tBxtHHuIV1J21V76eYrK4AvYtoFxfRFvpZYDxzC0tx+foUY7kRF67exER0jfMah+Kxo7viMG16HEkPJqqaunZTXzf1TQGNV2bmsw6nPX8weNf83xrX1rt2nts459J9jet4uFXrKCx6XIjOUvO988cFzKB9VfTY6zsITea75XcBRfNm7hRiK1uztiaqqQ3PXPWrSqBKoEqgSmAnSKCC406Y5TrGKoEqgdsnAVfahnblXZ421+a5ZodovLqGVw/n3jMj1oENduahtSuFZYjcU0giKABsqF0kjxok0ooXTGu6FUz7TYK5RE5Dwip1ex7j6avTabJ6CNhQ4zbE3sL7jvQAdOPxozfO4Sznalybno+rN9n39zrdph/DAN8wQDSERtCrz/1q1bKTXRxT4QH3aBjnFoGsVby3LsQ8EKZ6rBctXB+AeefekXjgyJ547J5DceTAnhgaGcZCdBfnI07G0PgYwDWQ/WnlWgC7yKmM4ta3cikSRrbcdQFsA+yP3HfoQHzxU31xJ95Vn/nFG/Hsa6fjOuD2ytsX4gU0qmL6YH9PjAPMvVxHGMcIzoD62Y/oXLi3VGBcoO/zi8sxt7gSc0vLPOPchniH69RNDvXimXZPfOzug+lESI2fnlTbvY3+GNCNfK9hBnyN4050ovPeIyljauepndlW02iqclAjmCbLXEvgnfC1oFLTzV/2aHpfWvK9WsPxkKasiCvzNoXzYpQfv9Kslhu6XEOVQJVAlUCVwA6SQAXHHTTZdahVAlUCf/sScG29rgtKwi0YKCtuNYwZB2F47SbCcxo7gI1hEwjM3hROM0MW/0KjexqXMJHsAejWgLE0UXU1nyv/rOI3/9oETBsq/bPw2eszcRUPossr4zFEv3owf+wb6M39hV/44HB8+gN3x/XphTh1+Xq8fuEGWkhAc34hbtzkbES9vQovEE7HryYIL5ppcmGPJFsGcbSj11Zh9O5DuzB93QNsDhbHM5i+Do2OxNAY2szxcTSamHjqmEczWCvIwDXHfKvfTcKtS5M3RWl/ANmhLvYvAkz3M64jB/fGUx99CHPZmXjj1MV47cwlxnQjrkwtxJlLl2MJ+aZyrqUq5Z+VgVob7FEE/vVvM4xGchyN6xHMdo/vHQOAx/CcOpjTp6mx41aGXotEADD3QqL+uwh8X2a/pVModBraEXqfGIg8f/XHgPZZYBTq3OeoqaomqoY0X6az3aT7jrTvR2qvmwZ8l5oWcl5KwYLb3putiLDRggOceSZkZqxfVQJVAlUCVQI7QQIVHHfCLNcxVglUCdxWCeS+MdfrbS/wntqFdicX7tKHRELIIxNY2AuM7h/rYFap5ijNV1nYbwCI7Qq+hSaPTxAGqI4GCgVstpO1/vqvFkra/ILMuvac/LN+TRKnOZLj1NWZeODoMofeDyQ8aGLZ3w8ldQ3lmMYm12M/GrWH71vKcwrVtqXWS1Cxcuppj39wTPlx/2bTTge1XB91DqKhHAIKhwGtfjSKPWj3Bthf6LEbfYOcGcmzUJ17DVto5JrY3bTRnj3YUM6WwdtYEZ+RCXHU1YXZqntJ+4C94YmxGEfjOLmPYzvuPx5zAPAi2sNl9hyu4whIeeTHOUo445iLrBOQ98p4lZvw38f8DVD/AJpKj9qwZbV63cxXQqGywZxUWTl5c5jsvoPp7w3AMefYEu0Y7TAhQbMB/K0QmfekO5/tjw1qFIVHxU9TgKr7ZX3vjCOn7wrt+uOFwXlyPhgZ/TPdLOUHjRJf0jOHmX1PaqgSqBKoEqgS2FESqOC4o6a7DrZKoErgb1sCYEICg8v6dm+YB6e7yN8MzYP8qN5HQGihob3vQkNFZC7u82gJ7l2/yxHuUyvanxYhNmvebKetzxS5wOD5kMkPSQZFu5QeSklvAWQFYHrnyjQmnAtxcHIU7VUDR4ItoNeDsxu9jU5SRpSw6tR8Wj952zFvQEumCdGpNU2QKc/QVJqxplMdPJ922E+YWjn2SPbilEZY7BDfRZuau1qPwavaNGEnx+e9gyv/TGz6REQzRgefeQUfot172d9Bi5nHaqjdXI7x5d2YcuJhNfcwrgB4HFEBiDmWLuvkugb0bfCxEqohbgtY8mww3rb8pBwoZ741jhtZY0/nsseoWCf91nvt2WuzMUub7ZyXyf3rGkbrNs/W2d76nO0x8nwnpMbGv2rxpMpz80MFXJn9cl+jc5fBG/vpg/Pn1YE0gaQMZV6bhzaxXqsEqgSqBKoEtrUEKjhu6+mtg6sSqBJ4P0jA5bVL8QQW7wU2n7NzzSKdFbmaKHVTuXB3hc6zZoiWa80RN59Tc+WSHw7Ao2qCjef1ZZ3NWp9yhrbdvN/Sss+2lwCSBHqrvO0Ijx7VcAZzVU1W7zqAN1MgL52iQCqaeQpevb04eOHquYwJuEm0jpH2G81UC4ymq3FM7Ze9tY+abdKeH4+96BYQievq5h7ANFsBxgJg62sAm0CEjNTGqiHrog8CaY6Y/BvCuZAq0BlJnFpCKBBPonh9FcRtnj/vc98hdfT2DybwmjfhEE1vXpHtumpd58V70gXKAuxWqxRpk/QctzKwAYL7UNfVAAqMmBYvA5z+QCDwNrMfJ9E2Xrg5m+PJMZSCm/Pu43uFFvBzDrdkMN7zHNX8uq/TkXp0insaE7TNq9zo75rabPuYMm240k4070+Ogvt2RG2flV0NVQJVAlUCVQI7RwIVHHfOXNeRVglUCdwWCQAHwIuYUBbgLU7YmbKgTzAUkFicC0zCmHCmOWVq5yRKgs+aQeYTX0Xz2M3R7RzFISQkGGUrVEU+8iTEZOlbX2qnMlgRcAUiJjwmYCb4CDsFRWzvJsduvHlxKh46PB93oAVcA9y6VzjqoUcQoz3qcW+d/daMVTgsmktBDmyB0RJ4AUYd05R0GzcUIMmx4vYz+51jJL0dg7CW/UKGghzw5fEdmmMqW01XO/1rBQbVTFrOehNahUvGwnjW8Ha6jlOeLvYhdvX10yf+F9gAZJpnCq/IQ9BUir2MrciBcfIvQc9+OGbPcMS81P7YBzVwRQtHwRSLMEY8c7JqX7uYI+TmW5D9sU8JoDrYWeOEk5scxbGkQOi/M2QP/uagTLeGnLGcN2Vbjs1IsLaqRn7C/kaHXtC2Pwo42GJGzK395a+dmZzYpoEyRo8+sV7yIONfbX9rX+p9lUCVQJVAlcD2k0AFx+03p3VEVQJVAu8nCdxahZcFOQv4rnLuBkvwElIzRmqHxXhxnNILDOJldDOdm0aDdmtVXypO2CNSD58u/KGBBBehq61fvEio4CoWJJiQLcsKFMCESCB3Uiwho4UCy+nl861LN+M0WscjOLDpAoDU+q2vAo9q3mybejJQV45HeBQUhRsq7fLICcCwG3DsRrNXxmFj2aC9yv7avjH54FiEFKq2H7axnlo7z6xcwpQUgKR990LmkRNAY3cv2kjaTiayHivLPgJwy0tA5zLQW9KDYyVbOG9lYX7jDAJVAqV62caTUQJiyhrcFsLU1AmOOAIyzVEIyp01tXu0SZq1CV5qHYumUo+1QCT1eL08NZ/ea5dIN2/m5yvh244QjMvJcb68f6+Q0EhCIzcaTbmkbE2jY9bpe7bK++X74rg7mg3rXTXnyj2XpYXWpDrbplrnpgTGuSmbNq5eqwSqBKoEqgS2uwQqOG73Ga7jqxKoEritEnDR7p+rbpfjxWySOxbnqcURNFyRs9h3D595RA2hSw1XKUpaOsYhn4t9nK6qLWO5b43AAdc8VuEWIr5LFwUQdCi3kdq1AjIpFMDCoFMXoUHtYjkHkkgpkjjPF+QuTrHP8bXzN+KRO/bFCP1V2yj0dADIDnnTJNK9lkIVZrSgkzXbPTSCW8YuPAIvOuFpDEsznz2xz16zV1bR9M9qjEwoU3vn+Y+LOK6ZnUPruBKD66Ox3q9DIaEQuSQAIR36nvBkcca4ssRRF5yvWMxghVr6glwTMm3DkGW5qlU05Fi4UpU9YOTkIfO6mlqeBCggUVi0rfVVK0IGdpg6co6RzwZaRbzVkA4w0mev5ltAa/n8uWtxkTMs1W4qmwRXmkmWy1Zp2feHj7X72QR17/kYHHfWwb3dTQdFwqNj4eM7lebEypC5WgVsrbHAs9rsDkDJQHxJbaQNNmD7XpvGvKTmtM1Tr1UCVQJVAlUC214CFRy3/RTXAVYJVAncTgkkQNgBV/KsvBNSXJWz8k79VC7GSXSBD1i4D87FfB5uDxxqblkgwdzACnCZGkPzAxmu8E1fx3QyTVUzJqsvEMSzYQOoSI+jQoRmsVuDQJmB40BIXgMS1Nq1cCK0LNKvVy9cj1+eux5PHN9PWwIjx4AAQAlDAKMatjTdXAPaPGBecFOXCaRYpWMnM8AipBLfwgmJZW+gozEj+RhTFkpSMQJIQz5r7CVc36AOPJ0uzs/FCuanpvUDZ5rKJhg18JXARKpBDenCzGwszc5Hr0591HyyJ1Pvpxwcma0UqdBuM1el08TmOKiEbjgLxrfmnZqCqo1MeLfLZFNuflY5hkVzWudlJecHJzt6nFVbq7xIu4rToWfevhQzC2gAKZtnXdoUcJqza1s8G6y7yMSLnSntZJumEVpNcXmS/4FD6kqHRfaVcjoa6sta+aEATWnOH9AqCGa9gjF5fVVK2zwneJY+ZNOkuFe3hiqBKoEqgSqBnSOBCo47Z67rSKsEqgRugwQShGjXJbafVAx6U1SP3uQi3VSP1sg/0r0KNqncygW/pp5kJ3+aGwJ/AqT7HIWuBBjSMosr+4RK85eQ8dyqdWzvE5RaLRXdUHsGeaR2MuGR/GvCKtGaWJ7hWI6fv30x7js4GeP0bb0HgAMcewChVbyQdq9yfIgOcjTfXKVfvRKY4NOAKWAizMGUCVvKwCaFFG9aGMrnzV6SbiCf2r4Eb/pif9QgLkxPA23A2coojm0831EtIu0l/FjOzq/HMiaqc1M3AMcFzoQciYGRETSX7NfEgyubGelhgcdyrAn9sXxTNtv3i+jycQ4wMxUo6XsZgiBlcgHm1LzSL/tmX9U4tpC91pzbOL24HG9cmop3Lt7M+XOfaBusM818vbaRzdVcijBlxkVIzT4YtzUQ38K/Jrfer6/xw0S+NxwXgiOgRf7WNat1bIRWS1uejGhrVvo2yXtJeedxFRiuoUqgSqBKoEpg50igguPOmes60iqBKoHbIIHiQZX1d+6RA44SNugI6/GWQ4S1Hs4y7O8fwkOp4MPCPGkBIGCR7p60hATLGA/UaP5ZQMu6iCwMU0YoSBiXLXDNW1ojLrV+PBcuKgDQaqnygHiB0/qFP5sqNWYVM5zp+MrZq/Hy2WvxyRN4H22AaJ19hTqK0Xw1tVeUV8OZ9psA1obnVgIaHslBIfrq/jq8oNqvzGffbKi55niUjpF8vCTZKJcyjhwLCULY1NVrsUzfejkDsgcQdDw5/mygaN2WFuaBxjn6EjEyMc7YgKjUkBbBvQu9si9N81w8vqKdD+GrNT/NyoSxW9SV/dQsdo1xajYqNBZg9EgPTFRpUy2gaafxpPqzk5diHqhNDaOyJ9j37IKdbcZgvKnOybpypE9lfsnCeHO/IjFtUAab0EikMC085g8MgL6BWU5Z9aR22X5aI3/0rciX5xyvebPAZp0+ImUvNVQJVAlUCVQJ7BAJVHDcIRNdh1klUCVweyWQALkJAmre6E8uytHHAQK9ahcxTRUCCj8IKIJDruGz85qpuqA3f0kQDoAToYy8wkhqrRI2KCtb+OWVIhYTmMCFEkcZazKULKIEeYi0H4KkQdjwyAqB5+LNuXjmrQvxyJE9MUaF3e51hDg6CURoH9cxX13X+Yx9srywSu1ZKc8QWIIJLQrF9jv7YAcMbYfKU3m2PPEbalc11aVv9qcHz6ianC4AhCsLi5iqcuYjZqddrTZWUGIMxcR11Z7E4OgIpqqUo3zKOWVV+th2oW2azrWCyW5lunECM1f/sl9e+JQ4JIwZqnnSaRBX4VYZrTdax27K3phfTtPfty7eoDBypDMOvQVe9bRKJlEyJ67Iyz2nzmJqh7MEj9TXlkuAdExNHG9FwqZ1J0zSUaG2i3lYw+TXfLbjn/0iArmYO+8aYPahxNlXTVqFUPeI1lAlUCVQJVAlsHMkUMFx58x1HWmVQJXAbZRAs+zOhbqL9Qwu8Am56Oe6tobmCZPJPCYiiaFZwDcQ4L5ANUfCgFW0mjXX+QXSsrr8SjhsHbsQI0BJSubd1P7dyk67BBL905EO1otkpxPcb+4/pL8LHFD/yukr8RKax0fv3J/moWsrmG2ideywZ09I6+4IxvSSTooWosk64JFQSpxXwcgU7zI0l/LwK9/KifrSRFenQMigA2j3DQzEwPBQguPCLPsX2e9YwFsQKy1YzuC+vsGx4RjATFWT1qyjwz5HZJrV05dNkZcim53IR77SJFgQYwyb+yeNp2xCo/F8BKuERsx4N7WNeoNt9oM6eaevTqO9vRHTC8sF9tvW7AxBmMujM6hLyEsHSsQnbDMn2VfmK7WdTRnLtQCZtZSBvatvWbPx/nOO+KSkGIeiSoAUCNXGWmEb1Jg3ge5nml5Xa6gSqBKoEqgS2DkSqOC4c+a6jrRKoErgNknARXxaWGb7RWMkAJQ4ND9oqFZ0isMxET0dTEC35DePJTYX6YIcFebZhSzwOyzoU0NExgJkQpMQQEHNGc1LebVJ9iNVnZq5mkRFCSBEp3dQ8ggPmpS6D3Gdut37p1dVAVJQ1VPnJY6P+JOfvR27RgZjbHggQXcdrdp6B1DqAxxT24aGDyc5gnCnJ9ERqKRe/6+jTSQOcrLHwq194fNrA/3a9CiaDnDULK4AgDi5GRpGi7iQ41yam889l+s40Mk+U6Fy6iNf/8hwDI+Px8DQEGc+Wt6jO3SQUzSYNq9WuOCmfSIGGahos296m01ZAX+pTQSslIlOcRIWM7PADHDxWUUG7v9cUdvI3K4D3DroURs5s7gUz71zNV4/fz3BTQ1yC3ytDNp5YTKcPNriwzvjDwTCcWptjbMsca1JdPsjRCsvUnLOlImQ2N3LnFgPffTHAfvui8F3hnzmiSjqNdY7AhrjvLN9czsnCqeGKoEqgSqBKoEdI4EKjjtmqutAqwSqBG6bBAA0l+Dt4rwsxlmcEy/0JVaRiKFqmhF2Y+rZg8mlJVYAkIRM1ugrgEgvICQwadpKAjlEJBfwaJ/4CBxqqBI8hDLgBrJL7RJJJAqsxGcRvhIcfKBEeoaxfHnsBu60MFXzBQNmXaKg2rZXz12NH79xPnaNDsbR/b14OcV8tLc5DgONYO6pU/PYDSxJvprh0l+DcLIByHT10RD3xhuX6SWLQ78VlFP202p6MTUdIN09eSsxsMKZjqPD9ApERSZ6LXVfYRuM04vqIM5w+odG0FIORm+auAK8eZ4kGlxklJrKll6zD37d6pvjtl41t6nddVCCmx3Ni7MIeDfeUjfshxpGoFFwTE+qPK+ikf0pXlRfQmu7yLEiaXasXBrZNNSfPxakWSpzmKIQFmmt1Th6VbDd2YcCdbn3sRm4vc80u6fs7Hf23Ymkp8Yzuc6TU58QSNyqx4ZQZ6b7Zd7mu0Al7wTPZU9mSUtxEVdDlUCVQJVAlcD2lkAFx+09v3V0VQJVAu8HCbAAd/H9XsElv+vzNG9sl+jsPdsA2vIMQYACZigLeTRWazz0ikkAp7Ch4qutXSQQDKkttYNbl/V6bCUxy2Q+YTLbyyf0gvYQ+LM2wGFdTZcgJSCZBdgUOLN64paAoWfevBDH90/E7gm0eWg/BaQePK2u9QBEaAYFre4etWQAWAshzVi7SPM8wy7gjVQaKONQHiVwzTI+eV8uwmU32sKe9YHoow73Xa6izbOTatNWgbE0E6WsGlLBqH9oMPoGh/CkOhT9mLb26H1VjaMyUI6Ezb2BAhxlbdqU1OxxzfMt8UgqgDlX9sc8ji1B0nigMDV5AqN9Ax7X8DZbrmpZ1+PM9Zn4KXI7c2PaZmkf4VKHIhbIuhPqS5/synuF7JcJDTxmHu675eWcK2oyranAMXhuY08LulmWrI6fykiGV5krHjSH7uKsTRBys3zKPhvha0ufNB2uoUqgSqBKoEpg50igguPOmes60iqBKoHbIgFxoAmSRrPyLmt40Q5tFwt2P+nkBo3VBuaT6YAk1/8u4lncCxcs2jVZ9UgFjjEE0lj467HU1b9Vk0Fvq2lUaZ22RX5yeJwi3yz0zWfIK/GG5pJx1mU9RKvB7AAjIAXF3fdYsC5rRDt57tp0PP3Ls7F/YjQev2cwwWkVDaDmn8Kj+wfXBSPK93CVtzRdFZTAPY6ooFuZDAoDIY4ju6Wc2v7bP0LbxTS3JK/HivT09gOFRZNmmzrHsf0NAc8yQlCaqvYDjsDj0ADQOJDQWDR3yNI9mZqr0l7ZB0pLTE6agxJnSDC0XoBQxzKmC9f+ORYlJUDqMTW1i6lZVNO4lCbI6wCy+xtn2c/4g9fOYaLKsSBo9tJbLqWbZmwq760xW86EzaemLRIFPi6Zoiza/MqSkGd25p0/SugMh/7lMRxD1I/GkH46XqHe/hctqrU47vIuNj3I4ZnSBn9LsHXHrzluDVUCVQJVAlUCO0cCFRx3zlzXkVYJVAncBgmU5XjTMIv1sggvAMZyPhfmAoTmoEkN3nejDSvMkuv3Fj2NWkdztAx06cBmQ92j2iE1gZQ33Y9aIxVXAk1XejNtiMIo4hM0vTfYocxr39relfxaMWYcMCs0qqFKuCKb96v04ZUzV+Mne88BjyNxYM94gpJaPoGtmz2PkmGv5fgk1AIcjk0AWRfClvXYygP7DdkkmYoyWaxoKLOH5YtIogmCM/USBL6e9X7MUGmGNnsHATSIWkgTmOyjZ2F2qDvNe4FGPbGmls+aJFnOItRsNp3PUKZLk1oa2kizXTpquwmjaBA9t9BniiU4CmRZR4HGDfqV5qzua0TzKcSuLVE/gLXAmY0vYJ7685OXw/MbnYOch2yrvA/2N/cm0gaJKad0KkS8QdkrO+HZdGXU7mUkhji/ysXnVoapTc2fArL7fDHXjM/stuzMUx0PtqNTHJ7tg1HNJ98n22tjrSPv61eVQJVAlUCVwE6RQAXHnTLTdZxVAlUCt0UCLryFGL6zfc0B1RjlwfBEmSQHSATdfNy72FETxH1CCeUSCa2IZXzRFnHH8woarQ6g43K+dbBSMICFf7ZmW81dkoFV0I5pwgggIldmoA9tFruaiGCcEEgn7UOCC+XUhpY2u2N6fiGee/NiHEDr+EUc5QyrbUTD1u2+Ph3ZAI5CY9lDaH8cM0SKZ5b06olKU1CzG1040dEZTMKMnWn7Rlo6+7Fj5iM+YdosVNVFXR3aNX9eM1f5si41nwJk0SxSPxVswjPCX28PsgecwGGgirpsQHhEI5lmp8UmuMQLUE6c3bG8jmc0TeWjBlOnOGtA4ypnWyqLFe7Vzn7n5bNx4cYMc8+xKfQh58Ex0K5VcVuCA/TJPN7ShppAj1oxX4d+CXKin7KzI2Yzf3aKuPa5hVNH5nCUR+ajgmJyS/+dX0Efjt9QDZytlPocp5CsoliwtLl8N2jYftdQJVAlUCVQJbBzJFDBcefMdR1plUCVwG2SQFnb853A4cWFvn9ccyWOOSHwITLp/MV0n1naE8c9QNADOKjJy7r4vrXPTscrrPjhAbVu0ojmqz7/anDtbx+EwbzlK72tUn9qlLJFsIBn+0XWUg3wp6MV4c+2EmZS41TG8c7lG/HtF96KO/aMxeP3HqLv9HV5Kfdj5vmEghtOcnIc1L0OrWpyq2bUhnUcY2N6Y/UMRstsIAehJ/sAqAo9ahoTsgRX7pcXFmJ5fi4Wbk7FwswsacEexpEYHB9NxzdrS0uxjKdVYbunrw9T1cHijTUw2exDRj0UUNuoTJoxpxY0NwsiHDWt2Rbt01+1vTkBdio1vsVZzhr1q+XMvYxqPGk3tY1oHlVPXrwxGz8Grp9/5yJjdV4xq6VpZc6/Eux8Vm4qASEbpQOb3HLYNC1sF+gX5iXB/C5jsF8Evx2PWOfY1AynJpSyvkFC6AawnX1Os9WSP8foePOltCbb5ytfBodcgDmbaeY/M9WvKoEqgSqBKoEdIYEKjjtimusgqwSqBG6nBDRBTDChE2BA/rm8F7tkvNQstiBlHHBh8BtsZLFPnM5mWMVbVwJicgapggQh6xcWEkAyavMrTTOBhwQOYgUKg452uowntHmE1yaGuAIKeZ4g5LYmMVB/wh19NB+cl8c9nLpyM/7kubfiwORo7Ns9xrEiRdvWtQo0rqDHo60eyurzJ8GTXjjM7oRc6gRg1vHCqmmtclmanYuZGzdwfMPZjA6R9GWATE+lq1wX5wDGqZnwCI4Vzm8UgtTYCoeDo2PsaeyLFfYUmk8toLDU3QuwYao6ODrCXsdBjuYYyGM6+jjSw3I60OnqQ/NGWylVx8tHkRbYL3Ol6adz5nykt1Qgdk0N69JyrGiiipZRbWMX8demgMbXL8T32duoR1Xnz5ktomxnwndCWRag55bnnGD6XZ58NEbizL4IfwqzgTrnvw1b71oTZr3N9grensFhWafSGrkRCK1GjbLVrK364wG1mcl3IOGVCOMIHfvgX/PulNj6XSVQJVAlUCWw3SVQwXG7z3AdX5VAlcBtl0CuwekFuMBfs+jmzpBrcRfo+QBY4cQkc6l5Y4HfkEPCgt5CWc0TBYSpNWThDrKw2HfBr6ZOcERbB9BYY7POL1UnsMgMTSzXju1aj/CQNZcy4kuCAzVkPOkJIBRNr6ukr1tODSH3wtA8nk1fOHkxvvn8W/GlD94Tx/o5ogOYIpGy5KVfNl0Ah37KPHzcwyeg5pXuJDsBMoszU/H6T56JK2cuJKj0oYEUHtXUqXVbQ05rQJoaPMerIxzNdVfnFmLp5nTRziKf1KppbtpAjrLT42s3feoIkgP90cPxHjrNGeCsx360lbsOHoyRXZOZTy2jY0y4p12hrYXGBCf6tEb9aZKaHlQxUXXcgOwSWtcX3rkcP3r9XFy+OZdySg0s/S0CoGJqlw3tV04NX8J7tulVQb0r0D79SNPdTHLO35VBoWZ5Y83iuyDkuu+ya7CZC+NtkMLrK9QJVCojX4l3BZ+FR2rMsf9qW+/KXB+qBKoEqgSqBLazBCo4bufZrWOrEqgSeF9IwAW3ocWBhBCekwtN4Ma4VT1f4m1Tk9RuVEDrXQKO0FK0ernHDavUdY5O0D2MoNQDAOFqBcAokCEYpibReltYsomtACKkmJakUsBCwHAvYssNyQdNHmFPSOoAs6mLs0M+k3+Na0Gb7piaXYhvYbI6OtwfI+x33DvRyf197jE0r+cMrkuLQqR/js0v6dR4Wl/XzJUoYW4QkJubnolrF66581BERjYFqNPc0mKUt+4+9oX20k6vDnMwQTVdbezSMprKBhztq3GOYVX44qNceqmjn3qH0EAeOLo/BoeHY2Ryomh+GWsOVzjNe8ENwLI8sJX7GDFPXUHbqMZxFVj07MZl9jX+7K2L8VevnI5Tl6dyjHoxTbkmSNN5gn1XFv5z3OW+kSj9EtkyeCmFc2zCfZooZ3KTp+QsFWX2Uq9zlT86kC2B03Hbnl/uNwUMlzHZXePdy/2epPOrAjXwyTbbiukdUWluS9k8g7JNqtcqgSqBKoEqgW0vgQqO236K6wCrBKoEbrcE2v2IRY0HgLAgFxBSiwMc5N4xF/eaSKqBIy4DoEI0C3y+MBcs0MXKHRCCXFjcsw8Q8HATnMDYlttc65tGyDSum8c0WKnBdO6zHPfuMSxOaBIZMl1AEZTWhRj6o5msjn0ECkFuKzxa9gLnFD790juxe3QoPvbgHTHG2Ynu98u27Bj1CGrUwD0SoAvKwe927EqoFw3gsQfvj0X3Ma68HFcvXY1FtH8C8zJgs4hH1gVMKr3XlFcG0hRWx0LpYIiIFfq9uIQ2EOc3a5rA0p75DIlrPPcBm6NEjg/05jmPY7t2xQDgqIa13dOX5Ii8hThl4HymMxzPaQQaNZ1t9zUKkkLkL/Gg+q1fnIpXz1yLJaCsBzDNQB8VntCm5tT3QJkox7w1U/axdDTT2zimfhMkiSt7RO1XyWs/lZ25sg1uHUdqChttdTENLlnMmR/6ojzKXki1jrwfVNG+T1Sb0MzvGEby1YSt921cvVYJVAlUCVQJbFsJVHDctlNbB1YlUCXwfpKAS/lbi36h0cCS31U5/9SEdQGEXThxUVsmSIh3RVvZLNa9ELmm2ifZT/UjsGdVLOLzjzyJKLnaJ4Z4gTEB0XyG5vld9wKkSQ0MCIGlj1TdxMNeyat5PiKp3cCIQKn5JXelXep+4/z1+M4vTsY4WscP3n0kfdBkLXbMtimD4SnwCPBie5raUm1jC0Um4Amro7t3xb0ffDTNcs+8/HrMsKdxEZPYWUwub3Bd0lwViJ1FU7bMVTayzw7B4SewWydBE9cBtJFDmKcOce1v7vUCOzbUH3v3TsZdD98fdz32cIxMTGQZ+5AD5qnMgyDPSBtHOKtCo95T1TRqpur+S55PX7kR33rxZLx06lLMc/RGaubsFP8MQmPKmU7ZO/eM2nHnrwixZPQdcDy2aT7l79Rn4JpObATQYlecdQj6m8H8tKszpJRH0rrtC/+MwzqyBfIR548Qvkt29VawEp7MayVNSBi1ghqqBKoEqgSqBHaMBCo47piprgOtEqgSuD0SQJej1mez8bIqz7U4kS7UEwDUYkEGCVGZVw0fqS78XbsnVLHMZ7Ge8RwvgacTIIbyAIsr++INMzO3a/1c7GuW6kK/tFw4IJswXpAjZH8AjC4AzLwJNlxljdRqkjdzbZKLQIKWMsdAfpL1SGo9S2jdnn3tbAziaGaYvY4P3HGQfJhw0tZqA3JkCw8SMYDJgF2iEVRDDUQ7Zk0ix/bujhMf+1DsOXQgTj3/YkxfvpoaUDqZsKhGcRqInMYRzgKavWW0gIt8PJxeqO1nP6P9GEBek4O9MQkkCo16qTV0+npjfN+e2H/PXXEIDWf/4CDzwKh1JMNfApKypU/KPZ3zqGnEHFXNovsZ1zgWxf2DG8RfvDYT/+mZN+PHr56NOc9rFOyAsjyWhPEoV6RW2rYLpucEe2uLOfy8msnn1EYi/9T+tnlIMHeyoJl+TbCMR38Y0usr41LraL8cg1peCdJ9qD0eBdPB7NZpYcCO2d7kWaDeOsf5LnGTc1T6a1INVQJVAlUCVQLbXwIVHLf/HNcRVglUCdxWCQhct6Ct7UpZlBcwgPiIdhEumKA5Y1GfCjhhikW/p0B0gDJRywW/qic1RuyGBIDYZ5hau2Kumgt6G2lgIZGBPHkV/gTFhEB61cR7TUYwvgGTbNdq2o6Sx21va/TDvJqs5h67fCKGfnXT6dRAMo4lxvEsnkTdX/h7n+mKB48fQEOpt9EGNqyX9jTX7OmhXc9ZtB+q1QgJV+RRHn2Yre676072PI7E5bffievnzscCex/7KD8y3BW7yeffKjJZBR5XgSO4MYfaCzj2ocXtFXLlNfqY4M3z0NhoTB4+GHuOHY2RPXvS42qa4VpbU6dzp/Y1j7NwboB09zCqbVzxyBGueXbj0mq8evpS/AXOgX74yim0ogAaUs+9pU4Z4xTgEraps8CiOUgkPjWFdlCZZEeJ5r4FezuvZPwof4diyHLl9q99m9e688Nt7mmkBuHPmno5u5FupQbbHx+st9Pdy5EwmBanuao/OCgy+mQ9vnv5jy9CkVFG5XP9qhKoEqgSqBLY3hKo4Li957eOrkqgSuB9IAHhwHW3doYuzt8ViMgkMuTinGcdmQhm5dgNQEdVjyTJKt9LLtjV7vGw2ttLaqk3tZJWJhg0oGFbRml2KZB4bWHE+NLmln4JLQkWBU5MF1mLY5iiMRM71Hy28KhGTMczbSiOeNbj5vxiPPfWhRjo74l+NHt3HNwdQ9RXjpiXSKiDz+p6D0jMI5pAzV7t8ToqTJWb8ouU1Onti7H9+zg2YyjG9u2NmxcvxOzV67E8O597Lgv68i3kWZByyjy9mJaB5vi7AMn+0eEYxgzWeobZ09g/zPmOem1Vo6gsKZ7jZYziuc8J9GmausJ92ceoM5x1jt7Qq+orQOOfPvNa/PzNCzGzgJMj5WgH+CiP0gXgNaN4bvqkzFqN4lZoNN45FAxTBE1dFqM77w520NBeyduIjXZ4r4BkyxmXN3w5svRE69j4pNwYf0K1mfiXtVKn1aqZdM59l+38ljfGWmuoEqgSqBKoEtgBEqjguAMmuQ6xSqBK4PZJIBffNs9NWnm6EM8lebMybzJ4KSmtdrIs+F21JzwJnWkKKliV9b97+DY0kSSPjjoFLJRDJTQgV+olXpAhtNeS6da3qal5tJykALiqttPraTfPaRJpeYsIIxSw3bwnSjhSM2c9arTM52cGePzRK2fSa+dXP3QiHr/vKN5LMVldKs3Yllo++9lj25rL+vF8x6yBFLuEWa6NDnJcRs9gP+A3GXM3p2KBozeWpqbyvMbVBTSA7DFUU1cAp/Srg7mswNnP+Y0DY2MxND7OdSTNUjua/JI7z87MwfFon1qIBOLVNnoOYp7V2Ggb1Tp2Yye8xPXnb5yJbz77ZvzszXNA4xIiaQCRqgzpBEeNZz7wXYTItdE0Ou4tkGi2NBW2LJ/UKpK3Dbfumpg2rb1app2bZt6Vr+PavJSIlLvvl0WFbEaeeWxZsCx95f3CJjm13aUG8lNgS3tNoXqpEqgSqBKoEtjGEqjguI0ntw6tSqBK4PZLwEV+LvT5UlvYLrYTunhMDaLdTFhhsU6eYt7IDUDhQn5T45g1AUVSG6SY+xEFSv7kAxfz3ueDTVkvYfNq5Q1QiAetGarlhEPNTC2bTmGEr/YZcLKOVlMpyKSGkTKttjHrIr/5BCT3M+pqRQyeBh6ffulUNo0laTx29+EYHxsGxOwOHkfRONr+BlpJQa6HuDXB1fqEMGvlXyIN5XvQsvZMFvhb2783FqdnY2F2JrWPy4uLaUqaeVHvddBi9g0O4SmVD9DYOzSIx1YdEBXt5Bpt2evWwUzuE1XTKE0hg1VNU/m4hzHPhAQUN9AyqmmcmZmPF9+5GH/y09dTszq3uJRzV7ScyNTR007KtxlLDkW58kkgJN2g3DJfPpX7dh6bqMzT3r/XNec03xlSba9pu7Tp1BYhpmy4XdWLL/nX8E6rnFO3TKL7Vi2T72h5aYvs22/SS3/L9b36UuOqBKoEqgSqBLafBCo4br85rSOqEqgSeF9JQHAADnK13iy4WXaLeKDJrcBC33V9Lvj1ttks3MsaPhNIEgbKvfXxmAt4WVDOaYM52pBleGhB1XghWJt7sQAAQABJREFUbTN4T0VtcfMnPJoBqMhAnBqw9LTalE04JM56W3hMrSNlbH9rugCjE5kf//J0aujci/gkmscRjuxQO7jK5skERwaR3j7XcNLCvsduoI9OsAcPGTbmlvanOAgSSTnHkvMeR/oGMDmdKJpBzEZTg6n9JbJS9h7RIUAmbNs50jZ0gKMsHUP+NWOlb9avuXDuZwSa1TSqcfTcxtzfiFbz2tRcvPDm2fhToPFlzFQX3dPIOPOYDerMoKdSbtPpje9ANlfa3AqNOUr7wacpmcWVjXNhyHlp5ioj/Grmop3NzbL2o+0D2Yr572apfFlyzI4Tebn/srRcvvNHAeixqd6G+GzWnn3JNhMqt9Rbb6sEqgSqBKoEtrUEKjhu6+mtg6sSqBJ4X0gg6aEsv3MN7oJbSND80sCjXj5zT6OrdXhhQ4c3JvAsLKZGCHgCA7JIsUlN/OBZzZFZG4T4FQBJDSZpaUpKmlfbN1ibyNSCoXEtpFhb5mryt3kyXjgBbNaaNtPMkU6k5tJ+Al62YfkEVTSIi2jtfoLZ6vTcUlwBvJ564t6YQPOYWxITGvGzSrkuHbXgCbVrDQ0k5br1ymN6A3rZp6b/aWLaiKSb/Yu9aioJyqJoEQGgtBG2lPXQLfuX8OhDZiaaex6sb1WYAhZTw0hfdAKk8xg1j3pOPX/lZjz98sn44x+8EpduzGbZTVADEJ3eDl+aqNJ5PrRnH4RHQtPdvG9lvVk+Y80K7DpXW/O3z83VrG25nNOMsC064IfQelQlIrXEHcbX3VPSHHHB/ALsKRx6lz9pID/vLOeAlGfuf2Qe0uusKama5KaGKoEqgSqBKoEdIYEKjjtimusgqwSqBG6fBMpCm/V2YZTEjIIpCXoJOGXJ7rfAYNCEcF1zVO9z/U4ZYcylvgBFXMIRMJIaTUChYJrAVOpXE2gJK7AeISLb5LoVXrxPQCE+8/NsfvtSPHDyYKCeTXhUG2a99td7CmT7ghvPm/EUsy6HAAsntL5x9kpcnZ7L8w6/+rGH4jhOcwbwfOpZlrbXjbbRa6cDsOltlTrTG6kaPMdumwmDWyrn1mBbGRhL23DCWUbSC+SlNjE7pDy4T1nTXo5VDaOwmB8AkmfHw00sYgb73Btn4y9+9kY89+b5uD6zQDXIINsqMlX2ytPxG9+wbpGNXS93OQb7al7Hs9nv5t6ytEpFt+bEvO1PBXklTblmPvMScn68sR/KyeA1wdXeOk5myr5lhdbRwWyVMTNO053H8p5QVmC3BWnYXvq+MahsV3e/NVQJVAlUCVQJ7BgJVHDcMVNdB1olUCVwuyRQMI5vNTd2gkW7C3uvCUI8ai4oOHVxRMItk0fzuLp34W4t5S/9xLCIV8vUBRD4J6/pT0ZAzXptQzjwkc9maOKsz6yme59Xn0nPvY7EGS8A5Z5H0vIgeeAitYpSB/cCzJpt8awGsmg17XFp27pkDrVvG0IHdXpEh5q6b//8zbx+5pG74sMPHIv9u8ZyLO47FO42AEjNTtUkdrMPMj2tAo/dHWq3LtrOL9pQslu9fuazY2gHz62hjKXkNUrtmfCYx23QlvCohtEzD/NoDp6XME19+9zV+PEr78Qzr52Oty9cY9/mMjJjjJrQ0n7KWZnYF64FGpt7O8E/82d3TKft1BZm330oM5DfxFnP5j3JORfGG8xLvwxZT46z1O1cpIbXuWvizWdb3Wo/Cak59NgTANB3TY1sl+eBMm4dMGU6+X2XBMj8gYJyhT2tl7Jb6s5K61eVQJVAlUCVwLaXQAXHbT/FdYBVAlUCt1sCuRAvyJDrbRgnF/VlUV7W4CZ3VE8RBLR0ZCIoJBgKBc1inbIs5Uu+cuGexTx59YKp99EEEuvJXHw1UCJsbMaRX8+bjbEsmdv6C0QmllingCBcWFcDKwk1pJlHEFMjmFo8KxdCQB6Hoq6qWwC0KB+DoNwBWnyemlvIfY8Xrk/Hm8DYJx6+M+47vI+9j8MBK6YZrOBpP7s5niMhjTGuN+dW2uXUtjb1poxszQTK2HW7lKaqOQD6oQbNe9Ppux+hUVgs8FjieEgHODenZuOZ18/E937xdryEIxy1jGrm1OZat3LfDNwLYhnHfabYFpyXVsZNH4x3jjLduaE+71vYVDYJjdnRMoYcE/EZyJ9z2lzzvMccLO03ZTK/fbN+Qsof2ZcfKriaZnzCYrlHUMglo/OrPBpHHfxzZlN0pDa9v5W53lUJVAlUCVQJbHsJVHDc9lNcB1glUCXwvpKAC3bX4lxzEc5Cn8cCItCFTls2NvxPs7BGjgQCR+Civ8BKAU5RwJLCiiacmrWiNjJQtyktNOT9lmdhqdUkqoOyL4bUFnrDc2oVaVvsaDWQ9tdyCZBe7Xv7bF+ppkCkY+CZPhmp91dDagG5JsT4TDsC21vnr8XVqfm4zNEaH3/gaDx07EDsxdnNIB5QezBVVQ5qGTsbjhEwTtPVMoYugDIb1vyyqZPLu0LKixjThXi1u46haNjQvHrPJ/cy0p9Vzmtc5HzIhfnZuHhlKl547VT8jCM33JtpyL2oXIVjZaKMUg60YBu2JFjmnbJUvmYkmC+h0Tifadd5amFT2Re9YCaTodTZerTNWMsQrMFqN+fAehIEm3nx2YyETr4jzlnTLp13ilatqwHG7ENWSB7OdUlNpO376FcTzJLZtlzbtHqtEqgSqBKoEti+EqjguH3nto6sSqBK4H0jARfdLLX5lxo8nsq1XYznytzYkk+IScBoluhZFBhwEa9pJLnWOUJBxzSJl7myz0r5IkgEzUK/baEklAV/3jfA4n3CC9dWC2arGbbAQoIDfdpMI0NCJ9fsK/UlzgBlIpP50nSVa4KRqUQKcWrlcj8hSQVWNmJqdiGeefVMzM/MRtfCXNxzaH/s3jMRw5y32Nc/kEdwbGiyCgAlSAKRNJB1KRC7ui4AtePeOnDaTJC1L96zNy9hsdhiokH0nEbNU5fD4zzmbs7E1PXrMUdfbqBh7F/nbEhaTW2dY9CkWGrc2gbtJio6/ub/rDk24jObprXcFJy0I01x4c6EpvzWKtP+mHxC7dZQIJUYy5reJjb5sj7jeM408nX30jKZ02Pqmn2izizPHVpha7l1dKalyMNYNGj2ddoackTKcWtkva8SqBKoEqgS2PYSqOC47ae4DrBKoErgtktA80g74SqfT0KMQJFmnU3vzMBiXDDSnLKDsxIX9okauUgng6DDP80z1bpBQOR1X55XYJM0q2lhzJrz2RvhxIsUQL0Z34CFIJJaSOs0T6Mh3HSmYz8o13QfwODZspqocm3r7KacJz7afmmNpignllhz7oW0fcJWeFSDJ9D0AYYDveScm49zb52K2StXY3Lf7hjjzMbBkVHOYhQg+6Mbj6tCqe2sI4uOmlrHtzlYGmjGm63ZJh8vaaoKEJl5zb2NyG4NDeMCGsa5Gzdi+vrNhFcd4SRQ0c4IjnukKvf2FXikbf42JK0UiqMraTl25ZMtNJrH7KtZyNz0KzOQLz2nEufVqlrZ5j3y1Gy5vAdZ4taX9TSybOtSg5r5aS/TmrayLsZbvMuWl2RtzfFYkisAjhia4Fxza6IF/cJ7qq9jXjNNOfoKOLM1VAlUCVQJVAnsFAlUcNwpM13HWSVQJXD7JNBAQS7mm/V4OrYBRFyHl5W6V+CBvD04yOni7EHvU69jGevgYx2iWD4DTOs6cTGahX5qjKyNPJk3a7ReQgMZyQIlpgAJi//UaDWwk0BoW02eNK2kbCIC8S0ItvApIrX4kG16RAMdyaM6qHMTLqyfttzHmd5T2z5YvgEQj9IY41zGfeMj0aspJX8z167H/PRM9AFv/cODMTQyHEOjYzEwMhQ9ff18eqkA6lGjR5kybnqvPBSusuGmnP0IXOOYR1Bc4bM0Nxfzs7NA41wsLSzG8tJyelM1/8AAgEqfe616BvNY7hMa7TdtWXU3bSgfe+q3ob3ekn8rHdOyO2UunMvmIzQa8gcFZaGsCCU2b9/9Rf5Wc90m5FCtp5Fl1m2idSGf1fVO4F6IPvO/fTL7/m2srdAGZSyckbTtjxNqU7N12zGqyDXfK56zDPn9+7V9tMoaqgSqBKoEqgS2lQQqOG6r6ayDqRKoEng/SkANoWtzESIhJhfrZeH9rv7mKr2JsUCzKt8EFiP81y7sNYsEFtS6dXHAfVdnOYEgoUFq2gIhxlmd7aeWy2bM00BKaw65aQZpegMhQpAOagyp2RNQeE6IpHyHfNar0sq83rdhU7NIvA5/rLKAFlXknXntmYE+Up+Q2MtZg209VrcC1K1ytuIi2siZG1PRD2D29velx9Ve4FFNZAfYzr2F1JG18pWaWM9lzOM1VmKFOlYWqGt5iTq5X/W8RvZk2jHyu6fS7ijXDnJdx91oQqNzsyWYbv9B5MzvnKTDnEaeZvX4ESc9ZUr+TZhLWWTJHLnZ2j2M3rdh673tZWhkm+9Sm3Hr1fabeducW1rpoMktjoRSMpTgSl1W5xmc/vrg/k892m4G0hIwzUf7t2CVBLqztX+bZepNlUCVQJVAlcC2lUAFx207tXVgVQJVAu83CYhuuf53tb656uZ+MxSYdOmu8SOrdvIZV4Bqs4xFmvKCmmDSM9Aba4tA0DwaNcChZwvAWFUb2mZBhQTZFvSso+2JefJ+ax0tjFhR9r/UJExkd5p+lH2NpAmzmaL5bXf2KUsAWJvHOXhvLsbZtm31QljCDNfCZU0e2lLDugTouRdRYSbUonlVS0uhAkfGWxG12tUEIs1vGYMglCao9rtpN6EM8M76LGVhZU8QtvpNsp98NqVEGzlH5LGbtwK5KJNgSRst8Hndmq2VmW1uhUbzZBpXy/guOFcZeDY45rzmt+OzZwTH1MR5SW+rRpPYzfvhsSY5NvJlOvUUmPRqjHMm3JOesixtmeToPU6lNGTeGqoEqgSqBKoEdpoEKjjutBmv460SqBL4W5eAWi8X5Lk2t3VvMP8rq3yX5AQX/S7YiRasPKRdU8gMLShwLSaXxpPfevHEIqxFXyd6h/pibQlAWsB8tYGJ3LsoAPKcENPUZb0tmNidd2uUShdtJdMyc8EX9z+qsdQxj7TR7m+0HTWQ2WPbaNrLfE095s/jORwjcQ5XLaWaSJ/hPwDQPYs8b9IY97aDNvFdwbb8sE9xZXUdf7IrrTjL2C1DAT9kSzNe6xFIheSURaZlj0tZGzCzwea4tRuDfcIjR6RkbTnszCLwUSUfr6W9bLG9N9G2zMeHTZVlDyKlE/7MZ/zWQFwLpNlf+pN5yZNzSro9bEu18hWKUxvslfQct/Uiy+7+nujh08V9epU1XoI0E3137sqwlY/joAb/EZmf/PHCMmV+xdR8NUsha6uhSqBKoEqgSmAHSKCC4w6Y5DrEKoEqgdsrAZbjZdHtit7AVcc47eNmZJOWGVyks18wocO9jEAHfnASIlzMl0V80TTlmY/gRi/OY4SYpetzsbq4Ws4/7AJDzA8QCBntfrpskzhhz7Q0STSS+8zvbWbiC7DIIOSYTrAegaM9qiMjictUv/jIxjlWLoJO4jMk5tWyttMCkFf3OA5hUlnOsCTC+hhPwowE19RvtRl4TsBpJNkCVkbSRj6n6WWiUNO3Ujr7bh7KZoxy4GPfrC6d/jBH9mUQ89U+TT3ppHNgP+xvgl3pSfnODCWtjS4gxpPjtb8Ev1twzYgmzvtWHu1Y2mvOeZvZvhKaWWlGT4Tz08xVHqWhLAfo+xjvRYK3LZfxWd6CGzpX0kmQcsp6G7lt1l6iU0qkl5az6OZ4sq76VSVQJVAlUCWw7SVQwXHbT3EdYJVAlcDtloBaLhf5BfjaBXpZ+LsUT5xg0e9RGwKLWqEWTrzf4OiN1MC55kdNVyAiV/0lPgcI5KBR6kHrqGZp8cZCrAOP5k+NHtnlFjAB6KOt0uomCLg/b1N7JRglRJU+tmaseUYgaQkPAgplimkq/fr/2XsTKEmv8kzziyUj98rM2jeVqkqUCiEJCSEjkOhGSGY1xt3YWGN67GnAjdunx2Of4zY+Q5/2zLTH22Eaxm4jmzYY02fc3bYxM6w2O0gYIYRYLCShtUSVaq+syjX2Zd73u/8fGRGVFZWVWZUZkfHerIj//+/63eemFPHmd5dItMRXmkTRy37DCHdwsSC9YqFu1uM1+bEPVeTNQKANwHPq005RjKIJ/9DH4LFjP1inu7voMWN94Q2N4RnBp7iyEEUQo7wJ5vLMXh9vq9wl1YfCpWI0NmBFcYS+BT4cm4SLxiHYxY19SnEaRSJtQXAB6YbSVgpjek/DjqlMownuWSQzBPcMMn8UeOcCM46Lr3EGXGMByX55Xbg6R+alzbwwP64UiUnMr02PYIOffkxR9fWi/L2CdxF5PVv0O1CucF0sYqJ4/gHBQ5wvPKB9iEv8ccI9jSGH3kVABERABHqMgIRjjw24uisCIrC6BPg13L05+IIf9AC/2VMMIsG/sOOCb+MUKPi6D9EUJipSKPALfQKiq4xdMfsgqoJ4oNqJyuLLP4WAixNcgyhATfA09W8cxIYwaKfEtuAmo2gpU7SFstzZ1KeZ0hzG4UoZxDZZT5A4zIvyFEm4ekC6SyEIpMZ8KO6BV9bjjrnoPggTij70ycUTRA/SKBbdLsRTlPVjOuUwN72hd4x9Qx72LUz1JD8QQj4KtsACGWgNGbADzhh3rNx5MN2jPQ8rdFGOOArqRAUb31SZmc/sH+yLlJELSJwVSek7lO6zEWzE04f6i9EUT9xGtofyrCMOjGGyB5rFjjQEemuZ7oK4Ib5+6x2nsVEMM0f3FMBuKzgksS6TU3t5BEeCwhbHlPB4lnICU2IhFlM8UNLL8o19RHxUEcchCW80asGVnmOKczYS/R45RDyBI4v4zqrgHnJ4VFSTV603ERABERCBHiAg4dgDg6wuioAIrB0BFwh4izeR4Rd/Chx3d8Es/0oPMeKeRaTxizm+wrvBnM5KVxKze7prN5ZnQcgTXimQmJtvFDUIzJ+k0Exiemsf6i4jL4Uf64Lg4BpKX7HHZ5QP5xVCVOCZ1VBDUWRQReKIP1zx7FMavRH3gLoYDC2HptlwFFiEkbS5PgUWUcG6kMk9qKwkElE8l3IQu6mODGawOypth0jxtAbhGAlG97ihNlrDvjJQAJGEi0yPw1NUf2DkBqFvZEHhyrbxL3QEeb2TYZwgxth3iiZ66QYHDYIWnjuKNOyySpnqXsk+HAXCELoR3SM1MoocqMXcHBemIUvjO1oKfcUVWX182A7UnIt3rwrP/jvBzBSM+MMAd5Kl2E5C1HIdI4Ug2+PuqUV4EUvFMuIwhl4pEhA4pnx0i1Cxj3sUwzROh+ajF0vhDwa4Z37n4HfwxCLC/+Dg9ehNBERABESglwhIOPbSaKuvIiACa0KAnjJ+Afev4a4EKHHiQFkRxBs9hPy2Tn9fEiIu1houiqJ1aExNwVNUg5fIq3IhiCrw4AKFpVGWUx8r3i7qhLZgHbFgoMLgpjrchManfkIksS23BIYFkUjhGGykSHJRgSgXMHFetkXhBcFRqfAeblT0IXjrcDwH12WybuTj4fS4oE60AjHG1uhbdQGGPqQhwkaH+m0CZzWmIIbcG0ehyFdsJ/uIVzJJTxlJROKRlvu/kO5WU+FQ/Hmn2BDuKWRpD670slE8h/iQ7lnRV5rpQjIagCHw3r5zh92w8Ro7fuq0TZ46gmM9is6PnmB6QL198PbNfXg0CuxkPbyyXo5PuKBV3rMIL+gLvaiUdCkKROx8muI5nugzBRs9iCEjy6NGtJGipxG8KL7TFI4IPGakinMZ+1A2k8z4pkFFPFP8JaFegwj2rMEecODUZE5BdYHNjG4x8/A+Ch6PGPCMY32qrWePY+LMuoqACIiACKxnAhKO63l01TcREIEOIECphS/9UAnxd21+F6+5dy+SPhSJLmr4JR6qjB4kCAZE1YN/cUdB1gGZFjw/3PiGgQqEuoIBGbgbqws6VODRLIS7oAH4QFGKaZosD7HCKY4u7FgcApXpnAZJ64LRaA+eOsq1NIUOf9AmPVZBVNLbhTMAOTUWAhGaBCESaazPG2Yky0FcJbnujmsG+2xwYAxnMo6hrqLtGC/Z+CjW5UGM0QNJz1t8dQGJskE4kmkI8Zo8inMPcYLb7oZ4NEUjAznT5mSKDMkTP7zC6JgVzXUPb5SXfd66e78Njt1gm3CG5OTxJ62Qm4YtLFu0QilrhULOxZuLQQpHalK0x7r9BrVTDAYBz6hgGzUnRwmOYYhGckGfKZbx4zZUwrix3mAT8qPdCscPAryE+j3Na6kFG8A57m+9fZrBgWFmtO1jRCa4obeS3maX8pxDTZvddk9GFnho+fvKF9qOveehLs+qNxEQAREQgR4gIOHYA4OsLoqACKwdAXwtxw+/tQepwC/y+O7tX77dKn6Rh9fH0/EF3r/EVyHoOMcxfH9HUhAHzO9f+yMBUKXXjN67yKPkXi62g3ooErxFb4yqA9nYFH5YnHVWsMaPoQZvIeULirkgoS0uDBnLey/FPHzhCeZBQ3jghj6pPogdJKX6Qz30NCbh0UxTAPq1D/f91tc/YAMDI9gEZwjCCp6xzLBt2rTPBod3WyF70rbkH0X6WfekpSgc4X3jRjn0yuENOoWiCj2AAHMQtAb2sU/0vgWRtJDmBiKdmTkOvGV+djSdJnP+ww8EFLkzQ6gjXBlfofBFP4a37bVceq/t2rzB9h28Fesj56xUyVsue9bms6dtbgav+SkIyKwVS3mI6JKV4fErR15X2stNiYLYgj1oDzV7cJtoNpNhT6UcxsWfOI6w039NKNzYDzgh8Q8vjD8EfZieCwaooFgoou5wliftJ5syxGeChVga9blHGHlT4BqmJAcObI//GPi7FAc071z8ivtYrNc7EGfUVQREQAREYF0TkHBc18OrzomACHQEAQoSqoTwDZxf/cNsTcQFDxi+0EO8VSgmOKUTwaeQ+h3yIL+LJ9TD/JB+XhW9V1UKN4hHfs/3ki5++EWfhfGGG4oqnx4ZqwLmQTvUi2UsYuQOoJ4EkVnFDq4sU6PnizoCNnpVeHcxweR4WipaqMfx3sUdhB7WKg4Njdn4+DbbsGGLjY1ttZEN2+FZnMA0TIhG/JThnSxi054SRE2+UEP+bTaxmSL1ebOZY+6RdOFJ4QhhGmykTRSuFDVB2Pg6SNhEW2lnENkhDY+McRaMYRp/ADFw53P8onCkSHOhxvhITHI94cAWG6zttdrcAGwFNxezGxGfsQ2De2x8K6aZYj1puThjszNH7PTpH9nk5FGbn52ESCvgjMmyT9st10rgRU8ueMOzh6ZcvLErYZ0pjMR4wiS3y62HXRTivrKSfUdeepRd0HLcoKmRm28+9ZQCuAKu/KOAs3BO9DD7b52PF2cLo2T4HUBe3xDIOTCSLwpbMsAt4/mLVf+2wN+D6MW/FiiIgAiIgAj0DIH6R0HP9FgdFQEREIFVJEBxF3tv/Gu2e3LwxRvf0CFVPPArPT1TVWxoUqAQzASRloAg5NRAfpvnl3V+2a9A0FDUMI5f6pEAwYZpn6jRq6YIgTLwPIhgmQR22QzrHr2YCwwXFV6eGfDCW/AyBtGUKIW4UC/FTBAJSWyawjV2Pi0VioJtck3i0OAG27R1t23bug8CcLeNjm6H4BtxbxeFRy2RRrsUjEHE+JrICoQv4tinvsEhS41ss2wxh3V6Z3GeI6bFYqdQilF6xmKvGm7QZnjRbu8z6gh9iPoSkAV1xG7gObbfFbw/ByHVJBSjfGH6LcYE4raCabRDe19idgR2ztJ7R+Eeqk6AcxX9CuPCY1AGbHTjRhvbfL0dhIyfmzlqJ048aUeOPI51kce8n1V4Ivn7QHvcJu8DXbiol/ccSwpCjjvZUNhx4xrcx+KYIpAb4/T1ZawKbyinq/IPDsjF3lqlBKEK6Pwjg/81gUKPVqNKD94O2o9EJu1AFPJDkGIXXvYbstZ/94iWibygBGzmHeyjeYhllYxREAEREAERWP8EJBzX/xirhyIgAmtKgEIPX7L9i3Z0jZ/h1SnCw1cswqNUKGHKZxoeQHwZpzihSoi/krtopEgJgsMlDzUCt7+kiEqWsBFOEH78Jk9PFgOroHcpfLuH6GF9MIS2hEjeIM5FCvMzM8qwQFQHj8zw9YNeiOIUtkLccn3dxJatmGq62zZv22fjG3fbwOA4dvscxQsb3EBEVTiNEgKGwtCFIq4Uar5BDa90eaJJzgYtFqqW2TpsQ2O77PSZYzbWVwrrINmW2+CuNb8PCot2h1ewml1xeeR99I7QM0lOocMeFQvIcEX7EFxMpihybm4jvKFwtfZt2GT9W/ZbcWQzRNSMCysKJ5/WirbYDwp7luMa0DTaSyb6MR6DPn5DY4N21SAYbb3OZs49b8ePP2Enjj5jc7NTLvTI2KcCY6A4BPSkJrFrq3uOAYb9olD1MaN9iOGGNrS9ijLcTInTX6nl/A8FiOcmO/z9Yb0sQe3I3hGViz7Ee0A7NNzXpHoW3OO5WKpgLAoQo0G0Mruvr+UfIzBeXqfXjUJed6hO7yIgAiIgAuufgITj+h9j9VAERGCNCVAAuArgd3Z+gccP/2VzRXjYirZlZBACbMxGx0ctg7MMg8eJIs1LRl/6URSPlBPcUdW//7v3J8S78vBG0AbbYfD8vAZhEifX05HkJtEm3ITmPIalEWApIiEdUT02lOkbtCQ8cInh7VYb2GoDI1tsCKJqcGQT7N4AHZKwEoQwvV8FiGF6ylibCyyfKsl2UCdfFC0UHu68wto8cEhiGuvGbbtt9uyMHZt83HZtSNoAvGr0MgYIuEAwxcd0kA9tdsEbjKfRzQFF6yFqFwVC+3yGh883e8F9vP6RU0qTwxutf+dBS22+xuZnskEjoX9uN3nRdvKH4gteOApg7y08dhRw4IbdTfshHIdGttrGTXtty9YX2P59p6w8+7zZ/GGrzk9CjBUi8OzLgrGoldQp/VzgeTt49oGCrf67wf67GSGe4xQ80rSDCbQtusWFeVmdR+EtlEUeCk3EMm8ZwnF+Fhv9zM1bCf2ZyxVsjGdEQtRSnHITHQ/MTLWrIAIiIAIi0DMEJBx7ZqjVUREQgbUhQI8gv9LzC3v4ts77PL6gT8/nLYOdNF967S67etcmmxgbda8TNVDQQcxJ4RbK8mt6SMOXeE9BOsVByBy+x7MIAsWZCyt/iiJx8TP4kD+U90Q2wQaiNqM4RNJe5isnh6yYGrZS3yYrD+6wGtYjljI7LF/mmYFV7ORZxjUSipy+SZEI0UEd5d4x3PgPn/kDAULh5QKS01hhawnl6XnNZAZs98Eb7MgPsna2Mmlp1DXoR1QEhixIoUYBuSCe+Bz6UO9E1OW6V4zPtCMWOylaEtpGhUjimkYcawFPanVog2W27INo3I8+DyN+3vuDbO7x49mWRO6MEcl6GEJ9jMcDMrD/KOD2JlMjNrH1oO286nobYL9yR6wvd8wGSmesvzqHTW6KrAIBhcK/+mPclo9TeIvaorhEdry5xxL39DTy941GsHm+eXncUGZ6nMdTKCMZWSns48D7QrZgzx09bacmZy2P6dOZfMnSQ/RsojzL8I2V+nP4PYvL6yoCIiACIrB+CUg4rt+xVc9EQAQ6hAC/svOrOb/kU+zwK/1UNg/vXMX2bBu3A3u22u6tE1izFta6VbFhDDc9gTbyQJEVKwCviV/aGRN/38cjRYErCMQz1WUDb/i65IBC3Do1PWgVeBnnMjttJnOV5RJbbb486N7EcpbHhURTIik8aCMVDAVLbBivtNHthIXM5unBqJAMOsyGOvL5ouUwZXfrjm0QP7fZySe/b2cKp20bjs4YgMrhWscwhTcS4uDg4hFNcLdVNhOLSfbb66V3lrzYGPUU2nH7+IwS9Fbymeswy+hzcWDYBrZfgymq+6yEKaeF+XnkC4KRs0YZyJb9YJ2+WRG7E201y+mkLuL4FsNn/XiVsFsq17LmUhusf+jF2DjooI0mjtnm0o+sv4iNdEpzsAMCknVHIZBim6xtoU7PgTc/EoTtRPehWMjp2eNyUUWh7+HB+VBwNwYmTYzYxpEBe+b503bk5JR7xjNY19qPTYHYb+87svF3LmqpsQbdi4AIiIAIrFMCEo7rdGDVLREQgc4gEHagpDgJ9nD3zNkspqjO52xidMh2bh73swqPTWINHV1eCEEURJ4cfLun2HEREglGzxS98Yt7mCpJ8YAX8i98nYeMCRoh0hDRQ6it/l6vD5npraqlsE5vcMJqG/dhfd91VkiMWL6awXmFEFCc1sjOcG0dJzji1jUjKqEo4fRUNsbli2424nDr6xn57HFRXq7X4w6hbj9MY708iqKAhnbt3m3DA4N24tDjdmzqiG0yTJkcwEY5yOdHcsBW33kWz+wvO4r3YD/q4b3HRe27UWjf1wLCiGAr2sQ9J9QWUxmbS07YxL4bbGBiuxXQgRLXneJYEFaGakIVXgcfGIcrF5HS+8k1gGyUHjxEU5Txkb2vgRXvaaH/4QDlilifOFXJWK7vajub2WWD5UOWyf7QErNHrYZzIbn2MIgylMQ/thWeUdGid2yMjYaWcBce0T5j+FxPj/LRHtoXhzin14B6xkeGMOYVm5yac/E42N/HYQ8hZIqL6ioCIiACItADBCQce2CQ1UUREIG1IxCvf+vD8RZUFNNzeTuDKYAjQ/3wOMGjhbgcvpjzKA7/Yh+ZGn+dj4WGe9Jijxe+tFMfBDGx0DfqAUoEfqengnENgbvw7JFesP5M0UPhANNYKgmPUt/Gqy2186VmY9dAMA5YybDJje+jEjxNLoSQO27Fa42NxZU2NDyGZ/TRRRUSuVayyg1z2DIy0kPH/BUKr6hsBWJldi5nI+MTdu0td9jUmRN2+vDTNjl91DZmirZxGBvRwDsbhE/0TsGL8uTk/Y66xr6xgVgi+U61PkcTDlXYMlus2jw8gH2brrar9h7Eur4kvJ7YAAgC2Hdv9VZgJ9i7QEYjbjf6hJ6gerTL/rFOto3m2Jbvghq17QUQF9ZmerbQZ0ApYJfZUq3f5jPX2uCu/Zaeecaqx79txTPP4sQOrH9kIDxU74FXF9txPbQHXNlpZoMtvAlUGopEhWk787FCXijeeWW8c8O9M/QaEhDOSRsezNg81uPO4Hd3dHQAOUL+UIidVBABERABEegFAhKOvTDK6qMIiMCaEeCX8HSqD0LE7Ow01u2dy1JqYC1fH3awrGLaZw5ePO48CgEAKzktNRy9AfmBb/QUK/ReufDiA58bvqvzCz+9dvzyH8QCa2EIMX6HWz7FKYzzwAgkJNP9Nrh5j/Vvf5FVNl5jlaHtVkmP+GYv3PCFnkAKYGb3llCG01TZNq3mZj2eJ9QaCSzPHMp4ftYD3x7rotCiwPF6eYnqhniknZymS89lsVRyThs2bcMGPKOWn90LkX3aTsydtVR21jLY4XUIn2JwhOEeEg7uyEbx4wbSjAheGV7EItqk57SIgwkT/aM2gLo3jW+xFM6YRGuYSurnkMCbyfMWaTNHi6IUNpIFbXfhFvpQxfmNYborOxny+A63aJdjSnmLC17MjyvGrlrlkRvMjvrYY4LFsR45iPTU8DWW3DVqNor1lcf/0XKTR6xSyAYuyMZAu+oBHfbNfRjvDTEFdUYgfE0r0xgdGvK78IaaoqqcEX/RELhE0rPiyo2aMNJeXSGft2lsFDQEIcl4P1vUS+hNBERABESgFwhIOPbCKKuPIiACa0aAX+b5hfu5Q8/bM88cxiYyCRvsz2DjlyKEBAUTBVj4Au/CBN/5ubunSyhMgfRpoRQ+8Gi5qGzsCTVHSGoSFp4FGsAdinigHogFFfWaZ0Zl6f5+G9q008b33GJ9u2+xxOhOq2bC7qgu7mCdSwm0EQrxSvXDCKZRWFFM4YlX//ESUWrIE9VSj+M01tDnYEyoJwhRquK0r2WEekSd5QIkHjYQGoJwHBgcsZGJrVbKzUNMYdfPfNbmIbxni1iHWMbaQIg++O9cmFP7eKCwgshJQLxbX7/vDJsZ2GAbhkYsyWND+oeg2QbQD2yMg7ZoF71svsMqQSVxTiPq8H7SXLfdLyThG8xQWCUhYhk4LdWvng/jG1D5WMMwdiniQF8lqYYfDm6Vrl1sRFQb2YepwlstNQRB2/+oZQ9/x+bPHLUKdp4NA8lKfFCQH1G89/ZoUWgzSuWTpzE+usFtbAjIxxmRzUvjlyV4HENunxaMkpxiXSiWLMfdVrPzNn7VObua6zG9FMZKQQREQAREYN0TkHBc90OsDoqACKw1gQq8SbMFSpqMjWDn1L5MGt68sBaQ3+v9uz2UC7/Dhy/6PLydViOGig9qw8VXtAGMZ2RyiEVe5vOI8G3fK2SclwpxnoXiLuRNZ4asf/NVNrTnxTa07zZLj1/tnk8/QgPtuXige4x1se5YnOCWG8K48PP2Y/EI4QOj3SOHIi6QaDdeHkdVgnj2C//Cizesg3GsE08UaTybkNegwSDa4JEt5CB0sUHLMNbdpcbHEIedafMFvPJYE4njI/CqlvLoMjyGEUt69Fw0QogmIRrT2LG1LzNsg8MjePW75ikU8paHC5LrS32nVjeJ4osMkAWCyT2KsJ/i0b2I3n96Idl34OG44Cblx6RQlOGfM0MZ2oIozmSlwE7Gu7l6z5mJLxaBjWyQ9yhTg8c3vflGG8xgJ1scU1JOf8/yON+yUppHCeQjNF68LG9D2fDOeLTrdoS8jWLQs8JAWB/GGQaynjRL8YZ24Ca2hx1IIyo1CCGPNahzxQR2BU7hDxMUjPwdURABERABEegFAhKOvTDK6qMIiMCaEeC6wQ1b99oLb/tx27b/enjOhiE+XIXwa7p/86/CBefCiY+cAhr9JOuCMIgvVyP8Tu8CA5khalwE4Is9hQG9VkHMUEZA8MDTBvmAvrN+Vo4X8uUwRTYxst36tt9oqQ37/JD5CuK8OaTzh1NUK65+Ajo3OZjt1iHZhVCYBsr6MbWTag7lg7cyNMdyXh+3LUUZvihoGCjCoLlCPC7sQjLqS3yWJfMxE/tHLrkcvYoFX4+ZwbrRoXGsT0xPwDEYBEyoGjxoIAO8h2yCoVbFrqZYP1ksFW1udt7tZ3wCeXzqKGx1g3y9JftEm1CabeOBfWHfqtEaST6z8nDESSQkKUAhpigmKTQpvpgNtFA4sgUAKNooFj0N7ZCDt4e6fW0lHrDprqVHdtimm37KNu6/xconHrFa9qSNcG4uAzsGI3xnVzwkIJDrgfbi5UdzRFk5Dk6F5aLA7lEYclDYN+YJgZsP4R4ZfEyIl5nxO8ENjLa94AYbHN0c5dVFBERABESgFwhIOPbCKKuPIiACq07AhQ6+uCexK+e2XXvszjff454z/zIeaZpGo1zYIWKRJM8Wp/MhrnshdxBWTPNdQ/3LP2uKRQBTGCBX8MX/uZNZe/pEzmaKEC60EQIhbhePLmn4THnDdukpo8iLN/ChWKxiE5lw9iHTUd6FGsQTxCPL8FB5v7JNn5saPJIuvJiO/F43hUiknmgtbaFQwZ3rFBc2tI6Cst6fIJBKKFipFDCFEtlDxpCDegflAwukNQS3CdVRIkFXs4eu6dieF2bHWZ5CCvcUftwxlXKwvkaTCa4avbQzDUKVdSIn10Wyf6jHs5IXhDVi8Yz+emTUBprDCLiFbhu41rh1LIJPQYUdFfBLDG6z0Rdusz2bB+zGvRtcaIbfA4cX8js3v0U77AjbCHWF2KW/N5ZqrYN1ZzDNOTOAab4OMbS19NqVUwREQAREoBsJSDh246jJZhEQgY4nwC/X8RdurtEb3TC+JJv5FbzxS3u7Qo1f1+MyjXEsy/ggbyjQqnbqXN6OzRQwdRb/+8cUWnq3XLB4RgobvCB6fFMYCBZf20dnIeNRF4VnmJKKOPfMRWnu+aJoZDoEXWQI9RWKom00gHivh3VFLxdoTOM/iJwUhCNPwODczoRPhUR80FWI5L45kacOdbrJiGP9ePd8vOUzeuvpISXKS1uoD5nOwgguRv0e5RHP8kxzHYybuO0gdFk3+SAd9fg0VORlnXRCMlRRgAKR5Sn8aAvLJriBUIUb7rAPyMeWmYdtIY2V+PEi7AfifaMf7xjyuX19li3W7Pkps6FTZXvRvk02NMCPcE/EtfEO1XlMc1wUteglzs9E1sjnhZoZ2xKCUbAVOaP7lhx6FAEREAERWGcEJBzX2YCqOyIgAp1BgCKIIiMWj0v9ct32y3pL1xbL2xjHexcAeCuUa3bibN6e+NGUTUI4VqM1dZSDLg4hbuiho8Cjh5Fr+zwNm/lQcLIvQTDinnkp4LhLKu+ZH22EcpziinY9DrkoPpFQrZVDXs/HOOTB2kVsB4P6sfcojA1ikh45aBG8KMAYarA95gei8OShsCeES1A4EFgoiFT8hJL1/kfZ/AIRjBrxLyYV2mAURSyFcdilNKQHDyG5xB5TXJHkXkiwcYHr4ik0QoFYRh0UfugQoCCewhflOcWY6zcjnWwVqk3Yk4RoTCA/WdGDR8lLHu6ddlu55jMwns2W7IcYwy0bR2xnX9oyXHy4SFg8dpGMUVRr/tbnC5Zs/T2/YEYliIAIiIAIdDsBCcduH0HZLwIi0LEEYtEYX1fbUApCCoBcqWbHJnP2xOFpe/4EXFbJPktho5kgDINYotCjYHPBh3uWpSisH8eBuJCGeNxwLSbTQhxFZJhSWSqWXWRxemUVawqZLwhHlg91BqEKgYWdUDmllFNOLVnCJjVpm8dGNX0QR5xHSm6xgAl3ru5cj1FYMcQewboQpOKsi0JmCM9xPV6o9Y15GGCGizxc2C/WXcaRIPN57CZawPrIIs93ZP+RzvyolPX6pjowyKfZ4tyVZAoveBcZz6muvksrNsVJQShyXWMaLwpcppF5goxw7/WwXdRJQZ1EWxSgKRe0IZ4C9uxMzh55ZtJKENRXbR2xgQyVaRCbuFn1sFa/36veUTUoAiIgAj1OQMKxx38B1H0REIH1ScC9negar5OzBXvm2JwdOT1vuXzZ+gcxRRXx0CVB2MHjxXMmqYa4NtE9ixSCsUhCvIs9ZMCt39dFY5SvhA1TihBYeexSWsKuLmW8fL0j6gg7tdKzyPbYCOMgPP3MREw+RVwpUbKp2ZqdPDdv2Sp2PIUQcq8d7+gFpDZCOd8YiLZCTNWFJZQW7eJbEDF8ovwKV97FAfrMmSwmdsgqDuGWYrjiNs1ArM1ng0AuQQCm0hDffWXXmbFw5DVs6gPRi41q0nhmO0ke74FXBmX6+lPWj111awmURxodkzgJEm98T1kZ/QorOCktYSueqYOZj7YzkmNx6NgMlGUf6uqzXVj36Jv7IG2xfsV90lUEREAEREAEVkJAwnEl9FRWBERABDqUgAsfCI0iFOHkTNFOTBUjT2DZp0RWcO4g/GJBzHG+JMQYBVyTlxFChPW41kM/Y08h5SMFiosair4yPHIQpPPzWcvNz1kRB9ZXIByr1RK8dEUrwWvHyaNhN1jUAzcbRWUFSnIIYiqJtZY8pmL+TM5OHjGbnco7VYotD5Fw9GmoXFDYGCCsGGgTxaKLq3DH6MUDO0UR5vn9JiofsqOWIC7xWMHZipOnJi03ddaq84iH3ZzSmkhn8Eq7k5J1xbvAcvYqa+R6xUrkNaX3FGeBWG5g2IYgit0T6edD0tOINHYJV3o5k6gb8hSsaUUC3kn0DelcU0mh6WMBfhhFOw4v8saxrI0NJW3DCMQ2+hX6H/qEogoiIAIiIAIicNkISDheNpSqSAREQAQ6g4BLKGgHCsFT53J2ZrrgwoTaqgzPVgmvKjxiLlFiLyBM92mpVCYIfKdQ9HtcKfK4ptElGqKDiIRoLJVxxiKPt8ja7PQZmz57HAfEn0MGCNXCjGXnzkBMTkNYJXCGIj2JaAfeyTI8jvSi7Z3YYrtwrEN/qs9GTiVsvDzs5y0GC4IAohW+uynsqLnrEd1x25BCAetmUnVF028RQT1Jces18I1CDFcKTO6ASo8nhZ+LOhrFHrOPLAFbo0qtD3VNZHN2YCZreezeynYpYLlbLtvz4OKVXkaIPxqD+hM8moR1gVkRqu9s/7id3bgfUWOWgZcwk2Ff8EJ+2hK2zMEFApHdoa1JeDbppWX1PJrF82Ps+MeADNdHYvOg42fmbGzQ7LrBDKbEBhYsqyACIiACIiACl5uAhOPlJqr6REAERGCNCYRpqJRIZicgHE/B28gNY6gJS/BWJSE8ahAe1BfUOS5eoivLhLhwZSVhCidvKIwgiHiLekoQPIViyWZz2KV16oydefYf7Mlvf8aGhoYgEjOueNIQM33wtnEKbD4bdjrlWkCK2n5UdHCobLcPFWwolbF8DuLuBKbNIr4KgcQFgJRUFHJJGBXEFMShGxjsYg7WlYaIo3AroVNcB8hOUDZyAx6KwyAig1BjGsWa72JKsUehyAAby67UUMTpUMA5JdsJgez18ZEg+Q8ilkd0BBEboiv0DiJLbWbW+s9OWgZe08RAvx3PTNjXOCW474CVBnGWJ4QjlpnCE4kXBSS8vjUK30j1eauoiJ7GcA8GyMWzHQvw8JIvheL0XMlOn83Zvh0lG0oG5qjEWaFmBREQAREQARG4bAQkHC8bSlUkAiIgAmtPIIgqqBrIjem5vJ3DDqrc2CUBj14C0yMx8xJeQk63hMcNoonBhQ7FGPUWnllHeDXcezyf8aKgg3gqYSObbDZvMyeesRNPPWBHnn4IojFt+665yva/4IBt3rzFBgYHra8PIgdt8ZVO89gJKCYIpAye90Bkjg4N2gAOtR+EjYznmkgXZpy7SdnkyolJFET0qlFo0UaYjDzM5vVSyDEd+Tnl1tdRYrOeJNpMUVjih+UY75v2sC+shE1EddOzyuboIWWCt8X8+CEvik1OoaUXsIKGufss7zn91AUo60mlbe70GZv58pet8Mj3LDNzBsK2YMO7pmy2UgztO0jaiWYo4mkzRChqhEDkoSNuFupGfazfxSXXhWKKLzY7Csee8Fqzs3MFOzk5b/t3QTiy8yzgvcBFQQREQAREQAQuEwEJx8sEUtWIgAiIQCcRoHQ4cXYeG+MUrYiFdxnoiSSmNtIPxzWJSYg0Hg3hwogaCa4t7pYK5eFx7lTDG9MrLl5YI8WNOwJ9qmkBwnF+8nk7/vh9duTJB7iMz15w8Dq7+ZaX2oFrD9jo2AZ4xTgtMwNhmLF+HhqP6aEUX2m423i+ZQ0iy6d44mgJeg0ZKAYp9BhoGkO8UQ4sahK4FG9J9INTWesBzxRPVbrnkJ/9jkUy+8hptszvu6RCrJFDui8IS0pETmVly74LLDtMOeeMsHENpqim0Qeef1mBwKVnlOIuRftRB+tiqwMQ1LWr9tjkX/43K33zS2BeMJ6cEdpHKxFHnizigpbNUMfDrmrkDWY8013DogBt46ZD3IWWx6n4GCF+ajZvz5+es2t2b0QOVqQgAiIgAiIgApefgITj5WeqGkVABERgTQlQ/nFn0+OTWZuex/EYtbTv1gnFBm8YhA3Symns4onpoRSGnILqagwFKZAYRVXCNHoWqZkYFfIyHp4v1JGfOWknn/y6Tf7oO5iOWrWD17/YXnrrj9n+/fttDKLR3WgoSR1XRl1c91ehiKV3jvEUfXix8hrXPXKSKtrztZUu5IIAdGEFw3wtIAShC0FY5cKQ3jX0iy96EVnedzml2kJ7npfeVbTj6bQDfaHwS0A0pvGc4lEZiKMhToIZcJQI7aIepcikmKVd7APPcKQHkMdlUIqn4NHsQ0HuokqPYHZmxqeUjhx8oU3dcL0Vv/uAJbAOtILyKOL8ggwMjIkAg4EENowQIkK/WAbRZMCxKKN9RHm/uIEONxoqwIM8OY1dX7EIcwA7tjaJ6FCj3kVABERABERgxQQkHFeMUBWIgAiIQGcQcGEHUygspjB98Sy8jVlMU6V3L+nTLSFAMF2V6/IoQigAKeAw99SnRlJR8TnEhWuYGkpxB8HSkMbdRmeP/sBOPPEAzjqct2uuv8Guv/Eme9GLXoSpqWiDAoueRRwZwVCiAILY7KMSg3jktE5O8+zD7q59FE0UchBfbMPXaCJfJKNwhY2wN55GynWBrKYGEey2Mie8ilR9tJeJvHIDm1hEkY1LRiRzF1eK0ATSwxRalqHHlVcIQopEGu1TSGkb6kNcMokybidsRVwf2scOOy4Y097nhM2XivDyzlsJQrGKcx9LOHKkSjvYOtJ4ZqP3EjwhN72P3PSGU4c5buwHPZjeJUYQvT8AG6epYn2q50GdXK9axbRVxs3lytgIac52bNpg/RDdCiIgAiIgAiJwuQlIOF5uoqpPBERABNaYQBEC7dDRaaxxhHiB44xnAyYggqhLOEW1Wi242Kj2MR7CBAnQQbjihvd+gVhCmQrECXVM0DD0k0HLQBRW83N29qlvW2X+rI1t2WIbRkchTsv2o+ee9p1bmT8FwcrNXlAcO6eO2bbxMT//sAZBWcU0VbYJCyGs+iBu8YIYYpt0sSVciPEe6zNZAd4oNLlJjm9TQ48i/lXpiaTYg/hEbs/H7HHgpjxuQFBgfp/glF0u9qSRPAoE9rANimnIM7woInllACDaCKYszPWLJeRn814eyWk+w/YZHEkyiY2CysPD8MCmsBaxiKmlwXNJ69LYSIjeRHpFWdY9oKiIQjEW/ey+U65iKmoKtlObsgPIQ7HraypdaEMcIi9t5isL4fij4zO2acOwhCOGRkEEREAERODyE5BwvPxMVaMIiIAIrDqBWHiwYW5+88SRaZuZL/k0VQoUehwpkLjDaRLipwJhVMZGLekEpqvih+KE4onSiVLFPXR4oD5iYLx7wih4ILqK547ZPHZSLeWylsbuoacf/4GdffYpn5LKMwYps+AMo1yznX399os7ttv20REIrqTlvTJ4BF2hUthBqKEh+PPgAYRWckHHqaScYgrBhPgyjIPcxPOCTT511PNi3SbifcZt3AmUo+08d9ED4umHowzkzqcJ6Dl6UZlKcYvWPM3FHOLcs4grG6RQhTrjE9YfBpEZuEAEUjQyAZ7cfBbnWMLTeO4FB230ta+1geEx1EP3aBC12E4ocHTDQg1uJG32wL5inDwddnkfODroHBqhcKSQT2OzITLj7rHx9Ncs1psePj5nN+yP+ov6OIbugY1q10UEREAEREAEVkJAwnEl9FRWBERABDqMANe7nZrK2TlMU8UsVRccnOrp3jToD27sQgFZLGGzFtzDseeBWgWaxIUNI1iGmsxFC8QMg2s0xuGmUMhiGmwBZwpiSursnJ2ew/RM5CkgzcVYdE81OgKhM1IqWQmNca0jp62GQHFH/1pomE/BkYi4hjyUtRRIPPcxbIQDEeV2xKKQHsJgKQUo++IhspURLgwjgcbWmSdsVIO6cM8prYyjPQzhnhNMQ70eiTfmjcszLggzlGUbENSD8HCeOQvRfs0BG9i521LYMZail91J0Pvp9eEZ+VmP1+WJiPA4KER6JSlu3evJG7SLumt0H+Oeu9R6HPuMiirIzHGfms9jTWvBxob7kUfTVTk+CiIgAiIgApePgITj5WOpmkRABERgzQlk8/A8nZi1XAniA4ICOsNFC4UVj6qAuxFePexmWsN0VayZYzyDC0vPzKcQx8JhGmWICkdCUNRh7V4pD48XT1yE4ITnq4ybEr2Yfg3isVpjOryb8I4ViwWbwYs+N7ZFMdgYPIbt+Q8FWhBsrN9feKP5KQglX1qIeIrHOB/rYo0uKPnAwPpYDrexMGRdfGZwbx0eXLcxAveQaR5IjvmYnyEuw/Zi8ch4J4xGeM2QMSOnzlr+zGnvQSrTZ2UIddaTLOM8Te87jYprZvuRxxd5EljzyY56e7EByEpPaNnXN8JrjKNLGBAV+ovpumVwyWFN5eRU1rZN4BxNCUdnpHenlxQAAEAASURBVDcREAEREIHLR0DC8fKxVE0iIAIisCYEYm8bG5/LQTienIWHCsILwoOHTFC1UKyluLsopk2mcDxGssA1eNhxNYOpkdxkhuLE1QpriRQLtY2/6AWLvXusq2rF3BwSWR/ycvqmV4Bpm8jPqaYsVi2jHP8hSwl10mPoXk1cKZW4NjGWZ0Ew4h1pLIz9c2ATH6KAvJyyGoRbqMvzIo5eTK+JEbjnDqgMXicaD35D+hKDsKONTGX1nKaKW09jLNvgEwO1HfNQPLv083y0IQR2mTuesgIvhXxsg1Nha/DoshquifTNbJArhThOt2UIJuKerkXYGO8G6yLSO8Y0VhBq5wY4nKaawlRjvpgURCjSkYdHphSR59S5rO3dWbbhQXgl2Q4yarqqo9CbCIiACIjACgngE0tBBERABESg2wlQuFBcTM8X7TSmqlLQhHV6uIE4caFCtQHh4mcWcp0jxGQJXjAXFq6SKDQgYihK+KIgYnHcu5bhlfEQjvnsvAsZisIQOM00fKQwii+u/2N+nm3I9YxpVMKzDFkGSRBR0dRUT+eRFhSEjKNQY5kgyFgXa2TtbAJVuqeNsRTGzBtaZAzrD2XZHqUc22EdUQ3OhjuZsn5O6AyvkBpq4jvaoZ0s64WRjhumBBuR4vWzjqTbQU+oBwCjF9bTsWkOxSkFXBprSml9VB3uowIOOLTnjZIxK6KoRGA93FHWxT/qS2PsfEdcH1TUDS8yx7oKtT05k7cc1jv6mLKs16A3ERABERABEVg5gfhjbuU1qQYREAEREIE1JTCPjVkmp7NWhMjwjWwoWNwiTn/ktisUHzgCAzt+8sgMyhMeKB88lkFc0bPm3kMoDpb2KayeM4gZrw/19mF9JO/pQeQHSV1AUtkhncHfvR7es7Z4/SO8dlRlCJRRXM/oNuDKtpnTc9c9h2yDcbhS2OJKD2YBN0XkYb3uB2zIT0HpayJDiq+7ZE7aGtJCf/HogSZTJDIE3yZFYpyHLSKeF2bElV5V77MXoQVBzIacyOb9QCQUchWK0g8z4REl7rhloaituE7GRIX9Qj5RRKWKTY64uywaTIJ7AudOhvWZzOkVsCEI4pSdmynYPHZYpXCNU5GoIAIiIAIiIAIrJiDhuGKEqkAEREAE1o4ABVccZiAcT0xikxqIE/ezQcS5houEHPPFnigKxyR2AvXpqlzrGM2/5BrBSNN4nOukoHGQEAQSj58YGxt1TxqnicZ1sn7mYfbGDxd6GfujOlg/Vz663EOci0aUoJ7iOj1eXfDgza94pnyLRRzjQojEGtLYYmgvlpwuO90rSfuZFpoPNdY9mUgMG+4gA5LopQ0YYk9kVCpc6msr2T5rCtNyQ93MQiFJUUpJzl1sCZC2sU/sdw2b2tTg3gxjxhoiu3xWKvsT4njxJiNxXcaA1qDQ0zi2pD9DbyO3IQqcQ17cowA3PZrPVyEe85bHzkheh7eiNxEQAREQARFYOYHGz/aV16YaREAEREAEVpUARVssHmezRTs7jamn3BTHpy9SpMAcKgj8397lGiIYF9bKpd2jWCoW66KIkosZXEB6TyDbWMaVT2jL64Gn0o/DiHq7IFLCHQUYm+aHDIVjBnZmKGQ9P9/xQoYgCoMIonBiM/Ri+lTbkAux/KGoYwGWpSwO3sMgCpGOaG/L47lS0XvCVrwIr7yptxfVw5b9h2ku1PgUBCRb9fKI55Xc+GKIbYrvWV2wBWyRiUeJMBe9hJ4Hl8zAAG4pLUM9jtTv434xJ9uiukQueBzJulIpOetUPE0VgFg9p9syj5fi3F+Uw55IdnY252tdGR88ubxTEAEREAEREIGVEeAnjYIIiIAIiECXEohFI6egzmJ94xSOY+BCwOAFDIKnqWsUNZAjaYgQ32QF+qTEIzWoZhB4pRSJ9IgLD0/wN5RFBorIEjZ64XER1EVRURdO4UMleADjeF4pl3gN+UMKLeFOraHtBSnmd5GIi9tmnAfYH3rAJwqn6Oo3TGEUbES9LEFxxuDxnj2KQD0UqBUoMO6uGnsBw5RYyjWkoQbm9j7ByFjoMY3SNFxDXlThedmObw7kO9YiDoLONSByp9D5cHYlMjG4uMTVobOC8HKxFzJ4HNc3MqQwxZhexRpUtVuGin0DoQAQaXxO2ulzeZzhmfcy3vFwp3cREAEREAERWBEBCccV4VNhERABEVhbAi54IBxymJo4A49joUQhFgvHoEmCmIKdVYgOd1UFsZaGEElxkxycEbiwlpG5oVcgdFzAUFS5RAoiinEUUOUSNnqBWKUP8WIfJN5+qNbrpjjji/KHXssgyKJ265ndv+i6h/WzHSah6SiwNIVSLArDc5gGizgXnmFNZSweOS2VwtdFIdLrAhDx8RRZCkLWG9UaN+Ys4vzNNtCmqE7kDlYjB72CMM41Id5YJsGzGNlfPjEtrp2dCP+iGFjDApDb3IiIwjHJY1TS4RiOMIZIZh3IxzYYKBwTEJanp3GOJ6arevveilcWMuldBERABERABJZJ4GKf98usVsVEQAREQARWg0CYqspjOAo2m8UpiRQSdOtBecTvddlABUVvlYsWeh3TlsZmKxSNZU6HpAsuEjF0k7kgwaY6voNn1BnWyWmYJZzJyCM6Fj5E6q1421F2v0STM71d5qeA81dDJlrrXlI3ga2E4O3hzfvpPfKYemp4oqikAGXfmLQgynhHwUck3D2VgR5GCkUGlmRfQz5egcj7jhhWjsC42PsY1836mByyUAyGOliP1+E1smpKZFzxRq8wb7xMKBgSkO4hzhhRZZYS1zdifFJYH5nuw/mb2F3Hs7EAMtDG0GdeIcjxPJetYHfdEv6IEHrJeAUREAEREAERWCmBhc/8ldak8iIgAiIgAmtCgMJgCtNUZ3GGo/vmIFaCqsA7bl2QwdvogpJTOKEjXIxAvWUgSJhexBEOLihjtYQamvVGEB/xDqjFAtZSQmjGAsrbu0DvaY17DKO6KYj4YmDblFa+RyvtQlx4DhvUxPUHOcZ0ysAgvuCPq9fDupIQuczPwDWSPAaD5Vgnemc8DMM9i54n8qB6Ou/Dj8s/Z4Q7CEjWE+QXblgnfvjOf54X7/wJ5zOGVMZjW9OQk312O2BbuYA4/CDKE/2e/YkeWT/TPCAfBqBSLnt5HqHCqa6hY7E8xSPyBu8t+w9LuNYRg84py1NzBffo+vhHteoiAiIgAiIgAsslwM9zBREQAREQgS4kQGHBF8PMbN7mIBaClILAoAihvgm6JNx7XioTly8uJOlxTGK3znKJ0gqZWYZVcg4k84ULI1ilB5auVCnDospZCCFOb7zzeAgaevtCqyiFSt35GZWJJ7tSNFF0UR/xTMQ0NZDnCfWzLsaE6bFBtsXmhbYX4lg/beeVwUVg9FzxNYJB8IVehdKs20Wf9ydYy7MuQx0RC9aFF0uQNT9EuflPYz3epHtvkYfKPdqoyHdada9jVAkuTYEFvTBjUSmFK/Knkn3WB+9wWMMI5hSjnjdk5iiEsyJhRVR+erZg0zPzYfyY7gPJehVEQAREQAREYHkEuGu4ggiIgAiIQJcRiIUAvUk8i3FqrojpqvBOuUiBpIFQSMCjSI1BkeFXvPljJDoYl4Q6o9exUOZUVW52A/8V012b0GvGwqzD78ItlFSYqgpR49yYOaTzkdNQg4D1RC9TpaLBP4qceGMZplKoufDBXRBtXlUkNDnNlPWGFAo0HoHBDXW8Htwz4GRDz0HBWaLYQhxu/eXHgOCBOo4GsC565hotpPhz4zzdH1wQMp4eR9rrRTxbaCvuLaslL9rpteKBdnAybBXnNvLBPX6wi15e7wkz8uUBvkqUZ7+4oQ/bqWEKsNcYMc3gCI4UxpXrHdkWa+fF87NG7qKL5wTP6kQFXA85h/Wu9EKHApENLKogAiIgAiIgAsskIOG4THAqJgIiIAJrTYDikaIkly/5tMRsHmf3wTPlwRVIEDzBL+aKhE4sBIoNSA8oFu7yybMBS4WUHxqfpLiBYgoCBTld0MTPeIjWPBZzs4hkTfzBFW/M6yEuHD0yveKCDoKH9fmPG1Jvh7EudlEbN7gpUOnBkD5UmqYYQnZuLlpANJP68EwhyXMkZyCoyoxDJsbToipcrYhCXRBleA7TWoOtbGthzWKIS6GNYBvLe9dcDEa1IQJQ2EEkwlLPS5u4HpIxbCsuj1s31qf18j7B1nHxzXFw4w9M8OqcnT8gnnWQJTNVsAERRW4fhH0Sapzex/AHg5CJx64wXxCmcTlcYdgcpi1P448JFKVsPdTrFXtTehMBERABERCBSyUg4XipxJRfBERABDqEAAUDhcF8rmjzmKZaKlctAzWFKIQgEoJgoA8ueBJd5jCSYiLK6burYodVbpLjYtTzwuvFbHU1iMeoXEhAIiuDDbxQwgQBgzoQF/IisiGEVkNpRvOZlXg1eOKVoQzBeBrPXN83gTmrExBNKWTOIe0kvHisewJeNYrEOdicRTx9dAl4TEfR9hjKUFTOoMw5pA/Ag7cJcQMQVCTh3XCLwzRU92DSI4i00A8wRbk8XhSuo1g3GHtMkcXzBVtjm0PJQDR4Bekd9HnCsAduXWCEAMQGRJEFkQ2sC/5MCEvvPKphveTIDYnKqCOFacRpeIW5U26NytnFa2DGVpHAdy/IYzo4nbWKaw5/RJiDx7FYKltfOuPMvFSUPRTSuwiIgAiIgAgsnQA/IxVEQAREQAS6lgAEThE7aBZ51IPLw7oAi0WIyyF/oGzijUsOXPARAEFDbxbXOlKTsI5Yi/gB88gfhBbjwz1rGBgY9Fpizx6nSDIwjzflT/FbSKOHjqFenz8xMhJgbBuJPK8wl8nYGYin6eg8RH5Y4YAJOw0xNQsBlYWAPIPrCXoW4WUdyvRZFm0zPYd4bPljZRxfcQaCaxoCkGsVKT4jE9grNhv6hCf6J/miJzKP+NNo9xg8hNNoh1NvaVf4YZkgMBHhQpMeTdZHcUnCDDXaDddoErYl+vCC+EyjPqg6b3ehNuaOrIougbtni85uZAKsg7c3zh0L80A7qgHZSJJjQI1JryNf5O1jR4MVREAEREAERGCZBORxXCY4FRMBERCBTiBAUZDFGY4FCBWKC04lpYihF80DLi44cA26hDceiVwow0g89kFkuZeMcRCTjA7v0Zo9FmEcEihCcPqjH3TP1lL0hsEQrgdk20n8uA1JroHk9FFONUXiQgUL4pENIX+QRBB3eOqHB25Tf7/lsGFPGWKL000pBIuoIwFP4xDsx2EgNo92KXivHsjYBsQNI+557PZ6FpUNQu0N4PiKfvQlTRFHGyHe3C7ko12x0GM/aR/Nm8XbFATfNOICgdAb9pEC0vuJFFSLHDEhJ++R7CZkG4YBQg9M/GxF7IgK3Yo2MZWY7PFygc3MDIxjoLLmPzwyJgUOaZ7dSNs40IyM7PSSuOdUY9bNwHtmY0aYa0WsfZ2bz9u2jSTDolF7/qQ3ERABERABEbg0AhKOl8ZLuUVABESgYwi4mIA1XOMIjQXRADEE0VOGDOPxFhQtFGWUN/RwQTqEK1UPHnlhoNhIQdwkKxBwEDwUPniDGgqlfAor7zHlkxuxVJAnm8tbFp5OrsOrQKBQ3JXg/fMjL1g37nk4CBVMrlSwPKZp5iDgsnCFcY0i1/8xUIyFxniltVhzWStbBuWr2LBnFh2bQ5vJVMpmMBW3hKmXZcxDzcH4HKoY4/mTtZJ7IwcS2HkU5aZRph9xGYinEkR1Dsdg5FBzXy2sNXSRiPZ5nqMzQD2MS/IoC3R8GDYXkTZXxhRgpGUN4o0BtlKkxSI3ttu7gjTazjpzyEHBNo+zNQ1jM5/HWsNKweZx5EkZizFTFYwDKiJX9wTy6mNF3vQW4kpIsCOBKbbMFzUfBgsKnXZTB9KzGAfawemsrLsIVvNoO4uXl3YjWQaFFERABERABERgGQQw+yb6NFlGYRURAREQARFYOwL83zdFwbmZvJ2ZzmK6KgQjpnnW4KmiEKFECO8XtpHlmY81VSA2uDOnT2GNirguafyYoCCEEJw+9qzNTZ2xmm/gEjyG8WYwrNPrw5W7hW7BVM1r4BmkFqLAbPzYCW3HNoSrCztMPZ2n1w4exyEYwQ1ysvSiwZYM7mkXfaz9qHMQLxdtEJdZtMy1gRnEpeFxzMLeJMRUP8phwihSQxu4+D2vcSCzGl6UzmWUK6KeFBoaqhNCThhcx+EiDD1Fpe5BxA37RrtK27bZ8DXXWK2IdYbPHrLK5BmrDo3YzKarLTU8gp1suVMqhCHqcAYUibCI9wy0wS2Bx9HjwkBEAjPkcfVIwAzxhcZA4Jcg5gf7U7ZtfMjGRvpDv93ekF3vIiACIiACInCpBCQcL5WY8ouACIhAhxCgSHGPFcUCQqQd6uJjOWbGdVy0bNTmRfNFGWJBtNT86yJfLNQaWLk0vAQYl5C1CRnHMS5LCUohL29jEyI9iIAIiIAIXCIBTVW9RGDKLgIiIAKdQiAWAvE1tisWDPFzJ1w70aYrziUWjk0NgcQlwLiErE2t8KGx7MU8z+cVVoQIiIAIiIAItBCQx7EFiB5FQAREQAREQAREQAREQAREQASaCXAnAAUREAEREAEREAEREAEREAEREAERuCABCccLolGCCIiACIiACIiACIiACIiACIgACUg46vdABERABERABERABERABERABESgLQEJx7Z4lCgCIiACIiACIiACIiACIiACIiDhqN8BERABERABERABERABERABERCBtgQkHNviUaIIiIAIiIAIiIAIiIAIiIAIiICEo34HREAEREAEREAEREAEREAEREAE2hKQcGyLR4kiIAIiIAIiIAIiIAIiIAIiIAISjvodEAEREAEREAEREAEREAEREAERaEtAwrEtHiWKgAiIgAiIgAiIgAiIgAiIgAhIOOp3QAREQAREQAREQAREQAREQAREoC0BCce2eJQoAiIgAiIgAiIgAiIgAiIgAiIg4ajfAREQAREQAREQAREQAREQAREQgbYEJBzb4lGiCIiACIiACIiACIiACIiACIiAhKN+B0RABERABERABERABERABERABNoSkHBsi0eJIiACIiACIiACIiACIiACIiACEo76HRABERABERABERABERABERABEWhLQMKxLR4lioAIiIAIiIAIiIAIiIAIiIAISDjqd0AEREAEREAEREAEREAEREAERKAtAQnHtniUKAIiIAIiIAIiIAIiIAIiIAIiIOGo3wEREAEREAEREAEREAEREAEREIG2BCQc2+JRogiIgAiIgAiIgAiIgAiIgAiIgISjfgdEQAREQAREQAREQAREQAREQATaEpBwbItHiSIgAiIgAiIgAiIgAiIgAiIgAhKO+h0QAREQAREQAREQAREQAREQARFoS0DCsS0eJYqACIiACIiACIiACIiACIiACEg46ndABERABERABERABERABERABESgLQEJx7Z4lCgCIiACIiACIiACIiACIiACIiDhqN8BERABERABERABERABERABERCBtgQkHNviUaIIiIAIiIAIiIAIiIAIiIAIiICEo34HREAEREAEREAEREAEREAEREAE2hKQcGyLR4kiIAIiIAIiIAIiIAIiIAIiIAISjvodEAEREAEREAEREAEREAEREAERaEtAwrEtHiWKgAiIgAh0I4FTn/mUfffuO+zM5z/XjebLZhEQAREQARHoOAISjh03JDJIBERABERgpQTO/f1nvYrJT3/CcoeeXWl1Ki8CIiACIiACPU9AwrHnfwUEQAREQATWH4FapeKdyj76fXvqV395/XVQPRIBERABERCBVSYg4bjKwNWcCIiACIjAKhCo1eqNVHLZ+r1uREAEREAEREAElkdAwnF53FRKBERABESgkwlUq3XrEolE/V43IiACIiACIiACyyMg4bg8biolAiIgAiLQwQRqDcKxlkx1sKUyTQREQAREQAS6g4CEY3eMk6wUAREQARG4FAINwjGZ0EfdpaBTXhEQAREQARFYjIA+TRejojgREAEREIGuJtDocUyk9FHX1YMp40VABERABDqCgD5NO2IYZIQIiIAIiMDlJNAoHE0ex8uJVnWJgAiIgAj0KAEJxx4deHVbBERABNY1gYZdVU1746zroVbnREAEREAEVoeAhOPqcFYrIiACIiACq0mgYY2jaXOc1SSvtkRABERABNYpAQnHdTqw6pYIiIAI9DKBWqVc735CU1XrLHQjAiIgAiIgAsslIOG4XHIqJwIiIAIi0B0EtDlOd4yTrBQBERABEehoAhKOHT08Mk4EREAERGA5BGqVSr1YTYsc6yx0IwIiIAIiIALLJSDhuFxyKicCIiACItCxBBp3VU2mUh1rpwwTAREQAREQgW4hIOHYLSMlO0VABERABJZMIFGr1vMmkvqoq8PQjQiIgAiIgAgsk4A+TZcJTsVEQAREQAQ6mEC1Vjeups1x6ix0IwIiIAIiIALLJSDhuFxyKicCIiACItCxBGrVhTWO8jh27DDJMBEQAREQgS4iIOHYRYMlU0VABERABJZGIGELHkfTGselQVMuERABERABEWhDQMKxDRwliYAIiIAIdCeBWqVxjWOiOzshq0VABERABESggwhIOHbQYMgUERABERCBy0Og1rA5jmmN4+WBqlpEQAREQAR6moCEY08PvzovAiIgAuuTQKJhcxytcVyfY6xeiYAIiIAIrC4BCcfV5a3WREAEREAEVoFAtcHjmNAax1UgriZEQAREQATWOwEJx/U+wuqfCIiACPQggVplYVfVWkJrHHvwV0BdFgEREAERuMwEJBwvM1BVJwIiIAIisPYEGndV1VTVtR8PWSACIiACItD9BCQcu38M1QMREAEREIFGArWa1aqNu6rqo64Rj+5FQAREQAREYDkE9Gm6HGoqIwIiIAIi0LEEqg2ikUZqjWPHDpUMEwEREAER6CICEo5dNFgyVQREQARE4OIEEi3C0bTG8eLQlEMEREAEREAELkJAwvEigJQsAiIgAiLQXQQap6m65dpVtbsGUNaKgAiIgAh0JAEJx44cFhklAiIgAiKwXALnCcekPuqWy1LlREAEREAERCAmoE/TmISuIiACIiAC64JA61TVREIfdetiYNUJERABERCBNSWgT9M1xa/GRUAEREAELjeBasMZjl63PI6XG7HqEwEREAER6EECEo49OOjqsgiIgAisZwK12sJRHOxnQpvjrOfhVt9EQAREQARWiYCE4yqBVjMiIAIiIAKrQ6BWrjQ3pM1xmnnoSQREQAREQASWQUDCcRnQVEQEREAERKBzCSRqtWbj5HFs5qEnERABERABEVgGAQnHZUBTEREQAREQgc4lUK02exwT8jh27mDJMhEQAREQga4hIOHYNUMlQ0VABERABJZEoNrscUxoc5wlYVMmERABERABEWhHQMKxHR2liYAIiIAIdB2BWsuuqvI4dt0QymAREAEREIEOJCDh2IGDIpNEQAREQARWQKBlV1Vsq7qCylRUBERABERABESABCQc9XsgAiIgAiKwrghUK83HcZjWOK6r8VVnREAEREAE1oaAhOPacFerIiACIiACV4pAy66qiYQ+6q4UatUrAiIgAiLQOwT0ado7Y62eioAIiEBvEKiUm/pZ0+Y4TTz0IAIiIAIiIALLISDhuBxqKiMCIiACItCxBGraVbVjx0aGiYAIiIAIdC8BCcfuHTtZLgIiIAIisBiBavMax0RKH3WLYVKcCIiACIiACFwKAX2aXgot5RUBERABEeh4ArWWXVV1jmPHD5kMFAEREAER6AICEo5dMEgyUQREQAREYOkEauXmNY6mNY5Lh6ecIiACIiACInABAhKOFwCjaBEQAREQge4k0LKpKg6e0kddd46krBYBERABEegkAvo07aTRkC0iIAIiIAIrJ9CyxjGZTK28TtUgAiIgAiIgAj1OQMKxx38B1H0REAERWG8EatVKc5fkcWzmoScREAEREAERWAYBCcdlQFMRERABERCBDibQMldV5zh28FjJNBEQAREQga4hIOHYNUMlQ0VABERABJZEoNzscdSuqkuipkwiIAIiIAIi0JaAhGNbPEoUAREQARHoNgJVHcfRbUMme0VABERABLqAgIRjFwySTBQBERABEbgEAi2b42hX1Utgp6wiIAIiIAIicAECEo4XAKNoERABERCBLiXQIhw1VbVLx1Fmi4AIiIAIdBQBCceOGg4ZIwIiIAIisFIC1UrrGkcdx7FSpiovAiIgAiIgAhKO+h0QAREQARFYXwRadlWVx3F9Da96IwIiIAIisDYEJBzXhrtaFQEREAERuFIEWjyOOo7jSoFWvSIgAiIgAr1EQMKxl0ZbfRUBERCBHiBQ0xrHHhhldVEEREAERGC1CUg4rjZxtScCIiACInBFCSRahWNKH3VXFLgqFwEREAER6AkC+jTtiWFWJ0VABESgdwjUWtY41hKJ3um8eioCIiACIiACV4iAhOMVAqtqRUAEREAE1oZA666qyZR2VV2bkVCrIiACIiAC64mAhON6Gk31RQREQAREwBK1ahOFRFLCsQmIHkRABERABERgGQQkHJcBTUVEQAREQAQ6l0CrxzEhj2PnDpYsEwEREAER6BoCEo5dM1QyVAREQAREYCkEWndVXUoZ5REBERABERABEWhPQMKxPR+lioAIiIAIdBmB1l1VTR7HLhtBmSsCIiACItCJBCQcO3FUZJMIiIAIiMCyCdSqtaayiaQ+6pqA6EEEREAEREAElkFAn6bLgKYiIiACIiACnUugVqk0Gac1jk049CACIiACIiACyyIg4bgsbCokAiIgAiLQqQQSOsexU4dGdomACIiACHQxAQnHLh48mS4CIiACInA+gdZdVXWO4/mMFCMCIiACIiACl0pAwvFSiSm/CIiACIhARxOotZzjaFrj2NHjJeNEQAREQAS6g4CEY3eMk6wUAREQARFYIoHzdlWVcFwiOWUTAREQAREQgQsTkHC8MBuliIAIiIAIdCGB1nMckwl91HXhMMpkERABERCBDiOgT9MOGxCZIwIiIAIisDICrWscLZ1aWYUqLQIiIAIiIAIiYBKO+iUQAREQARFYVwRad1W1RGJd9U+dEQEREAEREIG1ICDhuBbU1aYIiIAIiMCVI1CtNtWd0BrHJh56EAEREAEREIHlEJBwXA41lREBERABEehcAi3nOJrJ49i5gyXLREAEREAEuoWAhGO3jJTsFAEREAERWBqBVuEoj+PSuCmXCIiACIiACLQhIOHYBo6SREAEREAEuo9AteUcRy1x7L4xlMUiIAIiIAKdR0DCsfPGRBaJgAiIgAishEDLGkdtjrMSmCorAiIgAiIgAoGAhKN+E0RABERABNYVgdYVjYmUjuNYVwOszoiACIiACKwJAQnHNcGuRkVABERABK4UgVqLxzGR0EfdlWKtekVABERABHqHgD5Ne2es1VMREAER6AkCrcJRU1V7YtjVSREQAREQgStMQMLxCgNW9SIgAiIgAqtLoNa6q6p2x1ndAVBrIiACIiAC65KAhOO6HFZ1SgREQAR6mEC11tz5ZOuqx+ZkPYmACIiACIiACFycgITjxRkphwiIgAiIQBcTSMrj2MWjJ9NFQAREQAQ6hYCEY6eMhOwQAREQARG4PARaNsepJbWr6uUBq1pEQAREQAR6mYCEYy+PvvouAiIgAuuQwHmb4yT1UbcOh1ldEgEREAERWGUC+jRdZeBqTgREQARE4AoTaNkcR1NVrzBvVS8CIiACItATBCQce2KY1UkREAER6CUCzZvj1LQ3Ti8NvvoqAiIgAiJwhQhIOF4hsKpWBERABERgjQi0rHFMaHOcNRoINSsCIiACIrCeCEg4rqfRVF9EQAREQATMWqaqWkIfdfq1EAEREAEREIGVEtCn6UoJqrwIiIAIiEBHEWjdHCeZ0q6qHTVAMkYEREAERKArCUg4duWwyWgREAEREIELEmiZqmpa43hBVEoQAREQAREQgaUSkHBcKinlEwEREAER6EoCNa1x7Mpxk9EiIAIiIAKdRUDCsbPGQ9aIgAiIgAiskMB5U1W1xnGFRFVcBERABERABMwkHPVbIAIiIAIisL4ItExVlcdxfQ2veiMCIiACIrA2BCQc14a7WhUBERABEbhCBGotu6omtDnOFSKtakVABERABHqJgIRjL422+ioCIiACPUAg0Sock9odpweGXV0UAREQARG4wgQkHK8wYFUvAiIgAiKwugRa1ziatlVd3QFQayIgAiIgAuuSgITjuhxWdUoEREAEephAi8fRtKtqD/8yqOsiIAIiIAKXi4CE4+UiqXpEQAREQAQ6gsB5axyT+qjriIGRESIgAiIgAl1NQJ+mXT18Ml4EREAERKCVQOtU1YSEYysiPYuACIiACIjAJROQcLxkZCogAiIgAiLQVQQ0VbWrhkvGioAIiIAIdCYBCcfOHBdZJQIiIAIisFwCtWpzSXkcm3noSQREQAREQASWQUDCcRnQVEQEREAERKCDCVRbhGMHmyrTREAEREAERKBbCEg4dstIyU4REAEREIGlEWjdVVUex6VxUy4REAEREAERaENAwrENHCWJgAiIgAh0IYEW4ZiUcOzCQZTJIiACIiACnUZAwrHTRkT2iIAIiIAIrIxA61RVbY6zMp4qLQIiIAIiIAIgIOGoXwMREAEREIF1RaDpHEeKRgnHdTW+6owIiIAIiMDaEJBwXBvualUEREAEROAKEWg6x1Gi8QpRVrUiIAIiIAK9RkDCsddGXP0VAREQgS4jMP2dh+3Ih//MipOTS7O8aaoqPI4XCdlnn7HnP/SfL5JLySIgAiIgAiLQ2wTSvd199V4EREAERKDTCZz4iw9b9tHvW2Vqyvb++m9ckrmJ5MWF4+Hf/x3LPfOEDe7bb5vu/vFLql+ZRUAEREAERKBXCMjj2CsjrX6KgAiIQJcSGHzBAbd89oH7rZrLXbwXjbuqJtp/zE1+7asuGr3SVOridSuHCIiACIiACPQogfafqD0KRd0WAREQARHoHAIbbn+lG1M+N2mTX/3KxQ1rmKqauIhwPPeFz3l9yYFB23Tnqy9et3KIgAiIgAiIQI8SkHDs0YFXt0VABESgWwiM3/pj1rd5m5s7ff/XLmp2466q7aaqzj/xhM0+cJ/XN3bX6y5arzKIgAiIgAiIQC8TkHDs5dFX30VABESgSwgM3/QSt3T2wa9b9vDhtlYnGqaq1trknPxi8DYyy9jtd7TJqSQREAEREAEREAEJR/0OiIAIiIAIdDyB4Zturts4/Q9fr98vdtN4HMeFpqqWpqdt+itf9OJ9W3fYxCtuX6wqxYmACIiACIiACEQEJBz1qyACIiACItDxBDa85KV1G2ceuIhwbPA42gV2VZ384heMayYZRl8u0ViHqxsREAEREAERuAABCccLgFG0CIiACIhA5xAY2LnThq670Q3i0Rxcn3ih0OhxNFv8OI6pL32+Xnz85ZqmWoehGxEQAREQARG4AAEJxwuAUbQIiIAIiEBnERi+8aa6Qee+0cbr2OBxTCTP/5g7h6muuSce9boyV+21sdtuq9erGxEQAREQAREQgcUJnP+Jung+xYqACIiACIjAmhIYuTlskEMj5h584IK2NPoYa4nGp1Dk3Je+UC87etsr6ve6EQEREAEREAERuDABCccLs1GKCIiACIhABxHY8JJbLLVh3C3KPfW45Q49u6h1jVNVEy1TVXPPPmMz93+5Xm4sOiOyHqEbERABERABERCBRQlIOC6KRZEiIAIiIAKdRiCZydjQ9S+umzX98MP1+6abanXhsWVznMkvfN5iYTmw/1oba9itdaGQ7kRABERABERABFoJSDi2EtGzCIiACIhAxxIYic5zpIFz311cONYa1jjWEgsfc9V8zqaiIzhYfvQV2hSHHBREQAREQAREYCkEFj5Rl5JbeURABERABERgDQmMNngIcz/4nlXm58+zJmG1elyiYY3j6c9/zkqnT9TTNt756vq9bkRABERABERABNoTkHBsz0epIiACIiACHURg+NprrR87oTKU52Zt6uFv+33jW6PHsXFX1akvLhzBMXr7q2xo/zWNxXQvAiIgAiIgAiLQhoCEYxs4ShIBERABEeg8AoM33lw3avbbD9Xv6zeNaxwjj+PUgw8az3+Mw8Sdd8W3uoqACIiACIiACCyBgITjEiApiwiIgAiIQOcQaJyuOvuN+62CtYuNobqIcDz3pQVvY2bX1bbp7h9vLKJ7ERABERABERCBixCQcLwIICWLgAiIgAh0FoGJO15p6Y1b3KjyuTN27mtfazawYXOcBDbH4TrIuW8tnPs49k/vbM6vJxEQAREQAREQgYsSkHC8KCJlEAEREAER6CQCqcFBG/snr6qbNPX1++r3vGncHMeSSZt66FtWnp2u59l41931e92IgAiIgAiIgAgsjUB6admUSwREQAREQAQ6h8D4q+6yyU98zA2ae+gByx87ZgM7dwYDW6aqzkI4xkGb4sQkdBUBEehmApxY8eBTZ+3TD52wJ56ftbOzRXvZCzfae376oI0ONn+9f/+nnrK/+coRe/sb9tu/es3ebu62bF9jAs2/WWtsjJoXAREQAREQgaUQ2HDTTTZ0/U2+4U2tVLSp+++z7ff8D6HowmkccD8mbO7hBeE4cfdrllK98oiACIhARxI4NV2wj33jefvbfzhmc/PFJhu//PAJu273qP3CnXua4j/74AmrQGl+5O+etbfffbWlk4mm9PX6UKnW7AvfP2mf+tZxO3muYNNzJSuVq7Z5vN9eecNm+/lXXWWbRvvXa/evSL8kHK8IVlUqAiIgAiJwpQmMwesY75Q6g01yYuFYq1XrTXPjnMrkKX8ePHCdbdLZjXU2uhEBEegOAhQ7n/nOCYjFo/bk4Zm2Rg9mUuelx/9HpHg8M1Ow7eMD5+VZbxHfPTRl7/7wIzbTIq7ZzyMny/bfTs7bX3/5sP2vb7vOfvLWHeut+1esPxKOVwytKhYBERABEbiSBDZCBJ766Iew+c2szf/gezb76A9s9Pobmtc4Fgt1E8bvfm39XjciIAIi0OkE6DH7228etXs/+YzlCuXzzE1hRsWb7thlb7ltpz19Ys6SeH7DLdvPy1eB8IxDNl+Jb9ft9WuPnrZ3f+gfL9o/Cun/8y8fs61j/XbbgY0Xza8MZhKO+i0QAREQARHoSgKZTZts9I5X2dTnP+32Tz/wDReONXzZikMtn/fb9MQm2/y618fRuoqACIhAxxI4ciZrf/6lH9l9j5w5bzoqjR7IpO2eO3fbz995dX094wsxRfVCgesh4/DRrx62vVuH7LlTWTs1lTcKyZHBlE2M9Nuv/9QBXPvirF15zRUr9lv/5bEm2zfDw/ov7tpjezYPWaFUsT/73HN26NhsPc+9n3nWbvs1Ccc6kDY3Eo5t4ChJBERABESgswlMwOsYC8e5hx40+8V3mTV8S6qWgsdx7K7XWHrDhs7ujKwTARHoeQLZQsV+7vcftFJlwUsYQ9m7Y8Te9uo99kZ4FftSi69TLFVq2CinYKexWc7Tx+fsMWycky8ueCv//sFjcXXnXfdvH7Z3YA1kN4fDZ3JN/d06MWgf/3evaOJ1141b7T1/+ahxTSjDD3+0sOt2N/d9NWyXcFwNympDBERABETgihAYv+3lNnDNQcs/84Tlnv6hzf7gEQjH879wbXrtG65I+6pUBERABC4ngZPT+fNE457tI/a+d77Yrto82Laph54+Z79673d9I5y2GVsS+1JJ27tzxO66IZyP25LcVY9Xw6vI/sTC+9ffcqBJNLIzmNHrGwjFwrGrOrjGxko4rvEAqHkREAEREIGVEdj4hp+wY3/8hFcy/c0H4HBsmJeF2OFbXmbDL3jByhpRaREQARFYBQKLbVxzGOsX73/8jN1zx25LJRf3NNK0P/rU0xcVjVwXuQeey1sPTOA1bjfvHbfx4aVPT+VU0EPYWGYqW7Qb9ozbhpajP1YBUdsmBjJJe98v3Wwf+uIh6wOrO67bvGj+5yezi8Yrsj0BCcf2fJQqAiIgAiLQ4QS2/ORP2ZlPfNyKR54zTldNtAjHDS97RYf3QOaJgAiIQCDAXVH/AN7F3/xw8+Yuf/jxJ+3DWJv3jtfutZ9++S6sc0yeh2yxHVUbvW8s8Nv/8ga7+8Vbzyt7sQju7PqRrxy2v8CRHtxUJg47tw7bu163d9FNeZhnJle2D/79s/b5h09aLl92z+Ybf2y7vfUVu6wvfX4f4npXcn0ZRDFf7cLXHz9bTx4ZztTvddOeAD5fG0a/fV6lioAIiIAIiEBHEjjx139lxz/4R25bIp22WnlhTc/Nf/cVS2T0xaAjB05GiYAILErgyWNz9lvY8bNxE5c4I8XgW161295+116baPAWnsHaxt/4ix94tn96/Wb7iZdu9x1D3/6fHrbHnp3y+Pf/65vs9oOLe+Hi+luvtOVdf/Twoju7xnl/42dfaD8DMdgYfoj1lb/4f3+7Pm20MW2wP23vfutBeyNsXO3AdaCv/s2v1u269YUb7QO/9JLVNqMr25PH8TIMG6X3g0+dtU8/dMKewH8kZ7Eg+WX4JXzPTx+s73YVN/P+Tz1lf/OVI/b2N+y3f/WavXH0ur2exWGrH/rCIe/fu//5teu2n+qYCIjA2hLY+uY32+Qn/18rHj9i1rCramp4VKJxbYdGrYuACCyDwLVYc/jff+Nl9m2sW/wAvHyx8GNVXL/3VziDkK83377L3v2Wg76ObzMOs//Ir7z0vNa4NjIuPzW38Ee18zIuEvHsiXl7x/seqossZqGHbmQgZScmc/USf/yJp5uE4xx2a72QaGQhHi/yf/w/j2LKa8ne9k+uqtezGjefxZmY8RpItvf6NRCvq9HPK9GGhOMKqJ6aLtjHvvE8DmQ9dt52yVxwex22Rv6FO/c0tfDZB0+4i/8j+J/A27FzVbrNXPWmgl34QD4/+3vfrP+FSsKxCwdRJotAlxBIDgzaONY6nvrzP8XeOAvnlKU3buqSHshMERABETifwK0vmHAx+CR2SP0Ijuj42ndONk0V/eQ3jtojz83YB//NLTY2tPjX+tGBhfjJueL5jVwghtNM3/7+Zo/hv31r8Cxyg5nvHZqyX4InkoFC8BmIzGuwMyvDf/7Cs03izCPxRu/eM0fn7Ry8owycgrtlQ8Zec9M2f77Sb2X8YZEiNw70fL7+5tVpO26zm69XZnJxNxO5iO2c4/3/feuY/fz7H7Kf/N+/bh/9PNbUzC/+H+Fic83jvf44P/zMTPiP5iJNdmUypwG86wPfqYvGruyEjBYBEegqAtuw1rFvS/O0p+TIhc8266rOyVgREIGeJnAtNrT5vf/xevvi773Kfg0z2jbibMI4cDrrz/zuA3b07IIHME7jNUGVF4WGCRlx1AWvf4jNdhqP8uB01LfCw8nqKMDuf2yyqWzjRjlf+M6ppjQ+3IOzFDkllMdj8GzFOPxvH33UeAzJaoQ/geNmpuF7+69x19UrtNZyNfqz2m0s/AlitVvusvYq+A/kb7951O795DOLiiHuUvWmO3bZW27baU9j96sknt+Ac3ZaQwXCMw48dHW9hn//Xx+z46e1Y9V6HV/1SwQ6kQDPaZx4w5vs1H/5UN28/p3Na27qCboRAREQgS4kMNSfsp975W77mZfvtPfgu9Z93z3pvaAY+uDnDtl/+LkXnderYnXhu2cFf9hfSjiC8xA//cDRpqzv/esfurdu28YBTFPNN4lKCsEtY/2en06Ws1P5prIbML311950wOPYhw/865fY2/7gm+49pTPl898/af/sZTubyiz3oYj2P/3tE/ZJOHpOnytYvlS1Ml7pvmSTs2cgk7afvHXHcpvpyXISjhcZ9iNnsvbnmBpw3yNnmn7Z4mL8pbvnzt3283deXV/P+EJMUb1QaNyK6KNfPWx7tw7Zc6eydgr/gVFIjgymbGKk3379pw7guvTtkS/U3lrEf/ybx+wrmD+uIAIiIAKrTWDLm95cF46D191oO/7lO1fbBLUnAiIgAssmwI1oRnHExY6JBY/cYpXRS/beX7jBfhvTUGOB99CT5xbL2hRHEdUa7v27Z+wr/3jGfucXrjd6Nhn+FDuhxoHOkXgnVU5JfQ7TZhsD03/v7TfUo55qSWfCG2/bDqdKPYvt3TZkb3rlLvvE/c975JHTi3tLF0os7W4ensv/CbMCj+DIkPNCywRBelPvfs99WCe603759fttsZmC59XR4xESjm1+Aeg2/7nff3DROdp78R/W2169x94Ir2JfquG/hIb6OF3zLOZwn8ZmOU/jP6LHsHFOo8v/7x881pC7+XY/5oi/A2sguy2chAD+v/AXKQUREAERWAsCmU2bbPjmW6147Ki98I//dC1MUJsiIAIisCwCH4OHj149hte9bIf9+5+97oLfMeMGtjcIzBzWJC4WdjRMC51qmKbJvNPZsi+74v2ffe45ey+O62D42vcWppr+ya/cYs/BA/knn3qmvjbRM+Ft385R+w//4kXGzXzicKLF28j47x+ajpPr16s3D9Xv8zgf8nKEf/PB7y4uGi9QOYXwX+GYkc9864S995032i37xi+QU9EkIOHY5vfg5HT+PNG4Z/uIvQ/n63CHqnbhIeyC9av3frf+F5p2eRvTuMXyXvzHd9cNWxqju+b+OUxPjf8qxWkJjfPIu6YTMlQERKCrCVz7H/+wq+2X8SIgAr1JgBvgxOFz3zpuX8Ru/XfgzMWfuHWb7dw46Mug8uWKzUEgPgHPJD2MDz1+Ji5ir37J4ucz7pgIU0iZ8Shm0jWGh59ZOM8wnQ6OEIrJeNdRzqy7CWKKrzdjWudhlP/h0VnbMNRnB3YMG3dybQ2Ts6XWKHscwvF3P/6EvQc7wDJw453/CsEWh8U8oXHaUq+zqJPtxIHfqe/9X26xQyez9ruY1tsucL+SX8ZGP7+OI0J+9vbd7bL2dJqEY5vh397wF5o422GsX7wf/5Hec8duSzX63OMM0fWPsKA4FlAtSfVHuvb3wHN5Kw4pvfXAuN28d9zGG87jqWe8wE0Of505BFf8VLZoN+wZt8ZFyRcocsWjbzuw0b7+3lfjfwgl/E8lY6/8t1++4m2qAREQAREQARFYjMCTTz5p999/vz399NPG+3w+b1u3brVt27bZO9/5TjtwIKy5Wqys4kRgtQm84SXb6lM32Ta/R96HtX98XSxwjSGPgVssNH6fffTQDERhzT2Z3Eznd/7qiXqR19wUnBY5TOGMA2fKPY9jN3ZvGvRNca7eMmR8tQsUcIsFTkv9EjbNuWrroD19eLYuTpn31Teu3GEyiLWTjYHi91c+8L2m2X5M51mWB7aP2iOHp+1j/3DUHn5iQTz/x795woZRz0+8VGsfG1nG9xKOMYlFrpzr/AfwLv7mh/+xKZVbB38Y7vx3vHav/fTLd9lA5vzNaRebJ82/fMR/wWGFv43pAHfjL0mXGrjo+CP4K81fYGeoRnG6c+uwvet1exfdlIdt8K87H8Sc9c8/fNJy+bJ7Nt/4Y9vtrTiw9XLuKMW6NuEvUOdwhqOCCIiACIiACFyIwPe//3177LH2noDWsvfcc48lk+d/7jbmK5VKdu+999r73ve+xuime9ajIAKdROAl8Or96luutXtxXETj98WL2Xg7RNdvcVrrBXYHbVwvyamZr/l399kENrI5dmphHSCF56tvCN9Jt2xo9iL+wd8+af/pXTddzIx6erVhQ4//+Z8dwLF1R+sbJtKz9/ih5sWG92Dp1yuvW/nRSTzijiy+8cjpui2NS8QY+fbX77PbD2729Ltu3Gp8fRfHirz7w4/UZ8l9FhvrSDjWETbdJGoITTF6OI8AFyr/1l8+ZtzuuDVQDP7/7J0FfGRn9fd/cXeXzWqylnV3qbuwQA1paaF/SoEW+lIKFNpCKVootEAp1KhRly1bW/eua9aTbNzd5X3Ondw7kolPdkd+D5/hPn6f53tn0zn3nOec65el4taVoxBloS0sU2cb73vukNZ96eRY9QVMRLz6R3rrX3YbQVjljYf+5bWdt6eyrOWbSpUu//B7SuIueZUSBi1Tljpf2VMgVolh8/+Uav5yBwdAFY9cq361VVuGaFe3/nGl5ZKYJwESIAES8HACjz/+OP70pz8NiMKpU6fg69v7e+977rkHb731Vq/z7ty5U9M89tqJjSRwHghIqIvtSgu2RsVsPJxbowSaVjSrF/568lUCYoRyoDhTWazJ70uJ9dhX+tqfdiErx2zGadlffqM9d99cwzGOtP3x/RN4ba3ZlPRqFYbjfqXR7M3aLkcdVyqsbMLGw6V4c2OedouHlMOdpZPi8Ogbx/DJrkLL2yJ9RDi+fYVZkLNqHGRBwo08/fFpvLL2rJWmUX6v331dumYxaG/qcvW7/ZY/7tK8wS5VMSX1s572+npyHQXHATz9Xerc4pNKy3fkdJXdUfKP6v8p2+2enOXIoAdfOQKxW5f0cxWPZyDC2mkVWPWrv99p9RYqVJ0jDA30UW6Rzd6oRBBc/9gy7R7yf3XKW+ulP91oNc5otMjIW66bloywqBlaVuzvv/q7Hdok8g928+9XDG1CjiYBEiABEnArAsMhOB48eBBXXnmlFaf7778f06ZNQ1RUFBoaGlBWVoYLL7wQPj7Wpm1Wg1ggATciIAqEr/1hp9WO5LfZRcry7O4rxiHaxpO/hLS47BdbrCIKSOzIb102GqIZFS1mcVWzcvxYg/WHypSWr8wQ1PzVuciWLnPXXyrruouUICZJ5pRIAiHqd2uCmks0hMOVRC1WXtesFC0d6neyr3YUTMnHvSaxlNulznzOTY9BRHDvL6d6nciNG0llAA9X3ug8e/csyOHlZ1WIjg3qTZClqeh7W/NxMLsG/7hrZo9fuDD15dVTeZ21ql6vt3cVM9NbH99lJfz98IsmzaL8Q9in1OzfUppISaKNPKWEzLHKM6ukpz85bTVOq1T/N3tCNE7l1xsessQENy7c3/gHrvcb7LXeQisaYLHvwc7HcSRAAiRAAu5FQIS522+/3WpTzzzzjFG++OKLkZaWZpQlYxnM3Kqhq2CrwVy7di3Gjh1rryvrSMBjCEiouGfvnYPXlNmoCHDLJseo41IJPSo7/JVW87nvz8Y3n9xjxGSU2Iy/fuVon8wstZIinOpJ5rT0vqrXD8dVfhtrjnt6jpDX7bYSBk8Xcrs1skIjYJZiCKTfBCTGza+VtrBBCW7vfl6IF5QQqQc6FXPWVY9uw3P3zkaK8oBlmyz/gyfq9P6mPytnO5Z22pbmqGLSsOlIudVUlo5yPlEHkW3Tl1em4d6r0iEhR7742HaUdblO/vnzh7Ho17GQ4KxDTRKXUk+BKugqEwmQAAmQAAlYEli5ciXkY5lE0Dt9+rRWdfPNN2P58uWWzX3m9+7da/S54447KDQaNJjxdAKTlGnoQzeE9xuDRBB48/75eFiFCOlPfG4REpdMj1fayAC89GmOdh/L8479vjE7Oi0BCo5DeDQiXN24OBWr5ifjAeXmd+Nek9crCUHxj4/O4OEbJ3WbvaWjw6hrV16t+pPkrKAe3FXvL3F+/qoOTydEByoz1SYroVIOOMep85SSxJGOLtTqYyVMxvevNHmSkz08eecM3PSb7Zr2VDSoHyvvXdfOTda7D/pq6ZXLXzkaYiIBEiABEiCB4STQ2NiI8nLzi9SBCp2DWVtBQQEOHz5sDF2yZAkCA3sO3n7ixAlkZ2dr/SMjIzFnzhxjrGWmvr4eR48exbFjxzRBOjo6WvMCm5mZieTk/v03WpwEff7558jPz0dVVRUqKiog84aFhUHuLZ8pU6ZgwoQJlrdmngQMAvI78bGvTEbp1ePw0qazOJJTA/ld2tLagegwf6StJBQTAABAAElEQVTFB2Pq6AgsmxiLUQkmb6uvKws8PdkLzaG38ep6BCg49vLMxBFNWJCvZsfdSzfNi9XvvpqJR5Q5pi7gSWydvpK9mDVP/e8U1h0ow6/UYWLRbEr6u/KEqic5wKybx4pJarZFzB/pI+2/vjVT744TNu3ScPm8RBULyOii/UO/cnGK4QL6bKn5vKS518BzDS1mIXk47dgHvjKOIAESIAEScEcChYXWzjcSExOHfZt1dXVW5rZPPPEErrnmmh7v+8tf/hLr16/X2mfPno0333yzW9+tW7fiO9/5jpUQbNnpvvvuw5133tmjkyA5x/noo4/i7bffhqyvt/TTn/6UgmNvgNimERCFxPevHNcvGgmR/ka/EhUTncl9CNB+sIdn+ca2fHxFOXa59uEtmkMbiXnTV0pUB4X11NhDDJskpQ3UU5XSTFomCbj6/MfZkFiR//wo22jasM9savq3u2figZsmIcpOwNXRyWF47odzMTUtwhhb1GWCalSozH6L4Kh6/chY01siKTep+JCOSJbzWNq7O2JuzkECJEACJEACtgRshaSICPN/D237OqqckZEBEQD19Oqrr+rZblcR6HShURqvuOKKbn1eeOEF3HjjjT0KjTLgd7/7Hb773e92GysVbW1tuPvuu/Hiiy/2KTRK//5qL6UvEwn0h0BilPmoVnaxY5QR/bkv+ww/AWoce2AsDnD0JF5QP/28CItUzMUrZicgWZ1d9Faavaa2dtQpAVG8h4qG8fOjZfoQrJhhPz6j2H3rKb+sQc9q193Kk5OefH1NKkERJvVYPoHKS9U05clKPlfPTkKuGp+VX4vwYD+kJ4WYDgHrE3Rd7ZkIHFWC46NvHcMDygOsJHG887KKC6kne5pQvW0g12ZlxqAnv6796GVeSYAESIAESMBdCNxyyy3YtWuXth3RFubk5GDkyJHdtichQCzTBRdcYFnUvL3+7Gc/s6r72te+pmkExQT39ddf1+aWDqtXr8aOHTswb948q/7//e9/8emnnxp1oaGh2jnRiRMnIjw8XDOjFRNWMVkV09XJkycbfZkhAUcQsPTxseVAieZPwxG+MxyxNs4xNAIUHHvgd9mMBMN0U7qIeehGdfZPPn0lOWP4gIp1Yy8lWmgcD5+pUUJhp+bRKr+iEb967Zgx5KJpcVre8pygOMfJU2E3UmOClFc5YGRcsPYxBtnJ1Pag+Xx3Ux4+U05zRsQH4WRurSGcyhQrVPBUR6QGC6+qEnOIiQRIgARIgAQcSUBCaliap9pqHHs64/jSSy9h+vTpDlvKpZdeChHQ9PuLiej3v//9bvOvW7fOqBNBzla4fOqpp4x2yaxZswbST0+33XYb5LN9+3atSjzIvvLKK3qzdpVwJHqSNX322Wc4Fya7+j15JYEQdS5SYjSeOFuj/X4WR5LiE4TJ9Qnw13wPz1Bi1EhcQ0s3wj10tapeqISu//xgrnbu0aqhqyBxb/QkZxQv+slGXPfr7bj+ka1GrBwRPFdkmjSWceFmDaWM+82bx/Xh/bpaerP6zrXpSFLCpp7qlKmsaB91jabUf3lFGhZPjNG7DOmaXWI2T/CzPFQ5pFk5mARIgARIgARMBMTRjAhr+seWi15ve+2UIG8OTEFBQbjhhhuMGcXcVExGbdNHH31kVNkzU/3www+N9ocffthKaJSGkJAQPPjgg0afAwcOGHk9U11tHeTd39983kzvwysJDDeBy2abzxe/uTlvuG/H+c8RAQqOvYC+ackIrP/tcvzhm9NxkTINTY4PQajySCrCpP4JCvBFotIAXq48qz6p4jc+fttUSByYnpIEPJ0w0nzmQoTHgpJ6o7s4t3n8m9M0jaJUytlACZ2hp53KHPZXr2ehvY9YHjmlDdh+vAIVtc36UC1G48tKqJW92CZ5M/T4ndNw79Umb6u27YMp63EkZWyE4sZEAiRAAiRAAo4kIB5Gx4wZY3ySkqz/+yYaPct2PR8VFeXIZWhzrVq1yphTzErFZNUynTx50ko7KtpSyyTmo5baU4lhaS9NmjTJqBaBWLylWqbx480WT9I+Y8YM3HPPPZoTHt2bq2V/5klgOAhcNtMsODaq0G9M7kGApqp9PEfxBioaOEdp4eR2P141Hl/7g/U5BxFEL5qTiLuvGIdoG8HzO5eNxeodRYZG8j3l5nizitv4rctGQzSjosUsrmrGkbwarD9Uhq0Hy4zwHP7qXKSeRAgVG/Nf3jwJD355ArJLGhAS6AMRZofD6+k1c5Ow73QliiubcfPyEfoyeCUBEiABEiABhxCQc36WSTRwV111lVElHkvj4hxz/MKYtIeMmJTOnDkTe/bs0Xq89tprWLp0qdF748aNRl4EXEsTVGmQkBmW6dvf/rZlsce8CJsSVkNPct7y+eeft3Ku89Zbb0E+kmJiYnDttddqnmDpGEenxqujCchv2V+oMB5Pf5SN71011tHTc77zRMAsVZynBXjibSekhuHZe+fgtS35aFFxFpdNjsEFUxO0s472ePir84HPfX82vvnkHiMmo8Rm/PUrR+11t6qz9GZqaXYrc2Ykm8J9WA1wYCFeuW5+8lszHDgjpyIBEiABEiAB5yUgQpsuOH7wwQd45JFHIPEXJa1du9ZYuAhutkm0g5ZJn8eyrj95EQzlXOOTTz6pnX+0nVe0of/617+0z+23344HHngAPj6MtdwftuwzMAKidbTUPA5sNHs7IwEKjufpqUxSpqEP3RDe77uPiA3Cm/fPx8P/zcK6PUV9jhMhccn0eKWNDMBLn+Zo/S3PO/Y5ATuQAAmQAAmQAAkMiMBll12mnUHUhTURHr/61a9qZzA3bdpkzHXRRRcZeT2TmmrtPETMbIODzX4J9H62V3shR8QUV+Iz3n///ZrnVTGblfvv37/favgzzzyjOc654447rOpZIAESIAF7BCg42qPipHViZvqYUvuXXj0OL206iyM5NThb1ogWFfYiOswfafHBmDo6AssmxmJUguk/Nq8rs1Y92QvNobfxSgIkQAIkQAIkMDQCIujJWcfnnntOm+jll1/WBEfdC6pUiqdTex5dxdzU0jOreGW9/vrrtXkG+3++vr5YtGiR9rnvvvtQU1OjeWp96KGHDA+w4pCHguNgCXMcCXgWAQqOLvi845QJ6PevHNevlSdEmp3SlFQ39WsMO5EACZAACZAACQyOgHhX1QXHo0ePQsJjbNiwwZjsuuuu69E0VBzZ6JpJCbWxePFixMebvKwbEwwhI3Ecv/SlL2lxHnUPr8ePD8xb+xBuz6EkQAIuToBeVV38Afa1/MSoIKNLdrE5PIZRyQwJkAAJkAAJnCcC7e3tWtgKCV2hfyyXotdZXi3bnTEvTm+mTZtmLO3111+HpROfnrylyoC77rrLGJeTk4Mvf/nLmpAnnGxTc3MzOjo6bKtRXFyMysrKbvVSIf137tyJXbt2Ge1Tp0418syQAAmQQG8EqHHsjY4btKVEmwXHLQdK0KBcIovJKxMJkAAJkAAJnG8CTzzxBESz1lP6xje+0a3p1KlTEBNMZ05f+cpXjPOE4uHUMs2fP9+yaJVfsGCBJiyKR1ZJp0+fhjAQE9aMjAzExsZqJqbigVUEyzVr1nTzzvqzn/0Mok2UMeK9Vc5ASixHMVOVcBz6+Uv9xvPmzdOzvJIACZBArwSc+y9vr0tnY38IhCghUWI0njhbg3YV8Pjdzwtx42LrA/j9mYd9SIAESIAESIAE+kfg8ssvxw9/+MNuneXMoghxvSVxaiOaQdFU6kmEPXteVs+ePdtNcMzNzdWGyZgTJ07oU9i9ihB755132m1jJQmQAAnYEqCpqi0RNyxfNtschPXNzXluuENuiQRIgARIgAQAPz8/p8AQEhKiOcWxXcw111xjW9WtLOcQf//730NiUC5cuFDTHHbr1FUhoTVsU0NDg21Vt/LcuXPxm9/8Bs8++ywCAwO7tbOCBEiABOwR8OpUyV4D69yHQEVdKy77mSnwcGxkIFb/fJH7bI47IQESIAESIAEnJfDqq6/iRz/6kbY6ia8o5wsHY2YrAmJRURFEKBSNpXhgTU5O7lFQFrPUgoICNDY2orW1FfJTTwRSiSkpYwMCApyUGJdFAiTgzARoqurMT8dBa4sO9cMvVBiPpz/KxveuGuugWTkNCZAACZAACZBATwTkPOEjjzxiNN9+++2DEhplAhE65dPfJEKifJhIgARIwJEEqHF0JE3ORQIkQAIkQAIk4NEE6uvr8fHHH2tCo25KKo5qRNsoJqxMJEACJOCqBKhxdNUnx3WTAAmQAAmQAAmcdwIiKO7YsUMzDd27dy/eeOONbmt68sknKTR2o8IKEiABVyNAwdHVnhjXSwIkQAIkQAIk4DQEJDzIrbfe2uN6fvWrX2H58uU9trOBBEiABFyFAAVHV3lSXCcJkAAJkAAJkIDTERAzVHtJhMXvfve7mDVrlr1m1pEACZCAyxGg4Ohyj4wLJgESIAESGAyBjuZmNKq4d37Ks6S/+jCRgCMIiBOaJUuWICkpSfN0OnbsWMycOROpqYyZ7Ai+nIMESMB5CNA5jvM8C66EBEiABEhgIARUkPQOFeS8U4UbqNi0AVVbN6Nu9w5ELlmJpGuvh5e3D3ySEuETFg4vFauu8OWXUPSvp7Q7+CemInhyJoIyJiA4PQOhGRnwDgoayN3ZlwRIgARIgAQ8igAFR4963NwsCZAACbg2gfbqKnRU16AlPw+tKrZduxIc2xrqUfDSs8bGfIOV6aAX0NbYAB//AHipj7d8AgLRlJ9j9LPM+ISq8AVLVyL2yqsQOn6CZRPzJEACJEACJEACigAFR34NSIAESIAEnI5Ac0kxmnJz0Zibg5a8s2g6m4u2sjK011QpYbEGnW1tw7Zmn6BgeCtB0jc8AgFj05F0y1cQmEKzw2EDzolJgARIgARcggAFR5d4TFwkCZAACbg/geo9u1G9ZROq136KtppKp9pw7E1fx4hv3OFUa+JiSIAESIAESOBcEqDgeC5p814kQAIkQAJWBJpLS1Hy3juoUcJiS1GeVZsUfMOjEJCQiIhZc1Dw6gtW7V6+vgjJmIRQdVYxavZcq7a+Cu3KUU6TcpTTpExem4oK0FJcjNayYrQ3N9kd6u0fiGn/+8xuGytJgARIgARIwBMIUHD0hKfMPZIACZCAkxHQBcaqD99DW1WFaXXKmU1w+ngEZ0xE2LhxgCo35Z9VTm+2KJPV08YOgtLGIGzqdIRlZsI/ynHeUTs7O9FUkI+6Y1mo3bcHzcUFxj0lE7F4BRJu+gpCxo+3qmeBBEiABNyRQG5ZIz7cXYik6EAkRARifkY0Glo7UdvciZpmdY68oxOxId6IC1aHypk8ggAFR494zNwkCZAACTgHAXsCo3g4jVp5IcLT0+Ht54+q3Z+jZu8eNGafNBYtDm9CJk9F+PQZCB2XbtQPZ6b2yGFUbtuC+uNHjNv4KAc7MatuRMpttxt1zJAACZCAOxDIr2jE4bM12Jddi6y8Whw+2fVSz87moqNCEB8TgvSUUKQnhSJzRChGxwUi2I9CpB1cblNFwdFtHiU3QgIkQALOTaBk9fso/vfThobRNywCkcqTafTceWitrUW1Ehhr9+1Ga2W5sRFNuzhthhIYp8MvNMyoP5eZpqIiVG3bjMrtm43bhmROR/Id/4dQpfVkIgESIAFXJvCXNTn4RGkWi8vqh7SNgABfxEWHIXNsNOZPiMG8MaGIDqIgOSSoTjaYgqOTPRAuhwRIgATckUD+88+i5IVntK15+fggcsXFiFLnFjubGlGh4i/W7NmJzvZ2oz1s6kyEz5yNsAznMQutPrAf5Z+sQXNJoWmd6oxlnHKak/K1W93xkXFPJEACbkzg4NlavLK5EHuPl6GiqrHbTuNiQxEWEoiQIF/tunV3ttYnNSlSuzY0taK2phGt7R3dxuoVUZHBSEsMx+RR4Zg/LgLzxobrTby6KAEKji764LhsEiABEnAVAtm//y0q//euttygcROQcM318FZCV+WmDajaucUIreEXFaM0i7MQrgTKwLg4p9xee2Mjit5Xznx27zDWJ9rHlLu+pxz1ZBh1zJAACZCAsxGoUWcT9+c34r1tediyJx/tHd2FvhHJUZicHo+Z4xOslr9+dy461f9WzBppVV9T34Ly6iZU1DSgpKIeOflVqKi0r7kM9PdF+shIzMuIxPjkUAT6eau5OjE33XFn1a0Wx4LDCVBwdDhSTkgCJEACJKATOPnAj1C7w2TiGT5vMRIvvxJl69eiZvsWtDWaflwEJKQgcv4CRM6drwmU+lhnvpat+wyla943lugXm4AR9/8UETNmGnXMkAAJkIAzEMit6sCZija8tz0Pew7lo6Gxpduyxo+Nx7TxiRiXatIoduswgIrahlacKazUhMjcgipU19j3Vq1P+ZObJuHqOUl6kVcnJkDB0YkfDpdGAiRAAq5M4Pi930X9/t3aFsQ0NUw5tSn76H+Gh1RdYIxeuNglt1lz8ADy//NvY+0+IaEY+YtHETFzllHHDAmQAAmcLwIVjZ04Ud6OD3YW4POD+aisarBair/SAE5OT8CUjASkxoVatTmycLakFmeUJjK3sBq5ed0d7vh4eeG2y8fi9guttZmOXAPncgwBCo6O4chZSIAESIAELAhYmqdGXXIVvFuaUb7uY62HlwqzEXvJFYhdvtJihGtmm1T8x+wnfmeY23p5e2P0b/5E4dE1HydXTQJuQyCrrB27cprxyfZTyDpZYrWvsNBATFHaxWnKHDUqNMCqbbgLucW1OHKqFFmnSrppPkND/fGl5aPwrQtGDPcyOP8gCVBwHCQ4DiMBEiABErBPoPyzT5H76M+1xtirv4D2igpUbl6nlQOT0xB3xVXnLKSG/RU6tlbiP5763a/RWm7+cTbmd09QeHQsZs5GAiTQTwK7Ctrx2YFSrNt+BpXVZi1jXEyoJjBOV0Kj6XxhPycchm71zW04pATaIydLUVhcbXWHhNgQXL8oBV9fTgHSCowTFCg4OsFDsLeE6oY2VNSpN/Q16tBxXYvKt6BSfZKigpASrT4xgdrV3ljWkQAJkMD5IlC9fx9O33uXdvuo5RehUzmTqeo64yieUpO+8CX4BAaer+UN631P/f4xtJQWGfeg8GigYIYESOAcEGhTvm5WH2nE+n0F0L2gym2Dg/2xYHoa5k5OhrIKdbp0Mq9KxYwsxuHjxVZrGz8qGvevSsckFSuSyTkIUHA8z8+hoVm9FTpYgvUHy5BX2oCqeuXeuLYF7eoNdn9SnBIkE6MDkawEycTIAJQoQXPZ5FjERwRg8gi6Pe4PQ/YhARJwDIGmvLM4/q1b0a5CbEiMxog581G+9iNt8kjlGCfp+lWOuZETz3L8wfvR3mx2BJH5+vvwi6bHQCd+ZFwaCbgFgdqWTjz2Xi627sm1MgFdNHs05k9JQYDmwdS5t3o6vxrbD+Qh+6w5lm9wkD++cWUGbllo7eXVuXfivquj4Hgenm2OEhB3nKjAliNl2H7Y/I9jOJYSHR6AqDB/zB4fhVuWpmkC5XDch3OSAAl4NoEOpVk8ef8PUX9onwYi4Us3o/i/L2n5aKV5TLjsCo8A1FJZgVOPPWzsVRzmTH3PJDwblcyQAAmQgAMJHCloxGNvncQxdW5QT5OVw5sL543V4jDqda5y3ZNVhB1KgLR05nP5otH4+aoxrrIFt10nBcdz9Gi3HivD+58X4+DpapRWdg+0arkMH+VcwdfHGz6+6qOuvurqL3kV96yzoxNtKkh2mwq42qHi77S2dmhxeDqUfUK71FlOZJO/65p0fHV5mk0tiyRAAiQwdALZv/01Kj/6oNtEMcqbavyll3erd+eK6gP7UfDSs8YWI5ZegDE/NwuTRgMzJEACJDBEAqt3F+Lxd06jts5s6TAjMwWXLRw7xJnP7/DGlnZs25+HXerT1tGuLWbK+Hg8c+eU87swD787Bcdh/gLkKu3ic+tysXpbfrc7RYQFIHNsNOKiQuAfGICw4EBEhAUi0N+nW9/+VlSqc5Gn8yqRrdwen1Wxc2xj9WSkheOOS0Zh6STnDK7d332yHwmQgPMQqFHnGk91nWu0XJUnCo36/ks++wTlH6/Wi0j65t1I/PINRpkZEiABEhgKgXalSPjNO8fx7qY8q2nmTh+BC+eOtqpz5UJhWR0+2XYaeYVV2jamT0rGn26diCBfV96V666dguMwPbs29Q/6BSUwvqI+NfXWgVZTEsNw6eIMJESHDNPdzdO+8P4B7R+buF62fBuVMVKZri5PwSXTaTNupsUcCZDAYAic/NkDqN26wWqop5xptNq0TaHo3bdRacEl7We/RMzyFTa9WCQBEiCBgRGobWzDAy8exs6jZVYDF84aheWz3M+yrLWtE++sy8KJM6XaftNSovH0XdMRFeSEnn6snoj7FSg4DsMz/WB3Ef6jBMYz+bXdZh87KhZXLsk4ZzbnLcqUNb+0DqOTw3FAuTzeujcXFZX1xroyx8XgK8tSsDyTGkgDCjMkQAL9JlC5ZTOyH/yRVf+AhGSMuff/WdV5aiH/1ZdQs/dzbfty3nHMb/+E0AkTPRUH900CJDBEAkVVTZrQePi0SQOnT7di/lgsmJqiF93y+t7G4zikzj9KEqc5Hz+yBH6DN9JzS0bDvSkKjg4kXN7QjsfePIGNe7qbpY5Oi8GMicmYoDR95zvtP1GCXYfyUVxqFmwvmZ+Cuy8djTjljZWJBEiABPpL4PgPv4/6LsFIxvgGhyL957/s73CP6Hfmz39EU0GutteQ6bOR8Yc/e8S+uUkSIAHHEjhdVK8JjWcKzL/f5A6XLM3ArAmJjr2Zk872yprDOJNrciwZER6Ijx9a5KQrdc9l+fxCJffc2rnblYqogfcPVOPRV4/i4HGzRytZgQRbXTF/jPJsNRqxkUHnblG93CkxJgQzJyYhOCQAhSW1aG1rx6m8WnyigsUGB/lgQkpYL6PZRAIkQAImAmUfrUHZm69Y4Ui++esIiKMFgyUUL38/1B0+oFW1FhXAKyKKWkdLQMyTAAn0i8CDr2Th8OlKq75XXzgR09M959jRlHHxOFNQjRrlDKi5uQ2HCxtx6Yx4KyYsDB8BCo5DZFtc14Gn1xfiv5+cQEmZ+Q2QqNDnzUjD1csnIDl2+M8yDmYbybHKbCotGhU1TaiqaVSOdNqw+VAZTpc1Ykx8CKJC/QczLceQAAl4CIHcP/wGbeWmMyey5fgrr0fkrNkesvv+bzMwKRmNOTlorTCdR2rOyUXkBRfBJzCw/5OwJwmQgEcTePrj0/jAxtHi3OlpmK88qHpayhgdq46DVaG+oQV5xXVo7PTGvPRIT8NwXvZLwXEI2LdnN+Jva3Lw2dYzaGxq1Wby9/fFrCmpuGJZBsYroczbyc/thgT5YUp6PFraO5FfVK3t4Uyh8mC1r0QLBzJ1ZMQQCHEoCZCAuxIo/d+HqHj/LWN7EXMWqliNnhV2w9h8PzLe6nxjzd5dWs+O+loVTqkTEXPm9mMku5AACXg6ge3HK/DoK0etMIxIjsJ1K8Zb1XlKwU+Fqhup9p9dUKn9/j54qhLhymw1cwQt5ob7O+A93Ddwx/kbWjvxq/fz8NN/78Z25WymXcVT9PHygsTN+fp1M5Ub5FGICHEtbd0Fas1XXzgJ4n1VkniCfeLt43hqzSl3fITcEwmQwBAJVG8ye1H1CQhE9JJlQ5zRvYeHjZ+A8BlzjE2Wv/Vf1B09bJSZIQESIAF7BJqVk8O/fXjaqsnf1wcr3CjkhtXm+lmIiQjEwhkjjd5/fP0oDp+tMcrMDA8BCo4D5PpZVi3u/MchvLf2GGprm7XR6aPj8LUvzNSCrcaqL7KrpswxsbjpiqkYpzy/6un5j7IpPOoweCUBEtAINCizy9odmw0aEQuWIDDBc87YGBsfYEaEay+fLheAKqB10csvDXAGdicBEvA0Ak98cBJZOSaLMH3vS5TfjNT4UL3osdcp4+KQMdZ8vvH59Xkey+JcbZyC4wBIP/VJLh55fi+OnTI7wFkwcyS+eNFEJJ6DmIwDWOqgu8obnC9eNAniBVZPIjz+/SPrt116G68kQAKeR6Bywzpj076R0YhdSm2jAaSXTFBKKiLnLzF6SOzLmgMmpzlGJTMkQAIk0EXgrPI58b7NucZJ6njRvMnJZNRFYNG0VBWSwyTObNhTiMO51DoO55eDgmM/6JZUN+Pufx7A8x+eMM4yyjAJtLpitllN3o+pXKKLsrrF9RdMxIgUc+iQZ9ecofDoEk+PiySB4SdQu2WTcZPohUsh8QmZ+kcgZuly+AQFG52rt2w08syQAAmQgCWB/+0thpiq6ikqIhgr543Vi7wqAknK0eMc5SRIT8+uo9ZRZzEcVwqOfVDdeaICX/nj59h5xOw5UIaI0Lh8lvmL2sc0Ltcc4OeN61dORFKC2TmOCI8vrM91ub1wwSRAAo4jUL3rczSezNIm9IuORcyy5Y6b3ANm8ouMRPDYDGOnNVu3GHlmSIAESMCSwCd7ii2LWDZ3DMKD/azqWAAWTRuhhb8TFpv2FWJvNrWOw/W9oODYC9m/KfPMu5/aq0JVNBtqcOk+d/oItxYadSQhQb64TgmPEotST898eAr7z1TpRV5JgAQ8jED1zu3GjkPHTzLyzPSfQLBylKOnloJcVGwxnxfV63klARLwbAL/21OE3KI6A0JyYiQmjY42ysyYCfj5emG+Eh719MIGah11Fo6+UnDsgehdT+/Hc0rDJklMNtvbTaYCUyYkKq+po3sY5X7VkWH+uFaZrereVsVk4kkb717ut2vuiARIoCcCzWfPGk2hmVOMPDP9JxA2YaJV5xoL01+rBhZIgAQ8lsBHe83+NATClAyzExiPhdLLxsVRju6fY9ehElQ1dvbSm02DJUDB0Q65X791DLuOlmktYpIqAUZFbBRvo1ctNZsY2RnqllVxkUFYMst8lnP/yUo8sZphOtzyYXNTJNAHgZY8k+AoZqqh49L76M1mewT8wiMQMsb835LabVvQVmfWLNgbwzoScDSBwldfQaXSdrc3Njp6as43RAIHlYOXbYfMR6Qiw4MwdRw9V/eFdcZEk9OglrZ2rD1S0Vd3tg+CgO8gxrj1kHd3FuCdTSYV98zMVJRV1qNCfUJDAnDBvDFuvffeNjd9fAJyCqtw+LjJ3v6lT7MxdWQYlmfyDVhv3NjmXASq9+xG0XP/gv+IkQibOg1Ri5coxy4hzrVIJ15NR1sbWovztRWGTsx04pU6/9JCJk5G/enj2kLbaiohZ0djlq9w/oVzhW5BIO/fz6D0pWeNvQRPyETIrDkImz4DYcqSwNvftWJRGxtxk8xuFdDeMk0cFw8xx2TqncCEkWanjv/bVYjrZ5kjBPQ+kq39JUDB0YbUaxtNQuOMiQmIiw7GnkOm8nIlNEqoCk9Oy2aPQm5BNWrrmjQMz3ycQ8HRk78QLrj3mr170HB4v/apWvMeCv8ehdD5CxG+YBGilRDp5U0jjN4ea1NuDjrb27UufjHmeK+9jWGbfQKhkzNRsvpto7HxzGmAgqPBg5nhJRCivn916RPReOKodqOGrEOQjwiT3gGBCJ46EyFTpiI0cyrCp04FxN060zkjcMDCuYuEmpiaTm1jf+FLbPUTZ0pxIKsYpbXKT0eYT3+Hsl8/CPj8QqV+9POILq9vzcfq7QWYPj4WFy+eiBfe3qPte9aUVOWxKdUjGPS2yUB/XwSoz4mccq1bhXIaNDYlFKMTqLHpjRvbnIdAxMxZaG9tR8Oh/dqiOpqb0HTqBKo3fIbyj9agMT9PCY8+CExJcZ5FO9FKalXMweoNa7UVRc1bhIB4WhwM9vH4BgejZv8+tDeYTFR9QsMRvfKCwU7HcSQwIAJBqamIvfJqhM6eB++oWHQ2NaOtwnREp7O9DS0FZ1G/dxcqP1qNsvffQ92RI5o5ta8ys/YNNTvMG9BN2bnfBP7w1gk0t5he0k1WFl9i9cXUPwLt6mjj8TOm77J3gD/mjTNHB+jfDOzVGwEKjhZ0fvVaFiprW3DdxZn495u70aJ+YKYmRSrPohP4sq2LU6KKl1NR24TS8nqtphVeuHgafzxafI2YdXIC4Up49B81Fg1Hj6Kj3nyuTPKNx46i8rOPULFuHVqKlVm2evMekMD/YOuPtHLjetTvN71Qi162En7h4XoTr4MgUJ91FC3lXeeY2joQf/2qQczCISQweALy8kdeqIkQGT5/MXwT5IyYF9rKlGOWDpNTwI6mBjTnnkHt9i0ofecN1O7fj+ayMk0z6R9Ly4PB07c/cn92Nd7YZHZCduGCcYgMC7DfmbXdCMRGhWDLnhytPq+sAbcsN3tb7daZFQMmQMGxC5mubYxV5ql1TW3IyauEv68PrlThKCJDaetv+c1KjA1D1plytLQoTkX1WK4ExxjlfZWJBFyFQPCoUYhccQG8gkPVmb0StNdZx3xqr6lS5qwHULnmA1Rv347mykpNSPKLMp+fcJW9OnKd1Zs3KoH7sDZl3CWX8xzUEOHWHT+G5kLTmVH5DsavuoFMh8iUwwdPQITAMGWWGnPxpYhS/74DRo9DJ7zRctb0I1ybubMTrUUFmjayfPW7qNq8GU0F+ehUMiYtNQbP3nLkhyp24+7jFVpVWmo0ls6k4GPJp6+8t7KqPqOOVdWoY1WN6vd8YlwwxidTS94Xt/62U3DsIqVrG69YPgF7jxSqL1srZk8dgel0f9ztu2Rrshqk4j3Oy2BsoW6gWOHUBHyUqWD4tOmIveIqeEfGoL2yyjDVslx4m9II1e/bjbL33kbtoUNoq6+H/MDyRKc65R9+gCalefDy9UX8pVdaYmJ+EAQa1LnGptxsY2TonAUISEw0ysyQwPki4BsSipD0dM18OmLJcniFRai/j5Vor622WlJbZTkajhxElVhqfPopWpQm0icsHP4xdEpiBWoAhWc/y0Z+aaM2YpJyijM6OXIAo9lVCOQU1aCk3GRRVN3Ugavm8O+qo74Zvo6ayJXnEbfHp/JrTVtQB8DFi6qPcpIxbTy/aD09V7G335tVhMLiany2pwTfungMAvzoWKQnXqx3XgLegYFI/MIq7VO1YweqNm1A7ZaNEE+Xtql+z07Ip/iZvyFE/ciPWLgYUUuWwicoyLarW5ZbS01xxXzUj0imoRPwDQuzmqRBnbcNnzbNqo4FEjjfBILHjIV88I07UP7Zp6hcvxa1Wzd0W1ZLQS5KX3tR+4TMmo+o5SsRvWy5R75k6wZnABUHTpktYFISeBxgAOiMrtER5v8mn8rr+n1vtDIzFAIUHBW9Q7mmN2jpY2JxLNt0oHai0jTGergX1b6+WBmjYjTBsaiiEe99XogvLqRDkb6Ysd25CUTOmwf5tH3zW6jYsB5VGzcobeMuddbH5KRAX317UyNqNq3VPoVPxyBUOYqJVF5ZoxYs1Lu45VU0jZL8KDg65Pnaaq1blUk0Ewk4M4GYCy6EfBpycpSZ6ibUbtuszNcPdlty/e7tkE/Rv59G2OKlmhAZoUJ9MPVOoLS6WbN403ulxlu/XNLree2dgGUUhDoVi/14YR0ykmiu2ju1/rVScFScjp41vY3IGBmL1euyNHJT06lt7OsrNC0jARt2KBfyKr2tPNJScOyLGNtdhYB4Doy/6hrt06yc5FR/vgO1Ks6emKzaM9WS0B7yKRqdjvClyxGjzk8GjnC/cyk+wSYPym11fIPriO+ymAMykYArEggeORLywc23KI+rh7SzjtVbN6vzkGesttNWWYbK99/SPqKFjL36Wi30kVUnFgwC+epFvJ4S4yMQHMCf6TqPgVyjI4OtumcprSMFRyskgy7wG6nQ7TlRpQHUhcYxSoAclUTzgL6+VaFBfhiRHIWzBZWaqe//9hThspnuIXC3KJO849+5E60V4vHQC14+pjhAcvXyUia56vS1FvNP8hLfSuL/qbxWZ5P3Un07VZu3qtfmMfp3zavKRr3cp6tvp9TLXDLOYqzeV66d+tq0OUxr0du9VVgJbW0ypz6HunZKvmutMq9WVld9T/o9ocZra9b7qFhSsjZtLVp/09q8VZ2y7e5ioNYsZdmjtn7ZQ9f+u+q1ewoy1d6pr1HlvbV+qr/6n7fsTcarfto9pa+UNT4ytcxvuo/kvdT9NRZqXVKWvrJ2bQLpO4QkXlXjlcdB+bSr841VSoDUhMg9u9BSlGc1c9OZE5BP6X/+jbAFS5UDnpUqqPtKqz6uXPAOMQmOHfUUHB3xHDs7lN94JhJwcQKhkzIhn9Rv3onKHdtRvXUL6nZuQ2tJodXOdC1kGQVIKy6WhfyKJqOYkkBtowFjgJnoMLOpqgw9oTSOTI4h4PGC49myRpRWmt/wCNZpdIjT72/XyJRITXCUAbtOVrmN4Nh49ixay1Q4hq7U2WWq2Nmq1/DqMgREsFRCpvxEFyFWEzi1vEkI1up1YVc2pQmfNv1F+JR5dMFUG+8D3xgViqalGR3yUTEh9dTZ3o6azeu0z9nHHoF3aJhyGBEBccgjSeYJX7AYSTfdrA9xiasmuKuVtqu9drS2wtvPzyXW7ayLbK02vbR01vVxXSQwUAJR89TZRvWRVLltq/JKva2bEEkBsmeqeeUNRmNaIhUYBowBZvx8vRASHID6hmZt5LE8Co4DRNhjd48XHHedsj5TkqRMAyaOZlyiHr8xNg1jR0Rh8+cm05Sdx0zuo226uFyx/JOPUfL6qy63bi64BwLKfXxnp+mMoriM19O50vV0trYoj63l2ke/t1wbjhxwOcExIDbO2EJrTQ0C6DnR4DGYTFuNtYfKwcxxLsaUrH4fLSUlmsbfy8fXZBEgL1GUBYCXClulXVW9vBCBXifWBJJXH836QeunfnLIOF+xwFD9pU6VO7vm9JYztDKH+nj7SV6NVX1UJ0C1iSWCuyd5SdnZpj7qxYy8gOpQsRS92tpUuAuVb21Tx63b4dUuV/XHTOU721vRrmJOa+ewpb/6QPpLW9dV4jF2tJnmM9XpY7v6dY2R+TrkJak2rsM0h7qXdh99LukjeYnxqLfJeKmXq9Ym6zLfQ/r7KLPsjpYWVa/mk7Eq6QJkQcpIZL7wsrs/2n7tL7vU/AIyNYFOyPoFrYdOUcpBji44niqg4NgDpgFXe7zgmF1ifrsj9MaPjhkwRE8ekBIbitCQANTVN6NE2eZL4Nppo1z7j13uYw958iPl3s8RgZDps8/RnRx3G0svoG0UHIcM1hUEx8JXX0HRP/865L06agIvXSAVAVTyclWCp5jai+m7CJdaWYRMVectAq3k5SNWA0oA1cvm8V3Cb5fgqrWruUx9TYKsJgDLfWQelTQrg6ZmdGrWBurabLI86FTCUV9ClJcIZ0p40ixZNKHOJMBpwp+jQLnQPG02Jq0utHSHLzW/zCQ4RoQHIiyYFh2OAlxX34KCyiYkRwU6akqPncfjBceaBmvbQwluzzQwAqNUgNpDx0xnGXYcL3d5wTHl3vtRseZDeImmSt6MqquW5D/0Kq/XS15rU2osyVrVyw8DSapBe2OsrqaxXfNpQ9WbZGVAqZ1z0ufSyqpetXdo80reNNZ0L9XA5BYEms+ccrl9BKWNNNYsQb9DRo82yswMnEB7tWtoHH3Do+yGpxn4joc+wiRsKY1WD1N1/eXtoZXVzkTANyoGAWMzMOYnDzrTss7rWoq7nONEhVs7dzmvi3KTm2fl1VBwdMCz9HjBsapBmU10JYndmELXxzqOfl/T06IMwfGz/WX4porp6MopXgWEl4/TJnlDrYRJXVA1BEoRMEW4NYRdEVpVvy6BV+o1QVWEVFWv9+tUAqomqHaN8xKBVRtjIQzLzzRtnPrBJmPlV5v0Uf+zWocOrese0lF6SVKnBLU5zAK3jLauN9q6+spFDdLWK3Np7apG5jL6aovRelnVm9pNJlGW/aXech5jXplHFiRXOdOolaWuq7+6ak55LMraXGqQ0mOY1tnV1lZViSblrr41X52VtX2brjQZPurMY9qPfqbGuFbyjY2FrwQCV0HAtcD1ixa71gacbLVtteZ4bbI0PxU43ZnSse9+Gw2H9zvTks7tWkRDqTSMorWEpeayK+/l62cyt5WyaCu7tJryt1H7+yF/IyXfddX/5opGUvu7ol4wai8Wtf7ST/7WyN9jGafyql3+vkofVTDmOrcQhu9uaQ/8HBEzZw3fDVxw5qoak8YxMNDjf547/Oll5ddj5RSHT+txE3r8N7OyTpmVdKW42BAGsddhDOBq6fY4u6AWEocoLiJgADOw64AIyPmfrgEirjA5B4GmggJUKWcQNdu3on7Pjm6LElf0EUrQily4GAFx5rOC3To6cYWXOnfmGx6pCY6NuTlOvFLXWJqt4BiQ4lyxcNuqBum8p8t8VIQuzezTR5nciQAmZqJ2hC3NJFQTvLrOMWr9TMKYyVxU/VRRddp5R7kqQa1TK4uw5gsf5aRJzkua+qi83Fe1m8oW83Td21tdIecrLcra+Un1/dbOcMp8klcveZwyKUFSEyZFoNSESiVcijCqBE3tZZY6I2l6odf1ok+1iUCqvzDUzhnKSzKpl/+pM4jay0OjX9e86pyiZvmi6jUBVl1F4NUFX4k7Wp91BE0nT6AlL7tXVJp2MTUN/qNGq78hEQgaOYpCYy/EggJoptoLnkE1+fvwF9OgwNkM8njBsbrebKoaTzNVm69H/4qhwf5WHffnVOPCqcrbJBMJuDmBltJS5TlwiwqCvRV1SliUH2CWyS8hBZHLVyJKBcwOGTvOsskl895+/vCNUGeY86GFqmlVgoVfZKRL7uV8L7q9qQntjfVWywhIHWFVPt+F9D/8CU356mEr4Uyc1WjnBbsELk0QVHVamwhx4mG3SzjUHNyc78W78/3l5aH6WCbrkmWLY/Md6ntbuWkjKjdvRL0KudHRYnbmYnsnv9gEhC5YhKjFSxExe45tM8s2BGobzf/9CGT8Rhs6Qy/6KadcTEMn4PGCY4OFqWoqzVQH9Y0KUX/gxMy3Xd58qpSrQpwwkYC7EpAQCpVbtqBGCYz1u3dYheHQ9yzaxaiVFyJGCYxuFbIiIAB+EWZBseFsLiIoOOqPfUBXYWebgkak2lad17J/XDzkw0QCVTt2oGrLJtRs3aQ8RJf1CES8p4bOWaCsK5YgeslSpbml5qxHWDYNNY1mRYb+e8qmC4tDICAhOpiGToCCY7MyvehKI+j6WEcx4Guw0jrW1pnePOaWUnAcMEAOcG4C6qVI+cYNKi7jRtTu3Ir2+u6uveXteviyFYhesRKhEyc7934GuTovpXEMHDkS2GaaQMxVI6ZMHeRsnj2sycbUV2KCOq1ppGc/Ko/dvXj9LXr9v6jduhlN2Sd75CB/F0Jnz1exaRciUgmM/nyZ1COr3hpqLRQZra2mF/G99WfbwAj4U+M4MGA99PZ4wbGtxWQaEBToh5gIuunt4XvSZ3WoCrRqCI4l1uZXfQ5mBxJwUgI1Bw6gerMSGLdsRktRnt1Vhsycp8xRV2jaRZ/AILt93KXSOyQE4VOmofDVF7UtteSddZetnfN9NNloHAOSks/5GnhDErBHQJz4FCuBseydN7o799IHKCsj+dsXoYTFqIWL4B+foLfwOkgClhrH1jYKjoPE2OMwCo49ohlQg8cLjj4+3uo8eQdio0MHBI6drQkEW8QbKizv+cyD9SiWSMD5CMiZrkrRLm7ZiIajB+0u0D8+CWFLlrm1dtHexsXszEedcQwZOx71p46h/vRxNObnISjFuUws7a3d2eqalMddy+SXTIaWPJg/PwRKPlyNirffQKP6t20vhUydibD5i5RmcRGCnOxMrr31ulJdVKjZqWCbOCJiGhKB2nrr36IUHIeE0xjs0YJjfWun8tKmDssqwTHA38eAwszACYSFmP/gVVQ3ifM2ib3MRAIuQUA8ElZuWI+qjetRu30zOlvN3pb1DWhnd+YtMp3dWarO7jirx0V9wcN0FcExNHOKJjjKLWoO7KfgOEDWTSUlKi6itcfSoNFjBjgLu5OA4wiIKX65EhjrD+zpNmnQ+MnKDFX97VOaRXdw8tVtg05SERls/klOjePQH0pdbbPVJP4842jFY7AF87d0sDO48LgGdQ5ZnLpI8vczXV14O+d16cHK1Ncy5ZU3IC2WAWwtmTDvfARqDx9C1aYNqFZeAlt7MEUNm7MQ4RJGQ87uREc73ybO8Yq8lSv9sKnTUPzuG9qd6w4dQMJlV5zjVbj27ZrycrttIHze/G51rCCB4SZQs28vSt5Q5xi3bbS6lX9CMsKVVUXUsuUInZRp1cbC8BCIDDV7qG+nqeqQINepH/jtEjrGIvlLyB6mIRPwaMFR6ImpqiR/f2vBR6vk//WbgK/NoeOy2hYKjv2mx47nkoA4fCj/7FNUrfusx+DmQRmTlLC4BJHqh1OwOINhMgh4h4YqATrGMFdtKStGzaFDCM/kj0sDUh+ZhtOnrXpI2BZ+z6yQsHAOCJz5zaOo+ni11Z3C5qu/e11OvtzKI7TVLp2z4KfiDAYG+KBJOW1sU5ZwTIMnUFVvrW2UmWiqOnieliM9XnD01QVHP76JsPxiDDSvc9THhag/fkwk4GwERGAseOrPaKuq6La0gNRRCJk1G1FLlyN8+oxu7awwE/AKD0fIhEmGuWrtwX0UHM14es1J7Mu6g3ut+oROn2lVZoEEhptA3bEsQ2j0CQ5F5EWXIubSKxCSkTHct+b8vRAID/FXgmMjCotreunFpr4I6M4aLfuNTaQVnCWPweYpOHZpyvh2Z7BfIdM428CqEtuRiQScjUB7U6MhNPopBzeBo8cqRw8LlKfQqeAZs/4/LZ+oKBVyZBJKVr+tDao/egitSpPrp8xYmXonUL1vD+R7aJnCGBzdEgfz54BA6PgJiL5mFbxV3MU4ZWoemEyvvucAe5+3iAjxQ0lFoxYXu7CsDkmxdNzYJzQ7HarqrDWOidFBiAkz++KwM4RV/STg8b/ufZRpgKRamy9ZP/mxWxcB366zojqQ0ECP/2rpKHh1IgLxV1wFCXsQlJpK9/FDeC4+EZEIHDtWaSeU1vH4EbQ3N6Fm/37EqIDfTL0TqNlr7XzEW4VwiVIhDZhI4FwTGPnde871LXm/Pgikp4TixFmTtjG/tJaCYx+8emqusflNnzEyvKeurB8gAY/3COPrYzKprKm1fgM8QI4e393H5owjBUeP/0o4LYCImbMoNDrg6fjGxSFKnQHVU/XObXqW1x4IVB88gGYbJ0zBU2bAO8i943/2gIPVJEACNgQWT4wxavJormqwGGim1uaMY+YICo4DZdhTf48XHHUw5co0gGnwBHy7NLcyg586N2pZHvysHEkCJOCsBHyiohGuhPDwGXO0JTaXFKJMORxi6plAzd7d3RrD5s7rVscKEiABzySwbHKcctZostgqLK71TAgO2HVpRb3VLFNHUXC0AjKEgscLjoFdZ/HaOtpRWtUwBJSePVTX3AqFIJqpevaXgbv3GAI+sXGIVlpHry7Ljcptmz1m7wPdaH12NuoO77ca5heXiLhLLrOqY4EESMBzCchL91njozQAldUNqKixPqvnuWT6v/PSqkZUWvyeDw3yw5Q0Co79J9h7T48WHINVBI6IsECDUFG59RsKo4GZAREIDqRH1QEBY2cScFECvrGxCE7PQOS8xdoO2qorUbzmQxfdzfAuu3LT+m43iLzgYviEhHSrZwUJkIDnEpg/3hwvOL+U3lUH+k04fKrUasiEtDBlBefR4o4Vj6EWPJpksJ8XIsPNgmMJBcdBf58s7clDqHEcNEcOJAFXI+CbkoJoFSTcNzRMW3r1dmodbZ9h7eHDqD20z6ray88f0dQ2WjFhgQRIAFg03nzOMTu/kkgGSODoqRKrEUunxFmVWRgaAY8WHMWfamKUWXAss1BtDw2r542uqW8xNh1MwdFgwQwJuDsB8bAaPGkyIhaaPKq2NzZQ62jz0CvsaBsjVlyE4LQ0m54skgAJeDqBEbFBuHBWoobhYFYRzpbwrGN/vxO56lyopZlqoDovujIzvr/D2a8fBDxacBQ+KTFmb3YVFBz78ZWx36WuwWyHPzs90n4n1pIACbglAd/kFMRfcTX81Zk9SRUbPnXLfQ5mUxU7tqHhzIluQ6MvvrRbHStIgARIQAhcNTfJAPH5oXwjz0zvBI5ll1t1WDglFnER/lZ1LAyNgMcLjiNjgw2C1UpwbGhpN8rM9J9ArYXGcW6G2T6//zOwJwmQgCsTCMzMRNTylaYtdHQg919Pu/J2HLL2jrY2VG3Z1G2usLmLEDFjZrd6VpAACZCAEJivfkfNnWQyscw6WYLiSjpv7M8342ROmVW3ZZNjrcosDJ2AxwuO4UHeCA02vY3oUDzL+I9zUN+q+q6YOT7eXsp7VcSg5uAgEiAB1yaQdNsdCJ89X9tE/fEjKFr9vmtvaIirL9+4Hs3FBVaz+EbHIeX/vmNVxwIJkAAJ2BK4ck6CUbVpd46RZ8Y+gSylbbQ0U42NDMSKTJ5vtE9r8LUeLziGKZkxLMxsrnoyt2LwND14ZG2Xqeq09CgVx1FOjzKRAAl4GgEvf3/Ef+lG+IaZXJ9XbvwM1QesQ1B4CpOm4mJUbd3YbbtJd/wfgni2sRsXVpAACVgTuGR6AiaMNL2IP366FJUMzWENyKLU2taJLXushetFmbEI8PN4MceCkmOyHk80Wmkco8IDDJq2am6jgZleCTQ0mJzjzBpnij/Ua2c2kgAJuC2BsFmzkXDj14z9Fbz0LNqbzWegjQY3zrTWVKPw1ZfQVmvtSj/mui8h9uJL3Hjn3BoJkIAjCXxxcYox3erN3c9KG40entmohMbisjqDQniIP25eOsIoM+M4Ah4vOAb4imdVs8axrKIep/KrHEfYA2YS8wA9ZTLIqo6CVxLwWAJxX1iF2EuvMvZ/+g+PGXl3z3S0tqLg5f+gqSDXaqvBk6dhxLdpomoFhQUSIIFeCVw5OwnXLEnV+uTmVWDdLmutWq+DPaTxTEENduyz/nt78wVpGBln9mHiISjOyTY9XnAUyiunW7vqPW4hCJ2Tp+DiNzmeYzLvFbfH00bxfKOLP04unwSGTMDL1xepd38PIeMna3O1VVci76UXhjyvK0yQr/Zp60XVJygYKXd+G17ePq6wBa6RBEjAiQhcNzfZWM02pVk7mUflhgFEZTbbmKhOGRuJr68YadmFeQcSoOCoYM4bE4bIcLPW8WROOdo7HUjZzafKKzL9EZs8OhxB/vxh5OaPm9sjgX4R8AoMwuifP2L0rT2wB0UfvGeU3TGT/9rLqDt6sNvWUu75EUInZXarZwUJkAAJ9EVgYmoY7rom3ej2zqdH0NQq7hyZNu/Lw9mCSisQX79glFWZBccSoOCoeIYHeCFznNllb21dE45nW7v0dSx295lNAtNWVTdqG8ocaXKI4T67405IgASGQsAvIQFjf/MnY4rKTWtR+NYbRtmdMkXvvo2aPTu7bSntgYcQc8GF3epZQQIkQAL9JfDV5Wm4bL5J89jS0oYX39+HNg+XHQvVmcbtNiaqVy9MweKJMf3Fyn6DIEDBsQvavAnWX7TjSuvI1DeBg8cKjU7fvmyskWeGBEiABIRA+Ow5iL1mlQGjasdm5CvHMe6SxPFP0TtvonLrhm5botDYDQkrSIAEBkngvmszDC+rpUpoevadPWjy0NjjVSp2+Acbj0OEaD0lx4fgtgtH6UVeh4kABccusMsnRiE0xOxd9bQKy1FtEdR+mPi79LT5pXXYd7RI28PM8dEuvRcungRIYPgIjPjuPQidvcC4Qc3ez3H2+X+jvanJqHPFTO2xLOT+/UlUbtvUbflpP/4FNY3dqLCCBEhgsARCAnzwg+vTERTop00hwuOLH+xHXUPrYKd0yXGNSlh+57MsyP71pEKI4xc3TkRSVKBexeswYXXk+QAAHrFJREFUEaDg2AU2PtQbU8aZtY6NTa3Yuv/sMGF3j2n3Z5kDW08bTac47vFUuQsSGB4C6b/5PeK+/BVj8rojB3D2X/9AU0mJUedKmeI1HyLv33/v5j1V9pB4+12IufAiV9oO10oCJOACBKamReB7140zVirC0ytrDqKyzjNCHnUq/yPvrM1CQZdvDR3Eb2+fTueMOoxhvvr8QqVhvofLTN+sDMY3HTT/iClS5/dSk6MQFWbWRLrMZoZ5oaJt/GiTKaZQtIqD+cCq8QgJVLFNmEiABEigBwLhKsajX+pI1G7bDHR0oK26CnVHDsE3LAKBiUk9jHKu6vrsbC1Go2hN7aXk7/4Qiau+ZK+JdSRAAiQwZAITU8LQrDw4HjhtckwocbRzCqqRlhypfoeZtJFDvomTTvCWEhpP2vggefCWSbhwmnV0BCddvlssixpHi8e4YlKM8q5qrebeTq2jBSFz9shJk4mq1Fy1IAnxkdbczD2ZIwESIAEzgVjlKGb8359FQNoYrVJCdRS88rzTe1ztaGtD2YZ1OPvMU93CbchGfJTwm/7Uv5BwzXXmzTJHAiRAAsNA4DtXjMWXV6QZM5eW1+G/SvOYlWPtYdTo4AaZ99WZxmOnzMod2dIt6kzjFbNc46WjGzwCbQvUOFo8yQA/b4QE+WLLYbNHVfEYGhoaiKTYUIuenp0VbeOHG45rEETb+JMvTqC20bO/Etw9CQyIgF9UFOKuvR6NZ/PQnH1KG9uUewaNZ87APyERfuHOY/reXFGBik0bUPTW65CQIuho77bXkBlzMOnfL8I/1uydu1snVpAACZCAAwksGB+DUnW+8VhujTZrc3MbjirBysvHB2mJ7uXl/uPtZ7DvsPl4lGx4ZkYUHr7JFCvYgVg5VR8EKDjaAJJ4OceKG5BbVG+0lCvhcUp6Anx9qKAVKHuP5OJsUa3G50vLR2B5Jk0EjC8LMyRAAv0mELV0ObyjYtBaUoq2qgq0Vpajauc2tFRVwS8i4rwKkPVKiC1f+ylK3n0D9Sey0NHYYHdfcV+6BaMf+JndNlaSAAmQwHASWDIxFvnVzTiZZ/pNJvfKya9EeU0T0pKi4Ofr2r9ba5VgvHZnNvYcyrPCeN3SEXjsK5lWdSycGwJenSqdm1u5zl2yleB4+193o7auxVj0/BkjsXLOSKPsqZnDJwrw7rqT2vZF2/j8PbNppuqpXwbumwQcSKDs009Q9M+/obWs2Jg1bMoMhIyfgPDMafAJGn5z+ObyctSfPI76I4dRl3XIWIe9TMis+Yi9+lpEL15ir5l1JEACJHDOCNz/4iGs22P+2yk3TogNw4WLxmFkQtg5W4cjb7QnqwjbVJzGaiUE68lPKXDuv2ECrpxN81Sdybm+UnDsgfirW/Lw+BvHjFZfbx985dppHm2ymqViW7710WGDyQ++OB5fWphqlJkhARIggaESyP7j71H5v3c15zn6XF5+/ggZm4HQzCmImDkb3soUy1Gp/uRJ1B49jMbsM2jKy+5zWgqMfSJiBxIggfNA4NdvHcM7m6w1c77qb+X8mWlYMG0E1Gksl0h56jjUlr25OGXjBGei8t7/8I2TkBYX7BL7cNdFUnDs5cne++xBbDlgPog7IiUKt1w+BV4qXoynpULl8vn1jw6hriu25TVLUvHA9eM9DQP3SwIkcA4I1J84gbLV76Hq4w/R0Wx+26zd2tsboeMnIzh9PAKTUxCUkgJvf/9+raq9sRFN+floKipU5ylPoeHUCbQ3mo8l9DYJBcbe6LCNBEjAGQh8tK8YT35wGsXl1qb1sdEhmDs1FdMzEpxhmXbXoBzFYtOeXOzcexZtNmfJVynT1Puuy7A7jpXnlgAFx154Hy+ow51P7kG9RXDVmVNScekCkzfAXoa6VVN9YxteW3MAReotkKSpYyPxz+/Mcqs9cjMkQALOR6AhNxflSoCsXLMa7XUmBxD2VukXHYuAxGQEiCCZnAqvAJMg2VJehpbCQrQUF6G5tAhttT3PYW9e/5FjEaZCiITNmoOo+QvsdWEdCZAACTgVgbLaZjz+3il8uquw27pGjYjB3CkpGJca2a3tfFZkZZdrWsbiUvNZTVnP8pkJuHGxEnhHO9d6zyer831vCo59PAF5e/Pg89ZnXS5dNh4zxzvvW5s+tjSgZhXaEm9/egQnukwGwkP88ckveaZnQBDZmQRIYEgEWkqKUfLeu2hQJqUNh/ahU4XGGK4UpLSZEYuXIWLOXKXVTB+u23BeEiABEhhWAu99Xoi/rT6Nimobqw1118zxSZg0Nu68CpDi+ObAiWJknSpFcZm1wDh7fDRuWJaGJRNjhpURJx84AQqO/WBmT3j86rUzkRrv3iE6Kmqa8cGGY8grNAWZHauCzr78w7n9IMYuJEACJDA8BNrr61Fz6CDq9+1F7a6daDptCg000Lv5Km+u/ko76Z+SioDUEQhQ17ApU+Efwx8qA2XJ/iRAAs5JoFR5XH1lawFWby9AlYWTGX21URHBGDMyBpPGxGHEOfpNezq/WgmMRTh+qgxt7dbhjSaMjMCX1VGoy2cl6kvk1ckIUHDs5wOxJzzed9sS5erYPQ88nimowf82HlN/aBo1QtNUvJyn/29mP2mxGwmQAAmcPwIdSiPZ2dyM9qYmdUayGR0tLUbZJyQEQSNGwDsg4PwtkHcmARIggXNIoLaxHa8op4/vKwGyxOb8o74MOQc5fkw8piuLughlXebIJNrFo2fKcPB4EWzNUb3ghYVT47BscgyumZvsyNtyrmEgQMFxAFBthcfI8CB8+4Y5A5jBNbqKC+S1W0+hpc38JmjH4xe4xuK5ShIgARIgARIgARIggW4EWtT5o/9uzcc72wpUPG6T34punVRFalIk4pQgmRATol3jo0IQ0A+3rA0t7SivalTmsQ2oVBpOUT7ItbC4utttxo4Ix4XTYnHB1ASMpKfUbnyctYKC4wCfjAiPj76ShaYW0xmbxPgI3KbCdLhDEvOB7QfykH223NjOXBVc9i/fdI/9GZtihgRIgARIgARIgAQ8mMDeM1XYlFWFPaercPx0Jdo7lFOLXpKYtYYE+6t+nWhTioW29g51lU+7Vtfa2o6+QsMnxgRhSWYclmfGYva4qF7uxiZnJUDBcRBPZo/6x/aHt07gZJ7JQ19yYiSWzBqJsSkRg5jt/A+prGvGtv152Hc432oxjNNohYMFEiABEiABEiABEnA7AqKJXHukEhuOVODImUrUqd+FdQ0tQ95nclwI0lPDMC8jEmMSQjCD3lGHzPR8T0DBcZBPoKG5HY+pYKsf7TS7OxYvVUtnjkRkmGNtwwe5xH4N26oExp1Ky9jQaP4DEejvi9/dPgVz06P7NQc7kQAJkAAJkAAJkAAJuAeBZmVUV9fSgZyyZuSWNaKoqgnFlU0oU04Ta+ta0Kq0kyEBvggK8EFIkB8ClRmr+umIxAh/TEpVjiM7O5GZFoFg1c7kXgQoOA7xeT615hSe/yjbmCUo0A9zpo7A4umpRp2zZSQu437lAvmQOqRcVmEd/HqeOpz84y9MQFJUoLMtm+shARIgARIgARIgARIgARI4TwQoODoAvK3wKFMmJURgwbRUTBjlPK7dS9SB5QNKWDx0rNhKw6gjuG7pCNx/XYZe5JUESIAESIAESIAESIAESIAENAIUHB30RbAnPMrUUZHBSFfCY/rIWIxMCHPQ3QY2jYTWOHC8EEeOl6BT/c82iQOcLyxMUoeV422bWCYBEiABEiABEiABEiABEiABUHB04JegJ+FRv0VCXBjGqUCr49UnUbk4Hs5Uo2LmHM8pw/HsCisvqZb3pMBoSYN5EiABEiABEiABEiABEiCBnghQcOyJzCDrP9lfjH/87wzOFlufHbSdbkRylBIiozEqKQKx0aHoR3gc2ym6lVtaO5CVU66ExTKcUoFW29XhZHuJAqM9KqwjARIgARIgARIgARIgARLoiQAFx57IDKG+QrkxfvLDM/hgm3V4i1Urx+CNtae7zezr7YOoqCDERAUjVgVZjYsM0q6xEUHw8urWXQmE0OJINjW1adfa+mZkKUHxpBIaW7riS3YfBVBgtEeFdSRAAiRAAiRAAiRAAiRAAn0RoODYF6EhtH+wqxD/XHMGReWNxizRkYH4+U2TER8bjh/8fRcKSmqNNtuMt6qQM5J+/j5obmlXnzYlGKqgqyrYan9SXHQQJo2KwJyxEZiuYuekJykXyUwkQAIkQAIkQAIkQAIkQAIkMEACFBwHCGyg3QtV3Jtn1+Zg4/5SVNY2G8NFy7j+N0txpqwJx4ubcCi3HoezK5GTX4lmCaAziBQWFoCMtEhMHxOJJROiMDF5eM9RDmKJHEICJEACJEACJEACJEACJOCCBCg4nqOH1tbWgdV7i/Hp3hLsPFo25LuKkDg6JRKZSqM4SQmI6YmBGBUXPOR5OQEJkAAJkAAJkAAJkAAJkAAJ2BKg4GhL5ByUaxra8OHeImTl1SKvtBF5ZQ2orDZrI+0tIS4qEFOUJnHWWNNndDy1ifY4sY4ESIAESIAESIAESIAESMDxBCg4Op7poGZsVh5Rs0sb0KjMVAP9fBAc4KOu3ggK8EWQOuPo62PHS86g7sRBJEACJEACJEACJEACJEACJDAwAhQcB8aLvUmABEiABEiABEiABEiABEjA4wiI404mEiABEiABEiABEiABEiABEiABEuiRAAXHHtGwgQRIgARIgARIgARIgARIgARIQAhQcOT3gARIgARIgARIgARIgARIgARIoFcCFBx7xcNGEiABEiABEiABEiABEiABEiABCo78DpAACZAACZAACZAACZAACZAACfRKgIJjr3jYSAIkQAIkQAIkQAIkQAIkQAIkQMGR3wESIAESIAESIAESIAESIAESIIFeCVBw7BUPG0mABEiABEiABEiABEiABEiABCg48jtAAiRAAiRAAiRAAiRAAiRAAiTQKwEKjr3iYSMJkAAJkAAJkAAJkAAJkAAJkAAFR34HSIAESIAESIAESIAESIAESIAEeiVAwbFXPGwkARIgARIgARIgARIgARIgARKg4MjvAAmQAAmQAAmQAAmQAAmQAAmQQK8EfHttZSMJkAAJkIDTEti7dy+ysrK09fn4+GDVqlXw9u79feChQ4dw8OBBbUxgYCCuu+46p92fpy+spaUFb775poGht+f1yiuvGM/02muvhZeXlzGOGRIgARIgARJwBAGvTpUcMRHnIAESIAESOLcEdu/ejeuvv9646T//+U9cfPHFRtk209HRobWfOHFCa1q5ciWeffZZ224sOwmBmpoaTJkyxWo1a9aswcSJE63qpDBy5Eij7siRIwgJCTHKzJAACZAACZCAIwj0/mraEXfgHCRAAiRAAsNCYNasWRDhT09//vOf0du7wE8++QS60ChjfvCDH+hDeXURAi+//LKLrJTLJAESIAEScDcCFBzd7YlyPyRAAh5FwFL4EzPUDRs22N2/CJRPPPGE0XbFFVcgMzPTKDPjGgReeOEF1NXVucZiuUoSIAESIAG3IkDB0a0eJzdDAiTgaQRE+BMhUE+idbSXRKAUwVJP3//+9/Usry5G4P3333exFXO5JEACJEAC7kCAznHc4SlyDyRAAh5NQITA1atXawz27NmDLVu2YNGiRVZMLAVKOReZkZFh1W5ZaGtrw7Fjx3D8+HHt6uvri/T0dG2MvfN1lmP1fGtrKz7//HPk5+ejqqoKFRUVqK+vR1hYGCIjI7WPnN+bMGGCPmRYrtXV1Th8+LDmRCgvLw9paWkYP348Jk2ahIiICLv3zM7OtjLp1TstW7YM/v7+KCsrw8cffwzpFxAQoLGRtp7m08c76vr888/jxhtvHNR0g+ExqBtxEAmQAAmQgNsRoODodo+UGyIBEvA0AiIEijD41ltvaVsXk1RLwXHz5s0QgVJP3/ve9/Rst+vZs2chguiuXbu6tUnFNddcg4cfflgT/Ox1aGhowKOPPoq33367T5PKn/70p8MqOMqZTtmLPdPO0NBQzXT3ggsu6LaNtWvX4qGHHupW/+mnn6K8vBzf+MY3us2ZlJSkORrqr2DdbfJ+VCxfvhzr16/H0aNHIR51Z8yY0Y9R5i6D5WGegTkSIAESIAFPJkBTVU9++tw7CZCA2xCwFAa3b9+uafv0zVlqG2+55RaMGjVKb7K6ijCyePHiHoVG6fzuu+/i0ksvhQiItkk0lXfffTdefPHFboKVbV8pJycn26t2SN0//vEP3H777T2uQ4TJ2267DeKJtr/p9OnTPQqihYWFePDBB/s71aD63XTTTca4l156ycj3JzMcPPpzX/YhARIgARJwHwIMx+E+z5I7IQES8HACP/nJT/Cf//xHoyDaKTFp3LlzJ774xS8aZLZt22ZXYBPnOaJN3L9/v9FXQnuIICkCoWjbtm7darT98Ic/1IREo0JlxOPnj3/8Y6NKtHqyDtHChYeHQ+IQigmrmKyK6eoNN9zQoxBrTDKITHFxMebOnWs1UvYmZrGirXvvvfes2sSkNj4+3qgTE9SNGzdq5aeeegoiFErStbpjxozRTEXFhPfpp5822qXPZ599hnHjxkl2yMk2HMepU6e0fYnWU5I8KzH7ldRbOI6h8tBuwP8jARIgARLweAI0VfX4rwABkAAJuAuBu+66yxAcxaRRNIiWnlRFA9eTlk/O7FkKjX/84x/xhS98wUBz66234re//S3+9re/aXW///3vNZPN4OBgo8/BgweNvAiNIkQlJiYadecq8+STT1rd6i9/+Quuvvpqo05CmFg6BxLh8Be/+IXRLhpZXSsr2lv9/KiYAovQ+PrrryM2NlbrL9rXBQsWGGPlHKWjBEdj0q6MCKpf+9rXIM9G0jvvvIOvf/3rWr63/xsqj97mZhsJkAAJkIDnEKCpquc8a+6UBEjAzQmIUCjCoZ7uvfdebNq0SS/izjvvNPK2mXXr1hlVl1xyiZXQKA3e3t4Qc9iYmBijX25urpGXjDhesUziSOZ8JBGC9SSaR0uhUeqvu+46zJw5U+8COdPY3ySaVl1olDHC3JJJSUlJf6caVD9L7bFolHuL26nfYDh56PfglQRIgARIwP0JUHB0/2fMHZIACXgQAUvhUM7k6Um0kXFxcXqx2/XMmTNG3ZVXXmnkLTNBQUGYNWuWUSXaNcsk3kr1JGcIxXnLPffcgzfffFPzQKq3Dee1paXFynRUzEvtJcv6nJwczRzXXj/bOtFW2ibR6orJqnxWrFhh2+zQsgiqYkIsSZ6vaER7S8PNo7d7s40ESIAESMC9CNBU1b2eJ3dDAiTg4QREOPzOd76Dv/71r1Yk7rjjDquybeHEiRNG1eOPP655CDUqLDKW3lkLCgosWgBxvCNaMP0MnjSKeafu7VU0c9dee62mFe3JZNZqwkEUbNck3k7tpZSUFKtqOQdoW2fVQRUkZqYIz7ZJzoGey3TzzTdr4UDknnKm1dJU1nYdw8nD9l4skwAJkAAJuDcBahzd+/lydyRAAh5IwNJcVbb/gx/8AFFRUb2SsBT2RJMlAqK9T2+TiGAo5xpFSJUzjrZJ7vGvf/1LE3QeeeQRtLe323YZcrm2ttZqDokbaS9Zns2UdnFE01eydKDTV9/hbF+yZAl0gfiDDz5Ab+axw8ljOPfIuUmABEiABJyPADWOzvdMuCISIAESGBIBERLT09ONIPaiKesriedT8TgqSYS+ESNG9DXEruMbubfEZ7z//vuxY8cOzROrnLO0dLwjEz/zzDPa+L40oX0uwqZDQkKCVU1ZWZlVWS+IV1fL1B+h0PIso+XYc5338fGBOCuSeJmS3njjjR6XMJw8erwpG0iABEiABNySAAVHt3ys3BQJkAAJDIxARkaGIThefvnl+N3vfjewCWx6iwfQRYsWaZ/77rtP0+itWbMGDz30kBFb8cMPP9S0kzZDh1S0dFzz/9u7f5U49igO4GMRFCwSrIIWeQIlNmIfX8BGUlkYSGHlC/gAlnkCIXY21r6DpQRMJYK1pMifQnLvPXOZZdysxng298azn4HLrjN7Zvd8fhfCl5n5/eJEHz9+bGKyn+Ht7Ozsxq77hMKYIOhP2WLG2y44vn///taf9Ts9bv1SBwgQIECgpMCf869gSV5NESBA4HEI9Ce2OTw8bNd/HOcvj3UcNzY22iDZnTdC3bi3CHdxtbXb9vf3f5j4JtaSPDg46D7SPrs4+OORvIlAGGtTxtatMznqp0+Kx6je7SNAgACB8QoIjuP1dDYCBAg8SoHXr1/feC4xln2IcPXly5cf+rm+vm4ifA1vMcHM1dXV8O727+/fv7dh9OTkZHB8aWlp8H6cb7a2tgani+cqYxmRr1+/tvviNWaY7T/Tubm5Ofj8Y3oTk+TcZ5sUj/tY+AwBAgQIPFxg6p81oP56eLlKAgQIEPgTBdbW1gbPOMZVt1HLSAz/7qOjo2ZnZ2d4d3tF7vnz5+2Vu8vLy/a8MbnNcOB6+/Ztc3x83AbQmLzl6dOnTazlGBPPnJ+fD25R7b4gluoY9X3d8Ye+Rqh99epVE8ts9LcXL16M3BfrOMattbFFbVwZ7ba4KhpLi8QWz37GLb3dFs9y9pcn6faP6zXcFhcXB6cb7if++Y5x7S+7Eh/+8OFDMzs7O6jLeAxO4g0BAgQITLyAZxwn/n8BAAQIEPhXIJbKiNse9/b2bpCcnp428V9/Gw4xcezi4qL9SASt/vIe/bru/erqatNfc7LbP47XJ0+eNO/evWvevHlz48ri8G+O5xpjDcYuNMZ3x5XR/pIj/d8TffWPffr0qX/4P38/NTXVTpKzu7t753dnPO48sYMECBAgMFECblWdqOHWLAECBG4XiCCyvb3dzoS6vr7e3DVhTP9Wz+6Mo25r7Y51rysrK20wjaugMzMz3e6xvy4vL7dLg8RzgMNLg8Tf0V8sHfLy5csb3x0zlt53m56evu9Hf9vnuuccf/YFD/X42XkdJ0CAAIHJEXCr6uSMtU4JECDwywKfP39uryR++/atrY11Eefn55vhdRC7E8ftlbHofDxLGLdIxu2UMTHO3Nxc8+zZs+b/CFvxG+JKatxmu7Cw0K6BGCF5Ujcekzry+iZAgEBOQHDM+akmQIAAAQIECBAgQIBAeQG3qpYfYg0SIECAAAECBAgQIEAgJyA45vxUEyBAgAABAgQIECBAoLyA4Fh+iDVIgAABAgQIECBAgACBnIDgmPNTTYAAAQIECBAgQIAAgfICgmP5IdYgAQIECBAgQIAAAQIEcgKCY85PNQECBAgQIECAAAECBMoLCI7lh1iDBAgQIECAAAECBAgQyAkIjjk/1QQIECBAgAABAgQIECgvIDiWH2INEiBAgAABAgQIECBAICcgOOb8VBMgQIAAAQIECBAgQKC8gOBYfog1SIAAAQIECBAgQIAAgZyA4JjzU02AAAECBAgQIECAAIHyAoJj+SHWIAECBAgQIECAAAECBHICgmPOTzUBAgQIECBAgAABAgTKCwiO5YdYgwQIECBAgAABAgQIEMgJCI45P9UECBAgQIAAAQIECBAoLyA4lh9iDRIgQIAAAQIECBAgQCAnIDjm/FQTIECAAAECBAgQIECgvIDgWH6INUiAAAECBAgQIECAAIGcgOCY81NNgAABAgQIECBAgACB8gKCY/kh1iABAgQIECBAgAABAgRyAoJjzk81AQIECBAgQIAAAQIEygsIjuWHWIMECBAgQIAAAQIECBDICQiOOT/VBAgQIECAAAECBAgQKC8gOJYfYg0SIECAAAECBAgQIEAgJyA45vxUEyBAgAABAgQIECBAoLyA4Fh+iDVIgAABAgQIECBAgACBnIDgmPNTTYAAAQIECBAgQIAAgfICgmP5IdYgAQIECBAgQIAAAQIEcgKCY85PNQECBAgQIECAAAECBMoLCI7lh1iDBAgQIECAAAECBAgQyAkIjjk/1QQIECBAgAABAgQIECgvIDiWH2INEiBAgAABAgQIECBAICcgOOb8VBMgQIAAAQIECBAgQKC8gOBYfog1SIAAAQIECBAgQIAAgZyA4JjzU02AAAECBAgQIECAAIHyAoJj+SHWIAECBAgQIECAAAECBHICgmPOTzUBAgQIECBAgAABAgTKCwiO5YdYgwQIECBAgAABAgQIEMgJCI45P9UECBAgQIAAAQIECBAoLyA4lh9iDRIgQIAAAQIECBAgQCAnIDjm/FQTIECAAAECBAgQIECgvIDgWH6INUiAAAECBAgQIECAAIGcgOCY81NNgAABAgQIECBAgACB8gKCY/kh1iABAgQIECBAgAABAgRyAoJjzk81AQIECBAgQIAAAQIEygsIjuWHWIMECBAgQIAAAQIECBDICQiOOT/VBAgQIECAAAECBAgQKC8gOJYfYg0SIECAAAECBAgQIEAgJyA45vxUEyBAgAABAgQIECBAoLyA4Fh+iDVIgAABAgQIECBAgACBnIDgmPNTTYAAAQIECBAgQIAAgfICfwMGruVLbADQUgAAAABJRU5ErkJggg==" - } - }, - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# How to add breakpoints\n", - "\n", - "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions). \n", - "\n", - "Breakpoints are built on top of LangGraph [checkpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer), which save the graph's state after each node execution. Checkpoints are saved in [threads](https://langchain-ai.github.io/langgraph/concepts/low_level/#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.\n", - "\n", - "![Screenshot 2024-07-03 at 1.32.19 PM.png](attachment:b5aa6d4c-8dfd-490d-a53c-69c1368cd5b5.png)" - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install --quiet -U langgraph langchain_anthropic" - ] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for Anthropic (the LLM we will use)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdin", - "output_type": "stream", - "text": [ - "ANTHROPIC_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", - "_set_env(\"LANGCHAIN_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "131fd44d-c0f8-473a-ae80-4b4668ad7f47", - "metadata": {}, - "source": [ - "## Simple Usage\n", - "\n", - "Let's look at very basic usage of this.\n", - "\n", - "Below, we do two things:\n", - "\n", - "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` the specified step.\n", - "\n", - "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9b53f191-1e86-4881-a667-d46a3d66958b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", 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" + } + }, + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to add breakpoints\n", + "\n", + "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions). \n", + "\n", + "Breakpoints are built on top of LangGraph [checkpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer), which save the graph's state after each node execution. Checkpoints are saved in [threads](https://langchain-ai.github.io/langgraph/concepts/low_level/#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.\n", + "\n", + "![Screenshot 2024-07-03 at 1.32.19 PM.png](attachment:b5aa6d4c-8dfd-490d-a53c-69c1368cd5b5.png)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from typing import TypedDict\n", - "from langgraph.graph import StateGraph, START, END\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "from IPython.display import Image, display\n", - "\n", - "\n", - "class State(TypedDict):\n", - " input: str\n", - "\n", - "\n", - "def step_1(state):\n", - " print(\"---Step 1---\")\n", - " pass\n", - "\n", - "\n", - "def step_2(state):\n", - " print(\"---Step 2---\")\n", - " pass\n", - "\n", - "\n", - "def step_3(state):\n", - " print(\"---Step 3---\")\n", - " pass\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(\"step_1\", step_1)\n", - "builder.add_node(\"step_2\", step_2)\n", - "builder.add_node(\"step_3\", step_3)\n", - "builder.add_edge(START, \"step_1\")\n", - "builder.add_edge(\"step_1\", \"step_2\")\n", - "builder.add_edge(\"step_2\", \"step_3\")\n", - "builder.add_edge(\"step_3\", END)\n", - "\n", - "# Set up memory\n", - "memory = MemorySaver()\n", - "\n", - "# Add\n", - "graph = builder.compile(checkpointer=memory, interrupt_before=[\"step_3\"])\n", - "\n", - "# View\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "d7d5f80f-9d8c-4a39-b198-24fe94132b41", - "metadata": {}, - "source": [ - "We create a [thread ID](https://langchain-ai.github.io/langgraph/concepts/low_level/#threads) for the checkpointer.\n", - "\n", - "We run until step 3, as defined with `interrupt_before`. \n", - "\n", - "After the user input / approval, [we resume execution](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) by invoking the graph with `None`. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "dfe04a7f-988e-4a36-8ce8-2c49fab0130a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'input': 'hello world'}\n", - "---Step 1---\n", - "---Step 2---\n" - ] }, { - "name": "stdin", - "output_type": "stream", - "text": [ - "Do you want to go to Step 3? (yes/no): yes\n" - ] + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "---Step 3---\n" - ] - } - ], - "source": [ - "# Input\n", - "initial_input = {\"input\": \"hello world\"}\n", - "\n", - "# Thread\n", - "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "\n", - "# Run the graph until the first interruption\n", - "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", - " print(event)\n", - "\n", - "user_approval = input(\"Do you want to go to Step 3? (yes/no): \")\n", - "\n", - "if user_approval.lower() == \"yes\":\n", - " # If approved, continue the graph execution\n", - " for event in graph.stream(None, thread, stream_mode=\"values\"):\n", - " print(event)\n", - "else:\n", - " print(\"Operation cancelled by user.\")" - ] - }, - { - "cell_type": "markdown", - "id": "3333b771", - "metadata": {}, - "source": [ - "## Agent\n", - "\n", - "In the context of agents, breakpoints are useful to manually approve certain agent actions.\n", - " \n", - "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n", - "\n", - "We'll add a breakpoint before the `action` node is called. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6098e5cb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up the tool\n", - "from langchain_anthropic import ChatAnthropic\n", - "from langchain_core.tools import tool\n", - "from langgraph.graph import MessagesState, START\n", - "from langgraph.prebuilt import ToolNode\n", - "from langgraph.graph import END, StateGraph\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "\n", - "\n", - "@tool\n", - "def search(query: str):\n", - " \"\"\"Call to surf the web.\"\"\"\n", - " # This is a placeholder for the actual implementation\n", - " # Don't let the LLM know this though 😊\n", - " return [\n", - " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", - " ]\n", - "\n", - "\n", - "tools = [search]\n", - "tool_node = ToolNode(tools)\n", - "\n", - "# Set up the model\n", - "\n", - "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", - "model = model.bind_tools(tools)\n", - "\n", - "\n", - "# Define nodes and conditional edges\n", - "\n", - "\n", - "# Define the function that determines whether to continue or not\n", - "def should_continue(state):\n", - " messages = state[\"messages\"]\n", - " last_message = messages[-1]\n", - " # If there is no function call, then we finish\n", - " if not last_message.tool_calls:\n", - " return \"end\"\n", - " # Otherwise if there is, we continue\n", - " else:\n", - " return \"continue\"\n", - "\n", - "\n", - "# Define the function that calls the model\n", - "def call_model(state):\n", - " messages = state[\"messages\"]\n", - " response = model.invoke(messages)\n", - " # We return a list, because this will get added to the existing list\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "# Define a new graph\n", - "workflow = StateGraph(MessagesState)\n", - "\n", - "# Define the two nodes we will cycle between\n", - "workflow.add_node(\"agent\", call_model)\n", - "workflow.add_node(\"action\", tool_node)\n", - "\n", - "# Set the entrypoint as `agent`\n", - "# This means that this node is the first one called\n", - "workflow.add_edge(START, \"agent\")\n", - "\n", - "# We now add a conditional edge\n", - "workflow.add_conditional_edges(\n", - " # First, we define the start node. We use `agent`.\n", - " # This means these are the edges taken after the `agent` node is called.\n", - " \"agent\",\n", - " # Next, we pass in the function that will determine which node is called next.\n", - " should_continue,\n", - " # Finally we pass in a mapping.\n", - " # The keys are strings, and the values are other nodes.\n", - " # END is a special node marking that the graph should finish.\n", - " # What will happen is we will call `should_continue`, and then the output of that\n", - " # will be matched against the keys in this mapping.\n", - " # Based on which one it matches, that node will then be called.\n", - " {\n", - " # If `tools`, then we call the tool node.\n", - " \"continue\": \"action\",\n", - " # Otherwise we finish.\n", - " \"end\": END,\n", - " },\n", - ")\n", - "\n", - "# We now add a normal edge from `tools` to `agent`.\n", - "# This means that after `tools` is called, `agent` node is called next.\n", - "workflow.add_edge(\"action\", \"agent\")\n", - "\n", - "# Set up memory\n", - "memory = MemorySaver()\n", - "\n", - "# Finally, we compile it!\n", - "# This compiles it into a LangChain Runnable,\n", - "# meaning you can use it as you would any other runnable\n", - "\n", - "# We add in `interrupt_before=[\"action\"]`\n", - "# This will add a breakpoint before the `action` node is called\n", - "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])\n", - "\n", - "display(Image(app.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", - "metadata": {}, - "source": [ - "## Interacting with the Agent\n", - "\n", - "We can now interact with the agent.\n", - "\n", - "We see that it stops before calling a tool, because `interrupt_before` is set before the `action` node." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cfd140f0-a5a6-4697-8115-322242f197b5", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "search for the weather in sf now\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "[{'text': \"Certainly! I'll search for the current weather in San Francisco for you. Let me use the search function to find this information.\", 'type': 'text'}, {'text': None, 'type': 'tool_use', 'id': 'toolu_011ezBx5hKKjVJwqnECNPyyC', 'name': 'search', 'input': {'query': 'current weather in San Francisco'}}]\n" - ] - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n", - "\n", - "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n", - "inputs = [HumanMessage(content=\"search for the weather in sf now\")]\n", - "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "1bca3814-db08-4b0b-8c0c-95b6c5440c81", - "metadata": {}, - "source": [ - "**Resume**\n", - "\n", - "We can now call the agent again with no inputs to continue.\n", - "\n", - "This will run the tool as requested.\n", - "\n", - "Running an interrupted graph with `None` in the inputs means to `proceed as if the interruption didn't occur.`" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51923913-20f7-4ee1-b9ba-d01f5fb2869b", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for Anthropic (the LLM we will use)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: search\n", - "\n", - "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Based on the search results, I can provide you with information about the current weather in San Francisco:\n", - "\n", - "The weather in San Francisco right now is sunny. \n", - "\n", - "It's worth noting that the search result includes a playful reference to astrology, suggesting that Geminis should \"look out.\" However, this is likely just a humorous addition and not related to the actual weather conditions.\n", - "\n", - "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n" - ] + "cell_type": "code", + "execution_count": 2, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ANTHROPIC_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "131fd44d-c0f8-473a-ae80-4b4668ad7f47", + "metadata": {}, + "source": [ + "## Simple Usage\n", + "\n", + "Let's look at very basic usage of this.\n", + "\n", + "Below, we do two things:\n", + "\n", + "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` the specified step.\n", + "\n", + "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9b53f191-1e86-4881-a667-d46a3d66958b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from typing import TypedDict\n", + "from langgraph.graph import StateGraph, START, END\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from IPython.display import Image, display\n", + "\n", + "\n", + "class State(TypedDict):\n", + " input: str\n", + "\n", + "\n", + "def step_1(state):\n", + " print(\"---Step 1---\")\n", + " pass\n", + "\n", + "\n", + "def step_2(state):\n", + " print(\"---Step 2---\")\n", + " pass\n", + "\n", + "\n", + "def step_3(state):\n", + " print(\"---Step 3---\")\n", + " pass\n", + "\n", + "\n", + "builder = StateGraph(State)\n", + "builder.add_node(\"step_1\", step_1)\n", + "builder.add_node(\"step_2\", step_2)\n", + "builder.add_node(\"step_3\", step_3)\n", + "builder.add_edge(START, \"step_1\")\n", + "builder.add_edge(\"step_1\", \"step_2\")\n", + "builder.add_edge(\"step_2\", \"step_3\")\n", + "builder.add_edge(\"step_3\", END)\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Add\n", + "graph = builder.compile(checkpointer=memory, interrupt_before=[\"step_3\"])\n", + "\n", + "# View\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "d7d5f80f-9d8c-4a39-b198-24fe94132b41", + "metadata": {}, + "source": [ + "We create a [thread ID](https://langchain-ai.github.io/langgraph/concepts/low_level/#threads) for the checkpointer.\n", + "\n", + "We run until step 3, as defined with `interrupt_before`. \n", + "\n", + "After the user input / approval, [we resume execution](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) by invoking the graph with `None`. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dfe04a7f-988e-4a36-8ce8-2c49fab0130a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'input': 'hello world'}\n", + "---Step 1---\n", + "---Step 2---\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Do you want to go to Step 3? (yes/no): yes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---Step 3---\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"input\": \"hello world\"}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)\n", + "\n", + "user_approval = input(\"Do you want to go to Step 3? (yes/no): \")\n", + "\n", + "if user_approval.lower() == \"yes\":\n", + " # If approved, continue the graph execution\n", + " for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)\n", + "else:\n", + " print(\"Operation cancelled by user.\")" + ] + }, + { + "cell_type": "markdown", + "id": "3333b771", + "metadata": {}, + "source": [ + "## Agent\n", + "\n", + "In the context of agents, breakpoints are useful to manually approve certain agent actions.\n", + " \n", + "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n", + "\n", + "We'll add a breakpoint before the `action` node is called. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6098e5cb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set up the tool\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_core.tools import tool\n", + "from langgraph.graph import MessagesState, START\n", + "from langgraph.prebuilt import ToolNode\n", + "from langgraph.graph import END, StateGraph\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " # Don't let the LLM know this though 😊\n", + " return [\n", + " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", + " ]\n", + "\n", + "\n", + "tools = [search]\n", + "tool_node = ToolNode(tools)\n", + "\n", + "# Set up the model\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", + "model = model.bind_tools(tools)\n", + "\n", + "\n", + "# Define nodes and conditional edges\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "def should_continue(state):\n", + " messages = state[\"messages\"]\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"end\"\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return \"continue\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " response = model.invoke(messages)\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [response]}\n", + "\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(MessagesState)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", tool_node)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + " # Finally we pass in a mapping.\n", + " # The keys are strings, and the values are other nodes.\n", + " # END is a special node marking that the graph should finish.\n", + " # What will happen is we will call `should_continue`, and then the output of that\n", + " # will be matched against the keys in this mapping.\n", + " # Based on which one it matches, that node will then be called.\n", + " {\n", + " # If `tools`, then we call the tool node.\n", + " \"continue\": \"action\",\n", + " # Otherwise we finish.\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "\n", + "# We add in `interrupt_before=[\"action\"]`\n", + "# This will add a breakpoint before the `action` node is called\n", + "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])\n", + "\n", + "display(Image(app.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", + "metadata": {}, + "source": [ + "## Interacting with the Agent\n", + "\n", + "We can now interact with the agent.\n", + "\n", + "We see that it stops before calling a tool, because `interrupt_before` is set before the `action` node." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cfd140f0-a5a6-4697-8115-322242f197b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "search for the weather in sf now\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "[{'text': \"Certainly! I'll search for the current weather in San Francisco for you. Let me use the search function to find this information.\", 'type': 'text'}, {'text': None, 'type': 'tool_use', 'id': 'toolu_011ezBx5hKKjVJwqnECNPyyC', 'name': 'search', 'input': {'query': 'current weather in San Francisco'}}]\n" + ] + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n", + "inputs = [HumanMessage(content=\"search for the weather in sf now\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "1bca3814-db08-4b0b-8c0c-95b6c5440c81", + "metadata": {}, + "source": [ + "**Resume**\n", + "\n", + "We can now call the agent again with no inputs to continue.\n", + "\n", + "This will run the tool as requested.\n", + "\n", + "Running an interrupted graph with `None` in the inputs means to `proceed as if the interruption didn't occur.`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "51923913-20f7-4ee1-b9ba-d01f5fb2869b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: search\n", + "\n", + "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Based on the search results, I can provide you with information about the current weather in San Francisco:\n", + "\n", + "The weather in San Francisco right now is sunny. \n", + "\n", + "It's worth noting that the search result includes a playful reference to astrology, suggesting that Geminis should \"look out.\" However, this is likely just a humorous addition and not related to the actual weather conditions.\n", + "\n", + "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n" + ] + } + ], + "source": [ + "for event in app.stream(None, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" } - ], - "source": [ - "for event in app.stream(None, thread, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/human_in_the_loop/edit-graph-state.ipynb b/examples/human_in_the_loop/edit-graph-state.ipynb index a5d4ef6db..7f29740a3 100644 --- a/examples/human_in_the_loop/edit-graph-state.ipynb +++ b/examples/human_in_the_loop/edit-graph-state.ipynb @@ -1,575 +1,575 @@ { - "cells": [ - { - "attachments": { - "49539520-097a-43d5-94b4-2b56193a579f.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# How to edit graph state\n", - "\n", - "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Manually updating the graph state a common HIL interaction pattern, allowing the human to edit actions (e.g., what tool is being called or how it is being called).\n", - "\n", - "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state and then resume from that spot to continue. \n", - "\n", - "![image.png](attachment:49539520-097a-43d5-94b4-2b56193a579f.png)" - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install --quiet -U langgraph langchain_anthropic" - ] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for Anthropic (the LLM we will use)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "ANTHROPIC_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", - "_set_env(\"LANGCHAIN_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "035e567c-db5c-4085-ba4e-5b3814561c21", - "metadata": {}, - "source": [ - "## Simple Usage\n", - "\n", - "Let's look at very basic usage of this.\n", - "\n", - "Below, we do three things:\n", - "\n", - "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` a specified step (node).\n", - "\n", - "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n", - "\n", - "3) We use `.update_state` to update the state of the graph." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "85e452f8-f33a-4ead-bb4d-7386cdba8edc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", 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+ } + }, + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to edit graph state\n", + "\n", + "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Manually updating the graph state a common HIL interaction pattern, allowing the human to edit actions (e.g., what tool is being called or how it is being called).\n", + "\n", + "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state and then resume from that spot to continue. \n", + "\n", + "![image.png](attachment:49539520-097a-43d5-94b4-2b56193a579f.png)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from typing import TypedDict\n", - "from langgraph.graph import StateGraph, START, END\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "from IPython.display import Image, display\n", - "\n", - "\n", - "class State(TypedDict):\n", - " input: str\n", - "\n", - "\n", - "def step_1(state):\n", - " print(\"---Step 1---\")\n", - " pass\n", - "\n", - "\n", - "def step_2(state):\n", - " print(\"---Step 2---\")\n", - " pass\n", - "\n", - "\n", - "def step_3(state):\n", - " print(\"---Step 3---\")\n", - " pass\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(\"step_1\", step_1)\n", - "builder.add_node(\"step_2\", step_2)\n", - "builder.add_node(\"step_3\", step_3)\n", - "builder.add_edge(START, \"step_1\")\n", - "builder.add_edge(\"step_1\", \"step_2\")\n", - "builder.add_edge(\"step_2\", \"step_3\")\n", - "builder.add_edge(\"step_3\", END)\n", - "\n", - "# Set up memory\n", - "memory = MemorySaver()\n", - "\n", - "# Add\n", - "graph = builder.compile(checkpointer=memory, interrupt_before=[\"step_2\"])\n", - "\n", - "# View\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'input': 'hello world'}\n", - "---Step 1---\n" - ] - } - ], - "source": [ - "# Input\n", - "initial_input = {\"input\": \"hello world\"}\n", - "\n", - "# Thread\n", - "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "\n", - "# Run the graph until the first interruption\n", - "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", - " print(event)" - ] - }, - { - "cell_type": "markdown", - "id": "4ab27716-e861-4ba3-9d7d-90694013e3c4", - "metadata": {}, - "source": [ - "Now, we can just manually update our graph state - " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "49d61230-e5dc-4272-b8ab-09b0af30f088", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current state!\n", - "{'input': 'hello world'}\n", - "---\n", - "---\n", - "Updated state!\n", - "{'input': 'hello universe!'}\n" - ] - } - ], - "source": [ - "print(\"Current state!\")\n", - "print(graph.get_state(thread).values)\n", - "\n", - "graph.update_state(thread, {\"input\": \"hello universe!\"})\n", - "\n", - "print(\"---\\n---\\nUpdated state!\")\n", - "print(graph.get_state(thread).values)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cf77f6eb-4cc0-4615-a095-eb5ae7027b7a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---Step 2---\n", - "---Step 3---\n" - ] - } - ], - "source": [ - "# Continue the graph execution\n", - "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", - " print(event)" - ] - }, - { - "cell_type": "markdown", - "id": "3333b771", - "metadata": {}, - "source": [ - "## Agent\n", - "\n", - "In the context of agents, updating state is useful for things like editing tool calls.\n", - " \n", - "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n", - "\n", - "We will use Anthropic's models and a fake tool (just for demo purposes)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6098e5cb", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up the tool\n", - "from langchain_anthropic import ChatAnthropic\n", - "from langchain_core.tools import tool\n", - "from langgraph.graph import MessagesState, START, END, StateGraph\n", - "from langgraph.prebuilt import ToolNode\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "\n", - "\n", - "@tool\n", - "def search(query: str):\n", - " \"\"\"Call to surf the web.\"\"\"\n", - " # This is a placeholder for the actual implementation\n", - " # Don't let the LLM know this though 😊\n", - " return [\n", - " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", - " ]\n", - "\n", - "\n", - "tools = [search]\n", - "tool_node = ToolNode(tools)\n", - "\n", - "# Set up the model\n", - "\n", - "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", - "model = model.bind_tools(tools)\n", - "\n", - "\n", - "# Define nodes and conditional edges\n", - "\n", - "\n", - "# Define the function that determines whether to continue or not\n", - "def should_continue(state):\n", - " messages = state[\"messages\"]\n", - " last_message = messages[-1]\n", - " # If there is no function call, then we finish\n", - " if not last_message.tool_calls:\n", - " return \"end\"\n", - " # Otherwise if there is, we continue\n", - " else:\n", - " return \"continue\"\n", - "\n", - "\n", - "# Define the function that calls the model\n", - "def call_model(state):\n", - " messages = state[\"messages\"]\n", - " response = model.invoke(messages)\n", - " # We return a list, because this will get added to the existing list\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "# Define a new graph\n", - "workflow = StateGraph(MessagesState)\n", - "\n", - "# Define the two nodes we will cycle between\n", - "workflow.add_node(\"agent\", call_model)\n", - "workflow.add_node(\"action\", tool_node)\n", - "\n", - "# Set the entrypoint as `agent`\n", - "# This means that this node is the first one called\n", - "workflow.add_edge(START, \"agent\")\n", - "\n", - "# We now add a conditional edge\n", - "workflow.add_conditional_edges(\n", - " # First, we define the start node. We use `agent`.\n", - " # This means these are the edges taken after the `agent` node is called.\n", - " \"agent\",\n", - " # Next, we pass in the function that will determine which node is called next.\n", - " should_continue,\n", - " # Finally we pass in a mapping.\n", - " # The keys are strings, and the values are other nodes.\n", - " # END is a special node marking that the graph should finish.\n", - " # What will happen is we will call `should_continue`, and then the output of that\n", - " # will be matched against the keys in this mapping.\n", - " # Based on which one it matches, that node will then be called.\n", - " {\n", - " # If `tools`, then we call the tool node.\n", - " \"continue\": \"action\",\n", - " # Otherwise we finish.\n", - " \"end\": END,\n", - " },\n", - ")\n", - "\n", - "# We now add a normal edge from `tools` to `agent`.\n", - "# This means that after `tools` is called, `agent` node is called next.\n", - "workflow.add_edge(\"action\", \"agent\")\n", - "\n", - "# Set up memory\n", - "memory = MemorySaver()\n", - "\n", - "# Finally, we compile it!\n", - "# This compiles it into a LangChain Runnable,\n", - "# meaning you can use it as you would any other runnable\n", - "\n", - "# We add in `interrupt_before=[\"action\"]`\n", - "# This will add a breakpoint before the `action` node is called\n", - "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])" - ] - }, - { - "cell_type": "markdown", - "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", - "metadata": {}, - "source": [ - "## Interacting with the Agent\n", - "\n", - "We can now interact with the agent and see that it stops before calling a tool.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cfd140f0-a5a6-4697-8115-322242f197b5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "search for the weather in sf now\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "[{'text': \"Certainly! I can help you search for the current weather in San Francisco. To do this, I'll use the search function to look up the most up-to-date weather information. Let me do that for you right away.\", 'type': 'text'}, {'id': 'toolu_01FSkinAVXR1C4D5kecrzAnj', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}]\n", - "Tool Calls:\n", - " search (toolu_01FSkinAVXR1C4D5kecrzAnj)\n", - " Call ID: toolu_01FSkinAVXR1C4D5kecrzAnj\n", - " Args:\n", - " query: current weather in San Francisco\n" - ] - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n", - "\n", - "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n", - "inputs = [HumanMessage(content=\"search for the weather in sf now\")]\n", - "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "78e3f5b9-9700-42b1-863f-c404861f8620", - "metadata": {}, - "source": [ - "**Edit**\n", - "\n", - "We can now update the state accordingly. Let's modify the tool call to have the query `\"current weather in SF\"`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1aa7b1b9-9322-4815-bc0d-eb083870ac15", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'configurable': {'thread_id': '3',\n", - " 'thread_ts': '1ef3e229-4126-628c-8002-2a809f9bb238'}}" + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# First, lets get the current state\n", - "current_state = app.get_state(thread)\n", - "\n", - "# Let's now get the last message in the state\n", - "# This is the one with the tool calls that we want to update\n", - "last_message = current_state.values[\"messages\"][-1]\n", - "\n", - "# Let's now update the args for that tool call\n", - "last_message.tool_calls[0][\"args\"] = {\"query\": \"current weather in SF\"}\n", - "\n", - "# Let's now call `update_state` to pass in this message in the `messages` key\n", - "# This will get treated as any other update to the state\n", - "# It will get passed to the reducer function for the `messages` key\n", - "# That reducer function will use the ID of the message to update it\n", - "# It's important that it has the right ID! Otherwise it would get appended\n", - "# as a new message\n", - "app.update_state(thread, {\"messages\": last_message})" - ] - }, - { - "cell_type": "markdown", - "id": "0dcc5457-1ba1-4cba-ac41-da5c67cc67e5", - "metadata": {}, - "source": [ - "Let's now check the current state of the app to make sure it got updated accordingly" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a3fcf2bd-f881-49fe-b20e-ad16e6819bc6", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "[{'name': 'search',\n", - " 'args': {'query': 'current weather in SF'},\n", - " 'id': 'toolu_01FSkinAVXR1C4D5kecrzAnj'}]" + "cell_type": "code", + "execution_count": 13, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "current_state = app.get_state(thread).values[\"messages\"][-1].tool_calls\n", - "current_state" - ] - }, - { - "cell_type": "markdown", - "id": "1bca3814-db08-4b0b-8c0c-95b6c5440c81", - "metadata": {}, - "source": [ - "**Resume**\n", - "\n", - "We can now call the agent again with no inputs to continue, ie. run the tool as requested. We can see from the logs that it passes in the update args to the tool." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "51923913-20f7-4ee1-b9ba-d01f5fb2869b", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: search\n", - "\n", - "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Based on the search results, I can provide you with the current weather information for San Francisco:\n", - "\n", - "The weather in San Francisco is currently sunny. \n", - "\n", - "It's important to note that the search result also included a playful astrological reference, which isn't directly related to the weather. If you need more specific weather details like temperature, humidity, or forecast, please let me know, and I can perform another search to find that information for you.\n", - "\n", - "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n" - ] + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for Anthropic (the LLM we will use)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ANTHROPIC_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "035e567c-db5c-4085-ba4e-5b3814561c21", + "metadata": {}, + "source": [ + "## Simple Usage\n", + "\n", + "Let's look at very basic usage of this.\n", + "\n", + "Below, we do three things:\n", + "\n", + "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` a specified step (node).\n", + "\n", + "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n", + "\n", + "3) We use `.update_state` to update the state of the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "85e452f8-f33a-4ead-bb4d-7386cdba8edc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from typing import TypedDict\n", + "from langgraph.graph import StateGraph, START, END\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from IPython.display import Image, display\n", + "\n", + "\n", + "class State(TypedDict):\n", + " input: str\n", + "\n", + "\n", + "def step_1(state):\n", + " print(\"---Step 1---\")\n", + " pass\n", + "\n", + "\n", + "def step_2(state):\n", + " print(\"---Step 2---\")\n", + " pass\n", + "\n", + "\n", + "def step_3(state):\n", + " print(\"---Step 3---\")\n", + " pass\n", + "\n", + "\n", + "builder = StateGraph(State)\n", + "builder.add_node(\"step_1\", step_1)\n", + "builder.add_node(\"step_2\", step_2)\n", + "builder.add_node(\"step_3\", step_3)\n", + "builder.add_edge(START, \"step_1\")\n", + "builder.add_edge(\"step_1\", \"step_2\")\n", + "builder.add_edge(\"step_2\", \"step_3\")\n", + "builder.add_edge(\"step_3\", END)\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Add\n", + "graph = builder.compile(checkpointer=memory, interrupt_before=[\"step_2\"])\n", + "\n", + "# View\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'input': 'hello world'}\n", + "---Step 1---\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"input\": \"hello world\"}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "4ab27716-e861-4ba3-9d7d-90694013e3c4", + "metadata": {}, + "source": [ + "Now, we can just manually update our graph state - " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "49d61230-e5dc-4272-b8ab-09b0af30f088", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current state!\n", + "{'input': 'hello world'}\n", + "---\n", + "---\n", + "Updated state!\n", + "{'input': 'hello universe!'}\n" + ] + } + ], + "source": [ + "print(\"Current state!\")\n", + "print(graph.get_state(thread).values)\n", + "\n", + "graph.update_state(thread, {\"input\": \"hello universe!\"})\n", + "\n", + "print(\"---\\n---\\nUpdated state!\")\n", + "print(graph.get_state(thread).values)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cf77f6eb-4cc0-4615-a095-eb5ae7027b7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---Step 2---\n", + "---Step 3---\n" + ] + } + ], + "source": [ + "# Continue the graph execution\n", + "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "3333b771", + "metadata": {}, + "source": [ + "## Agent\n", + "\n", + "In the context of agents, updating state is useful for things like editing tool calls.\n", + " \n", + "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n", + "\n", + "We will use Anthropic's models and a fake tool (just for demo purposes)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6098e5cb", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the tool\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_core.tools import tool\n", + "from langgraph.graph import MessagesState, START, END, StateGraph\n", + "from langgraph.prebuilt import ToolNode\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " # Don't let the LLM know this though 😊\n", + " return [\n", + " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", + " ]\n", + "\n", + "\n", + "tools = [search]\n", + "tool_node = ToolNode(tools)\n", + "\n", + "# Set up the model\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", + "model = model.bind_tools(tools)\n", + "\n", + "\n", + "# Define nodes and conditional edges\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "def should_continue(state):\n", + " messages = state[\"messages\"]\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"end\"\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return \"continue\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " response = model.invoke(messages)\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [response]}\n", + "\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(MessagesState)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", tool_node)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + " # Finally we pass in a mapping.\n", + " # The keys are strings, and the values are other nodes.\n", + " # END is a special node marking that the graph should finish.\n", + " # What will happen is we will call `should_continue`, and then the output of that\n", + " # will be matched against the keys in this mapping.\n", + " # Based on which one it matches, that node will then be called.\n", + " {\n", + " # If `tools`, then we call the tool node.\n", + " \"continue\": \"action\",\n", + " # Otherwise we finish.\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "\n", + "# We add in `interrupt_before=[\"action\"]`\n", + "# This will add a breakpoint before the `action` node is called\n", + "app = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])" + ] + }, + { + "cell_type": "markdown", + "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", + "metadata": {}, + "source": [ + "## Interacting with the Agent\n", + "\n", + "We can now interact with the agent and see that it stops before calling a tool.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cfd140f0-a5a6-4697-8115-322242f197b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "search for the weather in sf now\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "[{'text': \"Certainly! I can help you search for the current weather in San Francisco. To do this, I'll use the search function to look up the most up-to-date weather information. Let me do that for you right away.\", 'type': 'text'}, {'id': 'toolu_01FSkinAVXR1C4D5kecrzAnj', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}]\n", + "Tool Calls:\n", + " search (toolu_01FSkinAVXR1C4D5kecrzAnj)\n", + " Call ID: toolu_01FSkinAVXR1C4D5kecrzAnj\n", + " Args:\n", + " query: current weather in San Francisco\n" + ] + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "thread = {\"configurable\": {\"thread_id\": \"3\"}}\n", + "inputs = [HumanMessage(content=\"search for the weather in sf now\")]\n", + "for event in app.stream({\"messages\": inputs}, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "78e3f5b9-9700-42b1-863f-c404861f8620", + "metadata": {}, + "source": [ + "**Edit**\n", + "\n", + "We can now update the state accordingly. Let's modify the tool call to have the query `\"current weather in SF\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1aa7b1b9-9322-4815-bc0d-eb083870ac15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'configurable': {'thread_id': '3',\n", + " 'thread_ts': '1ef3e229-4126-628c-8002-2a809f9bb238'}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First, lets get the current state\n", + "current_state = app.get_state(thread)\n", + "\n", + "# Let's now get the last message in the state\n", + "# This is the one with the tool calls that we want to update\n", + "last_message = current_state.values[\"messages\"][-1]\n", + "\n", + "# Let's now update the args for that tool call\n", + "last_message.tool_calls[0][\"args\"] = {\"query\": \"current weather in SF\"}\n", + "\n", + "# Let's now call `update_state` to pass in this message in the `messages` key\n", + "# This will get treated as any other update to the state\n", + "# It will get passed to the reducer function for the `messages` key\n", + "# That reducer function will use the ID of the message to update it\n", + "# It's important that it has the right ID! Otherwise it would get appended\n", + "# as a new message\n", + "app.update_state(thread, {\"messages\": last_message})" + ] + }, + { + "cell_type": "markdown", + "id": "0dcc5457-1ba1-4cba-ac41-da5c67cc67e5", + "metadata": {}, + "source": [ + "Let's now check the current state of the app to make sure it got updated accordingly" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a3fcf2bd-f881-49fe-b20e-ad16e6819bc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'name': 'search',\n", + " 'args': {'query': 'current weather in SF'},\n", + " 'id': 'toolu_01FSkinAVXR1C4D5kecrzAnj'}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_state = app.get_state(thread).values[\"messages\"][-1].tool_calls\n", + "current_state" + ] + }, + { + "cell_type": "markdown", + "id": "1bca3814-db08-4b0b-8c0c-95b6c5440c81", + "metadata": {}, + "source": [ + "**Resume**\n", + "\n", + "We can now call the agent again with no inputs to continue, ie. run the tool as requested. We can see from the logs that it passes in the update args to the tool." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "51923913-20f7-4ee1-b9ba-d01f5fb2869b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: search\n", + "\n", + "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Based on the search results, I can provide you with the current weather information for San Francisco:\n", + "\n", + "The weather in San Francisco is currently sunny. \n", + "\n", + "It's important to note that the search result also included a playful astrological reference, which isn't directly related to the weather. If you need more specific weather details like temperature, humidity, or forecast, please let me know, and I can perform another search to find that information for you.\n", + "\n", + "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n" + ] + } + ], + "source": [ + "for event in app.stream(None, thread, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78780afe-409d-46cd-a734-e82538cdd8de", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" } - ], - "source": [ - "for event in app.stream(None, thread, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "78780afe-409d-46cd-a734-e82538cdd8de", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/human_in_the_loop/review-tool-calls.ipynb b/examples/human_in_the_loop/review-tool-calls.ipynb new file mode 100644 index 000000000..5a4aeaf56 --- /dev/null +++ b/examples/human_in_the_loop/review-tool-calls.ipynb @@ -0,0 +1,683 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# Review Tool Calls\n", + "\n", + "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:\n", + "\n", + "- A tool call to execute SQL, which will then be run by the tool\n", + "- A tool call to generate a summary, which will then be saved to the State of the graph\n", + "\n", + "Note that using tool calls is common **whether actually calling tools or not**.\n", + "\n", + "There are typically a few different interactions you may want to do here:\n", + "\n", + "1. Approve the tool call and continue\n", + "2. Modify the tool call manually and then continue\n", + "3. Give natural language feedback, and then pass that back to the agent instead of continuing\n", + "\n", + "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state taking one of the three options above" + ] + }, + { + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic" + ] + }, + { + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for Anthropic (the LLM we will use)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "035e567c-db5c-4085-ba4e-5b3814561c21", + "metadata": {}, + "source": [ + "## Simple Usage\n", + "\n", + "Let's set up a very simple graph that facilitates this.\n", + "First, we will have an LLM call that decides what action to take.\n", + "Then we go to a human node. This node actually doesn't do anything - the idea is that we interrupt before this node and then apply any updates to the state.\n", + "After that, we check the state and either route back to the LLM or to the correct tool.\n", + "\n", + "Let's see this in action!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "85e452f8-f33a-4ead-bb4d-7386cdba8edc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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tA6kknuCrm54NnGUtwKsrs2djdbaCyov1vsNOH0l2mHZksD5PPZFtkg13lsnXqAVBeI2b8FvBv4q1mU5dW19oyfK6XsnVMtNX1NLJTsDGuawMY6EaMcZc1vnjnBIAf1CdZLx+w+0cPajMLbWS5dZo6oUIdjEfj75JidcjQw6PUjrvXUdV2cOf4vcX/uul/QtVU2Dw3/BpxW2tt1kyihs9ACSKS349WwRAnvIaynA30CsTDsiho8PsroKO43C0SUMMtuuFFQSytqqRzGmF5Y1nPG4sLdse0EEH0aK9DRvzUVURvzH19WUbYwwtPwDtlsymqu9nyTJrDSVleLpVWS23BsdBPU8wc95YWFzedw29rXta7Z2Oqxb/AAX8eMlPHHkGSwW2hurb1bbVFXMFLb6oT9vzxM7PbgXl/myF4Ae7QB6iw/LOn+Cr9+ZKv6NPLOn+Cr9+ZKv6Nb+wr8pqzwRO6+D/AI1eIs4pp6i6C3Zg5k9woI6rlhjqGhv/ADiEa2yU9nGSdkEsb0XpdU8Q8dZDa7Xj1qyGgo4o4IrreMlkhrKoNYAZJmMoXNDyQSdHR7+m9CR+WdP8FX78yVf0a4TZzR08T5ZbbfI4mNLnPdZaoBoHUkns+5OwueFMpqywVBw+kv2X2POr3DJYMqoaWa3z0dpuRqaOppnOcWxyl8LC8AkSDTWkO9JHfj8W8HvHsQ8lGUNwvElLi9fVV1qpampZIyATwPhMGyzmMTWyOLRvYJ6uI6KQWHinY8qtFNdbKy6Xa2VLS6CtorVUzQygEglr2sIPUEdD6FkPLOn+Cr9+ZKv6NOwr8smrKL3LgRYLpimY4/LWXJtFlN2N4rZGSxiSOYmE8sZLNBn2BnRwceruvdqwK/8AzGo/7t3/AKLD+WdP8FX78yVf0a+SXuqvlPJR2u1XIVUzTG2WuopaaKLfTnc6Ro2BvegCTrSsWqqZzMYXEpFw6/i+xj+66X9E1SFa12jw8fB/sdporazPiWUcDKdvNZa8HTGho6dh07lPuFHhRcNeN2QVVlwzITd7hT076t0Ro54NxNdG1z/sjG6AdLGOut7Ot8rteXdqiquqqPGZSdsrWREWtBERAREQEREBERAREQEREBERAREQERYHLM8xzBIqOTIr5b7K2smFPTePVDYjPKSAGMBO3O69w2gzyKHQ8UrXUcTp8Fjobs66wUgrJasUEniTGkAhpn1y8x30HxH0qMW/JuKeecOshlpMWoeHWWNqxDaW32rbXwSwBzOaaQQ6LSR2oDCOhDSdgoLYXkrLrR29lQ6oqooRTwuqJQ542yMd7yO/XQ9VBqzhrfcjmwSvu+bXiguNhYyS5U9glFNR3icCMu7ZhafsRcx/mDXSQjfQLJWPg/iGOZ1kOY2+zMhyO/sbHcqwyyP7dgDQG8jnFgHmN7gN9d96DAVvhEY5V8OfLPEaG8cRLaa33PZBi1Eaid0u9E8jyzzAdbd3aIPULL3HIc5fxCxykteL0kmF1NKZ7pd6ytEdVTSFr+WFkGtkg9mSTsaLh0I2plb7dSWmkjpKGlho6WMaZBTxiNjB6g0dAvQgre2cPMvuHl9SZVndRcbRfjJBaae00jKCostOe0A7OoYeZ8nK9nnOHQxg9QSF6aXgZh7sNsONXi2+VdvskpqKSTInePTCUlx53Pf9sfPcOvo16gp+iD4GgOLgACe86719XhqL3QU9wFvdVwG5ugdUsoBK3t5ImkBz2s3stBIG+7ZA9KqqHKsz468MfHsL90eFVwluXYtqcntAkqXUbR1ljgc7QLuYFvNseaR6QQFszXSjgr4aF9VA2vmjfLFSmVolkY3Qc5rSdkAubs9w2N961H8KDDKvww/B0rLxY8Uu1gvmN3Cast1PkFtdDXV0EcJ7VkDGh79TbbytGud8Me9dCNk4+FGL++GzPZrRBLmIom0HuqebmEQ5ujWlxa3fM4Egb1oEkBS9B+SHgGeCceN+X+VOTURdg1mlBMcoIbcakdRCPWxvRz/xN/lEj9bWMbG0NaA1rRoADQAXhsGPWrFLRT2qyWyjs9rpwRDRW+nZBBFtxceVjAGjZJJ0O8krIICIiAvhAIII2D6F9RBW/BK5UrbbkON2/CJcFtWM3ee10NN2RZT1kI08VUPmNHK9z3nQ5uuyTsqyFCLdb8ypuLt3rK280U+CVNthZb7ZoNqYKxrj2rujBzMc3R2Xkg9wA6qboCIiD8yf8or4JpxG7VPFPFaQCyXCYG9UcLf81qHn+HGv5Ejj19Tz6ecBue8Cy18SPBy4Y+W7eGrssxfLQyrqXWiaUXulgiL2wuNLIAyWNwc+RnZ9XNlDi4jlA/Q+426kvFvqqCvpYa6hqonQVFLUxiSKaNwLXMe07DmkEgg9CCltttJZrdS0FBSw0NBSxMgp6WmjEcUMbQGtYxoADWgAAAdAAgg3Cnj5gvGile7Fr7DVVsIPjNrqAYK2mI6ESQv04aPTei3fcSrCVWcVvBpwTi7VMuVztslryWEh1NkdklNHcYHDucJWfba9AeHAegKvfHeO3ALpVwM44YbF/P0rRS3+mZ8bPtKnQ9XnuPU6QbKoq04T+EVgnGXtKfH7wI7zDsVNjuLDTXCncPtg+F3U69Jbtvxqy0BERAREQEREBERAREQF5rncaez22rr6uQxUlLE+eaQNLuVjQXOOgCT0B6AbXpWOyOC4VWPXSG0zx0t1kpZWUk8zA9kcxYQxzmnYIDtEhBW9d4Q1vu3C2mzbAcfvPEWkq6vxOmpbVSuhlcfOBkcJg0sjBb9sW/ym9NdVILhVcQp+JViFtpLFFw/NIZbpJWvlFz7ciQNjhDdx6B7Iku7/ADgD3LL8O4MipMHslPl1XR12TwUscVyqbf8AwEs4GnOaOVut9+uUAb6ABSJBW9Dwou1bQZzbsozi65FbckfIymgZFHRutVO4v1HC+MbLg17RznqTGDobKyVk4MYbZMTsGOGxUt0tlicZLe27s8dfBIS4mQPl5iHbc7r6N6GgpsiAiIgIiICLpnqoqcHnd5wY6Ts2gue5rdbIaOp7x3D0j1qofLbJePnDWmuvC+4zYQZrn2L7lklmcZJKRm+aWnicQHc5LeVzuhHMPNcOgWpVX6hpq2SgFTFNdG0zqttuilYamSJpALmsJBI5i1u+7bgN9VVtTd89438MaKsxV1w4QXKpuH2V2RWxk9YKJvN1bDzaY9+2dHdRpw9TlO6Phni9FndZmsVjo25ZWU7KWouwj+zPjaAA0Ek8o0ADrqQ0A70FJ0EYp+GeL0+dz5q2x0flbUUzaSW7dnuYxNGg0Ek8vToSOpAAJIAUnREBERAREQEREBERBVHGKkwnGs04f8QMprK6guNpr32e1yUjS6KSWuaIuScBp8zTdgkgNO/SQrXVf8arrXWjHbTLQYWzOZZLxSRPoZIu0FMxz9OqtcrtGPv3oa9YVgICIiAiIgIiIK04seDrgnGXs6i/2cRXmHRpr7bnmmuFO4falkzep16A7mb8SrTxTjtwC60szOOGGxfzFS4Ut/pmfE/7Sp0PX57j0GlssiCueD3HrFeNtNcRYn1tHdbW5jLnZrrSPpqyge7fK2Vjumzyu6tJHQ9VYy124Qfdj+ED+C4/+qPWxKAiLhJKyIAve1gP9I6Qc0XT45B9/j/LCeOQff4/ywriR3IunxyD7/H+WE8cg+/x/lhMSO5F0+OQff4/ywnjkH3+P8sJiR3KtePvHqw+DrhdNk+R0F1uFvnrmUHJaIY5ZGPcyR4c4PewBv2Mje+9zRrqrE8cg+/x/lhYXNMYx/iFi1yxzIIKa42e4wmCop5XDTmnuIPeHA6IcOoIBGiExI/M2x/5Qp/DC33uy8MMKLYLlkVXdo6jKK01Z7Od5PZMp4Gxdn15CAZZNeeNv2HD9TKSSWWlhfPEIJ3MaZImv5wx2urQ7Q3o9N6C/KC3eB/dOGHhmYPidS51xxequjbjQXNwBZU0sH2aSOQjoHhrORw6d4IGnBfq/wCOQff4/wAsJiR3IunxyD7/AB/lhPHIPv8AH+WExI7kXT45B9/j/LCrO/8AGc5Dhl4r+E7LTxAvVBXNtz6X3SbTwRyHl5nmQjT2sDw48p84AgO2NJiRaLnBrST3Ab6Kn5eLt24vcOaq78E3W251rbn7miuyKGenpGsbrtZmDlDpA3mGtdCQ7v1oyJuAQXDiVbc7r7/dIq2ltvibLDDcibbE92+1k7PQ53nbRs6HmNOt61N4p6SCNscckMcbRprWOAAHxBMSInBwmx/3zBxCqKaWTLPc9tuE5qpHQwxbJcI4yeUcx1s6/kjuO9zRdPjkH3+P8sJ45B9/j/LCYkdyLqZUwyODWysc49wDgSu1TcCIiAiIgIiICIuL5GRDb3NYPW46QckXT45B9/j/ACwnjkH3+P8ALCuJH508dv8AKP5hZb1V4lT4S/Dr7ZLwxtbUR3cVInjieRJCA+lbpsg7nj0aIB2tnPBK8Keq8KO25JcH4Y/FqK0ywQRTm4eNsqpHh7ntB7KPlLA2Mnv/AIQd3p1l/wApjwAluGT2DiFjdKaypu0sVnuNPTDmc+o+1p5ND0uA7Mk6A5Ix3uW5Hg4cKLdwI4PWDE4pad1bBF29wnjcNTVb+srt+kA+aCf5LW+pMSLSRdPjkH3+P8sJ43Af56P8sJiR3IiKAiIgIiINduEH3Y/hA/guP/qj1sStduEH3Y/hA/guP/qj1sSg8t0rfc22VlXy83YQvl5fXytJ1/5KvbXidqv1upLlebfSXi5VULJpqmtgZM7bmglreYeawdwaNDQ9eypxlX+jF4/A5v8AgKj2M/6OWr8Ei/4AvS0eZotzVTOJyy3Q8XvfYt7N2j5hF+6nvfYt7N2j5hF+6qw4R+E7j+ZUlBb7/c6SgymsudbbmUtPTTtpy+Opmjhj7VwcwSujjY7kL+Y82wNEBT+r4tYpQ4zkmQT3XktGOVM1HdKjxeU+LyxECRvKGcz9cw6tBB30JW2L9yf956pmeLIe99i3s3aPmEX7qe99i3s3aPmEX7qjOUeELgGG3e52y7351NWWt8ba9jKGplbSCSNkjHSuZGWsY5sjdPcQ3exvbSB7sY41YZmNfXUVpvQqKmjpfH3skppoeem3rt4jIxoli3/Lj5m9R16hO3ueeepmeLMe99i3s3aPmEX7qe99i3s3aPmEX7qwWFccsI4hV1XRWK9eNVlLT+NyU89JPTSOg3rtWNlY0yM3oczNjqOvULoxLwgMBzq6Wu32S/isqLpEZqFzqSeKKqDWc7mxyvjDHPa3ZcwO5m6IcAQdO3ueeepmeKSe99i3s3aPmEX7qe99i3s3aPmEX7qz6gll44YVkV9rLRbrw+qrKKeppqxzaKoENLJAXCVs0xjEceuR2uZw5gNt2CCr29yP956mZ4s5732Lezdo+YRfup732Lezdo+YRfuqLWnwi+Ht8fVtosgMrqekmrtOoalnjEETeaSSDmjHjADRv7Fz9O5Zm48WcVtlrsFwlubpaa/QOqbZ4rSzVD6qNsBqHOayNjnaETS7qPUO8gGdvc889TM8WQ977FvZu0fMIv3U977FvZu0fMIv3VDMY46WSPhVZMuyW+W8x3SWWKnltNJVctS4SyNayGnezt3ODWecOTe2uP2vVZux8a8KySWyxW6+MqZLxVT0VIzsJWuNRDGZJIZA5g7J4YC7lk5SR3Ap29zzz1MzxZj3vsW9m7R8wi/dQcPcWHdjVnH/AMBF+6vJXcUsWt1kuN3mu8RoLfcRaamSKN8ro6syti7Hka0uLud7W6APfvu6qVK9tc809TM8WA977FvZu0fMIv3U977FvZu0fMIv3VGKvwhuH1Beai1z5AI6umrvc2pPidQYaap5+QRzSiPkiJcQAXuAd6CV6cx474LgN8daL7f2UdfHGyWdjaeaZlKx50x0742ObC0+gyFo117lO3ueeepmeLPe99i3s3aPmEX7qe99i3s3aPmEX7qwee8bcO4cz09Jeb1HBcKqmdVQU0MEtS8xD+deImuLI99Od2m9D16KN4b4Q1n96TCMozKtgt11yKhZUtorbSzzukfygv7KGMSSFjdjZ6gbGz1Tt7nnnqZnisEYBjAB1jlpbvXVtFGD37HUN9YB/EsxglbM5l3tssz6htrrBTwyyuL5DG6GOVoc49XFvaFuzskNBJJ2Vj8Vyyz5xYqa82K4Q3O2VIPZ1EJ6EgkOBB6hwIILSAQQQQCvRgf/AE3mP95RfqdOsblVVy1VrTnG34xC5zvTFEReUxEREBdc88dNDJNNI2KKNpe+R7g1rWgbJJPcAuxVnx1vD6ew2+0Ru5fdOoLZ/jgjbzOH43dm0+sFy6dGsTpF6m1Hj/ZWEUzDincsmqHw2epmtdmB0yWHzKipH9Iu1uNp9Abp2upI2WiBy2OgqJXSz0kVTM77aWob2r3dd9XO2T+Mr2ovpFixb0anUtRj5z72OtPgx/k7avgyj+bs/Ynk7avgyj+bs/YsgotlHFDGMNrxRXa59hVdn2zooqeWcxR9dPk7Nruzb0PV2h0PqW+q7qRmqrEe81p4sz5O2r4Mo/m7P2J5O2r4Mo/m7P2LAXni3idiqYqequwfPNSMr4o6SnlqXSU7y4NlaImu5m+Y7ZHd0J0CN9934nYxY7JbLtVXaPxG58viT4GPnfU7HMOzYxrnO6dTodPTpYdvTt/Pu5mZ4sx5O2r4Mo/m7P2J5O2r4Mo/m7P2KNcKc+fxFs93uJ7A00F2qqOlfAxzOeCN+o3ODiTzEHr3f2BTVZUXe0piqmdkmZ4vlodPjkgkstVNZ3juFI7ljP8AbH1Y78bSrq4c8SvKlzrbcmx094jaXtMfSOpYO9zd9xG+rfxjY7qWXXLcJ7M+G60v+d294qouutloPM3fqc0uaficVwaZoVvTKJiY/N4T/fBYnOyW1SLqpqhlXTRTxHmjlYHtPrBGwu1fN9wIiINduEH3Y/hA/guP/qj1sStduEH3Y/hA/guP/qj1sSgxeVf6MXj8Dm/4Co9jP+jlq/BIv+AKSZHC+ox66RRtLpH0srWtHpJYQFGsXe2TGbQ5p211HCQfWOQL0bP7M+/6MvBrXRYXfo/Bxxu3mxXFt1p87ZXOpfE5BPHEL8+TtizXMG9kefm1rlO96WH4kUmQWXhdxxwqLDskut4v14rbjbpLbbJJ6aennETg8TNHLzN04GPfPsdGna2/RY6rFrZmGJXuqoPCebDZq+Z14tsMdtDKV7jWuFobGWw6H2Qh+26bvzunevbllky2izPFLnjdonkutFw/ulNTyy057FlcRSmCGVxHK0l7DpriN8rvUVsMiuqNSMDtF3q+KmGXp1r4gVjnWC5UN2ueUQThja2SOKQMZE7pCzcTxtjWxkljWlxWbxrD75S8JvBrpn2S4RV1ou1G+4QupJGyUUYoaprzM3W4xzOa0l2htwB71s4uqqpYa6mmpqiJk9PMwxyRSN217SNEEekEJqjtWuNm4ZX6/wDg/cX8apaOa03q+3vIHUoqozB4wJamTsnbcBtkjeUB/cWkHuVljwduFrSCOHmMgjqCLVD+6rDVxneNc+D+NY/kOR49NW4lxGt98stM+cPymtr5aCinMfYyRxmeZ0cnM2R4aWBwLQd66BezgVwmveLcQbs29QkY/iMc1nxNz9nnpqmXxmSQb9LGGnpwR95ePWtgEU1RphbOHN/tWE8HL3dbDlclssgvNJdbdYJKmkulJ4zUF0U7Y4nMle3zAHNb15Xg6IU2yLhrHNwfueUYDj2SUuW0d5gyOjp8olqJq6tqKXUfnNmkfJqSDnjDSQ4ggaHRbMopqQNWMX8HW/4hxJwi1CV1diUpjyfIKh2yJL5TMcwv33ASyTxScvefFifQVtOuE0TKiJ8UrGyRvaWuY4bDge8FV9/ydeFv/V3jH5qg/dWURjcKcyrDL9UcBuPlBFYrjLcLlk9dU0FKykkMtUwupyySJoG3g8p05ux5p13L3ZELxgNz4zWeXC77k8+aSOqbTWW2hNRT1AkomU4p55R5sIjex2zIQOV2xvuWzDGNjY1jQGtaNADuAX1TVGrWB2i/8BcouVNfsavmTi74zZqClutioXVrY5qSkME1PIW9Yw5/2Rrnaaec7IIUOxbh5ecWoeFl/wAisGbPs0eFx2Sqp8XlrKe4W6rbMZdzQ072Slj2u5T0OnRt2B0K3WRTUEH4OY1aMbwtjrNarxZoLjUzXCalv80ktb20jvPfKZHvcHO0HaLt9eujtSrA/wDpvMf7yi/U6de5eLA2H3Wy6QdWPubADr0ikpwf/P8A/u9bJ2Wq/d9YWPFMERF5iCIiAqk490jxLjVd/NMmnpXHXc6Rge3+z+BI/GPWrbWHy3GqfLsfq7XUuMbZgCyZo26KRpDmPHxhwB16e70rt0K/GjaRRdq3R8p2LDXFF33O21lhucltucQp66Prob5JW+h8ZP2zT/iO46IIUKqeD+DVlRLUT4hZZp5Xl8kj6GMuc4nZJOupJX0fWmqmKreJief2lhjCXKg8hx6Sw8Tcurbza8wuFBeTT1FDUYvU1QYeSFsb4ZWQyNDSC3Yc/oQ7vCsn3l8B9jLH+b4v3VLKCgprVRQUdHTx0tJAwRxQQtDWRtA0GgDoAB6FpuWqr2NeIjG3jy4QKyw3EG41xVhZb7XV0Vip8TpqOndM1zmxuFTK7sjISQXgEEjZOiPQoJgVsvGBR8Pr/cscu9dRU1prbXLS0tE+WpoJX1IeyQw65+VzG8uwOg16Ctj0WE6LGzE4xu6xP0FdcEKesjs+S1FZbqy1mtyKvq4oK+ExSdm+Tma7lPoI9I6KxVgshwTHMtnimvdit92mibyRvrKZkrmN3vQLgdDaxXvMYFrXkbY9f3fF+6ttFNdumKIiJiOf2EyXku7zHa6staXvMTmtY0bLnEaAA9ZJAWPx7Csew81D7LZbfZzOB2zqOnZFzhu9c3KBvWz/AIqzuGGDS5JdKW81cZZZ6OUTQB4141K3qxw/1GnTt+lzRroDuXtIp0a1N27sx8eSxG1c1loTbLPQ0bjzOp4I4iR6S1oH/wCF7URfMZmZnMqIiKDXbhB92P4QP4Lj/wCqPWxK124Qfdj+ED+C4/8Aqj1sSgKJ1XD9nbvfbL1crHC9xe6lohA6EOPUlrZYn8uz100gbJOuqliLbRcqt/4ysThDvICv9s738jQ/Vk8gK/2zvfyND9WUxRbe83OXSPRcyh3kBX+2d7+RofqyeQFf7Z3v5Gh+rKYonebnLpHoZlDvICv9s738jQ/Vk8gK/wBs738jQ/VlMUTvNzl0j0Myh3kBX+2d7+RofqyeQFf7Z3v5Gh+rKYonebnLpHoZlDvICv8AbO9/I0P1ZRXhphXECsxhsmc5PU0N+8YmBhtMdG6DsQ89kdugceYs0T1799yttVl4O9qslm4bspsfyubM7d4/VvF0qHFzi8zOL49n0MO2/iTvNzl0j0MyznkBX+2d7+RofqyeQFf7Z3v5Gh+rKYonebnLpHoZlDvICv8AbO9/I0P1ZPICv9s738jQ/VlMUTvNzl0j0Myh3kBX+2d7+RofqyeQFf7Z3v5Gh+rKYonebnLpHoZlDvICv9s738jQ/Vk8gK/2zvfyND9WUxRO83OXSPQzKIMwGsB+yZde5WHvaY6Nvp9bacEf4qSWq1UtkoIqOii7Gnj2QC4uc4kkuc5xJLnEkkuJJJJJJJXrRa671dyMVTs/iPkkzkREWlBERAREQYy/41a8opBTXWhirYmnmZ2g86N39Jjh1afjBBUGqOAtne9xprxeaJh7o2TRSBv45I3O/wASVZiLrs6XfsRi3XMR8Oi5VZ7wND7S3z/dfoE94Gh9pb5/uv0CtNF0fiel+0+XoZVZ7wND7S3z/dfoE94Gh9pb5/uv0CtNE/E9L9p8vQyqz3gaH2lvn+6/QL77wNB7S3z/AHX6BWkifiel+0+XoZQGz8FMbtsrZaptVepG9R7pSh7PxxtDWH8bSp61oY0NaA1oGgB3BfUXHdv3b85u1TPvM5ERFoQREQa7cIPux/CB/Bcf/VHrYla7cIPux/CB/Bcf/VHrYlAREQEREBERAREQEREBVl4O91sl54bsqcfxSbDLd4/VsFrqGlrg8TOD5NH0PO3fjVmqqbdeeJuAYLktyyu30ee3Skrea2UWLRGCoqKMuYPPEpDe0bzPPK3vDBoklBayKDnjLi9DXYjbL1XHG7/lFO2e32W7N7KqJIZuJ4G2tkBeG8vN1IIG9FThAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEUVzziph/C+h8byzJbZYISOZgralrHyf9hm+Z5+JoJVKyeGDXZ691Pwf4aZDxA5iWtvNXH7mWr+0TzDbtd/LppPrQevhB92P4QP4Lj/6o9bEqifB94WZ5j+f59n/ABCmscV8y1lCz3NsJldFStpo3saC6TvcWubvWxsEg9dC9kBERAREQEREBERAREQEREHhr7HbbrU0dTW2+lrKijk7Wmlnha98D+7mYSNtPxhQi38GaXFrznN+xm83K2X7KInEyVk7qykpKnTuWeOncQN7c0lu9EMAHKN7sVEFUXTJ+JPDfAsdNZjrOJ2RPrfF7rPY3MoGxQFz+WdsbyS4gdmHNb02XHYAUog4s4nUcSqnAGXiM5dT0ra19sMbw7sSNhwdy8p6EdAdjY6dQpevO630r65la6mhdWRxmJlQYwZGsJBLQ7vAJAJHxBB3tcHNDmkEEbBHpX1VOeA0eF8Nb1jPCy91GAVddWivhr3h1xFNJzMLmtZO8+Y7k0W70Odx11WVrMizvHMlwuyNxluU2mrphDesmjrIqbxSoa3rJ4vrbmuLXHTdAFwG0FhooXjPGLE8tvmV2ihuTmXDFpDHd46unkgbTDb9PL3tDSw9m8hwJGmkqYwzR1ETJYntkie0Oa9h2HA9QQfSEHNERAREQEREBERAREQEREBERARR/MuIGM8PLabhk9/t1go+upbhUshDiPQ3mI5j8Q2VSFR4Z1Hl9RJRcJsFyPifVBxYK6mpzQ2xru7T6qYAN6/6uj60Gx6jmbcR8W4b27x7KchttgpdEtfX1LYi/XoaCduPxNBKpAYF4QvFXzsozi08LrPJ32vEKfxmuLf6L6qTox3+tHsfEpJhPgb8L8QuPutWWabMsgcQ6S85ZUOuNRI70OIk8wH4w0FBHpfDG8tpX0vCLh7kXEmXZY259j7nWsHu61MwHd6uUb13rr97Tj9xV87MeIdv4b2iTvtGEQF9WW+p1XJ1Y742bHxLZCKJlPEyKJjY42ANaxg0GgdwA9C5oKVwPwPOFmC13umcf8pr6488l4yaU3Cpkd/SPabaHfG1oKuhjGxsaxjQ1jRoNaNAD1LkiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgx2QY9bspstwtF1pI62218Dqaqp5N8ssTgQ5p110QT/iq7uXBGpseJYpjnDbJ6nh3a7DWduKamp21jKmFzy6SF/bEnR53kOJOiQdHQVqog0O/yl/GPLcVxGfAp7Ha24zlL4nUd2bVPdVFtM6KWZhi0Ax3amEB2yOXfQk7ZEvAW8Of3L8Q4c8R7iBQgNgs99qna7DXRtPO4/wAjuDXn7XuPm6LdruO/giYf4RWUWq85bdch5bbSmlp7bRVrI6Ru3lz5OQxuIkd5rXOBGxGwa80LFWLwBOBlicHjCmXCYEHtK+uqJd6/1efl/wDpQWtUZrcK2ab3CtENfSxOMfjVZWGmjlcDp3Z6jeXNB2OYgAkHWxonp8qMt9nLP+epfqqx3Dhxfw/xt51zOt1O46GtkxtJP+Kka9eui1bqmjUicbPH1ZTiJxhjfKjLfZyz/nqX6qnlRlvs5Z/z1L9VWSRY4tezj/r1M8mN8qMt9nLP+epfqqeVGW+zln/PUv1VZJcWyMc9zA5pc3XM0HqN920xa9nH/XqZ5Mf5UZb7OWf89S/VU8qMt9nLP+epfqqySJi17OP+vUzyY0ZRlm+uO2jXxXqUn9VXKt4tY3jlqqKzKbpRYmaYgTsutVHE0b3yljiQJA7R0W9ehBAcCB2Wy82+9xTS26upq+OGaSmlfSzNkEcrHFr43FpOnNcCC09QRoqG3vhViHEjinTVGU47QX99stjZKRtwhErI3OmcCeQ+a7uH2wOvQsK6LddFU004mIzsz9Zk3ohWeGracnqpbfwqwzI+Kdex3Iai3UrqW3Md6pKmUAN/t5SPjXV5G+EZxY65Fl1m4S2aTvt2MQeO3Et/ovqHnlY7/WjP4lsdRUVPbaSKlpKeKlpom8scMLAxjB6g0dAF3rzWKi8N8C/hjjNy917tbarO8gJ3Jd8vqnXGZ59Za/7HvfXfJv41d9NTQ0dPHBBEyCCNoayONoa1oHcAB0AXaiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiCuOGv8XmMf3ZTfomqlbPlef3ew8W8omzKSno8XuV8pLZbKe30vK5kET+yMr3Rlx5HFpAGt8nnF/NoXVw1/i8xj+7Kb9E1efHOGVnxu1ZNbmGeto8huFXca2Krc1wL6n+FY3la3TNdADs/GV7GkRm7V75Wd8oCziJkJrOAcZuHmZRG913HYx/85Itck4/k+Z9kaHeZy92u7ooJw94lcQRhvCHNL1lovMGV3SG0V9oNtp4YWtlbMGTMexoeJA6JpPnch5jpreiszG/Bxs+OX7Eboclya6nFO1baKS4Vkb4KeJ8DoDHytiaXAMd0c4l/mt84jYPvouBNjtOC4ZjNLU3CWkxGuhuVvdLNGJJpou0LGzOEei0mQ75Wg934+bFSLKWsmGS3bBc68IPLpciuFxobHWvrpbMaembHVltsilYC8Rc4LG8rG8rhsNBdzEkm1/KLil7C4x/81z/AP69c/eZtZzy55Sy43WkdeI2tu1kinY63V7hCYQ6WN0ZcTyEDzXNB5Wkg6VnbuFW0/EPPeHtFw6yrJ8miya15c8Q1lmht0UDaGSWkkqYzTPYOdzW9kWHtC7YO+i6cf4kcQbfjXDTiFeMlp7ja8yulDSVGNR0EUcNFDWkiEwzAdq58ZdHzc5cHDm6N6KxMR8HPH8TvVmrnXe/XumsTHx2a2XetbPS2xrmcn2JoYHOIYSwGRzy1p0NLrx7wa8bx272eoZdL7W2iyVTq20Y9W1okt9vmPNyviZyB55Od3IHvcG76ALHFQ6fBl/0XzD/AGzvv69IrLsv8ZNX/dMf6Z6w+EcNrbgFyySqtdVXGC+VzrlNQ1EwfT087yXSuhbrbOdzuZwJI33aWYsv8ZNX/dMf6Z63U7Ldfu+sLHim6Ii8tBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERBWttqhg9sp7NcqWsaKFgggqaejlninib0Y4OjaQ13LrbHaIIdrmaA493l3afVcfzXVfRqxEXoTpNFU61dM5nn9pZZid6u/Lu0+q4/muq+jTy7tPquP5rqvo1YiKd4teSev2Niu/Lu0+q4/muq+jTy7tPquP5rqvo1YiJ3i15J6/Y2K78u7T6rj+a6r6NPLu0+q4/muq+jViIneLXknr9jYrwZ1aidBtxJ9QtVUSf8A7ay2J0FRV3utvs1PLRwy08dJTQztLJXNa57nSOaRtuy4ANPXTdkDelLUWFd+maZpopxnnn6QmY8BERcaCIiAiIgIiICIiAiIg//Z", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from typing import TypedDict, Literal\n", + "from langgraph.graph import StateGraph, START, END, MessagesState\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_core.tools import tool\n", + "from langchain_core.messages import AIMessage\n", + "from IPython.display import Image, display\n", + "\n", + "\n", + "@tool\n", + "def weather_search(city: str):\n", + " \"\"\"Search for the weather\"\"\"\n", + " print(\"----\")\n", + " print(f\"Searching for: {city}\")\n", + " print(\"----\")\n", + " return \"Sunny!\"\n", + "\n", + "model = ChatAnthropic(model_name=\"claude-3-5-sonnet-20240620\").bind_tools([weather_search])\n", + "\n", + "class State(MessagesState):\n", + " \"\"\"Simple state.\"\"\"\n", + "\n", + "def call_llm(state):\n", + " return {\n", + " \"messages\": [model.invoke(state['messages'])]\n", + " }\n", + "\n", + "\n", + "def human_review_node(state):\n", + " pass\n", + "\n", + "\n", + "def run_tool(state):\n", + " new_messages = []\n", + " tools = {\"weather_search\": weather_search}\n", + " tool_calls = state['messages'][-1].tool_calls\n", + " for tool_call in tool_calls:\n", + " tool = tools[tool_call['name']]\n", + " result = tool.invoke(tool_call['args'])\n", + " new_messages.append({\n", + " \"role\": \"tool\",\n", + " \"name\": tool_call['name'],\n", + " \"content\": result,\n", + " \"tool_call_id\": tool_call['id']\n", + " })\n", + " return {\"messages\": new_messages}\n", + "\n", + "\n", + "def route_after_llm(state) -> Literal[END, \"human_review_node\"]:\n", + " if len(state['messages'][-1].tool_calls) == 0:\n", + " return END\n", + " else:\n", + " return \"human_review_node\"\n", + "\n", + "\n", + "def route_after_human(state) -> Literal[\"run_tool\", \"call_llm\"]:\n", + " if isinstance(state['messages'][-1], AIMessage):\n", + " return \"run_tool\"\n", + " else:\n", + " return \"call_llm\"\n", + "\n", + "\n", + "builder = StateGraph(State)\n", + "builder.add_node(call_llm)\n", + "builder.add_node(run_tool)\n", + "builder.add_node(human_review_node)\n", + "builder.add_edge(START, \"call_llm\")\n", + "builder.add_conditional_edges(\"call_llm\", route_after_llm)\n", + "builder.add_conditional_edges(\"human_review_node\", route_after_human)\n", + "builder.add_edge(\"run_tool\", \"call_llm\")\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Add\n", + "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_review_node\"])\n", + "\n", + "# View\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "d246d39f-4b36-459b-bd54-bf363753e590", + "metadata": {}, + "source": [ + "## Example with no review\n", + "\n", + "Let's look at an example when no review is required (because no tools are called)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1b3aa6fc-c7fb-4819-8d7f-ba6057cc4edf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'messages': [HumanMessage(content='hi!', id='393fa21d-4bfb-445b-8faa-78e22b92e346')]}\n", + "{'messages': [HumanMessage(content='hi!', id='393fa21d-4bfb-445b-8faa-78e22b92e346'), AIMessage(content=\"Hello! Welcome to our conversation. How can I assist you today? Is there anything specific you'd like to know or discuss?\", response_metadata={'id': 'msg_017S671xYvZm1mi9EcsKvPzF', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 355, 'output_tokens': 29}}, id='run-8ec507a1-5caf-47d6-89eb-1a2e8f38423c-0', usage_metadata={'input_tokens': 355, 'output_tokens': 29, 'total_tokens': 384})]}\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"messages\": [{\"role\": \"user\", \"content\": \"hi!\"}]}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "d59dc607-e70d-497b-aac9-78c847c27042", + "metadata": {}, + "source": [ + "If we check the state, we can see that it is finished" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "213323cc-0320-4313-ab11-19042e28b495", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pending Executions!\n", + "()\n" + ] + } + ], + "source": [ + "print(\"Pending Executions!\")\n", + "print(graph.get_state(thread).next)" + ] + }, + { + "cell_type": "markdown", + "id": "5c1985f7-54f1-420f-a2b6-5e6154909966", + "metadata": {}, + "source": [ + "## Example of approving tool\n", + "\n", + "Let's now look at what it looks like to approve a tool call" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2561a38f-edb5-4b44-b2d7-6a7b70d2e6b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b')]}\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b'), AIMessage(content=[{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_019FjC1prjVv8BuQX7DmF65F', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 80}}, id='run-1b580410-173c-4fe0-a149-22e8f516b259-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440})]}\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"messages\": [{\"role\": \"user\", \"content\": \"what's the weather in sf?\"}]}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"2\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "4ef6d51c-e2b6-4266-8de7-acf1a0b62a57", + "metadata": {}, + "source": [ + "If we now check, we can see that it is waiting on human review" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "33d68f0f-d435-4dd1-8013-6a59186dc9f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pending Executions!\n", + "('human_review_node',)\n" + ] + } + ], + "source": [ + "print(\"Pending Executions!\")\n", + "print(graph.get_state(thread).next)" + ] + }, + { + "cell_type": "markdown", + "id": "14c99fdd-4204-4c2d-b1af-02f38ab6ad57", + "metadata": {}, + "source": [ + "To approve the tool call, we can just continue the thread with no edits. To do this, we just create a new run with no inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----\n", + "Searching for: San Francisco\n", + "----\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b'), AIMessage(content=[{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_019FjC1prjVv8BuQX7DmF65F', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 80}}, id='run-1b580410-173c-4fe0-a149-22e8f516b259-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}), ToolMessage(content='Sunny!', name='weather_search', id='835b0fe3-8aa0-45d5-ac29-03bbe57cc767', tool_call_id='toolu_01MW3ETLpq4b8s6VaAMgDBZP')]}\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='8bda37cc-4bd3-4a14-bca5-b992934e710b'), AIMessage(content=[{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text'}, {'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_019FjC1prjVv8BuQX7DmF65F', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 80}}, id='run-1b580410-173c-4fe0-a149-22e8f516b259-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01MW3ETLpq4b8s6VaAMgDBZP', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}), ToolMessage(content='Sunny!', name='weather_search', id='835b0fe3-8aa0-45d5-ac29-03bbe57cc767', tool_call_id='toolu_01MW3ETLpq4b8s6VaAMgDBZP'), AIMessage(content=\"Based on the search results, the weather in San Francisco is sunny! It's a beautiful day in the city. Is there anything else you'd like to know about the weather or any other information I can help you with?\", response_metadata={'id': 'msg_01UY2d6RCzvwagwMb1J5etek', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 453, 'output_tokens': 49}}, id='run-7137f52c-abe6-4dc1-b536-92dd1d9187b0-0', usage_metadata={'input_tokens': 453, 'output_tokens': 49, 'total_tokens': 502})]}\n" + ] + } + ], + "source": [ + "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "8d30c4a7-b480-4ede-b2b4-8ec11de95e30", + "metadata": {}, + "source": [ + "## Edit Tool Call\n", + "\n", + "Let's now say we want to edit the tool call. E.g. change some of the parameters (or even the tool called!) but then execute that tool." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ec77831c-e6b8-4903-9146-e098a4b2fda1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597')]}\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Mv7iqdtPgZEX2LiBBqWDuY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 88}}, id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 88, 'total_tokens': 448})]}\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"messages\": [{\"role\": \"user\", \"content\": \"what's the weather in sf?\"}]}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"5\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "edcffbd7-829b-4d0c-88bf-cd531bc0e6b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pending Executions!\n", + "('human_review_node',)\n" + ] + } + ], + "source": [ + "print(\"Pending Executions!\")\n", + "print(graph.get_state(thread).next)" + ] + }, + { + "cell_type": "markdown", + "id": "87358aca-9b8f-48c7-98d4-3d755f6b0104", + "metadata": {}, + "source": [ + "To do this, we first need to update the state. We can do this by passing a message in with the **same** id of the message we want to overwrite. This will have the effect of **replacing** that old message. Note that this is only possible because of the **reducer** we are using that replaces messages with the same ID - read more about that [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#working-with-messages-in-graph-state)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "df4a9900-d953-4465-b8af-bd2858cb63ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current State:\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Mv7iqdtPgZEX2LiBBqWDuY', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 88}}, id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 88, 'total_tokens': 448})]}\n", + "\n", + "Current Tool Call ID:\n", + "toolu_01CpbVmprQnjxpQzx8MzE1g8\n", + "----\n", + "Searching for: San Francisco, USA\n", + "----\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}]), ToolMessage(content='Sunny!', name='weather_search', id='ff968b9f-9b87-4893-9f32-dfb88dbe0536', tool_call_id='toolu_01CpbVmprQnjxpQzx8MzE1g8')]}\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='0c488edd-7b9c-4416-ba02-8a2d7e9f2597'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search tool. Let me fetch that for you.\", 'type': 'text'}, {'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], id='run-52a09799-efb5-4fff-82c3-884e20119ad3-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01CpbVmprQnjxpQzx8MzE1g8', 'type': 'tool_call'}]), ToolMessage(content='Sunny!', name='weather_search', id='ff968b9f-9b87-4893-9f32-dfb88dbe0536', tool_call_id='toolu_01CpbVmprQnjxpQzx8MzE1g8'), AIMessage(content=\"Great news! The weather in San Francisco is currently sunny. It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?\", response_metadata={'id': 'msg_01PhwUeRWkSJB6kzHZS361XZ', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 464, 'output_tokens': 50}}, id='run-5aebcf37-626e-4675-b225-476bc99bdbb8-0', usage_metadata={'input_tokens': 464, 'output_tokens': 50, 'total_tokens': 514})]}\n" + ] + } + ], + "source": [ + "# To get the ID of the message we want to replace, we need to fetch the current state and find it there.\n", + "state = graph.get_state(thread)\n", + "print(\"Current State:\")\n", + "print(state.values)\n", + "print(\"\\nCurrent Tool Call ID:\")\n", + "current_content = state.values['messages'][-1].content\n", + "current_id = state.values['messages'][-1].id\n", + "tool_call_id = state.values['messages'][-1].tool_calls[0]['id']\n", + "print(tool_call_id)\n", + "\n", + "# We now need to construct a replacement tool call.\n", + "# We will change the argument to be `San Francisco, USA`\n", + "# Note that we could change any number of arguments or tool names - it just has to be a valid one\n", + "new_message = {\n", + " \"role\": \"assistant\", \n", + " \"content\": current_content,\n", + " \"tool_calls\": [\n", + " {\n", + " \"id\": tool_call_id,\n", + " \"name\": \"weather_search\",\n", + " \"args\": {\"city\": \"San Francisco, USA\"}\n", + " }\n", + " ],\n", + " # This is important - this needs to be the same as the message you replacing!\n", + " # Otherwise, it will show up as a separate message\n", + " \"id\": current_id\n", + "}\n", + "graph.update_state(\n", + " # This is the config which represents this thread\n", + " thread, \n", + " # This is the updated value we want to push\n", + " {\"messages\": [new_message]}, \n", + " # We push this update acting as our human_review_node\n", + " as_node=\"human_review_node\"\n", + ")\n", + "\n", + "# Let's now continue executing from here\n", + "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "e14acc96-3d50-44b1-8616-b8d9131e46c4", + "metadata": {}, + "source": [ + "## Give feedback to a tool call\n", + "\n", + "Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert these feedback as a mock **RESULT** of the tool call.\n", + "\n", + "There are multiple ways to do this:\n", + "\n", + "1. You could add a new message to the state (representing the \"result\" of a tool call)\n", + "2. You could add TWO new messages to the state - one representing an \"error\" from the tool call, other HumanMessage representing the feedback\n", + "\n", + "Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_node` and how it handles different types of messages.\n", + "\n", + "For this example we will just add a single tool call representing the feedback. Let's see this in action!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d57d5131-7912-4216-aa87-b7272507fa51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24')]}\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458})]}\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"messages\": [{\"role\": \"user\", \"content\": \"what's the weather in sf?\"}]}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"6\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e33ad664-0307-43c5-b85a-1e02eebceb5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pending Executions!\n", + "('human_review_node',)\n" + ] + } + ], + "source": [ + "print(\"Pending Executions!\")\n", + "print(graph.get_state(thread).next)" + ] + }, + { + "cell_type": "markdown", + "id": "483d9455-8625-4c6a-9b98-f731403b2ed3", + "metadata": {}, + "source": [ + "To do this, we first need to update the state. We can do this by passing a message in with the same **tool call id** of the tool call we want to respond to. Note that this is a **different** ID from above." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3f05f8b6-6128-4de5-8884-862fc93f1227", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current State:\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458})]}\n", + "\n", + "Current Tool Call ID:\n", + "toolu_014UTKh5uqfc885Fj4RRqGdg\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458}), ToolMessage(content='User requested changes: pass in the country as well', name='weather_search', id='e20ceddc-a0d3-469d-b31e-512f3042a07e', tool_call_id='toolu_014UTKh5uqfc885Fj4RRqGdg'), AIMessage(content=[{'text': \"I apologize for the oversight. It seems that the weather search function requires more specific information. Let's try again with a more detailed search, including the country. Since San Francisco is commonly associated with the one in California, USA, I'll use that. Here's the updated search:\", 'type': 'text'}, {'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_018rErqC2cLe2VVhebdJf81e', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 480, 'output_tokens': 116}}, id='run-fcba65ed-400a-4783-9ecd-e22051682399-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 116, 'total_tokens': 596})]}\n" + ] + } + ], + "source": [ + "# To get the ID of the message we want to replace, we need to fetch the current state and find it there.\n", + "state = graph.get_state(thread)\n", + "print(\"Current State:\")\n", + "print(state.values)\n", + "print(\"\\nCurrent Tool Call ID:\")\n", + "tool_call_id = state.values['messages'][-1].tool_calls[0]['id']\n", + "print(tool_call_id)\n", + "\n", + "# We now need to construct a replacement tool call.\n", + "# We will change the argument to be `San Francisco, USA`\n", + "# Note that we could change any number of arguments or tool names - it just has to be a valid one\n", + "new_message = {\n", + " \"role\": \"tool\", \n", + " # This is our natural language feedback\n", + " \"content\": \"User requested changes: pass in the country as well\",\n", + " \"name\": \"weather_search\",\n", + " \"tool_call_id\": tool_call_id\n", + "}\n", + "graph.update_state(\n", + " # This is the config which represents this thread\n", + " thread, \n", + " # This is the updated value we want to push\n", + " {\"messages\": [new_message]}, \n", + " # We push this update acting as our human_review_node\n", + " as_node=\"human_review_node\"\n", + ")\n", + "\n", + "# Let's now continue executing from here\n", + "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "2d2e79ab-7cdb-42ce-b2ca-2932f8782c90", + "metadata": {}, + "source": [ + "We can see that we now get to another breakpoint - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a30d40ad-611d-4ec3-84be-869ea05acb89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pending Executions!\n", + "('human_review_node',)\n", + "----\n", + "Searching for: San Francisco, USA\n", + "----\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458}), ToolMessage(content='User requested changes: pass in the country as well', name='weather_search', id='e20ceddc-a0d3-469d-b31e-512f3042a07e', tool_call_id='toolu_014UTKh5uqfc885Fj4RRqGdg'), AIMessage(content=[{'text': \"I apologize for the oversight. It seems that the weather search function requires more specific information. Let's try again with a more detailed search, including the country. Since San Francisco is commonly associated with the one in California, USA, I'll use that. Here's the updated search:\", 'type': 'text'}, {'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_018rErqC2cLe2VVhebdJf81e', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 480, 'output_tokens': 116}}, id='run-fcba65ed-400a-4783-9ecd-e22051682399-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 116, 'total_tokens': 596}), ToolMessage(content='Sunny!', name='weather_search', id='3f3ee262-70f5-422c-8e3f-6a9af758514d', tool_call_id='toolu_01AaipBbWDLjHnPcoApx8wRq')]}\n", + "{'messages': [HumanMessage(content=\"what's the weather in sf?\", id='601c4c75-f506-4d91-896d-5e382123de24'), AIMessage(content=[{'text': \"Certainly! I can help you check the weather in San Francisco. To get the most accurate and up-to-date information, I'll use the weather search tool. Let me fetch that for you right away.\", 'type': 'text'}, {'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_013nHyPYxNXFSoXeS6q4oWua', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 360, 'output_tokens': 98}}, id='run-0537e15e-86a4-4c6f-8dfb-6e4c160812c4-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_014UTKh5uqfc885Fj4RRqGdg', 'type': 'tool_call'}], usage_metadata={'input_tokens': 360, 'output_tokens': 98, 'total_tokens': 458}), ToolMessage(content='User requested changes: pass in the country as well', name='weather_search', id='e20ceddc-a0d3-469d-b31e-512f3042a07e', tool_call_id='toolu_014UTKh5uqfc885Fj4RRqGdg'), AIMessage(content=[{'text': \"I apologize for the oversight. It seems that the weather search function requires more specific information. Let's try again with a more detailed search, including the country. Since San Francisco is commonly associated with the one in California, USA, I'll use that. Here's the updated search:\", 'type': 'text'}, {'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], response_metadata={'id': 'msg_018rErqC2cLe2VVhebdJf81e', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 480, 'output_tokens': 116}}, id='run-fcba65ed-400a-4783-9ecd-e22051682399-0', tool_calls=[{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01AaipBbWDLjHnPcoApx8wRq', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 116, 'total_tokens': 596}), ToolMessage(content='Sunny!', name='weather_search', id='3f3ee262-70f5-422c-8e3f-6a9af758514d', tool_call_id='toolu_01AaipBbWDLjHnPcoApx8wRq'), AIMessage(content=\"Great news! The weather in San Francisco, USA is currently sunny. \\n\\nHere's a summary of the weather information:\\n- Location: San Francisco, USA\\n- Current conditions: Sunny\\n\\nIt's a beautiful day in San Francisco! The sunny weather is perfect for outdoor activities or simply enjoying the city. Remember to wear sunscreen and stay hydrated if you plan to spend time outside. \\n\\nIs there anything else you'd like to know about the weather in San Francisco or any other location?\", response_metadata={'id': 'msg_017Pnjyte2ZXAREgUvEqbUVt', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 609, 'output_tokens': 107}}, id='run-30c0d0ef-09a3-40ad-b410-80019b284983-0', usage_metadata={'input_tokens': 609, 'output_tokens': 107, 'total_tokens': 716})]}\n" + ] + } + ], + "source": [ + "print(\"Pending Executions!\")\n", + "print(graph.get_state(thread).next)\n", + "\n", + "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/human_in_the_loop/time-travel.ipynb b/examples/human_in_the_loop/time-travel.ipynb index fdfd454c6..765a1b221 100644 --- a/examples/human_in_the_loop/time-travel.ipynb +++ b/examples/human_in_the_loop/time-travel.ipynb @@ -1,571 +1,571 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# How to view and update past graph state\n", - "\n", - "Once you start [checkpointing](../persistence.ipynb) your graphs, you can easily **get** or **update** the state of the agent at any point in time. This permits a few things:\n", - "\n", - "1. You can surface a state during an interrupt to a user to let them accept an action.\n", - "2. You can **rewind** the graph to reproduce or avoid issues.\n", - "3. You can **modify** the state to embed your agent into a larger system, or to let the user better control its actions.\n", - "\n", - "The key methods used for this functionality are:\n", - "\n", - "- [get_state](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.graph.CompiledGraph.get_state): fetch the values from the target config\n", - "- [update_state](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.graph.CompiledGraph.update_state): apply the given values to the target state\n", - "\n", - "**Note:** this requires passing in a checkpointer.\n", - "\n", - "Below is a quick example." - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_anthropic" - ] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for Anthropic (the LLM we will use)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdin", - "output_type": "stream", - "text": [ - "ANTHROPIC_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\nimport os\n\n\ndef _set_env(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "e36f89e5", - "metadata": {}, - "source": [ - "## Build the agent\n", - "\n", - "We can now build the agent. We will build a relatively simple ReAct-style agent that does tool calling. We will use Anthropic's models and a fake tool (just for demo purposes)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f5319e01", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up the tool\n", - "from langchain_anthropic import ChatAnthropic\n", - "from langchain_core.tools import tool\n", - "from langgraph.graph import MessagesState, START\n", - "from langgraph.prebuilt import ToolNode\n", - "from langgraph.graph import END, StateGraph\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "\n", - "\n", - "@tool\n", - "def search(query: str):\n", - " \"\"\"Call to surf the web.\"\"\"\n", - " # This is a placeholder for the actual implementation\n", - " # Don't let the LLM know this though 😊\n", - " return [\n", - " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", - " ]\n", - "\n", - "\n", - "tools = [search]\n", - "tool_node = ToolNode(tools)\n", - "\n", - "# Set up the model\n", - "\n", - "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", - "model = model.bind_tools(tools)\n", - "\n", - "\n", - "# Define nodes and conditional edges\n", - "\n", - "\n", - "# Define the function that determines whether to continue or not\n", - "def should_continue(state):\n", - " messages = state[\"messages\"]\n", - " last_message = messages[-1]\n", - " # If there is no function call, then we finish\n", - " if not last_message.tool_calls:\n", - " return \"end\"\n", - " # Otherwise if there is, we continue\n", - " else:\n", - " return \"continue\"\n", - "\n", - "\n", - "# Define the function that calls the model\n", - "def call_model(state):\n", - " messages = state[\"messages\"]\n", - " response = model.invoke(messages)\n", - " # We return a list, because this will get added to the existing list\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "# Define a new graph\n", - "workflow = StateGraph(MessagesState)\n", - "\n", - "# Define the two nodes we will cycle between\n", - "workflow.add_node(\"agent\", call_model)\n", - "workflow.add_node(\"action\", tool_node)\n", - "\n", - "# Set the entrypoint as `agent`\n", - "# This means that this node is the first one called\n", - "workflow.add_edge(START, \"agent\")\n", - "\n", - "# We now add a conditional edge\n", - "workflow.add_conditional_edges(\n", - " # First, we define the start node. We use `agent`.\n", - " # This means these are the edges taken after the `agent` node is called.\n", - " \"agent\",\n", - " # Next, we pass in the function that will determine which node is called next.\n", - " should_continue,\n", - " # Finally we pass in a mapping.\n", - " # The keys are strings, and the values are other nodes.\n", - " # END is a special node marking that the graph should finish.\n", - " # What will happen is we will call `should_continue`, and then the output of that\n", - " # will be matched against the keys in this mapping.\n", - " # Based on which one it matches, that node will then be called.\n", - " {\n", - " # If `tools`, then we call the tool node.\n", - " \"continue\": \"action\",\n", - " # Otherwise we finish.\n", - " \"end\": END,\n", - " },\n", - ")\n", - "\n", - "# We now add a normal edge from `tools` to `agent`.\n", - "# This means that after `tools` is called, `agent` node is called next.\n", - "workflow.add_edge(\"action\", \"agent\")\n", - "\n", - "# Set up memory\n", - "memory = MemorySaver()\n", - "\n", - "# Finally, we compile it!\n", - "# This compiles it into a LangChain Runnable,\n", - "# meaning you can use it as you would any other runnable\n", - "\n", - "# We add in `interrupt_before=[\"action\"]`\n", - "# This will add a breakpoint before the `action` node is called\n", - "app = workflow.compile(checkpointer=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", - "metadata": {}, - "source": [ - "## Interacting with the Agent\n", - "\n", - "We can now interact with the agent. Let's ask it for the weather in SF.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cfd140f0-a5a6-4697-8115-322242f197b5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Use the search tool to look up the weather in SF\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}]\n", - "Tool Calls:\n", - " search (toolu_01Bpq6yiKqk9moPuGYKdLr8r)\n", - " Call ID: toolu_01Bpq6yiKqk9moPuGYKdLr8r\n", - " Args:\n", - " query: weather in San Francisco\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: search\n", - "\n", - "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Based on the search results, I can provide you with information about the weather in San Francisco:\n", - "\n", - "The current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\n", - "\n", - "However, there's an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you're a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the report.\n", - "\n", - "To summarize:\n", - "1. The weather in San Francisco is currently sunny.\n", - "2. It's a good day for outdoor activities.\n", - "3. There's a playful astrological warning for Geminis, but this shouldn't be taken seriously in terms of actual weather conditions.\n", - "\n", - "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n" - ] - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n\nconfig = {\"configurable\": {\"thread_id\": \"1\"}}\ninput_message = HumanMessage(content=\"Use the search tool to look up the weather in SF\")\nfor event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "1c38c505-6cee-427f-9dcd-493a2ade7ebb", - "metadata": {}, - "source": [ - "## Checking history\n", - "\n", - "Let's browse the history of this thread, from start to finish." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8578a66d-6489-4e03-8c23-fd0530278455", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StateSnapshot(values={'messages': []}, next=('__start__',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-b80d-6e18-bfff-c903ebc4bdfd'}}, metadata={'source': 'input', 'step': -1, 'writes': {'messages': [HumanMessage(content='Use the search tool to look up the weather in SF')]}}, created_at='2024-06-28T14:29:14.932371+00:00', parent_config=None)\n", - "--\n", - "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577')]}, next=('agent',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-b810-60dc-8000-a9c67d8cc5e0'}}, metadata={'source': 'loop', 'step': 0, 'writes': None}, created_at='2024-06-28T14:29:14.933257+00:00', parent_config=None)\n", - "--\n", - "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'), AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444})]}, next=('action',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-c6b1-6028-8001-82bd095f9a87'}}, metadata={'source': 'loop', 'step': 1, 'writes': {'agent': {'messages': [AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444})]}}}, created_at='2024-06-28T14:29:16.467180+00:00', parent_config=None)\n", - "--\n", - "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'), AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444}), ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', id='b5aa87cb-335a-4ee0-8809-381e33d0f02e', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}, next=('agent',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-c6ba-63a8-8002-48d076c3c4b7'}}, metadata={'source': 'loop', 'step': 2, 'writes': {'action': {'messages': [ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', id='b5aa87cb-335a-4ee0-8809-381e33d0f02e', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}}}, created_at='2024-06-28T14:29:16.470958+00:00', parent_config=None)\n", - "--\n", - "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'), AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444}), ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', id='b5aa87cb-335a-4ee0-8809-381e33d0f02e', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r'), AIMessage(content='Based on the search results, I can provide you with information about the weather in San Francisco:\\n\\nThe current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\\n\\nHowever, there\\'s an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you\\'re a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the report.\\n\\nTo summarize:\\n1. The weather in San Francisco is currently sunny.\\n2. It\\'s a good day for outdoor activities.\\n3. There\\'s a playful astrological warning for Geminis, but this shouldn\\'t be taken seriously in terms of actual weather conditions.\\n\\nIs there anything else you\\'d like to know about the weather in San Francisco or any other location?', response_metadata={'id': 'msg_01NWeLrkQRLiGsVsxnepzq3p', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 217}}, id='run-09f1b7d5-50ec-4f00-a31b-c4dec858b312-0', usage_metadata={'input_tokens': 486, 'output_tokens': 217, 'total_tokens': 703})]}, next=(), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-f0f5-6942-8003-b79974988738'}}, metadata={'source': 'loop', 'step': 3, 'writes': {'agent': {'messages': [AIMessage(content='Based on the search results, I can provide you with information about the weather in San Francisco:\\n\\nThe current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\\n\\nHowever, there\\'s an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you\\'re a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the report.\\n\\nTo summarize:\\n1. The weather in San Francisco is currently sunny.\\n2. It\\'s a good day for outdoor activities.\\n3. There\\'s a playful astrological warning for Geminis, but this shouldn\\'t be taken seriously in terms of actual weather conditions.\\n\\nIs there anything else you\\'d like to know about the weather in San Francisco or any other location?', response_metadata={'id': 'msg_01NWeLrkQRLiGsVsxnepzq3p', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 217}}, id='run-09f1b7d5-50ec-4f00-a31b-c4dec858b312-0', usage_metadata={'input_tokens': 486, 'output_tokens': 217, 'total_tokens': 703})]}}}, created_at='2024-06-28T14:29:20.899258+00:00', parent_config=None)\n", - "--\n" - ] - } - ], - "source": [ - "all_states = []\nfor state in app.get_state_history(config):\n print(state)\n all_states.append(state)\n print(\"--\")" - ] - }, - { - "cell_type": "markdown", - "id": "0ec41c37-7c09-4cc7-8475-bf373fe66584", - "metadata": {}, - "source": [ - "## Replay a state\n", - "\n", - "We can go back to any of these states and restart the agent from there! Let's go back to right before the tool call gets executed." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "02250602-8c4a-4fb5-bd6c-d0b9046e8699", - "metadata": {}, - "outputs": [], - "source": [ - "to_replay = all_states[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "21e7fc18-6fd9-4e11-a84b-e0325c9640c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'),\n", - " AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444})]}" + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to view and update past graph state\n", + "\n", + "Once you start [checkpointing](../persistence.ipynb) your graphs, you can easily **get** or **update** the state of the agent at any point in time. This permits a few things:\n", + "\n", + "1. You can surface a state during an interrupt to a user to let them accept an action.\n", + "2. You can **rewind** the graph to reproduce or avoid issues.\n", + "3. You can **modify** the state to embed your agent into a larger system, or to let the user better control its actions.\n", + "\n", + "The key methods used for this functionality are:\n", + "\n", + "- [get_state](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.graph.CompiledGraph.get_state): fetch the values from the target config\n", + "- [update_state](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.graph.CompiledGraph.update_state): apply the given values to the target state\n", + "\n", + "**Note:** this requires passing in a checkpointer.\n", + "\n", + "Below is a quick example." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "to_replay.values" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d4b01634-0041-4632-8d1f-5464580e54f5", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "('action',)" + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "to_replay.next" - ] - }, - { - "cell_type": "markdown", - "id": "29da43ea-9295-43e2-b164-0eb28d96749c", - "metadata": {}, - "source": [ - "To replay from this place we just need to pass its config back to the agent. Notice that it just resumes from right where it left all - making a tool call." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e986f94f-706f-4b6f-b3c4-f95483b9e9b8", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'messages': [ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}\n", - "{'messages': [AIMessage(content='Based on the search results, I can provide you with information about the weather in San Francisco:\\n\\nThe current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\\n\\nHowever, there\\'s an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you\\'re a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the weather report.\\n\\nTo summarize:\\n1. The weather in San Francisco is currently sunny.\\n2. It\\'s a good day for outdoor activities.\\n3. There\\'s a playful astrological reference for Geminis, but this shouldn\\'t be taken as actual weather information.\\n\\nIs there anything else you\\'d like to know about the weather in San Francisco or any other location?', response_metadata={'id': 'msg_01S4uzzxbGwvsk1vfoLJAD7Z', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 214}}, id='run-570bd10f-4007-4b33-8c29-44b9a19b6978-0', usage_metadata={'input_tokens': 486, 'output_tokens': 214, 'total_tokens': 700})]}\n" - ] - } - ], - "source": [ - "for event in app.stream(None, to_replay.config):\n for v in event.values():\n print(v)" - ] - }, - { - "cell_type": "markdown", - "id": "59910951-fae1-4475-8511-f622439b590d", - "metadata": {}, - "source": [ - "## Branch off a past state\n", - "\n", - "Using LangGraph's checkpointing, you can do more than just replay past states. You can branch off previous locations to let the agent explore alternate trajectories or to let a user \"version control\" changes in a workflow.\n", - "\n", - "Let's show how to do this to edit the state at a particular point in time. Let's update the state to change the input to the tool" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fbd5ad3b-5363-4ab7-ac63-b04668bc998f", - "metadata": {}, - "outputs": [], - "source": [ - "# Let's now get the last message in the state\n", - "# This is the one with the tool calls that we want to update\n", - "last_message = to_replay.values[\"messages\"][-1]\n", - "\n", - "# Let's now update the args for that tool call\n", - "last_message.tool_calls[0][\"args\"] = {\"query\": \"current weather in SF\"}\n", - "\n", - "branch_config = app.update_state(\n", - " to_replay.config,\n", - " {\"messages\": [last_message]},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bced65eb-2158-43e6-a9e3-3b047c8d418e", - "metadata": {}, - "source": [ - "We can then invoke with this new `branch_config` to resume running from here with changed state. We can see from the log that the tool was called with different input." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9a92d3da-62e2-45a2-8545-e4f6a64e0ffe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'messages': [ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}\n", - "{'messages': [AIMessage(content=\"Based on the search results, I can provide you with information about the current weather in San Francisco (SF):\\n\\nThe weather in San Francisco is currently sunny. This means it's a clear day with plenty of sunshine, which is great for outdoor activities or simply enjoying the city's beautiful views.\\n\\nIt's worth noting that San Francisco's weather can be quite variable, even within the city itself, due to its unique geography and microclimates. While it's sunny now, it's always a good idea to be prepared for potential changes, as the city is known for its foggy conditions, especially in certain areas and during specific times of the day.\\n\\nThe search result also includes a playful reference to astrology, mentioning Geminis. However, this is likely just a humorous addition and not related to the actual weather conditions.\\n\\nIs there any specific information about the weather in San Francisco that you'd like to know more about, such as temperature, wind conditions, or forecast for the coming days?\", response_metadata={'id': 'msg_01AoFmmzZxbMLuu3npVXJKG7', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 211}}, id='run-ccb9e786-c23e-4017-bf29-8405f17cec9f-0', usage_metadata={'input_tokens': 486, 'output_tokens': 211, 'total_tokens': 697})]}\n" - ] - } - ], - "source": [ - "for event in app.stream(None, branch_config):\n for v in event.values():\n print(v)" - ] - }, - { - "cell_type": "markdown", - "id": "511e319e-d10d-4b04-a4e0-fc4f3d87cb23", - "metadata": {}, - "source": [ - "Alternatively, we could update the state to not even call a tool!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "01abb480-df55-4eba-a2be-cf9372b60b54", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.messages import AIMessage\n", - "\n", - "# Let's now get the last message in the state\n", - "# This is the one with the tool calls that we want to update\n", - "last_message = to_replay.values[\"messages\"][-1]\n", - "\n", - "# Let's now get the ID for the last message, and create a new message with that ID.\n", - "new_message = AIMessage(content=\"its warm!\", id=last_message.id)\n", - "\n", - "branch_config = app.update_state(\n", - " to_replay.config,\n", - " {\"messages\": [new_message]},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1a7cfcd4-289e-419e-8b49-dfaef4f88641", - "metadata": {}, - "outputs": [], - "source": [ - "branch_state = app.get_state(branch_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "5198f9c1-d2d4-458a-993d-3caa55810b1e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'),\n", - " AIMessage(content='its warm!', id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0')]}" + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_anthropic" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "branch_state.values" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5d89d55d-db84-4c2d-828b-64a29a69947b", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "()" + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for Anthropic (the LLM we will use)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ANTHROPIC_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\nimport os\n\n\ndef _set_env(var: str):\n if not os.environ.get(var):\n os.environ[var] = getpass.getpass(f\"{var}: \")\n\n\n_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "e36f89e5", + "metadata": {}, + "source": [ + "## Build the agent\n", + "\n", + "We can now build the agent. We will build a relatively simple ReAct-style agent that does tool calling. We will use Anthropic's models and a fake tool (just for demo purposes)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f5319e01", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the tool\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_core.tools import tool\n", + "from langgraph.graph import MessagesState, START\n", + "from langgraph.prebuilt import ToolNode\n", + "from langgraph.graph import END, StateGraph\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " # Don't let the LLM know this though 😊\n", + " return [\n", + " \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", + " ]\n", + "\n", + "\n", + "tools = [search]\n", + "tool_node = ToolNode(tools)\n", + "\n", + "# Set up the model\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", + "model = model.bind_tools(tools)\n", + "\n", + "\n", + "# Define nodes and conditional edges\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "def should_continue(state):\n", + " messages = state[\"messages\"]\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"end\"\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return \"continue\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " response = model.invoke(messages)\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [response]}\n", + "\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(MessagesState)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", tool_node)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + " # Finally we pass in a mapping.\n", + " # The keys are strings, and the values are other nodes.\n", + " # END is a special node marking that the graph should finish.\n", + " # What will happen is we will call `should_continue`, and then the output of that\n", + " # will be matched against the keys in this mapping.\n", + " # Based on which one it matches, that node will then be called.\n", + " {\n", + " # If `tools`, then we call the tool node.\n", + " \"continue\": \"action\",\n", + " # Otherwise we finish.\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "\n", + "# We add in `interrupt_before=[\"action\"]`\n", + "# This will add a breakpoint before the `action` node is called\n", + "app = workflow.compile(checkpointer=memory)" + ] + }, + { + "cell_type": "markdown", + "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", + "metadata": {}, + "source": [ + "## Interacting with the Agent\n", + "\n", + "We can now interact with the agent. Let's ask it for the weather in SF.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cfd140f0-a5a6-4697-8115-322242f197b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "Use the search tool to look up the weather in SF\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}]\n", + "Tool Calls:\n", + " search (toolu_01Bpq6yiKqk9moPuGYKdLr8r)\n", + " Call ID: toolu_01Bpq6yiKqk9moPuGYKdLr8r\n", + " Args:\n", + " query: weather in San Francisco\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: search\n", + "\n", + "[\"It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Based on the search results, I can provide you with information about the weather in San Francisco:\n", + "\n", + "The current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\n", + "\n", + "However, there's an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you're a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the report.\n", + "\n", + "To summarize:\n", + "1. The weather in San Francisco is currently sunny.\n", + "2. It's a good day for outdoor activities.\n", + "3. There's a playful astrological warning for Geminis, but this shouldn't be taken seriously in terms of actual weather conditions.\n", + "\n", + "Is there anything else you'd like to know about the weather in San Francisco or any other location?\n" + ] + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n\nconfig = {\"configurable\": {\"thread_id\": \"1\"}}\ninput_message = HumanMessage(content=\"Use the search tool to look up the weather in SF\")\nfor event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "1c38c505-6cee-427f-9dcd-493a2ade7ebb", + "metadata": {}, + "source": [ + "## Checking history\n", + "\n", + "Let's browse the history of this thread, from start to finish." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8578a66d-6489-4e03-8c23-fd0530278455", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StateSnapshot(values={'messages': []}, next=('__start__',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-b80d-6e18-bfff-c903ebc4bdfd'}}, metadata={'source': 'input', 'step': -1, 'writes': {'messages': [HumanMessage(content='Use the search tool to look up the weather in SF')]}}, created_at='2024-06-28T14:29:14.932371+00:00', parent_config=None)\n", + "--\n", + "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577')]}, next=('agent',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-b810-60dc-8000-a9c67d8cc5e0'}}, metadata={'source': 'loop', 'step': 0, 'writes': None}, created_at='2024-06-28T14:29:14.933257+00:00', parent_config=None)\n", + "--\n", + "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'), AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444})]}, next=('action',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-c6b1-6028-8001-82bd095f9a87'}}, metadata={'source': 'loop', 'step': 1, 'writes': {'agent': {'messages': [AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444})]}}}, created_at='2024-06-28T14:29:16.467180+00:00', parent_config=None)\n", + "--\n", + "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'), AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444}), ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', id='b5aa87cb-335a-4ee0-8809-381e33d0f02e', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}, next=('agent',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-c6ba-63a8-8002-48d076c3c4b7'}}, metadata={'source': 'loop', 'step': 2, 'writes': {'action': {'messages': [ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', id='b5aa87cb-335a-4ee0-8809-381e33d0f02e', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}}}, created_at='2024-06-28T14:29:16.470958+00:00', parent_config=None)\n", + "--\n", + "StateSnapshot(values={'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'), AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444}), ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', id='b5aa87cb-335a-4ee0-8809-381e33d0f02e', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r'), AIMessage(content='Based on the search results, I can provide you with information about the weather in San Francisco:\\n\\nThe current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\\n\\nHowever, there\\'s an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you\\'re a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the report.\\n\\nTo summarize:\\n1. The weather in San Francisco is currently sunny.\\n2. It\\'s a good day for outdoor activities.\\n3. There\\'s a playful astrological warning for Geminis, but this shouldn\\'t be taken seriously in terms of actual weather conditions.\\n\\nIs there anything else you\\'d like to know about the weather in San Francisco or any other location?', response_metadata={'id': 'msg_01NWeLrkQRLiGsVsxnepzq3p', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 217}}, id='run-09f1b7d5-50ec-4f00-a31b-c4dec858b312-0', usage_metadata={'input_tokens': 486, 'output_tokens': 217, 'total_tokens': 703})]}, next=(), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef355ac-f0f5-6942-8003-b79974988738'}}, metadata={'source': 'loop', 'step': 3, 'writes': {'agent': {'messages': [AIMessage(content='Based on the search results, I can provide you with information about the weather in San Francisco:\\n\\nThe current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\\n\\nHowever, there\\'s an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you\\'re a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the report.\\n\\nTo summarize:\\n1. The weather in San Francisco is currently sunny.\\n2. It\\'s a good day for outdoor activities.\\n3. There\\'s a playful astrological warning for Geminis, but this shouldn\\'t be taken seriously in terms of actual weather conditions.\\n\\nIs there anything else you\\'d like to know about the weather in San Francisco or any other location?', response_metadata={'id': 'msg_01NWeLrkQRLiGsVsxnepzq3p', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 217}}, id='run-09f1b7d5-50ec-4f00-a31b-c4dec858b312-0', usage_metadata={'input_tokens': 486, 'output_tokens': 217, 'total_tokens': 703})]}}}, created_at='2024-06-28T14:29:20.899258+00:00', parent_config=None)\n", + "--\n" + ] + } + ], + "source": [ + "all_states = []\nfor state in app.get_state_history(config):\n print(state)\n all_states.append(state)\n print(\"--\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ec41c37-7c09-4cc7-8475-bf373fe66584", + "metadata": {}, + "source": [ + "## Replay a state\n", + "\n", + "We can go back to any of these states and restart the agent from there! Let's go back to right before the tool call gets executed." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "02250602-8c4a-4fb5-bd6c-d0b9046e8699", + "metadata": {}, + "outputs": [], + "source": [ + "to_replay = all_states[2]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "21e7fc18-6fd9-4e11-a84b-e0325c9640c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'),\n", + " AIMessage(content=[{'text': \"Certainly! I'll use the search tool to look up the weather in San Francisco for you. Let me do that right away.\", 'type': 'text'}, {'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r', 'input': {'query': 'weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], response_metadata={'id': 'msg_011ae64fY2jEcfS8kgrt4Fn9', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 363, 'output_tokens': 81}}, id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0', tool_calls=[{'name': 'search', 'args': {'query': 'weather in San Francisco'}, 'id': 'toolu_01Bpq6yiKqk9moPuGYKdLr8r'}], usage_metadata={'input_tokens': 363, 'output_tokens': 81, 'total_tokens': 444})]}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "to_replay.values" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d4b01634-0041-4632-8d1f-5464580e54f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('action',)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "to_replay.next" + ] + }, + { + "cell_type": "markdown", + "id": "29da43ea-9295-43e2-b164-0eb28d96749c", + "metadata": {}, + "source": [ + "To replay from this place we just need to pass its config back to the agent. Notice that it just resumes from right where it left all - making a tool call." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e986f94f-706f-4b6f-b3c4-f95483b9e9b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'messages': [ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}\n", + "{'messages': [AIMessage(content='Based on the search results, I can provide you with information about the weather in San Francisco:\\n\\nThe current weather in San Francisco is sunny. This is great news for residents and visitors who want to enjoy outdoor activities or explore the city.\\n\\nHowever, there\\'s an interesting and somewhat humorous addition to the weather report. It mentions, \"but you better look out if you\\'re a Gemini 😈.\" This appears to be a playful reference to astrology, suggesting that Geminis might have some challenges despite the good weather. Of course, this is not a scientific weather prediction and is likely just a fun addition to the weather report.\\n\\nTo summarize:\\n1. The weather in San Francisco is currently sunny.\\n2. It\\'s a good day for outdoor activities.\\n3. There\\'s a playful astrological reference for Geminis, but this shouldn\\'t be taken as actual weather information.\\n\\nIs there anything else you\\'d like to know about the weather in San Francisco or any other location?', response_metadata={'id': 'msg_01S4uzzxbGwvsk1vfoLJAD7Z', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 214}}, id='run-570bd10f-4007-4b33-8c29-44b9a19b6978-0', usage_metadata={'input_tokens': 486, 'output_tokens': 214, 'total_tokens': 700})]}\n" + ] + } + ], + "source": [ + "for event in app.stream(None, to_replay.config):\n for v in event.values():\n print(v)" + ] + }, + { + "cell_type": "markdown", + "id": "59910951-fae1-4475-8511-f622439b590d", + "metadata": {}, + "source": [ + "## Branch off a past state\n", + "\n", + "Using LangGraph's checkpointing, you can do more than just replay past states. You can branch off previous locations to let the agent explore alternate trajectories or to let a user \"version control\" changes in a workflow.\n", + "\n", + "Let's show how to do this to edit the state at a particular point in time. Let's update the state to change the input to the tool" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fbd5ad3b-5363-4ab7-ac63-b04668bc998f", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's now get the last message in the state\n", + "# This is the one with the tool calls that we want to update\n", + "last_message = to_replay.values[\"messages\"][-1]\n", + "\n", + "# Let's now update the args for that tool call\n", + "last_message.tool_calls[0][\"args\"] = {\"query\": \"current weather in SF\"}\n", + "\n", + "branch_config = app.update_state(\n", + " to_replay.config,\n", + " {\"messages\": [last_message]},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bced65eb-2158-43e6-a9e3-3b047c8d418e", + "metadata": {}, + "source": [ + "We can then invoke with this new `branch_config` to resume running from here with changed state. We can see from the log that the tool was called with different input." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9a92d3da-62e2-45a2-8545-e4f6a64e0ffe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'messages': [ToolMessage(content='[\"It\\'s sunny in San Francisco, but you better look out if you\\'re a Gemini \\\\ud83d\\\\ude08.\"]', name='search', tool_call_id='toolu_01Bpq6yiKqk9moPuGYKdLr8r')]}\n", + "{'messages': [AIMessage(content=\"Based on the search results, I can provide you with information about the current weather in San Francisco (SF):\\n\\nThe weather in San Francisco is currently sunny. This means it's a clear day with plenty of sunshine, which is great for outdoor activities or simply enjoying the city's beautiful views.\\n\\nIt's worth noting that San Francisco's weather can be quite variable, even within the city itself, due to its unique geography and microclimates. While it's sunny now, it's always a good idea to be prepared for potential changes, as the city is known for its foggy conditions, especially in certain areas and during specific times of the day.\\n\\nThe search result also includes a playful reference to astrology, mentioning Geminis. However, this is likely just a humorous addition and not related to the actual weather conditions.\\n\\nIs there any specific information about the weather in San Francisco that you'd like to know more about, such as temperature, wind conditions, or forecast for the coming days?\", response_metadata={'id': 'msg_01AoFmmzZxbMLuu3npVXJKG7', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 486, 'output_tokens': 211}}, id='run-ccb9e786-c23e-4017-bf29-8405f17cec9f-0', usage_metadata={'input_tokens': 486, 'output_tokens': 211, 'total_tokens': 697})]}\n" + ] + } + ], + "source": [ + "for event in app.stream(None, branch_config):\n for v in event.values():\n print(v)" + ] + }, + { + "cell_type": "markdown", + "id": "511e319e-d10d-4b04-a4e0-fc4f3d87cb23", + "metadata": {}, + "source": [ + "Alternatively, we could update the state to not even call a tool!" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "01abb480-df55-4eba-a2be-cf9372b60b54", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import AIMessage\n", + "\n", + "# Let's now get the last message in the state\n", + "# This is the one with the tool calls that we want to update\n", + "last_message = to_replay.values[\"messages\"][-1]\n", + "\n", + "# Let's now get the ID for the last message, and create a new message with that ID.\n", + "new_message = AIMessage(content=\"its warm!\", id=last_message.id)\n", + "\n", + "branch_config = app.update_state(\n", + " to_replay.config,\n", + " {\"messages\": [new_message]},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1a7cfcd4-289e-419e-8b49-dfaef4f88641", + "metadata": {}, + "outputs": [], + "source": [ + "branch_state = app.get_state(branch_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5198f9c1-d2d4-458a-993d-3caa55810b1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'messages': [HumanMessage(content='Use the search tool to look up the weather in SF', id='9558b3e7-fa30-4e8b-9587-d58ae3491577'),\n", + " AIMessage(content='its warm!', id='run-cfef25ca-d1be-4e79-8798-3bb9a7002287-0')]}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "branch_state.values" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5d89d55d-db84-4c2d-828b-64a29a69947b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "()" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "branch_state.next" + ] + }, + { + "cell_type": "markdown", + "id": "cc168c90-a374-4280-a9a6-8bc232dbb006", + "metadata": {}, + "source": [ + "You can see the snapshot was updated and now correctly reflects that there is no next step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74a7a5ed-0c14-4883-a16b-d70aaf40f7ea", + "metadata": {}, + "outputs": [], + "source": [ + "" ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "branch_state.next" - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } }, - { - "cell_type": "markdown", - "id": "cc168c90-a374-4280-a9a6-8bc232dbb006", - "metadata": {}, - "source": [ - "You can see the snapshot was updated and now correctly reflects that there is no next step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74a7a5ed-0c14-4883-a16b-d70aaf40f7ea", - "metadata": {}, - "outputs": [], - "source": [ - "" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/human_in_the_loop/wait-user-input.ipynb b/examples/human_in_the_loop/wait-user-input.ipynb index 6f41e65e4..eda7f0e6b 100644 --- a/examples/human_in_the_loop/wait-user-input.ipynb +++ b/examples/human_in_the_loop/wait-user-input.ipynb @@ -1,670 +1,670 @@ { - "cells": [ - { - "attachments": { - "02ae42da-d1a4-4849-984a-6ab0bbf759bd.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# How to wait for user input\n", - "\n", - "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \n", - "\n", - "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to stop graph execution at a specific step. At this breakpoint, we can wait for human input. Once we have input from the user, we can add it to the graph state and proceed.\n", - "\n", - "![Screenshot 2024-07-08 at 5.26.26 PM.png](attachment:02ae42da-d1a4-4849-984a-6ab0bbf759bd.png)" - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install --quiet -U langgraph langchain_anthropic langchain_openai" - ] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for Anthropic and / or OpenAI (the LLM(s) we will use)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdin", - "output_type": "stream", - "text": [ - "ANTHROPIC_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")\n", - "_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", - "_set_env(\"LANGCHAIN_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "e6cf1fad-5ab6-49c5-b0c8-15a1b6e8cf21", - "metadata": {}, - "source": [ - "## Simple Usage\n", - "\n", - "Let's look at very basic usage of this. One intuitive approach is simply to create a node, `human_feedback`, that will get user feedback. This allows us to place our feedback gathering at a specific, chosen point in our graph.\n", - " \n", - "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` our `human_feedback` node.\n", - "\n", - "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n", - "\n", - "3) We use `.update_state` to update the state of the graph with the human response we get.\n", - "\n", - "* We [use the `as_node` parameter](https://langchain-ai.github.io/langgraph/concepts/low_level/#update-state) to apply this state update as the specified node, `human_feedback`.\n", - "* The graph will then resume execution as if the `human_feedback` node just acted." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "58eae42d-be32-48da-8d0a-ab64471657d9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", 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" + } + }, + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to wait for user input\n", + "\n", + "Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding. \n", + "\n", + "We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to stop graph execution at a specific step. At this breakpoint, we can wait for human input. Once we have input from the user, we can add it to the graph state and proceed.\n", + "\n", + "![Screenshot 2024-07-08 at 5.26.26 PM.png](attachment:02ae42da-d1a4-4849-984a-6ab0bbf759bd.png)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from typing import TypedDict\n", - "from langgraph.graph import StateGraph, START, END\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "from IPython.display import Image, display\n", - "\n", - "\n", - "class State(TypedDict):\n", - " input: str\n", - " user_feedback: str\n", - "\n", - "\n", - "def step_1(state):\n", - " print(\"---Step 1---\")\n", - " pass\n", - "\n", - "\n", - "def human_feedback(state):\n", - " print(\"---human_feedback---\")\n", - " pass\n", - "\n", - "\n", - "def step_3(state):\n", - " print(\"---Step 3---\")\n", - " pass\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(\"step_1\", step_1)\n", - "builder.add_node(\"human_feedback\", human_feedback)\n", - "builder.add_node(\"step_3\", step_3)\n", - "builder.add_edge(START, \"step_1\")\n", - "builder.add_edge(\"step_1\", \"human_feedback\")\n", - "builder.add_edge(\"human_feedback\", \"step_3\")\n", - "builder.add_edge(\"step_3\", END)\n", - "\n", - "# Set up memory\n", - "memory = MemorySaver()\n", - "\n", - "# Add\n", - "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_feedback\"])\n", - "\n", - "# View\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "ce0fe2bc-86fc-465f-956c-729805d50404", - "metadata": {}, - "source": [ - "Run until our breakpoint at `step_2` - " - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "eb8e7d47-e7c9-4217-b72c-08394a2c4d3e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'input': 'hello world'}\n", - "---Step 1---\n" - ] - } - ], - "source": [ - "# Input\n", - "initial_input = {\"input\": \"hello world\"}\n", - "\n", - "# Thread\n", - "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "\n", - "# Run the graph until the first interruption\n", - "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", - " print(event)" - ] - }, - { - "cell_type": "markdown", - "id": "28a7d545-ab19-4800-985b-62837d060809", - "metadata": {}, - "source": [ - "Now, we can just manually update our graph state with with the user input - " - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "2165a1bc-1c5b-411f-9e9c-a2b9627e5d56", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Tell me how you want to update the state: go to step 3!\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "--State after update--\n", - "StateSnapshot(values={'input': 'hello world', 'user_feedback': 'go to step 3!'}, next=('step_3',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef3e216-b8a2-6db4-8002-966ecca671d0'}}, metadata={'source': 'update', 'step': 2, 'writes': {'human_feedback': {'user_feedback': 'go to step 3!'}}}, created_at='2024-07-09T18:31:13.083519+00:00', parent_config=None)\n" - ] + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] }, { - "data": { - "text/plain": [ - "('step_3',)" + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic langchain_openai" ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get user input\n", - "user_input = input(\"Tell me how you want to update the state: \")\n", - "\n", - "# We now update the state as if we are the human_feedback node\n", - "graph.update_state(thread, {\"user_feedback\": user_input}, as_node=\"human_feedback\")\n", - "\n", - "# We can check the state\n", - "print(\"--State after update--\")\n", - "print(graph.get_state(thread))\n", - "\n", - "# We can check the next node, showing that it is node 3 (which follows human_feedback)\n", - "graph.get_state(thread).next" - ] - }, - { - "cell_type": "markdown", - "id": "ccc4a84a-02f2-4b79-a5a5-22173645526d", - "metadata": {}, - "source": [ - "We can proceed after our breakpoint - " - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "3cca588f-e8d8-416b-aba7-0f3ae5e51598", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "---Step 3---\n" - ] - } - ], - "source": [ - "# Continue the graph execution\n", - "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", - " print(event)" - ] - }, - { - "cell_type": "markdown", - "id": "a75a1060-47aa-4cc6-8c41-e6ba2e9d7923", - "metadata": {}, - "source": [ - "We can see our feedback was added to state - " - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "2b83e5ca-8497-43ca-bff7-7203e654c4d3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'input': 'hello world', 'user_feedback': 'go to step 3!'}" + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for Anthropic and / or OpenAI (the LLM(s) we will use)" ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.get_state(thread).values" - ] - }, - { - "cell_type": "markdown", - "id": "e36f89e5", - "metadata": {}, - "source": [ - "## Agent\n", - "\n", - "In the context of agents, waiting for user feedback is useful to ask clarifying questions.\n", - " \n", - "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n", - "\n", - "We will use OpenAI and / or Anthropic's models and a fake tool (just for demo purposes)." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "f5319e01", - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 2, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "ANTHROPIC_API_KEY: ········\n" + ] + } + ], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")\n", + "_set_env(\"ANTHROPIC_API_KEY\")" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up the state\n", - "from langgraph.graph import MessagesState, START\n", - "\n", - "# Set up the tool\n", - "# We will have one real tool - a search tool\n", - "# We'll also have one \"fake\" tool - a \"ask_human\" tool\n", - "# Here we define any ACTUAL tools\n", - "from langchain_core.tools import tool\n", - "from langgraph.prebuilt import ToolNode\n", - "\n", - "\n", - "@tool\n", - "def search(query: str):\n", - " \"\"\"Call to surf the web.\"\"\"\n", - " # This is a placeholder for the actual implementation\n", - " # Don't let the LLM know this though 😊\n", - " return [\n", - " f\"I looked up: {query}. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", - " ]\n", - "\n", - "\n", - "tools = [search]\n", - "tool_node = ToolNode(tools)\n", - "\n", - "# Set up the model\n", - "from langchain_anthropic import ChatAnthropic\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", - "model = ChatOpenAI(model=\"gpt-4o\")\n", - "\n", - "# We are going \"bind\" all tools to the model\n", - "# We have the ACTUAL tools from above, but we also need a mock tool to ask a human\n", - "# Since `bind_tools` takes in tools but also just tool definitions,\n", - "# We can define a tool definition for `ask_human`\n", - "\n", - "from langchain_core.pydantic_v1 import BaseModel\n", - "\n", - "\n", - "class AskHuman(BaseModel):\n", - " \"\"\"Ask the human a question\"\"\"\n", - "\n", - " question: str\n", - "\n", - "\n", - "model = model.bind_tools(tools + [AskHuman])\n", - "\n", - "# Define nodes and conditional edges\n", - "\n", - "from langchain_core.messages import ToolMessage\n", - "\n", - "from langgraph.prebuilt import ToolInvocation\n", - "\n", - "\n", - "# Define the function that determines whether to continue or not\n", - "def should_continue(state):\n", - " messages = state[\"messages\"]\n", - " last_message = messages[-1]\n", - " # If there is no function call, then we finish\n", - " if not last_message.tool_calls:\n", - " return \"end\"\n", - " # If tool call is asking Human, we return that node\n", - " # You could also add logic here to let some system know that there's something that requires Human input\n", - " # For example, send a slack message, etc\n", - " elif last_message.tool_calls[0][\"name\"] == \"AskHuman\":\n", - " return \"ask_human\"\n", - " # Otherwise if there is, we continue\n", - " else:\n", - " return \"continue\"\n", - "\n", - "\n", - "# Define the function that calls the model\n", - "def call_model(state):\n", - " messages = state[\"messages\"]\n", - " response = model.invoke(messages)\n", - " # We return a list, because this will get added to the existing list\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "# We define a fake node to ask the human\n", - "def ask_human(state):\n", - " pass\n", - "\n", - "\n", - "# Build the graph\n", - "\n", - "from langgraph.graph import END, StateGraph\n", - "\n", - "# Define a new graph\n", - "workflow = StateGraph(MessagesState)\n", - "\n", - "# Define the three nodes we will cycle between\n", - "workflow.add_node(\"agent\", call_model)\n", - "workflow.add_node(\"action\", tool_node)\n", - "workflow.add_node(\"ask_human\", ask_human)\n", - "\n", - "# Set the entrypoint as `agent`\n", - "# This means that this node is the first one called\n", - "workflow.add_edge(START, \"agent\")\n", - "\n", - "# We now add a conditional edge\n", - "workflow.add_conditional_edges(\n", - " # First, we define the start node. We use `agent`.\n", - " # This means these are the edges taken after the `agent` node is called.\n", - " \"agent\",\n", - " # Next, we pass in the function that will determine which node is called next.\n", - " should_continue,\n", - " # Finally we pass in a mapping.\n", - " # The keys are strings, and the values are other nodes.\n", - " # END is a special node marking that the graph should finish.\n", - " # What will happen is we will call `should_continue`, and then the output of that\n", - " # will be matched against the keys in this mapping.\n", - " # Based on which one it matches, that node will then be called.\n", - " {\n", - " # If `tools`, then we call the tool node.\n", - " \"continue\": \"action\",\n", - " # We may ask the human\n", - " \"ask_human\": \"ask_human\",\n", - " # Otherwise we finish.\n", - " \"end\": END,\n", - " },\n", - ")\n", - "\n", - "# We now add a normal edge from `tools` to `agent`.\n", - "# This means that after `tools` is called, `agent` node is called next.\n", - "workflow.add_edge(\"action\", \"agent\")\n", - "\n", - "# After we get back the human response, we go back to the agent\n", - "workflow.add_edge(\"ask_human\", \"agent\")\n", - "\n", - "# Set up memory\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "\n", - "memory = MemorySaver()\n", - "\n", - "# Finally, we compile it!\n", - "# This compiles it into a LangChain Runnable,\n", - "# meaning you can use it as you would any other runnable\n", - "# We add a breakpoint BEFORE the `ask_human` node so it never executes\n", - "app = workflow.compile(checkpointer=memory, interrupt_before=[\"ask_human\"])\n", - "\n", - "display(Image(app.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", - "metadata": {}, - "source": [ - "## Interacting with the Agent\n", - "\n", - "We can now interact with the agent. Let's ask it to ask the user where they are, then tell them the weather. \n", - "\n", - "This should make it use the `ask_human` tool first, then use the normal tool." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "cfd140f0-a5a6-4697-8115-322242f197b5", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Use the search tool to ask the user where they are, then look up the weather there\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " AskHuman (call_LDo62KBPQKZWxPI5IHxPBF0w)\n", - " Call ID: call_LDo62KBPQKZWxPI5IHxPBF0w\n", - " Args:\n", - " question: Can you tell me where you are located?\n" - ] - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n", - "\n", - "config = {\"configurable\": {\"thread_id\": \"2\"}}\n", - "input_message = HumanMessage(\n", - " content=\"Use the search tool to ask the user where they are, then look up the weather there\"\n", - ")\n", - "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "cc168c90-a374-4280-a9a6-8bc232dbb006", - "metadata": {}, - "source": [ - "We now want to update this thread with a response from the user. We then can kick off another run. \n", - "\n", - "Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "63598092-d565-4170-9773-e092d345f8c1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('agent',)" + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tool_call_id = app.get_state(config).values[\"messages\"][-1].tool_calls[0][\"id\"]\n", - "\n", - "# We now create the tool call with the id and the response we want\n", - "tool_message = [\n", - " {\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": \"san francisco\"}\n", - "]\n", - "\n", - "# # This is equivalent to the below, either one works\n", - "# from langchain_core.messages import ToolMessage\n", - "# tool_message = [ToolMessage(tool_call_id=tool_call_id, content=\"san francisco\")]\n", - "\n", - "# We now update the state\n", - "# Notice that we are also specifying `as_node=\"ask_human\"`\n", - "# This will apply this update as this node,\n", - "# which will make it so that afterwards it continues as normal\n", - "app.update_state(config, {\"messages\": tool_message}, as_node=\"ask_human\")\n", - "\n", - "# We can check the state\n", - "# We can see that the state currently has the `agent` node next\n", - "# This is based on how we define our graph,\n", - "# where after the `ask_human` node goes (which we just triggered)\n", - "# there is an edge to the `agent` node\n", - "app.get_state(config).next" - ] - }, - { - "cell_type": "markdown", - "id": "6a30c9fb-2a40-45cc-87ba-406c11c9f0cf", - "metadata": {}, - "source": [ - "We can now tell the agent to continue. We can just pass in `None` as the input to the graph, since no additional input is needed" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "a9f599b5-1a55-406b-a76b-f52b3ca06975", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " search (call_LJlkCFfHvAS2taKHTaMmORE5)\n", - " Call ID: call_LJlkCFfHvAS2taKHTaMmORE5\n", - " Args:\n", - " query: current weather in San Francisco\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: search\n", - "\n", - "[\"I looked up: current weather in San Francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The current weather in San Francisco is sunny. Enjoy the good weather! 🌞\n" - ] + "cell_type": "code", + "execution_count": 3, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "e6cf1fad-5ab6-49c5-b0c8-15a1b6e8cf21", + "metadata": {}, + "source": [ + "## Simple Usage\n", + "\n", + "Let's look at very basic usage of this. One intuitive approach is simply to create a node, `human_feedback`, that will get user feedback. This allows us to place our feedback gathering at a specific, chosen point in our graph.\n", + " \n", + "1) We specify the [breakpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) using `interrupt_before` our `human_feedback` node.\n", + "\n", + "2) We set up a [checkpointer](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer) to save the state of the graph up until this node.\n", + "\n", + "3) We use `.update_state` to update the state of the graph with the human response we get.\n", + "\n", + "* We [use the `as_node` parameter](https://langchain-ai.github.io/langgraph/concepts/low_level/#update-state) to apply this state update as the specified node, `human_feedback`.\n", + "* The graph will then resume execution as if the `human_feedback` node just acted." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "58eae42d-be32-48da-8d0a-ab64471657d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from typing import TypedDict\n", + "from langgraph.graph import StateGraph, START, END\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from IPython.display import Image, display\n", + "\n", + "\n", + "class State(TypedDict):\n", + " input: str\n", + " user_feedback: str\n", + "\n", + "\n", + "def step_1(state):\n", + " print(\"---Step 1---\")\n", + " pass\n", + "\n", + "\n", + "def human_feedback(state):\n", + " print(\"---human_feedback---\")\n", + " pass\n", + "\n", + "\n", + "def step_3(state):\n", + " print(\"---Step 3---\")\n", + " pass\n", + "\n", + "\n", + "builder = StateGraph(State)\n", + "builder.add_node(\"step_1\", step_1)\n", + "builder.add_node(\"human_feedback\", human_feedback)\n", + "builder.add_node(\"step_3\", step_3)\n", + "builder.add_edge(START, \"step_1\")\n", + "builder.add_edge(\"step_1\", \"human_feedback\")\n", + "builder.add_edge(\"human_feedback\", \"step_3\")\n", + "builder.add_edge(\"step_3\", END)\n", + "\n", + "# Set up memory\n", + "memory = MemorySaver()\n", + "\n", + "# Add\n", + "graph = builder.compile(checkpointer=memory, interrupt_before=[\"human_feedback\"])\n", + "\n", + "# View\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "ce0fe2bc-86fc-465f-956c-729805d50404", + "metadata": {}, + "source": [ + "Run until our breakpoint at `step_2` - " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "eb8e7d47-e7c9-4217-b72c-08394a2c4d3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'input': 'hello world'}\n", + "---Step 1---\n" + ] + } + ], + "source": [ + "# Input\n", + "initial_input = {\"input\": \"hello world\"}\n", + "\n", + "# Thread\n", + "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n", + "\n", + "# Run the graph until the first interruption\n", + "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "28a7d545-ab19-4800-985b-62837d060809", + "metadata": {}, + "source": [ + "Now, we can just manually update our graph state with with the user input - " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "2165a1bc-1c5b-411f-9e9c-a2b9627e5d56", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Tell me how you want to update the state: go to step 3!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--State after update--\n", + "StateSnapshot(values={'input': 'hello world', 'user_feedback': 'go to step 3!'}, next=('step_3',), config={'configurable': {'thread_id': '1', 'thread_ts': '1ef3e216-b8a2-6db4-8002-966ecca671d0'}}, metadata={'source': 'update', 'step': 2, 'writes': {'human_feedback': {'user_feedback': 'go to step 3!'}}}, created_at='2024-07-09T18:31:13.083519+00:00', parent_config=None)\n" + ] + }, + { + "data": { + "text/plain": [ + "('step_3',)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get user input\n", + "user_input = input(\"Tell me how you want to update the state: \")\n", + "\n", + "# We now update the state as if we are the human_feedback node\n", + "graph.update_state(thread, {\"user_feedback\": user_input}, as_node=\"human_feedback\")\n", + "\n", + "# We can check the state\n", + "print(\"--State after update--\")\n", + "print(graph.get_state(thread))\n", + "\n", + "# We can check the next node, showing that it is node 3 (which follows human_feedback)\n", + "graph.get_state(thread).next" + ] + }, + { + "cell_type": "markdown", + "id": "ccc4a84a-02f2-4b79-a5a5-22173645526d", + "metadata": {}, + "source": [ + "We can proceed after our breakpoint - " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "3cca588f-e8d8-416b-aba7-0f3ae5e51598", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---Step 3---\n" + ] + } + ], + "source": [ + "# Continue the graph execution\n", + "for event in graph.stream(None, thread, stream_mode=\"values\"):\n", + " print(event)" + ] + }, + { + "cell_type": "markdown", + "id": "a75a1060-47aa-4cc6-8c41-e6ba2e9d7923", + "metadata": {}, + "source": [ + "We can see our feedback was added to state - " + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "2b83e5ca-8497-43ca-bff7-7203e654c4d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input': 'hello world', 'user_feedback': 'go to step 3!'}" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph.get_state(thread).values" + ] + }, + { + "cell_type": "markdown", + "id": "e36f89e5", + "metadata": {}, + "source": [ + "## Agent\n", + "\n", + "In the context of agents, waiting for user feedback is useful to ask clarifying questions.\n", + " \n", + "To show this, we will build a relatively simple ReAct-style agent that does tool calling. \n", + "\n", + "We will use OpenAI and / or Anthropic's models and a fake tool (just for demo purposes)." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f5319e01", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set up the state\n", + "from langgraph.graph import MessagesState, START\n", + "\n", + "# Set up the tool\n", + "# We will have one real tool - a search tool\n", + "# We'll also have one \"fake\" tool - a \"ask_human\" tool\n", + "# Here we define any ACTUAL tools\n", + "from langchain_core.tools import tool\n", + "from langgraph.prebuilt import ToolNode\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " # Don't let the LLM know this though 😊\n", + " return [\n", + " f\"I looked up: {query}. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n", + " ]\n", + "\n", + "\n", + "tools = [search]\n", + "tool_node = ToolNode(tools)\n", + "\n", + "# Set up the model\n", + "from langchain_anthropic import ChatAnthropic\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n", + "model = ChatOpenAI(model=\"gpt-4o\")\n", + "\n", + "# We are going \"bind\" all tools to the model\n", + "# We have the ACTUAL tools from above, but we also need a mock tool to ask a human\n", + "# Since `bind_tools` takes in tools but also just tool definitions,\n", + "# We can define a tool definition for `ask_human`\n", + "\n", + "from langchain_core.pydantic_v1 import BaseModel\n", + "\n", + "\n", + "class AskHuman(BaseModel):\n", + " \"\"\"Ask the human a question\"\"\"\n", + "\n", + " question: str\n", + "\n", + "\n", + "model = model.bind_tools(tools + [AskHuman])\n", + "\n", + "# Define nodes and conditional edges\n", + "\n", + "from langchain_core.messages import ToolMessage\n", + "\n", + "from langgraph.prebuilt import ToolInvocation\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "def should_continue(state):\n", + " messages = state[\"messages\"]\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"end\"\n", + " # If tool call is asking Human, we return that node\n", + " # You could also add logic here to let some system know that there's something that requires Human input\n", + " # For example, send a slack message, etc\n", + " elif last_message.tool_calls[0][\"name\"] == \"AskHuman\":\n", + " return \"ask_human\"\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return \"continue\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state):\n", + " messages = state[\"messages\"]\n", + " response = model.invoke(messages)\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [response]}\n", + "\n", + "\n", + "# We define a fake node to ask the human\n", + "def ask_human(state):\n", + " pass\n", + "\n", + "\n", + "# Build the graph\n", + "\n", + "from langgraph.graph import END, StateGraph\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(MessagesState)\n", + "\n", + "# Define the three nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", tool_node)\n", + "workflow.add_node(\"ask_human\", ask_human)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + " # Finally we pass in a mapping.\n", + " # The keys are strings, and the values are other nodes.\n", + " # END is a special node marking that the graph should finish.\n", + " # What will happen is we will call `should_continue`, and then the output of that\n", + " # will be matched against the keys in this mapping.\n", + " # Based on which one it matches, that node will then be called.\n", + " {\n", + " # If `tools`, then we call the tool node.\n", + " \"continue\": \"action\",\n", + " # We may ask the human\n", + " \"ask_human\": \"ask_human\",\n", + " # Otherwise we finish.\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")\n", + "\n", + "# After we get back the human response, we go back to the agent\n", + "workflow.add_edge(\"ask_human\", \"agent\")\n", + "\n", + "# Set up memory\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "memory = MemorySaver()\n", + "\n", + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "# We add a breakpoint BEFORE the `ask_human` node so it never executes\n", + "app = workflow.compile(checkpointer=memory, interrupt_before=[\"ask_human\"])\n", + "\n", + "display(Image(app.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", + "metadata": {}, + "source": [ + "## Interacting with the Agent\n", + "\n", + "We can now interact with the agent. Let's ask it to ask the user where they are, then tell them the weather. \n", + "\n", + "This should make it use the `ask_human` tool first, then use the normal tool." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "cfd140f0-a5a6-4697-8115-322242f197b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "Use the search tool to ask the user where they are, then look up the weather there\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " AskHuman (call_LDo62KBPQKZWxPI5IHxPBF0w)\n", + " Call ID: call_LDo62KBPQKZWxPI5IHxPBF0w\n", + " Args:\n", + " question: Can you tell me where you are located?\n" + ] + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "config = {\"configurable\": {\"thread_id\": \"2\"}}\n", + "input_message = HumanMessage(\n", + " content=\"Use the search tool to ask the user where they are, then look up the weather there\"\n", + ")\n", + "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "cc168c90-a374-4280-a9a6-8bc232dbb006", + "metadata": {}, + "source": [ + "We now want to update this thread with a response from the user. We then can kick off another run. \n", + "\n", + "Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "63598092-d565-4170-9773-e092d345f8c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('agent',)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tool_call_id = app.get_state(config).values[\"messages\"][-1].tool_calls[0][\"id\"]\n", + "\n", + "# We now create the tool call with the id and the response we want\n", + "tool_message = [\n", + " {\"tool_call_id\": tool_call_id, \"type\": \"tool\", \"content\": \"san francisco\"}\n", + "]\n", + "\n", + "# # This is equivalent to the below, either one works\n", + "# from langchain_core.messages import ToolMessage\n", + "# tool_message = [ToolMessage(tool_call_id=tool_call_id, content=\"san francisco\")]\n", + "\n", + "# We now update the state\n", + "# Notice that we are also specifying `as_node=\"ask_human\"`\n", + "# This will apply this update as this node,\n", + "# which will make it so that afterwards it continues as normal\n", + "app.update_state(config, {\"messages\": tool_message}, as_node=\"ask_human\")\n", + "\n", + "# We can check the state\n", + "# We can see that the state currently has the `agent` node next\n", + "# This is based on how we define our graph,\n", + "# where after the `ask_human` node goes (which we just triggered)\n", + "# there is an edge to the `agent` node\n", + "app.get_state(config).next" + ] + }, + { + "cell_type": "markdown", + "id": "6a30c9fb-2a40-45cc-87ba-406c11c9f0cf", + "metadata": {}, + "source": [ + "We can now tell the agent to continue. We can just pass in `None` as the input to the graph, since no additional input is needed" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a9f599b5-1a55-406b-a76b-f52b3ca06975", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " search (call_LJlkCFfHvAS2taKHTaMmORE5)\n", + " Call ID: call_LJlkCFfHvAS2taKHTaMmORE5\n", + " Args:\n", + " query: current weather in San Francisco\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: search\n", + "\n", + "[\"I looked up: current weather in San Francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini \\ud83d\\ude08.\"]\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The current weather in San Francisco is sunny. Enjoy the good weather! 🌞\n" + ] + } + ], + "source": [ + "for event in app.stream(None, config, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6f972d1-3d99-4fc1-8b33-92b71e74835d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" } - ], - "source": [ - "for event in app.stream(None, config, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6f972d1-3d99-4fc1-8b33-92b71e74835d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/input_output_schema.ipynb b/examples/input_output_schema.ipynb index 59f902361..16779ba6e 100644 --- a/examples/input_output_schema.ipynb +++ b/examples/input_output_schema.ipynb @@ -42,7 +42,6 @@ "def answer_node(state: InputState):\n", " return {\"answer\": \"bye\"}\n", "\n", - "check = SqliteSaver.from_conn_string(\":memory:\")\n", "graph = StateGraph(input=InputState, output=OutputState)\n", "graph.add_node(answer_node)\n", "graph.add_edge(START, \"answer_node\")\n", diff --git a/examples/introduction.ipynb b/examples/introduction.ipynb index a0720fdc9..d0f269946 100644 --- a/examples/introduction.ipynb +++ b/examples/introduction.ipynb @@ -84,8 +84,9 @@ ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "ef7bcad1-1274-4b7c-a2e9-365180ef3a31", + "id": "9c374e41-f9b7-439e-a520-6d8c853c5220", "metadata": {}, "source": [ "## Part 1: Build a Basic Chatbot\n", @@ -120,13 +121,24 @@ "graph_builder = StateGraph(State)" ] }, + { + "cell_type": "markdown", + "id": "31c755cd-8994-4867-bdff-96a55d7beae7", + "metadata": {}, + "source": [ + "
\n", + "

Note

\n", + "

\n", + " The first thing you do when you define a graph is define the State of the graph. The State consists of the schema of the graph as well as reducer functions which specify how to apply updates to the state. In our example State is a TypedDict with a single key: messages. The messages key is annotated with the add_messages reducer function, which tells LangGraph to append new messages to the existing list, rather than overwriting it. State keys without an annotation will be overwritten by each update, storing the most recent value. Check out this conceptual guide to learn more about state, reducers and other low-level concepts.\n", + "

\n", + "
" + ] + }, { "cell_type": "markdown", "id": "4137feed-746e-4c72-a34a-f7a699ad5dcf", "metadata": {}, "source": [ - "**Notice** that we've defined our `State` as a TypedDict with a single key: `messages`. The `messages` key is annotated with the [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function, which tells LangGraph to append new messages to the existing list, rather than overwriting it.\n", - "\n", "So now our graph knows two things:\n", "\n", "1. Every `node` we define will receive the current `State` as input and return a value that updates that state.\n", @@ -836,7 +848,7 @@ "\n", "We will see later that **checkpointing** is _much_ more powerful than simple chat memory - it lets you save and resume complex state at any time for error recovery, human-in-the-loop workflows, time travel interactions, and more. But before we get too ahead of ourselves, let's add checkpointing to enable multi-turn conversations.\n", "\n", - "To get started, create a `SqliteSaver` checkpointer." + "To get started, create a `MemorySaver` checkpointer." ] }, { @@ -846,9 +858,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")" + "memory = MemorySaver()" ] }, { @@ -856,7 +868,7 @@ "id": "08d3d11a-1b42-4cbb-8e11-2a4294263d90", "metadata": {}, "source": [ - "**Notice** that we've specified `:memory` as the Sqlite DB path. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to connect to your own DB and/or use one of the other checkpointer classes.\n", + "**Notice** we're using an in-memory checkpointer. This is convenient for our tutorial (it saves it all in-memory). In a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect to your own DB.\n", "\n", "Next define the graph. Now that you've already built your own `BasicToolNode`, we'll replace it with LangGraph's prebuilt `ToolNode` and `tools_condition`, since these do some nice things like parallel API execution. Apart from that, the following is all copied from Part 2." ] @@ -1187,7 +1199,7 @@ "from langchain_core.messages import BaseMessage\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode\n", @@ -1265,12 +1277,12 @@ "from langchain_core.messages import BaseMessage\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph, START\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode, tools_condition\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "\n", "\n", "class State(TypedDict):\n", @@ -1496,7 +1508,7 @@ "from langchain_core.messages import BaseMessage\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode\n", @@ -1531,7 +1543,7 @@ "graph_builder.add_edge(\"tools\", \"chatbot\")\n", "graph_builder.set_entry_point(\"chatbot\")\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "graph = graph_builder.compile(\n", " checkpointer=memory,\n", " # This is new!\n", @@ -1581,7 +1593,7 @@ "from langchain_core.messages import BaseMessage\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph, START\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode, tools_condition\n", @@ -1615,7 +1627,7 @@ ")\n", "graph_builder.add_edge(\"tools\", \"chatbot\")\n", "graph_builder.add_edge(START, \"chatbot\")\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "graph = graph_builder.compile(\n", " checkpointer=memory,\n", " # This is new!\n", @@ -2080,7 +2092,7 @@ "from langchain_core.messages import BaseMessage\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph, START\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode, tools_condition\n", @@ -2277,7 +2289,7 @@ "graph_builder.add_edge(\"tools\", \"chatbot\")\n", "graph_builder.add_edge(\"human\", \"chatbot\")\n", "graph_builder.add_edge(START, \"chatbot\")\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "graph = graph_builder.compile(\n", " checkpointer=memory,\n", " # We interrupt before 'human' here instead.\n", @@ -2527,7 +2539,7 @@ "from langchain_core.pydantic_v1 import BaseModel\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode, tools_condition\n", @@ -2614,7 +2626,7 @@ "graph_builder.add_edge(\"tools\", \"chatbot\")\n", "graph_builder.add_edge(\"human\", \"chatbot\")\n", "graph_builder.set_entry_point(\"chatbot\")\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "graph = graph_builder.compile(\n", " checkpointer=memory,\n", " interrupt_before=[\"human\"],\n", @@ -2653,11 +2665,11 @@ "\n", "from langchain_anthropic import ChatAnthropic\n", "from langchain_community.tools.tavily_search import TavilySearchResults\n", - "from langchain_core.messages import AIMessage, BaseMessage, ToolMessage\n", + "from langchain_core.messages import AIMessage, ToolMessage\n", "from langchain_core.pydantic_v1 import BaseModel\n", "from typing_extensions import TypedDict\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import StateGraph, START\n", "from langgraph.graph.message import add_messages\n", "from langgraph.prebuilt import ToolNode, tools_condition\n", @@ -2744,7 +2756,7 @@ "graph_builder.add_edge(\"tools\", \"chatbot\")\n", "graph_builder.add_edge(\"human\", \"chatbot\")\n", "graph_builder.add_edge(START, \"chatbot\")\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "graph = graph_builder.compile(\n", " checkpointer=memory,\n", " interrupt_before=[\"human\"],\n", @@ -3056,9 +3068,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "langgraph", "language": "python", - "name": "python3" + "name": "langgraph" }, "language_info": { "codemirror_mode": { @@ -3070,7 +3082,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.1" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/examples/lats/img/lats.png b/examples/lats/img/lats.png index 79e96a304..2570635be 100644 Binary files a/examples/lats/img/lats.png and b/examples/lats/img/lats.png differ diff --git a/examples/lats/img/tree.png b/examples/lats/img/tree.png index 6b3dd0c02..36e40e7a2 100644 Binary files a/examples/lats/img/tree.png and b/examples/lats/img/tree.png differ diff --git a/examples/learning.ipynb b/examples/learning.ipynb deleted file mode 100644 index 2588a7d28..000000000 --- a/examples/learning.ipynb +++ /dev/null @@ -1,477 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# Get/Update State\n", - "\n", - "When running LangGraph agents, you can easily save good threads and use them in the future.\n", - "\n", - "**Note:** this requires passing in a checkpointer.\n" - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - } - ], - "source": ["!%pip install --quiet -U langgraph langchain langchain_openai tavily-pythonvily-python"] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OpenAI API Key: ········\n", - "Tavily API Key: ········\n" - ] - } - ], - "source": ["import getpass\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nos.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [], - "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"] - }, - { - "cell_type": "markdown", - "id": "21ac643b-cb06-4724-a80c-2862ba4773f1", - "metadata": {}, - "source": [ - "## Set up the tools\n", - "\n", - "We will first define the tools we want to use.\n", - "For this simple example, we will use a built-in search tool via Tavily.\n", - "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", - "metadata": {}, - "outputs": [], - "source": ["from langchain_community.tools.tavily_search import TavilySearchResults\n\ntools = [TavilySearchResults(max_results=1)]"] - }, - { - "cell_type": "markdown", - "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58", - "metadata": {}, - "source": [ - "We can now wrap these tools in a simple ToolNode.\n", - "This is a prebuilt node that extracts tool calls from the most recent AIMessage, executes them, and returns a ToolMessage with the results.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", - "metadata": {}, - "outputs": [], - "source": ["from langgraph.prebuilt import ToolNode\n\ntool_node = ToolNode(tools)"] - }, - { - "cell_type": "markdown", - "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5", - "metadata": {}, - "source": [ - "## Set up the model\n", - "\n", - "Now we need to load the chat model we want to use.\n", - "Importantly, this should satisfy two criteria:\n", - "\n", - "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n", - "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n", - "\n", - "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", - "metadata": {}, - "outputs": [], - "source": ["from langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(temperature=0)"] - }, - { - "cell_type": "markdown", - "id": "a77995c0-bae2-4cee-a036-8688a90f05b9", - "metadata": {}, - "source": [ - "After we've done this, we should make sure the model knows that it has these tools available to call.\n", - "We can do this using the `.bind_tools()` method, common to many of LangChain's chat models.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", - "metadata": {}, - "outputs": [], - "source": ["model = model.bind_tools(tools)"] - }, - { - "cell_type": "markdown", - "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4", - "metadata": {}, - "source": [ - "## Define the nodes\n", - "\n", - "We now need to define a few different nodes in our graph.\n", - "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", - "There are two main nodes we need for this:\n", - "\n", - "1. The agent: responsible for deciding what (if any) actions to take.\n", - "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n", - "\n", - "We will also need to define some edges.\n", - "Some of these edges may be conditional.\n", - "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n", - "The path that is taken is not known until that node is run (the LLM decides).\n", - "\n", - "1. Conditional Edge: after the agent is called, we should either:\n", - " a. If the agent said to take an action, then the function to invoke tools should be called\n", - " b. If the agent said that it was finished, then it should finish\n", - "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n", - "\n", - "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "3b541bb9-900c-40d0-964d-7b5dfee30667", - "metadata": {}, - "outputs": [], - "source": ["# Define the function that determines whether to continue or not\ndef should_continue(state):\n last_message = state[\"messages\"][-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"end\"\n # Otherwise if there is, we continue\n else:\n return \"continue\""] - }, - { - "cell_type": "markdown", - "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b", - "metadata": {}, - "source": [ - "## Define the graph\n", - "\n", - "We can now put it all together and define the graph!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "812b4e70-4956-4415-8880-db48b3dcbad2", - "metadata": {}, - "outputs": [], - "source": ["from typing import Annotated, TypedDict\n\nfrom langchain_core.messages import (\n AIMessage,\n AnyMessage,\n HumanMessage,\n SystemMessage,\n ToolMessage,\n)\n\nfrom langgraph.graph import END, StateGraph, START\nfrom langgraph.graph.message import add_messages\nfrom langgraph.managed.few_shot import FewShotExamples\n\n\nclass BaseState(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]\n examples: Annotated[list, FewShotExamples]\n\n\ndef _render_message(m):\n if isinstance(m, HumanMessage):\n return \"Human: \" + m.content\n elif isinstance(m, AIMessage):\n _m = \"AI: \" + m.content\n if len(m.tool_calls) > 0:\n _m += f\" Tools: {m.tool_calls}\"\n return _m\n elif isinstance(m, ToolMessage):\n return \"Tool Result: ...\"\n else:\n raise ValueError\n\n\ndef _render_messages(ms):\n m_string = [_render_message(m) for m in ms]\n return \"\\n\".join(m_string)\n\n\n# Define a new graph\nworkflow = StateGraph(BaseState)\n\n\ndef _agent(state: BaseState):\n if len(state[\"examples\"]) > 0:\n _examples = \"\\n\\n\".join(\n [\n f\"Example {i}: \" + _render_messages(e[\"messages\"])\n for i, e in enumerate(state[\"examples\"])\n ]\n )\n system_message = \"\"\"You are a helpful assistant. Below are some examples of interactions you had with users. \\\nThese were good interactions where the final result they got was the desired one. As much as possible, you should learn from these interactions and mimic them in the future. \\\nPay particularly close attention to when tools are called, and what the inputs are.!\n\n{examples}\n\nAssist the user as they require!\"\"\".format(\n examples=_examples\n )\n\n else:\n system_message = \"\"\"You are a helpful assistant\"\"\"\n output = model.invoke([SystemMessage(content=system_message)] + state[\"messages\"])\n return {\"messages\": [output]}\n\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", _agent)\nworkflow.add_node(\"action\", tool_node)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n # Finally we pass in a mapping.\n # The keys are strings, and the values are other nodes.\n # END is a special node marking that the graph should finish.\n # What will happen is we will call `should_continue`, and then the output of that\n # will be matched against the keys in this mapping.\n # Based on which one it matches, that node will then be called.\n {\n # If `tools`, then we call the tool node.\n \"continue\": \"action\",\n # Otherwise we finish.\n \"end\": END,\n },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")"] - }, - { - "cell_type": "markdown", - "id": "bc9c8536-f90b-44fa-958d-5df016c66d8f", - "metadata": {}, - "source": [ - "**Persistence**\n", - "\n", - "To add in persistence, we pass in a checkpoint when compiling the graph\n" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "6845ed6a-d155-4105-9160-28849877248b", - "metadata": {}, - "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "79d29875-8aa8-434c-9f20-1c58346a6249", - "metadata": {}, - "outputs": [], - "source": ["# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=memory, interrupt_before=[\"action\"])"] - }, - { - "cell_type": "markdown", - "id": "e8aff75b-563e-42b1-969b-742201514fc3", - "metadata": {}, - "source": [ - "## Preview the graph\n" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "c9ab60eb-679b-4eef-9e64-5ffbf3dffc70", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 156, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["from IPython.display import Image\n\nImage(app.get_graph().draw_png())"] - }, - { - "cell_type": "markdown", - "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", - "metadata": {}, - "source": [ - "## Interacting with the Agent\n", - "\n", - "We can now interact with the agent. Between interactions you can get and update state.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "cfd140f0-a5a6-4697-8115-322242f197b5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}])]}\n" - ] - } - ], - "source": ["thread = {\"configurable\": {\"thread_id\": \"1\"}}\nfor event in app.stream(\n {\"messages\": [HumanMessage(content=\"what's the weather in sf?\")]}, thread\n):\n for v in event.values():\n print(v)"] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "4479f8ae-7c46-4117-8ca2-0a9c2ef9785b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'messages': [HumanMessage(content='what's the weather in sf?', id='1cfd0c2f-9b60-48da-8938-408fd6aeda13'),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}])]}" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["current_values = app.get_state(thread)\ncurrent_values.values"] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "1a0cdb78-40c6-4550-8c27-8f1b02d9e678", - "metadata": {}, - "outputs": [], - "source": ["current_values.values[\"messages\"][-1].tool_calls[0][\"args\"][\n \"query\"\n] = \"weather in San Francisco, Accuweather\""] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "652f699a-89bc-4277-b37a-c3d94b835df5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'configurable': {'thread_id': '1',\n", - " 'thread_ts': '2024-04-20T01:13:15.108790+00:00'}}" - ] - }, - "execution_count": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["app.update_state(thread, current_values.values)"] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "e2b29825-a108-4d40-b377-22e8f4629d64", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StateSnapshot(values={'messages': [HumanMessage(content='what's the weather in sf?', id='1cfd0c2f-9b60-48da-8938-408fd6aeda13'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco, Accuweather'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}])]}, next=('action',), config={'configurable': {'thread_id': '1', 'thread_ts': '2024-04-20T01:13:15.108790+00:00'}}, parent_config=None)" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["app.get_state(thread)"] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "f0aad8a6-056e-42ca-bbc2-b45f768da75f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'messages': [ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1713575495, \\'localtime\\': \\'2024-04-19 18:11\\'}, \\'current\\': {\\'last_updated_epoch\\': 1713574800, \\'last_updated\\': \\'2024-04-19 18:00\\', \\'temp_c\\': 16.1, \\'temp_f\\': 61.0, \\'is_day\\': 1, \\'condition\\': {\\'text\\': \\'Sunny\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/day/113.png\\', \\'code\\': 1000}, \\'wind_mph\\': 16.1, \\'wind_kph\\': 25.9, \\'wind_degree\\': 300, \\'wind_dir\\': \\'WNW\\', \\'pressure_mb\\': 1015.0, \\'pressure_in\\': 29.97, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 67, \\'cloud\\': 0, \\'feelslike_c\\': 16.1, \\'feelslike_f\\': 61.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 4.0, \\'gust_mph\\': 20.6, \\'gust_kph\\': 33.1}}\"}]', name='tavily_search_results_json', id='8c7e9af3-6569-4982-a83d-be1ec02f828a', tool_call_id='call_yQrJa8CEOfKBdpVl80jzWf5h')]}\n", - "{'messages': [AIMessage(content='The current weather in San Francisco is sunny with a temperature of 61.0°F (16.1°C). The wind speed is 25.9 km/h coming from the west-northwest direction. The humidity is at 67%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 476, 'total_tokens': 535}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7d652a0d-cb00-4857-b9fb-d3e37b0a6d23-0')]}\n" - ] - } - ], - "source": ["for event in app.stream(None, thread):\n for v in event.values():\n print(v)"] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "84748206-975e-4a33-a178-d43df683298c", - "metadata": {}, - "outputs": [], - "source": ["chkpnt_tuple = memory.get_tuple({\"configurable\": {\"thread_id\": \"1\"}})\nconfig = chkpnt_tuple.config\ncheckpoint = chkpnt_tuple.checkpoint\nmetadata = chkpnt_tuple.metadata\n\n# mark as \"good\"\nmetadata[\"score\"] = 1\nmemory.put(config, checkpoint, metadata)"] - }, - { - "cell_type": "code", - "execution_count": 130, - "id": "ce7fa228-8c37-4001-afd4-0001b268e1db", - "metadata": {}, - "outputs": [], - "source": ["examples = list(memory.search({\"score\": 1}))"] - }, - { - "cell_type": "code", - "execution_count": 131, - "id": "0543a501-b4cb-4890-8236-3350ebee5af9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[CheckpointTuple(config={'configurable': {'thread_id': '1', 'thread_ts': '2024-04-20T01:13:36.933600+00:00'}}, checkpoint={'v': 1, 'ts': '2024-04-20T01:13:36.933600+00:00', 'channel_values': {'messages': [HumanMessage(content='what's the weather in sf?', id='1cfd0c2f-9b60-48da-8938-408fd6aeda13'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h', 'function': {'arguments': '{\"query\":\"weather in San Francisco\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 94, 'total_tokens': 115}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-3764f79e-17b4-4aa3-bbe4-4f92b11ca52c-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in San Francisco, Accuweather'}, 'id': 'call_yQrJa8CEOfKBdpVl80jzWf5h'}]), ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'San Francisco\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 37.78, \\'lon\\': -122.42, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1713575495, \\'localtime\\': \\'2024-04-19 18:11\\'}, \\'current\\': {\\'last_updated_epoch\\': 1713574800, \\'last_updated\\': \\'2024-04-19 18:00\\', \\'temp_c\\': 16.1, \\'temp_f\\': 61.0, \\'is_day\\': 1, \\'condition\\': {\\'text\\': \\'Sunny\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/day/113.png\\', \\'code\\': 1000}, \\'wind_mph\\': 16.1, \\'wind_kph\\': 25.9, \\'wind_degree\\': 300, \\'wind_dir\\': \\'WNW\\', \\'pressure_mb\\': 1015.0, \\'pressure_in\\': 29.97, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 67, \\'cloud\\': 0, \\'feelslike_c\\': 16.1, \\'feelslike_f\\': 61.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 4.0, \\'gust_mph\\': 20.6, \\'gust_kph\\': 33.1}}\"}]', name='tavily_search_results_json', id='8c7e9af3-6569-4982-a83d-be1ec02f828a', tool_call_id='call_yQrJa8CEOfKBdpVl80jzWf5h'), AIMessage(content='The current weather in San Francisco is sunny with a temperature of 61.0°F (16.1°C). The wind speed is 25.9 km/h coming from the west-northwest direction. The humidity is at 67%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 476, 'total_tokens': 535}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7d652a0d-cb00-4857-b9fb-d3e37b0a6d23-0')], 'agent': {'messages': [AIMessage(content='The current weather in San Francisco is sunny with a temperature of 61.0°F (16.1°C). The wind speed is 25.9 km/h coming from the west-northwest direction. The humidity is at 67%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 59, 'prompt_tokens': 476, 'total_tokens': 535}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7d652a0d-cb00-4857-b9fb-d3e37b0a6d23-0')]}}, 'channel_versions': defaultdict(, {'__start__': 1, 'messages': 6, 'start:agent': 2, 'action': 5, 'agent': 6, 'branch:agent:should_continue:action': 4}), 'versions_seen': defaultdict(, {'__start__': defaultdict(, {'__start__': 1}), 'agent': defaultdict(, {'start:agent': 2, 'action': 5}), 'action': defaultdict(, {'branch:agent:should_continue:action': 4}), '__interrupt__': defaultdict(, {'messages': 4})})}, parent_config={'configurable': {'thread_id': '1', 'thread_ts': '2024-04-20T01:13:35.392072+00:00'}})]" - ] - }, - "execution_count": 131, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["examples"] - }, - { - "cell_type": "code", - "execution_count": 157, - "id": "336a70d3-d8c7-4310-a373-df2be3320030", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_amNrYLgitup6hCDiHUYKwodH', 'function': {'arguments': '{\"query\":\"weather in Los Angeles, Accuweather\"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 25, 'prompt_tokens': 296, 'total_tokens': 321}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-6b852685-e84f-48e7-b8d0-5b0a44ac9776-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in Los Angeles, Accuweather'}, 'id': 'call_amNrYLgitup6hCDiHUYKwodH'}])]}\n" - ] - } - ], - "source": ["thread = {\"configurable\": {\"thread_id\": \"7\"}}\nfor event in app.stream(\n {\"messages\": [HumanMessage(content=\"what's the weather in la?\")]}, thread\n):\n for v in event.values():\n print(v)"] - }, - { - "cell_type": "code", - "execution_count": 158, - "id": "ab245e7c-e47d-45e4-8b79-361fab359e76", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'messages': [ToolMessage(content='[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{\\'location\\': {\\'name\\': \\'Los Angeles\\', \\'region\\': \\'California\\', \\'country\\': \\'United States of America\\', \\'lat\\': 34.05, \\'lon\\': -118.24, \\'tz_id\\': \\'America/Los_Angeles\\', \\'localtime_epoch\\': 1713576177, \\'localtime\\': \\'2024-04-19 18:22\\'}, \\'current\\': {\\'last_updated_epoch\\': 1713575700, \\'last_updated\\': \\'2024-04-19 18:15\\', \\'temp_c\\': 17.8, \\'temp_f\\': 64.0, \\'is_day\\': 1, \\'condition\\': {\\'text\\': \\'Partly cloudy\\', \\'icon\\': \\'//cdn.weatherapi.com/weather/64x64/day/116.png\\', \\'code\\': 1003}, \\'wind_mph\\': 4.3, \\'wind_kph\\': 6.8, \\'wind_degree\\': 250, \\'wind_dir\\': \\'WSW\\', \\'pressure_mb\\': 1014.0, \\'pressure_in\\': 29.94, \\'precip_mm\\': 0.0, \\'precip_in\\': 0.0, \\'humidity\\': 65, \\'cloud\\': 50, \\'feelslike_c\\': 17.8, \\'feelslike_f\\': 64.0, \\'vis_km\\': 16.0, \\'vis_miles\\': 9.0, \\'uv\\': 5.0, \\'gust_mph\\': 10.3, \\'gust_kph\\': 16.6}}\"}]', name='tavily_search_results_json', id='6477bd73-bdf1-46c5-ab0a-cc808b1a183e', tool_call_id='call_amNrYLgitup6hCDiHUYKwodH')]}\n", - "{'messages': [AIMessage(content='The current weather in Los Angeles is partly cloudy with a temperature of 64.0°F (17.8°C). The wind speed is 6.8 km/h coming from the west-southwest direction. The humidity is at 65%, and there is no precipitation at the moment.', response_metadata={'token_usage': {'completion_tokens': 60, 'prompt_tokens': 679, 'total_tokens': 739}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-a604c04e-c3c3-4545-804f-a89aee6bf516-0')]}\n" - ] - } - ], - "source": ["for event in app.stream(None, thread):\n for v in event.values():\n print(v)"] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ab115de-9b11-4e8b-8ace-c23e1369300b", - "metadata": {}, - "outputs": [], - "source": [""] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/llm-compiler/img/diagram.png b/examples/llm-compiler/img/diagram.png index 01655a5ec..d5d82411b 100644 Binary files a/examples/llm-compiler/img/diagram.png and b/examples/llm-compiler/img/diagram.png differ diff --git a/examples/llm-compiler/img/llm-compiler.png b/examples/llm-compiler/img/llm-compiler.png index 3c9ec7a57..acc50e146 100644 Binary files a/examples/llm-compiler/img/llm-compiler.png and b/examples/llm-compiler/img/llm-compiler.png differ diff --git a/examples/many-tools.ipynb b/examples/many-tools.ipynb new file mode 100644 index 000000000..b415f5065 --- /dev/null +++ b/examples/many-tools.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5fa317ef-b9a7-4432-ba85-ce71b8dfbdc6", + "metadata": {}, + "source": [ + "# How to handle large numbers of tools\n", + "\n", + "The subset of available tools to call is generally at the discretion of the model (although many providers also enable the user to [specify or constrain the choice of tool](https://python.langchain.com/v0.2/docs/how_to/tool_choice/)). As the number of available tools grows, you may want to limit the scope of the LLM's selection, to decrease token consumption and to help manage sources of error in LLM reasoning.\n", + "\n", + "Here we will demonstrate how to dynamically adjust the tools available to a model. Bottom line up front: like [RAG](https://python.langchain.com/v0.2/docs/concepts/#retrieval) and similar methods, we prefix the model invocation by retrieving over available tools. Although we demonstrate one implementation that searches over tool descriptions, the details of the tool selection can be customized as needed." + ] + }, + { + "cell_type": "markdown", + "id": "1a417013-ddc4-463b-8ea0-0904bd232827", + "metadata": {}, + "source": [ + "## Define the tools" + ] + }, + { + "cell_type": "markdown", + "id": "24708f3b-18b1-4b42-9f6a-0d4827222918", + "metadata": {}, + "source": [ + "Let's consider a toy example in which we have one tool for each company in the S&P 500 index. Each tool will fetch information, and is parameterized by a single integer representing the year.\n", + "\n", + "We first construct a registry that associates a unique identifier with a schema for each tool. We will represent the tools using JSON schema, which can be bound directly to chat models supporting tool calling." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "da30c3f1-127f-4828-8609-94e16719f0be", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import uuid\n", + "\n", + "from langchain_core.tools import StructuredTool\n", + "\n", + "\n", + "def create_tool(company: str) -> dict:\n", + " \"\"\"Create schema for a placeholder tool.\"\"\"\n", + " formatted_company = re.sub(r\"[^\\w\\s]\", \"\", company).replace(\" \", \"_\")\n", + "\n", + " def company_tool(year: int) -> str:\n", + " return f\"{company} had revenues of $100 in {year}.\"\n", + "\n", + " return StructuredTool.from_function(\n", + " company_tool,\n", + " name=formatted_company,\n", + " description=f\"Information about {company}\",\n", + " )\n", + "\n", + "\n", + "s_and_p_500_companies = [ # Abbreviated list for demonstration purposes\n", + " \"3M\",\n", + " \"A.O. Smith\",\n", + " \"Abbott\",\n", + " \"Accenture\",\n", + " \"Advanced Micro Devices\",\n", + " \"Yum! Brands\",\n", + " \"Zebra Technologies\",\n", + " \"Zimmer Biomet\",\n", + " \"Zoetis\",\n", + "]\n", + "\n", + "tool_registry = {\n", + " str(uuid.uuid4()): create_tool(company) for company in s_and_p_500_companies\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "ba17b047-73ed-4385-adc2-f02012db2206", + "metadata": {}, + "source": [ + "## Define the graph" + ] + }, + { + "cell_type": "markdown", + "id": "2055548d-3d14-4aaf-9588-abf70f28b5d6", + "metadata": {}, + "source": [ + "### Tool selection" + ] + }, + { + "cell_type": "markdown", + "id": "8798a0d2-ea93-45bc-ab55-071ab975f2c2", + "metadata": {}, + "source": [ + "We will construct a node that retrieves a subset of available tools given the information in the state-- such as a recent user message. In general, the full scope of [retrieval solutions](https://python.langchain.com/v0.2/docs/concepts/#retrieval) are available for this step. As a simple solution, we index embeddings of tool descriptions in a vector store, and associate user queries to tools via semantic search." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "435b0201-7296-4617-abf8-2c757a71f6b5", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "from langchain_core.vectorstores import InMemoryVectorStore, VectorStore\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "tool_documents = [\n", + " Document(\n", + " page_content=tool.description,\n", + " id=id,\n", + " metadata={\"tool_name\": tool.name},\n", + " )\n", + " for id, tool in tool_registry.items()\n", + "]\n", + "\n", + "vector_store = InMemoryVectorStore(embedding=OpenAIEmbeddings())\n", + "document_ids = vector_store.add_documents(tool_documents)" + ] + }, + { + "cell_type": "markdown", + "id": "e9ce366b-b5e7-41e9-b4a9-d775b9be0d09", + "metadata": {}, + "source": [ + "### Incorporating with an agent\n", + "\n", + "We will use a typical React agent graph (e.g., as used in the [quickstart](https://langchain-ai.github.io/langgraph/tutorials/introduction/#part-2-enhancing-the-chatbot-with-tools)), with some modifications:\n", + "\n", + "- We add a `selected_tools` key to the state, which stores our selected subset of tools;\n", + "- We set the entry point of the graph to be a `select_tools` node, which populates this element of the state;\n", + "- We bind the selected subset of tools to the chat model within the `agent` node." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d319fea9-e8ae-4763-a785-b2bf72239ae4", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Annotated\n", + "\n", + "from langchain_openai import ChatOpenAI\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph import StateGraph, START, END\n", + "from langgraph.graph.message import add_messages\n", + "from langgraph.prebuilt import ToolNode, tools_condition\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list, add_messages]\n", + " selected_tools: list[str]\n", + "\n", + "\n", + "graph_builder = StateGraph(State)\n", + "\n", + "tools = list(tool_registry.values())\n", + "llm = ChatOpenAI()\n", + "\n", + "\n", + "def agent(state: State):\n", + " selected_tools = [tool_registry[id] for id in state[\"selected_tools\"]]\n", + " llm_with_tools = llm.bind_tools(selected_tools)\n", + " return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n", + "\n", + "\n", + "def select_tools(state: State):\n", + " last_user_message = state[\"messages\"][-1]\n", + " query = last_user_message.content\n", + " tool_documents = vector_store.similarity_search(query)\n", + " return {\"selected_tools\": [document.id for document in tool_documents]}\n", + "\n", + "\n", + "graph_builder.add_node(\"agent\", agent)\n", + "graph_builder.add_node(\"select_tools\", select_tools)\n", + "\n", + "tool_node = ToolNode(tools=tools)\n", + "graph_builder.add_node(\"tools\", tool_node)\n", + "\n", + "graph_builder.add_conditional_edges(\n", + " \"agent\",\n", + " tools_condition,\n", + ")\n", + "graph_builder.add_edge(\"tools\", \"agent\")\n", + "graph_builder.add_edge(\"select_tools\", \"agent\")\n", + "graph_builder.add_edge(START, \"select_tools\")\n", + "graph = graph_builder.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "35cab3b2-4d03-4cb5-ba10-f7d3a5ad5244", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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W93G+ZNW3wUzai7XWsBrIvFyXU5ifGxgjMbnFwIGySeYlT52VOpQGVtlvNPUjo+OG3TVLQfTp8TXNI+Ha/PlnTe5l++ZKv6pehzNc/wDOV0ZRWDgZb6nEsix2/wCR5JltDfKZtLUOvdc174mNDtdkI2Max23b5uXZIbsnQXnvHg/2661dnuUOUZPasjt9ALY+/wBBXRtrK2nB5gyoLo3Mk87bt8gIJJBCmXlnTe5l++ZKv6pPLOm9zL98yVf1ScxX2ZNGdyL5JwNtWRQY5I295Ba75YIDTUeQUNf+33ROAD2Svka9sodytJ52nqNjS4vnA2336x45STZHkcd1sFTJVUOQsrmuuLXyB7ZA572Oa5rmvLS0s0AABoAKU+WdN7mX75kq/qk8s6b3Mv3zJV/VJzFfZk0Z3I4+68Sra40dHiViuVJTnsoa2tyiWOeoY3o2SRraAhr3AAkAkAk9SvxcuFXldkljzGurrjimVUtIKOqZYLg2SGeHtO07CR0kPtjA7ZBDWO84rKY9xYx/LbVHc7GbleLbI5zWVlBa6meJxa4tcA9sZBIcCD16EELJeWdN7mX75kq/qk5i52ZTRlBJfBpxmW7uldc757AOufsw7FvHG+xZq+07Xn7Pk5+XtPbOz5+Tm68q/WT+DbjmU3a8Ty3W/UFpvdQ2qu9goK0RUFxlAaC6VnIXjmDGh/I9odrrtTnyzpvcy/fMlX9UnlnTe5l++ZKv6pOYr7K6M7kduPBa1VWTZBfKG73uw1d8t8VBWMtNYIYyYtCKdg5SWSsaOQOB1ykggr3cOuFlv4cSXmqhuVzvl2vE0c9fdbxO2WonLIxHGDyNY0Na0aADR6e9ZTyzpvcy/fMlX9UuRmNO46ba76TroDZasb/GY9JzFe3RlNGXrx7+Ue7/ANE0n/OqVNlFsStdU+6XC91lO6idVxRU0FLJrtGxRl7ud+u5znSO83Z0Gt3olzRKVxcpmJuat0ekEiIi5UEREBERAREQEREBERAREQEREBERBrv4Af3MOPfHbj9NmWxC138AP7mHHvjtx+mzLYhAREQEREBERAREQEREBERAREQEREBERAREQEREBERBrv4Af3MOPfHbj9NmWxC138AP7mHHvjtx+mzLYhAREQEREBERAREQEREBERAREQEREBERAREQERdbp4mO06RjSPQXAIOxa0+Fj4YVx8F292OnfgJyS1XanfJFchdvFQ2ZjtPiLOwf3NMbt7G+cjXm7WyPjUP+Wj/2gqZ8LfgzTce+CV6sMJide6UeyFpfzDYqo2nlZ+B7S6P4OffoVxI1E8CPwzq21twrg9QYA+6z1lzla66Mu3IYopp3zSy9j2J2ImOe4jnGww92+n0mXz9/YwOBos9DeuJl7gENXUOfa7VHOOVzI2n2+UA+twEYPQjkkHcVv741D/lo/wDaCYkdqLq8ah/y0f8AtBdqYBERQEREBERAREQEREBERAREQEREBERBF89rpoqa1W+Gd9N7KVwpJJoXlkjYxFLK8McOrS4RFvMNEBxIIIBGFOAYw4Dmx21PI9L6KNx79nZI2epJ/GsjxB/hPDP6Yd9Bq11ZNkttw+xVd5u9T4nbaRofNNyOfygkAaa0FxJJA0AT1Xq26qrdqnQnGdfGYZZxGp4/sfYt72rP8gi/RT7H2Le9qz/IIv0Vh8c42YTlWP3m90F+hFuswJuT6yKSkfRjl5tyxzNa9oLeoJHX0bXlxbj7guZTXaO2Xp/7k0gr6+StoqikZTQHenvdMxgAIBI9YBI2ASsufudufNMzvSL7H2Le9qz/ACCL9FPsfYt72rP8gi/RUcsHH/AsnbcTQX3mdQUT7jNHUUdRTyGmYNumjZJG10rB/jMDh1HrC7MZ48YNmFyjoLRezV1MtK+spx4nUMbVQsAL3QPdGGzcuxsRlxHpCc/c7c+Zmd7Pjh/i7SCMbtAI6gihi/RXfj8UWMZXR2qgYKe2V1LNJ4nH0jhkjMejG3WmgteQQNDYaQNlxMJ4G8c7dxssdTV01FWW+rgqKiN8E1HUMjEbKiSKNwmkiYx7nNYHOa0ksJLXAEKbO/lDx34pW/8ARViuq7TMVTmMTwiZWJmdqdoiLx2IiIgIiICIiAiIgIiICIiAiIgIiIIdxB/hPDP6Yd9Bq1DOPNVlFHwzuEmJNrDc+2p2yutsQlrGUpmYKh1Ow7DpRFzlo0evd10prxAYTXYlITpkV3JcdH00lSwf2uA/GvYvTjXao8J9ZWepphWYFV3ql4zxUtkzwWy947bprbV3qmnqqyrnpZZXEcszuf7d0ftL+VxbzcrdaXXe7deeOds4hMqnTUfE64Y5RwUmPG01tpjloqWsE0hZJUta6QySPMfMNBnM0eklbhZFj1uyyx1tnu1K2tttbEYaineSBIw94JBB/qUbwTg1h/DavqK6wWl1NX1EQgkrKmrnq5+yB32Ykme9zWbAPKCBsDp0WrQRS1Di9jy+zZBcKHFOJMOQ0OO3COlfl1TcJmRyTQGN9PC2eV4ke7p1jaWnlHXelnrXjF3huHg1PNprWC00E0VxcaZ48S3aSzlm6e17kAbp2vOAHetgEWWiKW8G2evxq03PB7tYbxbrjbbnc6rx6ooXtoamKWukljdDP9o8lszTyg7HK7YGlaLv5Q8d+KVv/RWYWJDDJxDsPL15KKtc7p3DcA3/AFkLbb1RPhPpKwnKIi8pBERAREQEREBERAREQEREBERARcEga2e/u+FUq+5XjwmMEqo7JXZZwogpb52D6yWkZBWXCmhPn9mHbMbXu2ObvBjIIIJaQzWVcQ7RnV3zPhljlZTVGdW61eNmKuppjSU0rgDB2krNEODjG8cjuYdHN6tOvVinDjJ6PGrbBf8APK+svUcIbV1FBSUscMknpLWvie7XcNk9db0N6Fgw0kMEssrImNml12soaA6QgaBcQOvRdy3UXq7cYpnV/E+q5whvkBcPfne/yFF+rqPcQ4mcM8HveU3nOL1FbbVSvqZT2NCC7Q81jf2v9s52mgekuAVprSL9koq+IGXWawYDhuHZHfbZUfundKy02qephcWuLYYC+NpGwQ97mnr+9FbOk3O7yj2Mpb4HPF+4eExgdxrrhlNwt2SWurMNbRUcNH2Yjft0MjQ6AkAtDmnZ+2jcegICv7yAuHvzvf5Ci/V18vPAJ+yLgPGfHr7bMNyO4YffJfYe41tNbJ30gjfIGGV0obyARSAOc4noGvHTZX11TpNzu8o9jKHDAbgCCcyvR16DBRdf/rrD5Vw/yunitVRhOUQ2+6x18MlzqL3Rtq/ZCkG2ugJby9mAHOe0RhoLh3jmc5WSiwrv3K40ZnV4RHoZQvHuKltyLiLkmFx2670d1skcU8k9ZQPjpamKQDUkMvc4c3M3roksdrYBKmixWVY3RZjjV0sVx7bxC5U0lJUCnmdFIY3tLXae0gg6J7lAqa0ZdwppsBxnFLbJmOMQvdR3e5Xu7E3KmiJBZOHOGpWt2/bR10GBoAGxoRaSLA43nmO5hW3ajsl7obpV2mpdR19PSztfJSzNJBZI0dWnYI6+o+pZ5AREQEREBERAREQEREBRXMc3Nks+Qiw0keUZPaaLxwY7S1cbKmTm5uzBB2WB3K7RI68p0Cei8/F6pzWj4f3Gfh7S0Vblsb4HUlNcCBDK3tmdq1xLmge18/Xf4Oq78a4ZYxjOU3zKrbYqagyK/Fj7lWtHNLKWtaA3ezoeaCQ3QJ847PVBG7bw0dnV9wTiBmNPXWjLrLQOHsJR3Rz6GmqJW6kdyt6PcAXt3vRDtHm00iz0RAREQERVJx08IOg4SCgsdroJcq4gXn2uz4xQnc07jv2yU/4OIaO3n1HXc4gIj4Af3MGPfHbj9NmWxKqjwXOFl14NcEbBi18mp5rvA6eoqfFCTGx80z5SwE9/Lz636SFa6AiIgIiIIRlPDZs1kyd2F1NLg+V3sRvlyCioIpJXSsO2PlaRqToXN87rpx0drw0/EWtxXKcNwjIbbdbtertb+abI7dbiLYamNhMjXOBPZF3I9wB6a161YqIOAQ4dDv0dFyqZteLu4A3qyWTCsdu17suU36eouk9TXTVEdna6MOL28wdysc8E6J2XOJLj0AuZAREQEREBERAREQeG+2z2bslwt3jM1H43TyU/jFM8sli52lvOxwIIcN7BB6EKveFuVYrhvCGnbU59SXm04y72Ir8kutQ2BgnjeIy2V73aDtuY3qepI6klQ7w0sD4j5lwsbVcNMou9jvFnldWS26zzugluTOXRYJGak5mguLWA8rydEFwYW/KDhFilZxa444xZLk+ouFTer1EK+aoeZJZGulDp5HuPVzuXncSep6oPvAiIgIi1w4i8b8k4o5fW8M+Cz4pLnTO7K/5pI3tKKyNPQsjPdLUd+mjoCPgcWBm+NHhB11oySPhzw0t8WVcTqxnM6Fx/admiP/eKx4+1A2CGd52PW0OzHAzwe6HhO+vv93uEuWcQrz593yauHtsp6e1RD/BRDQAaPUN9A0NzfBjghjnA/HJLdZI5aqvrJPGLnea5/aVlxnOy6WaQ9Sdk6HcNn0kk2CgIiICIiAiIgIi8V6s9FkVmr7VcqdlXbq6nkpamnkG2yxPaWvafgIJH40EUz615RX5ThM9hyGls1rpbg+S70c+ue4QcmhEzzT1DuvePwqcL4QcfOElbwQ4s5Dh9ZzyMopyaSdw/f6d/nRP7tbLSNgdzgR6F9WPAN4Q1nB7weLVTXLtI7pfJ3Xypp5BowGVkbWM1rYPZxxkg9Q4uHoQbDoiICIiAiIg4JAGz0ChsmbXW4O7Wx2SnrKA/vdVXVzqYTD/GY1sUhLT6Cdb7wNEEyDJ3mPGrs5pIcKSYgj0HkKjuMtDcbtIaA0CkiAAGgPMC7rFFGhNdUZ146/1herJ5UZb73LP89S/qqoSk8GaS0+ExS8YLVY7PbqhsU7qizx3OTspauRjo3VAd4uOQlr3bbynbjzbB3vYtkjJC4Nc1xaeVwB3o+o/1hfpb/pfjj/17me5jfKjLfe5Z/nqX9VTyoy33uWf56l/VVkl47RebfkFuhuFrrqa5UE4JiqqOZssUgBIPK5pIPUEdD6E+l+OPOr3M9yt+NNl4k8TcdgsdtrafELRPLq7yWWrNRcaqm/nRU8kjImREjYLjvex1ABDrH4TYXimA4HbLRhdBFb7DGzmjYxha97u5zpebzjJsadzedsaOtaXtXl4aHdjrx0Abdq8AAaA/bMh/4kn8a1XqKJtzXTTjExv6875ldsJaiIvPYiIiAiKH8Tszfh9iYKQt9lK1/YUvMNhh1t0hHpDR1+Elo9K22rVV+5FujbI7ct4l2XD5fFqh8tZcC3mFDRND5det2yGsHq5iN+hQibj1cC4+L4tEWb6eMXPkdr4Q2Jw/tKrlrCHSPc90ssrzJLLIdvkee9zj6SV+l9vZ+D8mt04uRpT4zHpgzCwPs83j3q0fzu79XT7PN496tH87u/V1X6Lo+V8i/Hxq900u5CuPGBUPHviDhWVXfHKOmksEuqumFeZBc6cOD2wPcYBytDubro9HvGuoIvX7PN4H/hWj+d3fq6r9E+V8i/Hxq9zS7lgfZ5vHvVo/nd36uuRx5u++uK0mvgu7ifo6rW3XKjvFFFWUFVBXUcw5o6imkEkbxvWw4Eg93oXpT5ZyKdcW+NXuaXctyycc7VVysivFDUWNziB28hbLT7PoL29W/hc1o+FWPFKyaNkkb2yRvAc17TsOB7iCtXFN+EuYyWK8U2P1DybXWlzaQHup5tF3IP8AMeA7Q9DgAPtunj8v+E0UUTd5P1bY7u5dq70RF8mMXlX8WLx8Tm/uFR7Gv4uWr4pF/cCkOVfxYvHxOb+4VHsa/i5avikX9wL0bP2Z8f0vU1awS/3/AIO8LOM+Z+UFZkL7ZkN4hhtlbT0zIH1XjTWNqXujja/ZJ25ocGaJ00dCJfkOf5rwIyCgiyjJBndFc7FdbgYzQQ0j6aqoacTlsRiaNxPbzN0/bgQ3zjvSnjeAGP8Asjlbn112msmTmd9yxySoabfJLM0Nlla3k52vdrew/QPUAFcYrwBsmP3j2TuV3vmYVcdBJa6U5HVMqW0tLJrtI42tY0eeGtDnO5nEAAu1tYREwiCWDM8/xSo4W3zI8pgyG3ZvUxUVXaWW6KCOglnppKiJ1O9g53NYY+R3aF2wd9CodheQZhjngx8LanHaiut1k7Wp9nrnaLdHcK2kpw+YsfHA8ODmc+uchrnBvUDvVy4f4OtgxC+WW4ezF/vUFhY9lltt2rhNS2wObye1NDA4kMJY0yOeWtOhpcUXg+UNkxuOx2LL8tx6ghrp62mbbriwGnbL9tTt543Awg8zmtcHFpcSHd2mJE3wK7w37C7JcYL3FkkVTSRyC7wRiJlWS0bkDB0Zs7PL6O70LJ8M/wCBLh/S1d9Iesbg2F2vh1iNrxuyxPhtdthEMLZHl7yN7LnOPeSSST6yVkuGf8CXD+lq76Q9bK/sVeMftepLkRF5qCIiAqO42VDpc6tsDjtkFtc+MeoyS6d/y2K8VU/HSxSfuVkETdx0vNSVWh3RyFpY8/A17df6zfo6ex8Jrpo5XTpdeY/mY/0LCsUX5kLhG4saHPAPK0nQJ9W/QoeL1nu+uJ2PX/uCX9TX3tVcU7fSZ9GtMlr9U8QuIeU1t/uGM0d0fBb7hUUNFRQUdC+jnML+Q9vJLM2YFxB3yAcoI0Ha62d7NZ9707F/vDL+pryDhDRC91N1o7verCa+dtZXW211wZSzz9OZ5BZzAu0OYtLebXULkvRVexFEzG/bHrCode8nzSuruJVRR5AbLFjNPBVU1AKKGUOeaJkz4pHuBJZzc32unecfO0ABkqHK8l4m5MbfZby3FqO32mir6qWOkjqJZ5qpjnsYBJsCNrWHeupJ1sKaTcO7bPLmD3T1QOURtirNPb7WBAIB2fm9Dyjfnc3X4OixVVwbtj6y21lDdrzZa2joIra+ottS2N1VBGNMbMCwgkddOABGzojotc2r0TnMzGZzr75xjd1bN2B5vByBHBPFNnZ8Wds/6x6sdQKz2K+8ObJbsdxizUd3tFDDyR1V1vDoJyS4khzWUzge/v2PwL1ezWfe9Oxf7wy/qa6LVUW7dNFUTmIiNk+wma6KyodRtgqo/wB9p6iGeM/5zZGuH9oXix6qvNXSyOvVto7ZUB+mR0da6qa5uh1LnRR6O99NHu71JcWsUmUZZa7cxpdCyZlZVO10bDG4O0f9Jwa38Z9RW2u5TRbm5VsiFp2tlURF+WK89wo23CgqaV5IZPE6JxHoDgQf+Kr6ivgxihprZd6Wugq6WNsJlgopp4Zg0AB7HxsLdHW+U6I7iFZKLptXotxNNUZjyXKvPLu0+q4fNdV9Wnl3afVcPmuq+rVhot3SLXYnz/o1K88u7T6rh811X1aeXdp9Vw+a6r6tWGidItdifP8Ao1K+ZmlDUeZTU1zq5z9rDHbagOcfVtzA0fhcQB6SFJcNs09jsYhquQVc081VM2M7ax0sjpOUHQ3y8wbvXXW/Ss4i1XL0V06FMYjxz+oM7hERcqCIiAuuppoqynlgniZPBK0skikaHNe0jRBB6EEehdiJsFH5XwiutjnfNYojdrYSS2lMoFTAP8UF2hI0esuDtaHnnZUKlgraZxbPaLtA8d7ZLbOP6jyaP4traVF9FZ+N3rdOjcpirv2SuqdrVfc/ufcvm+f9BNz+59y+b5/0FtQi6Pn1X4+P9JiGq+5/c+5fN8/6Cbn9z7l83z/oLahE+fVfj4/0Yhqvuf3PuXzfP+gg8YcdC3XMn1C3Tkn/AOC2oRPntX4+P9GIa42XCMkyKVraW0TUMJI5qq6MdTsaPTphHaE/BygesjvV24VhVFhVtdBTuNRVTEPqayQAPmd6PwNGyGtHQfCSSZCi8rlfxK9yuNCdVO6P2vgIiLykf//Z", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(graph.get_graph().draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "66f62a69-989b-46ce-80b3-97a867e36782", + "metadata": {}, + "outputs": [], + "source": [ + "user_input = \"Can you give me some information about AMD in 2022?\"\n", + "\n", + "result = graph.invoke({\"messages\": [(\"user\", user_input)]})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "479a459d-6896-4960-aae9-9f1259fb47d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['3b7d1528-6007-4473-a92f-b9b3341c3bfe', '8d77b753-c58a-41bf-9649-ad1a7326bc27', '514a6fc3-03d1-4e73-b410-c39309ad7b2f', '83c5cc8f-5111-46ed-874a-e0b883265ff6']\n" + ] + } + ], + "source": [ + "print(result[\"selected_tools\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "376f28fd-3f7f-4ae5-a34c-baef1778e82b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "Can you give me some information about AMD in 2022?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " Advanced_Micro_Devices (call_Htbv7Imx4BwSsYWhZvSSs6yW)\n", + " Call ID: call_Htbv7Imx4BwSsYWhZvSSs6yW\n", + " Args:\n", + " year: 2022\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: Advanced_Micro_Devices\n", + "\n", + "Advanced Micro Devices had revenues of $100 in 2022.\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "In 2022, Advanced Micro Devices had revenues of $100.\n" + ] + } + ], + "source": [ + "for message in result[\"messages\"]:\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "3bd847ef-4627-4fc2-99f9-c2b17cf83f95", + "metadata": {}, + "source": [ + "## Repeating tool selection\n", + "\n", + "To manage errors from incorrect tool selection, we could revisit the `select_tools` node. One option for implementing this is to modify `select_tools` to generate the vector store query using all messages in the state (e.g., with a chat model) and add an edge routing from `tools` to `select_tools`.\n", + "\n", + "We implement this change below. For demonstration purposes, we simulate an error in the initial tool selection by adding a `hack_remove_tool_condition` to the `select_tools` node, which removes the correct tool on the first iteration of the node. Note that on the second iteration, the agent finishes the run as it has access to the correct tool." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1954a5f1-91e4-4b32-9be9-c8bc1cc43cb5", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n", + "from langchain_core.pydantic_v1 import BaseModel, Field\n", + "\n", + "\n", + "class QueryForTools(BaseModel):\n", + " \"\"\"Generate a query for additional tools.\"\"\"\n", + "\n", + " query: str = Field(..., description=\"Query for additional tools.\")\n", + "\n", + "\n", + "def select_tools(state: State):\n", + " last_message = state[\"messages\"][-1]\n", + " hack_remove_tool_condition = False\n", + " if isinstance(last_message, HumanMessage):\n", + " query = last_message.content\n", + " hack_remove_tool_condition = True\n", + " else:\n", + " assert isinstance(last_message, ToolMessage)\n", + " system = SystemMessage(\n", + " \"Given this conversation, generate a query for additional tools. \"\n", + " \"The query should be a short string containing what type of information \"\n", + " \"is needed. If no further information is needed, \"\n", + " \"set more_information_needed False and populate a blank string for the query.\"\n", + " )\n", + " input_messages = [system] + state[\"messages\"]\n", + " response = llm.bind_tools(\n", + " [QueryForTools], tool_choice=True\n", + " ).invoke(input_messages)\n", + " query = response.tool_calls[0][\"args\"][\"query\"]\n", + " tool_documents = vector_store.similarity_search(query)\n", + " if hack_remove_tool_condition:\n", + " # Remove needed tool\n", + " selected_tools = [\n", + " document.id\n", + " for document in tool_documents\n", + " if document.metadata[\"tool_name\"] != \"Advanced_Micro_Devices\"\n", + " ]\n", + " else:\n", + " selected_tools = [document.id for document in tool_documents]\n", + " return {\"selected_tools\": selected_tools}\n", + "\n", + "\n", + "graph_builder = StateGraph(State)\n", + "graph_builder.add_node(\"agent\", agent)\n", + "graph_builder.add_node(\"select_tools\", select_tools)\n", + "\n", + "tool_node = ToolNode(tools=tools)\n", + "graph_builder.add_node(\"tools\", tool_node)\n", + "\n", + "graph_builder.add_conditional_edges(\n", + " \"agent\",\n", + " tools_condition,\n", + ")\n", + "graph_builder.add_edge(\"tools\", \"select_tools\")\n", + "graph_builder.add_edge(\"select_tools\", \"agent\")\n", + "graph_builder.add_edge(START, \"select_tools\")\n", + "graph = graph_builder.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9110789a-843a-4c21-aeff-8841b24f7674", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(graph.get_graph().draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bee04c3d-0e36-4443-b0c8-10986a5f6e39", + "metadata": {}, + "outputs": [], + "source": [ + "user_input = \"Can you give me some information about AMD in 2022?\"\n", + "\n", + "result = graph.invoke({\"messages\": [(\"user\", user_input)]})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6906fb50-435c-4473-bbb6-5353433b9199", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "Can you give me some information about AMD in 2022?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " Accenture (call_L82JRUyIFilhzeTmPnNbPeVD)\n", + " Call ID: call_L82JRUyIFilhzeTmPnNbPeVD\n", + " Args:\n", + " year: 2022\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: Accenture\n", + "\n", + "Accenture had revenues of $100 in 2022.\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " Advanced_Micro_Devices (call_k3zR9zS98gjiejmNgq6aVsXL)\n", + " Call ID: call_k3zR9zS98gjiejmNgq6aVsXL\n", + " Args:\n", + " year: 2022\n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: Advanced_Micro_Devices\n", + "\n", + "Advanced Micro Devices had revenues of $100 in 2022.\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "In 2022, Advanced Micro Devices (AMD) had revenues of $100.\n" + ] + } + ], + "source": [ + "for message in result[\"messages\"]:\n", + " message.pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "177aedfa-cec5-45d0-82ad-efc0233aa6b4", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "This guide provides a minimal implementation for dynamically selecting tools. There is a host of possible improvements and optimizations:\n", + "\n", + "- **Repeating tool selection**: Here, we repeated tool selection by modifying the `select_tools` node. Another option is to equip the agent with a `reselect_tools` tool, allowing it to re-select tools at its discretion.\n", + "- **Optimizing tool selection**: In general, the full scope of [retrieval solutions](https://python.langchain.com/v0.2/docs/concepts/#retrieval) are available for tool selection. Additional options include:\n", + " - Group tools and retrieve over groups;\n", + " - Use a chat model to select tools or groups of tool." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/memory/add-summary-conversation-history.ipynb b/examples/memory/add-summary-conversation-history.ipynb index 5b5a19156..10584bc21 100644 --- a/examples/memory/add-summary-conversation-history.ipynb +++ b/examples/memory/add-summary-conversation-history.ipynb @@ -105,10 +105,10 @@ "\n", "from langchain_anthropic import ChatAnthropic\n", "from langchain_core.messages import SystemMessage, RemoveMessage\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import MessagesState, StateGraph, START, END\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "\n", "\n", "# We will add a `summary` attribute (in addition to `messages` key,\n", diff --git a/examples/memory/delete-messages.ipynb b/examples/memory/delete-messages.ipynb index feda6f023..dd4cab2eb 100644 --- a/examples/memory/delete-messages.ipynb +++ b/examples/memory/delete-messages.ipynb @@ -112,11 +112,11 @@ "from langchain_anthropic import ChatAnthropic\n", "from langchain_core.tools import tool\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import MessagesState, StateGraph, START\n", "from langgraph.prebuilt import ToolNode\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "\n", "\n", "@tool\n", diff --git a/examples/memory/manage-conversation-history.ipynb b/examples/memory/manage-conversation-history.ipynb index 2cdeb85d0..5b5129a15 100644 --- a/examples/memory/manage-conversation-history.ipynb +++ b/examples/memory/manage-conversation-history.ipynb @@ -103,11 +103,11 @@ "from langchain_anthropic import ChatAnthropic\n", "from langchain_core.tools import tool\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import MessagesState, StateGraph, START\n", "from langgraph.prebuilt import ToolNode\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "\n", "\n", "@tool\n", @@ -234,11 +234,11 @@ "from langchain_anthropic import ChatAnthropic\n", "from langchain_core.tools import tool\n", "\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import MessagesState, StateGraph, START\n", "from langgraph.prebuilt import ToolNode\n", "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "\n", "\n", "@tool\n", diff --git a/examples/multi_agent/img/hierarchical-diagram.png b/examples/multi_agent/img/hierarchical-diagram.png index b3fa81275..7c25ac0ab 100644 Binary files a/examples/multi_agent/img/hierarchical-diagram.png and b/examples/multi_agent/img/hierarchical-diagram.png differ diff --git a/examples/multi_agent/img/simple_multi_agent_diagram.png b/examples/multi_agent/img/simple_multi_agent_diagram.png index 3da47ba9b..d28f9e59b 100644 Binary files a/examples/multi_agent/img/simple_multi_agent_diagram.png and b/examples/multi_agent/img/simple_multi_agent_diagram.png differ diff --git a/examples/multi_agent/img/supervisor-diagram.png b/examples/multi_agent/img/supervisor-diagram.png index 84497aa4c..8aaad4759 100644 Binary files a/examples/multi_agent/img/supervisor-diagram.png and b/examples/multi_agent/img/supervisor-diagram.png differ diff --git a/examples/node-retries.ipynb b/examples/node-retries.ipynb index 1af599840..cca3b352f 100644 --- a/examples/node-retries.ipynb +++ b/examples/node-retries.ipynb @@ -8,7 +8,7 @@ "\n", "There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. \n", "\n", - "In order to configure the retry policty, you have to pass the `retry` parameter to the `add_node` function. The `retry` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters:" + "In order to configure the retry policy, you have to pass the `retry` parameter to the `add_node` function. The `retry` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters:" ] }, { diff --git a/examples/pass_private_state.ipynb b/examples/pass_private_state.ipynb index ca177a180..2e60d805e 100644 --- a/examples/pass_private_state.ipynb +++ b/examples/pass_private_state.ipynb @@ -7,7 +7,7 @@ "source": [ "# How to pass private state\n", "\n", - "Oftentimes, you may want nodes to be able to pass state to eachv other that should NOT be part of the main schema of the graph. This is often useful because there may be information that is not needed as input/output (and therefore doesn't really make sense to have in the main schema) but is ABSOLUTELY needed as part of the intermediate working logic.\n", + "Oftentimes, you may want nodes to be able to pass state to each other that should NOT be part of the main schema of the graph. This is often useful because there may be information that is not needed as input/output (and therefore doesn't really make sense to have in the main schema) but is ABSOLUTELY needed as part of the intermediate working logic.\n", "\n", "Let's take a look at an example below. In this example, we will create a RAG pipeline that:\n", "1. Takes in a user question\n", diff --git a/examples/persistence.ipynb b/examples/persistence.ipynb index b99767a2c..5592ed25d 100644 --- a/examples/persistence.ipynb +++ b/examples/persistence.ipynb @@ -1,589 +1,589 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# How to add persistence (\"memory\") to your graph\n", - "\n", - "Many AI applications need memory to share context across multiple interactions. In LangGraph, memory is provided for any [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) through [Checkpointers](https://langchain-ai.github.io/langgraph/reference/checkpoints/).\n", - "\n", - "When creating any LangGraph workflow, you can set them up to persist their state by doing using the following:\n", - "\n", - "1. A [Checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver), such as the [AsyncSqliteSaver](https://langchain-ai.github.io/langgraph/reference/checkpoints/#asyncsqlitesaver)\n", - "2. Call `compile(checkpointer=my_checkpointer)` when compiling the graph.\n", - "\n", - "Example:\n", - "```python\n", - "from langgraph.graph import StateGraph\n", - "from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver\n", - "\n", - "builder = StateGraph(....)\n", - "# ... define the graph\n", - "memory = AsyncSqliteSaver.from_conn_string(\":memory:\")\n", - "graph = builder.compile(checkpointer=memory)\n", - "...\n", - "```\n", - "\n", - "This works for [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) and all its subclasses, such as [MessageGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#messagegraph).\n", - "\n", - "Below is an example.\n", - "\n", - "
\n", - "

Note

\n", - "

\n", - " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n", - "

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "7cbd446a-808f-4394-be92-d45ab818953c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we need to install the packages required" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install --quiet -U langgraph langchain_anthropic" - ] - }, - { - "cell_type": "markdown", - "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", - "metadata": {}, - "source": [ - "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"ANTHROPIC_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", - "metadata": {}, - "source": [ - "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", - "_set_env(\"LANGCHAIN_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "4cf509bc", - "metadata": {}, - "source": [ - "## Set up the State\n", - "\n", - "The state is the interface for all the nodes." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "14619607", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Annotated\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph.message import add_messages\n", - "\n", - "# Add messages essentially does this with more\n", - "# robust handling\n", - "# def add_messages(left: list, right: list):\n", - "# return left + right\n", - "\n", - "\n", - "class State(TypedDict):\n", - " messages: Annotated[list, add_messages]" - ] - }, - { - "cell_type": "markdown", - "id": "21ac643b-cb06-4724-a80c-2862ba4773f1", - "metadata": {}, - "source": [ - "## Set up the tools\n", - "\n", - "We will first define the tools we want to use.\n", - "For this simple example, we will use create a placeholder search engine.\n", - "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.tools import tool\n", - "\n", - "\n", - "@tool\n", - "def search(query: str):\n", - " \"\"\"Call to surf the web.\"\"\"\n", - " # This is a placeholder for the actual implementation\n", - " return [\"The answer to your question lies within.\"]\n", - "\n", - "\n", - "tools = [search]" - ] - }, - { - "cell_type": "markdown", - "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58", - "metadata": {}, - "source": [ - "Now we can create our [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/?h=tool+node#toolnode). This \n", - "object actually **runs** the tools (aka functions) that the LLM has asked to use." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.prebuilt import ToolNode\n", - "\n", - "tool_node = ToolNode(tools)" - ] - }, - { - "cell_type": "markdown", - "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5", - "metadata": {}, - "source": [ - "## Set up the model\n", - "\n", - "Now we need to load the [chat model](https://python.langchain.com/v0.2/docs/concepts/#chat-models) to power our agent.\n", - "For the design below, it must satisfy two criteria:\n", - "\n", - "1. It should work with **messages** (since our state contains a list of chat messages)\n", - "2. It should work with [**tool calling**](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling).\n", - "\n", - "
\n", - "

Note

\n", - "

\n", - " These model requirements are not general requirements for using LangGraph - they are just requirements for this one example.\n", - "

\n", - "
\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_openai import ChatOpenAI\n", - "\n", - "# We will set streaming=True so that we can stream tokens\n", - "# See the streaming section for more information on this.\n", - "model = ChatOpenAI(temperature=0, streaming=True)" - ] - }, - { - "cell_type": "markdown", - "id": "a77995c0-bae2-4cee-a036-8688a90f05b9", - "metadata": {}, - "source": [ - "\n", - "After we've done this, we should make sure the model knows that it has these tools available to call.\n", - "We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", - "metadata": {}, - "outputs": [], - "source": [ - "bound_model = model.bind_tools(tools)" - ] - }, - { - "cell_type": "markdown", - "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4", - "metadata": {}, - "source": [ - "## Define the graph \n", - "\n", - "We now need to define a few different nodes in our graph.\n", - "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", - "There are two main nodes we need for this:\n", - "\n", - "1. The agent: responsible for deciding what (if any) actions to take.\n", - "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n", - "\n", - "We will also need to define some edges.\n", - "Some of these edges may be conditional.\n", - "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n", - "The path that is taken is not known until that node is run (the LLM decides).\n", - "\n", - "1. Conditional Edge: after the agent is called, we should either:\n", - " a. If the agent said to take an action, then the function to invoke tools should be called\n", - " b. If the agent said that it was finished, then it should finish\n", - "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n", - "\n", - "Let's define the nodes, as well as a function to decide how what conditional edge to take." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "3b541bb9-900c-40d0-964d-7b5dfee30667", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the function that determines whether to continue or not\n", - "from typing import Literal\n", - "\n", - "\n", - "def should_continue(state: State) -> Literal[\"action\", \"__end__\"]:\n", - " \"\"\"Return the next node to execute.\"\"\"\n", - " last_message = state[\"messages\"][-1]\n", - " # If there is no function call, then we finish\n", - " if not last_message.tool_calls:\n", - " return \"__end__\"\n", - " # Otherwise if there is, we continue\n", - " return \"action\"\n", - "\n", - "\n", - "# Define the function that calls the model\n", - "def call_model(state: State):\n", - " response = model.invoke(state[\"messages\"])\n", - " # We return a list, because this will get added to the existing list\n", - " return {\"messages\": response}" - ] - }, - { - "cell_type": "markdown", - "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b", - "metadata": {}, - "source": [ - "We can now put it all together and define the graph!" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "812b4e70-4956-4415-8880-db48b3dcbad2", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, START\n", - "\n", - "# Define a new graph\n", - "workflow = StateGraph(State)\n", - "\n", - "# Define the two nodes we will cycle between\n", - "workflow.add_node(\"agent\", call_model)\n", - "workflow.add_node(\"action\", tool_node)\n", - "\n", - "# Set the entrypoint as `agent`\n", - "# This means that this node is the first one called\n", - "workflow.add_edge(START, \"agent\")\n", - "\n", - "# We now add a conditional edge\n", - "workflow.add_conditional_edges(\n", - " # First, we define the start node. We use `agent`.\n", - " # This means these are the edges taken after the `agent` node is called.\n", - " \"agent\",\n", - " # Next, we pass in the function that will determine which node is called next.\n", - " should_continue,\n", - ")\n", - "\n", - "# We now add a normal edge from `tools` to `agent`.\n", - "# This means that after `tools` is called, `agent` node is called next.\n", - "workflow.add_edge(\"action\", \"agent\")" - ] - }, - { - "cell_type": "markdown", - "id": "bc9c8536-f90b-44fa-958d-5df016c66d8f", - "metadata": {}, - "source": [ - "**Persistence**\n", - "\n", - "To add in persistence, we pass in a checkpoint when compiling the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "6845ed6a-d155-4105-9160-28849877248b", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.checkpoint.sqlite import SqliteSaver\n", - "\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "79d29875-8aa8-434c-9f20-1c58346a6249", - "metadata": {}, - "outputs": [], - "source": [ - "# Finally, we compile it!\n", - "# This compiles it into a LangChain Runnable,\n", - "# meaning you can use it as you would any other runnable\n", - "app = workflow.compile(checkpointer=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "7654ebcc-2179-41b4-92d1-6666f6f8634f", - "metadata": {}, - "source": [ - "
\n", - "

Note

\n", - "

\n", - " If you're using LangGraph Cloud, you don't need to pass checkpointer when compiling the graph, since it's done automatically.\n", - "

\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "0d49697f", - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/jpeg": 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- "text/plain": [ - "" + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to add persistence (\"memory\") to your graph\n", + "\n", + "Many AI applications need memory to share context across multiple interactions. In LangGraph, memory is provided for any [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) through [Checkpointers](https://github.com/langchain-ai/langgraph/tree/e4ca7ab69c599fd77dd4f0d47280849d715392cc/libs/checkpoint).\n", + "\n", + "When creating any LangGraph workflow, you can set them up to persist their state by doing using the following:\n", + "\n", + "1. A [Checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#basecheckpointsaver), such as the [AsyncSqliteSaver](https://langchain-ai.github.io/langgraph/reference/checkpoints/#asyncsqlitesaver)\n", + "2. Call `compile(checkpointer=my_checkpointer)` when compiling the graph.\n", + "\n", + "Example:\n", + "```python\n", + "from langgraph.graph import StateGraph\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "builder = StateGraph(....)\n", + "# ... define the graph\n", + "memory = MemorySaver()\n", + "graph = builder.compile(checkpointer=memory)\n", + "...\n", + "```\n", + "\n", + "This works for [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) and all its subclasses, such as [MessageGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#messagegraph).\n", + "\n", + "Below is an example.\n", + "\n", + "
\n", + "

Note

\n", + "

\n", + " In this how-to, we will create our agent from scratch to be transparent (but verbose). You can accomplish similar functionality using the create_react_agent(model, tools=tool, checkpointer=checkpointer) (API doc) constructor. This may be more appropriate if you are used to LangChain’s AgentExecutor class.\n", + "

\n", + "
" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "try:\n", - " display(Image(app.get_graph().draw_mermaid_png()))\n", - "except Exception:\n", - " # This requires some extra dependencies and is optional\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", - "metadata": {}, - "source": [ - "## Interacting with the Agent\n", - "\n", - "We can now interact with the agent and see that it remembers previous messages!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "cfd140f0-a5a6-4697-8115-322242f197b5", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "hi! I'm bob\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Hello Bob! How can I assist you today?\n" - ] - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n", - "\n", - "config = {\"configurable\": {\"thread_id\": \"2\"}}\n", - "input_message = HumanMessage(content=\"hi! I'm bob\")\n", - "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "08ae8246-11d5-40e1-8567-361e5bef8917", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what is my name?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Your name is Bob.\n" - ] - } - ], - "source": [ - "input_message = HumanMessage(content=\"what is my name?\")\n", - "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", - " event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "3f47bbfc-d9ef-4288-ba4a-ebbc0136fa9d", - "metadata": {}, - "source": [ - "If we want to start a new conversation, we can pass in a different thread id. Poof! All the memories are gone!" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "273d56a8-f40f-4a51-a27f-7c6bb2bda0ba", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": null, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_anthropic" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what is my name?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "I'm sorry, I do not know your name as I am an AI assistant and do not have access to personal information.\n" - ] - } - ], - "source": [ - "input_message = HumanMessage(content=\"what is my name?\")\n", - "for event in app.stream(\n", - " {\"messages\": [input_message]},\n", - " {\"configurable\": {\"thread_id\": \"3\"}},\n", - " stream_mode=\"values\",\n", - "):\n", - " event[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "e833f994", - "metadata": {}, - "source": [ - "All the checkpoints are persisted to the checkpointer, so you can always resume previous threads." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "8578a66d-6489-4e03-8c23-fd0530278455", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "You forgot??\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "I apologize for the confusion. I am an AI assistant and I do not have the ability to remember information from previous interactions. How can I assist you today, Bob?\n" - ] + "cell_type": "code", + "execution_count": 21, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"ANTHROPIC_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "4cf509bc", + "metadata": {}, + "source": [ + "## Set up the State\n", + "\n", + "The state is the interface for all the nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "14619607", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Annotated\n", + "\n", + "from typing_extensions import TypedDict\n", + "\n", + "from langgraph.graph.message import add_messages\n", + "\n", + "# Add messages essentially does this with more\n", + "# robust handling\n", + "# def add_messages(left: list, right: list):\n", + "# return left + right\n", + "\n", + "\n", + "class State(TypedDict):\n", + " messages: Annotated[list, add_messages]" + ] + }, + { + "cell_type": "markdown", + "id": "21ac643b-cb06-4724-a80c-2862ba4773f1", + "metadata": {}, + "source": [ + "## Set up the tools\n", + "\n", + "We will first define the tools we want to use.\n", + "For this simple example, we will use create a placeholder search engine.\n", + "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " return [\"The answer to your question lies within.\"]\n", + "\n", + "\n", + "tools = [search]" + ] + }, + { + "cell_type": "markdown", + "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58", + "metadata": {}, + "source": [ + "Now we can create our [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/?h=tool+node#toolnode). This \n", + "object actually **runs** the tools (aka functions) that the LLM has asked to use." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.prebuilt import ToolNode\n", + "\n", + "tool_node = ToolNode(tools)" + ] + }, + { + "cell_type": "markdown", + "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5", + "metadata": {}, + "source": [ + "## Set up the model\n", + "\n", + "Now we need to load the [chat model](https://python.langchain.com/v0.2/docs/concepts/#chat-models) to power our agent.\n", + "For the design below, it must satisfy two criteria:\n", + "\n", + "1. It should work with **messages** (since our state contains a list of chat messages)\n", + "2. It should work with [**tool calling**](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling).\n", + "\n", + "
\n", + "

Note

\n", + "

\n", + " These model requirements are not general requirements for using LangGraph - they are just requirements for this one example.\n", + "

\n", + "
\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "# We will set streaming=True so that we can stream tokens\n", + "# See the streaming section for more information on this.\n", + "model = ChatOpenAI(temperature=0, streaming=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a77995c0-bae2-4cee-a036-8688a90f05b9", + "metadata": {}, + "source": [ + "\n", + "After we've done this, we should make sure the model knows that it has these tools available to call.\n", + "We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", + "metadata": {}, + "outputs": [], + "source": [ + "bound_model = model.bind_tools(tools)" + ] + }, + { + "cell_type": "markdown", + "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4", + "metadata": {}, + "source": [ + "## Define the graph \n", + "\n", + "We now need to define a few different nodes in our graph.\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", + "There are two main nodes we need for this:\n", + "\n", + "1. The agent: responsible for deciding what (if any) actions to take.\n", + "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n", + "\n", + "We will also need to define some edges.\n", + "Some of these edges may be conditional.\n", + "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n", + "The path that is taken is not known until that node is run (the LLM decides).\n", + "\n", + "1. Conditional Edge: after the agent is called, we should either:\n", + " a. If the agent said to take an action, then the function to invoke tools should be called\n", + " b. If the agent said that it was finished, then it should finish\n", + "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n", + "\n", + "Let's define the nodes, as well as a function to decide how what conditional edge to take." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3b541bb9-900c-40d0-964d-7b5dfee30667", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the function that determines whether to continue or not\n", + "from typing import Literal\n", + "\n", + "\n", + "def should_continue(state: State) -> Literal[\"action\", \"__end__\"]:\n", + " \"\"\"Return the next node to execute.\"\"\"\n", + " last_message = state[\"messages\"][-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"__end__\"\n", + " # Otherwise if there is, we continue\n", + " return \"action\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state: State):\n", + " response = model.invoke(state[\"messages\"])\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": response}" + ] + }, + { + "cell_type": "markdown", + "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b", + "metadata": {}, + "source": [ + "We can now put it all together and define the graph!" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "812b4e70-4956-4415-8880-db48b3dcbad2", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.graph import StateGraph, START\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(State)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", tool_node)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.add_edge(START, \"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")" + ] + }, + { + "cell_type": "markdown", + "id": "bc9c8536-f90b-44fa-958d-5df016c66d8f", + "metadata": {}, + "source": [ + "**Persistence**\n", + "\n", + "To add in persistence, we pass in a checkpoint when compiling the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6845ed6a-d155-4105-9160-28849877248b", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "\n", + "memory = MemorySaver()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "79d29875-8aa8-434c-9f20-1c58346a6249", + "metadata": {}, + "outputs": [], + "source": [ + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "app = workflow.compile(checkpointer=memory)" + ] + }, + { + "cell_type": "markdown", + "id": "7654ebcc-2179-41b4-92d1-6666f6f8634f", + "metadata": {}, + "source": [ + "
\n", + "

Note

\n", + "

\n", + " If you're using LangGraph Cloud, you don't need to pass checkpointer when compiling the graph, since it's done automatically.\n", + "

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "0d49697f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(app.get_graph().draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159", + "metadata": {}, + "source": [ + "## Interacting with the Agent\n", + "\n", + "We can now interact with the agent and see that it remembers previous messages!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "cfd140f0-a5a6-4697-8115-322242f197b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "hi! I'm bob\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Hello Bob! How can I assist you today?\n" + ] + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "config = {\"configurable\": {\"thread_id\": \"2\"}}\n", + "input_message = HumanMessage(content=\"hi! I'm bob\")\n", + "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "08ae8246-11d5-40e1-8567-361e5bef8917", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what is my name?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Your name is Bob.\n" + ] + } + ], + "source": [ + "input_message = HumanMessage(content=\"what is my name?\")\n", + "for event in app.stream({\"messages\": [input_message]}, config, stream_mode=\"values\"):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "3f47bbfc-d9ef-4288-ba4a-ebbc0136fa9d", + "metadata": {}, + "source": [ + "If we want to start a new conversation, we can pass in a different thread id. Poof! All the memories are gone!" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "273d56a8-f40f-4a51-a27f-7c6bb2bda0ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "what is my name?\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "I'm sorry, I do not know your name as I am an AI assistant and do not have access to personal information.\n" + ] + } + ], + "source": [ + "input_message = HumanMessage(content=\"what is my name?\")\n", + "for event in app.stream(\n", + " {\"messages\": [input_message]},\n", + " {\"configurable\": {\"thread_id\": \"3\"}},\n", + " stream_mode=\"values\",\n", + "):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "markdown", + "id": "e833f994", + "metadata": {}, + "source": [ + "All the checkpoints are persisted to the checkpointer, so you can always resume previous threads." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8578a66d-6489-4e03-8c23-fd0530278455", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "\n", + "You forgot??\n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "I apologize for the confusion. I am an AI assistant and I do not have the ability to remember information from previous interactions. How can I assist you today, Bob?\n" + ] + } + ], + "source": [ + "input_message = HumanMessage(content=\"You forgot??\")\n", + "for event in app.stream(\n", + " {\"messages\": [input_message]},\n", + " {\"configurable\": {\"thread_id\": \"2\"}},\n", + " stream_mode=\"values\",\n", + "):\n", + " event[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb20430f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" } - ], - "source": [ - "input_message = HumanMessage(content=\"You forgot??\")\n", - "for event in app.stream(\n", - " {\"messages\": [input_message]},\n", - " {\"configurable\": {\"thread_id\": \"2\"}},\n", - " stream_mode=\"values\",\n", - "):\n", - " event[\"messages\"][-1].pretty_print()" - ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb20430f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/persistence_mongodb.ipynb b/examples/persistence_mongodb.ipynb index ef96b17f5..99ce73057 100644 --- a/examples/persistence_mongodb.ipynb +++ b/examples/persistence_mongodb.ipynb @@ -2,423 +2,21 @@ "cells": [ { "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", "metadata": {}, "source": [ "# How to create a custom checkpointer using MongoDB\n", "\n", - "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions.\n", + "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions. \n", "\n", - "This example shows how to use `MongoDB` as the backend for persisting checkpoint state.\n", + "This reference implementation shows how to use MongoDB as the backend for persisting checkpoint state. Make sure that you have MongoDB running on port `27017` for going through this guide.\n", "\n", - "NOTE: this is just an example implementation. You can implement your own checkpointer using a different database or modify this one as long as it conforms to the `BaseCheckpointSaver` interface." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Checkpointer implementation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph pymongo" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "from contextlib import AbstractContextManager\n", - "from types import TracebackType\n", - "from typing import Any, Dict, Iterator, Optional\n", - "\n", - "from langchain_core.runnables import RunnableConfig\n", - "from typing_extensions import Self\n", - "\n", - "from langgraph.checkpoint.base import (\n", - " BaseCheckpointSaver,\n", - " Checkpoint,\n", - " CheckpointMetadata,\n", - " CheckpointTuple,\n", - " SerializerProtocol,\n", - ")\n", - "from langgraph.serde.jsonplus import JsonPlusSerializer\n", - "from pymongo import MongoClient\n", - "\n", - "\n", - "class JsonPlusSerializerCompat(JsonPlusSerializer):\n", - " \"\"\"A serializer that supports loading pickled checkpoints for backwards compatibility.\n", - "\n", - " This serializer extends the JsonPlusSerializer and adds support for loading pickled\n", - " checkpoints. If the input data starts with b\"\\x80\" and ends with b\".\", it is treated\n", - " as a pickled checkpoint and loaded using pickle.loads(). Otherwise, the default\n", - " JsonPlusSerializer behavior is used.\n", - "\n", - " Examples:\n", - " >>> import pickle\n", - " >>> from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat\n", - " >>>\n", - " >>> serializer = JsonPlusSerializerCompat()\n", - " >>> pickled_data = pickle.dumps({\"key\": \"value\"})\n", - " >>> loaded_data = serializer.loads(pickled_data)\n", - " >>> print(loaded_data) # Output: {\"key\": \"value\"}\n", - " >>>\n", - " >>> json_data = '{\"key\": \"value\"}'.encode(\"utf-8\")\n", - " >>> loaded_data = serializer.loads(json_data)\n", - " >>> print(loaded_data) # Output: {\"key\": \"value\"}\n", - " \"\"\"\n", - "\n", - " def loads(self, data: bytes) -> Any:\n", - " if data.startswith(b\"\\x80\") and data.endswith(b\".\"):\n", - " return pickle.loads(data)\n", - " return super().loads(data)\n", - "\n", - "\n", - "class MongoDBSaver(AbstractContextManager, BaseCheckpointSaver):\n", - " \"\"\"A checkpoint saver that stores checkpoints in a MongoDB database.\n", - "\n", - " Args:\n", - " client (pymongo.MongoClient): The MongoDB client.\n", - " db_name (str): The name of the database to use.\n", - " collection_name (str): The name of the collection to use.\n", - " serde (Optional[SerializerProtocol]): The serializer to use for serializing and deserializing checkpoints. Defaults to JsonPlusSerializerCompat.\n", - "\n", - " Examples:\n", - "\n", - " >>> from pymongo import MongoClient\n", - " >>> from langgraph.checkpoint.mongodb import MongoDBSaver\n", - " >>> from langgraph.graph import StateGraph\n", - " >>>\n", - " >>> builder = StateGraph(int)\n", - " >>> builder.add_node(\"add_one\", lambda x: x + 1)\n", - " >>> builder.set_entry_point(\"add_one\")\n", - " >>> builder.set_finish_point(\"add_one\")\n", - " >>> client = MongoClient(\"mongodb://localhost:27017/\")\n", - " >>> memory = MongoDBSaver(client, \"checkpoints\", \"checkpoints\")\n", - " >>> graph = builder.compile(checkpointer=memory)\n", - " >>> config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - " >>> graph.get_state(config)\n", - " >>> result = graph.invoke(3, config)\n", - " >>> graph.get_state(config)\n", - " StateSnapshot(values=4, next=(), config={'configurable': {'thread_id': '1', 'thread_ts': '2024-05-04T06:32:42.235444+00:00'}}, parent_config=None)\n", - " \"\"\"\n", - "\n", - " serde = JsonPlusSerializerCompat()\n", - "\n", - " client: MongoClient\n", - " db_name: str\n", - " collection_name: str\n", - "\n", - " def __init__(\n", - " self,\n", - " client: MongoClient,\n", - " db_name: str,\n", - " collection_name: str,\n", - " *,\n", - " serde: Optional[SerializerProtocol] = None,\n", - " ) -> None:\n", - " super().__init__(serde=serde)\n", - " self.client = client\n", - " self.db_name = db_name\n", - " self.collection_name = collection_name\n", - " self.collection = client[db_name][collection_name]\n", - "\n", - " def __enter__(self) -> Self:\n", - " return self\n", - "\n", - " def __exit__(\n", - " self,\n", - " __exc_type: Optional[type[BaseException]],\n", - " __exc_value: Optional[BaseException],\n", - " __traceback: Optional[TracebackType],\n", - " ) -> Optional[bool]:\n", - " return True\n", - "\n", - " def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", - " \"\"\"Get a checkpoint tuple from the database.\n", - "\n", - " This method retrieves a checkpoint tuple from the MongoDB database based on the\n", - " provided config. If the config contains a \"thread_ts\" key, the checkpoint with\n", - " the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint\n", - " for the given thread ID is retrieved.\n", - "\n", - " Args:\n", - " config (RunnableConfig): The config to use for retrieving the checkpoint.\n", - "\n", - " Returns:\n", - " Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.\n", - " \"\"\"\n", - " if config[\"configurable\"].get(\"thread_ts\"):\n", - " query = {\n", - " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", - " \"thread_ts\": config[\"configurable\"][\"thread_ts\"],\n", - " }\n", - " else:\n", - " query = {\"thread_id\": config[\"configurable\"][\"thread_id\"]}\n", - " result = self.collection.find(query).sort(\"thread_ts\", -1).limit(1)\n", - " for doc in result:\n", - " return CheckpointTuple(\n", - " config,\n", - " self.serde.loads(doc[\"checkpoint\"]),\n", - " self.serde.loads(doc[\"metadata\"]),\n", - " (\n", - " {\n", - " \"configurable\": {\n", - " \"thread_id\": doc[\"thread_id\"],\n", - " \"thread_ts\": doc[\"parent_ts\"],\n", - " }\n", - " }\n", - " if doc.get(\"parent_ts\")\n", - " else None\n", - " ),\n", - " )\n", - "\n", - " def list(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " *,\n", - " filter: Optional[Dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " limit: Optional[int] = None,\n", - " ) -> Iterator[CheckpointTuple]:\n", - " \"\"\"List checkpoints from the database.\n", - "\n", - " This method retrieves a list of checkpoint tuples from the MongoDB database based\n", - " on the provided config. The checkpoints are ordered by timestamp in descending order.\n", - "\n", - " Args:\n", - " config (RunnableConfig): The config to use for listing the checkpoints.\n", - " before (Optional[RunnableConfig]): If provided, only checkpoints before the specified timestamp are returned. Defaults to None.\n", - " limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.\n", - "\n", - " Yields:\n", - " Iterator[CheckpointTuple]: An iterator of checkpoint tuples.\n", - " \"\"\"\n", - " query = {}\n", - " if config is not None:\n", - " query[\"thread_id\"] = config[\"configurable\"][\"thread_id\"]\n", - " if filter:\n", - " for key, value in filter.items():\n", - " query[f\"metadata.{key}\"] = value\n", - " if before is not None:\n", - " query[\"thread_ts\"] = {\"$lt\": before[\"configurable\"][\"thread_ts\"]}\n", - " result = self.collection.find(query).sort(\"thread_ts\", -1).limit(limit)\n", - " for doc in result:\n", - " yield CheckpointTuple(\n", - " {\n", - " \"configurable\": {\n", - " \"thread_id\": doc[\"thread_id\"],\n", - " \"thread_ts\": doc[\"thread_ts\"],\n", - " }\n", - " },\n", - " self.serde.loads(doc[\"checkpoint\"]),\n", - " self.serde.loads(doc[\"metadata\"]),\n", - " (\n", - " {\n", - " \"configurable\": {\n", - " \"thread_id\": doc[\"thread_id\"],\n", - " \"thread_ts\": doc[\"parent_ts\"],\n", - " }\n", - " }\n", - " if doc.get(\"parent_ts\")\n", - " else None\n", - " ),\n", - " )\n", - "\n", - " def put(\n", - " self,\n", - " config: RunnableConfig,\n", - " checkpoint: Checkpoint,\n", - " metadata: CheckpointMetadata,\n", - " ) -> RunnableConfig:\n", - " \"\"\"Save a checkpoint to the database.\n", - "\n", - " This method saves a checkpoint to the MongoDB database. The checkpoint is associated\n", - " with the provided config and its parent config (if any).\n", - "\n", - " Args:\n", - " config (RunnableConfig): The config to associate with the checkpoint.\n", - " checkpoint (Checkpoint): The checkpoint to save.\n", - " metadata (Optional[dict[str, Any]]): Additional metadata to save with the checkpoint. Defaults to None.\n", - "\n", - " Returns:\n", - " RunnableConfig: The updated config containing the saved checkpoint's timestamp.\n", - " \"\"\"\n", - " doc = {\n", - " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", - " \"thread_ts\": checkpoint[\"id\"],\n", - " \"checkpoint\": self.serde.dumps(checkpoint),\n", - " \"metadata\": self.serde.dumps(metadata),\n", - " }\n", - " if config[\"configurable\"].get(\"thread_ts\"):\n", - " doc[\"parent_ts\"] = config[\"configurable\"][\"thread_ts\"]\n", - " self.collection.insert_one(doc)\n", - " return {\n", - " \"configurable\": {\n", - " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", - " \"thread_ts\": checkpoint[\"id\"],\n", - " }\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## MongoDB connection" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "MONGO_URI = \"mongodb://localhost:27017/\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic example using graph" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StateSnapshot(values=4, next=(), config={'configurable': {'thread_id': '123'}}, metadata={'source': 'loop', 'step': 1, 'writes': {'add_one': 4}}, created_at='2024-07-09T15:56:06.885848+00:00', parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3e0bc-09c1-6c26-8000-b9e1d26417ff'}})" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "checkpointer = MongoDBSaver(\n", - " MongoClient(MONGO_URI), \"checkpoints_db\", \"checkpoints_collection\"\n", - ")\n", - "builder = StateGraph(int)\n", - "builder.add_node(\"add_one\", lambda x: x + 1)\n", - "builder.add_edge(START, \"add_one\")\n", - "builder.add_edge(\"add_one\", END)\n", - "graph = builder.compile(checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"123\"}}\n", - "graph.get_state(config)\n", - "result = graph.invoke(3, config)\n", - "graph.get_state(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'v': 1,\n", - " 'ts': '2024-07-09T15:56:06.885848+00:00',\n", - " 'id': '1ef3e0bc-09d1-6a75-8001-8f750e9a0782',\n", - " 'channel_values': {'__root__': 4, 'add_one': 'add_one'},\n", - " 'channel_versions': {'__start__': 2,\n", - " '__root__': 3,\n", - " 'start:add_one': 3,\n", - " 'add_one': 3},\n", - " 'versions_seen': {'__start__': {'__start__': 1},\n", - " 'add_one': {'start:add_one': 2}},\n", - " 'pending_sends': []}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CheckpointTuple(config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3e0bc-09d1-6a75-8001-8f750e9a0782'}}, checkpoint={'v': 1, 'ts': '2024-07-09T15:56:06.885848+00:00', 'id': '1ef3e0bc-09d1-6a75-8001-8f750e9a0782', 'channel_values': {'__root__': 4, 'add_one': 'add_one'}, 'channel_versions': {'__start__': 2, '__root__': 3, 'start:add_one': 3, 'add_one': 3}, 'versions_seen': {'__start__': {'__start__': 1}, 'add_one': {'start:add_one': 2}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 1, 'writes': {'add_one': 4}}, parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3e0bc-09c1-6c26-8000-b9e1d26417ff'}})\n", - "CheckpointTuple(config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3e0bc-09c1-6c26-8000-b9e1d26417ff'}}, checkpoint={'v': 1, 'ts': '2024-07-09T15:56:06.878338+00:00', 'id': '1ef3e0bc-09c1-6c26-8000-b9e1d26417ff', 'channel_values': {'__root__': 3, 'start:add_one': '__start__'}, 'channel_versions': {'__start__': 2, '__root__': 2, 'start:add_one': 2}, 'versions_seen': {'__start__': {'__start__': 1}, 'add_one': {}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 0, 'writes': None}, parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3e0bc-09bc-6e04-bfff-5342ac1ccc10'}})\n", - "CheckpointTuple(config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3e0bc-09bc-6e04-bfff-5342ac1ccc10'}}, checkpoint={'v': 1, 'ts': '2024-07-09T15:56:06.877337+00:00', 'id': '1ef3e0bc-09bc-6e04-bfff-5342ac1ccc10', 'channel_values': {'__start__': 3}, 'channel_versions': {'__start__': 1}, 'versions_seen': {}, 'pending_sends': []}, metadata={'source': 'input', 'step': -1, 'writes': 3}, parent_config=None)\n" - ] - } - ], - "source": [ - "list = checkpointer.list(config, limit=3)\n", - "for item in list:\n", - " print(item)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CheckpointTuple(config={'configurable': {'thread_id': '123'}}, checkpoint={'v': 1, 'ts': '2024-07-09T13:22:19.610402+00:00', 'id': '1ef3df64-4ba9-6b58-8001-ab084cc01a30', 'channel_values': {'__root__': 4, 'add_one': 'add_one'}, 'channel_versions': {'__start__': 2, '__root__': 3, 'start:add_one': 3, 'add_one': 3}, 'versions_seen': {'__start__': {'__start__': 1}, 'add_one': {'start:add_one': 2}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 1, 'writes': {'add_one': 4}}, parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3df64-4ba2-660c-8000-569999697ff3'}})" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get_tuple(config)" + "NOTE: this is just an reference implementation. You can implement your own checkpointer using a different database or modify this one as long as it conforms to the `BaseCheckpointSaver` interface." ] }, { "cell_type": "markdown", + "id": "456fa19c-93a5-4750-a410-f2d810b964ad", "metadata": {}, "source": [ "## Setup environment" @@ -426,9 +24,29 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, + "id": "faadfb1b-cebe-4dcf-82fd-34044c380bc4", "metadata": {}, "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U pymongo motor langgraph" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eca9aafb-a155-407a-8036-682a2f1297d7", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "OPENAI_API_KEY: ········\n" + ] + } + ], "source": [ "import getpass\n", "import os\n", @@ -444,6 +62,580 @@ }, { "cell_type": "markdown", + "id": "ecb23436-f238-4f8c-a2b7-67c7956121e2", + "metadata": {}, + "source": [ + "## Checkpointer implementation" + ] + }, + { + "cell_type": "markdown", + "id": "922822a8-f7d2-41ce-bada-206fc125c20c", + "metadata": {}, + "source": [ + "### MongoDBSaver" + ] + }, + { + "cell_type": "markdown", + "id": "c216852b-8318-4927-9000-1361d3ca81e8", + "metadata": {}, + "source": [ + "Below is an implementation of MongoDBSaver (for synchronous use of graph, i.e. `.invoke()`, `.stream()`). MongoDBSaver implements four methods that are required for any checkpointer:\n", + "\n", + "- `.put` - Store a checkpoint with its configuration and metadata.\n", + "- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).\n", + "- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).\n", + "- `.list` - List checkpoints that match a given configuration and filter criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "98c8d65e-eb95-4cbd-8975-d33a52351d03", + "metadata": {}, + "outputs": [], + "source": [ + "from contextlib import asynccontextmanager, contextmanager\n", + "from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple\n", + "\n", + "from langchain_core.runnables import RunnableConfig\n", + "from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorDatabase\n", + "from pymongo import MongoClient, UpdateOne\n", + "from pymongo.database import Database as MongoDatabase\n", + "\n", + "from langgraph.checkpoint.base import (\n", + " BaseCheckpointSaver,\n", + " ChannelVersions,\n", + " Checkpoint,\n", + " CheckpointMetadata,\n", + " CheckpointTuple,\n", + " get_checkpoint_id,\n", + ")\n", + "\n", + "\n", + "class MongoDBSaver(BaseCheckpointSaver):\n", + " \"\"\"A checkpoint saver that stores checkpoints in a MongoDB database.\"\"\"\n", + "\n", + " client: MongoClient\n", + " db: MongoDatabase\n", + "\n", + " def __init__(\n", + " self,\n", + " client: MongoClient,\n", + " db_name: str,\n", + " ) -> None:\n", + " super().__init__()\n", + " self.client = client\n", + " self.db = self.client[db_name]\n", + "\n", + " @classmethod\n", + " @contextmanager\n", + " def from_conn_info(\n", + " cls, *, host: str, port: int, db_name: str\n", + " ) -> Iterator[\"MongoDBSaver\"]:\n", + " client = None\n", + " try:\n", + " client = MongoClient(host=host, port=port)\n", + " yield MongoDBSaver(client, db_name)\n", + " finally:\n", + " if client:\n", + " client.close()\n", + "\n", + " def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", + " \"\"\"Get a checkpoint tuple from the database.\n", + "\n", + " This method retrieves a checkpoint tuple from the MongoDB database based on the\n", + " provided config. If the config contains a \"checkpoint_id\" key, the checkpoint with\n", + " the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint\n", + " for the given thread ID is retrieved.\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to use for retrieving the checkpoint.\n", + "\n", + " Returns:\n", + " Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"].get(\"checkpoint_ns\", \"\")\n", + " if checkpoint_id := get_checkpoint_id(config):\n", + " query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " else:\n", + " query = {\"thread_id\": thread_id, \"checkpoint_ns\": checkpoint_ns}\n", + "\n", + " result = self.db[\"checkpoints\"].find(query).sort(\"checkpoint_id\", -1).limit(1)\n", + " for doc in result:\n", + " config_values = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": doc[\"checkpoint_id\"],\n", + " }\n", + " checkpoint = self.serde.loads_typed((doc[\"type\"], doc[\"checkpoint\"]))\n", + " serialized_writes = self.db[\"checkpoint_writes\"].find(config_values)\n", + " pending_writes = [\n", + " (\n", + " doc[\"task_id\"],\n", + " doc[\"channel\"],\n", + " self.serde.loads_typed((doc[\"type\"], doc[\"value\"])),\n", + " )\n", + " for doc in serialized_writes\n", + " ]\n", + " return CheckpointTuple(\n", + " {\"configurable\": config_values},\n", + " checkpoint,\n", + " self.serde.loads(doc[\"metadata\"]),\n", + " (\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": doc[\"parent_checkpoint_id\"],\n", + " }\n", + " }\n", + " if doc.get(\"parent_checkpoint_id\")\n", + " else None\n", + " ),\n", + " pending_writes,\n", + " )\n", + "\n", + " def list(\n", + " self,\n", + " config: Optional[RunnableConfig],\n", + " *,\n", + " filter: Optional[Dict[str, Any]] = None,\n", + " before: Optional[RunnableConfig] = None,\n", + " limit: Optional[int] = None,\n", + " ) -> Iterator[CheckpointTuple]:\n", + " \"\"\"List checkpoints from the database.\n", + "\n", + " This method retrieves a list of checkpoint tuples from the MongoDB database based\n", + " on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to use for listing the checkpoints.\n", + " filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.\n", + " before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.\n", + " limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.\n", + "\n", + " Yields:\n", + " Iterator[CheckpointTuple]: An iterator of checkpoint tuples.\n", + " \"\"\"\n", + " query = {}\n", + " if config is not None:\n", + " query = {\n", + " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", + " \"checkpoint_ns\": config[\"configurable\"].get(\"checkpoint_ns\", \"\"),\n", + " }\n", + "\n", + " if filter:\n", + " for key, value in filter.items():\n", + " query[f\"metadata.{key}\"] = value\n", + "\n", + " if before is not None:\n", + " query[\"checkpoint_id\"] = {\"$lt\": before[\"configurable\"][\"checkpoint_id\"]}\n", + "\n", + " result = self.db[\"checkpoints\"].find(query).sort(\"checkpoint_id\", -1)\n", + "\n", + " if limit is not None:\n", + " result = result.limit(limit)\n", + " for doc in result:\n", + " checkpoint = self.serde.loads_typed((doc[\"type\"], doc[\"checkpoint\"]))\n", + " yield CheckpointTuple(\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": doc[\"thread_id\"],\n", + " \"checkpoint_ns\": doc[\"checkpoint_ns\"],\n", + " \"checkpoint_id\": doc[\"checkpoint_id\"],\n", + " }\n", + " },\n", + " checkpoint,\n", + " self.serde.loads(doc[\"metadata\"]),\n", + " (\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": doc[\"thread_id\"],\n", + " \"checkpoint_ns\": doc[\"checkpoint_ns\"],\n", + " \"checkpoint_id\": doc[\"parent_checkpoint_id\"],\n", + " }\n", + " }\n", + " if doc.get(\"parent_checkpoint_id\")\n", + " else None\n", + " ),\n", + " )\n", + "\n", + " def put(\n", + " self,\n", + " config: RunnableConfig,\n", + " checkpoint: Checkpoint,\n", + " metadata: CheckpointMetadata,\n", + " new_versions: ChannelVersions,\n", + " ) -> RunnableConfig:\n", + " \"\"\"Save a checkpoint to the database.\n", + "\n", + " This method saves a checkpoint to the MongoDB database. The checkpoint is associated\n", + " with the provided config and its parent config (if any).\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to associate with the checkpoint.\n", + " checkpoint (Checkpoint): The checkpoint to save.\n", + " metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.\n", + " new_versions (ChannelVersions): New channel versions as of this write.\n", + "\n", + " Returns:\n", + " RunnableConfig: Updated configuration after storing the checkpoint.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = checkpoint[\"id\"]\n", + " type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)\n", + " doc = {\n", + " \"parent_checkpoint_id\": config[\"configurable\"].get(\"checkpoint_id\"),\n", + " \"type\": type_,\n", + " \"checkpoint\": serialized_checkpoint,\n", + " \"metadata\": self.serde.dumps(metadata),\n", + " }\n", + " upsert_query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " # Perform your operations here\n", + " self.db[\"checkpoints\"].update_one(upsert_query, {\"$set\": doc}, upsert=True)\n", + " return {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " }\n", + "\n", + " def put_writes(\n", + " self,\n", + " config: RunnableConfig,\n", + " writes: Sequence[Tuple[str, Any]],\n", + " task_id: str,\n", + " ) -> None:\n", + " \"\"\"Store intermediate writes linked to a checkpoint.\n", + "\n", + " This method saves intermediate writes associated with a checkpoint to the MongoDB database.\n", + "\n", + " Args:\n", + " config (RunnableConfig): Configuration of the related checkpoint.\n", + " writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.\n", + " task_id (str): Identifier for the task creating the writes.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = config[\"configurable\"][\"checkpoint_id\"]\n", + " operations = []\n", + " for idx, (channel, value) in enumerate(writes):\n", + " upsert_query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " \"task_id\": task_id,\n", + " \"idx\": idx,\n", + " }\n", + " type_, serialized_value = self.serde.dumps_typed(value)\n", + " operations.append(\n", + " UpdateOne(\n", + " upsert_query,\n", + " {\n", + " \"$set\": {\n", + " \"channel\": channel,\n", + " \"type\": type_,\n", + " \"value\": serialized_value,\n", + " }\n", + " },\n", + " upsert=True,\n", + " )\n", + " )\n", + " self.db[\"checkpoint_writes\"].bulk_write(operations)" + ] + }, + { + "cell_type": "markdown", + "id": "ec21ff00-75a7-4789-b863-93fffcc0b32d", + "metadata": {}, + "source": [ + "### AsyncMongoDBSaver" + ] + }, + { + "cell_type": "markdown", + "id": "9e5ad763-12ab-4918-af40-0be85678e35b", + "metadata": {}, + "source": [ + "Below is a reference implementation of AsyncMongoDBSaver (for asynchronous use of graph, i.e. `.ainvoke()`, `.astream()`). AsyncMongoDBSaver implements four methods that are required for any async checkpointer:\n", + "\n", + "- `.aput` - Store a checkpoint with its configuration and metadata.\n", + "- `.aput_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).\n", + "- `.aget_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).\n", + "- `.alist` - List checkpoints that match a given configuration and filter criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "888302ee-c201-498f-b6e3-69ec5f1a039c", + "metadata": {}, + "outputs": [], + "source": [ + "class AsyncMongoDBSaver(BaseCheckpointSaver):\n", + " \"\"\"A checkpoint saver that stores checkpoints in a MongoDB database asynchronously.\"\"\"\n", + "\n", + " client: AsyncIOMotorClient\n", + " db: AsyncIOMotorDatabase\n", + "\n", + " def __init__(\n", + " self,\n", + " client: AsyncIOMotorClient,\n", + " db_name: str,\n", + " ) -> None:\n", + " super().__init__()\n", + " self.client = client\n", + " self.db = self.client[db_name]\n", + "\n", + " @classmethod\n", + " @asynccontextmanager\n", + " async def from_conn_info(\n", + " cls, *, host: str, port: int, db_name: str\n", + " ) -> AsyncIterator[\"AsyncMongoDBSaver\"]:\n", + " client = None\n", + " try:\n", + " client = AsyncIOMotorClient(host=host, port=port)\n", + " yield AsyncMongoDBSaver(client, db_name)\n", + " finally:\n", + " if client:\n", + " client.close()\n", + "\n", + " async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", + " \"\"\"Get a checkpoint tuple from the database asynchronously.\n", + "\n", + " This method retrieves a checkpoint tuple from the MongoDB database based on the\n", + " provided config. If the config contains a \"checkpoint_id\" key, the checkpoint with\n", + " the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint\n", + " for the given thread ID is retrieved.\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to use for retrieving the checkpoint.\n", + "\n", + " Returns:\n", + " Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"].get(\"checkpoint_ns\", \"\")\n", + " if checkpoint_id := get_checkpoint_id(config):\n", + " query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " else:\n", + " query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " }\n", + "\n", + " result = self.db[\"checkpoints\"].find(query).sort(\"checkpoint_id\", -1).limit(1)\n", + " async for doc in result:\n", + " config_values = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": doc[\"checkpoint_id\"],\n", + " }\n", + " checkpoint = self.serde.loads_typed((doc[\"type\"], doc[\"checkpoint\"]))\n", + " serialized_writes = self.db[\"checkpoint_writes\"].find(config_values)\n", + " pending_writes = [\n", + " (\n", + " doc[\"task_id\"],\n", + " doc[\"channel\"],\n", + " self.serde.loads_typed((doc[\"type\"], doc[\"value\"])),\n", + " )\n", + " async for doc in serialized_writes\n", + " ]\n", + " return CheckpointTuple(\n", + " {\"configurable\": config_values},\n", + " checkpoint,\n", + " self.serde.loads(doc[\"metadata\"]),\n", + " (\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": doc[\"parent_checkpoint_id\"],\n", + " }\n", + " }\n", + " if doc.get(\"parent_checkpoint_id\")\n", + " else None\n", + " ),\n", + " pending_writes,\n", + " )\n", + "\n", + " async def alist(\n", + " self,\n", + " config: Optional[RunnableConfig],\n", + " *,\n", + " filter: Optional[Dict[str, Any]] = None,\n", + " before: Optional[RunnableConfig] = None,\n", + " limit: Optional[int] = None,\n", + " ) -> AsyncIterator[CheckpointTuple]:\n", + " \"\"\"List checkpoints from the database asynchronously.\n", + "\n", + " This method retrieves a list of checkpoint tuples from the MongoDB database based\n", + " on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).\n", + "\n", + " Args:\n", + " config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.\n", + " filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.\n", + " before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.\n", + " limit (Optional[int]): Maximum number of checkpoints to return.\n", + "\n", + " Yields:\n", + " AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples.\n", + " \"\"\"\n", + " query = {}\n", + " if config is not None:\n", + " query = {\n", + " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", + " \"checkpoint_ns\": config[\"configurable\"].get(\"checkpoint_ns\", \"\"),\n", + " }\n", + "\n", + " if filter:\n", + " for key, value in filter.items():\n", + " query[f\"metadata.{key}\"] = value\n", + "\n", + " if before is not None:\n", + " query[\"checkpoint_id\"] = {\"$lt\": before[\"configurable\"][\"checkpoint_id\"]}\n", + "\n", + " result = self.db[\"checkpoints\"].find(query).sort(\"checkpoint_id\", -1)\n", + "\n", + " if limit is not None:\n", + " result = result.limit(limit)\n", + " async for doc in result:\n", + " checkpoint = self.serde.loads_typed((doc[\"type\"], doc[\"checkpoint\"]))\n", + " yield CheckpointTuple(\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": doc[\"thread_id\"],\n", + " \"checkpoint_ns\": doc[\"checkpoint_ns\"],\n", + " \"checkpoint_id\": doc[\"checkpoint_id\"],\n", + " }\n", + " },\n", + " checkpoint,\n", + " self.serde.loads(doc[\"metadata\"]),\n", + " (\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": doc[\"thread_id\"],\n", + " \"checkpoint_ns\": doc[\"checkpoint_ns\"],\n", + " \"checkpoint_id\": doc[\"parent_checkpoint_id\"],\n", + " }\n", + " }\n", + " if doc.get(\"parent_checkpoint_id\")\n", + " else None\n", + " ),\n", + " )\n", + "\n", + " async def aput(\n", + " self,\n", + " config: RunnableConfig,\n", + " checkpoint: Checkpoint,\n", + " metadata: CheckpointMetadata,\n", + " new_versions: ChannelVersions,\n", + " ) -> RunnableConfig:\n", + " \"\"\"Save a checkpoint to the database asynchronously.\n", + "\n", + " This method saves a checkpoint to the MongoDB database. The checkpoint is associated\n", + " with the provided config and its parent config (if any).\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to associate with the checkpoint.\n", + " checkpoint (Checkpoint): The checkpoint to save.\n", + " metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.\n", + " new_versions (ChannelVersions): New channel versions as of this write.\n", + "\n", + " Returns:\n", + " RunnableConfig: Updated configuration after storing the checkpoint.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = checkpoint[\"id\"]\n", + " type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)\n", + " doc = {\n", + " \"parent_checkpoint_id\": config[\"configurable\"].get(\"checkpoint_id\"),\n", + " \"type\": type_,\n", + " \"checkpoint\": serialized_checkpoint,\n", + " \"metadata\": self.serde.dumps(metadata),\n", + " }\n", + " upsert_query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " # Perform your operations here\n", + " await self.db[\"checkpoints\"].update_one(\n", + " upsert_query, {\"$set\": doc}, upsert=True\n", + " )\n", + " return {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " }\n", + "\n", + " async def aput_writes(\n", + " self,\n", + " config: RunnableConfig,\n", + " writes: Sequence[Tuple[str, Any]],\n", + " task_id: str,\n", + " ) -> None:\n", + " \"\"\"Store intermediate writes linked to a checkpoint asynchronously.\n", + "\n", + " This method saves intermediate writes associated with a checkpoint to the database.\n", + "\n", + " Args:\n", + " config (RunnableConfig): Configuration of the related checkpoint.\n", + " writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.\n", + " task_id (str): Identifier for the task creating the writes.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = config[\"configurable\"][\"checkpoint_id\"]\n", + " operations = []\n", + " for idx, (channel, value) in enumerate(writes):\n", + " upsert_query = {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " \"task_id\": task_id,\n", + " \"idx\": idx,\n", + " }\n", + " type_, serialized_value = self.serde.dumps_typed(value)\n", + " operations.append(\n", + " UpdateOne(\n", + " upsert_query,\n", + " {\n", + " \"$set\": {\n", + " \"channel\": channel,\n", + " \"type\": type_,\n", + " \"value\": serialized_value,\n", + " }\n", + " },\n", + " upsert=True,\n", + " )\n", + " )\n", + " await self.db[\"checkpoint_writes\"].bulk_write(operations)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e26b3204-cca2-414c-800e-7e09032445ae", "metadata": {}, "source": [ "## Setup model and tools for the graph" @@ -451,16 +643,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install langchain_openai" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 5, + "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", "metadata": {}, "outputs": [], "source": [ @@ -483,423 +667,109 @@ "\n", "\n", "tools = [get_weather]\n", - "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='a624d383-13c6-499c-8f03-31ed11fa0cfb'),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_wapm4s91KQUQqE9y1L53QmmE', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 58, 'total_tokens': 72}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-614bc54a-ad37-4f17-9047-b80752bdf66e-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_wapm4s91KQUQqE9y1L53QmmE'}]),\n", - " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e58dd97d-b50d-4b0a-9492-7155106c975a', tool_call_id='call_wapm4s91KQUQqE9y1L53QmmE'),\n", - " AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 86, 'total_tokens': 96}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0')]}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CheckpointTuple(config={'configurable': {'thread_id': '1'}}, checkpoint={'v': 1, 'ts': '2024-07-09T13:22:49.794047+00:00', 'id': '1ef3df65-6b84-63fd-8003-888bcef289e3', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='a624d383-13c6-499c-8f03-31ed11fa0cfb'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_wapm4s91KQUQqE9y1L53QmmE', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 58, 'total_tokens': 72}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-614bc54a-ad37-4f17-9047-b80752bdf66e-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_wapm4s91KQUQqE9y1L53QmmE'}]), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e58dd97d-b50d-4b0a-9492-7155106c975a', tool_call_id='call_wapm4s91KQUQqE9y1L53QmmE'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 86, 'total_tokens': 96}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0')], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {'start:agent': 3, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 3, 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 86, 'total_tokens': 96}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0')]}}}, parent_config={'configurable': {'thread_id': '1', 'thread_ts': '1ef3df65-6252-6b30-8002-2c9e1e68364b'}})" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get_tuple(config)" + "model = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0)" ] }, { "cell_type": "markdown", + "id": "e9342c62-dbb4-40f6-9271-7393f1ca48c4", "metadata": {}, "source": [ - "### Checkpoints saved in MongoDB" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'_id': ObjectId('668d398bb975d3e766de42ce'), 'thread_id': '123', 'thread_ts': '1ef3df64-4b98-68b4-bfff-592f97570cf6', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:19.603371+00:00\", \"id\": \"1ef3df64-4b98-68b4-bfff-592f97570cf6\", \"channel_values\": {\"__start__\": 3}, \"channel_versions\": {\"__start__\": 1}, \"versions_seen\": {}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"input\", \"step\": -1, \"writes\": 3}'}\n", - "{'_id': ObjectId('668d398bb975d3e766de42cf'), 'thread_id': '123', 'thread_ts': '1ef3df64-4ba2-660c-8000-569999697ff3', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:19.607399+00:00\", \"id\": \"1ef3df64-4ba2-660c-8000-569999697ff3\", \"channel_values\": {\"__root__\": 3, \"start:add_one\": \"__start__\"}, \"channel_versions\": {\"__start__\": 2, \"__root__\": 2, \"start:add_one\": 2}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"add_one\": {}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 0, \"writes\": null}', 'parent_ts': '1ef3df64-4b98-68b4-bfff-592f97570cf6'}\n", - "{'_id': ObjectId('668d398bb975d3e766de42d0'), 'thread_id': '123', 'thread_ts': '1ef3df64-4ba9-6b58-8001-ab084cc01a30', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:19.610402+00:00\", \"id\": \"1ef3df64-4ba9-6b58-8001-ab084cc01a30\", \"channel_values\": {\"__root__\": 4, \"add_one\": \"add_one\"}, \"channel_versions\": {\"__start__\": 2, \"__root__\": 3, \"start:add_one\": 3, \"add_one\": 3}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"add_one\": {\"start:add_one\": 2}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 1, \"writes\": {\"add_one\": 4}}', 'parent_ts': '1ef3df64-4ba2-660c-8000-569999697ff3'}\n", - "{'_id': ObjectId('668d39a7b975d3e766de42d1'), 'thread_id': '1', 'thread_ts': '1ef3df65-585c-6abb-bfff-634fac15b6b3', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:47.785541+00:00\", \"id\": \"1ef3df65-585c-6abb-bfff-634fac15b6b3\", \"channel_values\": {\"messages\": [], \"__start__\": {\"messages\": [[\"human\", \"what\\'s the weather in sf\"]]}}, \"channel_versions\": {\"__start__\": 1}, \"versions_seen\": {}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"input\", \"step\": -1, \"writes\": {\"messages\": [[\"human\", \"what\\'s the weather in sf\"]]}}'}\n", - "{'_id': ObjectId('668d39a7b975d3e766de42d2'), 'thread_id': '1', 'thread_ts': '1ef3df65-5863-6fbc-8000-d45480f1ccc0', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:47.788537+00:00\", \"id\": \"1ef3df65-5863-6fbc-8000-d45480f1ccc0\", \"channel_values\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"what\\'s the weather in sf\", \"type\": \"human\", \"id\": \"a624d383-13c6-499c-8f03-31ed11fa0cfb\"}}], \"start:agent\": \"__start__\"}, \"channel_versions\": {\"__start__\": 2, \"messages\": 2, \"start:agent\": 2}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"agent\": {}, \"tools\": {}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 0, \"writes\": null}', 'parent_ts': '1ef3df65-585c-6abb-bfff-634fac15b6b3'}\n", - "{'_id': ObjectId('668d39a8b975d3e766de42d3'), 'thread_id': '1', 'thread_ts': '1ef3df65-6248-6ee8-8001-d5eef3fad087', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:48.826032+00:00\", \"id\": \"1ef3df65-6248-6ee8-8001-d5eef3fad087\", \"channel_values\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"what\\'s the weather in sf\", \"type\": \"human\", \"id\": \"a624d383-13c6-499c-8f03-31ed11fa0cfb\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"\", \"additional_kwargs\": {\"tool_calls\": [{\"id\": \"call_wapm4s91KQUQqE9y1L53QmmE\", \"function\": {\"arguments\": \"{\\\\\"city\\\\\":\\\\\"sf\\\\\"}\", \"name\": \"get_weather\"}, \"type\": \"function\"}]}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 14, \"prompt_tokens\": 58, \"total_tokens\": 72}, \"model_name\": \"gpt-3.5-turbo\", \"system_fingerprint\": null, \"finish_reason\": \"tool_calls\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-614bc54a-ad37-4f17-9047-b80752bdf66e-0\", \"tool_calls\": [{\"name\": \"get_weather\", \"args\": {\"city\": \"sf\"}, \"id\": \"call_wapm4s91KQUQqE9y1L53QmmE\"}], \"invalid_tool_calls\": []}}], \"agent\": \"agent\", \"branch:agent:should_continue:tools\": \"agent\"}, \"channel_versions\": {\"__start__\": 2, \"messages\": 3, \"start:agent\": 3, \"agent\": 3, \"branch:agent:should_continue:tools\": 3}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"agent\": {\"start:agent\": 2}, \"tools\": {}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 1, \"writes\": {\"agent\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"\", \"additional_kwargs\": {\"tool_calls\": [{\"id\": \"call_wapm4s91KQUQqE9y1L53QmmE\", \"function\": {\"arguments\": \"{\\\\\"city\\\\\":\\\\\"sf\\\\\"}\", \"name\": \"get_weather\"}, \"type\": \"function\"}]}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 14, \"prompt_tokens\": 58, \"total_tokens\": 72}, \"model_name\": \"gpt-3.5-turbo\", \"system_fingerprint\": null, \"finish_reason\": \"tool_calls\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-614bc54a-ad37-4f17-9047-b80752bdf66e-0\", \"tool_calls\": [{\"name\": \"get_weather\", \"args\": {\"city\": \"sf\"}, \"id\": \"call_wapm4s91KQUQqE9y1L53QmmE\"}], \"invalid_tool_calls\": []}}]}}}', 'parent_ts': '1ef3df65-5863-6fbc-8000-d45480f1ccc0'}\n", - "{'_id': ObjectId('668d39a8b975d3e766de42d4'), 'thread_id': '1', 'thread_ts': '1ef3df65-6252-6b30-8002-2c9e1e68364b', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:48.830033+00:00\", \"id\": \"1ef3df65-6252-6b30-8002-2c9e1e68364b\", \"channel_values\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"what\\'s the weather in sf\", \"type\": \"human\", \"id\": \"a624d383-13c6-499c-8f03-31ed11fa0cfb\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"\", \"additional_kwargs\": {\"tool_calls\": [{\"id\": \"call_wapm4s91KQUQqE9y1L53QmmE\", \"function\": {\"arguments\": \"{\\\\\"city\\\\\":\\\\\"sf\\\\\"}\", \"name\": \"get_weather\"}, \"type\": \"function\"}]}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 14, \"prompt_tokens\": 58, \"total_tokens\": 72}, \"model_name\": \"gpt-3.5-turbo\", \"system_fingerprint\": null, \"finish_reason\": \"tool_calls\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-614bc54a-ad37-4f17-9047-b80752bdf66e-0\", \"tool_calls\": [{\"name\": \"get_weather\", \"args\": {\"city\": \"sf\"}, \"id\": \"call_wapm4s91KQUQqE9y1L53QmmE\"}], \"invalid_tool_calls\": []}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"ToolMessage\"], \"kwargs\": {\"content\": \"It\\'s always sunny in sf\", \"type\": \"tool\", \"name\": \"get_weather\", \"id\": \"e58dd97d-b50d-4b0a-9492-7155106c975a\", \"tool_call_id\": \"call_wapm4s91KQUQqE9y1L53QmmE\"}}], \"tools\": \"tools\"}, \"channel_versions\": {\"__start__\": 2, \"messages\": 4, \"start:agent\": 3, \"agent\": 4, \"branch:agent:should_continue:tools\": 4, \"tools\": 4}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"agent\": {\"start:agent\": 2}, \"tools\": {\"branch:agent:should_continue:tools\": 3}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 2, \"writes\": {\"tools\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"ToolMessage\"], \"kwargs\": {\"content\": \"It\\'s always sunny in sf\", \"type\": \"tool\", \"name\": \"get_weather\", \"id\": \"e58dd97d-b50d-4b0a-9492-7155106c975a\", \"tool_call_id\": \"call_wapm4s91KQUQqE9y1L53QmmE\"}}]}}}', 'parent_ts': '1ef3df65-6248-6ee8-8001-d5eef3fad087'}\n", - 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] - } - ], - "source": [ - "client = MongoClient(MONGO_URI)\n", - "database = client[\"checkpoints_db\"]\n", - "collection = database[\"checkpoints_collection\"]\n", - "\n", - "for doc in collection.find():\n", - " print(doc)\n", - "\n", - "# The checkpoints from both the examples have been saved in the database." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Asynchronous implementation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Async package for MongoDB\n", - "%pip install motor" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "from contextlib import AbstractContextManager\n", - "from types import TracebackType\n", - "from typing import Any, Dict, Optional, AsyncIterator\n", - "\n", - "from langchain_core.runnables import RunnableConfig\n", - "from typing_extensions import Self\n", - "\n", - "from langgraph.checkpoint.base import (\n", - " BaseCheckpointSaver,\n", - " Checkpoint,\n", - " CheckpointMetadata,\n", - " CheckpointTuple,\n", - " SerializerProtocol,\n", - ")\n", - "from langgraph.serde.jsonplus import JsonPlusSerializer\n", - "from motor.motor_asyncio import AsyncIOMotorClient\n", - "\n", - "\n", - "class JsonPlusSerializerCompat(JsonPlusSerializer):\n", - " \"\"\"A serializer that supports loading pickled checkpoints for backwards compatibility.\n", - "\n", - " This serializer extends the JsonPlusSerializer and adds support for loading pickled\n", - " checkpoints. If the input data starts with b\"\\x80\" and ends with b\".\", it is treated\n", - " as a pickled checkpoint and loaded using pickle.loads(). Otherwise, the default\n", - " JsonPlusSerializer behavior is used.\n", - "\n", - " Examples:\n", - " >>> import pickle\n", - " >>> from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat\n", - " >>>\n", - " >>> serializer = JsonPlusSerializerCompat()\n", - " >>> pickled_data = pickle.dumps({\"key\": \"value\"})\n", - " >>> loaded_data = serializer.loads(pickled_data)\n", - " >>> print(loaded_data) # Output: {\"key\": \"value\"}\n", - " >>>\n", - " >>> json_data = '{\"key\": \"value\"}'.encode(\"utf-8\")\n", - " >>> loaded_data = serializer.loads(json_data)\n", - " >>> print(loaded_data) # Output: {\"key\": \"value\"}\n", - " \"\"\"\n", - "\n", - " def loads(self, data: bytes) -> Any:\n", - " if data.startswith(b\"\\x80\") and data.endswith(b\".\"):\n", - " return pickle.loads(data)\n", - " return super().loads(data)\n", - "\n", - "\n", - "class MongoDBSaver(AbstractContextManager, BaseCheckpointSaver):\n", - " \"\"\"A checkpoint saver that stores checkpoints in a MongoDB database.\n", - "\n", - " Args:\n", - " client (AsyncIOMotorClient): The Async MongoDB client.\n", - " db_name (str): The name of the database to use.\n", - " collection_name (str): The name of the collection to use.\n", - " serde (Optional[SerializerProtocol]): The serializer to use for serializing and deserializing checkpoints. Defaults to JsonPlusSerializerCompat.\n", - "\n", - " Examples:\n", - "\n", - " >>> from motor.motor_asyncio import AsyncIOMotorClient\n", - " >>> from langgraph.checkpoint.mongodb import MongoDBSaver\n", - " >>> from langgraph.graph import StateGraph\n", - " >>>\n", - " >>> builder = StateGraph(int)\n", - " >>> builder.add_node(\"add_one\", lambda x: x + 1)\n", - " >>> builder.set_entry_point(\"add_one\")\n", - " >>> builder.set_finish_point(\"add_one\")\n", - " >>> client = AsyncIOMotorClient(\"mongodb://localhost:27017/\")\n", - " >>> memory = MongoDBSaver(client, \"checkpoints\", \"checkpoints\")\n", - " >>> graph = builder.compile(checkpointer=memory)\n", - " >>> config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - " >>> result = graph.ainvoke(3, config)\n", - " \"\"\"\n", - "\n", - " serde = JsonPlusSerializerCompat()\n", - "\n", - " client: AsyncIOMotorClient\n", - " db_name: str\n", - " collection_name: str\n", - "\n", - " def __init__(\n", - " self,\n", - " client: AsyncIOMotorClient,\n", - " db_name: str,\n", - " collection_name: str,\n", - " *,\n", - " serde: Optional[SerializerProtocol] = None,\n", - " ) -> None:\n", - " super().__init__(serde=serde)\n", - " self.client = client\n", - " self.db_name = db_name\n", - " self.collection_name = collection_name\n", - " self.collection = client[db_name][collection_name]\n", - "\n", - " def __enter__(self) -> Self:\n", - " return self\n", - "\n", - " def __exit__(\n", - " self,\n", - " __exc_type: Optional[type[BaseException]],\n", - " __exc_value: Optional[BaseException],\n", - " __traceback: Optional[TracebackType],\n", - " ) -> Optional[bool]:\n", - " return True\n", - "\n", - " async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", - " \"\"\"Get a checkpoint tuple from the database.\n", - "\n", - " This method retrieves a checkpoint tuple from the MongoDB database based on the\n", - " provided config. If the config contains a \"thread_ts\" key, the checkpoint with\n", - " the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint\n", - " for the given thread ID is retrieved.\n", - "\n", - " Args:\n", - " config (RunnableConfig): The config to use for retrieving the checkpoint.\n", - "\n", - " Returns:\n", - " Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.\n", - " \"\"\"\n", - " if config[\"configurable\"].get(\"thread_ts\"):\n", - " query = {\n", - " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", - " \"thread_ts\": config[\"configurable\"][\"thread_ts\"],\n", - " }\n", - " else:\n", - " query = {\"thread_id\": config[\"configurable\"][\"thread_id\"]}\n", - " result = self.collection.find(query).sort(\"thread_ts\", -1).limit(1)\n", - " async for doc in result:\n", - " return CheckpointTuple(\n", - " config,\n", - " self.serde.loads(doc[\"checkpoint\"]),\n", - " self.serde.loads(doc[\"metadata\"]),\n", - " (\n", - " {\n", - " \"configurable\": {\n", - " \"thread_id\": doc[\"thread_id\"],\n", - " \"thread_ts\": doc[\"parent_ts\"],\n", - " }\n", - " }\n", - " if doc.get(\"parent_ts\")\n", - " else None\n", - " ),\n", - " )\n", - "\n", - " async def alist(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " *,\n", - " filter: Optional[Dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " limit: Optional[int] = None,\n", - " ) -> AsyncIterator[CheckpointTuple]:\n", - " \"\"\"List checkpoints from the database.\n", - "\n", - " This method retrieves a list of checkpoint tuples from the MongoDB database based\n", - " on the provided config. The checkpoints are ordered by timestamp in descending order.\n", - "\n", - " Args:\n", - " config (RunnableConfig): The config to use for listing the checkpoints.\n", - " before (Optional[RunnableConfig]): If provided, only checkpoints before the specified timestamp are returned. Defaults to None.\n", - " limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.\n", - "\n", - " Yields:\n", - " AsyncIterator[CheckpointTuple]: An Async iterator of checkpoint tuples.\n", - " \"\"\"\n", - " query = {}\n", - " if config is not None:\n", - " query[\"thread_id\"] = config[\"configurable\"][\"thread_id\"]\n", - " if filter:\n", - " for key, value in filter.items():\n", - " query[f\"metadata.{key}\"] = value\n", - " if before is not None:\n", - " query[\"thread_ts\"] = {\"$lt\": before[\"configurable\"][\"thread_ts\"]}\n", - " result = self.collection.find(query).sort(\"thread_ts\", -1).limit(limit)\n", - " if limit is not None:\n", - " result = result.limit(limit)\n", - " async for doc in result:\n", - " yield CheckpointTuple(\n", - " {\n", - " \"configurable\": {\n", - " \"thread_id\": doc[\"thread_id\"],\n", - " \"thread_ts\": doc[\"thread_ts\"],\n", - " }\n", - " },\n", - " self.serde.loads(doc[\"checkpoint\"]),\n", - " self.serde.loads(doc[\"metadata\"]),\n", - " (\n", - " {\n", - " \"configurable\": {\n", - " \"thread_id\": doc[\"thread_id\"],\n", - " \"thread_ts\": doc[\"parent_ts\"],\n", - " }\n", - " }\n", - " if doc.get(\"parent_ts\")\n", - " else None\n", - " ),\n", - " )\n", - "\n", - " async def aput(\n", - " self,\n", - " config: RunnableConfig,\n", - " checkpoint: Checkpoint,\n", - " metadata: CheckpointMetadata,\n", - " ) -> RunnableConfig:\n", - " \"\"\"Save a checkpoint to the database.\n", - "\n", - " This method saves a checkpoint to the MongoDB database. The checkpoint is associated\n", - " with the provided config and its parent config (if any).\n", - "\n", - " Args:\n", - " config (RunnableConfig): The config to associate with the checkpoint.\n", - " checkpoint (Checkpoint): The checkpoint to save.\n", - " metadata (Optional[dict[str, Any]]): Additional metadata to save with the checkpoint. Defaults to None.\n", - "\n", - " Returns:\n", - " RunnableConfig: The updated config containing the saved checkpoint's timestamp.\n", - " \"\"\"\n", - " doc = {\n", - " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", - " \"thread_ts\": checkpoint[\"id\"],\n", - " \"checkpoint\": self.serde.dumps(checkpoint),\n", - " \"metadata\": self.serde.dumps(metadata),\n", - " }\n", - " if config[\"configurable\"].get(\"thread_ts\"):\n", - " doc[\"parent_ts\"] = config[\"configurable\"][\"thread_ts\"]\n", - " await self.collection.insert_one(doc)\n", - " return {\n", - " \"configurable\": {\n", - " \"thread_id\": config[\"configurable\"][\"thread_id\"],\n", - " \"thread_ts\": checkpoint[\"id\"],\n", - " }\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example with basic graph" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, START\n", - "\n", - "checkpointer = MongoDBSaver(\n", - " AsyncIOMotorClient(MONGO_URI), \"checkpoints_db\", \"checkpoints_collection\"\n", - ")\n", - "builder = StateGraph(int)\n", - "builder.add_node(\"add_one\", lambda x: x + 1)\n", - "builder.add_edge(START, \"add_one\")\n", - "builder.add_edge(\"add_one\", END)\n", - "graph = builder.compile(checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"123\"}}\n", - "res = await graph.ainvoke(3, config)" + "## Use sync connection" ] }, { "cell_type": "code", "execution_count": 6, + "id": "5fe54e79-9eaf-44e2-b2d9-1e0284b984d0", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "res" + "with MongoDBSaver.from_conn_info(host=\"localhost\", port=27017, db_name=\"checkpoints\") as checkpointer:\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"1\"}}\n", + " res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n", + "\n", + " latest_checkpoint = checkpointer.get(config)\n", + " latest_checkpoint_tuple = checkpointer.get_tuple(config)\n", + " checkpoint_tuples = list(checkpointer.list(config))" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, + "id": "c298e627-115a-4b4c-ae17-520ca9a640cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'v': 1,\n", - " 'ts': '2024-07-10T11:34:28.485660+00:00',\n", - " 'id': '1ef3eb05-e0d1-651b-8004-15f129f5f4fb',\n", - " 'channel_values': {'__root__': 4, 'add_one': 'add_one'},\n", - " 'channel_versions': {'__start__': 5,\n", - " '__root__': 6,\n", - " 'start:add_one': 6,\n", - " 'add_one': 6},\n", - " 'versions_seen': {'__start__': {'__start__': 4},\n", - " 'add_one': {'start:add_one': 5}},\n", - " 'pending_sends': []}" + " 'ts': '2024-08-09T16:19:39.102711+00:00',\n", + " 'id': '1ef566b2-d2a8-6cdc-8003-cc4d1980d188',\n", + " 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f4227353-e0e5-43a9-984a-e4b9e2d8e7b8'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd1d3187-470f-4ebd-938f-527a61824045-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", + " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='2d124101-696d-450f-bc9f-d8fdcc564101', tool_call_id='call_Y7PzHb7LrIdiTnO5UiSfelt3'),\n", + " AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-87c76dd2-33f4-433e-986a-9405cfe88c88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})],\n", + " 'agent': 'agent'},\n", + " 'channel_versions': {'__start__': 2,\n", + " 'messages': 5,\n", + " 'start:agent': 3,\n", + " 'agent': 5,\n", + " 'branch:agent:should_continue:tools': 4,\n", + " 'tools': 5},\n", + " 'versions_seen': {'__input__': {},\n", + " '__start__': {'__start__': 1},\n", + " 'agent': {'start:agent': 2, 'tools': 4},\n", + " 'tools': {'branch:agent:should_continue:tools': 3}},\n", + " 'pending_sends': [],\n", + " 'current_tasks': {}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latest_checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "922f9406-0f68-418a-9cb4-e0e29de4b5f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-d2a8-6cdc-8003-cc4d1980d188'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:39.102711+00:00', 'id': '1ef566b2-d2a8-6cdc-8003-cc4d1980d188', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f4227353-e0e5-43a9-984a-e4b9e2d8e7b8'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd1d3187-470f-4ebd-938f-527a61824045-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='2d124101-696d-450f-bc9f-d8fdcc564101', tool_call_id='call_Y7PzHb7LrIdiTnO5UiSfelt3'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-87c76dd2-33f4-433e-986a-9405cfe88c88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-87c76dd2-33f4-433e-986a-9405cfe88c88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-cdf7-6b98-8002-997748cc5052'}}, pending_writes=[])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latest_checkpoint_tuple" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2ce743b-5896-443b-9ec0-a655b065895c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-d2a8-6cdc-8003-cc4d1980d188'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:39.102711+00:00', 'id': '1ef566b2-d2a8-6cdc-8003-cc4d1980d188', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f4227353-e0e5-43a9-984a-e4b9e2d8e7b8'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd1d3187-470f-4ebd-938f-527a61824045-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='2d124101-696d-450f-bc9f-d8fdcc564101', tool_call_id='call_Y7PzHb7LrIdiTnO5UiSfelt3'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-87c76dd2-33f4-433e-986a-9405cfe88c88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-87c76dd2-33f4-433e-986a-9405cfe88c88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-cdf7-6b98-8002-997748cc5052'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-cdf7-6b98-8002-997748cc5052'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:38.610752+00:00', 'id': '1ef566b2-cdf7-6b98-8002-997748cc5052', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f4227353-e0e5-43a9-984a-e4b9e2d8e7b8'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd1d3187-470f-4ebd-938f-527a61824045-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='2d124101-696d-450f-bc9f-d8fdcc564101', tool_call_id='call_Y7PzHb7LrIdiTnO5UiSfelt3')], 'tools': 'tools'}, 'channel_versions': {'__start__': 2, 'messages': 4, 'start:agent': 3, 'agent': 4, 'branch:agent:should_continue:tools': 4, 'tools': 4}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='2d124101-696d-450f-bc9f-d8fdcc564101', tool_call_id='call_Y7PzHb7LrIdiTnO5UiSfelt3')]}}, 'step': 2}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-cde3-6c60-8001-28d4cc36978d'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-cde3-6c60-8001-28d4cc36978d'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:38.602590+00:00', 'id': '1ef566b2-cde3-6c60-8001-28d4cc36978d', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f4227353-e0e5-43a9-984a-e4b9e2d8e7b8'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd1d3187-470f-4ebd-938f-527a61824045-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})], 'agent': 'agent', 'branch:agent:should_continue:tools': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 3, 'start:agent': 3, 'agent': 3, 'branch:agent:should_continue:tools': 3}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd1d3187-470f-4ebd-938f-527a61824045-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_Y7PzHb7LrIdiTnO5UiSfelt3', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}}, 'step': 1}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-c72c-6fca-8000-aac6e4f4b809'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-c72c-6fca-8000-aac6e4f4b809'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:37.898584+00:00', 'id': '1ef566b2-c72c-6fca-8000-aac6e4f4b809', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f4227353-e0e5-43a9-984a-e4b9e2d8e7b8')], 'start:agent': '__start__'}, 'channel_versions': {'__start__': 2, 'messages': 2, 'start:agent': 2}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': None, 'step': 0}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-c72a-6af4-bfff-919b9dc6abfe'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b2-c72a-6af4-bfff-919b9dc6abfe'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:37.897642+00:00', 'id': '1ef566b2-c72a-6af4-bfff-919b9dc6abfe', 'channel_values': {'messages': [], '__start__': {'messages': [['human', \"what's the weather in sf\"]]}}, 'channel_versions': {'__start__': 1}, 'versions_seen': {'__input__': {}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'input', 'writes': {'messages': [['human', \"what's the weather in sf\"]]}, 'step': -1}, parent_config=None, pending_writes=None)]" ] }, "execution_count": 9, @@ -908,92 +778,126 @@ } ], "source": [ - "await checkpointer.aget(config)" + "checkpoint_tuples" + ] + }, + { + "cell_type": "markdown", + "id": "c0a47d3e-e588-48fc-a5d4-2145dff17e77", + "metadata": {}, + "source": [ + "## Use async connection" ] }, { "cell_type": "code", "execution_count": 10, + "id": "6a39d1ff-ca37-4457-8b52-07d33b59c36e", + "metadata": {}, + "outputs": [], + "source": [ + "async with AsyncMongoDBSaver.from_conn_info(host=\"localhost\", port=27017, db_name=\"checkpoints\") as checkpointer:\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"2\"}}\n", + " res = await graph.ainvoke({\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config)\n", + "\n", + " latest_checkpoint = await checkpointer.aget(config)\n", + " latest_checkpoint_tuple = await checkpointer.aget_tuple(config)\n", + " checkpoint_tuples = [c async for c in checkpointer.alist(config)]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "51125ef1-bdb6-454e-82cc-4ae19a113606", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "CheckpointTuple(config={'configurable': {'thread_id': '123'}}, checkpoint={'v': 1, 'ts': '2024-07-10T11:34:28.485660+00:00', 'id': '1ef3eb05-e0d1-651b-8004-15f129f5f4fb', 'channel_values': {'__root__': 4, 'add_one': 'add_one'}, 'channel_versions': {'__start__': 5, '__root__': 6, 'start:add_one': 6, 'add_one': 6}, 'versions_seen': {'__start__': {'__start__': 4}, 'add_one': {'start:add_one': 5}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 4, 'writes': {'add_one': 4}}, parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1'}})" + "{'v': 1,\n", + " 'ts': '2024-08-09T16:19:48.212051+00:00',\n", + " 'id': '1ef566b3-2988-664c-8003-5974c59c6bda',\n", + " 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1ae4b12f-b1cb-4d55-a754-42cf1c2fbcd5'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b5da58b5-8f75-485d-af29-bfdeb09b0d94-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}),\n", + " ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='56d4e46b-6cb3-4efe-b369-27b666e62348', tool_call_id='call_IJvXEELx7Ir3kASCqr9dbvhU'),\n", + " AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-dcacbc70-b213-4ddc-ac08-c0d17b2766d8-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})],\n", + " 'agent': 'agent'},\n", + " 'channel_versions': {'__start__': 2,\n", + " 'messages': 5,\n", + " 'start:agent': 3,\n", + " 'agent': 5,\n", + " 'branch:agent:should_continue:tools': 4,\n", + " 'tools': 5},\n", + " 'versions_seen': {'__input__': {},\n", + " '__start__': {'__start__': 1},\n", + " 'agent': {'start:agent': 2, 'tools': 4},\n", + " 'tools': {'branch:agent:should_continue:tools': 3}},\n", + " 'pending_sends': [],\n", + " 'current_tasks': {}}" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "await checkpointer.aget_tuple(config)" + "latest_checkpoint" ] }, { "cell_type": "code", "execution_count": 12, + "id": "97f8a87b-8423-41c6-a76b-9a6b30904e73", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "CheckpointTuple(config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3eb05-e0d1-651b-8004-15f129f5f4fb'}}, checkpoint={'v': 1, 'ts': '2024-07-10T11:34:28.485660+00:00', 'id': '1ef3eb05-e0d1-651b-8004-15f129f5f4fb', 'channel_values': {'__root__': 4, 'add_one': 'add_one'}, 'channel_versions': {'__start__': 5, '__root__': 6, 'start:add_one': 6, 'add_one': 6}, 'versions_seen': {'__start__': {'__start__': 4}, 'add_one': {'start:add_one': 5}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 4, 'writes': {'add_one': 4}}, parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1'}})\n", - "CheckpointTuple(config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1'}}, checkpoint={'v': 1, 'ts': '2024-07-10T11:34:28.477660+00:00', 'id': '1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1', 'channel_values': {'__root__': 3, 'start:add_one': '__start__'}, 'channel_versions': {'__start__': 5, '__root__': 5, 'start:add_one': 5, 'add_one': 4}, 'versions_seen': {'__start__': {'__start__': 4}, 'add_one': {'start:add_one': 2}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 3, 'writes': None}, parent_config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3eb05-e0bb-659e-8002-de83b4764141'}})\n", - "CheckpointTuple(config={'configurable': {'thread_id': '123', 'thread_ts': '1ef3eb05-e0bb-659e-8002-de83b4764141'}}, checkpoint={'v': 1, 'ts': '2024-07-10T11:34:28.476662+00:00', 'id': '1ef3eb05-e0bb-659e-8002-de83b4764141', 'channel_values': {'__root__': 4, '__start__': 3}, 'channel_versions': {'__start__': 4, '__root__': 3, 'start:add_one': 3, 'add_one': 4}, 'versions_seen': {'__start__': {'__start__': 1}, 'add_one': {'start:add_one': 2}}, 'pending_sends': []}, metadata={'source': 'input', 'step': 2, 'writes': 3}, parent_config=None)\n" - ] + "data": { + "text/plain": [ + "CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-2988-664c-8003-5974c59c6bda'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:48.212051+00:00', 'id': '1ef566b3-2988-664c-8003-5974c59c6bda', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1ae4b12f-b1cb-4d55-a754-42cf1c2fbcd5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b5da58b5-8f75-485d-af29-bfdeb09b0d94-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='56d4e46b-6cb3-4efe-b369-27b666e62348', tool_call_id='call_IJvXEELx7Ir3kASCqr9dbvhU'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-dcacbc70-b213-4ddc-ac08-c0d17b2766d8-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-dcacbc70-b213-4ddc-ac08-c0d17b2766d8-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-23c9-64ea-8002-036c32979035'}}, pending_writes=[])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "list = checkpointer.alist(config, limit=3)\n", - "async for item in list:\n", - " print(item)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Checkpoints saved in MongoDB" + "latest_checkpoint_tuple" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, + "id": "2b6d73ca-519e-45f7-90c2-1b8596624505", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'_id': ObjectId('668e57930f55bbe62f358531'), 'thread_id': '123', 'thread_ts': '1ef3ea0c-18a5-67a6-bfff-0d85b77e4a09', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-10T09:42:43.453328+00:00\", \"id\": \"1ef3ea0c-18a5-67a6-bfff-0d85b77e4a09\", \"channel_values\": {\"__start__\": 3}, \"channel_versions\": {\"__start__\": 1}, \"versions_seen\": {}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"input\", \"step\": -1, \"writes\": 3}'}\n", - "{'_id': ObjectId('668e57930f55bbe62f358532'), 'thread_id': '123', 'thread_ts': '1ef3ea0c-18a7-6ea3-8000-9a52ba553d0c', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-10T09:42:43.454326+00:00\", \"id\": \"1ef3ea0c-18a7-6ea3-8000-9a52ba553d0c\", \"channel_values\": {\"__root__\": 3, \"start:add_one\": \"__start__\"}, \"channel_versions\": {\"__start__\": 2, \"__root__\": 2, \"start:add_one\": 2}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"add_one\": {}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 0, \"writes\": null}', 'parent_ts': '1ef3ea0c-18a5-67a6-bfff-0d85b77e4a09'}\n", - "{'_id': ObjectId('668e57930f55bbe62f358533'), 'thread_id': '123', 'thread_ts': '1ef3ea0c-18bc-6b54-8001-ef5781939492', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-10T09:42:43.462843+00:00\", \"id\": \"1ef3ea0c-18bc-6b54-8001-ef5781939492\", \"channel_values\": {\"__root__\": 4, \"add_one\": \"add_one\"}, \"channel_versions\": {\"__start__\": 2, \"__root__\": 3, \"start:add_one\": 3, \"add_one\": 3}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"add_one\": {\"start:add_one\": 2}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 1, \"writes\": {\"add_one\": 4}}', 'parent_ts': '1ef3ea0c-18a7-6ea3-8000-9a52ba553d0c'}\n", - "{'_id': ObjectId('668e71c4171972a41a226373'), 'thread_id': '123', 'thread_ts': '1ef3eb05-e0bb-659e-8002-de83b4764141', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-10T11:34:28.476662+00:00\", \"id\": \"1ef3eb05-e0bb-659e-8002-de83b4764141\", \"channel_values\": {\"__root__\": 4, \"__start__\": 3}, \"channel_versions\": {\"__start__\": 4, \"__root__\": 3, \"start:add_one\": 3, \"add_one\": 4}, \"versions_seen\": {\"__start__\": {\"__start__\": 1}, \"add_one\": {\"start:add_one\": 2}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"input\", \"step\": 2, \"writes\": 3}'}\n", - "{'_id': ObjectId('668e71c4171972a41a226374'), 'thread_id': '123', 'thread_ts': '1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-10T11:34:28.477660+00:00\", \"id\": \"1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1\", \"channel_values\": {\"__root__\": 3, \"start:add_one\": \"__start__\"}, \"channel_versions\": {\"__start__\": 5, \"__root__\": 5, \"start:add_one\": 5, \"add_one\": 4}, \"versions_seen\": {\"__start__\": {\"__start__\": 4}, \"add_one\": {\"start:add_one\": 2}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 3, \"writes\": null}', 'parent_ts': '1ef3eb05-e0bb-659e-8002-de83b4764141'}\n", - "{'_id': ObjectId('668e71c4171972a41a226375'), 'thread_id': '123', 'thread_ts': '1ef3eb05-e0d1-651b-8004-15f129f5f4fb', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-10T11:34:28.485660+00:00\", \"id\": \"1ef3eb05-e0d1-651b-8004-15f129f5f4fb\", \"channel_values\": {\"__root__\": 4, \"add_one\": \"add_one\"}, \"channel_versions\": {\"__start__\": 5, \"__root__\": 6, \"start:add_one\": 6, \"add_one\": 6}, \"versions_seen\": {\"__start__\": {\"__start__\": 4}, \"add_one\": {\"start:add_one\": 5}}, \"pending_sends\": []}', 'metadata': b'{\"source\": \"loop\", \"step\": 4, \"writes\": {\"add_one\": 4}}', 'parent_ts': '1ef3eb05-e0bd-6c9c-8003-aa9cb0fdedc1'}\n" - ] + "data": { + "text/plain": [ + "[CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-2988-664c-8003-5974c59c6bda'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:48.212051+00:00', 'id': '1ef566b3-2988-664c-8003-5974c59c6bda', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1ae4b12f-b1cb-4d55-a754-42cf1c2fbcd5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b5da58b5-8f75-485d-af29-bfdeb09b0d94-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='56d4e46b-6cb3-4efe-b369-27b666e62348', tool_call_id='call_IJvXEELx7Ir3kASCqr9dbvhU'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-dcacbc70-b213-4ddc-ac08-c0d17b2766d8-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-dcacbc70-b213-4ddc-ac08-c0d17b2766d8-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-23c9-64ea-8002-036c32979035'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-23c9-64ea-8002-036c32979035'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:47.609498+00:00', 'id': '1ef566b3-23c9-64ea-8002-036c32979035', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1ae4b12f-b1cb-4d55-a754-42cf1c2fbcd5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b5da58b5-8f75-485d-af29-bfdeb09b0d94-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='56d4e46b-6cb3-4efe-b369-27b666e62348', tool_call_id='call_IJvXEELx7Ir3kASCqr9dbvhU')], 'tools': 'tools'}, 'channel_versions': {'__start__': 2, 'messages': 4, 'start:agent': 3, 'agent': 4, 'branch:agent:should_continue:tools': 4, 'tools': 4}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='56d4e46b-6cb3-4efe-b369-27b666e62348', tool_call_id='call_IJvXEELx7Ir3kASCqr9dbvhU')]}}, 'step': 2}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-23b5-6de6-8001-a39c8ce6fd93'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-23b5-6de6-8001-a39c8ce6fd93'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:47.601527+00:00', 'id': '1ef566b3-23b5-6de6-8001-a39c8ce6fd93', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1ae4b12f-b1cb-4d55-a754-42cf1c2fbcd5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b5da58b5-8f75-485d-af29-bfdeb09b0d94-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})], 'agent': 'agent', 'branch:agent:should_continue:tools': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 3, 'start:agent': 3, 'agent': 3, 'branch:agent:should_continue:tools': 3}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}, 'agent': {'start:agent': 2}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b5da58b5-8f75-485d-af29-bfdeb09b0d94-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_IJvXEELx7Ir3kASCqr9dbvhU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})]}}, 'step': 1}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-1d6c-6a02-8000-158f156ffce3'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-1d6c-6a02-8000-158f156ffce3'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:46.942389+00:00', 'id': '1ef566b3-1d6c-6a02-8000-158f156ffce3', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1ae4b12f-b1cb-4d55-a754-42cf1c2fbcd5')], 'start:agent': '__start__'}, 'channel_versions': {'__start__': 2, 'messages': 2, 'start:agent': 2}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': 1}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': None, 'step': 0}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-1d67-61e2-bfff-d91abbcc3a09'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef566b3-1d67-61e2-bfff-d91abbcc3a09'}}, checkpoint={'v': 1, 'ts': '2024-08-09T16:19:46.940133+00:00', 'id': '1ef566b3-1d67-61e2-bfff-d91abbcc3a09', 'channel_values': {'messages': [], '__start__': {'messages': [['human', \"what's the weather in nyc\"]]}}, 'channel_versions': {'__start__': 1}, 'versions_seen': {'__input__': {}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'input', 'writes': {'messages': [['human', \"what's the weather in nyc\"]]}, 'step': -1}, parent_config=None, pending_writes=None)]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from pymongo import MongoClient\n", - "\n", - "client = MongoClient(MONGO_URI)\n", - "database = client[\"checkpoints_db\"]\n", - "collection = database[\"checkpoints_collection\"]\n", - "\n", - "for doc in collection.find():\n", - " print(doc)" + "checkpoint_tuples" ] } ], "metadata": { "kernelspec": { - "display_name": "myenv", + "display_name": "langgraph", "language": "python", - "name": "python3" + "name": "langgraph" }, "language_info": { "codemirror_mode": { @@ -1009,5 +913,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 5 } diff --git a/examples/persistence_postgres.ipynb b/examples/persistence_postgres.ipynb index 5874aa819..7b62defef 100644 --- a/examples/persistence_postgres.ipynb +++ b/examples/persistence_postgres.ipynb @@ -5,629 +5,11 @@ "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", "metadata": {}, "source": [ - "# How to create a custom checkpointer using Postgres\n", + "# How to use Postgres checkpointer for persistence\n", "\n", "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions.\n", "\n", - "This example shows how to use `Postgres` as the backend for persisting checkpoint state.\n", - "\n", - "NOTE: this is just an example implementation. You can implement your own checkpointer using a different database or modify this one as long as it conforms to the `BaseCheckpointSaver` interface." - ] - }, - { - "cell_type": "markdown", - "id": "ddc5cc5f-89fa-4d7a-9f2b-5b099feb6ecc", - "metadata": {}, - "source": [ - "## Checkpointer implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "faadfb1b-cebe-4dcf-82fd-34044c380bc4", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U psycopg psycopg-pool langgraph" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a35dba8e-5562-4803-ad80-160f53592dd7", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"Implementation of a langgraph checkpoint saver using Postgres.\"\"\"\n", - "from contextlib import asynccontextmanager, contextmanager\n", - "from typing import (\n", - " Any,\n", - " AsyncGenerator,\n", - " AsyncIterator,\n", - " Generator,\n", - " Optional,\n", - " Union,\n", - " Tuple,\n", - " List,\n", - " Sequence,\n", - ")\n", - "\n", - "import psycopg\n", - "from langchain_core.runnables import RunnableConfig\n", - "from langgraph.checkpoint import BaseCheckpointSaver\n", - "from langgraph.serde.jsonplus import JsonPlusSerializer\n", - "from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, CheckpointTuple\n", - "from psycopg_pool import AsyncConnectionPool, ConnectionPool\n", - "\n", - "\n", - "class JsonAndBinarySerializer(JsonPlusSerializer):\n", - " def _default(self, obj):\n", - " if isinstance(obj, (bytes, bytearray)):\n", - " return self._encode_constructor_args(\n", - " obj.__class__, method=\"fromhex\", args=[obj.hex()]\n", - " )\n", - " return super()._default(obj)\n", - "\n", - " def dumps(self, obj: Any) -> tuple[str, bytes]:\n", - " if isinstance(obj, bytes):\n", - " return \"bytes\", obj\n", - " elif isinstance(obj, bytearray):\n", - " return \"bytearray\", obj\n", - "\n", - " return \"json\", super().dumps(obj)\n", - "\n", - " def loads(self, s: tuple[str, bytes]) -> Any:\n", - " if s[0] == \"bytes\":\n", - " return s[1]\n", - " elif s[0] == \"bytearray\":\n", - " return bytearray(s[1])\n", - " elif s[0] == \"json\":\n", - " return super().loads(s[1])\n", - " else:\n", - " raise NotImplementedError(f\"Unknown serialization type: {s[0]}\")\n", - "\n", - "\n", - "@contextmanager\n", - "def _get_sync_connection(\n", - " connection: Union[psycopg.Connection, ConnectionPool, None],\n", - ") -> Generator[psycopg.Connection, None, None]:\n", - " \"\"\"Get the connection to the Postgres database.\"\"\"\n", - " if isinstance(connection, psycopg.Connection):\n", - " yield connection\n", - " elif isinstance(connection, ConnectionPool):\n", - " with connection.connection() as conn:\n", - " yield conn\n", - " else:\n", - " raise ValueError(\n", - " \"Invalid sync connection object. Please initialize the check pointer \"\n", - " f\"with an appropriate sync connection object. \"\n", - " f\"Got {type(connection)}.\"\n", - " )\n", - "\n", - "\n", - "@asynccontextmanager\n", - "async def _get_async_connection(\n", - " connection: Union[psycopg.AsyncConnection, AsyncConnectionPool, None],\n", - ") -> AsyncGenerator[psycopg.AsyncConnection, None]:\n", - " \"\"\"Get the connection to the Postgres database.\"\"\"\n", - " if isinstance(connection, psycopg.AsyncConnection):\n", - " yield connection\n", - " elif isinstance(connection, AsyncConnectionPool):\n", - " async with connection.connection() as conn:\n", - " yield conn\n", - " else:\n", - " raise ValueError(\n", - " \"Invalid async connection object. Please initialize the check pointer \"\n", - " f\"with an appropriate async connection object. \"\n", - " f\"Got {type(connection)}.\"\n", - " )\n", - "\n", - "\n", - "class PostgresSaver(BaseCheckpointSaver):\n", - " sync_connection: Optional[Union[psycopg.Connection, ConnectionPool]] = None\n", - " \"\"\"The synchronous connection or pool to the Postgres database.\n", - " \n", - " If providing a connection object, please ensure that the connection is open\n", - " and remember to close the connection when done.\n", - " \"\"\"\n", - " async_connection: Optional[\n", - " Union[psycopg.AsyncConnection, AsyncConnectionPool]\n", - " ] = None\n", - " \"\"\"The asynchronous connection or pool to the Postgres database.\n", - " \n", - " If providing a connection object, please ensure that the connection is open\n", - " and remember to close the connection when done.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " sync_connection: Optional[Union[psycopg.Connection, ConnectionPool]] = None,\n", - " async_connection: Optional[\n", - " Union[psycopg.AsyncConnection, AsyncConnectionPool]\n", - " ] = None,\n", - " ):\n", - " super().__init__(serde=JsonPlusSerializer())\n", - " self.sync_connection = sync_connection\n", - " self.async_connection = async_connection\n", - "\n", - " @contextmanager\n", - " def _get_sync_connection(self) -> Generator[psycopg.Connection, None, None]:\n", - " \"\"\"Get the connection to the Postgres database.\"\"\"\n", - " with _get_sync_connection(self.sync_connection) as connection:\n", - " yield connection\n", - "\n", - " @asynccontextmanager\n", - " async def _get_async_connection(\n", - " self,\n", - " ) -> AsyncGenerator[psycopg.AsyncConnection, None]:\n", - " \"\"\"Get the connection to the Postgres database.\"\"\"\n", - " async with _get_async_connection(self.async_connection) as connection:\n", - " yield connection\n", - "\n", - " CREATE_TABLES_QUERY = \"\"\"\n", - " CREATE TABLE IF NOT EXISTS checkpoints (\n", - " thread_id TEXT NOT NULL,\n", - " thread_ts TEXT NOT NULL,\n", - " parent_ts TEXT,\n", - " checkpoint BYTEA NOT NULL,\n", - " metadata BYTEA NOT NULL,\n", - " PRIMARY KEY (thread_id, thread_ts)\n", - " );\n", - " CREATE TABLE IF NOT EXISTS writes (\n", - " thread_id TEXT NOT NULL,\n", - " thread_ts TEXT NOT NULL,\n", - " task_id TEXT NOT NULL,\n", - " idx INTEGER NOT NULL,\n", - " channel TEXT NOT NULL,\n", - " value BYTEA,\n", - " PRIMARY KEY (thread_id, thread_ts, task_id, idx)\n", - " );\n", - " \"\"\"\n", - "\n", - " @staticmethod\n", - " def create_tables(connection: Union[psycopg.Connection, ConnectionPool], /) -> None:\n", - " \"\"\"Create the schema for the checkpoint saver.\"\"\"\n", - " with _get_sync_connection(connection) as conn:\n", - " with conn.cursor() as cur:\n", - " cur.execute(PostgresSaver.CREATE_TABLES_QUERY)\n", - "\n", - " @staticmethod\n", - " async def acreate_tables(\n", - " connection: Union[psycopg.AsyncConnection, AsyncConnectionPool], /\n", - " ) -> None:\n", - " \"\"\"Create the schema for the checkpoint saver.\"\"\"\n", - " async with _get_async_connection(connection) as conn:\n", - " async with conn.cursor() as cur:\n", - " await cur.execute(PostgresSaver.CREATE_TABLES_QUERY)\n", - "\n", - " @staticmethod\n", - " def drop_tables(connection: psycopg.Connection, /) -> None:\n", - " \"\"\"Drop the table for the checkpoint saver.\"\"\"\n", - " with connection.cursor() as cur:\n", - " cur.execute(\"DROP TABLE IF EXISTS checkpoints, writes;\")\n", - "\n", - " @staticmethod\n", - " async def adrop_tables(connection: psycopg.AsyncConnection, /) -> None:\n", - " \"\"\"Drop the table for the checkpoint saver.\"\"\"\n", - " async with connection.cursor() as cur:\n", - " await cur.execute(\"DROP TABLE IF EXISTS checkpoints, writes;\")\n", - "\n", - " UPSERT_CHECKPOINT_QUERY = \"\"\"\n", - " INSERT INTO checkpoints \n", - " (thread_id, thread_ts, parent_ts, checkpoint, metadata)\n", - " VALUES \n", - " (%s, %s, %s, %s, %s)\n", - " ON CONFLICT (thread_id, thread_ts)\n", - " DO UPDATE SET checkpoint = EXCLUDED.checkpoint,\n", - " metadata = EXCLUDED.metadata;\n", - " \"\"\"\n", - "\n", - " def put(\n", - " self,\n", - " config: RunnableConfig,\n", - " checkpoint: Checkpoint,\n", - " metadata: CheckpointMetadata,\n", - " ) -> RunnableConfig:\n", - " \"\"\"Put the checkpoint for the given configuration.\n", - " Args:\n", - " config: The configuration for the checkpoint.\n", - " A dict with a `configurable` key which is a dict with\n", - " a `thread_id` key and an optional `thread_ts` key.\n", - " For example, { 'configurable': { 'thread_id': 'test_thread' } }\n", - " checkpoint: The checkpoint to persist.\n", - " Returns:\n", - " The RunnableConfig that describes the checkpoint that was just created.\n", - " It'll contain the `thread_id` and `thread_ts` of the checkpoint.\n", - " \"\"\"\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " parent_ts = config[\"configurable\"].get(\"thread_ts\")\n", - " with self._get_sync_connection() as conn:\n", - " with conn.cursor() as cur:\n", - " cur.execute(\n", - " self.UPSERT_CHECKPOINT_QUERY,\n", - " (\n", - " thread_id,\n", - " checkpoint[\"id\"],\n", - " parent_ts if parent_ts else None,\n", - " self.serde.dumps(checkpoint),\n", - " self.serde.dumps(metadata),\n", - " ),\n", - " )\n", - "\n", - " return {\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": checkpoint[\"id\"],\n", - " },\n", - " }\n", - "\n", - " async def aput(\n", - " self,\n", - " config: RunnableConfig,\n", - " checkpoint: Checkpoint,\n", - " metadata: CheckpointMetadata,\n", - " ) -> RunnableConfig:\n", - " \"\"\"Put the checkpoint for the given configuration.\n", - " Args:\n", - " config: The configuration for the checkpoint.\n", - " A dict with a `configurable` key which is a dict with\n", - " a `thread_id` key and an optional `thread_ts` key.\n", - " For example, { 'configurable': { 'thread_id': 'test_thread' } }\n", - " checkpoint: The checkpoint to persist.\n", - " Returns:\n", - " The RunnableConfig that describes the checkpoint that was just created.\n", - " It'll contain the `thread_id` and `thread_ts` of the checkpoint.\n", - " \"\"\"\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " parent_ts = config[\"configurable\"].get(\"thread_ts\")\n", - " async with self._get_async_connection() as conn:\n", - " async with conn.cursor() as cur:\n", - " await cur.execute(\n", - " self.UPSERT_CHECKPOINT_QUERY,\n", - " (\n", - " thread_id,\n", - " checkpoint[\"id\"],\n", - " parent_ts if parent_ts else None,\n", - " self.serde.dumps(checkpoint),\n", - " self.serde.dumps(metadata),\n", - " ),\n", - " )\n", - "\n", - " return {\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": checkpoint[\"id\"],\n", - " },\n", - " }\n", - "\n", - " UPSERT_WRITES_QUERY = \"\"\"\n", - " INSERT INTO writes\n", - " (thread_id, thread_ts, task_id, idx, channel, value)\n", - " VALUES\n", - " (%s, %s, %s, %s, %s, %s)\n", - " ON CONFLICT (thread_id, thread_ts, task_id, idx)\n", - " DO UPDATE SET value = EXCLUDED.value;\n", - " \"\"\"\n", - "\n", - " def put_writes(\n", - " self,\n", - " config: RunnableConfig,\n", - " writes: Sequence[Tuple[str, Any]],\n", - " task_id: str,\n", - " ) -> None:\n", - " with self._get_sync_connection() as conn:\n", - " with conn.cursor() as cur:\n", - " cur.executemany(\n", - " self.UPSERT_WRITES_QUERY,\n", - " [\n", - " (\n", - " str(config[\"configurable\"][\"thread_id\"]),\n", - " str(config[\"configurable\"][\"thread_ts\"]),\n", - " task_id,\n", - " idx,\n", - " channel,\n", - " self.serde.dumps(value),\n", - " )\n", - " for idx, (channel, value) in enumerate(writes)\n", - " ],\n", - " )\n", - " conn.commit()\n", - "\n", - " async def aput_writes(\n", - " self,\n", - " config: RunnableConfig,\n", - " writes: Sequence[Tuple[str, Any]],\n", - " task_id: str,\n", - " ) -> None:\n", - " async with self._get_async_connection() as conn:\n", - " async with conn.cursor() as cur:\n", - " await cur.executemany(\n", - " self.UPSERT_WRITES_QUERY,\n", - " [\n", - " (\n", - " str(config[\"configurable\"][\"thread_id\"]),\n", - " str(config[\"configurable\"][\"thread_ts\"]),\n", - " task_id,\n", - " idx,\n", - " channel,\n", - " self.serde.dumps(value),\n", - " )\n", - " for idx, (channel, value) in enumerate(writes)\n", - " ],\n", - " )\n", - " await conn.commit()\n", - "\n", - " LIST_CHECKPOINTS_QUERY_STR = \"\"\"\n", - " SELECT checkpoint, metadata, thread_ts, parent_ts\n", - " FROM checkpoints\n", - " {where}\n", - " ORDER BY thread_ts DESC\n", - " \"\"\"\n", - "\n", - " def list(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " *,\n", - " filter: Optional[dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " limit: Optional[int] = None,\n", - " ) -> Generator[CheckpointTuple, None, None]:\n", - " \"\"\"Get all the checkpoints for the given configuration.\"\"\"\n", - " where, args = self._search_where(config, filter, before)\n", - " query = self.LIST_CHECKPOINTS_QUERY_STR.format(where=where)\n", - " if limit:\n", - " query += f\" LIMIT {limit}\"\n", - " with self._get_sync_connection() as conn:\n", - " with conn.cursor() as cur:\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " cur.execute(query, tuple(args))\n", - " for value in cur:\n", - " checkpoint, metadata, thread_ts, parent_ts = value\n", - " yield CheckpointTuple(\n", - " config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " },\n", - " checkpoint=self.serde.loads(checkpoint),\n", - " metadata=self.serde.loads(metadata),\n", - " parent_config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " }\n", - " if parent_ts\n", - " else None,\n", - " )\n", - "\n", - " async def alist(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " *,\n", - " filter: Optional[dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " limit: Optional[int] = None,\n", - " ) -> AsyncIterator[CheckpointTuple]:\n", - " \"\"\"Get all the checkpoints for the given configuration.\"\"\"\n", - " where, args = self._search_where(config, filter, before)\n", - " query = self.LIST_CHECKPOINTS_QUERY_STR.format(where=where)\n", - " if limit:\n", - " query += f\" LIMIT {limit}\"\n", - " async with self._get_async_connection() as conn:\n", - " async with conn.cursor() as cur:\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " await cur.execute(query, tuple(args))\n", - " async for value in cur:\n", - " checkpoint, metadata, thread_ts, parent_ts = value\n", - " yield CheckpointTuple(\n", - " config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " },\n", - " checkpoint=self.serde.loads(checkpoint),\n", - " metadata=self.serde.loads(metadata),\n", - " parent_config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " }\n", - " if parent_ts\n", - " else None,\n", - " )\n", - "\n", - " GET_CHECKPOINT_BY_TS_QUERY = \"\"\"\n", - " SELECT checkpoint, metadata, thread_ts, parent_ts\n", - " FROM checkpoints\n", - " WHERE thread_id = %(thread_id)s AND thread_ts = %(thread_ts)s\n", - " \"\"\"\n", - "\n", - " GET_CHECKPOINT_QUERY = \"\"\"\n", - " SELECT checkpoint, metadata, thread_ts, parent_ts\n", - " FROM checkpoints\n", - " WHERE thread_id = %(thread_id)s\n", - " ORDER BY thread_ts DESC LIMIT 1\n", - " \"\"\"\n", - "\n", - " def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", - " \"\"\"Get the checkpoint tuple for the given configuration.\n", - " Args:\n", - " config: The configuration for the checkpoint.\n", - " A dict with a `configurable` key which is a dict with\n", - " a `thread_id` key and an optional `thread_ts` key.\n", - " For example, { 'configurable': { 'thread_id': 'test_thread' } }\n", - " Returns:\n", - " The checkpoint tuple for the given configuration if it exists,\n", - " otherwise None.\n", - " If thread_ts is None, the latest checkpoint is returned if it exists.\n", - " \"\"\"\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " thread_ts = config[\"configurable\"].get(\"thread_ts\")\n", - " with self._get_sync_connection() as conn:\n", - " with conn.cursor() as cur:\n", - " # find the latest checkpoint for the thread_id\n", - " if thread_ts:\n", - " cur.execute(\n", - " self.GET_CHECKPOINT_BY_TS_QUERY,\n", - " {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " },\n", - " )\n", - " else:\n", - " cur.execute(\n", - " self.GET_CHECKPOINT_QUERY,\n", - " {\n", - " \"thread_id\": thread_id,\n", - " },\n", - " )\n", - "\n", - " # if a checkpoint is found, return it\n", - " if value := cur.fetchone():\n", - " checkpoint, metadata, thread_ts, parent_ts = value\n", - " if not config[\"configurable\"].get(\"thread_ts\"):\n", - " config = {\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " }\n", - "\n", - " # find any pending writes\n", - " cur.execute(\n", - " \"SELECT task_id, channel, value FROM writes WHERE thread_id = %(thread_id)s AND thread_ts = %(thread_ts)s\",\n", - " {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " },\n", - " )\n", - " # deserialize the checkpoint and metadata\n", - " return CheckpointTuple(\n", - " config=config,\n", - " checkpoint=self.serde.loads(checkpoint),\n", - " metadata=self.serde.loads(metadata),\n", - " parent_config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": parent_ts,\n", - " }\n", - " }\n", - " if parent_ts\n", - " else None,\n", - " pending_writes=[\n", - " (task_id, channel, self.serde.loads(value))\n", - " for task_id, channel, value in cur\n", - " ],\n", - " )\n", - "\n", - " async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", - " \"\"\"Get the checkpoint tuple for the given configuration.\n", - " Args:\n", - " config: The configuration for the checkpoint.\n", - " A dict with a `configurable` key which is a dict with\n", - " a `thread_id` key and an optional `thread_ts` key.\n", - " For example, { 'configurable': { 'thread_id': 'test_thread' } }\n", - " Returns:\n", - " The checkpoint tuple for the given configuration if it exists,\n", - " otherwise None.\n", - " If thread_ts is None, the latest checkpoint is returned if it exists.\n", - " \"\"\"\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " thread_ts = config[\"configurable\"].get(\"thread_ts\")\n", - " async with self._get_async_connection() as conn:\n", - " async with conn.cursor() as cur:\n", - " # find the latest checkpoint for the thread_id\n", - " if thread_ts:\n", - " await cur.execute(\n", - " self.GET_CHECKPOINT_BY_TS_QUERY,\n", - " {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " },\n", - " )\n", - " else:\n", - " await cur.execute(\n", - " self.GET_CHECKPOINT_QUERY,\n", - " {\n", - " \"thread_id\": thread_id,\n", - " },\n", - " )\n", - " # if a checkpoint is found, return it\n", - " if value := await cur.fetchone():\n", - " checkpoint, metadata, thread_ts, parent_ts = value\n", - " if not config[\"configurable\"].get(\"thread_ts\"):\n", - " config = {\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " }\n", - "\n", - " # find any pending writes\n", - " await cur.execute(\n", - " \"SELECT task_id, channel, value FROM writes WHERE thread_id = %(thread_id)s AND thread_ts = %(thread_ts)s\",\n", - " {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " },\n", - " )\n", - " # deserialize the checkpoint and metadata\n", - " return CheckpointTuple(\n", - " config=config,\n", - " checkpoint=self.serde.loads(checkpoint),\n", - " metadata=self.serde.loads(metadata),\n", - " parent_config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": parent_ts,\n", - " }\n", - " }\n", - " if parent_ts\n", - " else None,\n", - " pending_writes=[\n", - " (task_id, channel, self.serde.loads(value))\n", - " async for task_id, channel, value in cur\n", - " ],\n", - " )\n", - "\n", - " def _search_where(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " filter: Optional[dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " ) -> Tuple[str, List[Any]]:\n", - " \"\"\"Return WHERE clause predicates for given config, filter, and before parameters.\n", - " Args:\n", - " config (Optional[RunnableConfig]): The config to use for filtering.\n", - " filter (Optional[Dict[str, Any]]): Additional filtering criteria.\n", - " before (Optional[RunnableConfig]): A config to limit results before a certain timestamp.\n", - " Returns:\n", - " Tuple[str, Sequence[Any]]: A tuple containing the WHERE clause and parameter values.\n", - " \"\"\"\n", - " wheres = []\n", - " param_values = []\n", - "\n", - " # Add predicate for config\n", - " if config is not None:\n", - " wheres.append(\"thread_id = %s \")\n", - " param_values.append(config[\"configurable\"][\"thread_id\"])\n", - "\n", - " if filter:\n", - " raise NotImplementedError()\n", - "\n", - " # Add predicate for limiting results before a certain timestamp\n", - " if before is not None:\n", - " wheres.append(\"thread_ts < %s\")\n", - " param_values.append(before[\"configurable\"][\"thread_ts\"])\n", - "\n", - " where_clause = \"WHERE \" + \" AND \".join(wheres) if wheres else \"\"\n", - " return where_clause, param_values" + "This example shows how to use `Postgres` as the backend for persisting checkpoint state using [`langgraph-checkpoint-postgres`](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres) library." ] }, { @@ -640,12 +22,23 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, + "id": "faadfb1b-cebe-4dcf-82fd-34044c380bc4", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U psycopg psycopg-pool langgraph langgraph-checkpoint-postgres" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "eca9aafb-a155-407a-8036-682a2f1297d7", "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stdin", "output_type": "stream", "text": [ "OPENAI_API_KEY: ········\n" @@ -675,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", "metadata": {}, "outputs": [], @@ -685,6 +78,8 @@ "from langchain_core.tools import tool\n", "from langchain_openai import ChatOpenAI\n", "from langgraph.prebuilt import create_react_agent\n", + "from langgraph.checkpoint.postgres import PostgresSaver\n", + "from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver\n", "\n", "\n", "@tool\n", @@ -699,7 +94,7 @@ "\n", "\n", "tools = [get_weather]\n", - "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)" + "model = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0)" ] }, { @@ -712,12 +107,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "2b9d13b1-9d72-48a0-b63a-adc062c06c29", "metadata": {}, "outputs": [], "source": [ - "DB_URI = \"postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable\"" + "DB_URI = \"postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3fe36f67-073a-4fd7-a8f8-da196dd46a0d", + "metadata": {}, + "outputs": [], + "source": [ + "from psycopg.rows import dict_row\n", + "\n", + "connection_kwargs ={\n", + " \"autocommit\": True,\n", + " \"prepare_threshold\": 0,\n", + " \"row_factory\": dict_row,\n", + "}" ] }, { @@ -731,7 +142,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "2657c1c4-d8a5-4fe3-8f77-95415a98ed6c", + "id": "bd235fc7-1e5c-4db6-a90b-ea75462ccf7d", "metadata": {}, "outputs": [], "source": [ @@ -741,40 +152,37 @@ " # Example configuration\n", " conninfo=DB_URI,\n", " max_size=20,\n", + " kwargs=connection_kwargs\n", ")\n", "\n", - "checkpointer = PostgresSaver(sync_connection=pool)\n", - "checkpointer.create_tables(pool)" + "with pool.connection() as conn:\n", + " checkpointer = PostgresSaver(conn)\n", + "\n", + " # NOTE: you need to call .setup() the first time you're using your checkpointer\n", + " checkpointer.setup()\n", + "\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"1\"}}\n", + " res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n", + " checkpoint = checkpointer.get(config)" ] }, { "cell_type": "code", "execution_count": 7, - "id": "6d388241-de57-4b4e-af7b-eb1081fb8f36", - "metadata": {}, - "outputs": [], - "source": [ - "graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, "id": "a7e0e7ec-a675-470b-9270-e4bdc59d4a4d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='bc87fac7-1da1-4818-a43b-6ba7c9b9b3e4'),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_MjkmibJlXeuNchL6B8qpIjOW', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b9de0cab-f310-4f74-897e-97014072c001-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_MjkmibJlXeuNchL6B8qpIjOW', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", - " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='8d8f9596-a683-4644-a898-1e303b5a01ea', tool_call_id='call_MjkmibJlXeuNchL6B8qpIjOW'),\n", - " AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_dd932ca5d1', 'finish_reason': 'stop', 'logprobs': None}, id='run-4b5282a3-e7a6-42ee-ad0f-e6013a745a88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}" + "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='735b7deb-b0fe-4ad5-8920-2a3c69bbe9f7'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_lJHMDYgfgRdiEAGfFsEhqqKV', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c56b3e04-08a9-4a59-b3f5-ee52d0ef0656-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_lJHMDYgfgRdiEAGfFsEhqqKV', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", + " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='0644bf7b-4d1b-4ebe-afa1-d2169ccce582', tool_call_id='call_lJHMDYgfgRdiEAGfFsEhqqKV'),\n", + " AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-1ed9b8d0-9b50-4b87-b3a2-9860f51e9fd1-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -785,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "96efd8b2-97c9-4207-83b2-00131723a75a", "metadata": {}, "outputs": [ @@ -793,32 +201,35 @@ "data": { "text/plain": [ "{'v': 1,\n", - " 'ts': '2024-07-12T15:21:51.891852+00:00',\n", - " 'id': '1ef40627-6fb2-6962-8003-b74d816658c5',\n", - " 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='bc87fac7-1da1-4818-a43b-6ba7c9b9b3e4'),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_MjkmibJlXeuNchL6B8qpIjOW', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b9de0cab-f310-4f74-897e-97014072c001-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_MjkmibJlXeuNchL6B8qpIjOW', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", - " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='8d8f9596-a683-4644-a898-1e303b5a01ea', tool_call_id='call_MjkmibJlXeuNchL6B8qpIjOW'),\n", - " AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_dd932ca5d1', 'finish_reason': 'stop', 'logprobs': None}, id='run-4b5282a3-e7a6-42ee-ad0f-e6013a745a88-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})],\n", - " 'agent': 'agent'},\n", - " 'channel_versions': {'__start__': 2,\n", - " 'messages': 5,\n", - " 'start:agent': 3,\n", - " 'agent': 5,\n", - " 'branch:agent:should_continue:tools': 4,\n", - " 'tools': 5},\n", - " 'versions_seen': {'__start__': {'__start__': 1},\n", - " 'agent': {'start:agent': 3, 'tools': 4},\n", - " 'tools': {'branch:agent:should_continue:tools': 3}},\n", - " 'pending_sends': []}" + " 'id': '1ef559b7-3b19-6ce8-8003-18d0f60634be',\n", + " 'ts': '2024-08-08T15:32:42.108605+00:00',\n", + " 'current_tasks': {},\n", + " 'pending_sends': [],\n", + " 'versions_seen': {'agent': {'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8',\n", + " 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'},\n", + " 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'},\n", + " '__input__': {},\n", + " '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}},\n", + " 'channel_versions': {'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af',\n", + " 'tools': '00000000000000000000000000000005.',\n", + " 'messages': '00000000000000000000000000000005.b9adc75836c78af94af1d6811340dd13',\n", + " '__start__': '00000000000000000000000000000002.',\n", + " 'start:agent': '00000000000000000000000000000003.',\n", + " 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'},\n", + " 'channel_values': {'agent': 'agent',\n", + " 'messages': [HumanMessage(content=\"what's the weather in sf\", id='735b7deb-b0fe-4ad5-8920-2a3c69bbe9f7'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_lJHMDYgfgRdiEAGfFsEhqqKV', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-c56b3e04-08a9-4a59-b3f5-ee52d0ef0656-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_lJHMDYgfgRdiEAGfFsEhqqKV', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", + " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='0644bf7b-4d1b-4ebe-afa1-d2169ccce582', tool_call_id='call_lJHMDYgfgRdiEAGfFsEhqqKV'),\n", + " AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-1ed9b8d0-9b50-4b87-b3a2-9860f51e9fd1-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "checkpointer.get(config)" + "checkpoint" ] }, { @@ -831,16 +242,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "180d6daf-8fa7-4608-bd2e-bfbf44ed5836", "metadata": {}, "outputs": [], "source": [ "from psycopg import Connection\n", "\n", - "with Connection.connect(DB_URI) as conn:\n", - " checkpointer = PostgresSaver(sync_connection=conn)\n", "\n", + "with Connection.connect(DB_URI, **connection_kwargs) as conn:\n", + " checkpointer = PostgresSaver(conn)\n", " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", " config = {\"configurable\": {\"thread_id\": \"2\"}}\n", " res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n", @@ -850,17 +261,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "613d0bbc-0e38-45c4-aace-1f6f7ae27c7b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "CheckpointTuple(config={'configurable': {'thread_id': '2', 'thread_ts': '1ef40627-7d58-6422-8003-de6e83a8c293'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:53.322868+00:00', 'id': '1ef40627-7d58-6422-8003-de6e83a8c293', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='8d0209ed-a8c2-42ae-8e77-cc71a9cca29d'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_BO4zHHp0JkEWtrtaEqFHkDjK', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-1f76b350-6a33-4de7-9276-59725b1ac101-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_BO4zHHp0JkEWtrtaEqFHkDjK', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='c1bb1a24-62a8-4b43-b90e-b00899c112a8', tool_call_id='call_BO4zHHp0JkEWtrtaEqFHkDjK'), AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_dd932ca5d1', 'finish_reason': 'stop', 'logprobs': None}, id='run-7576d437-4938-48b9-b2cf-e4809d92742d-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {'start:agent': 3, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 3, 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_dd932ca5d1', 'finish_reason': 'stop', 'logprobs': None}, id='run-7576d437-4938-48b9-b2cf-e4809d92742d-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}}, parent_config={'configurable': {'thread_id': '2', 'thread_ts': '1ef40627-775a-6746-8002-a3967bf0eae6'}}, pending_writes=[])" + "CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-4650-6bfc-8003-1c5488f19318'}}, checkpoint={'v': 1, 'id': '1ef559b7-4650-6bfc-8003-1c5488f19318', 'ts': '2024-08-08T15:32:43.284551+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'channel_versions': {'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'tools': '00000000000000000000000000000005.', 'messages': '00000000000000000000000000000005.af9f229d2c4e14f4866eb37f72ec39f6', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'}, 'channel_values': {'agent': 'agent', 'messages': [HumanMessage(content=\"what's the weather in sf\", id='7a14f96c-2d88-454f-9520-0e0287a4abbb'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_NcL4dBTYu4kSPGMKdxztdpjN', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-39adbf2c-36ef-40f6-9cad-8e1f8167fc19-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_NcL4dBTYu4kSPGMKdxztdpjN', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='c9f82354-3225-40a8-bf54-81f3e199043b', tool_call_id='call_NcL4dBTYu4kSPGMKdxztdpjN'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-83888be3-d681-42ca-ad67-e2f5ee8550de-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, metadata={'step': 3, 'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'logprobs': None, 'model_name': 'gpt-4o-mini-2024-07-18', 'token_usage': {'total_tokens': 94, 'prompt_tokens': 84, 'completion_tokens': 10}, 'finish_reason': 'stop', 'system_fingerprint': 'fp_48196bc67a'}, id='run-83888be3-d681-42ca-ad67-e2f5ee8550de-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-4087-681a-8002-88a5738f76f1'}}, pending_writes=[])" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -869,6 +280,56 @@ "checkpoint_tuple" ] }, + { + "cell_type": "markdown", + "id": "49fb52fd-af31-4603-889d-66d783244bce", + "metadata": {}, + "source": [ + "### With a connection string" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5fe54e79-9eaf-44e2-b2d9-1e0284b984d0", + "metadata": {}, + "outputs": [], + "source": [ + "from psycopg import Connection\n", + "\n", + "with PostgresSaver.from_conn_string(DB_URI) as checkpointer:\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"3\"}}\n", + " res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n", + "\n", + " checkpoint_tuples = list(checkpointer.list(config))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b2ce743b-5896-443b-9ec0-a655b065895c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CheckpointTuple(config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-5024-6476-8003-cf0a750e6b37'}}, checkpoint={'v': 1, 'id': '1ef559b7-5024-6476-8003-cf0a750e6b37', 'ts': '2024-08-08T15:32:44.314900+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'channel_versions': {'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'tools': '00000000000000000000000000000005.', 'messages': '00000000000000000000000000000005.3f8b8d9923575b911e17157008ab75ac', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'}, 'channel_values': {'agent': 'agent', 'messages': [HumanMessage(content=\"what's the weather in sf\", id='5bf79d15-6332-4bf5-89bd-ee192b31ed84'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2958adc7-f6a4-415d-ade1-5ee77e0b9276-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='cac4f90a-dc3e-4bfa-940f-1c630289a583', tool_call_id='call_9y3q1BiwW7zGh2gk2faInTRk'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-97d3fb7a-3d2e-4090-84f4-dafdfe44553f-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, metadata={'step': 3, 'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'logprobs': None, 'model_name': 'gpt-4o-mini-2024-07-18', 'token_usage': {'total_tokens': 94, 'prompt_tokens': 84, 'completion_tokens': 10}, 'finish_reason': 'stop', 'system_fingerprint': 'fp_48196bc67a'}, id='run-97d3fb7a-3d2e-4090-84f4-dafdfe44553f-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}}, parent_config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-4b3d-6430-8002-b5c99d2eb4db'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-4b3d-6430-8002-b5c99d2eb4db'}}, checkpoint={'v': 1, 'id': '1ef559b7-4b3d-6430-8002-b5c99d2eb4db', 'ts': '2024-08-08T15:32:43.800857+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'channel_versions': {'agent': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8', 'messages': '00000000000000000000000000000004.1195f50946feaedb0bae1fdbfadc806b', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'}, 'channel_values': {'tools': 'tools', 'messages': [HumanMessage(content=\"what's the weather in sf\", id='5bf79d15-6332-4bf5-89bd-ee192b31ed84'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2958adc7-f6a4-415d-ade1-5ee77e0b9276-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='cac4f90a-dc3e-4bfa-940f-1c630289a583', tool_call_id='call_9y3q1BiwW7zGh2gk2faInTRk')]}}, metadata={'step': 2, 'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='cac4f90a-dc3e-4bfa-940f-1c630289a583', tool_call_id='call_9y3q1BiwW7zGh2gk2faInTRk')]}}}, parent_config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-4b30-6078-8001-eaf8c9bd8844'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-4b30-6078-8001-eaf8c9bd8844'}}, checkpoint={'v': 1, 'id': '1ef559b7-4b30-6078-8001-eaf8c9bd8844', 'ts': '2024-08-08T15:32:43.795440+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'channel_versions': {'agent': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af', 'messages': '00000000000000000000000000000003.bab5fb3a70876f600f5f2fd46945ce5f', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, 'channel_values': {'agent': 'agent', 'messages': [HumanMessage(content=\"what's the weather in sf\", id='5bf79d15-6332-4bf5-89bd-ee192b31ed84'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_507c9469a1', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2958adc7-f6a4-415d-ade1-5ee77e0b9276-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})], 'branch:agent:should_continue:tools': 'agent'}}, metadata={'step': 1, 'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': '{\"city\":\"sf\"}'}}]}, response_metadata={'logprobs': None, 'model_name': 'gpt-4o-mini-2024-07-18', 'token_usage': {'total_tokens': 71, 'prompt_tokens': 57, 'completion_tokens': 14}, 'finish_reason': 'tool_calls', 'system_fingerprint': 'fp_507c9469a1'}, id='run-2958adc7-f6a4-415d-ade1-5ee77e0b9276-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_9y3q1BiwW7zGh2gk2faInTRk', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}}}, parent_config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-46d7-6116-8000-8976b7c89a2f'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-46d7-6116-8000-8976b7c89a2f'}}, checkpoint={'v': 1, 'id': '1ef559b7-46d7-6116-8000-8976b7c89a2f', 'ts': '2024-08-08T15:32:43.339573+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'channel_versions': {'messages': '00000000000000000000000000000002.ba0c90d32863686481f7fe5eab9ecdf0', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='5bf79d15-6332-4bf5-89bd-ee192b31ed84')], 'start:agent': '__start__'}}, metadata={'step': 0, 'source': 'loop', 'writes': None}, parent_config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-46ce-6c64-bfff-ef7fe2663573'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '3', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-46ce-6c64-bfff-ef7fe2663573'}}, checkpoint={'v': 1, 'id': '1ef559b7-46ce-6c64-bfff-ef7fe2663573', 'ts': '2024-08-08T15:32:43.336188+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'__input__': {}}, 'channel_versions': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'channel_values': {'__start__': {'messages': [['human', \"what's the weather in sf\"]]}}}, metadata={'step': -1, 'source': 'input', 'writes': {'messages': [['human', \"what's the weather in sf\"]]}}, parent_config=None, pending_writes=None)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_tuples" + ] + }, { "cell_type": "markdown", "id": "c0a47d3e-e588-48fc-a5d4-2145dff17e77", @@ -887,8 +348,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "20cea8b7-8f13-4dc7-a3c9-825040eb4c57", + "execution_count": 13, + "id": "4faf6087-73cc-4957-9a4f-f3509a32a740", "metadata": {}, "outputs": [ { @@ -907,49 +368,105 @@ " # Example configuration\n", " conninfo=DB_URI,\n", " max_size=20,\n", + " kwargs=connection_kwargs\n", ")\n", "\n", - "checkpointer = PostgresSaver(async_connection=pool)\n", - "await checkpointer.acreate_tables(pool)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f889dce6-7ec1-4277-b8af-ace7811733fa", - "metadata": {}, - "outputs": [], - "source": [ - "graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"3\"}}\n", - "res = await graph.ainvoke(\n", - " {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n", - ")" + "async with pool.connection() as conn:\n", + " checkpointer = AsyncPostgresSaver(conn)\n", + "\n", + " # NOTE: you need to call .setup() the first time you're using your checkpointer\n", + " # await checkpointer.setup()\n", + "\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"4\"}}\n", + " res = await graph.ainvoke(\n", + " {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n", + " )\n", + "\n", + " checkpoint = await checkpointer.aget(config)" ] }, { "cell_type": "code", "execution_count": 14, - "id": "ed58c722-1662-4ae2-9bb7-4872158a5b29", - "metadata": {}, - "outputs": [], - "source": [ - "checkpoint_tuple = await checkpointer.aget_tuple(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, "id": "e0c42044-4de6-4742-8e00-fe295d50c95a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "CheckpointTuple(config={'configurable': {'thread_id': '3', 'thread_ts': '1ef40627-8b0e-6b02-8003-68a7a04ea6a5'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:54.760751+00:00', 'id': '1ef40627-8b0e-6b02-8003-68a7a04ea6a5', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='108ac72d-f658-4ae0-af57-af481adc8aa5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_XY5TyZEwF5nbdNTWjjiqGtdS', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-454e2142-6f18-4676-ac4b-91f89ea7a6d4-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_XY5TyZEwF5nbdNTWjjiqGtdS', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='2d86514f-b8f0-439b-ab94-68c731309c63', tool_call_id='call_XY5TyZEwF5nbdNTWjjiqGtdS'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'stop', 'logprobs': None}, id='run-90ae3622-b480-4964-b689-9c1a572112f1-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {'start:agent': 3, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 3, 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'stop', 'logprobs': None}, id='run-90ae3622-b480-4964-b689-9c1a572112f1-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}}, parent_config={'configurable': {'thread_id': '3', 'thread_ts': '1ef40627-860c-63d4-8002-49a92ae87052'}}, pending_writes=[])" + "{'v': 1,\n", + " 'id': '1ef559b7-5cc9-6460-8003-8655824c0944',\n", + " 'ts': '2024-08-08T15:32:45.640793+00:00',\n", + " 'current_tasks': {},\n", + " 'pending_sends': [],\n", + " 'versions_seen': {'agent': {'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8',\n", + " 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'},\n", + " 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'},\n", + " '__input__': {},\n", + " '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}},\n", + " 'channel_versions': {'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af',\n", + " 'tools': '00000000000000000000000000000005.',\n", + " 'messages': '00000000000000000000000000000005.d869fc7231619df0db74feed624efe41',\n", + " '__start__': '00000000000000000000000000000002.',\n", + " 'start:agent': '00000000000000000000000000000003.',\n", + " 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'},\n", + " 'channel_values': {'agent': 'agent',\n", + " 'messages': [HumanMessage(content=\"what's the weather in nyc\", id='d883b8a0-99de-486d-91a2-bcfa7f25dc05'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_H6TAYfyd6AnaCrkQGs6Q2fVp', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-6f542f84-ad73-444c-8ef7-b5ea75a2e09b-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_H6TAYfyd6AnaCrkQGs6Q2fVp', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}),\n", + " ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='c0e52254-77a4-4ea9-a2b7-61dd2d65ec68', tool_call_id='call_H6TAYfyd6AnaCrkQGs6Q2fVp'),\n", + " AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-977140d4-7582-40c3-b2b6-31b542c430a3-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}" ] }, - "execution_count": 15, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint" + ] + }, + { + "cell_type": "markdown", + "id": "56552584-9eb8-40df-a6a0-44151018b509", + "metadata": {}, + "source": [ + "### With a connection" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "386b78bc-2f73-49ba-a2a4-47bce6fc49b7", + "metadata": {}, + "outputs": [], + "source": [ + "from psycopg import AsyncConnection\n", + "\n", + "async with await AsyncConnection.connect(DB_URI, **connection_kwargs) as conn:\n", + " checkpointer = AsyncPostgresSaver(conn)\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"5\"}}\n", + " res = await graph.ainvoke(\n", + " {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n", + " )\n", + " checkpoint_tuple = await checkpointer.aget_tuple(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d1ed1344-c923-4a46-b04e-cc3646737d48", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CheckpointTuple(config={'configurable': {'thread_id': '5', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-65b4-60ca-8003-1ef4b620559a'}}, checkpoint={'v': 1, 'id': '1ef559b7-65b4-60ca-8003-1ef4b620559a', 'ts': '2024-08-08T15:32:46.575814+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}}, 'channel_versions': {'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'tools': '00000000000000000000000000000005.', 'messages': '00000000000000000000000000000005.1557a6006d58f736d5cb2dd5c5f10111', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'}, 'channel_values': {'agent': 'agent', 'messages': [HumanMessage(content=\"what's the weather in nyc\", id='935e7732-b288-49bd-9ec2-1f7610cc38cb'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_94KtjtPmsiaj7T8yXvL7Ef31', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-790c929a-7982-49e7-af67-2cbe4a86373b-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_94KtjtPmsiaj7T8yXvL7Ef31', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='b2dc1073-abc4-4492-8982-434a7e32e445', tool_call_id='call_94KtjtPmsiaj7T8yXvL7Ef31'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-7e8a7f16-d8e1-457a-89f3-192102396449-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, metadata={'step': 3, 'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'logprobs': None, 'model_name': 'gpt-4o-mini-2024-07-18', 'token_usage': {'total_tokens': 97, 'prompt_tokens': 88, 'completion_tokens': 9}, 'finish_reason': 'stop', 'system_fingerprint': 'fp_48196bc67a'}, id='run-7e8a7f16-d8e1-457a-89f3-192102396449-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}}, parent_config={'configurable': {'thread_id': '5', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-62ae-6128-8002-c04af82bcd41'}}, pending_writes=[])" + ] + }, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -960,25 +477,22 @@ }, { "cell_type": "markdown", - "id": "56552584-9eb8-40df-a6a0-44151018b509", + "id": "2f7e486a-3e63-41d7-b84b-6743f0a5764c", "metadata": {}, "source": [ - "### Use connection" + "### With a connection string" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "386b78bc-2f73-49ba-a2a4-47bce6fc49b7", + "execution_count": 17, + "id": "6a39d1ff-ca37-4457-8b52-07d33b59c36e", "metadata": {}, "outputs": [], "source": [ - "from psycopg import AsyncConnection\n", - "\n", - "async with await AsyncConnection.connect(DB_URI) as conn:\n", - " checkpointer = PostgresSaver(async_connection=conn)\n", + "async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:\n", " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - " config = {\"configurable\": {\"thread_id\": \"4\"}}\n", + " config = {\"configurable\": {\"thread_id\": \"6\"}}\n", " res = await graph.ainvoke(\n", " {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n", " )\n", @@ -987,21 +501,21 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "d1ed1344-c923-4a46-b04e-cc3646737d48", + "execution_count": 18, + "id": "2b6d73ca-519e-45f7-90c2-1b8596624505", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[CheckpointTuple(config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-96b9-6682-8003-134aebfec1e9'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:55.984109+00:00', 'id': '1ef40627-96b9-6682-8003-134aebfec1e9', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1c1e48ba-fa25-4190-a847-459828f44579'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-07c7ee03-64f7-462a-9249-615572156216-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='50e612d7-c770-44dd-b128-4bfdbd7d5b7d', tool_call_id='call_pS4ybOXkIDOmS93jZ8wOYGfU'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'stop', 'logprobs': None}, id='run-924e10c9-7005-4cbf-a92e-3ce63b54092f-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 5, 'start:agent': 3, 'agent': 5, 'branch:agent:should_continue:tools': 4, 'tools': 5}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {'start:agent': 3, 'tools': 4}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 3, 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'stop', 'logprobs': None}, id='run-924e10c9-7005-4cbf-a92e-3ce63b54092f-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}}, parent_config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-96b9-6682-8003-134aebfec1e9'}}, pending_writes=None),\n", - " CheckpointTuple(config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-91a0-6100-8002-e404dda477d4'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:55.449447+00:00', 'id': '1ef40627-91a0-6100-8002-e404dda477d4', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1c1e48ba-fa25-4190-a847-459828f44579'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-07c7ee03-64f7-462a-9249-615572156216-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='50e612d7-c770-44dd-b128-4bfdbd7d5b7d', tool_call_id='call_pS4ybOXkIDOmS93jZ8wOYGfU')], 'tools': 'tools'}, 'channel_versions': {'__start__': 2, 'messages': 4, 'start:agent': 3, 'agent': 4, 'branch:agent:should_continue:tools': 4, 'tools': 4}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {'start:agent': 2}, 'tools': {'branch:agent:should_continue:tools': 3}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 2, 'writes': {'tools': {'messages': [ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='50e612d7-c770-44dd-b128-4bfdbd7d5b7d', tool_call_id='call_pS4ybOXkIDOmS93jZ8wOYGfU')]}}}, parent_config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-91a0-6100-8002-e404dda477d4'}}, pending_writes=None),\n", - " CheckpointTuple(config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-9194-66de-8001-86c8d77c2d7c'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:55.444687+00:00', 'id': '1ef40627-9194-66de-8001-86c8d77c2d7c', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1c1e48ba-fa25-4190-a847-459828f44579'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-07c7ee03-64f7-462a-9249-615572156216-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})], 'agent': 'agent', 'branch:agent:should_continue:tools': 'agent'}, 'channel_versions': {'__start__': 2, 'messages': 3, 'start:agent': 3, 'agent': 3, 'branch:agent:should_continue:tools': 3}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {'start:agent': 2}, 'tools': {}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 1, 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d33f7b429e', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-07c7ee03-64f7-462a-9249-615572156216-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_pS4ybOXkIDOmS93jZ8wOYGfU', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})]}}}, parent_config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-9194-66de-8001-86c8d77c2d7c'}}, pending_writes=None),\n", - " CheckpointTuple(config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-8b8a-6b1c-8000-55b423aa733b'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:54.811566+00:00', 'id': '1ef40627-8b8a-6b1c-8000-55b423aa733b', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='1c1e48ba-fa25-4190-a847-459828f44579')], 'start:agent': '__start__'}, 'channel_versions': {'__start__': 2, 'messages': 2, 'start:agent': 2}, 'versions_seen': {'__start__': {'__start__': 1}, 'agent': {}, 'tools': {}}, 'pending_sends': []}, metadata={'source': 'loop', 'step': 0, 'writes': None}, parent_config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-8b8a-6b1c-8000-55b423aa733b'}}, pending_writes=None),\n", - " CheckpointTuple(config={'configurable': {'thread_id': '4', 'thread_ts': '1ef40627-8b88-62b8-bfff-9922bbf9342b'}}, checkpoint={'v': 1, 'ts': '2024-07-12T15:21:54.810527+00:00', 'id': '1ef40627-8b88-62b8-bfff-9922bbf9342b', 'channel_values': {'messages': [], '__start__': {'messages': [['human', \"what's the weather in nyc\"]]}}, 'channel_versions': {'__start__': 1}, 'versions_seen': {}, 'pending_sends': []}, metadata={'source': 'input', 'step': -1, 'writes': {'messages': [['human', \"what's the weather in nyc\"]]}}, parent_config=None, pending_writes=None)]" + "[CheckpointTuple(config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-723c-67de-8003-63bd4eab35af'}}, checkpoint={'v': 1, 'id': '1ef559b7-723c-67de-8003-63bd4eab35af', 'ts': '2024-08-08T15:32:47.890003+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}}, 'channel_versions': {'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'tools': '00000000000000000000000000000005.', 'messages': '00000000000000000000000000000005.b6fe2a26011590cfe8fd6a39151a9e92', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'}, 'channel_values': {'agent': 'agent', 'messages': [HumanMessage(content=\"what's the weather in nyc\", id='977ddb90-9991-44cb-9f73-361c6dd21396'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-47b10c48-4db3-46d8-b4fa-e021818e01c5-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='798c520f-4f9a-4f6d-a389-da721eb4d4ce', tool_call_id='call_QIFCuh4zfP9owpjToycJiZf7'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-4a34e05d-8bcf-41ad-adc3-715919fde64c-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, metadata={'step': 3, 'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'logprobs': None, 'model_name': 'gpt-4o-mini-2024-07-18', 'token_usage': {'total_tokens': 97, 'prompt_tokens': 88, 'completion_tokens': 9}, 'finish_reason': 'stop', 'system_fingerprint': 'fp_48196bc67a'}, id='run-4a34e05d-8bcf-41ad-adc3-715919fde64c-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}}, parent_config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-6bf5-63c6-8002-ed990dbbc96e'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-6bf5-63c6-8002-ed990dbbc96e'}}, checkpoint={'v': 1, 'id': '1ef559b7-6bf5-63c6-8002-ed990dbbc96e', 'ts': '2024-08-08T15:32:47.231667+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}}, 'channel_versions': {'agent': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8', 'messages': '00000000000000000000000000000004.c9074f2a41f05486b5efb86353dc75c0', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.'}, 'channel_values': {'tools': 'tools', 'messages': [HumanMessage(content=\"what's the weather in nyc\", id='977ddb90-9991-44cb-9f73-361c6dd21396'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-47b10c48-4db3-46d8-b4fa-e021818e01c5-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='798c520f-4f9a-4f6d-a389-da721eb4d4ce', tool_call_id='call_QIFCuh4zfP9owpjToycJiZf7')]}}, metadata={'step': 2, 'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='798c520f-4f9a-4f6d-a389-da721eb4d4ce', tool_call_id='call_QIFCuh4zfP9owpjToycJiZf7')]}}}, parent_config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-6be0-6926-8001-1a8ce73baf9e'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-6be0-6926-8001-1a8ce73baf9e'}}, checkpoint={'v': 1, 'id': '1ef559b7-6be0-6926-8001-1a8ce73baf9e', 'ts': '2024-08-08T15:32:47.223198+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, '__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}}, 'channel_versions': {'agent': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af', 'messages': '00000000000000000000000000000003.097b5407d709b297591f1ef5d50c8368', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000003.', 'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, 'channel_values': {'agent': 'agent', 'messages': [HumanMessage(content=\"what's the weather in nyc\", id='977ddb90-9991-44cb-9f73-361c6dd21396'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-47b10c48-4db3-46d8-b4fa-e021818e01c5-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})], 'branch:agent:should_continue:tools': 'agent'}}, metadata={'step': 1, 'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'type': 'function', 'function': {'name': 'get_weather', 'arguments': '{\"city\":\"nyc\"}'}}]}, response_metadata={'logprobs': None, 'model_name': 'gpt-4o-mini-2024-07-18', 'token_usage': {'total_tokens': 73, 'prompt_tokens': 58, 'completion_tokens': 15}, 'finish_reason': 'tool_calls', 'system_fingerprint': 'fp_48196bc67a'}, id='run-47b10c48-4db3-46d8-b4fa-e021818e01c5-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_QIFCuh4zfP9owpjToycJiZf7', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})]}}}, parent_config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-663d-60b4-8000-10a8922bffbf'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-663d-60b4-8000-10a8922bffbf'}}, checkpoint={'v': 1, 'id': '1ef559b7-663d-60b4-8000-10a8922bffbf', 'ts': '2024-08-08T15:32:46.631935+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}}, 'channel_versions': {'messages': '00000000000000000000000000000002.2a79db8da664e437bdb25ea804457ca7', '__start__': '00000000000000000000000000000002.', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='977ddb90-9991-44cb-9f73-361c6dd21396')], 'start:agent': '__start__'}}, metadata={'step': 0, 'source': 'loop', 'writes': None}, parent_config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-6637-6d4e-bfff-6cecf690c3cb'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '6', 'checkpoint_ns': '', 'checkpoint_id': '1ef559b7-6637-6d4e-bfff-6cecf690c3cb'}}, checkpoint={'v': 1, 'id': '1ef559b7-6637-6d4e-bfff-6cecf690c3cb', 'ts': '2024-08-08T15:32:46.629806+00:00', 'current_tasks': {}, 'pending_sends': [], 'versions_seen': {'__input__': {}}, 'channel_versions': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'channel_values': {'__start__': {'messages': [['human', \"what's the weather in nyc\"]]}}}, metadata={'step': -1, 'source': 'input', 'writes': {'messages': [['human', \"what's the weather in nyc\"]]}}, parent_config=None, pending_writes=None)]" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1009,14 +523,6 @@ "source": [ "checkpoint_tuples" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a39d1ff-ca37-4457-8b52-07d33b59c36e", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/persistence_redis.ipynb b/examples/persistence_redis.ipynb index e42963f21..3b2ad1170 100644 --- a/examples/persistence_redis.ipynb +++ b/examples/persistence_redis.ipynb @@ -7,19 +7,19 @@ "source": [ "# How to create a custom checkpointer using Redis\n", "\n", - "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions. Make sure that you have Redis running on port `6379` for going through this tutorial\n", + "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions.\n", "\n", - "This example shows how to use `Redis` as the backend for persisting checkpoint state.\n", + "This reference implementation shows how to use Redis as the backend for persisting checkpoint state. Make sure that you have Redis running on port `6379` for going through this guide.\n", "\n", - "NOTE: this is just an example implementation. You can implement your own checkpointer using a different database or modify this one as long as it conforms to the `BaseCheckpointSaver` interface." + "NOTE: this is just an reference implementation. You can implement your own checkpointer using a different database or modify this one as long as it conforms to the `BaseCheckpointSaver` interface." ] }, { "cell_type": "markdown", - "id": "0aac2830", + "id": "456fa19c-93a5-4750-a410-f2d810b964ad", "metadata": {}, "source": [ - "## Install the necessary libraries for Redis on Python" + "## Setup environment" ] }, { @@ -33,420 +33,20 @@ "%pip install -U redis langgraph" ] }, - { - "cell_type": "markdown", - "id": "a6a4e417", - "metadata": {}, - "source": [ - "## Checkpointer implementation" - ] - }, { "cell_type": "code", "execution_count": 2, - "id": "a35dba8e-5562-4803-ad80-160f53592dd7", - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"Implementation of a langgraph checkpoint saver using Redis.\"\"\"\n", - "from contextlib import asynccontextmanager, contextmanager\n", - "from typing import Any, AsyncGenerator, Generator, Union, Tuple, Optional\n", - "\n", - "import redis\n", - "from redis.asyncio import Redis as AsyncRedis, ConnectionPool as AsyncConnectionPool\n", - "from langchain_core.runnables import RunnableConfig\n", - "from langgraph.checkpoint import BaseCheckpointSaver\n", - "from langgraph.serde.jsonplus import JsonPlusSerializer\n", - "from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, CheckpointTuple\n", - "import logging\n", - "\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "\n", - "class JsonAndBinarySerializer(JsonPlusSerializer):\n", - " def _default(self, obj: Any) -> Any:\n", - " if isinstance(obj, (bytes, bytearray)):\n", - " return self._encode_constructor_args(\n", - " obj.__class__, method=\"fromhex\", args=[obj.hex()]\n", - " )\n", - " return super()._default(obj)\n", - "\n", - " def dumps(self, obj: Any) -> str:\n", - " try:\n", - " if isinstance(obj, (bytes, bytearray)):\n", - " return obj.hex()\n", - " return super().dumps(obj)\n", - " except Exception as e:\n", - " logger.error(f\"Serialization error: {e}\")\n", - " raise\n", - "\n", - " def loads(self, s: str, is_binary: bool = False) -> Any:\n", - " try:\n", - " if is_binary:\n", - " return bytes.fromhex(s)\n", - " return super().loads(s)\n", - " except Exception as e:\n", - " logger.error(f\"Deserialization error: {e}\")\n", - " raise\n", - "\n", - "\n", - "def initialize_sync_pool(\n", - " host: str = \"localhost\", port: int = 6379, db: int = 0, **kwargs\n", - ") -> redis.ConnectionPool:\n", - " \"\"\"Initialize a synchronous Redis connection pool.\"\"\"\n", - " try:\n", - " pool = redis.ConnectionPool(host=host, port=port, db=db, **kwargs)\n", - " logger.info(\n", - " f\"Synchronous Redis pool initialized with host={host}, port={port}, db={db}\"\n", - " )\n", - " return pool\n", - " except Exception as e:\n", - " logger.error(f\"Error initializing sync pool: {e}\")\n", - " raise\n", - "\n", - "\n", - "def initialize_async_pool(\n", - " url: str = \"redis://localhost\", **kwargs\n", - ") -> AsyncConnectionPool:\n", - " \"\"\"Initialize an asynchronous Redis connection pool.\"\"\"\n", - " try:\n", - " pool = AsyncConnectionPool.from_url(url, **kwargs)\n", - " logger.info(f\"Asynchronous Redis pool initialized with url={url}\")\n", - " return pool\n", - " except Exception as e:\n", - " logger.error(f\"Error initializing async pool: {e}\")\n", - " raise\n", - "\n", - "\n", - "@contextmanager\n", - "def _get_sync_connection(\n", - " connection: Union[redis.Redis, redis.ConnectionPool, None]\n", - ") -> Generator[redis.Redis, None, None]:\n", - " conn = None\n", - " try:\n", - " if isinstance(connection, redis.Redis):\n", - " yield connection\n", - " elif isinstance(connection, redis.ConnectionPool):\n", - " conn = redis.Redis(connection_pool=connection)\n", - " yield conn\n", - " else:\n", - " raise ValueError(\"Invalid sync connection object.\")\n", - " except redis.ConnectionError as e:\n", - " logger.error(f\"Sync connection error: {e}\")\n", - " raise\n", - " finally:\n", - " if conn:\n", - " conn.close()\n", - "\n", - "\n", - "@asynccontextmanager\n", - "async def _get_async_connection(\n", - " connection: Union[AsyncRedis, AsyncConnectionPool, None]\n", - ") -> AsyncGenerator[AsyncRedis, None]:\n", - " conn = None\n", - " try:\n", - " if isinstance(connection, AsyncRedis):\n", - " yield connection\n", - " elif isinstance(connection, AsyncConnectionPool):\n", - " conn = AsyncRedis(connection_pool=connection)\n", - " yield conn\n", - " else:\n", - " raise ValueError(\"Invalid async connection object.\")\n", - " except redis.ConnectionError as e:\n", - " logger.error(f\"Async connection error: {e}\")\n", - " raise\n", - " finally:\n", - " if conn:\n", - " await conn.aclose()\n", - "\n", - "\n", - "class RedisSaver(BaseCheckpointSaver):\n", - " sync_connection: Optional[Union[redis.Redis, redis.ConnectionPool]] = None\n", - " async_connection: Optional[Union[AsyncRedis, AsyncConnectionPool]] = None\n", - "\n", - " def __init__(\n", - " self,\n", - " sync_connection: Optional[Union[redis.Redis, redis.ConnectionPool]] = None,\n", - " async_connection: Optional[Union[AsyncRedis, AsyncConnectionPool]] = None,\n", - " ):\n", - " super().__init__(serde=JsonAndBinarySerializer())\n", - " self.sync_connection = sync_connection\n", - " self.async_connection = async_connection\n", - "\n", - " def put(\n", - " self,\n", - " config: RunnableConfig,\n", - " checkpoint: Checkpoint,\n", - " metadata: CheckpointMetadata,\n", - " ) -> RunnableConfig:\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " parent_ts = config[\"configurable\"].get(\"thread_ts\")\n", - " key = f\"checkpoint:{thread_id}:{checkpoint['ts']}\"\n", - " try:\n", - " with _get_sync_connection(self.sync_connection) as conn:\n", - " conn.hset(\n", - " key,\n", - " mapping={\n", - " \"checkpoint\": self.serde.dumps(checkpoint),\n", - " \"metadata\": self.serde.dumps(metadata),\n", - " \"parent_ts\": parent_ts if parent_ts else \"\",\n", - " },\n", - " )\n", - " logger.info(\n", - " f\"Checkpoint stored successfully for thread_id: {thread_id}, ts: {checkpoint['ts']}\"\n", - " )\n", - " except Exception as e:\n", - " logger.error(f\"Failed to put checkpoint: {e}\")\n", - " raise\n", - " return {\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": checkpoint[\"ts\"],\n", - " },\n", - " }\n", - "\n", - " async def aput(\n", - " self,\n", - " config: RunnableConfig,\n", - " checkpoint: Checkpoint,\n", - " metadata: CheckpointMetadata,\n", - " ) -> RunnableConfig:\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " parent_ts = config[\"configurable\"].get(\"thread_ts\")\n", - " key = f\"checkpoint:{thread_id}:{checkpoint['ts']}\"\n", - " try:\n", - " async with _get_async_connection(self.async_connection) as conn:\n", - " await conn.hset(\n", - " key,\n", - " mapping={\n", - " \"checkpoint\": self.serde.dumps(checkpoint),\n", - " \"metadata\": self.serde.dumps(metadata),\n", - " \"parent_ts\": parent_ts if parent_ts else \"\",\n", - " },\n", - " )\n", - " logger.info(\n", - " f\"Checkpoint stored successfully for thread_id: {thread_id}, ts: {checkpoint['ts']}\"\n", - " )\n", - " except Exception as e:\n", - " logger.error(f\"Failed to aput checkpoint: {e}\")\n", - " raise\n", - " return {\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": checkpoint[\"ts\"],\n", - " },\n", - " }\n", - "\n", - " def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " thread_ts = config[\"configurable\"].get(\"thread_ts\", None)\n", - " try:\n", - " with _get_sync_connection(self.sync_connection) as conn:\n", - " if thread_ts:\n", - " key = f\"checkpoint:{thread_id}:{thread_ts}\"\n", - " else:\n", - " all_keys = conn.keys(f\"checkpoint:{thread_id}:*\")\n", - " if not all_keys:\n", - " logger.info(f\"No checkpoints found for thread_id: {thread_id}\")\n", - " return None\n", - " latest_key = max(all_keys, key=lambda k: k.decode().split(\":\")[-1])\n", - " key = latest_key.decode()\n", - " checkpoint_data = conn.hgetall(key)\n", - " if not checkpoint_data:\n", - " logger.info(f\"No valid checkpoint data found for key: {key}\")\n", - " return None\n", - " checkpoint = self.serde.loads(checkpoint_data[b\"checkpoint\"].decode())\n", - " metadata = self.serde.loads(checkpoint_data[b\"metadata\"].decode())\n", - " parent_ts = checkpoint_data.get(b\"parent_ts\", b\"\").decode()\n", - " parent_config = (\n", - " {\"configurable\": {\"thread_id\": thread_id, \"thread_ts\": parent_ts}}\n", - " if parent_ts\n", - " else None\n", - " )\n", - " logger.info(\n", - " f\"Checkpoint retrieved successfully for thread_id: {thread_id}, ts: {thread_ts}\"\n", - " )\n", - " return CheckpointTuple(\n", - " config=config,\n", - " checkpoint=checkpoint,\n", - " metadata=metadata,\n", - " parent_config=parent_config,\n", - " )\n", - " except Exception as e:\n", - " logger.error(f\"Failed to get checkpoint tuple: {e}\")\n", - " raise\n", - "\n", - " async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", - " thread_id = config[\"configurable\"][\"thread_id\"]\n", - " thread_ts = config[\"configurable\"].get(\"thread_ts\", None)\n", - " try:\n", - " async with _get_async_connection(self.async_connection) as conn:\n", - " if thread_ts:\n", - " key = f\"checkpoint:{thread_id}:{thread_ts}\"\n", - " else:\n", - " all_keys = await conn.keys(f\"checkpoint:{thread_id}:*\")\n", - " if not all_keys:\n", - " logger.info(f\"No checkpoints found for thread_id: {thread_id}\")\n", - " return None\n", - " latest_key = max(all_keys, key=lambda k: k.decode().split(\":\")[-1])\n", - " key = latest_key.decode()\n", - " checkpoint_data = await conn.hgetall(key)\n", - " if not checkpoint_data:\n", - " logger.info(f\"No valid checkpoint data found for key: {key}\")\n", - " return None\n", - " checkpoint = self.serde.loads(checkpoint_data[b\"checkpoint\"].decode())\n", - " metadata = self.serde.loads(checkpoint_data[b\"metadata\"].decode())\n", - " parent_ts = checkpoint_data.get(b\"parent_ts\", b\"\").decode()\n", - " parent_config = (\n", - " {\"configurable\": {\"thread_id\": thread_id, \"thread_ts\": parent_ts}}\n", - " if parent_ts\n", - " else None\n", - " )\n", - " logger.info(\n", - " f\"Checkpoint retrieved successfully for thread_id: {thread_id}, ts: {thread_ts}\"\n", - " )\n", - " return CheckpointTuple(\n", - " config=config,\n", - " checkpoint=checkpoint,\n", - " metadata=metadata,\n", - " parent_config=parent_config,\n", - " )\n", - " except Exception as e:\n", - " logger.error(f\"Failed to get checkpoint tuple: {e}\")\n", - " raise\n", - "\n", - " def list(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " *,\n", - " filter: Optional[dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " limit: Optional[int] = None,\n", - " ) -> Generator[CheckpointTuple, None, None]:\n", - " thread_id = config[\"configurable\"][\"thread_id\"] if config else \"*\"\n", - " pattern = f\"checkpoint:{thread_id}:*\"\n", - " try:\n", - " with _get_sync_connection(self.sync_connection) as conn:\n", - " keys = conn.keys(pattern)\n", - " if before:\n", - " keys = [\n", - " k\n", - " for k in keys\n", - " if k.decode().split(\":\")[-1]\n", - " < before[\"configurable\"][\"thread_ts\"]\n", - " ]\n", - " keys = sorted(\n", - " keys, key=lambda k: k.decode().split(\":\")[-1], reverse=True\n", - " )\n", - " if limit:\n", - " keys = keys[:limit]\n", - " for key in keys:\n", - " data = conn.hgetall(key)\n", - " if data and \"checkpoint\" in data and \"metadata\" in data:\n", - " thread_ts = key.decode().split(\":\")[-1]\n", - " yield CheckpointTuple(\n", - " config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " },\n", - " checkpoint=self.serde.loads(data[\"checkpoint\"].decode()),\n", - " metadata=self.serde.loads(data[\"metadata\"].decode()),\n", - " parent_config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": data.get(\"parent_ts\", b\"\").decode(),\n", - " }\n", - " }\n", - " if data.get(\"parent_ts\")\n", - " else None,\n", - " )\n", - " logger.info(\n", - " f\"Checkpoint listed for thread_id: {thread_id}, ts: {thread_ts}\"\n", - " )\n", - " except Exception as e:\n", - " logger.error(f\"Failed to list checkpoints: {e}\")\n", - " raise\n", - "\n", - " async def alist(\n", - " self,\n", - " config: Optional[RunnableConfig],\n", - " *,\n", - " filter: Optional[dict[str, Any]] = None,\n", - " before: Optional[RunnableConfig] = None,\n", - " limit: Optional[int] = None,\n", - " ) -> AsyncGenerator[CheckpointTuple, None]:\n", - " thread_id = config[\"configurable\"][\"thread_id\"] if config else \"*\"\n", - " pattern = f\"checkpoint:{thread_id}:*\"\n", - " try:\n", - " async with _get_async_connection(self.async_connection) as conn:\n", - " keys = await conn.keys(pattern)\n", - " if before:\n", - " keys = [\n", - " k\n", - " for k in keys\n", - " if k.decode().split(\":\")[-1]\n", - " < before[\"configurable\"][\"thread_ts\"]\n", - " ]\n", - " keys = sorted(\n", - " keys, key=lambda k: k.decode().split(\":\")[-1], reverse=True\n", - " )\n", - " if limit:\n", - " keys = keys[:limit]\n", - " for key in keys:\n", - " data = await conn.hgetall(key)\n", - " if data and \"checkpoint\" in data and \"metadata\" in data:\n", - " thread_ts = key.decode().split(\":\")[-1]\n", - " yield CheckpointTuple(\n", - " config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": thread_ts,\n", - " }\n", - " },\n", - " checkpoint=self.serde.loads(data[\"checkpoint\"].decode()),\n", - " metadata=self.serde.loads(data[\"metadata\"].decode()),\n", - " parent_config={\n", - " \"configurable\": {\n", - " \"thread_id\": thread_id,\n", - " \"thread_ts\": data.get(\"parent_ts\", b\"\").decode(),\n", - " }\n", - " }\n", - " if data.get(\"parent_ts\")\n", - " else None,\n", - " )\n", - " logger.info(\n", - " f\"Checkpoint listed for thread_id: {thread_id}, ts: {thread_ts}\"\n", - " )\n", - " except Exception as e:\n", - " logger.error(f\"Failed to list checkpoints: {e}\")\n", - " raise" - ] - }, - { - "cell_type": "markdown", - "id": "1d142495", - "metadata": {}, - "source": [ - "## Checkpointer implementation" - ] - }, - { - "cell_type": "markdown", - "id": "456fa19c-93a5-4750-a410-f2d810b964ad", - "metadata": {}, - "source": [ - "## Setup environment" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "id": "eca9aafb-a155-407a-8036-682a2f1297d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "OPENAI_API_KEY: ········\n" + ] + } + ], "source": [ "import getpass\n", "import os\n", @@ -460,6 +60,669 @@ "_set_env(\"OPENAI_API_KEY\")" ] }, + { + "cell_type": "markdown", + "id": "ecb23436-f238-4f8c-a2b7-67c7956121e2", + "metadata": {}, + "source": [ + "## Checkpointer implementation" + ] + }, + { + "cell_type": "markdown", + "id": "752d570c-a9ad-48eb-a317-adf9fc700803", + "metadata": {}, + "source": [ + "### Define imports and helper functions" + ] + }, + { + "cell_type": "markdown", + "id": "cdea5bf7-4865-46f3-9bec-00147dd79895", + "metadata": {}, + "source": [ + "First, let's define some imports and shared utilities for both `RedisSaver` and `AsyncRedisSaver`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "61e63348-7d56-4177-90bf-aad7645a707a", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"Implementation of a langgraph checkpoint saver using Redis.\"\"\"\n", + "from contextlib import asynccontextmanager, contextmanager\n", + "from typing import (\n", + " Any,\n", + " AsyncGenerator,\n", + " AsyncIterator,\n", + " Iterator,\n", + " List,\n", + " Optional,\n", + " Tuple,\n", + ")\n", + "\n", + "from langchain_core.runnables import RunnableConfig\n", + "\n", + "from langgraph.checkpoint.base import (\n", + " BaseCheckpointSaver,\n", + " ChannelVersions,\n", + " Checkpoint,\n", + " CheckpointMetadata,\n", + " CheckpointTuple,\n", + " PendingWrite,\n", + " get_checkpoint_id,\n", + ")\n", + "from langgraph.checkpoint.serde.base import SerializerProtocol\n", + "from redis import Redis\n", + "from redis.asyncio import Redis as AsyncRedis\n", + "\n", + "REDIS_KEY_SEPARATOR = \":\"\n", + "\n", + "\n", + "# Utilities shared by both RedisSaver and AsyncRedisSaver\n", + "\n", + "\n", + "def _make_redis_checkpoint_key(\n", + " thread_id: str, checkpoint_ns: str, checkpoint_id: str\n", + ") -> str:\n", + " return REDIS_KEY_SEPARATOR.join(\n", + " [\"checkpoint\", thread_id, checkpoint_ns, checkpoint_id]\n", + " )\n", + "\n", + "\n", + "def _make_redis_checkpoint_writes_key(\n", + " thread_id: str,\n", + " checkpoint_ns: str,\n", + " checkpoint_id: str,\n", + " task_id: str,\n", + " idx: Optional[int],\n", + ") -> str:\n", + " if idx is None:\n", + " return REDIS_KEY_SEPARATOR.join(\n", + " [\"writes\", thread_id, checkpoint_ns, checkpoint_id, task_id]\n", + " )\n", + "\n", + " return REDIS_KEY_SEPARATOR.join(\n", + " [\"writes\", thread_id, checkpoint_ns, checkpoint_id, task_id, str(idx)]\n", + " )\n", + "\n", + "\n", + "def _parse_redis_checkpoint_key(redis_key: str) -> dict:\n", + " namespace, thread_id, checkpoint_ns, checkpoint_id = redis_key.split(\n", + " REDIS_KEY_SEPARATOR\n", + " )\n", + " if namespace != \"checkpoint\":\n", + " raise ValueError(\"Expected checkpoint key to start with 'checkpoint'\")\n", + "\n", + " return {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + "\n", + "\n", + "def _parse_redis_checkpoint_writes_key(redis_key: str) -> dict:\n", + " namespace, thread_id, checkpoint_ns, checkpoint_id, task_id, idx = redis_key.split(\n", + " REDIS_KEY_SEPARATOR\n", + " )\n", + " if namespace != \"writes\":\n", + " raise ValueError(\"Expected checkpoint key to start with 'checkpoint'\")\n", + "\n", + " return {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " \"task_id\": task_id,\n", + " \"idx\": idx,\n", + " }\n", + "\n", + "\n", + "def _filter_keys(\n", + " keys: List[str], before: Optional[RunnableConfig], limit: Optional[int]\n", + ") -> list:\n", + " \"\"\"Filter and sort Redis keys based on optional criteria.\"\"\"\n", + " if before:\n", + " keys = [\n", + " k\n", + " for k in keys\n", + " if _parse_redis_checkpoint_key(k.decode())[\"checkpoint_id\"]\n", + " < before[\"configurable\"][\"checkpoint_id\"]\n", + " ]\n", + "\n", + " keys = sorted(\n", + " keys,\n", + " key=lambda k: _parse_redis_checkpoint_key(k.decode())[\"checkpoint_id\"],\n", + " reverse=True,\n", + " )\n", + " if limit:\n", + " keys = keys[:limit]\n", + " return keys\n", + "\n", + "\n", + "def _dump_writes(serde: SerializerProtocol, writes: tuple[str, Any]) -> list[dict]:\n", + " \"\"\"Serialize pending writes.\"\"\"\n", + " serialized_writes = []\n", + " for channel, value in writes:\n", + " type_, serialized_value = serde.dumps_typed(value)\n", + " serialized_writes.append(\n", + " {\"channel\": channel, \"type\": type_, \"value\": serialized_value}\n", + " )\n", + " return serialized_writes\n", + "\n", + "\n", + "def _load_writes(\n", + " serde: SerializerProtocol, task_id_to_data: dict[tuple[str, str], dict]\n", + ") -> list[PendingWrite]:\n", + " \"\"\"Deserialize pending writes.\"\"\"\n", + " writes = [\n", + " (\n", + " task_id,\n", + " data[b\"channel\"].decode(),\n", + " serde.loads_typed((data[b\"type\"].decode(), data[b\"value\"].decode())),\n", + " )\n", + " for (task_id, _), data in task_id_to_data.items()\n", + " ]\n", + " return writes\n", + "\n", + "\n", + "def _parse_redis_checkpoint_data(\n", + " serde: SerializerProtocol,\n", + " key: str,\n", + " data: dict,\n", + " pending_writes: Optional[List[PendingWrite]] = None,\n", + ") -> Optional[CheckpointTuple]:\n", + " \"\"\"Parse checkpoint data retrieved from Redis.\"\"\"\n", + " if not data:\n", + " return None\n", + "\n", + " parsed_key = _parse_redis_checkpoint_key(key)\n", + " thread_id = parsed_key[\"thread_id\"]\n", + " checkpoint_ns = parsed_key[\"checkpoint_ns\"]\n", + " checkpoint_id = parsed_key[\"checkpoint_id\"]\n", + " config = {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " }\n", + "\n", + " checkpoint = serde.loads_typed(\n", + " (data[b\"type\"].decode(), data[b\"checkpoint\"].decode())\n", + " )\n", + " metadata = serde.loads(data[b\"metadata\"].decode())\n", + " parent_checkpoint_id = data.get(b\"parent_checkpoint_id\", b\"\").decode()\n", + " parent_config = (\n", + " {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": parent_checkpoint_id,\n", + " }\n", + " }\n", + " if parent_checkpoint_id\n", + " else None\n", + " )\n", + " return CheckpointTuple(\n", + " config=config,\n", + " checkpoint=checkpoint,\n", + " metadata=metadata,\n", + " parent_config=parent_config,\n", + " pending_writes=pending_writes,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "922822a8-f7d2-41ce-bada-206fc125c20c", + "metadata": {}, + "source": [ + "### RedisSaver" + ] + }, + { + "cell_type": "markdown", + "id": "c216852b-8318-4927-9000-1361d3ca81e8", + "metadata": {}, + "source": [ + "Below is an implementation of RedisSaver (for synchronous use of graph, i.e. `.invoke()`, `.stream()`). RedisSaver implements four methods that are required for any checkpointer:\n", + "\n", + "- `.put` - Store a checkpoint with its configuration and metadata.\n", + "- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).\n", + "- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).\n", + "- `.list` - List checkpoints that match a given configuration and filter criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "98c8d65e-eb95-4cbd-8975-d33a52351d03", + "metadata": {}, + "outputs": [], + "source": [ + "class RedisSaver(BaseCheckpointSaver):\n", + " \"\"\"Redis-based checkpoint saver implementation.\"\"\"\n", + "\n", + " conn: Redis\n", + "\n", + " def __init__(self, conn: Redis):\n", + " super().__init__()\n", + " self.conn = conn\n", + "\n", + " @classmethod\n", + " @contextmanager\n", + " def from_conn_info(cls, *, host: str, port: int, db: int) -> Iterator[\"RedisSaver\"]:\n", + " conn = None\n", + " try:\n", + " conn = Redis(host=host, port=port, db=db)\n", + " yield RedisSaver(conn)\n", + " finally:\n", + " if conn:\n", + " conn.close()\n", + "\n", + " def put(\n", + " self,\n", + " config: RunnableConfig,\n", + " checkpoint: Checkpoint,\n", + " metadata: CheckpointMetadata,\n", + " new_versions: ChannelVersions,\n", + " ) -> RunnableConfig:\n", + " \"\"\"Save a checkpoint to Redis.\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to associate with the checkpoint.\n", + " checkpoint (Checkpoint): The checkpoint to save.\n", + " metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.\n", + " new_versions (ChannelVersions): New channel versions as of this write.\n", + "\n", + " Returns:\n", + " RunnableConfig: Updated configuration after storing the checkpoint.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = checkpoint[\"id\"]\n", + " parent_checkpoint_id = config[\"configurable\"].get(\"checkpoint_id\")\n", + " key = _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)\n", + "\n", + " type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)\n", + " serialized_metadata = self.serde.dumps(metadata)\n", + " data = {\n", + " \"checkpoint\": serialized_checkpoint,\n", + " \"type\": type_,\n", + " \"metadata\": serialized_metadata,\n", + " \"parent_checkpoint_id\": parent_checkpoint_id\n", + " if parent_checkpoint_id\n", + " else \"\",\n", + " }\n", + " self.conn.hset(key, mapping=data)\n", + " return {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " }\n", + "\n", + " def put_writes(\n", + " self,\n", + " config: RunnableConfig,\n", + " writes: List[Tuple[str, Any]],\n", + " task_id: str,\n", + " ) -> RunnableConfig:\n", + " \"\"\"Store intermediate writes linked to a checkpoint.\n", + "\n", + " Args:\n", + " config (RunnableConfig): Configuration of the related checkpoint.\n", + " writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.\n", + " task_id (str): Identifier for the task creating the writes.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = config[\"configurable\"][\"checkpoint_id\"]\n", + "\n", + " for idx, data in enumerate(_dump_writes(self.serde, writes)):\n", + " key = _make_redis_checkpoint_writes_key(\n", + " thread_id, checkpoint_ns, checkpoint_id, task_id, idx\n", + " )\n", + " self.conn.hset(key, mapping=data)\n", + " return config\n", + "\n", + " def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", + " \"\"\"Get a checkpoint tuple from Redis.\n", + "\n", + " This method retrieves a checkpoint tuple from Redis based on the\n", + " provided config. If the config contains a \"checkpoint_id\" key, the checkpoint with\n", + " the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint\n", + " for the given thread ID is retrieved.\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to use for retrieving the checkpoint.\n", + "\n", + " Returns:\n", + " Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_id = get_checkpoint_id(config)\n", + " checkpoint_ns = config[\"configurable\"].get(\"checkpoint_ns\", \"\")\n", + "\n", + " checkpoint_key = self._get_checkpoint_key(\n", + " self.conn, thread_id, checkpoint_ns, checkpoint_id\n", + " )\n", + " if not checkpoint_key:\n", + " return None\n", + "\n", + " checkpoint_data = self.conn.hgetall(checkpoint_key)\n", + "\n", + " # load pending writes\n", + " checkpoint_id = (\n", + " checkpoint_id\n", + " or _parse_redis_checkpoint_key(checkpoint_key)[\"checkpoint_id\"]\n", + " )\n", + " writes_key = _make_redis_checkpoint_writes_key(\n", + " thread_id, checkpoint_ns, checkpoint_id, \"*\", None\n", + " )\n", + " matching_keys = self.conn.keys(pattern=writes_key)\n", + " parsed_keys = [\n", + " _parse_redis_checkpoint_writes_key(key.decode()) for key in matching_keys\n", + " ]\n", + " pending_writes = _load_writes(\n", + " self.serde,\n", + " {\n", + " (parsed_key[\"task_id\"], parsed_key[\"idx\"]): self.conn.hgetall(key)\n", + " for key, parsed_key in sorted(\n", + " zip(matching_keys, parsed_keys), key=lambda x: x[1][\"idx\"]\n", + " )\n", + " },\n", + " )\n", + " return _parse_redis_checkpoint_data(\n", + " self.serde, checkpoint_key, checkpoint_data, pending_writes=pending_writes\n", + " )\n", + "\n", + " def list(\n", + " self,\n", + " config: Optional[RunnableConfig],\n", + " *,\n", + " # TODO: implement filtering\n", + " filter: Optional[dict[str, Any]] = None,\n", + " before: Optional[RunnableConfig] = None,\n", + " limit: Optional[int] = None,\n", + " ) -> Iterator[CheckpointTuple]:\n", + " \"\"\"List checkpoints from the database.\n", + "\n", + " This method retrieves a list of checkpoint tuples from Redis based\n", + " on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to use for listing the checkpoints.\n", + " filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None.\n", + " before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.\n", + " limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None.\n", + "\n", + " Yields:\n", + " Iterator[CheckpointTuple]: An iterator of checkpoint tuples.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"].get(\"checkpoint_ns\", \"\")\n", + " pattern = _make_redis_checkpoint_key(thread_id, checkpoint_ns, \"*\")\n", + "\n", + " keys = _filter_keys(self.conn.keys(pattern), before, limit)\n", + " for key in keys:\n", + " data = self.conn.hgetall(key)\n", + " if data and b\"checkpoint\" in data and b\"metadata\" in data:\n", + " yield _parse_redis_checkpoint_data(self.serde, key.decode(), data)\n", + "\n", + " def _get_checkpoint_key(\n", + " self, conn, thread_id: str, checkpoint_ns: str, checkpoint_id: Optional[str]\n", + " ) -> Optional[str]:\n", + " \"\"\"Determine the Redis key for a checkpoint.\"\"\"\n", + " if checkpoint_id:\n", + " return _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)\n", + "\n", + " all_keys = conn.keys(_make_redis_checkpoint_key(thread_id, checkpoint_ns, \"*\"))\n", + " if not all_keys:\n", + " return None\n", + "\n", + " latest_key = max(\n", + " all_keys,\n", + " key=lambda k: _parse_redis_checkpoint_key(k.decode())[\"checkpoint_id\"],\n", + " )\n", + " return latest_key.decode()" + ] + }, + { + "cell_type": "markdown", + "id": "ec21ff00-75a7-4789-b863-93fffcc0b32d", + "metadata": {}, + "source": [ + "### AsyncRedis" + ] + }, + { + "cell_type": "markdown", + "id": "9e5ad763-12ab-4918-af40-0be85678e35b", + "metadata": {}, + "source": [ + "Below is a reference implementation of AsyncRedisSaver (for asynchronous use of graph, i.e. `.ainvoke()`, `.astream()`). AsyncRedisSaver implements four methods that are required for any async checkpointer:\n", + "\n", + "- `.aput` - Store a checkpoint with its configuration and metadata.\n", + "- `.aput_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes).\n", + "- `.aget_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `checkpoint_id`).\n", + "- `.alist` - List checkpoints that match a given configuration and filter criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "888302ee-c201-498f-b6e3-69ec5f1a039c", + "metadata": {}, + "outputs": [], + "source": [ + "class AsyncRedisSaver(BaseCheckpointSaver):\n", + " \"\"\"Async redis-based checkpoint saver implementation.\"\"\"\n", + "\n", + " conn: AsyncRedis\n", + "\n", + " def __init__(self, conn: AsyncRedis):\n", + " super().__init__()\n", + " self.conn = conn\n", + "\n", + " @classmethod\n", + " @asynccontextmanager\n", + " async def from_conn_info(cls, *, host: str, port: int, db: int) -> AsyncIterator[\"AsyncRedisSaver\"]:\n", + " conn = None\n", + " try:\n", + " conn = AsyncRedis(host=host, port=port, db=db)\n", + " yield AsyncRedisSaver(conn)\n", + " finally:\n", + " if conn:\n", + " await conn.aclose()\n", + "\n", + " async def aput(\n", + " self,\n", + " config: RunnableConfig,\n", + " checkpoint: Checkpoint,\n", + " metadata: CheckpointMetadata,\n", + " new_versions: ChannelVersions,\n", + " ) -> RunnableConfig:\n", + " \"\"\"Save a checkpoint to the database asynchronously.\n", + "\n", + " This method saves a checkpoint to Redis. The checkpoint is associated\n", + " with the provided config and its parent config (if any).\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to associate with the checkpoint.\n", + " checkpoint (Checkpoint): The checkpoint to save.\n", + " metadata (CheckpointMetadata): Additional metadata to save with the checkpoint.\n", + " new_versions (ChannelVersions): New channel versions as of this write.\n", + "\n", + " Returns:\n", + " RunnableConfig: Updated configuration after storing the checkpoint.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = checkpoint[\"id\"]\n", + " parent_checkpoint_id = config[\"configurable\"].get(\"checkpoint_id\")\n", + " key = _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)\n", + "\n", + " type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint)\n", + " serialized_metadata = self.serde.dumps(metadata)\n", + " data = {\n", + " \"checkpoint\": serialized_checkpoint,\n", + " \"type\": type_,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " \"metadata\": serialized_metadata,\n", + " \"parent_checkpoint_id\": parent_checkpoint_id\n", + " if parent_checkpoint_id\n", + " else \"\",\n", + " }\n", + "\n", + " await self.conn.hset(key, mapping=data)\n", + " return {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " }\n", + "\n", + " async def aput_writes(\n", + " self,\n", + " config: RunnableConfig,\n", + " writes: List[Tuple[str, Any]],\n", + " task_id: str,\n", + " ) -> RunnableConfig:\n", + " \"\"\"Store intermediate writes linked to a checkpoint asynchronously.\n", + "\n", + " This method saves intermediate writes associated with a checkpoint to the database.\n", + "\n", + " Args:\n", + " config (RunnableConfig): Configuration of the related checkpoint.\n", + " writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair.\n", + " task_id (str): Identifier for the task creating the writes.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"][\"checkpoint_ns\"]\n", + " checkpoint_id = config[\"configurable\"][\"checkpoint_id\"]\n", + "\n", + " for idx, data in enumerate(_dump_writes(self.serde, writes)):\n", + " key = _make_redis_checkpoint_writes_key(\n", + " thread_id, checkpoint_ns, checkpoint_id, task_id, idx\n", + " )\n", + " await self.conn.hset(key, mapping=data)\n", + " return config\n", + "\n", + " async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:\n", + " \"\"\"Get a checkpoint tuple from Redis asynchronously.\n", + "\n", + " This method retrieves a checkpoint tuple from Redis based on the\n", + " provided config. If the config contains a \"checkpoint_id\" key, the checkpoint with\n", + " the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint\n", + " for the given thread ID is retrieved.\n", + "\n", + " Args:\n", + " config (RunnableConfig): The config to use for retrieving the checkpoint.\n", + "\n", + " Returns:\n", + " Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_id = get_checkpoint_id(config)\n", + " checkpoint_ns = config[\"configurable\"].get(\"checkpoint_ns\", \"\")\n", + " if not get_checkpoint_id(config):\n", + " config = {\n", + " \"configurable\": {\n", + " \"thread_id\": thread_id,\n", + " \"checkpoint_ns\": checkpoint_ns,\n", + " \"checkpoint_id\": checkpoint_id,\n", + " }\n", + " }\n", + "\n", + " checkpoint_key = await self._aget_checkpoint_key(\n", + " self.conn, thread_id, checkpoint_ns, checkpoint_id\n", + " )\n", + " if not checkpoint_key:\n", + " return None\n", + " checkpoint_data = await self.conn.hgetall(checkpoint_key)\n", + "\n", + " # load pending writes\n", + " checkpoint_id = (\n", + " checkpoint_id\n", + " or _parse_redis_checkpoint_key(checkpoint_key)[\"checkpoint_id\"]\n", + " )\n", + " writes_key = _make_redis_checkpoint_writes_key(\n", + " thread_id, checkpoint_ns, checkpoint_id, \"*\", None\n", + " )\n", + " matching_keys = await self.conn.keys(pattern=writes_key)\n", + " parsed_keys = [\n", + " _parse_redis_checkpoint_writes_key(key.decode()) for key in matching_keys\n", + " ]\n", + " pending_writes = _load_writes(\n", + " self.serde,\n", + " {\n", + " (parsed_key[\"task_id\"], parsed_key[\"idx\"]): await self.conn.hgetall(key)\n", + " for key, parsed_key in sorted(\n", + " zip(matching_keys, parsed_keys), key=lambda x: x[1][\"idx\"]\n", + " )\n", + " },\n", + " )\n", + " return _parse_redis_checkpoint_data(\n", + " self.serde, checkpoint_key, checkpoint_data, pending_writes=pending_writes\n", + " )\n", + "\n", + " async def alist(\n", + " self,\n", + " config: Optional[RunnableConfig],\n", + " *,\n", + " # TODO: implement filtering\n", + " filter: Optional[dict[str, Any]] = None,\n", + " before: Optional[RunnableConfig] = None,\n", + " limit: Optional[int] = None,\n", + " ) -> AsyncGenerator[CheckpointTuple, None]:\n", + " \"\"\"List checkpoints from Redis asynchronously.\n", + "\n", + " This method retrieves a list of checkpoint tuples from Redis based\n", + " on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first).\n", + "\n", + " Args:\n", + " config (Optional[RunnableConfig]): Base configuration for filtering checkpoints.\n", + " filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata.\n", + " before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None.\n", + " limit (Optional[int]): Maximum number of checkpoints to return.\n", + "\n", + " Yields:\n", + " AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples.\n", + " \"\"\"\n", + " thread_id = config[\"configurable\"][\"thread_id\"]\n", + " checkpoint_ns = config[\"configurable\"].get(\"checkpoint_ns\", \"\")\n", + " pattern = _make_redis_checkpoint_key(thread_id, checkpoint_ns, \"*\")\n", + " keys = _filter_keys(await self.conn.keys(pattern), before, limit)\n", + " for key in keys:\n", + " data = await self.conn.hgetall(key)\n", + " if data and b\"checkpoint\" in data and b\"metadata\" in data:\n", + " yield _parse_redis_checkpoint_data(self.serde, key.decode(), data)\n", + "\n", + " async def _aget_checkpoint_key(\n", + " self, conn, thread_id: str, checkpoint_ns: str, checkpoint_id: Optional[str]\n", + " ) -> Optional[str]:\n", + " \"\"\"Asynchronously determine the Redis key for a checkpoint.\"\"\"\n", + " if checkpoint_id:\n", + " return _make_redis_checkpoint_key(thread_id, checkpoint_ns, checkpoint_id)\n", + "\n", + " all_keys = await conn.keys(\n", + " _make_redis_checkpoint_key(thread_id, checkpoint_ns, \"*\")\n", + " )\n", + " if not all_keys:\n", + " return None\n", + "\n", + " latest_key = max(\n", + " all_keys,\n", + " key=lambda k: _parse_redis_checkpoint_key(k.decode())[\"checkpoint_id\"],\n", + " )\n", + " return latest_key.decode()" + ] + }, { "cell_type": "markdown", "id": "e26b3204-cca2-414c-800e-7e09032445ae", @@ -470,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", "metadata": {}, "outputs": [], @@ -494,7 +757,7 @@ "\n", "\n", "tools = [get_weather]\n", - "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)" + "model = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0)" ] }, { @@ -505,82 +768,53 @@ "## Use sync connection" ] }, - { - "cell_type": "markdown", - "id": "e39fc712-9e1c-4831-9077-dd07b0c13594", - "metadata": {}, - "source": [ - "### With a connection pool" - ] - }, { "cell_type": "code", - "execution_count": 5, - "id": "a1710e2f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Synchronous Redis pool initialized with host=172.25.0.4, port=6379, db=0\n" - ] - } - ], - "source": [ - "sync_pool = initialize_sync_pool(host=\"172.25.0.4\", port=6379, db=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2657c1c4-d8a5-4fe3-8f77-95415a98ed6c", + "execution_count": 7, + "id": "5fe54e79-9eaf-44e2-b2d9-1e0284b984d0", "metadata": {}, "outputs": [], "source": [ - "checkpointer = RedisSaver(sync_connection=sync_pool)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6d388241-de57-4b4e-af7b-eb1081fb8f36", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 1, ts: None\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 1, ts: 2024-07-09T08:22:48.417492+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 1, ts: 2024-07-09T08:22:48.420714+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 1, ts: 2024-07-09T08:22:49.458951+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 1, ts: 2024-07-09T08:22:49.465101+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 1, ts: 2024-07-09T08:22:50.084141+00:00\n" - ] - } - ], - "source": [ - "graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)" + "with RedisSaver.from_conn_info(host=\"localhost\", port=6379, db=0) as checkpointer:\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"1\"}}\n", + " res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n", + "\n", + " latest_checkpoint = checkpointer.get(config)\n", + " latest_checkpoint_tuple = checkpointer.get_tuple(config)\n", + " checkpoint_tuples = list(checkpointer.list(config))" ] }, { "cell_type": "code", "execution_count": 8, - "id": "a7e0e7ec-a675-470b-9270-e4bdc59d4a4d", + "id": "c298e627-115a-4b4c-ae17-520ca9a640cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='64df8e19-0b9f-47f7-928f-4db3255485aa'),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_n2XQOZHfpXpaNaviJakmjo82', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_ce0793330f', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-9057607f-6fa7-452b-95c7-f8f9832cb343-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_n2XQOZHfpXpaNaviJakmjo82'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", - " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='444d80db-8230-440a-b0eb-46a3f4db1006', tool_call_id='call_n2XQOZHfpXpaNaviJakmjo82'),\n", - " AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_d576307f90', 'finish_reason': 'stop', 'logprobs': None}, id='run-676a1ee4-7301-4405-93a4-87af27a92614-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}" + "{'v': 1,\n", + " 'ts': '2024-08-09T01:56:48.328315+00:00',\n", + " 'id': '1ef55f2a-3614-69b4-8003-2181cff935cc',\n", + " 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n", + " ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I'),\n", + " AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})],\n", + " 'agent': 'agent'},\n", + " 'channel_versions': {'__start__': '00000000000000000000000000000002.',\n", + " 'messages': '00000000000000000000000000000005.16e98d6f7ece7598829eddf1b33a33c4',\n", + " 'start:agent': '00000000000000000000000000000003.',\n", + " 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af',\n", + " 'branch:agent:should_continue:tools': '00000000000000000000000000000004.',\n", + " 'tools': '00000000000000000000000000000005.'},\n", + " 'versions_seen': {'__input__': {},\n", + " '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'},\n", + " 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc',\n", + " 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'},\n", + " 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}},\n", + " 'pending_sends': [],\n", + " 'current_tasks': {}}" ] }, "execution_count": 8, @@ -589,33 +823,19 @@ } ], "source": [ - "res" + "latest_checkpoint" ] }, { "cell_type": "code", "execution_count": 9, - "id": "96efd8b2-97c9-4207-83b2-00131723a75a", + "id": "922f9406-0f68-418a-9cb4-e0e29de4b5f9", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 1, ts: None\n" - ] - }, { "data": { "text/plain": [ - "{'v': 1,\n", - " 'ts': '2024-07-08T12:21:14.392158+00:00',\n", - " 'id': '1ef3d249-1acf-60ed-bfff-248e42e4d9f5',\n", - " 'channel_values': {'messages': [],\n", - " '__start__': {'messages': [['human', \"what's the weather in sf\"]]}},\n", - " 'channel_versions': {'__start__': 1},\n", - " 'versions_seen': {},\n", - " 'pending_sends': []}" + "CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3614-69b4-8003-2181cff935cc'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:48.328315+00:00', 'id': '1ef55f2a-3614-69b4-8003-2181cff935cc', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.16e98d6f7ece7598829eddf1b33a33c4', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-306f-6252-8002-47c2374ec1f2'}}, pending_writes=[])" ] }, "execution_count": 9, @@ -624,50 +844,32 @@ } ], "source": [ - "checkpointer.get(config)" - ] - }, - { - "cell_type": "markdown", - "id": "967c95c7-e392-4819-bd71-f29e91c68df3", - "metadata": {}, - "source": [ - "### With a connection" + "latest_checkpoint_tuple" ] }, { "cell_type": "code", "execution_count": 10, - "id": "b7d3687b", + "id": "b2ce743b-5896-443b-9ec0-a655b065895c", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 2, ts: None\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 2, ts: 2024-07-09T08:22:50.132262+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 2, ts: 2024-07-09T08:22:50.135993+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 2, ts: 2024-07-09T08:22:50.875540+00:00\n", - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 2, ts: None\n" - ] + "data": { + "text/plain": [ + "[CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3614-69b4-8003-2181cff935cc'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:48.328315+00:00', 'id': '1ef55f2a-3614-69b4-8003-2181cff935cc', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I'), AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.16e98d6f7ece7598829eddf1b33a33c4', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in San Francisco is always sunny!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad546b5a-70ce-404e-9656-dcc6ecd482d3-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-306f-6252-8002-47c2374ec1f2'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-306f-6252-8002-47c2374ec1f2'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.736251+00:00', 'id': '1ef55f2a-306f-6252-8002-47c2374ec1f2', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}), ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I')], 'tools': 'tools'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000004.b16eb718f179ac1dcde54c5652768cf5', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000004.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='e27bb3a1-1798-494a-b4ad-2deadda8b2bf', tool_call_id='call_l5e5YcTJDJYOdvi4scBy9n2I')]}}, 'step': 2}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-305f-61cc-8001-efac33022ef7'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-305f-61cc-8001-efac33022ef7'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.729689+00:00', 'id': '1ef55f2a-305f-61cc-8001-efac33022ef7', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f911e000-75a1-41f6-8e38-77bb086c2ecf'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})], 'agent': 'agent', 'branch:agent:should_continue:tools': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000003.4dd312547dcca1cf91a19adb620a18d6', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4f1531f1-067c-4e16-8b62-7a6b663e93bd-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_l5e5YcTJDJYOdvi4scBy9n2I', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}}, 'step': 1}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a52-6a7c-8000-27624d954d15'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a52-6a7c-8000-27624d954d15'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.095456+00:00', 'id': '1ef55f2a-2a52-6a7c-8000-27624d954d15', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in sf\", id='f911e000-75a1-41f6-8e38-77bb086c2ecf')], 'start:agent': '__start__'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000002.52e8b0c387f50c28345585c088150464', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': None, 'step': 0}, parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a50-6812-bfff-34e3be35d6f2'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-2a50-6812-bfff-34e3be35d6f2'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:47.094575+00:00', 'id': '1ef55f2a-2a50-6812-bfff-34e3be35d6f2', 'channel_values': {'messages': [], '__start__': {'messages': [['human', \"what's the weather in sf\"]]}}, 'channel_versions': {'__start__': '00000000000000000000000000000001.ab89befb52cc0e91e106ef7f500ea033'}, 'versions_seen': {'__input__': {}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'input', 'writes': {'messages': [['human', \"what's the weather in sf\"]]}, 'step': -1}, parent_config=None, pending_writes=None)]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import redis\n", - "\n", - "# Initialize the Redis synchronous direct connection\n", - "sync_redis_direct = redis.Redis(host=\"172.25.0.4\", port=6379, db=0)\n", - "\n", - "# Initialize the RedisSaver with the synchronous direct connection\n", - "checkpointer = RedisSaver(sync_connection=sync_redis_direct)\n", - "\n", - "graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"2\"}}\n", - "res = graph.invoke({\"messages\": [(\"human\", \"what's the weather in sf\")]}, config)\n", - "\n", - "checkpoint_tuple = checkpointer.get_tuple(config)" + "checkpoint_tuples" ] }, { @@ -678,151 +880,116 @@ "## Use async connection" ] }, - { - "cell_type": "markdown", - "id": "ee6b6cf7-d8f7-4777-a48d-93b5855fe681", - "metadata": {}, - "source": [ - "### With a connection pool" - ] - }, { "cell_type": "code", - "execution_count": 13, - "id": "20cea8b7-8f13-4dc7-a3c9-825040eb4c57", + "execution_count": 11, + "id": "6a39d1ff-ca37-4457-8b52-07d33b59c36e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Asynchronous Redis pool initialized with url=redis://172.25.0.4:6379/0\n" - ] - } - ], + "outputs": [], "source": [ - "# Initialize a synchronous Redis connection pool\n", - "async_pool = initialize_async_pool(url=\"redis://172.25.0.4:6379/0\")\n", + "async with AsyncRedisSaver.from_conn_info(host=\"localhost\", port=6379, db=0) as checkpointer:\n", + " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", + " config = {\"configurable\": {\"thread_id\": \"2\"}}\n", + " res = await graph.ainvoke({\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config)\n", "\n", - "checkpointer = RedisSaver(async_connection=async_pool)" + " latest_checkpoint = await checkpointer.aget(config)\n", + " latest_checkpoint_tuple = await checkpointer.aget_tuple(config)\n", + " checkpoint_tuples = [c async for c in checkpointer.alist(config)]" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "f889dce6-7ec1-4277-b8af-ace7811733fa", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 3, ts: None\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 3, ts: 2024-07-09T08:22:50.949172+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 3, ts: 2024-07-09T08:22:50.951824+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 3, ts: 2024-07-09T08:22:51.698633+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 3, ts: 2024-07-09T08:22:51.702156+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 3, ts: 2024-07-09T08:22:53.530983+00:00\n" - ] - } - ], - "source": [ - "graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - "config = {\"configurable\": {\"thread_id\": \"3\"}}\n", - "res = await graph.ainvoke(\n", - " {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ed58c722-1662-4ae2-9bb7-4872158a5b29", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 3, ts: None\n" - ] - } - ], - "source": [ - "checkpoint_tuple = await checkpointer.aget_tuple(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e0c42044-4de6-4742-8e00-fe295d50c95a", + "execution_count": 12, + "id": "51125ef1-bdb6-454e-82cc-4ae19a113606", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "CheckpointTuple(config={'configurable': {'thread_id': '3'}}, checkpoint={'v': 1, 'ts': '2024-07-08T12:21:18.866666+00:00', 'id': '1ef3d249-457b-62d3-bfff-b0e787336a7c', 'channel_values': {'messages': [], '__start__': {'messages': [['human', \"what's the weather in nyc\"]]}}, 'channel_versions': {'__start__': 1}, 'versions_seen': {}, 'pending_sends': []}, metadata={'source': 'input', 'step': -1, 'writes': {'messages': [['human', \"what's the weather in nyc\"]]}}, parent_config=None)" + "{'v': 1,\n", + " 'ts': '2024-08-09T01:56:49.503241+00:00',\n", + " 'id': '1ef55f2a-4149-61ea-8003-dc5506862287',\n", + " 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='5a106e79-a617-4707-839f-134d4e4b762a'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}),\n", + " ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9'),\n", + " AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})],\n", + " 'agent': 'agent'},\n", + " 'channel_versions': {'__start__': '00000000000000000000000000000002.',\n", + " 'messages': '00000000000000000000000000000005.2cb29d082da6435a7528b4c917fd0c28',\n", + " 'start:agent': '00000000000000000000000000000003.',\n", + " 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af',\n", + " 'branch:agent:should_continue:tools': '00000000000000000000000000000004.',\n", + " 'tools': '00000000000000000000000000000005.'},\n", + " 'versions_seen': {'__input__': {},\n", + " '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'},\n", + " 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc',\n", + " 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'},\n", + " 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}},\n", + " 'pending_sends': [],\n", + " 'current_tasks': {}}" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "checkpoint_tuple" - ] - }, - { - "cell_type": "markdown", - "id": "56552584-9eb8-40df-a6a0-44151018b509", - "metadata": {}, - "source": [ - "### Use connection" + "latest_checkpoint" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "a7bf32bd", + "execution_count": 13, + "id": "97f8a87b-8423-41c6-a76b-9a6b30904e73", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Checkpoint retrieved successfully for thread_id: 4, ts: None\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 4, ts: 2024-07-09T08:22:53.585109+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 4, ts: 2024-07-09T08:22:53.587207+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 4, ts: 2024-07-09T08:22:54.932663+00:00\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 4, ts: 2024-07-09T08:22:54.936425+00:00\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", - "INFO:__main__:Checkpoint stored successfully for thread_id: 4, ts: 2024-07-09T08:22:55.982495+00:00\n" - ] + "data": { + "text/plain": [ + "CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-4149-61ea-8003-dc5506862287'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:49.503241+00:00', 'id': '1ef55f2a-4149-61ea-8003-dc5506862287', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='5a106e79-a617-4707-839f-134d4e4b762a'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.2cb29d082da6435a7528b4c917fd0c28', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3d07-647e-8002-b5e4d28c00c9'}}, pending_writes=[])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from redis.asyncio import Redis as AsyncRedis\n", - "\n", - "async with await AsyncRedis(host=\"172.25.0.4\", port=6379, db=0) as conn:\n", - " checkpointer = RedisSaver(async_connection=conn)\n", - " graph = create_react_agent(model, tools=tools, checkpointer=checkpointer)\n", - " config = {\"configurable\": {\"thread_id\": \"4\"}}\n", - " res = await graph.ainvoke(\n", - " {\"messages\": [(\"human\", \"what's the weather in nyc\")]}, config\n", - " )\n", - " checkpoint_tuples = [c async for c in checkpointer.alist(config)]" + "latest_checkpoint_tuple" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2b6d73ca-519e-45f7-90c2-1b8596624505", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-4149-61ea-8003-dc5506862287'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:49.503241+00:00', 'id': '1ef55f2a-4149-61ea-8003-dc5506862287', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='5a106e79-a617-4707-839f-134d4e4b762a'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9'), AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})], 'agent': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000005.2cb29d082da6435a7528b4c917fd0c28', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000005.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000005.'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='The weather in NYC might be cloudy.', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 88, 'total_tokens': 97}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'stop', 'logprobs': None}, id='run-69a10e66-d61f-475e-b7de-a1ecd08a6c3a-0', usage_metadata={'input_tokens': 88, 'output_tokens': 9, 'total_tokens': 97})]}}, 'step': 3}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3d07-647e-8002-b5e4d28c00c9'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3d07-647e-8002-b5e4d28c00c9'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:49.056860+00:00', 'id': '1ef55f2a-3d07-647e-8002-b5e4d28c00c9', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='5a106e79-a617-4707-839f-134d4e4b762a'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73}), ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9')], 'tools': 'tools'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000004.07964a3a545f9ff95545db45a9753d11', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000004.', 'branch:agent:should_continue:tools': '00000000000000000000000000000004.', 'tools': '00000000000000000000000000000004.022986cd20ae85c77ea298a383f69ba8'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'tools': {'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'tools': {'messages': [ToolMessage(content='It might be cloudy in nyc', name='get_weather', id='922124bd-d3b0-4929-a996-a75d842b8b44', tool_call_id='call_TvPLLyhuQQN99EcZc8SzL8x9')]}}, 'step': 2}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3cf9-6996-8001-88dab066840d'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-3cf9-6996-8001-88dab066840d'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:49.051234+00:00', 'id': '1ef55f2a-3cf9-6996-8001-88dab066840d', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='5a106e79-a617-4707-839f-134d4e4b762a'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})], 'agent': 'agent', 'branch:agent:should_continue:tools': 'agent'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000003.cc96d93b1afbd1b69d53851320670b97', 'start:agent': '00000000000000000000000000000003.', 'agent': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af', 'branch:agent:should_continue:tools': '00000000000000000000000000000003.065d90dd7f7cd091f0233855210bb2af'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'agent': {'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': {'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'function': {'arguments': '{\"city\":\"nyc\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 58, 'total_tokens': 73}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': 'fp_48196bc67a', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-0d6fa3b4-cace-41a8-b025-d01d16f6bbe9-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'nyc'}, 'id': 'call_TvPLLyhuQQN99EcZc8SzL8x9', 'type': 'tool_call'}], usage_metadata={'input_tokens': 58, 'output_tokens': 15, 'total_tokens': 73})]}}, 'step': 1}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-36a6-6788-8000-9efe1769f8c1'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-36a6-6788-8000-9efe1769f8c1'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:48.388067+00:00', 'id': '1ef55f2a-36a6-6788-8000-9efe1769f8c1', 'channel_values': {'messages': [HumanMessage(content=\"what's the weather in nyc\", id='5a106e79-a617-4707-839f-134d4e4b762a')], 'start:agent': '__start__'}, 'channel_versions': {'__start__': '00000000000000000000000000000002.', 'messages': '00000000000000000000000000000002.a6994b785a651d88df51020401745af8', 'start:agent': '00000000000000000000000000000002.d6f25946c3108fc12f27abbcf9b4cedc'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'loop', 'writes': None, 'step': 0}, parent_config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-36a3-6614-bfff-05dafa02b4d7'}}, pending_writes=None),\n", + " CheckpointTuple(config={'configurable': {'thread_id': '2', 'checkpoint_ns': '', 'checkpoint_id': '1ef55f2a-36a3-6614-bfff-05dafa02b4d7'}}, checkpoint={'v': 1, 'ts': '2024-08-09T01:56:48.386807+00:00', 'id': '1ef55f2a-36a3-6614-bfff-05dafa02b4d7', 'channel_values': {'messages': [], '__start__': {'messages': [['human', \"what's the weather in nyc\"]]}}, 'channel_versions': {'__start__': '00000000000000000000000000000001.0e148ae3debe753278387e84f786e863'}, 'versions_seen': {'__input__': {}}, 'pending_sends': [], 'current_tasks': {}}, metadata={'source': 'input', 'writes': {'messages': [['human', \"what's the weather in nyc\"]]}, 'step': -1}, parent_config=None, pending_writes=None)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_tuples" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "langgraph", "language": "python", - "name": "python3" + "name": "langgraph" }, "language_info": { "codemirror_mode": { diff --git a/examples/plan-and-execute/img/plan-and-execute.png b/examples/plan-and-execute/img/plan-and-execute.png index 829f2aee8..819ab9858 100644 Binary files a/examples/plan-and-execute/img/plan-and-execute.png and b/examples/plan-and-execute/img/plan-and-execute.png differ diff --git a/examples/reflection/img/reflection.png b/examples/reflection/img/reflection.png index b3fdc455f..d7e2e0c46 100644 Binary files a/examples/reflection/img/reflection.png and b/examples/reflection/img/reflection.png differ diff --git a/examples/reflexion/img/reflexion.png b/examples/reflexion/img/reflexion.png index 929be2e64..a1d5393dd 100644 Binary files a/examples/reflexion/img/reflexion.png and b/examples/reflexion/img/reflexion.png differ diff --git a/examples/rewoo/img/rewoo-paper-workflow.png b/examples/rewoo/img/rewoo-paper-workflow.png index cdfc7261f..476abee61 100644 Binary files a/examples/rewoo/img/rewoo-paper-workflow.png and b/examples/rewoo/img/rewoo-paper-workflow.png differ diff --git a/examples/rewoo/img/rewoo.png b/examples/rewoo/img/rewoo.png index 48af25d57..e719a0c21 100644 Binary files a/examples/rewoo/img/rewoo.png and b/examples/rewoo/img/rewoo.png differ diff --git a/examples/storm/img/storm.png b/examples/storm/img/storm.png index 696592181..8c36d189e 100644 Binary files a/examples/storm/img/storm.png and b/examples/storm/img/storm.png differ diff --git a/examples/storm/storm.ipynb b/examples/storm/storm.ipynb index 1138bf826..67e56dbf7 100644 --- a/examples/storm/storm.ipynb +++ b/examples/storm/storm.ipynb @@ -1,920 +1,1004 @@ { - "cells": [ - { - "attachments": { - "bdc25ea2-123b-46b1-b9f5-fdd345ecbc73.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Web Research (STORM)\n", - "\n", - "[STORM](https://arxiv.org/abs/2402.14207) is a research assistant designed by Shao, et. al that extends the idea of \"outline-driven RAG\" for richer article generation.\n", - "\n", - "STORM is designed to generate Wikipedia-style ariticles on a user-provided topic. It applies two main insights to produce more organized and comprehensive articles:\n", - "\n", - "1. Creating an outline (planning) by querying similar topics helps improve coverage.\n", - "2. Multi-perspective, grounded (in search) conversation simulation helps increase the reference count and information density. \n", - "\n", - "The control flow looks like the diagram below.\n", - "\n", - "![storm.png](attachment:bdc25ea2-123b-46b1-b9f5-fdd345ecbc73.png)\n", - "\n", - "STORM has a few main stages:\n", - "\n", - "1. Generate initial outline + Survey related subjects\n", - "2. Identify distinct perspectives\n", - "3. \"Interview subject matter experts\" (role-playing LLMs)\n", - "4. Refine outline (using references)\n", - "5. Write sections, then write article\n", - "\n", - "\n", - "The expert interviews stage occurs between the role-playing article writer and a research expert. The \"expert\" is able to query external knowledge and respond to pointed questions, saving cited sources to a vectorstore so that the later refinement stages can synthesize the full article.\n", - "\n", - "There are a couple hyperparameters you can set to restrict the (potentially) infinite research breadth:\n", - "\n", - "N: Number of perspectives to survey / use (Steps 2->3)\n", - "M: Max number of conversation turns in step (Step 3)\n", - "\n", - "\n", - "## Prerequisites" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": ["%%capture --no-stderr\n%pip install -U langchain_community langchain_openai langgraph wikipedia scikit-learn langchain_fireworks\n# We use one or the other search engine below\n%pip install -U duckduckgo tavily-python"] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": ["# Uncomment if you want to draw the pretty graph diagrams.\n# If you are on MacOS, you will need to run brew install graphviz before installing and update some environment flags\n# ! brew install graphviz\n# !CFLAGS=\"-I $(brew --prefix graphviz)/include\" LDFLAGS=\"-L $(brew --prefix graphviz)/lib\" pip install -U pygraphviz"] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": ["import getpass\nimport os\n\n\ndef _set_env(var: str):\n if os.environ.get(var):\n return\n os.environ[var] = getpass.getpass(var + \":\")\n\n\n# Set for tracing\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"STORM\"\n_set_env(\"LANGCHAIN_API_KEY\")\n_set_env(\"OPENAI_API_KEY\")"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Select LLMs\n", - "\n", - "We will have a faster LLM do most of the work, but a slower, long-context model to distill the conversations and write the final report." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": ["from langchain_openai import ChatOpenAI\n\nfast_llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n# Uncomment for a Fireworks model\n# fast_llm = ChatFireworks(model=\"accounts/fireworks/models/firefunction-v1\", max_tokens=32_000)\nlong_context_llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Initial Outline\n", - "\n", - "For many topics, your LLM may have an initial idea of the important and related topics. We can generate an initial\n", - "outline to be refined after our research. Below, we will use our \"fast\" llm to generate the outline." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/wfh/code/lc/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n", - " warn_beta(\n" - ] - } - ], - "source": ["from typing import List, Optional\n\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\ndirect_gen_outline_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a Wikipedia writer. Write an outline for a Wikipedia page about a user-provided topic. Be comprehensive and specific.\",\n ),\n (\"user\", \"{topic}\"),\n ]\n)\n\n\nclass Subsection(BaseModel):\n subsection_title: str = Field(..., title=\"Title of the subsection\")\n description: str = Field(..., title=\"Content of the subsection\")\n\n @property\n def as_str(self) -> str:\n return f\"### {self.subsection_title}\\n\\n{self.description}\".strip()\n\n\nclass Section(BaseModel):\n section_title: str = Field(..., title=\"Title of the section\")\n description: str = Field(..., title=\"Content of the section\")\n subsections: Optional[List[Subsection]] = Field(\n default=None,\n title=\"Titles and descriptions for each subsection of the Wikipedia page.\",\n )\n\n @property\n def as_str(self) -> str:\n subsections = \"\\n\\n\".join(\n f\"### {subsection.subsection_title}\\n\\n{subsection.description}\"\n for subsection in self.subsections or []\n )\n return f\"## {self.section_title}\\n\\n{self.description}\\n\\n{subsections}\".strip()\n\n\nclass Outline(BaseModel):\n page_title: str = Field(..., title=\"Title of the Wikipedia page\")\n sections: List[Section] = Field(\n default_factory=list,\n title=\"Titles and descriptions for each section of the Wikipedia page.\",\n )\n\n @property\n def as_str(self) -> str:\n sections = \"\\n\\n\".join(section.as_str for section in self.sections)\n return f\"# {self.page_title}\\n\\n{sections}\".strip()\n\n\ngenerate_outline_direct = direct_gen_outline_prompt | fast_llm.with_structured_output(\n Outline\n)"] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Impact of million-plus token context window language models on RAG\n", - "\n", - "## Introduction\n", - "\n", - "Overview of million-plus token context window language models and RAG (Retrieval-Augmented Generation).\n", - "\n", - "## Million-Plus Token Context Window Language Models\n", - "\n", - "Explanation of million-plus token context window language models, their architecture, training data, and applications.\n", - "\n", - "## RAG (Retrieval-Augmented Generation)\n", - "\n", - "Overview of RAG, its architecture, how it combines retrieval and generation models, and its use in natural language processing tasks.\n", - "\n", - "## Impact on RAG\n", - "\n", - "Discuss the impact of million-plus token context window language models on RAG, including improvements in performance, efficiency, and challenges faced.\n" - ] - } - ], - "source": ["example_topic = \"Impact of million-plus token context window language models on RAG\"\n\ninitial_outline = generate_outline_direct.invoke({\"topic\": example_topic})\n\nprint(initial_outline.as_str)"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expand Topics\n", - "\n", - "While language models do store some Wikipedia-like knowledge in their parameters, you will get better results by incorporating relevant and recent information using a search engine.\n", - "\n", - "We will start our search by generating a list of related topics, sourced from Wikipedia." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": ["gen_related_topics_prompt = ChatPromptTemplate.from_template(\n \"\"\"I'm writing a Wikipedia page for a topic mentioned below. Please identify and recommend some Wikipedia pages on closely related subjects. I'm looking for examples that provide insights into interesting aspects commonly associated with this topic, or examples that help me understand the typical content and structure included in Wikipedia pages for similar topics.\n\nPlease list the as many subjects and urls as you can.\n\nTopic of interest: {topic}\n\"\"\"\n)\n\n\nclass RelatedSubjects(BaseModel):\n topics: List[str] = Field(\n description=\"Comprehensive list of related subjects as background research.\",\n )\n\n\nexpand_chain = gen_related_topics_prompt | fast_llm.with_structured_output(\n RelatedSubjects\n)"] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RelatedSubjects(topics=['Language models', 'Retriever-Reader-Generator (RAG) model', 'Natural language processing', 'Machine learning', 'Artificial intelligence', 'Text generation', 'Transformer architecture', 'Context window', 'Impact of language models'])" + "attachments": { + 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SAYKb67kwa621lnn++eejg5988slm++23z/VFL774ovnMZz4ztM+dd95pxo4dm+sYbIwAAggggAACCLRJgODmNl0N8oIAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLRUwA5u/shHPmIWWGCBzJwecMABZosttsjcbjhvQHDzcL76nDsCCCCAAAII+AQIbua+QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMgUsIObb731VrPkkktm7sMG2QIEN2cbsQUCCCCAAAIIDC8BgpuH1/XmbBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUICdQQ3v/jii2bSpEnmnXfeMUsssYRZdNFFzYwzzpiYv5dffrnrM80iPWrUqOjfp0yZYp5++mkzffp08/GPf9wsvvjiZuaZZx7aR9/zxhtvRP8/22yzmdlnn9385S9/ifb57Gc/a+aaa67oM33PAw88EM1OveKKK5qZZpqp63ufeuop89Zbb0WfKc/Kw5gxY8zIkSNTfadNm2b+9a9/DW2jma1feOGF6P+POOIIs9VWW3n3n3feeYdsXnvtNfPee+9F2/3jH/8wm2222dA+V111VXTebpphhhnMRz/60dS8/ec//zGTJ082zzzzjJGrjrPggguaESNGePebOnWqeffddzs+k3fsqM+ffPLJyFM2usZzzDFHofuPnRBAAAEEEEBg+AgQ3Dx8rjVnigACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIFBaoMrj5pptuMocffrh59dVXu/JzwAEHmH322ccbUKyAZTftt99+Zv/99zfHHnusueCCCzo+VrD02WefbcaNGxf9+/9n707grZj/P45/7m1Pi7JU5Jct6RdF+lmyJVrsS5Twy1aUpEWSXUS2RIVIimiR7FlCylIpW8qWJJEfsiRLdVvu//H++s+YM/fs59zbubfX9+Hq3nNm+c5z5pwzZ+Y9n3nppZfswgsvdL+feuqptmrVKlMVaq9NnDjR3n33XRs6dKj/2L777mtPPPFEkf6ccMIJtmDBgiL9adGihR199NH23//+1ypVqlTk+Ztvvtnuv//+lNfDnDlzXEBYrX379vbpp5+mNI169erZ3Llzo46jkPagQYNMwehwUyD61ltvtTZt2hR57uyzz7aZM2dGPC6vp59+2p588km7+uqrXQA82IYNG2annHJKSn1nYAQQQAABBBDYsgQIN29Z65ulRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE0hLIVrj5+uuvt7Fjx8btg6ooP/TQQ656cLBFCzer0vHBBx9sAwYMiDpNhXMVDFbQOBhu1rTDwduGDRuaKjKH26hRo1xgOdiCHtFmrDDx448/7qpRB1uuhZtVpVkh5eXLl8ddJz169LCBAwdGDBMt3CxXhZujhaG9kbUeGjdunNZ2yEgIIIAAAgggUPYFCDeX/XXMEiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAhkLZCPcPHv2bOvcuXNEXxRkrlatmgsgB8PG/fv3t169ekUMq+rMv/32mymQqwrLas2aNbMKFSq4v1u2bOlCswrXBqtCjxgxwlRpORhu1rjqi+bfu3dvfz7t2rWzY445JuKxcF8KCwvtggsusHXr1rmfNWvW2LffflukEnW0qs8zZswwVa72mgLQXlPV51133TXqurryyiutVq1a7rnRo0fb4sWL3e+//PJLRPVphbCrV69eZBoKeYfDyRqoS5cuNmvWLH94BbxlWlBQYK+99lrEOgmHkidNmmTvvfeeyWPKlCn+NM455xwbN26caVqHHHKIzZ8/3xYtWuQ/f9ZZZ9lNN92U8TbJBBBAAAEEEECgbAoQbi6b65WlQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEsiqQabhZAVhVWV6wYIHrlyr8jh8/3vbbbz/39/fff28KvQYrJ3/wwQdWu3btIsuhIHOHDh386SgUfcUVV1j37t3dYwrmHnnkkX414osvvtguu+yyIuHm999/3xT6bd++vX366adu3Jdfftn23HNPF3xWGFutW7dudvXVVyf0XLlypQv1jhw50h/WC1bHGvnQQw/1+3n77bdbx44dE84nOIDcDjjgAP+hmTNn2i677JLUNN566y0788wz/WGvuuoq69q1q+Xn57vHfvzxR/e3t85at24dter2pk2bIuYpU63X++67z8qXL++mpYC4F4BWePrZZ59Nqo8MhAACCCCAAAJbngDh5i1vnbPECCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAygLBcPP+++/vqi1HawrGjhkzpshTX3/9tR122GH+4wMGDLCePXtGDKfqzaeffrr/2PDhw10gOtyC4WY9pwrJqtYcbAojKzytdtppp9kdd9wREW5WuPqTTz5xz6uqsxfgVUXkSpUquaC0V2E52XCzN/9gkLdHjx5RKyZ7w27OcLPWweTJk11X2rZt6ypCh5sqLh977LHu4aBZcLhwuFnPLVy40GrUqOEP9txzz5lC5moKPytYTkMAAQQQQAABBKIJEG5mu0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIKBAMNycaWEHmcFMVZFVD9pqCzDvssEPEYKrurIq/P//8s3tcIeFevXoVmVY43HzzzTdHVCDWCHPnzvUDy40bN3bB6pdeeskuvPBCN71Y4WZVjq5YsWJK4WZVilbV5o0bN1qdOnVs2rRp1rdvXzefVq1a2cMPPxyTbHOGmxX6njdvnuvbQw895KpdR2vNmzf318mHH35otWrVihgsHG4+7rjj7J577okYZvny5X5YfOutt7ZOnTol2ox4HgEEEEAAAQS2UAHCzVvoimexEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBVAQyDTc//vjjdtlll/mz/Oqrr0xVnsNNAWgFodUUgL3tttuKDBMON7/wwgvWpEmThIuTzXDzZ599Zg8++KDNmDHDD/5G60C0qtLB4TZnuDkYWlY/tttuu6iGL7/8sv3555/uuWjW4XDzoEGD7Jxzzkm4PhgAAQQQQAABBBCIJkC4me0CAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIKFAMNx86623WqNGjaKOU6VKFdtzzz2LPHfvvfeaxlMLVk0OD3jRRRe5ysdqrVu3trFjxxaZVjjcrCrN9erVS7gM2Qo3qxLztddem3B+GiBXw83hQHJSC2Nmzz33nDVt2jRi8PC0RowYYSeccEKyk2Q4BBBAAAEEEEAgQoBwMxsEAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQEKBYLj51VdftYYNGyYcJzjA1KlTrV+/fv5DX375pZUvX77INFStWWFltbPOOstuuummIsOEw82qoqxQdaKWjXDzvHnz7LTTTvNnpaB2u3btrE6dOm55fv31V3vnnXfsiy++cMPkarhZfQtWjdbfWpZE7fnnn7ddd901YrBwuPmxxx6zQw45JNGkeB4BBBBAAAEEEIgqQLiZDQMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgoUCm4WYFfjt27OjPZ+bMmbbLLrtEzHf6H++cAAAgAElEQVTDhg22//77288//+weHzhwoPXo0aNI38Lh5lhB6fCI2Qg3Dx482EaPHu0m3bJlS1dZunLlyhGzCga5E4WbjzrqKD8IfeWVV9qFF16YcF0EB5BV8+bN/YeefvppF6hOpnXr1s2mT5/uBu3bt6/16dMnmdGKDBMON0+ePNkOPPDAtKbFSAgggAACxSNQWFho+vycPXu2tW3b1ho3bhx3RgsXLjR9Lqs1adLEKlasmJWOrVq1yp599ll3UdJxxx2X1MVJWZkxEylVAoSbS9XqorMIIIAAAggggAACCCCAAAIIIFCaBFauXGnffvttzC7r9qzJVBSKNoFUD0KWJjf6igACCCBQMgJ//vmnLVu2zJYuXeo+r1ShbZtttrH69etbs2bN4nairJ6EUiBm9erVbtlVdW+HHXYomZVRiubC/k0pWll0FYGAQFl93y6tK7mgoMBeeeUV++677+yEE05wnzk0BEqLQKbh5hUrVrgwsNd69uxpAwYMiFj81157zc477zz/sXvvvdeOPfbYIkSbM9ysqs2q3qx2yy23WOfOnYv0TyHhp556yj2eKNwcDBhrWbXMqTQdJ9p55539UW688Ubr0qVLUpO47bbb7J577nHD6jvBtGnTigTOk5kQ4eZklBgGAQQQiC+wZs0a0767Wrly5Wz77bdPSKbwsb6rq+nuAdttt13McZ588kl3IYvXEl0ME/zcf+CBB9xdCjJtGzdutIMPPtj+97//uUkdfvjh9sgjj2Q6WcYvgwKEm8vgSmWREEAAAQQQQAABBBBAAAEEECiLAjr5q1t5KnSkoJGCwdWqVcvpRdXtN1VtJ1Z7+eWX3XKk01I9CJnOPBgHAQQQQCB9gV9++cXWrVsXdQIVKlRwIeK8vLz0Z5DBmDqJNH78eLvuuuuiTqVFixamKnOxWlk+CXXuuefajBkz3KL36tXL+vfvn4F02RyV/ZuyuV7L2lLpu4PCc/r56aefbKeddrLdd9/datasWdYWNanlKcvv20kB5OBAl112mT3++OOuZwoSvvfee2lf+JqDi0eXyrhApuFm8Sh0O2vWLF8qGJZavHixnX766X7VZr1G5s+f714r4bY5w83B/UaFtSdOnBjRPf2titNeSxRuHjJkiI0aNcof/vbbb7cOHTq4YFuyTaHoRYsW+e8t2m9LpnqzLnRUyMxr+q6i/hx55JEuJBdsCjDre0y07zKEm5NdUwyHAAIIxBbQOYMLLrjAfy//5JNPEnJ9+umn1r59e384jRPtczPaZ3DXrl3tmmuuiTmP4gg3a9/3lFNOiZjn+++/746V0RAIChBuZntAAAEEEEAAAQQQQAABBBBAAIGcFlCYWRV8dMIq3HbddVd34K1169bFvgz333+//frrr24+OlC4zz77JJxncYZ/wicCEx2ETNhZBkAAAQQQyKqATkTphFS8plt/Hn300aYqbVWrVs3q/GNNTNV8zjzzTJs7d27M+ekE07Bhw2I+X5ZPQm1p4Wbdkv3DDz9061q3fk9mn4r9mxJ5qTKTNAXWrl1rkyZNMlWgVHX6cKtXr55dcsklrrrm5rrAJNlFS+f1GWvaZfl9O1nPXBpu/fr1LmwfbMlWwUvne2kuLTt9KRsC2Qg3f/TRR3b88cdHgPzrX/9yQVrdVSTYwhWIdWGAbmOvpuM0XphXfytk7IWBDz30ULvwwgujous91ntO4S8vOKZK6gsWLHDj6AL7ihUrWvfu3e3FF190j2m//eqrr3a/33nnnXb33Xf709e+/QEHHGC6kFHHsD744AMXLKtcuXJEUHuPPfYwLdPee+8d0Tftk5144okRjynk1bBhQzcNLatCyFdccYWpanS09uCDD7ppB5uOnekiH7Uff/zRli9fHrUy87hx44pc/Kj5N2rUyFUAVSVRVZuX9zPPPOMfF9O61Oeu1958803/97322stq1arl/q5du7YNHz68bLwIWAoEEECgGAX0+XHSSSf5c1iyZIn7bInX3nrrLXesx2tff/11zMFHjBhhd9xxh/98rLsjeAMUR7j5jz/+sCZNmvh90GeVLjTP9e9oxbjamXQMAcLNbBoIIIAAAggggAACCCCAAAIIIJCzArNnzzaFdqMFE4KdVuXJ4O1Ki2OBdFJMJ4DUVD2nY8eOCWezcOHCiEpEOhE0cuRIf7xMKjenehAyYWcZAAEEEEAgqwLJhJu9GSo0oErKwRM7We1MYGIKg6hapNcUuNAtRRWayM/PN1WcbtasWdTbfnvjlOWTUFtauFnhHG17aolC7d76Z/+muF6dTDdTAVUnPvvssy0Yqoo1zVatWrkgVp06dTKdbbGNn87rM1ZnyvL7drGtgGKecKdOnSIuNJozZ467Q1Gils730kTT5HkEUhXIRrhZ81So/6abboo7e4V9dQymUqVK/nBDhw5NKiSr4zYaN1rLRrhZYWNVNv75559jLoPCvPfdd5+pomawPfzww6bPonC7/vrrbezYsXFNdJHOpZdeGnUYXch43HHHFZlfeGBdrHbIIYdEPKxxFXZTfxM1HZNSEFwtHKiLNa6+86gqJw0BBBBAIL7A//73PzvwwAP9gd555x2rW7du3JGeeuop69OnjxtGQeHXX3895vC//fabu1PA22+/7S641/mXeOHp4gg3q3MzZ850n3lVqlSxHj16uGNRNATCAoSb2SYQQAABBBBAAAEEEEAAAQQQQCAnBVSp54gjjojomw5w6XaaquYcDi3ohFG4wk02FywbJ5F14itY8TmTcHOqByGzacG0EEAAAQQSC4TDzTq55DW9h4dDEHpeIYtgcCPxXFIbQhfZ6HbT3rw1z0ceecSv5JbK1MrqSSjCzbErdsfaPti/SeWVw7DFKTB69GgbPHhwke8PLVq0sMWLFxf5/tC3b18/AFCc/Up32tkMN6sPZfV9O13fzT2eKvDdc8899v3339tZZ50V96KiYF+z8b10cy878y/9As2bN/f3J2fNmmU777xz2gulyvKDBg3yqyV7E1IV5169ekW9sDx8sXesmScbbg6GblURed68eW6SOvakStKxKjdrGFXG1B1PFCoLNl1AqMrQvXv3tg4dOhS5G1mscPOmTZvsiSeecMFvVY6O1tTHYMXN8DAqEKAL6ydPnhwzeK3xY1V/1nx1AZBCb7GKDdxwww3ugiI1he6SKQBAuDntlwkjIoDAFiagi0122203f6lfeOGFhBfDBy8Y0sUz+pzJViuucHO2+sd0yrYA4eayvX5ZOgQQQAABBBBAAAEEEEAAAQRKrYBuo6nbaXotWBVGj6nqjU4QeSdadKvLadOmFdvyZuMkcjbDP8W2oEwYAQQQQCArAsFws25Zrc+sYFOYSZXZvFtc6zlVrlPAqbjaxx9/bMccc4w/+VihiuKaf2mYLuHmzRtuLg3bCH3MXQFVkFywYIHrYOPGjW3ChAlWu3Ztv8M//PCDq1yvIN4ZZ5zh3nNVsT5XW7bDzbm6nPQrNYFsfC9NbY4MjUDJCKxfv950kbvCvQo2KxxcmpouIvzuu+/s999/txo1arhK7JUrV3aL8NNPP7nlqlixov+j0HSipjuqrFixwhRy0/hVq1a17bbbzhQSzsvLSzS6e17jr1y50goLC93fcq1Xr55Vr149qfF1Uea3335rBQUF7jOzZs2abtm0LDQEEEAAgeITCF5IFDx2o/dlvSerVatWzVU9VtNFnrrYU03HlYJ3RtDnie5yE63p82TbbbeNuyDJhJvVJ/VNrVy5chHfw/TYunXrbPXq1THnE1yW4lNlyqVRgHBzaVxr9BkBBBBAAAEEEEAAAQQQQACBMi4Qvm1yv379XLWbcAvf+vLpp592lZ29phM4XtMJoGgtOMzWW2/t34JNfdDJKa8df/zxplvCqV1zzTUxq0QrQKEDeNFaJuHmTA5C6iSYV6UzeMBSJw6XLVvmDi7usssu7pZ1yZ6g0okxnbhTRR+dhFQ1CZ2ATOYEXRnffFk8BBBAwAkkCjdrGJ38adeunQtyqClsN2TIkJiCCjZ89dVXbngFE/Teq1uTJhtu0B0D1C81ja+wczLjZnoSKji+PmcUitCJNc1fn626/amWpX79+jE/Q8MomVooeKILpRQy150hGjRo4GZR0uFmLcc333zjPk8VgGnUqJHVqVMn7qsoeDIzuO8SHGnVqlXu81lNQRgvIBRel7fccourDqjWunVrV6UvWlMAxgvohJ/fXPs36ofmLUO1WrVquf2QH3/80VXplZPCN7vvvnvSAR5NR/tMqqiqf7VdaP/IO2HM21vuCoSrm917771RK+Fqv1jfIQ455JCEweZs7e9qW9Q+t8Jhei3qe4le6+H95my+PjN93472npvqe1VxvD5T2QJjBT+C/Qq+hwa/b3nf6bRdafhw895v9Ljeb1S5Vd8B9X6h70Th0Hzw+1i0ZfA+G6M9VxzfSzWfVLfvbFmksg4ZFgEEEEAAAQQQ2FIFglX/g9X2O3XqZHPnznUsl156qV1yySXud/37zDPPuN/79+/v7n7gtaOOOirm3QA0jO4ipotDY7Vkws1Tp041ncPxmvaRg3cmCx6PijYfHRtT9WkaAmEBws1sEwgggAACCCCAAAIIIIAAAgggkHMCOhDnHZhT5+bMmePCKeGmk8QtW7b0Q8e6JaZujek1L6ikvx9//HE74IADIiahYJVOQHstOMzNN99s999/f8o2sfqqCWUS/snkIKSqIuy9997+sijM1bNnT5sxY0bE8qn6jw6WKtwUq8l87NixNnTo0Ki3Jx0wYIC79Soh55Q3HUZAAIEyJpBMuFmLrKrOd999t1t6XaCjC3XCTXcp0O26dWvpcNN796233mpt2rRJKKg7IujOCGqp3PEg05NQ+rxRaFhNnzHXXnutnXfeeX6o2+u4Qsa6rXfw9qvZtlBgtW/fvq5ya7Bp3jIePny4//mok4E6KVgcTSEx3S5cyxttnSrkrpN70Zr2d2bOnOmeCp+09Ibv3LmzzZ49u8gwr732mrNPtcnlxBNPjDra5tq/UWeOPfZYW7RokeuXXjuvv/66/3oKdvbKK6+0888/P+7+iQKv8vQuZguOr2XXPqaCkLTcFND7pE66e03Bfb0O0mnZ2t/Vdxq9r3gXGQb7opBz165d3T65d9I/m6/PTN+3vb5m8l6VzddnOutRr+cpU6a4UVUFu1u3bu73YOXjhx56yI488kj3eLA6noIVeg/+4IMP7KSTTioy+8cee8wFQHQB7ptvvhnxvKav9/bgxbXaBjT9eE0B6Wgt299L092+s2WRzrpkHAQQQAABBBBAYEsTCIaVBw4caD169Ciyz3rKKaf4xxSCoefw3cMSnVd49tln3UXfsVoy4ebgXTh1sV94HznR9xMdK9I5BxoCYQHCzWwTCCCAAAIIIIAAAggggAACCCCQcwL33HOPXzkwVtDL67SCC/fdd5/7s1WrVqbbtHktGG7WCWhVaAu2shRujncQMhxuVigq6BTeABSeO/DAA4tsF6pkreByOBAWHrBFixY2adIkvwp2zm1gdAgBBBAoAYFkw81jxozxL8yJFjhWlWa9by9fvjxur3WiSye84jVV5dVnrFoqJ44yPQkVDDdrGVUB+N13343aVQX+FMqNFiLN1EKVU08//fSo4VV1Rp9fuo24d/FPcYWbVTVaAeN58+bFXV8K4imQF27BcLOCdcHqSN6wZSXcnOgkazA8mWj/JtadQGSmi7YU4I7XdCHBk08+aTvvvHMJvIMwi3QEguFUvdc8+uijrqJ3Ki0b+7va99b7h3cRQrz5K0SgfW9VBy/JcHMynwGZvldl6/WZyvoLDhu8eMj7jFRFe1Vz95rC5+ecc467m0DwolcF0/fZZ5+Y4WZ9B33nnXfsqaeeitq9o48+2kaNGuU/lyvh5ky271jh5lQt0l2fjIcAAggggAACCGxJArqIXXejUdOFurpIPHxBp75L6DuzWvACvvB5kBEjRkRUbta+7/PPP+9zJvrenUy4+YgjjvAvYNfFweHv17o7UvixhQsX+uMk8/1kS1r/LOs/AoSb2RoQQAABBBBAAAEEEEAAAQQQQCDnBHSwzgvf/ve//7XBgwfH7KNOKPfp08c937BhQ3v11Vf9YTMJNyvY9OKLL/rTUlVnryn8FDz5HeycKgPGClFkUtkwk4OQ4XCzgmM6GKrg+Pbbb28KrQVbrGqeOqCqA6teU8jnsMMOs7y8PBdSCwbvVK2hS5cuObdt0SEEEECgpASSDTerirACk2pt27a10aNHR3RR76XBi0r0WacTWAUFBS4Ip/dzryW6lWgw3KzKPQpWJ9MyPQkVDDcH53f44YebKkiGK/p0797drrjiiiJdy9QiWPlIE5ejTgAqwDd9+nQXevY+I/V8cYWb77rrroiKzZrnQQcdZH/88Yd/e1lv4aPdeSKTcLMC3vo8LywsdLPQNuRVlVV1pWgXN2m4M844w+03RGuba/9GfQmGJ711p+XQHSt0q95wxdz58+e7fZ9ge++990wVr4JNFVtr1qzpTgAr0Oe1cGAxmdcPw5ScwHXXXWfjxo3zZ6h91Ztuusm9t5YrVy6pjmRjfzfcD81Yr58dd9zRtL2Fq4N77zXZfH1m+r6tPmf6XpWN12dSKy3GQBMnTvQv+jn11FPdRQwy1meP1xRsVsD5p59+sv32289//P333zdtPytXrrTbb7/dvWfqs8pbd8GLKbScujBG8ws2Xaijda62du1au+aaa2zdunX+ID/88EPEe36sys3Z/F6ayfadLYtM1injIoAAAggggAACW4rAI4884vYf1Y477jh3obruxti+fXufQN+BP/nkE/d38DyIvucHL+gLm/3111/uLiReyzTcrJCy+ug1ncc44YQTEq4q3UFSw6oRbk7ItcUOQLh5i131LDgCCCCAAAIIIIAAAggggAACuSsQDITFqkjo9V4V0XRyWS14QC98UC/Vys1hnWD1A53g7tixY8qAmYR/MjkIGQ43a1rBWzDrgObll1/uV3rQ8wsWLIiomvnLL79EhJp0wFIhOZmrKWSnULp3Ul+PK7yhKnQ0BBBAYEsUSCbcHK7Sqffiiy66yOd666237Mwzz/T/vuqqq6xr166Wn5/vHvvxxx/d33rPVgueDPrqq69MF9wEmx4LhupatmxZZNUccMAB/kVD8dZbKiehwuHmevXqmT6Xd9ttNzcLBbwUPPMuktFJNgW1gy0TC00nHGg77bTT3AU7XuBRweKePXtGVFotjnBzOECnsPqECRP8wK2qOatvXtMFVVOnTo2wyCTcHF6nqgw9fvx493DwlrapvGY31/6N+hgMT+pvrTNdMKD1qmpUCrrecMMN/uIo0K9gv9cUWNS251US1/rQMMETwwqYX3bZZf44iU78pmLHsNkVUJi9TZs2RULtuijx4osvdifYK1SoEHOm2djfXbRokdsuvaZtSkGERo0a+Y8pMK/3dl2cor7pLjR77rlnkX5l4/UZnGgq79vZeK/K9PWZ6dYR/J6oQLMCIuHPEu/OP8GgSPg7pdeP4N2FFHzW9ha8440+YzU9r+m9VReCxmpvvPGG6UJer8UKN4fHT/d7aTa27+KyyHRdMz4CCCCAAAIIIFDWBF555RV3vEfNOy6g4yS6q6IunPQuwtVFebo7lqore03fSapXrx6TJJvhZn3v1nmS4N25Pv74Y6tWrVrCVZLK95OEE2OAMitAuLnMrloWDAEEEEAAAQQQQAABBBBAAIHSK6DggRfUUiDFCy9HW6IPP/zQdKszr3322Wd+oDaTys3heaV7Ejk4nc0V/gmHm1V9cciQIRGLOGfOHDv99NP9x3RrOlU99FqwQrZO5qtymRds9obRgdFDDjnED5QkqiBaerdQeo4AAggkFgiGm1UFVlWCvaZKwTrZFK5YrGBrnTp1/OEGDBjgglNq0ao66/FgkC4YyNLJpGOOOSZxR0NDqHKvN894I6dyEiocbtatSIOf3ZrP3XffbXfeeac/S4WRdWcAr2VioWmoInbwThAfffSRq8wbbOEAdHGEm6dMmWL9+/f3Z6vgsk5UBlvYQp/RO+ywgz8I4eZ/tILhSYVEdQK4fPny/gDh2/bqQizd0tdr33zzjdt38Vp4/8d7vFu3bq66txp3p0j5baVER1ixYoX77qCq2+Gmqt6qqhwMuAeHycb+7vXXX29jx471J/v6669HveOLvrMoOH/ppZcW2af2Rt6c4eZsvFdl+vrMdMMJBpa9i2Z0MYnuDOAFQnSxjaq8B4PGwdt7B/sQDDfrcd0NQOsv2ILfGfU5GbxYJbw8JR1uzsb27S1Dti0yXdeMjwACCCCAAAIIlDUBHbM4/vjj3WJ5+6yjRo1yx/Q7d+7svp/qYrunn37aateuHXFRXaKL5rIRbtaFwvo+re/H3ndl9TVRoZrgekrluFJZW78sT/IChJuTt2JIBBBAAAEEEEAAAQQQQAABBBAoIYHgifBEAZLwbc90KzYvdEu4+e8VFg4360CobqsebBs2bPAraOrxcKWxYNBKFSLC1UC9aala4pNPPun+fPDBB131PBoCCCCwJQoEw83JLH+4oqzGUShKgWe1YMX98PSaN2/uX1iii35q1apluRxuVkWf7bbbLmIx9FiHDh38xxRKq1q1qv93JhaaSDBw6N3SNdp6Ubjbq25dHOHmu+66y4YNG+ZmraBlOOCux3UiMljtUyHD/fff3+8u4eZ/1lxwn/Gcc86xQYMGFVmtZ511lu8cXqfBi7uiVQz3Jhas3qygs0KntNwVWL9+vauIrursCriHW6dOnezmm2+OCMJrmGzs7wa3N13EoYs50m2bM9ycjfeqTF+f6bp54wUrFXsX/9xyyy2uUraqsetuPGpLliyx5557zlV9V4tVxT4c6NXFFHvssUdENxUg1l0V1BSi9+5QEG1ZSjrcnI3t21uObFtkuq4ZHwEEEEAAAQQQKGsCusNV8DiAjhN4F33rgmndpUTHUfR9o379+m4fVk13jnn11VfjcmQSbtbF2VtvvbXpu3T4u5aOcajYSbggSqzOEG4ua1tt8SwP4ebicWWqCCCAAAIIIIAAAggggAACCCCQgYAq6nkH4XSSuU+fPjGnFu+kMOHmv9nC4WZVdFC1snALeoXDzcHQsg5UhqtMetNSqG7p0qXuTwWMFDSiIYAAAluiQLLhZlXDV3g1WgAqGFpWNchwINhzffnll/2TSi+88II1adIkKvltt91mCiSpKXSlQHW6LZWTUOHKzV999ZXl5+dHzFp3bNCdG7wWDjdnahGsvBsvtKwKSLNnz3bdKI5wc/DztFWrVvbwww8XWQXhC44UMjz55JP94Qg3/0MWDE9eddVVptdduJ177rmmbTDaOg2GlvW8d0I4PI1vv/3Wv9AgVhX1dF9LjFd8AmvWrHEX3enCvuXLl0fMSAFnvScGWzb2d4PvVQof9OzZM+0F3Jzh5my8V2X6+kwb7v9HVDW5nXfe2Z/Ml19+aRdddJHpM1MXDOmiF20Xs2bNshdffNEUfFbr16+fqzgXbuFAb6KKeIn6X9Lh5mxs394yZdsikRXPI4AAAggggAACW5pA+LiAKjmfd955LtCsYwTal9R3He277rnnnv534WSO9WQSbo63HnQsZccdd0x6VaVyXCnpiTJgmRMg3FzmVikLhAACCCCAAAIIIIAAAggggEDpFwieyO/SpYu7vVmsFry9brgyAeHmv9XC4eZYwbd44WatB534T6Vdc8011rVr11RGYVgEEECgzAgEw80KsR5++OH+silo6VXsVbj57bfftipVqkQs+6ZNm2yXXXZJ2UPVJ5s2bRp1vFwJN0cLhOniGFU59Vow3JwNCwWnFaBWGzhwoPXo0SOqUbwgbMorI8oIwaquCizrpGS09u9//9sPrGu/SOFsrxFu/kcsGJ684YYbTDbhFm+dqoKrF2hMdv0mc7I42WkxXMkIKBjw/PPPFwms6rG9997b70Sm+7vh96rBgwfbf//737QXcnOGm7PxXpXp6zNtuMCIRxxxhH/hpYIguoDliy++sNdee81uuukmd+HDY4895v71LvhRheOTTjqpyOyDgd5YlfdT6XNJh5sz3b6Dy5Zti1TcGBYBBBBAAAEEENhSBIJ3lnr99ddNd6FSteTJkye7i7K136oLdFWExLvLYqJzKbIrrnBzuFhKovVEuDmREM9LgHAz2wECCCCAAAIIIIAAAggggAACCOScwIgRI0wHt9R0cG7q1Kkx+6jbTd97773ueQXHHnnkEX/YROHm3377LSIApgODOmgYraliplfxTbcw7tixY8puv/76q+2zzz7+eKoapsoK6bRUDkJmI9ysaoiPPvqo39Vkbi+nQEesCojpLDPjIIAAAqVJIBhuvvPOO61Dhw5+9xcvXmxt2rTx/44Vygx+9mjgZN57Fdbbddddo1KV1nCzFiZTi2DANd5dIYo73Kzbx6pSt7dMwc9Wb6WtX7/edt99d38dar8oWNU6mXBz+/btTQFxtUsvvdQuueSSqNtENsKTm2v/RguUaXjy2WefdRW6vZbMa0yhdIUiaaVPQKFWhdO9Fr5wIBv7u8H3qu7du9sVV1yRNlQ2Xp/BmacSHsjGe1Wmr8+04QIjBgO9ukW23hvVPv/8c/d9c/To0XbzzTe7i4ymTZvmnot1l5tgoDfRd9Rk+l7S4eZsbN/ecmXbIhkvhkEAAQQQQAABBLY0Ad1tZu7cuW6xH3jgAb86s/Yj9bjuFNOsWTNr3bq1DRs2zA13+eWXu7uVxGupnFfQdIIXX+sujTq/UbNmTXdHruAFxroAUHfjrFSpUlKrKpXvJ0lNkIHKpADh5jK5WlkoBBBAAAEEEEAAAQQQQAABBEq3QLAas5ZEJ5vr169fZKFUHU0nln/++Wf3XLgyQTDcrNu+q3JmsC1ZssSOPPJI/6F44WYFIRSIUFMlhAsvvDBl5NQSsb0AACAASURBVHDIWKFt9T+dlspByGyEm3Xr5kGDBrmuZuNkfjrLzDgIIIBAaRKIF27WcgSfj1W9WdV6p0+f7hY7XiA3WZfSHG7O1EJ3E/AugDrttNP8i6jCdsUdbg5ewKX1rkqiOiEYbOEApm41u99++/mDBMPNqkCtStThFjz5GC/crGC9V61U+0naX0q1ba79G/Uz0/DkokWL3DS8pr+rV6+eKgHDlyKBYBV3VScOBtWzsb8bfA9R0OCZZ56xvLy8tISy8foMzjiV8EA23qsyfX2mhRYaSe+PEydOdI9q+RXarlevnguDqGKzvtcphP7OO+/YBx984IbT+/J2221XZPbBQK8uiNV3x0xauuHmdL+XZmP79pY32xaZODJuagK6U4Yusta/33//vamyvSqXV6hQIbUJMTQCm0lA7519+vSx7bff3r2fH3zwwdauXTvbaaedNlOPmC0CCCBQfAI6DqTjAWo6JqIL89R0zED7s95dYs444wybMGGCe053h9IFufFaKucVNJ3g8QWFrPW+6zUVgRk5cqT/t/a3gxcQx+tHKt9Pik+ZKee6AOHmXF9D9A8BBBBAAAEEEEAAAQQQQACBLVDgjz/+sCZNmvhLrmoDqjoQbq+88op17drVfzhcZat58+Z+8FlBIAUEgk0HBFVd2Gvxws3BUJVO1HvVolNdPcHA9fDhw+3EE09MdRJu+FQOQmYj3Dxz5syISgzhA5lpLQQjIYAAAmVYIFG4ecGCBRHVeK+99lo7//zzI0SCYWRVlFVVyV122SVttdIcbs7UYtSoUTZkyBBnJ8v58+dHrYR9xBFH2NKlS91wOiGnE3PZbNpX6d27tz/JaBdfqYro/fff7w8zb948q1Onjv938ASnqnTr9rTBpsBScP8iXrg5GHZT2HrOnDlJV1kKznNz7N9o/pmGJ8P7nNqvVBCeVnYFgsHQ8EUj2djf1fuM3m+8phP9xx9/fFqg2Xp9ejNPJTyQjfeqTF+faaGFRtL3raFDh7pHO3fu7ILO3oUcuoBWQZC2bdvae++9539vXLZsWdRAerYDvemGm9P9XpqN7dvjzbZFNtY104gv8O2337rPNwWZw+3rr78uMb6vvvrKdJcRXfyhUGq5cuViznvhwoUufK2m40MVK1bMSj9XrVplunNDlSpV7LjjjnP/0uILrFy50rQNxWq6I1lJOX700UdRP1d1oYr2satWrcrqRAABBMqMQHD/fa+99jJdjKvv7e+//74Fi7Y0btzYv3NTvPMbHkwq5xU0Trxw859//unutOUVn9Hw2s8NHiOItUJS+X5SZlYqC5KyAOHmlMkYAQEEEEAAAQQQQAABBBBAAAEESkJAoWOvGoHmp6rBCih7lc9UnUC3ZvOaTo7pBFWwBW/dpiDTpEmTbO+993bTUGWuM88803QAzmvxDv6FgwqqStChQ4e4J+OiOQVDUwokKTig27ipqRL18uXLrW7dula5cuW4zKkchMxGuFknFXXiz7vFvTp33XXXuXUQ7RbuGzduTNmmJLYr5oEAAgiUlECicLP6oaqhb775puuS3ktVLTJ4Ql4hAgU/vKaTWPo80l0HypcvH7Eo+gzR51u8CqGlOdycqUW4GrI+vxQi9hwLCwttypQpdtlll/muxRFu/v333+2AAw7w9z9UcU4VlrRPoPbCCy+YqjF7TbeXHTt2bMS6Dob19IQqM+kzWlUXf/jhB1fRSeF5r8ULNyvkpEqzXtNdMFTpNNpne7zXzubYv1F/shGe1P7MuHHj/MXT67Jnz562ww47FFlk7Q+FX3sl9Z7CfOIL6AS6To6rMnP79u2L3PVF604XDeh90Gvhi/Wysb+rYOzhhx8e0VkFCnX75mjbTkFBQczAXrZen15nUgkPZOO9Khuvz0y3e72vexep6H1WF694FzF4nyve45qXQiO6kChay3agN91wc7rfS7OxfXsu2bbIdD0zfnwBhZ/03hg89uGNEdzmtY0oxOo1VTBP5jNP+x7aD1VTNd1YgWWFqA877DB/+ldccYWrnB6rxQtSpbvOdZxC+9b/+9//3CT0fu3d2SPdaaY6nsJfev9ZsWKF823YsKGrOpyMdarzytbwXqX7WNNTNXAFnEuiaXvbf//9o85KdxnT+kx1P7Yk+s08EEAAgXQEor3/endU1Oe6PivDTRe0BS+K1x1ZdJeSYFuzZo07/uQ1nVepUaOG/7fe06+++mr/70SfyeELI8PHMfSZF61wjULa3v6J3rtVqCbYrrrqKlNwm7ZlCxBu3rLXP0uPAAIIIIAAAggggAACCCCAQM4KhE98qaM6yHXQQQfZ4sWLXQg42FQh7eijj4547L777rNbbrkl4jGFiKpVq+Zu3xY8ke0NpJN7Gkch6GALV0HUcwqZ6USUgsi//vqrO0GlE3S63X2sFry9tDeMDtIp4KzKRDqgN3XqVNOBSq9lehAyG+Fm9SVWhZx9993XdtxxR9OJQp0MVQBalfAUrqIhgAACW6pAMuFmVck9/fTTfSKdPAq/dyp0qfBlsOnzp1GjRqbQiU5Kfffdd66CzzPPPGP77LNPTPJ0ws3ZOAkVDuhFqxAY/pzVZ0m48lqmFgqsqlqg1/71r3/ZgQceaAo2q2qnV7HZe744ws2a9pgxY4rcTUL7JFqXXtjG64PCzsG7Wehx9fWUU06JWM/aR9p99939ULP2T7Sv4zVNX6Hpjh07RoynAKOCPcEqS5rWHnvs4fZztA/xzTff2CGHHOKCo7m0f6O+ZCM8qerNClsFDTRt7RNq/0ah8d9++80++eQTV2kyHDbfUt/jcm25H330UdPJb69p+91tt93cOtT+dbAyr4bR86+99prVqlUrYlGysb+rffdorxdtUwr+6SJFbW96jarKui5YiNbSfX1m4307G+9V2Xh9ZrqdhQPEmp4uotUtvBXk1DYSbOH1oc/fL7/80g2izy7vO2g4fKHP/GBoM9xvPa/1HmwKnAbf81XxLtj0Ph9+r9fzmXwvzWT7zpZFpuuU8VMTiPYZp+/vqlquzzTtO3hhZL0vBUNFeo/U84lasDLj+PHjY74WwsdntK/y6quvxpx8oiBVon5Fez7aPpSCVfpMKO6mfVvti0ernq15Kwim9yd9bsVqL730knsPUFPAV+OURMulcLOWV/vM2i/TutN2FdyHa9euneniJRoCCCBQFgTCx1K0TNo/HDZsmFu84Gelt7x6fwxe5KHjGuGCMIls9BmjiwS9lugzWRc56XxIMDAdvJA0fLF5ovl7zydThTrZaTFc6RUg3Fx61x09RwABBBBAAAEEEEAAAQQQQKDMC6jqoKoJhsMm4QVXxcKTTz65iIdOeKiSYDgo5A2oE28KQYSD0tFuEa9xrr/++oSBlksuucRUITFWC1eejDbc3XffbSeddJL/VKYHIbMVblaHVInhyiuvjFr1Kbgsqnp40003lfltlAVEAAEEYgkkE27WuHq/190E1HQCav78+REnohS+UkhOJ+4TNQXqVJkvVksn3JyNk1DZCjdnaqFgmj6fwp/7npf8td/gBaCLK9y8du1at68QDFpHW2fXXnutnX/++VFXZ7x9Az2ni64U9gw2hbsHDBhQZHovvvhi3MqJGkFBKO0D5NL+jfqSrfCkAn/9+vWLCIRHW1YF4r1q64lejzxfsgK6ODGZ90mvV9qetV1Ha5nu765bt85uvPFG03eNRC3a3WeC46Tz+szG+7b6kOl7VbZen4kM4z2vi2LbtGkTMYhCerpgQ+2oo46KeN336dPHXaTpteBndLz5KGASLYjsjRMteJJoufSe1Lt376iDZfK9NN3tO1sWiZab57MroLthKTDrtbZt27oLKqpUqVJkRgomBSs9JhMo0jGXYMXeaBdleTNS1cjgRVa6W4TeK2O1REGqdKQU9g5eNKaLv7SfGu/OJ+nMJzxOtAstok1X+6JaX7HeT3QhpPfZEgy3ZaOP8aahC/FnzZrlD6L1PnLkSP/vkqzcHO5ntP37eNthcVsxfQQQQCCbAh9//LEdc8wxEZPU/qH2E9V0/Cd41yZ9jijcHGy6i0kwqJxM/8J3NtDFT975mfDdb7zp6bNCd5TymgrM6IJ+fcZGu7tNMv1IZl8kmekwTOkWINxcutcfvUcAAQQQQAABBBBAAAEEEECgzAvolpMKyc6bNy8ipKyDdf/5z39MJ6BjBROE89NPP7nbqCkYEGxeCDl8EFDDxAo362TfE0884arABKshBqerKgXxKhtqWB2Y1IHIaNNQxSCdzAoeuMz0IGT4NnWqjqQqSeEWrLgU7+DhqlWrXIUIncCKFRxXFW1V06YhgAACW6pAMHwaL3SsqnjnnXeez6TPPAVww02fGQonv/322zEvMNHdAc4+++yY5OmEm7NxEuqtt96yM8880/Ur2sk2Pa5qdu3bt/f7rkBapUqVoi5LJhaqwKv9gnDlIlUm0sU72t+4+eab3XyLK9zsLdSkSZPsnnvuKRK2Vthx0KBBcfdvCgoKXKhEF0QFm4J6Ci3de++9EaETDRMr3KzntNy33367+zda0/6JquPFayW9f6O+dOjQwa8ONWTIEFeJMtyCFxooVK59wGhN4XndylyhoXAVb294bb+qkp6fn7+lvrXl7HJrnemCAe2fah1Fa1p/CtPpfVIn2+O1bOzv6sIVha69u7NEm5+Cda+//nrcvqT6+szG+3awQ+m+V2Xz9Znuhqf3/KZNm0aMrgsUdKGC2kUXXWTTpk3znw+HlDt16mRz585NOPtE4eZgICThxP5/gIEDB7qK+9Fapt9L09m+s2WR7PIzXOYCutBCd77ywkiqaKv9g/Lly8ecuCqIexeBaT/j+OOPj9uR8MXbqtioO4tEa7pThj5jdacRVbLX669OnToxp18c4WbNbObMme6idQW89RrTfldxNgWqtZ+p4zLB/QkFzStWrOiCX8EL7/RZpTD0tttuW6RbmyvcHO6ILqIL3i1mc4ab1bcff/zRBfy841MK12kfm4YAAggggAACpV+AcHPpX4csAQIIIIAAAggggAACCCCAAAJbjMAvv/xiOnmmgM0OO+yQUnUdVZbRiX6dCK5fv77VrFnTuSn8rMd0Usn7iXeyz8NWX3TLZwVhNH7VqlXdSTz1LdmqPzqprGkooKQTWBq3JG6Hms0NRhXddLt6nbBTq1atmls3wdvfZXN+TAsBBBBAwExhLX0e6vNDIUt9pum9V59jW1pL12Ljxo2mSm8K/ihc6AWpFTz566+/3Oe6Qi8lEWLVPBVq0bzUlwoVKiS9GrUfonH1Obz99ttb3bp13bi6a4P2fTQtLZv+1U+ifRRvf0mf7zKSQa1atdy0k9k/0rzLwv6NXL/77js/EFa5cmUXwKpdu3bS64YBN5+Atl8Fr7S/rvfJ6tWru224Ro0aaXUq0/1dBfpWrlxpumhT7zl6r9a+sgLWeq9JtmXj9ZnsvKINl8l7VSbzZdz4Apl+L810+2b95K6ALky4/PLL/Q6+9NJL1rhx47gd7ty5s82ePdsNowutzjnnnLjD6yKO4F2nvvrqq6ztOxVXuLmk11j4QkZdDK+LP4L7mFOnTnVVOPXZoN9jrSfCzbHX3pgxY0wXenpNIfZgJfKSXu/MDwEEEEAAAQSyI0C4OTuOTAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENjsAscee6xfyV6Vg5O5Jf1ll11mjz/+uOu7Kit74WhdXOFd0KwLNLyLxXVXqPPPP98Nr4roqozuNQ2v8WI1XTwV78KpZMLNuoBFF7qplStXrshFSLqgRBd8xWq6OFsXchVnU5hZd3FRi1etXxeNq7+77767351w/3U3AN1NTK1169buri7Rmi7q0YVZalp+TUdNF7ptvfXWcRdXBQB0UY5arHWUrcrNuphMgXjdfUHB7t12281dqJfoYrxoC6CL64JVuC+++GLT9kxDAAEEEEAAgdItQLi5dK8/eo8AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPgCDRo08H+/9957TWHnRG3YsGF21113ucFOO+00UzBXTeFc7/cDDzzQJk+e7B4PVodu2bKlTZw40Z+Fquiqmm6sduONN1qXLl1iPp9MuNmreOxNZPHixf7dOPTYyy+/bBdccEHMebRr184eeOCBRCwZPX/77bfbyJEj3TR0p6758+e7IHYyLVz1OZlxNMzw4cPtxBNPdIPffffdduedd7rfFSB+//33/eBzeHq6w4CC8F578MEHrU2bNkVmm2m4WXcCUGVwbzsKzkBGt956a9T5Jlr+gQMH+tugtndt9zQEEEAAAQQQKN0ChJtL9/qj9wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgBMIV7F9/fXXXdXgRC0YVj788MPtkUcecaP07dvXnnzySfe7wqcKyKopRDt06FD3ezAMrb8ThZuvvvpq69atW8wuJRNuVkBaAVy1cOVoPZYo3Kzqx2PHjk3EktHzqpjdv39/fxoKicsqmZaNcLMqQh9yyCH+7OIF3cNh8YULF1qNGjWKdDWTcLOqNJ999tm2fPnyuAQ9evQwhZVTaY8++qhdddVVbhRVcX722WdTGZ1hEUAAAQQQQCAHBQg35+BKoUsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECqAp9++qm1b9/eH+3jjz+2atWqJZzMzJkzXfBUrWHDhvbqq6+630844QRbsGCBP/4nn3ziqgBfc801fgC6d+/e1q9fP38YBYunTZsWMU9NT1V71bIRbj7iiCNMYVk1VSpW2DrYlixZUuQxBXa9cUoi3Pzdd9/ZQQcdFNEvhZsvvfRSq1evXtx1smzZMld9uLCw0A2nsPPPP//sfleYW1W0o7UzzjjD9t13X/8prVOtW7VgaD08bs+ePe355593D8ezySTcrGrds2bN8met7UxB5IKCArd83vahAV566SVr3Lhxwu3WG+CVV16xrl27uj+1fWo7pSGAAAIIIIBA6RYg3Fy61x+9RwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEnEAwpKy/v/7666RkgqHoYDi0QYMGEeN7oVNVXp4+fbp77pZbbrHOnTvHnc9JJ51kH3zwgRsm03CzQsrHHXecP78RI0a4EHaipsrJGlatJMLNmo8CyrfeemuRrnXq1Mm6d++eVFVtz2z8+PFuOqeccooNGzYs0eK658MVrN9++22rX79+xLgbNmywpk2b+uHi22+/3Tp27Bh1+umGm9966y0788wz/WmqyrLCyPn5+e6xH3/80f3tBelTXT8aL7gNfPbZZ1alSpWkjBgIAQQQQAABBHJTgHBzbq4XeoUAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCKQkMHHiRBs4cKAbZ9ddd7XXX389qfHDodXFixfb77//bvvtt58bX9WAFU5+4IEHrF27dhEVnR9++GFr1apV3PlkK9y8ceNGF7x99913/fklW516c4Sb1clRo0bZkCFDovqomnGfPn1sm222ieunQHg64WYFl/fff3+/6nP//v2tV69eEfOaN2+eqaK012S73XbbRe1PuuHmAQMG2OTJk90027Zta6NHjy4y/UWLFtmxxx7rHk+1+nK4SrYqRO+8885JbfsMhAACCCCAAAK5KUC4OTfXC71CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRSEnjyySetb9++bpx69erZ3Llzkxq/sLAwIgw6Z84c++GHH0yhZE3n4IMPtieeeMJUcfeCCy6w5s2b+4FZr5pzvBllGm5W/7755hu78cYb/YrRml/v3r2tX79+SS3j5go3q3MKiyvgPGPGjCJ9VZD3sccecwHyWC3dcLOmN3ToUBs+fLi/Tah6c7ly5fxZBV1atGhhU6dOjdmPdMPNCk8rRK320EMP2ZFHHhl1HsHt6sMPP7RatWoltW6XLVtmhx9+uD+stt8ddtghqXEZCAEEEEAAAQRyU4Bwc26uF3qFAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgikJDB79mzr3LmzP85XX31l+fn5SU3jiCOOsKVLl7phn3vuOfvyyy9dVeGWLVvaAQccYMOGDbMzzjjDBg8e7KpCe00VnWvXrh13HumGmxW23XrrrU1h1T///DNiHv/6179MwWqFg5NpmzPc7PVPVaYffPBBUwg93FRlO+gafD6TcPPXX39thx12mD+5CRMmuLC614466ij74osv3J/XXXednXfeeTE50w03B0PLhx56aMzK0C+//LK/nl944QVr0qRJMqvWVfLu0KGDP6y23fLlyyc1LgMhgAACCCCAQG4KEG7OzfVCrxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgZQEFE5WSNlryQSPvWG7dOlis2bNcn+OHTvWPvroIxdo7tSpkylkfNlll9n+++9vo0aNcpWbvaaquXl5eXH7mW64Od5EFeTecccdk/bJhXCz19nvvvvOBYmnT5/u918h4zFjxkRdnkzCzZpgcN2efPLJdtddd7n5qB8HHXSQP8833njDGjRoENM0nXDzpk2bbJdddkl6PXkDKmDftGnTpMabNm2aXXTRRW7YbbbZxt5///2kxmMgBBBAAAEEEMhdAcLNubtu6BkCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJC0gKob//vf//aHV3i2UaNGSY0/cOBAmzhxohv29ttvtzfffNOeffZZu/TSS12Y+cwzz3TB0UmTJlmbNm3ccKqerOESteIIN48fPz6iInGiPuRSuFl9VehXtsEqzgqnlytXrsiiZBpuVoXrCy+80J+ugus1a9a0xx9/3IXW1Ro3buwqYcdr6YSbNT1Va16+fLk/6WSqbT///PMxK1mH+zhu3DgXFldTAH/KlCmJNgeeRwABBBBAAIEcFyDcnOMriO4hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALJCijcrJCz2uDBg+2///1vUqMOHz7chg4d6oZV4FXh0k8//dTuvPNO23ffff2K0A888IBdcMEFbjiFVh999NGE00833HzOOefYPvvs44K4+fn5dvbZZ/vzUrD61VdftUqVKiWcvwbItXCz+jRz5syIZZo7d67Vq1evyPLccMMNflXnVq1a2cMPP5zUMnsDrV+/3po1a+ZvFzfddJOdddZZ1r17d3vxxRfdYP3797devXrFne7q1att77339oeZOnWqq+qdqHXr1s2vUt23b1/r06dPolFSej44/VNOOcVVHKchgAACCCCAQOkWINxcutcfvUcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBHyBzp072+zZs93fu+66q82YMcPy8vISCgWr+Hbp0sUeeeQRN87kyZOtadOmrrKvmoKko0ePdr936tTJbrvttoTTTjfcrCB1u3bt/OmrovTIkSP9v5MJ5HoD52K4+eWXX/aD4urnZ599ZlWqVCni+dBDD9mgQYPc46qePWfOnKRD3d7EgnZ77bWXPfXUU9awYUN/Xqra7K3jeCu0QYMG/tMKxJ944okJ17+2kXvuuccNp6rN06ZNs1122SXheMkM8O2339rBBx/sD3rllVdGVKlOZhoMgwACCCCAAAK5J0C4OffWCT1CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTSEnjttdfsvPPO88dVOPnAAw9MOK033njDr/Ks8OuiRYvcOLNmzbKdd97ZvIrQCsCqorNav379rHfv3gmnna1wsypSq1r0zz//7M9T/Q4GbmN1pqTDzQqI161b14455hgXvq1QoUJE15YtW+a8ly9f7h5X0FiVqKM1BdTPPfdc/ylNe+DAgS4onGzT/A4//HB/8KuuuspUwVlN1aJVNTqZdsQRR9jSpUvdoArPP/30066yttqmTZvc8mi5K1eu7E8uHEBWQHvIkCF25JFHWvny5SNmq2kojJ9MIF8jqtq4QtZe++CDD6x27drJLArDIIAAAggggEAOCxBuzuGVQ9cQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFUBBQOVWjUC6Duu+++rgpzjRo14k5m8eLF1qZNmyLDfP755y6oesIJJ9iCBQsinlc14I4dO/qPKVg9duzYItN48803/cf+9a9/FQkjjxs3zg+5eiFqjRCu3KzHFKYNBqpbt24dMc8VK1bY5ZdfXqQP77//vikcraZQcPPmzSOGUdg3mcrFya6L4HJoHIWXVa1YlZnVx3fffTdiUvEqDv/+++8umBwMdWsZ9thjD1fJefXq1fbNN9/YIYccYgpxx2rBqt7BYXr06OHC0sm0G264wcaMGRMxqNwUcF64cKEznjp1qrVo0SJiGK3j6667LuIx9b1Ro0a23Xbb2Zo1a+y7775zofpnnnnG9tlnn4TdUci+Q4cO/nrt2rWrXXPNNQnHYwAEEEAAAQQQyH0Bws25v47oIQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkLTAU089ZX369PGHVyVmBZwVJo3VfvvtN2vatGnE0xpeoWC1nj172vPPPx/x/Pjx4+2www7zH5swYYJdccUVSffTG3DJkiV+ZeNE4WaFt0877bSIcHAwBP3FF1/YUUcdlXIfkq1wncyEFUaWebKtbdu2dv/991t+fn7MUV588UXr3r173EkqyK7wd6w2bdo0u+iii4o8/eSTT9p+++2XVHfDVZijjXT38jYz1gAAIABJREFU3XebqnUH24YNG1zw+r777ks4nxEjRrgwfbz23nvvucrXXmBdw7799ttWv379hNNnAAQQQAABBBDIfQHCzbm/jughAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQtMD69evt+OOPN1W29Vq9evVclVuFWJs1axY16NygQYOIeaj6rqrwqt1666127733Rjw/ffp0V3nXa1OmTLH+/fsn3U9vwC+//NKv3KyKyl6F4miVmzWOKgQfd9xxEcs2Z84cy8vLs2XLlrkqx6m2bIabFeR94YUXbMaMGSajYAA32C/5KrCs6tPlypVL2OV58+aZqmXr32gtGEaP9nxBQYGriBzsj7aL2bNnxw1Wh6f18ccfu+rZCpKHm/owePBgO+aYY6L2UePcdtttLogcy0XVoc8+++yI8QsLC+3rr7+2Dz/80BRsVlg/2KjanHDzYQAEEEAAAQRKlQDh5lK1uugsAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQWEAB4VNPPdWWLl1aZOBdd93VXn/99cQTYYiMBVRpevXq1fbLL7+YKjorgL3ddtu5n/Lly6c1/TVr1rgQ99q1a23jxo1WpUoVq1Wrlm2//fYJp6mQsLaLd999181bYfRevXql1Y9Vq1bZihUrTKHprbbaygXm41UHD89E1cJVCVrjq2p1zZo1bYcddrCKFSsW6c93331nBx10UNR+nnjiiXbnnXcmXPa0FpKREEAAAQQQQGCzCBBu3izszBQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECheAVXGHTp0qI0ZM6bIjFQFl7blCTz88MN27bXX+guuKtB16tTJeYgPPvjATjrppIh+KlB9zTXXWKdOnVKqPJ3zC0sHEUAAAQQQQMAIN7MRIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACZVhg5cqV9uqrr9pHH31k33//vf3xxx82YcIEq1ChQhleahYtKLB48WJ7/PHHbfTo0f7DF198sV122WWlAurDDz+0m266yVWGrlu3rqvifNhhh7mq1TQEEEAAAQQQKHsChJvL3jpliRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS2YIFly5bZ/PnzbcWKFfbKK6/YokWLIjQaN25sTz31FOHgLXgbYdERQAABBBDIZQHCzbm8dugbAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIpCqhC8+DBg6OO1bBhQ3v44Ydtxx13THGqDI4AAggggAACCJSMAOHmknFmLggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIlIjAxIkTbeDAgRHzatGihbVv397OOussKjaXyFpgJggggAACCCCQrgDh5nTlGA8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgRwUmDdvnj3zzDNWt25da9CggbVs2dK23XbbHOwpXUIAAQQQQAABBIoKEG5mq0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBnBAg3JwTq4FOIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggADhZrYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKLMChYWFtmHDhoifn376ybbZZhu3zHl5ef6/wd+Tfa6goMAqV64cMR1v3GjT8x4rs+AsGAIIIIAAAggggAACCCCAAAIIIIAAAhkKEG7OEJDREUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoHgFRo4caSNGjLAqVapYfn6+CxJ7IWGFl72fjRs3mn42bdrkh5nVs3Llyln58uX9n2233dZ++eUXN56aN36i36M9rz799ddfCacTHje4DPq9UqVKpqC0t3zB5fR+1zS88aIFp7UcwfC0fveWUeOuX7/eGWh6cgr2ScPp+SZNmtjUqVOLd4UydQQQQAABBBBAAAEEEEAAAQQQQAABBOIIEG5m80AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGcFjjllFNs1apVduKJJ9qff/5pf/zxh/vR797f3u/evwodV61a1f3od1VX1o9CxPqpWLGiVahQwf0o8KsAtPcTDhB74WeFpjP9Wb16tZungsyJftQPr5/B/gZ/95ZB/6pvW221lb9ca9assVq1arnl0/MrV660+vXr26+//mp169b1w95e+Hvp0qU2ceJE0780BBBAAAEEEEAAAQQQQAABBBBAAAEENpcA4ebNJc98EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAICkBhZoV0h03blxSw2sgVVNW0FkBX+9n3bp15v0oWKxKxfrZsGGDq2Ts/QSrQWtaXthZFY8z+VGI+OOPP7b99tvPhZbDPwogBx/T8CXZXnvtNevevbt98cUXJTlb5oUAAggggAACCCCAAAIIIIAAAggggECEAOFmNggEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIaYFjjz3W6tWrZw8++GBO97O0d+6JJ56wgQMH2pIlS0r7otB/BBBAAAEEEEAAAQQQQAABBBBAAIFSLEC4uRSvPLqOAAIIIIAAAggggAACCCCAAAIIIIAAAggggMCWIHD00UdbgwYNbNSoUVvC4m62ZZw+fbr17NmTys2bbQ0wYwQQQAABBBBAAAEEEEAAAQQQQAABCRBuZjtAAAEEEEAAAQQQQAABBBBAAAEEAgKbNm1yt6YP3qbeu12997j+9X50+/rw78Hb2kf7XfPQj57zfg8/VlhYaN5PeAXl5eWZ95Ofn2/BH92+Xj/ly5f3f3SL+/Bt7itVqmTeT5UqVaxy5cqmf6tWreqmTUMAAQQQQCCXBNq2bWt77LGHjRw5Mpe6Veb6ct9999mwYcNs8eLFZW7ZWCAEEEAAAQQQQAABBBBAAAEEEEAAgdIjQLi59KwreooAAggggAACCCCAAAIIIIAAAjEE1q1bZx07dnQhXwWC1RQW1u9egNgLGSuIHAwke8FkL8Cs8RQMVhg4GBD2fveCw16IWPPU717AOBg8DoaEg2Fl/R4MNwf76D0e7TEvAO2N7y1jOAjtGUTjUp/Cz3vja3pqwXB0xYoVTT8KP+tnwoQJts0227AtIoAAAgggUKICRx55pO2999521113leh8t7SZvfTSS9arVy8qN29pK57lRQABBBBAAAEEEEAAgTIrEDymHFzIZApcJDNMGC6dcZLFL85pJ9uH4HDxjsOnM71sjLM5+7Q5553ILpf7lqjvm+v5Qvv7XFu46fF4nhttk4Zwo1UqV9EqlKuQ9iIQbk6bjhERQAABBBBAAAEEEEAAAQQQQCBXBA488EAXyN1ll13cQZVY1ZCjBYrDgWFvfO/gjPdvOLTshZm9kHOq1ZK9sLD3b7CSsn5XoNirthwOW3vzCgaro4Wqg8sQzcQLenvBbq9a9erVq00/v/32m/vxfv/ggw/sp59+Mv1LQwABBBBAoCQFWrVqZS1atLA77rijJGe7xc3rhRdesN69exNu3uLWPAuMAAIIIIAAAggggAACZVFAx3/feuste+edd+KGEaMtu449q8hFsoFiDVe9enVXIKM4mu46WK1ateKYdNrT/P33323t2rVpj5/tEXU+QMfzCwoKsj3ppKan8wurVq1KeVtLauIMlLJA9GiyN5lEz8Z+fqPuSmobY/Sn0AoK9ewmyzez5k32tROPOcGd70qnEW5OR41xEEAAAQQQQAABBBBAAAEEEEAgpwQOOOAAO/744+3qq6/OqX6Vtc489NBDduedd9qiRYvK2qKxPAgggAACOS5w2GGHWcuWLe2WW27J8Z6W7u5NmzbN+vTpQ7i5dK9Geo8AAggggAACCCCAAAIIOAEFXfv3729z5syxGjVq+EHl4J0FNZwKYygI7TU9r79VhENNodVYLVjdtWKFiu4Oh8XRVPBDxUBypXlGugNjLjUFm707NJZ0vzRfbStUSS5p+TTml/fPOHkW+CM0KV20kF/+79d0Xp5Znu5i6n4x91rPK/f3uBpOr1E3kP42s7/+WGO77LSzjRkzxurVq5dGJ80IN6fFxkgIIIAAAggggAACCCCAAAIIIJBLAvvvv7+ddNJJduWVV+ZSt8pcX6ZMmeIC5J9//nmZWzYWCAEEEEAgtwUOPvhga926td1444253dFS3rvnnnvO+vXrR7i5lK9Huo8AAggggAACCCCAAAIISODXX3+1Hj162Op1v9s5fc63ChXL+zBr1qyxP/7806yw0NZv2GCrfv3FvFqtGzdssF9+XWVecPmXn3+xwk1/P6vHCvWr+9/frdA2RYAXFgbDkhr+n2E3FmywdX+t84dXOLjgz3+qH2vYgrXrbf06L1BdaOsLNtj6Nf+M8/c8o7TCYNTa71zUjSHQpX+WY9NGK4xclL+Xr3DT34HhKDNVfz2b8Iy8EHn0zma2jUZZ0swmmFNjx4vbpt/R/HL5sSuR/39YN+rUXXBXAd9QCFjjRHncDRUtL1x0dD8MHJxvxGB5ZhUqVrAKlSr6w+aXy7PK1ar8HTJ2Lc8q16hiQbWKlStY+cr/VEvOt3wLvizz8oIbs8b8/2nlFVpenpb17+fzy5ezrbb+u2K6hqhavaqVq1jeOVbaqrJVqKILDvKsfLlyVrl6lf/30Oh59sZjr9pPn/9ojz36mO20005prTjCzWmxMRICCCCAAAIIIIAAAggggAACCOSSgMLNJ598sl1xxRW51K0y15cXX3zRevbsaUuXLi1zy8YCIYAAAgjktsBBBx1k7du3t+uuuy63O1rKe/fMM8/YpZdeakuWLCnlS0L3EUAAAQQQQAABBBBAAAEEvHDzHxXXWLehF1n5Kv+EHW1TMHRcGFHtV8HfTcHw8qbI6sQu2xyHNyI3GRpu4/oNtnZdgf9o4fpNVrB2nT9BhXYVbFYI2msb1m+w9YFx3OOuA5Fp0eQrBv9/QDvQN02u4M81tm7NP33znl6/tsDW/LGmSEVizX3dmnW2NhDODi6uQs9/rf7TNq6PX93ZLUq0tHUM478HjbcGSv+2r/BsKi3R8Hq+ao2/g7nRmkLKW9WsZnn5Redbrlx5q1Kzyt+ViQNNFY2r1qhm+VHG+bv7iZchar9DLyD1uWLFf1676qOCxcEwc6XqVfzZaa4uEF0xdqXziOsPQiCRtdeV4PYGyPs7HO5VZ/7/Cs56Nl/Lqhl7zxUW2qQbH7Fv3llmjz1GuDmVbZlhEUAAAQQQQAABBBBAAAEEEECgjAn85z//sVNPPdUuv/zyMrZkubU4M2fOtHPPPddVbg4eTMutXtIbBBBAAIGyKMBdGkpmreqE0y233GILFy4smRkyFwQQQAABBBBAAAEEEEAAgWIT8MLNv1f4y867s7tVqFyp2OZVFiacVxgZ8vaXKRT2jlhWlwSPUu75/+PHCjgnl1su22Hl4t8+EgSJXSY3z6LkkF3XpJ8XCi8H+6xAcdE55Fmeq9xMCwvotTThurH27bvLbcKECVRuZhNBAAEEEEAAAQQQQAABBBBAAIEtV6BFixbWsWNHGzBgwJaLUAJLPmvWLOvatau99dZbVqdOnRKYI7NAAAEEEEDgbwF91nfq1Mkuu+wySIpRQJWb+/bty10aitGYSSOAAAIIIIAAAggggAACJSVAuLmkpJkPAggEBbxw8/J5y+yRRx6x3XbbLS2gvMLk67GnNQNGQgABBBBAAAEEEEAAAQQQQAABBIpbYL/99rPTTz+dwFMxQ7/xxhvWvXt3e+qpp6xRo0bFPDcmjwACCCCAwD8CzZs3ty5dulifPn1gKUaBZ5991i655BJbtmxZMc6FSSOAAAIIIIAAAggggAACCJSEAOHmklBmHgggEBbwws3L5i61sWPH2p577pkWEuHmtNgYCQEEEEAAAQQQQAABBBBAAAEEcklAgaczzjjD+vfvn0vdKnN9efPNN61nz542evRoO+CAA8rc8rFACCCAAAK5K7DPPvvYeeed54K3tOITUOXm3r1725IlS6x8+fLFNyOmjAACCCCAAAIIIIAAAgggUOwChJuLnZgZIIBAFIHCjZvs0avH2LfvfU24mS0EAQQQQAABBBBAAAEEEEAAAQS2bIF9993XzjrrLLv00ku3bIhiXvq33nrLhcpuvvlma9++fTHPjckjgAACCCDwj0DTpk3t4osvtgsuuACWYhR48skn3Z0wPvroI9tqq62KcU5MGgEEEEAAAQQQQAABBBBAoLgFVq1a5S5g/b5gpXW7u6dVrFK5uGfJ9BFAAAHbuH6Djb/yQfvfgm8JN7M9IIAAAggggAACCCCAAAIIIIDAli2gao66VX2/fv22bIhiXvq3337bevXq5ZwVJqchgAACCCBQUgJ77bWXu0PDOeecU1Kz3CLnM2XKFLvqqqts7ty5Vrt27S3SgIVGAAEEEEAAAQQQQAABBMqKwB9//GGXX365fbLiM+t5X1+rWK1KWVk0lgMBBHJYQOHmR6580L4n3JzDa4muIYAAAggggAACCCCAAAIIIIBAiQg0a9bMhZ369u1bIvPbUmcye/ZsZ6zqzWeeeeaWysByI4AAAghsBoF///vfdvXVV9sZZ5yR1txffvlle+WVV2zOnDl23nnn2aeffmoK8oZbjRo1TPNSq1mzpl177bVWv379mPO866673DTjtZ122slNR9OO1RTcjtaf4PDq1+jRo61Tp07u4dNOO81OPfXUuP1LFWvSpEk2aNAgmzFjhtWrVy/V0RkeAQQQQAABBBBAAAEEEEAghwQUbh44cKAt+vZTu5hwcw6tGbqCQNkWINxcttcvS4cAAggggAACCCCAAAIIIIAAAikIKNx87rnnWp8+fVIYi0FTFVB4S+Hmbt262fnnn5/q6AyPAAIIIIBA2gKNGze2G2+80YV5U20KDSs87LUDDzzQPv74Y/v9998TTmry5Mmm4WO1Bg0aJJyGBsjWdO64446IZVFg+uSTT7bPP/88Zj8OOugg91yiIPS3335r48aNs8cee8yGDBlidevWLTJNBb3jhb2TwmAgBBBAAAEEEEAAAQQQQACBEhEg3FwizMwEAQRCAhvWrrOHrxhtP378vY0dO9b23HPPtIzyCgsLC9Mak5EQQAABBBBAAAEEEEAAAQQQQACBHBFo2rSpC9v27t07R3pUNruhW9T369fPVW3u2bNn2VxIlgoBBBBAICcFGjVqZLfffrudcMIJKfdv7733ttWrV1v16tWtXbt2ruKxWrSKyxruk08+cc+rUrIu6olXcXnYsGGmz8d4TdO57rrr4g6jytKaTrx5KVSsvmtYzVfVp1Npuggs3l0uPKdE01QV6ngXOSVjomrWqqDtVcmONk+FrfUTrxG2TrS2eB4BBBBAAAEEEEAAAQS2ZAHCzVvy2mfZEdh8AgV/rbFxA0bbz4t/JNy8+VYDc0YAAQQQQAABBBBAAAEEEEAAgVwQUBina9euhJuLeWW88847Ltx8yimn2KWXXlrMc2PyCCCAAAII/COwxx572PDhw619+/YpsSgIfMEFF7hxVPXYCzanNJEcHdi76GjFihWuh1WrVnVh4fLly0f0WMPtuOOOLmCtcHesttdeeyVVzVqh5Hhh7WSnkyhsffDBBycMN2tZkglbT58+PW5wPNmwtYLviQLoVLbO0RcM3UIAAQQQQAABBBBAYAsUSDbcvPaPv2xDwfqYQhUqVbRKW1Vxz69Z/adt3LChyLB5+flWtUY1y8vPMwUbC9YWhIbJswoVK1ilalWscNMmW/vnGtu0YWPUeZavVMEqVf17fjQEECh9Al64eeVn39uYMWNMx4rSaVRuTkeNcRBAAAEEEEAAAQQQQAABBBBAIKcEdGBEwaVLLrkkp/pV1jozb948F24+5phj7Morryxri8fyIIAAAgjksEDDhg3t/vvvt9atW6fUy0GDBtlDDz3kxlm4cGHcYGpKE86RgVVpWhccKbyrligwnKjbV199tU2bNs3tUzVu3Djq4ApQxwv4KlD+xBNPuGrZ8ZoC0vEqN3fq1ClhVWxNP1th60TTOfroo/2q3vGWK5mwdaJq302aNHHrMp4zla0Tbc08jwACCCCAAAIIIIAAAkmFmwsL7ekRj9tn8z61DevW26ZNm3y4/PLlXCB5r0P3sWMvONGFkR8dPNa+/Xz5P2HovDwrV76c1a67jZ0/pLtVqFjRZkyabvNfmGvrCwqscFOh5eXlWYXKFW3vQ5rZsd1Ptt9/+tWeHP6Eff/Vd7Z+3d/DqGk4BZv3OWI/a9PlaCtXIfLiXdYoAgiUDgEv3PzDJ9/ZqFGjbL/99kur44Sb02JjJAQQQAABBBBAAAEEEEAAAQQQyCUBBUAuvPBCws3FvFLmz5/vAlStWrWyG264oZjnxuQRQAABBBD4R2D33Xe3hx9+2FTNN5XmBVIPPPBAmzx5ciqjlqphg0Hgt99+29KtHqwTTuPHj7dbbrnFDj300M1uoErJiULSWrfxmsLWCn8rDByvKdwcr7J1t27d/BB5vOkkCjer+vinn36a0DbRdJKtbK3tPp7RlClTXBg9XkumsrXWk9ZXvKawdrxAe0IUBkAAAQQQQCCHBNauXevueqEQYLVq1WyrrbbKod7RFQQQQOBvgWTCzXmFhfbpO4tcYHnGpFftk9mLrLDw70Dy3ofva+3+286237me7fGff7sQ8kcz3rOFby2wZ+6ZagXrCiy/XL4ddkorO+TUVvafo1uapvflR0ts8fzP7KkRU1yAeevtt7Zjup1k+7Te1xr9p4kV/LnWPpm70H78+nt79r6n7ZvPv3YB6Z332sUOP/VI+3fLJrZ78z1N1aBpCCBQ+gT+Djc/YN9//J0rVkC4ufStQ3qMAAIIIIAAAggggAACCCCAAAJZElC4uXv37tarV68sTZHJRBOYNGmS3XXXXXbYYYfZbbfdBhICCCCAAAIlJrDbbruZPof+85//JD1PhS333ntvN3ymFY2TnulmGlDBXS/4/cADD8QN6cbr4j333GOPP/64DRgwwI499tjNtDS5O9tkqiUnE7ZOprL10KFD44bUk61sfccdd9hpp50WEzXZ6bRt29ZGjx4dczrBKunx1mCi6SggrWnFa9kKW+sigHQvBMjdrZSeIYAAAghkW2DChAlWrlw5W7JkiX355Ze2bt06e+ONN+z777+3ihUrWn5+vv3111+28847W8uWLe30009nPyrbK4HpIYBA2gJ6f7r++uvt7QWz7ZKHBliVGrEvxCjctMmWfrTEBne61n7530+Wl59nJ198qnW58QLLszzTf64VFtrqn3+zO7vdYh+89q4LJauq8/lDeqj08v8PY7b29z9sWPfb7b3p86z9+cdZl+vOt4pVK/+zLIVmmzast0m3jLcpQyfZPq2bW9ebu1vd3Xdy77v+/NJeekZEAIHNJbD2j7/soX6j7KcvfiDcvLlWAvPdfAJr1qwxVYtS0wfxq6++avvuu6/74lC9enWrU6eO6WB37dq1N18nmTMCCCCAAAIIIIAAAggggECJCagCXI8ePQg3F7P4e++95yo3N2vWzO6+++5inhuTRwABBBBA4B+BXXfd1Z5++mlr2rRp0ixz5841BTfVElWvTXqiOTyggtwKdGcS5B4+fLg988wz7vP+mGOOyeGlpWsSyEZla71OxowZk7BCdqLK1ppGMnf2aNy4sb300ksxV+CwYcPcxXSJWqJq7NqGE1Wk1jy0XNddd13M2SVT2bpmzZrudRevKjWVrROtUZ5HAAEEckdAFZkfe+wxGzdunH322WdWuXJl22GHHaxq1ap+hWZVat64caNt2LDBBfCqVKli5cuXt59//tkWLFhguuvIyJEjTRfkJGwrVij0YHM//9yWrlxpBXl51qhRIzvooIMSjsoACCCAQCIBXZAx+KbB9vKb063f+Cusas3qcUdZv7bAHrn+QXvuvqdc9eY99tvThrw0zMpXqhA53qZCm/3sLLuz2622oWCD/atxAxv05C1Wa4dt3XCq3vz5e5/akDMHWa3ta1m/0QOt/p47F5n3uj/X2D2977R3p8+3S0b0swNOONRVjE63bVy/wb6Y/5mtXPaDNT54L9u2QR379X8/20cz3rfadbexJq2aWb6C03FawZ9rbP4L71jBnwXJdSPfrGGLhlb/37vY1x99aUs/+NK2rrO1NWvTwlW1LsmmgPryhUvtyw+XWu26Na1Zm/3/j72zAKtq6cLwB9IpXSqggO3FFkVF7EawAxWvghiIYDd2tyIY1+5GEUzswE4UEQGR7s7/WaP4iwGHw+Fe1FnPPQ9XmFkz8+7N4ey9v/kWE6mXNIjBkyuPkRydBGk5KdRu/ReUtVRKmqb49nl5eHzpAWI/xEGnqjZqmtf9v0C++N5fWuRkZuHp5UdIiEqAUQMjVK5TtQS9edOyIJCRlIqtzhsRFxTLxc1lAZjnLJ8E6ALg48ePrCQeuXJVrFiRXUSEh4ezf9MFBJV+oX9TWTdTU1P07NkTw4cPZxccJY74eCAxEUhKAqSkACMjQEKixGl4B06AE+AEOAFOgBPgBDgBToAT4AQ4gbIlQCIJR0dHLm4uW8x4+PAhnJ2d2UM2KiXGgxPgBDgBToAT+LcIGBoaYs+ePV/ciQUZl8SEBQ6w5IL7O8fXzs2zZ8/GiBEjhFruqlWr4OPjwzaNWVlZCZWDd/pzCQjibF2cWzIJtkkoTbmKiuLE1oKKpItzkhbU2VpUYuvi5kO/n9u3by+SDYmtbWxsinRwp/dHEn8rKSkV6V5dnBP5n3uT3PgXAAAgAElEQVS285VzApzA70qAhMn29vbIzs6GvLw8Hjx4AH19fbbJm95fdXR0mJCZNAoqKipM0JyXl4eUlBRERkbi9evX8PX1ZYZs2traoM+g31YeuXnzJk6fPs1+/jYgAKlhYTh44QKq6emhpqEhpBUU8OTtW6SkpWHMmDFsAw0PToAT4ASEJVBScTON8/reC8zoNglZ6ZmQkpHC7MMLULd1g++mkBgdjxV2C/HE7xGkZaUxfKE9Ov/dg7XLzszCrtme8Nl5Fn0mDoCNy4DvRcX5QOirYLj1mQ5do8qYtGMGFFSUhF0q65cck4hd0zwREfQRf68egyp1DfHg7B3snb0DzazMYT11ACQki9Z9xYfHYI3tEiREJwg0F9rk0tO5NywGt8M5Dy94bz4Fo0bV4bDR6XtRuEAZhW+Ul5UN321ncW6LF4xpDu7OzFm7pEEi7e2u7kiIjIeMvAxsJvdDk57mQgmP2fmQkYXYsCjk5+VDy0Ab4lKfxPJ5OTnYMnY9Am69QMMuTTB4wQiICSEIT41LwtYJmxD87B26j+8Fy2GdSrpk3l7EBLi4WcRAebryT2DgwIGIi4sD3dgZO3YsK/HCIisLyMgAcnOBnBz2NS87G6kALt+9i+NeXti1axdzmhCkZO6HDx+QEBSEj2/ewLJWLeYG/SWqVQPU1Mo/LD5DToAT4AQ4AU6AE+AEOAFOgBPgBP4wAlzc/O8c8EePHjGxEz2AO378+L8zKB+FE+AEOAFOgBMAQOJmEvWZmJhwHj8gICqX6uXLl8PPzw9Dhgz54nrNgXMCvyoBQcTW5LZMAt+fBYmtSShNYuCionfv3ujTp89Pm4hKbD1y5EgmmisuinPIFtTZmvh4e3v/dDgSopPTdgEfYvmtg7UgztbfDkBGRrSxMiAgABERESAXVfo7MHjwYOagyoMT4AQ4gbIiUKdOHSZYzsrKYhvobW1tmci5pEH6hMmTJzMn5xUrVsDJyYml8PLyYhvIWrRowYTTJIpuUrs2RllZwap1a6gpK38aSlwct8LDsWz7dkRFR2P69Ono2rVrSafB23MCnAAnAGHEzWlJKVg+dAEeXPRnBC36WsJpy5TvxMnkEnz5wHmsH7OSiVZrN6+LOccWQUpWBqEv32Ge9QzIV5THlF2zoGtc5bujkZuVjVMbj2Lvol0YuWQ0Oth1K5VrMw0Q9iIYW503Qb6iAv5e7QgldWUcW3YAN45cxZCFdmjQuVmxTsbx4dFYOWgxEqMTmBBa8lvX6m9WQk7QJKg172sBn61n4OPhBaMGJhi5bty/Lm4mpuc8veDj7gXjxtXh6OFScnFzXh7Ob/fG2U0nmSs3OWk3tWqBPtMGQkpOuM/iEYEfsHfOP0zkbL9+3BeHb3La3uq0Aa/vvkKDTo0xcN5wocTNKbFJ8By/nombezjZoK1dZ/7b/x8T4OLm//gA8OH/HQJ0sUA3RCxbtcIWT09cOnaMCZifP30KsexshL1/jwM+PtDT1ER4dDQi4+IQk5CA+ORktpORhMn0JqusqAgZWVmkpqczxwr68E+uzxSBgYHspvju3bvx7NkzZGdlQVJCAtk5OcjKzoaSvDy0VFXRtHZt9OzWDb3HjPl3Fs9H4QQ4AU6AE+AEOAFOgBPgBDgBToATEJhAjRo1mJsNPXjiUXYEnjx5wh7IaWlp4cCBA2U3EM/MCfwHBC5evAgS8JMzObl1qamp4f79+6hSpQpz7aKg+0lVq1Zlzlt6enqfKn6lpQEpKQAJo7S0/oOZ8yE5gT+DAJX2PnHiBEhwUtIggSO5xf7O8bVw8unTp0WKNYviQFUT79y5A2trayZw5sEJcAKiIyAqsbUgztZkFFSUg7ugYmv6vEMOoz8LQcXW5DrauXNnxISEwLxtW+AHpcBfvXqFlUuXMlHz/cePYaCnBzUlJchJS0OiQgVEJyUhKCwM9g4OIJf5H4WbmxsTJFIJdRLyBAUFISoqijmpJiQksGeG9FmuSZMmTFzYq1cv5rj69u1b9jlPQ0NDdAecZ+IEOIFyTYA0CIU2tyQlYYSjI06cOYOxw4ZhnqurcPOn9zdpaUBSkr0amJmx60tycKbN4nR9Se9TZOw2euBAqCUmYtTixYhLSkJweDiMKldGUmoqxMXEUEVbG3q6ughLSID/kyeg99w5c+YINy/eixPgBP5YAsKIm+mz1K0TV7Bs+CImWlZSU8aqKxugoa/zHce4jzFYZjsfL+88h6yCHJw2uaBpN3McWLILx9cfwaAZQ9FzbG+IfW0s+TlLQmQc6xsfGYf5p5dCvbJ26Y5Tfj7unrqJw4v2on7Hxug/2xbpSanYaL8a8RFxGL1lAqrUMix2jAJxc1JMIupZmqJJjxYQL8pNWFwMlYz1UFFHHeEBIQh9GQIldSXUMKsjlFC32AkW0UAU4ub0xBRsm7gJAXdfQVJKEjnZOcxteeTasdA0/P4cEGS+Ic/fYf2I5chKz8LskwugZvD5WOfn49WtZ0iITIB6ZU0YNTQRyh2ai5sFOQr/bpuMpBR4OG1EQnAcqwLasGFDoSYglk/vSDw4gXJGgHYoVqtWDavmzcPhQ4eYAzOJjRvUqIE3oaGobWiIvPx8VNbURCUtLVaihX2419CAtpra/3c0fl5XTm4u7j5/js2nTuHUlSusr6urK2JiYrBz5052g6NxnTro0qQJahgYQIYuOADEJyXhVXAwrjx4gPCYGIRFRbF50M0NAwMDWFpaokuXLmjZsmU5I8inwwlwApwAJ8AJcAKcACfACXACnMCfRYCLm/+d401iKRI30wNAEpjx4AR+FwLkpDV37lxWfrjAnZAEzXSPqnLlyuycz8nJYWWIY6Oi8OTZMzSvVw9jbWzQzdz8/xi0tYEq3zvR/C6c+Do4gf+SAN2PvXLlCrsvW5IoEN7R73lRrqolyVke2xa4sFIJdDLxEDYWLlzINnp06tSpSGGksPl5P06AEyhfBIoTXBfnbE39Dx8+/GVRJBYkt+uvgz5H0cYwp/HjmVCZDIboORy9XyWTgE9cHB+jooD8fNBD6ypaWqhTrRp73qeiqAhZemYnJoaMzEw8CwrChXv3mGvgrFmzMGnSpC9D0WZXEgzq6+pi4dKl7Hle3bp12WYN+ltAG9dI4Exi52vXrmHHjh1IS0tjAmh1dXVmtkSVY+fPn1++DhKfDSfACYiMALnA03uHu7s7+/0nJ+SJEyfCwsIC/yxZgjFz52KRoyOc+vcX2ZjkwKzbpQsTLJs1b45rV6/iwoEDMK9RA0hO/m6cy/fvo6KiIl6HhEBaUhKhkZF49f49zt25g/fh4Wyz+cjhwzF3xgxAVlZ08+SZOAFO4LclIIy4mWAkfIzB5PZOiAyJBMSAPhMHYNAsu+9cj/Nz83Dl4HmsG7MK5ORc37IhRi0fizm9pkBGXo45Oavpfr+BjETTj6/cx4J+s9B2YAfYrxr/nTN0SQ9KTlY2zmw4jqv7L6PbOCu0se2I90/ewmPceqhV0oDDJifIKSsUm/ZrcXNb247o7mwDcYkKxfZjDfLpv09STNpYV1SI0eff/E9a3vxi2rJ8n9t/yv3jPqIQNwf6B2D7xE3IzsxBLfM6eHnzOTu2vSb3RwvrlgLN9ROLfDp1WPufips/tysQr/6M2ae1f25Fi/+GV4nEzXn5X7oLwl2wA89bfUsgJS4RW8atR2p4MjZv3syuCYUJLm4WhhrvU2IC5OxENwuohIuuru6X0oHkAEE3EMLDw1FRXBy1TExQo0EDZJNlf/fueHDmDHrZ2qK1qSkWicgxmW5mdHFxweO3b5Gamorc3FwcmD+flXkRJE48eoSDFy/C7+pV1j89PR0knjbU18cwOzsMHz78t3cBEYQTb8MJcAKcACfACXACnAAnwAlwApzAv0mAnFbpQTR3bi5b6s+fP2eMpaWliyxPXbaz4Nk5AdESoPOaHmbTw5769eujadOmTNBMApj3799DQUEBkR8/IujlS6QkJCAyKgph0dH4GB0NeVlZdr+rUY0aMK1eHTUNDGBiZoZKtWp9EWCeOnUKly9fhqKEBKpUqgQxWVm0tLBgD6VJLF0oyAk6IQGIjPz/t8kNmiqQfdtWtBh4Nk6g3BPQ19dn95LJ7a4kQf0oyDXU2dn5S9dp06bh+vXrTFRHL1lZWUhKSrLfafpKD9RkZGSY2E1HR4d9j0QwJICTkpJi/6afaWtrIzIykjmB0vcKfkbGGjQ2/Zs2S5AjKP2cxqIICAhgTvFfB41Jbandj+LNmzcgB2t6oEdOo18/GOrXrx9u376NZs2a4eDBgyVBVKgtuZ6Se6q5uTkcHR2FzsM7cgKcwJ9DgDZF/PXXX8wYiD5H/ShIYHzi+HFYWVgwsfLDgAD2PkvvZ3+ZmCA3Lw+rnJxQSVNTIHD93Nxw9f59yMvLY8aMGezZ3OTJk9kmmOYmJlgzfjzLs/3cORzx84Ovnx/7vEfzpHFpE8etW7fQvn179lmvQoUKiI2NZQ7OgwcPZjl5cAKcwG9AIDcX6bGxSIuKQkxUFBxnzvy0AWL8eOhoaMDz8GGMdnPDBjc3nPXzw6WrV5Hi5yfahZMGYuBABLx9yz6/tTQ1xdUtW4QaIyYlBZM3bMCukyehr6eHo6dOwdTUVKhcvBMnwAn8OQSEFTfn5eTi6OoD2DN/B4NVpaYBFp5ZASX1it/Bi/0QjaW2bgi49xKyinKo36YBHl66D9t5f6PTiO5froO/7piRnIZ/Znvi1ulrmLRjJuq0FPL9jK6Pc/PY58qU2ETsnrkDEW/DMWz5KBjUrYq7XrdwyG0PGvcwQ9/pg1BBUuKTC3MRYuLSiJsToxOQEBkPGXkZaOprMzF4Tnomgp+9Q25OLirVqAJxcTG8f/YOQY/eIj8PkJaXQk2z2tCupocKUhKF+eaDuU+Hvw5F4P3XyMnOhRjyIS0vA8P6RtA10oOMovyXPqUVN5NY/cz6Y7jwjw90jfXQw8kGJ1YfQfjrMDTq2hT9ZtmysQtFfj4TL6cmpECrqi4UlBXw5n4AQp4FQ9NACxW1VBD2KgSn1hxFTmYOBsyxhbKWCqqaVoO0vByiQyKQnpwOeWV5qFUufD2QlZaB0Bfv8ebuK+Tk0NoBWSU51GxeG5oG2qgg9en+SXHi5tzMLESHRoOE2+TITU7iapXUYdy4BipqqX4n2v9z3iHKbqUF4uak0ASsW7dOaONYLm4uu2PEMwNstzM9XA4MDGSiZtoxTTdgk5OT2R8vuklLO7Y1VVSQEBeHD9HR0FVXZ7s2ImJi0KJpU7SuWROz7OxEzrOCmRm7gKhtbIx9c+agnrGx0GOQK/TwRYugrqvLypVu3LgRQ4cOFTof78gJcAKcACfACXACnAAnwAlwApwAJ1AyAlzcXDJewrYmFza6zqebxSTW5MEJ/A4EdLS0ICsjg0vHj8PAxASQkQEkPj9ISEn5JDSOi2NuI18HVQa7/ewZjl2+DPejR9GwZk0Y6uri6bt3CImKYvedKOir+GenksysLCbeoZ8UCCnpnpk8lVvPz0e/Nm3YBny6P/Zd6OkB9OLBCfyhBEgofPPmTeiV8PfgZ+JmEsCRcww96CVndhK20e8rvcgQg170945+Tvey6ft0f7vg/+nfJEQuECR//X3qR/2/vAeIi7O21Ifa04vaf/2VRNXU72c/p74FomdqY2hoWEjELCpxM5U6Dw4OZps9SBDOgxPgBDiBHxGgDRpU9YLelysqKaGKjg7eh4ZCtWJFLJo7F80tLIDPVVLp/VBFRQWNq1fHxY0bRQa02ejRUNHUBDmx0otcpCXFxRH0lZN0wWDRiYm4HRSEl6GhEJeWRk1TU3To1OnL+yoZNFH1DnJzvXfvHlvbzJkzRTZXnogT4AT+XQLkKL9zyxb4P3yIxJQUtqmCvtJmCgVZWcQlJaGylhZS0tKQnpmJgPfvkZGVBbOGDXFmyRIoyv9fJCaKmU/duBHvIyJw0NcXNQ0NcWDhQtStVk3o1AnJyejo5MQqVy9atAi0aY8HJ8AJcAI/I0DXtLQZ7dxVH0zcMw1yyooCw/oYGArnVo5IT0mHhJQknDa6oFXftt8Jg0kI7fPPGXi4bvh03SsuDpOG1THjgBuUNVS+Hy8/H++evcXC/rNhZGoC1x0zWH5hgoS/D339mbA2JSEFd0/eZGma9mwOOUU5vPYPwOs7L1HbvC6q1jeCnLI8TNs3hLyK0k+HK424+eq+C7iy5wITVg+cP5ytKy4sCp4TyAk5E+3tuiDwwRu8uvkciVEJbA4kttYy1EZ7u85o0Lnp/12i8/NBwvHzW88y92QSTTP34nywexgq2iqo0aIO2tl1gqreJ3fs0oqbSSDuMX4Dgp++Q6sBbdDZoTtOrz2Om0evQkNfC3+vdoSOcaVC7PJycrBj0ha8exKEtkM7Ijc7B1f3X0JCVDyqN6uFrPQMRAVHsWNErtaKqkqQkJSAw4Zx0DaqhH2z/0HQozeoY2GKXq592flDkRqfjAs7vPHg3D3ER9C92U/DVpCoAO1qumg10BJNujdngvWixM3piam4c+oG7p66hajgCGRlZDE3chkFGVQyqYyW/S1Rp40pJKWlhDkFeZ+fECgQNyeGxDNxc6tWrYRixcXNQmHjnQQhQDdA27Rpg9GjR7Pd0vTQk/5g0q5neuhsZWXFHC1evnyJZgYGMFRXZ+VVdnt7o1mdOth77hz6d+iAbWW0O1rZ0hISkpKIvXULSEoSZElFtqESMbP27MHKVavQunVrhIaGsl3qPDgBToAT4AQ4AU6AE+AEOAFOgBPgBMqegImJCXMU5s7NZcuanBzJxZHEAyRk4MEJ/GoEqAoXOfwVxOLFizFv7lykXb0KcQHKP/5svdHx8ZiwejV7YD116FB0GzgQG48cYQ+ZM9LTWfn1Z/v3Q05GBlnZ2YhPSkJYbCwevHkD3xs3mOjxY0wMElNT2QN2Elt3bdECI3r0QLuvS/ZxgfOvdsrx+YqIAImPybH4xo0bIhM3i2hqJU5Dv+8kkqYXPWQu+H/aQESu8d9+v6ANifZUVVW/CK9JKNi3b98v44tK3ExupSTyMzY2Zvf1eXACnAAn8C2BKVOmYMOGDRhnb49Bbdui7udnYfdevsSkdevYs75qlSqxaq2NmjVDtVq1MGPuXAQ8f464CxdECrTD1KnIystjrswk8FNRVMSmyZNh06ZN8ePIyQEKCp9e9P9SUmyDm4ODA9s8Qm7T27ZtKz4Pb8EJcALlhsC7d+/Qv39/hIeEwLJBA9SvXh3+L17A+9YtWDRsCPcpU+D/8iUkKlRgVXhkpKSgrKiIscuXw+f2bfSxtMTBRYvKZD1HL13CqMWL4TF9umDvUQLMwnnLFmzcvZt9fuTBCXACnMDPCNB7xJIlS3Dq/Gm47psBuYqCi5tzs3Kw2XkNzu8+x9LXbfkXpu93g5zS95tAyH13ns10hAaEsM28vV36Y+CMYV+Eql/Pj8SvZz1PYu+Cf+CwchwsBnQQ+gBGBn7AzmlbkRybxETYmekZzDVZRk4WuTk5SE1IZa68CioKEBMTh55JZQxeOBwKat9UU/tqBoXFzR3QbYLN/wXHP5gpbUCmEMvPx1n30/DedBLGjavDYbMzJKUlER0cgTXDliAtMQ1qlTSYYFnTUAda+lrISs9E0KNANndyev5aPEyuxafXHoXfvkuQUZBlTscyinLIz8tjQmdyNCats8Xgdug2rhcT+ZZK3Jyfz0TX21w2s43XQxbaoa5Ffdwj9+uFe9m4Vi69Yd6/bSEKudnZ2DhyFXOWNjSthqj3kcjOyGJrr9GsFqQVZNn3gh68Yfc0apjVZseop3NvqOiowX30GgTceYmGXZrAdtFIiFUQR05GJs5sPImL//gwMXPlmlWgoquOvNxcfHgVgtgPMcxxefACOxg3rflTcXN2Rib89lyAj+dZdj5oV9WFemUN5vb9MfADYsKioaiiCCvXvp+E5eTqzUMkBLi4WSQYeZKyJNCjRw+YmZmxhziTJk36UpqPdjt/ibQ0ID4e+PCBfetlcDCGzZuHh69fQ0leHjG+vmU2RRlzc0wfPhyzqRShCMTNNFGZVq3YA14qU0hi7ho1apTZ/HliToAT4AQ4AU6AE+AEOAFOgBPgBDiB/xMgcTM5Co//XH6YsykbAuTQRpuYExIS8ODBg7IZhGflBEREgISCz549w/bt29mNczpn6b6NoqIi6tatywQw3bp1Q7v69bHXzU0koy7dtQseJ05g3ujRWLpvH9vk36ZhQ3Rv2RIO1tYCjeF7+zYOXbyIE35+SMvIgLyMDPq2b491EycyVxbUrAkoCv4gSqBBeSNOoJwToN9dutd6/fp1VKpU2CGouKn/zLm5uH6/2s9HjhwJX19fNGvWrJCjc0nXMXXqVISEhEBTUxNr1qwpaXfenhPgBH5jAnQNQJsqtLS0sGraNGikpODo5cs46efHRIFqyspoVKsWQj5+RFJqKpLS0hCbmIha1aohJz8fQe/f48jixWjdoIFIKTm6u2P/8eNISU6GhooKws+cET6/uDjypaSg2KwZWjZsCIuWLTGljISOwk+S9+QEOIEfEbh27RosLS1RuVIlDLCwYO8/mdnZMNDRQS8LC9QzMvohuJjERDSwtUVoRARm//035o0cWSaAzUeOxNsPH/Dx7FnR5RcXxyh3d+w7cAApVHWIByfACXACPyBAG2yXLl2K4+dOwHX/TMiXQNxM6Z7feIzZPaYgJzsHMnIycDu5FNWb1v5upLzsHOxdtBNHVu5nYuJ2AzvAfvUEJnD9NhIiY7HSbjFSElMw78QSKKlXFPrYZaakITzwAzLTMnH94GW8vPEc5n0tUNfSFCHPg3Fu4ymoGWqhp5MNJKUkmDBbq5pekSLWr8XNph0ao4VNS4jTPbkfhLi4GCrVrMIEvMWJm5NikiAhJYFGXZoyB2fNqjrM1fjy7vM4536aZSfn4lYD27H/j3z7AZscViM5NpkJmDs5dIeUnAz7WVxYNI6vPIRH5+9Dv44h7FaOhqqeeqnEzXQMj604hGv7L6FSzcoYuWYsKuqoITIoHJ5OG5nrcf2OjTHQbRikP8+D5lIgbn7jH8AckbWqaMG8fxvmrqysURFaVXUR9jIY6/9egaz0LMw8uQCa+lrIFxNjLs8/EjcH3n0Jd8d1bANP/faN0XOiDXOnzs/Nw6ubz3Bg/m7m5mzWyxwD5g3/obiZjgfN6Z/JHkhNTEGDjo3Ryb4bNAx0QG7jwY8DcXrdcSbKJvG0/YbxUNZSFfpc5B0LE0iJSYT72HVI+pDAnZv5yVH+CFy9ehX29vZ4+PAhbGxsoKysjOXLl39y1aAP1vRKTPz0+kFU6tYNNfT1cUGE5am+Hubc7duwcnXFq0OHYKCrKzKAKh064P6DB8yx+s2bN5CiXd48OAFOgBPgBDgBToAT4AQ4AU6AE+AEypwAuQuSazMXN5ctarrWpet9qsT0/Pnzsh2MZ+cEhCCwa9cuHDt2DJcuXWLlz0nUTA95yQ2VKon17NkTo0aNgq2tLc6fP88qbz3aswe1DQ2FGO3HXa49eoQ5Hh649fQpdNTVsWHSJHRp3lyo/I9ev8bMzZtx4+lTSFaogNpVq2L2iBFoY2cHyHx6mMGDE/gTCNDvcZ06dbi4uYiDffv2bVAJ9hEjRqBWrVpCnxZkVEIPD+mevpuINn4IPRnekRPgBMoVge7du7PPU64DBmDjxo3YeuoU6lStChtLS3Ru3hz62tqF5ksVT2e6u+PmkyeQlpKCtpoafNauRXV9fZGuKzIuDjpdujAX1vUuLhjWrVup88/28MDxK1fYvP1JiGhgUOqcPAEnwAmUHQEnJycmmmnRogWue3sDL18KPNiHqCj0nj4dd549Q/CJE6jyzXuZwImKaJiTmwupFi0wecgQLBkzRhQpv+R4EBeH1n36YP/+/WzzLg9OgBPgBL4lUFpxMznwLuo/Gw8u+TM33/ZDO8N+xThISBUWLefn5MJ7hxe2uKxnU1DRUsXcY4tgULfw5hJy/33gewerHZaj7aAOGL7QQSQHLTk2ATuneCLyXQRGrHJElbqGuHnoCo4s2Y/mNq3QZ+ZgNn9BokDcnBidwFyDxSUqkGb3hyEjLwuHjU6oXMegWHEziZTJ2dh+/bhCDtpJ0QlYY7sEseExaNmvDXpPH8TGCnkahB2TtkBBVRFDFo2ApoHO/+eQn49bx67i4IK90KikgRFrHKFdTa9U4ub4j7FwH7OWORpb2nZAt3HWkJCWRFZqOo4sOYBbJ65BXU8Do9aPh46x3pe5fC1uVqiogD4zBsK0XWOIS/5fEB7y/B3Wj1jOxM2zTy6AmsGna4cfiZvJnfno0v24dvAKlDUrwmXPdObaXBDUx2vdMVza5Qv92gYYt20SE7d7jl+P4Gfv0MPJBm3tOiMnMxvHlh3A9UNXoF+3KoYu+RvqVbS+5KFz8dWN5/B02gBxCXEMnDeMuTfzEA2BxMg4bLZfjbTYNC5uFg1SnkWUBFxcXFCxYkWQyJmccFbNmwfExHwSMwtQFkW9QweM6dOnzHZGTt+0CRsOH0bS5cuiXDbMRo+GYfXq0NXVxYoVK0SamyfjBDgBToAT4AQ4AU6AE+AEOAFOgBP4OQEubv53zo7AwECQMyQ5OpIjLQ9OoLwQIHfRRQsXolmDBuhnZQV9Q0OMdnWFSfXqmDhxInvIXRDt2rUDVRybMWMGunTpgoOzZwOpqSJfirG1NVIzMxF2+jQrx1na2HryJBbu2IGQyEhsnT8fw6dMAX7iGlPasXh/TqC8ESC30Pr164Mc+bhzc9keHarQQO9ZSkpKWLx4cdkOxrNzApzAL0Ng1qxZuHLlCiRzc5GbkcGcUEmkRxuviorsnBwm6KuqpwfvNWtgUqWKyNe8ZOdOzN26FRtcXVHL0BDN69Ur9RjB4eHoPW0aAsPCkHD37qfKGTw4AU6gXBIIDw+Hqakp1NXVsXnzZpG5BlsAACAASURBVLQmh+bPVaNLMuEKZma4u2MHGpZBZebHb96gxciROLViBSwbNSrJtIpte/XlS/SbOhUODg6YM2dOse15A06AE/jzCJRW3EzE7p27haVD5iM7MwtquuqYddANhn+ZFIKZGB2PlSMW4YnfI+Tn5zNBsI1TXwyYMYwJhAsiIyUN22d64Mnl+3DdPgNGDWuI5KCEPnuHrc6bmBh2+MrRkFeUw+HFe/HgnD8GL7JD/Q6NBR7na3GznLIcFMjt+ifCaGk5aQyebwfd6pWLFTenJKSi21grtP+7a6G5ZKdnYNPoNXj74M0XJ2JqkJmchoh34UxgrFutEptDXl4ecrKykZqQghuH/XBhhw80qmhixGpH6BgJL26mY/bkwn3snOrJ3KWHLh2F2ub1mBNzfl4+bp28jmNL9iM/H+jpbIPWA9oy52WKL+Lm+69RvVlNDF9u/51DeEnEzamJqVhntxQf34ajQYfGGLbcgbmBfx0Rb8Lw7OoTyFdUQIPOjZGdnv2duDkpOp65RUe/j0KbIe3Rfbx14Tz5AAnYPcZvQFhAKHN17uTQo0hXb4FPIt4QXNzMT4JyTaBRo0bQ1taGrro6PGbOBOLiSjRfpTZtMKF/f7jZ25eon6CNuzo745K/P9KvXRO0i0DtpmzdipXbtyMtLY27NgtEjDfiBDgBToAT4AQ4AU6AE+AEOAFOQDQESNw8duxYkFsPj7IjEBQUxBwh379/j9evX0NCQqLsBuOZOQEBCHh7e7PffVU5OdQlMUudOhATF8ek9esxxsYG8x0cADk5QFb200tODm2trdHTygqurq7w9/dHvdxcIDtbgNFK1qShrS0U5OTg5+5eso5FtE5JT0fNvn0hLSmJwOvXuYugyMjyROWdQExMDJo0aQI/Pz/mxC5ohIWFfdncQGYUffr0EbTrL9uO1lxSAfjXi50wYQJkZWVBD8C5gccvexrwiXMCIiVw7949WFtbs+deU/r2Rc/WraGlKli5ZhL0dRw/Hnbdu2ORo6NI51WQjASD4mJiuObhIdL8nidOwHHZMmQHBwOVKok0N0/GCXACoiMwefJkkMD5+vXrCKbfV6oyJcTmVe3OndHJzAz/0OZXEcfHmBhU7t4dT/fvR00RO8E/ePUKnVxc0KdvX+aqz4MT4AQ4gW8JiELcnJ6UiuldXBD0JJAJP3uN74Mhs+0g9nnTfX5uHu6cvYFVI5egcg19vH30hgliK5lUwexDC6BVVffTtPKBkFfvsGL4IlSpoQ9nz6moICn8/eXczCxkpGcyMfVD73s4tfYo6lrWRw8na6QlpmLn1K1IiUuC3UoH5mosKSUJKfniK6EViJuTYhLRrGcLWAztAPGfGAyIi4tBRUcVktJSxYqb05PTMWDuUDTuXrjCW05mFra6bMaLq0/QzMocA92GfzmM5FKcGBXPxLexIVHIyckFzetDQCg+vvmAlIQUaBnqlFrcTA7dhxfuwe0TN6CsqYxO9t2hpK78ZR5R7yNxfutZpCWloW7b+hi68O8vLAvEzYH3X6NF71awnjIAkjJShU7FkoibY8OisKjXHFaRr/s4a7T7u0uxv9gpsUnfiZvDX4Vgg/0qdi7UblkX1RoUFuTT+ZidlQX/s3cQ8S4CzXq1QN8Zg9mx5FF6AlzcXHqGPEMZEaA3F7rB0eSvv3BLyIc3xjY2UJSTwxV3dyjJy4t8pj0nTWKlZSKoLI0IY/X+/Zi6aRMsLS3ZS0FBAbGxsazEaU5ODjQ0NNjO0f79+4twVJ6KE+AEOAFOgBPgBDgBToAT4AQ4AU7AyMgIgwYNwjyqHMSjzAi8e/cOw4YNYxuaN2zYwK5zeXAC/wUBut8ybdo07N69G2oqKmhgZARy2NNQUUGr+vXxl7ExrFq3/uHU7ObPxy4qLw4g/dEjSKanl8kSaPM+bdynDfyiDJr78PnzkXnrFiSaNBFlap6LEyi3BCIiItC8efMSi5tv376Nfv36sXUdPHgQzZo1K7drFMXEaK20Zg8PD3Ts2FGolOPHj4eysjKSkpKwdu1aoXLwTpwAJ/B7EaDP/vr6+rhz5w5ALsYliPuvXsF68mS0b9oUW2fMKEFPwZtqdOyIMb17Y+7IkYJ3ErClVqdOqKimhoA3bwTswZtxApzAv02ANr7RBjiqOLFxwQJIvX8v1BReh4Sg3qBB6GZujvWurtBRUxMqz886UeXqwZ07Y42zs8jy7jh9GqevXYP3rVtQVFbG6tWrIScnB9oYSBW3EhMTUaVKFdSqVYt9NqzAK/+IjD1PxAn8SgREIW4Wy8/HyU1H8c8sT+Tm5EK/tiFmHpgPTX1thoIExOvHrcSLW8/huHocdszyRGRwBBMTD5o1DFbj+jAzAhLqntvhhUPL9mLkUkeY27QRGiXN6cnlh3h04T7ycnLx4fUHRLwNh3Y1HehVrwwSEgf6vwaJj40aV4eMnDRqt/4L9Ts2/qlQuWAyX4ub29p2RHdnG+ZEXVzQnM66n4b3ppMwblwdDpudISktiejgCKwZtgQZqRkY6DYMDTsXvjdB4uZtrpvx3O8JmvU0x8D5n8TN2WmZeHrlEa4fuozIdxFIiU9GBQkJ5uQsJSMFCSlJxH+MgYa+dqnFzVHvPmLz2HWIeR/JnLbllOQg9pVbdW5uHtKT05CXmwdVXTU4rB8HneqfqrJ8LW62GNwOPZx7s7l9HSURN38MDMPSPm7MZbnfrMEws/nxPd6v8/9I3Bz04DU8xq0HOUHT8Suysp4YUL9dQ/SfMxRSctLFHWr+cwEIFIibU2NSQVUX27QR7vddLJ+2L/DgBERIwM/XF+27doXXqlXo0LSpUJn3nDsH27lzoaKoiDUTJ2JI585C5flZp1X79mGmuzvSrl4VSd7QyEhYT5mC+y9fQlNVFVOcnBCenIz09HR2I9jAwIC5WdFNeLr58+zZMyxbtoztdOfBCXACnAAnwAlwApwAJ8AJcAKcACdQegIkbh4zZgycRfiQqPSz+v0ykGPzkCFD2IMycnCTL4MNyb8fNb4iUROIi4tDr1692Dk4f84cuJTwxui9ly8xePZshEREiLyq19drFW/WDG+PHoWhnp5IEfSaPBm3nj6Fr7s76v0BLrQihceT/bIEyI24devWuHz5MhNoCBpfi5tv3LhRKkdjQcf8L9t16tQJL1++hJ2dndBlyceNG8fKukdGRmLTpk3/5XL42JwAJ1AOCGzevJlVx8nKyvo0mxKKm5PT0qDSti201NTwwcurTFYkYWYGj+nTmTu0KMP39m08ePMG0zZsgIWFBfsbxIMT4ATKF4EnT57AxsYGd+/eRVVDQ8T7+pZqgusOHsTUjRuRmZWFOtWqYcbw4ejbrl2pchZ0HrloEY5dvozY8+dLnW/LsWNw27YN4TExqKSpicGdOmHd4cNoZmYGRUVF0MZ0qrxFkZmZyQTPGRkZsLe3x5QpU6Cr+9lBtdQz4Qk4AU7gVyAgCnEzrTMmNALTu7gi8n0Ec7a1XzYG7Yd1IfNbPLrkj6VD3FCvdX1M3T0Hh1buw8HFu5GXl4dazepgyq5ZqKithoSIWKwatQTZGdmYuncOlDVUhEZIbtHnt53B0yuPkZ2excTUJGDV1Ndk4tyEyHgkxSShopYKlDWUAXExdBrVDbVa/sVEs0VFWYqbB7kNR4POhbV0PxI35+flMSfnPbO2M3EuuUPrVNWFup4G9OsZoqKWKt4/e4czG09CXU8ddqsdoWOkh9ysbJzz9IKPuxcTWDt6uDAeRQWNddfrFvbP/ocJmlV1VCEu+X2f3KwcxH2MY+7d3cdbo83QT5uqRS5ufhOGpX2LETeT3LVA8ioujqLEzekp6ahqagQdo59XYxETy0eV2gZo1M2sVG7iQp/Qv2FHEjdvHLUGKVGJWLlypdCb8Lm4+Tc8Of7TJaWns/KAQaGhiCnlxYPP7duYv307Hr1+DVlpaVhbWGDzlClF76QQcPFP374FlQb137kT9YyMBOz142YT16zB2oMHUadqVXaB47B0KaxatUL3jh3xl7k5tE1MEB8fz0r5kcBZWloaFy9eZCVT6YEwdxUrFX7emRPgBDgBToAT4AQ4AU6AE+AEOAFGgMTNDg4OcHV15UTKkAA5/wwcOJBd59LDQy5uLkPYPPVPCZDA5uHDh9DT08N+KrsbGFhiWkPmzMF+X18mbpaUEL785c8GfhcejmrW1oj09mZu0qKKy/7+GL5gAXt4HRYfj3+OHxdVap6HEyjXBKjEeNu2bUssbqZFjRw5Er6+vpgwYcJvvwmqwLmZHKrJqVqYoM1i9P5KohhPT09hUvA+nAAn8BsRqFOnDhM2v379GoiJAT6L5UqyxPErV+LghQtQU1LC/V272DM/UYZuly5oYWqKw4sWiSTtJX9/0LO/8OhoNK1fH0McHDB16lT2jG/t4sXo3Lw5QGugF9/sKRLmPAknICwBFxcX+Pj4YNmkSRg7fTqCRHB99OLdO+w+exZHP29oOL9hA/S1PzmTliYysrKgaGGBWSNGYPaIEUKnajd2LC7eu4fGtWrhxLJl0P1cUWvTkSNYvHs3c/TsYWmJCXZ2qFqrFqChAW9vbyxduhSBgYGgzcLr16/HiFLMQejJ846cACfwnxAQlbiZBLB73Lbj6OqDIB/V2s3rYtqeOcxF2H3iOvj73MUEj8lo3MkMH9+GMiF03MdYyCrIwX7FWLTp3x4PL/tjneNKdP27O3pPGlRqHmmJKcjOyELIi2Dsm7MTGpW10G/mIEjKSOHMhuN4fvUZrFz7oF6bvwCIQb6iAip84yj8o0mUB3FzVmo69s/bBf+zd5hge+C8YdCvV405YJO4WBzAzSNXcXDhHqhX1iyVc3Nmchr2zf0HD3z8UbmmPvpMGwBpJdnv0CRFJ+LIov2ICo5A7Zb1YLdiFCTlZEUubo4Ni8Ii6znIyclBV0crdLQvvImRHLLjImIR/PQd5BRkYdSoOnPq9hy/HsHP3qGHkw3a2nXGx4AQrB+1ChnJ6eg8pgcsh3b6bk1iefnITMtAbm4upOWkISkrU+rzkif4RCAhPBrrRqxERkI6Fzfzk6L8EDiyaRPsp01DXSMjXNm8WSQTe//xI3pNmYKA9+/RqGZNHFmyBBoVK5Y6N5WS6mRmhp1z5hTKlZObyy4IcvPycOjCBTSoUQPj+/b94XidnJxY2wWjR2PKkCGsTVhUFOt348kTvAkNRVxSElIzMliZl+zcXLbLpW7dumjatCkuXLgAc3NzVsqXByfACXACnAAnwAlwApwAJ8AJcAKcgPAESNxM4iVyoOFRdgTIOZOEUwkJCVzcXHaYeeZiCNC9FBKXODo6om+zZkBERImZ7fPxgf3ixRjXty8WOTqWuH9xHRKSk6HWoQN8169H20aNimsu8M9P+Plh+qZNrOz6nO3b0cjMDFu2bGFOXFFRUYyLqqqqwPl4Q07gVyHw9u1b5vBCphH6+volmnaBuLlSpUog9+bfNZKSktCiRQvQ1969e7MHR8IEvbdSJcIXL17gn3/+ESYF78MJcAK/EQEy7pkxYwZmTpoEPH8O5OQItbrd3t5wWbsWsYmJGN6tG1ZNmAAlEQmDHZYswT5fXyRduiTU3Ao6PQ0MZI6tTwIDkZCSgpE9e8LV3h66NWvixdOnaGljA0VZWSgrKGBw587obm6OGk2aAJqagJxcqcbmnTkBTkA4AkbVquHoihUIfvuWXd9FeHsLl+gnvQ5fvIg+bduKLOeE1atZBaFjS5d+lzMiNhbBHz/iQ3Q0LBo0gJqy8ndt2o8bh2uPHuHWtm2ob2Ii2LwUFQEtLUBVFVu3bmX3zmgjG1X6cHNzEywHb8UJcAK/NAESNy9fvhxHTh/BpAOzIK+qJPR63j58jXk205EYk8BEy6NXjYd6JXUssZ0PI1NjTN8/H5LSksjJysbWqZtwbtunyh0N2jXC6NVO8PI4iYcX/TFp+wxUrmkg9DwKdczPx82jfji+/DDMrM3Rw7k3kmMS4TlhI1ITUuCwwQk6Jj937P3RJMqDuJnWsM1lE949fIumPc0w0M0OEPu/4zQ5NJ/ddBK+W89Cy1CnVOLmD6/ew9NpA+Ii4tF2WEd0H2fNXLC/jey0DBxfeRjXDl2GskZFOLpPgK5JFZGLm9OT07DJfiXeP3+PWuZ1Yb9hfKH55OXk4vy2s7i00wdVahlg1IZxyEzN/E7cnByTgA1/r0RE0Ec07tYM/WbbMuH710FtfDzPIjUhGU17NEeNFnVFc17yLIj9EIV1w1cgOzmTi5v5+VAOCCQk4KCnJ4bNmoUhnTqBPnyfXLGCCXlFGQ8CAtCgenWRpDxw/jxuPnkCTVVV0A2LoA8f8PLdO6RlZkKiQgUmbqaLhuZ162LrjBlQ/0ZQvWTXLszYvJmJo8ktR9CISktjN1kU5eWx99Qp+Pn7o1G9ejh/6RKUROjkI+h8eDtOgBPgBDgBToAT4AQ4AU6AE+AEfgcCJG4m15lp06b9Dsspt2sgt9y5c+cytx/u3FxuD9NvPzESON6/fx/Hjx9HSzU1ICVFqDUPnTcPJLShTfqjrKwwpndvofL8rJNOly7sofT+BQtKnZc2409evx4bDh+GuJgY20BvWKkSImJikJaezvKTsJkcTbS1tdGqVSuMGzeOCR15cAK/A4GAgAB07dpVKHHz4cOHv1R28PDwELoMZnnmSIJm2nxEgmQKcm0m92ZhgiphmJiYsPfZvXv3CpOC9+EEOIHfhMDly5cxdOhQdO/eHRvHjBH6M9fXOOZt3YqVe/eyzywe06czkXBpIyUtDTX69oVFw4bYM2/eD9PR88CrDx+ipakpWvxFzn2F48bjx+ji7Myea5Ko2c3e/juHaXJzHTBzJlSUlGCoq4sTV66gS4sW6E9C50GDAHX10i6F9+cEOIESELjh5YV+I0Yg7PRpVrH5ZXAw9s2fD6vWrUuQ5d9reu/FCyZMJnM0+pqXn4+Y+Hhk5eQgOS0NslJSSM/KgrysLP4yMsI1D49Ck3sRFIR6gwfj4saNaF2/fsknTjqEKlXg//QprKysmCu/s7Mzv49WcpK8ByfwyxGgz120+fXAsYOYfKh04uacjEysHLkEN09eY5+bTC0bQl1PHTdPXIPD6vFo1efzhpD8fDy8eA/Lhy9EamIqFFUU0WVUD9z2uolaZnUwatnYH4pnhYGbm5mNw0v24f6Z2+g1uR+a97bAm7svsXOKB3SMKsFupT1klRRKlLpciJtjE7HD1R1v77/BX+0bwnahHSSkpYB8ICc7G0GP3+LY0gP4EBAKTQNtJm7WNdJDbnYOznl6wcfdC8aNq8PRwwUVfiBULgBCQuEbh6/gyJL9kJGXZXlMmtb8Ia/8vHzGeb/bLuTl5qL7eGtYDu2I3JwcbBy5CoH3X8NicDsmMJf4xiE77EUw1totR1Z6Jlz2TkelmvoQF6/A+rqPXoOAOy/RsEsT2C4aifz8PPhsOQ1vdy/IKMpi1LqxqGpqzFyr6Wdhr0Kwd+Z2Jlo269US/ecORUps0nfiZhLZn1p1GH77LkFVVxUD5g5jLs/kfE2RlZqB2yev4/iKw5BVlsPwpaNg3LRWic4V3vjnBLi4mZ8d5YdAYCCOHzsG23nzIC8jw3ZEVmzbFk1q1WLuNOUtfG7fxo7Tp1kpGSohQ2LmtIwMKMrJoU61arCxtETjmjXxl7FxkVNX79ABlo0a4VApy1w9DwpCi5Ej2U2Ss0ePon45veAqb8eRz4cT4AQ4AU6AE+AEOAFOgBPgBDiBrwkYGxuzB+8zZ87kYMqQwEeqrtSrF5KTk3Hnzh0oKJTsxnAZTo2n/oMIzJ49GwsWLEBuYiLEXr4s1cpPXL0Kt61bQfdnyIWP7mftnjcPKuRuVcqYsGoVPE6cQKqfX6kMAI5dvgy7BQtAwh0S0DgPGIDKWlqISUhgZUBjEhOxx9ubVRIbO3Ag2nTtivC4OMybNw+TJk3C5MmTS7kS3p0T+O8JPH/+nIkwzp8/z1yFSxqdOnXCy8/vF7RBokOHDkz8S27Ov3KQqHn79u0gATdVV6AojWsz9R81ahR0dHTg7++PM2fO/Mp4+Nw5AU6ghATS09Oxfv16nDp1CjIyMpCUlARtbszLzsbHM2dQQfyTCEAUsWb/fkwYMEAUqVgOcm+OTkjA0SVL2L+pyuqFu3ex6+xZvI+IQFZ2NvR1dGBjYQHngQO/G7dW//4IDg/HgQUL0KNVq5/Oiyq1Oi5dyoSJnc3M2Gc8rxs3kJqejt7dumHY2LGscisPToATKDsCoaGhmDJ2LA55eUFHXR3ea9fCde1a3H3+HDUNDXHD07PsBhcic2hkJJxWrcJlf3+oKiuz6zjavEoVq0mPQI6qI3r2hLaqKts8kZOXB+UfONs3s7Nj7vdvjh4VYhafu0hKAoaGuODvj+HDhyM2NhZXr15FIxFWGxJ+crwnJ8AJlBUBUYqbxfLzce/cLaz8ewnSktMgJSMFSSlJGNarhhkH5kNOSf7LMtKTUrDy78Xw97nLvierKAdZBVmMXT8RDdo3Edlyk6LisX2SOxI+xmP4Sgfm5Ou39wJOrT2Klv3boJdrv0KOx4IMXB7EzVnpGTi2/CBuHr4KRTVFtB3eCSaNayAzPRMhz4Nxz+s2okOimFhYQUWRCYqrN60JRVXFEombU+OTsXfODjy9/IgdR/sN4yCv8nN376h34dgydj2iQiJRw6w2Rq5yRAVpiWLFzdRv1eAlSEtKZYJkg7+qoq5lfXZOfCtuFqsgjo+BYdg6YROiQyJRuZY+WvWzgIqeBhLC43Dv7G28ufMKyprKGDhvGKo3r/NDcTOdr++evMWeWTsQExKFag1NYNbLHNpVdZCZlolA/wBcO+iH1PgkNOraDL2nD4S0vKwgpwhvIwABLm4WABJv8i8QoBJN8+ezXdZVdXURcOQIG3TM8uXYeeYMUq5c+RcmIfgQaw8exFxPTyZqfhcejt6WlmyXNomUq2hrC5zo8v37aD92LLt4oN3ZpY1L/v7oOnEim9erZ88A/nC4tEh5f06AE+AEOAFOgBPgBDgBToAT+MMIkLh5yJAhINEjj7IjEBkZiR49eiAlJYWLm8sOM89cDAEXFxccOnQIL8+dg0Jqqkh4PX7zhpUxpg3wT/btE0nOhORkaHTsiGnDhsFt1Kgf5iQRTmhEBBrUqPHDn19//BgWDg7o3Lw5qx6mqvTzhwtUSW35nj34x8sL9U1N0WfgQKxdu5a51K5evVoka+JJOIH/isDjx4/Rp08f+Pr6CiVuJuFv37598eHDh0JLIIHznDlzsGbNGvj4+BS5PCUlJXh7excpiHZ1dWVC46Jck2vXro0JEyaA8v0sbt++DXoVBImYSeD9dSgrK4PEPQVuzfSz0gqbKQeVKm/YsCHjQQ75PDgBTuDPIEAuzbQRhATNtPEjLDQUqWlpUKLnVfn57HmYZjmuPhr44QMOnz8Pv4cP8SQwEHFJSVCSl0d2Tg4zN4qOj2eVXOubmODE8uWFDurZmzfR09WVuaG2EtAN9dS1a1i1bx/CIiOZqJqCnpOGxcbCytoaq1atgqIINsv9GWcfXyUnIDiBc6dOobuNDfpYWoKe1wcdP/7FZd108GB8jInBjtmz0aV5c8GTlmHLgJAQdj0nLi6O6lWqwK5HD6ZPkJGSKvGoypaWGGllhRXjx5e473cdKlfG05gYtGnTBvr6+qxiBw9OgBP4fQmIUtxMlFLikzC/z0y8uvupcpCMnAxGLBmNDsO6FoJIwtIbJ69h7ejlyEhNh5i4GEwt6mOi5zQoaaiIDPj7J2/h4bQBmvrasFvhAGlZKSYKfujrj/5zbFG/Y8mF1PHhMVg1ZBGSopNgObQDujvZCOQ0TWv2JsfhTadg3NgE9pucISktiej3EVg7fBkyUjIw0G0oGnQqvBkuJzMLOyZtwbOrj9GsZwsMmDecGRq8vR+AfTN3IDY8lrkvq1XRRHZ6FpKiE6BWSQ21W9bD40uPEBceA1VdNbSz68z6+5Bzs4cXjBvVxOgtzkU6Nwc/DsR2F3ckxyWhzZAO6D7BpkiThqyUDBxYuBv+Z24zIfUYdxdoVdXGRvtVePvgDXNuJl7fOjenJ6bAfew6BD8OYmsj92SXvdOga1IFWxzX4tWdF2jUuSmGLPobJG7Oz8vDo/P+OL78EBKi41nFOgVVRaTEpzB3akU1JbTqb8mODzlaM+fmCRsR8uzdJ0fp4Z3YOUZt752+BR+P04iLiIeUtCRUtNWQnZmJuI9xkJCUYKJnm6n9oWVYev2fyE7s3yDRJ3HzcmQnZzH3eLpHLUyI5dMZw4MTEIZAUhJcRo/GuoMHmSg38NixL1mCwsNhYmPDXI2t27QRJrvI++z39YXjsmWQEBfHKmdnDClFqas+06fjYUAAAkuzM/KbFVIprs1Hj2LZxImwnTQJEOKiRuTQeEJOgBPgBDgBToAT4AQ4AU6AE+AEfhECJG4eOHAgcyrlUXYEoqKi0LVrV6SlpXFxc9lh5pm/JlAgRJSTA5SVERkdja2enli3Zg3qGxvj3Nq15ZrXgu3b0dXcnAlpHr1+jcMXLzIBDD2Ij01IYAKi3Nxc3Pvnn+820D99+xamgwZh3qhRmGlnJ/A6c/PyMHLhQgRERDDXwpCQENSvXx9LlixhbrU8OIFfkQAJLujv/Llz52BoaCjUEkggvG3bNiY+/lrkTBuj3NzcBMp58ODBIoXLJA4RJDw8PIp8qFO3bl3QfIsLPT09thYSU5OwmQTgpY0RI0bAzMwMJ06cgJeXV2nT8f6cACfwixBo164dE9/VrFmTbY7C69dY7+GBradO4U1ICDo1a4Zjy5aVu9XQxi7PEycQGBbGxILKiopo37gxMzZKSk1lG8TkZGRgoKPz07m3trdHUloaHu7eLfT6SFB57MoVJniuWLEixKSlceToUaE25Ag9Cd6RE/hdCcTFISM0FD0cHaEkI8M2EpCh2V43+EoWPAAAIABJREFUNyYULgiqePP3woXlyr25dv/+eBkcjN1z52JQp08iK2HC8+RJjFu+HK8OHy7y/axEuatXx74zZzB48GBcv34dzcuJILxEa+CNOQFOQCACohY35+fmwcv9GHbM8kRuTi5MGtbAzMPzoaz+vWA5MToBbn2nI/D+a+bQO2z+KHQa0V2geQva6N2D17i46zyMG5mgRZ/WyMrIYuLitMRUdB3XE6p6moKm+tIuNTYRR5cdQGpiKkzbNUTTXi2ZGLe4IHGz/9k7uHv6JnSNK6HbeGtUkJRAUmQ8jq88zFyW29i2h1HjwiYHJMD19TiD4KdBqNG8FtrYfhKB5mXn4NWtF7h64BLiPsSAjqW8sgK0q+rCzLoFdI0r4/bx63h8+SET/5pZm6N++4a4730H97zuoFKNKug63vqncyfJaMCNZ7i85wITYXcY2QVV6lQtcpkkOn56+SGuH76KCuJisBjSHsaNquP0umMIf/MBpu0boknP5qggIVEoD/UjsfaFf3yQlZEJCXEJ9J4xEBqVteC1/hjCXobCuEl1tB3emQnh2fpzc5lD87WDF5EYk4SsjGxISkmgoqYqGnVtilrmdSAlJ8PaZiSnwXvzaUQGf0TTXsSh0ZfxiW/AnRe4ffwGooIjkJGUBik5aeZ4Xa2BMcxsWkJVVwP4NCwPERGICYvEumHLkZOazcXNImLK05SQQCNTU3wIDUU9Y2P4rFv3XW8jGxvk5eWxHZP/daRnZkKrUyfoaWjg4qZN0FVXL9WUKFfPVq3gMX16qfJ82/mvQYOQkp6Ot7duAZUrizQ3T8YJcAKcACfACXACnAAnwAlwApzA70yAxM39+vXDggULfudl/udri4mJYY5uVLL6zp07UOCVh/7zY/JbTyAwECeOH2eOeNtOn2YlePMAaFasyAT2dJP8+YED5RbBlfv3cer6dfjevo2MzEwmapaUkEBmVhYTMteuWhUdmzWDjLQ0+rdv/906WowcieS0NDzZu1f4NeroYPWRI/D09ERwcDBzcLa3txc+H+/JCfxHBOhvjq2tbanEzV9PndyOyZmYHJ3JufnWrVuFHJB/tMxatWoV6zJDOSn3167L3+Yix2Yak5xRfxa0WWv79u1ffkzun+T4/HWQoJmEyDt27GAOzjQ/ehXlGi3I4bOzs0Pr1q2xb9++Yt2sBcnH23ACnED5J/Do0SO2ASouLo4JJljExwNv3iAqPh7WkyfjWVAQEi5eLDeLYcJlJyc8evMGZnXrYvOUKTAW8rkaVdoY1q0blo8bV+r1ZWZnMxfopPR05EtLw9vHh4mdeXACnEApCLx+jVZ9+yIuORnh0dGwbNwYkwYNQtM6db5LOmHVKrbRYJGjIwaXQkxcitl+6br+0CG4rF2LUytWoJOZWalSdnF2ht+DB0j18ytVnoLOt548ATneD3FxQSMLC2RmZuLp06ciyc2TcAKcQPkjIGpxM63wY2AYFg+Zh9gP0Rg82w6d/+7xw4Xn5eTi7NaT2D1vB6rWqwpnj2nMYVmUkZuVg8yMLCZ6lZSRYo6/mamZyEc+ZORlICZevCj52/nk5+Uzt2n6SqJfyitoZGdmITsjm7klS5PwVgxsThmpGawiipSsNBM8fxuZaRnIzc6FhJQEa1MQebl5yEhKRVxkHHIysqCspQI5RXlI0drExJCTmY3M9Eymy5WUlWbzLTQH+U/i359FTlY2stKzWC5peWmIV6hQ7FJzsnKYUJtCSkYKEtKSyExNR25OXpG8iEN6yieuYmKAjIIcE15npmYwoTy5KJPouFDk5yM9OR1Z6RnISEmHlKwMZBRlmWM4OTwXBAm1iSHxYnOSkiycho5BSjoSo+KRFJcMOUVZKGuoQEZBtkTHt1g4vMEXAiQ0XzNkMZANLF++HF26dBGKDnduFgob70TOEaHv32PLlCno94OHL+yPWWwsKnfrhmVjx2LioEH/GrQ3oaHf3cCYtWULyCkn4PBhmFSpUuq5KFhY4MCCBehmbi50Lip3SqLrey9e4EFAAOoZGUFLVRWD58xBpK8vNGh3JLkS8eAEOAFOgBPgBDgBToAT4AQ4AU6AEyiWgImJCWxsbLB48eJi2/IGwhOIjY1F+/btkZWVxYRbXNwsPEves2gC90+cwHBXV1AZX42KFSEtJQWdz5vV6WY9ORKnZmRgvr097Hv1Klc46Z7PuBUrQF9p47+epibaNGzIHASr6OjAvF49geZbwcyMOVO3b1Ly8pmFBtDSwr4bN+Dg4AApKSksWrQIo0aNEmgOvBEnUF4I3Lhxgwl5z549i6pVi3YRKi9z/rfm8SO3aBI5kxhaGMHzsGHDmMiRXK4vliMh47/Fk4/DCfyJBNzd3bFhwwaQG7wPVWkl53h6JSYyHE8DA9Fw6FAcXbIE3Vu2LBeIGg8bxqpi7Js/H33athV6Tif8/NB72jSEe3lBU1VV6Dzfdmzj6IiM3FxUMjJiFQN4cAKcgPAEOllY4PLNm1jt7AyLBg1Qq4gqHqQTMB08GIpycojw9hZ+0BL2fPHu3XfzqtarF2pVrYrTK1eWMNv3zVs5OMDUxATrJk4UOtegWbPwIjj407V0ejoa1KiBbW5ueJ6RgRH29pg0aRKmi9jYTejJ8o6cACcgUgJlIW7Oy8nB5f0XEB0WiS6jrKCkpvzTOceFx8B722kY1DZEc6tWQomNRQqEJ+MEOIF/hQATNw9ejLysPPbczsrKSqhxubhZKGx/dqdWrVrh5YsXiD57tlgQQ+bMwYHz53Fx40a0ql+/2PalbdB46FD2gfzo0qVo/tWDoqq9eqFutWo4uWJFaYcA7bruN20ads6bB2V5+RLnIx7Ldu/Gi6AgyMrIsIsHZQUFjOvbF1OHDoV0ixZwHTwYy+fPBwwMSpyfd+AEOAFOgBPgBDgBToAT4AQ4AU7gTyRA4ma6ObKsHJZK/p2OB7m5tW3bFtnZ2Vzc/Dsd2HK2lsNbt2KAvT17IP23lRVsO3dGXSOjQrMk4XADW1vUNDDAs/37y80K9vv6YvDs2ZCSlITLoEGYNHgwu+9T0li2Zw/mb92K5CtXStr1x+01NHD80SMmDs3IyMCBAwfQo8ePXXVEMyDPwgmIloCfnx/GjBnDxHcWFhaiTf6LZyOXZxLOJScn/3Ql5BJNAnFBYujQoejatStjffXqVUG68DacACfwixMwMzPDvXv3EHjxIgxkZX+4mg7jx+NtWBjekvj5P465np7wOHECs0eMgIO1dalmM2n9emw/dQoxvr7MrU7YeB0SgpjERBjo6DBHPtqY12zECMgrKsK6Xz/Mnj1b2NS8HyfwRxOg65bRDg4Y0rEj1rm4CMTiwt276Obigmp6evDfuROy0t84QAqURbBGl+/fh9PKlcjMycGF9etRWUuLdczKzoa0uTlubduGZj9wmBYs+/9bUUUfuj4WJhbu2MFM4Oh9qXndukzUTO9V9O8hnTsjX0MD6o0asQpdtLGN/ibw4AQ4gd+LQFmIm4lQbnY2crJzP7kTFxXkqJueyVx5f+RY/HvR5qvhBDiBAgIF4ubczFwmbu4lpEEJFzfzc6rEBOTl5eG7fz9aaAtWKqCpnR3bPd2leXP0a9cOahUrsl2VVIZTVOG2dSs2HzuG5NRUrJww4TvHHsnmzbFt5kzYCmlxLqp57vb2hsuaNcjIyoJV69bo2bo1uxAh954CHo2GDmWuRJc2bULj3r0BIQTUopovz8MJcAKcACfACXACnAAnwAlwApzAr0KgevXqTIizatWqX2XKv+Q8ExISWKn63Nxc3Lp1C4qKir/kOvikyzGBnBx0a9sWT1+/xiw7O/zds+dPJzvbwwP7fXwwomdPTLW1/c8Xdf7OHfScNAm5eXk4s3o12jVuLPScKvfoASU5OTw/cEDoHN91VFXFjkuXMG/RIqSmpuLatWuoUaOG6PLzTJxAGRK4dOkSEzd7eXmhWrVqZTjSr5s6LCwMz58/x4sXL9gGJPr/rwXPJG4mkfPPgvrTz4cMGQJra2u2YYz+1vPgBDiB359A5cqVUeV/7F0FVJXZ9910l3QIiISIqNiFYtc4dow6tjPG2Pqzdezubsfu7hwLsRWVVkCQlO7+r3Mc/KMSj7DwnrXeAt8799x79318ft+9++yjp4c7GzbkOdknnp6cWLZh4sRiE4olRfRNaCgniX0qNKTTogXfb0VduSJpqDz9Zm3ZAkUFBUws4r3k8n37MH/nTh6PjLQ0n/WVMzKC89atcHZ1RbO//oK8ggKOHD2KZs2aFXu8IoBA4GdD4MKFCxg0aBBa1q6NbRMnSjx936AgtBk9ms/cy+rpwdzICInJybA0McEjDw8mIfsHB6NGxYpcrbmwtuXECWw9dQquPj4w09fH0tGj8Uv9+h/CUOXm2v37I93FBdLFSJwo7Lg+9SfRtX6zZzN3YlU+qs8T9+7FUw8PhIeH4+HDh5CWli5u16K9QEAg8B0hQPu4tGe+9/Be/O/wTKiWUf+ORieGIhAQCJRWBAS5ubSu7Hc+L4f/1JdXjBwJJzs7iUc7bPFibDt1CipKSkhKSUFKaiqUFRX5QZ9esrKynD1J2YuFsfCoKAxdtAgnb95EXXt73Ny06bPm8aSM3KQJLq9ZgyY1ahQmfIn60rzLtmsHNRUVHJw7F7XywI+yJ49cu4b6lStj7ZIlQr25RFdBBBMICAQEAgIBgYBAQCAgEBAICARKKwJEbm7VqhVWrVpVWqf4XcwrJiYGjo6OyMzMFOTm72JFSt8gwl1d4dSxI5NCzhRQvpfUq6r06oWA0FBEXL4M9a+UIO7u58eK0Z8alT+OiY/Hor/+Qrdiklc0mzZlNcKxPXsWeZGvPXqEsIgIXHBxgV9wMMwNDLBzzhxM370bxy5dgqmpKc5/xVLNRZ6IaCgQAHDp0iWMHj0ap06dguUnSu7FAYiUzEn5OFutkxStFGnfWkaGX0TsyP6ZlZUFeXl5/jf507/p/0M6KM7+PSQkBDo6Ovxe9mevXr0CEQcp9ps3b6Cvr8+/k09ERASUlZX531QVgZKIFBQU+HdKQiCj31NTU/knkY+9vb25P3oR6btcPqXZswnPGhoaqFOnTp5QrVixAitXruTPybdatWpMbDl06BAqVqxYHIhFW4GAQOAHQICqjC4eNQojOnfOd7R/LFjAZ30Lhw/HHx06fFSdIi09HT6BgXztU1VSghkpGBfDSGV0w9GjcKpeHVumTOEzRTIiDPaeOZPv+x7s3FmMHorftO2YMTjn7Ay7cuXQpWlTvpZDSgo2Zmbo2bIln38S+fmfS5cgq6SER48eFb9TEUEg8BMi0LhxYzx/9gzvLlwo9OzPOzuDxMdcvb0RHR/P1y1SWVdRVGR15crW1jhXyCT56Zs2Yd727ShrYIBD8+ahdi7KzCdu3EDH//0PWffuFXrMJdnAoHVr6Jcpg2d79+Yb1i8mBhYtW6J69eq8tzaHKkwLEwgIBEoNAnR/Rnvm2/fswKSjf0NNW6PUzE1MRCAgEPh+ERDk5u93bUrtyBKp3ImaGqZMmIDEgAAsGzWqUHONTUgAZXaHRkYymfm5jw88/Pw4S5I2IcoZG2P2H38UKma7sWNx98ULTOnXL8/DHiIVqzZqhH1z5qB78+aFil+Sziv27weVyjq6cCGa1aqVZ2gq61Wld29Ur1ABS0eNEurNJbkIIpZAQCAgEBAICAQEAgIBgYBAQCBQahEg9dHmzZtjzZo1pXaO38PESAEyu0SpUG7+Hlak9I1h16pVWL1xIyqVL4+dEpTvvujigjZjxqChgwOur1//RQE5e+cO+s2aBS11dRyYNw/VbGw+9Hfq1i1M27CBD8tvbd5crHEQQZoOzElZqyjl0e+4umLi2rVcSc1YRwcRsbFMCOrWtCnvNZG1nzwZbm/eoH///pgyZUqxxisaCwS+BgLnzp3DhAkTcPLkyRIlN69fvx5XrlxhMh7tf2eTlIlITC/ax46Pj2fCWvYrm7icTYDO+VNOTo7hIFJ09islJYUJzPT3TGTq7N/Jjz4jMnW20RjocxoHEZqp/2wiM42RXkSU9vf35/dHjRqFXr16FXsJxo0bhyNHjuQZh4jRdnZ2GDBgQL7qz8UeiAggEBAIfHUEqDQwKaMm3bghUd9EcN519iwnehAxUFZGBg42Nkw6Lm9iwgJHRG6+sXEjdDQ1JYqZ0+no9et8HxMVG8uE4cUjRnyk3EyEwcW7d0NVWRmXVq8udPySarD5xAn8b80abJ82DZ0aN843bKvRoxGWlIQaNWticzHvE0tq/CKOQOBHQoDuf+ie6ND8+ejSpIlEQ7/7/Dn2XriAsKgoTpIYVkDyhkRBKeksPR3y9etjZLduWDVuXJ7N6Pmw+5Qp8D95EnplykgavkT9aP5/LV2K/XPmoFXdugXGnrBjB24/fcqJGK6urqLKT4GICQeBwI+DgCA3/zhrJUYqEChNCIT4BmFFr/nISsvCggULQM+eRTGpLNoBEyYQkBABUsK6tns3arRpg+Bz5yRsBVCG9aErV2Blaoqp/fqhWgmUvAyJiIBZ+/ZMbJ45aFC+Y1F1ckLftm2xbsIEicdc0o40ViMdHYnUqak8THBEBBTl5XHyn3+EenNJL4aIJxAQCAgEBAICAYGAQEAgIBAQCJQ6BGxtbUFqPkRSEvblECCCV+3atWFlZcVYGxkZfbnOROSfEoEBffvC9+VLyMvK4qKEhJVj//6LHlOnooK5Oe/9UMJ4trpfSYBISs0zN2/Gmdu3oaWmhvG9e2PMb799FHrcqlWc1N+4enVMHziwJLotUoxn3t6o9vvvTDQiQlAVKyu4+fqiZZ06PDYDbW2OS8Qjsw4dEBkTA2dnZ9TKJxG/SAMRjQQCJYwAKTYTEf/48eP8f1BJ2f79+5ksQyS9yMhIVjQn5WQiHNPPt2/fgu4x6PecL1JwLm12+PBhuLm5Mck5NjY21+kRyfngwYOlbepiPgKBnxqBGjVqINDPDyESnvkRsY+IzHvOn8etp0/5LIvey8jMZKViSlAjUaNZf/yBXx0dC4Xtgn/+wdI9e/g+bniXLpjUt+9n7U/fvo0527bxvY7z1q2Fil+SzjZduzK52/fEiQLDPn/1CrUHDEAjJydQMkmzYlb4KLBD4SAQKIUI0L1XzxYtCkyApeehMStXwuX5c5gaGKC2nR2i4uI46YKE0CQxup6FRUbCUEfnM3d6Jvx1/HhkurjkG4quk81HjMDSkSMxqH17SbotcZ/GQ4fCw99fYk4HPSNW6tMHterU4SS9vQWoPZf4gEVAgYBA4IshIMjNXwxaEVggIBDIB4Fgr0As+W0OZKVlBblZfFO+DgI9evQAKTSdnTMH+s2b4+HOnSirr19g5+3Hj+eHhm3TpnE29X03Nzzbs6fAdtkOo1es4PJWFT8pr+fq44Oa/frBde9eLvGUn9UdOJBLlAaeOSNxvyXp+NjTEw0GD+aHpg6NGhUY2jcoiDc6dDQ0+EDstzFjgK9UWrXAwQkHgYBAQCAgEBAICAQEAgIBgYBAQCDwHSJAJdMdHR2xadOm73B0pWdIpChJBAg66BLKzaVnXb+nmXTq1AlNHBwwc9kyRFy6JPHQXr99i04TJ8LT35/LgVN5cKoaRntGVCWLFFNN9PQ4+X1yv34Sx7338iW6Tp7MpXyb1KiBRX/9lWvbzpMmMVG4f7t26NOmjcTxS9rRvH17mBkY4MyKFVBTVs43/LwdO7Dj/HlUqFQJZ77RnllJz1/EK70IHDt2DDNmzChxcnPpRazoM+vevTv+/PNPLF++HH379mXCM71cXFwwevRojKG96jzs4sWLcHd3B92X0cvExKToAxEtBQICga+CQJdff8Wjhw8LJOlSEtf8nTtBSWUNqlRhwl7NihVLdIxWnTtzssmo7t0xNA+VVSIudp44ESlpaQg4fbpE+5c0mLuvL2r174+Jffpg2oABEjU75+KC8evXQ1VdHffv35eojXASCAgE3iPw8OFDNGzYEBN69cKswYNzhYV4AxuPHeOXgpwcRvfogVp2djhw6RJevX0LOVlZTlDtJkFywc0nT/DbtGl4e/bsZ31df/SInw+n9u//WcLrp87GbduyavOT3bu/yVIqN2zIitXZ1XskGcTJBw8wfuVKUKUSUlik+0JhAgGBwI+PgCA3//hrKGYgEPgRESBy8+IesyEnIyfIzT/iAv5IY05KSmJVjMePH2PzsmWQ9vdHhW7dEP/vv1BSUMh3KqOWL8etJ09QxdqaVWLCo6K4/EsZdXU82rULuhKUpFJt1AizhgzBuE8Ucahjg9atMaBdO8wfNizfcSzbtw8L//kHRxcu5DKlRbUbT56gURHa0wEXlcpKu3OHD4AlsSNXr+KPhQuhrqICt4sXoWxvL0kz4SMQEAgIBAQCAgGBgEBAICAQEAgIBH5KBKhUOqkJbtu27aec/9eaNO0RODg4QE5ODnfu3IG6uvrX6lr085MgULduXSyfNw9dunfH7S1bUK6Q6uAuL17goosLK1Q9dHdnUjKpB9IeVHxSEqrZ2GDN+PESo1mjb1/4BAbixf79TI7Oy4hYTQTrkd27817Vt7DDV6+ygnVGASpiOcc2cOFCHL12DfPnz8ewAvbXvsWcRJ8CgWwEDh06hDlz5uDo0aOwtrYWwHxBBLp27cok5v79+8PLy6tQPdnb23+k+kz3CURypmu7IDwXCkrhLBD4agiYGhlBV0MDj6iKaB5GysxVe/dGlyZNMOH337laK6mX0v1RYSwuMZGFiD4VM6IYJI5E52jVbW2xZty4fMOW79SJzxt9jh+HngTnjIUZoyS+pFo9Ytky3Ny4EfaWlpI0YZ86gwYhTVYWLVu14nsvYQIBgUAuCKSnA4GBQHLyhw9bDRqEqy4u8Dl6FGaGhh81io6PR5dJk/Di1Ss4WFsjNiEBy8eMYcXmbBs8fz7uPn/OlaV3zZwpEeyy9erh1NKlaFOv3kf+pOqs17IlPxvOGTIkX4X6Rbt3Y8HOnTizfDknhRTF6Fp75Nq1AhWrP41NqvqVfvsNl1evLnRF7RkHDuD45cug5PZXr14VZdiijUBAIPCdISDIzd/ZgojhCAR+EgQEufknWehvPc2YmBg0bdoUVatWxeLFi3F9/36MmTMHHZ2csGrs2HyHRw8Sd1xdWX2YsiApG1JVWRnJqal800/vbZ06tcApqjRqxGUz6cb/U6s/eDDehoVh9p9/5quK4xMQgMq9eqF1vXpMcC6K0cNR21Gj4FitGhYOH16oEFV69eK539mypVDtluzZA3r90qABtlN5rXwO0QoVWDgLBAQCAgGBgEBAICAQEAgIBAQCAoFShkClSpVYUXjnzp2lbGbf13SSk5NRpUoVUElYQW7+vtamtIymUaNGTPYY0qcP9syejSpWVhJP7f7Ll3jg7s5lzEvKaF9qXK9emP3HH/mGJBVkKpPesnZtLsP+LazJsGGIiY9nQQFJLTI2Fra//QYZOTk4OzvD3Nxc0qbCTyDwVRHYR+IVCxfiyJEjgtz8hZHv0qULxo0bhz59+sDb27tQvc2aNQvbt2/Ptw2pOR88eFCoOhcKWeEsEPgyCPzeuzeOHT0Kj0OH8q3U2mjIEKRnZPAgXJ4/h7WpKV4HBcFAWxtbpkxBi9q1JRrgtI0bsZ9UVI8d+8yfFJmr9+mDZaNHs3JzfjZ1wwYcu34do3r0wJBOnSTquySd/t6yhQWVkm/fLlTYW0+fotOkSbCpWBETJkxA+/btC9VeOAsEfgoE/PyAsDCeanxiInrNmIHzd+/C3NAQzWrVwvr//e8jGOjvavf589BUVQUlum6fPh2WuVSOqNSjB8oZG+P0smUSwajepAlmDByI8b16feZPCR77L19mMvWeWbPyjEecCFJvtixbFvcKuD/KLUjwu3f4/e+/kZCcjHr29lg2apREYyenEUuXYueZM4j791+J23xwlJbGuF27sP/IEYwcORKTJk0qfAzRQiAgEPiuEBDk5u9qOcRgBAI/DQKC3PzTLPW3neiAAQOgo6ODsWPHol+/fkh+9w7yMjL8cOB74gS0NTQ+G2BoZCRqDxiAwNBQdGjUCP/MnAkVJaWP/IhorKqkxA8QucXI6WzYpg16tmyZ6w07KedQmSrKjCbicH7lNn8dPx7Orq64sGoVatjaFhrY2du24fStW3js4YG9c+agR/PmEsWgDR9VJyc+2Jr4++8StcnpNGXDBqw+cACdmzfHP4cPAwWoZRe6A9FAICAQEAgIBAQCAgGBgEBAICAQEAiUAgQqV67MpNvdX6ncJ20KZ2VlgX4S4ZeUjOn3nO/T75GRkdDQ0Pjo/ex2b9++hYGBwYc4OduSj4+PD8qVK/chbvbnUVFRUFNT+9AuOx59/uzZM5CKNZUQpVerVq1KdHVTU1M5vqKioiA3lyiyIlg2ArKyskj28oJFvXr4d8MGWBgbSwROv9mz8SowEOqqqlwFa/+cORK1IydSZ14wfDia16r1WRvNpk0xrmdPTB84MN94jzw80HLkSN5zor2nb2E01sl9+3KJ9MLYqWfP0G/iRDg6OuLkyZOFaSp8BQJfDYFdu3Zh2bJlIAVnGxubr9bvz9hRp06dmMTSs2dPvhcpirm4uODly5dwc3Pjn+7u7h+FmTFjBgYWcF0tSr+ijUBAICA5ApTIcOLECSwbOZJJwnkZiQy5+/qiR4sW6Nq0KYsRkZ29cwdjVqyAX3AwLq1eDaf/3s9vBFcePEDzv/5C5OXL0MqlAkyLkSPhHxyMDZMmoUk+8ehs0LpLF06Ce1KM57+Ja9di0V9/SQ7af56/jB0LVx8fvDl1qtBtqeKtb0QEHrm5ccVcfX39QscQDQQCpRoBumeIi0NETAxajRoF4h30adMGCUlJWHPoEM6vXInmnyRU+IeEYO2hQxjWpUuelX/UGjdG/3btsHrsWFCCQjtHR1SvUCFPKB1+/x2yMjJ4kEcCfZ+//8b9d9lKAAAgAElEQVTFe/fQrkGDfMXcdp07h6GLFjEJmsTjCmvjV6/GvRcvQMr3lMwhaUIHKdwTR8G/qM93mpqo3KULPD09ER8fz3tewgQCAoEfFwFBbv5x106MXCDwIyOQTW6WlZbFPKrUWERBEqksOgETJhDIBYG0tDRoamryBiZlD//Spg1m/PILkJkJ8w4dICUlBd/jxz9qSRsBm44fh52FBeYOGfJhk+PT8LotWqBFnTqcRXny5k3c3bYtz6xw3ZYt8VuLFlidRxkqKlM1aN48yEhL4+Lq1aiaR1nCc87OGDxvHhOhi3rI1HjYMH6IonE7k5KyBOY0ZAjcfH0RdvGiBN65u3SeNInLcbleuAA7CUnVRe5MNBQICAQEAgIBgYBAQCAgEBAICAQEAj8gAkRsJtItKTuWlFWvXh3R0dFMIialYiIxk9G/6ZmYjH4qKSnxZ/R7zvdp41hBQYFJxjnfl+R3IhFT3Oy+csam92RkZD70n/1ZQkICtyFyqLS0NFq3bo0pU6aUFBwch/YKbG1tuR+h3Fyi0IpgABPgWjVrhvUTJmDOtm24v2NHgbg89fJC27FjIScjw4pYCYmJrN5MB+Ck2iWJGbRujSY1a2Lf7NmfuXeYMAE3Hj+G36lT0FBRyTdcoz//xG1XV2TcvStJt3n60OF17UqVChUj6N07lG3XDlfXrYNTtWqFagtZWbSePBn3Hz7E+PHjMXny5MK1F94Cga+AwObNm7Fu3TpBbv4KWHfs2JHvH3r06FGipcizCc80ha5du0I9F2IjfRYbG8v3MGR0b1exYkXUqVOHf+bV5ivAIroQCJQqBKhiK93Lr5g6FecvXaKHGpxauvSzOa48cAB7zp/H9fXroZbLfVD/OXPg6e+P8iYm2P3330hJTYWCvHy+WMnUrYsDc+cyUfpTIzXkfTQeANMHDMjVJ7vNuFWrsP/iRayZMAGdGzcu9PoEhoXBtnt3GOvqsnJ1YYzuHTs3aYJ1EyYUphn7RsfFoVyHDujSvj1eBQXh2rVrhY4hGggESjUCb98Cb9+i3bhxrNycmp7+oTIyCZldffAAS0aMYHKyppoaHrq7o+2YMZwUS8TnsrkkDEzfuBFL9+7FtfXrkZSSwmrItKNzY9MmlM8jmdasfXvUr1wZ+/JJmm0zZgwuubjg7IoVaFmnTp7LQkkd993cWLVeR1Oz0MvXc/p0pKSlMeF7av/+uSbl5gy69eRJDFm0iHkcueEh8QBMTTnpWFdXF/fu3ZO4mXAUCAgEvj8EssnN23Zvx+Sjs6Cm87mI5fc3ajEigYBA4EdHINgzAIt/mwOpLCnMnj2bk+iLYoLcXBTUfpI2T548Qa9evdC2bVveUNxEN+9UCgYAlYeijMVmNWti6oABfEi778IF3uSoWakSrq9blytKd1xdMWndOjzz8uLNgjIaGqy8TP+ZXli9Gvbly3/WTqp2bc5m7FWA2hQd4CjIycEnl3JW2UEp65s2Wuihp1uzZoVeSSrVWbNfP97sIDL19Q0b8oxBpWaoxNbyffsw5rffClUqJreglB3vGxyMoOBg4L9D7EJPQDQQCAgEBAICAYGAQEAgIBAQCAgEBAKlFAEHBwcuU09lzkvKDhw4wKHomff+/fuo/Z86EP07O1ecfgYFBcHQ0JB9c75PbWrWrPnRcHLmmCcmJjJJ+FMjHzo4yu4vZ1wiSmcTm3O2y60NVWAqaaP+raysoKKiIsjNJQ3uTx6PFIMz37zBlgMHEBoRASVFRd6/qWtvnysypJZHh9Je/v7Q09LC//r04f0aXU1NjF21CvKysnzITCrOBVn1vn05Bh2Gf2qkAjZg7lwkJiVxcn5+duTqVQyaPx/bpk5lwktRjPaRqHIYkZQpSUFSO+/sDDrsT3N2lrTJR35J+vowq1mTry90TctOwihSMNFIIPAFENi6dSuTm/fv348K+ajsfYGuf7qQJHQyc+ZMVtR5/fr1V58/kaC7d++ea78mJiYfCM80Pvq3MIGAQKBwCNDfjgupLg8ejHEdOmDv2bPoP3s2mtSo8ZEwEJ3nTduwgatSLBk5MtdOnJ8/57bmhoYY27MnRi5bhuHdumFk1655DkqnRQsM7dwZc/78M1cfuu96ExLCSqWnly/PM2mLhIgqdO0KbRJpOnq0cCD8533m1i10nDiRx5OXwNKngSnpreWoUdg4aRL6tW1bpH4X794NStDzjYzEH8OGoX///kWKIxoJBEolAnFxOL5hA4iQnJaRwerqVCk620gQ7OSNG5zUTcrEyMpCzYoVcWPjRsjnoS5MCQkVzM25MhDZqZs38ceCBSAFwO3TpqFt/fof4ickJ2PIwoWgZzu6BhEXIj+TqVMH43r3xuICVODp2lfLzg7nVqwo0rIRx0FNSQl03d03dy4a55LQGh0fj3k7dmDZ3r2c7Ltzxowi9fWhkZwcroSEoHOPHhg1ahSTkoQJBAQCPyYCxMdavXo1Nm/fgqkn5whys4TLmJX5n8DJe42Tr2f0H9TX7vPrzU709BMhEPjSF8t6L4AUpDCLeJ+9ehVp9oLcXCTYfo5G586d45tUKjNybOdO6EZFsWpztm08dgzDFi/mf9J1lco/0QbGsf/e+xQl36AgJkRT2RSXbdv4QYPsiacnGg0ZwuVkFo8cyaU+ySh7mYjEqWlp8JegtBM9aOw+fx4P//kHtubmuS4SkbJbjx7NDzDX8iBgF7S6lBFKmx06GhqYN3Qoq0p/ajHx8Vwqh0qSTurbF7P/+KOgsBJ9rt28ORzr18eJc+ck8hdOAgGBgEBAICAQEAgIBAQCAgGBgEDgZ0GAVJbLlSuHI0eO/CxT/ibzpM3w8uXLQ01NDbdu3YKGhlD6+CYLUQo77de1K46dOwcNVVUm1zz28kLou3d5VsLyCQhA96lT0alxY1avymm090P7NzHXrkFJQYHVtKiCWF7W8X//A+1bPd2zJ1eXnWfOYOjixbwXdGLJknzLF1fs0YOT74taIr3uoEFISUlB4xo1CpUoX6NvX642FnD6dNG+Herq2PvoEUaPHo2hQ4eKg+uioShafUEEtmzZgvXr1wty8xfEODv0r7/+yteATp06fRNyM43j8OHDuHTpEiv6vyUFxzzM39//KyAiuhAIlB4E1q5di1VLl8LGxARnli//MLEDly5x0liDqlUxa/BgNHRwwMajR1np9HVQEI4uXIiOTk65AkH3IESoI4Ixke+eenqy4M/kPBIt9Vq1wuD27fl8LS/bd/Eihi9eDCNdXbz8L+E0N98tJ09i8rp1GNW9O6YPHFikhZq9dSsW79mD9f/7H5MBc7Og8HAER0Tg8r17mLF5MyuhkgJrUY0SU41/+QVzhg7FpPXr4ebuzsqowgQCAgEAGRlo36QJvN68QbemTTErlzP24HfvuGoy7U/YlisHEz29fKFTdXLihAq6NuW0liNH4ubTpzDV14ecrCwnvN5+9ozjLhw+HON79843Ll37DNu0QYMqVXB5zZp8fW8+eQKnoUNxde1aftYrrFFfTYcNQ0hEBF+Dbm7a9BmZe/zq1aAk4MqWlliaR1JKYfuFoSEsGzdGfHw8Nm3axNW+hQkEBAI/HgJ070HJwus3r8eUE3Ohriv2cwtaxaTYBESGREJBUR5lDLUhLSdbUJOS+TwrCzGhUVDSUIa8ogLSUlKZkZeanAIVTbXc+8jKQmpSCmTkZPlVkpaekoqM9AwoqCgBmVlITkiEvLISpGUkF2QoyngyUtKQmpIKJfXPRSsy0tKRmpQMJXVViUNncpsUKOYSj4NkZiIlKeWD2IO8smKusbMyMpGUkAjlQvQt8SCL6JiVmYmk6Hgol1H/ECE9JQ2ZGRnIax5F7KrQzXyf+GD1gMWC3Fxo5EQDiRE4duwYpk2bhiaOjlg7ZAjVnv2sLf0nSBmMqrkoTeXWUZnmzfFXly6Y/UlG9v2XL9Fzxgykp6cz+VlWRgZhUVF88/1kzx5I/1fuN7/B95g2DWdu38abkydZETovaz9+PO6+eIH9c+agaQHZlnnFWLpnD3adP88HX4fmz0frunU/cz1x4wasy5ZFRQsLiTEvyPHkzZugAzcqU+WUx0ZSQTHE5wIBgYBAQCAgEBAICAQEAgIBgcDPg0BCQgLOnDnDpb1LexnvKlWqMLn5xIkTP88Cf4OZ0j6Aubk5k5oFufkbLEAp7TLC0xO6trZoVbcuPPz98frYMbh6e6PhkCGcSP8p6ZgObQ9fuYLRv/322QE1QUQqXNtPnUL63bsYumgRaI+mZ8uWeZKFCyI3U0zar6ozYABeBQZyJa+8FKXP3rnDCsrk07Bq1UKvGB16/z5zJrfbNn16gUph2R0oNmiAPm3bYvPkyYXu80MDOzu07tIFFy9ehJubm1DHLTqSouUXQIDIFPTau3cvbG1tv0APImQ2Ar/88gvmz5/P5BVfX99vDgxVlaRr0t27d/lnNuHZ2NgYzvmo1VM7SnqrWLEi6uST4PLNJygGIBD4igjoaGlBW10dtzZvZhJfTnvo7o42o0cjJiGBRYfISGWUFI1r29nlOUoS+qHzvMe7drFPhwkTcPr2bQzp2BHr/ve/j9pde/gQTYcPx+Pdu+FgbZ3vzIkE6Pv2LfbMng3HfO6pfhk7lpPavI4cYcJfUazf7Nk4cPkyE75zU2mdsn49jly7BjNDQ74XHN2jB0bmoTAvaf+Ldu2Cu58fwmNjoW9hge07d0raVPgJBEo1AhERETDQ12fCcH4VlAsDAqkm927VCivHjv2s2drDh7Fi/368i47mRFt6JqUEjGyRtvz62XT8OAuvUdWga+vXFzikKr16QYXUl7duLdA3NwdSrB61YgVszcwQm5CAO0WMU6jOpaXxv337EEiK+vfu4cWLF7lWICtUTOEsECgmAlTphar5aGpqFjPSz9Oc9nMpWXjdpnWC3CzBshMR1vnITSTFJyIlMQU12tSGdlldyMnLIz0tjYoGICMtjUVASR5UVk4W0lLSINItkIWMzCx+T1ZBHsnxiUyYVVBWZIIykWhlFOQgIyPLseQUFd6ThuMTAWkpZKZn4MrOS6jfpSG09LXw8vZzyCvKI8wvhMchr6QAaRkZpCQkMZFZTkkBRG4N8g6EiroKlNWUkEkDlCKtXCnIKcojLSmZx0wEZSK8piWn8vtEUE5JSOZ4sgpy7CclJc0xUxKTeH4RwZGIfBsOqxo2HMPNxQ1W1awgryDP41VUVkSWlBTPnYjYjFEqEbIBeSVFpKWk8JyobyJJU98KKgoApJGamAwpWen3bZLfk7PT0zOQlZGB6LBovHF7A3unylBSVQakpSGVlYWkxCREvY1A0KsgVG1ajQnLNBcad3pKChPBFVWVkJqcymNSUFGElIwMooLe4dUTb1RyrMxzpcnQ+tCYaS6JsQlwvfYEmvpayMjIhEWV8tw2PS0DGalp73GXk0VSTDxcrz9BrV/qIZnWQJawex+H1pawy8zIBJHCqcoEZKQhIyvLuBPOackp/Dt9HzIzs9iP1oFiMB7S0vxZTkwJS6kssB/NNSXx/XoqqhC+aUiMjcfTy4/h2KPJh+/Fu4BwxIZHwcLB6j3JWYUI6TISfPtL1kWQm0sWTxEtFwROnz6NYcOGYd+cOXD8T2W5OEC9fP0alXv2RMCZMzDS0fksVFp6OqgUk8uLF/x5z1at0MjBQeIu7Xv2hK6WVoGKzBS/6+TJ6Ny4ca4PMZJ22GvGDD5wU1FU5OzIr2WOlJUZEwNvb++v1aXoRyAgEBAICAQEAgIBgYBAQCAgEPhBERg3btwHUke3bt1Yga+0Ku3Wrl0b+vr6OCVB5Z8fdDm/m2ETuZnI8oLc/N0syY89kHfv0Pv33+EfHAxpaWmsmzABlcqX5zm9eP0a9QYOhKKCAjo5OSEsMpKVtRJTUtDe0RH7587Nde6k6mzVpQsiLl8GJagTeURVWRlT+vXDxD59Pmtj1bkz7C0tcWzRonyxfOzpifqDB6NpjRofqR1+2qjuwIFITE7Gs717i7Q2t54+RcM//4SliQm88ymzHh4VxSqCRCJKSU3F27Nni9Tfh0bGxrju5YUmTZqAStaTcqowgcD3gsDGjRuxefNmQW7+CgvSpk0bLF68GERy9vPz+wo9Fr4LIi4XlLi3YsUKrFy58kNwIjnb2dkx2VkQnguPuWjx4yOwY8kS/DllCm5s3JhnkhbNkkR96J7MWFeXhYgKshYjRrDCqt/Jkx9c6axv8vr1HOOvrl2RkZmJo9eu8b0dqUJfWr26oLAYsXQpdpw5w771KlfO05/uh4zatsWIbt2wfPToAuPm5VC+Y0dExcXB/dAh6Jcp85kbJcsRyXlo587cV3EtPikJei1bwv3gQTgOHYq99NPRsbhhRXuBwA+PwIEDBzBy2DBsnDiRq/SUhNl06QKvgAAk377NVXZKwuhaSRWoe7ZogfDo6DyfTXP2RUkklDBLSbhFtX8fP0aPqVM5CbiOvT1WjhlTYCgiYP/eunWBfnk5XPH2xt8bNvB9lJaWFhYuXFjkWKKhQKC4CBCxuXv37lxRrlq1ah+9VFUlV3Et7jh+tPaC3Fy4FUtLSsXZdcdRtqIZlNSUoWmghRCfYFSoVxHe9z2QnpaOqJAoJgYrqSkxwVdVWw1RQRFIiEmEipYKE4Ota1eEp4sbE6Qr1rfHy9uuUNdRZ/KtkU1ZhPq8ReVmNRDyKghut12ZfFrGsAwenL6LzpN6Mnn14uYzUFJXRujrYJhWKge1MmpMwPV77gslVSVUa1mTCa/e992RkpSKIO+3kJGRYuKsrLwszCqZw8PFAyoaKjC1M0dcRAyigiOhoa8JDR0NvHnpz2RaXTM9BHoEQEZWGuWqWOLVY28m4mqX1YPPAw/omhtC27AMosOiYFDeCG/d30BaVhqVm1WDloEO/J754NVDL5jal0OwTxATai0cLPH6sQ+Db1nDGkE+b5EQnQBNwzIoo0dzeM2Eb6saFeB1zx1lK5kh9FUIE7PVtdXhev0pzOzMYFWzAkzszJEQFYd7J+8gmQnZ0jCwMERMeDSUVJShZaCFV098IKsgi8pODvBwcUNmejpMbM1gWaMCwvyCcXXnRRhZGkFFS426gGUNG/g88ELVljXw7k0Yzm88BXN7C7wLDIO2sQ4sqlnjnX8oYt/FQK+cIewaVkZ8ZCycj91CxQaV4On8EpCSRvlqVvB+6MmEdmNrEyTEJCAqJJLHqGWkDaPyxogJi4ayhjJeP/VBYkwCzO3LIyEmHjHhMcjKzGDMfZ/6MBG6cnMHaOpr49VDD/g9e43k5FSoqCszSd6imhWIMEwE6vI1rPHmhS+SE5MR8SYMNdrWwRv3N+ynb2HA38fE2ETIyMjAqpYNjCuYFe4PoQS8Bbm5BEAUIfJHYPv27Rg+bBiSbt4sEago45FKswxo1w6biqMik8toouPioN2iBbZPm4a+bdsWOF7TX3/lLEqfYpSNopI3XadMQVJyMpe7Obl0aYH9uvv6cnmc4ph/QgLKNW2K48ePi9IvxQFStBUICAQEAgIBgYBAQCAgEBAI/AQInD17lkkdXl5ePFsjIyO0atUKzZo1Q/369UsVAnXr1kWZMmVAcxb2ZREghWw6RBDk5i+L808RPSoK8PZGu3HjWAWveoUK2D1r1mdTbzRkCDz9/ZGUksJqeoPat0frevXyhIj8VBo1wpEFC/hA/KmXF5cbv3r/PkwNDDBj4EA42NiADpcnrV3LBGEi5Gip5VHeMUdP1fv2ZaLPve3b8+yfiDHazZvj/MqVaFlEtVAqDz9i2TL8Ur8+dsyYkWtfl+hQb9o0WBgZ4djixTAzMCje14YOAitWxPTp07FgwQJOrKe/d2ECge8BgQ0bNmDLli3Ys2cPE1OFfTkEqOLHsmXLuPKHv7//l+voC0emBI3x48fn2wt9l0aPHo2WLVt+4dGI8AKBb4zA27ewc3SEjqYmk5tL0kjxdPa2bYi6cuWjsEQGnrl5M0j4iAg1VqamGN+7Nwb9+qtE3VMlCyLxBZw+XaB/z+nTcf3RIwSfO1egb34OZh06IDEpCSHnz0NG+suW2qZxjFq+nJVidTQ04PLmDfaJxLJirZ9oXDoQ+OOPP7Bz506k3r5dYhOav3Mntp48yWf0Z5cvL1Lc9IwMuPn6clIpXZs2HjuG4V26YO+FC/h78GC0kzA5Qa5ePX7m7dG8eZHGQY1IBb/vrFl8bW3fsOFnKvk5A5O69PSNG+F55IhEz7u5DSo5PR3qTk5c0cPExASBgYGgChrCBALfAgHa46Z7/GfPnn3UPRHvKVG7adOm/FJUVPwWw/tu+xTk5sItTUZqOtydXyA6JArR76JhbmfOxN96nRrgwWkXZCILOmWJDPwGlRpWxtPLj6ChpwVDS0N4P/BEtVa14HzkBuybVENEQBgC3P2ZSPvmpR8adHWC8/FbMCxvDD1zfVhUscTd47egV84A0UERiIuKYxJvq2HtgYwsOB+9yeRm/xe+qNe5IZyP3YSCkgK09MswgbZ2+3ooY6yDx+cfICk+CUmxCdA21uXfidzLSsiZWTCtaAbPe+5MqDW2MUVkUATHKVe1PJOgiURrbFMWckryrK4c/iYUgW7+0DUzYJKueWULJiCT6jEpMFNMIgibVjSHrWNluF59jKS4RP6/KTo0ClLSUlDVVEWYXyg09TShrq8Jt1svUKG2LXxf+KJCHVskRMczCdmuQWVEhUTAwMKI3zOrbIGw1yF46x0Am7p2CPIMQKPezeHl4oaIgHBoGZWBp4s7FJQVmNBLWJcx0mHlZyL2EtnX/9krWNexRdy7WDQd2BqRAeF4dOEBKjWqgmdXH0FdRxNyinKMddP+rREfGYMbe6/CoLwxY0dk4oigCCZz01oRqblJv5ZMaL9z9CaToEN9gxHg5s/k6/ioOOiXM0CQVyCyiNheqwKeX3/KRGrLalYI8Q2GWhl1xjX2XSxjSi/b+nZ48e8zyMjJsNI2qW5bVLGATT17PD5/j1W5w/3DeJ287nlASUMFBuUMkJGWgQAPf8bZoXlN3Nh3lcnwRtYmPCYiltOYIgLfQUNPE2aVyqF8DZvC/SGUgLcgN5cAiCJE/ggMHzIE23bsQPKtWyUG1egVK7Dp2DF0cHLCrMGDYW1qWuTYVA5UTVkZt58+Rb85c/gi+UpCsjKpPMfGx8O/mIperj4+6DFtGtLT09GoWjVsmTIlz/mcd3ZG/9mzsb64maaamqjZqxffkNFBsjCBgEBAICAQEAgIBAQCAgGBgEBAIJAfAgkJCdi2bRu/oqOjP7gSmYNIzrThWzWfMr8/CrpE1ibC7YULF36UIf+w4yxfvjxUVFQEufmHXcHvZOBU7tzDAxkJCVCoXx92FhZFVjrObUaaTZsygfl6jvLApCK4bN8+xCUkICMjA6np6VxmnUjIWurqBQJD+0AtR47ELw0a5LsHRIGsu3SBsZ7eR/0X2MEnDrO3bgUdxJNCdUcnp8+aB717h+sPH6JXq1aFDZ23f6VKgLIySPGod+/eILVcYQKB7wEBKqFL9zK7d+8W5OYvvCBE9F21ahUnxBGJhQ4Hf1Qjhee7d+/Czc0NpPL28uVLxMXFfTSdOnXq4ODBgz/qFMW4BQIFI5CQgMznz0GEujPLl+ebIFZwsM89KKlMq1kzjOnRAwuGD//MgT6n8zvlQpCMSI2ZFFHrVq6M/XPmFDis6Ph4aDVtiqx79wr0zc+BEuKoeoaNmRke7NxZrFiSNL58/z5O3byJxSNGQK1xY4SEhEAnl8q3ksQSPgKB0oIAKbHGRUXBu4T/b/7fmjVYeeAAP8sVVLEnJ5b07Dh140bsu3iRqwGZ6uvDwtiYFd7vvXzJiabbp0+XCH7yp8pEd7dvR61iJutRpeqhixbhXUwMmlSvjn9mzsx1DJR0W97EBPd37JBojHk5mXbsiFvOzliyZAkMDAwwbdq0YsUTjQUCxUXg5s2bvAd8/vx5REZGfhTO0NCQ97uzyc7F7as0tBfk5sKtIhFY7524DdMqFnjz/DXSU9KZKGpd25aVm4kYa2ZfDr7PfFC9VS3cPHCdFZWrNHHAk6uPUKN1bVzaeg56ZgZMno2PiYeRpTESYxPQ+PcWuLz9PBJjk9B+TCfIyMjC5fgtaJfVRURgON83E8G32cC2TCJ+eNoZUtLSiAqOQIOujXBp+3kmI2sZaCM+Kh612tVhYvWTSw+ZeCsjKwNNfU0eM5GNo0IjISUlDSMrYyZIR4dFw8TGFEkJSZBCFoysTJggG+zzFnrmRGSWYWXnhOg4xEXEsdI0vWdia8pkbiIQk6Lze2VoOSbilq9RAc+vPYGqlioi3r5D+JswJh6raasjKSYRcVGxkFWQR5hfCKxrVkB4QBjkFeQgp6iIQE9/WFa3hpy8LFS0VJl0rWduiNiIaCTHJfFnL266MgGZyM1EEiZit5/rK8jJy6FyMwc8OnuPlZU1dDSRkZGOkNfBCHQPYIJxanIqHHs0RkRgGO6fcYFtXTv4PnvFZG+Xk3dQrVVN2DWqiuTYeFzfcwX65oaQV1KAvJI8/F1fIzYylpWqSc27QXcnJCck4e7Rm5BTVOD1CfUPQTm7clBQVYKuqR487rrxGhKZ2O32cyZdq2mr8Zj0TPWZeExq4KlJyZBTUoBFlfJMOif1bWRlQU5BHhYOVqzQ/PTSfVbFJhJzeQcrPLv6mNdC11SXlZvD/EOZ5FzJqQruHbvF8QwtTRDxNhxlK5oiNjyG/eIi46Bnpoda7RsU7g+hBLwFubkEQBQh8kegd6dOOHzmDN5dusQk4pIyyuKmbG2yJjVrYuZ/ajmSxu89cyZO37oFIjfTxiptrZLqzqmlSz+ULS0olv1vv0FNRQXOW7cW5Frg50+8vDB6+XJQyVNdLS3smD6dD89y2rZTpzCMFHT09eGVTznRAjsjhzJlsOb8eUyePBnx8fESNRFOAgGBgEBAICAQEAgIBAQCAgGBgECAyEXhB58AACAASURBVClECqISn2lEKsxhROpo3rw5WrRoAdNiJKF+S5QbNmzISaCXLl36lsP4Kfq2tLSEsrKyIDf/FKv9BSfp6wuEh6P3jBk4desWYq9fL9HOSJ1q9aFDiLx8mcvvZVtsQgJcvb2hoqTESff0U1Kj0ujjVq7kimRdmzbNt9mWkycxZOFCLjVenOT+7lOmgJQPU+7ckXSYxfMrWxYwNMTw4cNx6NAhhIeHFy+eaC0QKCEE1q5dix07dmDXrl1cElvYl0OA7gfXrFnDasavX7+G9FdQL/1ys/k8MqkNEsk5m/A8YMCAfJWbV6xYwcTosmXLgu6ZKUFQqId/zRUTfRUbAT8/nDpyBJ0nTUKas3Oxw+UWgAh2pA66b86cYqmRZseu1b8/Hri5we/ECZgZGhY45sv37qHTxIk4umgRWtSuXaB/fg4kqEQEZ3MjIywfPRodGjXK052UYEl5uaD7wvz6cxo6FEM7dWLiZK/Bg9Ht99+LNX7RWCDwoyOgr68PXQ0NvNizp8Snsvv8eQycO5d5D7+1aIG1EyYU2EfjYcPYhxSbKdmVKkMbaGujipUV2tavj96tWxcYI9tBv1UrZGRmMvfiUxs0bx6GdOqEGra2EscLCg/Hkj17sO7IEWhraGBSnz5wdHBgUtXd589BnIzImBi4HTwIw2ImTtQZNAgrN27Eu8hI0H25EBaQeJmE4xdGgIjNRHCm7yQRnj81Emig5xt6tnFwcPjCo/l+wwtyc+HWJis9Ay9uubJKMBF/bepWxJvnvkhKSIayugrKGGmjjKE2k0iJ9PvqsTcUld+r5gZ6BrxXSb77Eoqqygh+9ZY4q9A10WVCrFVNGzw+fx/hgeFoNaQdDyz0dRB8Hnoy/82qVgVWZLauXZFVe4lgGx0ezaq85apYwvuBOxRVlRD6OgRyCnKo0tQB8kpEEn6DjNQ0VgNWUFUGzSE1JY1Jy76ur2FibQLTSuaIDo1GTFg0qz0rqysx2VZZQwXaRjoIeOkHORVFlDHQQqhvKPevpqOBuHcxTJo2sDSGtLQUZOVkuJ2UtAzsnSpDTVcLId6BkFdWQFYm4HH3JaRlpBgPUjImErB+OUPEvothVWRSciZ54+jwKKQlpcHQyhgqGipQ19XEi3+f8v9jRMAmBWSdsjoI8QmCVe2KTBR/dO4epGWloWVQBulpGYiLjIWqphqrYMvJyfL/sykJKQjyCmBir6GlESyq2bDy8tPLD5koblDeEGpaaqzU3HrYr1DV1kB6Sipcrz1hLDR1NZnETYR2GmNiVAK0TXRQoV4lpCalMKk9PTUdUSFRSE9J477VdTSY4B4eEM5qyUQqj4uIhUPz6gj0CuD5WlS1hO/z15CRkWZidlpKGhJiEhgXUvsO9gmAtKwsKjs5QFVbHUEe/qzUHBMSBW0TXbz1CoSyujIrgEvLyjAx+o2bH8eRV5SHTlldBHm/hay8PMwrmSEuKh7h/qGME5GdzatYFu4PoQS8Bbm5BEAUIfJHoJ6DAyIiIrBv7lwuC1qS9tzHh8ulaKqp4eaTJ9j199/oWUDpN8ruptKfASEh6Nu2Ldo2aMAH8vaWlihnZCTx8OihZeeZMzhXjNKguXU2d/t2kAIQjZMeEKiMlKKCAp6/egWrsmVRVl8fu2bO5I2OYpmODt6pq0NXV5cv6sIEAgIBgYBAQCAgEBAICAQEAgIBgUBhEPD29saxY8dw9OhRhIaGftRUVlaWN3yJ6EzqFpqamoUJ/U19nZycQOO/8kk55G86qFLaubW1NRPJ6dDgR/qOlNLl+DGnRSryXl48do0mTdClSRNs+wLKT4qOjujWpAl2zZpVIjhZdenCSfZeR44UGI8Omit064Y148fzPlZxjNS9Xvr64vn+/awUlp+RKACph83+88+idUkK1hUqIDU1FQq0r/X8OSqRmrMwgcA3RoDItv/88w+/BLn5yy4GVfbYsGEDpkyZgv379/P91c9sdA38VO2Z8CCiM30XiehMv1OZdmECge8SgefPMWb+fCbPhn7BKjdWnTsz4a9ZrVqYOWiQxFCQcNCl+/fx+u1bBISGMiHvbXg41o4fj6GdOxcYJyUtDY2HDkVgWBirPNevUqXANgU50Hnf31u38n3f/37/HTMGDoSCvPxHzSJjY2Harh06Nm6M3X//XVDIPD9f+M8/PG+9MmWQrqqKOcuXFzmWaCgQ+NERoOo6cnJy0NfWRvDZswVOJ/jdO4RFRaGCmdlnf6N5NabE0b+3bOG/b0pi2DNrVp4Jr8f//Rd/kbiahQUm9umDJjVqFDim3BwOXLqEoYsXM2nt4Ny5aP5JEsbeixexcv9+vta0c3TMsw/foCBM3bCBE0lympuvLyauXYurDx5AR1MT6RkZ7/kK2tr4d8MGvr4U19qMHo0RY8bApnp1rgJHCXDCBALfGwLPnj1jkjO9cvuO0j07kZxp7/tnu3cX5OaifVvTEpIgLS/HRFdkZiEzIwPS9LuEJpWVhbTUNMjKybEqL2SkkRKfiNsH/4WtY2Um/2ZbRsp7YrKU7P8LNPBnWVnIysyClIz0R72mJ6dCRlYWUrIfv/+RUxaYhEwqyBXqVISMvBzPIy01lRWCISWFzLR0SMlIMVGZfucxSEn9/7gpoBSYKEyfURsyIlKTX2540GfkR7hlpKZz9TwFRXnmt6WmpEJBUYFjfMCG/iH9Pm5WRgbPNy+cM9Mz+P9wwon80nPM5aO5Z2Yy6ZfUjD9YVhYTraWlpODh8hLxkQmo+Wvd//88M5PnlJWjelVWRibS09OZjJw9d4aE1jYt/b/9kizgv6TwtKQUuF5/jKTYJFZzJmVvWkNSXKY5ZhDG0tLvydT/PkVyfDLkFGRRvWUtZElL8dgK+o5RDBoiEaGRmclz4rUlTJNT3o/1v/HQmpI/qVHnnJeEX+Fiu71+7I01A5eAZGtnzZqFXr16FSmmVJZgRxYJuJ+hUcXy5aGnqYkqlpZYNW5cnlOmEip003zj8WPUsbNDbXt7OFhbSwRR/zlzuB1tArRv2DDPkikUbMDcuaCb/5cHDhSKzJw9ELqJp9Khd1xdMa5nTy7z9KlRZvnJmzexberUfDMYX75+zRe1iuXKfRbjgosL9p4/j6sPHyIhKYlLbVGZmtVjx5ZMuS89PcDcnC94pLxmZmYmEdbCSSAgEBAICAQEAgIBgYBAQCAgEBAI5ESAlC2I4Ewvd3f3z8Ah0ioRhhs3bsw/v3cSK5UbzMzMxPUSVn8V35rPEbCxsYGqqipOnDjBKobCBAKFRoCIzdHRoEpXY1euxMXVq1HnCxBou02ZwvtOgzt0wNwhQwo1TDo4fuzpydcVdz8/7Dx7Fn5BQbiydq1Eh9qkFKLYoAGrgf3ZsWOh+s7NuXqfPnjq7Y3V48ZheJcuecZTdXJCJycnFhIostnbA0pKnFhPm84rV64scijRUCBQUgisXr2aVZt37twpCPclBWoeceieatOmTXzoT0lxOdXvv3DX32X4ixcvYvv27az2nBvJOXvQpBgnFJ2/yyUUg3r4ED2nTsX1R48QfO7cF8Pj8v37oIoTlIjVrVkzTOnfv8C+iOy35fhxJiWSKiqR8ogcvfivv2Ckq1tge2dXVyYM0v2aFokpbdr0WRuq2qGuolJgrE8diAy+8sABhEVGIujdO/T/5Re0qluXzycjYmIwaP58Jla8Pn680LFzNvAJDET9QYOwYPhw3PbwwPbDh4sVTzQWCPzICFDVmAoVKqBXs2aYPmAAV0z+1MKjojBp3TocuXYNyampTBiiM3vLsmVR1doa84cO5YrP+dmuc+cwYulSGOvqcjtKjMitog9xEw5fvYqoK1cgm6MaUGEwpr/xPn//zdehC6tW5dr0j/nzcfvZM74OEF8iL6Pr0sZjx+BUrVquyazEhTh18yY/v/49eHBhhlmgb+eJE/nZsGGHDrC1tRUVfgpETDh8SwTovoDu4bOJzsnJyR8NhxK56VmndevWaFvMZPRvOc/C9J1Nbl67aR2mHp8Ndb3Pr6+Fifcz+xIplUi+TCzNx7LJvxlEjFVS/OBJJOawgFAYWBizKjSRT2MiYkAUZVUtNaQmpzJZlcirskSslpVFXHQcKybT50QSTYpLQBakICsrAwVlRaQkJkNeWZHHlZyQxOTWbLJramIS0tPSoayh9tlomRycnPKB+EpzI2VjMiZ052aZWUxWzvPzPDAhUjLNVTYn2TiHL5GaSd34M3J3Dp/0/wjVRNCWUXhP5i2KkSJ1+JtQKKsps2qzJEYkZ1rTvMb/IUZWFmLColhlmtSxFdXyeA7JykJseDRiI2OhbagNJY2PRVKJJE3r/ympPbsfIizT+hdEWCZSOslpF/R9lQSDQvtkZbGK98bhqyCVJYUZM2agb9++hQ5DDQS5uUiw/RyN7Gxs0KFePX5I3zhp0meTXnPoEObv3Mnv62pqIi09ncu4kD89NPzasCGa16qFSuXL5wvYwStXMH7VKlB25fbp09GnTZtc/XVatICdhQVubNyYZzwiHdMGSLVclKY9/f25/MrI7t1zJSVTUPp81tatiL56Nd8xtxs3DiZ6etgwcWK+fjQnGg9lfpaYkUKPmRmTm11dXcVmfokBKwIJBAQCAgGBgEBAICAQEAgIBH5eBEiBN/vl6en5GRA/AtGZNqVTUlJw48aNn3chv9LM6SCL7NGjR1BWVv5KvYpuShUCDx+yskTbMWOYaJOYS+nQkpjvsevXmXiiX6YMBrRrhwm9e0sUtnb//njk6QlZUtIg9TBZWT4oXzh8OBpVqyZRDFLhm7x+PTwPH4a1qalEbfJzio6PR+8ZM/jQW09LC5P79uUSyDS2bJu3Ywdmb9uG2OvXoUCqMEU1Gq+BAWrXrs3X1adPnxY1kmgnECgxBIhkv3fvXuzYsUPsh5YYqrkHosS2bdu2cSUPui/82ZWbc6Lk5ubGJGf6SS8XF5cPHx88eJAVnPOywMBA/uhnU4n7wl9XEV4SBO7fR5fJk3Hm9m0k37olSYsi+yzbuxfbT5+GpqoqZgwahJb5/E0s+OcfzN66FR2dnLB01ChuQ2JBkpp/SAh+nzkTfsHBfOaXG2lw5cGDWPTPP/A4dKjIVVXp3HDJnj18/ikvK4uE5OT35a5TU+F74gSUFHKowUk6+E/8Kv32GxMa/cLCsFcCtdoidiOaCQS+ewSCg4PhULUqbMuWxZR+/ZhnkNNIBK3/3LlMAOvRogU6N2nCqs1E5qW/02deXkjPzERnJydMHzgQ5fOpqjB90yYQ14EqLndr2hRLRo78DJ/mI0YgNDISrnv35oodPSsOmjePx9mrVas88U1MSYFyPteKv5Yswfm7d7Hor7+4qlFe1vF//2MRt46NGmHT5MlfdT07T5qEXp07o2rr1kK5+asiLzorLgJ0D55NdL5///5n4aysrJjgTC+qVFdajcjNVJ1n9YY1GL9nCvQsSpC/VBpBI4Xf1HTIysuyOi9xs0gRV1ZOFmH+oVBUVWKiMZFk5RTlkckKwmmQkZWBrII8iAjrev0JtI20EREQjgr1K7EfJeOkJiUz8ZiIq5Sg4/3QE8FegUwYNihvhNTEFAS/CoKCsjz0yxkiOTEF8e9iifcLDR0NGFub4NHFB9DU10JyfBKsatjA9doTVG1eHWWMdHD78L8oX80K5lUsWaU5lcj9WYCsogKTi9mkwGq+qclp8HnsxarONJ53gWHISM1AbEQMrGvbggjJRKqWkZdnpWGab0JMAkJeB8OqVgXeK6Bx07xYTZjUl7NoyzeTlaFzEnMjAsIQ7PMWFepVeq9eLCfDeKYmpTApm8jGqUmpMLUzR1pyGmTkZCAtK4P0lDRWaKa1eHzxAQwsDJGWlAqzKhZM2KU5EZ6ssEzE8/8UnVktWkmRx0fv07oQMZxUq4kwHh0SASkZGWgaaPH6ZqZlIAvvlZ3lCStql5bO86CYsWFR8H/pD+taNu9Vr/9TSSaSN6ky0/rT3Pn7kP6+rex/BGzGUZbUmtP5p4ycHBOlaf3Jh0S9qTHNh+ZB35FXT3xgWtEMCsoK/P1SUFJARvYcpaXh4eLGa09zp/ikzJyRkc4K2zQ/mjONLTwgDInRCSjnYMm40Xv0Sk1MZtw/UrYu4b9luu543HmBTX+t5nFNmzYNgwpR4SfncAS5uYQXpzSFq1+vHoa1aYNZmzd/Vm6z9oABeOjmhrr29nCqXp2zGb3fvOFyUZQVaENqwllZ8A0Oxq3Nm/kQKT+jjQA6oKHsxyrW1tg5fTrsLS0/akLKM3QIlV85K3rIUFFUxIklS4q0FBuOHcPIpUuR5uycZ/uXr15xCZo9s2cjOi6OCdclbTHx8XlvtBgaIgBA+fLl8fDhQ1SuXLmkuxfxBAICAYGAQEAgIBAQCAgEBAICgZ8YAdrozSY6Uzm/Ty2b6EyqflSK8nsht5LaBqnp3b59+ydeva8z9W7duuHJkydM7pH/pDzz1xmB6OWHR+D5cyApCdZduiA1LQ1+J09+sSlR2d5bT5/ixatX2DRlCrrmc1hMg7Dq0gWBoaHYOXMmE3IomZ+S+gtjo5cv5z0uSvzPLTG+7sCB/BkRlAtrO86c4TLKUbGxTK4h9UAi1RAZyNXbm8nXeSmCSdyXhgZgY4POnTvj5MmTSE1N5Y1+YQKBb4nAihUrsG/fPlbQtSd1cWFfDAGq2EEkciI3e3h4CHJzAUgTyZksP9VmIlXUr1+f/dTV1dm3bt26/JNegvD8xb7OIjAhcP8+Bs6di7N37uD+zp2srJybkbooEYHP3LnDZD55OTlUsbLiChSUJCapzdi8GUeuXmUl5RubNuWpdmrYpg3KqKtztdbczN3Xl8dBZ5B5mYefH9z8/LhqRW624ehRPs/zo4ozecyb2lF12aV792LekCFMzvjU6H7w2L//gpSiw6KiGMMhnToVqcpsbuOctnEjHnl4wNzQEDRmYQKBnxWB2NhYGBsZoZatLXML9s2Z8wGKPefPo+/s2Wjv6Ij98+blmsw5bcMGXLp/n3kKuyWoZOP15g3+XLCAE0hn/fknpnzyfNZ96lTcefYMgWfO5Loky/ftY4I0XSuLyk2gwFfu30fvmTOxZMQI/J6HCBz5lWneHFYmJri3Y0eJf0VGLV+Odg0asHp+btZ27FgM69ULUmZmXNnn0qVLJT4GEVAg8KURoL3Ma9eu8evFixefdZdNci6Nas5EMqQqSEtWLMVfW8fBxFZUh8/v+0aquW63X8DO0Z6VZxVVlRH6OghahmXeqy2nZyI6NIpJosbWxoiNiGOlXiKf1v61HhNdD87di/LVLOH/3BfmlS1gaGXCXQZ5B0K1jBoqN67Giro3D16DspoKVDRUIKsoB31zQzy5+ICJykSgdr32GA1/a4qUhGTcOXIDDi2qw935JSo5VYXr1ccoW9Ec90/egX3jqihXtTxOrTqGWr/WZ7Jz3LsYuN1yRXpqOiwcLPHGzR9q2uocKzkhGaplVOF+5yXK2prx+3KKcoh8Gwn/F695HmH+IUxANrIuiyCvAI6TlZWJNy/9UblpNRhZGSMqJBJycnLwdX0FPXMDViNOS0lD2YpmMLYxBUtQAwh084XnXXfomOkhKS4R0lLSMKpgijcvXkNRRQkxYdFMdLapY4tg77eQU1RAWTtTeDi7MWnctJI5Lmw8gyrNqyE5Lgm6pnowsTVFkPd7wnREYBheXH+KclUtEeobAlLMNre3QFRIBGLCYqChr8Vx3r0Nh6auFhRUFBkfmre6rgYiAt/xuEgFm0jlcZFxiI+MhUkFU5jaWyDcNxg3D1yDsU1Z6JbVY4XpMN9gKKkqQcOgDIK8Avk5wqySOSKDIhAfHQ8tAy0mpceEx0K3rA5CXwezMjZh4/f0FRTVlGBY3ggZ6ZmMs4GVMYI83iDA4w38XF+jQXcnvPUMQFZGFkztzPg7RzipaKri2dXHqN2hPhPRiZCuX84AkcERTOBW1VLFu8BwJmETpjSeSo2q8HdPWV0FJjZl8fzmM6iWUUeN1rXzVIcu7jX5U3Lz1KlTMbiIlSUEubm4q1GK23fv3h2dHR0xfu5cLrmZrTLTeNgwPPbwwKN//uFSLZ8aPQjQJgCV/STV5qMLF0qMUqdJk+Dx34ZFzYoVsX3atA+lp4zatmVFGipHlZvdfPIELUeNwtIRIzC8a1eJ+8zpSIo+9PDwfN++PLM5Z27ezBsda8aPx4A5c/jwiEqMlpRRmauj164xKTxXMzbGYWdnDCCC+cOHoHLAwgQCAgGBgEBAICAQEAgIBAQCAgGBwJdAgDZ679y5A2dnZ/6Zlpb2UTcGBgZMcKYXqfx9S2vXrh0iIiJ4rMK+LAJ2dnZISkqCj4+PIDx+WahLb3RfXyA8HHY9erDyHanpFWQP3NxYkYv2YWiPqvInSfH5ta/csydILcvW3Bynly3L03X7qVP4c9EiBJ4+XWCifl5Brj54gMHz56ORgwN2zJiRq5t28+Zo5+iInXl8XhAW9PkTT0+sOnAAqenpIPKPqrIymtSogVl//CFJ84J9rK3xS+/erEx6+PBhVM+HWFRwMOEhECg+AsuXL8exY8cwbtw4dOzYsfgBc0TYtGkTE/llZGRAJYtVVFQ4eYdI/fQe/aRXWFgYjIyMPns/JCQEVNpYV1eXP6MDreyf9Pvr169haWn54f/M7GQB+kmHPXS/lVPAIptYl5CQAEVFRR4D+ZFl/ySyrKGhISsikRLRokWLSgyTRo0aYdeuXaBENnd3dz6oFFY8BOhaSol4eVk24Zn2/Fu2bFm8zkRrgcCnCDx9ionLl+PKgwcY3rkzBvz662cYEbmWKkAoyMujWc2acHRwQGhEBE7fusUEY3qfVFBHdusmEb6/jB3LhMC///gDo7p3/6zNvkuXWHWZqqiSInJuNmndOib80b1N2/+SAyTqPIcTJbg1HT4cb8+ezTdZbejChbjz/Dnq2dvnWsm2sP1+6k9nikTkzsuuPXzIeFDl2Ylr1xa3O9FeIPBDI6ClqYllI0Zg38WLzE8ge+rlhRp9+3Iiw6EFC/KdX3JqKrwDAmBfQGXpnEHqDRrECQa9WrbEmJ49P7Rde/gwxq1ahSe7dqFiLmJndH156u2N1WPH5qvcXNCC3HdzQ4sRI7jCdV7JGpfv38ev48Zh67RpPM6SNMK39ejRLOiWjfmn8esPHozFY8bgtIcH35/+LQF5vCTHKGIJBEoagXv37n0gOnt5eX0UnhScieBMz51mJOpYCoyeIylZeP7iBRi+ZawgNxewpokx8bi0+Sxa/fkLLm07B01dTcRHJ7DKLyk6Ezk4NTUNJhXKMpE29l0MarWri2u7LqPn331ZEffUyiOwcLCCzyMv2NavhDcvfFkpWc9MD0E+Qfh1ZCfIKcjB+5EX3rr7M9mYyLoVG9jjwRkX6BCBVgpwv/MCbf7qiLTEFFzcegaWNWzw+Nw9XkNSXa7fuSErN6vraqKMkTZ8n72Cde2KqNqsOkL9gvDi5nNEBoYzSTfkVRCsatog0DOAlZeJvOvz0BsVG9jB79lraBlqMzKRQeGsRiwjL4uk2CTIyctyfF0zPVYwDgsIh4aOJiyqWsLfzQ9KKop4/dSHlYRJUZgUq0kV2bGbEysLkxG5mYjUpERdqWFlPL7wgInC4f6hUNfTYHIvEceJ6BwfFYc3L/1Qo20dvHrsDT1TfT6X8nviw6TumPAYRIdFoaytKVISU1C7fQP4ufrg0fn7qNy4Kh6evQ+jCsZMgqZ1q9aiBhN/aY565fQRGRQJ3bK6SEtNR2J0PLTL6vK6+r/wg5G1MRObicAuryTPY6Q+w32DcO+UMyxrVoDfs1esfk1r9fLmc1ZXJuK0oooiK1vT9o2ZfTl4OL+EvKI89Mz1WaE5KjgSfs98YFPPDu/8w5jkHB0azUrWtC76Fka4c/gGDK2M4Hb7JQwtDZlQTordAe5vkJ6aikqNqiIxNgFe9z1YoZu+f/Q9e3LpAQvQ1m5fn9c5JSkVr594w66BPaLDo5EQHc/fD1IeN7MrB5+HntAtpw/Hbk1YbfpL2P+xdxVgUW1ddEl3SomKhd3d3S12J3Y+2/dssbsLn89uxcbARsVCEQtUQhQFpCSk/L+9BX/EAYZhBkHO/r75gJlz9tln3TuXe89Ze21BblYEqsLnLwhMnToVRjo6OH30KJOY/505E9tOnMD4VatwZNEitKpdO13UfPz9UdDcPN12yRtQSRXKfC6WPz8r6/w9cCAGt2+Pura2+Bobi3s7d0r012rcOLi9ecMbULJa71mzuBwqZYI2TKXMaIXevVk9mrI+qw0YgMOLFsm8sCIpzsCQEJi3aoXJffti0ciRvzYpUgR9J0zA8ePH4eXlhbx588o6XdFPICAQEAgIBAQCAgGBgEBAIJCtEUhJ4hB//5/U8juwIOLwkydP4Obmxi/6O7mR4hyR34oUKYJhw4ZBU1MzS8+vjh07gkqY0uK0MMUiQORmIlzRM6kwgYBMCISHA8+fo+W4cbwG9HDXrlTdUGI5KeWpKCvD1NCQVQQ/BAayQjGtVxHJuUezZmmGcfDSJfSfMwcmhoZc9rx706YS25OiMm2GP9q9W+LnX6KicPrGDS6BnJrdefoUF+/eZQJQambWsiXKW1vj4rp1EpvQxjWpHv6dWIqZCEXyNiJDEmEyVTMwQPkuXZg8OXDgQPTo0UPeIQh/AoEMIbBixQocOnQI27Ztk3slO6r6QAp0RBSm+xsiN6ckFNO9V2hoKKvu0u/JX6QySO3p3iflZ/Q3VZYgn+Q/+edJf9P/VOqb/HP6naplUDxJ79PPpN8NDQ3x+fNnxpC+o7Nnz84Qnmk1rlevHvbu3cvkZlGlQW6wIqkkNhGdCVcijqc0up+mhEJhAgG5IuDpiY3btsHewYErrz47ePAn901HjcLVhw9ZNGj52LFQVVH56XNSYV62dy/fs709flzq0Iise/zaNdzdseOXCqizt23DMlOb/AAAIABJREFUfHt7JNy5k6q/moMGITQiAvUrVcKWadOkHjd5wy+RkdBt1Ahfrl7lCrSSLDg8HPlat0a7+vVRoVgx3pOUl9Ee5/pDhxATH4/j6SShKNWsCft//sHAefPkNbzwIxDIkQg0qF0bNjVrwunBAzgkVmqmvfk3fn6cEJHmM0wmZkxjUMXm/KamMDM2xtbp0xEQEoIq/fphYu/emD9s2E/eH1PVnOHD+Zn0fircBWnDoQQHSgq5umkTqpcpI7Fb2R49OPkurWdnaceT1G7AvHk4ffMmnDZulJhIXNTGBhfWrUOvxYuxbNky1K9fPzPDib4CgWyFAD0PJik6v6Vk/ESjaoU2NjZc0Yaq2+RkE+TmjB29qNAInN1wArU718Nth1tM4g3yC2JVYn0TfSTEJ7CKs0VRSzy6dJ9JtA16NcHZjQ7oOWcAK+ae2+SAAmUKsVpv+caVcOfYDURHfUXhCkXx0csfzYe0ZkLvg/P3YFWqIAJ8A+D5wANtRnfg9/LmN4G2vhbunLiFBr2bIDI8EneO30Tl5tXhce85KrashodnXWBmZQY/j3fQ1NVCoO8nmBe1ZCJyhSaV4eHyHM+dn7J6sqGZIeK+fldwfvPYE2XrlkfQhyBWZK7SqgZuH7sOA3NjaGhpIMD3E74Eh0HXWJ8Js0S8JuItKTWHBYbhg8c7Vpo2sszLCsNmRSwQ/CEI+YpZwvXSQybnqqio8LwpLiVVFbx77oXnN92ZFFyvWwNc3XsZ5sUsEfLhM8dhXticE8LDAkOgqa8Nv+e+rFIdHvyFVY/9PN7jw6t3KFmnDKK/RDPBl5SIG/RqCoti+eHt9hrvXviwEvLt4zf5mH0J/sIk6AY9G+PDmw94dv0Jq1uT+rEOKTYb6zHpnMjIZRtWwK1D1xifiNAv0NLTRVhAMCs8N+jdFJ/9AvDc+RmreTsfu8HE89pd6sPFwZnJweaFLZis7vnQA1p6WqxATSR1Ut8uWas0Xt59iYRvcfjo8R6FKhaFupYGilUrAaedjlDTUEfj/s2ZIH11vxNK1yoL9xuu0NDRhI6xPvSMdPHshhsfxxodayMyNBIvb7t/V5gOCkOJ6iVxde8lVgRvOqgVzm46BVMrM7x++IoVvD/7BTKpuWCZwgjxD4JlKSsmlQf4fGICv76ZUca+IFK2FuTmZEAlZcokLxMkfv8OUGZx+PjxIy+QDu3QAV5+fqwmY9qiBYpYWqa6ySPlOZxmMyJE95szB1VLl0bZIkWw9tAhFDA1RbMaNfDX6tVw3bMHpQsX/skHZY4Xs7HhjPM1f/0lcxjtJk6E78ePWDx6NFrWrPmLH4fr10ElZq5t3sxka3NjYxzJgDK1tIHRwxCpEb0+duyXrHXbdevw7/79fGHv1avXD5eZPd5JjoQf6b4/AifpcJLHtSitc1Mch993HBSNvbTXy5zW7ncQ0QijP2ncP2kuv+vYCAx/VjyTBQ9Z+vyu451dx5UWw5x2nRfx/h8Bela/fv16lkLSpUsXeHt74969e1k6bm4crGzZsqxsScrNwgQCMiPg4YFmffqAKnF9vXnzFzfL9uwBVc/S0dRExwYNULFECYR++YInHh649ugR9HV0oKGmxpvPaakxJzmmEt+r9u8HlRWn8uekAJ3c6H31OnVYCaxL48YSp3Xg4kWMXraMSS8TevaUeeqFO3XiNa/U4h6/ciVeeHt/L5E+diyTeuRptHFNK/c70yBDxsTGYtTmzdAwMOCklQkTJsgzBOErkwgk3UuldJOT308v9jVr1rC68oYNG1C1atVMIvhndE9OlJYn0adu3brYv38/GjduzCXHiUiT3NI7VrK2Tf7skhPP7YzgkjRXShwkZe/Xr1+D9mNIwbt3794ST1DyTwmG9D2g63K5cuX4J5EuJJks8UjrR5G+5XUepIwxO/+t6NgKqKvj9YMHmLRuHbw/fGCiLyWLkdmfPIkhdnZMqh3Url2aF0dKLrPIoNhOoY4dEfblC5aOGYMhHTr88D9ryxa+L/M5dQqGurq/jLtw505QldO8+vq/kLEzcgWne0eDJk3gefRoqtVa5+/YgWdv3sDZzQ237e2RL4NzTC8eqiASHhWFns2aYaEkQSMAQaGhKNKpE/bMnQv/RIVnRZ8XmfGfmb6SvuPZ2V92jk3eWGaXuQZ5e8PL2xvRX79i+oABeOHlBUpWJUVjuxEj0vvKyfz51QcP0HPmTNQpXx6Geno4ePEihnbsyAr2QWFhzI0oYGb2w3+Pv/+G2+vXGNm5s8wVpZOc0bPv3O3b+fosyc46O6P7jBlYMmYMj6cII/E1y7ZtMaBt218SSujcUKlVCze2bWNCd+vWrX+E8DvPm985try/fyn9/clzyylzTXkMKBn1xYsXivj6ZYlPQW7OGMzxX2Nx8/BVfEv4BhU1FSb5hnwMxrc8gEn+vPy+jqEO9E0NmVBLSsDxcXF4/eAVBiwbDjV1VTw478J9lVVUUKCMFd4+9ATtThJ5V0lJGXW7fyfMv7z9jEm6ZCYFzVjV+LWrB49pZG4Et6uu+Pw+kJWUibhL6slBfp9RokZJvHR5joS4eERHxUDXQAf+b96zGjANVKBsYfi6vcWzW25QUlGGobkRE40LlraC+40nTFg2zm8CZWUlJlx7uLyAlr4u1LXU4PPMC3p59RHo84mJ2vmK54fvM2/kUQITZN+/escqzxQ3KQuXrluWCcGWxQvg0YX7iI9PgFWZQlBRV0He/KbQNtRlcvC7V77MYyxZvRSeObszGfjjG38kJMRzXP5vPxDTkVWTiWBeqFJRVj7WM9bFZ/8QvPfwg1khM2jqaLAC9BOnh7CZ0hMqaqoI8v2IQD8iWOdjQjgRjvMVzYfgT8GIJNVtInfHxOFLaDi09XU4LiIPkxJ1ZHgEStYuC5eTt4E832BkYYyosEhEhkVyv4rNqrLKtZ/nO+QvURCvH3ngW0ICE71VVJRhXjQf1DTVoaKqjGD/YD5XYr7GMHlax0AXeQua4PUDD4R/DmUivGXJgtDNq4+Cpaxwacc56Jsbokb7OnzcXt55jo9v3zOBm4jUpM5NRkrQRJSPCo9iMjnNSUNPE5+8PvK4JvlNmDxfrkFFXD/gxNiGB4ahRK3S8Pf0g7aRLsdGatyENSk3J8TGo17PxtDU087YF0TK1oLcnAwoKkN35MgRKaETzTKCQExMDC+ota5XD0GfP7OqzYp9+zDH1paVlBVpkdHR6DJtGm80EXm4fO/eqFi8OPacOwfbDh2wZfr0n4anuKZv2AC/06dZhUcWozGrDxyIJtWqwbZjR95kSmmk7EzKQKRuQw8S0myeyRILLRLRAwSV25o5aNBPLgyaN+fxSXbfLNlDlCzjiD4CAYGAQEAgIBAQCAgEBAICAYGAQEARCDRv3pyVFbPSSFWUygg+fPgwK4fNlWORcjM9l2Z2UZ/IOVQSMaX9DsLM7xiT5v07xv0dY0qaa1krK5ioqzOpZtmYMejapMmPU2HTkSMYu3IlBrZti60zZkj8nlHlLVI4ppLpRMiRxkYuWcLlhq0LFmTySHIjUolF69ZYM3EiRtjYSHRnu3Ahq1mdWbUKlUuUkGZIiW1Kde/O/femosw3dsUKEjLh8vFExJa3TVq7lquz7Z8/H61TKfNOpeRHLVuGkOhorqRmamoqMYzfcT79jjGz2/dV3udETvI3ePBgzJo1KyeFnONiJXLzgQMHUCeV60OOm5AIWCCQyxHQ1dLC6cWLmTzbtHp1vs/plKg+qNe4MQqZm+OJhHtyecB29MoVjFy6lPez+rdpg2E2NihpZYUjTk4Yvngx3wu1kCA0NG3DBr5HJDGjXi1ayBzKbTc31LG1hbeDw0+kxCSHlNyWv21bFnWqU6ECJ5XJ22gvkTCgarWX1q+X6H73uXOYs20bhtvYYL2Dg7xDEP4EAjkKgW6NGuGQoyM/H248cgSNq1Zl1eZdJIxWqpRC5+L66hUmrlmDuPh41ChTBv5BQXy9IkX7ptWq4WgyBXaqMHTg0iW47t6N4gULZiouqw4dWC3aZccOiX5IyZ6SXx/v3QurDFbMzkhgxIMgobeUJOtnb9+CKm83qV4dO8+cgYmJSUbcirYCgT8CAV1dXdjZ2aFDsmStnDQxQW7O+NGKiYhCTHQs1LTUoZQnD74EhzMZVkX9e5IgkXSVlJUQExkN18uP8C0+nsmwjfo2Qx5lZcRGf2USNBGLSZE3Pi6ehQYiQyNYDVhd+3tVkfjYOER8DmfiMxGmldVU+b08SuRfGXFfY7gP+SESKo1LxFplVRVux2tk38DtSVGaxqK3lFWV+fPIkC9MdCVfSqoUiwpio74iJjoGGjoarAZM7yWNSYrEMVExTMwmIq2KmjLUtTTxNSKK/RIG1JdIvdGR0fxTVUv9OxFc9Xuf2JgYJhBTbJSInUdZiWNLiI/nOVM7IhrT4ifNnTBlv1ExUFIC465KxHBVFZ43zY38x8XE8jxo/k+cHkFTVxNlGlRkn+w/IYHjIbziYmOhbaDLx4WI3Iz3t2+I+hIJDW1NJvnmQZ7v1bUSSezRdMyjYqClr424r7H4GhnNmBNxmcb8XgVPhYnshHlE2BdoamlARV2Njx+t5ZI/6hsT/ZXJ5HmUlPg8offIv6qa6o950bF5eN4FpeqWg2lhC54H4RIVFsHt1LTVmQxNc9My0ObPvkZEszI0vUdj0nEj31oGOjx/6hf9JQpxsXGMBWFLn1OMkWFfoKahATVNNSZa03EnZWmWoVaACXJzIqhRUVHYs2cPPxQvSaecjwKOQ65w+enTJ6irq6NUwYL8RaWT+taWLVBNzOpWNAiT167lBYx5w4bB/c0b/nIeungRU/v141eS5W/Xji8UvqdOyRzSrnPnsP3ECQRQOdTdu6Gprv6TL29/fxTq0AHhV6+i0YgRWDRyJC8EKco6Tp6MN+/f48nevT+GeOXjgzI9e0JDQ4OVm6k0oTCBgEBAIPCnIUA35dn9RddgWWKkY5W0ES9+Zl49NzU8FYXtn/ZdE/MRCAgEBAKKROB3lNTu06cPnj59CldXV0VOTfgGQMrN9P+WSqpnxogAv2DBgsy4EH1zOAKl8+fnhW0qkX59yxaeDZFq6w8bhi5NmuCgnV26M0xaPE63YWKD2kOGgBLcqUIYkaqTm3GzZlwG+Nzq1RLdFWzfHvnNzOCcyeQNIxqndGmcX7NG4ji0qfzazw8GOjqptpF2vqm1s+7SBVHR0Xh3+rTEJqSW6OnrCw8/P9x99gz6+vqZHVL0FwjIBYGhQ4fi77//losv4UQyAkRqPnjwoCA3ixNEIPAHIVDR2hrPPTzQtk4dePj6snDPsEWLsP/CBTw9cAAFFSikQ1UvSNBHW0MDt9zcmCxI9zhhERGspkzVWlNarcGDER4Ziaf792fqKFA1WKreEXXjhkQ/NP//zpzh5LcDCxaw+JEibNCCBTh5/TqLOTWsXPmXIXr+8w/Pt1C+fDh1964iQhA+BQI5BgFKuFg2YgS6TpuGiOhoJjcHhITgysaN6c6BKt843buH83fucCLFSx8fDO/UCVUySIree/48tjs48LWhde3a8Pn4Ee8+fcKUvn0xumtXjmOevT2/4pyd040rrQYvvb1RoU8fbJg0CYMlkCbXHDyIw5cv48Hz56leyzIVQIrOmvXrc6LFqvHjf3xCXA3HO3dw7/lzRMTGghRshQkEcisCVDUwJ5ogN2f8qJEiMhmRa9MyIsZ+evseIZ9CkK+4JXSNFcTh+vbtO7lWRUXqyVBsRMpNbw5SO8wmDRNi41gx26ywBdR1cu7/pIjgcIR+CoZ5kXxQUpX+uGaTw5BuGHTdeX7TDVvHrGMyNq3l2drapttPUoM831KTupDJXdZ2unPnDrp37/7ToEQ0Ikv6mfz3lJ/J0ja3+QsODuZSZ+qqqlzms2XNmliZ7GY2tSP+PjCQy4TGJyRAT1sbX2Ni+NWoatVfyn6md9ZQqZmdp07h3Jo1IHJvlX792KeXgwNnStIDRTEbG5QtWhQPd+1Kz12qn5+8cQOk4FO6cGFcWLful3a0yEHK0bOHDEHLcePw+dIlmceijvSwUsLKKlUflIFJc6VyXaO6dOF2s/79F3scHUGq2jVr1kShQoV+nOuynM8Z+Z7ktnM/t81XnAv//78hvkupYyELNrJ+lzJ1gRWdBQIKQkBRpOmc6DcnxkynRU6KOyfFmtOwTYpXYCx9kgllfAcGBvIrKCiIM8CTm7a2Nit60osSgMiopPaMVNRWFXSZxsCBA/HgwQNQeW9hikWAyM2kuvD48eNMDURJ6+TDyckp074yFYjo/NsQeOPpiZrFioFKAM8ZMgSdGzdGs9Gjcfn+fficPMlrUfK2i3fvou+cOShqaQkdLS1M7N0bzWvUwIegIIxdvhznb99G8OXLUFH+efOCPi/csSOm9+uH2TIuxCbNRbdRI94sTl6ePfk8awwaBK/371khWlHqZIt37cICe3tWve7erNkvMNNxKGRpCQ01Ndx4+RIlMqFULe9jKPzlTgR8fHz4PuSvv/7C8OHDcycIWTTr2rVr4/Dhw6hXrx6qVKny4/4ui4YXw0iBQFxcHCIjIxEWFoaIiAj+/evXrz96UqIhvYR9RyD52rc0mOTk9mnFfuXKFVade//+PfR1viupkYDPoYULpYElU23+3rQJK/ftQ71KlaCppoa8hoZMzjh48SITftskU4qPiY0F3SudWLoUrWrXztS4Bdq3Z8Lwjc2bJfoZvGABEr59Y9Gj0ytXZmqstDrff/4cjUeOROF8+Vh5NblRhYymo0ezGquRkRGitX8tC52Tz8mMfgfFXFM/k3IyNhmJne737t68iZDgYP7OkPgY7ZMnFz2ThNK87duZbFy/UiVOZjXU1UVoRAQWjxrF78lir3x94fryJcyMjNBk1Ch2cWLZMrStW5d/N2nRAiHh4ejXujVfx+pVrJihCtN3nj5F+0mToKaqincSBNxIQZqqTq+ZMAEzt27F1U2bZJnGjz4bjx5F9Nev+KtXr1T9NBwxAh4+PvA7c+ZHG6osFPrlC649eYL+ySpOZ+S4ZvRaoOj2OTn2jGIj5pr61yYgIICrAHp4eCA0NPSne+miRYuiWLFiIMXmJCtevDjatGmTqe/h7+osyM0ZQ56ImB4uL6Bvpg+zQhbflW1JtpieLZSUEtWSSS75+/v0XpJ9V1L+hjx5lPAN9PO7OjAxGem+k+7HyVgxmMjT1DfRN/njtsn6fP/ouxowEXoLVSj6fTz6IFHFOel7nuSGZYUBEHk2LDAUOgY60DHWg5JyEoE2MfaMwSJaCwQyhACdy6Sw/e/ETbmb3Hz79m1Q2VnaQCWFYSJ7kqKAMPkiEP7pEzp264ai+fLhpKMj/M+dS3MAKq9EJZTyGhjgc2gojPX1eWGgRa1aWD95MsyNjDIVIBGQiWhMiy6HFy1iX2atWqF8sWIIDgtD39at0aBSJZQqXJhJ2dIYZTwu37OHidIzBw/GyM6df+k2a+tW/kdDiym02XX333+lcS2xDZUxffr6NW7b26fpo2TXrtDW0sKD//7jdj3s7KBuYAAiD2yUIkNV5gBFR4GAQEAgIBAQCAgEBAICAYGAQCDXI3Dv3j04OjryiwhFKa1JkyZo3bo1bGxssgXphbK+aZ2A1JvlbZT4S1WjoqOjmdxNG/FEKkn+k8a1trbm91N+RmrSpUqV4vYpX+SXFnokfUbv0QI7JbbS7zR20vhJpHNSU035PiXEcsm3xMXSLYmKuPLCpVy5clzR6eHDh/JyKfzkUgSWLl2KIF9fKIeHg5LOSZXrzK1baFSlCrYrUJl119mzIDLL5mnTsP7wYXz6/BmmRkaoU748tjk4oFuTJtg9d+5PR+WSiwuTom9s2YJiBQrIfMRuurryBrbH0aO8ZibJyvXqBf/AQARcuCDzONJ0zNemDYhAFChhHCIVeR49iikbNqChjQ0nkAgTCPxOBBYuXIizZ89i5cqVqK7Aanq/c47ZZewkcnPTpk05cUxLSyu7hCbiSAMBIjo/e/aMK2t07doVenp6qbYm0SASDypdujTKlCnDP+lFe2zC/lwEKKmwVq1aKFiwIF6+fImqVarA/elTRF6/LtWkqdLGcy8vvm+KS0hA02rVOAlKWqP+V+7fh9ODB5xENq57d9Be2ZUHDxBx7doPNy7u7qg3bBiTrjvUry+t+1/anbh2DV2nT8d/c+agV/PmEv1U6d8fqsrKaFuvHv5R8L3O7K1bsWT3blxYu/YnouXWEydAyXcxcXHo16cPOmcyiU5mwERHgUA2QYD+R1Fy1bBevbBq7lxsOHIEpQoV+lHpR1KYA+bNw92nT0HJArQOQpwBIz09TqTo1rhxhpWbJY2xcOdObDxyBMULFoRT4h49qSmPWrYMj169YjI1kZFJlZ7Iyvra2tDV1mbVevqbOAax8fH4EhXF/AniTtC6DflzXLsWliYmvwxL5Ocu06fjzbFjnPTx9eZNmY8SxWbduTMqFS+OY0uXpurn1pMnnIxBVYbouZyMrpUm+vqwKFkS/+7cKXMMoqNAIDsgQGuop06dgoODAy5fvvwjJDMzM7Rv3x4tW7ZE1apVs0Ooco0hObl55JbxKFCmsFz9/2nOIkPC4bDqKKq2qQE1DTVo62sjKiIKsdFxsChmgdBPofgaEQUVdTVER0TDolg+aOnrIE9CAvy9PiAqNAJ5C5giMiIKJpamCHz3Ceramgjy/Qh9MyOoaagiwCeA+clW5QojPCAEmvraUNfUQND7AOga6uFLaDiUlJQRFhCM+Lh4GFnmhed9D+QrYQkzK3N8/hAEdU11BL0LhJa+FrT0tPH5fRBUNVQ5JhVVVWjoaMDP0w/vnnqhTP3yMLUyY8I1KTrr5lWQwvSfdjKI+ciMAF13XB1d8N+07UxuJjEkqsYmi+Vo5WZnZ2f07NmTy6HSJqIgN8tyCqTf583Fi6hsYwNNdXVWLaZSJKmZzdSpnM335v17Xpjo1LAhKpUoweo3BeSotjPYzg6HLl7EATs7zoTcc/48xq1YwSWsdLW0OCOTSs9QtiQ9JNDDDMntU+aKspISKz7TfKitQWK2FbWtUrIkZ4lLsklr18LC2JgfSs44O+N+Jm7eqQQqKfC47t0LU0PDVPH8Z/NmULmZ8CtXuE2jiRNRpUYN0Ma6fTrE6PSPrGghEBAICAQEAgIBgYBAQCAgEBAICAR+RoBIzEmEZiI3p7QiRYqgVatWTGqmZ/HsZCNHjsS1a9eY0CFP69+/P27cuMEbZEkv8p/87+QVLlL+nkQ8Vkvc+E/+OS3wkDpDykoq6urqrL5H79NPDQ2NH1NKUm6gN4gYTSTjJEWHpEY0pqGhIT5//szEZ4pfR0dHbrDQsSeik4uLi9x8Cke5E4FevXrBy8sLzvb2WL55M+Zu384bwuupJG/79goFhTamp23YgK5NmuCokxOvYbk8e8abzEQuOb1iBepWrPgjBiKjzNi4EfG3b2cqLhpz9YEDiE6lPDo5N2/VChWLF+cNZUWaw7VrTNjeOmMGeiRTbyZRASIR3Ny6FY1Gj8YMOzs0k6DurMjYhG+BQEoE7OzscP78eSxfvhw1atQQACkQAUFuViC42cQ1kTaeP38uMZokwjPtt3VJrCiZTcIWYcgBgZMnT3K11hUrVvBzwqqxYzGwbds0PdufPIl9jo64+vAh76lRlVZDPT2snTgRXRo3znRURTp1gramJtz27WNfsXFxfC8UHRPDsdl27IgK1tbpjkN7gS+8vOD94QMc797Fv6dOcfXUp/v3p9q3QLt2CAgJSfO+LN2BAU6WI1yoIkhqRp8bNWvGiq8H7ex+NKtja8sqqn1mzYLX8+cwK1JEmiFFG4HAH4sACdvVrVsXZ0+cgLWxMVRUVFCuWDEMSEUtte/s2Th3+zYnyk7p21dhlW8I8FqDB/PzKnELLiarAv3o5Ut4vnuHD4GB8AsIQBBVVoiKQkRkJBOaqU/S+g8Js+loarIadK8WLVC9TJlUj+XDFy8wYP583Nq2De0mTsy0cnPJbt1YPf/yxo1M/k7N8tSogQXDh+PvxKSPOkOGwC8oCHZLl6J3Gte5P/akFBP7IxC4f/8+r3mfOHGChTOTrHHjxqzGTImdBgZ/Ltkzidxst9gOA1eMQIlapf+I46qoSXz5HIqTq46iVJ2yeOXyAlblCyP6SzRzzTR0NeH/9gMMjPXh+8IH+Yrnh46hLqq2rcVEZMctZ2FkacTk4sjwCFRqWgXuN6nSpBJUVJURGhCCIhWL4eWd59A3NUCRSsXg89SLidSautq4few6rGuUwusHL6GqroZ3L7xhaG4MbX0dBPh8hJqmOso3rYQnlx5BTUsdUeGRiI+Jg4mVGXyevoV50Xz4+PoDTItawDhfXnx88wEfPN+jcMUiUNfRYKJ0XksTFKpQTFHwCb8CAUYgJbmZKrGNHj1aJqGkHE1uvnXrFmgTpFGjRqBySoLcLP9vSHh4OEpZW8Pv40d8OHsW5sbGqQ7Sc+ZMkIpN69q1sWT06DTbyiPSMj16gDK9H+/Zw5mPL7y9uYyp66tXeP3uHd4HBjLBmR4eSIWGJP7JKDOSNn+1NDRgYmDAhOXSRYqw2rNNo0aphrb95ElcvX8fo7p2xYTVq3EnE+TiIXZ2uPn4MZPFe6aSMU6BTF2/HluOH0fI5cu44uqK6fb20NTS4vKL7dq1kweMwodAQCAgEBAICAQEAgIBgYBAQCAgEGAlRHrRIi+p/qY0IjMnvUhZJjva2LFjceHCBbx48UKu4SWRpV+/fg0qSUjm7e0NKyurH4s09Evyz5MHkLwtvZ+cyEzkYyo9nPL9N2/egIjkKUtH0rEh4nNKkrWvry/Hk/x9Pz8/fo+OF1X/IYVneRkpN1NZRko6FyYQyAwCjx49ws6dOzGgc2csnj+fCc0dJ09GxPXrXK4xLbvt5obg8HAY6OigTJEiXF49o3Y14LLFAAAgAElEQVT90SM88fRkRSryQfb2/XtQUjqN73HsGCttkR2/ehWkCLZ0zBgM69Qpo0P9aF++Vy98Cg5OszJawXbtULdSJeybN0/mcajjjcePuYzymG7dUvVTunt3LjGcvOwwzZMEBGYOGgTztm1xx8WFFeSFCQR+JwLz58/HxYsXQYrvQl1WsUeClF2PHj0KShzbtm0bTCQoCSo2AuFd0Qi8e/cOhw8fZvVmutelfSBJNmvWLAwePFjR4Qj/WYzAvHnzWAW/U+vWWNS3b5p7ed1mzMArHx/eQwuPiEDvli250oVF3rysviwPo30+Iv22q1uX1ZrJ6B5t+d69uPX4Md/v0cY4qUSrq6nx7yRe9KP8dkICJ4OSojQpoNL7FBslrB1atAjVS6dO3ilqY8OVNFwyUan10KVLmL9jB1rVqsX3iWnZzK1bse3ECfifPcvNTt+6hXUHD7KIk7O7Ow6cPy8PSIUPgUCORuDq1aucVG+eNy/eHj2a5lyW7NoFUkW3GzEizeQCeQFC16aJa9bg3adP2DZjBicrKNryNm/Oiu8dJk/GzW3bYGVuLtOQ8QkJqNq/P569fcvXIEpSSc2UatbEhsmTMaJzZ34+JlI3VYR49fatTGOLTgKB34UArRGTOvOlS5d+qj6XP39+JjTTq0KFCr8rvCwdl+6f9u/fjwWL7NBvyRCUrlc+S8fPaYPFRn7FRfszKFyxGAJ8PkHHUAdahrpQ11CD+w03qKiqoGyD8nC9+ABlG1XE20eeaNS/BXzcvHDB/jSTl3mdXikPYmNioWOgizePPJC/VEEE+39GgVKFEB8bB8vi+eFy6jbyKCuhzeiOzGVz2nUBpWqXwdNrT6BnogdlVVUYWRix7/CQMOQvXuC7arOWJoI/BkFDSxNxMbGs7JwQF49CFYrAzckVSirKMM5vgrCAEMRERqNMwwp4fOkRVNVUULVtTRhZ5OVxhQkEFIVASnLzuHHjQHt4suwv5mhy882bNzk7jErh0j8lQW6W/ylHCxyXHBxgpqeHf2fOTHUAKsUyZ9s2fnAf36OH/AOR4JGyIKkMSr2KFXFt82apxqSMbfonQspYVAImLdXklA6JKG3Zti1OLlvGm1nJN32kGjxZIyq1OmTBAt60WzRqVKrdS3TtioJmZri4fj3aT58O86JFubQcPdhRpqowgYBAQCAgEBAICAQEAgIBgYBAQCAgKwKk0kxl+OglSbmtWrVqrNRJLyLaZnejJNAzZ85wiWVhikWgfPnyTJYmRWhhAoHMIkAl6ZycnPDs6VNM6tULz7y8cGDBglTd7j1/njeUicxMlbuCQkM5+b1a6dIICgnBi8OHuWJXZoxIKgPnz0fHBg2wNxnBuPrAgVyJK6+BAasItq9fH0UtLaUayv3NG1DspNr896BB+HvAgFT7WXXogIrW1nBYvlwq35IaERGpQp8+qFmmDK5s2pSqn7POziDi0rElS9A8UQ23Ut++2DhlCizMzVF/+HD4+PrKHIfoKBCQFwJExqM9gEWLFoGUhYUpDoEkcnODBg24YiYlVgn7sxEgsjORnGnvITnh+eDBg2kmE1Bb6ktqz/QSlv0R2LNnD/r27YsWLVrg/Pr1wOfPqQbdYdIk+Hz8yIleMwYOZGEjRdnKvXsxffNmLB8z5qekLErA+vj5Mysyv/bzg39gIKujaqirc3JWeGQk3w9S9VYiNBPpmdRQa5Urh/5t2qS7B1hv2DBERUfj/n//yTw1SoCje1MaM/l9oySHHr6+KNWtG+ISK4E0HzsWZYsUweHLl3Fw0ybUTqNyrswBio4CgRyIwI4dOzB82DAm2NJzlyQL+fIFpi1aYFjHjlg3eXKWzZKqLhPJmezY0qXIK8dEckmTIBG0hf/+i3LW1mhevTrGdu8u01z/WrMGBy5cYFG4fwYNwuQ+fST6ufv0KWoNGYK3J04wkZrI4ztPn0bvnj2xcNUqmcYWnQQCWYkArXfTcyO9Uq5bJqk0E6lZU1MzK8P67WMRydDBwQH/zP4HfRYNFuTmdI5IXHQMru29hIJlCyHscxiMzIzh+8KblZRV1FRYLblkrTJwv/4EJWuVxuuHHqjbozGCPwTi6u5LsLC2hIGpAROQ7zjcROO+zfD48iPom+ojLjYOhubficjW1Utg/5ydKF23PKq3r4OE+HjcOU7r7XnwwfMdCpYtDE1dTega68Hr8Vset3LLqjiz/gSa27aF7zMvxMXEQVlVGbpGeoiNjoWFdT54PvBAZMgX6ObVZxJ11JcolGtYgYnUWnraKFa1OPTy6sPI0uS3n5sigD8XAUFuTjy29M+oT58+XCKAsm0EuVn+J/3ChQvh4uQENeBHxnTKUWhxo5iNDUZ37YqV48fLP4g0PHIpz/37Mal3bywYMULhY1PJ0lX79+ONnx+ib96UebOMymMZN22KepUq4RItIEmwmoMGwdXDA+9OneJNM7M2bVC3fn3QJvLs2bMVPlcxgEBAICAQEAgIBAQCAgGBgEBAIPBnIkCqh+fOnWMicHR09E+TLF68OJOZ6Tm7cuXKOQqAqVOn4tixY/Dw8MhRcefEYEm5OW/evFxFS5hAQB4I0AaTka4ubzztnDULrVIhz+w8cwbT1q9H+WLFmMxC6oH1K1XixHAi6VKVLCqRLg8lwbnbt2PF3r3YOn06eiSrumX377/Yc/48PH19OYGeSgyTkiBVCCNCDSXTk9IKKWMR6ZrUBulzE0NDLrNeyMIi3XLCTUaNYnXnpPLssmBMJBmqCJbf1BTXt2xJ0wWVHaZN8jV//YUvkZEo3KkTAhwdMf/IETx+8wZHjhyRJQTRRyAgVwTmzp3Lgg8LFixAnTp15OpbOPsZASKPk6ovlYT39PSUSVVHYJo7EKB7wrCwsB+TpT26MmXK/CA7C8Jz9joPSDCKkhaqVq2KQYMGYVilSqkGOGntWpxzdmYl5F1z5kBXS0vhk6k/bBgevHiBfq1bY9PUqQofjwag+72V+/Yh1MlJ5vECQkJQuEMHVkH1PXUqTT87Tp3CsEWL8P7sWa4sa9ysGSu/ampoYPPu3UAW4CzzREVHgUAWI9CjQwc4ODri0e7dKJlYOSt5CHTN+BAYCI901J0VEXYdW1uuGj2hZ0/0atFCEUP85NPh+nUMmDuXK1lfXLcOJSTgkVYQp2/eZOXnzo0a4eHLl4iOiWEC3eS+fTGuRw9+fiUjkbiGI0ZwYsjdREV7qkZ95cEDbN66Fc1at1b4XMUAAgFZEAgKCmK+WBKpOS4u7oebihUromHDhqB1p9yi0iwJQyIZ0n7A1BlT0XvhIEFuTudE+xYXD79XPtAx1kds1FcYmBvh9f1X+IZvrLocFhQKQ3MjBL0PhJG5EUI/hcCieAF8i0+A12NPRIRGoHCFolDVUGMCcpFK1gjyC8S7l74wyW8CbX1tJCQkQE1THZd3OqJhn6bQNzMCvn1DoO8nvPd4Bw0dLRiYGDCZWk1DDaFBoYiPiUW+4gXw5pEn+4wI+wJfd2/oGutC38QQsV9joKOvjTeP30BFVRkmhcwQE/GV1aOV1VTw9MpjjotI1hramtAzNZTlKyf6CASkQkCQmxNhun79OmcYN6dyHBcuCHKzVKdPxhpRFvecv/9GyQIFcHrlSomdS3brxptGT/fvz5jzxNa0CURkXyojKosV79KFN4po0yUrbP3hwxi/ahWm9euHBcOHZ2hIKlNDm0pk9DBAm3KVS5ZE18aNYaCriza1a+PRq1e8uEEZpwtHjsTYbt2w/cIFHL5xgzeOP3z4AGNj4wyNKxoLBAQCAgGBgEBAICAQEAgIBAQCuRsBFxcX3Lp1C+fPnweV5EtuhQoV4kXeRo0a8c+caqT+eujQISbhCFMsApR0a2pqyhsHwgQCmUWAqoadPXsWxa2soB4ZiVUTJkh0+dLbG+V69UKlEiVQtWRJbJgyJbNDp9ufNq0fe3jAZccOLsee3EhFkDbTST3Q29+fS6HTe6QiSOtcpAIYGx8P2iYmYjOpCNatWBED2rRJd9yGw4fj5pMniLx2jcnSsti527cxaP58dGncGOsmTUrTBZU6tmnUiInctFa14+RJXN+1Cy0nTUL7Dh0wcuRIWUIQfQQCckWAxB4oAYJIzvXq1ZOrb+HsZwSI3Ez3VITzW1F+XJweaSDQsmVLiRVgknfp2rUrZs2axeXshf1eBHr06MHfa3ounGRri8ra2hIDuvXkCRqPHImBbdti87RpWRZ0UoIV3UdtnjqVlZcVbVQBxKRFC+xfsADdmzbN8HBExlZVVcUxJyfMt7dHjbJlWRG1drlyvOeX3Igs3mnqVBjp6eH9mTMYt3IlEwbj4+Nx39ERmtbWGR5fdBAI/MkIfHBzQ6EqVVClZEk4b9/+01RJrV2zbl0cXLSI99hlsYRv3+B4+3aqibVp+STFdhd39zSrM8sSU1p97j9/jrq2tlBTUUHv1q1h2749cwzSs3vPnqH1hAlMEL+xdSsnkZCC8/Fr1/D63TtOYqMEXB1NTTx9/ZoTNdwPHPihfK/doAGqVaqEq87O6Q0lPhcIZCkCrq6uuH//Ph4+fMjPickT7pIIzbTWTb8LI86sIDdnx/Pgo9d7fHr9AWUaVoKSspJCQwwPCMZr19coWbMMNHRzl3K5QoEVzlNFQJCbE6G5du0a+vXrx+WTHB0dBblZAV8ab29vFLe2xsIRIzCxd+9fRlj0339YvmcPDtrZoWn16hmOYNfZs7A/eZLLXv49cGCG+1OHbwCOXL6Mrk2ayNRflk41Bw/GEw8PjOrSBcvGjpXKBSk+04OC3fDhrNTTuGpVEH6kPE0PUPTFpkxJPW1t6Glpwdnenhc5oKSECgMGQEdfHzVq1ABt+gkTCAgEBAICAYGAQEAgIBAQCAgEBALSILB69WouOffmzZufmv8phObkkyLS0+7du3+ZqzQ4iTYZQ4BU+iwtLZksL0wgkFkEzM3N+bs7YuhQuO7cyZuqkqzV+PFMJq5XsWK6ZN3MxpTUn8jKRTt1QslChTJVrjyj8fSdMwfP377F0E6dMDSVMsyp+bzt5gZTIyMUtbRE0cTS5pc3bOANa0lGaoNmLVvi7KpVaFmrFq/T3XB1xVw7O1Ru3JiT7NXUqKabMIHA70WAyJFEyKP/9/Xr1/+9wfzhoxO5ed++fVzJQySN/eEHWw7Te/bsGdzd3UE/6XXnzp1fvB48eJD374T9XgSo0g1VnaBy7Y3KlIFts2YSAyrSqRPMjY1/IRNmJPoXXl58/5RRoz0zewcHThA7s3IlyhYtmlEXGW7fYuxYEPmPyHwWefNK3Z9UWwt17Mjk5Cl9+zLhcPqmTWhWvToc79xBg8qVUb1MGRjq6uLRy5dwun+fhZrWTZyIge3aoYiNDT4EBODGzp2o2rkzIO63pMZeNMwlCISEoGWrVggMCUHVUqV+SrawmToV1x89QuCFC1KDQc+SpFp88sYNXHJxAVWmJuL0HXt7qX1Iakh7+1Q1h3gKVLFHkUZK82sPHUK1UqVAiSh0zSK1+yZVq0q8XlLiKl1TwyIi8Hjv3l9Co+vYhiNH+PnPPyiIE4nnDxv2nZ8AYK+jIwuyHTl2DJTQJEwgkB0QoIp9kyZNApGbk1u1atW48gwJdwhC869HSpCbs8PZK/m4kFpzHiXFEpt55G/fmJemlBVjZU+4RVRZjIAgNycCTmXo+vfvzzdTtKFGiyO0SCJMvgjkMzdH5WLFJCo352vThstVhmWwFO3GI0cwf8cOfpCvUaYMZ2APatdOLoGfv3MHLRW8UEblW6i0Z9/WrXH21i3MHz4cDVIp4RUZHc0Z2PSQRe3WHjzI2Y7Hlizh+dKDg9vr16DFHhUVFeQ3MUGdChW+Y2FggOVHjuDE2bMgpTVaTC5YsKBccBJOBAICAYGAQEAgIBAQCAgEBAICgT8fASI3r1q1Cvny5QMt8lapUoUXeP/EMnyk5Hjx4kVQuWVhikWAlJsLFCiAM2fOKHYg4f2PR4AIUGPHjoWNjQ2iAwIwp3t3iXNed+gQlu/dixIFC+LCunVZisuCHTswz96e130url+fJWOv2LcP1x4+xBNPT7jv3w/tVAjfkoIp27Mnpg8YgN4tWmDW1q3Y7uDAhICZgwdj5qBBP7oQqXnv+fOYvmEDShUqhIdUCh1A+4kT0bBBA3z4+pUT8ZcvX54lcxaDCATSQ2DmzJm4ffs2/vnnnxxdaSK9eWaHz+vUqYP//vsPbdu2/aXiR3aIT8SQ/RFITnimaCkpITV79+4dJk6cyB/XqlULpUuX5lf+/Pmz/0RzWIREBipTpgzWrl2L6VOn4tqmTShfrNhPs9h87BgmrV2Lz5cuMVk3I0b7XEt37cJZZ2eoqqjA/9y5jHT/0fbk9eusHEoJbVlhVKWjav/+KJY/P44tXcr3RdJYlf79mbS8bcYMDG7fnrss3b0bpNDs7ObG6s2kiEokkuCwMMTExGC2rS26NW2KfnPm4MDFi3xf27BrV0BUa5UGctEmtyHw7RvG9O6Nx8+f4+bjx/A5efJHdeQKvXtzZRxpnw3rDxuGe8+fQ1lJiQXGdLW10btlS9StUIHFyDJjVKFav1EjdGjYEPvmzcuMK6n6kuoyPS9+jYlBCSsr5hloamjg1uPHKFesGMwMDVlMjYjcdA2ipNlL69ejSbVqUvlPauQfHAx6trQqXBgPHjzIUF/RWCCgSARoHal74toR8cNatWrFpOZiKe5pFBlDTvQtyM058aiJmAUCORsBQW5OPH5XrlzBgAED+B/WuXPnBLlZQed1k8aNcdvZGX5nznCGcZJRuaTBdnYoV7QoHJYtk2p02pTpMm0a3n36xGVeSBGabrzlZZ/Dwlht5urmzahTvry83Er003naNBS2sECpwoWxcOdOWBcowKU+6UGoUL58vHG0/8IFUBZlh/r1oa2lhS1Hj/Jm0tR+/VKPjcqNUjkwU1PsPHECU6ZMgYWFBWxtbTF69GiFzkk4FwgIBAQCAgGBgEBAICAQEAgIBP48BGjRNzcopdnZ2WH79u2ifHoWnMJEbi5cuDCrggsTCGQGgcuXL4O+u0WKFEGNEiVg26CBRHe0qfrS25vVpkoXLizzkES2aV27dob7D7Gzw1EnJ+ybP1+mssUZHdD340eQamK/RFEBqpgmjRER+9iVK0xUjvr6FZrq6thx6hRWHzgA9zdvoKqsDCsLC1Zp8fH3h46WFkoXKoQrmzb9cF+sSxesWbsW7Tt35iR7+q4LEwhkBwSI1Hz37l3MmDEDVF5YmOIQIHKzvb09unTpgqdPnypuIOFZIADg8OHDrL6X0vT09JjknER4bt68ucBLDghs3boVixYtQtGCBVHKzOyXahiWbdrAzNgYD3ftkno0VvV0cmJRny9RUahcsiQm9e4tF3IyKSpXK11a6lhkbThl3Tpcd3UFEZ1XjhuHEaSknIqR+mu/uXNx9cEDzB06FDMGDPilZeiXL5xkpq6mhoioKFQqXhzt6tWDsb4+RixZAqpqO6ZrVyxfuBCQ4x6prPMX/QQC2RYBf3/UadGCn1+0NTRwfs0aDlW/cWNM6tPnp+RNSXMg8TFSUk9ISEDxggUxuU8fVlWXp30ICkLJrl2ZLL1xyhR5uk7T14W7d3Hl/n3ccXfnatNU6Zp4HGqqqpykSgJzxGNYOHKk9ARu4ijo6eG+pye6DRsGXz8/3L9//48USMiyAyUGUggCtNZtbW0NY5EcJDW+gtwsNVSioUBAICAnBOi68+i8C3ZN346E+ASMGzeORU4o+Sqjlucbecuh5uTkhIEDB6I1qeeePSvIzQo6jkSofe7mBkRF4XIyhZpV+/dj97lzTOiV9PCeMpxBCxZgFxGk9fTgtGEDZw/K2ygr+u9Nm7BhypQMl+3MaCy0SUQEZyo5Nb5nT9BixekbN3D32TPQJpSWhgaXhaEHLlrkKFagAP6bMwcFChaEiZkZQF9YepHsP/3U0vr+0tDA+/fvsWzZMhw7dgy6urpo06YNliQqPWc0TtFeICAQEAgIBAQCAgGBgEBAICAQEAjkBgQWL16MLVu2CHJzFhxsIjeTIgo9swoTCGQGASqLTkryffr0QVEzM4yXQFw6ce0abBcuZEW8xaNGZWg4SrIn9T8qE/zaz49VujyPHeM1m4zaqZs30a5u3Yx2k7k9JdLThjWVGg4IDubywLXKlUvTX6tx49Cufn28Dwjg9apFKfCy+/dfJh8R0VldVRVjundn4YEku/roEf7auBFGJiasjEtkUmECgeyCAJ2PVNlu6tSpaNKkSXYJ64+Mg5TPNm/ezNfmlOWe/8gJi0n9VgTCwsJAFVjc3d3x/PnzVGMhcvO2bdt+a6x/yuDr16/HjOnTYaCtjVMrVqCCtTVPjQR7TFu25KqjHVNJOEuOwaajR7nCKW1O23bowKRBE0NDucH08fNnUPVYuldJUzBIDiOSymnjkSOZmO394QNuuLqiRc2arGxNpG1SZvULCGCl5ksuLvz38WXL0L5ePalHJzGkfzZv5pLg7evXxypKXitSROr+oqFAIFciEB+PY2vWYOrq1axQ7O3ggPiEBKjUqoVnBw+mqbQ+bcMGXL53j6vhbJ0+natIy8NIpd7p/n3cffoUr3x88ODFC5QtWhRu+/ZJdE8JDoGhodDX0YGBjo48QpDog5KBiWgdHhHBPwkncyMjVnEmleeLLi5oU7cudA0N4RsQgJaNG0PP0BBK6uqIiImBX2AgHr98CUpAPn36NPLkycNrPi1atFBYzMKxQEAgkHUI/ERuthuE0vUVKxSZdTMTIwkEBALZFQG67txzuIW9s//Dt4RcTG6mm6tBgwYx8ZNKoZIS1cGDB7PrccuxcVEJuj179nCGsam2NnbQxkZ0NMavXIkLLi5YOno02qazuTNzyxaQekyHBg1wYulSuWORtChAN+y0eOJ+4MBPYwSFhvJiA5Wokbdtc3Bg0vb9Fy84+5oeELz9/bnMlJKyMvKZmaFR/fpwdXfH5+BgLqcWGBjI5b9I/aZZs2bImzcvgoOD8fjxYzg6OuLWrVusSvDkyROsWLGCsxeECQQEAgIBgYBAQCAgEBAICAQEAgIBgUDqCFCC6IYNG+Dl5SVgUjAC9LxasmRJQW5WMM65xX3btm1hZWWFixcu4NXevb9Mu8XYsVyGmFSbqVx4eubm6Yl/T5/G3vPnWSkvNj6eScFUVYuUqzo2bJieizQ/JxIPkY4f7d6dKT/SdKay8PYODmhavTqevnkDXS0tJtEQ2SaliuG1hw/Ravx4GOnro6SVFVaMG/eDrCTNWNSm7pgxePryJYYOHYqlCli/kzYO0U4gIAkBIjffu3cPkydPRtOmTQVICkSgXr16WLduHYYMGcJqfcIEAlmJACnxEdH52bNnPxGeSUmc9kpSMyJJUx+6TyXVZ2FpI0DiUYP694epnh5c/v2XG5+5dQu9Z83Cvnnz0LpOnTQdzN2+HXO2bcOg9u1h//ffCoG718yZuPXkCUZ16YIpffsqZIzkTomkSGOSMJNNw4YICQ/HMy8vEMmaxI1I8IgSyGLi4pgoSVVc07LYuDjcf/6cydCUXJakUFaxeHHsICGpggUVPicxgEDgj0Dg/Xt06dOHybkX1q3DWz8/9Jo1C7HOzlAmATEJRqTmvrNnI+TLF1AVHFJOl8W8PnzAjpMnmST9wtsbVUqW5GsBvUjcjEjEpFQ/rnv3n9zTM+leR0dcuHOHydWUsEqxFC9QAA8yoIyfkZgPXrrEqvCk5kzqzfTS0tRkcnMeJSVERkdDX1+fScshISHQ0tJCTEwMqzyrUlstLaioqODTp09o2bIlFi5ciKJFi2YkBNFWICAQyMYIJJGbp0yfgm4ze6Nii+rZOFoRmkBAIPAnIEDXndtHr+Pg/N34lvAt9yo3X7p0CYMHDwZtglAGmSA3K+70rlKlCiZOnMgqWCYmJjhib4/2XbrgwZMnuLJhA5dySc1mb90KUpuhxQe7ZGowskS7/vBh0M25tro6KpYogdM3b0JNRQVe/v5QV1FhBeWkDG5ST17033+s0BMQEsI350RwpvZU+qlZjRq80UO/y8OOX73KWep6urpo0aQJWnXogPxFi+LRo0dMWn758iV8fX0REBCA0NBQfP36FdHR0fyih4UkEXV6eChVqhQaNGgAUs0uKBY45HF4hA+BgEBAICAQEAgIBAQCAgGBgEDgD0dg5cqVWLt2rSA3Z8FxJuVmStjdv39/FowmhvjTEaD1PVIHJcJ8zbJlsbhfv5+mXLhTJ17LeXn4cLpQzN62Dav370cBMzNEfv2Kuba26NuqVbr9MtKAiCoa9ephev/+WDB8eEa6ytSWVMH+O3OGq6dRCWUqNewfFMTKgVQ2nhL5ScXM9dUr5DMxwc5Zs1jBjMqql7CygoqyMlRVVGCkpwdNdXWOgeZAm+t+nz7xJj2pju08exbx377x95qEJIQJBLIbAkRuJqLtX3/9BVJwFaY4BOrXr88k0jFjxoCIpsIEAr8bAToPaf8vLbO1tcWFCxe4CQnM0L0qEZ2pnyA8S0Zux8aNGDJ6NDo3aoTVf/2FZbt3Y+3Bg4i/c4fJb6nZgYsX0Wf2bEzr108h90Lnb9/GX6tXM6FYR0tLqntAeZ6jJNREZOTnXl68p2hubIyilpas4lynQgX0TPwfRFUyrj16xARmUkx9HxiIL5GRTCSkqhuk9FyuaFEUsbREwrdvoD3ExWPHYuqMGQBVdxUmEBAISIdAbCzw7BkM69Vj5eOJvXtzFedQJ6dU+7eZMAEevr7MGxjZubN046RoRYkJXWfMYEJyeGQkSEiNqlOTmjs9V1Hy7MguXX7xPWXdOmw6dgylCxfmawGpwVNyg562NgpZWPB1hdTb5w0dKlNcKTsduHABMzZtYvIyPeO1atwYdC9XuFQpaGtr89j0IkG1fPnysQCbm5sbE5uJ5BwXFwdNTU2Ym5vz/0tKcitevLhcYssmeK4AACAASURBVBNOBAICgeyDQBK5efLUKegwpQtqdpQt6SP7zEhEIhAQCGR3BFKSm0eMGMFrepRUlVHL8y2JUZnRntmg/cWLF1k9oF27djh16pQgNyvwmFy9epVJ5FeuXGHVBlLCohveN2/eIPDVK2gEBABfv/4SwaFLlzB00SJeHLHPRCnLL1FRqNy3Lz84UOlQXW1thH35wg8AVhYWvKGUvEwWbcp0nDIFJQsVguvLl6hbsSLa1KmDCsWL49Pnz0yKppKkCQkJOLp4MROdM2PrDh0CqVNvXrECJatWxdHjx1lN3NvbG7Vr10alSpVQokQJViIicriOjg6TmdXU1DhLUj1xcykzMYi+AgGBgEBAICAQEAgIBAQCAgGBgEAgNyOwevVqrFq1ip/DhCkWASI3V6hQAbuzQLlWsTMR3rMLArNmzcKRI0fw8eNH7F66FK3LlePQSBHLok0b9GzWDJumTk0z3P5z5+Kljw8o2X1w+/Zy2yxOOSipd1Xq2xddmzRh1b6sMtqcJ7IPqUZfefAApiYmUFJRQWhYGK/REQmJNqZ1dXR43axYgQLw8/fn90i9Oio6Gl9jY78rc6mo8OY6kXVMTU0REB6OoJAQXl+lhHthAoHsiACRmx88eIDx48fLvTw2kTzou0EvUquzsLD4QQQhVT76HiWRQ9Ii/GVH3GSJqWHDhli0aBGrZN+8eVMWF6KPQCDLEUhObpY0OBGea9WqBbrnEMrO/0eoT7t28Pb15WoXtA9H9urIkVSPn/ubN6jQuzeaVK8OxzVrZD7ORAL+a80aOD95wklbtNcXn5DAe3ZEGi5uZYVhnTqhd8uWTGaUdD927vZteL9/j0/BwXz9pqQvSgRrVauWzHEl70ix0D2UeorNd/uTJ7HtxAkmNetoauIbwBVdKQYNdXVoa2qyoithSu9VKlECzapXR68ePVCoenVAQ0Mu8QknAoFchUBAAK4fO8aVakwMDJjEG3L5skQIfPz9Uap7d1ZXd1y7ViaYSLG99pAh0FJXR+3y5VmlnhJI6fpE16xShQtL9Nvzn3/g/vYtE6sn9urF/Ijy1tasME1CbHfc3LDu8GH+uWDECIzt1k2m+KgTXV+Ig3Hyxg1EU2Lv5MkYOWkS1LS1ZfYpOgoEBAJ/LgL/JzdPRofJXVGzkyA3/7lHW8xMIJA9EEhJbibx4qlTpzJPMqOWo8nNlIVNCxbt27fHyZMnBbk5o0c/g+137tyJCRMm4OjRo5g/fz5n9RUpUgQuLi6AqysQE/OLx4Lt23OmoP+5c6wUI4u99vNDrcGDedNl45QpaJ5IRKYsSSrHKcmqDxzIqjVF8+fH4lGjYF2gwC/NqHRU4xEj4OzmhvNr1vzwm9EYl+7ejZ2nT2PmtGmwP3wYr1+/Rrdu3fi8rJNO6a6MjiXaCwQEAgIBgYBAQCAgEBAICAQEAgIBgYBkBEi1mVQGBblZ8WcIkZsrV64MWicQJhCQFwKtWrWCp6cnfHx8sHLKFIxq1w67z57FhNWrWRVwuI1NqkONW7kSD54/x+2nT3HIzg6dGzeWOSxS3iMyS/IN62sPH+LO06c46+yM225u0NfRQYCjo8xjZKbjXmdnrNq9G+8/fICRkRFvsFO8UYkEmtjYWNCLEus5qV5XF5oqKihiYQFdNTUm2xBp3D8khEutf42LQ+/evTFnzhxW7BImEMiuCMycOZOVm8eOHQu6XsjTKLmC/JPRd4SU7IjETN8t2owxNjbmanxJBGhqQ4IV4eHh3I7+ps9IFS+J/Ezv0e9JL/quUvW+lO/T5/SdTdrcSWpPsdDvSf2S/qZqgBqJxLikftSOKi7KKzmhUaNGmDt3LmNy7do1eUItfAkEFIoAKTzfvn0bz549g7u7O/z8/H4Zj8jNtKkq7DsC7levon6HDtgyfToGL1jA17Lb9vYoU6SIRIjaTZyIG66uuL5lCysZy2Junp6oY2sLdTU1VClZkvcOaVxSPKYqsaQinZJQTON4+vpi1LJlPD4lbKkqKzOZmIjFJIpEiqhhkZFoVKWKQhLQes2ciRPXrvHeY4USJWDTogVqVa6MIoUKIa+xMVTU1RHx5QuCg4ORJz4eupqaMLWwAGgfU08PEPdZspwuoo9A4P8IvH6NvFWqcOJAo6pVcXbVKono7Dp7ltXfqaJN27p1ZUKQlKEPXb7MyRJrJ06UysfENWvgeOcOtDU0cGjRIliZm6far+/s2SAV/Md797LCc0aNyNfdZ8yAz8ePqFiuHLba2yOvpWVG3Yj2AgGBQC5C4Ae5edpkdJgkyM256NCLqQoEfhsCgtycCL2joyOGDh2Kjh074sSJE4LcnAWnJC30LlmyBH379sXs2bOxfv169G7SBPDx+WX0pMzIOba2mNynj0zR0SJFq3HjYJ43LzyPHpXKx6ajR7Fw5060rlMHW6ZNS7dPjUGD8OztWzzes4fLQ2XE5m3fzmVBWzZrhn0ODqxoQeekMIGAQEAgIBAQCAgEBAICAYGAQEAgIBDIWgQ2bNiApUuXCnJzFsBO5Obq1atj+/btWTCaGCI3IbB161bMmzePCYO3tmzBwxcvMHX9euyaPTvVqlsO169jwqpV+BAUhONLlqCljGp9VPq85bhxXFacEuKJAJxEttFQU2M1QSpBTOpbU/v1g4mh4Y9DExYRgZ1nzuDk9evw9vdnQg6RglrXro3+bdrI5RC+fvcOY9atw4eQEFaQJdGBTp06cdlgKh9MKpREtqTSfvQKDQ1ltVn6SSQvImmGh4UhISaGiZGmlpawtrZG2bJl5RKfcCIQUDQCREi8ceMGevTogWHDhsl9OCIHJ5XtpmqF9P1IIi4n/0lkY2pHxGIiKycRnl1dXfn7RH8TsY2+k/S9SyJIk0/6rECBAj+SEpI+JwImVftLUixN+kljvX37FpaWltyH1QL9/X8kNgQFBXEiA71Pa/XyIjc3adIEM2bMwMKFC3E5FVVEuR8A4VAgoAAEwsLC+H8gEZ6J+ExGyZCk4pyaTZo0iT+i/630qlmzpgIiy0Yuo6PRuXVr1K9YEasPHADd8xBBePWECVylIrldffgQA+bORT4TEzhn4jmg8ciR+BwWBtc9e6QGgkiARAY00NXFhJ49Ma1/fyjlyfOjP1XWoHvC+fb2KGhujsolSmD7339L7T+thqv278eMjRthqK+PUQMHYsasWcgjlFHlgq1wIhDIEAJRUejftSvOOTujWqlSOJMKuZm+s5PXrkXEtWucRJFRo+sTJXK88vZGwIULUnW/6OKC3jNn8r0h8Q2SPyum5qBKv34Ij4rCq8OHpRojqVFAcDDaTpyIz6GhaN6iBTZk4nqcoYFFY4GAQCBHI0DPohcvXsTEyRPRbmJnodyco4+mCF4gkDMQEOTmxON0/vx5Xsikhfzjx48LcnMWnb8HDx7kUr9U8vLgtm2ooK4ucWTaDCrfuzfu7dwpcwY3ZTkecXKCw7JlqFi8uFQzLNG1Ky/oeh47JlX78IgI5G/XjhV57tjbS9WHGjndv49OU6bA3NQUVWrWBJVAplKewgQCAgGBgEBAICAQEAgIBAQCAgGBgEAg6xHYtGkTFi9eLMjNWQA9kZuprDcRwYQJBOSNwMOHD2FjY4PQkBD0bNaMCcOkypWaOmDNwYMRERmJYTY2GN21q8zhDJg3D3fd3ZnU06lhQx6Pypy/8vFhUvOQ9u1hpK//i//1hw/z5jmRHak0ORF+qIrZGz8/rjhGfY8vXYqShQrJHNuVBw/QY+ZMlC5XDk+fPsU///yDUaNG8ea5MIFAbkGAyLv37t3DyJEj0bZt29wy7d8yz6ZNm2Ly5MlYuXIlSGBGmEAgtyBABOju3bv/Ml0iOJcpU+YH4ZlIz3+S3di/H4OmTkVwWBjyGhhwJdRTK1b8MsVJa9fiyOXL6NG8OVdMlcW6TJvGyqY3aW/R2loqF489PEAkQLo/O7xoUZp9PN+9Q5sJE3iPcHLfvhjWqZNUY6TWqPO0aTh+9SpG9OuHDf/+S5L6mfInOgsEBAKyI0CJKWGhoTDR10fhfPlwaf16ic7o+WzM8uX4dveuzIN9DAqCq6cnWiRWk07PEfEFHr58yYkhdK2Sxt4HBMCqQwdO2Fg6Zow0XbhN6/Hj8cLHB02aNcM2ui4JEwgIBAQCUiJw8+ZNjBozGm0mdBTkZikxE80EAgIB2REQ5OZE7M6dO4fhw4fzhsexY8cEuVn2cypDPc+ePYtp06ahlLU1OlWrhh6plPr0CwhA/rZtEXPrFlQzsdnyxNNTanI0lQhtOHw49i9YIPXDA02eFid6zpyJzg0bYu/8+eni4fX+PSr3748vkZFYtWIFRo0dm24f0UAgIBAQCAgEBAICAYGAQEAgIBAQCAgEFIfA5s2buZqOl5fXj5Lwihstd3smcnPdunWxcePG3A2EmL1CESAFVFI6RUIC3PfvR+FUqm0VbN+ey5BTeXRZjdSvTFu2xIjOnbFxyhSp3YxbsQI7Tp9G/UqVsHDEiF9IOqdv3oTtwoWsAn13xw6UsLKS2ndSw2sPH6Ll+PEoVbo0K8lSIoeRkVGG/YgOAoGcjsDcuXPh4uLCYift27fP6dPJ1vE3b94c48aN4//zZ86cydaxiuAEAvJEgJSe6VpDpH6qIpGaEcHu1q1b8hz69/oKDET5WrVQq2xZ2J86hc8XL0JPgjLx6GXLcP3RI8waMgRdUtkXTGsigSEhGLFkCe49ewYvBwep51y2Z08oKSnhyd69UvW5//w56g4dynOgirCS5pKeo5jYWFACHVV9PbRnD9pnIoEuvbHE5wIBgYD0CFBC6beEBJS3tk5V/X01KTevWwf/c+dgLCE5VfrRpGtJFX7U6tRB7XLlcGPrVuk6Jbb6Z/NmLN+zBxunTsWgdu3S7dvj77/x2s8Phvnz44KUqtLpOhUNBAICgVyDgLOzM0aMHok24wW5OdccdDFRgcBvRECQmxPBJ5LtiBEj0LlzZxw9elSQm7PopDxw4ABsbW1RplgxaCkrwymVzUxaqKCNoVAnJ1aqyQqjzPEtx44h/OrVDA9HJbeotFTDKlU4K11ZSSlVH5RJSYrPJ/bvR305lRfNcMCig0BAICAQEAgIBAQCAgGBgEBAICAQEAj8QGDr1q2ws7MT5OYsOCdIua5GjRrYsWNHFowmhsitCERFRUFPT4+V94jQEufs/AsUK/ftw5qDB7F09Gh0b9ZMZqhajR+Pd58+wW3fPql9zNm2DRuOHEH/Nm2wPJ2kd1Ib9Pb3R2AGN6BvPX6MLjNmQNfAADadO7M6vTCBQG5FYP78+SBV1aFDh6JDhw65FYYsmXfLli3RqlUrUOVMEpgRJhDIjQi8e/cO7u7ufN159uwZ/0xubm5ufJ/yp1iv9u1R2NgYZ27dwtqJEzlxK6VN37gRhy5dwrk1a1C8QAGZpn7gwgW4v32L+cOGSdX//O3bILXnI4sXo2WtWlL1oUa013fo8mUMbNsWfw8cKHU/augfFITagwcjNCoKd1xcOLlMmEBAIJA9ELh8+TKomkeFEiXQrVUrNEi6LoSGAl++cJDP375Flf79cX7NGonXMnnPhCpZV+3fH/OGDWMV5oxa6wkTcNPVlfvOtrWFUjKFeFKDJvV6upYNsbODm6cnAiIjccvZGRYWFhkdSrQXCAgEcjkCgtycy08AMX2BQBYjIMjNiYCTagCVoevSpQuOHDkiyM1ZeCJSVvqk0aPx5MULLj81plu3X0ancipFOnXC0SVL0KZOnSyJrs6QIYj/9g137O1lGm/3uXOsqEPWtFo19GvdGh0bNICaquoPf5X69gXN7eGpU7DMonnJNBnRSSAgEBAICAQEAgIBgYBAQCAgEBAI5CIEtm3bxuRmUnolIqQwxSFQrlw5NGrUCGvXrlXcIMJzrkfg+vXr6NevH+b99ReGT5mCCsWL4+aWLSC1riSbtXUr1h44AM9jx7iMuiz24MUL7Hd0ZPUvWgeSxo44ObHyYH5TUzzavVuaLijepQu+AfA4ckSq9tSo+ZgxcPf2xoDBg/n6JkwgkJsRoO8AbcYOGTIEnTp1ys1QKHzurVu3xsCBA0EiJyQqI0wgIBD4jgCRnInwXKBAAd6PTM1WrVqF1atXM/m5dOnSqFWrFv+kF6k+Z0e7cPIkxo8fzxVwiuTLxwJAKW2bgwNIvfnT+fP/Y+8+oJsq2ziA/5Ome+9JB6WUvYUyBQGRLSAgW1mCKIg4+EREEBAQRZS9h8gespQpe4+yobule+82o8l3nrdNLZuWtnQ895ycpM297/jdFNrkf58X5iYmpTKND3/4AXtOnMDdrVvhaGPz0n1S5eV6gwYhMDxc/K5Wx9PzpY4NiYpCi5EjoZFIcP3mTTg4OLzUcbwTC7BA6Ql88skn4oITuuh6/fr1/3X88CEQFQX6+Tdp2xYdmjbFwQULSnxgdEGEY5cu2D1vnsgUFHZTqlTo9dVXoNWpqbCaq4MD5EolUjMyxFwMDQxQxc4OcoUCDs7O+GHuXLGSFm8swAIsUFgBDjcXVoz3ZwEWeBUBDjfn6e3fvx/jxo1D3759sX37dg43v8qrqgjHRkdHY9iQIbh46ZJ4I2NAp06Y0LcvHK2tRWvJaWnil3m60nD2xx8XoYfCH1L7/ffFQXe2bCn8wXlHqNVqzN24EZv++QcB4eHijwWq4kw3CjnT8jIxR47AtEkTwNCwyP3wgSzAAizAAizAAizAAizAAizAAsUnsHr1alBVx5IIN0dERID+VqRbWFiYCDRQgFp7oxAAPU5KSoKNjc0jz9H36TiFQgF9fX0xYXpjh270fapKS/dRUVGwtrbO/572+3Svfezn5wd3d/en7qNti/bNzs4WwQSVSiVu7+f9rVxc2hRu7tChAyi4wRsLlJTAzZs30bVzZyz67DNcvHMHB86dw40//nikuznr12P2unUI27cPFq8QsknPzIRJIVYdqzdwoPg5/+aDD9CvQ4eXIqA+PHr1EiHsaxs2wDDv34NnHTxp4ULsPHECnbp1w/Lly1+qD96JBSqywI8//ogzZ85g+PDhYiVH3kpOoFu3bhg0aBD27NmDrVu3llxH3DILVFCBSZMmiYJMT9sKBp6pcFNZCjuPHzECm3ftQi13d5x8yu8evn5+ohpq+P79+Z8DlvQp9HrvPbSsVw/fjxoF90JWKb12/z7affyx+J2LVqCt5eHxyHDps7+M7GxY5VXgplU2mo8YIYLNvrduwd7evqSnx+2zAAsUQUAul4vqzbS6BF10TReT5G8hIUBsLJy6dkVdT08cKqULsqnYW9NatbDlFS5IPX7lCk77+sIvLAyJqaloXqcOYpOSoFCpsOPYMbi5umLUxx+Lwn+8sQALsEBRBDjcXBQ1PoYFWKCoAhxuzpPbt28f6Oq8fv36Ydu2bRxuLuor6hWPo0pNEydOhLubGxxsbNDU2xvtGjQQb25QFWT60ObookWv2MvLHd5/yhTsOnECijNnxAe5xbFly+W4dOcOBk2bhvjkZMwcMwaTvv4acHIqjua5DRZgARZgARZgARZgARZgARZggWIQWLNmDWbMmIHAwMBHKrsWQ9No27YtaGlqekNGJpOJwDBt9HXBe23ImZ7XPkfPUwhSG2h+2njoOGNjY2RkZOT/Lav9m5buKRRN4Wja6Gvtjb6vVCof+R49R2OkkDM9rlevHja+ZGXZl7WicHOnTp0wf/78lz2E92OBwgtkZmL/smWYt3Ej1k6dihGzZqF+tWpYOGlSflu03Ph3y5dj9ZQpaFm/fuH7KMIRf587h6nLl+NhbCxi/v67UC1QNUCf4cNFJa4N06ahe+vWTz3+0/nz8eehQ/Bp3hwH/vmnUH3wzixQUQXmzZuHkydPiorCFAjkreQEevbsKQLk//zzD/7888+S64hbZoEKLEAXXmorPd+7d++pM6Xqz2XtAoIZ48dj1rJlz6zOLPXxwZkVK9CiXr1SOXtWHTuitocH5o0fL4J+hd3uhYSg/zff4FZgICxMTeFgZQVdmUwEBun2VpMm4vPLuORkNP3gAyRnZuK6r6+4oJQ3FmCBsiuwe/dujBo1SlQwvnr1qrgAPT09Haampqjj6ooLV67gw+7d8VkxX+j9LBH6W5VWA8o8dapY0f4+fx4Dv/0Wb/r4wMLZGevWrSvW9rkxFmCByiUgws3jxqLTuB5o1b9t5Zo8z5YFWKDUBTjcnEe+d+9efPrpp+jfv794A6AsvhFQ6q+O19Thpk2bcOTIEbH8i5GRkagSJZVIYGdlhdYNGmDLzJmlMrL1Bw7g60WL8POECRj0zjvF1ufIWbOw99QpfD10KCZ99BFQq1axtc0NsQALsAALsAALsAALsAALsAALvLrA2rVrMX369BIJN1MgQUdHB7GxsaK6Gj2mwDLdU7VmOzu7/O9RFSH6QE37PN0/6+JbbTVous/MzISenl5+hWjtcxRSjomJEVWdtdWeC1aRdnZ2fuIYf39/eHl55Yeea9as+erABVqgcHPXrl0xZ86cYm2XG2OBJwQCAvDzokXY9Pff+HvhQnSfNElcTP9XXrD++oMHaP/JJ5g/fjyGd+9eKoA/bdyIE9evw8TQEFuLUJkrLTNTzOPU9eswNjBAVWdneDg5icBNeEwM/B4+RGR8PKq6ueHG7dsw5FXDSuW8cidlX+Dnn3/G8ePHMWzYMFHshLeSE+jVqxeoejOFyTds2FByHXHLLFCJBC5cuIA7d+48EnimSvRUffRZGx1DN/rss1atWqCqzyW+KZXwcHMTq5hu/uEHNKpR478uZTLoNmuGoQMHYvWKFQCtQiGXA3QRZmwskJIC5F0EWlzjbPPRRzA2NMQ7zZtjQv/+RW42JiEBa/bvx92gIHGRmYO1NepWqwafOnVEddc6778vfgc7f+ECGjduXOR++EAWYIHSE1i0aJFYvWvy5Mlo0KABkpOTxXsjdGH6mYMHERMbCwM9PQzu3BlDu3R5qYHR+y9BkZGITUxEtkIBQwMDtKhb96WOdejcGc3r1sXuefNeav8X7aTWaNBixAjY2dsjUS4H5WKsrKxedBg/zwIswALPFKBw85ixY9D2gw7oMLIrS7EAC7BAiQpo1Bqc23kS22b+AXo8YsQIfP311+IzsMJuEk3BUkKFPfo17//XX39h/PjxYnnVLVu2cLj5NZ8P6v7+/fuimhR98EJXDy5ZvFhUnqI3Hj7q1QuNa9QQbxq8aKM3FwIePkRSWhpu+PsjKCJCXEVtamSEKR9+CGdb22c2Ydepk6gWfbeYlswbNn06Lty+jYbVq+cuJ+PlBVhavmgK/DwLsAALsAALsAALsAALsAALsEApCtDfoBRuDggIKPbKzaU4jXLRVZ06ddCjRw/Mnj27XIyXB1nOBYKDMeWHH3Dx9m1MHjoU01auhI5UilPLlyMiLk5U2aMKyMsmTy6ViS7fvRt069SsGX4cN67IfdLqYOv278fJ69cRGhWF9KwsKJRKUTmQPlS/eeuWCDLxxgIskCuwcOFCHD58GEOGDBGfB/BWcgJUGbtDhw64dOkSaGUM3liABV6PAF1QmJqamt85XWRZu3Zt8fsBBZ7pVhLbysWLMf+nn5CYkoKW9eqhc/v20DExQVBsLBYtWQIDAwNR5OiLL74Qq+vmb0olEBcHxMcD2dnFMrTFO3bgl02bxKqxZ1euLJY2H2+kzoABiExMxA8zZ2LcK/xuVyKD40ZZgAWeK0BZkV9//RU3btwQAWfKJ9AF5hYWFnDT00N6ZibSMjLEqs8D3n4bfdq1Q/s33shvk6q73w4MxILNm8XF5NcePBAXVOjr6ooibi52drhy/z7+N2yYWNn5eVu/KVNEobQH27fDzcHhlc/c0O+/h1omw/6TJ7Hnr7/EimK8sQALsMCrCGjDzW2GvoW3R5dOgYBXGW95Olai0Yj/R6jAiUYiKU9D57GyQMkJ5Khx/I8j+OuX7ZU73Lxnzx5MmDCBw80l91IrlpYXL16MFUuXQiGXIyExkdbthZeLC1wdHETQ2crMDEaGhkhITkZGVpb4w+HszZui6jP9wUEb/SdgbmKCJjVr4lZAAI4tXiyq2jxt+23rVkxftQozP/oIY19hiUK6uvO9yZNFqJqW9N0zbx4sPDyAqlWLxYUbYQEWYAEWYAEWYAEWYAEWYAEWKD4BWkno+++/B1Utpr/heCs5AQo39+7dGzNmzCi5TrhlFigokJSECePH45KvL3bOmYNvli4FVW1uWb8+aBUva3NznF6+HG6OjiXuRh+A1xs4EKPffReLv/qqWPv79OefsWrPHmxYsQJ9hw0r1ra5MRYo7wJUne/gwYMi3DxgwIDyPp0yPX5aKZOWeb916xZWUHVW3liABV6LAP0sUuXm520UdKbK9sV9QVT3bt1ga2UFRU4Orly9iqCgIBEWNDY2FpWNW7RogR07duCtt97CzMdXblWrc0POdMvMfGW7VqNHi9/7fP/4A15VqrxyewUbGDRtGvaePo1fFizAqFGjirVtbowFWKD0BKKiokR1fLpPT08XK2OFhIRANyMDZy5eRGBEhLhgg1bfos//aTWg6MREcW9lbi6Kq9HFpwqVSlzEWtPDA4b6+giLjsa6AwfEMUM6d8bcghd0PGV6Tl26iHB1+P79rzR5WuknS6GA1MwM7Tt0EFUOeWMBFmCBVxWoNOFmjQZp8SnISs+CtbMNhc2QEBEPfQM9mDs8VgFfA6TEJkKRJYe1ix00GjXiH8bB0NQQZrYvKHip0SArLRORfuGIfRgLtTIHUpkUppamcK3jAVMrM0h0pK962gp9vDwjC4mRCTAw0oel87OLhha64SIeoFGrochWQKYrg44uf2ZTRMbyeViOGsc2HMLeBTtF+J9WYvvmm2+gTysAFXIr15Wbd+/ejc8++0y8mbl582au3FzIk1+au9MfFAMHDkS7du3w5ZdfIsjPD+HBwYiOjERSXBwyabkqWgrKxgZZcjm2bfb4WQAAIABJREFUHjmCalWq4KvBg9GiXj0x1GOXL+PrRYsg09ERz/0xffozp0BvdtAfHMF79ohqPoXdgiMjMXDqVBHA3n7smPiArGWjRgBVzOHlQAvLyfuzAAuwAAuwAAuwAAuwAAuwQIkLaMPNd+/eFdV6eCs5AaoY169fv+cuo11yvXPLlVlg3pw5+GHWLHh5euLO3bviPSIK3qhzcmBjbg57a2t83KePqORcktu7X36JW4GBCNy1q9i6SUlPh90776BujRq4cvNmsbXLDbFARRFYtmwZqDrf4MGDMWjQoIoyrTI5D3ofv0mTJuKCsaVLl5bJMfKgWKCyCFDlZvr75vz58+KePmuLiIh4ZPr0OenEiROLlSQ7O1v8/D98+BCurq4ixFyvXj1s374dp0+fFpXdPTw8cPz4cfH5LD3/xKZQAKGhuHHpEo5euoSr9+8jNDpaFDVyd3JC+yZN8NYbb6Cup+dzx37a1xcU9Bvbpw9+/PjjYpvnuHnzsOnoUUyePFnceGMBFqigApRByMpCRlqa+PcnPCoKN2/fhl9gIGLj42Gsrw8PZ2c0q1ULLevWBXJynoAY99NPWLZzJy6vX49G3t7PhLp67x7ajBmD2WPHYkIRVxqhivWLt29HQx8fPIyIwKlTpyroieFpsQALlLZAZQk3K7PkOL7hMOJCY9B94nvISs3AvoW7ULt1XbR4700RdtZuKrkCh1YeQFJkAnp//T7SElKx5+ftqNeuAZrTvs/aNBrEP4zFyT+P4cH5O0iJS4FUmnsBjZ6RPtxqu6HFe23g7VO7RAO9GUlpSIpOEEFqc/vc4HaIrz/2/LIDHg2qoefnfUv7ZfZEfwkPY3Fu1ylUb1oT3s1rv/bx8ABKUeCxcDOtwjZt2rQifXZXrsPNu3btEn+wc7i5FF98r9AVXSU5fvx48WbH559/jqFDh8LMzEy0GBcXh53bt2PVypXQl8nwxbBh6NWsmajyXHCjND99gHTg7FkRXK5ib//UEYXHxorKPe80b47GNWoUatT7Tp/G8JkzMaJHD+z8919MHT4cQ7t0AVxcgGdUiy5UB7wzC7AAC7AAC7AAC7AAC7AAC7BAsQts2LBBvDni5+cHXV3dYm+fG/xPgMLNFHyaMmUKs7BAqQvQe0ihoaGoWbOm6JuqFS5fvhxe7u6wMzXFbfo3QEcHn/Tti2Fdu0LvRf8e0IcaJiaAsTGgo/PffB4LDhWc6Plbt9By1CjsnjsXPd98zocdhdCpPWAActRq3A8MBIpwoX4huuJdWaBcCqxatUpUCaVwM914KzkBqo5NqzSEh4fj999/L7mOuGUWYIEiCVDgWRt2pscjRoyAC31+9ZSNnqcK0LTR7/A+Pj6iyvOrVnqm8POvv/6aX7V59uzZ4vO/gtumTZtEQDosKAiejo5ISE3FveBgEWyWSiTwf/hQVEadNHAgZnz00XMtun/+Oc7cvImko0eLZPb4Qb2/+grHrl9Hw4YNceLEiWJpkxthARaoIAKUTVCpckPOdE8V6JOS0LR3b9zw98fqb7/F4HfeeeZkqRo8ZROcbQtfLXPz4cOYuGABho8YgcPHj+PMmTMwMDCoILA8DRZggdctUFnCzUmR8VjzxTJYOlpjwPfDcOfkDez4cTMGz/wQddo1fOQ0JMckYO2k5TC3NcfAGR/g9okb2D1/O/pOGYgGb7/xzFOWlZKOPb9sx9WDl2DhYAXX2u6wcrJGSmwSIh6EIzowEhYOlhg04wN4NilcXq0wrxPfI5dxbM0/aNylGdoOeVsc+uDCHWz43yrUaF4bQ2aPLExzxb5vjlKFcztPYd9vu9BxeGd0HNm12PvgBsuwwGPhZvq7lFZeLUphonIdbt65c6cIyXK4uQy/WJ8ytJMnT4o3NOgKb1reLiYmRty6du0KSup369btv6PS04G0tP9uOTniahfLDh0w4O23sayYr6b+ZskSbPz7byz9+mv8umULXO3tsWbq1NwPuKhqc4GreMqXOo+WBViABViABViABViABViABSq2AIebS+/8UhiCLljmCmelZ849vViAKrrSe05XL14UlbhS09NFcMbKzExcHO9kawtbCwsERUejhpcXdA0NIdHTg4mlJZydnUU1Qgrz0ZLrYktOBvz8ntmxS7duolL01fXrXzy4F+xBK5Bdu38f0f7+MCvm5dZfeXDcAAuUEQFaoeHPP/8UVZvp/yDeSk7ggw8+gJeXF+Lj47FgwYKS64hbZgEWKHGBCxcu5IebH++Mgs4UeKbf7enxswLSzxskhZyHDx+OrVu3QkdHR/xOJZFIxIVoDg4OkEqloH06tWuHfq1bo+sbb+SvtnovJATvT5mC2KQk/P7FF3jvadWfC3Tu2qMH5n36Kd7v2PGV3CYvXoyle/agd+/eWLt27Su1xQezAAtUHoGo8HC0btMGmRkZqF2tGib27YsuLVoUG8A/58+LKvVjRo7EoePHxYol2gt6i60TbogFWKBSC9DvhaPHjEabIW/h7dHdK6xF8DV/rP9mFXzebYGOH3bG/sV/4do/l/Hx0gmw93R+ZN5B1/ywZtJStBvSEW8ObI99i/bA98hVTFj7Jaycnn6RikSjwf0Ld7F60hKYWZtj0IwP4exdRVRoVitzkBSXhD3zt+H2yRto/E5TDJ49EjqyAsUUikmexnF2xwnsmLsFbw7qgJ4Tc6s0p8YlIeDyA1jYW6Fq4+rF1NvLN0PjykjNQHpKOsLvhOLwygOICopEl7E90emjivu6e3mhSrQnh5tzTzZVapg0aZKoFkRvbNIf3/QHNG/lQyArK0sk8m/evCk+QHqpLTtbXCE5eepULFq7FtM++ghfDhjwUoc+b6dDFy6Ags013N0x75NPMOrHH+Hh6IjFX32Ve1i1aoBVbhl/3liABViABViABViABViABViABcqegDbcTEuoy2SysjfACjQi+oCNKsR98cUXFWhWPJWKJpAYF4fg+/dB1V6v37mD+/7+SEtPF/8+UOhGpVLBxMREhG7kcjnofSr6PgVxxo0bh48//hgWQUG51bqesk1ZulSs+NWvQwfMGD26yHw/b9qEL377DVPHj8eMhQuL3A4fyAIVXYDe/1+3bp34LIDCt7yVnMDIkSNFyDEjIwM//fRTyXXELbMAC5S4AFVupkJDhw8fxp07d5BGxYSestEqq2fPns1fbbWwA6OVNVauXInAwEAoFAq4u7uLUB4Fpxs0aJDbHFVBpZUxYmLyV22NTkhA3YEDxYVo1zZseG63D2Ninrma68uO9+vFi7Hu77/Rt18/LFq06GUP4/1YgAVYQAjQv3Vff/21yKiYmppCIZdj0LvvwsXKCl+8916RlbYfO4YBU6di9g8/YNqMGeLfU16ppMicfCALsMAzBG7fvo0hw4ag5cA3K2a4WaOBRq3Gqc3HcGzdYfSfOgRVarlh7aRl0DXQw4hfxkDfyBCQSsTvohq1Bqe3/ouja/5GvymD4FrHA2u/XAaZni6Gzx8DI1MjaJ5W/FKjwZE1f+Pgoj14c1B79PjsPUgLhpc1Gjw4fxerv1gKK0drjFs2Eaa2eYUUACgyskWF54yUdMj09GBqbQozGwtIdKSPnDkaX3pSKuIfxiE9MRWGpkawqWILc1tLSKQSpCek4uzOkzi04gCa9WiBt0d1ham1uTBIS0qFLrVtY57fplqVg5T4JCSGJyAzNQOmVuawdrGBiZUpJHkryGly1EhLSIEGGphamiE9JQ2pcSlQypUwsTSFha0ldI30n/szppIrsH/RHtz+11f0k5GcAUiALh9zuLnS/ePE4ebcU05/kNMHaRxurnQ/AuIDp6pVq8LOzg5ODg6Y/OmneLNGDSA19ZkfOj1Nacfx41j1118IjozE96NGoaqTEz6aMwft33gDP0+YkHuIoyPAVXMq34uMZ8wCLMACLMACLMACLMACLFCuBCjc/N1334kP1KlqGG8lJ1CjRg2MGTMGn332Wcl1wi2zQAkIUKCZwnr0bwQFmx/fTp8+jVmzZolwj0ajwdwJEzCuZ8+njmTpzp1YvGMHLq5dC+MiLhVMSxb3+vJLTPzgA8xfvZpXDCuBc85NVhwB+ixg+fLlonLzhx9+WHEmVgZn8tFHH8HGxgY5OTmYM2dOGRwhD4kFWKCoAnfv3hUhZ7qnG1Xwo41CerRUOYWcn7ZRSJr2pwsfilLh+ZE2MzMpIQjEx4vA8/UHD9B85Ei0qFsXx5csKerUXnjcmPnzsf/sWfTq3Ru///77C/fnHViABVjgWQJUbG/jxo0IDw9HVFSUuICWLpodOXgw+nfqhBq2tkBGxksBfrtypVhVesTo0eJvUbqYj1Yt540FWIAFiluAfpcbPGQwWgxsU+HCzVkpGbh5/LoI0945fRMR9x+iRZ/WkOpIcW7naVg52aBJl2awr+oItzoeuHXKF5lJGbhx7Bqig6LQondrSHQkOLPtJBw8HNCgYxM4ejnDu3ntJ0+DRoP9v+8WoehW/dqi58T3oGv4aOCXxnHyj6MihNz6/XYwtjAVger4sBic/PMYAq/6Q6lQivFRuLjZu63QoH0j6BkZiP7UKhWCrgfgxB9HERMUCYVcCZmuDBb2lmgz4C14N6+FQ8v249qhy0iKThQBaadqLuj1ZT/Is+TYPX8bqjashncn9RftqbIV8D16Fed3n0FKTCJUCpUIfNt5OKBlnzao0bw2pLoyZKakY+ePm0WYuU7b+vA9fAUJEfFQ5+RA38QA9d5qJOZjaGr8zJenSqHAiY1HEX4vFBpAnIu4h7Ecbi7uH+jy0B6Hm3PP0rZt2/Dll19yuLk8vGhLYIwHDhxAjx49xPm/evUqlEolmjdvjj6dOqF1nTqwoqtLsrIe6Tk0Ohrnb97E0cuXsffUKdStVg0fdOuGBl5e4gMpCjvP/eQTjOjRI/c4+uPDw6MERs9NsgALsAALsAALsAALsAALsAALFKeANtwcFBQkPlTireQEaKn6Pn36cOCp5Ii55dcssH//fgwZMgRmhoZo16gR5owbBwdr6ydGder6dbRp2LBIoz136xbajhmDXm3bYuvBg4CubpHa4YNYoLII7NmzB7/99psIN9PqAbyVnABVrzc2Noaenh5mzpxZch1xyyzAAmVCgIIuFGp+XmiZVtGlSqW00b70WRxVZaYVden+WaHo506QVmrNCzlf8vVFzy+/RGpGhig+tOrbb2Fn8V+Fu1eBogs1Ok6ciAs3b+Lzzz/nf9deBZOPZQEWeEKALpClzMrevXuRmZkpbq6urujYvj0GvvsufOrUyc0r0L95dK/RIDwpCVuPH8eSzZvF6tYWVlY4efIkli5dik6dOrEyC7AAC5SIQEUONyc8jMX+xXuQFJWA8HthosqwnbuD+JrCuVVqusLI3Bi1W9dDjea18PeyfUiMSEDY3RCY2ZjD1tUeCRFxSIxKhGstNxiZGaNO23po2a/dE+dCotHg0r7z2PrDRhhbmKDNwLfg2dBLtKFrqAddfT1RHFqpzF0JjkLJkEiQGpeEHXO34O7Jm3DydoGtqx1UchWCbwSIKtJdP3kXTXu2glQqEcHg7bM3ISogEm51q8LczhxpSWkIuOIHS3srDJ75IYJvBOLKwYtivnbu9vBs5IX2w95B7MNYrP5sEWq1qoeRCz8BctQixL37522g6s0eDapB39hAVGgOvRUCczsL9PqiH6r71EJGQiqWjluImOAoMTcTSxPYVLGDPCMbQb4B0DPUR78pg1H3ree8F6oBsjMyoVLkiMIRfy/Zg7M7TnG4uUR+qst4oxxuzj1BdFXcV199xeHmMv56LcnhnT9/Ht9++y0uX74MT09PsdwVLasVHR0Na2tr6MlkkOnoIEehQFJqKvR0dVHfywuNvL3h6eKCyLg4EXQOjYrCyJ49MXHAAJhrK/c4OACuriU5fG6bBViABViABViABViABViABVigmAQo3Dx16lSEhIRA8rQl44qpH24G8Pb2FsEAquzIGwtUVIGAgADM/PZbbNm9W7zf1L11a3zarx86NG36ylPefPgwhk2fjrebNcP+Q4cA42dXPHnlzrgBFqggAgcPHsS8efNEuHnUqFEVZFZlcxoTJkwQFe4prPj999+XzUHyqFiABUpVoGC4+fGO6d8K+ryWQs5F2hQKICFB3L6cMwfbjh1DVnY25Eol3qhVC9+NGFHki8m2Hj2KsfPmQaFSYe3atejbt2+RhsgHsQALsMDLCFy5ckWsArR69WrQhfdUmI0usHB0dISDg4P4/YoyDPHx8aAVsajiM1XTHzt2LKZMmQJzc/OX6Yb3YQEWYIEiCVTkcHOOQon0pDQEXffHrp+2oVmPFmjaowUOrzqIwGt+GDRzOKxdbGFsbACpnq7YN/h6ALbN2oS2g9qjYaemOLR8L4JuBGLonFGwcLCCkZEB9E2NnmqdGBmH7TM34cHFe+JzCEsHKzh4OsLOjULG1cXXtm720DXQByQQ4eUzW//FrnlbRAi575SBopq0WqXG9SNXsGP2nyKMPXbpZzA0NcRfv+7AmS0n0ODtJuj5WR8YW5ogMzkDexfuxJWDl9B1XA+8NfRtnN15Cn8t2IFW/duh+6e9INPTxd2ztx8JN2ckpmD158sQfj8Mncf2QPNeraCjJ0NWaiaOrz+M09tOoG7b+hgwfRhUWUoRbg69EwzvpjXR/bPecPR0RnZaFv5auAPX/r6MDsM7iyA2BbZftNG898zfihObjnK4+UVYFfF5DjfnntUtW7bg66+/Fm9obtq0SVwhTH9A81b5BMLCwkAfOtHSL7Q8Fv2xQH8M0B8F2dnZSE9PR2pSEuJjY8UVkwa6unC2sUE9T0+82bAhOjZrlotG1b2srAAbG7r8vPJB8oxZgAVYgAVYgAVYgAVYgAVYoJwKaMPNoaGh5XQG5WfY1atXx+TJkzF8+PDyM2geKQsURUAux+1du7Bm3z4RtImKi4Nao4G9lZW4cN7N0RET+vdHDXf3l2o9MTUVH8+bh21Hj2Jwz57YsHNn7ntRvLEAC7xQ4OjRo5gxYwYGDx6M0aNHv3D/srSDWq2GSqUS4Ra60eOMjAzx3jU9Lnjz8/ODm5vbE9+/ffs2aOUE7b5JSUkwNDQU4RgTExO0bNkSVatWLZZpU4iRLuqws7MTF47xxgIswAIkQIEYKjhE9/T527179/JhKMz3vMrPLy2YmAikpiI8MBBTfvkFp319ERIVJYoYOdvaom3jxmjfpAnqeXmhprs7dGWyJ5qm6s8/b9qE9QcOgFZz7dKlC+hvRSqIxBsLsAALlKaAv78/Ll26JP7tpLBzSkqKWGmMgs4eHh4i29K9e3fo8io+pXlauC8WqLQC9DvcoMGD4dO/JTp//G7Fc9Bo8O/GI+L2/tTBcPZ2xZpJS2FsZYIP5o6GnqFB/pyp+vLJzcdwZPVBDPhuGFxqumLNF8tgaGqEYXNGweAZoeb8BjQaxIZE4/zu07h39g6SY5KQlZYJSjLr6ctgZmeBem0bos2At2DpbAN5eiZWfrYYYbdCMGjWh6jfoUl+U4qsbCz/5HeE3ArC2MUTYONii2XjFiI1IRUfzBuN6s3yLiDUaBByMxBbZ25C7VZ1RFD54l9nRDXoNwd1QM+JuRfx3Tl9879w86/jEHjVH4s/+lnMceSCcTCzzVsdRaNBxIOHwogqLI/67ROYWZqJcHN0YCSGzBmJem81EuFt8rp84AK2zfoDjTs3Q5/JA0SQ+kUbh5tfJFTBn+dwc+4J3rx5s/gwjcPNFfwFX5LToyvCaRkY7WZq+lJXmJTkkLhtFmABFmABFmABFmABFmABFmCBwgtQJa7p06eLys28lawAhau+++47DBkypGQ74tZZoCwIUMgmPFy8f0RhmY1//40//v4bD0JDoVKrIZVIkKNWw8PREc3q1IGLnZ240cpgthYW4hj/hw+x4/hxnL91C5bm5pg3YwZGjh9fFmbHY2CBciNw6tQpfPHFFyKkVhLVhLt16ybCevTBnb6+PuRy+RMrQdAHfgVXh6CvqQIfBZYpqEL32o2e09PTE+3Qpj2u4L32MYWcKQBNG7Wj7afgvvQ89SU+WMy70f60L4WcqfgLXXxUHBsVlImJiYGzszNmzZpVHE1yGyzAAhVQgAoNUUiGKjcXuWrz81xUKiAtDYlhYZj72284fOYM4pOTEREXB309PVHkyMLUFJZmZiL8nJmdjYSUFHFvZmKCtzt0wJIVK2BDxYx4YwEWYAEWYAEWYIFKLpAbbh6Epn1bousnvSqMhiZHjezMLCizFdi7cBdCbwVh6I+jkJWSgT+/34A6beui67h3IdPXg45MBkVmNlRKFbbN/EOEeIfOHYWMlHT88b/VaNzVB2+P7AI9Az2x/3M3jUb0mZ6chogHEYgJjkJMUBSCfP2RFJUo/sav36Ex+n87BJnJafh99M/ITM1E24HtYW77X6V+tVoD3yPXRIXp3l/2g2ttD6yYsAhWjlYYvXg8zKzzwsgA1EoVMhLToGOgCyNTI5zbefK54eYRC8bhwq7T2DpzI94c0B49Jr4nqjZrt/T4FKz9cjkiA8Ix+vfxsKtiJ8LNybFJGLNkPFxq5BZzoHDz7dM38ceUNajzZj0xJ5nBC3zo/Qqu3Fxhfs6KNBEON+ey/fnnn/jf//7H4eYivYr4IBZgARZgARZgARZgARZgARZgARaoOAJr1qzBDz/8gODg4IozqTI6k2rVqmH27Nno169fGR0hD4sFilmAwjUpKUBWFpCZCWRkAEolLt+9iy1HjuCGvz98/fyQkZ0NLxcXZCsUYvlzJd1ycpCang43Fxd8PGoUJkyeXMyD4+ZYoHIIXLx4EZ999hmGDh0qlu4u7o2qjmqrIl+9ehWNGzcWIWIKG2tv4eHhoqoyfa0NGMfFxYmlxqmCsna5cXqegsj0YSYFpeleG1qmcVMgueA9rTrh6uqaPyXt8/QNqvD3+BLl9Dwd4+7unh+apv+bi2ujZdEjIiLQsGFDTJgwobia5XZYgAVY4NUElEpR1VmdnIxL587h+u3buO7nh9jERPHvqrWtLerVrYte778Ptxo1Xq0vPpoFWIAFWIAFWIAFKphARQ03RwdE4MSfR5GVmoVg3wAoFUpUa+KN9KQ0hN0KhmM1Jzh6u6DxO01hbmOBE38cgTxTjoCrflDnqOH1Rg2kJ6Ui+EYQnL2c4ejlgoYdG6Fm6/pPfQWoc3KQkZwOCQATSzNAKhEhXo1ajeyMbKTEJuH6ocuigrREKsXYJROgZ6iPXz+YC6VcAUs7S/H+QMFNrlCAQtodPuwMB08nrJq4BFUbeGLs0omQynSeOg4KHJ/dceL5lZt/GYsja/7G/kV70HPie2g3rNMjF2xnpqRj45TVCLzmj9ELP4FjVScRbqb5jV06AXYeTqJv6uvumVvY8M1qDjdXsH8XSnQ6HG7O5aVqBN988w2Hm0v01caNswALsAALsAALsAALsAALsAALsEDZF1i1apUI3NJSn7yVrICnpyd+/vlnvPtuBVzCsGTpuPWKJEDhRKrSStVW6T4nB4lJSdi4bRtMqWJzWhoMDAxQpUoVNGrUSIQfeWMBFii6gK+vL0aNGoXhw4eXSLi56COreEdOmzYNfn5+aNeuHUaPHl3xJsgzYgEWYAEWYAEWYAEWYAEWYIFKJlBRw80P74Xgwu6ziA2KQvDNQFSp5QYrZxsEX/NHVno2vJvXgrG5MVq81xqaHA3O7jyN+LBYEeit2tATFg5WCLrmJ/at2aI2DM2M8EY3H7jXe/oFxKmxydizYDtylDl4b/IAmNr8V4VZ+5KSp2diyccLEeIbiCGzhsPJuwp++3AeZPq66DWpL/SN9B959eWu5CSBQ1UnJEYlYPXEJXCt7YYxSyZCt0CF5ByVCpnJmZDp6cDIxBBnX1C5eeQvY3Fs/SFR0ZqqV3cY0QVSnf+C1WnxKVj9+RLEhETjo0WfwtY5t3IzVbIeu2Qi7DwcxDg53FzJ/rEorulyuDlX8o8//gBVERg8eLB47OPjg61btxYXM7fDAizAAizAAizAAizAAizAAizAAixQTgSWL1+On376CQEBAeVkxOV3mFWrVsWiRYvQpUuX8jsJHjkLsAALsEC5Erh3754ocjJy5Eh8/PHH5Wrs5W2wM2fOxPXr19GzZ09RKZs3FmABFmABFmABFmABFmABFmCB8i1QUcPNVDU5R6kUlZLPbPsX/acOhb27A9ZPXgETS1MM/mE49E0MoCOT0TJKUKlycGrTUfy78SgGTh8G+6qO2PC/VTA0NcSgH4bD0NQIUh0dSKRUm/nJLS0uWVRWjg6OwtDZI1D7zQZP7KTJyRH73D5xQ+zj2aQ6loxZgIzkDIxZNB6utd3zj6Fgc+BVf6TGJ8PbpxaS45Kx/JPfYGCkj5ELP4Wde27AmMYeejsIBxf/JfZrO6gDLuw5/fzKzQs+xtVDl7Hxf6vQsFMTvD9tGPSNDPL7jrgXitWfL4VUJhWVm43NTTjcXL5/zMvW6LXh5l93iurm/fv3x/fffw9DQ8NCj1OiKbjGWaEPf70HbNy4Ed9++y2Hm1/vaeDeWYAFWIAFWIAFWIAFWIAFWIAFWOC1CyxZsgQLFiyAv7//ax9LRR8AhZtXrFiBDh06VPSp8vxYgAVYgAXKiACtzNCrVy989NFHHG4u4XMyd+5cnD59WgSb+/XrV8K9cfMswAIswAIswAIswAIswAIswAIlLVBRw83kpsqSY/OMDYgMiBAh3ZS4ZKyfvBKN32mGbhN6P0Kbo1Bi/dcrkBAej9G/f4qkmEQR8G3Zty3eHt0VUul/lY2fdk7o+G2zNuHinrOo3ba+qN5saW8JSKTQQCNWeAu9FYw1XyxDdnoWxq34HM41XPHHt6vhe+Qq3h7eGe+M7QmpTAcU14wNicKq8YugVORg3IqJMLEwwarPlyL0ZhB6ftEXLd9rC6lUgqy0TOz9dSfO7TyF7hN6o8O9WhqPAAAgAElEQVQH7+D8zlPYPmczWr3fDr0n9afiz7hz5hZWf7YItVrVw8iFnyDyfhgWj/4ZuoZ6GDZ3NNzrV4NEIoFKrsDJP4/j4OI9qNOmPgbP+hCKTAWHm0v6B7EytZ8Xbt7/225xsUC3Lt0wa9YsGBsbF1qhXIebN2zYgKlTp3K4udCnnQ9gARZgARZgARZgARZgARZgARZggYol8Pvvv4tqwg8ePKhYEyuDs/Hw8AC9J9O6desyODoeEguwAAuwQEUUiIyMRMeOHTFu3DgON5fwCf7ll1/wzz//4NNPP0X37t1LuDdungVYgAVYgAVYgAVYgAVYgAVYoKQFKnK4OTE8DisnLoZTNRf0/WYArhy8iEMrDuD9qUNQu+2jlZWTYxKx4pPfYO/hgAHThuHyAdp3P/r+byDqtm/0UqchxNcff3y3FonhCajasBq8mtaAvYcjFFkKhN8Lwb2zdxAXHoeGHRuj/3dDYWBsgAcX7orANYU8W/drh2pvVEdiRALO7zmDkBuBaNWvLXpMfA8ymQxX/76I7bM3iUrKPu+2hK2bPfyvPMDlfedhZmOOUQvHwdHTGdcOX8Gf362DjYstGndpije6+iDcP/yRcLMyS449v2zHuV2n4VG3Kpr1aglrZxsEXQvA+V2nkaNUoc83g1C/fSNkJKRyuPmlXgG800sJaMPNi/ZAJtNBh3YdMGfOHJiamr7U4QV3Ktfh5vXr1+O7777DkCFDQFWcfXx8sHXr1kIj8AEswAIswAIswAIswAIswAIswAIswALlW+DXX3/FsmXLcP/+/fI9kXIwend3d2zbtg1NmzYtB6PlIbIAC7AAC1QEgcTERDRv3hwTJkzgcHMJn1C6YGzXrl345ptvRKCcNxZgARZgARZgARZgARZgARZggfItUJHDzWG3g7B5+ka06tsGTXu0wLG1/4gw8aAZw2HjZv/IiQu7FYRN361FuyEd0aSLDw4u3YuQm4EYMmsELJ1sXuokq5Uq3Dp1Ayc2HEGUfwRUKhX0jfShztFAma2AsaUJareph7cGvw0b99z+VXIlLv51Bic2HkVqQgoMTAyhkquo4DNqtqiDrp/2goWDldg3Oy0Tp/48hvO7z4iKzTI9GVRKFawcrNFpdFfU69AYEqkUsaHR2PzdOlGx2tzGHENmjxTVojdNW4vqzWph4PQPRHvxYTHY//tu+F26ByrvrGugK4LYVCW6Zb+2aNmnDXQN9ZGZlIYN36xGVloGhv44CtZV7MTxEo0GfpfuY8eczfBuXhM9JvSBTF/vxVYaDf5ZtlcEq9sP64Q3B/P7Cy9Gq0B7FAg368p00L6yhpvXrVuHadOmcbi5Ar22eSoswAIswAIswAIswAIswAIswAIsUBSB+fPnY82aNaA3ankrWQEKN//111+oX79+yXbErbMAC7AAC7BAnkBWVhbq1KmDzz//XFRv5q3kBOhiMSomM3fuXLRq1arkOuKWWYAFWIAFWIAFWIAFWIAFWIAFSkWgIoeb5WmZiI+Ih4WdBYwsTJAYlSDCu3audtDR033El4LDcQ9jYeNsAwNTIyRGxEOpUMLOzR5Smeylz4UmJwfxD+MQ7BuAh/fCRPhYqiOFqbUZvJvVhGM1Z9F+wS1HoUT4/TDcPXMbGcnpIrTs9YY33OpWhYmV2SP7yjOyEH4vFP6XHyArPQs2Ljao1qQGHDydRLCZNnVODqL9IxDx4CF0jfRFvxKJBLGhMTA0M4Kta16wW6NBSlwyAq88QOidYOQo1TC1Nkf1Zt5w8XYVwWbRnipHHEv3dh4OkBWwIzeaL7Vr5WSdP4YXgSVHJYgwt4WdJczsLF+0Oz9fkQRy1Di6/h8cXPyXqEjevl37ylm5ee3atfj+++853FyRXtw8FxZgARZgARZgARZgARZgARZgARYoggAFcCiIc/v27SIczYcURsDNzQ2HDh1CjRo1CnMY78sCLMACLMACryRA//989dVXHG5+JcUXH7x69WosX74cS5YsQZMmTV58AO/BAizAAizAAizAAizAAizAAixQpgXu3buHgYMGoWnfFuj6Sa8yPdbyNDiqapyj0QCa3FFLJHhh8FejVovdJdBAKpFCQwc9a1OroQYgFQ0/Yz/qX9v58/A0Gqjz9hXx6LyQdHny5rGWHwG6AOCfZftwbP0h6Bnq483mbSpnuJkqMk2fPp3DzeXntcsjZQEWYAEWYAEWYAEWYAEWYAEWYIESEZg1axa2bduGGzdulEj73Oh/AhQuO3HiBDw8PJiFBViABViABUpNwNPTE59++ik+++yzUuuzMnZEF4v98ssv4qIxqpbNGwuwAAuwAAuwAAuwAAuwAAuwQPkWCAgIQL/+/fDGexxuLt9nkkfPAuVDgMLNBxbvwalNx6FnpI/WzVpVznAzVRCYMWMGhg4dig0bNsDHxwdbt24tH2eRR8kCLMACLMACLMACLMACLMACLMACLFBsArSy0969e3Ht2rVia5MberoAhZsvXLgAR0dHJmIBFmABFmCBUhOgFQOGDx8uqjfzVnICW7ZsAV00tmfPHlCgnDcWYAEWYAEWYAEWYAEWYAEWYIHyLaANNzfp3Rzdxvcu35Ph0bMAC5R5AQ43552iVatW4YcffuBwc5l/yfIAWYAFWIAFWIAFWIAFWIAFWIAFWKBkBb799lscOnQIly9fLtmOuHVQuJkqZFtYWLAGC7AAC7AAC5SaAFURHjBgAKZMmVJqfVbGjnbt2iWMjx49Cmdn58pIwHNmARZgARZgARZgARZgARZggQolIMLN/fqhYc+m6Pl53wo1N54MC7BA2RPgcHPeOVm5ciVmzpzJ4eay9xrlEbEAC7AAC7AAC7AAC7AAC7AAC7BAqQpMnjwZJ06cEBWFeStZAQo3379/H4aGhiXbEbfOAizAAizAAgUEGjZsiJ49e4JWa+Ct5AT279+PiRMnit+prK2tS64jbpkFWIAFWIAFWIAFWIAFWIAFWKBUBPLDzT2aouckDjeXCjp3wgKVWIDDzXknf8WKFWJ5tGHDhmH9+vXw8fHB1q1bK/FLg6fOAizAAizAAizAAizAAizAAizAApVTYNKkSSKEc/bs2coJUEqzVqvV8PDwQEhICCQSSSn1yt2wAAuwAAuwANC0aVN07NhRfCbAW8kJHD58GGPGjMHt27dhZGRUch1xyyzAAizAAizAAizAAizAAizAAqUiwOHmUmHmTliABfIEONycB7F8+XLMnj2bw838o8ECLMACLMACLMACLMACLMACLMAClVxg/PjxuHHjBk6ePFnJJUp2+pmZmahZsyZCQ0NLtiNunQVYgAVYgAUeE2jVqpUocDJ//ny2KUEBWgnjgw8+QFBQEKRSaQn2xE2zAAuwAAuwAAuwAAuwAAuwAAuUhgCHm0tDmftgARbQCnC4OU9i2bJl+PHHHznczD8bLMACLMACLMACLMACLMACLMACLFDJBcaOHQs/Pz8cO3askkuU7PQTEhLQuHFjUbmZNxZgARZgARYoTYF27dqhXr16WLhwYWl2W+n6ogvFKNwcHBxc6ebOE2YBFmABFmABFmABFmABFmCBiijA4eaKeFZ5TixQdgU43Jx3bpYuXYo5c+ZwuLnsvlZ5ZCzAAizAAizAAizAAizAAizAAixQKgIjR45EeHg4/vnnn1Lpr7J28vDhQ7Rp04YDT5X1BcDzZgEWYIHXKPD222/D09MT9LkAbyUncOrUqfzKzSXXC7fMAizAAizAAizAAizAAizAAixQWgIcbi68tCIjG1KZFDJ9vRcfrNYgOS4JFraWgFTy4v1LYQ+VXAG1Kgd6xob5veUolMih7xkZlMIInt6FMlsuntA10H9kB3VODtITUmFoagRdfV2oFEpIZTIkRSXCysUGEslLuGo0yErLhKGZMaDRIDU+BfoGepDqyZAjV0Kmr4vMlAwYmhk90j+NSaLRQGZYPC6aHDUk9Dp4ypgp9CvR0XkSR0NDVkOZJYeOni50dGUvPEfUVnZ6FgzNTV64b2nvwOHmPPElS5Zg7ty54o22devWiSXptm7dWtrng/tjARZgARZgARZgARZgARZgARZgARZ4zQLDhg1DYmIi9u3b95pHUrG7f/DgATp37iyWqueNBViABViABUpToFu3brC3t8fq1auf261cLgfdsrOzxT3dFArFI4/p64I3pVIpvqZ7uqlUqvx7eky3nJycR25qtRoFbxqNRoxLe08fvGlvUqkUdIEQhbN1dHQeudFzL/pewX18fX3RokUL6Ovrw8DAQNwXfPy071H7L7tR5eYRI0aAPvzmjQVYgAVYgAVYgAVYgAVYgAVYoPwLcLi5kOdQA9w+6QsLews4eDiJQLCuAYWcNVBkKaBnoAeJjhQUgKYQanZGFv5Ztg89P+8rAsU6Mh0RUKWArkRKAWldQK2BPDMLUh0d0ZYiSy4CuNrwsVqpEv1QezlKlRgw7afMVkCjVotAMj1PAWVdQ33x3oMyk4KwMtEmHUOBWgrWqtUaxIZGIzk6Cd4+NaFraCCeSwyPRVpSOpy9XJCjUok2aXw0DupHnaOGvrEBlCIYrYaeob6YD+0r1ZWJeSkyKfStA1393DlIpZL8uWrUGtEeHaNrqAcJJKDgsExPVxxD+4fdDhah5aoNqz0S/s1ITMXpLf+icddmIrAbExKNOm3qY/+iXXh3Yj/IDPSEgzzPXKYrE740Zj0jffFcZnIGbhy/hhbvtYVKLsfxDYfh2cAL8ZHxkGdmw9rJBolRCahS2x0e9auJc0Jjeng3BGqNBlUbeuX6GuiJdslbmKo1wlkikYr569JjmQ5yFCrxHJ2PHEWumQYahN9/CEcvZ+jSvKUS0RaNj86t/1U/VK3vKTzE2CnkLZUgIykNEQ/CkJWeDVtXW9i42Al/6oP8qQ9xrgBQcF2lUEGRKce1Q5fx5qD2kGfKhTm9/0NjFK8fQ32xX25w+9EweSF/Igq9e364efO/MDA2QNP6b2D+/PkwMzMrdFsSjfadtkIf+voPWLx4MebNm8fh5td/KngELMACLMACLMACLMACLMACLMACLPBaBQYOHChCTLt27Xqt46jonVOgqk+fPggMDKzoU+X5sQALsAALlDGB3r17486dO7C2tn4iaEzBY20Ame61YWCZTIaCN11dXRE4pjAwPX7RTU9P74l9qD06rmC79OERfU332r6pH3qsrS508eJFNGjQ4Kkh6acFpwt+j0LU2q/v378PV1fXJwLcBcPc9Ljg1zQGmgvNu+D4qV36XsEKSOnp6YiLi+MLmcrY65+HwwIswAIswAIswAIswAIswAJFFeBwcyHlNBpc2n8eJpamSIlJRI5KDVs3O6iVasSGxcDJy1kENimoq2dsAHNbC5zbcQrvjOmGgMt+kOpIUbWRFx5cuAe3Wm5wb+iFpMgE3D59AzpSHdRpVx/XD12BnoEuGr79BgxNDBF0IxARDx6iSi03RAdGQCLVgUuNKgi+HgiZng683vBGxP2HSEtJh4t3FUglUgTfCoKxubEI7YbeDYFtFTskRsZTBhtG5sYIvOYH+2ouqNmsJmzc7BEfGo248DhkJKdDmSGHZxNv2FV1RHpiKm6dvCHCst7NaohwLj12reOOuLBYZKZmwNjCFFZO1gi+GQgjE0MxzpAbQZDq6sCtjgf8Lt2HgYmhCAtTKLhKTVcRbo7wC4eZjRmcqjnD7/IDxEfEicBxrdZ1Hwk3p8Ym48rBi/Dp1RJntp5AakIK3vt6AE78eRQt+7wpgrrRQZEIuHxfBILJJuCKvwgfe9T3RFpiKhIi4hAVEIH3/jcIqiw5Tm77F7Va1BXhZWMLE0T6hYvjdPR14d2sFhIj4nDv3F2kxqXAvqq9CGynJ6XDwdMRGSkZSIlJFu/F0Hs9Ni42MLQwRtitYBiZGcO6ii3C74ZSLhwe9asi0j9ShI4t7C1x64QvqjeribTENNi62iEtLgVZ6Vlwq+OOk38eR+v+bZEcmwx5lhweDarBwd0BQdf9cXbnaTE+pVwpQuYN2jdC4PUAUY3ao64HHKtXEcfcOeGL1IQ02Ls7wP/KfTTs0Bhh98JgYmUCj3qeuHP6FlRyJWq2qiPOi4WdBWq1qV/IH4JX210bbj699V+YmBujTrW6+PXXX2Fubl7ohst1uHnRokX46aefONxc6NPOB7AAC7AAC7AAC7AAC7AAC7AAC7BAxRLo27eveJNpy5YtFWtiZWw2586dA1XJ9vf3L2Mj4+GwAAuwAAtUdAEKLU+bNu2JYLE2VKwNGNMHT49XVS74NYV3KdBbMAxd8PHjQWnt149Xb37R1wXb0QaTaRw0Tg8PD4SFheWHigtWeabz+KyvHz/H1B7VrykYftaGuwsGtynYrA1qF7ynatUWFhb5z2mP6dChA7p06VLRX1I8PxZgARZgARZgARZgARZgARaoFAIcbi7kac4LN+sb6cPv4n3Ub98QAZceQKVUwtDMWIRFk6KTUKtVXVHh2am6CwKvBaBWy9pIiEyA/+UHqNeuAQKu+uOdj7rB1NocobcCEXjNX4RvG3dphgu7z8CzUXXUfbMeLBysceGvM6ISsY5MJqou6+hIoaOnh/jQGFRrUh3Glia4cuAiqjX2QkJEPNzqVkV6Uhoi/SPg5OWClLgk2LnaiyC2ub2F2IdCu47VnSEB0OidZgi5EYjAq35IjkmGjasNXPMqGN8/dxcxgRGwreqA2JBoEfK1dLBC6K1gUdGZ+g/2DRRViSmMS5WV4x/GQqIBUhJSRdCZgtnePrUQcPWBCNhG+ocjKzUTVo7WIpRs5+EImyq2SIyIh62bA2q1qffIhdbacHO7IR2RFJ2IfzceQp+v/gs3U7Xsu2dviZA4BcGrNfFGdGCk6CvsboiocNzwnWa4uOc0+n47OD/c7NO9pQhK0zmh86arryvCzV5NauDKgfOiGnJmaiZU2QrEBEfBoZozUmKToW9iALsq9gi+ESCC6lH+EdAz1EX1ZrXw4OI9SKCBTF8PhqbGUGRlIy40FvaudrB0tkHEg3BYOVuL9uq2bYCg6wFIiUtGlRquIoTsXKMKwu+GwdjCCAbGhmg94C3EhUTj0r4LsHS0EqF3Cj9TaP3+hbuwd7cXVaHbD+8sXj+3jvuiuk8NxARFISowSrxuqtRyhf+lB2jVrx3C74cKmxqtauPBubto/2FnWLvYFvKH4NV253Bznt/vv/8uSlZ/+OGHWLt2LXx8fLB169ZX0+WjWYAFWIAFWIAFWIAFWIAFWIAFWIAFyp1Az549xVXfGzZsKHdjL08DPnr0KMaNG4cHDx6Up2HzWFmABViABVigTAhQEJmCzpmZmSJgTV9rbzTApz3WLr5Z8DnaVxuApurQ2mrRFJzWVo8uExPmQbAAC7AAC7AAC7AAC7AAC7AAC7x2AQ43F/IUFAg3U+i0aTcfXN53AbZVHUU1Xwr2GpoaomnPFvA9fA0OVR0QfDMIZtbmkOhIEHjFD3Xa1kdWWhZa9W9Hf+zj3M6TSIlJEdWFfXq1EuFTqrJMFZlrtqyHywfOw9HTGeH3wxAXFiMqAFMYNz05A0nRCaJacfCNQBGWpTBrenI6KPAbHRQlqiRTpWYdmY6YqLWzjWhfoiMVFYcTIhLQuIuPCDc/vB8GEzNjxD2MhbmNOZr2aoW7p26KysfUDoWYqRq0haMV7p+5DV0jfTTs0AQ3jl8TYer67RtBkaVAwJUHkOnJoKMrE9WJKUxdrZE3Qu8EiQD0jSPXkJaYAqfqVUQVaI0E8GrsJULABmZGIuhrZmMOSKVizI+Hm09sPIzeX73/X+VmfV0c23AYMpkOQu+EwKuJt6ja7OxdBZf3noNMTxdtBr6FfzcewbuT+uWHm+u0qY9Le8/DvZ4HPBtXR+iNwNxwc2NvXNhzRoR+0xOo6nMCYoIjxT4aACq5Cu4NqopzWb2JN26e8BXvtzTq1gy3jl2HSpkjqizLdGUijE0WcaExMDIzEs9ZOlpDkZ0NWxc73Pj3OjRqwK2mG6KCo2BbxVaEwW3d7UX4u1arekiKSsCFvedEqNyttpuonp2jVIlgtHt9T/F6a9CxiahC7X/FT4TnI/wjRFidQuTezWsiOToBVk62ojp2UkQCvJvXEq+3jiO7FfIH4NV353BznuFvv/2Gn3/+mcPNr/6a4hZYgAVYgAVYgAVYgAVYgAVYgAVYoFwLUHVBZ2dnrFy5slzPo6wPft++ffjqq69w7969sj5UHh8LsAALsAALsAALsAALsAALsAALsAALsAALsAALVHoBDjcX8iWgAe6fuy0q81JgtEaL2rh39g6kUomoAmxgbAAzGwtEBYRDz1BfBE3vnb8LAxNDUS05IykN9u4O0DPSR923Golw862Tvoi4FwaJjo6oxEtVfaU6UtRuXQ/O1avg7rnbsKliJwKtt0/cAFWNrlLTDUHX/UV1YZcaFHgNhY6uDhyqOoowbnZGlggam9tZwtrZCma2FiJUrGugK4LSFP41szUXY/JuXhvRARGi8i+FrrMzs+Fc3UV8PykyHlcPXoRUpoOqjaoh4Ar1CbjV8UB6QhqqNvAU1ZLp+ZjASBiYGcKpmosIW1Ool0LNcQ9jRNA4NjgGjp6OCL0TDB2ZLhKjEmBqaQKHqk64c/om1BoNqr9RA6G3g9Gqb1voGRuIk0PhZgp4txvyNjKS03Htn0t4c2B7HP/jMFr3bQddAz1c2X9BBKxp3lbONjCxNBHh4UBff1FFOjtTjqyMLHQa1R2qrGyc3HwcrnU9hKeVgxXsPOyhUWsgM9ATFZgj7ofh3ulboiK1s7erCAJrctRw8HSGPFsOe3dHRDwIE0Hs0FtBMLY0E+ecxmLv4SjC5XQ+qIp3WlwysrPkcKzqhLSkNBEsN7EwgYV97jnRMzKAmbUZMtMy4ODhiKjASFCK2qupN5xruInXzPndp0XY2bWOh6igbWJliqBr/mI/t7oecK9fDdlpGbi077x4nVDF8PiHcSIEnxidKKpS27jYiyrhdIE8vR6kMine6NaikD8Ar747h5vzDBcuXIhffvmFw82v/priFliABViABViABViABViABViABVigXAt07NgR3t7eWLRoUbmeR1kf/LZt2zB9+nTcuXOnrA+Vx8cCLMACLMACLMACLMACLMACLMACLMACLMACLMAClV5AG26u360xen35fqX3eBkARZZcrJiUo8qBvqE+6GtIJchMyYChqZEIkqbGp0DfxBAGRgZQZGaLKsYUvqXwM4VLqaovhVppo2rLaQmp0DcyEMFhRZYSGrUa5vaWoh9qnyovU+A5LTENUokExhYmor2cHLWoskyBZHlGNkytzaCUK8VjXUM90b6unq4IGtP+1J6hiaE4jqoN0wpSFMKmQCzdlAoVlNkKEZ6liscUvqZK0DQeY0tTZCani3mbWJqKcVOYV5mtFOPLSEkXbdGc0xJSROCZPMR+erpQKlVi3vQ17Z+elC6qDtMx5CWV6kDfWB9hd4JRtYGXOJ62jMRUnNl+Ej49WsDMzgLZ6dkimHtwyV50/7SXCCSTEfnrGemBSkGLytEyHSgVSjFnqmCsb2wgxqPMysaJP46ierOaIvwtVsPKUePeuTsiFEzVpdU5anFOdGRScV5USpWoMm1saQK1KkecT5oHGankSuiQb2Ka6IP6loiRS6BWq4UnuVKgmY7JUatzz4muTFTFpjYkdIBEAplMBqVcIc6FiZWZMKDQdWZKunhMoekclQo6MhnkmXIoshUwtTIV46EtKzUDCrlS9EXP6RnoIjUuFUbmRqIfmpOeoZ6YM7VBofvS3jjcnCf+66+/YsGCBRxuLu1XIPfHAizAAizAAizAAizAAizAAizAAmVMoG3btmjSpAnmz5//WkZGbxAqlUqoVKon7oODg0VVaXqTi95Qonvt7XlfP+05uVwulp7XLlGvnax2aXq6f3x5enqzjG66urripqenJ276+vriZmBgIL5+mW3dunXiQvObN2++zO68DwuwAAuwAAuwAAuwAAuwAAuwAAuwAAuwAAuwAAuwwGsUoHBz3359Ua9zQ/SZPOg1jqT8dC3ef1flADpSUTn5iY3CsmoNZVuf/vzzpkrH0qGUds1NyD665T751BYogPzIeLRtSZ/cv+C+4rHo7+ntPr2zvHE+pe2XOpM0NpHnfbJPCg9rg83UFn2dGJkAEysTGJgYieZzFErEhMTAqZoT8LRz8DhbARs6N4mRcTAwNhSB7dz2VEiKToSpjZkIMz9zK+j/HN+XMnhijJr8kHNRjn/hMTR27fay5/o5r7cX9veMHTjcnAdDwWYKOA8fPhxr1qyBj48Ptm7dWlRXPo4FWIAFWIAFWIAFWIAFWIAFWIAFWKCcCjRu3BhmZmZo0KCBmAG9+RgaGgoXFxfxtTZYnJmZKYK8qampMDIyEt+nYDLdUzBZe0/foxt973nBZW2gmQLFFBzWhogL3js5OSE+Pl6EjguGj7Uh5Me/V/Drx58TFR9ksvw3BOl5bdD58TD04+OnsdJNoVCIGwWls7OzxY3GYmhoKG4Udtbe02NtCJrmFxISgqioKFy/fr2cvlJ42CzAAizAAizAAizAAizAAizAAizAAizAAizAAixQeQQ43Fz4cx3/MAYp0UkwtjaFvZujqNosIrp578dnJqeJarkSqQQOHrnP5wd5C4Z6NQBFfLUB3xy5EvGRcZBCAmMrUxiaGec+l3cMNZIakwgDc+P8AK72/X86NjowEs41XcU4aDxJMQniWAsHmyfaiQ6KgLWTNfQNDRDlHwELR6vcKr4F+ivYd94Mc8cLIDs9E5lpWbB0sM4NcReYf8HHud/+73MK7VzTE1NFtWRTG/NHQs40H23cWWuW/728diQUuKVAM91rH+d97pMfllarc+dCx6hyEBceCzt3x/zPSwqGqrWGz/ye9pxpgOTYRFE1W9cgt2I3Vai2drbNnYP2paQNDoss8X8h7qfNTRSI2DIAACAASURBVPt5FfUdExIFE3OT/MB1/isz73VS0PJxI+1r5Fn2ZBYdHAWjvNeUqTW55732tPMrcA5F3xQCj4iFpZMNpDo6zwyjF/YniMPNeWIcbi7sS4f3ZwEWYAEWYAEWYAEWYAEWYAEWYIGKKXD8+HHMnDnzv/eCaCm19HQYGxs/Ev6lUC8txfZ4ELhgyFkbdtYGnOk5OoZCxdr7gtWQtY+19xQIpmO0weRXuacAMoWwtZWZC549sZRa3q1gNWjtuAsGtAtWlS4YcC4YdKb9KcisreysDWjTnLXBbAp7b968GRTY5o0FWIAFWIAFWIAFWIAFWIAFXkXgwoULopDVhx9+iE6dOr1KU3wsC7AAC7AAC7AAC7DAMwQ43Fy4l4ZGpf4/e/cBHWWVvgH8mT6TNpn03kOA0JsB6UVpUgQVBBULuLirK6uuurZ1de36313Xte/KqiiKuiAqTar0TighIaT3NslMMiVT/udeEjYqrgQpAZ7vnJxMZr7v3vf+vjs5Sp68wZr3vpbB45DoUBkQFiFfjV6D0JhQVBZUoPx4GRrrrQiNDUNYYiRslkb5M4HolFiU55XC3exGQIgRzQ4nGs2NCIkPh5/JH2XZRfh24UrEdUuEMdQIU7gJ0Z3iUFFYAWejHQGhRuxcthWpA9KQ1DdNhmsrcktk0FetUmHfmt0YNH0oastq4Gv0RXl+OcylNUjokYyQuDA0mi1orLMiMNyEvat2o+foPjKcvO79VQiJCUVIbBhCokNQXVSFZpcLwdEhsFTXo9nulEFupUqJ+op6qLVqGAIMqK80wzfAV67f3+Qng8oV+eXQaNRyrIrjpfDAi4i4CFSVVMHt8iAiKRJaHz2q8sths9rkzxZczS4ERZig0qjl9X5iLqUStoYm6WQur4MhwAdBUSEoO1Ys/aI7RaOmpBY2SxMiU6PgsDtRV1KDwPAgqDRKVBVWwRgSgOC4cNnlOXdPjpy7vqoe8HoQnRYHpUYNZ6MNZcfL5M92IpIjodZqpVNNcZUM80YmRaKurBa2RhuMIUZsX7YFna9MR0KPFDTVW1F2rETeJ0utFRqdGnp/H/j6+8DpaIbX65H3V3SFDooJQUN1g5wbHsBmsyM8PhwOmwPmCjOCo4NxcN0BGCNMSOyRKDLRcHs8ECFkc1kNLNVmGTAWrlVFldD56+GyNcPe5EBYfBistRbYGx3SKTAsUAbdvUogKjkapUdL4PF6sOHDNRg+a5QMsYux5N6LC4Gj0SEdVSolIpKjUZxVCI/bC2N4INa9twJDZo6U98jldMmAuM73f3S2Po23E8PNLUgMN5/GbuEpFKAABShAAQpQgAIUoAAFKEABCvxiARHqPd2Pw4cPIyUl5WSAujVI3d7P4h9D6+rqYDQaT4aY2y6kNfD8w+7OrWHktmHstl2lxePWAHPrZxFqFs/zoAAFKEABClCAAhSgAAUocL4ErrjiCpSXlyMiIgLbt28/X9NyHgpQgAIUoAAFKHBZCRw/fhzTp09H93G9MO2hWZfV2s9kse5mF7557T+ISI1GXWktHE12GeoVAdiEnomoLqiSoWW1TgOXo1kGSTV6LaqLK5E+tAdytmVBa9DKQLK1rgH+gf4IT4pCtxG9UJh5HCvfWo7kfmkyzOx0NqPf2AE4tPkg7FabDBI31NQjLaMLOg/qhmM7jyJvXy5czmYEhBlRW1qDoIggqHVa1FfXQ++rQ3VhFYKiQ6DTa2XAutFslS2GNToteolwc2QIvnnrS6iVKhmATe3bCQUH82VtoXFhKDpSjJiusTI4bPA3wOXywO1ywxRhkmu31FjgZ/KF3WpHcEu4W8yT2D0JmRsOICQmBLFd43Fo434YQ03SIDIlGjk7j6DsWBkKD+YhsXcyrOZGKBUKGepurG9EU32TfCzCvyJYK9bffXhvZO84IkPPyf06Ye+qXYhJi0V0pxgc23UUPkY/NFlt8Df6wVxVh9C4cAyaPgwOqw3ffboOASGBqCmpgcvhxICJGQhLjkbRweM4svUQmsyN6H/NIER1ikHWloM4uvWw7KAcFBmEirxyGTq2N9hQW16DrkO6IzIxWoabc/dkw+0WPZA9sNZaZUhZhMdFiFoEtMW+EI2cxT2oyC1FREoUirOK4B/kj/DESBQfLZRhYa/HC7VGLW2DIoPlZzFOSr80bP50PRpqGuR4YYkROLzpANIH95AhejGO8C4/XorI5Gh53yOTI1FXYZYh6rDEcGxc9C0G3zAcmz/diAHXDJSeoq4gEcyvb5TGIhRdV1qDQdcNxY6lW6V3Uu8UHNqYiW7DeqDoSCF0PjoZEO85pt+ZvHVOXsNwcwsFw82/aB/xYgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgALnXUD8lZqePXvCZrPJX76cN28eHnjggfNeByekAAUoQAEKUIACl7pAfn4+pk2bhm5jezLcfBo3WzQp2b5sM6KSonFw4wHZnbfv2AHIXL8fTocDcV3iZQi4IDMfjXUW+Jh8kTF5MPas3AmFQim7L/uZ/HB8zzFodBo4bCdCwf0mDIS5vAar3lqOmM7xiO4ah5KjIgQbILvwilC1+BCdjEX4OSIpCju/3AJjeBCcNjvqymtRU1wtg9ODpw/DgXX7YG+yy67CofEROLhhn+wOLcaoLa1FUHQweo/pK8PN6z9YjdT+aSjIzIOP0VeOJULQIszqsDkx7MaRWP/BGtndOG1gV9kluLKwQnb6FcldEVg+8O0eGTr2CxJzuGWYt6qwEl4vEN8tQYZ4vW4vugzuhvhuicjaehCl2SWyY/CgaUPx7cJV8vHU312H+qo6ZG7Yjx4jemPTR+sQnRYDswztRsmu0qJjdWBEkAzw6g1aGS4/tH4feozpi/KcEoTEhspwsd5Pj2E3jkazzYF1H66WnZ9FoFeE0kXH4sSeqcjZcQSFhwtQX1GHXmP6IbF3CrJEmLzRLgO/2dsPIygyBJ2v7Iatn22Ej78P4rsnorqoWnbrriuvgVavk9fl7s6W3a7zDuRCZ9DLcHRtSTUCQgLkX98UnY97jOyFzHX70exslqH30qxCpA1MR1VBBXR+BtlBuzS7SO7EjGuHwBQRJMPNYXHhstO16AwuwslJvVNRX1mH+PQE7Fy+Te6jETeNwe6vd8BSa0Hvq/vB0WiXXZhLjhbjxqduxadPf4iuw7qjNLtYrqPXmL5Y/8G38DH6oPfV/bF35S4k9kzEsd05cq6olGjUFFfCFBWCkqwihCdGyFC8qOuXHAw3t+gx3PxLthGvpQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKHD+Bb766ivcddddJydOTU3FmjVrzn8hpzHj0qVLsWjRIiQlJeHxxx+HwWA4jat4CgUoQAEKUIACFOgYAgw3t+8+iHDzvtW7EJ4QjqytR6BUKtB9RC8c2XxIhn2bGpqg1KhkEFh2Ww70Rf9rBmLf6j3wM/qgJLsYHrcHLrcbvkY/eD1ueJo9GHX7eFiqzfjm9WUy/JvUJxVFhwtkR9+8/bkwhgXKjtDGMBNCY8OQOqAzjmw+KAPQIlRsigqCrb5JBmb9gwNQX2mW4dya4ioZBBbdnc0VdQgMM8nwshij+4ieMIYHY9t/NiGlXycU7M+T3ZirS6pkYFckk0WAVnQPriqqhPhrkEqVEgqlAv4hAXA0OaFUK5F2RRdkbT4kOwg7mhzwejyyO3N1cRUaqusREhuGZrtThqJFCDh9cHfk7D4qOyK7nS70urofNn+6AfACobGhsNZb5dzdhvXEgbX75HzN9mbZBTrvwHH4BvjI+uw2Owx+PjLsK8YOTwxHU4NNhrhFALqmtBoT754qvbd+sUl2tQ6OCUNNSTVM4YGI6ZqAXV9vQ1VBpQx9J/fthC5XdkPu7hwZPtYZdCg6kg+bxYbgqGC5NhHmFt2Q4zrHyy7J2TuyoPfRI65bAvIP5CI+PRHblm1BTKcYBEYGIWd7lrwP4p6INST3TcWR7w7Ke+m0N8Npd8gO006bE3ofHaI6x2L/6j0yID989miotRpsXLQG9ibHyc7NDVX18n5lfrsPfiH+8Lg8KDqcj8SeyairqIMpMhh2a5MMk4fGhyNn11FM+u21WPLsR0jp30muw1xeJ19rrLVApVWj5+g+2L9mT0vwPkd2HBd7zlLbIO9fWXaxDNaLa7oO7dm+N80Pzma4uQWE4eZftI94MQUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIHzLrBgwQJ8/vnnJ+cNDg7Gnj17znsdpzPhX/7yF4ifS4vj0Ucfxdy5c0/nMp5DAQpQgAIUoAAFOoQAw83tvw3W2gbZdbnJ0iQv9jf5o7G+EWqtGuXHSqBUqWSwFR4vVGoV/IONENe43W7ZUbmpvlGGi8OTI9FQaZZBYNEd1+NyoTirSHb69Q30k92DRcffirwyGSrWG/TQ+enluEExoajMK8PhLYfQaLYguVeqHE+ef7wMAeEm+AX6obqwCh6vBxGJEagpqkJzs1uGdkVHaWOwEWqNGvXVZhj8fWGzNsmxRZBZrEEEfg9tykTnQV1lKPrYrqMAlIjpHIvQ+DAZ0FVAAUOAL2wNjXKtZbmlUGlUiEqORnF2EZqdLsSkxaKyoFyGe2O7xsPH6Ad7QyOcdic8Xq/sTm2uqoNWo0aJ7LwcBo1ODZ8APxm2FgFuEVgW3aYLD+ZD76uHVq9Fs6MZVrNVji8ei67GoXFhUGtUKD1WImuOSouD1+1BfXUdNBoNtDoNnI5mqHQa6H0Nskt02bFSGPwMMtgbHB0Gm6URIsSuVCrhcjTDXGWGrb4RkZ1i5JpFWFqEpN3OZljMFnmeGMthbYLe3weWmgb5tc6gReGRQnhcbkQkRcr77xPgi7JjJbDWWRGREoVmm1N2dw4TXZG1Gnmfd321XXarThnQRYa8tyzZIO9TaEI4olNjZGjeL9iIoiMFco9EJEZiw6LVSO7TSXanNkUGyWC8SqNGXNf4E0HmuDDZxVl05tYadCe6c9dZEJUWK+sT90AExFVaFYqPFMj9oDXooVBCdvCurzDD6XDK8XR+Pu1/07S5guHmFgyGm3/RPuLFFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABc6rgNlsxrBhwyA+tx4ipLF+/XokJCSc11pOZ7K24WYRyr733ntP5zKeQwEKUIACFKAABTqEAMPN5+82iADwzi+3yk65PUb0kd2PRbdiKE6/BoXXC69CIa+rLa2S3XZFd+heY/pB66M/9UBer5xCXvc/jpNjt5zjaLTh2J4cdBvcXYZ9c/cdgynChKCo0P/+d7pXLODnxz79FX7/zB/W9KNxfspP1PUz6z2jmjxiwpaxT8dTyLfj/prLanBsdza6De8JvQgRe704vjcHwdEhMIYHnbJkT7MLhzcfRJdB6VBpNd8751R+P2t6RjCnfxHDzS1WDDef/qbhmRSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUutMB//vMf/Pa3v/1RGY888gjmzZt3ocv70fxtw82/+93vTll7hyuaBVGAAhSgAAUoQIEWAYabf9lWaBsU/WFo9FSvNdtFt2NArdP8KHwrOv+KrsAGox9+eK21zgKFUgGbpQnBseGyaBE4FuOJbr/qNqHWH9ZhrWmQ3XlFN+dTBVtddgccNidcLhd8/H2h0WtlqFYcbrdHdmQWhxhD1KBQKk+O01RvQU1JNaJSYk4Ga1vn+F82bdV/KoDrcrpQV1aD4LgwOedPXXOqeSw19dDotND7GWStLpcbltoGBIYHndLA6/GiyWyRHZdFV+dT1eRssstu1E11VtlFOiIpGsoWG1HbD+uoLq6SAXbRAfnk61CgqcEKjUYNzQ/C6OL62vIa2XE5ICTw5HLbuouO12JOEWRvO5/osi2uaz3EayKHXVdRjaDwEJnzFuF2EYSuq6pDcFToKdf4y94Np3c1w80tTgw3n96G4VkUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFOoKACDaLgLPJZEJdXd3Jkvr3748lS5Z0hBK/V0PbcPN9992He+65p8PVyIIoQAEKUIACFKDATwkw3NzOveEFGqrNaDRboVIp4fF44RPoC41WA3N5LbQGLfxMAairqIUIzAZHhcBcWXciOBzgC1+jL2pLqmUY2S/IH/WVdSeeN/nj0Ib9sFka0XVwDzRUmeFj8oMx1IRmhxNblmxEYFggqour0XVINwSEBkKlUqGuvEYGko0RJ7r6iu7Qog6djx7+Qf6yjsLDBdDptYjrloimhkYZnHXaHHDanVBr1LK+stxSRCZHITQmDE6HEyIsawwzyXW6ml3QGfQIjAySoWcRlhZBYR+jL4oOF6DsWAmGzR4NpUKJuhYDsXZbow1BEcFwNNnQaG5EQKgRCpUSDZVmGMSaA3xRXVwpQ94h0aGoKa2GVq+VoW271QatQYe9q3eh65XdZM3+IUY5f31VHZrMjdJMrVWjvqIOBqMPtDotzJVmOa6lrkHW7PF4ZJjc7XJj51fbcNUdE2RgWlwnDOurzPI1vUGPzA37EN89CT7+PvK6kJjQ/3ZC9nhxeHOmXGtguAmNdVbZXdnjdp8IFXsVsFmb5D2CQinv66FNmeh9VV8Ex4RJM2ttPXS+BmRvz4IxPFB6BwQb0VBbD72PAebyGuTuPYaotBgEBAei2eGQ919Y2Fo88vYdg6/RD+FJEXA02mGKDIbe3wcuR7PcCyLQ7fV64Gy0wz80EMVHCpDcKwVVxVVQKJQICAlA4aF8xHZNkPtE7Ed9gA9qS2ug99XL8c5Jx+s2bzOGm1swGG5u5zdfnk4BClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClDgAgmYzWYMGzYM4nOvXr2wb98+WYlSKYIjHixfvhzdu3e/QNWdetq24eb7778fd999d4eqj8VQgAIUoAAFKECB/yXAcHM794fbg++WbIDTZkdpTikiU6JkCNcUGYSSrCL5fPrQHjiwdh/8Av0QkRyFvSt3IalvKizVDYjtGofS7BK4nM1I7JmCzPX7MGj6MESmRGLzpxtgrqiVQV6drx4NVfUYfvMY2W13+atfICQ2TIZrI1Oi5Zw6g1aGnaHwYtQt46DRqnF480FZh8PuQFx6AipyS2E1WxEUGSSD2CKELXoyi/Cv3qCTIWfRBdhcUYeIpCgZfq3IK5fdoP1M/ijJKYYpPAiia/Go28bJuncu2wq32w23ywWP2ys/j7jpKhka3rxkI1L6dULx0SKolEoEt4SWRUBadHcW4WsRjBaB4NR+aTi48QDs1ib0GX8FNixai54jesk1ihCwMSQAWVsPIyo1Bhq9BldOHy7n3b70O9gabHDY7AhPiITokOwb6AeNToPqoiqYIkwyOC46P+fty5VfG0MDsWfVToyeMxZHdxyFrcGKHiP74ODG/fA3BUCt16CqoALhiRGoKaqWHZdFeDksIVJuEJfDifUfrkH64B7Q+mhRcrQIBn8fZG05hPgeSaivNMNaa4Ex1Ai1RiODzsVHCzHm9gkIjgnFri+3yPC2KTIE1QUVcnxxXDFpELYt3Sz3j7msDhX5ZUjqlSotPQBCY0JkZ2wRWhYhZxHEFteKgLjYA4m9U5HaPw11pTXY+PFaJPVKQUl2sdwLvkEBcDubEdM5Frl7cuBxeRDbNR7leeXSs9nmkCF7YVeZXw6xMYbffJXce+fyYLi5RZfh5nO5zTg2BShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgbMn8Nlnn+F3v/udHHDixIkyzCyO0NBQVFVV4aGHHsL8+fPP3oRnYaS24eYHHngAv/nNb87CqByCAhSgAAUoQAEKnB8Bhpvb5yyCmeveX43uw3rg24WrMfTGETi4fj8CI4JkULggMw99xw5AbVkNYrrEyS69Imw884mb8OnzH8HgZ0BobDgaahtgigiCpboeV/9qkuxInLXlIKoKq2RwdvpDM7HireUYPnsMfAIM+O6TDQiKCpYdlnuN7o3NSzbJ8G58twSUHC3GqFuugl9QADLX7UVDdT0KD+XBFBEsg69NDU0oOHgcvqYA9B83QIZ0vQoFBlwzEDk7s+BudsugrMfllgHa0PgwBEWGYP/avRCJ1yunD8PmTzZg/K8nw2q2YPt/NmPANYOwbdkWmMIDZedgERQuzynGkS0HkdQ7VdaX1CcF9WW1gPLELyuKjsPFWQWI7ZaA6qJqJPZIRE1JLcqPl6LLoHQc2ph5wnPDfgy+brgMWR/ddhgDpw7GjuVbMeb28XA5XNj6n02y47QIPvcY1Qc1JVUw+PnI50THa98gf/lYoVSgNLtYuoXGhuHI9sPoMayXDPLmH8hDr6v6yDo6DeiMI5sPQaNXyzB27t4c2fU6tW+avIdQKmUQWLj1GN4T9bUNOLhuP0LjwuBocmDAxAwcWLcf9kabDCXr/QwYNmsUtn3xHXpf1Q9B0aHY8812GUQXtbia3TIEnX/gOIbMGI5176+RXcAHTBqInB1ZsNQ0wGa1IywuHNZ6q+w83Xf8Fdj11TZ5j8Piw5G9Iws6X50MpIswfXVhJfZ/uxfJfVKx9YuNSOmThvK8Muj0Gmh99eg+tKfckwWHCmC3NMru3FffeQ1qi6plR+u4rvHSO2P6YIRGhQEqZfveGO04m+HmFiyGm9uxa3gqBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgQsoMG/ePKxcuRIZGRlIS0vDwoULZTV9+vTBnj17MHjwYHz44YcXsMIfT9023Pz73/8ev/71rztUfSyGAhSgAAUoQAEK/C+BvPw8TJs2Dd3H9sK0h2YR6+cE3G5s+mQj0od0k51yr7xuGPav3i0DxSLsWpJTgi6DusHd7EJESiSObj0iuwlPvf86fPbiYtlBWXThdTmaZSfmRrMFV94wUoabRbD1+L4cWGss6DG6D7I2H8TouROh02uxZckGGUB2NJ3oDC26/SpVChhDAmGuMGPkLVdBpVZh9btfy3BtVUEVjKEBsoOxvdEhuyuLEHNcejyKjxSisaEJcV3iUFVUKbsV15bVQqvXQaVSwGlvhq/JF031TTJMPGjaELnWMXdMgKPRjk0fr0VUWizKj5UgNC5cdj3uNqwXKo6XyE7Jopvw+g++RXzXONlp2OP2wOv1SgelWoGw+Eg0mq3QGrSyg3Sz3YWoTtEoP1aKoTNGYOPidYjtHAeXyw1rnQX9JmRg1/Ktsjt0Y30jljy7CF0Gd8PBtfvRY0wf2dlazBuZGi07QpdkFSI6LVZ2JW6yNKHJbIEx3ITiI8UIiQ2R3aurCitk+LrZ4UJSr2RkbT4MpVop702TpRH2hiYEx4chtks8IpKi5f3Z/c0ONJobZDfno9sPy3C6Si06UHfCxo/XydB6VXEF/Iy+iO2aIO/n4Bkj5HmZ6/ahvsqMhmozjOFBMPgaUJ5biqhOsTi+N0eGnsV5pTkl8DP5wVJnQWC4SXa/Fl2ve47qjT0rdsEn0BcKhUL6ifC20+7E6FvHoba0BtnbjyCpZzI2L1mP6C7xsFltcDbaYYoOgVqlgrXOCh+jAdWF1XI/xHRNQH1lHeyWJtnl2VrTgCsmDYS90Y7ITrE/904449f/G25eL626pXSD+H8qo9HY7jEVXrGzLtKD4eaL9MaxbApQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQ4LITiI+Pl2t+7LHHILoIvv/++/LrESNGIDMzE9XV1VixYgW6dOnSYWzahpsffPBB3HXXXR2mNhZCAQpQgAIUoAAFfk7geP5xTJ82HelX9cJ1f2C4+ee8RMi1qqACxrBAVBSUISwuAnXltbI7cmOdRYZdjREmGHz1MPj7yDCrvcmBuC4JKDqUD/8QfxRk5sPH6IOI5CjZ+Tc0PkJOa62pR3F2sQy3ijmCY0IR2yUBCgVkEFZ0BvY1+iIwLAg1pVUngrxHi+BnCkByv05QeL0oOlwg69HoNAgIMaKuohYKhRLhCeGor6yHtd6C8PgIbF++FZFJkQgMD0J0pxiUHSuBj8kXPv6+KMsphcftlgFmR6NNBpgr8ssRlRIt6yw6nI+aslqExoTCL9hfNHeWgV0RkhWdnYPCg5CzOwc2S6PsCGyuNMNcZUZwlAgWq2Wg2j84AAFBASjJLoZGp4NvoC+UWiViUmORuzsbVrNVBrFFuDooOkR2Zw5PjJIBbREslpFWr1calQmz4ABZu7gnAcFGmCJM8Hi8snMzlAoZ5C7PLYN/mFF2OdbqtPAPEdf4wM/kL8PpbpdHdq4WAWp3c7MMNjscToQnRMkgsa3eity9x6RJQEggTJFBsgb/YKN83utxn1hLkI8MEOt9dEjomQytQY/S7CJU5pXJa3wD/eX6vPDAWt0g758xPBDFWYXQ6vWISBQh6SoZTo5OjZbrEMHnupJqGSQ3l9eh2dEs91x4UiQiU2PgbLKjoaYeprAg5GfmyjWI8Py+VbsxdOZI5O07JvdLQo9EWKobAJVC3ueQmFAY/A0ozhL76EQou668BhHJMT/7VjjTE1rDzVuWbEKAKQDx4XH4xz/+AZPJ1O4hGW5uNxkvoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECB9ggsX75cdj328/PDqlWr8Nprr53s0jxy5EikpKTgrbfeguju/Mgjj7Rn6HN6bttw80MPPYT58+ef0/k4OAUoQAEKUIACFDibAq3h5i4j0nHDE3PO5tCX1VitYVvRVVd0khWff+oQwWGFUnnKc8Q44kpxjlKt/u8Qsj2tVwz8vWE9LjeUKuV/n/d64fV4ZRhXXiFCwArFibm8Xng8wfhoiwAAIABJREFUHii8wL41u9FlUDp0/j4nX2sd2+vxnJhKjHuqwwt4PG4olSrIYk9xiHm9bg+UapWsQdQpOkuLOdquX65TqRSlnaxZfCHX8BPzy/og1iShZRBZhMrFc6IjsZiz1V+ML9YinjsxyYn5xZzyaOvZYizCxOJcec0PDlGXmP+Hr/2wJjmHqs31Xq/sYt1qIGtpuT9iL4jjZF1iD4l5RA2nMBCvyWvlHjlh+r1DeLs9aKy34uC6fciYOqRlTwrj/95TcX9ajdvOfa7fuK3h5q2ffQdTcCDCA8Px5ptvIigoqN1TM9zcbjJeQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIAC7RG49dZbsXbtWsycORPPPfccRFD4o48+kkOMGjVKhoanT5+OkJAQrF69+ox++N2eek733Lbh5ocffhi/+tWvTvdSnkcBClCAAhSgAAUuuEBruLnziHTMYLj5gt+P81WAo9EOnUELtAm7nq+5Oc/5ERBB72Z7M/R+hvMz4WnOwnBzC9T//d//QfzP5G233YZ//vOfyMjIwOLFi0+TkadRgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoMC5FqisrET//v3lNJs3b0ZMTAx+//vfn/zZ7ujRo/Huu++ib9++qK6uxjPPPINZszrGn01vG27+wx/+gDvvvPNcc3F8ClCAAhSgAAUocNYEGG4+a5QciAIUOA0BhptbkBhuPo3dwlMoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKXECB1oDwuHHj8MYbb8hK7r//fnz66afy8ZgxY/DOO+9AdEZetGgRhg4divfff/8CVvzfqduGmx955BHMmzevQ9TFIihAAQpQgAIUoMDpCDDcfDpKPIcCFDhbAgw3t0gy3Hy2thTHoQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKHD2BUTX5pEjR8JisWDhwoUYPny4nOR3v/sdPvvsM/n4qquuwttvv41ly5bh7rvvls+Jxz179jz7BbVzxLbh5kcffRRz585t5wg8nQIUoAAFKEABClw4AYabL5w9Z6bA5SjAcHPLXWe4+XLc/lwzBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwsQj8/e9/x4svvoiEhARs2LDhZNn33nsvvvjiC/n11Vdfjbfeegt1dXWya3NDQwPmz5+Phx566IIvs224+bHHHsMdd9xxwWtiARSgAAUoQAEKUOB0BRhuPl0pnkcBCpwNAYabWxRbw82333473n33XWRkZGDx4sVnw5hjUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKDALxBwOBwYO3Ysjh8/LkPBIhzcetxzzz1YunSp/FKc8+abb8rHv/nNb/Dll18iMTERa9asgVqt/gUV/PJL24abH3/8cYifTfOgAAUoQAEKUIACF4sAw80Xy51inRS4NAQYbm65jww3XxobmqugAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUuPYFFixbh4Ycflgv79NNPMWDAgJOLbA0xiyfGjRuHN954Q772ySef4IEHHpCPX3vtNUycOPGCwrQNNz/xxBO47bbbLmg9nJwCFKAABShAAQq0R4Dh5vZo8VwKUOCXCjDc3CLIcPMv3Uq8ngIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoMC5EZg+fTp27tyJvn374vPPP//eJHfddRe++uor+dz48ePx+uuvy8cVFRUYOnQo7HY7pkyZgr/+9a/nprjTHJXh5tOE4mkUoAAFKEABCnRIAYabO+RtYVEUuGQFGG5uubUMN1+ye5wLowAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFLmKBlStXYt68eXIFonvzr371q++tZv78+fj666/lcxMmTMA//vGPk6/PnTsXq1atQmBgIDZs2CA/X6ijbbj5j3/8I2699dYLVQrnpQAFKEABClCAAu0WyM3PxfRp09FlRDfMeGJOu6/nBRSgAAXaI8Bwc4tWa7j5jjvuwDvvvIOMjAwsXry4PZY8lwIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQqcF4Ft27ZBfIifZ4gPHhSgAAUuZYF77rkHS5cuhVKpxNq1a5GYmPi95d55551YsWKFfG7ixIl47bXXTr7+wQcf4JFHHpFfi87NooPzhTrahpuffPJJzJnDUNCFuheclwIUoAAFKECB9gsw3Nx+M15BAQqcuQDDzS12DDef+SbilRSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCpxfgdafa9x7771YsGDB+Z2cs1GAAhQ4jwKVlZUYPnw4GhsbMXbsWLz55ps/mr21O7N44ZprrsHf//73k+cUFhZi2LBh8Hg8MtgsAs4X6mgbbv7Tn/6EW2655UKVwnkpQAEKUIACFKBAuwWOFxzHtGunsXNzu+V4AQUocCYCDDe3qDHcfCbbh9dQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwIQRE11IR1BPdSv/whz9ciBI4JwUoQIHzIrBo0SI8/PDDcq5XXnkF06ZN+9G8t99+O9asWSOfnzRpEl599dXvnSM6JK9btw6BgYHYsGGD/Hwhjrbh5qeeego333zzhSiDc1KAAhSgAAUoQIEzEsgvysO1U6ah84h0zHiCf4HijBB5EQUocNoCDDe3UDHcfNp7hidSgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwAQXWrl2LW2+9VVYwYsQIvPfeexewGk5NAQpQ4NwKtAaXxS91rFy5Ejqd7kcTiu+J4nujOCZPnoy//e1v3zvnk08+wQMPPCCfe+211zBx4sRzW/RPjN423Pz000/jpptuuiB1cFIKUIACFKAABShwJgL5xXm4djLDzWdiB68X8kOhOPFxOkfr+adz7sV0jhfAaRJcTMtirWdfgOHmFtPWcLP4k0Vvv/02MjIysHjx4rMvzhEpQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgwBkKWK1WjB8/HgUFBXKEmJgYbN68+QxH42UUoAAFOraA+F43dOhQWeSCBQtw7733nrLgW265BevXr5evTZkyBX/961+/d5743jl69GiUlZVBdHF+8sknL8jC24ab//znP2P27NkXpA5OSgEKUIACFKAABc5E4JION3u9qCqsgFank38lyeNxIzgmTDK5nS5UHC9BSEwYqosrERwVAl2Ar3ytqc4CS10D/IONMJfXIjwpCiqN+nu8zkYb8g/mo76yDiq1ChEpUYiIj4BSq/nJ2+BpdiEv8zhi0+Kg9dWfye1q/zVeL9wuN5QqFTzNzSjPK4MpIgg+gf7tH+snrrBbmlCWWwpjSACCWnzP2uAc6JITYLi55ZYy3HzJ7W0uiAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwyQmIjqMvvPDC99YlmrWIpi08KEABClxqAv/+97/x2GOPyW7Nomuz6N58qkN0QN64caN8aerUqRAh4h8eItD8z3/+E+np6fj6668vCFXbcPMzzzyDWbNmXZA6OCkFKEABClCAAhQ4E4HWcHOnIZ0x60+3wXu6HYjPZLLzfE2zzYGVby1HTOc41JXXQuejw6DrhssqqvLLsPqfKzF81kh8+94qjLr1akR1ipWdmHd/sx01RVWIS0/Aoc2ZGH/XFBj8fU5W73I4sXbhKhzdfhgGPwMcjQ5ACWRMuhK9xw2QQeJTHSIE/NZv/44bHpmN8OSo86IhgtqHtxxESr/OUCqBVW9/jX7jr0BCr5SzM7/Xi8y1+7DizWVIG5SOCfMnQ6X76YD32ZmUo1zMAgw3t9w9hpsv5m3M2ilAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACl76A2+3G2LFjkZ2d/b3FXsgupJe+OldIAQpcSIHbb78da9aswfXXX48XX3zxJ0sRIeHvvvtOvn7ttddC/Oz3h8fOnTsxffp0+fTWrVsRFXV+QiJt62gbbn722Wdx4403Xkhezk0BClCAAhSgAAXaJSDDzZOmIb5/EuY8Nw8K9amDue0atAOc7HG7UVtcjS9e/gQZU67E/m/3oOvg7uh9dX943B5krt+H3V9tx9CZI7Dyza9w41NzYIoMQbPDiaUvf4KQ2DA0O5rR1NCIib+ZArVed3JVJUcL8K8H3sTwG0ej86B0NJqt+O6TDTBX1EpDnZ8eXrcHGp8T3ZndzmZ4PR453kuznsUtz92GoMgQQKGEwU8P1Sm6PYv67VYbmu1O6Hz10PnooRDpZK8XIrTtsDmhUiuh8zVA2XLP3C4XbPWNcLs8MnSt0WtRnFWAL//6OcbcPk6GvHN2HUVM53gERYfA3eyCraEJHpcben8DtAaxRgVcTie8Hi/Ev1e47M3QGLRyfpwi+N7cZMfSvyzB/tV7YIoKwi3P34ngmNAOsANYQkcVYLi55c60hpvnzZuHt956S3Y4EJ0OeFCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBjiCwZMkS3HfffT8qJSEhARs2bOgIJbIGClCAAmdVID4+Xo734YcfYvDgwT859syZM7Flyxb5+rRp0/DKK6+c8twZM2bIYPO9996LBQsWnNVaT2ewtuHm5557DqJuHhSgAAUoQAEKUOBiEWgNN8f1S8Sc5+88GZS9WOo/ZZ0eL/IOHEPWtiM48O1epA/phkMbM2XIuevgbijPL8eB1XtgrqxDRHIU8vYdw/j5kxGaEIG8vTnY/NlGGQSuKqhEYLgJw2ePQlRa3MmpDm7Yh0+e+gC3vnQnYrsmQKVWofhIAfau2oVhM0ehJKcY9RV1uGLKYChUShzddhgNVfXoNrQ7Xp71DPpOuAK2+iY5Xlz3RPQa1QcaGSxuOdwe2RU6a9thGW42BPii37gBCE+Khrm8BrtX7EB9hRlqjQqJfVKRPri7yDxD1JWzK1uGloOigtFtaA/sXbMbO5ZuRrdhvTBw6mDkH8hF8oDOCI0OxcGNB5C76yg8Hi+MYYHoOaoPgqNDcOi7TNQUV8Fpc8JS0wD/IH9kTB0MY3jQj7grjpdi8Z/eR3RarAxOj7ljPPqOu+Ki3j4s/twKMNzc4stw87ndaBydAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBXyYwe/ZsbNq06ZSDLFy4EMOHn/iTuTwoQAEKXCoCDz/8MMLDw2UY+X8dN9xwA7Zt2yZPEd2ZX3755VOe/tVXX+Guu+7qEOHm559/HiJszYMCFKAABShAAQpcLAKXYrhZdE0+sH4f1n+wBlqdGmqNGhazFb3H9EOnAZ2RveMItizZiMTeqSg4kIuYzrHoMqgbIlKjsPWzTcjdmY34nknI3ZWNXmP6In1YT3lu61GWXYJ/PfC6DEYn9U5BTFocAiNM0Pno4Gv0wzevL0PJ0WLc9sp8qLRqrH7nK5TnlGHaQzfgpVl/hm+ALzpf2Q2N9VY5x7SHZyItI/3k+Pb6Rvxt7kuI6xqPsIQIGY6O6RKPq++YgDXvrUD29iPodEUXNFSZUXSkCDc8NhsqlQofPPouEnomwz8kAFlbDqPLwK5QqpTY9sVmdB/VGz1H9sI3r3+JkXOugp/JHx8/9QESuiUgKCoEOTuPwhQdjHHzJso5RChcBKI1OjUOrd+PEXOuxsBpQ6Fo273Z48X2Lzdj22ffYcr91+Hbf62AIcAH1z8y+5TdqC+W9wTrPLcCbcPNgSFBiDCG4s0330RQ0I/D8z9XicLrFbn+i/NguPnivG+smgIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwOQhs374d119/vVyq6NScn58vHwcGBsJsNmPOnDl48sknLwcKrpECFKDAjwSuu+467NixQz4vHr/00ksdUqlt5+YXXngBIpTNgwIUoAAFKEABClwsApdiuBkeD5oamvDFCx8jtlsiakurofXR4arbxsuwb2lOMZb95TOMmjMWK99ejom/niJDwV6PB9uWbkbhoXwZgt6zcheue2QWjKGBUCiVJ2+px+XGgW93I3NDJsqOl0ClVELno0ffcf3Rb3wGVr391f8MN4+8+SoMnDYMzkYb3nvwLUR1isGU+//735ANlXV45tonMOKWMeg1ui9qS2tgt9oQlRKN9x/9F/qMHYDeY3qjodaCpS99irSMrhCZ4yNbD+G2l+bD4O+DA2v3wt5okwHpz1/8BBPungJTWCAWPbEQw28ajYLMPLnO2U/fDt9APxzaeAAr3voS0x+cITtQ15TW4MY/3gKlUoXFT78vz5n+8CwolIqTDg6rDUueXQSP14vxd03C9qVbZCh63qu/QVB06MXyFmCd51mgNdy844utMIWZEKgz4p133kFwcHC7K2G4ud1kvIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK/LzAE088gffee0+eOHPmTHz00UfycXp6Og4dOiQDzxs2bPj5gXgGBShAgUtQYNq0adi1a5dcmfhFkBdffLFDrrJtuFnU2PpLKx2yWBZFAQpQgAIUoAAFfiBwrCgX102ejvh+SZjz/J1QqlUXvVFVfgUObdqPXcu3yXBzxbEShCaEY9C0oXDYnMhcvw/H9+QgtX8aDm/KlM93HdwNhYcLsH3pZvgE+AJKBZrMVgy5YQTSh3SHSqc96WKtbYDL4YRap0NNcSXqymtwYO0+GWie+cTNMih8snOzRo1Vby9HRW657Nz8f7c8h1uem4vY9ER4PV4sefZDWGobcNvLd50c3+Voxoo3l+PY3qPw9feFX5A/eo/pKztQv/fg27LTtF+wESIkWpFXjpR+nWCz2qBUKjD94dlQaVQQY3jcbtSUVGPJsx9h4j1TERgagA8fX4ihM0dgz6pdCIoIwoR7pkGlVqH0aBE+fPxfGHfXJOTsyIJoh3vtAzPgdjrx+UufwO1y4YbHbvleuLk0qxALH3wbar0aoXERqK+sRWVeOa59cCb6Tsi46PcRF3BuBFrDzbuWbUNweAi0bo38t7HQ0PYH4i+JcPOdd94pW1dnZGRg8eLF50ado1KAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECB0xRoaGjA6NGjUVFRIa8Qf41ywYIF8vHYsWOxYsUK+XjhwoUYPnz4aY7K0yhAAQpcOgJTp07Fnj175IJEN2TRFbkjHm3DzaK7tOgyzYMCFKAABShAAQpcLALZRTm4YfL1l1S4uba4Etv+sxnZO7KQ2r8zjmw+iEHThyCpZzIa6xuxYdG3UGnUsttybXE1hswaieiUaBzbk4NNH61F50HpOL73GGLTE9B9eE8k9EiW54tDdHfe8eUWVBVW4uo7JkBj0MnnCg4cl+Hg8b+ejNKcEhRm5mHu334DpUqFz1/4GE3mJhlufuXm5zDj8ZuQOqAL3M5mfPjEe9AadJjx+M0nt4zd0oS8/blQKIHa0loc3LAPXrdXruGLlz5F/wkDEZUWDXezG+XHyxGZEoWcnVlwNtkx44k5UGtUsju10+6UY3/23MffDzffOBL7v90Dva9BBphVWjWO7z4qQ8xT778emev2QqFQYvJ918PtcOKLVz6Fy9n8/XCzx4NNi9dJ537jr4DeVw9Xsxv71+5BYFgQZj5+E1Q6zcXyNmCd51GgNdy8e9l2BEeEQONSM9zMcPN53IGcigIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgf8p8PHHH+PBBx+U54wcORK/+tWvTnb7FE1bFi1aBIvFgttvvx2PP/44NSlAAQpcdgKTJ0/Gvn375LpnzJiB559/vkMatA03v/zyy5g+fXqHrJNFUYACFKAABShAgVMJXIrhZni8+Pa9FWiqb0R4YiRy9mTj2vuuh8Hoi8baBnz2/EfoMaoPcndnIywhAkNmjAIUQN6+Y1jz7tcYftMYbPhwDcbOn4SYzvHfZ/N4seub7Vj51nJM/PUU2UXZ5XLj2O4cbPzoW0x/cCZKcoqx7YtNuPGPc+BxufHRU+8jOiVWhptfmvUMulyZjuE3joK50oxlf1mC4bPGoN81A0/OU1lQhn8/+A6u+8NMBIYHYefybTiwdi+m3n8dvnn9SyT0TMEVkzJQV16Hdf9ehYypg2Gts8pg9rSHZkDv5yPrD4kLQ4/hvfDx0x9g6IwRiO4Ug0+fWYThN4+Roe4dy7dh2u9vgH+QP7Z+8R1Ks4sx7aGZ+O6TdT8bbrbVW/HRn/4N/2AjJt87TYaoRafoTYvXY+uSDZj76t0Ijgnjm44CPxJguLmFRHQ5EP8zyc7NfJdQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABSjQkQRuvvlmbNiwQZb017/+FcHBwZg9e7b8+t5770VOTg6++uorpKamYs2aNR2pdNZCAQpQ4LwIXHPNNThw4ICca+bMmXjuuefOy7ztnaRtuPmVV17BtGnT2jsEz6cABShAAQpQgAIXTOBSDDe7m11Y/rfPEdctAbUlNdD66DD4uuFQqJSoLqrAqreW48rrh2PTR+sx/ObRJwPMmd/ukR2TOw/sir2rd+Ga306Dj9HvR/fGWtuAr1/7D0qzSxAQaoTT4YTdYkNq/zSMmjMWlYUVWPLMh9D7GKDVa2C3NyMiKQoT7roG7yz4B5QqpQwgN9ZZEBhhwlQZMDaenMdhbcKix/8Fh60ZwbEhqDhejtR+nTDi5quwb/VubPlsIwLDAmGz2hAQFIBJ910Hr9uDz57/GE3WJugNerhcLtlZ2hQZhE+f+RAKpRJDZoyUoetB04YiKCoYS579CG6XC3p/Axqq6nHltKHoPrK3DEYrlAqMuWOi7C69+p2v0Ox0YeI90+Tz4qg8XoovXv4EA68dgh4j+wCKE88XHcrDZy8sxlXzxqPrlT0u2L7mxB1XgOHmlnvDcHPH3aSsjAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwuQrs378fkyZNksvv1KkTVq5cifXr1+PWW2+Vz4lwc1RUFH7/+9/LrxcvXoyMjIzLlYvrpgAFLlOBCRMm4ODBg3L1N954I5599tkOKdE23Cx+Pn3ttdd2yDpZFAUoQAEKUIACFDiVwKUYblZ4vagtq4be1wCn3Qm1Vg1fU4BcfrPNCUu1GX7BAWiorkdguAlqnVa+Jro6u5vd0Bq0sFmbEBgeLAPRpzpsZiuKsgpRkVcGtVaDyNRoRCRGQu/vIwPDZceKUVVYBWNIIEzhgfAqFDCFm2TwWQkFio8WybETuyfCGBH8oynqy2uRuycbllorwpMiEJ+eAIPRD802B4qyClB6rATGYCPiuyUiIMwEeIG68hoUHSmAWL8IUwfHhsmO1JV5ZbCaGxGZHAmbRQSi/aH10aOmtBp5+3LhtDkQlRaLmNQYqPVaNFTWwasAjGFB8Hq9J772eBAYGXKyTmeTHXXltQgMM0HnZzj5vNvpQlVRBXwD/WRXZx4U+KEAw80tIq3hZvGn3N544w35D3/iHwB5UIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIELJSA6e4puzeK4//77cffdd2PFihXyL1GKQ4SbRZfSoUOHwuFw4Ne//vXJoPOFqpnzUoACFDjfAuPGjcPhw4fltLNmzcIzzzxzvks4rfnahpvF46lTp57WdTyJAhSgAAUoQAEKdASBSzHcfL5cvR4vPC63DBCr1KqT3Yvl/F4vPB6P7JisaOlq3Lau1uuUKtVPlivOEWOIscU4rYcIHIvXxHOiC/T3xnV75Jffe97rlSHltmO0XiPGEa8pxRynqPN8WXKey0eA4eaWe81w8+Wz6blSClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoMDFIjBx4kRkZmbCYDBg1apViIuLw7Jly2TIWRwi3LxgwQLMnTtXvt69e3csX778Ylke66QABShwVgSuvvpqZGVlybFmz56NP//5z2dl3LM9SNtws/jFlSlTppztKTgeBShAAQpQgAIUOGcCDDefM1oOTAEKnEKA4eYWFIab+f6gAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCgIwls374d119/vSxpxowZeP755+XjJUuW4L777pOPW8PN77//Ph599FH53NKlS9GrV6+OtBTWQgEKUOCcCowZMwbZ2dlyjptuuglPP/30OZ3vTAdnuPlM5XgdBShAAQpQgAIdQYDh5o5wF1gDBS4fAYabW+51a7h5/vz5eP3115GRkYHFixdfPjuBK6UABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKBDCTz77LN44403ZE2LFi3ClVdeKR9/9NFHeOihh+Tj1nBzYWEhhg4dKv9ErAg+33PPPR1qLSyGAhSgwLkUGD16NHJycuQUN998M5566qlzOd0Zj9023Py3v/0NkydPPuOxeCEFKEABClCAAhQ43wIMN59vcc5HgctbgOHmlvvPcPPl/Ubg6ilAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACHU2gtRPpDxuyLFy4EI8//rgstzXcLB7PmTMH69atQ//+/WV3Zx4UoAAFLheBkSNHIjc3Vy73lltuwZ/+9KcOufS24eZXX30VkyZN6pB1sigKUIACFKAABShwKgGGm7kvKECB8ynAcHOLNsPN53PbcS4KUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAX+l8CmTZswe/ZseYroQCo6kbYeb7/9Np5++mn5Zdtw8wcffIBHHnlEPr9y5Up07tyZyBSgAAUuC4Hhw4cjLy9PrlX8oseTTz4T56lDAAAgAElEQVTZIdfdNtz897//Hddcc02HrJNFUYACFKAABShAgVMJHC06hhmTr0N8vyTMef5OKNUqQp2GgMvhhNPmgEKphN5XD68X8q8uqTRq+Vm8rtVp4VUo4LI7oNZpAYXiNEb+wSlerxzP3ew6MUYHOLxuD9wuF9QaNTweL8TXKp3mf1amEOsApItKq4VCeQYWZ7p2jwdQKn90tbu5GQqFAkq1+tQje7xwOZ1Q63X/fV3caHG0uZcelxset1vuAY1WA5u1CUqlEjpf/Znd8zNd50VyHcPNLTeK4eaLZMeyTApQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKDAZSAggnn//Oc/ERwcjNWrV8vPrcdrr72GF154QX7ZNtxcXl6OESNGoKmpCY8++ijmzp17GUhxiRSgAAWAoUOHoqCgQFLceuut+OMf/9ghWdqGm8X38okTJ3bIOlkUBShAAQpQgAIUOJXAkaIszJx8AxL6pTDc3I4tkrX5IEqPFslQb3hCOIKiQ2TINywhEvZGG7K3H0G3YT2hUquwb80edB3cDVofvTzHI8K24vB6ZThaBMpFeFnEfeVjl/tEYFijQvnxMhj8DagpqUFS71S4nc1QalRQKJTwuF3wegC1Vi1DtCJgqxTPezxQqk58VkAhg8gqrUYGeeW0bjdcIiyt1cgaREBZhHHF9a31iPNEaFd87RXjtHwWQWtrTQNqyqqh0Wlh8NXDZrUhunMcPG6PHEfM7W52QyxIrF/UVl9eA6ejGcVZRUjukwpfkx9UarVctzjEeW4REHZ5oNZp4PV4WwLUGkAEoUXA2+2Wn5WqE14qjQZer0eeK8cQYetml1y3WJuoR5wnDKM6RUOmq0UAvSUkXpZTDI1ODVNkiBxDeIt1tq7dWteAvL3H0H1knxO2KhWstRZ5f0Pjw6WheE6srba8Vr4Wn56AQ98dlNY9RvaCf0hgO3bV5XEqw80t97k13HzXXXfhH//4B374J94uj+3AVVKAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKHChBcQP14YNG4bCwkLZsVl0bm57tA3HtQ03i3PuvvtuLFu2TAb93n///Qu9FM5PAQpQ4LwIDB48GEVFRXKu2267DU888cR5mbe9k7T9/i1+Jj1hwoT2DsHzKUABClCAAhSgwAUTOFR4GDdOmYlEhpvbdQ+2fr4RgWGB8DMFYPeK7UjpmwY/k68ME9eUVKPocAHG/2aK7G68+t1vMGTGCBgCfFGWU4Tqomq4nM0y0Kvz1SGmczyO782WHYRjO8fi6PYseV1QdDAOrN2LxB7JMpcbEhuKsmMlCAgOgDHUhJKjJ/5bOaVvJ/gHB6DoaAF8/HxQeqwEUZ1iYKltgNftRX2VWYZxo1Jj4XW5UXgkX9YQFhcKt9uDhmoLgqKCUFdWA52vAQk9EmXwuDirAAZ/H9SV1srrG2rqUV9RJwPMDpsDBQfz0GlAF2h0Gvkh5vPx90FYQgQKD+cDHiCpdzL8QgKxa/lW+XpDVQPCkyNhDDEirmu8HEPUIELBhYfy0WxvRlKfFNQUV8v5whMiEJkaI71EYFyEiX2NPqgpq0VwdIgMMluqGiD6Qif2TEbhwTw47U7EpMWisqgKDqsNx3YfxcApg1FVVA2VRom0K7pC52dAwYHjcDocsFvsMkgdnhCJyE4xcNmcOL7/GMwVtagsrES/cVegIq8UvqYAWGrq5f3tOqQ7KnLL4BPgA99AP1SXVqMyrxxpA9Nhq7ei8FABOg3sgoTuye3aV5fDyQw3t9xlhpsvh+3ONVKAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFOr7A119/jfnz58tCFy9eLBuytD1eeuklvPrqq/KpH4abRbBZBJzF8d133yE2NrbjL5gVUoACFPiFAoMGDUJJSYkc5fbbb8fjjz/+C0c8N5e3DTe//vrrGD9+/LmZiKNSgAIUoAAFKECBcyDAcPOZoW77YiPiuibIwPCa91bI7sYiCNxQZUZMlzgc3pSJaQ/N/FG4efvS72Q3YRF+FYHeivwKxKcnIv9gLnR6LYJiw7B/9S4MuWEE8vblQqFWyvOKswplsFeEdvMP5iEg2Ii68loERQbDP9gfPUb0xoF1e2Ug9+jWI+g+ohca6xtloFica29y4Op5E+ByurD7q20ynFxfaZbhXK1eC1ezW3Yntlls6DO2HyKSY7D7m+2wWZrk3El9OkGlVKLwUJ58XFdeA0tNAxK6J8Fa24Amix2myCAZAg4MM0Hno0NlQSW6j+yFhPRE7Fm9E421FlQVViE6LRbVRZUIT4pEQWYefAIMCAgJxLHd2YhLT5CB5sx1+xCaEI4mcyPG3TVZdkv+5vVl6JzRVa4zLj0RRUfyERwVgpC4MFTmV8DP5A97kx1BUcGoLalGQ1W9DFfn7jmGnqP6YMtnGxDfLRndhnVHUEwY9n+7W54jzo3pHAer2YJhs8ag4ngJdi7fJuvM2XUUAaYA6Pz00lsEoB1NdoTFR6CyoEJ6x3aNl9YumwPDb74K1YWVyFy/H1dMHoSAMNOZbbBL+CqGm1tuLsPNl/Au59IoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAheRwIIFC/D5559DdCL98MMPf1T5888/L/8KpTh+GG5uamrCiBEjUF5ejhdeeAE33HDDRbRylkoBClDgzATEL4GUlZXJi++44w489thjZzbQOb6qbbj5jTfewLhx487xjByeAhSgAAUoQAEKnD0BhpvPzFJ0bo5KjUZITCjWLlwlQ6wi9OqyO5ExdTA2f7oeY3816Ufh5h1LNyOmcywObcpE54Hp2Ld6N3T+elgrzQgIDZTjFGQex/j5k7Dq3a/l+NFpMTi48YDspjx05ghs/WIzVCqlDPEaQwJRW1aNfhMHoeJYKVa8vVx2lBZdj9MGdEHunhwZ3PV6vLjyhmEyvLxh0bfQ++hRVVSJ0LgwJPVJlWFs0UlaBJi7DumBqLRYHN50AAfW7IZPoB8sdQ0YMGEQynJLkNQ7Bbm7stHsdCEyNRrVhRUyeJ12ZToy1+xBs6MZ3Yb3RtHhPER1ikVCz2TkbD8MS61FdkLuP34A9q3ZA6/bg+qSGoTGh8I/2AhbQyMa6xqh0WtkoDq5TyfZsXn47DGwW23YtHgdBlwzEO8/+i76jb8CNcU10Ok16DshAzk7j6I8rwxxXRIQnhyBPSt2QKPRYNScsVj3wSr0Gdcf+9fsRVN9I9KHdkdy707Ys3KXdBKdqDtndMGRLYcw9MbRKDh4HDnbj6DH6D7Y9PFa2U06MjkKjkY7QhMj0NTQKDt0i3B3XUUd4rrEy1C1x+XC6DsmyrUq1SoZAlcoFGe2wS7hq74fbg6FxqXCe++9h9DQ0HavWuH1ekVX84vyYLj5orxtLJoCFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUocEkJ2O129OvXDxaLBS+++CKuv/76H63vmWeewZtvvimf/2G4WTz3yCOP4IMPPsDUqVMhgnQ8KEABClzqAgMGDEBFRYVc5ty5c/Hoo492yCW3DTeL7+Njx47tkHWyKApQgAIUoAAFKHAqgdZwc0LfZNz6/J1QatSEOg2BvSt3oKa4GmqNClofgwwJi86/DZVmaHRa1JbVYOydE6FWq7H5843oN+4K6PwMyFy3BxFJ0cjdk43EnsnI2nwQsekJOLbzKHQ+eiT0TEL29iwMuWEYNi/ZBFOkSYZvLXUWaHQa2XnZ5XQjIikKCoUXviY/mCvqZJjY3mDFyne+RtfB3XFww34MnDIYWdsOQ6lSIjAiCD1G9oG1pgGbPlqLgFAjakprZEA6tX8aqgorUZFXBoOfAd1H9v5/9u4DPKoqfQP4OzOZ9N4LIR3SaAkd6U0QRbFgwQqINAtiL3911XVdu2BDRewNFRUpooj0HlqAkEB678kkM5n2f85J2QApgyQhhPfu46KZe8/5zu/cDJB55xsZNi5IzcHuX3ciqHcwTu5NxqBrhiEjMQ0BEd2QcTQdWk0V7JwcZMdnEeYN6xeO47uOy0C36GCdeyoXQ68ZjuC+Ycg+lo6TB1JgMprQZ1w/HN2WCHcfN9mF2sbORnZiPplwAkqlAk7uzjI0bOtoC1cfN8SO7Aedpgr71u5B/KSB2PLNRrluk9mMysIyWNvaokarQ2h8BFIPpMDa3hYuni4yTD1s2nBs+3EzXP08kHM8A2aYENE/CsF9w3Fs6yFUllRKn5A+oTi5PxnxVwxBWV4xdv28DbbO9tCWV8HF21WGwq3trOEXHoBTB1KgUChhMhpQXV4Fd39PKK2UMBlMGHrtCJzYlwRndyd4h/hbcCddeqc0Djd7+/lAqVdc2uHm+fPnY+nSpfIj3sRHvfGgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAh0l8N1332Hx4sUICQnBunXrYGNjc9bU//rXv/Dhhx/KrzcVbt64cSPuuOMO+Pj4YMuWLbC2tu6o8jkPBShAgQsiIN4UUlBQIOe+++675Zs8OuPRONz8wQcfYOLEiZ2xTNZEAQpQgAIUoAAFmhSoDzf7RwVi9pvzYWXDv2tacqtUFJahNL8EVlYquPl5AArIcKteVwNNaSVsHe3gLr+uRFl+MZw9XaBQqaApLoe1vY0Mxdo52aOytBJO7k4oTM+HQqmAR4CX7Cbs5uMuxxdB6ZpqLVRqNdTWVjI07eThLIPQJpMJVioVavR6OLo5AyYTSnKL4SA6LReVy0BzZXEFKorL4e7rDns3JxkuLs7Ig8FokiFklVoFR1cniP63hRn5sLG3gbu/FxQqJYw1elmLvbMDNKUaOHs4o7qyWp4julSbDEYYxTjWVrJDsViPOK8wIw/Je5Kg1Wgx9LoR8AryhV5bg/L8Etkd2snTBZqyStg62KM0rwhGgwlegd4ozi1CTZVOBsXFtaJ2jwBP2DrXBqiFuYu3m6xJhMhF8Hnfut0IjA6SaxZ2RZkFMOgN8jrR7VmeX1gq96a6sgomkxk+IX6wslajqrQCBoOxtnZHO1RXVMHJ01V2lC7JLoS2WifXLtZblFUgnRxcHVCWXybDzGJ8azsbGY4WIXfRQtjFxx2aknI5vo2DnSW30iV3Tn24ed/Pu+Alw81guJnh5kvu+4ALpgAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQp0CgERShbh5AceeEAGl5s6nnnmGSxfvlw+1FS4WXx98uTJOHLkCD7++GOMHTu2U6yNRVCAAhRoL4G4uDgUFRXJ4efMmYPHH3+8vaY6r3Ebh5uXLVuGCRMmnNd4vJgCFKAABShAAQp0pEB9uNknwh9zliyE2vbsN+N2ZD2c6+IXqCgsReaxdBkKFh2Z2zMwL7pLuwd4QcWO4xfNjcNwc91Wvf766/Kj2di5+aK5d1koBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKBLCZSXl6NXr15yTatWrULfvn2bXN+TTz6Jzz77TD7WXLj5rbfewquvvipDzu+++26XcuJiKEABCpwpIJ4vS0pK5JfvuecePPbYY50SqXG4WXTgHz9+fKesk0VRgAIUoAAFKECBpgQYbuZ90R4CCrMZZoWiPYbmmBe5AMPNdRtYH25esGABlixZgsGDB+Obb765yLeX5VOAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKHCxCHz++ed44oknZKhZhJubO0Ro78svv5QPNxduTkpKagjNiU7QoaGhFwsD66QABShwzgK9e/dGWVmZvG7u3Ll49NFHz3mMjrigcbj5o48+wrhx4zpiWs5BAQpQgAIUoAAF2kSA4eY2YeQgFKCAhQIMN9dBMdxs4R3D0yhAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFGgXgenTp2PHjh3NBpbrJ3344YcbGrQ0F24W586ePRvr16+Xv4puzzwoQAEKdFWB2NhYVFRUyOXNmzcPjzzySKdcauNw88cff4yxY8d2yjpZFAUoQAEKUIACFGhKgOFm3hcUoEBHCohw829LV2Hvqp3w8vOG0qDAJ598Ai8vr3MuQ2E2m83nfFUnuYDh5k6yESyDAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKHCJCjz44IP4/vvv8euvv6JXr17NKtSfJ05oKdy8cuVKLFq0iJ9WeYneT1w2BS4lgejoaGg0Grnk+fPnQ7wJpDMejcPNy5cvx5gxYzpjmayJAhSgAAUoQAEKNCnAcDNvDApQoEMFjEb8vnwNtn6xCR6eHrBWWTPcvGTJEv6gr0PvQk5GAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKCApQIi0Pzjjz/K01sKN4vHRSdod3d39OjRw9LheR4FKECBi04gMjIS1dXVsu4FCxbgoYce6pRraBxuFh3HRo8e3SnrZFEUoAAFKEABClCgKYFD6Ucw4+qb4RPhjzlLFkJta0MoClCAAu0nYDThj0/XYdOKDfBw94Ctte2lHW5euHAh3n77bYab2++W48gUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQqch4B4LePnn3+WI7QWbj6PaXgpBShAgYtGQLyBQ6fTyXrFc+TixYs7Ze2Nw80rVqzAqFGjOmWdLIoCFKAABShAAQo0JZCQfhC3XT0DvhEBDDfzFqEABdpfgOHmWuPVq1dj3rx5+Pe//43HHnuM4eb2v/U4AwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAv9AYO7cufjtt9/klQw3/wNAXkIBCnQ5gfDwcOj1ermue++9Fw8++GCnXGPjcPOnn36KkSNHdso6WRQFKEABClCAAhRoSoDhZt4XFKBAhwow3Hw6t/h4tunTpzPc3KF3ISejAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClDAUoG7774b69atk6cz3GypGs+jAAW6skBoaCiMRqNc4n333YdFixZ1yuU2Djd/9tlnGDFiRKesk0VRgAIUoAAFKECBpgQYbuZ9QQEKdKgAw82nc1dXV+Pzzz+Ht7c3pk6d2qF7wckoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQGsCM2fOxIYNG+RpDDe3psXHKUCBS0EgODgYZrO50z8vNg43i9ekhw8ffilsD9dIAQpQgAIUoEAXEWC4uYtsJJdBgYtFgOHmi2WnWCcFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUAO644w5s3LhRUjDczDuCAhSgABAUFNTA0JmfFxlu5t1KAQpQgAIUoMDFLMBwczvsntkM8R49hQJ1/9cOc3BIClysAgw3X6w7x7opQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhS4FAVuvfVW/P3333LpnTnEdynuDddMAQpcGIHGnZvvu+8+LFq06MIU0sqsjcPNX3zxBS677LJOWSeLogAFKEABClCAAk0JXArhZm1FFWqqdXBwc4JSqURlcTmUahUcXJ3OIqku10CvrYGjuzOgADTF5bCysYads0OrN5BBV4OSnCKU5JXAoNNDqVLC0dURHoE+sHWyg0KmnTv2MOkNqCypqK1Frqnja6hfsclohMlokv+pUCqhUikvaD0duxOcrUGA4WbeDBSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClDg4hG4+eabsXXrVlkww80Xz76xUgpQoP0EQkJCYDLVhh/uvfdePPjgg+032XmM3Djc/OWXX2LYsGHnMRovpQAFKEABClCAAh0r0NXDzWajCdtWbsKp/cmYvPAaWKmt8OvbP8I3zB9jb5sAc6Owrzj3jxXrkJ+aiysWXA2TwYhf3lyJ8AGRGDptBKBsPhhcVVqJHT9uwf7f98jrTSYzlADsXBwQMTASl103Gt17BclQb3sdMshcWglrGzVsnRxkOLsoLQ+/vf8LnDxccMX8qVDbWrfX9M2PazKjMCsfpxJOIudEJgw1engH+yI0LgK+IX5Qqq06vibOeOEEGG6+cPacmQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQqcq8ANN9yAnTt3yssYbj5XPZ5PAQp0RYHQ0FAYjUa5tIULF2Lx4sWdcpmNw81fffUVhg4d2inrZFEUoAAFKEABClCgKYGuHm7WVmjw9bOfoSS3GHe9cg8KM/Lx2VMfY+i1IzBh9pTTSKorqrB88XsQHYbv+u89SD18Cl8/swLjZ03G8JvGNnsDGbQ1WPfhamz6YgPUNtYI6NENrj5u0JRWIic5G8W5xfAJ8sFNz9yG4D4RMnTcHkfW8TT88voPCB/YE6NvnQCV2gpZx9Lx6WMfwt3fQ65fbWfTHlM3P6bJjKRdR7HmnVXIOZkjA+NmsxkKpQJuvh4Yd9fl6D9pEBRWqo6ti7NdOAGGmy+cPWemAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAucqcO2112LPnj3yMoabz1WP51OAAl1RICwsDAaDQS5twYIFeOihhzrlMhuHm7/++msMGTKkU9bJoihAAQpQgAIUoEBTAl093JyfmoOP7l+K8P49MfWB67Dthy3Y8PFvuP2l2YgYFH0aSXZSBj68bwn6ju+Py++5UnZxFt2YRSg4qHdYszdQxuFT+OjBd2HUG3HVA9ciPL4HbOxtodfpUZxdgN/e/RlJO44hfvJA3PzsnbCyUbfLzXhi91F8cO8SxE3oj+ufmAErazUqi8px6K8E2Dvbo9foflB2cIi4PL8EnzzyPtIPp6Hn4CiE9A2Xaz91IAXHth2BRzdP3P7S3QiI7N4uJhy0Ewow3NwJN4UlUYACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFmBKZOnYqEhAT5KMPNvE0oQAEKAOHh4dDr9ZJi/vz5ePjhhzslS+Nw8zfffIPBgwd3yjpZFAUoQAEKUIACFGhKoMuGm01miP8d3XwIX//rM9kheMCUIfjxv18jPTEds96YD89unoBCCdSdm/D7Hnz7wpe47pHpiBoWi6+f+wwlOcWY/dYCOHs6157bxLFr1RZ8/dzn6D2mH25+5jZYO9j97yyzGTJ0vOBtOHm64P4Vj8LZy7XhcWONHpUllaiu0EChVMLR1RF2Lg5Qqs7oZGw2Q1tZjdL8ElSXa6C2tYartzvsXR2hVCqg1VTj+I5EfPb4R4gd1RdXP3AdHN2coLJSQlOmgdJKCXtnR0BR2zbabDKjqqwSZfkl0FVpYetoBxcvN9g528s65DliznINTEYT7JzsodfVoKK4AjXaGtg52sHR3VnW0dJx8I99+PTxDxEYFYQb/+9WeHbzlqcXZRXgu+e/QPLeJBkknzjnSn6DXioCDDdfKjvNdVKAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKNAVBKZMmYJDhw7JpTDc3BV2lGugAAXOVyAiIgI1NTVymHnz5uGRRx453yHb5frXXnsNb775phz722+/xaBBg9plHg5KAQpQgAIUoAAF2kNgb3oC7rh6BvwiAjFnyUKobW3aY5oOHdOkNyD1QAoqSypwcGMCDm8+iOE3jIK7vwc2LF8Leyd7jJoxXnYNDujZHaf2J0NbWYWE3/fi6PYjGHXLOBkM/v2jNXDzdcNl00fBK9gH3aOCgbrgb/2CFGYzNn6xAT+/9h0GTh2GaQ/fCGu70w1rNNX47sUvZQj5qgeug4O7s7y8qrQC+9fvwcE/E2RgWalUwtnbFf0nD0L08F6wsqkLDpvMyEvNxpZvNyH14ElUV1bD2kYNr+4+GHLNZYgYEIkt32/C3tU7kHYkFc6eLgjqFYKRN42VaxbrcPRwwuV3T5FjmgwGnDyQjO0rNyMvJQe6ah1sHGwR0CMQg6YOQ3DvUNnhWV+lw7plv6A4twTxkwbi2ObDyDyRCb1WJw1jR/bBoKlDYePk0OT+Cpu1H/yKte//givvnSbN6ztHm00mrPvgV6x552f0v2IwbvnXnR3eVbpDb0pO9j8Bhpt5N1CAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBi0dg0qRJSExMlAUz3Hzx7BsrpQAF2k+gR48e0Ol0coK5c+fi0Ucfbb/JzmPkV155BW+//bYc4bvvvsPAgQPPYzReSgEKUIACFKAABTpWYEfqPsyadhv8u1C4uaKwFN+98AWyk7NRmlsMpUoJV29XaDValBeVyw7KDs4OiBrWCyNuGoVv/vU5CjMLUZJbCJVKJc8VAWLRpdjF00V2M+43oT/G3z1FBpAbHyLAu2/dLnz+1CdwcnfCpLlXIbx/DxkwFkFiRV2nZNH1WJwrA8sKBQzaGhn83fTlH7B1sIVXd28YDUbkncqV11x5/7UyuCy6KJfkFsku0id2HoN7gDtcvdxQVa5B7slceAZ64tYXZiJpeyJ2/LId+ady4eDqCL8wf4ybOUnW9PGD78E9wANz3loIKzsbpCak4MunP0Zpfqk8z97FARXF5chPzYNviC+mPXozQvqGQ1tehQ8XLcWpAylw9fOEtqIKngEeMOgNsk5rW2vc8NSt6DsuvqEj9Jk2f6xYj0Mb92PKfdcgLK5nw8OiG/S6936WBgOuHIybn2G4uWO/8y/gbAw3X0B8Tk0BClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFzlFg/PjxSEpKklcx3HyOeDydAhTokgI9e/aEVquVa7vnnnvw2GOPdcp1vvzyy1i6dKms7fvvv8eAAQM6ZZ0sigIUoAAFKEABCjQl0BXDzcYaPYqyC5F3MgffPv85IofFYvj00di5aiv2r9uNax+9Cd16dIOtswMcXBxQnF2I7KRM2V25z7h4DJ52Gf76bAOS9xzHtY/eCN9gXzi4OcLRw7XJm6g4Mx+fP7kcKSfvG24AACAASURBVPuTYWNnA68gL3SL7g7fYD8E9w6Dm787nD1cGzoTi5Dz0e2HseKRD+X81z1+MwKjg2AyGJG45bCsw8nDCYtWPApHDxf8sXwdfnvnJ4TGhWPaQ9Ph6uMGTUklVr+zCgc27MOEWZMw5o7LcWxHIj577EP0HtMXUxfdAAcXR+SmZJ0WbjYYjPj2hc+RsH4vRt08FqNuGye7dVeXV8mu1rt+2Y6+4+Jw3eO3QGGGDDcn7Twmw9ei+3JY/x4w6Gqwbtlv0nPotOG4+sEboD6jW3U9lK5KK8+3c7SHUm1V+2WzGbkp2fjiqeXIOp6BqfdPw8jbJvIb9FIRYLj5UtlprpMCFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIGuIDBmzBikpKTIpTDc3BV2lGugAAXOVyAyMhLV1dVymDlz5uDxxx8/3yHb5fqXXnoJ7777rhx75cqV6N+/f7vMw0EpQAEKUIACFKBAewh0xXCzdDIDR/5OwJfPfIrJc69C3OUD8P2/v0TuyRzMfH0u3P28AEWdqBlIWL9LdnC+8enb0GNQJD5/8mNoyjSY9fo8OLq7/O/cJjbBbDIh/Ugqtn3/N5L3JqGyuBw12hqorFSyi7Krrxt6jeyLAVcOgYu3G8xGI754ejn2/rYLU+69BmNunyg7NIujRlONz59ejkN/JmDGC3chZlgsls55HbmpubjluTvQd3z/2i7JJhOS9x6XHZ3D+/fEjU/fjhN7EvH+giWInzgA1z85A1bWamQmpjYKNy9ATkoO3pv3Juxc7DF36f3wDPKpXZEZyDyaio8fel+yzHx9Htx9PWS4OWXvCUxZeDVG3zqhNqBsBhI3H8CKxz5EaN9w3PriTNi7OFp0e4qgc9qhk9jwyToc35aIoN4hmPH8XfDo5m3R9TypCwgw3NwFNpFLoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQ4JIRGDFiBNLS0uR6GW6+ZLadC6UABVoQiIqKQlVVlTzj7rvvxhNPPNEpvV588UW8//77srYffvgB8fHxnbJOFkUBClCAAhSgAAWaEuhq4WazyQxDTQ3MRhPWLVuNvb/txE3/ugPObs5Y/vAH6BbZHdc9eiNsHOygVKkazl31xkokbj6I216aDSsrKyx/5ANEDo7ClIXTYOtg+7+uw83dRmYzqso0KMoqQObxTGQdT0dRRgGyk7NQUVQuw8vxlw/EtY/cCLPZjLfu+q/sLD1xzhTZRbr+MJnN2Lt2F/at2Y3L75mC/lcMwZt3/gfWtja455374B3s23CuXqtD/qlc2NjbwDPIFyd2txxunv3GfBzbdgQfLX4PfcbG4c6X74FSrWoYT1NaIbspn0pIxm3/noWQ3uEy3JyRmIa7374XYfE9Gs5NO5iC9+a/hYCegbjjv3Pg6ObU4jeY2I/i3CIkrN+DnT9vQ0lOEUL6hGH8rMkIj4+EQlmfNOf3aZcXYLi5y28xF0gBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAXEhg2bBgyMzPlihhu7kIby6VQgAL/WCA6OhoajUZeP3v2bDz55JP/eKz2vPD555/HsmXL5BQ//vgj4uLi2nM6jk0BClCAAhSgAAXaVKCrhZvLC0qw7fvN0FVpcWTzIVQUlqHfxAGo0epw8M/98A0LQMSAnjJY698jALtWbYe2qhoJ6/dCrzOg38R4aEoqcHBjAkL6hCOoVwgiB0cjYlBU0+5mM/TVNTDDDLWttQwxi4C1yWiUYefck9k4sGEftv2wGQqFAvPffwCegV54e+YryEvNhZO7M6ysrU4bW3R9FmOOumUs+oyLxztzX4d3kA9mvbkQTh7Oze5/a+HmWa/Nw/7f9+Kr5z7FmFvHY+qi6xs6RotBdZpqfP/Slzj4RwJufPpWRA2NleHmnKQszFl6L7r3Cm2YO/3wSbw7700E9Gg93GysMSB5zzFs+upPpOxNgr2LA/qOj8fQacPh2d2XweY2/Y6+CAZjuPki2CSWSAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAXqBAYNGoTc3Fz5Xww387agAAUoAMTExKCyslJSzJo1C0899VSnZHnuuefw0Ucfydp++ukn9OvXr1PWyaIoQAEKUIACFKBAUwJdLdycm5SJn17/HpVllcg6mg7Pbp5w9XVHfnoeKosq0C26O+ydHBA/eSA8A3yw7v1VKCupkOf6hvrD2dMZOclZ0Gp0CIwKgq2jLYZcexl6j2n60zm0FVX4/cPVqKqoxvi7JsG9m9dZzDUaLT58YCmObU/EVfdfiwFXDcGSma+gNL8UI6aPhpufxxnXmKGAAv49A6GyUuKdeW/Au7sPZr+1AE4eLg3nyi7VWp0MKFvZWrfauXnW6/Owf90efPWvzzB6xjhc/eANp4Wbq8sq8dUzK5C0+xhmPH8XwuN6ynBz9oks2TW6e0xIw9wi3PzevDfh30q4WXRsTtiwBz+/vhKiM3TPQdEYfuMYhMWHQ2VtzW/KS1GA4eZLcde5ZgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABS5Wgf79+6OgoECWz3DzxbqLrJsCFGhLgdjYWFRUVMghZ86ciaeffroth2+zsZ599ll8/PHHcrxVq1ahb9++bTY2B6IABShAAQpQgALtLdDVws0wmqDT1uDI5oP47oUvcOW90xA7ohd++O83yE/Lx23/ng03P3eora1kJ2XRJVl0bV75n69w8/N3IrhXCL585hPoKmtw+0uz4ODmBLWNFZRWp3dXrt8XbbkG785/EzknsnDbS7MQO7IvoFCcvm0mE75/6Sv8/fWfuHLBNRh+02i8ddcryE/Px6zX5yJyUHTD+aIDdP6pPBRlF8A/zB8GoxFv3vEyVNZqzF16H3zC/GvPNQP5qdlYv2w1/MICMPauyTixJxHvL1iC+IkDcP2TM2BlrUZmYio+fvA9uAd4YPYb83F0+1Esf/BdxIzojZmvzZXj1h+F6fn46IGlqCqvkud6Bnqfd7g5LyUHHz6wBGUFpRhz+0Rcdv1IOLo7n23U3jc6x+88Agw3d569YCUUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoDUBEYYrKSmRpzHc3JoWH6cABS4FgV69eqG8vFwu9a677sL//d//dcpli7o++eQTWdvPP/+MPn36dMo6WRQFKEABClCAAhRoSqDLhZvFIk0m/PLmSuxbtwd3vjJHdmr+cNE7CIoNxnWP3Qy1rU0DhdlglMHmxK2HcffbC2A2K/DhfUsQPaIXrrr/OqhtWu4ubDIY8c1zn2Lnz9sQP2kgrnrgOrh4uf4vvGsyoSA9X4aE807lYc7bCxE5JBpfPfspdq7ailEzxstuzip1bXi6oqhMdk/OOp6J216ciYDIQCy7bylSD6Zg2kPTMfTakVColKip1mHjinVY894vGHnzWFzz8I04sfso3l/wNvqOj8P0p26TtTcON895awHy0vJkx2WllQozX5uHoNgQWatw2P3bDqx86Sv5tVtfnAW1tfq8ws0Ksxl/fLoev771AwZcMRhTFl4De2cH4Izst+g8LerhcYkIMNx8iWw0l0kBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoECXEGgc4mO4uUtsKRdBAQqcp0Dv3r1RVlYmR7nzzjvxzDPPnOeI7XP5U089hU8//VQO/ssvv0DUzYMCFKAABShAAQpcLAJdMdysq6zC8oc+gFZTLTsQpyem4etnP8WY28Zj5K0TTtua6rJKLJ37Buyd7GTY99j2RHz7r88w9cHrMfDKoRZ1GE7efRxfPfOJ7E4cO6ovoofGwM3fA0a9EQVpuTi48QBO7D6G4N7hmPXGXNm5OGX3cXzy2DKYTSaMvX0iwuIiZL371+2Voeew+AjMfH0+bO1tsPuXHTKA7eLjhlG3jIVnoBfSj6Ti76/+hNlslt2owwdEIu1gCpbe/Ro8unnKwHPEwChUl2saOjfPeWshTCYTfn7zB2xb+TciB0fjshtGwdXHDTnJ2dj05R8ozCrAlAVXY+h1I6Gr1J5XuNmkN+C9BW8haedR9BkXB59gv7ODzVAgMDoIsaP6WGR9sXxfsc4WBBhu5u1BAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABS4egaioKFRVVcmCGW6+ePaNlVKAAu0nIDogl5aWygnuuOMOPPvss+032XmM/MQTT+Dzzz+XI/z6668Qb1bhQQEKUIACFKAABS4Wga4Ybi7IyMOH9y2VIePJ86/G319vlIHhG5++DaFxEadtTU5yBj68/x3EXz4QE+++Eus++BUHN+7HjH/diW7RwRZto1FvwL61u/DH8nUoyMiHSqWUHYpFkFhTWgkrazWCY0MwecFUBPUKlSFek16PbSs3Y8Mna1FVpoGrtxtqdHpUFpfDP6Ibrn34RgT1DpNhYF1FFdYtWy27Q4u5bJ3sUF1WBTtnewyfPgqjbpsIlZUKZXnFWHb/O8hJzoSDiwOmPXQjvIN98OljH8Ld3wN3/neO7FqddzIXP77yNVL2nYCto52sVcyrUCgQf8VgTJg5CQ7uztBWVGHFY8uQm5KNma/OQ7fooAYP0RH6wwfegX94AG5+4S44ujqdZaWtrMI797yO7BNZcuymDwUGThksO2qLjtQ8LgEBhpsvgU3mEilAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFOgyAj169IBOp5PrYbi5y2wrF0IBCpyHQN++fVFSUiJHuP322/Hcc8+dx2jtd+ljjz2GL7/8Uk6wevVqxMbGtt9kHJkCFKAABShAAQq0scCO1L2YNe12+EcEYs6ShTL8erEfohvzyYRk+Ib5w8PfE9nJWagoLkdIn3BY252+vsriMqQePImAnt3h7uuOtMOnoKvWIbRPGFQ21hZTmI0mZJ/IxNGth5F66BT0Wh2UKiVcvFwRNTQGYfE94Ojhctp4RoMBqftTkLBhL4qzi6C2UyMoJhixI/vBK9jntHNrNNVI2nUMR7cdgaakAp4BXug5LBqh/SKgUqtrzzWZkHE0TZ4n5u49qi8cXJ2QdvikXHdw77DaALEZKM0rxoE/9iJlbxIMeiOcPVwQNSwGPQdHw9bJvnY4gxFph05Bq6mS19o5OzTUJDpCnzqQAjtHe3SPCYLKuq6GRlUbavRI2X8COo22WUcReXbz80BgZHeYmw1AW7wNPPFiEGC4+WLYJdZIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABWoFQkNDYTQa5b8z3My7ggIUoADQr18/FBcXS4pbb70Vzz//fKdkeeSRR/D111/L2n777TfExMR0yjpZFAUoQAEKUIACFDhTwAwzRLh59rQ7ulS4+ULutAg5m8Tf7c2QXZdFx2KllUp2a27uMOkNMJvN4nQorFRQKJvpYGw2y8AxzGZAqawdt4lD1CDnbm6cumvMJlPteGJehaJ2bgaML+Ttc2nMzXDzpbHPXCUFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAJdQyAo6H8f78pwc9fYU66CAhQ4P4G4uDgUFRXJQWbMmIEXXnjh/AZsp6sfeughfPvtt3L0NWvWIDo6up1m4rAUoAAFKEABClCgbQUYbm5bT45GAQpYIGA04e9v/sTv7/4GV1dXODo44pNPPoGXl5cFF59+isIs3hbAgwIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoF0EDAYDwsLCGsZmuLldmDkoBShwkQnEx8ejsLBQVn3LLbfgxRdf7JQrePDBB/H999/L2hhu7pRbxKIoQAEKUIACFGhGgOFm3hoUoEBHC4g48vaVf2P16z/C2clZBpwZbu7oXeB8FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUMACAa1Wi549ezacuWjRItx3330WXMlTKEABCnRdgf79+6OgoEAu8Oabb8a///3vTrlY8Zy9cuVKWdvatWsRFRXVKetkURSgAAUoQAEKUOBMAYabeU9QgAIdLcBwczuJC9g9e/Zg27ZtmDBhAv9i2k7OHJYCFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUocCkJVFZWIiYmpmHJixcvxsKFCy8lAq6VAhSgwFkCAwYMQH5+vvz6TTfdhJdeeqlTKolu+z/++KOsbd26dYiMjOyUdbIoClCAAhSgAAUocKYAw828JyhAgY4WuCDhZjFpbm7uP1qrtbU1PDw8/tG1HXnRDz/8gAceeKBhyp9++gn9+vXryBI4FwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAl1MoLS0FH369GlY1cMPP4z58+d3sVVyORSgAAXOTWDgwIHIy8uTF9144434z3/+c24DdNDZ9957L1atWiVnW79+/Wmd+DuoBE5DAQpQgAIUoAAF/pEAw83/iI0XUYAC5yFwQcLN1dXV//hdqOIvpt999915LLljLr3tttuwadOmhslmzZqFp556qmMm5ywUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQp0SYGioiLExcU1rO3RRx/F3Llzu+RauSgKUIAClgoMGjSoobnW9OnT8fLLL1t6aYeet2DBAvzyyy9yzt9//x09evTo0Pk5GQUoQAEKUIACFPinAgw3/1M5XkcBCvxTAYab/6lcK9e9/fbbeOWVVxrOeuedd3DFFVe002wclgIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgUtBQHQmFY1g6o/HH38cc+bMuRSWzjVSgAIUaFZg8ODByMnJkY/fcMMN+O9//9sptebNm4fVq1fL2jZs2ICIiIhOWSeLogAFKEABClCAAmcKMNzMe4ICFOhogQsWbp48efJZazUYDEhPT2/4emho6FnnDB8+HM8991xHO53zfGVlZXjvvfewdetWTJo0CaJzs1qtPudxeAEFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUqBfIysrC0KFDG0CefPJJzJ49m0AUoAAFLmmBIUOGIDs7Wxpcf/31pzWh6kww99xzD9asWSNL+uOPPxAeHt6ZymMtFKAABShAAQpQoFkBhpt5c1CAAh0tcEHCzc0t8tixY5g4caJ82MHBAYmJiR3twfkoQAEKUIACFKAABShAAQqcJZCZmQnxT0uHs7MzoqOjWzynvLy81b/ndOvWDeKflg5LxhG1iJpaO3bs2NHiKec6juiUxIMCFLi0BMTPb8TzUv1h6fPGmdedqWbpOG3xPNZWz6uWjGPJ87ywaG1dlozTVvVY8vugJfW01TiWrMuS35fbqp7W7mWxn5b4WDJOW63Lknvsuuuua/XPJJfWsx1XS4HOI5CWloYRI0Y0FPT0009j5syZnadAVkIBClDgAgiIN32IN3+IQ/w55tVXX70AVbQ+5d13341169bJE//880+EhYW1fhHPoAAFKEABClCAAp1AgOHm898EY40eJoMJanubswYzG4wwA6ip0sLW2UE+rqusgrW9LRRK5blPbjLBZDTDaDBAaaWCSm117mNYeIVBWwOT2QyzySSvsLa1gUL1v5p1ldWwtrf5Z+s4swYxj9EEhZXq7OpaeswoajOjWqOFraM9FEpFk6szm2rXIczOPIw6vVyn2tbaQpnmTzMbDKipMcDG3va8x2o8gF6rg0KhhJXN2U14xT7VVGth7+oEKJpef5sW0waDdblwc25uLpKSkqDT6eQ7Xbt37w6VqombuQ5PnFf/Apy1tTVcXFzkI1VVVdi3bx9EN2nxYpq3t3eL3MXFxTAajU2eo1Ao4OnpeU7bJTYmPz8fqamp8lc3Nzf4+/ujqW7W5zQwT6YABShAAQpQgAIUoAAFGgREqEkEicQ/UVFRDW+2PJNo2LBhrYabxTWvvPKK7A7U3GHpOB988EGztYixe/XqdVqQsLn5RCeglgLXlo4jPpGmpcC1JeOIMJaop6VxLPGxZBzx6TmtvVlWjPPNN9+06NP4BcfmjMU4y5YtQ0uhbkvHaa2e6dOnWxR2FPW0tO+WjNORPpbUI+4b4dPS/dOR47SlT2v7bun93FHfX5Z8n4rvl9aefyzZL0vGseT5R4zT0vOY+LmQGMeSoy2eDy1ZlyX7LsZp6feLtlqX+L1S7Lslx6FDh5p9Y8251NPa/WPpfXgx+ZyLc1vdh63tqXjOFXPxoAAFOp9ASkoKxowZ01DYM888gzvvvLPzFcqKKEABCnSgQOM/I1577bV47bXXOnB2y6cSnfbXr18vL9i4cSNf+7WcjmdSgAIUoAAFKHCBBWrDzXswe9qd8I8IxJwlC6G2PTuke4HL7NTTZxxJRXlBCXoOiZF1KpVKKNVWMOkNOLr1EHzDA3B8eyL6TxkMkWM8uv0IQvqGw0qlgEJlBZV1bUBZhHsNuhoobdSwUipRo9VBpVZDpVJCX6OHyB1WFJQh92QO7F0d4erlAkdPF8BoksFco94Ao8kElZXVaSFeMa4cX4R7VUqYxK9KJfS6GlhZW9cGlk1m+XWRhxRhYTFXTko2tJValOYWQ2mlRHh8Tzh5utTWqa3BgT8TEDUsBjaOthBXiXlF6NpkFuOrYDIaYW1jDaM4X6+H2toaMJtlFlMBBaysraDX6SEu1pRWIiMxFdGX9ZZ1WFlZyTWLeiuLK5B5PANRw2LlmCLALFzEkXU0DXqtHhnH09FvfBzsnZ2gsqr1EmuxUlvBDAUK0nNRlFWIHgMiG9Yvgs5iLWLsqnINegyMlEFtk7EuBG0yy7qFhViHqEuMa1U3t6hN2ioUct3i2tK8YmQeTUfvcfF1lrX7Kh4Xh9paLT1MBoNcgxhb1CHuFXG9mE/unxjTaJTrV6hUOLk3Se5TYGR3qG1qxxD7bWWtlveYpqwS/SYMgAhxC1ezGdJKrF/ULHK24p4Uh8JsRo1O+ABWYn/0BnlviNpEPTU1eqjrahNjiEPM05bB6S4TbhYvejzxxBMoKio660lq0aJFmD9/vryZzzzEO2Lrf+gnfhj41ltvYe7cudi8efNpp4qQ9HvvvYeYmNonlzOPcePG4cSJE80+Qa5du1aGJVo7tFot3nnnHbz55ptNnirCzQsXLsS0adNaG4qPU4ACFKAABShAAQpQgAJnCIjgkHgBSXTIObM7oggrilBWU4elIbzWQsmNuwi1tDki7NhSWDY2NhYVFRWt7m9L4bC2Cpl1tnEESlBQUKs24oTW9stS59Y65Vk6TmvheEvHaav7p7V6LL2fWxunrdZl6b635mPpOK3dP23lY2k9ne37va18OiKcei7PY20Vkm4tvHv55Zfj6NGjrT6XdYTPuYRuOyrcbOn3V2v3oaXPPy2ty1IfJycnHD58uNk9PZf7sKV6xASW+rR2g4k/i4jnzJYOsf7WPnGitXn4OAUocO4Cx48fx4QJExoufO6553D77bef+0C8ggIUoEAXErjsssuQkZEhVyReR3399dc75epEp/0NGzbI2v766y+EhIR0yjpZFAUoQAEKUIACFDhTQISbt6XuxpxpdzHc/A9vj6QdichPzZFdlI01Rtg42CBu8iDoKrVY+Z+vEDuqL3b/tgM9+/dEYEwIirMK4B7gifTDqbB3cUD85EEyxHp062EUpuXBPdALzp6uSE04AVsne4TGReDwxgMyQGxlY4WMxHT0HBIFZ08X5KZky0BrYEwQso9nQKfVw9ndCX3G96/t6mw2I+3IKRhrDMg7lYuAnt1QUVgua8w+ngk3Pw/EjOoDbUU1UvadgLWNWoae7VwcZVBXbWONwtRcQK1C3Lh4uPp5IGlnInJScpCdlIEh1w5HTlI2TCYjAqODcXLfCRlWFrUaDSb0GtUH6ccyoCkuh0+IL3RVOpTklcj1hvULR9LOozKYbe9sjyObD8m6S3OKEBAZiKxjGTKk6+LlgiN/H8KQaZchIzFNhnfjJg2ErZ0t/vpqAzTFFSgvLId3qB8cXR0RHhchLUUX6F4j+8LJ0xl7ftuBk/uT0XNAJKortXB0d0RE/55IS0xFVVkVirOL4BfmB7+Ibsg5kYno4X2Ql5qNQ3/sl92SI/r3QEFGAcoLy+Di5QqlUoFukUHIOZUNa2s1spOzYW2rhleQLzKPpWHETeNkCFsEiY8Lr+RsueYeA6OQcSwNVWUa+AT7oqqiCj0HR+H49qNwcHFAzskcOHs4w9XHDelHUhE5NAbeQb44sGEvUg+mwM3PE1FDY5CVlIGKwjK55mPbjsDD3wMObk7QlFTCs7u3DFOX5ZfCv0cg0g+dlOHkQVOHyXuisrgce37bKbtu9xwUhRO7jsFgMCK0b5gMkuel5klzsSfi/hJB6v5XDIGju9M//A45+7IuEW4WXQmWL1/eIor4YfzHH38MB4fatu31R+Nws3hBQ/xAsKV38q5YsQKjRo06a67Wws0///wz+vTp02KN4kWrefPm4eTJk61usPhBpfiBJQ8KUIACFKAABShAAQpQoHWBN954oyHQ3NzZd911F/7v//6v2cFa+6h4Sz5uXgy+Y8eOFgu2ZBwRgLKkM3FL3XtFESKQJP5p6RCh79bGaWyzffv2JoebOHGiRePUfzRrczWJWsRYLR2ino4aR+yXqEc4NXdYUo8l44h7pznf+rnFOC11D6+/By0Zp6PWZalPW6yrre4fS5wtXVdbOLfVuv7pOOK6+k/Cqr8XW3qDRls+/7TV85gl41jy/NxW41jyPG9JPZaMI/avpecwsV+dbZy2+v3UknHayqe174m2dLZkXWLPxT3U2t639Pvts88+K3/eKtYm/gzV2p8XWv8TG8+gAAUsFThy5AgmT57ccPrzzz+PW2+91dLLeR4FKECBLikwfPhwpKeny7Vdc801ED8L6oyHaLolXp8Wx6ZNmxAcHNwZy2RNFKAAO9qqQAAAIABJREFUBShAAQpQ4CwBhpvP/6YQ4WYRAi3MKkTfsf2QtPMYxtwxUXbu3bB8DWKH98Lf32zCsOtGIGnXMeg01fAK8kF+ai5cfd0xYMoQ2NjbYuOn62XnXDtnexRlFsI72Fd2M/YJ9YWtgy3cfD1QWVKBvLrOzTVVOhlQ9Q32ReKWQ7KzcPykQTj4536MvWsS7J0dZLj55P4kHNuWKOvzDfWHUgE5jneInxzr8jlTZNfgLd/+Jb+u19bI8LW41tbJAbpyDRRqFfqNi4eDqwP+WLEekUNjcXDDXji4O6K6vFp2n7axs5FdnsP695R1ewf5QFullYHb2JF9cfDPfbKLcvfo7tLL1dtNBrXdu3nIAG/awRTYOTvAN8xfBoOLMguQvCdJBqRF4FZ0ZDabAF21DjEjeiEsrgcO/LEPVWWVSE9Mw+CrL8P+dXvg6u0qOxmLMHH3mGDEjOiD5D3HcHJfMgx6A6Ivi0XKnhOyk/aBDftg7WALraYaOo0WnoFesotx/BVDcHLPCRzfcQTdorojPy1XdoiOGdEbh/46AGs7a7h4u6Isr6S243KNHoYaA9y7eUq/xuHmrd9tgtrOGppSjeziXJxbDHc/d2g1WrlO//AAFGUVoDCzAD4hfijMLJSPq6xUCIoNRllBudwX0VFaBIxzk7OQn5qH0LhweZ+IoLaDqxPKC0oROSQGBzful6F0ryBv5J/KlQF5ca/FXd4fZfllcHRzRMKGffAJ8pHh+pMJJ+Ed5A1NmUbOI0LOJxNS0C2yO+yd7FBeVI7QfhHoHtt2b+C86MPN27Ztw0033XTas4f4gbqjo6N80Vej0TQ8tnjxYtn1uPHRONwsgs/154sXTQMCAmRHlfp3z4rr/Pz8sGXLlrO6QL/99tundW4W7b5//fXXhqlaCzcbDAZMmTLltK48op64uDjY29vLORuvRQz82WefYcSIEef/zMkRKEABClCAAhSgAAUo0IUFRCBUdF5ufIg/6w8ZMkSGcMSns1gSOOrCRFwaBShAAQpQgAIUOGeBxh+pLi4WP0+9//772cn5nCV5AQXOXeDAgQO46qqrGi588cUXccstt5z7QLyCAhSgQBcSEK+ZpqWlyRVdffXVzX5K7oVesmhgJTo2i+Pvv/+2+NOnLnTdnJ8CFKAABShAAQow3Hz+94DoZJyXkiPDocNvHI0dP23B8OmjZQ5RdBaOGBCJwxsTMOyGUdj23Sboa/QI7hMmw7tF2YUYceNYeAR44tDG/SjJKZZBV4VCicDYYBSl58uws6uvG1y83FBdoUHmsQzYOtqhulwjuzd7dffGwT8TYOdoh+E3jcLW7zYjbtIAOLk5wcbRHiW5RVizdBU8unmiID0f0Zf1Qsr+ZIT0DkVxThFG3jgG1va22PLtRtndWalSwdrWCj6h/tDr9NCWV0GpVqHv2DhZy8bPfkffcfHYu3Y3lFZKGHR6WYfowCxqihoWK7tAiwB3ZmKaDBT3HR+Pfet2y9BtzPBeOJmQDDsnB1QUlaEktxiBUUEozMiHvYsT+ozti0MbD8jAcPaJTDleQUY+aqp1sLazkSFnYdo9NhQiWF6SU4Tc1FyMvW0C/v76Txk2Fofw6NYzEOEDImXY+tj2I4AZGC3O+/IPGcI++Md+eAf7yCCxtrIaWUmZGHHLWBkCP7UvGYWZefAN8UPilsPiUvQdF4dDfyXAP6IbDm06gLC4CNmFWQSaRcBbdF3WlFeh3/j+sou0GHfXL9tkN2URRC7KKpQBYr/wABl+F92Rj249gvjJA7Fv7W50i+6OqpJK2Lk6wN3PA96BPijMKpBdmMUh9lR04s5PzUf0sBhUlFTA1tFW3g8l2cWIuiwWCb/vgZ2TvezKnPD7XnnvVBSUIXJYrJzb3tURGUdSUZRRAAdXR5QVlCKgZ3doK6tkB+qoYTEyDO3m446gXiFyD1x83WWnaSd3Z0Ah+nKf33FRh5tF8VOnToX4QZ44RBhYBH7j4+Plf+fm5mLGjBmnhY73798Pd3f3BrXG4WbxRRFe/vbbb9G9e/eGcz7//HM88cQTDf8tgsyNf3DY1BZUVVUhKiqq4aHWws2iI7T4KOP644orroD4gaSrq6v8kghLr1y5Eg899JD8b9Ep+tVXX214/PxuA15NAQpQgAIUoAAFKECBrisguk2K8E1ZWZkM3dSHmrvuirkyClCAAhSgAAUo0P4C4s9Y4uPeRffm+kN0gn7llVda/ZSF9q+OM1Cgawvs27dPdiWtP1566aWzmsB0bQGujgIUoMDZAiNHjkRqaqp8QLx+/NZbb3VKJtFpX4SaxbF58+bTXpPulAWzKApQgAIUoAAFKFAnwHDz+d8K2UkZsoOv6FIsgsPHdx5FzLBYKNVWsruxlY0auuoaRA2NkUFWk9EoQ605J7KgsFJhwJTBcHBxxL61u1BZWgkrlRJugV7IP5kDK7UVQvv1QOLmAzIoGxbfA+mHU+Hk4QxbJztkHk2XXw+MCUJFUbkMAosuzWobKxliDekTDn21Dnt/2wnvEF8Z3o0eHouMw2kyVCxCsQOvHAortQpph0+hMKMAahs1TCYzXLxcZJdkESpWKpUI7hMKexdH7F+3W4ZtjQYjwvqFI/VAilxHYHQwNKWVCIwMRM6pHLh6ucr15CRnw6g3yLCzCPQG9OgmQ9QiN5lzIlOGkcP6RSB573FZY3h8TyTtPIrirEJoq3Syk3BBWr4MZ2cdz4DKWo1BVw2Bg5szCk7m4Oj2I7BxtEWfMXE4uuUwnL1ccOpginSJHdkHXkG+KM4qwN41u2Q36d6j43BkywHkJGXBZDYjtF8YVGor6Cqqkbg9EVcvul5em5eaI0PBosN0XlouygrKZABa1BHaNxx71+5CzGW9oSnX4MTu47Cxt0FQTDAqistRrdEiekgMHN2ccGLPcRn+rigph0lvqvUwGtEtKlAGmBPW78FQ0dV79zEUZRTCzslO1mnrYIeAqCB5g57cl4TjOxKhtrVBzPDeSN6ZCKPRJDtsi/vA0d0Zp/Yno6ZK1OcFKxtr+PcIQElWEU4lpMBoMmPkzaNhbW+HwrRc2fFapLWDeocg7VCqXK/oUF2SW4LKknIZYnf3FR21vWUAXmFlhaL0PMRNGiTD7+d7XNThZvHu28adix9++GHMnz//NBPRvfnGG29s+Jr4i6z4C239cWa4+czH688TYWPRxVkc4iNjP/jggxbtzzXcPHr0aJw8eVKOKYLVoi61Wn3WHL/88gvy8/MhPrJIPBnwoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShwoQQyMzPx7LPPYv369Q0liICzeFMZDwpQoH0Edu3addr32Msvv3zWp9W0z8wclQIUoEDnFRg1ahROnTolCxRNqkSzqs54iE774hN7xSF+DQwM7IxlsiYKUIACFKAABShwlgDDzW14U4jWvmc2tTWZYDajtpuweEycIw4FoKusgpVaDZVNbZbQbDJBV1kNGwc7eb6uqho2tjaASgmjXg8FFDIwbTYa68ZTwFSjlyFXtZ3NaQvJTcmSnYtdfOqaxdbXVv+rmEt0Qra1gUJ1jllFsxkG0anYxhoKpQIGXY2cWwRqmzrMRhP0Oh3UdrYy3NxwmM0yeC0sxGMmgxFKq7rgrMkMva4Gamt1LZnZLOsUHZKVKqUMONcfJr24Tvgqan0VaLKmxuMrxNw1BtltWdRUU6WVHY9FCDlyWK8W1lEjreU6GkxFrXoolQpZl1hv2qFTMiwsgu1nHqJeo0Ev11y78eIGUUDUJPZEhMsVTQSIxbgmk0kGsWE0oUarhdrersHUZDTBKMzEvVDvbIY8T2VlVXtd3SEC6+IQnbBNegMMBgOs7WxhNpmlhbgHG9/L+iqt7OAc1DuMnZu3bdt2WjcCEWT29/c/bZ9Felt0ci4qKpJfX7x4MRYuXNhwzpnhZvFDQR8fn7NulsadlUVH5rVr1zZ5c9Z/8VzCzWLTw8LCGsb7z3/+c1ogu8WJ+CAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgQsssG7dOjz44IOoqKiQlYhwswg586AABdpeYMeOHaeFmfmGgrY35ogUoMDFJ9C4kdSVV16JJUuWdMpF3HTTTRCvcYtj69at6NatW6esk0VRgAIUoAAFKECBMwUYbu6Ye0IEV8UhOiK3FCYWAdbmHhdjmBuHg88oXTwuOhGLYLToDq1QKGX4+HyO2jFFOLt2HBHAlkHtZo76GkUI91wbvIq1CyURXrb0aM2kqXGaWoOxRo/CzAK4+bnXhXxNUCoUtd4CQFRWv26xD3XOTY0vajLoDbVh4hb268xrG+9f48fkGutD8XX7IILMTTqZTP+rs9EgzZ5ff05dwLpZd3EfiDnrw+eWblAz513UnZu//fZbPPTQQw1LE+/Gbepmb/yXxOnTp0N0Mag/zgw3NzdG4/McHByQmJjYIv25hJvFRySJj0qqP1auXIn+/fuf59bycgpQgAIUoAAFKEABClwaAqJb4Pfff4/rrruOLwhdGlvOVVKAAhSgAAUo0EkFxM9Mb7jhhoaA85o1axAdHd1Jq2VZFLh4Bc4MN7/22mu49tprL94FsXIKUIACbSAwZswYpKSkyJGmTJmCpUuXtsGobT+EeK1aPI+LQ4ScAwIC2n4SjkgBClCAAhSgAAXaQYDh5rZBFYFZ0epWhoBFGNVklp18ZSzWZEZpbjHsnOyQevgUeg6K/l8nZzF93fki8JmyLwnh/SNlUWIsOW5t62IUZRbAwc0RuqoauPm41YaO5Ym1zX+rSitQXlQGv7BuEPsquwuLgK4IJIuTGgWdG9db/+91pTdcK+qpKq1EQXo+gnqFypxu2uFT8AsLqOsUXRukrq9fnFBeWCpDtyXZhQiKDa0NW4uuxCLYXbeO08LRptqgsDghec9x2DvaISA6uHZMcW193XXnyeB3nZfwKc4SgWTP2kB4QwJYmNSh1PnXPy66N586mIzg3uFQqlR1+1XrW38YDUZkJKbCs7s3HFycUFFUBm1lFbyD/Wo7G+tqkHk0XXZmtmncJRmQnaUri8qgqaiSnbPd/b1q5zCZIaLbKrEfdQ2+5Yxyf2rnFvuXn5aH7jHBDZ2pRei6vLAMOo1O7otvmD+MNQa5D2FxPepC1rX7LDouF6Tlwr+H+BSZuntRjG8wInnfCYTGR0AlOkLXBZlr9110uTagsrgMbgFebfPNYMEoF3W4+Z133oHociyOlgLH8+bNw+rVq+V54i+2y5cvb6A5M9yclpbWJNv+/ftx9dVXNzx2/Phx2NrWtfxu4opzCTfv3bsX06ZNaxhFdDmJjKx98uFBAQpQgAIUoAAFKEABCjQvUF5ejkmTJkEEnCdMmIBly5aRiwIUoAAFKEABClDgAgqIP5/Nnj1bVvDNN99cwEo4NQW6rsCZ4eY33ngD11xzTdddMFdGAQpQwAKBsWPHIjk5WZ55xRVXQLyO3JHH66+/LsMY999/f4vTijeC7dy5U57T1KcS118snuvFP2ItERERHbkUzkUBClCAAhSgAAWaFGC4+fxvDJ2mGtknMqGyUsEnxA+FGfmoLK2Eb7AfNOUaaMo0OL79CKKGxmDfut3oMy4O3t194Orr2RBaLkjPg5ufB/at3YXoYb1kMNqvZzcZYhUdc0WYedMXfyK0X5jsMDzwyqEoyiqEjb0N3HzckZ2cJfO8+hoDnNycUFVZJX91dHdGQVqeDPJ6B/nAzsVBhmgzj2fAxt4WXoFeyE7JglqthnuAJ4qzCmEwGGBlZSW7D6tt1Ti88QDC+/eEV6AnMo9lytBvdWW1SG3DN9QfZQWlqCiqgKuvGw5vOgjfEF8YjUYE9w5D7oks2Ls6wNHNWQa89Xo9/MMDYO/qBIOuBllJGaiproGHvwf+/PR39J88CMF9w1GcWYCyghKR8IZ/RACET1WZRgarhWl5QSm8unlh0zcbMWL6GFl7XmoOHN2cUJxdBHc/d+lu0OmhKa2Ed5AvqiqqUFVehVMJJ9B/ymBoK6rhHeIHbd3XRchYbaNGdUUVDv99CJ6BnnD3dYedmyM0heWwd3FASW4xHNyccPivAzKE7BHoBd8Qv4Ywdc6JTOxftwdOXi5QqZTwC/eXjxdkFaKmSgev7t7Qa/Vw9nKRbnZO9shPza3rwqxA4uZDCO0XLkPMrj7uKMzIw+5ft8PZyw3iPguMDYZPkA9O7D6OkF6hKMgogGegFzy6ecl1nDqYIucoyiyCe4AHvIJ8YdIbsOe3negeGwyvbt4ozS+GjYMdclOyYaVWSeMjmw9iwszJdaH18/+eaG2EizrcLDocL1q0qGGN4t244hvmzKPxO2BnzJiBF154oeEUS8PNf/31F26//faG65oLQdefcC7h5uzsbAwZMqRhbBG+FiFsHhSgAAUoQAEKUIACFKBA8wIiOCP+rF//qSr8KGbeLRSgAAUoQAEKUIACFKDApSBwZrj5rbfewtSpUy+FpXONFKAABZoVGDduHE6cOCEfnzx5Mt59990O1QoKCpLzffTRRxC1NHeITx7bvXu3fFg8n/v5+TV56tNPP40VK1bIsPQDDzzQoWvhZBSgAAUoQAEKUKApAYabz/++OPJXAtKPpMJgMCI8PgL5qXmorqiGk5sjygrL4ObrjozENPQdH4/N32xEzIjeqK7UIn7iANmxeMeqrSInDGfP2iCyT6gfck5kYcTNY7D7l+2oKtcgtF8EEn7fh56DIpGTnAWfMD+U5ZfCaDAhMCoQBzbsR+yYvtAUVaAouxDu/h6oLKmUv1aVVaG6QoPYUX0RGBWEAxv2oiSnSHYQFuFao8GAGo0OTp4uMmxs7+yA6nINbB3s4N7NA6kJJxEYEyzDxaLrr62TnQwzizC16BIsQsgVhRWwd7FHZUmFDHiX5pXIzsVqO2vZcVqEb0XQ2c7ZHoHRQYgc1gtZR1NxePMhODg7wGQ0I/dkDkbeNFoGe7eu3CQ7VIugsQj7Co/qyiq4eLigtKAMju6OcPJwwbEthzB+9hXwCvTBjlVbYO9sjyN/H0LPwVEozS+R6xLB54ricuira+DoLjoxl8Pazhr+Ed0QOSxWdjtO3HIY2spqOLo6yoC4CEbbOthK+x6DomTouzSvVO6RePNjVlImfEJ8YajRY8TN46C2tZY24j7Y8eMWGTAW44lu2SIgXVFcIcer7WQNRA2JweHNB6W1pqRSdmf279ENKfuS4RvqK536Tx6MwvQ8bPl2kwyUizC7o5ujDHhnHEmFraMdRJdpEWLuN2EANKUVSFi/V3aW1uv0MuA9YMpQub9r3v1Zdv0eOGUoDv65X/77id1H5b6IcPyJXccwddH1sLZvvinw+X+n/G+EizrcLN7VKt7dWn+IAHJISMhpPuIdAgMHDkRRUZH8+qOPPoq5c+c2nGNpuPnrr7/GI488Iq8T747dsGFDi/twLuFmk8l0Wt2i03T9XG252RyLAhSgAAUoQAEKUIACXUlg8eLF+O677+SSxAtDr776aldaHtdCAQpQgAIUoAAFKEABClCgSYEzw81LlizBlVdeSS0KUIACl7TA+PHjkZSUJA3Ep3y99957HepR32xLdJD++OOPm51bfJqv+FRfcYjXun19fc86t7KyEmKc3NxciGZf/fv379C1cDIKUIACFKAABSjQlADDzed5X5jMWL/sVxm8VVqpZPBUBJRrtDpY29lApVZj5M1jsHXlZvQe2Qs7ftqGkTPG4e8v/4RPsA+sbKxlQDbvZI4Muopg6pg7JuKvFesRKTo9r90tA7aRg6ORlpiK2NF9kZqQIjsIi67N4nD385Bh1p6DYnB060HotDUYeNVQ7Fy1FSqlEr1G9UHuyVy4+rsjKDoYf65Yh5iRvSHSzXvW7MSQa4bLLr7ZSZlw9HCGb6gfirKKYGOnRkleCZQKBeIuH4C/vvxTBntF12ER7DXWGFBeUIbyonLZEdnOyQ5OHs7w7OaFY9uPwGwyybXIwK22RgZpRfdj0Uk5bnx/GbIVHZ7FWHvX7IKtvS2GTx8tQ7siBC5Czfnp+TL8K8LYhhoDbB1ta8coqZSdrkWwetydk6CyViNpxxHsW7NbBo3NZpPsWiwCx73HxmHjp+tlB+kxM8Zj9dJVKM4uxKS5V8G/Z3dUlVZi/UerodXoZLdl0eFaoVQgon9PJG47DGdPVxSl58sg8cgZY5CTnIPUg6fQe0xfHNqYgKHTLoOdsyMUKiWKM/Oxd+1uuHq7wc3fXQbQReA5rG84PIN8sW/NTtja28iA+961u+T+9x7dD5nH0mAyQ3Zf7jkoCsl7jmPwtBGoLq/CntU74OLjBqVKATtHBxRl5CE3NQ9BsUEySC+6VQ+7bgQqSzTYLc71dkHeqVwZhB5x81iY9Eb8+Oo3cPJ0xZCpQ7Fj1TbZdTplXxIUUCA0LhyZxzIwftYV5/nNYPnlIty8++dt+Ok/38HB3h6enp745JNP4OXlZfkgdWcqzGK08ziOHTuGiRMnyhEcHBwaOrA1N2RWVhaGDh3a8PD8+fPx8MMPn3b6H3/8gbvuuqvha+IjiMTH99QfZ4abDxw4AFdX17OmvP7667Fr1y75dfFuW/Gu25aOcwk3i3FETYcPH24YcvPmzejevft5aPJSClCAAhSgAAUoQAEKdF0BEWoW4WZxTJgwAcuWLeu6i+XKKEABClCAAhSgAAUoQAEKNBI4M9x85usexKIABShwKQqInw8dP35cLv3yyy/H+++/36EMosuy6LYsjpa6N1999dXYv3+/PE+89uzj43NWnY0/vTg5OVl+9DcPClCAAhSgwP+zdx/wVVRpG8Cf29J77wRC7703AUEFe8He2669r7v2su7qrrqW/SzsWkAUsIuKiEpv0kvoIYEkpBDSk9vv93sPzN2bAiQhPc/5PhbInTlzzv/MnUjuM+9QgAItLXA83Lwed1xyC+K6JeKOt+6Byed4aJatbgLbf92EY9kFMBgNqvpx+rY0VeFXKh1LhdzRl47HhoVrEZkcjYztB1WYeN23qzBu5iQVAJaQr/wulYuDIoIx/spJWP7pb4hICkf6tnRIdDMiKRJHD+ehy+DuOLTjoKowLGFfOWZ0cow6fvKgFOxdtwsOqwODpg7BliWbYDAZVcA3/3AeBk4ZguT+Kdj68waUFpbBaXeoCsY6vR7mknIERYaoas6RSdE4dqQAJi8TivKO4WhmPuK7JcBcZoalwoyAiGBEJUXDYbUhJyMHJXlF8JVgdqUFwZHBKtRdmF2ggsj+IYFqXtKnjNk7wFuFm/tNGIhDqRnYu34X/AL9VCC5oqQSIy4cpULOyz75BU6XEzazFSExYcjac1jZStg3NCYUJm8v5B/OhX9wgApqRybFoDivEF//Yz76Tx6Efev3YNSl47Bz2VZEJUejOLcYOoNOma+cvxQxKbHI3nsY466arCopr5y3VFVslqCzhKqlcrP8LhWNjweyy1EqVbhjw5SR2Pca0wepy3cgvkcCvPy8kNArGcW5BVj75UqExIYjpnMsiguKUXTkmApGe/v7QG/Qw1xuVkHl/Iw8JPRIVH1l7slE54Fd4LQ70WVACtK27seQ6SNhrbBgzZcr1HkhVae9fLxRkJWHgsNHEdkpSpZWVXeefPN5sFaasfmnDfAN9oPL4UT+oXycc+f56s9f/XO+Gqes6f6Ne9B/0mBVXbvkaAniusUje18WJt80DV6+zVe5efOP6zH/uTnw8fZRT75pM+FmuSxcf/31WLZsmfsK8d5777kD0nJ37pVXXumu2iyBaXnMj/yuterh5uuuuw7PPPMMjEaje5tPP/1UVXzWWl0ed13fcPM333yDe++9132M8PBwvPrqq5g4cWKNq5/ciSAXI88x1u0Sya0oQAEKUIACFKAABSjQ9gVKSkowZswYyO/x8fFYtGgRgoKC2v7EOAMKUIACFKAABShAAQpQgAJ1EKgebpbqpFKllI0CFKBARxaQAlpSSEua/Fk+M27Otm/fPlUgS9qpqjdfcMEFkGJb0k4Wbr799tvx008/ISUlBfJZNhsFKEABClCAAhRoDQISbl51cC3uvPQ2hpsbuCCWskoV1DV5GRHVOUZVQNYb9ao3qcYcnRyrKg+bK8yqIrAEgyVsHJMSr6on52ccURWKw2PCVPXnqM6xqpJzYEQQ0rceUOFaCQxLANrH3welRWUIDgtGXkYufIN8EREfqSonB4QFoORoMVxOF8LiIlR1YqnwfDg1A5ZKCwZPG464HomoLCrDodR0+IcGqrBuZuph+AR4Izw+ApYKC3wDfNXvEsC1VlpRdqwEFosNsV3jUVlcDqO3Cb7+PnA6napidFFuEWQiRi+jGp/FbFMWfsEByNqThaDwAFURWgeoMLbNakNIjFSbtiIzNUP1kdi7E0oLShCRGAmDwYDVX66AwWRQVaBjusYhc9dhGL2N0Ov0MJoMKpQb0yVGhYZ1egMikqLgcjiQseMgIhKjVNBZfCUUXVlagbiUeFSUliO6cyzy0nNU1eectGzEdkuA0WTEsax8FVp2WOzwC/ZTXoGhQSg+WqzmJaZSfVoqMUtYWgLJEuSWgHB5SYUKLXfq10UFvjN3ZcDLz1sFkq1mmwqXy7pYKq1I6J6AsuIyHMsqUGtnq7Ti4JYDKCsux8CzByMwLAiBoYEoLSxFeHykWvPsA1mqYraEw+X8sJSb1bnk7eOF/EN5ai4xKXFqTIVHjqpt5fwKjQ5T8xPXn2d9j8k3TMORA9kqZC1VtI/sk/PUgPjuicg7lKvW19uvGcPNi9Zj/rNtNNy8bdu2Go9bk4rHEvxNS0urcil5/vnnVRjas1UPN8trEiweO3YsAgMDsXPnTvfds/KapL+lqrLnHbJvvvmmemyQZ6usrMSGDRvcXxowYECVwEXPnj3xxBNPuF+XsPLVV1+N1atXV+lHgti9evVS+8ojiOTxQ4cOHcLf/vY3XHXVVQ28VHI3ClCAAhSkmChhAAAgAElEQVSgAAUoQAEKtF2BZ5991v14T8+bG9vujDhyClCAAhSgAAUoQAEKUIACdReoHm7mv4vqbsctKUCB9isg1Zp37dqlJthST/nyrMos4WT5PLh6mzFjBrZv366+LEW5oqKiqmwilZolHC1NPguWz4TZKEABClCAAhSgQGsQcMKJ5ftX4+7L70Bc9yRWbm7ORZE0rSR+m6q5XMg5kIX07QcRGBaIbsN7qZB0q2vVHHQuFw7tykB4bDj8QgNb3XCrD8h6IrRe74rnUlE7IwcHNu+Hb6CfqgTdFJWTJbh95EAW+owdAOib8oSr+1JJpnZzWw43y1TlB3cvvvjiKWd94YUX4pVXXoG3d9U3XvVws4SQtbtla+tw9uzZGD9+fJWX7rnnHnz77bd1VwcwfPhwyKO0PVt2djYeeOAByA8mT9fuuusuPProo6fbjK9TgAIUoAAFKEABClCgXQlkZmaqqs3SRo4ciXnz5rWr+XEyFKAABShAAQpQoL0KyFM35FdCQkJ7nSLnRYFmE6gebp41axbOPvvsZjs+D0QBClCgNQpIBfvU1FQ1NLkmyrWxudtbb72lPo+W9tBDD1V5aq82lvPOO08V15ImhbIiIyOrDNOzD9680twryONRgAIUoAAFKHAqARVu3rcad1/BcHNTnikuhxMVRaWQUKcEdvUGA0qPFqvqyVL1t0mb0wWdDnDJ/5ymmUvLYTAaYWriELSt0gKb2QKnw6UqRMvxXC60zvD16dDO9HWnq9WEjs90KnXdv1WFm6XS8llnnaXGLhWS6xLy1Sa6ceNGSAW36sFkqeIs4eMrrriiVpPq4WapnPzMM89g1apVKC8vd+8zdOhQ9Y/RLl261Ojn4YcfrhFUPt0CTJgwAR9//HGNzWRBvvrqKxXY1u4urq2vG2+8Uc2XjQIUoAAFKEABClCAAh1JQD6k0h63/OOPP6J3794dafqcKwUoQAEKUIACFGizAnKDmtyoJj93ZcC5zS4jB95KBKqHm//73/+6q3y2kiFyGBSgAAWaXcAz3DxlyhT85z//afYx7N27132zSb9+/bBw4cIaY/Acp3y+HRERUWUbz8rOmzdvRlhYWLPPgwekAAUoQAEKUIACtQkw3Hzm54XD4YC5tAIGnQ7egf4qSCzBXZOXFywVZhXcrSgux6HUgyjIOorB04bDaDJg/6Z96DqsJ7x9vCDVf+12BwwGA7z9jm8PuOAXHACr2Qq71QajyQiTtxcqyyrV/l7+vu7BS7VjS3nliT706nefAF+47E6YK83w9vGGFEiW/e02mwow6/U61bcErSVZLP+XvTcTfkH+CAoPgszL288HBi8TJJxtt1rV/jaLTVWclrn5+PvC6O2l9pc5yGtyXNkHzhNjstnh4+8Dp9MFo5cRNqsNuWlHkJueA2ulFeHxEer1/Ixc9Js0SM1VL5YBfjCXVUJv1MPL2wuWSrMat5efz5kvGntoUYFWFW5uDAmbzQYJSTudTkiw2d/f/5TdVg83Z2RkqO3lTSeP/bHb7ejWrRu8vLwaY3j16kOOLdWcCwoK1Hx8fHwQEBCAuLg4mEymevXFjSlAAQpQgAIUoAAFKNBeBOSxnsHBwapyMxsFKEABClCAAhSgQNsQ6NSpkxrozTffjKeffrptDJqjpEArFagebv7www/dhWNa6ZA5LApQgAJNLuBZEXny5MmQGz9aosl/6/zyyy/q0J988gnGjh1bZRjTpk3D7t271dc2bdqE8PBw9+vLli3D9ddfr/4u+8n+bBSgAAUoQAEKUKC1CDDcfOYrkXcwG6krd6pw7/ALxiAoMhhbf92EqE5R2PTDegw5dwTy0nNUKLkw5xjiuifAYDSgOK8IQ6aPhLevN1bO+xWSGJYs4cApQ7D2m1VwOpwYfO4wpG3YC0ulhJD1SOybjIOb96uw7+DzhsM/JFBNwFZhxsr5S1WAubywDH7B/ojvkQhrpUUd2z8kQIWWo7rE4MjeLHQd1h1GoxHbV2xTwWIJL8uYygrL4B8agLy0IzCYjIjqHIM+4wegJL8QaZv3o8ugrkjbtB/mCrPaNqFHIrqP6AWHxYbNP29AcUEJ4rrGoc+EgapS9dI5S+Ab4IfQ2DCV2+wyqBsyth9UFaxLC4phLjMjJCYUDqsdBzbtx4iLRuPglv2qonO3YT1waGe6qmrcuX8X7Fy2DX0n9Edy/5Q6VaE+85VlD00l0O7CzfWFOlm4ub79cHsKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSoXWDmzJnqSX1StVmqN7NRgAINF6gebpanRMrTItkoQAEKdGSB6dOnY8eOHYpg0qRJ+OCDD1qE47PPPsNjjz2mjl3bk3jPPvtsSIVnadUrMz/++OOYO3eueu2BBx7A/fff3yJz4EEpQAEKUIACFKBAbQIMN5/5eZGx9QAOpaYjY0c6Rl08FimDu2HD92uRfzgfmbsy0G14L8R1i8PedbtRWVqBssJSTL31PGz4cT0m33gOfAP9sPCNLzBsxijsWLYNib2TcCj1EIpyjiGxZxIKc49h4NlDsP7bNfD294HJ2wRLuRn9Jw1Cp76dVdDXXFqORe98h0FTh2LNVysxYPJg5KbnquBy6bESdcwuA7uqQLW53IxJN0yD027HLx/9BEuFBS6nCwGhgSpAbfKV/i0qyJy95zDGXT0FBYfzsGXxBvSdMghbf9oIl9MJKVEdlRyNfmcNVNtv/HEdKkoqVB9Tb5uB4txjWPLBIoy5bALWf7dGhaeHnDMM25ZuRXBksKr8LJWZdQY9ojrF4Mj+LIy9YgJWzluGmJQ45B48ApOXEWVF5YhMjET+oTycc8f58PJn5eYzP2tbtgeGm3/9FTfddJN7FbTKzS27LDw6BShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIH2IyCPhn/uuefUhCTcLCFnNgpQoGEC1cPNc+bMwbhx4xrWGfeiAAUo0E4EZsyYge3bt6vZnHXWWZCq9i3Rjh07BqkcLb8nJydDqjF7NnlNnh4sbcuWLQgNDVV/LioqUuOW/eRJvosWLUJiYmJLTIHHpAAFKEABClCAArUKMNx8ZieGzuXC+oVrUJRbiMKcAgyYNBg9RvXB4dR0fP/2N+g3YQDStuzH9HsuwtovVsJqtaowc0RcBLL2ZWLSDRJu9sXCf32BAVOHYM/qVARHhaqgr4SEgyKCVCC6+4je2LpkE/xC/FW42aDXo+uwHvAL8kN4YjTMpRVY9skSDJ0xEmtPhJtTV+2ApcyMgPAgFOcXYui5w/Hb7CXoMaIXhl80FnA6sfyz33D0UC6g1yM4Ihj+wQGwO+wwGg3oOrQ79qzZhTEzz0JR9lEVmk7o2Qnp2w8gMikaFcVlsNsdGH/VZFWFefUXyxEQFqTC0ufeMQMlR4uxeNaPGH7+SOxcsV2Fm2O7xePAxr2qqvTxcLNZzTOuWwLSt+5HfI8k7P19D0ZfMlYFvWWuTocDobHhKC8qw7DpI9Ux5ThsbVeA4WaGm9vu2cuRU4ACFKAABShAAQpQgAIUoAAFKEABClCAAm1CIDMzE2PGjFFjfeqpp3DLLbe0iXFzkBRojQJyg8DVV1/tHppU+dTeX61xvBwTBShAgeYQOP/887Ft2zZ1qIkTJ+Kjjz5qjsPWeoy//OUvkBtPpMk4ZDxakwBzWlqa+uvWrVsREhKi/vz111/jvvvuU3+ureJzi02GB6YABShAAQpQgAInBBhuPrNTQcLNaVv2IXP3YUhgs1OfzujUvwvKi8uxZfHv6Dm6Dw78vhcDpgzG/o37VJA3OiUGGdsOQqfXoffYfjCYjPjmnwsQ1TkaJpMRSf06I3XlDnj7esM/NAAFmUdht9lgLjVjyHnDcXBrGnyDfJHUOxkZ29Mw/MKxsFts2P/7LiT17YyDWw4gvmcicg5koyi3CE6psgygy6AUrPhsKcZePgGx3Y/fcHdkb6YKZev1enj7e6ux2K12Nc7IpCjkHsxBl8HdYa0wY+OP62GtsCA4OgQuJ1BWVIqQ6BD0GtNPhau3LNkIvcGggtGDpg1DRUk5vv3XF+jUtwtiusRCb9Qje0+mmndSn2R1HJvVBp1ej9CoUOxbvws6o0HNMyQmBKGxYcjcfUg5JPZKRnlpOcJjw2GttCA6Jf7MFo57t6gAw80MN7foCciDU4ACFKAABShAAQpQgAIUoAAFKEABClCAAh1DYPTo0cjKysLUqVPx/vvvd4xJc5YUaAKBpUuX4oYbbnD3/Nlnn2HUqFFNcCR2SQEKUKDtCFxwwQUqLCxtwoQJ+Pjjj1ts8J43oVQPKsvY0tPT1dgkjB0cHKz+LMFmCThL++6779C/f/8WGz8PTAEKUIACFKAABWoTYLj5zM8LCThLNWEJ9bpcAPS6enXqsNux9suVGHnxWBUqlrCv3WpTQWdzhRnrv10Fg9GIgNBA9J88GA7b8fCxVD4uzClEbLeTP0nN5XAALvX/yNpzSFWUHnnxeOiNhnqNUW3sdMLldKkAssvpVIFqqawsVZ/Vy3YHdDoddDqo38sKS7H9ty0Yet4IGH285Itw2B0wGAxAbUQSwj7RlzY4cTCIiezD1m4EGG5muLndnMycCAUoQAEKUIACFKAABShAAQpQgAIUoAAFKNB6BW677TYsXrwYvXv3xo8//th6B8qRUaCVC/z888+49dZb3aOcP38+RowY0cpHzeFRgAIUaFqBCy+8EFu2bFEHGT9+PGbPnt20BzxN75dccgk2btyI5ORkLFu2zL31uHHjcOjQIfX37du3IygoCEVFRSqQLb/LPN54440WHTsPTgEKUIACFKAABWoTYLi5FZwXLhfKCkoQEHH8BrkqzeVCcV4hzGWVCI+PPB4S1prLpcLUUgn5tM3lQnlRmdrMPzTwtJs3xgYuuwPlxWUICAuuPczcGAdhH21SoMOHm/Pz8yFVDqT5+flh+vTpbXIhOWgKUIACFKAABShAAQpQgAIUoAAFKEABClCAAq1Z4LXXXsPrr7+uhpiRkdGah8qxUaBVC/zwww/4wx/+4B7j559/jmHDhrXqMXNwFKAABZpa4KKLLsLmzZvVYSRAPGfOnKY+5Cn7nzVrFp5//nm1zUcffYSJEyeqP48ZMwaZmZnqzzt27EBgYKCq2CyVm6VJxWkJOrNRgAIUoAAFKECB1ibAcHNrWxGOhwLtX6DDh5vb/xJzhhSgAAUoQAEKUIACFGj7AiUlJUhNTcXIkSPb/mQ4AwpQgAIUoAAFKNBBBdauXYuZM2eq2c+bN4//bddBzwNO+8wFvvnmG9x7773ujr788ksMGTLkzDtmDxSgAAXasMDFF1+MTZs2qRmMHTsWn3zySYvOJisrC1OmTEFFRQVuvPFGPPvss2o8o0ePhrwmbefOnQgICFDBZgk4SxV+qcbPRgEKUIACFKAABVqjgISbl+1bhXuuuBNx3ZNwx1v3wOTj3RqHyjFRgALtRIDh5naykJwGBShAAQpQgAIUoAAF2rOA9gjzm2++GU8//XR7nirnRgEKUIACFKAABdqtgNywJoEeaYsWLUJCQkK7nSsnRoGmFJBKzQ899JD7EBKIGzRoUFMekn1TgAIUaPUCl1xyCTZu3KjGKdWR586d2+JjfuSRR1RYOTk5GcuWLVPjkRv3jxw5ov4sN/JbrVZV1bmoqEgFoCUIzUYBClCAAhSgAAVao4CEm5fuW4l7r/gDw82tcYE4Jgq0QwGGm9vhonJKFKAABShAAQpQgAIUaG8C5557rrtys1T5Y6MABShAAQpQgAIUoAAFKNBRBT799FP86U9/ck//22+/xYABAzoqB+dNAQpQQAlceuml2LBhg/qz3Ewl18qWbitWrMC1116rhvHVV19h8ODBGD58OHJzc9XXdu3ahe+++w6PPvoogoODVQA6NDS0pYfN41OAAhSgAAUoQIFaBRhu5olBAQo0twDDzc0tzuNRgAIUoAAFKEABClCAAvUW6NSpk9rn/vvvxwMPPFDv/bkDBShAAQpQgAIUoAAFKECB9iLw8ccf48knn3RPZ+HChejXr197mR7nQQEKUKBBApdddhl+//13te+oUaPw2WefNaifxt5p5syZWLt2LW699VZ17R46dCjy8/PVYXbv3o27774bS5YswVVXXYW//e1vjX149kcBClCAAhSgAAUaTYDh5kajZEcUoEAdBSTcvGXR75j37Gx4e3kjLi4OH374ISIjI+vYw/8207mkNzYKUIACFKAABShAAQpQgAKNLMBwcyODsjsKUIACFKAABShAAQpQoM0K/Oc//8Fzzz3nHv8PP/yAPn36tNn5cOAUoAAFGkPg8ssvx/r161VX119/PZ5//vnG6PaM+5g7dy4ef/xxJCcnq8rMUr25oKBA9fv9999j+vTp6s/z58/HiBEjzvh47IACFKAABShAAQo0lQDDzU0ly34pQIGTCUgcecfSLfj4sVkwGYyQzADDzTxfKEABClCAAhSgAAUoQIFWJcBwc6taDg6GAhSgAAUoQAEKUIACFGhBgXfeeQcvvfSSewSLFi1Cr169WnBEPDQFKECBlhe44oorsG7dOsybNw9dunRBVFRUyw8KQFlZGaZOnYqsrCxkZGRg4MCBKCwsVGN78MEH8eqrr2LQoEH4+uuvW8V4OQgKUIACFKAABShwMgGGm3luUIACzS0g4ebdq3bgP/f/Gwa9Qf1bj+Hm5l4FHo8CFKAABShAAQpQgAIUOKUAw808QShAAQpQgAIUoAAFKEABChwXePPNN/GPf/zDzbF48WL06NGDPBSgAAU6tMDMmTOxdu1aFW4eOXJkq7J44oknMHv2bDz55JN44403UFxcrMbXt29f7NixAy+88AKuu+66VjVmDoYCFKAABShAAQpUF2C4mecEBSjQ3AIMNze3OI9HAQpQgAIUoAAFKEABCtRbgOHmepNxBwpQgAIUoAAFKEABClCgnQq89tpreP31192zW7JkCbp169ZOZ8tpUYACFKibwDfffIO8vDxce+218PX1rdtOzbTVjz/+iDvvvBMjRozArl27UFJS4j5yeHg4fv31V4SEhDTTaHgYClCAAhSgAAUo0DABhpsb5sa9KECBhgsw3NxwO+5JAQpQgAIUoAAFKEABCjSTAMPNzQTNw1CAAhSgAAUoQIFmEJCqigkJCeoXGwUoUH+B6uFmCcWlpKTUvyPuQQEKUIACzSIglZonTJiAwsJCFbyurKx0H/emm27CM8880yzj4EEoQAEKUIACFKDAmQgw3HwmetyXAhRoiADDzQ1R4z4UoAAFKEABClCAAhSgQLMKMNzcrNw8GAUoQAEKUIACFGgygQULFuDhhx9WweZVq1Y12XHYMQXas0D1cPPSpUvRuXPn9jxlzo0CFKBAmxd48MEH8cUXX8BkMsFms7nns2jRIvTq1avNz48ToAAFKEABClCg/Qsw3Nz+17hJZ+gCHHY7XE4nDEYjdAZ9kx6OnbcPAYab28c6chYUoAAFKEABClCAAhRo1wIzZ86EVPibN28eRo4c2a7nyslRgAIUoAAFKECB9izgGcrMyMhoz1Pl3CjQZALVw83Lly+HdkNokx2UHVOAAhSgwBkJLFy4EHfddRd0Oh3kA3ppU6dOxfvvv39G/XJnClCAAhSgAAUo0FwCDpcTS/etwH0z/4i47km44617YPLxbq7D8zhtVcAFWMoqkJeRi0O7MmAuq0REQiSS+iQjJCYMOj1Dzm11aZtj3Aw3N4cyj0EBClCAAhSgAAUoQAEKnJHA7bffjjVr1qgPfBhuPiNK7kwBClCAAhSgAAVaVIDh5hbl58HbiUD1cPPKlSuRmJjYTmbHaVCAAhRonwKVlZWYOHEicnJy3BP897//jenTp7fPCXNWFKAABShAAQq0OwGHy4Ff9i3HgzPvZri53a1u00zIabOrUPOWJZuwc/lW5B/Kg91mh1+AL3qM6oNpt01HRKfouh3c5YLL6VLVn21mK2wWG3R6Hbz9fGHyMTEkXTfFNreVFm6edd/b0EGHnj174r///S8iIiLqPRedS7vNtN67cgcKUIACFKAABShAAQpQgAInF9CqkN1///144IEHSEUBClCAAhSgAAUo0EYFGG5uowvHYbcqgerh5tWrVyM+Pr5VjZGDoQAFKECBmgKPP/445s6d634hLS0NBoOBVBSgAAUoQAEKUKBNCNhdDizZtxQPz7yX4eY2sWItO0hrhRm71+zEis9+w8EtB2CptFQZkJePFyZcMwXn/uH8k1YAlyiqy+6ApcKMkmMlOJZ5FEcOZKMo9xjKCsugNxoQkRCBHqN6I6F7J5h8vVp20jx6owto4eb37nkTTocTQ4cOxbvvvouwsLB6H4vh5nqTcQcKUIACFKAABShAAQpQoC4CDDfXRYnbUIACFKAABShAgdYvwHBz618jjrD1C1QPN69duxaxsbGtf+AcIQUoQIEOLrBkyRLccsstSiEyMhIbNmzo4CKcPgUoQAEKUIACbUlAws0/7/kVD1/5AOJ7JOKOt+45aSi1Lc2LY21kAZcLpQXFWP/tGhVsPpZToCou19bC4sJxzQs3o/vwXlVedjmcMJdVoCC7AId2HMTh1AxkbD+I0sJSlBWWwmF3ACe61Bv0iEyKwsiLx2LQ1KEIiQ6F3mhs5Emxu5YS0MLN7979hlr34cOHM9zcUovB41KAAhSgAAUoQAEKUIACtQsw3MwzgwIUoAAFKEABCrQPAYab28c6chYtK1A93Lx+/XpER9fxMa4tO3QenQIUoECHFnA6nejcubMy+PzzzzFs2LAO7cHJU4ACFKAABSjQtgQk3Lw4dQkevvohJPRkuNm9ek4X7DYbrJVWWMxmOCwOSCDTaDLC298HPgG+qsJwYzcJAMPlgoR7XTpdY3ffoP7sVhuydx/Ginm/YdvSLagsKZchnrTJ2PudNRDn33sJAiOC4LTacSynEEf2ZWL/hr3I3JuJY5n5sJgtcNgcpxyTX5A/ugzuip6j+iA2JQ5BEcEwmgzKXnR0pqproNfplZ28JtsYjce3NZiMQCvxbNAitLOdGG5uZwvK6VCAAhSgAAUoQAEKUKA9CjDc3B5XlXOiAAUoQAEKUKAjCjDc3BFXnXNubIHq4Wap/CkVQNkoQAEKUKD1C4wZMwYWi4VVm1v/UnGEFKAABShAAQpUE2C4uSqIy+lEeVEZMncfUr+O7M9GRXEZHDa7qlRs9DLBN8gPvUb3Re+xfeAfGnTmoVmXC9ZKC45lHUX2/kw4rA4ERYUgoWcS/EMCzrz/hp71LhfKC8uQunIbVn++HOnbD8Jus9foTafTqeC3Z/Py9UZy/84IDA+C3eZA4ZFjKMjMh7ms8niF5no0CSt7+3kjMDwYQeFBMHoZYZAAs16CzFXDzTq9ToWbJd1s8vZSYejQqDCEJYYjpkscQmNCYZAK0Aw612MFGn9Thpsb35Q9UoACFKAABShAAQpQgAKNLMBwcyODsjsKUIACFKAABSjQQgIMN7cQPA/brgSqh5s3bdqE8PDwdjVHToYCFKAABShAAQpQgAIUoAAFWpcAw83/Ww+72YaDW/dj8+IN2LtuF4rzi1TlZnlSh2eTMG9AeCCGTR+J8VeehfCEqAaHZZ02O/IP5WHbr5uwe00qsvdnqcrNvkH+6DmqN8ZfOQkxKXEN7r+hZ5tUVM7PyMH671Zjww/rUZRzrEaA2eRtQmRSNLz8vJC9N1NZtaYm6ySVto3eRvj4+yKhVyL6jh+AzgO7IiopCgZvr9Y03IaPxeU6UUn7fwFzmXtrDnAz3Nzw5eaeFKAABShAAQpQgAIUoEAzCTDc3EzQPAwFKEABClCAAhRoYgGGm5sYmN13CIG3334bL7/8snuuW7ZsQWhoaIeYOydJAQpQgAIUoAAFKEABClCAAi0jwHDzcXe72YotP2/ALx/+hOwDWXDaqwaaa1sdLx8vDJo6DOf+8XyEx0fWK0wq4U5zSTn2rt+NlfOX4eCW/bBUWqocRioQ950wEJc9NhOh0n9TtRNVl1U09kQV6f0b92LlvKXYs24XbOZqoWUd4Bfoj74T+2P0pRPg7eeFH976GjtW7IBUvq5Pk0rLBpMRPn4+8A30VdWYS44WwVJuqRGmrk+/tW6rxu2HuO4JGH7BGPQZ2weB4SHQSaXnNtTk3JEq4nazBZVllagoLYe51Hy8urispU4HH19vRCbHwCfQr1XOjOHmVrksHBQFKEABClCAAhSgAAUo4CnAcDPPBwpQgAIUoAAFKNA+BNauXYuZM2di5MiRmDdvXvuYFGdBgWYWmDVrFp5//nn3Ubdt24bg4OBmHgUPRwEKUIACFKAABShAAQpQgAIdSYDhZsBpd2Dzzxvw3Wuf49iRmhWKT3U+mLxMGH7+KJx394UIiqzbDcouuwNHDmRjzZfLseXnjSjKLwL+V3S3yuEMRgNGXzoO5993qarm3FjN5XDCWmGGpdyMirJKOOwOOOx2lBWWqaD1xkW/o+Bwfq1Vq8NiwzDqsvEYddFYBEWGqCHtXr0T81+cg/zDeSedi+fYZV4BoYGI6RqLpN6dEdMlBrEp8dAbDNi1ZgdWf7EcBZlH4XTULyxdV5+AkAB0G9FTVd/uOqgbfIL8zyjkrHO54HS64HQ4lJkat6qo7FIeBi8jTF5egF5X1yHW2E7Cy5byShzLLkDWnsPITc9F1t5MFB45CrvFBofdqU4jOYKXrxcGnzMcU26cBpOfT4OP2VQ7MtzcVLLslwIUoAAFKEABClCAAhRoNAGGmxuNkh1RgAIUoAAFKEABClCAAm1cYPbs2XjiiSfcs9ixYwcCAwPb+Kw4fApQgAIUoAAFKEABClCAAhRozQIdPdwsIcvMnQcx9+kPkbU3q0q1YL1er0KpEsSVZrfaYLfaayynVB2edNM0TL5hKrz8fE+63BKALTlWgh1Lt2LNVyuQuesQbBbbaU8PvyA/TLn5XIy7clyoCHwAACAASURBVBJ8Ak7e/yk7crnUsSqKypB/OBfZ+7JUQDb/UD4sFZUqjCvVqi0VFpQeK1HbqmCuRxOHxD6dMOWGaeg5ui+8PcZiM1uwYeFa/PrxYhVwdtgcNYYjnj7+PojuEoPOA7ui27AeiOkSh+CoUGUslaql6rBU0U7bsh+bFv+OtE37UZxXCKvZeiIwLFnhkyTBoVPBXmmqunG18VcfkN6oV8eWcHPKkG4qXB0UEQwvPy8YjEbodDpIZWmdRx5ZBZjtDthsNlgrrKgsrUBpQan63VxWqfyslWZUlFaqMUsgWapRB0eFIK5rPDr164LQmFAYvb1Ou+5qHg6n6jsvI1eFztO3pyEnLRtFuUWwVlrUmkmYurYWHBmCG/52G7qN6FWnYzXrRi4X9q3fjbfvfE0ZDR8+HO+++y7CwsLqPQyd63QrXe8um26H8vJybN++Hf369YO/f+PdrdB0I2bPFKAABShAAQpQgAIU6LgCWrj5vffew7Rp0zouBGdOgQ4gYLVaT/uDJI3B29u7A4hwihSgAAUoQAEKUKCqwIIFC/Dwww+7v5iamsrPOXiSUIACFKAABShAAQpQgAIUoECTCnT0cLPDYsPnf5+L1V+scFcJNhgMCIkJRae+yYjsFIOQqBAVqM07mKsqFOcdyq1RUTg0OhQXPXwFBp49FPoTYWjPhbNVmpGxIx3rvlmNXSu3o6SgpNbwcEBoEPwCfHHkYHaVdQ+JDMGEa6dg2IxRCIoMVoHZ0zWpImwzW1FytBj5GbnI3H0Y6dvScPRwLgpzClVQWyo21yUa6u3rjV5j+2LSdVOR1K8zDCZjjcNLJegDm/dhy88bkLUnU1WBlirVOoMe/iEBiEyKQsrg7ugyMAXhiVHw8vU+HmiupbmcTlQUlyP3YDZyDhzBkbRsVBRXqLGqQG+Nis4uFZCWY8EpQW4rLJVWlB0rRVFuIcqLy05aBVr28/b3QUhUKALDAuHlYzq+hjodTD5eMHnM1WaVULMFdptdhZctZWaUFZWpULN4q1C1BKAlcCxFm08ErKU/Wde47gnoOaYPug/viUhl4FOjYrTsb7dYUVJQjEM707F7dSoOp6bjaGa+ClQ7HDWD47UZevl44cqnr8ew80ef7lRpkdfTNu3Dm7e8oiw7TLj5888/x0MPPYR//vOfuOyyy1oEngelAAUoQAEKUIACFKAABeomII8vP3z4MC6//PK67cCtKECBNisgP8iRO9nr0hhurosSt6EABShAAQpQoL0JfPvtt7jnnnvc09q9ezd8fRtYkam94XA+FKAABShAAQpQgAIUoAAFKNAkAnaXHYtSf8GjVz+EhJ6JuOOte2Dy6TgFSCzllZh139vYvTZV+Rq9jOgxohdGXjwWXQZ2VZWStSCvVMqV8O7i939Axra0qhVzdUByvy648qnrEN8jSQVjpUmV34LMPGxZsgkbvl+H3PQcVanWs0l1YAnVdh3SHf0nDYLRy4QPHnlXBY89mwSE+00cgAFThiCqUxT8ggJUZWmjUSoey6/jx7OUm1WguSAzH+k705G95zByD+agrLAU1sq6F6JRx9YBfoH+GHj2EEy56RxEJkUDeo9SxtXOSgklVxaXoyD7KEryi2B3OGAwGBEYHojw2Aj4BvmrMdeniZeElSXQrMLC6v9rVm/WQwfXiaHJdnazDcV5RThyIBs7lm1V1aDLi04ecq5tTMcrN/8vgO1yOVV4uaFNqkH7BvgitmscOg/qii4DuiIsLky5SKsoKUdJfvGJIPoBHN6VgdKCMjjsNSuGn2oMcpzwuAjc/NofkNg7uaHDbdL9Dm7ejzdufrljhZvfeOMNFWyWqs2vvPIKpk+f3qTI7JwCFKAABShAAQpQgAIUoAAFKECB2gW0O9LlhyjS7HZ7ne4oZ7iZZxQFKEABClCAAh1R4KeffsLtt9/unvrevXvB/y7qiGcC50wBClCAAhSgAAUoQAEKUKD5BGwuG77fuRh/vuYxJPVJwu3/uhvGDhRulsrCSz9Zgp//8wOsFitSBnXDBQ9cirhuibVXR3Y6sX/jXnzx0lxk7cuqUvVYKgCPuGgMzr3zfASFB8NcYUba5gNY8dkvkCq15gpLjYU1eZmQ0CsJIy8eg74TBx7fr6wC7979pjpO9SbHkCC0hIzDEiLg7eMNk+/x6sJ6vR6VZZUoPVaCnINHUJxbhMrSChUebUiTz3YCI4Iw/PzRmHDNZIREhzWkm5bfx+FEUX4hdq3agQ0/rFdVkM3l5jMKKTfGpCRI7xfkh6CIEITGhKrAdlFOkarYbC6rVJWha8lwq0PL2hhMBnj5eMMnwEdVrfZsEoQfet4IjL1iIgxepsYYbqP30SHDzW+//TZefvllhfnggw/ivvvua3RYdkgBClCAAhSgAAUoQAEKUIACFKBA3QWkarP8UE2aPC5LQs6nagzx1N2WW1KAAhSgAAUo0H4Eli5dihtuuME9of3798Nkap0fQLUfdc6EAhSgAAUoQAEKUIACFKBAxxawumxYuHMRnrjmcXQd1h03v3wH9Kb6VdZt64LmkjJs/WULyopL0XtsP8R0iYdU7D1pczqx7bfNmPf8HFUh2bN5+3qj36SBiOsaj4LsAuxcthVF+UU1Q6o6IDA0EAOnDsXYyycipms89Ibjn6NI4Zg9a1Px8WPvofRY6al5TwxTryo36+B0Ok4aiK3ekXxuIwFbo8kInUEPp8MBm8UGp90Jk48J0Z1iMOrScSok63OisnCbXmunE/mZedj+y2Zs+WUTcvZnHw85SzXoM206wGAwqNCxdu5Ivy6Hq05Ff+p6eJ1eD/9gP0R3jkXKwK4q4B6ZHA0fPx/opMy2NB3gG+SH0OiwVhtslmF2yHDzu+++i7/+9a9qnR599FHcdddddV17bkcBClCAAhSgAAUoQAEKUIACFKBAEwjID3C06s3SvcVSszqB52EZbm6CRWCXFKAABShAAQq0eoG1a9di5syZ7nGmpaWpD8bYKEABClCAAhSgAAUoQAEKUIACTSXgGW7uPqIHbvz7HdBXqwLbVMduTf067Q4VcpW5e36ecbIx2sxW/Pz+91jywSLYrLYqmxkMehUWdjldcNgdNbqQEHNirySMu3IS+k0cAN/ggBrHdFhtWPvVSvz4zncqQN0YAVwJM0v42j8sADFdYhGRGI3gyGAER4XA29cH5vIKFGQeRUVpBUJjw9B9eC9Ed46BqT1V8na5VID7WNZR7NuwB3vW7ET2/myUFZbCWmlR6yXrdrJ2PLwsoXCDChQHhAYiJDpUefkF+8NgMqnXZDuHVcLiVhTmFuLglv0oyi2s9Xyoy/tAzpnQ6FCkDOmhwvPx3RPU33XG4xW7JdBcJY5/4omqdem7pbbpkOHmWbNm4fnnn1fmjz/+OO68886W8udxKUABClCAAhSgAAUoQAEKUIACFKhFgOFmnhYUoAAFKEABClCgpkBlZSW+/PJL/PnPf1Yvpqen1+kDVVpSgAIUoAAFKEABClCAAhSgAAUaKsBwc0PlgKLsfHz8l/9i/4a9dQ4fe/v5oNeYPph0/VQk9kmG0evkT2yyVpix+affsfSTX5B78IgK5da3GYwGePv7qABzXLd4JPfrooKx4fGR8A8LVNWi5ZeEcSXUK9WbnU6XutlaqjpLNej22uxWG8oKSlCQfRQFmfnI2puJ8qIySHDdWUs1Z6mMbPIywuTnraoix6bEIjgyBAGhQfAPDTgRjBetE0ljqdzsAizllUjffhBbft6IAxv3ofhoEex1XEtZh8DwQHQd1gODpw5Dcv8u8A8NbBc3IHTIcPMHH3yAZ555Rr2nnnzySdx6663t9f3FeVGAAhSgAAUoQAEKUIACFKAABdqcgFQXsFqtpxw3Kze3uWXlgClAAQooAak6m5CQoH6xUYACDRfQqjfPmzev4Z1wTwpQgAIUoAAFKEABClCAAhSgQB0EGG6uA9LJNnG5sP23zZj/4hwU5RadsiMJDwdFBGHERWMw+pLxCIuPhE5/+uCwBHAztqWpYOze9btQmHMMNrMNTodTBarl/6RJ8PZ4VWEd9EY9fPx9ERoTiqjkWHQd0k1Vao7qFAPfYH8YTe07tNyQFZXK3VJlWVxVsLmW4s1qtXRQlZINJqMKf0sV57o0l8OJ0oJiZOxMx541qUjfnoZj2QUwl1W6A+XaU1BlHSVwHhAWiM4DUtBv4iB0H94DQREhqiJ4e2kdMtw8e/ZsPPHEE2oNJeR80003tZf15DwoQAEKUIACFKAABShAgTYm4HQ6IRVqfX19m2Tk5eXl8Pf3b5K+m7rTtjz2prZp7/3L+8JmO3V1AYab2/tZwPlRgALtUWDBggV4+OGHVbB51apV7XGKnBMFKEABClCAAhSgAAUoQAEKUIACFGh3Agw3n9mSSpXfn979Dkvn/gJLubnWzkxeJkSnxGLCVZMwcOpQ+AT41eugOpdLhWCz9h7G4d2HkHMgG8eyClQY12ZzqL4keCtVoQNCAxAWH6HCzLEp8QiLDYNvoB90RkO9jsmNm0hA1rK8EoVZR5G5J1NV5M49mIPy4jLYrXYVPJd1DEuIQNfB3ZAyqBsCI4KgM7S/9euQ4ea5c+fi8ccfV2fXCy+8gOuuu66JzjR2SwEKUIACFKAABShAAQq0Z4GioiI4HA4EBATgZEHLsrIyd3jZz6/qDyKKi4sxY8YMHDp0CO+//z6mTp1ahctsNuPss89WX3vnnXfQp0+fenF++OGHePrppzF06FDMnz9fPR6qNbTrr78eBw8eVOGmCy+8sNYh/fGPf8T333+Pu+66C48++mhrGDbH0IwCDDc3IzYP1SgCBQUFp+0nKCgIJtPJHx942g64AQXagcBrr72G119/Xc0kIyOjHcyIU6AABShAAQpQgAIUoAAFKEABClCAAu1fgOHmM1/j0vxCfPfW19jy0wZUlle6q/5KZd+Q6FD0mTAAI84fhYQeSWccMnY5XbCZzR7Vm50nJnC82q+EY40+XjB68efVZ76yTd+Dw2KFpdIKh82uKnGrytt6Pbx8vWDy8TmeWm+nrUOGm+VD/UceeUQt6UsvvYSrr766nS4vp0UBClCAAhSgAAUoQIG2LzBmzBiUlJSo6n4SDGtNbdy4cSqY/Morr+CKK66odWhaSPfWW2/Fk08+WWWbX3/91f0kGQn5vvHGG1Ver6ioQK9evdTXvv76awwaNKhe058+fTp27Nih9pFjpaSk1Gv/ptr4rLPOQlpaGl588UVce+21NQ4joe/+/furr0vV6dTU1FqHkpOTg6eeekq9JmsQHBzcVENmv80swHBzM4PzcGck4HmtPlVHcpPKueeee0bHai07y80p8r35jjvuwJAhQ1rLsDiONiDAcHMbWCQOkQIUoAAFKEABClCAAhSgAAUoQAEKVBNguLkRTgmXCyX5Rdi+dCv2rElF8dEihESFILZ7IroP64HY7gnw8feFTteOk6qNwMguOpZAhww3f/HFF3jwwQfVSp8qhNCxTgXOlgIUoAAFKEABClCAAq1PYO3atZg5c6Ya2Lx58zBy5MhWNcirrroKq1evVjdP3n333bWO7dJLL8WGDRvwxBNP4LbbbquyTWVlJW688UZVuVCqGFaf35mGm7/55hv11JopU6bgX//6V6v5gcjpws2C9Pzzz+PTTz9VVZvFqLa2f/9+TJ48Wb20fv16REdHt6rzg4NpuADDzQ23457NL1DXcPN7772HadOmNf8Am+CIvXv3Rnl5Of79739DbqRho0BdBRhurqsUt6MABShAAQpQgAIUoAAFKEABClCAAq1HQMLN3237EU9e/zi6j+iJG/9+B/TG1vG00NajVLeRSPXdsmMlqCythG+QH/yC/WE0mVQ1XjYKUKCqQIcMN0vFs/vuu09JvPrqq5CwARsFKEABClCAAhSgAAUo0PoEWnu4WSpXLliwADfccAOee+65WgElsHzkyBG89dZbOP/88+uFfKbh5nodrBk3rku4uS7DYbi5LkptcxuGm9vmunXUUXteq9esWYO4uLh2T8Fwc7tf4iabIMPNTUbLjilAAQpQgAIUoAAFKEABClCAAhSgQJMJSLj5280/4KkbH0f3kb0Ybm4yaXZMAQp4CnTIcPPChQtx1113KQd57LM8/pmNAhSgAAUoQAEKUIACFGh9Aq093KwFdM4991y88847qgLz7NmzodfrVcVkacnJyep3eYLM0KFDkZubi/fff78GtvQxZMiQKl8/Vbh5586d+Oqrr9T2f/rTn2A0GtWfJWy9Z8+eKv34+fm5n15zpqvscDggFaE3bdqEQ4cOITExEcOHD8cFF1xQa2Xo77//HitXrsThw4dVZepLLrkE11xzDdLS0vDiiy/i2muvdQ9JnqxjsViqDFECdLKPZxNjsS4oKMCXX36pXpIq2gEBAe7NYmNjccstt1TZz2634/PPP1djz8nJQbdu3TBhwgSMHz++ynbSZ2pqqvtrf/jDH2A2m/HDDz+oucicpZp0165dG8T597//HTabDX/84x+xbt06LFu2TM2lX79+qlJ5bRWoS0tL8dlnn6lxHTt2TI1dKsAOGzasyhjkBl45b6SJx/3334/t27dD/h28e/du9O3bF7fffjuCg4Pd+1mtVlWBXOaWl5eHoqIi9VpISAgGDBjQYEcJ9f/nP/9BYGAgbrrpJnz77bfqOPJINTkXZM2081YbzNtvvw0JrcvXZYx9+vTBuHHjEBERUat1fcfeoAXjThQ4jUB9w83yXpbzXN6j99xzDwyG/1U4kacU7Nu3D76+vuq6Le8XuU78/PPP6hoh3ye+++47Va1eQtRy/TpZNWh5v8n1Rd77YWFh6v109dVXQ74naG3jxo348ccf3X+XKsxybZPjyddljPJzs4kTJ2L58uXqlzTt+5h8Xa5Hnk1u/PHx8WnQecPrY+3XR5fL5f4+bjKZ1PeJ/v37q/X39vZukHVL7cRwc0vJ87gUoAAFKEABClCAAhSgAAUoQAEKUKDhAsfDzd/jqRv/zHBzwxm5JwUoUE+BDhlulg9n7rzzTkUlH5zOmDGjnmzcnAIUoAAFKEABClCAAhRoDoHWHm6WgNpjjz2GQYMGQZ4QI+FNCapJk+CZhLskfCRNC8VKKPm8886rwVc96CsbnCrc/Ne//hXvvvsuunTpgt9++83d32233YbFixdX6d/f379KWLeha1dWVgbpXwJz1dukSZNU2M0zrPrUU0/ho48+qrKpBLwlnFtbuLlTp041+pV/r8m/2zzb5ZdfrnxP1STEK8FqrRUXF+Pmm2/Ghg0baux266234sknn3R/XW6GlTCw1ubOnavWVQLInm3Lli0IDQ2tN6c2TwkA/uMf/6iyf3h4OObPn18lOC1hdakOLmHh6u3RRx9137wrr2nVVLXtFi1ahHPOOafGGkjYXlplZaUKmNfmIq9LaP3NN99skKOEqrV/b0tA/P/+7/+qjEMCk3LDsWeTse7atavGPGu7Mbm+Y6/3QnEHCtRRoL7h5s2bN+Oiiy5SvT/xxBPquipNQsxXXHGF+vPf/vY3dQOANLlJQJ4OINfZkpKSGu9X2V/68Wza94jqU0hKSoIEqLXq0nPmzMFf/vIX92YPPfSQuj5rN89oL3zyySeQILTcQHG6tm3btio3UJxue8/XeX08rlH9+lhYWIiBAwfWoJQbeT744AP06tWrPswtui3DzS3Kz4NTgAIUoAAFKEABClCAAhSgAAUoQIEGCVhcVny1+Ts8d+OTDDc3SJA7UYACDRE4vPMgXr3uJditdlVsSz4bl2Iu9W06l5QQaSNNPujXPjiSCVf/oLeNTIPDpAAFKEABClCAAhSgQLsXaO3h5qVLl6rQqYSLZKwSwPznP/+p1kXCoxJ8lTCatL1796rqihJMW7FihXvtJFQrodn6hpu1EKhUEH722Wfd/Un4TKoSS5M/SyiuscLNnoEkqRQqFTslWK2FqaXyshbM8wzvSUD24osvVqFoCQqXl5er8VWf808//QSprizt008/VU61hZt///13FZDOzMxUgT9pEgT0/MesVP2V6sBae/nll90haakiLKFzqdCsrYUE1UeNGqU2l9CyVNiWPiXkJ3OVwK1UyM7OzsYLL7zgPqYWPqzPm9EzxC1ViSXEJv+kfuSRR1Q3U6dOrVLd+9JLL3WHGeV8k6qqEk7csWOH2v7XX39FSkqK+88S+pWq0NJk7FIlWqplyzrJftJ++eUX1Y8EjmWe0qRi6+TJk1UoUaqPS5hdzm3P6tD1cfQMN2tjGTt2rKr8rYWpZUw9evRw80l1bKlSLeeIrIPcFKCFyuVckzC/1uo79vqsEbelQH0EPMPNUlVZCw5rfUgVZrkOeza5aUC7uUHeB/Hx8ZgyZYq6iUHei//+97/dm2vhZvmC9HP99derJwFIJXqt6rK8d6Qys7RVq1apCs3SpPq6XEMOHDjgvtlEquHL9VyaVNWXivDyvpQbQuR7loSs5XonVdeff/559R6U69R9992nKk5Lkwrw0uTmEPmBomeT60j1qux19eT1sfbro1wT5WYVqVYv3//kBh/5bxBpcl2U19pKBWeGm+v6buB2FKAABShAAQpQgAIUoAAFKEABClCg9QiYXRZ8sekbvHjTMww3t55l4Ugo0O4FjuzJxMtXPafCzfK5r3w22O7DzfLBr3yYLW3WrFk4++yz2/1Cc4IUoAAFKEABClCAAhRoiwKtPdwsgVftZsn09HQ88MADKuQs4TSpbpmQkKDCvqcKF5911lm1VjGW9TpZ5WYJoGnh1ffee08FWGtr2lNrGiPcLKFjLUD74IMPqpCb1rQKzRJwW7BggfqyVBSWIG23bt0goWWDwaC+7vkkndoC3VqfTz/9ND788MNaw83aNhKykxCdNAl6RUdH1+rgOXbPUJ98XULOEhoTQ7H0bJ4VopcvXw4tdCcBxH379qlqzlJ9ub5N66d6dWmp/q25avORgK+ECqWJp2dgWzt3qq+HhJkluCxNQm/i7+XlpQJxUmVc2uzZszF+/Hj3OknFaDnmqQKJ9XX0DDd7WklwXAtDSoBTgpwnaxJ0HjFihFojzwq3nudYXcZe3zXi9hSoj4Dntbq2/W655RbIddKzyftp5syZKugv14Lu3burGy7khgJ5z8pNBlrzDDdLlV7tphnpQ64Dhw4dUjfaaDd7yM+85Gdfntdk6cvze4d2w412jH/961/uqswSPpVrpTQJN8vPzrQnFGjba1XiT/cero+jbMvrY92vj3Kjz2WXXaaI5ckREmRvC43h5rawShwjBShAAQpQgAIUoAAFKEABClCAAhSoKiDh5s83fYO/MtzMU+M0Ai6nCy6nE9ABep1e/ueUezisNljKK1FaWI7K0nLI/n5BfgiOCoVPgC+gO/X+XJD2LXBkbyZevvJ4uHnChAmq0FhISEi9J92mKjcvW7ZMVbmRJh+WywdBbBSgAAUoQAEKUIACFKBA6xNo7eFmz7Do1q1bVXVcCZNKyOjee+9Fz549VQhZAkfytdpaXcPNEmyVsJuEmaTaszQJrkmF45MFUhsz3CxVkseMGaOOKxVCExMT3dOR6pESXpWQ6aZNm9TXtWrDUo347rvvdm8rlav79eun/t5c4WapTCoVg6VJpVQJLWvtrrvuwsKFC9W6SWVgz6aFm0ePHq2ctSZVqY8ePaoMZI3r27TwXvVQslRc1vqTyt9SmfWrr77C/fffr2xlfDqPH+JIlXD5N61nYFvG4hlulvC1rI3WJPDocDjUORkVFaVClFoFVglYSlhfzquBAwfWqAJaX0fPcLOE3j2ru0rFaglk1nYOSLVsuVlAqsUGBQVh/vz5WLJkCaRKtlZlWuZTn7HXd424PQXqI3C6cLM8PUzC+dWbvAfkveDZJOA8ZMiQKl/zDDfLTTV+fn7u1+VGGgkmSz9z5sxRX5ebIOQmGwkmX3TRRVWuv9p1XK7bnTt3dr/mGW7evXs3pNq0tIyMDPXkAani7HlzRVOHm3l9rHl9lGv3zp071doWFxera7g4ybVS1s9zretz/jb3tvLfdlLxW56W8P777zf34Xk8ClCAAhSgAAUoQAEKUIACFKAABShAgQYIMNzcALQOsIvT7oDdZoelzIzyolIU5xehrLAMlnIzdEY9fPx9ENM5BpGdYmD09nKLSIBZAs35h3Kxf+NepG9NU/uayyrVk059/H2RPCAFw88fidiUeOhNxg6gySnWJuAZbpbCL6+//nqV4jB1VWtT4WZ5rK2EDqRp1arqOlFuRwEKUIACFKAABShAAQo0n0BrDzfLP7CTk5MViIQvpaKvhGUXL16MHj16qDCnVOuUyrRS3bK2Vtdwc237io9U+TxZa8xws2eFyFOdARKEkzZ48GAVuHrllVdU9WrPdqo5a9s1ZuVmz7Fra6MdxzM0qI1de00LN3tWRG2Ms18LN//3v/91V57W+q0eGHzrrbeU4ama3Kn88ccfuzfxDDefrqqqVH597LHH8Pnnn9c4hFQilwBaQECAeq2+jp7h5m3btlX5YUNt50BhYaG6GWD16tW1Trd6iLs+Y2+MdWMfFDiZgGe4ec2aNYiLi6szlrx3tRtWpOLyM888U2Nf7TrVq1cvLFq0qMrrcuPFn/70JyQlJWHFihXwrLB+qkFIpXitkrtsp4Wba7vRo7Z+mjrczOtjWpWbP6SKv1yT5eaP2lpt32vrfBJyQwpQgAIUoAAFKEABClCAAhSgAAUoQAEKnEaA4WaeIhJIdjocsJmtKC8qU2HkY1lHkb0/W4WUS+TvOYXHA8pOlwIzmPSI6RyHUZeOx6Czh8A7wBd2sxWHd2Vg66+bsG/dbuSl58BqtsEp1Z49msnbhM4DUjDq4rFI6JkE32A/6HU66E88Kbb6iugNeneVZ71eD4OXEfK1k23PFW0bAh0y3OwZkJAPgaQKFxsFKEABClCAAhSgAAUo0PoEUlNTce65gmBQ6QAAIABJREFU56qBrVq1CgkJCa1ukFpIUx6DI9Wa//rXv0IqYkqV28mTJ0PCqSer2imTOZNws1TylSDayVpjhpsPHDgAuSNWmgTipIp09ebl5eWuwqjNSyrtSsVdz9YU4eZTBQql0qlUJJb20UcfYeLEie7hSGXTWbNmVak6rb2ohZslUCbVkxuraeHmDz74wG2q9V39tc8++0yFj6V5jttzLBIkv++++9xf8gw31/XfvHK+ylOOJFgs5295ebnqT6o+S/VnafV1rG+4WZylaq2/v7+6UUCCnBLU/P7779Wxq4ebtQnXZeyNtXbshwK1CTQ03Cw/rL3lllsgFdWlyc0qUpG8+vVVCzdLdXV5P3g2qd4uN4N4vqYFj6U/udGmtvbss8+6b86R17Vws1SMl8rxp2vaMd58801ccMEFp9u8zq/z+ljzvwukqv/UqVNVtXv5ni/Vt8VJqjdLCFyu1ww31/kU44YUoAAFKEABClCAAhSgAAUoQAEKUIACDRBguLkBaO1gFynyZDNbUFFUhqNZBchMTVdh5qOH8pB/OBfmUjNsVpt6YiiO55lrNHkiaVBkMCbdMBUDzhqEvRv2YOnsJchNPwKHzXFKJdnXJ8AX4XERCI0Nhc6gh+kkVZwNJiN0ep3qT8LN/iEBSOzdCbHd4hGZEAWjt8kdfm4HS9NhptAhw82e1abk8bYjRozoMAvOiVKAAhSgAAUoQAEKUKCtCSxYsABBQUGYNm1aqxz69ddfr0Kh1157LebMmaPCs8uXL4eE0S677DJVEVeCZzfffHOt469ruFn6lX+7SJhJ+zfMuHHj1DFP1hoz3Gy1WtGtWzd1qDvvvBOPP/74KddDqh1LSPa6667DCy+84N5WwqpS0VqqOr/44ovKrbZWl8rNOTk5botTVSguLS1VwT9pEkB/6KGH3IeU0LMEZ2sL9GnhZgn3Ssi3sZoW3nv11Vdx6aWXuruVytHjx49Xf5fqrBLuXbdunbvydW1h6NrG5Blubsi/eWWNnnvuOXUuS9BYqi4bjUbU17E+4WbPtfzkk08wduxY99S00PPJws2eBicbe2OtHfuhQG0CDQ03a8Fk6VPeaxJSnTFjBt5+++0qh9HCzRJWlhsQ5AezWpPrk3yflO+R7733nvryzJkzITf2DxgwAF999RUMJ6lk4XkQLdw8cuRIzJs377QLLd9/JGzbVJXteX38X+Xm7777Dnfffbdak61btyIkJET9WT5YGDJkyEmfknDaReQGFKAABShAAQpQgAIUoAAFKEABClCAAhSoo4BnuLnHqD644W+3QW801HFvbtaWBBw2OypLylGQVYCMnQeRsy8TWXuzUJBdAHNZBWxmm/rZpPySJgFkqZAswWL5s/xy2B3ql7vpgIDgAER2jlHB6LJjpe7962IjfeJ4brmOTcakg6+/L/xDA9B7TF/0nTgQSb07wSfQjyHnOiq2hs06ZLh506ZNuPjii5W/VIWSDwLYKEABClCAAhSgAAUoQAEKNETg0UcfVUEwqaYoj4uXqpsrV66EVAROSkpS4a933nnHXYG6+jHqGm7++uuvVcVkaf/3f/8HqYgs7VSh3sYMN8uxpHqxBOWkSfhVwm1a5WipLCkBOqneLO3dd99VVaylSTCrf//+kIC0VCJ+8skn1dfPNNwsVU87d+6s+pLAtPTbp08fNQ4JG/r5+bm5tbBfeHi4GkPXrl0hPn/84x/VNn/+859xxx13VFmepg43S1hcgrzR0dGQQLL4Lly4UIUc5d+tPj4+8KzYKcHGv/zlL5DwYWRkpBqrhN2rV3mtT7g5NzcX3t7e7rCc9FlYWIhbb70VGzZsUMeQ81oLR9bHsT7hZhmHrKE0zxC3VOS+8sor1denT58OqZAuQWtp9R17Q97f3IcCdRHwDDf/8MMPiIuLq3W3wMBA9/m7c+dOnHfeeWq7l156SVXivfrqq9Xf//GPf0CuP1rTws3y97///e/qpgiTyYTNmzfjoosuUpvJNVmCxtLke5J8b5ImN+DIjQH9+vVTx5YbACwWi7rOeLb6hptvuukmVXFa+pFQtfxszdfXV13nJXytvU/r4ue5jXbzB6+P/ws3S7Vu7XvVxo0bERERocjkvy3k3JEm/80hNxOpH/KzUYACFKAABShAAQpQgAIUoAAFKEABClCgkQU8w80DpgzCVc/cXM+waSMPiN01qoDNbEVpQTGy92Yia89hZOw4iLz0XJQUlMBaaYHT4VTHM3oZYfI2qWrKAaGBCIkORUhMKHz8fOHl4wWDyaB+RllytASpK7epPuSzPK1JANrlrFniWfYx+ZhU/w6rA1aztV7h51Ni6ACjwYCQmDB0HdYdfcYNQKd+neEf7A+jl4kh/UY9kxq/sw4ZbpaqU+eff77S/OabbzBw4MDGl2WPFKAABShAAQpQgAIUoECHENACYdpk5d8bUllTqhtrzTOYLOEzeZqM1qRSp9a0sJkElKQysmdgzrMPCaZNmTJFBaclrCuVogMCAlSlTs8K0bX1LceSispRUVH1Xp+jR49i6tSpqkqk55glhCtfkwCejEuaVPmVCtPaGCSYe+TIEfWaVqHUM9wslUo9q5XWNnYJJH/77bdVxi0BaglSVzeU/VNTU90BPs8QYPWJi6EE0iUMXVJSosLD0mobgwR+PUPT9UYEVIhRmhxX3OR3s9nsPl71St9yPl111VVVDiWeMlYZo1bJU84D7byrPnapTP3xxx/XGK4E5iQ4p43HcxzyNQk5a2F0+XtdHWXb+oSbZXst6C9/lmCjPMZMgtUTJkxQ1dG1Ju85CXTWd+wNWSvuQ4G6CHheq0+1vXaji2wvYX3t/JYq6fLDW6ly//7776sufvvtN/fNI1q4Wbt2yuue11TPa5i8Jj8svuaaa9T3Is+m7XPbbbfhiSeeUC/JDQvyXq3teifHHTVqVK1T+uWXX2o8kUAbn8xn4sSJdaGrsQ2vj/+7FmrfI7OysjB69Gi3lVTkzszMVN8/ql8f5VqZnJzcIHvuRAEKUIACFKAABShAAQpQgAIUoAAFKECBkwl4hpsHTh2CK5++kVhtXMBps6OiuBzZ+zORtuUA9m/Yg7yMPJQVlsJutcPldMJgNMA7wAfhseGI7hyL8PgIRHWOQXhcuKqAHBASAL8gf+j0eqi6CyeKL0j1573rduH7t75G9r6sKgFnTzajyYigyGAk9emMhF6JiEiIwtHDedjwwzrkph1pvIDziYPKfALDghCTEov4HseP5xvsC28fL+i1JyC6XHC6XKqIh8FkREBoAEJjwuEb5Kfmyda8Ah0y3OxZHUc+xNUeT9y89DwaBShAAQpQgAIUoAAFKNAeBObPn49HHnlETUWCXRKo3bJlCy688EL39KT6rFbJU0KZEhA9VZNws4TcpGpvz5491aae4Wb5+5IlS3DLLbeo1+Rx9TIGCehKoO10bd26dYiJiTndZrW+LpWCX331VSxYsKBKGE42rl6JOT09XVUj1uYrPvfee6/ykarJntv/85//VFV5T9WkEvaKFSuqbCJGn376KWbPnq2Cgp5NqoqmpKS4vyTjEKd9+/a5vyZhbal8qVXClPlJlemTNc/AdIMAPcLNr7zyCubMmaPCydr589BDD6nAYPXqmwcOHFDnhMypelu0aBF69eqF2sKG2rZS9VvOoepNq05d/esSlpRz6b777qtRgbUujtLfjh07VIBTmoQng4KC3IfRgswSTtfOWVk/CXZK9XMJ7cn5ImF5CTFPmzbNve/LL7+sApkNGXtD14z7UeBUAp7X6lNtJzdinHPOOXjmmWdUhXI5x+Varn1/kBtX5D0j1yj5WZVUcpdrgRZulq/JuS+V+7Uwsty4IIH/hISEKoeWCs1SGV4q/Ws3lmgbzJgxw30ziRxP3qu1Nbmujh8//qRTkirVsk31ELVUl9Yqrtf3zNHCzbw+pqmnH2jXR7l5Rb5fyPVRmgTV5aYXWfcHH3zQzewZiq+vPbenAAUoQAEKUIACFKAABShAAQpQgAIUoMDJBBhubh/nhgSWpRLz0cx8HN6Zjl2rd+LQzgwU5xUeDzSfCPV6+3kjLC4Cib2S0H1EL8R2jUdEYiSM3ib1pE+d4fQhX4fdgR1LN2Phv75CbkaOu2Kz/Mxbb9AjODJY9T1o6lAk9kmGX5CfChg7bLLfFix65zu1n/RTW7XnM1mR42MwwGDUQ6pJSwVng8ecxEG2kXBzYHggEnt1wuBzhiG5fwq8/HzO5NDct54CHTLcvGfPHlVtTJr2IXA93bg5BShAAQpQgAIUoAAFKECBDi9QVFSEY8eOqbuXIyMj3VWSq8OUlZVBQsMS4JMfBkgoT/aRis/VQ7wNRZUfNEglS5vNpn6wImFlrRJ29T7l+BKelfEYjcaGHrLB+2nhPQk3Tpo0SVVgll/x8fGn9ZCKrHl5eaqqt7e3t6rAbTKZGjwW2VEspNK29C0eoaGhqhL46damqRxlHBLGjI6OVuORCs7i4+XlpX55zrehYz8jMO5MgWYW8Aw3y0368h45fPjwKa9znkOU64VcN+Q6GRISot7jjdmkb7nOy3U9ODj4jPrn9fHUKyMBeO37l2wpf5eK+9q1sSW+pzXmucS+KEABClCAAhSgAAUoQAEKUIACFKAABVqnAMPNdVwXlwvWCguKjxbD4bDDL8APQeFBgFYVuI7dNPZmLrtDVWSWKso7V2zD3nW7kX84FzaLzR0clsCxVClO6t0Jfc8ahM79OiMyOQYmLy9AL2WZ69+kgvPWJRuwbO6vKMotVB34BfsjvnsiBp49BN2GdoePvy9cJyo+a0eQqtIHtx3A1iWbkLU3ExUl5bCrsTpPOgipqixzcNqdsFRaUFFSAavZ0ijBaAlzh8aEYfzMszDmsvHwCfKvPwb3aJBAhww379+/H5MnT1ZgUiFHHnfLRgEKUIACFKAABShAAQpQgAIUaA6B6uG95jgmj0EBCrRdgerh5rY7k9OPnNfH0xu19S3kZpXFixejd+/e6hcbBShAAQpQgAIUoAAFKEABClCAAhSgQOsXqHSZ8en6z/HKbS+qSrtXPn1j6x90c4/Q5UJeRg6Wf/IL9v6+G9ZKKyITozD+2inoO64fdPUIOEu4V4K5qmqxCyq06+3noyoJ16fZKszIy8zHnrU7sWdVKg7tTIe53Ax58h9cx3tSVZSjQ9F9aA8VOO48IAW+QQHQG3RAtdBxfY6tbeu0OVCcX4ji/GLIQUMiQ+Ab5A+pDg39KSpAS1DcbIXNbFGBZfmzeJysGU0GeHmbYKm0origBLlpR5Cx46AKchfmFKjK1Gfa/EMCMOrisZh4zRSERIfWCGWfaf/cv6ZAhww3Hzx4EBMnTlQafFwj3xYUoAAFKEABClCAAhSgAAUo0JwCDO81pzaPRYG2L8Bwc9tfQ87gfwILFizAww8/jISEBKxa9f/snQV4XFX+hr/RZOKeNEmbuhv1QouWlsUXtsAuUvhTFrqwyCKLLItDcdfCYl2glKVIcStSd3dNGnefmczM//nO7Q2TySSZtEka+Z2HPElnzj33nPdeZq689zuLBY0QEAJCQAgIASEgBISAEBACQkAICAEhIASEQAcgUOmuwps/z8Xz1z2JUX8YK3KzzzbjrHm5uzPx+QsLsPmXDXA5KSV7YDAa0H1QGmY+8zdEd4trfEu7PagoKUf6lv3YvWYHCg4WoKqsEh6PG5bgICT2TERyv1SkDeulBGFTkLV+ex4PahxOlBWUKrF358rt2LN2J/Iz8mGvrK6TYmy2mBHXIx4DJgzGsBNHInVgD9giQ9UMfa1RyIOlqVlDG133ISHbbx2vcGmuiynP9opqFB7Mx5bFG7Hlt83I3p2JqvJKtX0OtwSH2TD0+OEYc8Z4pA7ogaDQIJisFsXNaDRo42slhofb5468XJeUmzl156RJk9R2++WXX6DfWO7IG1L6LgSEgBAQAkJACAgBISAEhIAQEAIdg4DIzR1jO0kvhUB7ISByc3vZEtKPliDw9NNP45lnnlFN7d+/vyWalDaEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBFqZAOXm//z0Dp7/+9MYfbrIzb64S3OLsODJD7Hu29WocdZNCKZEPGP2VRhx6hglO/sWD6XmojLsWr0da79eif2b96Mktwhut7s2XZnLMGHZFm5Dtz7J6Dd+EHoO64X4tERYrFa1zuqySuQfzMPB7enYu3Y3Du7KQHlh+aF2freCTWYTYpNjMeSE4Rg5dQyS+6YiKNTmt2+tvFu1TfMeD1wul5KcM3cexJ61u5CxLR0FGbmoLq+qTcdWvA8Vj8ut5PSGkqKZoB0ZF4mYlFjE90hAVFIMbCE2GM0GWIKsMFstiie3PXk3VNi+2l88gMFsQFCQFSFR4YhMiERoZJhK6zb6Wd5ZaUfGjgMoyipERFwkug/sgaDwkLbh2cZrodz8+J8fgNPuxMknn6yuLUdGRja7FwaPrtc3e9G2XyAzMxMTJ05UK16yZAlSUlLavhOyRiEgBISAEBACQkAICAEhIASaJLBlyxZceOGFatryefPmNVlfKgiBjkCAoiIvpEybNk0etu0IG0z6KASOMoENGzZg2bJliIuLw3nnnXeUe9O6q5fPx9bl2x5aF7m5PWwF6YMQEAJCQAgIASEgBISAEBACQkAICAEhIASaR0Dk5oZ5GTwerPt+Nd675y1UllbWq8gk3/HnHIsL774M5iBL7fuUmpnMvG/Dbqz+Yjm2LduKssKSOunKftdqAMxmM8JiIhCbGger1QKn0wl7hR3FOUWoLK2A+5Cc6708JdvI+CgMGD8IY8+aoBKgrTYbUN+3bt7O0YFqk7m7pgYlucUozCpAeVEZygrKNH7l1dpI3G5UllWhuqIaGVv3I3tPVj1h3XvITGymyKwSm7X/mE+t/qPcbDQ1LDe7Xb/LzUazEZYgC2wRoYiIjUBc9wT0HzcAaUN7ISYlTgnT3K7F2YVqf1v+yWIUZWty8wl/noJjp0+G2V+adwfaPv66mr03E09f/LD6f6vLyM05OTkYN26c4rF8+XIkJSV18M0o3RcCQkAICAEhIASEgBAQAp2TgAgwnXO7yqiEgBAQAkJACAiBrklAju265naXUQsBISAEhIAQEAJCQAgIASEgBISAEBACHZuAyM0Nbz/KzSs+X4oPH5qrhFjKriaLSSXN6iWxVzfMevkGxKYmqETg6tJKHNyZjlULl2PTzxtQWlDctNR8mLsQpVtbeAj6jx+IY8+bjD5jBsBqCzrM1jrZYh4P4PHwP7Vd9OLxMLkZyNmbhe/e+Aobf1oLR5WjTQdPYTooOAiJvbth4MTB6Dm8Dwoz87HuuzU4sGUv7JV21R9u36TeSbj88WuQ3L97m/axLVaWsy8Lz1zyCMqLyzFlyhQ89dRTnT+5uaCgAKNGjVJ8V69erZJvpAgBISAEhIAQEAJCQAgIASHQ/giIANP+ton0SAgIASEgBISAEBACh0tAju0Ol5wsJwSEgBAQAkJACAgBISAEhIAQEAJCQAgIgaNHQOTmxtkXHszD589+jL3r9yCpbzeVrLz+hzW1C5mtZlx8/xUYduIIpG87gKX/+xXbl21BeWG5mumztYrFakHqoB449vzjMfykEbBFhWsJw1ICI+DxoLygFD+8/Q2WfvwrKkoqAluuJWsZAKZuM9XZ5XSjxuGsI2JzVaFRYbjkgSsw9KRjWnLN7aItXW4m+9NPPx2PPvoowsPDm903g8dbX2/24m27QHFxMUaMGKFWum7dOkRHR7dtB2RtQkAICAEhIASEgBAQAkJACAREQASYgDBJJSEgBISAEBACQkAIdAgCcmzXITaTdFIICAEhIASEgBAQAkJACAgBISAEhIAQEAJ1CIjc3PgOQW2SEmxpfgnCY8Kx6dcN+PCBuXDV/C4udx+chpT+3bFr9XYUZRXWec+7dS1p2QamPVNaLSsoRUluMcqLy1DjqAloz6QMG5caj9Gnj8eY08chNiUeRos5oGWlUn0CVSXlWPvtaiz5+Bdk7TwIh90B/B70fNSRJfRIxOWP/RXdh/Y66n1p6Q54y81nnHEGZs+e3fnl5rKyMgwdOlSx3LRp02ENuKU3hLQnBISAEBACQkAICAEhIASEQH0CIsDIXiEEhIAQEAJCQAgIgc5DQI7tOs+2lJEIASEgBISAEBACQkAICAEhIASEgBAQAl2HgMjNAW5rjwcwGJCxdR9euvpplBWW1S6oEpMNgMft34o1GA0IjQxDv3EDMXjSUPQ5ph+stmDYq6pRkluEbUu2YMtvG5Cfngd7pb1eei/Xaw22IDopFgMmDMKIU0ej17DesNiCAuy8VGuMgMtRg9x92djy20bs27gHeQdyYa+oRo3zd+HcaDTAaDapdGwzZfKmQrI9gMvpgsfjRk2NC85qBxzVDjir66cz++ubyWREaEw4jjvvBJz8f9MQHGrrdBuxS8rNVVVVGDhwoNqYW7duRUhISKfbsDIgISAEhIAQEAJCQAgIASHQGQiIANMZtqKMQQgIASEgBISAEBACGgE5tpM9QQgIASEgBISAEBACQkAICAEhIASEgBAQAh2PgMjNzdtmlcXlePGvT+LAlv1NLqhLzWlDe2LsmRMxaOJghESFAUZjnWVr7A7k7M3GvvW7sGPFdhTnFGrytNsDc7BVJTX3GNoTAycOQXL/VFiDrUq0ltKyBNwuF6pKKlCSXwJHlR1ur3Rug9EIo4nbjZKzsUn8dOE9LrcS1VW7ZdXI2HYAe9fvQsbWdJQVlvpN+KY8HRYVhv4TBmH4KaMwYPwghEaHt+xA20lrXVJudjgc6Nevn9oEO3fuhNVqbSebQ7ohBISAEBACQkAICAEhIASEgDcBEWBkfxACQkAICAEhIASEQOchIMd2nWdbykiEgBAQAkJACAgBISAEhIAQEAJCQAgIga5DoFZuvv5pjDt7AqbfeWnXGfxhjNRTU4NvXl2IL1/9vOGkZiYt26zoMaQnJk0/Af3HD0J4TAQ8TQnJHg9czhqUFpSiNL8EbpcbQTYrYrvHIyg4qJ4UfRjdl0WOJgG3BxWl5di3fjc2/LgO25dtRkmetp0N0PaZpL7JmHjuJIyYOhoh4aGdWmLvknKzy+VC79691W64Z88emEymo7lLyrqFgBAQAkJACAgBISAEhIAQaICACDCyawgBISAEhIAQEAJCoPMQWLZsGWbOnImJEydizpw5nWdgMhIhIASEgBAQAkJACAgBISAEhIAQEAJCQAh0YgLecvOEc4/F+bdf3IlH2zJDy9qZjlevfQ4FmQX1GrRYzUjql4IJZx+LEVNGIzI+SqTklsHeeVrxeMC07owdGTiwcQ+Ks4tgDQlCQq8k9B7RB5EJ0TB0Aee1S8rNjPLu2bOn2pn37dsHRnVLEQJCQAgIASEgBISAEBACQqD9ERC5uf1tE+mREBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRA1yEgcnPzt7Xb5cKqhUvx7RtfoSSnCB4PVOpuSv9UDDl+OAZPHoa4lHgYLebmNy5LdCkC3JfcThcMRgNM3F+6kOvaJeVmh8OBfv36qZ1ckpu71P/rMlghIASEgBAQAkJACAiBDkbgjTfewP3334/w8HBs2rSpg/VeuisEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEOjaBCncF3vjhXbx44zOQ5ObAt6XL4UTWzgwc2LIfbrcb0d1ikdK/OyLiI2HsAqm7gZOSmkLAP4EuKTdXVlZi0KBBisiuXbtgsVhk/xACQkAICAEhIASEgBAQAkKgHRIoLS3F/PnzMWTIEEyYMKEd9lC6JASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAQ6LwHKzS99Pgev3/4SJpw3CefffnHnHWwrjMzA2GYAni6UuNsKGKXJLkigS8rNFCSGDRumNvf27dsRHBzcBTe9DFkICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhEDDBCg3P7/gFfznrlcx8XyRm2VfEQJCoG0IdEm5uaCgAKNGjVKEt27dipCQkLahLWsRAkJACLQDAnv37kVxcbHfnphMJgwfPrwd9LJ9dWH37t3ggzEsiYmJSE5Obl8dbOe94f722WefwWaz4cwzz1S/pQgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEALtn4DIze1/G0kPAyegJ2m73B4YABiNBknVDhxfm9bM3Z+Fpy+ejYqScpxxxhmYPXs2wsPDm90Hg8dzKD+92Yu2/QI5OTkYN26cWvHmzZsRFhbW9p2QNQoBIdAlCVRUVNRKsr4AjEYjYmNjYTabW5XNtddei4ULF/pdR2hoKLZs2dKq6++IjV9xxRX48ccfVdf//ve/45ZbbumIwzgqfXa5XDjuuOOQlZWl1n/CCSfgnXfeOSp9OZKVut1u5ObmQj/ciY+Pb/X/V4+kv7KsEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIGWICByc0tQlDbanIDHA0dVNarLq1GSW4SinGIUZRWgsrgC1ZXVcNXUAAYjIuMjkdI/FckDuyMyLhoGI5VnKe2BQH56Np69/HGU5BZ3Hbk5IyNDiVYsGzZsQGRkZHvYFtIHISAEugCBt99+G//+978bHWnv3r0xduxY3Hjjja2SENwe5eZXX30VRUVFistpp52GkSNHtqu9oa3k5rKyMrz44otq7EzR5rbq6LMLrF69Guedd16d7blmzRol8neEwtTuzz//HB988EGtoM1+P/XUUzj//PM7whCkj0JACAgBISAEhIAQEAJCIGACfKjPaDBq9XkP2zCFAAAgAElEQVT91nPod8AtSEUhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBDobAZGbO9sWbefj8XjgqnGhxuFUP26nCzAYYLaaYQkOUr91/dhj0P5iGrPb40FNtQOVZVUoyspHxo4MZG7PQPbeTBRk5MNR7YCjyo4ahwtul6sWAtsLiQxF6oAeGH36OAwcPxhhseHK29Hbb+fEOm33Cg7m4rkrnlBSepdJbt63b59KjmRZt24doqOjO+0GloEJASHQvggEIjd797g1BMqvv/4au3btql3Njh078Omnn6p/H63k5smTJ+PAgQOqD48//jguuOCCdrXh2kpuzs7Oxvjx42vHvmjRIvTq1atdsWhuZ8rLyzFkyJDaxSjvMwXbcOgAs7nttVX9pUuX4sEHH8SmTZv8rvLRRx/FRRdd1FbdkfUIASEgBISAEBACQkAICIFWIVBdXQ2r1YpJkyaBDyampKSAx+wJCQlqfZyJ5aOPPsKAAQPA86KZM2eqkICmjue5HGcn4o8UIeBNoLS0VO1TgwcPxoQJEwSOEBACQkAICAEhIASEgBAQAkJACAgBISAEhEAHICBycwfYSB28ix63G44qByqKSpG5MxN56bkozMpHQXo+KkvKYTAZEZ0Yg8Re3RDXPR6RCVGwhdtgNBnhcQMVpRUozMhH9t4sHNi8D4WZBSjJK4az2tEsMiERIeg1vA+GnDgC3QenIaZbLIJDg2GymGE0mSTVuVk0j7yyt9x81lln4ZFHHkFYWFizGzZ49Hnam71o2y+wc+dOTJkyRa2YN27i4uLavhOyRiEgBLokAV+5mTeN9VJVVVUnGVZ//aefflI3l1urfPfdd+oGNYvIzf4pi9x8ZHsfJe0333wTNpsNs2bNwogRI46swTZY+vnnn8cTTzzR4JpEbm6DjSCrEAJCQAgIASEgBISAEGgxAt6X7Z599lm8++672Lp1q5qRZMaMGbBYLDCbzWp9rMsfisn8rYvM+usNPQDo3dmsrCxcffXV2L59u5Knm5KhW2yg0lC7JzB//nzccsstiIiIwMaNG9t9f6WDQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJAXbl5Ms6//S+CRQi0CAG3swZlxWXI2p6OLUu2YP/GPcjZnYWKsgptZkE/xWQxITwmEsFhwYfkZg+qyipQml8Kt8vdIv2iNB2THIfY5DikDEhFfFoiYpNjEZUYjdCoMFhtQTAr4dmohGcDgz7aechfi4Bp40a85ebp06fjvvvuQ0hISLN70aHkZt68Oe2009QgV65cWZtG0+xRywJCQAgIgWYS8Jab+aHrK09WVlbi9ddfx5NPPlnb8plnnokXX3yxmWsKvLrIzU2zErm5aUadrYa33Mxk8XPOOUc9AVZQUKCGKnJzZ9viMh4hIASEgBAQAkJACHROAhSSa2pqMG3aNCxZsgSczSwxMbGOsOw98qYkZG9Juqm6brcbxcXF6N69u/pNgVqKEHj66afxzDPPKBD79+8XIEJACAgBISAEhIAQEAJCQAgIASEgBISAEBACHYCALje/+a/XcNwFJ+LcW9rXbNwdAKF00YdAjcOJgsx8bFu8BRt+WoP0zftRXV6lAjdatRh8Wg9kdQYDuJglyKLk5siEaNjCgxEWFYHobjHo1jcFcakJiE+Lg9Vmg6ospUUIeMvNF154Ie69914VrNjc0qHk5g0bNoAx1SzLly9HUlJSc8cr9YWAEBACh0WgKblZb/S2227DvHnz1D+7deuGZcuWNbg+frFnZmaCqfROpxN9+vRBjx49alO3murokcjNXGdFRYVal8lkUk/HJCcnN3nTury8HEyq1gs/k5nsxXL33XcrkdRfiYmJUetpqPCm/d69e7Fnzx6VQk0W/Ixv6qa73l5ZWZlKL8vOzlbpwmlpaeqt1pSbCwsL1VTPLLm5uTj99NNrh8d9gGPwLUxQi42NbXTTcnrp3bt3K3mBDPr166eSsRorRUVFSnxgiY6Ort2HyJRcyKN///6Nbl+73Q5OM9xQ4fQQzT3Q4Fh405tj4baMj49X/eA2bq3y22+/qf2IEgjlD5aTTjpJvcby2GOPgQdOUoSAEGh9Alu2bFECzPnnn6/+n5QiBISAEBACQkAINE2A54nbtm3D+PHjUVRYpC4Im8ymgM+Nml5D0zX0i9BMyti4aSNOOeUUdc7T2Dld061KjY5OQOTmjr4Fpf9CQAgIASEgBISAEBACQkAICAEhIASEQFck4C03T7rwJJxz8/SuiOGIxmzgjHkdONmX/Xd73KhxuOBy1qjEYpPFrK47B5xY7HYrgfngzoPY8MMabFu6BXnpuXBWO46IbUML0y9hqrI1yAJbZChCI0IRHG5TkjLf47XryrJK5B/IRVVZ88Vqts3xM8E5JCIM/ScMxNgzJqDXiD6w2IJaZUxdrdEuKTevWbMGf/zjH9W2ZmpNSkpKV9vuMl4hIASOEoFA5WaKlRdffHFtLymW+sbqMwXrzTffVCnPFIx9CwVpTgOsTy3c0JCPRG4+++yzsX79+npNjxkzBn/4wx9w6aWXIiio/hf2ww8/jFdffbXZW2Hp0qVKnvYtHD+nHtCFcO/3KQEzZffUU09tcH1M473pppvw888/16lDwZntPvfcc/jxxx/Ve3//+9/VFLotVTiTALdvc0pjwjsF5bvuugtffPFFvSaHDh0K3kimGOyvnHHGGdCnmP7000+VMM/9SxfP9WWuuuoq3H777X73rW+++QZ//etfGxwOxcTXXnstoOFy36Ls7m8fYwPnnXee6ocuHwfU6BFU8pabJbn5CEDKokKgmQR0ASY1NRWLFy9u5tJSXQgIASEgBIRA1yNAqZgzAH344YfqwUJ1ATfAi+Zclg+jckahXbt24amnnqp3TqenQbN9Pnh5wQUXgOen/NufuOwtOc/62yzVF85ixIcVuYyUrkVA5Oautb1ltEJACAgBISAEOiKBdevWgdfZOfsIj40ZvsGAEt5bbqtr0R2Rm/RZCAgBISAEhIAQ6NwEyt0VeGHBK2Bys8jN9be1x0Xp1wlQAHa74apxocZRo347qh1w2J0qzDc0OgyRsZEwWswdY4fheFxuVFEATs9FxtYDyFMicCXMVgvCY8OROrA7Ugf1RGR8FAwm/9d73S4XyvNLsX/zPmz+ZT12rNyOwoP5io+/oq5nGwwwGrUf/u1RXN2NJzura+GAxWpBSGQo4nvEI2VADyT0TEJSnxTEJMfCbDFp16UNgMftgaOyGtuXb8PyTxfj4I4MJVofbno0243vHo+J503G2LMmIiI+KnDxu2PsEW3eyy4pNzOtmTdeWCgQcnpMKUJACAiBtiAQqNy8ceNGdTNaL0ycj4yMrP03U48pLvvKuL5joGT8wQcfNJq0eyRy8+DBg/2K1Xo/KOHyhjeTpL1LS8rNTNKdMWMGDhw40OgmnDVrlhJhfQvTgC+66KJ6Aq9ejwyZeNwR5GZK0pTiKWs3Vl566SVQZPYt3nIzZYN77rmnwe17wgknKOHBarXWaaYpufnkk09WUn5TZfbs2Xj55ZebqqaEiM8++wx9+/Ztsu6RVhC5+UgJyvJC4PAIiABzeNxkKSEgBISAEOh6BPQLrlFRUSgpKVEA9Ncakpv5PkUNdTHXA8z971z1kKpeuNxbb72Fyy67TL2kt8OLvouXLMbIkSOVQM3lKX+88MILuOOOO5Tk7Csu86I+C9fHcw8+dMkHSZsjX3e9rdr5RizHdp1vm8qIhIAQEAJCQAh0JAKcvTEjI0OFfPDeAu+xMKCFM4wwKIZiM+vwngB/eP2Z73PGQ1535/VwzjA2YMAA9V5CQgKCg4MDfpiwI7GSvgoBISAEhIAQEAJCwJtAubscLyx4VeRmP7uFvaIK25ZsVmIsE40d1U5Ul1eitKAMVSXlsFfZ4XJq10aju0Vj1OnjMPzEYxAaHd4+d7JDgra9ohqFmfk4sGk/dq/Zjn0b96IkpwjVlfbafjO9OTwmAj1H9MaJf5mCPqP6wcAkZxYP4HI6UZxbjP0b9mDd96uxZ/0ulOaVKlG5XjEYYDYZYQ0NRnRSDBLSEhERHwlrkBVGsxGOKgeydh1EfnoeKksrFWuPhxetAZPFpOpRaE7smYhufVNUn5J6pyAiNgLm4Lpei++6PTUuZO/NxvrvV2HH8q3I3pujBG5tHVxJ80pIeAhGnTYWJ8+YirgeiTBIyEfzAHrV7pJyMxPf/vKXvygMv/zyC9LS0g4boCwoBISAEGgOgUDlZgrBt956a23T+/fvr7MayqlMjtUL04mPP/54dQFt1apVdUTfBx54oPZGtL++Hq7czC9wJvTa7Xb1Q+GaFwV9xdpjjjkGH330UZ2UX4rCX331VW13OF69UCbu3bu3X6x33nknoqOj67zHm+zekne/fv3AxGWHw4Effvihjpz79ddfY9CgQXWWv/7668GUYr1w2cmTJ6sLmN9++62SnnmRUk/Hbunk5jlz5mDHjh1q9YWFhfj+++9r+8L06/Dw+ge03N7+RO3p06djxYoVtctTLh81apS6SOudDs3xMAXbW5jnQt5yM9fBbUkexx57rEqnID/vFGcK0Fynd2G6GwUF70JZn31gCURu5r5xzTXX1GmD25UXjCmj6+nSegXuY5988klz/lc8rLoiNx8WNllICBwxgZtvvll9j3C2Fc66IkUICAEhIASEgBDwT4DnaJQtmC7H375Cc0OiM1/n+ZPT6VQzBnlLyd5CMtvTZwbi61xOr0vZw2Tk9IPAxx9/rGZZ8ZWb9fXzN9vi+njux3MFi0WbBlBK5ycgcnPn38YyQiEgBISAEBACR4sAjzN5/Zg/vKfC60lJSUnqfglf47VtHrdGRkUhLzcPJpNRHdPyWJTF+9iXf/MnOiEZpYW5Sr7g8ao6BjablaTBoh/j6se+rMP2+LAff5stVlgOyR2lpaVKlOZx8IsvvoiwsDAlRlOizsnJwfDhw9W9CL4us5wcrb1I1isEhIAQEAJCQAg0RIBy83P/exlv3T0Hky86GefcXNcT6LLkPB5s+HEN5j/8HopzigLCYAu34fgLT8KUK/+A4PDQgJZp9UoUmmvcsFfbUZZfjIPbM7BzxVbsXrNLJTYzebqxYjAa0GNob5x3y3SkDe2tUqyLswqwa90urP9+DQ5s2ouK0golPPsWdQwdbEFsShy6D0pD71H90Gt4b0Qnx8IWFqquOevH3qW5RSjKKkDmrkwUZRXC4zbAYPQgKj4KcT0TERYVhpjkGNgiwg4PmduNguwCpG/chx0rtiL3QC6KMgtQVVqpSeo1LpVk7eFAmnCeLVYzhp00Eqddcza69UkBmEB9hEWdf3D9vMZuMnYJabpLys2U4PTEmZ9++qlBie4I9ydZXAgIASFQj0AgcjMvsvFGsC4JU7SdO3dubVsUYClz6oUJz4899pi62MXCm9L//ve/8f7776t/8/XVq1erNC1/5XDl5oY2b15enkr2YmKXXp5//nmcffbZDe4RHKOevPz444/Xpus3tQsxfZ9JxXq56667MHPmzNqb6Exb4L/Xr1+vqviKtWTNBGK9UNSlNK5PpUwp4Nprr8WiRYtq67S03Ow9xuzsbIwfP772Ja63V69eTWFQ77MuE6z1winyyFK/MPvGG2/g/vvvr33/hhtuwD/+8Y86bXvLzXyDQuF1111Xy5P75IUXXoidO3eq5SgiUMb2N/W0d8OUoLkP+NsGvoOjRM79Qd//uf/y5ve0adNqq/pu11deeQUUwVu7iNzc2oSlfSHgnwA/d5YtW4YJEyZg3rx5gkkIHFUC/J5i4igfTuL3Dx/q4Q1MyoDeRb/hWVlZqb6L33nnnYCPb47qAGXlQkAIdGgCjzzyCG655RYlIPsThSlRUObwPTektMEHVptKnGM9X2FZB8Z2dfGZF1Ypbvg+TMlzVd+ZX9jm1KlT1YOlDbXdoTeKdL4eAZGbZacQAoET4HkQH06X0jYEhgwZos47KRlKEQJCoP0SyM/Px7Zt29T5OAVmXWbmA3Pp6emIi4v7/V6J24CSglwVzKILwyGhoYhITEN1eQkc5UXqGJTHrpXVdlRWlMNiNoMBLGzLHBQCU5ANseE2GOABA0GsQTZUGGyIi9BeLyosQHJyMjLLXDA5SrFhzSokJiaqNmssodi0ZgXKS36XXHgtncfqXK8+gwmPn3VJWpeq9WNv1ucPj6P5Qzma18z5N3+zHmco5rjZj549e6qQkGHDhtVyaL9bU3omBISAEBACQkAIdCQClJufePcZzH3oLZzwlykiNx/aeB6XG588+QEWzf2x9mG5QLZrSEQIzrvlQow75zglqR52cWvHkvoMfW5HDWpqXOpBPqYqmyxm9ZuGsHe2BB1ZtYyzBtVllSjOK0b27kzsXLkd6Vv3I2dvNpxNCM2+fabgnDakF8adcyyy92Rh14ptyNmXrYRgv8UAWIOtKmX5mKljMPjYoUjqk/x78vNhQ2m5Be1llchNz0Vpfikytx9Q6dyUqsuLylBVVgUnQ0PsNXC7XOBsh77FZDZh6AkjcM5Nf0J8WiKnRmxW5wwqRdsDR3U1KoorldhdlFMER5Vdk8EHdkdIVHiz221WJ45y5S4pN1PEuvLKKxV6pnq2xVT2R3k7y+qFgBBoJwS85WYmCJ911lm1PeONXl6QW7hwYZ3e+oqbCxYswI033qjq8GLar7/+Wu8iFUWaSZMm1Qqi/hKL9ZW0tNyst8ub6vPnz1f/nDVrlt+kYb3u4crNt912W63oxhviFI18C1N+Ke2y8GLfli1baquw/oMPPlj77w0bNtS7Ae8rQLdXudmbN8e5cuXKevuFd7IzU515k8y7eMvNFJe5b+iCgl6PqdtXXHFF7WJffvklePOnsdIcuZn76tVXX13b3FNPPYXzzz+/XvO8OEwR/aqrrlIXbNuiiNzcFpRlHUKgPgGRm2WvONoE+J3DB35ef/11xMfHK5GZD05R2qPozIs/vt+Xep95ISO1e6pKYOLsEznZOYcuIh3tUcn6hYAQ6GwEKC4zme6iiy5SQ/MnN6t05UMyhT5+fo7xwf9TTjnlsJKTKVNs374dAwcOrF1eT2b2Zcw+sugPYPJv1mUbnJ2I51pSOj8BkZs7/zaWEbYMAe97GC3TorQSCAE+KKTP+hlIfakjBIRA6xDQE5gpMfNYk9ekOdMi/80ZBvv06VM780dJSQkchiAYrTZk7twIo9Gg6jAxeeSkqSjPO4CYiDD1gB+Ph+P7HQOzNRiFu9aoY1Ge8/cbfQK6j5iEVZ+9BTgrlMjca8xJ6D7sOKz74h1k5uYhOcKCoGAbhp7xf6guOIj1y35GTGwc0mKCET1wIoIi4pCzdTmq3GaM6RWDsG59UBmRhso9q9Gvb1/0jAtRwvQ3G7NgqC7BrpU/1orZ3YYeh7ikZOxZ+qUSlXkN4dwrbkBmTi5OGdFTLce+Z+QVY292MSI9pWq2Fo6dYTMUsyvKShUj3hvig4v6rC68FpGamoqhQ4eC17cZEpKQkNA6G05aFQJCQAgIASEgBDo1AcrNj771FN575B2ceLHIzbUb2+PByoVL8OmTH6GsqKz2Zf36bO1DbC53vf2j++AemDH7r0jsndz4vqNMZG2mkRq7A1WV1agqqURVWQWKc0tQnFOI4uwiVJSWw15pR429BmaLSc04EhJmQ0R8JKwhQbDagrQH7UxGVJVXojS3GOVF5SpxOj89D+XFZahxUNSt31d2UI2J/6kEYoOa3cRX6OV7ZotZtdGY1BwcEqxSmkecOgrDThyJyMQYUARuz4UiOMfkrHagOLsQpQWlqC6vQmFWAdK37MfuVTtQkl9S++CiPhZLkAX9xw3E+HOPQ1KvZATZglRStdGsCehGPtBoNMBjMMJd40KNswb2ympUl1ejJL8Y2TsPKuE8PyMfuftzNOmcszjagjBgwiBM+b/TkDowrdMKzl1SbvYWpyhu9e/fvz3/vyF9EwJCoBMR8JabAxkWk3V5g9c7werZZ58FhU8WSqB33nmn36ZuuukmNR0wC2WcU0891W+9lpKbKWfzQhpvjjOZ4IsvvgD7wHLiiSeCY2+oHK7c7C3r/uc//1E35P2VUaNG1Yre69atU3IRy7333os333xT/c0EbE4F568wtYVT1rG0V7mZAvCqVatUH3kThjdjfMv//ve/OmnNu3fvriNjecvNTIH2TnrW2+JFVE6Px+RKlsa468s0R26m1EBpmYXyPiXtppKhA/l/qSXqiNzcEhSlDSHQfAIiNzefmSxx5AT4fffBBx+oGX/4wM/atWvVQ0P6xSh/0mBDa9UlPz5Udccdd4AzEBhrH8I/gqfxj3yY0oIQEAKdiACPmfnZ1dDnk9PhVBeVfd+nABEeHq5NY9eM1Iji4mL1kAeX44w6fLhWT63nece5556rzjX0xDn+5vtKsDaa1EVbvT43A8/Z+FkrpfMTELm5829jGWHLENDPg1qmNWklUAIyY1CgpKSeEGh5AosXLwZn3v3ll1+UnLx37171AB3PySn7FhUVoQI27Nq0BmXFBeo+BIXl6LgE9B4xEeV5B3Fw/15Ulhejx4AR+NPf78PmRQtwcO8OREVFo0dCJHqPPw1RKb1xYP1vcLoNGJAQjODoRJh6jIU9cyvS80rQK8qI0JgkOMNTYMzbgV1ZRQgx1iDO6kRN8gi4XR5sX/EDDmZmoXdsMIIS+yE8bQjWffsBMrNyYawuQnBIGIad8X9Y/vU8/LRgLjwuFxIS4nH+DQ8hyBaKTT99jMjYeAzpEY9uw46DLbEXSnavQ0lxMZyF6chxhSN5wDH476P/wOZ1qxWH46b9EWPOvhz7V3yHP548FgzP4XWKdXnaLN0j4n/fJvtLgb2F1bDvWoolvy5SM3tu2rwFmZmZqHE6EBUVpWYHveCCC9QsKoHOHtnyW11aFAJCQAgIASEgBDoSAZGbG95alIzXf7saGTvSQZGVsqrVZlXXP6srqlFWUIq963Yj70BOHXHYbDXjpEtPxRnX/hEmq/n3FTBRucal0nkryypRXlCKgow85GfkKYm5OLcYBQfzUFFaoUTmGocTNXYnXH6kZPaBbfMhQKPRpMnJBqj2GxOZ9c7oMnRwaDCiEqOR0DMRib26qUYyNu/H7rU7UVVeFdiubAAsQVYk9e6GY04drdKao5PjVLp0Ry6umhpUl1ap1Osf3v5aic6+YreaBTYyBGFR4QiLjUBIuA2WYKvaX7gfmDgbo9GoxGmKzRSnS3KKlcBOYd1R7agnTZOZ0WzChHOOw/m3XQhrqK0jY2yw74UHc/HsFU+o1GpeL6Pn5Ts7ZSADN3j0Rw0CqX2U63z++ee47rrrVC8aSzM9yt2U1QsBIdAJCTRHbn7jjTcwZcqUehS8peUePXqoKdL8FUq8nIqN5b777sPll1/ut96RyM1MSqA4zeSEgoKCBrcYL5R98sknDb5/uHKzt7TMNpim6K988803tTKud9IwU385/TFLY9Lyn//8ZyxZsqTJeke6y2ZnZ2P8+PG1zSxatCjgC4veLCi8e6cf6w1SFP7Tn/5U2z4lBCZB6MVbbm6oDdb1rsfk60svvbTRoTdHbvZOoOY2nTt37pFibbHlRW5uMZTSkBBoFgGRm5uFSyq3EAGmGHE6Vx5PtWThE+xTTp2CRx97BKNHjYbB0L6fQm/JsUtbQkAItB4BXihVKfINXATmA4k8H9SnvfbuCZfVRePm9JAPtvJ85eyzz1apefoDufr0h1wXJWeep3DGNP1iLl9f+MVCtRyLfjmRqc58SJbTjLeXhxubw0PqBk6AsynNnDkT06ZNwz333BP4glJTCHQxAiI3H50NPnLkSHz66adHZ+WyViHQxQjweJL3a7/66is1gyCDLviAXFpaGjZkFGP9ooXYvm2bSiMOCgpC976DkNJ3CPasW4KD6fuV8Dt81FicMON2mCtyUVplx7A+qRjcOxV77eFIDQPKnYDdBYxOADLKtZ8IK1BQrb0WbAKWZgNRViCvCugXDZgNwK4SoHcEcLAcsJmBIbHAlkKgokarm1upLV/q0F5PCtVeGxwLxAUDy7OBYDNQVQNEBwE9gquwOsuBsko7fpj3GkIS0hDhyIPDGoHQnqOw9PO3sGntalQX5+H4cy5GbL/R+HrOQ+rBwMSEePQdNBxDplyEn//3OtYv/l6J3bmZ6bjwxvvRs/9gfPvmkzjjj9Mxflg/xPceikJrN/SPBmKCgRAzkF8FbCoA+kQC9pxdKoiGn3Wc9ZLH8mTO7x1e9+dDHlKEgBAQAkJACAgBIeCPgLfcfNIlU3D2P6YLKC8CTDF22h1KbFZCMVOIDQYw7ddZZce+TXvw+TMfI33rgTqSamxKLC596Er0Oqa/eiiuoqQCefuzkbnzIHL2ZiF7dyYKs4tQVVqBqvJq1V5rFyU0m42whdkQGR+FHkN6osfQXug1oo9KgQ6NDFOSNIXrX977Ecs+WYzqikYEZwPUteuYbrEYNGkYxp45ASn9U2EOsrb2UNq0fY/LjQOb9uKLlz7BjhXb4HK62mT9yf1ScfXzf0dMqn9fqk060YorKcrMxwszn0Reem7XkZsXLFiAG2+8UWH1ltxakbM0LQSEgBBQBLzlZorJV1xxRS0Zpuh6i5wNPXzBBEEmGDSn3H333ermob9yuHJzc0Tt1pCbmfx1OIkCvGDK5GEW3lDntPIst99+O2bNmuWXEbcTBW6W9pjc7MviscceU1/qvoUyOm8g6+Wzzz7DiBEjav/tLS0ztZnpzf6K9z7I71M9obuhfbI5crN32+wPk5zbSxG5ub1sCelHVyMgcnNX2+JHd7z8TmUSKVONWmeGH7e6aPXBBx9i/vz5SvxrTlLq0aUjaxcCQqC9EuAsNDzP8fd5wimpKYM0lMysy8W+y/LzcN68eeCDnv5KQ8vpdfk+29i3b5+aNpz9YGFfNmzYUHtO5t0O61utVi5vYVsAACAASURBVCVxSBECQkAIdHUCvnIzr2UxWIAPgeifqV2dUWPj54MyISEhKtVU//H+t/ffzz33XG1TTELlNVkpQkAItB4BXpP+73//q86JzzvvPBx//PEqpIWJzdu3b4cLBnTvPQAWVzWCgqzIyMjA1L9ci3GnngND9iZYuw3AtGNHIiXSilU5msBbVA3EBgM9I4A1eZpQbDJowvKYRKDIDjDNmKIyXxsZr9VhXfoGdjfQIwyIDAK2FgLdw4DCaoCTcR8TD2wt0taRGKIJz6MSgBo3sDYPSAoBCu1ArwggORTYXqRJzyYjYDECw2K117h8r0hgWyEwLB7go84UjmNtQGa5JkZzqvGdJcCACAeW78hGVVEOgtzVKA3viYLtKzH/7VcRGhkNZ3EO0sZORXRqX3z83L+QczAdoaEhSOzRD+dcdx++ffd57F6/FHHx8Rhz3IkYeNJ02KpyYCnPUsfo/KxD4kBEVGWqUJd3331XzcrJY3Pet+I5AK/TNxQm03p7h7QsBISAEBACQkAItGcC3nLzyTOm4qwbzm/P3W1/ffN4sP771fjwof+iNL+ktn8GowH9xw3CcdNPQEVJObYt2YyD29JRlFPUtMjMFGY1j0f94uHBpScwDHoohslqQnCoDVEJUUju3x19R/dH6qA0JKYlwBIS7Lex4uwCfPHip1j37SqVMOydjct2zRYTwmMj0X/8QIw+fRx6jeiLoE6aMExABo8H6dsO4JMnPsSu1TvqJTgHtkUaqcXYbU/dDRuVGIOZT89C2vA+R9x8e2ygJKcQL1/9NDJ3Z+Kiiy5SgRmdPrn5ww8/xK233qq2h7fk1h43kPRJCAiBzkXAWwiePn06KH3qxeFwqKfi9QRkSqivvfZaPQB33XVXHQmaF+ibKkzX5YVCf+Vw5OYVK1aA/dcL+8D+MmmLUw9zirjly5dj586dqkpryM1s1zvxmf8OhMXChQvVVHYs3tIyBV39wRdfTu1dbmZ/vZOb//nPf+Jvf/tbvc29bNmyOtLz0qVLVSqlXgKVm72l8MZSwfV2myM3e+/fQ4cOxRdffNHU7t1m74vc3GaoZUVCoA4BPWVfpgeWHaM1CfBiy6pVqzBu3DiUlJQgIiKilVbHW6MsRvB7mFPAHjhwQATnVqItzQqBrkBApbklJjY4kw7lBaYqNyQ3+2PEc9Ndu3Yp6cFbemYb/NFTmrkszyfXrl2rmpk4YSImHz+5TpOcEebhhx9WEoW+HFPieE7Btvk6U5spPbPowp53GnRX2I4yRiEgBISALwFfufm9997Dcccdp6oxsZMSWm5uLoqLi1FeXl7np6Kiot5r/KwN9OdwJunk9UD+UCrW//b+7e91vsbPe34H8IcPuOh/e//297r3a/4kZrYbSGGYgXf4BIMUODOBFCEgBFqewDvvvAM+TMD/98eOHYusSgOWfbsAWZmZCAsLU8eeE86eAUtYNN5+5GaMOf5UXHTmFAw7/nS4bLEqiXlfKRBvA9IigHV5QIgFqHRq6cSDYrTXKBRTeGb6MsVkpxvYWwKYjZqwPCgW6BaiCcf8N0uoRZOTNxZosjJlZwrKo+J/T3xOtAHZlcCQGMBmgRKrKVQzHZrSM8Xq7ApgW5EmSbs9WrrzjmIt1Zn9U+J0OJBg09bF/hdWAT3DgYhgYGO+lq7MpGUu3ycKWJsL9AgHXB5t/WMTgNwqjQXXuzO7DEmmEngqirEPCdi2/AcU5eUiJ303jAB6HXc2ln/zMVZ9vwB2O5P+HDj2nBkYNPZ4zHviNhTn5ygGPQcMVcfrhVkHVKjM5s2bVdDLzTffrFK1pQgBISAEhIAQEAJCoFZunv0OplxxGs647o8CpZkEnNV2zHtgLlYuXAq3S79fxIBnA8xWs3pNnwGvoaYNRiNMZiOCbUEIDrfBFh6ihGSjRZsp1F3jRlVFlUp6ri6rgr3aDs4qyif33IdsZ/qxlKI5o58l2Irw2AjEJsWgW/9U9B7ZB4m9kxGbGg+zNbBz65LsAvz64c/YsGgdSnOK4LQ71Xgo3fYc1hPDp4xWonRnlpq9txcF530b9+LjJ+Zh/4Y9TW7TxnYjyu88TmcSOJO0bREhKMoqhL1KCxNhCYsOx2WPzFSp2J2xeMvNnKHyjjvuqL2f0JzxGjyHc8WrOWtowbq8EMiBsvCGCqcakyIEhIAQaAsCjcnNXD8v8DFlWS8UOyl4ehdOKUyhlGXMmDEq8e9Iiu9FfCZI8+ZDY4Wy9Jw5c1SVY489Fm+++SaCg+s+qcV+/eMf/1B1mpKbp0yZUitC8+b31VdfHdCQdOGNlRuTkxtqjKzJnMVXNvdepq3kZortlJT18sknnyh2gRTvm14U2Z9++ul6i3l///FN320diNzMr/shQ4aAN8pYKOB7p0H762tz5GbuV9y/9LJmzZp2c/FU5OZA9kSpIwRangAfzHjjjTdw5ZVXytSYLY9XWjxEgEnNfGiKYoj+hHpbwOF3f7du3ZTMJwnObUFc1iEEOh8BTtPNhzy9hWPvUVJke/XVV3HttdcG9DnDi+dV1VVKMPEVov0J0pSTKVhTItPFZ15rO+ecc1Q3dLmZU14PGjhIXTDn553eFpfXhWamLfB1JsRR2pPPxc63v8qIhIAQCJyAr9z81ltvgdclpLQsAX5nXX/99bWN8tic56BShIAQaDkCvP/A671Mni8tLUVOTg6Cw6MwYMQ4BBtrkBQfh98W/YDpNz6MvgMHIynYiZiBEzE8ThOXC6qB6GBN+KW4PDgGWJMLBJkPBZYZgJFxmixsdwG9D4nPQ2KBIBOwt1SrR1mZyclp4ZpwnFUB2EyaqNw/CliXD4RbtGUoKTPdmUIxheOoYE2EZtsUp7n+MAvgcGsiM5evqAFWZWuiNZcblwRkVmiJ0f2jgQOHkqMHxGh9dbuBapcma1PcZgo0BWqnByixA8NjgTX5mmxNT4WCNhOjyWR/GRATpI2B4jOF6WVZWl+YKM0cN451c4HWPrmll2uydU4lkFEGGAt2Yu2uHCxZMAepab1hG3A81v38JX6aPwcOezUSEhIwcOBAbNmyBbNnz8Yll1zScjuFtCQEhIAQEAJCQAh0SAIiN7fMZtu3fhfeuf115KXnBtwgr5NSbg2Pi0C3vinoc0w/9Ts0OhTh0eEIjQqHyao5Pu4aFyqKy1FaUIqKwjIUZhegJLcYVaWVsFc51HQh5iALbCHBiEmJR3S3GMSon1hYQ4NhspgP67qss8qO3AM5KDxYAHtVNazBQUjokYCY5DhYbEGgpNulituNA1v245cPfsS2xZtQXlwBt8tVB8Hv178NoHDOEG71cHiQBcGhwUpaVuJ5ShyS+iQjvkc8gmzBmPfgXBzcnl7bFuvMmD0TAybWdcs6C29vuZm+AMMe+dB7c0uHkpu95cIFCxbUEcmaO3CpLwSEgBBoDoGm5ObKykolLOviKKVfCl3ehekhM2bMqH0pELm0sT5u3LgRnMZYL75pvv6WpQjM9GYWXtjyN1UxU5D5GcvSlNzsLSlTsH3ppZcCwvrYY4/hxRdfVHV5Q58yOFMFAi2vvPIKHnnkkdrlV65c6Tf92VtqZVrBLbfcEugqmlWPN/J79uxZu8wDDzygpn8LpNx+++14//33a6uuW7cO0dHRdRY97bTTsHXrVvUa06t/+umnOu97y83cJv/617/qrdo3/ZlJ2MOGNf4EWHPkZl/Z/tJLL60jOwfCorXqiNzcWmSlXSEgBITA0SXA5DueBFPO04u3ULd48WL1IFdWVpZKd6YoyO9spjvzhw+F7du3D9u2bVM3/5or43GK3UmTJmHv3r3NXvbokpO1CwEh0B4IMEWNokhDnz2UhyksX3/D9eo8q9F6LheMBmOtgOxvfP4EZ29RmZ+pTBGloMzXmQLN9E2VOGLQHrDs27dvvQdJ2E8WXXzmjC5MfG7uZ2p72CbSByEgBIRASxDwlZuP9PpfS/SpM7bhGzTBa2m8piZFCAiBliFw22234ZlnnlFTBldVVSExKQl/vfNRRCX3wfN3zVLn0zMu+Qsmn3UREJ6oxOGscoBico1HE5pDzZoETHl4aCywp0STmPVZtkcnArsOpSNT/l2ZoyU7U/7dXaKJxhSCk0KBflFaivLWIk34pcg8Ig7YUqi1SRGYwvDAGCDYBKzP01KdSxxAciiQHKa1T7G5ugYItwIDo7VUaL7ONigWj44HSp1AepkmHhdVAxVOYES81n+K0nQXwoO05Tfka21Qrs6ghBwPbCgAooK0n82FULI3l6EszfVT+raZNQmbYrTJoLXB9bBNis0RQUB0kJYmzXpMmWbi84AoYGeJlmDN9OdNBYDVCOzbsRlffPw+Fn7wJgZPnIJfPnsPI0eMAB+o5MxTvF8hRQgIASEgBISAEOi6BERubpltX2N34Ls3vsIPb35dJ33XX+u8NmoJtiC+eyL6ju6HwZOHIXVQGkIiQwNOVabsXOOsUbNAeXhwzGuwTANmarPVAqNZS3yW0vIEeH28LL8Yu1bvxL71u1FwMK9OYjdFcpPZjOCQYASFBqlU5tCIUPU7PCZcpWfbwkNhCTKrhG2jyYQahxNz73wDa79dpWZFZEnpn4ornrhGJW53xtIl5WaKgvfff7/anh999JGa/kiKEBACQqAtCDQlN7MPvOH86KOP1nbns88+w4gRI2r/TfGGMrIuqfKNe+65B7zpQcHXt/AghU/3NFSYlMAp2PXChC1ecNSTv3gzmtOl8wa0XryTjJnc7C3Vsg7/TdlWL03JzRSMKRrr5fHHH8f555/faL9ZlzKQPiUn/80b+2zrlFNOqZc+zS923yTGnTt3ggK5XsiQN9D15GoebMyfPx+33nprbZ3WlJu5Em/BmNvzv//9b0DpzUycZGKzXpg+SaZMXOPYOS49bZt1KC5TYPYuvutm+janA9QLpQmK9UxdY2GazW+//dZk0ndz5GYKY1OnTsWePXvqbBfKDZGRkfV2Y6a86VNYN7iTt9AbIje3EEhpRggIASHQzggcf/zxaiaC/v3710p0PH7iQ1P8PuX3D4+LeAzGYwRd4tN/6+Idb9TOmjVLnWNy5g3vyY24HCXphsrw4cPx8ssv1zmuaWeYpDtCQAi0QwJ8sCItLU31rDFpWReGN27YiMFDBquUZd/Cc4bq6mp1bM3PPH/t8XPtoYceAo/NvdfJ1/m5+cILL+C6a6/DHXfeAT6I6p3QzPqsx3Mwft76Fi6vT4XIekzR5Gw0Tc0q1A43i3RJCAgBIdAiBHzlZn7GnnXWWS3StjTyO4Hnn38evG6lFwqYfGhRihAQAkdGoKSkBCeffDLWr1+vrk0zLIXXrhf8tBIxPQcj2uLC6Ikn4LiR/VXSMIXehJDfU44p6VJC5m8mmJXZgT5RmuysS70UdY+JBwqqtCTjMQnA2nwgNkhLQqbYTHWDic1Mfh4SAxTbgfX5WmIzvQ7KxpnlQF6VJiEfLAd6RgBxNi2xmXUoWVM67hOpLUuJWJvGW0tNjg3W5Gj2jXUpETtd2pjYDuvx72Gx2vr5N0PrrCat/9uLgGIHkBoK7CzW+sS0aYsBSA0H1uUBA6K1RGhK3FyusgaIsGop1pSYq2o0iTm7ShPAdxYBdFRSQjVxeVCMtj3ZPttiCnSkVRszU6yZbM0k7HKHJntvLwaWrVyDG84/HlazUd134sPcvDegz9Z5ZHuILC0EhIAQEAJCQAh0RAKl7nI8/tZTeG/2O5hyxWk447o/dsRhtIs+F2UVYMHj87BtyWZUV1TXuZ/EA0henw0KCUZizyQMPHYwhp4wEom9khAcHtIu+i+daCYBjwf2ympUV9jrbGsTTwxMRphNJhgtJpgpO1u0BO6GisHjwbZlm/HFC5+iICMPtvAQHPun4zH5opOVAN0ZS5eUm72TOj/88EOMHz++M25bGZMQEALtkEAgcjMv/FFw0QsvAjIt0Lts2LDB7w0NSsQpKSnqxjKn8aUAfdNNN9WTWL3b4o3jU089Vd1k9i5MkKZoyguQLEzY0m8sP/XUU3j22Wdrq1OA5Wcpb5Iz0XDt2rXqgldwcDA43ToL/82b2Hy63zfpl4ko+rTFeqMUlfv166faKCoqUiLzHXfcoS6EehdOy0m527tw2QEDBqi0MIpGmZmZSsjlVJMjR46sU5fTMzN9WC89evTAhAkT1EEFhWFvyZZ1Wltufv311+slIDBluXv37qqLubm5Sjb3l1LNKRi+//77OuPjtmF9PQ2cb5IPBSvesPEu3nKz/jpZUGznNmBquHc7FBZ4s00vBw8eVFNA+JY1a9bULsf9YNSoUXWqUI7wlqiXL1+ukiB8Czkw2Zr7JfvDhEsWTo3X0oUCP/vhXbzHru/T+vt8AMFX8m/pPkl7QkAICAEh0PIEKC6ce+652LJ5S50psRqSBHVhuSHpj+/rD4h5y82szxu6+nv+RsLjKD5UJimlLb+dpUUh0FkJJCYmqlR5/SHOhlKVeY4xc+ZMdY7Dz6HG0pf9vefNz99nGZfhAyB8qNbj9sDhdKhzUv3hW/1zja/xWJ4CNV/j+dnpp59eT2Dme0x6Xr1mdZ0HcTvrdpRxCQEhIAT8EfCVm3ktjkEAUlqWAB+kYciDXvgdxIeHpAgBIXBkBHitl9fVOZMgH+TlTIjFNRbMuOFf6NmnH0IsmjDMVGSKvEFGLR2ZicuUbg+UaWIyhV5KzExNZlJyVqWW5My6oxO0lGLKwUxkTi/Xkph7RwK7iymEACV2Ld2YEjHrMuGYCcWUlikGUxKmBE1JmYnNSSFaH7YVAkV2Ta5mwjPlYErCbo9yD2DkzIiRmmDMvlI6Zn/ZJuvvK9PGwdRltk8xmt4CU5tZVLpzApBdCewpBvpF/z4OpjwzqZrrXJ0LdA/TJGkKyZSqKUiTHSXoHUVAfrX2PhOvh8Vp8rS9BugdpYnPHA+ZMa26b6Q2TvaRzJjizHVxnGQ1OFYTzJ1uoFcEsHDJJjx96xXIyc1B31GTsfq7j8HEex7Hz50798h2EllaCAgBISAEhIAQ6HAESlxleOLtp0Vubokt5wFy92dh5edLseW3TSjMKoTH7VJyqy0iFAlpieh9TF8MPm6YkpqNTQivLdElaaPjEHA5a5C9OxMHd2QgKiEKPYb1QnBoXfen44ym6Z52SbmZKQdMBWX54IMPMHHixKZJSQ0hIASEQAsQCERu5mr4GcXPKr188skn9dJ7+dqdd95ZRzb118VLLrlEpWs1Vj7//HNcd911jdZZsmSJEqdZKJYyHVkXl/0t+Nxzz6kLl94J06xHBieeeGK9Re699956ErdvJaZ33XzzzXVe5k10JqxwXU0VprGcffbZdart378fZEQB2F/hDXkm9uoCdGvLzf6Suf31i4nOnMLeu1AAp7Tgy9y7DsfDGQz8ffd5y82UmpctW9YgUiZek7nV+vvTX75J2E1tD/39efPmKaHcu7z77rsqXTqQQrnZX2p5IMs2VIc3DCnqB1p4wf7rr78OtLrUEwJCQAgIgXZCQAl0Llej0rG/rvJBMwqFUVFRKC0trTMTBOvraaW+yzYmLvN7mMcbIje3k51DuiEE2jEBfVYa/tZn6eHffADSX6qnnsisD8nf58zVV1+NF198scmk5MbkZ36eLl26FJzdR5smXJOp9fXxnI8PQ/IBVv01ys7eqfjen6GUt7Ozs5v9Gd2ON510TQgIASEQMAFfuXn27Nn485//HPDyUjEwAr5yM5fi9S3v612BtSS1hIAQ0AnwmO+hhx/GLTffrGZZZNDJuZdeg7NmXKukYArLFH2VsFsKhFk0WTkuWEsSpljMtDqmK1O8jQzS0pKZqsy6lHmZjszjzc2FQGIIUOkEHG5gyCE5l/IwJWGKyJSIHS4tAZlyMkVeysY2C7A6B+gWptXlegZEaSnQFHy5LqZKMwmZScZMY2YaMsVgytZMbGZas5Ke3ZpMzDYoDFOgTgzVJGv2j2Ndn6etm22OTgRcbmBNniYvc2zJYZp0zcTqUfHAunwgJhhIC9c4kR3FZArhYxK1PrKvFLP5fv9ojQPlZ6ZPM6U53qb9MHGaY6awTRbkR+laJVO7tDRsitmUrTnOXpHA1kIgKRTwOCrx+Cvv4PPXZiMnMx2XXXYZFixYoAJHkpM757TX8n+zEBACQkAICAEh4J8A5eZHX38cHzzxX5z6f3+Q5OYW2FFcjhrkpecgZ3cm7NUORMRFICw6ErHJMbBFhGnTgEgRAl2cQJeUm/kkPi9asbz33nsy9W8X/59Ahi8E2pIAP3OYPszSmHTM1GUmJ+vlzDPPVDeafUtxcbH6PPvmm2+UYOOv/OEPfwAT65sqn332GW6//Xa/sjQTlDldO5Nz9UIpmOvmhSzvQsmUN8ZvuOEGlSjjK4g2JDfzZjynced6fFOk9faZ2uw9VaT3erkMk4SZSOybsKvXu//++9XUab6FadkUacnAu4wbN04J5CtWrMDDDz+s3mptuZnrYP8pt1P6bUggJwffFGsuS3GBcjy3i++y06ZNU6nQlAT8FW+5maxYKBl7bw+mPjPVmInXvumTTLY54YQTmtrV6r3vT25mJe7TvAD+22+/NciB+xulYiZut2ThDUMK/YEWpqbzgQMpQkAICAEh0HEI8LOb33NDhw5ttNMU+X755Rclk1BkfvDBB3HjjTeqpCVeV+L7nL2AM2/wOMR7NoLm0Pjuu+9UW1OnTm3OYlJXCAiBLkiAnxU8Lufxvi4J83yRx+j+ii5D873GkundLjdMNDYaKY2l0PMc8NFHH1XJzCysSzmZ0gPXy89Pnl/pSdOsc9999+Huu+9W9fUHQ/Q+lpeXKxGayfZSOg8Bfpfy3H/w4MH1HnLtPKOUkQiBIyfgKzc3dE3ryNfUtVvwJzdv3LgRERERXRuMjF4IHAEBhlhwpkaeNzNQpNzpwV0vvK+Sl6ODtPNoSrMUcoNNQFWNJhJTTKZwW+rQBF5KuExi5nsHyoEIC5BXpSUaUyymsMuE4WirJucyyZiSsOuQ2EyRmWKzxwOszNUEabMB6BaqSdQrcrT3KFqzHwNjNMl5VwlUsjT7S+GXqcwUp1mH/aFwTGGZidJMgmYiNFOO2S5lZo8B6BmutcPEZCYksx6vIehiMQVvvsZxljuA8CAgyqotMyIO2FGsrY/rpyzNZXMqNXYUmylVMzGa/WBKNBOZWVgnJQxIL9MkcorPXA9/UxRnOvPYRG1ZsmPfC6uBIXFAcbWWZE1xm9I0uVM4p0B+cMUXmDH9bPTp00fNpsjjcwaspKWlHcGeIosKASEgBISAEBACHY0A5eb7n3sAHz8/H1OvOkPk5hbcgGomUI8HBoNRhOYW5CpNdQ4CXVJuphDG9E4WTpszefLkzrE1ZRRCQAh0aQKUWtPT08EbwCxhYWHqJnJzEm150JSTk6NuQLPwQj5F2MbaqKqqQmZmJsrKylR9rpM3oFny8/PVDW2mneg/TOVqqhQWFipJiCnGXD4kJATx8fHqBn4gaYaUlZlizKndKeBGRkaqfjWVusKkMUrbdrtdidz6TXnKxpWVlaofNputTZPDyIGyuz61PbdFt27dEB4e3hRGxZ/bJjo6WqVu+8rIvg34ys26CE6Jfvfu3UhNTW1QjG6yM0dYgTfAuX9zP2c6Hfcx7pscmxQhIASEgBAQAs0lwO98fq/zO99fCimPa3766SdcfPHFoFjy+uuv11mF+9C/mJykf0fr7SxatEjJfTyGcdbY4apxIS+vQH0n83jk1ltvVQIzv1e9i77cV1991dzhSH0hIAS6IAFKwvoDmPp5EyWDQM6XfHHxIVw+OMllAzlf05d3OpxKhuZyPGdq7LyRdfi5yKLLzfws5ucwz7H0ws9fnuvoQjYfdrzrrru64BbuvEPmTEKUNHn9gAKhFCEgBPwT8JWb+VD+VVddJbhamIA/uXnlypVISEho4TVJc0Kg6xCg8EqpmefcY8aOxcz7X8WA/gNgMWmSbo8wIPNQQjDFYqYHHxOvSbyUlCn5UrylnMzEY4q2lIAp3/aJ0uRbCscHKzSpmO8Pj9MkXcrDlKPLnMCYBE1G3lygyckUgynrsg2+RpE3yAjYDyU+M1F5YwEQYgJqPEDvSC0pmfIxRWMmPVP4pTxMUXhDnrYuJjT3PpSUzK3cIwLILNekbT31mYnKHOfAaC1JmanIFIsVEw+QFqG9xvHkVmljJ5OMcq0epW6nBxiXAFTUAAdKNcma/aNUzX6RB8dXaNfa5DiZVk1e5Ezxm0zImP0vc2h/Dzh0eZ38KI1zXSxMel6TC/SNAmZe8icUlpRj9a/fq2vzvO/CIDE+3ChFCAgBISAEhIAQ6DoEKDff99z9WPD8R5j21zNx+rXndp3By0iFgBA4agS6pNzMKdxefvllBf2dd945rJTJo7bFZMVCQAgIASEgBFqJQENycyutTpoVAkJACAgBIXDUCHB2BopVp512mt8+8GEvf0KDLg3yhix/vOVmvSHv5FG326metHe7AKPJWCtS+5MPKUcbjSZ4PMyZkrnGjtrOISsWAu2cANOVH5n9CG65+RZYg6xKFGYK/aWXXur3YY2mhkMxgUV/ENJXbuZnE38oIvM9p9OpHjpcuHChmtWGhX3gA6Z8qLShosvN+ucff/MBxrDQMOQX5KuHWVevXo1Nmzap2XY4+xBnBKLo3NDMPE2NTd5vnwS8RUI+XCxFCAgB/wR85ebbbrutwYR+YXj4BPzJzb/++muLzxB2+D2UJYVAxyPAUAqnswYejxtnnXUW3pz3KbIrAcrDFHGL7VrycGSQliRMMdlkBFblaMnOeqG8y3Tn6GAgv1JLKe4frQm9lH65PP9m4nOVU5N+KTVT2h0WC0Raga1FQIWDx7pAuBUYFA3sL9VE4JggTXBmsjIlaCYcWw8J2CmhWt8oCrMdvm41ahIyx7GpQJOA2TcKzHmVWt3UMG05SsNsN7NCk64pK1OATg3X+s5kZZtZcHkcRAAAIABJREFUk677RwHbijRZ2eX+XczOr9bkaUrdXNf4JI0MmVBmLmHiswVICNHaCzZr66GMTSGZY0+waQnOO4uBYxK0NGr2jdczciuBtHAtpZqJz2yT75Mj06MpRkdbXLj5qj+jzOGBtaYC33/7tTr258PachzX8f7flB4LASEgBISAEDhSArVy8wsf4Yy/nYOpfz3rSJuU5YWAEBACTRLoknIzp8GcM2eOgvPWW2/hpJNOahKUVBACQkAICAEh0NkJiNzc2bewjE8IdEwCTPfj1OWcznTatGkdcxDS63ZHQBeQvTumJy9z9odRo0YpiY/y89dff61mTViwYEEdyUFPbPYdXK0AzagkQKU+M9mIM0JQUuHfDcvNZrjdDhgMpnbHTDokBIRA+yBAuZizkfF7UU9AXrNmDQYPHlw7g06gPeXn2EMPPQSmgTJl3u1xqymm9cL0ZM5gw8+t+fPn49xzz1Vpu/4+O/WkZb7HachHjhxZp573567+GcjEaV2q1l/jZ+bMmTNVEhzH+PPPP+PEE08MdEhSrwMQELm5A2wk6WK7IOArN/Mz8aabbmoXfetMnfAnN3/33Xfo379/ZxqmjEUItCmBnj17qofgOGtRr1698O+nX8PAcaegZ4SWyEy5mGnHlGsp9jKdeWUOYDECZiNgMgBRQVoic4RVE3wpPTNheE+JJuJSBKYw3TdSSyWmWEzpuKhaSz9ODgN2FGkCMN+3mYABMUCpXROTY21aknL3MC2tmGIzl+dpPAVsJjdTQmYSMqVhnt1TTuZjyJSGM5gmbQXGJmj9pBRMyZhtMrW5RzhQ6tT+5rKUnpmETGF5d4kmSjORmWI2E6vZLiVkvs90ZSYrc/3sU1alJhvrkjJTpPk+WZHfziKg2g2EmYEih/baHgrQZq1PTKkeHKOlRJM5RfKDZdp7FLD5fnmNJnsX2bV1UYae/9YreOnhOzH1oplIiQrGr99/pWbv5DWTK6+8Es8++2yb7leyMiEgBISAEBACQuDoE6grN5+LqX898+h3SnogBIRApyfgLTdfc801uPnmm+vcxwgUgMHT0J3dQFtow3r33nsv3nzzTbXG//znPzjllFPacO2yKiEgBISAEBAC7ZOAyM3tc7tIr4RAVyeg39SfMGEC5s2b19VxyPhbiABFY7vdXq81Jjrz1HbFihXqPW8JuU+fPkq0P+aYY9R7jcnNfI8zBvHck+mjl19xBZwOB04//XRs3rwZmZmZ9drnMqGhoSgvL1MJzlKEgBAQAr4E+DnBdGSmJHt/PvF1ysWUkJtTKEqb+HlzKCzeAAMMtD8OFbbJ8sMPP9ReO9OFan/rYf0vv/wS2dnZmDp1Krp3715bTU9uZgK0LlAzNXrr1q2qfV3YYzI1k+6fe+45XH/99SoxOi8vTz1kIqVzEBC5uXNsRxlF6xPwlZv/9re/4Z///Gfrr7iLrcGf3MzZCYYNG9bFSMhwhUDLEaDcPHfuXFx00UWcwggVlVVISkrEZZfPxLA/XIaBKdGasBysichMCKZMTHmXYjMTlrMrALMJqHFpUi6TmCkqU+Sl1EtxmMJwhEVLRzYYtMRkpjtTeN5SqMnDasYlAzAoRhOX1+QCUcFawjHrJocCq3IBh0tLZ+a6Q0zArhJNeqZcTZGYYjPfp/R7oExrc0KSJlgzKZmyNdOf91EatmksKTFT2GY7XD/HzIRmLmt3acIxx8mkafaJf4+K12Rqrp99ZCLzwBitza2F2nj4Oo/YR8ZrKc6VTo0RE5kpgB8s1xhRVmY7vSM1OZwSNdOZKWPz30zMZl2OgSnSqh+OHLz26it4a87LOPbYSRh92oVY8vlcmD1OdW2DD4H37tMXD7zwNm69cnrL7TTSkhAQAkJACAgBIdAhCIjc3CE2k3RSCHQ6AqW5RXjxmqeRtfMgbrjhBnXfoLn3QgilQ8nNTKThlJ0sTIQ59dRTO92GlQEJASEgBISAEGguAZGbm0tM6gsBIdAWBERubgvKXW8dlIiZOKQnhpIA5bmCggIMGDDAb7Iy65SXlysB2V/yszfFyOgoFBUXw6iFN8PDO628qQqgqrIKIaEhSkT0TXBmImppaWnX2yAyYiEgBAIiQIl5165d4MMWjUnGDTXGz523334bM2bMUMvzgY3p0zUpQX9gw1eanjVrlhIZ9PeLi4sRHR3daH9ZV0/DZ3vbtm1TCZj8+9VXX8XVV1+t/mZ/mEJ93bXXKaH5kUcewT9v+6f6m8sMHDhQrSckJARVVVUBMZJK7Z+AyM3tfxtJD9sHAV+5man2d999d/voXCfqhT+5md+PY8eO7USjlKEIgbYlwLRmHnNedvkVePXbTfj8pXvw8rNPYsiQITiQkYFe/QbjxNPOxpUXnAVP0iCVehwZxANSIMisybrFdiDMqknHoxOAyhpgS8Hvic2UiSkmUzamdMxlKEcPi9Ok4vwqTXimLM3E5FALsCJbE5CZXMzl08I1YZhScJBRWzfTjLcVApFWIMYGVNVoKcxsmyIxxWbKzuMO9YlJyxSFmTTNpGj2mcI1050pKXO9w2O1Zdh/JjszvZp9cri1JGUuS0GbKc5MiqaEzPVyXRSTKYCzbb5GibncCYxKAPKqgMJqTaBmYnXfKI0b15Vk08TmpFAtnZpMKDZvLwIig4Ex8UCVSxPL7UXZ+PKT+fjty/nYs2snJp95AY4fPwbfLfoZiz7/EP+87TY10wvLZ0u24c/TxuHdt9/Ceeed17Y7lqxNCAgBISAEhIAQOOoERG4+6ptAOiAEuiSB8vwSvHLdcziweV/XkZtvv/12vP/++2qDv/baazK9dZfc9WXQQkAICAEh4EtA5GbZJ4SAEGiPBERubo9bpeP3SZfqfOXipkb2888/Y/HixbjjjjsarEoBb/gxI5WYZ2IKqsGg0pVY3C5XrVDtb90UBouKCvn8cFNdkfeFgBDoggQefPBB3HXXXbUj1z9HnnjiCTUVWyCfabqkzERkJjVTJNYf2Fi0aBFOOumkOmTLy8oRFh6mXjtw4AB+/fVX9O7dGxMnTqxTT0/D/+WXX1Ta8sknn6zSE/j3jh07MHjwYDAp+uKLL8b8+fM1WdoDvP3O27j88suxd89e9EjrodrkgyfeSdRXXXUV5syZE9D4uuBu0eGGLHJzh9tk0uGjRMBXbr7sssvwwAMPHKXedN7VPvroo3jppZfqDJCJs5MnT+68g5aRCYFWJsBjxUdf+g9umnUVlm3cqRKWt2/fjpfn/AfvzX0XkdExSEyIR3FBHvbtT8fYYydh1IRJGDPhOEz5f/bOA7qqYv3iO733TgKhht4hIFhRBHt9KOiTv4oNe+FhRQV9duwVRR+gCIjYAGmKCErvEHoLSUjvNzf9v/bMneQmBISYIOF+31pZSc6d8s0+J5N7z/nNnnMGYEeeq4J86dTcJxzgXiJbMvSnZILBBI3bBWkw2UR5hQZ+DxcCSfkaKiYY3dIfCPUC1qTq+gSb+RrdnQlGpxXp9viZvaWfdnymizGhYDoZR/tq0Jlux4kFQGEJ0D1MQ9OHCzSYHOWt6xGmNnC0txvg6qTdkXmcwHR+KcA8CRszB+OgnFQItAvUsDYhZzpQ07GZGvSK0JAyoWXmmVkE9LQDm5krweZYfw1O0x060B1IKdSu03R9pls0oW7jON0vEvj91yX437c/Y8UvPyM7IxVDLrsKF55zFjYczMTvc79Bbm4Orr7hFjx29y1Vu7FsywKOpOfgkq7hapHklVde2chXkjQvCogCooAoIAqIAqebAgI3n25nRPIRBRxDgYKsXHx8/7s4uHm/48DNjz32mHqQwqD7zCWXXOIYZ1tGKQqIAqKAKCAKHEcBwgx0rWTQoYZbCEqIAqKAKPBPKyBw8z99Bs68/svKyuDm5obDhw8r0M58LuTxmJgYXH311TUAOh4naMftVxl0Vub/SDo4G0jQXqXVq1fD2dUVPXr0UHAzwcF9+/YpUI+fPw0ASCfShx9+WOUSHBys2goICEReXg6cnOjxLCEKiAKiQE0FnnzySbz44otHQb50Uw4ICPhL+JfzDOc9zjtciOHp6VnDAdrevZlzlgnjcv/LL7/g/PPPV4ftne9Zj+BzixYtUFFeUQOYJohHpzf7RSWVFZWYOWsmhg0bpgDoxMREREdHK4D5zjvvVED0d999h+uuu071pUBsJ6d6bTUn19Dpp4DAzaffOZGMTk8FasPNN954IwjiSjSsAvw/xd097UN2+2xYjaU1x1OAcPPDr3yKN564Bwf27FQCcF0b3YMLyoCV87/BumULsGjRIpRVVCKmZRt4enqhpDAHOxIS4OXrD3cvXwy77mr07NYVztFdEdehEyr02jj0DNNgM92cCQkT/KW7c3axBpZDPLQrMcHkSG9gdSpUXboeuzkB7YM0qGwAYELBBJtZl+Wa++m2Qzz1FyFkAtOF5UBcgHZ9JjBMeJgOzKxXWAqE+wB7cwAfVw0aE2wmfEy35JwSKIdq5kOImc7OdHxOtWhnaELYdJvm7wScCU33jQAIPicXAH7u+vWOwRpU5vgJWFvKgWbeOhfqy3zzSqvBagLYlpJy/PrHGmzbsBbblv2I9WtXI7ZtB/Q+7xI0b9ECWckHsfKXuer9/NkXX4FzLr4S5w+9At1Dq69djoEu0c0rU9G5Yxy++uor0ChFQhQQBUQBUUAUEAUcSwGBmx3rfMtoRYHTRQGHhJsfeughzJkzR50DrsqXD2Cny+UoeYgCooAoIAqIAqKAKCAKiAI1FRC4Wa6IhlaArndTp05VoNzXX3+NgoICtahn06ZNCqAj0Ef4jzCzcTOt5Ha2Li74z2NjUFJSgvz8fAwfPlyBEKzPCA0NVa9ZLBbcNmoUli37DVkZmQgMDMRzzz0HPz8/BRQSCCQwzfasVivGjBkDgom+vr4oLCxUbf1r2PX4evrX4uDc0Cdf2hMFmrgC3Ins5ZdfPmoUs2bOUlZ0//rXv445QgLIhEp5T4zBuYi7mdEVuS7H58WLFyv3ZVOW35csWYILL7ywamGHfb3c3Fw1j61cuRIDBgyoatMA05wHuXV1t27dVH3Ote+88w7uu+8+NTcyCD2nZ6SrBR+TJ09WoLM6XlmJmTNngmCfRNNXQODmpn8OZQSnRoHacDPnUP79SDSsAs888wymTJlSo9H33nsPV1xxRcN2JK2JAg6kAHfsuPzG27Bg9lT1OZtB12A6KZdVatdkV2cNF//+52psXLsSB7asxvp1a3E4KQnRreIQEx0FPw9XtQguOycHycnJaNOxG/p074ywFnEIbNYKHdvEAsEtcXGXSOXovClDOycTIo7whnKM3piu3Z7dnDVwTLA5zQIkFQDBXkCpDYJOLwL4RVjZuEOzjX25wBGLzp1QcqsAYFM64O6iQWPCw0cKgShf4GCuhq05xg5BGlDelaOhYELTdHNu7a/BZJajMzRh5Lgg7VK9P1cDzHRo7heloW2C03RwZg6t/HV5ukQT8i4u14B0a+aUoaFqazmwatliFO7fgB17D2LNmjXYumEt2nbpifie3ZB+JAlRcT2wc9tmbFr1O9p06IwhF56PwRcOUu/9d+a5qTbomG2CwHaKRTtju1aUwMvLC/PmzZNdkR3ob1qGKgqIAqKAKCAKGAWyy/Mw4Z0JmPPeN7j83qsx+I7LRRxRQBQQBRpdAYeEm/ng5Mcff1Tiyo2qRr/GpANRQBQQBUQBUUAUEAVEAVGg3goI3Fxv6aTiMRTo2rUrtm7dWuUiaorZuzAT2COMTNiZD+6SjxyBG2FnW2HuBkQoj3AeoWZCyYSaGXR0fuvdd/B///d/cKazlA3iIyzNn/lV2/GU9QgSDh06VL0eGOSPrEzusSsOznIhiwKiQLUCdcHNnH88PDzU3GEg4bo043zGRRX2c92unbsQ1z6uTrjZODdzPjQQM+vyZ85/946+F/kF+TUco+2doU1/bId1li9fjrMHnq0gbLVwpELPhwcPHQTd/crLylFaVqoc7kePHg0XZxc4OVf3PWPGDIGbz5A/BoGbz5ATKcNodAVqw82XX3453n///Ubv19E6GDt2rFrwaB+vv/76cRcMOZpGMl5R4GQVaN++vXq/+OCDD2LHjh3KJbmoFCip0A7Fvm4a4iWwy71C2vhXOzAv2JSI0qStOLJ3G7Zv367ev86c9Q1c3T0R2zwa7m6u6rN6obUUaRlZyMtKQ35eLqKbx8LT1w9xXXqhdctY9Gwfi9KAWITHxKJVbHMFPLNvAsOJ+UC4F1BcAYR5Ac5OGlhuGwi4O+vf6fpMt+K0Iu2QTGfkLiHAtkygqAzoGgpkWDW0He4JHC6srkvYmO0STOY4CXLTuZp1duZwVxKgHBpiZpt0fSawnFeiIez4SKCsQoPZoV5AZhEQ5aMh5i2ZQH6xbtPTFfArPIQflq7BhnVrsWMTQeY16NG9G8orKhHWoh2cnF2QnZ2D5D1bcCQ5CX369EFsl3j07jcA3ePPRs9WoWpsjF3ZQKZV988xMw7maZdrulCznLmf8ck3i3DHdRed7KUh5UUBUUAUEAVEAVGgiSuQVZ6LCW+/iB8+mI2rHr4e599ycRMfkaQvCogCTUEBh4Sb77nnHrWqlMEP2FdddVVTOFeSoyggCogCooAoIAqIAqKAKOBwCgjc7HCnvNEH3Lt3b6xfvx7h4eE4cuRInVAfwTu6MBtQkA9U6cCckZGhHuY9+eST+Pnnn7Fhw4Ya+RJaJjhNpyq2kZaWpgBoOpVmZmaqn+kON378+KPG+euvvypHVMaoUaOUo6qEKCAKiAL2CtQFNxuneW4jHRsbe0zBCA8TLOZ8ZoIAMuetupybzWIM42BfXFysIOqlS5fi/PPPV02wrv2iDXsgmnMa3d/YB9ugC/OkSZOUU31AQICqy5wMwLxu3Tpwfn7llVdA0OzgwYNo0aJFVW4Ez+jcXFeucpU0LQUIKvH/3JAhQ/Dss882reQlW1HgFCpQG26++OKL1Twq0bAKPPzww/j2229rNPriiy/i5ptvbtiOpDVRwIEU6NKli9pthIuCV27agcP52mk4xk87DdOlmNAsg8AxoVm6Ba9NBaJt7shGLoLIhGv9LYeRvHc7EhISsGVbAtZs3g4vV2ds2bAGMTExCAoORa6lGJFRUfB2qVC7JKWnpyM1PRNZGWmIP2sgKkqLERwaCs+AMPgHhSIqPAztYkJR7BUGr4BQxMWEwjsgBN1bBCknZALRhI35HrpXmHZxpoNx7zCgoBQ4mK+h7HQLEOChyxNCbu6nweh0q3ZTthCGDgGSC/XY+bacAHX/KF1nSwZQVK7dnftGAF6uwOojWpf8Uu0AHRdQge+Wb8GqlX9i9+Z12J2wBSUFOeqehn9IOHy9vVFcVg5rfhYyMzIQ3bItWrZtj2atOyK+R2cM6tcNPC/Mm+NwcQYivDQ8zThAiDkf6B2hoWsG3a0P5AMdAoEQWznzGeDBZ1/Dm88+6kBXtQxVFBAFRAFRQBQQBahAVmkOnn9rAuZO+h7Dnr4J/a85V4QRBUQBUaDRFXBIuJlbbi5cuFCJ+9Zbb+Gaa65pdKGlA1FAFBAFRAFRQBQQBUQBUUAUOHkFBG4+ec2kxvEVmD17tnJie+KJJ0BwwYRxMyX4RxiP4e7ujrKyMhC6CwkJQVxcnHKJ4mfIP/74Q4HMDAPb8fvAgQPx+++/q7oEqLlbEIHAwRcNhqubqwIlCKeMHDmyRqIEBi+66CLVNz+n0uVKQhQQBUQBewVqw83G/ZiAcEpKCqKioo4J/y5btgznnlvzgQNhZHsnefu+OCfOmjUL119/vXZarqyEAZyrJ06CKtqN3pQx7s7Dhg3D19O/RqGlUDnaGwfoTz/9FLfffjtWr16Nfv36VTXFrcZ/+OEH3HXXXWpxSV5ennLGN/PrpZdeirlz5x4zX7lSRAFRQBQ41Qrk5+eDX5yvzM/2vxcVFan5jItQ+HW8n+vKnQsB2LYJLrTr0KGD+pXzLncFqf3F97F1HTfH+Lp9XVPevl5dP9sf+zs6186NCwnrytf++LH+T/2dPOzr3nvvvfjpp59qNMfFiFyEISEKiAL1U6BXr15qUe/Yx5/EjOUJCuSN8QVCPDXouztbA8MtfIEgT93H6lQN83YIqu6Tzs77c4FWAUCktz5eWgH8mQJEeAPtg4CCggLs2n8Iv21NRM6RQyjLTETS4UNqodyBQ4eRlHgIgUFBCA4KVO+VPXz84eTujczUZLSJjUa+pRj5hYXo07svvpk5HXnZmWruDQoKgrdfADx9AxAV7A8/P1/4+vrC1dMb3l7eKHLyhI+XJ+DiDg93N2zfshHx/fohwtcVaVYXlMIVAV4usJa7IMrfFds3rIZf695wLitCYZEVgc5FKC+xYm9GEUqKrbAWWeFVWYTKsmK1qDopNRPZWRmwWgpRbC1S/0f4vtjdwwPevv6IighHRHgYOnfuDI/gKHgENUN8h+bo3qENWrZsiYQsIKdEax4XqLUjQE6wmeeDWjfz0ccJXRPG7hFafT5SLbos3aKptYm9uUBcsAvmzV+AIReLc3P9/kKkliggCogCooAo0HQVINz83MTxmPfZDxj29M3of805TXcwkrkoIAo0GQUcEm6+9dZb8csvv6iTNHHiRFx33XVN5oRJoqKAKCAKiAKigCggCogCooAjKWDg5ttuu03c/RzpxDfyWAlo0BF03759VeAcIT8enz59OnjdmQeaTIUPEQk5Ezym+9OAAQMUrLdlyxaVqYHvnF1d4OnhgS+nfYnCwkLlMspyrMv2eYxAH+HmIksRXFyrHVQJVvCh6rRpU1FiLVagh4QoIAqIAvYK1OXczNcJzOXm5taAgWsrx4UW991331GCHg9wJszs6uIKZxcNL8+ZMwdXX311nSelNtz81FNP4YUXXlBbkbdp00Yt+GDs2rUL7dq1U+73BJZNsBy3MGcQkK4or0BuXi6Cg4Or6nGBiTg3y9+EKCAK/JMKEDh744038NVXX/2TaThU3/379wfdlfm9McLeCMe0P2bMmDr/ZzZG/9KmKHAmKsAFbI88+igef/pZTFmaoFyL6T5cVgHsyQWKy7VDs3EN3piugdtuodVqZFqB3Tm6HJ2QTWxIB1zsyhKW3pOj2ySIS5iXkWbRzsOEp90LkpGYmIhtew9h+55DSE1OxIHtG1BSWoaUI6nITD/CD/WIiohEZGQEIiIi4OoTiEpPf7QO84Gft5fa1Ymf0csqnZBqcUJlRTkCXKywFpcgOa8U+7auR98enVFuu2+Qby1DblE5PF3K4I5y7N+/H6ExrWEtd0KwRwVQUa7fwxcWIT2nAKUFOcjNzlS7P3GXk/DoWES3aouzenRG757dER8fjwKPMKRbnRUATj0Z6UXArmygbWA1hEydqamnK9AlRJfjMcLKTk7ajZqO2QzqTJfqTsHVAHlGEUCIOdpXQ+kmDhdora/qEognHn8c/GwiIQqIAqKAKCAKiAKOpYDAzY51vmW0osDpooA93MwdgkaPHl0vExSnSmNzdbqM7Dh5/Pvf/wYdaxivv/66cu2SEAVEAVFAFBAFRAFRQBQQBUSB00+Bw4cP488//1Rbl/v7256+nH5pSkZNTAE6wPGBYVZW1lGgHB9Y8nrjw09ueUugmR93CSSzHt2aCbYQ1jPOzWb406Z/hTtHjcKvv/yKESNGKBclOsEZx9KePXuqhbZ0fGLYQ3pDhw5FVEw0pk6ZgtLiEgH4mtg1JemKAqdCgXHjxmH8+PFHdcUFFIQjLBaLApzritTUVAVq1I5XXnkFY8eOPeo44WI66tnPVWbO4mtvTHwDvL9Gh3p7V01ze5DQNMvzNTrNcfGIcXVmmzxGSMTUNeXt++O4WI9B0JowIQ0LBHA+FVeb9CEKiAJ1KUAHeT5AkTi1ChBsnjFjRqN0yt1UuIOKfdx///3gwzIJUUAUqJ8C55xzDobcdA8+mzgB27YnwNu2bnd/HpBbDET5VIO4a9O4sA3oa/c2lVAyQdowLw0sm9ieBRSWAr3CNeBcCWBHFpBlBbqGajdiRnaxdnz2cQPaBWpwOrcEOJCn+yIITNg3pVDXYfkw5zzkZaTiyJEj2HEwFVsPHIF7YSpyM1MVcJyZmYnMzCxkZGUjPy8XpSXFaiGzm6c3PDw84e3pBjdXV/XetqyiElZaTJeXory0WL1HLygoVGQxHaHDQkMQFhaGkPBIuAY1Q/tWzdEjLhatWrVC27ZtcbjER42peyjg7abHlFwAJBYAbQKqoXBquTVTw98tbAA4NdmcAVRUAN3D9Nh5jGAzg2/xqamrM1BQCqxPA2L9gVhbfcLOe3O09nTMNsHjBKYJqscEuuPHH39U900kRAFRQBQQBUQBUcCxFBC42bHOt4xWFDhdFLCHm2mqwoXq9YkmBTcPHz5cbSHMePXVV5Url4QoIAqIAqKAKCAKiAKigCggCogCooBjKGCAO4JztSG5yopKrPhjBaxWq4LquAV495494WxzE6VCTz75pHIwpdOofRC+Y3k+vGS7K1asUK7N3BaWTtGMY0F5fAi6cOFCXHvttcjJyanXqmPHOHsySlHAcRWYNWuW2n3MHiamGgYoPh70S7iZEEXtuvZqElp2cnY65jxFAJl0xGeTP8Ptt9+ugGpPT5s9ni2PunKYMGECeC+OsAbnx40bN6JffD/lCG2AZ36fNGmSWhDCfriwhMAI3Z/bx7VXoDUXhtABX+Bmx/0bkJGLAv+0Aq+99hrohC9x6hXgjitmwUtD9m7/rMi0y4dkTz/9dEN2I22JAg6lwKBBg8CFA1xEt337djV2AsupFsDPXQPHjIQs7RzcJwLwtG1qRNB4dzbg4w501ht4qKCT8BEL0Du8uixhaboME/gNtr0lJfzMdt1coByOPVwAS6l2jGYQBHZ1Ag7ma2diAs4BHtohmpFWBGzPBNoHA1HeNU/bPhuc3dwX8HMpxbrDhcgpKEIrbyutHeWIAAAgAElEQVRcKkrVZ//CkgrsyXVCgIcz4kLd4OHhgSJnbySV+qJTuAcibG0yT8LC/J2wtQmOiePsFKTzYtCdmcfZL8FwBsFkOi5HeNWsT1iZXHWPMD12BsfKt/EFZVorwuaEu9ekalDanI9sq9aJ/cbZ5UT9CKEbCJo7svzwww/gAm0JUUAUEAVEAVFAFHAsBQRudqzzLaMVBU4XBRwSbqZTM7cCZrz88svqAYvEGaRAbi5QVARYLIDVCpSVVX9xa2fzxS1RvbwADw/93cd2V+AMkkKGIgqIAqKAKCAKiAKigCggCogCRytAMI5wBN2X6OBsH7U3JWLZ8spKZWDq7OSEb7/9FlOnTsXll1+u4L7aQXCQUB7hPHtHUvPzsaA85mNcShVAKCEKiAKiQC0FOD8ZGPhkAd8nnngC//3vf48LBr/11lt44IEHjglAm7nJ3sG5tKy0CnDmAg8CHLXD/rg9zMxydMm/7LLL1A5r/n7+cHHVFIZxcqbDc0FBgXK5W7duHXr37i1ws/xliAKiwD+mAOfRjz/+uKp/OgrbB9/vcdFHXV+Ewf7p4PxdVFSkFvGdzPfT4b3prl276vwf83c15aKhtWvX1miGOxNwcY2EKCAK1E8BOvryOezEiRMV3Exg+GAe0MxHA8UMOhHvytGOyyE2MDmvRDsx+7prMJmuwwweYxt0MjbAb3KhBqbpOBxuA4ZLyoEd2UBxOdAxGPB1A3iMUDJh3nAv3TZdjJkHoWH2bfq3lAHr0nS59kE1x04naYLU7Ivj2J2jHaPtnZSt5cC2TO2K3CkYcHMGOKYtGTXdlVluUzrg7wF0tOuHY0rMr9km+2BfhKBb2jYzIwBO7fzcdD8mOC62wdzpvMxg3oSdqUmkNxDooZ2c16UCXq5A5xBdLodQOd2uXXV9OmMzKiqB1alAMIFnW678f/bBhx9iVB33Q+p3xUgtUUAUEAVEAVFAFGgqCgjc3FTOlOQpCpxZCjgk3HzNNddg/fr16kzyhuRNN910Zp1VRx7NkSPAoUN/qUBKRgY27NqFfUlJyLdY4O3pibDoaLRo3x5te/dGZGTk0W2UlwOFhUB+fs3X3NyA2l/O9HWTEAVEAVFAFBAFRAFRQBQQBUSB01EBgieEUQgyZGVlKRj5ntF3Yd/e/WjTpi0IT/AYIz8/H3v37lU/G8fnzp07K+fRuuKhhx7CO++8owBE9sPv8fHxGDNmDC666CJ1rLyyAhV8nU8VbUFHVQKAffr0qfq8ejpqJzmJAqLAP6cA56XRo0fjo48+OmnA99dff1Vz2bBhw5Qjsr37Jeep2bNn47zzzkNubi5at26t2j8WQG0AZSoxd+5cBSczmJ+zk7Nyf64d9u7SzD87Oxtjx45VfdAxnytIykrL8MCDD6iqfJ1zIh3/4uLiVLnk5GRERUWd9Nj/uTMmPYsCosCZpsBzzz2Hzz//vGpYDz/8MPjeT6LhFYiNja3RaEJCAry9a9moNkC3V155JTZt2lSjJf6vpEu3hCggCtRPgSuuuAKXXnop3n33Xfy5YTu2ZwHNfDWIzCCwuzED6BJcDSbTiZiOy+4uGvjld/We0ApsyQRa+WtAmGGcjAkoG8dlHt+VDeSVAm0DNMTLt5h0PCZMHOiuXYr5OyFf5uDjpqFhRnmldjIO8jgabKbjNB2e2SYB40P52l2ZYLMBo1mf+ReWAV1DtDsyYWm6MxM0Nu7IhKzpuEwAuluoegtcNSbCyRwj4WkGwWi2yX4NbE1QmbA0v9Px2kDIhJgJkHcJqQbAmWNRmW6L0LKBwAlW81YE3Z1NP9SOMHaH4GrHZ762IU3nSgjdhLuHB17+bA4eufnS+l0gUksUEAVEAVFAFBAFmqwC1XDzjxgxfiT6XHZWkx2LJC4KiAJNRwFLdj4+euA9HNi4B0899RS441Z9wqmytr1VfVo5RXX4wXrz5s2qN67A50p8iaatwA033IBO7dpheI8eiLNt91x7RPuTkzFl3jx899tvOJiSgvaxsQgNDFQP9PYkJsLX2xvp2dlISk9HyxYt0KdfP/Tt21d9EUZw3b37aLC5VieZubmYNn8+VmzeDP7crFkznH/RRbj9/vubtsCSvSggCogCooAoIAqIAqKAKHAGKUBIbsmSJbj66qsVvKwgPq5PrHRSMLKXl1cVnGzcQ++//351jA5UBJBPJjIyMhSMaLFYlDtzuXqUCLhA98eg2+mECRMwYMAA/PHHHwLvnYzAUlYUcBAFOF/wHgYh4rrAY3vouC5J6ILs6uqq6r/33nu49957FYz81NNPqZ3NeJzt24PItdspLyuHvVvzm2++CcJ9Jnbv3o3mzZurOc0+R3vXTwNO13UrUcHOdMuna35ZuSI+zMIS9kFAu02bNg5yxmWYooAocLopwAco06ZNq0pL4ObGO0O14eatW7fCz89GNjZgt5dccolylrUPAs+EMiVEAVGgfgpce+21uOCCC/DBhx/h08XbFFRr4F7CtmtTgRZ+QKzNiZjH6LhMGJmOywSDGVaWTdMuy4SbGXQtppMxgWFzjMcTC4DUQt2mcS0+mA8QmibYS2iY8DDhZULHhIKNizTrr08D3Fw0mGwfhKsP5GuXY46BYDXhZoLaBoxm+T02J2dCyHSXpmM0IWaOxbgj85O/AZMJNhMmNmMiXEzH6Fa2jaWY49ZM3S9hbwLGrE9naOrF+h42AJyu1ruzgXZBug0GXabpxswyTk5AjM0xm7A0NSEYTbCaP+/M1nXolk3g24TqqxzoE159jM7QLYM9MW3GNxh29eX1u0CkliggCogCooAoIAo0WQUM3Dz/i59w22t3o/N53ZvsWCRxUUAUaDoKFOUW4NOHP8DuNTsdB262v2E1fvx4jBw5sumcMcm0hgJ8MNejRw8Qbj68axd+mD8fPem+HBOD+4cNw+8bN+LnlSux6+BBbN23D6EBAcqpmSBzTHg4woOCEODrq+Dmts2b40hmJlKzsnA4LQ1REREICA5Gbl4eKsrKEOLtjay8PPRq3x55BQUI8PNDgcWi6m/duxc5BQVIy85Gi8hIuDo7o9BqRUlpKYqKixEeHo5JkycrFyQJUUAUEAVEAVFAFBAFRAFRQBT4ZxUw8Nw/m0Xdvffr1w+rVq06HVOTnEQBUeA0UIBQ3YgRI+p0Vjbwcl3gM+Hi33//Heeee24VPEzH5LXr1ipHZIJcJuzr127rrbfewgMPaHdlOtFz4Qfd6k055sCfCUnb17UHmffv36/cFbibmlpwvmePuq/DMHWY7z333ANfX1+kpKTgq6++Unl36NABO3bskAUgp8G1WN8U8vLyMHnyZHTs2BHctl5CFGhKCnAnjpkzZ1alLHBz45292nAz3ZUDAwMbvEPurMKFOfZx8cUXY9KkSQ3elzQoCjiKAnxf1zc+Hh98Mhk//bkNnYL1yOluTLDZ3om4uFyDzXQijgsE/N2ry9JJmaAw4V4GQVwCv6xPENdEmgUgyEyAN8rmekwIlyC0uzPQOkADyQSNmQP7Mi7SbINtMo/edhCv6U85GrtoN2gCx3tzgChfoLkNFmY5uibT2ZmwNd2hCWkTYmZfhJAJJjM4TgLHPUIBTxvAnV8C5WzNcZoxEYzeTti7Ako7U3ZXDpBZpF2UfW0Qcm4xsDVLw+ImJ46bbtPUkoA4x88ggE0Qum+EBqs5HoLizJMu1AE27e3LxkdU50/Qe1sWcGFrT8ycMQNXXXWVo1zSMk5RQBQQBUQBUUAUsClgDzff/sZodDqnq2gjCogCokCjK2DNK8SnD7+PXasdCG4ePHiw2maYwa3kbr311kYXWjr4ewosXboUhw8fRmZmJqKjo3H99derBrkVNB+EpKam4pUnnsDEjz9GRHAwdicmoqSsDM5OTvDz9kbvDh1w5TnnoH/XrujQsqU6drygg/N3y5Zhd0oK3p8xAyUlJUBlJcrKyxEWFAR/buHq5KRgZx9vbxQWFaljg/r0QXRYmPoeHhys+ifwPObdd7F2+3bcOHy4cn1ojBuxf09hqS0KiAKigCggCogCooAoIAo4jgL1hZsJ1hUVFTXKlthGfe7+QtBQQhQQBUSBuhTgPMR7WnFxcVWArwGHCYzefvvtxxTueI7MppJxTTau9bXhZqvVqlyZc3Nz4e/vjxUrVuDss8+uAqbpTn/wwEG0bt0azi42ksMuo/T0dISEhCgw2h54Nq7R9nCzfd+EmhMSEkDnzq5duwrc3IT/PD777DPQbILXz5YtW5rwSCR1R1TgoYcewpw5c6qGLnBz410FteHmdevWITQ0tME75KKfgwcP1miXx6ZOndrgfUmDooCjKMDdcoNbdsFPs6Zg745tVcNedhgI8AS62/6UyyqAnTna5ZhQcqTdYzu6HvN1AxxbyzWETFiZwK8BhgkLE/q1d3KmuzLhXmcnDfYSfqZTMyFlwsQ8RtdihgKGrUDf8Oo2eZywMx2NSyuBdgHaNZmux4SXCTqbYD8Ep+3BapbLLdHjpPMyg2BxapE+ZsBkwsXbOSaX6jERNN5pc1fuFFJd9mAeQGCbztaEuxmEvTemA5E+1TlxfMyHeWZZNbTM9hPzgX15Wk/2T+iZY+e4mHuIZ/WYDucDe/OAXmGAnw14zivRYHOEF9Ax3AMvvfQSHnnkEUe5pGWcooAoIAqIAqKAKGBTILM0G89NHI+fv5gLgZvlshAFRIFTpYBDws2DBg1S21gynn32Wdx2222nSm/p5yQUmDFjBjZs2IDly5erLUcLCwsV2MxzR1efL7/8Um0h/eCDD+Luu+/GGy+/jD8/+UTBxwyCx8H+tr2qTqJf+6I3jRuH75ctQ/s2bbB1504Mjo/HTxMn1qu1/7z7LmYtW4ZKJyd88cUXOP/88+vVjlQSBUQBUUAUEAVEAVFAFBAFRIG/pwCBOQPunWhLhPDWrl0LLpbNyck5qpoBAs0L/KzCrbNrHz9ef4QChw8fjlmzZp1oWlJOFBAFHFABd3d3FBcXK8CXi7FfffVVjB07Vinh5la9l3R5WTlKy0rh6VlNK/Aey8CBA/8SDrafuzhfEkY2QbCZjsr2x7p06aJA1U6dOoHumpz/GBaLRZUzZdmuadseXr7vvvvUYvC6XKfZDnNgcJ50dXWt07naAS+FJjnkN998E3QAZ9QGCpvkgCRph1Jg9OjRmDt3btWYBW5uvNNfG25evXo1IiIiGrzDs84666iFhf379wefTUiIAqJA/RTgM9eIFm3w4zfT1cI0BkFhAr99wrj7h26XsC2dhwnXEk42QYdjwrQEcQkl02mZTsh8N9glBPB00SUJB29Ot7k729yh6VpMJ+XKSu2knF+q6/u5AYSeCTbTtZhBCPgAgd8IwMcGIfM46xL8LSwFWvpr52T2T9CXcLEBowkP78sFQrx0X4y9uRqs7hpSDQabfujibMBkAt0JNsfqzsE1IWiOoW0gEGSDmOkKTWdqgspGJ8LeHLuvuwajjR77c4Fwb50DcyfIzJ/pDt0tROfKvvfn6e8ertox20RGEbAlU+ts+iJEzfrUiOP39vJUu6pce+219btApJYoIAqIAqKAKCAKNFkFjhSnYfzrE7Bo2gKBm5vsWZTERYGmp4BDws32q/GfeeYZjBo1qumduTM4Y974oBsPnYB4c7Fv375HfUjmQ5D58+fjqaeewv/dcgsOHDqE7XPnokNISIMqYy0pgae7O/amp6PHDTfg7TFjcNsll9S7j6c++gibkpMxf9EizJs3T7bfrLeSUlEUEAVEAVFAFBAFHEUBuvutWrUK48aNQ0xMjKMMW8bZyAqcLNx8xx13YN++feCOMnQXrR327qMGzOMxAojff/89hg4dWgMCPNbw6Ap900034dtvv21kBaR5UUAUaMoKZGdnIyAgoGpeIfhLwIDzhz0c/PXXX4PbgtsfIxRNIJpw8rFAYmrD+zJ0S2YZwsyElfkzv7jgnIvQjQu+E5zw6muv4rHHHsN1110HQtVe3l6YMmUK7r//frUwhF8MuvUaQJmQcmlpqTpO4HrBggVVOa1ftx7LVyyvkSPnVW9v7yp36uPl35TP75meu8DNZ/oZPrPHx/eECxcurBqkwM2Nd75rw81//vknuMNJQ0efPn3AXQXsIz4+XhYbNrTQ0p5DKUAzosjISHzzzTcKbibcS+fhvhEaFGbQSTjDqt2a6dpsghA0Ydz4yGqImbBztlUDt8ZJWAHPmQA55c4hGlgmhEu4mEAvweCySu3UTBdjuhETbDZOyuyDTsR0UrZ3LWYeBIkzi4AYPw0j0zHavh+WYbvMy8MF6BKsge2kAj2uuCAg2La2kP0wJ7o9EzpmVNCdOVvn2yHIDoIuAFILNZRswGK6Su/O0QA4vxh0tCZsTWfqrqH6O0FlwuLhXtqJmmNiDnS23pAOtA8CmvnougZsDvLUx0wQ5l6XpvtvodcpoqhM98/26STt7Qr17Pa1T6bjgZECNzvUH7YMVhQQBUQBUUAUAJBCuPm18Vj85UKBm+WKEAVEgVOmgIGbd6/Zhaeffvq4u1ceLymnSvunuacs/fp1NGDAACQlJanKhGPvvPPO+jUktRpUge+++w6EV+hw9vjjj+O8885TD/UXL16MRYsWKZceggSd2rdHmxYtsOyPP7Drxx8RMmAAJj70EEZedlmD5mMay7dY0HXECJQ5OWHKe+9hUHR0vfspKi5Gv1GjcO7gwerB4+bNmwXSqbeaUlEUEAVEAVFAFBAFHEEBbj2fl5cHbgFNeEBCFGgIBTTcXAYnJz6iNL5LdbdMOHnhgoV4/4P3j9n1seBmVpg8eTJ69+6NHj16/GXqAjf/pURSQBQQBWwKtGvXDrt371a/GVdjAx8bkQxEbC8a5yt7J+bagLBxVS4rLYOrm6ZPat/yo2vpZZddpsBjwtIEpQlM8+vll1/GqNtHITpG3zsxfXHXLTo6b9y4UdXLzMxULtP8KsgvQFBwENavX4+rrrpKOU17eXopuJn3gV555RU8+uijasFI61atsW//PnFubsJ/CQI3N+GTJ6lj5MiRarGbCYGbG++iqA03c+eB5s2bN3iH3bt3P2pXFgLPs2fPbvC+pEFRwFEU4OI2LmjjZ+lFq7YqCJgQbqgN+KUT8ZFCDR03t0G01OaIBUjIqukaTGflFAvQKUg7NJvYngnkl2k3YgLLxYR7c4FKaLDX3RlIK9LgNOHqWL9qiJgO0hvSNIRsD/eybeZ2wFY+yls7FheWaSdmA0YT+OVxukrTNdndRbtCE2JmPwbWpgMzx0NQ2IDJ7IPl+Fpr/2oImv0Sjo72ra7PPFmfoDNdm00QtmYOBLPZd3klQMdmgtjM1cMZiPQBrGXA2jTdJp2lCVUTbKZWBMANwMx2CT2vSdPnqJ3NybmEIHSu7otlDbDN9+TPfTgdj916ndJZQhQQBUQBUUAUEAUcRwEDNy/5ciFue2M0Op3T1XEGLyMVBUSBf0wBAzfvXbcHzz33HP7973/XK5cmBTdz5X1qaqoa6BNPPAGuIpb4ZxXg9qkEmbm13/XXX4833ngDH3/8sXI2pstZiLc3hvbqhYLUVMycOxeZeXn4/McfkZyRAXdXV+yePRsx4eGNMojH3nkH0xcswA/ffoueLVvCOSXlb/WzNiEBt7zwAjp264bg4GBMmjTpb7UnlUUBUUAUEAVEAVFAFDiTFTAPtQVuPpPP8qkfm4aby22OoMeHm6dPn64+q8ycOfOYLqd1wc1mVNdccw1efPFFdOrU6S8HKnDzX0okBUQBUcAGG7/99tsgMMz57N1338V999131BxVF9xMATlnGYjZHm7mMe5w9sILLxyls/08R4iZUAPr0nHZ2dkZI0aMUGByXFycgsS4YH3Zb8vg5OykXmd91uN9kKioKOxI2AFPL0/VBhe6834QHaldXPQ+42w3KysL27dth7uH7osuzwTbEhMTVRlxbm6afw4CNzfN8yZZawU4161YsaJKDoGbG+/KqA03Eypv1apVg3fYsWNHWCyWGu326tULc+bMafC+pEFRwFEU4Gdgvk/buWs3PlqwRTkW0zmYQdCYzsIB7tpJ2URdwDGBX8K4rGvvrkw4OM1S7eRMaJeuxXQX9nfXbbMfgtPJBUCEj3ZyZrDM6lSgma+Gi+0jowggTB3mrSFlOhbzmL1jNCFgwtoEfgk2+7hpUJlOzIShY21t0tl5c4YGle3HSWfnZJs7c4TNyZlgNN2iCTGzXwZdlOlMzfGwHxO7bDkRbGbfDGpE8Jo50cWZbZRXaLCZwLPRnuUIPNNt2j4ntkHHZuVCbdsc1wDTxRVaOwOBM//Wwe74fOp03HzjdY5yScs4RQFRQBQQBUQBUcCmQBXc/NUi3P3eA2jbt4NoIwqIAqJAoytg4OZ96/fi+eefVztY1ieaFNzMm1N0iGEQquUDFIl/TgG6NNOdh24Ir732Gl566SU88sgjauvSGG9vgOcqO/uoBF3POkutwuZNkuxFi+DnY7d/UgMOp8OwYWgTE4O5Eyc2WKtvz5iBH9eswa8rVqht7/hwT0IUEAVEAVFAFBAFRAFR4GgFBG6Wq6IxFKDLKHeMOREwrqK8QrmXEsojoMcgpEcAr8KWnL1ZEeE7rhoeNWoULrjgAuVAai0pUX2diKmRBq8rTii3xtBG2hQFRIGmowBhLG9vb+VuzPmp9pxmtVrVttEnMteZURtw2fxuoGTeu4i27WTFedHZRffH1wlRc87jfRw6YHJ+JIB24MAB1Yzpn2XZxsGDB7F27Vp1nAs/OCeHhISoe3WmLNsYN26cAre5g0Pbtm2xbds2tGzZUrVb26W66Zw1yVTgZrkGmrICN9xwA1auXFk1BIGbG+9s1oablyxZov4XNHSwTb5/tw/uuELHWQlRQBSonwJ85peSkoKV67fgh2Xrq+DanGLtruzuCnQM0s7HDAK0a1M1HGyg2yyb6zEBZOOEzLJ0NyYI3CGo2kmYvxMGJuAb6a1h32gfIN0GURMaZvDzO/shMMz69kG4moBykDvQLgg4aHOMJhhswGiW35MDcBxtAvXxvBINOwe6aydoBiHjTRkaLLbvh7A2vwh7GydngtFsk2WNYzKdlenOTH0IG7vabiTQUZqwducQDXAzCBtbyrQLdrYVaOmvy69PA9yctWM2gxoZ+Jmamjb5Gvtin73t/KOoIZ2b3Zyqz0mqzVl7cCs3cBE4jaokRAFRQBQQBUQBUcCxFDBw86/TF+PByWMQ3amlYwkgoxUFRIF/RAGHhJu7deuG3NxcJfiYMWOUu43EKVbAagXy87Fg4UKMfvJJfPPpp7j3qafQulUrvPjss4gNDAQyMgCWqyPufvllfP7TT4gMCUFsZCSWffxxowygtKwMXuecg+9ffx2XDRzYoH10Gj4cAeHhaot13hiXEAVEAVFAFBAFRAFRQBQ4WgGBm+WqaAwF/Pz8FNx8IkEYr7CwEKxj3EfpKMqFmfH9+6sm7KFlBTE7O6svwnl0Y6500k9tTwRuZj3CzRKigCggCvyVAs2aNUNSUpICjM3iC/s68+fPx0UXXaTmorpeN5CyqcO5xyze4DH+bhZbfPHFF7j99ttVUXPMgMhmzgoICFD32+jgPHHiRLRr1+6oIRDG5tZxvBfi4uyCSlSqOjQe+Prrr6uA6bPOOgtz585FUFBQ1fjYT1hYmLqPN378+L+SR14/TRUQuPk0PTGS1gkpIHDzCcnUIIVqw80LFy5E+/btG6Rt+0Zq98PX+Pzoxx9/bPC+pEFRwFEU4E4ge1JysHbFUuxO2KKGbSkFNqQDvjawmC7BJlYe0S7EXW2uwQWlwMZ0DQAT1jWRZdUgcSs74JmwMEFogryxvsDBAiDUU0PH7MMejKaTcVk50D+q5pkg9EuXZG9XDSNnFmlAmgCzAaNZI7FAO0bTETrcS8PCrOfuonMnjFxaoR2bDVhs9olKKwIO52sg24yJ7s4J2YCPq3ZXJnBMx+TtWbpttklgm3HE5mJNgNq4WNMBmv0Rsj5cALQJ0GNmTqXlQC8brMy8qT8drlsFAJ522tOdmuPtE1ENPFNj5k/gmbA5yxPoJrDdNgDo2iIY77zzDm6++WZHuaRlnKKAKCAKiAKigChgU6AG3PzF44ju0Fy0EQVEAVGg0RVwSLiZrjB8OM149NFH8cADDzS60NJBLQV27gRyczH86afRvV07fPLdd7hv2DA8Mnz4SUlFV+W+nTph6nPPnVS9Ey28bMMGXHDPPSj47Td4edj2rTrRyn9R7rYJE4CwMOXcLDdLG0hUaUYUEAVEAVFAFBAFzjgFBG4+407paTEgQnTvvfdenbDfsRIk8Mev2g6ox3JErQ3/nejAudPQd999h9TUVBw+fBhXX331Sbmunmg/Uk4UEAWatgKcjzZv3ozOnTvD2clZOSnXDjpR0iG5T58+cHV1rZpLOD8RduYiDS76N1HbAZrlMtIzEBoWinXr1ql2jFsz69Se/95//33cc889mDZtmlrE7e5us5SrVZZ5MZ/abZg5lt+5w5XZdY3l7J3zQ0NDkZGRIXNjE72EBW5uoidO0lYKCNx86i6E2tAxF+zwuU5DBv/vtW7d+qgmu3btip9++qkhu5K2RAGHUmDsMxOw+1ASdq9fgS1btqC8QgO71nIN9toDw6tTNUjbM0xLRKB21REgwlsDvybyS4HN6UC0HfCcUaShXydnoI0/kGLRgDKB38pKDSGbYP8EfAnx2kdZBbAtU0PFdESmAzQdnFnXuCuz/BGLdo2mMzRzYB9sk98JIRMq5k6zWzL0sW6helwMQtkH8jTATXdmZycNL7M+f6aLtacNYmbfBIk7BQN+trfSrM/jsf5AM9sGtoSsqQnh7f25QAt/wM9Nl2N9jpOwNQHoghKtK8FmX7fq0R/K13nRsZm5MThG5m8t020HeGhN1ttgc4LlXDhJ5+Zhw4Y51HUtgxUFRAFRQBQQBUQBQOBmuQpEAVHgn1DAIeHmuLg4FHkDf38AACAASURBVBcXK71l67h/4LLLygL27FEdR15yCVpFR+Pa88/HmHqs8r3w3nux69AhJDaSk8LMxYtx50svIWfJkgYVauvevRjy0EP4cf58tWX1888/L1s4NajC0pgoIAqIAqKAKCAKnCkKCNx8ppzJ02ccM2fOREJCAp599tkTTsoeVCZ0x+CC2d9++w0FBQUNuhPL//73Pzz99NNYvnw5mjdvflIA9gkPSAqKAqLAGaPA8dzeOV/RnZkQspOzk/pOkKukuARe3l4gZOzmVk04fPXVV7jxxhur5h0z91EsJzihrLxMQckffvihgphrB/vLzs4GHZzvvfdeTJ06FYTRzj777BOay+zhZro7c542ALU9SE33Z869dblRnzEn9gweiMDNZ/DJdYChCdx86k5ybbiZ5iB0VG7IKCkpqXOXAS4cmjdvXkN2JW2JAg6lwMsvv4wdu/Zg/drVajHenhztrtw+UMOyJgyIGx/J95raWZjuzu7OQNfQ6nIEc3mcDsUGeM4tAQ7m6TIEbvk7g+7HBId5zMTeXCDVAvSNqAaOzWs7sjQMzP7YP12PCVbTBdlEpg1OZv90MuYdAdajOzSBaAMM022aUHCPsGpYmWU4fuZFV2i6PBMepjsy8yTEbOofyNfO0HRHpsMzg/W3ZmrQ2IyJsDPB7hZ+wCGbUzXLE3JOKtRgM92WCWSzPuFyQtH22tNJmlA3x02nawY1spTpnwmJUwfmuInae1Zrz/fgU7/8GjcNF7jZof6wZbCigCggCogCogAEbpaLQBQQBf4ZBTTc/AH2rd+j+MqbbrqpXok4VZonvPWqfmorcTU+H+YwHnzwQTzyyCOnNgFH7y0pCUhKQnJ6OrqOGIEAX1/smzOnXqp888svuPGppxDfuTNeu/9+DOzevV7tHKtSVm4uQocMQfLcuYgMse2J1QA9vDB5Mp6dNAmvvvoq+vbtq7aJPe+881TL3FLWarWqba8J4g8ZMgSjRo1qgF6lCVFAFBAFRAFRQBQQBZqeAgI3N71zdrpnzAdx/Dx4LMdl+/wJ9mVlZaFfv37Yu3evcm5u1qwZUtPTlFNqZUWFci3q2LEjNm7YqOBB+2D5sLAwpKWlabjQqebrdWmVm5uLmJgY5OTkqLYlRAFRQBQ4ngJcvM95Kiqq1v7aoGNdtds8f/7kk09w++23KyiYX/avc75LSUnBBx98gBdeeKGGQzP7t5+/Fi1ahMGDBx+VFtuj2zzd543D82233YbJkyef0PxnGjS3GOkGxwUptfv39fVFfn7+SbUpV9HpowB3JeBOev3791emExKiQFNSQODmU3e2asPN/N/Ss2fPBk3AYrGo9/G1g8d+/vnnBu1LGhMFHEmBiRMnKqh5/fr1WPjnZhDaJbAb6lWtAsFkgrkEcb1sH3vXpmnImS7O5qM1gWNCw4SD6YbMIHBLYJiQcXNfoKRCQ7l0Os62agCZrsWMxAJgXy7QK1w7G9vH/jwgtRDoGKyh400ZQIC7/t0E4eBdOYCPKxAXpNtlexlW7cJM4JlBgJou0ixjnKmVCzTzrNRgM92RmfOubCCvFGgXAATa6qcU6lxb+gHh3rpNOk0zJ4LL9lA33ZU5RtYxEHJygc6T2hFiJrycY9XthHgBITaAmb/T8Xl9mga4jTs1YensYt0eXawJTpeUA5szAY9asDnf5z/3wdd4bNQNVY7PjnR9y1hFAVFAFBAFRAFHVkCcmx357MvYRYF/ToHiQgsmP/oxdq/a4Thws/2Nsfvvvx+PPfbYP3cGHLFnG9y8fscOXDt2LPp26oRZL71UbyW++OknjHnnHRRYrQgLDMQtl16KF+6+u97t1a4YMngwLj/7bPzvJJzdjtX5m9OnY8Lkyci3WBARFoYOnTopiJlwxerVq5X7BK/HDh06KJhh27ZtmDJlCugi8emnn6JVq1YNNi5pSBQQBUQBUUAUEAVEgaaggMDNTeEsNZ0cx4wZo9zZ6IhMYIEPXY8HEBN4aNu2LZKTkxUEGBoaqlxJ8wsK8Myz49C9S1cMGDAAXbp0UQ6odKgKDw/H/v370adPH1x++eXqfT37YzvffPMN2rRpc1wgj/3QGTU4OFi5ng4dOrTpCCyZigKiwClXgHMGd4TifPFXCyjsYWYmWvt3QqdcwMH5zMPDAwSe63JH/uWXX9QCbfZX+3W2uWLFCuXWzHjqqadAF2Z7h+jaItm749u/xnsh69atUxCs/dg4j+7Zs+cvx3vKT4Z0KAqIAme8AgI3n7pTXBtunj17tnp/3ZDBhTJ8H1872rdvj4ULFzZkV9KWKOBQCrz77rtYuXIlNm3egk8WbkasPxDtUy0BHYW3Z2kQ18DBBHPpSNwrTIPGJuhabC0HeoRqwLmsAtiXBxSXaWdhN2cgvUgDvHQ9JvTrYavP45szNBRtgGPTLsFgtkPomEAzyxFc7hJaDUYTmE7I0qA1gWe6IRMsTi4EWvpXt3m4QLtI08XZwMp0mybETJdmQsTGNflAngajCTEb2Jt58jjdmQ1sbMBiatElWOdAWJpgdasAILdYg9Isz/p0YWaO1IRO05lFXJwI+LvrYyaYz5rUmu7UhJrp2sxzQZhbuVNXAtuytOsz3Z2pMyOloBzR/m74cPJU3PV/9XNLc6g/BhmsKCAKiAKigChwhimQYk3F869NwNIZS/DIlMcR2S7mDBuhDEcUEAVORwVKLEX44j+TsGPFNowfPx4jRoyoV5pNxrm5rKxMPUw2wW0y//Of/9Rr0FKpngrY4OYla9bgoYkT0btjR3wxblw9G6uu9v2yZRj73ntISktD6+hobJw2rUEedA158EH8unYt1k+Zgi52107thH9YtgyD+vSBr7fdnQK7Qv1uvRVrEhJww0UX4b+jR6MVXSE8PACzBWxpKZ59/XW89PHHOD8+Hp9Nnozm7durFghJcHtqbocngPPfvlSkAVFAFBAFRAFRQBRoQgqYh9rjxo1TbpMSosDfUYCf/+hKys+FdUF5pm3CeWPHjlWwIMFmRnR0tHJgthRa4O7hjoceekjBENxlhU6lhKXpoGrcSgn8EZzmMQZhwVtuuUW5kHLbpGeeeabOoUyaNAnTp08Hv7NPD3ePoxyh/44GUlcUEAXOPAW4+xMBYe9j3I+oa8ScC0ePHl3jpcTEROUcz3mKAB/nLc5j9gAz50fjrFxYWAi6KDM49/F35hAREYHU1NSqezJcKHK83OgYfeDAAcTHx9dYcEK4ma7UbM/AzZy/zaKUv4K57Qd3LFD7zLsaZESigCjQmAoI3NyY6tZsuzbczP9N3E2lIYOLELvXsRMkF0MuXry4IbuStkQBh1Lgo48+wpIlv2Djtp2Y+8cmxAVWD58AMqFZOhE3swHPxsW5d7h2DjaxOwegozAhaE/bcULA+SUaZia4y98jvYEUgs3+2h2ZUZc7sWmXMO+OLCjomjkogLpMQ7wGjCYEvDNbH2eudIUmAJyYDzTzrc6dDsm7s4G2gdUQMZ2P9+ZoN2nCxwZiJhhNsJugd6Rt7MyF46Q7M92tGXSrZk7MoWuIhr0JeBNsZl2C0wWlGkIm8EzH61b+2m2Z7dFBmnUIY0frt+oqCEOvTdUadbK5U1PLgxyTj4a22aaXK7AlQ+dPMNyck7xiYFVSMYa288bnn3+u7m9IiAKigCggCogCooBjKZBsPYJnXxqP5XOW4ulvn4dfhN2WF44lhYxWFBAFTqECBm7evXKH2nXyX//6V716bzJwMx/4cOW9iXvuuQePP/54vQYtleqnQMLGjXhnwgSUlpUhPTsbIQEBmHyMB/v16eHT77/Hpt278W4DOXLPXbECD7/1Fl665x5cN2iQemD4x+bNmP3rr/hjyxYk7N+vxuLm6orF77+P+E6djko79qqrYLFasebzz9GyWbPjDisxNRWvT5uGrxYuxKABA/D1l1/CKTBQARA7duxQoIOEKCAKiAKigCggCogCjqLArFmzlOMPty0ncCXRNBQwbqB871wd5mfnOp1AG3tk55xzDqKiopCeno7evXvjtddeq3MxJHdVcXd3V++96bbMsdCNmYAdg68TqHvwwQcVDHHbbbfhsssuAx1PN27ciFWrVilwmV+DBg3CWWedhWXLllUNj3WZB2G+2mHcSwk1Z2ZmoshSBGcXm0VSYwsk7YsCokCTVoDOyISRTzToqMwbgfaAMMFhOsdbi6zw9PJU9z845/GYKVdeVl41L5ljdLQPCgqqgp4nTJig5kC+XlFegQMHD6iF2seCkctKy1BRWaHm3u+//x5XXXWVaqtTp05qRyvWMwtH6IRPCJtxMnDz/n37MW/+PHCRi4QoIAqIAvVVQODm+ip38vVqw81fffUVBg4cePINHacG32/36tXrqBI0x+EuBRKigChQPwUmT56Mr2b/iEMH9mHXtk1VjRCWXZemIWADPBNs3p8HdAsDgj2q+zuUr8HlXuGArw1YJnxL2JkuyFHewN5cDQ7zGOFc445clzuxaZlQ8KZ07ZJMkJfuynRSpjO0t60fliVwTBdjQsMEj+mGvD9XQ9U8xqB78tYsDRU3t4OIOR7WjfCqhpgJRtPhmWM37swEixOy9fg6BmmnZcaObCCnuBosNm7VIZ7awdk4VLMsYWVq0C5Q90n4muOgCzXzsg+6UxO8JizO4PngmJgPoetwb+3eTF2pqXK1tp0TOknT8TnQqRA9WgTi448/VvdCJEQBUUAUEAVEAVHAsRQ4ZDmM8S+/gD++W4anvn8BfqG21Vlnmgw2cwcn5+pnM06VlaiorISzkxMqea+SW11wAZl5E/c3NGBbJ9QO+/yb/R3VF1fW8U3mMeKEc/sb45eqosBfKWDg5j2rduKll17CNddc81dV6ny9ycDNBQUF6Ny5c9Ug7rrrLjz55JP1GrRUOnkFbr75Zvzwww8oKy3FzUOHKkg4MjhYQcGna1hLSjDpu+9Urpv37EHCgQMI8PUFp/ee7dujbUwMhl98Mc7v3bvOITz85puYOn8+kubOhYdxaT6Bwc765RfMWrwYi1atwpi778YTL7+Mf48ciZCQELz99tsn0IIUEQVEAVFAFBAFRAFRQBQQBU6dAgTYpn89Hdwyes6cOcp5+Morr4SHh0cVgEb4rry8FG+/8zbGPfOc+hB6qnbSIaRHF2a6LLds2RLffPPNUeLQqXnkyJEK6DOgMaE+upbSQZTfjWMp4eYevXrilpEjccWll2H//v1ISEjAihUr8OKLLyqAjuDg6tWrMWXKlBrbJDm7uqCkpBRuLnb77QJKC4LSF110kXJt3rdv3z8Cgp+6q0Z6EgVEgYZS4IknnlBzjnE1/qt2v/32W3UT8FiAMJ2XubDjjTfewCOPPFI1/xFaJrzMMHUXLVqECy+8UM2PPMa5nfA0g3Mpwebdu3eD/wPq6o/1jAu0cWbmQhQubJo2bVpVXwSrl/yyBIMHD67Rf11jNQ7TTz/9tLqHwoXir7zyipqjJUQBUUAUqK8CAjfXV7mTr1cbbub79HPPPffkGzpODe7K0rdv36NK8P/W0qVLG7QvaUwUcCQF+Pc67euZOHL4EDZtqoabVx/R4G2XEK0GAd4N6RquJZxrgvAuXZM7Ble7HqcXAXQ+poNyqwAN5dJROLcECPMCCP6aIIRr705sjtPxeGM64O+hYWKC1YcLtTsyXaBNEKwm8EyXZMLIhJh35QC+7kCHQM2T0DGZuROSbmPH9BAupntykGc18KzA6DwNDpuy2VZgX57mSDoHa6dlBt2ZCUJ3tuVEZIZjJQBN7Qh8K3dlFw2Ke7np+syH7fm7aYCZGtkjKgSmOY4+ERp8JqzM8mZ8dMamAzYB7JRCoKW/1tXE6lTAzw2Ids1DaGgo3n//fdxxxx2OdFnLWEUBUUAUEAVEAVEAwAFLIl54+cUzHm7OTspARlI6Yru2QmlRCUqtJXDz9kDi9gNo0bkVvAN8kZOSqQwg/MOD1LVRkJkHdy93VdbL3wd8BvRXkXk4He6e7rDkFiC0RQRc3Oy2MamjcvLOQwhvGQlXD7s3r3/ViXm9shJ5GbnKiCIwUjtulxWXImXvYTSLawGXOvItLrAgKyVL3c+15BUirEU4/EIDUWIpRubhNJVzQVYectNzEBwVAu8AH6TuP4ISawkiW0XBK0BvV1JRVo7s5Ez4hvjBw8cLleXlSD+YCv/QAJDXTt2fAhd3VzRrG42c1GzkZ+YhtEU4ii1W9XNlRSV8An0QFhsJe+D8RIcu5Zq+Ag4HN9feaowfvvigQ6JxFRgzZozapohuPnffcQeevPVWRDs54e5XXsGUuXNhsXMya9xMTq71N778Eq9MnYoiq1U5M/fq0AGP3nQTBnbrBn8f275Rf9Fk4IUX4tIBA/CV7eHfyWUAbNmzB69Om4aV27bhxQkT8P5nn4FQ/ogRI062KSkvCogCjagA3Rc//PBD5fAoW7I1otDStCggCogCosBpo4DFYgG3mx07diz+/e9/44MPPoC7mzuc7FZ50+2Yjj4PPfSQevBF6Lm4uBh33HE74uI6KAdlgrx84EmHzsaIkuISdO7SWcHHdAXt0qUL3nrrLQXnMX8DThDAo6MygT6GAeMI4zGMYzMBPJYlvNe+U0fcfNPNuOFf/8LWrVsV3EyYmZDf119/rQBqtk/gMCIiQrk7O8FJ3agpLS2DvSczd2ohFE1AkeWWL1+O7du213lTqTF0+jtt8py++uqrePbZZxVEaA8vEtZu0aLFCQOXfycPqSsKOLICZlEGF2ScSHz33XfKIflYcDOd6OlOb4Ll8vLy4O3tXfX3XBtu5hzGe2z//e9/q+BmM5/SHTM4OPiYCzbMwhEDSJvv9mPhvRD+T7HP6XhjpbkB/w9x0U18fDycnZyr/kedjOvziegpZUQBUcAxFBC4+dSd59pw8xdffIELLrigQRPgvbz+/fsf1SYXQv72228N2pc0Jgo4kgIzZswA/2b5N8b3lIz16YCrk3YjZljLtetwtG+1EzKPE3imwzAhYL7GoCPxnhzAw1U7PhO+dXUGCCu7OVc7IZt+uIEUIV77IDSxKUODyd1CgNQiDSx3CgRCvatLEu5NKwLCvXS7hIa3ZwEeLtrJmBByFSTtrgFsE8ZZWgHYxt25RLtDE06OC9JgMZ2ld2QBeaXaMZogNoPw9qECoH2gdotmHMzXYwz11I7KxqHa3oW5iA7MeQBBaJZt7a/1MUEgmuOiJp4uGn4mMB3ooXPhF12eCVUTzm7mq/sxQSCc0SMM6r11s2bN8Oabb4I7I0uIAqKAKCAKiAKigGMp4AhwM52Kt6/YgvRDaeh8dldsXLwenn5e6HlRb/w553d0Prs7IuOisX35FhQXFKFZXIwCb9f/vEbBv+mJ6ejQryNc3d1QWlIK32A/uLm7Iys5HR5eHggID4Il3wJvXy+s+vFPxHZpib3r96DboB4KIPb08VLQb/YR7iRaqdrxCfBVrx3YvA9R7aJVG3SQJvzrGxKAEosVmUnp8PT2gn9YAHLSstX9WzcPNxTmFsDd0wO+QX7YsmwTgiOD4enjqe7Revh6YtlXS9D9wl7wDw1U48hOzaalBYKjgpG0KxG7ViWgpKgEvoG+CkQeOOw87F6zA9t+34Jzb7hAfef7UFc3F0THxWD3ml0K2PYJ9kX8lQPVgjtC4WvnrUL/a85GVJsYpB1IwdIvF2PAteci43Aaco5kIT+nEJ3P6YKdf26Hl58P3D3cEBITirSDaQqEjmwThf7XngPnWoZBjvUX6LijdTi4OSMjQz1AN3H77bdj3LhxjnsFNPLICTHwRmBubi78/Pww9a230C04GCguruo56MILMbBHD/z0xhuNnM2JN19cUoIhDzyATbt3o2vbtnjxnntwTo8eJ96AreTahASce9ddeP8//8Gtl19+0vXtK/y0fDkemDgRZ8XH49eVKxXs0Lp167/VplQWBUSBhlOAW90fOnRINfjzzz+jY8eODde4tCQKiAKigCggCpwmChBO69Chg7oxsmPHDgQEaIuiY0FiBk4jEOzj41PlhmzKE8bjTRSCwHQGTUpKOmZb9ZWAUDMBhe+//x6BgYEYNGiQymXIkCE4cOCA+rxicuMuKVyQWWHrzN3VVUHOxrGZ+Zowx/kanUbpUJ2YmKjqDzx7IGbMnInrr70OXGBrxltaWqrKEgTnZyUTYWFhuP7667F9+3blKrpy5R/qM9SgQRcdV9/6alLfegS8qQEdVRlDhw5VP/PhNZ30asOI/J0QPF1d77zzTlww6Hx8OmkSfHz8xJG6vidB6okCx1GgR48eVQCJfbG6QGHOfeedd94J6Wnq5+fnq/nSzGnmOz//XHzxxVXzFReP2N9r42uffvqpWkBiFozYd0xnfELZxwOOrVarWkTSp0+fY7o/1/X/iM75nG+ZO+cv9s95iXO3hCggCogCJ6uAwM0nq1j9y9eGmz/77DO1KLIhg+/dzz777KOa5MK833//vSG7krZEAYdSgDuE8DMvn8cSbl6bpl2Ee4dXy7DqCBDgoYFhEwZ4JlhLd2KGtUzDxYwOwRp05jFCwlxXbQBovp6QDeSXAPG1wGa+xjYIKhPQ5fdtWUBLv5r1M4o0JOzjDnQK0hAzIeSSCqBTsIaQyyqAzZm6f0LSZldwAtFHCgE6IBu42GIDo91sYDQBaQIhhKrzS4G2ARowZrDv3TnaMZlu0AzCztwlPMZPOzoHuGsna+PCTD35OnP2cgUKSrXbNHU1wZyoC8dN52iGKe/uDNBVmloTKifwHOJVEzY3uvW1acrP/9xl6oprrsPsGdMd6rqWwYoCooAoIAqIAqKAYzg3E27e9vtmFFuK0ap7G/w5ZzncvT1wwc2DsW7+SkS3i0WzDjHY+usG7Fi5Hf4h/giOCcWOFdsR1SYKKftS0L5fRyTvTERARJC6j0no+fD2Q3D1dkPPi/ood+NmbZth/cK1aNO7HfZv3IuAsEBkJWfCkluI/tedjUWf/Yw2PdshOzlDQb68l5l1JEuB1mEx4QomtuQVofM5XbF+4RoUZOfBmm9Fp/O6YfX3f6JFxxYKoqbrMcHoftcMxKZF61WbO1duQ4m1FO3i22PFN7+jRadY9b42ukNz7Fm7C35B/mh/Viek7ElCeVmF6tPbzxubFq9D/2vPxoYFa1GYU4BuF/bEwc370OfSfljz40qEt46CX7AfiixFOLz1IIbeQ2MN4ODW/VgxcynOHT4Ioc3Dsf7n1TiwaS/Ouv580B06KCoYm3/ZoEyBLDmFOGfY+fj1y8U4d/iFqEQl1s1diW4X9ERk2+jqN+DyB+lQCjgc3HzkyBH069ev6iTfeuuteO655xzqpJ+qwRIcJ4DLh/yT3n8f/cLDgdzco7q//vHHsWDlSnzyxBMYPmTIqUrvuP3E/9//Ye2OHfjkyScx6sor653T2HffxZT585Eyb16927CvmJWXh5HPP49dSUloFRenAEoJUUAUOD0UsH/oMnfuXOUKKSEKiAKigCggCpwpCtCdhw/5//jjD3Tr1u0oiNV+nLxZsmfvnhpD/+STT5TzMeFee8jOy8tLuT537dpVbYcV1SwKO3fuVBByYwThaS4QJJzHbagJZ3NshN14c+jqq6/Gl19+CS8fH5Wvh5ubuvlkHEWPlZP966q8WqtOj+aawfH/73//U/1ee+21VdD0G2+8gYcffhh0OV64cCHKykpQWVkBJ6fjw36NoVHtNulkTUdmnsOYmBgMHjxYuWwTEnz33XdBIHzWrFlo06aNqmrcXRcvXqygZoIohMsnTZqEc889Gwk7EnDlFVchMjIKP/zwg3JylRAFRIGGUYD3X+ikXjvMHGUPD9MlngAy5+G/CtYnXMyFLfYQsmnvvffew+jRo9W8wNfHjx9fBTez7sSJE9UCFjrU0+2feRq4mHP/goULcNttt6ntrTmnMA4ePKgWTvzw/Q9qi0cu/KgLQGNZAy3zO3Nau3YtevXqVTUswsyvv/66yov90P1ZnJv/6qzL66KAKFCXAgI3n7rrojbczPeiXJzYkMGFjnUt9OF7Xv7fkRAFRIH6KfDTTz+p915c6Dv9l40KSO4ZDhCmZWxIt8HBNhdnHqObMCFoArwGeCa4S5CXkDBdjwkWE8b1dtVuwwSBTRD+paNz38jqfsxrdDymI3G/SN0PnYhrO0bnFgN7cjUkTHdoQhh0XCYI3S6wGhjekqGh5+5h2iWZkWXVzsisw7J0Ry4p11Bxabl2dzbuzHRR5hhi/TSozCBYnJCl3ZJjbWM6YgHoyEwH6EP52omZrxNMJvTcJxwgNE0gmdB0TokuS4doE+xna6bWLsrmTs22CGYTqmZbBJuZI8Fq5tg+SEPjDOpGONpe010HU9C9Yzs88dwLGPef6gXb9btSpJYoIAqIAqKAKCAKNDUFqpybv1+Gp757AX6hthVpTW0gx8nXHm7uOTQe25ZuUu7C59w4qCbcvHSDAnwDw4OQuDNRuTi37tUOSQmHENOhOfau340B15+HBZ/MRUzH5shMyoR/qD96DY2Hp7cHjuxNRsbhDOVy7OzqjKI8C/Ky8nBg415ccvdVWD77N1x+/9VY/f0f2PLbJlz/+I1YPvM3tO3THkk7DsHL2xOdzu2GoOhQfPvqdFz14PVYO2+12rUuIzENPS/pi+3LtuCsawZi6ZdL0O2CHkjakYiYTrHYvWoH0pPTEdupJVL2JuOCWy7G5iXr4enlgeQ9SQpAbtG5JQ7vOITel8YjZXcy9qzbiZiOscg6nI6ysnJkp2QhtmsrWPIs6H/1QPzxzW9o1aOt0oROzR3O6oqW3VurN8mVvP/7yY/ofF53FGTmYs/6PagoLUPb+A5o1jYaO1duR056Lpq1iULqgVScN/xCzPvwewVD07WZ2vW/ZiCcT3DHwjPocpSh2BRwOLiZ2/sOHDiw6gIYOXKkergh0bAKLF26VD2Y5wpebs3skpgIpKUds5PwIUNQaLUiYcYMLMyWhwAAIABJREFUtIiMbNhkjtNaElcZh4XVKDHu44/x4hdfYN6bb2JIHdvSnUxydG3ek5iI5AaCm03fd/73v/jfvHn4bdmyOrfOO5kcpawoIAo0jALTp09XMBJdyYYPH94wjUorooAoIAqIAqLAaaDAfffdB19fX7z00ksqmxMBwggMMwivsTxBNk9PTwWgFRQUqNfYJgFiOjmNGjVKvcaFqCw7YsSIGp/bGkqGe++9F+3atcNjjz2GisoKJCUlIzJCWxAxz/UbNuDbOXPwwvjx4APZK6644oTGe6L5cYxPPvmkAvwICfKL4B8B39DQUOzbtw833ngjXn31VeVweqJ6n2j/J1OOcDIXrE6dOlWdC+ZaV3BMBAdnz56t4HUGF3nFx8dXXQMsw3Fy3K+99pp6r0Qd/vzzTwVEbt68WQHQJ3JtncwYpKwo4GgKcG4jSGKCf3dcUEEYry6ImSA0YWQzV9elV23XZ87bBkwm8Mw5vH379ggKClLuzHfccYfqi/OCWRxy1113KaCY0b17d1WHr9VePMLf2b6Zb9i36b9v374KWq4rysvK1dzy+BOPKzf5yMhI1TbnpFdeeQUvvvii2hnA39+OgHG0i+M0Gm9eXh4mT56sdjtqaFDxNBqmpHKGKiBw86k7sbXh5g8//BCXXnppgyawd+9etatL7WjWrJl6nyohCogC9VNgwYIF6rlrdr4Fny7YoBybCeAy1qVp8Pcs/XG3KuoCngnXZtuchd1dgIN5GjKmwzPdkQ2Em1yoQWQC1ISj7SOxQAPAdC5mGwSbCfbaO0Yrh+VswNVJH6f7Mvsm8NzCDkLema1BZrZFCJpBcJuQMB2Z2wZquJhQNsvSSZlgscmJUHKqRTszG3dmliEwTcdkQtUMujgTeG4VAKRZtHM0YWiOkxBy91A9BgLdBJwLSnR7xgWabRSVA2tTgRjfaidm1icUTndngtyR3hrQpjszwXM6Y5vzxFzZV69wwN+mKXWfv/Ew/n1+Rzz/3HN49NFH63eBSC1RQBQQBUQBUUAUaLIKGLh55U/L8eS34+Eb4rhw87alG+Hi5gpvf2/ldlxcZEXs/7N3HuBRFW0UPum9d5JQQui9iaCAVGkiiCioIFhARFQUFWmiguKPYEdEBKUqgvTeey8hgUAIhJCEkN57+58zd2+yWRJSWGrm48kTsjvzzcy5Ze/e+86ZxrUQeTkCbj7VcPnkJQH9HvhnD+q0rgfevwzxu4L2AzvAwtoCwScvw7m6C8IuhKJB+0YCXPZuVAPn9/uhy6u9EHjgHLqO6IntCzbhZkgkOrzYWQDI7Qd2xN5lu2HjaI3OQ7sjPTUDu//ahm6v9cLZbSdh52qP+IhYtOz1GI6vP4ymXVrg4L/74NO8NixsrRAfHoucrCykxKeKsoSZOw3ujMAjF+Dk6SwcqxMi48T1raOHI2q18MX+ZbtQ/4lGwjmZ8HZybDJCzgajwZONkXAjTrwXePA83Gq54ZrfVTTo0BjV6nqLi2QLa0uYWpgVws2ZKRm4cTkcYYGhQq+8vHxkpmag/uMNhJ5ntp1Aix6t4bfzNDoN7YYj/x1Cg/YN4d2o1kN73MiO37kCVQ5u1p2NT5ew6dOn37mSMkMxBejoNmjQIIwePRqTx40Dzp+/rUJDP/sMF0JCkJWTg33z5sFJs7z13ZJ1w4EDmL18OeysrPDbp5/CXeMMxPYcu3XDC926Yd6ECXfcfP1Bg9C8Xj38fQf72NItW7D75ElcDA0Fl8V+vEkTzBwzBtX69IGbkxMOnDwpwBAZUgGpgFRAKiAVkApIBaQCUgF9K8DrzODgYLi6uArnzDsNXffQ0hyRCcLRwZOOyvoMtkcH6sjISNjY2eJycDA6PdkBZ86cgampqfBbNjQ0EKD13j17BexQGnCr2/fygLmE9uhaStCObscc4759+8BJt4sXLxbjZRlGefLpUxvtXASs33rrLeFkV1Y/Stqmt9NMBRfpnD1gwAAQHqFzNSG3CxcuCMhZhlRAKlA5BbTBYP6f5xhOUFFhZO2sdEymvTyP4bVr12LgwIHFGuV5kLFnz55i58IJEybgq6++Kjw3cJlBcwtz8AR65OgRMQE7JCREuLmr54Jhw4aJcxyD7e3evRvt27cv5uBc0ojV8wt/0zGek+dLOr+ojs3a+QnApaSkiLFzNYC0tLTKiSpr6V0BOvoTeCJs7u/vr/f8MqFU4G4qIOHmu6lu8dy6cDPP63Xr1hWF2rVrpxfDj6CgILEqiW5wksyxY8fu3WBlS1KBR0wBXuuN+2gCMrJycPbMGVhqvtYLJ+B0xXVYhWg5dLoL0w2ZIK26+lFEGnAjVXEytjdVYGMbUwUYJthMUJlBd2ICy02cAFeNO7EqZ3QGcDFecU52NAf8YhQH5KZajtEEh1mGwHU9R8DGRHF5JmDsrgUhq47JBIvZDwZdnQkJs8/eNoCDmfI6+0oIurad0i6DUDPH5GyuANMMwsIEmwlKN3JUnJ8TspSyHGNyDpCogbv5OnUifO1mCYRqHJjp7uxgruRVgw7XdMFmf+jErLbP/tKpmVA0IWxbMwXCJvDMvKrrM8fuHwc0dgJcNO7SzHk6Bki5eQ0DOzUHV4HhvQ0ZUgGpgFRAKiAVkApULQVUuPnYpsP4dPXnsHZ69IwE6NwccOAcCOG26tUWEZeuIzkuGXXb1seJjUdRvUEtVKvvhfALoTAyNoKphSluXr2B3Oxc8QwtOyML2Vk5CD5xCd4Nq8PW2R52Lva4fiFUPHdq3rUFYsJikJ2RLdyRb1wOg0/zOji15Rhyc3IF6FurmS8y6ATdvDb89/rB3ccDyTFJyMrMQuOOzXDxyHmYWZnDvZY70pPSkRKfLHKy/YZPNEZkcATqtKmPI2sOIC8nDxFBYWjevRVqNvFBRFAEwgOvCcdoMysLpMQlw8TcBFZ21nCr5Y6rZ4PB1VltnGzhWtMdxsZGOLX1OOzdHWDn6ihAY/b9yqkgVG9cC1dPX0bCzXg4ebnAwsocV85chq2LHZy93WBuaQoHDyfYuzrCb/cpeDeoATs3B6EV6xOuDjlzGZnpWTC3MhfO19f9Q5CSmCocnX1b1sGFw+fRoF0jmNvoXOxXrUOvyo9WhZuvnrgszKH69u1bKU0MCspap7dSafVfiQ/mu3btWpj45ZdfFg9lZOhXATry8MstnVj6N20KxMbetgFCzW1HjIBPtWo4GRiIb8aOxTMdOsC6HMujVqTn3E0JUv+7ezeqOTtjweTJ6Nq6dWEK/ytX0Ozll5F/9GhF0pZaNjohAZZmZrC2rPiJdt3+/Rj59dcCbsjMyhKO1i6OjmjfpAm+fvttvDVzJrYdPYpuHTrg93/+0Ut/ZRKpgFRAKiAVkApIBaQCUgGpgKqAlZVVocsyXysLcuUER7p1tmjRQi8iEkhT4Tq9JNQkIdBnbGIsQAY6M3PCKwErOoXm5uYWjpNABVf+YVT0625pWvHanjrR1ZhO1nQVpWPc8uXLxes7duwQfSgJRNSnBqXlYv9cXFwQcjUEdvZ333VBBRI3b96MESNGIDUtFY6ODggPi7gXw5VtSAUeOQV4ruJxpbrCZ2dni4kbJZ2TWJbnG5ZlHZ53tM89rEs42M7Orth7U6dOBX9Ud2U6I380/iOx5OH+/fvRsWNHcQOcN9NVZ2a1T6rgbPunn34SE+LLmsTCsjxfZmRkFLo9l7XheG6lYyDP440aNUKvnr2wddvWMj/Hysor39ePAt999x2+//57kSw0NFQ/SWUWqcA9UkAXbv7oo4/EJBIZ+ldAF27WbYETZ7788ss7apiTZnr27HlLDldXV5w4ceKOcsvKUoGqrMC3P8zF/AULYGFUAL+zZ4QUBJWDdJyA+Todlwkot3ZTXIQZMRmKGzJdh6tZKbAw36NLMuFcS41rMmFdOkHXtC0ChlXdWZbQM92Pva0VWJcAdXNnBXBmcHIz3YlTNHkJA4enKmAzoWQVQmbfQ1I0kLQGYGYuQsK5BYoDsgoB0/E4Mk2pq8LWHB9dp+mATHdnBmHhgHiA8wmbOCvju5kGxGpgZr7PvhCQ5v85ToLedHDm63TSY9vUgrCzdpyOVvIxL4PtE7auZauA0xw3+0wwm67V1EgFs6kFIWbC1QS2hU4FgJ/mEa9N8hVwRRWuGMPVmGRIBaQCUgGpgFRAKlC1FFDg5uk4tunIIw03XzoeiJtXIvH4gCfFsyTe28xMScOxdUfQpHNzuNbyQH5uLp+aiRlq+Xl5KODqlVzCg9ezYdG4dvYKmnZrKVyaDQwNkRafAmMzEwHp5uXwORSEU3F+bp74Tcfk7MwsmJiaiPuq/CE8TeCZfSAQzOaMTU0L6zMv2+UbaYmpMLM0hamFOfJyc0XeE5uOims55n1y0FPCQZl50lNSYWZhLvrLe7q5WTkwMTeFsakJ0pNoEFEAc2sL0QfWz8nIEuMyMDaCmYVyQcw87FdOdjYyUjNhYW0uxpmbmS2uN41MjJR7ysZGMOSzt6xsMR4DzQqhedk5ojzrF/CCF4CJhZlwuM5My4ClrZVm3DmiX0IwGVVWARVuvnbqili5sqR7OeUR56GBmy9duoQePXoUjonL4c6cObM8Y5RlKqAAl1PmUsQ71q3DE+V0YSbU/PyECWjq64vE1FQc8fdH/Ro1hDsx41pkJFIzMjC4Wzd8/8EHFeiNUnTF9u14Z9Ys8QCuS6tW+HvGjFtyfLdiBT6bPx/Je/ZUOL8+K0RER6PWgAHw9fLCV2+/jf6dOt2Sfs3evRg7ezYcbWzw6dixGPLee/rsgswlFZAKSAWkAlIBqYBU4IFQYOTIkQgLC8Pvv/8OLy+vB6JPVaETXCZ+/fr1pUJxqgbp6emoU6cO+vXrJ2BhY2NjAeyeOnUKixYtgqenpwADygKjS9KUQFvz5s3h5+end8kJ+3F/euONN/D++++LlVD4PYEgHyE6xt69e9G5c+dCOK8yndAdN+FhvkaNCILztwr9qVAhb97whtK9jvPnz6NZs2YC+CZk3apVK9BBm9uVwHNFY8OGDZg1axaOHz8uxsjvhwQZ33zzzRJTETovQJ644dW69WNYsGCB3kD5ivZdlpcKPKwK8Lw5adIksUKZ9vlH+/83b96Em5vbbc/LBI9jYmKEu6525GTnKAA0b74bGd0y8ePnn3/GmDFjCnPf7tzPvnKix9GjR0XO0hyZExISYGNjIz6Pyht02/znn3/w66+/IisrS+SnsQHvVe3cuROXLl4SN9Nl3B8FJNx8f3SXrepHAV24meYeXO1Chv4VKAtudnJywunTp++o4YCAAPTp0+eWHM7OzuL7jAypgFSgcgrwWuy119+AmamJOE4J1p6NVdyJtUHca8mKS3IrtyJgOSkLuJgAuFoo0G1YKsAFR9JzFdiZgDAjj6uGRCr56miAYbW3mbkKDEznZcLBhKrjMoAWroC51iUg3Y8JMhPiZXuEqvmavRngq5nry3rsD/Mwn9o2wWYCzoSgCWAzCA6zv/xbfY3jIZxNp2q6KKtg9YV4BdamizQBZTowM6ezhTJG/p8ws6mhMha+znESnGa7RgYAbxt46izqquuCnZiluGUTbGZ7/OFYmJ//Zz9VCJsOzieiADcLoLaWpoHxQEqOAoaHBF/CE088gbfGvIvpn0+t3A4ia0kFpAJSAamAVEAq8NAqEJIWhi+++gIntx57ZOFmbpzUuCQkRifC3acajEyVmXWZqemIvhYFj9oeMLW0EPdFxVOcEqDbrLQMpCWkwdFLa8mQEra6moMAcXmeCRFkJhBcnijIy0fklQgBPbtWd4Odu6OYtUbwWPs+7G1zFijGP+yb+M2xanKIoWvWXWFWNafuSp9qXwvr8wXm0BrzLauDcpwGyj3o0vKVRwNZ5tFRICcjE39O+B0hx4PFSrB8hl2ZeGjgZj6w5RJmavCGJC2rZehfAbr7rPzmGzzdsmW5k8cmJmLW0qXYfPgwLoWGokW9ejh/9So8XVwQFhWF7JwctGnUCEcWLCh3ThZcuXMnhkyZIk6tM0aPxifDhpVYf/Jvv+GXlSuRsGtXhfLru3CPd99FcFgYrq5Zc9vUr0ydCrpNO9nZYc7s2WheAgSt777JfFIBqYBUQCogFZAKSAXulQJ0zeVDE8b8+fMr/WXlXvX3UWknIiICBw8eBL8rFbvhoDVAQsDVqlXDihUrBBBAaFd18dTWga87ODggKSmp3G7EV68G4+VXhsL/nD8IT9M9lNC0PoMrzYwdOxaJiYnw9vbGtdBQvD3mbYx5ewzaPvaYuBGzY8cu9OjR7Y6aLQ1upq4E9Qg3qzdnCBIT6L0b4y1rENTil19+wX///Ydnn31WFGffuf38zvrh9wW/Y968ecKh6bPPPisRAmHdr7/+GqHXr2HZ0qV44okOAuBWc6kusbt27RL16do5duwYTdduvSFHELpTp04YPHhwWd2X70sFpAIaBXic8XyputBzYgH/rw0GT5gwQRyr2ucn1blZFZLnJgbP6yzHz2BOTKDTPCe0qJ8Nwo1Eay5GZGQk2rZti+vXrxce+8zFPCU50jMPzwUNGjRA927dhRuIGpzw0Kp1KwE/m5mZVWiSjK+vr7hm+OOPP8TnCNvhZ9bKlStF/wnEMaeM+6OAhJvvj+6yVf0ooAs3T5kyRUyWk6F/BcqCm7t16ybO83cSZ8+eLbz21c6jD3D6Tvol60oFHnYFzpw5g+eeew6WlpY4c+48jt9UwGDVtZjjI6RLaLiZswIIMwgw0yGYLsL1HRRYODUbyAPgZK78qEHgl6ByI8UbqTBo+nbkJuBsrjgtX0tRAOoWLoC1SVG5G2mKUzL7RcCXEDDdok2NgDp2ym/Cv/6xCvysujgzA8sRRmY+9XW6IxMYpoMznaQZdJa+GK/wLuyLhea2Bh2TCVIT9ibIzD7rgs2EmQlZn4lRwOjGTkB0huIybW6s1KGLs3YQ4iZI3spVcW5OzQEIkBNsJgxOV2mCzYSdE7IU/QhcMwi5nIwCrEyAho5FWa8mK9uK24nj5STmMWPfRZcBwzDrq88LHasf9n1W9l8qIBWQCkgFpAJSgfIpEJIahs9nfI5T248/0nBzfHgMYsOjUc3XC4lRCTAwMoS1vTUiLoejVlMfWNhZI/FmnLjfaeuqXFClJ6XCxMwUeVnZMLW2LHafsyR1DQoKEHcjFiZmJkhLToOLtyuMTLQuWHUrFRQg4tJ1uNXygLFZ+U0gCtMUFCAlNhG5eflwcFcuoummHBkcgWp1vYV7tG5kpWYgKToBTt7OSLyZAMdqzoiNiEF6It2dATtXewE+J0UnwsnDCWbWFoi+fhN5WXlwIwRuoVzAp8QkIuFmPBw8HIUjc1x4tHB7dqnuhrz8PMSHxwsjCjcfN+FwTc0dqjnB0s4KUdduChjavaYHjDWu0eXbW2WpR0kBOn0vnvgHLh8KrBpwM12/6CqmxqBBg4RltQw9KxATA5uaNfHFyJEYN2RIhZLn5OYi9OZNLNywAbU9PfF8ly6Iio+Hl6srLDUuahVKCKDPuHE4dfEiFkyejL4aQKakHD+tXIn35sxB/tGjFW1Cb+XTMjNh16UL/pk+HQO7dLlt3vjkZLQZPhzO9vao4emJlRs3Arf7wNNbL2UiqYBUQCogFZAKSAWkAndfAQJNBAgYdGB8/PHH736jVbwFQnAEvlQwrjQ5CCzHxcaVeIPmllnWBQWwt7cXgHNZQcjWu7onPv7oYwCGwvG3YcOGuHjxYllVK/Q+XUAzMjJAl9GhQ4fC3MICQ4cNw9BXXkEvzdLUa9euw4AB/SuUV7dwaXBzXFwcmjZtCoLkahBqtrKyAlcXWrx48R21W5HKdLB+77338MEHHxQC6trbUP2/Cj8SFKTL3Q8//CBAbNazt7NHbd/aMDYyRj5yxW8DgyJLLD4A7du3b7Fu0U21SZPG6Nnrafz4w8+3gItsj/A5nfOioqIq5R5dER1kWanAo6AAj1eew3l88fzDvznBf8uWLYXD4/mF5z1tN43Lly+jbt26hWUyMzNFHjqvE1bm8aiGCikzt+o8z/dU4JnnCIIsanh4eODGjRulwsmsJyZS+PkJWILBv0eMGCEc9glrlwRG3257+fj4iEkU69auQ+j1UNE3nkfoaM1VBdLS0ioESz8K+8aDNAYJNz9IW0P2paIK6MLNn3/+OYYPH17RNLJ8ORS4HdzMyXicxFKS63I5UhcWoTszAUzd4Hcdgs8ypAJSgcopQJMpHqe2traYv+W0AGYJ96pBEJgQc0OHIjdkugafi1WcjQnyEuIl3Ex3YgLA2o7PBI5zCoCWJSwwRBiYdQjjEuK9nKhAxCpAzT4QLL6eorghExAmBHwpQalH2JdOygStz8YosLK2MzSBbM7vI3DsY6fM80vKVurbmQH1NI7HdFcOSgA4LkLdquM0nZ0jUoC6DkV9IthMcJgOygSn2T7HK8aZr4DZ8VmK+7SNqdI3tW1VU9UFu7WbAlELJ+hkwNtacX8mUE3omsA23arZHxXCZg5uD7rnNdfSlAD49eTifT12xh+9n+6Bl159DT/NunV13MrtMbKWVEAqIBWQCkgFpAIPiwIhqdcF3Hx6xwlMWP05rB01s7oelgGUo5+Eji8c8kdMaDRsnGwRFxGD7MwcuNV0R1JMAho80RQedTwReNAfmemZ8KrjBUsHaxxbcxBuPh6IvnYTjTo0hYmlGXIyskUOE1MTxEXEwtzSFDYuDsIF2szKAsfXH0bNpj64fOoSWnRrhbSEVJjbWsDGwRZJsUnI4/M6czMB+aYnp+H6+Wtwq10N9s4KVJyVngUrBxvkZmQhJjwaljaWYpskxyWJe5+m5qZIS0qDuaUZLGyt4L/PD86ezjC3NBf1zazMsW/FLjTr3FKAypb21kiKiheOzHR6jgi6jqgrkTAxN8HV01fQ7/2BCD4dhJjQKNwIChd9T45LVlblMzKAZx1vhJy7AmroUdcbTbu0BB139/29R/MssQDVG9VE8KkgAYKbmZvC3MYC8eGxsHd3hG+buji65hBsHG2QmpACz3reoh2aYNRuVQf12jUuxxaURR5FBfJycrF44gJcPlhF4GbdG1YDBw7EnDlzHsVte3/GxG+/YWHAzZtw6t4dbw8ciC/LsTQfH4h9/PPPiIyNxZq9e1GvRg2cDQoSdSePGFGusWw9ehTXIyMxcsCAW8p79umDJr6+2PrDD7fNdS44GI+NGIH/vvkGvdu3L1e7+i40df58zF62DGn79pUrNV2pP507FyZGRhg3ahRGTZxYrnqykFRAKiAVkApIBaQCUoEHXQEJN9/7LURn3sOHD8PkNhPm6Kg7a9YsWFvrrH+q6a4u3MwbD3ziR9fP27mrpaamYtWqVXj11VdFJhW869WrVzEwTx+quLm5ITY2VsB8bK9Lly7Cwfmll17Cnj17RBPsCyfD6iPUsRDY4/8JeterVw90OT106JCA9+mczCAIHh9Heyd9tHz7HHRj5vjNLMxhUKCMmSvw9OjRo8SKpTl56xZmuVatWiEwMFAs6V27dm3s3LlTTFYgwL1v3z7Ur19fAId0dTU3N8e///4Lfj8vKRwdHREaGgpC6TKkAlKB2ytASLhx48bKTWUN4KwNIZd0vPJY1AaI/f39RQ7tczEnGzAfIRX+5jmbx7N6fmMb77//vnBiVl8bP368+Lzg37qTPbT7wfMxXTJZhnmCgoIKzxG3q1eaEoS72fbcuXMFKM3geX7z5s34+OOP8eeffyI5OVnuSvdJAQk33yfhZbN6UUAXbp4xYwZeeeUVveSWSYoroAs33w2tOYmnpOt9Xg+fO3dObhKpgFSgkgpw4hzd1a3snLB052m0dC1KRDdjui4TrNV2Qw6IAzLzgCZOisvwlSQFtqVbs6fWrQdCxHQdbut26wrgag5Cz0lZAP8mWOxhVdQ+HZoJ+tqYKNBuNiHkRAUipquxnany2tlYwMq4uDM0YV/WNzJQ4GK6I6fnAAHxCpBMt2nC2XRVZv8JGNP1WXWcJlTN1+mkrMLahKyZj2MMT1X66WWtwNIcJ8dCmJnuyXS0Jkittq2Oiu9xDE2dAAdzZSyqizTdn9km+8B+Efgm/MwcbJcRmKDA5K1dFcCbQZdoAtOEo1X9mHfZrjOY8GpfDB82FDNnzqzkHiKrSQWkAlIBqYBUQCrwsCoQnBaCL6dPx7ndp/Hxv1Nh5fBows3nD5xDRnI6HKo5w8LGHEHHL8HYxEi4LHvVq4lq9b0QsOcMAo+ch72LPezdHBF0PBDuPh64eTUSdds2QNiFa3DyckFedh5ca7gi8uoNGJuaoHm3VqKMh281+O08DZ8WvgjxuwIbR1skxiQiNT4Z7Qd2xPbfN6F+u4a4GXJTAMlGxsaIuxEDC1tLOHu5wtjEGBkp6WjcsRlObT2OjLQMpMWnomGHJji+gdB0LaTGKxeYKXFJaP98J5zbdRo1m9XGxSMXkJudgzqt6+HQfwdRq0ktARBXq+uFK6cuCyC7btv6uHn1Bmyc7ZCVlonze/3w/MSXRb7Y61EI2OcHjzpewk26da+2OLbhCDzresPc2rxwPI8/1wHxYTE4tHofnnzhKQFzu/tWg62jHbLSM3HlTDAsbS2Rl5MHJy9X1GxaE5GXb8CxmiMOrd4P99rVkBgZj4L8fFRv4oNm3Vo9rIeO7PcdKlDl4OZjx47hhRdeKJRtwIAB4sGLDD0okMFvu9eAlBTQUdirb19Mff11TNDACSW1sPnQIfyyahX2nTkDbzc3eLm4YFDXrjgSEIDHGzfGqBJA5dJ6OmTyZBwNCEDI2rW3FPEdOFA8ILu6Zk2ZA63Zvz/qeHtjx08/lVn2bhRo8corsLOxwd5ffy13+t0nT6L/xx/D1soKf/32G7r2vzOHuXI3LAtKBaQCUgGpgFRAKiAVuIsKSLj5Loqrk5pgG+EyQqcD+g8QS0CVFgRMU1JSSnxbBZv5pi6Q1q5dOxw5cqSwni48qMHfAAAgAElEQVQoW6dOHQG06dZLS03Dnr17bnH+vRN1CNSampqKH67k8+677woAjlDv9u3bBeS3evVqPP/883fSTGHdkuDmFi1aIDg4GAQ36OBsZGQk+kCN6JR6L2LYsGF4qmsXEFj/fvYcjBo1SgDHhJLpmkrAMSYmRkCHdPNW4WztvlEr5omOjhZjYNnZs2eL36rbM8sX0N7KQPmdX5CP6tWro1OnTlixYoVoUxeu1G6D7xG4Zx9kSAWkAmUrwGNPBYp5/HzxxReYOnXqLefXvNw8TJk6BQTGtEFo1ucxpx6bdDwmFMxjnC7MPBfUrFGzcBIGJ2y8/vrrWLlypXBaZv2EhATRUU5OKAtQZnk6Z65du1a0yXt1nGhC6EyFri0sLISDO/tVlpMzz690/A8LCxMTRjih5fr166IvdM6vVauWmFzCvsq49wpIuPneay5b1J8CunDzN998g8GDB+uvAZmpUAFduPluuGTzuruk7cfvO1ypRIZUQCpQOQWuXbuGto+3h72LOy6cO10I0RKuPR4FuFoo0LEadDiOzVRcg+mIfCVRAZfpOFxDi5cR7sSpCoRLQFc7VBi4lasCJ5+OUSBhgsRqEKy+EA+YGwH1HRW4l6AzXydoTQCYX1vpYsz2CQsXwr7pSh/5PiFots92zscrEHZDJyUvg+7LdEgmwOyuWdCEoDL7WM2qCOomlJydr7hHR6cr7swEpNVxciwEvUOTFQdpOl5zPNpjZ146PNNdmhAy+0fHZkLarMO61LQACthMIJvaq30lBM1+tHEDTDX9Zzt8nc7W2vqdjAKuBpzE+BHPiefsclXkyh0fspZUQCogFZAKSAUeZgUupV7BVzNm4Nzus/j43ymPNNxMV2SCx5dPXEJybDIadWwsnJM969RQ4Oa9Z5CWmAYHdweEXwpDZkqGKB8eeB2e9asj+GQQnnzxKWybvxE1GtVEfGS8AHlb9GwjIGe6HyfciIOxubFYCTM9JR2Z6Rm4fOwSeo56BodW7cMzYwfg0H/7cfFwAPp/+CIOr94vgOTwi2HCdblxx6Zw8HDCmtkrRdlTW46JZ3t0j27Vuy3O7/dH22fbY++ynWj6VAtEBoeLvl09G4zYsBh40Rk5+AY6D+uOcztPCxflqKuRcPRwgmd9b9y4HIG2/dojOyNL5Ogz9jkYGhjg5JajMLcyh52zvQCZH+//JA7+uxe1mvsK+PrkhiOwd3PAk4O7IOrKDZzcdBRdR/TE/r/3wLdlHWRlZAqI2at+dWRlZIl7zITBGz3RFAUGBQjxuwoHVwdkZ2Uj/OJ1GBgawqu+Nx57pv2tMxwf5gNK9r3cCqhwc/Chi+L5Y2kGTWUlNCjQfopdVun7+D5dsejGpQaXRvrxxx/vY48ekaZzcgAuFZ2RgT/Wr8f8tWtx/Px5DOnRA8u//LLUQU5fuBAnAwMRHh0NM1NTDOjUCVuOHMHxCxfwRr9++G7cuHILNGnePMxdtQoJO3feUueX1avx+fz5mPTaa3hPs7R5aYm/WrQIk3/7Dcl79sDawqLc7WsXJNxtbmoKS3PzCtev3q8fhvfpgy9GjapQ3dOXLuHZ8ePhxGXzePP1AX5IR1CJIZeWr9AmloWlAlIBqYBUQCpQ5RSQcPO92+QEywj60u2S8FppoQ0k79ixQ0DBmZmZAhJjqHAcwbelS5YKJzRDI8NCKFYbtlPL8zfLE5gmhKYbbLNBgwYCVNNX/O9//8Onn36KsWPHCsfkLVu2oEvnLuLGjwoF3k24OT4xEVu2bsErQ14S7qSEifv3748NGzbgwIEDaH+XV5HJyMgQLsgdOnQodKpWtdV23tb+P93r6OydkZWF/s/2w9o1a8X2phvq7t27S9w03J+4n3Af4XdvbUdw5qbWTZs2FduXet8ujhw+gtZtWt/WVVxf+4fMIxV42BUICQkRAK8aPMfSeVmdqKBCvXv37hWTDHThY+1bfOL/pCEMlHO8WpbHNSetjBkzBq+99poAjrU/I3Rd/CuiqS6crX5e8PWuXbuKzyr2neeUksDpv//+W8DS1apVE6sM0KX5ySefRLNmzfD000+L8/7y5cvLhK4r0mdZtvwKSLi5/FrJkg+eArpwMx+o6Gsy3IM32vvbI124mZN0OJFGn8Hr7pKct7kywYULF/TZlMwlFahSCnAyXNOmzeHp5QW/s6cLx06w2doEaOhYJAfhWwLLTZ0BW1MF7M3KUxyQfWyL2IWbaQqYTADaUeex2/k4xeWYMDCBZYLNdDmu51DUDnNeTFBcjdk+nZbZFp2YCSC7a9ydCSsTdmY7hKsZ8ZkKAMxLYkLQ7CchYjoep2YDjZ0AKxOlLJ2YmZN9JFzNoCsyXaQJG6tQN12chQuzrfKbcDOhab4enAg0cgYsjDQOzJbK+3S7pn5qsJ+no4HqtgogzeCYCDMTdGafOV47M+V1LqpFt2o1R1iK4upMZ22OiUHH68uJgLUpUNe+CO4m8M0xZ105KiaFcDIir+lkSAWkAlIBqYBUQCpQtRS4lHIFX31VNeDmjJQMca8zMviGcEN2re6Ci0cvFMLN5/edFe7JFrZWuHIqCJnpmajesCZuXomAa00P8Vqbfu1weNV+1GlTT7gfE45u/3xHUSf4xCW4eLsg7OJ11G/bEAf/3QePOp64eCQAXV7ticDDAeg2vCd2LtyCyCuR6PDiUzi3+wzaDXgS+/7eDRsHG3Qe2l24N+/6azu6DO0Ov91nhJN0fGQcWjzdRjglN+ncHIdW7RfgtZWtFWLDY5CRlikcop2qOSPyyg088XxHBB2/CCdPZ+FYnXAzAYZGBnBwdxQu0inxydi7dCd6jemP7LQM7Fu+C22ffQIETk9sOoa6jzfApUMBcK7uKlyfc7JyEHk5XEDPWWlZOLbhMBo/1QwXD12Ah68HrvpdRZ3WdYUzc8SlMLhWd8XFo4ECqg4+dQnejWqiZmMfBOw/B2tHa5iYmog+dBzcFQZGmov0qnXoVfnRqnDz1SNBwsC4c+fOldLkoYGb6URGVyk1nnnmGfz888+VGrSspKVARAQQEYFvFi/GxF9/FVAwnZf9Ll/GiL598fWYMaXK9eZXX6Gpry/Gajlqt3/jDVwJD0fU1q3llvmvTZvAXNmHDpVY5+WpU7F6924MefppLJoypdS8Obm5cO7RAy927475n35a7vbVgrl5eag3aBAszczgv2JFheqvP3AAL0yciOOLFglNKhqXQkPRYuhQPNGmDXYcOFDR6vesvPogoGbNmmjYsCEaNWpU+JuOSjKkAlIBqYBUQCogFdCfAgSE3d3dwc/dhy0k3Hzvtlj37t2xdevWMsFmgs9cXpauu/N/my/AZRVgU6E21a2Xf/v6+mLo0KGYMGECzEuY+Feam6fIJUg6CAD2iSeeENCvvlw22X8+cKULaMuWLbF48WLh6Km6J7Nfdwtu5lbdtmO7cBF9Y8TrCLwYCC8vL/GAkBNvCW3s37//rm18wsgEH+nKTHCjLEdVtSOEI6l/Xn4+evfpjffffU9AgsyRlpYmiqn7ALVt0qSJgLbV63tuP37/Hj9+PCZOnFjMeZXb+IMPPhDOzLf7fs7tRSdpGVIBqcDtFeCxuW3btsJCqgMzz6sh10Lg4+MDujbn5eeVCgizMpcjZJl+/frBz89P5OM5g+dKwtO8x1bR4LmE/WGO8p5/SmqD5xLeTxj+6nDxWaQdhJ/NzMyEE/5jbR6Dbx1fLFu2TEwcoYv1woULxXnwTtqv6Lhl+SIFJNws94aHWQFduPmHH34QE9Rk6F8BXbh50qRJGDlypF4b4kSZV0tYdZKrBehzYqVeOy2TSQUeAgW4KhRXZuL14unTCtx8JgbgFVszl6IB0EmYIG8DR8U1mTAuIWW6JfvYFcHFfJ1gMl2NCe1qR0SqAuMSMCZQzHYIJTdxLipFKPdSIpCeDdRxUEBe1ovNUOp4a8Bg5iFcTLCZ8DOD4DEhYAMobatgNftNh2M6QNMlmXEjTclpY1rkeJyRq8DW9qaKuzPzEH7m2Ak207mZ4DHHSwA7IFYBoKkHHZgJaVMTAtj2ZkVjYlmCzSxHYJlBSJz5CVXTZToxU3GPZh6+znEyH4P9DIhXAGa6STNS6WGVAJgYAPUci9yd+Rp1aOMKHDl8SDxj7927N366T6vfPgSHgOyiVEAqIBWQCkgFHlkFqhLcnJrA1UsNEBcRCwsbC3jW80ZCZCy86tUSzs1h50NhZGIAU3Mz3LwSCa4WamhoIJyI87Jzcfn4RVSrXx1OHo6wdbFHqH+IWPKjadeWwo2Y90d9mvkK1+faLevg1KZjIKuWkZwGn+a+yEzLQK2mvvDfd1a4LUeHRiE/JxdNurTAxcMXYGZpJmDk1IRU5GZn48alCBibGQuImM7O9ds1xLG1h5CVnomIoHA069oSvq3qIizwOkLPX4WZhTnMLM2RkpAME2MTWDpYoZqvFy6fuAgDQ8DG0RYetT3h3bgWMlPSEXDgHFo+/RjSE1MQeOQCmnVrKa7v/XadQWxYFNx8vODk5YgL+/3Fc6RaGodmx2pOuBEUjuiQm3Cp4QpzSwtc9QuGpZ0lnLxcxXVq9LUoAYrXbl0HZ7afhJWdlXAFd3R3wPWAa+J4qt++Ebwb1EABl1iRUeUUUOHmkKOXxXPUjh07VkqDhwZu3rVrl3CUUaNPnz6YO3dupQYtK2kpEBCA6T//jJl//YXOrVvj85EjYWJsjCGTJiEwNBS7fvkFT7VsWUyyCyEhaFirFrJzc2Gq4zK8cMMGzFi0CO+++GKh0zLh5Vf79ClV9sBr19B48GDkaVyBSyrYY+xY7Dl1SvRlx22g9km//or/LVmCnMOHK7WZF2/ejOFffIFXevbE4mnTyp2j3/jxOOTnh7gdO8pdR7fgvP/+w9hvv8WMadPw8eTJlc5zNytu2rQJS5cuxeES9KVzHeEVujHxtwypgFRAKiAVkApIBSqvgDY8QpiTDotcOYHuha6urpVPfI9qSrj53ghNF9+BAwcK1+CSXJsJgK1Zs0bAxVx6u27duoVAc77mPoIhLYx0Q+smw8yvv8aUKVPEDZ7C0HICVV+Ljo7Gf//9hxkzZiA7O7uwHVtbW/ABaVxc3G0B7PIq9t5774HAxCeffIK+ffti1apV4qYIv5cQtGXoG25WnYqZ+/333wdhGL5G91PCzXSuJkRBF2NuE7qg3q0g9Ef4Tze0XVdLeo9wMbcDQXi6ofJ8Mn/+fLz11luFkCAhEW4nuqaqTq7MJVy98wvAfwRTNm/eLABrOsmqQeixNOdwQpbuHu6IioqSQOLd2jFk3kdGgdGjRwsnNe1JJdoTUXhs0rleXclMhXxZJiwsTExg4f95LiLYzONV13mdYlXmPMW86enpYrID79G1atUKXGWNqwfcbuUA3Y3D8wU/Jzw8PIQrdUnBJdHpUB8UFISXX34Zly5dwrFjx8TnDCfeSLj5/uzy4eHh+PDDD8VnyLgKrNh2f3orW5UKFFdAF26mEzyvJWXoXwFduJmTJfn5ps/QfVak5ua1Mj87ZEgFpAKVU4CrZnAFDd47INxM1+LMPKC11m0oArsEeWtr4Fq6I0coc2aFg7EKF2fmAiejFddjbSdmliOIfC4WaOIEuFoq+bLygRYuRY7DLBeSrIDItWwVGDgqHaATNCFkQsWM0BSALtKsy9cZdEYmGGxsoEDNBIUZdGcmcK1CyHwtOkPJSddkvk4H6dx8wD9OqcO+c0wEiFWYmXnpnEy4mE7VhKAJJtMdOiRJ6Qddnwk1O2stMptXoEDc5kYK1M1gfwg80905ORsIT1VyEeJmeerjqsmRkgOcjVHKemvcpQlhsy/MQeBZ1YD94HZp46aMjZMbX3/jTbTt2A3L/pDP1it3hMhaUgGpgFRAKiAVeHgVqCpw88Uj53H9wnW0fbadgG7FtWFiGk5sOopWvR6Dm0815GfnKjPIDAyQn5unGPVwqQxep16PQsiZYLTo+RjMrMzFPdT05DQYGxvB3NoCOZnZ4oKV91tp+GJsZors9EzkZuXAyNRY5GId3ivl+yampsjOzGZTMDE3FY7J/MPQwFAYUxgZKflNzEwFbJ2bk4u83FwcWb0fBXkFyC8oQIfBnQXMzL5mpLCsWeEqpmzD1MwUxqYm4j2GqbkpDI2MYMjxFxQgOzMLphbmKMjLR052jniffcjNzEZWRibMLC3EyqgZqWlixRNLKytlbMbsYwGyUjNhZmWmyZUt2jAyMYaRsTGy0jJE26bmJshIzaSNjnBoNjM3E+8xn4WNlXRtfnhPHXfc8yoHN9O5RnuGf69evTBv3rw7FrJKJ8jl1OPTsOzYEYO7d8dCHVfkLmPG4PC5c/hy1Ch89MorQqpFGzbgjRkzsPbbb/HMk0/eIt/RgAAM+vRTDOnRA/8bO1Y4Lr8+YwZ6Pv44/p4xo0S5/1i/HmNmzUJmGY7Fv69bh0lz52LiiBF4f/DgUjedZ58+aOTjg+2VnH387bJl+PinnzDnvffw/pAhpbYjHhhmZWH+2rX4bP58vP7MM5hzhw93Rn79NZZv24a58+YVcyp/0PbTU6dOCTBi+/bt4ENH3aCjM50B+cPlqmVIBaQCUgGpgFRAKlAxBfj5umDBAqxdu1aAk2oQdiLgrP7QUedBDAk335utYmNjg6SkpBIhNV6H7dmzR9zkoINZWmoaDGijpIl8cVsBMNCFm7XAZu151HQnJggbEBAg2uv7TF9U91YgOi5ram9vjz/++OMWwI3tE/olMLt8+XLRn9dffw3TxETCis/UZtvMQbCNk+sI2f6xaCG+nTULJ0+chJ2dnQCeBw0apJeNoAJ0vJljYGiId3WgwrZt2+L48ePC/YjfWVnubgXPCYOeHwRbO1sFOC4owJIlSzB9+nTxAHrjxo0CXNaNIUOGYNasWaJvqhM8/09gXQUo+RCbACFBeTW0ocnC/SY/X2z/bdu3o3//Z7Fn9x4x+eJ2cDXrErqkEzQhSBlSAalA6Qpw8gKPPe1jRT3nqcek+rc4h2vO2Tye6ayhnht+m/cbRo4aWWyignardwIH8/xBB3s6+umeLyqybQk5c7yWlgptoj1Jgs7yPB/xHMvPMAbPgXTI5znoTvpfkT7KslIBqcCjo4Au3Pzbb7+hZ8+ej84AH6CR6MLNH330Ed555x299lD3WZGanJ+fly9f1mtbMplUoCopwAm8/E7N5zt/7z4NgsuPuRLQUFRI1wDL1a0VwJYQMV2TeUlKAJnOygw6Lp+IVpyR6dqsHQR46VxMkJgQb1ACRDstXQFTo6KShHsj0xWIl3AyyxBOViFkE0MFBCYA3VDjIM3ahHyvJil9sjEBPDVfkaPTFeCZrssEhhnMqbomEww217g+n48HUrOBps6AhbECeDMnc3GM/D/Hxv8TbKarMiFo9oXlCRzzN12btYNAd24B0MJZ6R/BakLQ7BPh7iuJShuEv7PzFDha7T//JizOvvtqwG5C2ASb2R7dnQmAM26kAkGJipO16hq9dccuvPX2O2j3ZAesWDS/Ku3WcqxSAamAVEAqIBWQCgBQ4Wb/vX746J/Jwl33UYzM5DSkJabC3sNJALiMnPQsJEbHw8HDScDIt4ucjCwBG9u5Od43eQry8xETGoX05FQ4ejjD1lXngvq+9Uw2LBWouAJVDm6mY+zbb79dqBSX6qTTlIw7UCAlBW8OHSpg2rRSliR99qOPEHLjBhKSk4Vb8/Zjx9C6QQOc+PPPEhuOiIlBzWefxaKpU4X7MeOD777DjytXolf79tgwe3axejuPH8fT776LDs2bY285YHXj9u0xqn9//PLxx6UO/LC/P558800cW7gQbRo2rJRALYcOxeWwMMTv3CmcrEuKI/7+ou+1PD0Rn5iIo4sWwdNFa22uSrUMuPfqhcS0NAEzPQw32enwRnCGv0sCnfkwUgWduYSuDKmAVEAqIBWQCkgFyq/AjRs3hOvijh07QGBYN9q0aVMIOrdu3br8ie9ySTr7qSs5bNmyRSw/L0O/CvBakdufUKsu5EW3TupOR03uFy+99BL+/vvvYh3QdgIthFhZwsBAmbSu090zZ86A13I21jbIyc1BXEI8xr33Pnbv3o2IiAgxU7002Oyvv/5C9+7dhUsnI78gF9ZWNkhNTauQ2yfrEt4jaE13ZgK7dIYmrDL6rdEY+PxAcIWfuwI3a1yhzU1NBYyXlpYmljLniiaECgn6eXp6CjjvbkB3zDtx4kThwM0gZE7XbrqZqttyzJgxIKTMlVbUUCFI9o//1wbQuTKSCp906dJFbEtGXkGBGINBQQECAwOxcOFCoTf78Oabb4r3snKy8dKQl+Du5ibc5OnCertxs4+LFi0qthqTfo8ImU0q8GgowOOUYNbtJkqox7xyylbO1jzft2/fXvxNGJgOy3fjXKRPlTkOupio502eR+g4TaBmw/oNwlF1/Efj8e2334pmCTbzfCRDKiAVkApURgFduJnXRLxfKUP/CujCzR988AG4+oo+g9+RS3KD5neFK1eu6LMpmUsqUKUU4HUZr0XrNWqGX7ecFq6/qhNzTr4C1zpqQF6CtQRoc/IUsJYOyWrQXZiXqc2ci8tH8PhElOJ4TOdlOiETWG7lCliZFJUl9MvXCQcTgCYQTYiXV751NU7KdJA+HwfU0HIxptMxwWPO4TY1VABsBiFmQtQEhdlXBnMKCBpKX1TH4+BEBS6mszJfU3NyfISH2S+6NbNvHCf/Twiar9P1mWA3x85+a8fFBCApC2hBiNtQ6VNsBlDLTqnH8bEN9ouwspWxMjYGc56KVjQiyK0G+083Z8LfKkhNp2s6bhMqVx2rqcdP/2zHz9M+QIf2bcV9ARlSAamAVEAqIBWQClQtBS6mBGP6F9MReCQAH62YDEt7zUVR1ZLh4RltAY2SC4oZFj08nZc9lQoUKVDl4Ob169eLpTfV6NGjB37//Xe5T9yBAnvXrEHvIUPwYrduAkYuLdbu24fVGte3SSNGCMj5dkEn6O8/+AAj+/cvLLbp4EEMnjJFPFx774UX4OLoiH927MDJwEDU9vTEzp9/RrVygMHWTz2FN/r1E/lvF7Wfew6+Xl7Y9uOPlVao7vPPi7pBq1aVmGP/2bPo+e67aNekiXC3bq8nh+K/Nm3CnJUrkZaTg7Nnz5bovlbpQd3lioSc6eZM9wwuaa0bdFynqx1/ay+Ne5e7JdNLBaQCUgGpgFTgkVCA1wU7d+4UoPPFixdvGROXDdV2deaSvPczeD3A4KREGfpXgA8c6apEB0vtSEhIgLOzMwgR/O9//xNv0a2Xk9BUR0+1PCG6q1evCiCW0bJVS/EQUIGbi+PNJ0+eFKA06/BJHa/ntmzejNmzZ2P8+PFlgq3u7u6IjIxUoFk+8MvPh7m5hYBi+X/dcegqRtdoFxcX0GWvefPm4FLUrEcwjr95o4cA9dZt2wTc/OILL+hFdFWzPI1btBmXEcvOFkAz3yMgvGzZMvj6+grA+W4AhRzf448/LkBqjvfIkSPi/9Sd49YGHfkwmsA7J0bwepv7CJ2dDx48KPQnrMw+cyWWf/75R2g1c+ZMMSY1l6eXFyLCwzXbSnGIVnVQQUSxERkFBWJfmzNnjgAUbxd07/7vv//0sl1kEqnAo6yA6lCvHGLK8aeGerxrvxYfHw8HBwdRjueLn376Se8Q2d3SOzsrG4ZGylKNDILZqSmpYilE7aC7c2pqqnRtvlsbQuaVClQBBXTh5sWLF6NTp05VYOT3foi6cDPBZl4v6jN0nxWpufkZGhISos+mZC6pQJVTgNdlPg2a4tCJM3BVFtAQ9wnotkzXZEK/DILJiVkKqKw6IfP1C/FAeg7Q2q24dAUFwPEowM5MAW9vpilwNPNpg9GElgkY87Xadgroy3IEfPk33ZJTcxSwmACxUcoNhIaGITQsDJfDomBgaCyuKRv7eKJFs8awcnQTEDTz0V2ZQQCYztC8yibwrLYflqK8Xs9RcWNm0I2ZgDdB4cg0IDsfqGGjAMSEtemOHJUOEP4mtEyHZRVKVhUghHwjTXGnZq6kbKUdOjbTLZpa8jdBcY6NrtQErtXFtzhWBttSg32l/nRmVoFtOmkTgibUXV2LV+K2O7p7M+Z9NUHcT+FnoAypgFRAKiAVkApIBaqWAhcSg/DZpKm46hcs4eaqtenlaKUC91WBKgc38yHouHHjCkWns4KcXVr5fZDQQ/PGjeHj7o5XevXC6/36VT6ZTk2vvn1hZW6OSzpQcHh0NN6aORObDh2Cpbk5Gvn4iHZHDRhQrrYTUlLg3KMHNs2Zg57t2t22zvoDB/DCxImI3b4d1polTMvViE4hp+7d0bROHeyZO/eW6umZmeLBobVmCdXK5C+tTsthw+BSvTpat2mDGTNm6DP1PclFQGbr1q0CdCaApRu8ya1Czs2aNbsnfZKNSAWkAlIBqYBU4FFSYN++feIzlrAzgUXdcHNzE6AzwWIJFz9KW14ZC1fMIMBKoF07CL3RrYzbn3CrGnQ6pnMz3yd0TriVsDFdMAmcMggCfPLJJwI+Jchcp06dQqCO9d59913hAM1VOVSAjvDszh074VPbRwDUKoTn7+8vgGnm5LUeobQn2j+Bg4cOFoP02EcvLy/Url1bAPsq3KY7JsLaSUlJGiDaXFxn0qGZr7FegwYNBOxLmHvNurXYtn07+vTsVQz6vZO9gFB1dHS0SKGOfShXwFm+XPzt7e2NK8FXYGJqIhyV9e1UTl3pUk2Y+cMPPxRuy9we1PnXX38V7u7sByFrPqTcsGEDWrZsKbTh/wliN2nSRAHWtZh1Aj10e2VoA5QCYAfQqlUrAUgSKHz22WcFXM79a+TIkcLF+/PPPxdAMydSvPDCC9i/f79YBrwkt1jm575z/PjxO9kUsq5UoEoooA03607+4DHH97WPMx6jBDj4Ot8PCwtDrTImpT8oQnJ8dPcfMWKE6FJuTi4+mfBJoVuz2s8OHRY3RtIAACAASURBVDrgwIEDD7wb9YOiq+yHVKAiCvAah6uBWFlZiesF/vBahtdX/HlUQhdu5nWcutLMozLGB2UcunDzO++8g48++kiv3VuzZg3ef//9EnOGhobqtS2ZTCpQlRQgrGtraY469eoj4NzZwqH7xSpwcQsNXEvIlz9uVoCnVZFCgQmKG/Hj7gqgqx2Ebgn/NnEGCDD7xwJ17AEPrfoEewlH06GYADRh32spQFYu4GWjOCdn5gFno4GCtFis/uMnXLh4qdRN9NRTndDxxdGwMwUaaByPCSETNqZDMgFg1d04Oh24mqxAxSrUHRSVhi0b1iI6/Brik1KQnp4Ja0szOHl4w86tOnq2awxnLx8QKrY0ARIylX5rzU0UEDPzNnFSQGSW5d+1bAFrEwV6pgs29SKszGAfCJIzqAd1ae1aBDtTe+rMNpmHEDS3HSFmJwugrn2RJNSZQPaNYxvw2WefoVGjRliyZElV2q3lWKUCUgGpgFRAKiAV4DVFQhCmfDIJ1y6ESLhZ7hFSAanAPVOAcPOSiQsQcixYmLKQXahMGBRo2zxVJsM9qrNy5cpiN8G4bC6XtZVROQU+/vhjJIaE4FpYGCYMG4YuelzG/Md//sG477/HsYUL0bqEm+CZ2dngcs4VjVlLl+Ljn35C8OrVqO3lVWZ1syefxBcjR+KTYcPKLFtagTX79uGFTz/FsN698cfkybfNk19QgBXbtuHlnj0r3Z5accmWLVi4fTsCgoIQE6OZln3HWe9PArplELggSOPn53dLJwhV0PmPP/b2Wndd7k93ZatSAamAVEAqIBV4qBSgS6zq5szf/Fs36LY7cOBAAbGam2utE/pQjVR2VluBzp07Y/fu3be4eRISJuyu6x4cFBQkwOB69eoJcCUxMVHAwWoOwqnJqaniAZxBAQQ0e/78eQwZMkQ0m5ubCzs7O9ja2gpoWhuqI2jP/gwbNkzAvgTsCMy6urqKcgSt2TadxwneNdVa7YRfRfv07YMtm7eIMrpQbEFBPjy9PHEzMkr0g3AboWyC2nR/Y7+/njkTBfSRKgCeGzAAa9auwbGjxwSsokK6d7r3EK6O1lyTGxoYFDoUE/TldS61vHDhAl599VUxAZcPC/UV1GjatGmYMmWKAMi5Ogp1Ul27db/Oq5Cy4pBtAEdHRwEdE4TX1XfBggUYNWqU6Cq15DYmFMlxMfj9hiGcvLWfzgLiYSjdoNXVlFTnbWpOQOrnn38WTt/a9Xj+Yb9lSAWkArdXQJ1Ewd9ZWVliaXD1WOLfPI54vKrxww8/iJXO1Hp8vaTJIg+q7pwswQkiBoaKU3yhQ7xWh0+cOCHOKWW5/D+oY5T9kgo8yApwYhINRXj9pxucKNG1a1fwPvzDDgLrws1cwYIrY8jQvwK6cPPo0aMxYcIEvTb077//iol/uiGdm/Uqs0xWRRWwsbERk1nVZzkqXNvKFTAyAOIzFTjY2RKoqeUOHJ4KXEkCmjoXuR6rEp6LA3LzFOdiOjETdCYUXavoklZAyxfiFDC4oaMC97IduizTmdjdUoGACVpfPncSy+Z9V+bqQd37D0aXPv2FOzT7zmAf2RaBZzo/MwhbX0wAvKyKXJBvpgM3bsZi8ofv3HZPaPNER7zx2gikG1gIKFkb6iaATOC7rgPgZqG0G5KkuF3TwTomQ4GsrU2BuAwgl+7UtgrcrfaV/SDYrMLOzEm42Vjj7szX8wqAMzGAuZazNutfSlDG9pgbsG7tGnz99ddi1Sneu5EhFZAKSAWkAlIBqUDVUkDCzVVre8vRSgUeFAUK8vKxdPIfCDkajHnz5ol7/JWJhwZu5petTz/9tHCMTz31lHB3kVFxBcLDw8XNiZvbt6PbW29hybRpwkVZn2HTuTPcHR0RsGIFuHTzncaijRvx5owZ6NWuHTbMmVOudPZdu2Jg585lQsllJSOs/f5332FIjx5Y9sUXpRbffPgwJvzyC36bMAFtGjaEsWZZ1bLyl/a+09NPw9PbG3Pnzq307IXKtn236tEFcOPGjQJ01nYTZHt0giPgTNc3usTIkApIBaQCUgGpgFSgYgoQalVBZwKnusHrPwLOBJ3p+ivj4VSA4Ff79u2FU7E2OMrZrnyNcKm2C686yp49e2LTpk3o16+fuB4jwKpdPy0jA0aGhvh21ixMnDgRGRkZAnhl0CW4f//+Apx++eWXbxGOTqGEYrZs2SLgWPaB17B0hqbDePfu3UVbzLF27drC+hMnTxJuy2+NHIUff/zxFmht+PBhWLxkCQryFWdh/tC5ePbs2QKepatwTm6usCkyhIHovwo0E2zQ1zxews1RGrjZzMQE2VnZAqgm7N2jRw94enril19+EfA1x61PuJkwM6+T1W2l/qbmarA9Qut0PKWTc7PmzQQMGR8fDw8PD7ENtJddVzVavXo16OqtBh2ei0HMKtBcUFCqWyrLE3ImDD9mzBiRin1T83DyYkx0DAyNDAVozT7JkApIBW6vgDbczJI8tnkMqec1OlUSaGbwPLdu3TrxucBzto21jTg/PSxwM/vP+wRcHpurATD4mcFVoLRDPa88LOOS+7hU4GFU4Nq1a/juu+/ECiGcCKcbdHEm5Myfyj6IuJ+6SLj53qmvCzdzMh2/X+gzONmRq87oBr/jXLlyRZ9NyVxSgSqnAL+3cYWlc+fO4XKiAuC2dlNcl+k6fDJacTuuR4dijTqxmYoTcyOnItdjVThCwwRy23soEC7dhQkW19c4KYvvkAXARToU5wKNHBVH47AUBcw1NVLckBlsIzU9E7M+eatcE2cHj/oAL3Z/rBAMvp4CpNBl2VhxPGakZAP+cUq/fTXeO+wv265lW4B+A5VJ37cLcwtLLPjtFzjaWBQWY96zMUBNWwWYzswFriUDLpaKA3VSFhCZroDgBJsLDBTgm+7ODI6fIDaBcFvNI1Y6O3MM1Iuu19SJcS5WAb+buxS5O4ckQ7hGE4w2NwZWrVqFWd/ORs0a1cEJPjKkAlIBqYBUQCogFahaCpxPCMLUTyYhNPAaxi+fBEt7rVlqVUsKOVqpgFTgHipgUFCAZZ8tQvCBS/jtt9/EirGViYcGbubyunSrUqNjx45y6ZzKbHFAuIlt37YN/4wfD9eePbFyxgw8VckdqLQu9Hj3XcQmJMDT1RUbZs+ucE//3LgRu06eRGRMDE4EBsLexgaZWVk4+PvvqOPtXWa++WvX4pu//sIzHTrg+w8+KLN8WQV++PtvAThXd3PD4mnT0Klly1uqfPD99zgZGIjzV6/i6po1sLPWTPsuK3kp77/5v//h/I0bYnnWN998s5JZHtxqBJzXr18vQGeCFNrBJc/5IyHnB3f7yZ5JBaQCUgGpwIOtwMWLFwXQSNiZjrnaQbdHFXKWn7UP9nYsqXfBwcFITU1Fs2bNCgFSLi36xRdfFLoflwQ3p6WloXfv3gJEbdGihYDkCOcSNiUEkJ2dLcBYwggEYhkF+QXIL8gXwJwKvgrAzNgIBiBQXBRs08HBQcy8JQBNAIYr7bC/dIAmUGtpaVnsAaStnR3SUlMFDKv2mf+fPn06Pv/8c5Gcrshcdlp1E2Y/Ce5evXpV7MenTp8uhJvpqqxC0Pp09yTcrK6mQpcjHl+PPfaY+E0HVQLCy5YtE6APoet27drpbccaOXIk6LDM8fB4dnJyAl0UrbW+a6iu1yoQ2a1bN+HKzaBu1IT1+UMXblVr4XhNh+cCiPcyMzIFQMn+Hzt2rBA2p+sznakZ2vAz9wl1m6p6c/vt2rULffv2LdyufI9wNfcFLhOu6wKtN7FkIqnAI6IAj1Ee1wR5Vfd2nufV44wuzZwQwmOJ7/P8Q8d+3jvg3zzu9XkO1KesPJc0bNiwWEqO9a233hLnT57f9+zZI84Z2ucKjisgIKCY+78++yVzSQWkAkUKcGLVhg0bxA8nH5QUnJDAiXM0KeDKEg9DSLj53m0lXbj5jTfeKPZcRx89Wbp0KSZNmnRLKk6U4TWnDKmAVKDyCnAVJv5sOhKA0GQFriVES6CWcDHX9yHYTECYQbD5fByEa7G3ziOxwHjF6ZlwNB2G6S5MB2W6O2tHcKICExPYJdxLZ2JC1WyXeRl0IY7PAk6uW4gtW4tPhCtttHNmz0J9H+V5InOyPp2Va9sp/SBwTCdotkMwm0Ho+Eaa0i7LvDRiJFKTk8sUtPfT3fDOW2+IcnSnJsRNt+naGmCaLtQExL1sgPRcBVx2sVCcqdkOgWf+zYim43Mc0NCp6DWC5dSJc5AJSxOKVnWhdi1citydI9KAywlAC1cFJGes+PtvzJzzMzw83LF13aoyxyMLSAWkAlIBqYBUQCrwaCngHx+IzyZMRfjlUHy4ZBIs7O6MZXq01JGjkQpIBe6WAoSbl3+2CJerCty8cOFC8YCbN0zpJEFnMj7AkVFxBSZPngwLIyNM6tMHbj17wn/5crg6ak2TrnjKW2oc8ffHhz/8gBru7mI5439mzChX1j/WrcO4779HakYGanp4wN7aGh4uLujUogWG9+0LVwfNFO1Ssp24cAGTfv0VZ4OCkJ2bi7+nT0dPHbggPTMTlpVYkn3ToUP49JdfcDE0FA1q1sT4l1/G0N69RU/2nDqFp999Fw42Nvjnq6/wVAnwc7kE0Cq0bMcOfLlkCXgDuKRl9iqar6Ty6kNTFQJRnf4q+npl67EdugLy4SWdYej4ph2cteHt7Y2ZM2eKJc5lSAWkAlIBqYBU4EFUQP3c5OchwT7+1v0p6fWKlL2T3IRACRPxRwU0VR3p+MofPoBW+9y0aVPhyCbjwVRg2LBhYgUbFfpSwVUVcL1dr1mWcDBnx/KHddSH/4SlOQmSwCpfJxhH2IzOZ7ExsTAyNhJQLVeBydJMTNOGm9kuHcEjIiJEFwj9ElJTQTu+xu90BIEJxDLYBqFsfj9hEKDh8uCqKzHBPl03YeY1NzcXr/P31GmfYcrkKcIxSnU1Zf9VqFcfW1GFmwmV165dGzcibsDM3ExA4HyN7dHhkH0/ceJEMefiO2mf2qkupdzeHLMKLPL/hDeOHj0KuuEdOHBAwOrUjmXU/YMacZvzeprX1pwkrMaKf/7Bq8OGwtBAKU9tuW0IQNO5kY7UBIE4Pjp30Xmbr6ugOftH8HnoK0MxZaoyEVl7v+Tfp0+fFtuc+xn7y9yqc/SdaCPrSgUedQV4PlSdUUNCQsS9MPX44nFI92b1bx5XKckpsLO3E+dcOuJPmDDhgZxI4Obmhps3b94CLrPfnHyVnJwMrj6hCzdze/NahRMkZNw/Bbh9+FnOCUxPP/30/euIbPmeKcDjkZAz3dSTkpJuaZef7QSc+UPY+UGdWMGOS7j5nu024nytHZx8M23aNL12gN+Hpk6dektOTsoMCgrSa1symVSgqinA74HWdg74dXsAmjsDjuaKAkEJQFwW0My5CGzOyQdORgFWJrcCy3QYpnswXZfp9EwAOiMPaKnlLsy8dCi+ma44KTtbKC7PhIvp2EwImTAyIevwVKWN10a8hrT09Fs2y4ihL6H3010Rk2eF0MQcVDOIR21PF/HZRFA6WlOFsLG5keJ0TMdmRhMnwFjjTE0ImWAzx8T+//K/6bh6JRgWNvYwRD7iYqJL3SV++2k23Kt5CoibwHRDzWNXasH81ayUdoOTADuNYzPHZ2OqvMegOzOBa47dS8McZecpMDQBc1fLIndsOkFTF4LN7C9DddFm29Rdjem/LsO6pfPh5eooJpDLkApIBaQCUgGpgFSgailwNj4A0yZMw43LYfhw6URY2Eq4uWrtAXK0UoH7o0CVg5vnz5+PGTNmYPjw4fjzzz/FkpsrVqy4P+pXoNU7gT4rW7esenTBToqKwqJx49B59Ghs/O47WFUC9i1LhsGTJyMuMRFerq54oXt39CrDwSzw2jU0f+UVATJv/v57GBsZldVEsfev37yJ8T/+iLCoKBgYGuLDl17CwM6di5VZs3cvxn33HT4dPhyjBgyoUH618PZjx/DFH3/gVGAgMrOzxUM5LoH9ZLNmWP3NN3C01axnVansRZUIaLcZMQLVq1eHu7u7eEhZ1rataJk77OI9q073OC43KEMqIBWQCpRHAYJ3/OH5mT8q5KX+Xd7f97peWX1VwdN7BcRqw7n6aFMfOUoDfO93bn7+PkpBQFMuUfngblGem4R7Mu16NEAxJ38SLqmII672fltSvcOHD+Pbb7/Fxo0bxTXo2g3r0fvpnqJN9TyqqxKBOi5hW6dOHYwePVo4Dbu4uIi+MZhnzJgx+OmXX8S1ramxsYBdjxw5gg4dOhTrP8Fouk1rg7rMoUK9qhMxbYsaNm6EH777Dl27dEWuZpKBhZlZISR9p1tThZsJYTdq0hiDBg1CrRo1BchDV+V169ahb5++aPt4WwFo011ZdyJBZfpAjfg94Nvv5mDY0KHYvHGTaJM6crLvkWNHMXr025g3d67Qk2AgXbnpnEx98gE40B07LU3orBt0erWxUZaf4zYi3Myg+/Y777wjwDVuB+4D6kor/JsgtwqoE5KPiooqES7RbY/5CX+fOnVKgJkypAJSgdIVeOKJJ3Do0CFRICsrSxyLpZ3jVTd2deIAJzXweCsLMtR2fb9X24J94wTnksbCiRgEZ+nsrPuZwP5JuPlebaXS2+EkKE5K4mQaf3//+98h2YN7pgBX+uCqEOoPj2Pd4AQwFXRu3LjxPetbeRuScHN5lbrzcrpwMydnfvnll3eeWCuDej7STUpzDK6uIkMqIBWovAJNm7VAXFIKTl0IFs7DDAK0hJAbOCrOymqcilbcghtrXI/V1+mSTKflJs6KI/HlRMWJuZVrkbswy0alAwR/CfF6WClgb2iKkqWuPWBhrIDPVxKVtm2McvDsC0NvGVy/Pj3x1hvDC8vWd1TaZTBnWCrAW3cEqAkSMy7EA6k5CjBN2DklWxkn+8ExRrAOFECYsLKNCeBrD1yMzsSSX+cg4Ny5W/ox6o3hqPFYT+GuzLy8a0NQmxB4Dc3K74Sn2V5yjvI6nZWra97LzFMcn+mK3dxFSZ9fALBOVp7SL08Nh0SN+TohZgfNWDmG0zHKONWczEHH7W8mfwD/I7vEpEnew5AhFZAKSAWkAlIBqUDVUkDCzVVre8vRSgUeFAWqHNw8d+5cfPPNN8Itau3ataDbC6HPyjjO6hsQvR1Q+qDsMNr94AOjjNRUJPKh9+OPI/fw4ds+9Fq5cydWbN+OY+fPIzktDU19ffF8ly54Z9AgmGpAhdLG+fr06QgOD4eHszMys7KwdtasUiXxfuYZmJmaInj16lLL3IyLg7uTzp0SndILN2wQLs9cllo3/tuzB6/PmIG/PvsM/Tp0KLGdUxcvwj84WOQoKy6FhuJKRAR6t29fVtEKv3/p+nU0eOEFEKaoyq7FdKsbN24cuPSuDKmAVEAqUB4FCIMQbpYhFZAKVFwBAo5c4peAs4wHUwFeF6pAyfPPPy8gUYKu+gx+v+nTp49w6hMwtQbgN9K5vi4JTpsyZYqAYeko7Ofnh/feew8BAQECZEtJSQGdwR2cHAX0vPrfVYWAG7+j9e7dW0C09evXx/fff1/4nnY7fJ/OwXTvZC5XdzcsXrwE9erWhbOTE/Ly81FgANSvU1dvS1IT2I6NjRVLXzdu2lTAzcjPh5WVlegDgWZqxuPm2WefFeX4fTUiPAKGRkUuypXZRvzOG3r9OgwNDdCmdRsBjNNFmfB5k2ZNFY3yC8SywZGRkaIJQtjTp09HgYEBnu3/LHZs3VYILmv3ISEhQYDY3MaElQktM/g3AWmOhYAkX+d+J4Dp/PxCN2mW5cTZoUOHlgusZ91+/fqJ/UOGVEAqcHsFOMGXEz8Y2nDz/v37bwF/xX0xUg+aWyADBgwQbmxlwc1bt24VkxgqMjFGt9eq0395tyfP37qO/GpdjuPYsWNivHSZ1+4X36PzJ80OZNw/Begazs9nhnTRvn/b4X63TPd1bdBZXXFDu19cBea5557DM888c7+7W9i+hJvv3abQhZsJvXOlEU624/6ij++aqhGO7qh4fc4Vi2RIBaQClVMgORtoWK8OzE2NEXwpUCRJyFJAYDoJq7AzXycwSzi3pWvxtui8HBCvODazPF2XryYDbdwUN2M14jOBwATA0wqoaQuk5QBBicolLZ2TbU2Vtun4zL/pbHwt9Drefv/jWwY378dvYePqJcrWslNyMpiTLslGBkp91YWar9HJmQAy+6QCxHRRrmGrvEfwmbn8YpRcTV2AkCQFck5IzcTH77xZOAlY7dDjnbqh15A3BJjMXNEZCjTN/nNcBMT5FZ652SdDA6UNropFR2fV8Zkgtxp0Z07PVYBn6iS2SSZwIQHwsVVgbAbdnU9EAW5WgK9dUX1C5gS8z21ciH9X/l04gblye4isJRWQCkgFpAJSAanAw6qAhJsf1i0n+y0VeLgVqHJw848//ojZs2eDM/35AFXGnSlglJsrAOXZy5Yh7+jREpN98vPP+G3NGqSkp8PZzk5ABGkZGajh7o7IuDhkZWdjUJcu+Omjj0BntNJi1Ndfi/KEkq/duIF/v/4adtbFlzn4c+NGvDdnDn6bMAGDe/QoNVf3sWPRsm5dfFNJ0DUyNhY+zz2Hv6ZOxQvdupXYzuBJk9DIx0c4QE95/XV4u7ndmdgl1D5w9iw6NG9+27ynLl1Cm1dfFWAEXZqqYtA1Uh83vKuidnLMUoGqrADBOj5slYDzg7sXqLBKeX6XpwxHqu0kW5465SnzIOXVR1+Yg8uJEzDlb7q2agc14aQqgpr6iJEjRwpnP7r/ytCPAoS7OnXqBMJtebl5eH7Q8/jvv//uCEwrrWcHDx4UDsyEZtX9b+DAgYXLlxIQWb58uWib/SKMRkdOBvvWvUd3bN+1E4YGhuJBHSMuLg6ctEr4lpNWVXja3t4e//77LzrrrLpSUt+43xK0ZZutW7cWRU6dPCWgW2MTYwFMEOjz8fFBSEiIXoRnf+lU+cknn6Bxo8Z4+eWXMeqtUcJhlCBemzZtRDtt27YFnYzHjx9f6J5MKNrBwaHS/SDcHBIaCmNjIxgUQIDV6oomnASowotz5swRrth0SiTQIc5xBUBuXi7Wr18Pfp/eu3dvsX5QQwLyW7ZsEbCJ6u6susCysAo0q/vA77//LqButs0fdZ8gtFLWdxYVmt+8eXOl9ZAVpQJVRQFtuPnvFX/jxcEviuNaQIQFKDZxgpMQzMzMCj8LeP4LDg4uE26mlgSce/ZUXPkrE2+//bY4r5c3tM8pJdXhuZzXDslJyWKM2sEJGfy8uBMYu7z9lOVKVkDCzXLP0FXg+vXr4rs3P9t5TaQbnARGyJk/XGHifoaEm++d+rpws27LTZo0uePJbqoRjm5urkrCiZUyqp4CnHzDiWF169ZFgwYNwP2MPzIqroBvvYYwNsgXLuiZuQDdmT2sFZBWjaAEgHByazfAWOuSLSkLOBOrgNDe1opbc2C84rrsYlFUn8AvgWk6EddzUMBcAscEfOlMTNdlgsmEfatptX3s1Bl8Pv2bWwa1dMlfuJBsVqwsnY7pbMy52k4WigMzg4AxHZq1XagJENNN2d1KAaoJNxNIpuM0QeQWLooDM4FlgtB0dJ4/52ucOuNXrC/Vfevju6+nCcdpAsiEm5mH9ehATT0ZHGdugfKeqUa/szEKAN1M49jMcnSPTspW6lN/Xh4TQCdYTl3o0MzIK1Acn+miTWBbjZBkZaytXYElC+cL4zB+L+c9ABlSAamAVEAqIBWQClQtBQTc/Mk03LwajnGLP4WFbXFeq2qpIUcrFZAK3CsFVLj5yqHLYiVc3iusTBgUPCTraK9atQoffvihWO62UaNG4EPV1NTUyoxZ1gEwukcPDPrwQ8SnpCBP4wakLcxjw4cLx+WXe/bET+PHF9MsNy8P+8+cwVd//onzV69ixZdf4qlWrW6ra+/330fQ9etoUa8e6Ei8ZNo0NKtTp7DO5wsWYNGGDVj6xf/ZOw/wqIq3i5/0RgIhCaH3Jr0jKEgRBayfFEFAUVGqgiJSpElHpAkEEEUUFP4oKCgoHZTeew8l1FRCIL19z5m7k2w2u8nuppAyr88+kd0p75y79+4tvzkzCc/Wr2+yrTYDBiAiKgpzhw3Dc40aWbwtCWcXa98ea6ZORRcT8ESpzp3x7ssvY+uhQzj6448W95FZhYD799H03Xcx/r33MJiubyZiysqVmODnJ1yLZZgLYrF8dpbN7vb48IM3x/z9/VOWCyccwSUsq1atKv7yoWW/fv0yk1N9rhTIsgJ0OVy8eLH47nECjYqCoQDBZr4k8EqQQv5/Zn/NLWtuOfaXHWULxpZRo3gSChA42LRpkwAbCVoaRsuWLYVzI+GmkiVLZkuKdO0lQMDgQxMuMa8i6woQYOPyod27dxfAbGBgoADasjsIl124cEGslFOkSBFx/ORER/1Lx4ULF2LAgAHiM8K08oKUv6c8z5s1axZatW6Nps2bwU7aiepchek0TWdoHht//PFHvPXWW2YPYcyYMZg6dapwFJ44YSKWLF0COo6zvyFDhojvOCHrSpUqZZur5IgRI8QqQvxbv359ATfzfJXntFymXcI6hJt5cc6H6HSn4zUr9SEcPWHCBLNAQ30hqDfhkBs3b6JUqZIIDgoWzlA8b2YQcuRLLr1NuLlBgwZYu3Ztyr4utxm3Iyc1yOA2lr+HnExYo0YN0e6RI0dEewRD+G8eGzjGw4cPi/wJqMs2+TnHym3PyQw8lmQUrFe6dGncvXtXwYlmf+NVwcKqgD7czOtmut1zv+R+tHr1anHclNf83P+WLFmScozhvmnMSdVQS7bFydT8LbEWGGZdtmFuMDdOpMioP7rCcmKNofM0xySPf+b2p8plrwIKbs5ePQtaa/v27RPAKl/65xwcJ3//6SpPyJnnik8iFNyce6pnBjczE14LWDI5xjD7BQsWGJ1EywkyZ86cyb3Bqp7yjAKG+7hMLdMCUwAAIABJREFUjNczL7zwgjBQ4Qo4KjJXgNd3nLx86dIlATY72wO19ZyE6cQc8Aho7Ku5CcuIjgeOBmuuyYR2CQWzPqHccu6p5Qj4ngnV2n3KU3MvJoQcnQD4uGpuzwSTCfsWddIcoGX8tfck/GbPSDeIsQt/hpernQClGdKJOS5Jc4Auq2N3CC3THbpGMa0vBgHiJGgwNoFqAsHM/14kcD8SaFhCA5UJEDvaaXkSKl645Dts3rI9TS6lSpXC935zhVszNWI7BJ1DYzQYnAB1TKL2IgAu9TsfCkQmaC7YdHRmMFfC4Zy3TA2pFx2c6STt6ZxWF0LgrEbHaBmEsf0fAnW8AE8niOcfmzb/LVyn9+3amvkXQZVQCigFlAJKAaWAUqBAKXA8+DTGffoFHj94jCHLhsOpiN7MswI1UjUYpYBSIC8pIOHmGwevCcMoTka2JvIN3BwZGSluTPHhKZ2iVGRRgYAAfDVrFujOfHD5cjSvXTulwfcmT8bKf/7BiZUrUady5Sx2lFp9w7//4tO5cwUYQcD32zFj0KdTJ1Fg9dat+Ojrr/HzpEl40cQy6NsOH8aHU6fC1s4O/uvXW5UX4eai7drhn/nz8XyzZunaoKPysDlzxEO0Pp074+Pu3a3qJ7NKDfv0wf3QUNzLwLXsg3nzsGXvXgFNFISgCyRdBvfs2YOTJ0+mDKlx48ZiCXLeZCRAo0IpkNsKtGrVKmU/o3OZtT+ouZ236k8poBRQCmSkAOF+As18nTqV1kmG9SpWrChgZj7oa2TFhLHM1NeHm9VKDJmpZf7nEm4+d+4cevbsmaO/WWx/1epfRHKEk3kOzyAM9/nnn2P69OkpiRNW4XeNQWg2LiEBtWrXwqQJEwWIrR8S2CXg+umnnwpo2BKo7sWOHbFp8yYcOngIz7RsKc7bCTQXL15cXCcSviXcXKJESQQHB5ovbgYlv/jiC3w5ZTI+HzECjRs2Qo8ePYQehOxci7jhQdgD2NrYCOdmCTezOcKGH330kYAMV/y4HH16v2PRWFmPEwPOX7qIHdt34MsJEwRETI1nzJiBy5cvp5vQyH6lM6pwP42IENts1lezMPyz4Smj5Ln5w4cP0a5tO9jYau7b0oVbLhdOB0YJF8rP5Lbiw1/Rl4UK092ZD8ot2eYWdqGKKwUKhAIEcPhbyv2d+yf3S/7lPkRnwmHDholx8vhHB3VeZ8v9ytXVNd3qDMZEYXucHMJJE9buk8xPHnfMEZ6TQejAbAgu69e9desWypUrl645BTebo3DOllFwc87qW1Ba5wQFAs5coUP//h/Hx2OOdHLO7ZXaChPczGMzj+2cTMK/+v9v7D3CeNkJfZoDN/P7cPPmTau/9vwt5DHJMGiWYez61+qOVMV8owCvjehKu2XLFrGChbHgcYcTyHgfRN3/Nb1pqQ3PE9fvv4ioeA24JYDMCIwCLj7QgFm6K8uITwKOBmrvVfcE5L+9XYBqxVLLEVpmfToXsw06DROWfhQPFHXUIGiCyQR46VKs70JMB+ITJ09h8ezUexGy5cmLfkTj0qkTv+nETAiZQLB0Nw6P1dyiCTFL2JrjITDMMnSPJgxc2k3Lh22wf7bDMu4OmqszgWUCyAuXfI/NW7alEbJM6VL4Zv5cAWuXdwfcHTWnZebO+tGJQGKS1gfBbcbVcM3huXEJTQ8GQWjC1QSqmRvbYX6EwuniTO3kNjkXquXH+vI9wtTUuYqH5kbN4KSQ9f/sRPSjCBz8d0e+2bdVokoBpYBSQCmgFFAKZI8Cx4JOYfTgkYiLj8fgJZ/AyU3BzdmjrGpFKaAUyEgBfbj5hx9+QM2aNa0SLN/AzVaNTlUyrcDDh7i+axeqdumCVg0aYPfixaLs6atX0aB3b8z/9FN8lANgb0h4OL5YvBhbDh4UD89a1K2LUe+8g3pVq8KjbVt8+cEH+MSEc9vk77/Hsg0b8EqrVlg0YoRVW9f/9m3U7N4dp37+GbUqVUrXxmg/P1y4fh1RMTHYumCBVX2YU+n8tWto0rcvvvviC7z14otGq1Tq1g2eXl44fvy4OU3myTJczpxAM190/5NBFyYuXf3aa6+BcLMKpcCTVED/oQvBLC7nrkIpoBQwTwFOPKML15dffgkugS5BG/Nqq1I5oYB0aebxLDo6Ok0XBKIINEuoWcKqOZGHgptzQlUIt2ICbNOmTRNO3NbCaOZkx4eZnTp3FsuMcx3XcePGCbjulVdewTPPPJOuCV6Ucgnol19+Gbfv3UWrZ55FhfLlTeb43nvv4fvvvxftWDIOG1tbcQPO3c0NPNfkw+m4uDiRG92J6TJMsO/vv7egc+eO5gw10zJ0XR43cYKAm9s+10Zo0LdvX/z000+ISyAUaCsgX0O4mQ1zIoGYrGiTjMePHsPFxdWs8VJ/OkVPnjwZzi4uqFK1Cm5cu57itqzvvMx++G+eY/fp00esREGXVx4Ddu3ahffff198rq8zIRc6LXOCIfswBhrSTY/XInSgZn25CkFSYhJs7WyRiOQ0rtyZCgkICJ3bSIVSQCmQsQLSEZkOa3SC5/4r9z05GUE6ua9cuVIck+SxgJMipKN7ZjrzmMFjmSXHYcM2OVmiSZMmmXUljiOEGgkeZRT8veG5pXJuzlTSXC+QFbiZ5y4KOMz1TfbEOwwPDxeTqfjX8NqEkx1Onz6daznmJNzMax9zg+es1MKSlyGgnBmsLCeemJsTy3Xt2hWzZ8+2pIrJsubAzVyBQH+ypKUd6x+P9OvyXPPEiROWNqfKFzAF5ERvOQHX2PDatm0rIGe++L1RkaoAr2sDg0Oxau9VNPVNBW4fxgJ0CK7mqbkzyyCAezwIcHMAaumkpGMzQV1CuPpxIUxzIyaIy/J0TaYrsquDBg0zJKxLF2KCvIz7UZqr8rkzp7Fk9rR0m+t/q36Auw7QYZsEigkgs017W81F+lQI4OsKVNX1QwCYL7oiE6QmkEyXZzolXwzToGzWJWhc3FlzUmZ7EkCev2gptmzflSaXsmVKY/jkOSjhorkrE4wmMF3MScufjDjbIvTNuPVIc4omQF7EQXuPrs8Eq5kHIWiWJ/BNTVm/rndqDlfCgRCdnnSVZjyM0zRk3Yoeqel9PnUeThzah/iIYHE/SYVSQCmgFFAKKAWUAoVLAcLNowaPFJNfFdxcuLa9Gq1S4EkqoODmJ6l+Aek7+MQJNO7UCTUrVoS7iwvWzZyJ54cMwekrVxC0ZUuOjvLXHTtAkLhk8eLwv3MHT9epg6AHD3Dhxg2EbUs721km0rp/fwSzzNq1Vue2fvdudB01CnH79sFet5yzfmN0VA6LiBBw9+vPPWd1P+ZU5HjY19nVq9MVDwoLQ6mXXsI777wjrNnzUxB0o0MLXRKuX7+ekjoBKsITfBFqzsipKT+NV+Wa/xXgss5bt24V3026VKpQCigFzFOArjgdOnQQy8nKB0ZZcV4yr1dVypgCmbk0E2B8/vnnxXHOmBNiTqiq4OacUBUCgiAIkhvOt4Tnrly5gtq1a4OAAoHZ8ePHmxwYyzMeP36M6tWr4969exmKwHNDOsdZGszF//p18VCvU8eOINBPF2gvLy9xPPr999/h4uKCGzduiEkXMi9L+9EvT0iQY/rss8+wdu1aAUwQ8CZ8SAdSCQUag5vZf40aNYSW0rHUHIiQ9ehsLR3p5DjoBs3jr5OTk5hQEhwcLFY34ud0kuaKR3TfozMinZvZ17Vr18QKKXRQZFkZdo4OGEtX6gkTM9yuzJ1QvXRAIwjJ9i1dYYY5enuXQGhocFY2h6qrFCjQChB6k27K3Kf1JyZwH6Ib/MKFC4UGo0aNEmDYqlWrxOSXR48eYdjQYWjQsIFY+cyc4AQWTjw257hkqj3pEp9Zf8yfbvLy2GSq/IABA8Sy2YY5KefmzBTO+c+tgZvprnrgwAHhRK5CKWCoQG5eP+YE3MwJcCtWrCgwG3b79u2oVq1alsdjCDe3bt1anEfz3J/Hck7+46S8rIQpuNnb2xvHjh3LStOqbgFSgNdNvAbiswpeExkLgs0ScibwrAJo3vJZ3LpzD2cv+QuwlkFg9mwIUMotFUKWWh0N4hJPQBNf7Z2zoQAdmgkw6wchX4LNBKAJERMWpnMyodwqOgiZsC7LNPQBXLTFowS8S8dkxp0rZ/DVtKnpNtOaH5fBw8NdtBkSI+ZnCxCZbTAXgs2EhyV8Ld2UWcbZDgh4pIHMdFc+GaK5LhdzBO5GakA0/9JBmUC2jJnz/LBnz79pcilVpiymz/pawM10p+aYCW6zP0dbLQdqyGCfhJiZk4SdCUMTsub9Dr5XwlUre+mBBllXLQb46MDom48012t9MJoQ95FADWyu4ZmaGl2c/ebNRvDlo+A9xP/++0991ZUCSgGlgFJAKaAUKGQKKLi5kG1wNVylQB5RQMHNeWRD5Os0EhPxQqtW6Ni8OaYuX44Rffpg3JIleOmZZ/DHrFk5PrRbgYGYvmIFzl2/jqrlyuHyjRs4eO4cPnj9dfh9/nm6/kt26oRn6tfHuhkzrM6t37Rp2LxvH+7qls3Wb+jGvXuo3aMHirm7485ff1ndh7kVT125gmbvvovl48ahl4F785BZs+C3bp1wNeNyenk9+MBV3igkJKofDRs2ROfOnYVLJKEKFUoBpYBSQClQMBQ4fPgwunXrlmYwuflwumComLVR0LV2w4YN4jfY0AmNLvSEzwk1PwlHegU3Z23bmqpNeI0OyQMHDsyZDoy0KqHakJAQ4QZsKjh5jccFPhymg3NmoBzB3VmzZgkg15LgaiB3799Hm+eeQ0x0NF599VV06dJFTJzj927IkCFiZRD2z/eyAjfLMRDqrVSpEoYPHy5AZTqkurm5CfdRAiB8j2EMbub73D9ZnkFg78MPP8xQH+ZM97x169aZLQ3dq1m+VatWKFOmDGbMmIERn43QdLCzFY6JBMCrVq0q4GjGmrVr8dZbPXHpwkXxfmbbzOxkTBTkuHx8fBESwqffKpQCSgFjChD6kscv/c8lGOzu7p5uX42Pi4eDowMSExJhZ2+H+vXrm+2GyglqvF7Pyv5Px35CQ5kFx8Blzglec4KLqT6HDh0qJnbwc/0y0rE6s37U5zmngDVwM1dq2LFDLX2ec1sl/7XMSWi8V/jss89i8ODBuTaA7Iab9a93cm0QOdgRzxPp5MkJKFkNQ7iZvzM8B87OMAU383qFKwqoUAroK0B3Op6r8N5JRr9JTZs2FaYsr7/+OnjOVRgjPglo3qotQu7eRMB1DQinazABZjohGwLLp0M0iJeALeNqOBAUDTTz1WBhGbceA7cfacAtgWmCuXSCJsRLYJcQMl2MCfvSsdndUavJMgSLCUCzTPC1cxg9fnK6TfPzD0uQ7FwM9yIFZy3gZALUdJU+E6IVp+MxxxClA4jpakzYOCACSLYBfF00Z2ovF82ZmpBxmSKaa3TZIlp7MmISgHEzv8G5o/vT5FK2XHl8+81X4j3Wp/M0wWa6PfNVTve14rgIXJcrAlTSOUlTR9ZhzuyLfTOoCXOo6ak5QDMIhRNYpjO2lw5AJxhNsJyw9lPFU/Vn/duPgT0/zsTF82cF6L9v377C+PVWY1YKKAWUAkoBpUChVkDCzYmJCRjkNwyOulUvCrUoavBKAaVAjitAuHnt1FXw/++yMHatWbOmVX3aJGflaa9VXapKeUmB31etQt8BA1C9fHlcDgiAo709/vnmGzQ28wv1KCoKAffvIyIyElXLloWPp950YDMHuv3IEUz49lsBHhQtUgTbDh2C//r1qFCqVJoWPNq2FeDz7KFDzWw5fbGn33sP90NDcWPDhnQf7jhyBIO/+grVK1TAxq+/troPSyrW6dlTjHnfsmVpqlV4/XUU8/HJ80t2njt3Tjje8uagdHLjQAhk88Y1XwQsVCgFlAJKAaVAwVOAbkhcWlw/FNyc89uZjr0Emvki3KwffIhMmJkvujU/yVBwc86oT0iWzsRcxjm/B8/96TJcpIjuiZ0FA3qmdSvs3rkLtjY28PPzw+eff44ff/xRtFW2bFnhTpwdcDNTsrGzRWxsHOxtbdG/f380fbo5BvQfAF6QcyIinaMJWBOkbtasGb7//vs07shyWKfPnUX9unXh4uyCqKioDEfLiYOlS5fG/fv3zVIlCUDfvu/gpxU/ivIEvKkLgXS5xHIyn0jbAPPnzwfhHro78yE/AaNx48YJV+6swI3mJKrBzT4iLxVKAaVAegUIJ/uW9BX7PuFfYyEBX/5NSkwSkxcM99169erlKtzMyRR0kc4s/v77bzHp+eeff0avXr1MHnN4DD99+rRoTn9sHG9UdJRVvxuZ5aY+N08Ba+Dm0NBQMalGfGeTklJe+v829f8snx8+s2Rs5imdP0txEh5XtOCLLu2G4enpKSZa8TgwYsSIXB9kdsPNJ0+eFBBkQQlOrJs9e3a2DMcQbqYr7rfffpstbctGTMHNvr6+YsKlCqWAKQW470rQ2dT1Fq/FCDhzH7f2wWt+3gJc9Yur98hVOQnh0gmZ0LF+EBqO1jk0ExomQEunYgLQhIZl0E3ZP0JzUiZATHdhQtCEeKsV00BeujUT1q1JZ2KdW3FkPHAtAnCz10BdAsunz5zDKCNws5/fIjxy8IKtDVDSLRX4PR+q9VffR4OL43QAMd2YPZ00V2g6IrPts2FamerFNMiY0HB4rOagLB2sOSYC4CeDgd+WzcWZY4fSaFK5YgUsnDtTgNqErAkc29vwnDbV8ZrvHQvS3JWpiQz2GZcIONsDBK8Z9x4DAY81Z2vp7sycCJUTCmcbMieONSFZA545DlE/Erj8QBu/3+xpYrtywrhaUSM/76Eqd6WAUkApoBRQClinAOHm0YNHws7RHv3mDYKDs27WlHXNqVpKAaWAUsAsBfgsdf2sNbi884J4fmrtNbaCm82Su2AX+nfNGmz880/M+eUXVClbFn/PmyeclDOKflOnYufRo7h5/z6cHR0Rn5CA8iVL4qoF7mKG7X8wbZpo78Dp00hISsKtjRvhredaXPWNNxAbH4+106ahhd5yyuZuHdb1bN8eb3bogB/GjUtXbf3u3Zi2YgXK+PhgQxadq9nXW2PHonblypjUv7/JFI9dvIiW77+P23/9lQKGn7xyBW0HDcK0GTNy1ZXPXB25/DaBZr7+/Tftsltt2rRB+/btBdTMZQBVKAWUAkoBpUDBVcDYw1wFN+fc9uYkIj6Ao1OsfMDE3ggJSKCZTs2WuuDmVMYKbs4ZZQmNEGwnnJHfQ0xsLFrUKPyS2di8fbxx+MhRFC9WTLTB731CfAK2btsqlrhu1KiRgKEI0hKqykrY2NkhKSFBtDdgwAABN7/y8iso5esr+v3tt9/Ac2BO+OBy24SsCeYZBgFkZ2cnJMYnCLiX+66pIMA38cuJmDRpklmps+0G9evj1MmTKVA3gUA6wDJ4/k6YmWPgMuCEBqUTGcsRbja3L7MSMlFIwc1ZUU/VLQwK0LngnXfeEZMlTE02IOzJz/mX52INGjQQ/9aP3IabOTkis2MI938HBwfExsRi2CfD8M0335gcI8E0CRsZOjdzcoh0wi8M34m8NkYuY85VDJ5++ml88skneS09lc8TUIDnNNu2bcP27dvFyzAqV66cYn5Qu3btJ5BhapeGcPMvv/yCZ555Jks5rV27VjgSBwQEiEkpPM7pvwzfk//mOaSxFz839VlW62TUdpUqVcBjb3aFIdzM61U+wMrOMAU385xXQXvZqXTBbYuTMOjkzNeBAwdMDlQ6Obdr167gimEwMj7bOX/+PG7cuCGAY7oMN/WFAIdlEJgNjtHed7QFQmI0h2SCtT4uqeUexAIXwzTnY7oWxyYCVx8CCYlAWXcNII6IB04GaQ7GdDJmsBxhX0K6hIQJAbP7vcfOYtqUKem2xdivvoGnVwl4OgMldXC0f7jmeEwo281Ba4dtEqZmjoSE+XllD+BSuAYW0935ZoQGM4dGA672Wp4y2MbpYK2tDctm4dCRY2lyIdw8dspMxCRqjtcsR0dm5k8XZ4LRx4OAok6aE7MMwtCEsFmGOhAWJ/DNfKlJaZ0uKWB0ES1vBvvhdnoUB9T2SgXLJWxOWLuUG8T5+q1bt8Q1xJEjRwrN91kNVCmgFFAKKAWUAkoBnpMk41jgSYwZMhoOLo54b85AODjpLUuhRFIKKAWUAjmkgISbr+y6KFYm5mq41oSCm61RraDViYnB9X/+Qct+/dCpZUv4FCuGmUOGGB3l6atX8crw4QgMC8OLzZuj5wsvoJyvL67dvYtyJUqgTePGWVbnt5070XPsWDSsXh2HV6xIae+PPXvQZ8IEcZPX3s4OzWvXRrNatVC3alVUKlNGQMkJiYmIjYvD46gohEVE4G5ICK7euoW9p06BzsxexYoh8O+/jea469gxDJ0zB8EPHuDe5s1ZHgdB6pb16mHT3LkZtuXQsqUAoEe/844oR3B89bZtOHb8uNWzFrKcvIkGeOP4u+++w+PHj1NKEOZo27atgDoIS6hQCigFlAJKgcKhAJcU5zLk+qHg5uzf9oQFpKuQBBX5YLpTp04CaubvL1dMyGuh4Oac2SJxcXFYt24devbsmTMd5HKrBJPDw8Mtdgwm7EYX68V+i/HRxx/B0dFRuEBzKW3CzY111yR8YEc35awEocGEhATRBJ2bue+98sorcHJywtKlS1G1alVs3rwZo0ePRsmSJcWDwqeeespolwRfCI+UKVMG/v7+JicjEOqT7s3m5k7AkX1PnKhB0XT3XrVqFQg+E3uU8KMEBakNIXD+m46a1t5QyDw/ZsDQ4Esev+gYndMu0ZnnpUooBfKeAgQDCc4Ywsr6mXL1pFq1aoljBMtJsFjuU4SIJdxszn62adMmAR6aU9aUYs8++2y61SQMyzIvAs0fffSR+Gjw4MECCDQWnJgSHBxs9DMePwgOqlAKKAWenAKcZLBjx46UlzGXZp4v8djy0ksv5ZnJl4Zw88qVK9G6desnJ2QB7tkQbuZvGx9gZWeYgpt5nr1///7s7Eq1VQgU4ERVnofx2Hbp0iWjI+b9F15j0diloAeBbl5b/nvmpnANblpCcxOWcf0hcDsSaOwDuDoAUQnA0UDNbZgOyDII254JBUq4aC7DdH+mszPLl3DVIGRCzHQxJnBMMJpBWJdQLwFfOi1LMDgqEZjz3f+w/5/f022CL+ctRUmvoilw9C06HkdobRIkZtx8pMHDdDsmSHwjAqjkoTlOh8UCDX00p2MCzcyLbtHSGVl2KJygEzRgesrUGTh6/GSaXMqULYuRk78WdemiHJOguS672GvjPxmijaueni8Q+6S7taOdBjY72wGEwgmQUzu+xyAkfTxY00ofjCYsTvdpQszFdGOlxoSo6fYsy06YMAH7T5zH/ds3cea4crgv6PuxGp9SQCmgFFAKKAX0FdDg5hMYM2RM/oGbefKUSWReQq8BM9oz7M6KKromdLPcMhuAmZ9bNE4z2yyMxfTmamZt+FyWxYqwrBqXf8m8k0yLWNZp5h1aUULCzdf+vSKeWZbLxGjXVBcKbrZC/IJW5c6dO3i+VSv07dwZ0bGxuBUYiO/HjjU6TN+OHeHo4IAbf/yRozenv9uwAZ8vWIAP/+//MGPw4JRczvr7Y/+ZM9h55AguBwQgODxcgMwxcXGIT0wUD/gcCD/T6ULnXkQXZXdXV7z87LNYMX58hpuvZvfucLS3x/h+/dA1i7PxPdq2xcAuXUyC4jKRCq++iuZ16ghHakbHoUNx6c4dXA8IyHNfNQId//zzD1rx+9K3r3Cly05njzw3YJWQUkApoBRQCphUgC4ufGitHwpuzr4vzH///YdZs2bh1KlTKY3KCUVcHjUvAs36o9eHm/ft24eyZctmnziFuCXCzXQK5kPVJxE81zYVdE52tHC2/+TJk4VrsKXx888/w87WDuMnjMemvzbhw/4fomWLlmjVulWKc7NsMyvAHtsgnCHd0nnRzWsnBs+DCRG/+uqrArQjAC3/ZjQernzCffmnn35C7969jRZdtGgRBg0aZBFs2KVLF/HdIKR87do1cV0Un5gAG1tb2MFGtEUgm98h6foqrp0cHIR7tqurzt7K0o2Rafm0cPOaNWvQo0ePTGupAkqBwqRAYkIi3D3cxSRi7qsEgQnd0cXZML744gvw2Kl/bJPHDO7bTwJu5oQSQkEZBfO6ePGicK338vICHYC55Dsnpxgep3lc4rHK2PGbE1tcXPTsAAvTF0WNVSnwBBXgOQOhv507d4q/XBXCMHhuROiPQHP16tWfYLbGu+7evTsOHTqU8iFh28LkxJqbG8QQbn7uuefEuW92him4mefre/fuzc6uUtrihCBOCsxK8DeO5+T8/ZN/9f+f72UW/E3li/ulfD3Jfz/Jvjl+S/o3pyzPQfji9R3LGwse4zgRvaBG165dceDQYaw8EIAG3pqLsQxCwwSU+T5BWoLIhwM1B+ZqevPu6TB8OgRwdwRqFddqBzzSXKDprkwnZ9YlrOtipzkOyyB0TOkjEzSwWcDG8QnwW/MPtq1flU52JydnzPl2hQCVGXQ8JhhczVODgxl3IzU4mPA1XZUJT5cpork30zWZsPKDGM2dms//ZVn9zq6EaxByoxIarDzrm6XYtWtXunxmzl8Ml6Kewgma4ySMzDgSqPEJDUtogDMjJFprkyA0x1rEQXNgPh+W3t35RHB6MJqaBkUDFdxTHbMJhJ8w4g794adjxbX/hSN70txrLKjfYzUupYBSQCmgFFAKKAVSFdCHm23s7dB7cl/YOxoxD0hORmKSthqksUhKTNZO4kwE6yZn8Dmr8T6oWN7CZCfJiItLQLLOcMZUMTYRFxOHJLaXSYiy0bHi+snc4DjiouOQnGx+Hdl2YlwyYmNizO0q0+wTonl9kvk4s6nDgtmMDeDk6iyel2UleK3g5OIEW85mNDtsBFvp4GKeYQfv8Tu6OPKpXoY9cCxiTBlT9hYOAAAgAElEQVTsUA7M1c50rjZ2trDVX6JHr0fmYcqExcbWBjbyoka/jo1teo2Tk/H3oj9w89ANBTeb/Z1RBdMrEBeHQe+/j5jwcOw8ehQt6tXDxH79UKNChXRlfV58ER5ubvBfvz5XlHxr3DjsPn4cE95/H/3feMOsPq/cuoWIyEhExcQgXveD5+LkhBZGloY21uDmffvQbcwYlCxeHP6/p58BblYSukLF2rdHj+efx5LRozOsVrRdO/R88UUsGTlSlKv0+ut4pnVrrFqzxpLucqUsoTUPD48Ml9HOlURUJ0oBpYBSQCnwxBWgm80LL7yQJg8FN2ffZiHcTPCR7ozdunUTD97z2woJX375pThvUMuWZ9/34knBzXyo26tXL6xfv15MIiRE++DBA3gV98KSpUvQ9c3uAjbr3qUruDw5HZnNjSVLlmDAgAHmFhflYmNjBcRLB08uq0rYmzcHCAJ6e3ujSZMmKe3xIluEldPb6ZIqnZjZNjXg/rhx40bhSMgljOmANHbCeLRv1w4JcfEmx8IU+OratQs2/P6HcJ8mzJAaGqDQoEEjnD592iJNWJh1p02bhoULF4KwI/Whw1ilSpXwKOKReJJKh2u6W/PfkVGRQq+YmBiLQGrLEksLN//666/imKZCKaAUSFWA+6WcYECglys18K986WvF4zEnXHC/lqEPAVsKN/NYRjf6rE4EyWx7Mq+goCB4e2k2dUnJScL5nsCzs7NzSv88LlILUzARHWNzbjJGZqNQnysFCp8Cx48fx99//40tW7bA2LUeV6Qg0MzX008/nacF4kSwo0ePpuTIlek6dOiQp3POr8kZws10+OfkxOwMU3Bz+fLlwWvpnAjDceVEH6rN/KNAqVKlhOnLL7/8Ajc3t/yTuBmZdu3eE//u3YsTl28JAFjGvSjgUpjmhkxHYAaBW3sboK6eEzHBYILNvPbl+462miMyoWPCzhJCZhlCvXRMlo/t6aIcmwTEJgBedtG4fukcLl25ig2btiImOspo9q2f74jPBvYVbskRcVrfhJilizThYTo1Ex5mbtciNBg7Pgm4Gg48VRwCRKZbM0FqWVa/M0LEdH4m1M0x0Bl55fp/sGF16sqzsry7hwd6vNsfrZ9uLPphEIwmyFyLTtK6WwAEq+881nQiBE1YnFD4uVDNxZlQuGQm+B4/I1gtb3EQ2KaupdxSHaalOzTxhfo+qSO49ACYOn4UitvHYfu2rTh79qwZ3wRVRCmgFFAKKAWUAkqBgqKAgJuDTmLsx2Nw+9JtlKlRzijQmJiYhPgMnm8kxsZrcLKR4HkI6yZnAOGyTFxsLAQknVGICZ2Zq6/dPzSjIJsS91ozb1OWYNOZgdqiWa5ZaQCPirrmDMDMdJItgLLNbPKJFKOeSdmoi6kvYlKiESBdd6/dHDfkjMTh9hb30S34Lmnt2SDlWWUm6lvSh41NxpB1Zn3aO9rDwUFvmR693Gwd7OHgaPwzOwd72Bn5zM7OFg7pJk4kI/B6IDyc3MW9Ia64ZU0o52ZrVCtIdQ4fhleHDmjXuLEg6KV7sOEQ+06ahNVbt+L+5s3w9NBNgc5hHR5HR+Op7t1xLyQEm+fNwwvNm+dwj1rza7dvR6/x49G8dm2smDABVa10Gqzfqxeu3bmDX2fMQEcjN/gv3byJoXPmgG7UX3/8MXroADHb5s0xe84cBQLlytZWnSgFlAJKAaWAtQpcvXo13XKcCm62Vk1VTylgngKEetetW5drzs10ESUIS3iYENqoUaPw6NEj7NmzR7jyMei8PHHSJHFTau3//idW+fDz80Px4jp7pgyGxhs8zZs3x+HDli+JyhnDhIo/+ugjcHWRe/fugY6mPj4+aeBmBycXJMTHmH2PyzBdAsMS+qNj+vTp04WrclxsnLiBQXc1QsQL/fwwYsQIxGUwI1/eauPNCa6GQzcwOie5u8u1e7WbLp6eXgIetyTYFm8K0EmRusqcmRuB9G+++QZv93kbnsU903xuSR/WlU0LN//++++g+3xOg5TW5apqKQWejALcR2NjYsWNcO4bcr9lNvr7Cn8D9CdEGELN8t/16tUze4IEneTpOJfT+6SEm/mbwpDHKTps0sVZBn936O5MDQyD46fjpSnHiCez9VSvSoGCp0BYWBg2b94soGZjDrgE+iTQnJ+cj1977TWcPHkyZYNxgp3hSkQFb2s+mREZQsAtWrQAV+/IzjAFN3NCMK9VciLoPk33ZjqA87eIv53SySi7/51R2/I3m9cpnBDF6wD+1X8ZvmeqjCXOZTmhaUFokxNsuaJPQQnCvy+/2Qcn9+3E/bvaqkUMgrlnQ4EanhpMy6C7cFQ80MQ37ejPhGqgcD0vDdJl3TuRmttxlaKa+/ClcM0puXEJwEH3PJ4ALmFesi7ezsDpw/vw1dwFGUpbqVpNzJg0Fu7O9gI4JmxNt2bpIh0Wo0HVBJvZz/UIDWB2sgfOhmjvE74maEy4OCwWqOyRChWz8/uRwJWHGmxMWJmO0nR7LpIciaGDByPaxD2AMqVLoeXTTeFTsSaKVmkkIG46MzMi4zXImkMv4aq5LjN/Ok5zG9DJmnoxqCfdnKmVk532HoFtguDFXYCK8naCbpsQzmZfUtfrDzX9f5s9Ao52Nvjzzz9x4cKFgvKVVeNQCigFlAJKAaWAUsAMBQg3X3t8E//7+X84tes47O2MA4v6TdHZ2c4Md1pen9g78X6dHdhPRqwxmVAHZ0ejkKctbGAH3cmOwZhsHexMAphmDF/MDnNwzr57irbJmikFg9eCDhauaGpWztlQyA624r+8Erwu5QqwhkGHYlvCx5YTw+naEo7bMXHZNmReZ/OedFZgddalwVBWr7/ZDnPJajsUJ7tyMkdomnZRR674xmeoRYrozaA1pwFdGQU3WyBWQSwad+MGilSrhobVq2P9V1+hjI/edF7dgOno/NInn2BCv34YZWRZ1JzUZcvBgxgwcybcXV2xbsYMVCtXLie7S2n7wJkz6Dh0KBpUq4aKpUvjxwkTLO73xr17ePWzz1CncmXhit22cWO82aEDzvn747S/P37fvRvNatWCg709di1eLNr/etUqTFq+HBGPH1vcn6qgFFAKKAWUAkqB3FSAroEEYvRDwc25uQVUX4VRgdxybuaFLR2KhwwZIh56ubm7i9sKV69cEa7MderUwV9//SUugLk0+dixY/HZZ5/hq6++wocffggu8238Ajst6Mr6hPo4oz+jpZGMbeuZM2eiR48eePvtt0EnT7p/EpooWbIkGjduLKqw/efbd8TOXVut/roYws10Hu7YsSN2796Nhg0bgrDGqVOnBHA4cuRITJ061ay+jh07hqZNm4qbYISIpNs1+1uwYAGGDh1qVjssdOPGDSxbtkz0zTG3bdtWQB28sSa35auvvircWTf9tcmKWeVmp5JpQbrEMhcVSgGlQKoC3Fd5g08et3ijlxAvQx86lsdV+Z4pINlcuJnHBzrT8zifW3AzHQ71g67N/A3hS47fcNzyfU4G4YoQOZ2r+m4qBQqrAv7+/uB5DlfoCA4OTiMDJx3w2o8rQhBs5ood+S1efvllnDlzJiVtrnLB91RkvwKGcHPLli2xevXqbO3IFNxcuXJlMWlHhXkK8FxAAtISgDb219Rn1j5UZb+cyMR2zf1rbV+mlGC/fMDMh8OGf3ndzWCZhw8figmp/MuyMniuxmvP5cuXo3r16uYJnk9Kde39Hvbv/Ad3794VGRP8PRkMVCkGlNM9h772ELgfBTT1TYVoWZaAcmgM0MBHg4jppOz/UAOaq3sCznaaA/LNCA2KZhkG6/DFoLOxryuw+1/jcHOp0qVRodpTqFmvETo+2xgejpoLM3NkewSDGQSC6bhMgJmgMIFkuh57OgPHgzS34+LO2jjoRE1gmGWZowzCzudCNCi7dBFtFXZCyTScI0B9+ux5fDn9a0RHGXeVZjtOLq5Y9v3yFLdrgt/Uj0GgWsLi1C48DqjlqblDM5gzy9YsrmkitQogpO0IVC8qTAhFUGc6ORNsdtNB1HSGvhGhuW1PHPWJ+P3m7/zly5fzybdRpakUUAooBZQCSgGlQHYpEJ8cL1Z1jHwYaZZxAFfKtOVJnBlha29v3v06G8DOwc5oWeGAbALEpUmnOaC1GalmSxEbEMXO+5GXwObM1LLNxIE4s/o59TmvQ3ndmpXg9S/byI5rWmOGINbklp05ZdY/7zlw7DR5ooGUtff2FdycmdKF4PM61arB0c4Ox3/6yehoW7z/Poq5u2PT3LlixoSlsef4cazfvRulvL0x6u23La2OGT/+iENnz2Lup5+iYqlSFte3tsJfe/fi/z7/HC3q1hXLXH/61lt45dlnM2yOMDZB5plDhqSUi4iMROdhw3Dhxg0kJiWhUunS8C5WDGeuXkWnFi0w79NPUVQ3O+GtceNw1N8fl69etTZtVU8poBRQCigFlAK5okBAQABatWqVpi8FN+eK9KqTQqxAbsHNs2bNAt0033zzTdD9jPv23n370OrZZ01eeBLuJaxLV+NqnDzZsKE4h04baeFmfjZ+/Hh8+eWXFl/Qnjt3DgcPHhRALx2bP/jgAwFcE5yTcDPbX7P6N/R8q5vV3xousybBa7pR8+Kb7tVly5YVcA/drPk+xx8SEiIgZXMvzvkgvF+/fuJmIh+UU69bt26J9sxd2pjfCc52njdvngCZCSpeuXIl3XgJENIRlcvHc3LKkwq6RBGyVqEUUAqkKsDjFt3neSy4du2aOO6aApj1XSKyA27msSu7bopmtE2lc7Mh3DxgwADwN2fr1q144403Mjx+rly5Er179zb7GKu+Y0oBpYD5CowZM0bATvrgHs9FOnfujOeee068OLkgPwddms+fP58yhPnz54vVJFRkvwKGcDPvG6xatSpbOzIFN3O1GU6+VKEUsFYBXmfu378/5UXwWT8aNWokrmc4OUKuSGFtX3m1Hics87qN56cEhOmGXN4dqKj7Gbj7GLgcDjQqAQEWyyBES5i2rrf2fnQCcPGBBgITbKZrcWA0cDEMqOulgcUMAtB0ISZUTGfiMjqAet3Wffh+sXHnZu8SJdGsaRP0690Nzs5OOBWsgcf1fTSAmX0TCmbeBIUJ/cYlAqWKaBC0uwNQzl1zci7lqgHOHJ90VmZedFc+HqwB3XLshLLpRi2BZI45IT4We//bhzs3/XHgv91I0k1alLq40Ljp5+XinwKOfqjB2OyLOTCYBx2uCVFLXeg4fT4UqOWlOTszCJqzPl2Z6aLNXBh3I7U2ahfXgGkG2+N2YpsEoz/++GOxQsryn37Grevq+WNe3f9UXkoBpYBSQCmgFFAKKAWUAkoBpUB6BRTcrL4VePH553Hl4kVcW78+nRo7jhxBl1Gj0LJePWyeO9dstXYfP47xS5fi5OXLeBwdjVqVKqFPp074+M034eKku7o2u7UnV3Dp779j4rJl+LxPH6zcvBmf9OyJPp07G03Ib906fDJ3rgC474eG4um6dfGugQPIL1u24Mj583B1dsaLTz+N1g0bprTFzz6eMwdNmjcXy3mrUAooBZQCSgGlQF5W4M6dO6ADkwwCOU8SmMvLWqnclALZpQBB1nXr1qFnz57Z1WS6dk6fPo3Zs2eLB5mEzfhg87vvvsuwPwJ2fNGNmFAzQeOBAweiQYMGBvXSw80E3n788Uf07dvX4jHRKYsPm+l0/N577+Hw4cNISE5C40aNU+b4U6+uXbta3DYreHt7IzAwMMXJgNAxHbvoGk1oYvv27YiJixNQdZf/+z9w+0gtzO2QbREoYnD2NsFwuijxmJpZcLYzYaP//vtPOK7Vrl07Qwem0WO/wJRJk9Gnd29wWW1z+sgsB0s/V3CzpYqp8oVBgW+//VYcN3n8mDZtGkaPHp0ybHMnS8gKPKZK52Zz6vI4kB2uEeZsJx5PDeFmsRxhQoKY3MHjGWEiU0F3BzonmjMuc/JRZZQCSgFNAU4W44Q2BlfUILRHELh169b50qHZ1Hbt0KFDmvOkOXPmoEuXLuprkAMKGMLNdP3m+X52him4mQ6627Zty86uVFuFQIGzZ89i586d4nXixIl0I3ZychIwM4+PXCWnoMfgwYPFfYcbt+/jWJAGxlYtpo36QawGO9cqDpTUOQnzfQLP1x8BTxUHijsBCUnAlXAgJlEDgz2dNIiZjsl0PJYAc5QOQqbTMG2VJERMsHf9jsPY8P2cDOX2cHfHiKlzkeBYBA29NdiXfRMAphszQWG29TAOqOQBnA7R+qET8nVdGULAhJUlFMwO6a7MXL2cNTCbQXCbcLIEkvlvAtOHD+7HpvVrERJ032iubq6u+FUHNxOGjknQ8qRLNIPgNeHuCu5ACZ2mUiuCybK/x/HAZcLizN8z1Z05LEaDyNme3CZ8j2XpNk3Am8HVF+DsgfOnT+BuwJOb8FzQ9x81PqWAUkApoBRQCigFlAJKAaWAUiD7FVBwc/Zrmu9apFPOkUOH0LN1a3zWq1ea/IfPnw86L/f/v//DB2a4WWw9dAhDZs2C/+3bqFK2LN5o0waDu3VDOYOlR60V6d+TJ7H/9GmrHKCt7XPRr79ixIIF+PD117Fm2zaM6dtXQNoyth8+jDm//IJLAQHo0KwZ9p46hTI+Plg5cSJKFC9uVrf/HDiAVz77DM2bNxdud2+99ZZZ9VQhpYBSQCmgFFAKPCkF7t+/L363ZHBpw4sXLz6pdFS/SoFCoUBuwM0EeLkUOcHdjh07mgWRETSj+ycBZcLGdCT+999/BaiWNjS4OTlZg6G1/0/GDz/8IOBkS6NSpUrw8/MTLnybN2/GsGHDULpsWQFYSzSY0Jyrq6tVzqRs5/jx4ylp0eWIYB1fRYoUEe6qV69fQxEXVwEK0xnZGuguLCxMgNR0hSb4d+HCBbOkoOP12LFjhYbUP7NIttGWVr504aIAoXMqCLnru2fr97Nhwwa89tprOdW1alcpkC8V0Iebly5dikuXLuGrmV/Bzt74Mo2GgyScLCdWWAI3syyPXfpu0DkpIF1hCRLqHyfZN88heVzl8u50mecSmQQsDYPHSf4+WXOczclxFaa2uZ34G89VAl588cXCNPQCP1aCojVq1BDbNf3KGwVj+O3atYO/v3/KYL766qsUqLtgjDDvjMIQbqb2PN/PzjAFN/N7zAmaKpQCmSnA8y0JNHOSrLGoWbNmikszr/0KS/C6evXq1fjjZKCAg+kQzCDweyQQKKvnZMz3QwnXhmlwrXQ0poswXYYJMZdwAeKSgKOBGrxbVQf18j1CyHRrTkzS6tN1+REdk4OAKh50VU7E/cAg/LVlB/78628kJSWm2ww1GzTHlC8+gavu9JFt0hWZfRHGDozS2r4aDhAQrucN3HoEFHUCHsZq4DVBaBmJycCJIMDZHqjjpb3LNghiE5CW/6az86+rVmDP9oyNiipXrICFc2cKOJrQsp2t1g7dlwleU6sybqnAN4Hpo0FptSIQTVicuREAlyA22zsbqukucyPIze3hSTBbB6Uz557v9odn6Ur4c6UfbgUEFJavsxqnUkApoBRQCigFlAJKAaWAUkApUAAUUHBzAdiIWR3C999/jxUrVsDexgZjunVDBz1QqXX//oiIjMSbHTpg9DvvZNgVoeZFv/0m3IiXjxuHKmXKZDW1dPWb9O2LkAcPcHzlShTPxeUQD587h7lr1mDt9u1wd3UVr9Le3jh77RrKlyyJ2pUq4XZQEO6FhqJRjRpoUbcuXm3dWpTxcHMzqUNgWBjmb9iAJWvWYM7cuQLqoBscHYlUKAWUAkoBpYBSIC8rQLikSZMmKSkSyjt16lReTlnlphTI9wrEx8dh/frf04EYBMEI8GYF+CJgRodjtkNwl4CdJcEHwo0aNkK79u2Eg/Pvv/+uOQOliSQdRGebJtdly5YJ11JLgw9cCTjTRZ45E6xmHoaO0cZB68x7W7RwEQYNHpRSkP3Q3ZAacTniP/74QyyxPXz4cBA0lg7MmbecvgSd7ytXrgy6CpoDZHB7jRw5EgRzCCIRRjcEFOW/9b8XPXr0wC+//CI0oitZxt8ZwuiE0DUQPaNgXyEhIeJ3gUC4l5fuKbBBJWqmloDPTE31eWFTwBBupmM+J23UqlUrRYqM9lUeg2bNmpXixl63bl3Qhd9YHe6r8n1C0ZxQcfXq1TTuzXLCCkFjfYd3/brWbCNOjOPy7Yau8cyD55Hh4eGiWd4P4e+afn/8fx5nJ0yYkKXfOmvyVnVSFeD9w0mTJsHDwwNnzpxR0igF8pUCdKK+efNmSs50yu9lYPKRrwaUh5M1hJt5fpvZSjCWDscU3MzJF2pFREvVLDzlr127lgI079u3z+TA6V5Pl+aXXnqp8IijN9LPRozA8h9+xN7LQcKhWcbh+4CHo+Z6LONRHHAqBChXBKigA3/pQnz7EVDeAyitezRGWNfFHqitq5ucDFyL0GBmgruEjwk5JyQD7EcfgqZTMYHlyKhofDVuBMJCQ9JsF07K+fO3n8V7hJYJIRPIJiR9M0Jrm+7IfDUqAQRFaeBydIKWk74DNds4Gay5Izfw0a6ECW/zVdkDsLcF6IocEgPcu3kVMyeONfodqfpUXQx8uxtq1qgmzl0JR4dGa5fWhJDZL+Hvy+GAlxNQRQch0xmabtl0spbax+sgcMLlhJh9dCA2x0knaoLOdHJmELg+H6aNj/XtdJfydyOBD/r1Q8uGT8Fv/hxwNT4VSgGlgFJAKaAUUAooBZQCSgGlgFIgvyig4Ob8sqVyOM8hQ4YIAIGuYUvHjcPbOhDBq0MHuLm4YPvChahevrzJLPpOmoSVf/+N+Z98giHdu+dItl1GjsTZ69fxcffuGGzl0tJZTezh48fCvXne6tW4cuuWuDHhYG8PO1tb4dJcp0oVJCQl4W5wMKLi4nAvKEh0Wa1yZZT09QWXyYKNDcIjInDt5k0Eh4QIOIWOa1OmTBHuKAsXLsxqmqq+UkApoBRQCigFclwBnjPQ1VRGyZIlcejQoRzvV3WgFCjMChBu/vXX39Ks8kEAt0XLFihXrhx8fHwskodAMN2GZXC53YEDB2LBggVpQFljrp760Bz/f+bMmQJGI+z0v//9D7t370b79u0N8kkPN+/du1e4FtMVy9KgCzHBPOZXqlQp4XZKsNoQbpbupJa0zzHR9Vkfwvv8888xb9488R7HynN3/v/XX38tXKMNgT1L+mPZo0ePCkf8xMT0blSGbX3xxReYPGkybGxtRL/G3FeNwc1sh+3zZcwZNW0/6eFm6RBLF3Fet/AvYTcGtyW3R0YQJpc3VkvAW/rNUOULugKEmwk0M7g/cdIz91860nfu3BlRkVFYuGihOM5wvyUkXKZMGbGvsdxnn30mjkPy33Xq1AGXVze2L/LYxX2VS6s/CHsAewd7o+X4e3D79m2Rz4wZM8T9Ii7DTpd4ayfS3Lt7D94+3sKhWT/ksapq1apiokb58uXx999/o1WrVgJ05vGNx2OW4/it7b+gf49yY3z6MKE+JJobfas+lAJZVeCZZ54RxzUZkydPxttvv53VZlV9IwoYws2ciMffuuwMU3AzVyfh76cKpYC+Alu2bBHXqDt27DApDM8/CDTzHEl/gllhVLJF2444fWQfIh8/Shk+AWYCyQR+ZRAOPhGswbbVdHAuweEAnSuyfO9MKEBAt5Fe3ZuPAIK8j+M0+JgwL8MQguZ7LEsAmiD09eN7MG/B4nSbZdXyJYhzKibK0dmYfwlP02WakDPhaDo2E8YmuMwg98vP9YNgMAHhhj4ayEwXZDouM0dnO815mfC2uwPw+29r8c/G9ely6THgU7zVoZmozyAMfS9Kc6cm2OzuqPVBx2W2Q1icY2NQZ+rC/hkSAifI7OWcCovT9fqMjvGuz1wJiScCFx9o/RAiJ+As+ycEvWR0X7Rs2gic3MPrCRVKAaWAUkApoBRQCigFlAJKAaWAUiC/KKDg5vyypXIhz40bN4IPyekQNKBXLywaMgT2LVuiXpUq2LVkCYqacCBe9OuvGPL111j15Zfo1bFjljL987//hBvy46goXAoIQO3KlcVDrF937BAw8buvvILlY9PPhj515Qp+2bIFR86fR3RsLEp5e6NDs2YY2KVLlvLRr0yX5TF+fti8fz8GvvEGurVvj6TkZATHx8PGxQUVa9WCre7BG5fqpFMEg5DFgwcPEBQUlAKP0OWGN8zq1auHgIAAjB49Wjid8QFeVqGIbBuwakgpoBRQCigFlAIZKMCVBvg7JoMPMOmOqkIpQAUIDnACV4sWLQRwpSI7FCAYDPz22zp069YNdE/nw/vQ0FBcvHgRhML27NmDNm3amOiMoCpDe8LGc1Geh8ogNMZtRVdMOiDrv2+sQUO42dPTU0yU5PtJSBbfgTKlShtUTQ8389yXwKy14ebmJkA9Tq4YPHiwSbi5ebMWOHLUvAkYHMPs2bMFSKg/TsLNX8+ZjQcPwlHU3V2AvS4uLnj06JFwc85qcBsMGjQIRYsWFTBhRsHtRBdTAt6EECXUyDqGMLohCEhn6PSu2vq9pf2uyE+4nejkSpC9d+/eKO7jDZvEZNja2aZxWM0obzpd0yVRwYlZ/bao+gVJAX24mfs092FCxHJ/5r0CTgKRTsZy/+G/CSZcuHBBgMfyfe6n0dHRRvcz3qswXCnKlMOzocb37t0Tvz/sb8SIERg1apTIydx7GKy/ZMkSjB8/XkwOYV0eR+3t7FOOI8lJyXj5lZfFBBl+zvtBBJp5/JkzZ47RYw3779+/vyhjCE4XpO9JXhiLgpvzwlZQOVirACeQ6cNcPJfiZBIV2a+AIdzMa5Y33nhDHNP5O8dJiVwFhauWWBum4GauXvDXX39Z26yqV8AU+PPPP8HVfky5NJcuXRrPPvuseBHC5zlUYY/7kcCECeOxboWfeF7FIDBLqLeZb6o6hJXpcOxKN2bdoj0PYjWImPBuVQ/A1ha4Eg6ERJTnAwYAACAASURBVANNfTVYmEFYmCBuXKIG6xZ10t4ngJuoB/byPToOE0jm+wSML184j1HjJqXbTHO/WQBbdx9RRgDBDwFvFw0UvvAAqK6DrwkJE1KmCzIhaP24Gg4ERQONS2gu0gSQCUgTSC7ioDk9+z8EijoC0YnA9wtm48zxI2nacHP3wMrl36aAxdTtRoRW39MZ8HTS+j4XKjyQBITsaKc1IXSOBRr7pjou03macDYds8vrFnvl+CSEXddbc4FOSNK0jkzQxsryDOZMYJzO2uMG9xGrXvFcnPeTVCgFlAJKAaWAUkApoBRQCigFlAJKgfyigIKb88uWysU8p06dKh5YN6xTB0F378LV2RnHfvrJZAbubdrgTS4v98UXVmd54MwZvDJ8OCKjoxGfkJDiiOzu6ipmUtcoXx7TBw3Csw0apOnj+t27eHviRAE184FWlbJlhdN08IMHuHnvHoq5u2P9zJlorecsaU2Sm/btQ//p09G7Uyd0bdcO63buxI6TJ3Hq4kWxfCoftvEhGh+m0QWN/3/37l2x5Cr/v3HjxqCjJUEFlo2KihI31OmmRLcbghh0DFGhFFAKKAWUAkqB/KIA3V75kFJG9erVsW3btvySvsozhxWQy5azG+Xslz1iJyUlwNHJEUi2FTAYz30JBxCu5b/pkjxy5Eh0NDnZMC2wyvPTgwcPinNVAmSvvPIaNm9ODwIYc23miDKCU23smFuCDqPWYFutfHq4WcDQSUlWw64nTpxAjRo1xHk226H7sb6rvFR/9tdzMeLz4enAX2Nbh5MUOeHTMAg3hz+KwJLFS8TYqlWrJlyIp0+fbnX++n1Qp/nz5wtnO2P9y7IE/D799FMxllu3bqFixYppUs0MbuY2j42NzeCLmR5u5iRMPgDt06ePqJcstqdE5c3/jtNtVi2Ba75eqmThUEAfbuZxbPny5ejXr1/K4PmeMYBYujt3fLGjgHZaP9daHBd474H3G8yFli2ZbMD2CSDTNX7Xrl0CBOIEmypVqmS6sQg3c8UACVezLR6LfvtNW5GAeejnIie+mJMffw/pYL106VLhMk3Ha3GsSvn9yTQ9VcAMBRTcbIZIqkieVaBJkyZpYC6uovfBBx/k2Xzzc2KGcLOxsWT1/oEpuJkTsAm0qijcCqxduxY//fSTWFXIMLhikASauUpE5qvZFD4tJ02aJK5LOZGaYC6dipuUSHUC5vO6Uzo2lm7IdB1+HA9cDAOc7IEaxTRg99YjDQ4mLEy4l0FnZ+meXMxJA5AZlx4A4bFA85KpegdHa2A0weYKdDx2AI6eOofxE9M/Rxv3tR8aViwuwGWCzezP1UFzliYUzLohMRr0y1wrE77WuSWzR7pNc6x0ly7iqIHXzL2kK8A8CXOzXbbJ/Dm+7+Z/hdMnjqf5glSqVBGL5mgTlSUMTYtoXxfN4VrA1mFaDnW8Uh2r2dfdxxoETrCaQd0fxmq6E9qW6V59CIRGA08V18bD7cHcWLa8B+CtY/QJPB8PAjycgJqeEOfb7dq1A+9rcGK6CqWAUkApoBRQCigFlAJKAaWAUkApkF8UUHBzftlSuZznc889J2AH72LFUMzNDefWrDGawYhvvoHfunWI3LPH6gwv3byJ+r16CQD5h3HjUKZECeG+HPbwIc7fuIHnGjaEo8Gypezs4Nmz6DBkiICZF44YIaBj/SAk3XHoUOw9eRJrpk7F/5l0scs49eV//olhc+ZgxuDB2HnsGA6fO4e3e/dGx9dfB2+Mm5rRT8iEbs10RqOTG2EAurrRpYJ1CD4TiOCyiCqUAkoBpYBSQCmQ3xSgIyAfCslQDkn5bQvmbL4KfslefQlsEWilY/r69evx7rvvQrpvenl5CRfmw0ePCtjMLuWRl34O+s7ItgKcIwzNeprjbxLc3DwQExNNFMwsAJitmwLOCOHxnJchQWF9Z2H9ulwimHCctcGJhX/88YfQZc2aNSbh5hs3bgh3OFOwtuyfebJNY2PjQ0C2QYdQCeFl1p6l4yLQuGiRHzZu+BPduqdfhYYAH3Mg1MwgLExgUD8yg5tZNmO37PRw82uvvSZ0NgcyzGjMxYsXVw9SLf1SqPIFXgF9uFkOlvcO9CFgY8dcHg+uXr0qJnhwn6ab+/Dhw8UxTE6szg3xmAcnd/P+CPOg6zKXc6c7s37wWMX7IPL4mdXjiamxcfUB5kCXaf5Wyt+fnOovNzTOK32o87u8siVUHtYo0KBBA7Gyngy6zw8cONCaplSdTBQwB25mE/zd6tu3r1V6moKbuZ03bNhgVZuqUv5W4PLly9i+fbu4JjScYN2sWTMBNBNmpmu4iowV4OTdWbNm4eLtMJwNBRr6aK7DMug6TIdgvu9gq4HAdEeOSwJqe2oAMEHiMyEawEuolxEWo8HKdHCm43NJN+19YwA1XaDvRWqfl3LTHI8JBq/77yx+mDsl3QC+/XYpyvoUFZCynY0GTR8PBryctPoBjwEvZw2gJijsqHORZkN0q74YDtT10sqwH8LCEr6W8LCDneasTKdkjvsnv9nYfzCtc3PlihWwcO7MFBg6MUnTjjkw6A5NHWp4pjpWE2q+FK7pyT5FTlEawGxnq4HY0t2ZwDPLVymm5crgmENjNBBb9sP36axNILq+j1aue/fuaNzmJUweNRSPI8LVbqAUUAooBZQCSgGlgFJAKaAUUAooBfKNAgpuzjebKncT5U3At99+G2V9fND/tdfw8ZtvGk2g9EsvoW3jxvh5UvqloMzN2LdTJ+G4vH/ZMnOr4HZQEGp2746G1avjv2+/zbDeGyNHYvP+/fhv6VI0rVXL7D5YkGDzF4sXY8Abb2DK8uWYNmgQRsydCxg8pLOoUVVYKaAUUAooBZQCBUABTuLhJB0ZTZs2Fc57KpQCVEDBL9n7PdAHUeksRUCUq4cQyCD0HHAzQDz1qlO3Di6cPWek87RwMwEvwscSPCMMW6ZMWd1S3ebDzbIjQ2BMH25+9dVXBWAgy+iDt3SE4pLgWXXW5DgIRqxYsQJHjhwx+cDaHLCNQGB4ePoHfQTLCZnT8VrCzd99910ad9Xs2OqEm5cuXYa6devh/Pn0TmOGWhm6uRqDrY2N+3//+58A/4xHWrj59ddfF06o5uiXmQb8/hKEzI62MutLfa4UyC8KGMLN3I9v376NsmXLmtxX+LswevRoTJs6DbakHoA0x3U5oSO3NRDHKNhg3vx5IDg4YMAAkaOdvZ2Y2ODr6yuO+QSwHR1162XrJan/e2fMrdqc8cjVANhPZGQkqlatCurh5ORk1AHbnDZVGU0BdX6nvgn5WQFOxuWEBxkjRozAkCFDsm1IPG7zlV3BY1ZGL/ZD93tO6DD2l5OR+eLqgfr/LycgGsvz6aefBnVyc9NReFYOxly4uWvXrpg9e7ZVvZiCmwmu8rxVReFT4Pnnn8eVK1fEwGkso+/OzHMBFeYr8Mknn2DlylVYdzIYVT2BMnqHhCvhAB2V6XBMR2GCv3RdfhwHVCumAbtRCcDRQKCih+aazIiI05ycBRhsB5Qror1PgJlOxg1LaAAzg+7OQTqwlwBvCR0cfSYUuHj2DH6YNzXdYNb8tAyPbd2FqzLzoGMzgeCqRTVQ2ctFA5vZr5vORZqNEKKmC3V1T6C0HmxNB2gJXxMeZnBchJoJDBOQnv71fPy370CaXCpWKA+/eV+JPmMSAHdHoJxOA46f42Vd6VhN0Pl0iObC7OuqNUUAPDBKc6yuUjQ1X7538xFQvkhqbncjgeAooJgzUEHXD9ugptSiUQkNJmd0eq0Lmr3QBbNH9sfjx7pBmf+1UCWVAkoBpYBSQCmgFFAKKAWUAkoBpcATU0DBzU9M+rzdcZs2bfB8s2bYuXMndvr5mUzWpVUrLB09Gm/rlvy0dFTjv/0WyzduxMlVq4RLtLlRvWtX4eZ8dvVqs6rU6dEDQeHhuLh2LYp7eJhV58+9e7HlwAE4OTpi/a5dwv25OSFvV91dBrNaUYWUAkoBpYBSQClQMBUglEL3UBl8EEpYToVSgAoo+CX7vwdcEpbn5suWLcOdO3dw6dIllCtXTrhTTp06FStXrkSnTp2Eg3HaSAuq7tmzR7iuS/dMWVY6afLfyeIRpfgfsyIjuLlly5bYu3dvCqAnHTTv37+PSpUqCaiErlDWwq6E1wjIEZglOHvg8CE0btQYekZM2lCSk9PA3MYGxhz8/f1FXsbCwcEBCxcuxIcffmh1vpkJSriZ0DShxswAmaTEJAEMpoDjfMqanJxuuxnTVh9AT59T6neGui5atAjDhg3LLPVMP+c2oJP3hQsXcky/TJNQBZQCeVABY87NcqKCqWNjYkKigJo5GYOTXeRxTpbnvnbx4sU8MVqCdIQIlyxZgipVqohj8aFDh4yugiXHzRUG3nzzTRQpoiNfMhgJ67APWXf16tVisj7PVSUgzb779OmT4oadJ4TJh0mo87t8uNFUyikK8LhI0FcG4b3sOL9he1OmTBHn6AUhskMXc+Bmnuvy/gH/WhOm4GauIrBu3TprmlR18rkC/O5yJQmePxBs5vWuCusUmDN3HsZPmIiTAeGoqvfIjmDtzQigcYlU4Jb/JuxMkJnALi9HDwdqbsXVdXUfxACEcOnoHJsI1PTU8iJsTAiZ/5aOw/ycYHBskgY1S+CYzsZsx/beaUycNC3dwJYu/wFxti4CHD4WpAHIdGK+8QjwcADCYoGyRQAPvbl1UfHAsWANeGb+DDoj8zaIhK+Zd3wSkKC7ROZf9kHIedqchdj73940uVQoXxajp3wtwGKOt5KuXQEmRwDlPTSHZQb7PxqkQckVdOWoyR29HAiLMwhB+z/UXLBlrtSd73k7aznZctAArj8E7kRqYDMdshkEprka0/t9+2BIv7fT/B5a9y1RtZQCSgGlgFJAKaAUUAooBZQCSgGlQO4poODm3NM6X/XEG4tbVq7E89264d7mzSZzd23dGj9NnIiu7dpZNb7VW7eipJeXcH82N8YvXYqvVq5EzN60Nw4yqn/u2jU0fucdVChZEgeXL4enu940ZiMVD549i3aDBgnH5t9378a2hQtRtU0boHhxc9NU5ZQCSgGlgFJAKVDgFdB/aKng5gK/uS0aoIJfLJLL7ML//vsvpk2bhrZt26JXr15wdHBEyVIlERwcLOCAQYMGGWkrLdxM16rLly7DxtYmBTANDAwUYAFBMIalcDPrGAJ40n1z//79IOCs7zjM/+/YsaNYNlj2abYIRgr+9NNP8PT0FA/rRo8Zg8lTpqSDm1nt1KlTaNiwocmu6I5H6MUUTMgH5lzqmtCctTB2ZuP84IMPBBzD8egvnW6snj6wndF2MwlHJiaaGEfqd0bC1pnlbc7ndPw/efIkuCy0CqWAUiBVAWNwc0b6cN//+eef0bNHzzTHcv06dLP/4Ycf8pRTsaGjMn/T6Ih/9epVMTmFEzp47JOTbeTvCCdZzJ8/X5TlZzyWbNmyRbj0lypVKmXiCn8fR34+UmjCugSb9Y9/fn5+4neS+lnrCl3Yv7fq/K6wfwPy9/irV68uHI5lfPzxxxg+fHiWB0Vn+Oeeey7L7eSVBrLjut4Qbm7Xrp1Y+UQ/2E9WwhTczPPMX3/9NStNq7pKgUKvwMgZi7Bo2mg81nO7J+RL12YCw8WdNYnoQkwQl9CwdDk+GQJxLV7PWytDGJgAbkIy4Girwb50Eo5J1NydWVfCugSjZVk6PEsnYsK67J+w7vkzpzBu0vR022jW0pWo7u2AGxEayFvbCwiNAZzsgMfxGgAs85Z5HQkE6AxN12bG/SggOiEVSCY8TMdp5stx0ImZEDFz4/9PmrMEJw/sTpNL6bJlMWzi18I1urJurASTr4ZrzswSYmZ7B+4Dvi6p/T+KB248BOxsgBKuqe7OzIHu2HSBlmA4HacJm9vbaO7YEtqmToTDOX7phC0h6i/ffxkfD/wA3bt3T/N7WOi/8EoApYBSQCmgFFAKKAWUAkoBpYBSIM8roODmPL+JnkyCb7zxBho89RQWLVmCwL//NplEkTZt0LtjRywZNSrXEvXt2BGvtm6NZWPGWNTnn//9h9dGjEBpb29snD0bjWrUMFp/57FjePfLL/Heq69i4rJlOPzDD2jaqRPg62tRf6qwUkApoBRQCigFCroCdDeV4El2PAQt6HoVpvEp+CV7tzZBrJkzZwoYa9WqVTh79qzogC5xhDIqVqyI8+fPw9XVVSxfnTYILNulvPXyyy9j48aNKfAYP9i8eTP4PkM6X1o6An2AjG3Idvbt24cWzzwjgGnb5FQImmOpVauWWPb64MGDWYKFJeRLd65GDRthw8YNIDTXvn37NMMwhIENxxgWFpbigGps/ITu6KCt73JtqU6ZlacrNEFHgnxDhw7NsDhBbH1X08y2nf42ou6PHmW+FG2dOnVw5syZLG0fOQg6j7dt01aAhyqUAkqBVAUsgZt53kXne98SvsKSji7I3PffeustATPL3wA6GdNtPi+F/jEqxXE+OVlMcqGL/4IFC4SzMo9t/I3QL8/fOrr8r1ixQjgwE4aWZfTb4qSQ4sWLp0yo0W+D/89j2tkzZ4XrtQrLFVDnd5ZrpmrkHQW46pD+pDpOdhg5cmSWE5w3b55YtaagxPTp08VvSlbCEG5me2w3O8MU3Ny8eXOsXbs2O7tSbSkFCp0CcxYuxYTRn6VcLxIWPhumgbXSdZjgMCFaAruEaxkXH2gwcDO9x2jXIzQ3Z8LMBIOd7TRn5MP3gWJOQA0dWMz6hHUJF9MVmRA0LxvvRwJ0bZZQ9ZFjJzBhysx022Ttmp8RFmcn3JEbltBcoRnxiZqDMvPUD7o7MxdCwIyQGM0ZmjkSLiY8HBSlgcxxiZrjNMdJwJhg8qkQ4K+fl+HQvzvStOtTsjQ+nzoHVXRjpYPz+bD0Yz0epMHedXQQOMd9LULru6hjqpN1VAJwPhRwsANqFde0IQRNWJpRrRhQRHfKz5zPhUL0LZ2wE5MBQtwEuz/u0VHcY3jllVfEqicqlAJKAaWAUkApoBRQCigFlAJKAaVAflFAwc35ZUvlcp5HjhxBly5dMPnDD3Hm4kV8/fHHRjOo0a0bGtWsidWTJ+dKhpcCAlCnZ0+cXLkStStXtrjP7YcP461x4xAaEYEmNWuixwsvoH2TJnB1dsZpf3/h7Pz+lCko6+uL24GB+Kh7d7z/4YdA+fIW96UqKAWUAkoBpYBSoKArQAdYuukxFNxc0Le2ZeNT8ItlemVWulWrVti2bRuOHz8ultglmMHJiLdu3cKxY8cg3ZET4hNgZ58KMmvtEm4mxKUBpV988QWmTp2apkt9WDczQDajXPXhWTnxwRjcTBC7cePGAk6L0LlBZdUJ2dHRUeji5eUlAL979+6BrsOGYQpwJrRMeDmj4OcEoAls3Lx5M1uAX8P+BvQfgMVLFgvIj46CGUVMTIwA2mVktu30Ne7WrZtw+84ojIGImX1XM/q8WrVquHLlSlaaUHWVAgVSAbq107XdnEhK1JzVCefeuH4DFSpWEMciHv94fKKTMY8NhJ7pcEzAOS+7FPM8knB3z5494evrK/6frtOGvwl0W5VjIUjI46OxiSb6qwSIX8DExDQOzvydrFO3Di5cuJAjx3BztmF+LkN3bYLmPO/nagYqlAL5SQFD4JYTynhenNXgJDA5STCrbT3J+lzFZcyYMXjppZeynIah1pyUwkmZ2Rmm4GZeK3F1AxVKAaWA9QosX75cnGs9fvwYhHOPB2uwsYSYCTBfJLBL1+NiWj+EigMeA019NWiYQVdnQrsEm+nOLCHck8EauCzdnVmWLtCEc/k+HY/pfExA+VQwUM0TKO2mtfnv4dOYMX1ausEt/v4H3Ix2EQAwgV5CwXQ1ZntliqQtTjA5KRlo6KO9/zBWc4Ym2EynZ8LDHE9RJy1/wtk+LqnOz2dCgLgk4OAfy7Hp761pGvf2LQW/BXPFWFn3bKimB/OSc+vOhGrQNZ2oGfFJGijOvFlP6sw+qHNsAlDLC3Bz0LQk2MyyLCfdmelOTa1KF0l1nmbbJ4I1Hep6Ax06dBCTeiZNmiQmg6tQCigFlAJKAaWAUkApoBRQCigFlAL5RQEFN+eXLfUE8uQF7oB+/fAgLAzP1q+P6YMGoWq5cmkyqderFwLDwjJ0d87O1Dft24duY8bAf906lPLWTWu2ooMfN23Cdxs24MTly0hITISzoyNs+UAwKQleRYuK8RZzd8c3hLarV7eiB1VFKaAUyC8KEH5avHgxCGlymXkVSgGlgPkK1KhRQwA0DAU3m69bYSip4Obs28q///471qxZI1wo6eZLWIDgMMFguu14eHjgzz//RIsWLbBjxw4BHZ0+fVovAQ2EI+BMaIxLkBYrVgyp7yIN+JYZIJvRyCSMRlf3q1evCnCMcPMzdG7mE0Fd9O7dO2VMBNvsaE+ky9Fa5UaNGiVcPRnsi0CbKWCa+gkATgd801WaTsbR0dEZwm6Em1evXo3OnTunuNZbm6+xesxr8ODB8PPzw+HDh0HnuYyC+TJvOWZzcpGaHDhwINP2zWnPkjJ01g4MDFRAoSWiqbKFQoHWrVsbBQwMQd0d23egXft2IOBMuJnH+rp166Y7hhPqokumbDc34Wb+LtF5mXD1999/L1YWePXVV41uR15/cbILj638HWvUqBE+/fRTUc/w+C1XBOD7/P/x48dj8uTJ4v/1x7d06VIQWNSv/8svv6BHjx4pTs+Evm/cuIHq1aur41Gh2MPUIJUCmgKGwO17772HCRMmZIs8XIWE51amgsdETkjjeRv/yhffz6gO3fhNvViP1+LGXtY4cvL3RJ5XZlUUQ63feecdAdNlZ5iCm7lyC8FMFUoBpYD1CqxcuRIDBgxA2KNIHL0PlHTT3IDFcYdOwmGak/BTnoC9LRAcDRD4beCTCgDzvbAYzaWZbs90aWawbmS8BkHLoDN0YJQGHBMwJsRLp2Q6DpdxAyrp+ibAe+zqfcwcMyzd4N4bPRNP16oAV3uA7dGtmQ7LhKr140KYBi83KaGBz8yFYLHsl+Ojg7KHo/YZy9BJuYRuTvGlB0B4HFDfC1jk54cdu9JCwpwo8u2Cr0XfHCtdn+nOLIHvK+EAXa/ZP7WjPhJs5i0Tgtycms7/Z/3oRKBqUU0/As0sS2iamsqcCDwTbPZw0raJDI6VmjXWjbVdu3bo9+l49HmtvTCqyM1rBOu/jaqmUkApoBRQCigFlAJKAaWAUkApoBQAFNysvgUZKkD3iZljx2LvkSMIDA0VcHObRo3wODoa9nZ2iIyOxh979uDSr7+inK/eHYkc0nXvqVN4+ZNPsHzcOLzRtm229HL0wgXsO30aC9euRURUFCZ9+CGWbdiAo3Qyq1EDcHTMln5UI0oBpUDeVIBumAEBASK5f/75B0899VTeTFRlpRTIgwrUqlULkZGRIjMFN+fBDfQEU/r111/x2WefieXlz549+wQzKXhdDxkyBN99950Am+nQSYiLLs4NGjTAN998Ix5CpoXCUjFmghR0X+JDLH24WbpfZgVsptKyX7rXbdy4UUBnhJvpoKbfNl2W+W/CvJobpwQ7+BjP8mBbBNUIyjF4bCL0ZwpulvAz+2cuDH9/f3C5cmN1COTyISU1Z3m2TxftrLpNGxvpwIEDBdxsCOwZK7t7927wASXD3G3HnAkZc3JXTuSf0dYjwPjgwQPLN7CqoRQo4AoQJpPnU3KoYhJGUnIaN36+p7/f8v8JJtDRWD94PGCbPG5dvHgxW8EF6YS8YcMG8PcoKCgoZRUPusERUiZ8zVUECO3FRMegaLGiwkXaEKCQEzQ4htdffx0NGzZE//79Re6chGM4Jrry09WZ7aS4U8fGwdXNNUUXusPTJV4/mDNfXLWALq3Ui78Xly9fFjmqUAooBQqHAobALSe3c5KEiuxXICdBcpmtKbiZkxBpYKBCKaAUsF4BTuh9//33setalAB7axbX2iJ8fC5Ug4afKg442wNR8cCRIA1+LqtzSKYT8p1IgHOYiztrrscM/4fA/SigmS/goLv0fxgHBERon5f30EBi9nM0EHB31PphEHZm/VIuiej1Vq90g2v9fEcM/rCvaIsgMAFgAsuEk2UQDKZDdJOSgJOtBi8HPNJcoenSzHERbCYgTddoAsmEpKVr9PX/Z+88wKMq1jf+JiGQQnqj9957EREQC0qTJnIVFSyIXvyLYsF2FTs2FCsKIoIg0rwqCCp4FQu9E3qv6b2X//PO7GQ3SzpLyJLv0zwJZ+fMfPPOnjlbfvNOAnAmSTtOM7cpU/+Dvfv258ulW5dOeOGZJ5TjMiHoNkFWx+rjibqvhI1ZL4PH2DfCzMzX6MLjhLTpzmz0O5YA5aRNTY0bNYHnnVH8LAZoF2TtL3Olc3YXGyftHr2uwe1TXsFjo/urxYhcMC8hCogCooAoIAqIAqKAKCAKiAKigDMoIHCzM4xSBcjx/VdfxRdffYXA6tWRkpaGau7uCgQ+GxWF2MREdGzeHJ9NnYo2jRtf0mwzs7IQOmAAagQGInzxYoe19d8//sB9r7yCdyZPVr9/nzULXW65BfC1W9rtsBalIlFAFKgoCth+6fLjjz+iTZs2FSU1yUMUqPAK0N0pIUF/CyFwc4UfrnJNkM8LAs6tW7dWzw0JxylAwJaQKHVdtXIVDh85rABXbhVvojBolWMybNiwC77EchTcbNo3oC1/E9R98MEH8wlABzq6Em/ZskW56IWHh1+0QGyLkBr7Qpj6yJEjOHPmjPp3Qe51x48fVzoSVmY+BO/ousdFT7bQsJnf6CTarl079W1hBt2mLa7PF524TQXMk5Cfp6fl299iKmeedKez1bu4fIwel8OliXA9v0SVEAVEAasCvH4JFhC+tQ3jVFzctTpjxgz83//93wWLFTiXTJ06Vc3BhPc6d+6M6tXt9uS2Gwi2uW3bNrWAhu0SjCZo3LVrVwUDM0+6y/v5+eWry9xDFXTsBwAAIABJREFUChtX1suFDXTtXL16NRITE5WjP+FiLoDh3Mv54czpM9izd4+alydPnpy3+MTUy3maC2K4cMoskLFtm39zBwPOi7ZhnOnsQWbmxPuAhCggClQOBeyB29tvvx2vvvpq5eh8OffSXuv77rsPzz77rEOzKAxu5mIZ7nYjIQqIAmVXYMmSJbjjjrHYdCoVbW02Tz0QC0SnA20twC5dhzecA4I8gKaWdWmEigkhE9Kl+3FNvdGQAm3pWtwpVB9npLBsnAZzWY71MLZHQr3bbh+i/03YmWCyvwcQ7AGMGnsfkpMSL+jg7fdPQp/evRQATNi6qs0atlNJwCGb9lknXZiZiwGFD8QBnm7a2ZnnZucAzS0vFffFaidqgt6B1YCY2DjcMf6BC3IYOWwIrr3lX4hI0S7MARbHajpT04mZYLTpJzUhYJ2Zo8FmTwtrfCYZOJesHbMNWM2yMemAj3t+N2rWSXfm9sFANUt/CZCzb4SdAyyaxqYDvXv1wutvvoWRN/VDfHy8+hxEQhQQBUQBUUAUEAVEAVFAFBAFRAFnUEDgZmcYpQqSIx3L3n/rLcz58kuEBQSgZf36qF+zJvYfP459x47h+LlzaF6/Ph4aOVLBzueiozGwVy+HZz9t9my8MW8e/vr8c7S3c+QpS2MHTpxAz3vuwVcvvoiPlixBr/btMfWFF4AQy6cnZalUzhEFRAGnUYBuFGvWrMENN9yAMWPGOE3ekqgoUBEUoFOsceEUuLkijIjkcKUrULduXQWFEX41bpmE1QwgTNirKDdeun4+/PDD+OCDD/JJ5Ui4mXWxHQbzIbDGnI0r0O5du9GufTsFWc+aNUu5czrKOdPDw0NBynT0pDMnIb327dorB1E6hEZGRuLnn39WLlSEn4OCghQkt2HDBgWLE9jjnBYcHIzo6Og8l1HqS0hixIgRWLBoIW4ddavaKtbRMW/ePIwdO7bEjsp0kCZ8aKIo92bzvKA7LHUqb9dm5khH7x9++MHRskl9ooDTKzBu3DjMmTMn33VpXJqLu1YJInNOsy9nOx8Yh2dXl8JnLhdbWzuLE39xbZdGeJOPfZ28J9HxmbsO8H5GF34C1QMHDgS3RLed34rqI8vx8eysbLAvtlC4AqEtbtK2OROAW758+WWZD0ujnZQVBUQBxyhgD9yOHj0a06dPd0zlUks+Bey1piv/008/7VCVCoObZVwdKrNUVkkVeH/+d3j83tFIT0vLU+A4nYCTgdaB2uWYQQiZLyEJ7DLoIkywmeCwdxWgro8+Hp2m3YVbBQFhlnW8dElmWf4O8gRqeOmyhHUJ/NLd2Lw8ZduEjQlAE/xd8s0i/PT9igJHp4q7O/z9/JCclITUtDTM+uAdeATWUu0Tyg6xtEMXZDozEyBm0MGZeWfkaMdpBoFj/sn8CTfX99Hu1HHxCXj2hVdw5NjxC3K4Z8JE1O3QBw39rI7LdKfeFgE0C7DCyoSd6Q5NzVinAb4jUjWETai5gcV3iccIOxN+Zk4mP+rHeqh/dYsTdEKGHpcmfkAty7pGOkNvOg/8e0h3zPpoJvr164eoqKgSL6qupJeBdFsUEAVEAVFAFBAFRAFRQBQQBSqQAgI3V6DBcKZUNm7YgMP79sGrShVs3LgR58+dw6EjR3Dy3DmcjohA1SpV4O/jg/OxsWjdsCFqh4Tghu7d0bVVK3Rq3hxV7bZNLU3fYxMS0GTECAVWb503rzSnXlCWTtC9J0zA6OuuU1DFwjVr8PfKlUCdOhdVr5wsCogCooAoIApUBgXoAsgPxBkCN1eGEZc+Xk4FbEE3ulxeddVVePPNN5Xb565du3DL0FuQkpoCutBpqEsDxrDBcAl4/etf/wIX9tiGI+Fm1msgNkLGXEA0aMiQvEyYG3/4hdpff/2FzZs3o2XLlg6RduXKlRg8eLByXu7bt69yCXa3uBF1bN9euXm+9c7bCN8bDk8PDwU3EwqkHoT+ru59Nd6e8S5ys7Ph6+Or3FDXrl2L5557DgSP6Y791ltvKUdRRweBPB9fn1I5G9OJmwCHicLgZjO+u3fvdpjWZe0/n3+jRo1ScLuEKCAKaAXoYNysWbN8cnABRsOGDYsEb+lIzHI8t6wgMucN3kc4LxfnwOyo8eL9jE71XITCtnmv4Hz99ddf49FHH81boGIW7BQHNZu8TLn//e9/uOaaa/I0KQysNgtainPHdlS/pR5RQBS4vArYA7dctPbOO+9c3qSu0NbtteYuLk8++aRDeztz5kz1utw+7rzzTrVjgYQoIAqUTYGoNOCrpT/iqfHD1Y4ZDDoB02GZrsXGdZiwb0I60K2GtR0Cw2nZ2kG4oQXMpTvzlvNAPR+gvs0mqUcTgJRMDUoT7mXQnflsCtA11Oq6TJiZADTPpxsyXYpre6RjzLiJSEstflegx598HK51OysnZ9MOXZAJMhv4mv8mZJz3Y3FSpvs0XaCnPPMCTh89CH9fH7WQm3BzQcHvFye99ila16ye5wZtwGKCxo0s/ScsHcnUXYBQTyDQuCunAUcTAa8qGk6u4qoBaOrKv5v4ayCbQSfqk4l6TIw7dFoWsDUSCPPSztUM9on6+1YDHhjWF3TS56LCc+fOqQWFEqKAKCAKiAKigCggCogCooAoIAo4gwICNzvDKDlhjsuWLVNbkf/3v/9FgwYNEBYSolZLc7ujzIwMtGzUCIOvugoDundH/Ro2n4CUsK9PffAB3lm4EN+99RZu6tmzhGddWGz4k0+iblgYHhg+HG3GjMG2ZcvQbujQMtcnJ4oCooAoIAqIApVJgW7duoE7OzB69uyJRYsWVabuS19FgXJTgLAuXYj55eKKFSuU0zBhLDqgrVq1CrwWP/roIwUVWyGwC+FmJszzCJPZwmKXCm6uU6cOTp48iVzuM0tnp61b8f7772PBggUqB8JtjoTKCDcPHToU999/P5o3b66Av+tvvBGHDx9Gi2bNVHu5ri7YsmkzQkNCEBAQgNtuuw0PPfQQhg8frhZtznj/PeUofX3/6xSEy/c11157rVoI+cwzz2DWZ5/hnvHjywwSFvWkobs1cyxNMC8D7hXl3Ex4sqkDdr0pTW4FlS3OXfxi65fzRQFnVICQMkFf2/jyyy9BQMt2rub8YFzwWZaO9FWrVs0Dk8va961bt6ptqVu3bl3WKkp8HvvARSPcNYd/cw7j/cDX1zdv/iNoYcBnAiT8MX1lQ5xHOG/fcccdoIO9PZhN2O2xxx7LBzcb7Wz1JFTNoJu9hCggClz5CtgDt3zNyNelEo5XwF7rSZMmYcqUKQ5t6JNPPsFrr712QZ0E95599lmHtiWViQKVTYEff1qDYUMGqV2R4tOBndEalqWbMIMux8cTga5hVtiWEDJBXEK4hHj5m2AtHYMJ3zYPsKpIMJdlvd2tEDRdoQ/GAp1CNIjLiEwF6ERMt+JYAsGp+m+29cv6TVg66+1ih6b/8Dtwy5BBebAvXZCTLHXyZILbqVk639h0vTScbRAw5nG6IL/71P1ISigYaLZN4MaRd2LQoJvz2spl/yMAn6pAS0v/6eJMmJruy/7VNIjMIER9IE7n0SJAA+J0sN4fy91JgKb+VnfmqFTgYByUO7RxvKbWBJuZN921TWzXfhToEAxw9725c+eiT58+6nMavv6WEAVEAVFAFBAFRAFRQBQQBUQBUcAZFBC42RlGyYlzJICxadMmBWBwC2L+m6uC+cMPR/gllEe1amhQpw68q1bFs3fdhcZ16qBFgwZF9jo1PR3NRo5EaEAAtpTRvXnCa68hke5AL72EPg88gCHXXIPH3nwTuAhXaSceKkldFBAFRAFRQBQotQIEms+cOaPOo0Oe7fbhpa5MThAFRIFCFaCrr4uriwK6CIIdOnRIgbv8OykpSZ1HMM0+bGHXolw9CXY88sgjeZBsWYbC1P/333+je/fuqgoCYwTSzGN0a/7jjz/UewC+F3A06Eon5tjYWFUv33fQXX7Lli0qDwJwdLjmY/v370f16tXxxBNPKNC6SZMmyrWIj9808GYsX74CfXr3Vq7TdevWVYDExx9/jOPHj2Pxkm8xfPgIGz/ssqiloUS+PyIwffToUQVXE1RPKMGXpvYtsi+szx5uNvryvVdoaKgCCAlLzp8//5LA2SVVwtHjXtJ2pZwoUFEVKAhutnVTZt4EfOnU/s033+hFKlnZyMnNyYOdy+LczDoJhk2dOtWhC02K0pnXf1JiktppgPOSbd7MRzlWN2gItyp64UZOdo66/zFyc3Lh6uaqtDALY06fPq2cO3/88Ud1LusksMgFP7ZO1KwrOTlZ3SsJiPMxHvPy8lLHHbnQpqI+zyQvUaCyK2AP3A4cOFDNFRKOV8Bea+6GQmd+R8bs2bMxbdq0C6r897//jccff9yRTUldokClU2DdunVqIVpiWiY2nwdsXYfp7LwjUsOyQZ5aGsK2J5M0kEsw2LgLb4vUEG+7YKuE51MAAsYsw7J8PIZ1RgGtAq2wL+FngscsQ0diuhTzb8LOBH5bBwGnT53GO2+/hYhzZwsdo+69++E/j05QjxNepvsz66ErM2Fpwtte7hp4zsrV7s6+VYHMHGBnFODukoun7h9T7HMgtHY9THnhdbQOdqUhswqC0QST21v6T6fqIwlaJ0LItS2O1ewf+0RAuWkA4OMOZOQAh+KAjGygvg8QYFmLR13CY4FaXvmdsHdFA5nZQIcQwPLSGcpdOwPoGKL727ZtW7zz+dcYcWMfHD5yBCGB/sX2SwqIAqKAKCAKiAKigCggCogCooAoUBEUELi5IoxCJc2BX8DT7ZFOC9ySeufOneqNf25ODqp7eqJhrVp4dtw43NijR4EK/b1rFwg5X9ulS6kVfObjj/H9+vXYuWAB+Pe2Awew8qefAB+fUtclJ4gCooAoIAqIApVVgauvvlq5fTD69++POXPmVFYppN+iwCVVwECrhLHorEmA67rrrsMHH3yAxo0b5zlf2rv+lhRuZjnCXvxdlPtvUZ1kboStCRUbSKxNmzbYvXt33ml0GOU20bfffjtq167tcMCWMPKwYcOQkpKioO8aNWrg2LFj8PPzQ4uWLRG+d6/qJ11K/f39MWTIENAdlfPXZ599hltuuQVhNWuohZhLv12iFmkSelu8eHEedJyckoJqHh6lgpvPnj2r4JlXX30VNWvWVG6j/OFYEtYjuEdYj9BdSZ2b9+3bpxylCfYxWPfzzz+fb4jYf/M4wezNmzcriLosEGRpnuC8N6xfv77QUwRuLo2aUrYyKGALN6t5OCcX/I9zqm3YOjdznuOcynnvpptuKtF1zbpZBxeXfP/997j11lvVfF0e1yTnOO7sRfCYUdA8xDLs0+eff553T2I5gt5cgMMFLJyr+/bpq7bxZjB3+z6wHs79Tz75pJq/WQePMejWzP7zvsDjf/75p9r9gFoL4Fz81cYFOEuWLFH38BtvvLH4E6SEKFCBFLAHbgcMGIBPP/20AmV45aRirzXBZgLOjox58+bhueeeu6BKOvc//PDDjmxK6hIFKp0CfC/Xt29f/HkqC35VgRYWJ2A6GdOJuYEvUM/yNRqdiI/R1DgXaGzjLrw3RjsPdwnLe9mmIGZCym6uQBN/DTinZAFbzmtQ19TJ847EawiZcPDReP0YX80RGKaLMV2fz3Cdd24OjoTvRPL5Yzh89Lh6/56amoZsdy8Eh9bAsOt6oHXLFiqXowlWV2Y6QROypnsyoWdGqCcQaIGId0UB6TlAzZxI3DdxUpHPgWsHjQB/2oa4KoiYER4DJNn0n5AywWbC3CxDDRlZFog51QIxm/bZf+ZVtzoQanF3Zh8IgQd7AM1snLDp+ExtCTETnGYcSwROJQKdQjVIzWjeohWmfrwUj47sjYMH9qvX1hKigCggCogCooAoIAqIAqKAKCAKOIMCAjc7wyhVohzj4uLUl+6vPvss9h48iPikJGRkZSHAxwftmjRRP9d164ZBV19dZlUenzkTBKOXvfEGft20Cc998gk2/PQTgtq0KXOdcqIoIAqIAqKAKFAZFeBWhoRHGPLlcGV8BkifL4cCBLnoIhwYGIixY8cql0puv0yw1cBi5rc9qFwY1GocoQl3lRSute876/7XHXeAoIHl+7x8wByBKDokP/XUUwrsDQkJKRGMVxqN2Y/ly5dj5MiRqFOnDvjegnMUtbrvgQn46MOPUMXVFdu3b1dbsNJlOjg4GNOnT8cbb7yBdu3aoUOnjtiydSt6dO2GqKgoBbzR1TQxMVH9NoCcfV4GDuTv8PBwVd+KFSuU6zNhDgJ7tm6jBZ3PtgqrX5fXcJ7eLBfK0XTB1wtA0PmlaS8pR9O1a9eqftGV2s3VTYHThOCpOYHuSw0208X/999/L3LYygOkLM3zRsqKApdbAXu4ecKECWoRuD1sy8UjxqU/bzFKLlkSDfgWd33T7XnturUKVjHuxeXVd86HBJuLy5Hzc/t27fPcmtVcl5OT7/723nvvYfLkycrtnq6CBdVpXPobNWoEd3f3PKdmo7XtfZKPm53FyksPZ23HOKXyHsrdDiREAWdSwB645UJBPqclHK+AvdZTpkzBpElFw4GlzWLhwoXqfYV9PP300+B9VEIUEAXKrsA///yDXr16Ydv57HyuyxvPQ8HOzS1gLWFngrUMugsTFGYQzD2bDHQNA6paYFs6Np9J1o8TTq7uDuTkAqyTQG8zi4lwchZwPAGo5a0BZtZFuJeALl2ka3rrn8Pxuo5cAA0toLDpsQGr2T6DYDHLs06/atrNmE7ThJnp3kzgmLmHWSBiuigTFqYLck56Mr778SfsO3AYZ6PikJmZAb/qXmjasD4aN2wA7/qtUc2/BtoGWyFitsX+sn2CzOwn+2GC0LZxVyZwzXxqeFnbP5UERKcBgdWAuhaInH0gcE096VptzicsTpCZudLxmcG2D8ZpJ2wDSxOMbtGiOeYv/R6jbrgae/bsUZ/JSIgCooAoIAqIAqKAKCAKiAKigCjgDAoI3OwMo1QZc0xPBw4cQHpCAjbu3o35q1dj3ebNOHH+PHy8vBTsfH337nj41lvRvH79Eil07OxZPPz22+pLrbnPPYczUVH49tdf0W/AAPQZMaJEdUghUUAUEAVEAVFAFLAqQNfQw4cPqwODBw9WAJ2EKCAKXDoFCHKNGDECLVq0UE6Ts2bNwm233aa2XiZEQCf1mjVqIiRUg8OlgZt5/o4dOxQIXNaIjIpGUFBg3jasph7mwdzvuece5ejboEGDEoF4ZcnDgNoEqQk60704ICAA6//6E92798De3bsVwE0onM6dBIHp3vz1118rILptu3bKXXTuF1/Ao5oHHpvymHKfpkulh4cHYmNjFahsCzP/9NNPCmD28vJSUDHHhPrbuqyavhQG9hFU9vD0UK6ihbuH5oebbfWhIx7dqulUR0doQmdfffUVVq9ejR3bd+h8XV2KBQuL0pxfHJstdu3Lsf65c+eiS5cuarvbIuthLrTgkhAFRAGlAN34CeEyCoL/CffGxMSoxRgFXXs8ZhZGENLlPMuFHePGjVPXGoFe7q4x4f4J6iLmPOPp6VkuTsVse8OGDejZs2eJ5n2zwIbwdWFh3K25oIOLU3gvPHjgYD4gmm7PpF08vTzVjmHGqTkrM0uB3e+8+w44b5q5iO729jCePD0vVODdd9/FjBkz1APUTEIUcCYF7K9xLvTga0AJxytgrzWd9B988EGHNkQXebo028eLL76Iu+++26FtSWWiQGVTYMGqPzF+WH+kp6XldX1nlF5q28HycjQ7FzgUD6RlaWg4xFMXJcB8IBboGKpBaEZ6toagUzI1GG0g6K2RGv5tazEQ5vtNQsAeVYCaXtpp2bcqEOQBbInQsHNzfw0qu7tp12OCwoSTTTCnCBuwODdXl2ebwZ7aTZoOygSkzyfr9llv7eq6BrpQsw9tgnTbDNXXON1XlmM9DLYVnQq0DLSWJVR8Okk7JpvzWSdzzcoFGvlagW+CyTHpun91LO0TTCYYznyNuzPh6N3RQGaO1soA4wSzCWJTU6N/XLouS+Db9In6b40ARl/VGOt+WQPutMQFhWFhFvq7sj3Bpb+igCggCogCooAoIAqIAqKAKOB0Cgjc7HRDVokSTk3lt3xAsmVJt+WLvg+XLMEny5bhdESE+iKqad26qBUSgnGDBqFDs2aoV6NGPpHo0vz16tX4eOlSvHD//Xh23DgQdL7x//4Pj0yahIkFfBBaiVSWrooCooAoIAqIAmVW4Prrr8eBAwfU+cOHDweBBwlRgAqcOnVKfdlMR0UCrRKOU4BuwlxMQDclQrDcSpQQK7eHp3sZj69bty4P2LIFnIsCSun+S5dh1vntt9+WOGHWyR+CdbZtmb/52MGDB9GxY0dVZv78+Rg2bNglg1t//fVXLFiwQG1Hu/Drhfhq/lfgXOXi5oq4uHh8+803Cj6mU6ePj4/SiyB0s2bN1L+/+OILBZDTIZXO2HQbNZDyhx9+qFyyd+7ciZdeeknBg3Shu+uuu+BCYtBFg4kGTi6NQ/F3332HQYMGKcC8U6dOhehfONxsXFypN8HFvXv3gu6mhArpCM0w49SmTRt88803pQIb+UWzgZvtsWS2zcUuM2fOVGB1ceByaXQp8RNRCooCTqoArwfCtgrGJfyRnq5+vL29865dzp379+9Hy5YtC+xlSa8pWwdkMycUJ5txnDdzOnPjnEiHds7tXFTBILTGBR62iz94LiGzF1544YKdBQprl3D2ypUrccsttxSZmu0CE/7N+wp3NahVq5aah1mPcWxOTU1Vi1Ns3ZrNfMj5keWrV6+u7hsSRSsgcLM8Q5xZAXvglnAXXzNKOF4Be625+8kDDzzg0Ib42pmL+uzj9ddfx5gxYxzallQmClQ2Bdb8sxMDruqQt3iOYG9UGtDD5ms3ArvxGdr9mKAwIzYd2BEJtAjUTsQMgrmEi+nIXMfbCgbviQZSs4EuoVZ1TyQCVVw1LE3ImethCenuiOL7bO1OzHZZZ1q2Bps9LM7QrIWwMMFlgsXGxfh4IlDVVefIc1gvc4tK1e1WcwPqW5yfz6XoxwkLEzg2YdyV6exs+kV3ZULMTfytZQk674zWULJxk2YZOlyzrwSbCVIzziUDEamAT1VrWerHtryr6HoNtL0vFohNA9rTHdpyfmKG1qWej/5hENzeHgnU8AYa++ljBLM5JoS4B3dpgN9++025cm/atEm9bpYQBUQBUUAUEAVEAVFAFBAFRAFRwBkUELjZGUapsucYFwfExAD80sxmtfjSdeswa/lybD9wAPVr1IB7lSoKWk5NT0f9mjWRnZODY2fOoGGtWhjerx/uHToUtUNC8POmTbjnlVfwf5Mn47HHH6/s6kr/RQFRQBQQBUSBMiswYMAAhIeHq/NHjx6t3PMkRAEqYOAX2bbc8c8HW9CMsBbdhu+8807lpEjX3FtvvRX33nuv+iYwF7lwhQsWL16sjis4zeKYW5BvLmEvwtKs98yZM0Umb5yhCY8NuWUoTp44iX/+/jvvHAXbKt5XEb8KMmOu/H1g/4F8DpuOVImAIOHiatWq4ejRoyDIS2e3Pn374vXpbyApPgG9e/dWkKBydF7/B9zdqyA9PRP/+te/FNw8fvx4de6rr76Kjz7+GH//8w96dOuGyMhITJs2Dc8//7wCoRUoZ6NzWftBrZYtW6bgPLqpEk4uGBAuDG4mdgwkJiaBW3ETIKFjNWlkujUboNHFJRccFAUpW6D0gnPW9dn6NF94RIPcjK5du2Lt2rVWTYoRoqQgZln1lPNEAWdTgNc9AVzGokWL1HzNBSecp+yB3PLsG69VOkbzN+cRznuElzlPclGHm6tbPkd4M9ecPXsWdOkkbExouKSLbNg3e9i7JP01izs6dOigFogwCFtzQQr141zPfvBvwtlV3KrArYqGms1ilMTERHh5eqnjEoUrIHCzPDucWQF74LZHjx5qsZeE4xWw15oLKLlA0JHBhTATJ068oErOU1x4LSEKiAJlV4Cf87Vq1Uq9/jub4oKDsUDnMKC6BaylszHdkQkA17WAtXQH3nQeqO0NNLSAtcyAcDHBXILBBoKm43FkCtCthhXgpVtxRrYGjQkO851m3eramTguA+gcAtDVmJAwHZQJEJt82A5djOkuTQDYOCszTzomE/6l6/HReP0YoWy25eaqgWNXFw1mh8foeglXm2Au0Wn53ZUJJbMu5mpgZ4LFdJcm1Gw0Yb48l30h2O1XTdeqYO9MDV0TYibQzX+zfTpSE6420DZhbmrTKsjqhM3+b4sEgjyBZv66TurP/ntVAVpbnLB5fE+MdswmGN6ofl389ddfuOqqq9QOVvx8RkIUEAVEAVFAFBAFRAFRQBQQBUQBZ1BA4GZnGCXJ0apAZibAL/1S+ElGKm2N8MU33+DQsWP47++/IyU9HX06dkT31q3RrmlTtKhfHwG+vkhKTcUf4eFYsGYNft+0CW+/8w5GjRolyooCooAoIAqIAqLARSgwcOBA7N69W9Vwxx134JVXXrmI2uTUK0kBgV/KZzQJmRHAfeKJJxSwRdfMzMxM7QDKb+oY2TkKLqM7Jb+cHHnrKHwxdy7cCB3bBR2gDx06hIceegjXXXedcv/lOfZBIIyQMt2O6XQZlxCPQwcPISgwMK+oguEscPPZ06excOFCBZrR7djePdTRatFVlLAg3VCZB7Xhwsc27drihv7XKUCP7nHUa91vv6Jvn76488678dNPP+GHH35A48aNMWTIEBDQ69KtGw4c2I8zp04rbV9++WU899xzeSkX7qVcul7xi8WTJ0/i3LlzOHHiBLp3715ABYW1po9zqGxdoy+sIMcCSlqeG4WmWHyvDKDM5wEdUhnFOTab5gRuLt1zQ0pf+QoYuJlz45dffqnA4YiICL1IAcD//vc/teCErvMlvc4crZo9oEyHfy7I4DxbULAvnDMPHz6cD9IuSV7t27fHtm2AbOxaAAAgAElEQVTbSuwu/9lnn6lFPWyT8zNfj5p5hnoxD2pIh3nOkVu2bEHnzp3zypic3nzzTTwuC+CLHCJ5fVeSZ7CUqagK2AO3XBS4dOnSipquU+dlrzUXBjp6N5+ff/5ZL+i0C77f4G4oEqKAKFB2BfhaiQtYzyekYXdsVbQMskK8dDw+maRBWzonq/ehucDmCO2W3NL6kQAKgqCPxQOnkjVsa+BkukLHpen66J6caYGcCfayra6hQGKmBpDZFkHpAAsozPYTMoBtEUBjf6BOdZ0TYWceZ51ck0tHZLokE2omSEzgmGBzVTeALsgEg2tVt7oosw7CyQSLCSUbJ2bmcCBWuyPXt4DddEfeeB4I9gCaWmBjQs3GHZrnGwg6Ph04lqgdlVsGAJ5VNHhNiDs9C2gWoPNkUL/jCfqYcZLmeXRnZt5tLRAzj9EJOysXaBOkoWkGIWr2oV2w1ppOzav+2IJB1/bEn3/8jnr16pX9SSJnigKigCggCogCooAoIAqIAqKAKFCOCgjcXI5iS1OXUAF+k5+Whh3bt+PpF19EUnIytu7YATdXVwVypKSmolu3burLN7o60MlNQhQQBUQBUUAUEAUuTgHCf8Yhb9y4cWrbcQlRgAoI/HLpnwcEtwgt02mZgDHh2NU/rcbgIYPxyy+/wMfXB1FRUbjh+hsQGxuL2bNnKxB69erVqOLuXgDarHMm+EUnO4JgdHCmQzuhXwO2EQjbunUrvL29kZSUpJx/CBBERkWpOo1js6osl8BtDkaNHImZM2eqL88IXl9qOC8nOwf+Af4qPwLgTZs2VduOd+rUSUHNzH3y5MkKyqtVqyZSUlLg6emlgGg6MUdERiA4MAitW7dWgF1Obi6u6d0bf/zxh4LkbKMgR+OyjL5xFuW5LVq0wP79+wuoxrSmH7IYJ0M5MucFR8GA6/aQckmzzc2r21RrxsyA6XwOEPRevnx5qbsrcHOpJZMTrnAFONfSsZ1hrjE1V8IFrm6ual796KOP1MKT4ubP8rq+zD2BixsK+nzFPM7f8+fPV2A25+OCwj7n+Ph4tQjFLNYobvjbtm2LnTt3qmJ05uf9y5xrdhlgDnTn5/FVq1bhpptu0vernFycPXcWtWvXRqNGjXDkyJFiNS4unyv5cXl9dyWP7pXfN3vglm7v33333ZXf8cvQQ3utX3zxRdx9990OzeS3335Ti4Hs4/PPP8f111/v0LakMlGgsinAHUT4XnjNgXg0DKmuIGAGYeGDcRrIpWMwHYcZBIMJ2HYMsSpFsPdEEuBfTUPEDB7bEaVh2xBPa510R27sB8Sla/CYzs/nkzXwy7J0Viboy9+BHtZzWQPBYILFNb2ssDXrISRt4GVC0tXcrA7NdHMm9EyXY7ogb4/SebYIsOZPOPlEoi5Dd2U3FyApE9gdrcFquiuboGMz6ydYbHRin9xdNahs6+58IA6gbA38rE7MhJCZMwFq9o8Rk6b7T3doWyfprZH6cwA6VBv9CVtHpwPtggBvi7v26WTgZKIeJ1NnSGgYPl29C5OGdMHCBfNxzTXXVLantvRXFBAFRAFRQBQQBUQBUUAUEAWcVAGBm5104CTtkilAkINfWAXauMiV7EwpJQqIAqKAKCAKiALFKcBFQwTcGPfffz+eeeaZ4k6RxyuJAgK/XLqB5mtbAsbR0dHqh2AujxGGDQ4Oxrx585TrMN2KlaNmLtDv2n44ffq0KkcQeuPGjfD390fHjh0vSJQLFlg/ATDWQWiaDsb8t6+vr2qvTZs22LhhI26/43a1nTeBXy4oZBDMI1Lr4uqi3KEJB3MrasJkDEJ8zKE4QO9iFSRI3bRJU6xdt1ZBzXSZp9M0+/7oo4+qHwLFISEhIEjH39SrTv16al7b+Pc/iImJyXNKZb6E3gjAXYrw8/NDXFycqprj9Omnnxa41bZuO8cCm+tvk/PDzTxi68zM0SjOqfnCHtm6tOo2NDDN49Rq8+bNaNCgQZmkKC/4skzJyUmiwGVQwBZuNs1zLqWL8BtvvKHmX+WGn5OjXPMLCkLGvA+wLl6bhZVzdPeY15w5c4p15ExISFBwswGh8y2Y4IIM/m+ZZ9hP3ie4gKck94r+/furRT0sS+d+Bu9JtucyTy4G4n2TwfuW0YiLfm688UZ1L+B9rqRQtaO1dIb65PWdM4yS5FiYAvbALcG9lStXOpVgnB+5+EW93rb5u6BjpXmcZfk+gos8HBH2Wr/00ku48847HVF1Xh1//vmnWrRiH9yJRoA9h0otlVVCBTTc3AbrD0SiZ2NN8RK+PRKvAeEWgYCHfvuvXIzpZtwlTAPAjPgM4FCcBoPpOmzAYELABI7rWhyPU7O0u3ADXw0Zsx4CvgSct0XqcwkSs4y7m3aGtgV92dam8xrobWVxjOa5zJPwMo8T8OVLTOZC4JnOz2yfztOEnAlmc9MruiCzbwxC3MyfcDJzILiclg3sjtJ/twqy9nVXlAasO4Xqc1OydPvUx6OK1UmajtGH4tXGWqjpDQRb4O7TSTovOk7Tkdq0T4iawDZBbxMEywmI0/WagDnjeCJwLtmqFY9FpGh3aPaTdTCYI+f537bux6jruqsF7E2aNKmEz27psiggCogCooAoIAqIAqKAKCAKOKMCAjc746hJzqKAKCAKiAKigCggClQABUaOHIlNmzapTOgm+MQTT1SArCSFiqCAwC+XbhQIH5w4cUI5Ke3atUu5EhMWI6g1fvx4cIttbtHMfxNOrlGjBr744guMHj1agWXTpk1T0CxdMQuDxlJTU5W78fDhwxEWFgZu70ygme0RVqBjr2mDMJi3r49yGHXJzUVWZhbcq7orEK9Xr16qPL84Iww7adKkcoFIqFGfPn0wZcoUpQkXPNK1mk5uBrYmNEgIkM9Vujlfd911CgD//fffFfRN12tC3mfOnFHgBB3nCAyWBLQr7egTP/b390N8rIabjdPohW1pJ+bcXOPMbGBADTu7uFi+YQYwYsQILFmyxAI+G7hZYeeIjIxSX2wW1xdbwJmazpgxA+fOncObb75Z2i7mKy9w80XJJydfgQoUBDezm7zu6Ejcrl071euHH34Yb731lgJ3C4OXeQ7n2g8++KDYa9xRUrJNQsWFOTPreYuO8Ll5oLaZ5555+hm8/MrLKlfbRRS8d3G+KW6eKqgPfD1KKNwebmaOPEb9qNF7772n8jFwM/N77bXXZLFeEU8MeX3nqKtG6rkcCtgDt82bN8eaNWsckso///yjXlNu2LBBLZbjPGP7u7C/TTnzOOcpvn6nmz9/mx/zb4ckW0Ql3L2Fr3uHDh16UU3Za83X4bfffvtF1Wl/MhcjcrG1fSxevBjdu3d3aFtSmShQ2RRYtWE/Bvdqrd4Lh4ZqandvNBCXoZ2Uq1vW2tHZmK7IBJsJDzMI9+6NAarYQNDcdHVDBBDioV2QGQSLCS2HEvJ1Ac6naLCZQWC5tjdQz1eX4f5Dnm5APQsUbcaDLtB0MSbsy8jI0WAxnZLpxEy3Z0K9gdWAowna6ZgAcZDFHZkAMaFq9qmq5a00get9sXwdriFuAtJ0pd4TresnBE3AmUGwOyYd6BamwWi2xfYJSxPoNv0hUM3jBKTZNuFmBkFltkW4m3Azg+3viAT8PfI7SVNr/tCxmW7QDELRhJsb+gChFoiZjs/UjPC0aZ9lN5wDrm/ujxPHj6k58vvvv1eLvyVEAVFAFBAFRAFRQBQQBUQBUUAUcAYFBG52hlGSHEUBUUAUEAVEAVFAFKiAChCW5Be5jEceeQSTJ0+ugFlKSpdDAYFfHK864TFCWIRw58+fr6BlBkEtuikvWLAA9913H6ZOnYrXX38db7/9Nh577DEFFhNWZrlbbrklDzBjfeb8wuCxyMjIvC8zWZYQNSGLli1bYu3ateoxBai5uCgIr4qrK1asWIFRo0YpuJrHmGdiYqL6N8Ex5nnbbbc5XiC7Ggn23nPPPWAfmDfhEeZDTVq0aKHg36NHj+KOO+7AwYMHFXzN8j///LP6NyFCLt6YMGEC9u7di3Xr1l3SnH18fZAQn5A3JgU3diHczHIdOrTDtm3b4epqhZt5fODAwfjxR263ruHmwUMG4vv/fo97771fgd7Fhb17sylfFtjQti2Bm4tTXh6vbAoUBjdzviXEbK4545K//o/16NO3T4Eymet29uzZahFKeQTb5JzCe1BxwT79+uuv6n7Qu3dvtciEUJ8t3Mw6CB+///77ZYKbWR+1sp+r2CYBbOZAgHDp0qXqfsT70vBhw+Hh6aHconlPlShYAXl9J88MZ1bAHrht3Lixej17scHFf4MGDbrYairM+XS5/+2335STfVnDXmu+NxkzZkxZqyvwvAMHDuD666+/4DG+FylodxqHNi6ViQJXuAIL/tiPu69to94vcycnAsyEj42TMrtPMJdwcJtgINgCCxNYDo/REG/LQCsEvTVCA8Gtg6zCETYmEE0HYror06GYbsd0dyY8zfPZLuuq6qqdmG0jPBZIygC6humjBKAJEPtWBUI8db50cSZIbByn6aRsgN8DxgU5GPCywNqEkwksp2cDjf0Av2q6bgLIdJUm2GzAbkLFJxKAzqH6fNM+fxO4Zr7GyZr9SMrUTtDNtRG2codmvXSmbmoBvtk+naTZ3zZB2nGawb7QtZmwtdE62gIx05nZOGGfTgbOJgPeNo7ZPJ8u2MylV0MftStX165dsXz5cvW5iIQoIAqIAqKAKCAKiAKigCggCogCzqCAwM3OMEqSoyggCogCooAoIAqIAhVQAbo6cTtYBl1Q//3vf1fALCWly6GAwC+OVZ2QFiFdQsV0SjZwKH8T4uKX+zfeeCOOHDmi4F06e9J5mWVdXVyVExIhL7o1JyUlKXgrIyNDQV50BW3YsGGJEtZorY5c9fUdq9aOzfzis27dusrxko6Z/fv3V26YbJNQdnh4uGqvZs2aefmXqNEyFKIuu3fvBqEVLy+vPHCOx2vXq4uFXy9EfGysmreoX0JCAubMmYObb74Z0dHR+M9//qPgcOZNOPvkyZNlyKJ0p9A50AAahcPD2qGZsDLLENbm7+ysbKz6aRUGDx5sadSMFKFm/m11bia0/d57My+A/nKyc+BKiynbMVZt6Sgp0GzAyqLKC9xcuueGlL7yFTALR4rrKa+d7du3o3379hfAwPbncn7Izs4u1OG5sLYI/nLuK8wZurDz6HZPR/jign1YuXIlbrrpJtVOYcHced9o1apVkeUKOv/5559XbvsFzUNsn3Vz7lf3Mss8x2PMZ8iQIfjhhx+K60alfZyLfQjN8zUH75USooAzKWAP3NarVw9//PHHRXfB9n3PRVdWQSpYtWqVmn/LGvZaT58+Xe0i48igo2zPnj0vqPJic3dkjlKXKOCsCuzfvx9t27YFf1cPa4hDCUATPw0NMwgNE0ImkFzX4jjM4wSD4zM0rEvnZAYBaMLChIBNnErSfxHsJeRc30e7EdOxmO9A6cRM1+X4dO2ITFCYYLAJwsqEeAk2G8dluhrTmbmWtwavCSPT6ZkQcnaOhp5rW3JlWeZA2NrP4oLMug/FAwnpQH1fq7szwWQ6JLcIsPaJsDGdnNuHWMuxzvQcINMCRht359NJQFw6QLCa/SCvTHdm6sJjrQKtEPSuKO0OTSdp019qsCtaQ9kmf+bO/lNjA0ZzTAhL02W6XZCum0EXbT5GrfjZCD/v4G5fdLnnbmASooAoIAqIAqKAKCAKiAKigCggCjiDAgI3O8MoSY6igCggCogCooAoIApUQAXGjh2L33//XWVGt9gHHnigAmYpKV0OBQRudpzqBK8ICRNKptuwgbUIrhFmvuaaaxASEoIdO3bgxIkTaNCgAcLCwuDp6akArZEjR2LPnj0KVKNTZXJyMtq0aYPz58+rvw1oWhJ41RZuNj0k5OyaC1UXXdk+++wzBAQEKGiawF5sbKyaG+gM5O/vrxzep0yZkgeWOU6p/DWdPn1aacA2Ca9QG8LVcHVFTnaWgrLz+pCbq3Tl43QLJdhMMJpungTgysP9be7cucpF2gB3Beui4WYXF6tDMxeYcOwnPjARLvzmV4VxeDZwsv4m2MUl/79t+//JJ59g4sSJ+Zq1dW4uyfODJxfk9lyQe2pJ67tUzw+pVxSoSArQWZ5wbUnil19+UQ7+xS0S4OOcb3k/Vg77Foi3sGuPj//999/o0aOHSqMo8LigPO2h4eL6wvsSF+wU1g7r49x9/PjxfGXoot+vX78iqz98+LBatFNQ3VwsRJCDi4IYBKGfeuopBXuwTd6feA+QEAVEgStPAXvgtlatWmreu9g4duwY+vQp2E3/Yuu+HOfzPvDNN99cVNP2WnNe5XsSRwYXJxK+tA+6cfN1vIQoIAqUXQG+TuL1tWHbLiT7N1POwASFGYRnN50HAqtpJ2cThHsJ/RLCDbZA0HQbjkoDeoTldyFOydKOykfj9W9CuoR9UzKBLmFATJoGivnulkAw3Z1NnEkC6LrcMdQKJhP0JRRMSJpQs8mDgDRBYjoZE1hm8LHD8RoKNrA2j9M9OjINqO0NhHnpsszheILOwZQlvL0tAmjmD9SywNJshwAxIe4GvlZ3Z7YVmapBZQNoUz86KZPipjuzgZD3W9yh2wdb+8s6t0bqnIxzNbUjWM23/gaCZrsEy6kB+0WQm8F+MgdqSjdofj5z8EwsBvXroT7rKGgOLfuzRs4UBUQBUUAUEAVEAVFAFBAFRAFR4NIpIHDzpdNWahYFRAFRQBQQBUQBUeCKVuDuu+8GIRPGc889V27bn1/Rol4hnTNwc+3atfHXX39dIb0q324YcM3Dw0NBzAQwGISaCSZ/+OGH4DbYhIbpXEb3nZiYGAVnEZRjvPzyy8odc/jw4Qq6oCMn63nllVfUF1uEvGbOnKlc122BN7WVqnJl1j8m7OE4W5CV9UZERKi2U1NScfDQQbRr104B1i+99JJaADFw0EAsX7ECQwYOvsAl+FKou23rNnTs1BG33norFi1ahP/+979YtmyJxfHUzaKT1eXYaMB+ER784osvQEfl8gi2yW20hw0bVkRz1lxZnk7ZdJU24GJhLqWmwoIe5/OpZ8/uWP/nesTHJSoAnm7VtqGfiwaMtn9W6JImM7p4q39bnjgE3/M9t4oBLMtDa2lDFKhoCtjCx47Mjdczg4smeK2/9957eOSRR/KaMA7wvBcsXLgQd955J8L3hqNlq5aqTGkAZ8LZn3/+Oe6///4SO72fP3cewSHBqp3C5qfExER1r/r111/V60zOy8UBcikpKWpBT0Hu09ThhRdeUK7DbDMjPQPLVyzHbbfdpvrMe5bVBd+RoyF1iQKiwOVWwB64DQ0NxaZNmxyWFt//cL7la+GS/rYve7kXf1GTRo0aXbQm9lpTG74fcWRwPi9o95n169er18gSooAoUHYFDNz85Zqt6NiuNZrbQMwEc90sYK1pgQAtHZIJQNewgMGEhY8kAF1CAW93XZLQMmFnljuRAAR5audjgrkRqUDnMO18TCi3iosGkg2oy/Oj04DtkUDbICDU0g5dmumMTPg3KVNDynSUZlsEkT3cNHBNGJjljAtyHRvHacLR/CHUbNyR2RbhbDpTm2OEiDef10C2gY0JL7Mtvgtm341jNY+dTubiX6CJP+BlAbQJZsemaRC7hrfWhe7VdHimE7SPRSu2xb6y/y0DdbmsHCA8RkPUrYJ0nYSl6WTNvlNX6skgcM38CUubnHhvWnMkHZMGdcbXX32pdoOREAVEAVFAFBAFRAFRQBQQBUQBUcAZFBC42RlGSXIUBUQBUUAUEAVEAVGgAipwzz33gA6CjGnTpuGuu+6qgFlKSpdDATppzZ49W21nzK3LJUqvAMHlf/3rXwpgtg2CpqdOnUJgYCC8vb2xbNkyfPDBB5g3bx66du2KLVu2oGnTpkhLS0OnTp1wTe9rsHXbVuXeTGdKOlq++OKLanz++ecfNGvWDNdee60Cfw0UXRjczDxs3UIJFTzxxBPKnZkOb3Xq1MG+ffsQFBSk4OCre12NobcMxcMPP6yAsfTMTKxZsxp333mXgklYvjTwXOlV1Gfkh7KtgLB+1P7fQE52Dnpf0xuEI8oLMqGWBM4zMjLyuXPn1yd/rhzj66+/Ps9Bvzi4mTqwHY6zKTtu3Dh8/vksBAYGY/78+ejQocMFQEhZ4Wa6Y9Mzmufn5uRi0TeL8PPPPyuHqEcffbSswynniQJXnAKXCm7m9c7txOmQzMjOykZGZoZyqY+Pj1fHeB/hPEPYwcDOnFu48OXJJ58s1iHadjAIOPNcuuaXZG7PyszC6TOnERwcrO5P9nOYma8UpM2FEq4uGDp0KL7//vtinwOcS5VbfyHB+5afn5+am+Z8MSdvgV5Ri0WKbVQKiAKiQIVWwB645U4j27dvr9A5O2ty9lq///77av52dPC9JnePsQ2+xyCkLSEKiAJlV+DgwYNo07Ytvlz5N267tmNeRftigIRMoCudmC1HCfEeitNgsHFHJvC7M0o7CxvH48QMgO7OBI3PpGjXZT5GqJiuxXRCJuxMIJefR9Ty0v82QRfjLRHaGdm4SBNeppMz203LAs6mAPWqA3Q3Zg5VLI7JdC0m/MucQj01bGyCcPSxRCDYQ9fNSMgA9sbo/Br7WcvSsbo6YWML7K1copP5GlU7WZu+8nwCywz21wDaJ5M0xMy+BtpAyISTWwcBAdX0OTm5wA5C5K5aQxPUhoA2z/ezlGU7bI/9InTN4JjQCbtpAFDTAoEnZOTCr5objsTnYETfTmpRIj8vkhAFRAFRQBQQBUQBUUAUEAVEAVHAGRQQuNkZRklyFAVEAVFAFBAFRAFRoAIqQHe+1atXq8xeffVV3H777RUwS0lJFHBOBRYvXqwch0sSdOI8fvy4gpvpok7QeOLEiWrxwc6dO7F7924MGjQIAwYMUHAr4bPmzZuD7pZ0fU5KSkI1Dw8NsdGt2cXWr9keDtY4cEx0NNb++qtyuKQ7WlxcnDqfDmrcnpt10Pnt0KFDSMvIUN9+uuRoV2k+9vHHH2PGjBkKvCMQZ8DqkvT3UpZhH1q3bq1g8PICm01/CPhFRUWpfzIPAuwE8GyBctu+8zgBvpCQEJw4cQK+vr5FAoV0hV66dKm1X7nAut/W4cEHH1RQejY4/hpINqHA5NzcAuvl8b179yrHacKHBAkJkxBsN5GamqqcYl3cXBWQWMXVrVSw5KUca6lbFKgoClwquNlcv6y/sPmMZX788UfcfPPN6jq3deQ3+pR0LjQLKOLj4hEQGFCiOZT3JDozMziH2Du98zGC14SQCTdzNwPOe8XF22+/jccee6zQYqyH90LmzN0FnnrqKeX0zPsiH6so96Ti+imPiwKiQMkVsAdufXx81GtkCccrYK81F2JeClf8bt26qR1tbIPvfbh4RUIUEAXKrsDPWw5iUK92+O23/6Fnj26qIgLApxI12FxNbxQFOjYTriWoS+CWQYh4a4R2T6brMSMtW7sL0y2Zjsiebtq1mKAunaBbBOh/H44DUrM1JGwcoHk+Yd+N5zTs3NTSDgFm1kkg2d1V/x3sqV2lTyXpdo1jMuFfgsGeVYBWFhdkPh6fDtBJmfBxswANbKdmaQjap2r+snRRZoEOFtiY/TyWAFR1027LBizm+Yf0GkLU8rY6KROiPhgPNPLVIDiDLtZ0YmafbPtLMJmwdocQDWgz2CeC4ISl2U8GYWm6QNOZ2QDf1GXDOV3OwNrMdfu5LPRt5ImszEz1uRE/j+HuXxKigCggCogCooAoIAqIAqKAKCAKOIMCAjc7wyhJjqKAKCAKiAKigCggClRABQhPrly5UmU2ffp0jB49ugJmKSmJAleuAoSyatSogcTEROXgSxiLUG7Pnj2VMzNBLTqsE2olJDt27Fikp6ercr1790ZERAROnjypHM9cXF1VeX7ZxSDkmqs8kzTs/Pjjj+PNN99U/ybcvHfvHoy/e5yCQnjtjx8/Xv0QduUPXae5+IFbUG/ashkHDx5C65YtFUxm6mT+o0aNUq7RhGsbNGhQIrfPSzmiDRs0xJGjR0oE5Tk6D3uImf8mLMxtgekuWFiw3KJFi5SLNsefsA4BPTqhGq3NuTExMcplf/PmzcrBe8iQIQgLC9PjSgC9ALjZvg4DgHNsn376aeXSaht8XpmgGzUBE9bNcOPzihC9HUDvaC2lPlHAmRQoCm4m/FsYnJzflb7wHtMxnYsOigKcWdc777yTBwRfzDW6ZMkSdX+ZMGGCmtOLq4t9NPOCfV/5WOfOndU9onHjxgp0LonTKu9zXDhTWNDF2tVN58b5kHAHIe+BAwfihRdeUD8SooAocGUpYA/c8vUL5xYJxytgrzUhOi6icXRw9xnuSmMbXDjJRSoSooAoUDYFTicDv207iHuvb6cWS/fq1QsRqcCeaKB9sNVxOD0b2MV1uS5ApxDA1UVDyARrCd8aCDk7V4PHBKAJ4hIwrl0d4Pl0QubfBHHp6kzYmKBuXZ/8udOxmUB1myB9PDNH1xnqpcHiIwkaIqZDM4FjttnIT0PL/Juwcm4u0D5Ew88MOkHTMZpgdItA/ZtgMI+xCJ2UeYzBfiZnAT1q6H8zd7ZP8Jh5GbCYYDP7wfwIaBuImXA1geXa3lZ3awLHBLt5bn2b/hK2JghNTT2q6PYIkRNkJizNH8a5ZO1O7eWu9aP+bJea0oWasDaD7dCF2i07Hb0a+anPY7p37w466vO3hCggCogCooAoIAqIAqKAKCAKiALOoIDAzc4wSpKjKCAKiAKigCggCogCFVCBf//733lbg7/77rsKYpQQBUSB8lPA3mGT/6aD7tSpU5Vj859//qlgZgJeBMLo2jtw8GD6AsPNxVWV5TkEAPjb398fhF8ZCgzLyUF2Tjbeeett5WpJMOG+++5TsBodmunKS/CM7s90ZOOccPr0afUYQepmzZphwYIFysGZ257SYbgwN8zo6K9XhNsAACAASURBVGg0adJEfXn60UcfKZi3evXqxUJxjlKb/Wdu7E9Fi9tuu00ByXTeLg4S3LhxI7Zu3arGgOA5x/i3335T4Ed8fLxy9e7bt6/qZy6/AQXgqhl2FaZ+ewCZz6FZs2bh+Rf+g5dffhn33XsfC+dzeeb5Rj3j/lwQfClwc0V7hkk+l1sBXncGYrbPpajrhdciFxhwTi4q6HR86tQpNS8UCThb3JFt54KyaKMco3NyMXvObNx9991q4UxxwXO40GXo0KGFFmWZ8PBwBTlz4URRwQU/hLoLC+rNHQy4eGTu3Llqjt2yZYuCnLloyN4JtLj85XFRQBSo+ArYA7d8bWwPxlb8XjhHhvZaf/rpp+p1rKODi/R27NiR73XskSNHir0vOjoPqU8UuNIU+GfXIVzbvZ1a+NXt6n7YHAE08dMgMoNvH3dFA5nZQNsg7V7M2BqpAWMDIfMYYWNCutk5Gr6lmzBB400RGj6mazPhXf54uwON7YzXCQVnZAOdQq0qEyyuXlUDxEf5tzvgW027MxOwpmM0YWoGoWyCyQSzjeM06yPwS/CZTs4Erhk8FpMGdA61HiNsHJECdA7TjtOs/3C87jvhZgMR83zmReCYOtGlmkEHZro+B3gAzS3AMZ2s6W4d6qndpU0cT9RwdMcQ3ScGgXA6QRNYphs2g/DzmWQNXxPiNv0i2OxVRYPZDANw8+8GHimoWzNELWy/6qqrwF1OuCheQhQQBUQBUUAUEAVEAVFAFBAFRAFnUEDgZmcYJclRFBAFRAFRQBQQBUSBCqjA//3f/2HFihUqs5kzZyoHUAlRQBS4PAoQxKK7JSG2hIQEtdXo77//jocffljBwgxuB30+KhILv16IJo0aKacewmJ0TP7pp5+UAzTBA7o8E/Ly8fXB6bNn4VXNQ0GyBOjowluzZk3s3rUbcfFxCAoKQr9+/RRITRegG264QUHOTZs0xR1j71CAGN2hi4NybVUjYE1nTjq88Qs3ArmBgYEK0L5UYGxmZqaqvzR5XoqRZv/+/vtv9YUjg1AxdacLHcfm4MGDJQYF7fOzBZf5mHFTJtzMdr///nsFqd977735oBBC3xzna665BrmKh7bYXRGMtmvEHm4uSKNLNYaXYjykTlGgPBSoXbu2WhhSUHBOJxxc2Nz04Ycf4oEHHih04Yi61nNywDmOi0wemPAA3Kq4FTiX2i6Yudi5kHPJgxMfRFp6GtauXasckYurk+1v2LABrVq1UotbCoK2SzpX835UlHOncbzn4hHOq1yMY7RWuxhkZZXH0EsbooAoUI4K2AO3bPr48ePlmEHlacpea95/uOjE0cGdYvgexATnfb5mlhAFRIGLU+DQoUNo3749li5bDv/2NyhXZOPEzJoPxALRadoJmTAtY08MQOfiLjYQ8ukkDQO7u+nH6DDMIOzLjXwIHMemA8cTNCDNNoxbsmonDohOBbrX0M7EjJOJ+m8CxOY8OjgTOCZIXMNbQ8OMQ3HA+VSgQ7AGpxkEq/fFAnRTbhWknZ8ZPJ8QM8FgQtcMAsTsK8uZOglrE9JmPXRdNu7KzCs+Q7dj+sm+b48CqrgAbYP1u2gC1VvOA95VgdaB1nE6l6LhbMLW1JthXJeZIyFquk4zR5Zl+wSjTb/oAs36O4Toc1MydX1sTwHUaQmoW7cuTkfFY0C/q/HGG2+oheUSooAoIAqIAqKAKCAKiAKigCggCjiDAgI3O8MoSY6igCggCogCooAoIApUQAUeffRR5QTLuFRbzVbAbktKokCFV+CTTz5RX+xXrVoVb731lgLKCCDTOXn+/PngFs7r16/Hf/7zH8yePVsBZIQ7jHOoAdCq+1THgq8X4oP331db0hK05Q8hAgLU3Mp09erVmDdvHl599VXlgMl47733MGnSJAWHEYwtDmgrTtCzZ8+C8N+4ceMwbdo09TfDkZBsnz59lMPxxeZaXF+Ke5xjQEdsAn50VeLY0JmVwdx27dqlXEUNCHkxGvBcujw//vjjCoSns2tYWFheipdKi4vJuTj95HFRwBkVKGqupOM6F5UUFnFxcerxkl6vvP727t2r3I85l3MOeOSRRxRA7chrMzsrG6fPnFYQBYPzKxfUeHl5FTlEzIGQMe9fBfWJuxEsWrRIOS0XFYSguUCmKPdm3s86dOigXD9tF38YF/+SauqMz7my5sx7/5IlSxSA3qNHj7JWI+eJApdFAYGby092e63nzJmD/v37OzyB+++/X70XMcH7IXevkRAFRIGLU4Cu9u3atcNrny1GvxsGKjDXxIkE4GSShnUNBEwwmM7LhJAJ4DIiUoGkDMCnKhCfrl2H+Vh4jIZ2u4YBKVn63wSdm9uAujyfDsYEiemibABeQr0EmOn+fCpJO0jTpZnlCCsHewK1vHX7pywuyAST/W02/GCuBLOb+VuPE8Jme3RWNo7PdHAmhE3YOczy8pXlmDuB4Ua+VrD5bLKukw7KdFI2Guy1uEYTODbQNutkGAiZfxPwpkM1z61tyT/d4i7Nx1sGAh5uum26Q2fkAE39AD9Lv3gu4XE6PtNNmn8fSdBu2dSDunABO1//r9wbg0kjrsHbr72sFi9LiAKigCggCogCooAoIAqIAqKAKOAMCgjc7AyjJDmKAqKAKCAKiAKigChQARUgELd48WKV2aVyY6qA3ZaURIEKrwBd1QnFTpgwAVOnTlWuS3TmIezFoEMxYToCZLNmzcKE+ycgJzdHgcgExwi5VatWDZ26dEZ0TAxqhdVQrs5r1qzBfffdh2XLlql6nn/+eQVIs77Jkyfj3XffRceOHbF7926MHj0ahO5++OGHEkN3JRF22LBh+O6771RfmK8jgjBdly5dlFu1s8Qff/yh4D5Czhyvbt26YdqL0xAaFoqmTZsqzXOyc+Dqlt9b2TwHgoODcfvtt6vtaDnWLF9eIJ8jAUpnGS/JUxQoSgG6DHMe5Y990Ll+1KhRhV6jvKY5B5QmOIea3TbM9Xj06FE1xxMUc8R8YO8CzYUbvO8Yh/yi5huCaW3bti10TuKcxXtVUWFc8Lt07oKq1Sz2ewWcQMf6m2+6Wc2VrJNQNXMk+Fxec2Jpxu5yl/32228xZcoU+Pr6qsU2EqKAMykgcHP5jZa91l9++SX69u3r8ARsP49g5aGhodi0aZPD25EKRYHKpgDh5rbt2uHJ6Z/iPw/dkdd9Asx0A24RoIFZBoFfHusUaoWdCevSYTigmgZ3CTZXddVg7tkUDTbzXSqdmQnx0unYFkDmuQR22wVb2yE8TOC4sZ8GqXkeIWe2H5UKBHhoJ2VGZKrOqYkfQFdnEyzLH0LEJv+oNOBgrM6Brs8MwsGbI3R99S11MqeYdCArR7db3fLym20TuiZUzOOEkBmEqOnmTODb0/ISf28MkJyptTIANAFvAs81va2Oz7bu0oSr2Rah7sMWYJmu1cbdmc7S7APrZNtp2cCxeIA7KlF/1suIiopCsxYtsTY8EpNv7ac+x7kU83Jlu1akv6KAKCAKiAKigCggCogCooAoUD4KCNxcPjpLK6KAKCAKiAKigCggClxxCjz11FNYuHCh6tfcuXOVM6yEKCAKVAwFxowZg+HDhysojqBWkyZNMHHiRNBxnUFwi0HYYOzYscjNyUWHjh2U29ny5csxcuRIHDl+DA3r1Ue9evVA8M2AXjl0Y8rVX0iyHgPW8bepl/AZXUEJOzs6CK0NGDAAzZo1w8yZM0tZPb/msw1XBbMR9Hb2MDDh5s2bFdx+4403qi8xTbz++uv44osvsGLFCrRs2fKydVfg5ssmvTRcgRUgLEpXXNvgtbJt2zY1jxYF2pb2miJozOA8YYJzN4/zfsHfjlo8Yt8f9uOrr75S9xhPTwsVYzcuXBQzcODAQvs8ffp0PPHEE0WOJqFvzus+Pj6qP0XFnXfeqXYg4BbsjRo1Eri5CLG4iGnGjBmqBHd8kBAFnEkBgZvLb7Tstea8fykcQl955RW1UNMEdwvg7jQSooAocHEK/LHzMG7s2R5zvpiL224dqSqjM/KOKA3g1qmu6yfsuz0iv7txbJoGmIOqAdHpVofjM8kAQdyOoYBfVe22HJcO1KoOhNq8JEzMALZEamdiQrymbTo1E0pOSAcSM3W9dIemazIdpPkYIz5Dg9F0dDawM48TeCZcXc8XqGkBnlmW7soEgAkmM+gGveGcBoPp5MwgoH2WTtEu2gmZjzGYPx2feQ6hawM8M1f2r1Wg1QmasDVBaDpRV7UA0BnZwLZIDXabtljv0QSdb1N/3RaBauZOoJuwtnGSZhsEqOkCTYds1sdzGQSqbfu/+dB53HhVe5w9ew433dAfzzzzjNrRS0IUEAVEAVFAFBAFRAFRQBQQBUQBZ1BA4GZnGCXJURQQBUQBUUAUEAVEgQqoAD8Mnz9/vsqMv3v37l0Bs5SULocCxtlv/PjxyhFGovwVsHXNJLg898u5Clb7+OOPlbPvshXLERcbiyXfLkFYWBiaN28OnkO4y8vLC507d4aLm5uCld24Tyy4Xaz+reBmgnG5UGAzATICcU8//bQCxE6ePKm2POWih+7du5faVbQkajHXe+65B+Hh4QpiKCmIl5ub343zrrvGgW7Qt9xyS0madfoy9m6ql6NDpQUxL0eO0qYoUN4KDB06VLnS2wbnbrhY597CcirLNVXQOYSrCQOX1rFY3SdK6KRP0PjYsWOoXbu2co0vKOhk7eHhccFDyk1ZCWK5B9nA2faF2T8CG9xhJDAwsMj8uAvBtGnTkJ6WDveq7vD390d8fHypdSjv58zlaE/g5suhurTpKAUEbnaUksXXY6/1119/jV69ehV/YilLfPTRR2p3GhN8/7F27dpS1iLFRQFRwF6BD7/fiMdv64d5X36pFqTRDXhrhIZqCfEyCNpuOg/U8QEaWNyNM3O0Y7GvuxVs9nbXjsvbozTsW8MLIOh8PlmDugZgZp2EeDeez98OXZRZZ31fDe/SwZlgM2Hr44narZgQMJ2TjQtyiKc+ZoJwMsFq5m8gZpYlrB1oBxYTNq7iArQN1mcnZQJH4wFuiMR6+cOgAzOBZTowUwMDPEenAnRobhZghZCZ5/EE7a5sAGi+zKcmbIsO1SYIa59M0nka6JtgM/tNsNroRQ2ZF6FoulBTOwM2u7po2Nvyklmdu37vGUwc3A1nTp9Su1ZxEfyTTz4pT35RQBQQBUQBUUAUEAVEAVFAFBAFnEIBgZudYpgkSVFAFBAFRAFRQBQQBSqeAoRB6PrK4LbpPXr0qHhJSkaXRYHRo0fjn3/+Uc8JPjckLq8Cqamp2LdvnxoPbtVMx+N27dph3bp1yqmZDs/p6ekKZCbUlpmVpVyJCC/T2dMWdIs4H4HQsFCwTm9vbwU1Z2VlqXIE1QiEubnrPVqtnqCXpv+E1+rUqaPcPwllFxcG7M2DtLNz4Ofvh8TExOJOlccdqEBZQEwHNi9ViQIVUoHCgN7ikuXczUUmnNdKAyXbA8m8LqdOnYrXXnutRPXkZOfAxdVF3eu5iMXWBbqonNnOkiVLMGLEiELPYZ8KAp/VzgC5wKzPZiEuLk7lW1iwHTp4Hj92HF7eXuoeV1gkJSVh69atuPrqq1WRFi1aYP/+/SXSobjxudIeF7j5ShvRytUfgZvLb7ztteYOBEFBQWqhSVl/Cso+NDQ0372gVatWWLVqVfl1VFoSBa5QBbhrU9u2bdVi5eEjRmJLBEBImXCyiY3ntGNyC5tjhHDd3bS7cl0f/Tjh5M3ntYswAWWCtgR9Azy0C7RtbI4AvKpY2zGOxYR3CewS/G3oBxCiJqxMoLm5P+BRBcjIAXZE6vNbB1lrJZy8ixCzh9UdmefzGM9vE6TrZuyJ0fl2CdX/JsBNsJoP012ZDs8MQtY8npWrYW0DPBOYZl/Zz/oW4PtcMhAeqwHmIJu1ewTDuXa8Y4gGpBl0dqaGBJgNxExn6Ph0wK+a1YmZADXrJMBM4JmgNMFmOkhn5+jj7pYPY9j/ndzMKfYExtzUW+28wR2eHnvsMdxwww1X6DNYuiUKiAKigCggCogCooAoIAqIAleaAgI3X2kjKv0RBUQBUUAUEAVEAVGgnBR48cUXMWfOHNXa0qVLlfuHhChABQRurljPgw0bNijIq2bNmgre+vHHHxWQNnDgQLRv3x67du3CSy+9hClTpqBTp05YuWoVwveF45qre2PIkCGYPn06IiIi1BecdHkm0GxAtoYNG+LNN99ULpt0UeacsH3nTiXApYab2QZdQN966y0Fa/NLuqLgPnu4+fXXX1duRaUBAivWyDpnNgI3O+e4SdaXXgHOZ7aQMJ0ux4wZU+QctWPHDqxZs0bN36WZywpyW+ZCFUJnxdXDazgzM1NB1YScXd3yL4IpTimez74W5va8ZcsWtXtAQWHmcboxx8bGFtkU71dnTp9BZlYmYmJiUCOshsq1oPD19VWLcxjcyeDAgQPFdaNSPi5wc6Uc9ium0wI3l99QFqR1ebROiHrFihXl0ZS0IQpc0Qpwl43WrVsrM4NmfUciO1dDuCboeJybC3SwOXYqSbsH0+WZDsl0MmYZOjHzbzoZE7TdHQ34EIoOsEK9rHdXtD7fth06E3u56x8C0YSh+VJuXwxUTnQtphMyoV7CyoSN2wVpaJlBOJlgL92dCTwbiJk5EGJuHwxUddNlD8UDESlAtxraTZnAsAKYc3QbhLUZPE4Amf0kME24mEFgmnA2AeZmFtfoWItjNfM05ViWYDbdrJkTtWDEZwD7Y/X5xh2b7swEnlnGgODUcHskULe6hqgZxxJ0XuwT4W8C3gxC2Dujdf+9E46if//+2HXgCEYMGoDJkyerz08kRAFRQBQQBUQBUUAUEAVEAVFAFHAGBQRudoZRkhxFAVFAFBAFRAFRQBSogAq8/PLL+Oyzz1Rm3Eq9Q4cOFTBLSelyKCBw8+VQvfA2uWXzAw88gJCQEPy6dq2Cu86dOYsnnngCPj4+CnSmC6aXlxeqV6+Onj17KliOsBcdLDdu2IjDRw6rvwmWEUij+/P777+f575GGI7bTRcHxV0KZZgTYYb169er/PNHjvpnLq2oVeSoHPnv4OBgREVFXZacL4UOzlKnwM3OMlKSZ3krYH9t/P777+h9dW/lkFxQ0O2Zi01c4IK94XvVApSSBuFie7dnHqOTvZ+fZb/xIiobO3Ys5s2bl1fCkXM/F+TQDbqoMI7VRTlGf/vtt+jdu7e6991zzz34/PPPlV4FBcFu9oH1CdxcuPICN5f0CpNyFVEBgZvLb1QuF9zMe8fixYvLr6PSkihwhSpw4sQJtGzZEq99+CW6DxiJLmFWEJkAbly6hoDNK1RCwQkZGjImyEynZca2SH0eXYsJ/+4hwJyr4V2WM3EgTsO+3cKsADJhaQZhX8LEhIvpHk0wmA7JrINuyox9lpw6BGsXZwZhXwNMtw2yQswHLW3RsZn1MdgWQeZOoYCP5RhharbD+mwdpo8nAokZ2pWabtQmtkZo12q2xVCO1REaQm5g41BNEPlkItA5zAohsyzhbG8b6Juank0BvNm+n9aR5eiiXcMbaGJ5uU5nZ7o1M1fjlm1y2hujgXIC33/9vla9Hl7091E8MfZmPPXoJNx0001X6DNYuiUKiAKigCggCogCooAoIAqIAleaAgI3X2kjKv0RBUQBUUAUEAVEAVGgnBTg1uWffPKJau2HH34oFVRTTilKM5dJAYGbL5PwhTR73333KaDr448/Rja/cszll2MumDRpEpYtW6Ygsu+//x6Eu2rUqKHcOE+ePIlvvvlGOTrv2bMnD4Kje9OMGTOwe/du9dOoUaO8VjU0nFvusLBx8mQf6UaaPzTcbPWR1v9+8cWX8Oijjyq4W6J8Fbgcz5Hy7aG0JgqUXgFeF1WrVlWOyCYIG0dGRiI0NPSCeTUjI0PNd3TN59y7ZMkSjBw5ssQN2zvZmxOTk5PVQpfCYOWszCz8uPJHDBo4CG5VLFZ3RbRaEETNew0h2ccff7zAM9mvWbNmqV0g6NBcUGRnZSMgMEC5LReWK+shBO3pqQkbapWamnoB1G3qp87nzp1TC3nEubngQRW4ucSXmBSsgAoI3Fx+g3K54GbuLHX33XeXX0elJVHgClWAnwU0a94cz8yYhynjR+YBwycSAMK9hJ09LRAxQedzKRq+JfBL12YGwdqUTF2WsT8OiE/Xjs0sZ4L1EdDtEmqt83yKhnVrewNHEqxO0HRyjk3XULEBqI8mAKeTtIs0HZZN0N05LkOD1cbJmFDxiSSgTSDgZwGj6YxMd+e2wUCIBcqmYzL75e4KNLKAxayXxwlhs+88buBuQtsEjzuHAi4W1+eN57SzMx2rTfD8g7E6pwAPfZTO0HSSJhjeKhCo5qbboCZ0lSbEzN/GGdq/KtDS8vKY/aY7dUaOVaM8XROAiFSgib8Gyffv348Bg4biu7/3YepdA/HQQw/h5ptvvkKfwdItUUAUEAVEAVFAFBAFRAFRQBS40hQQuPlKG1HpjyggCogCooAoIAqIAuWkwPTp0/Hhhx+q1n766Sfl7CIhClABgZsr/vOAYNtzzz2nfkaNGqUWKNCRmUFX5qSkJJw6dUrBygQU6IpMSK1O7TrqWzxC0OfPn68wHWWeI0aMwOzZsxEQYPMNYl6G+SFnOpzu2rWrwuRfmRIRuLkyjbb0taQK8LqgY3JCQoKad3fs2KF2xOA8GxZmoUIslfHx+fPng+7JJjj3jR8/vkSLSwj9Gpdi+/wKg55ZjkDxjp07lFO+fRjA2MDMbGPFihUYNmxY3r3FnENHZc7XuTm5aicBeziZdbzx+ht48qknQYi7WrVqBZYhgMz7lb0DtWmHfaETNR392VZGZgZ69OiBbdu2XXAOyy5YsABjbhuDlq1aCtxcyBNX4OaSXtFSriIqIHBz+Y2KvdZt2rQpYHcVx+bDnWceeeQRx1YqtYkClVSB7YdOo0fbpvhkzjzcPUYvnotM1e7ChIgJ7TIIIB+O00Bu9apALW99nC7IBJS7hmlAmM7IBHvptkwnZhMssycGoOOyqZNgb1Sadjume7JxgibIS4i6dnWghgWgPpusXZ1bBgGBNk7QhKDpekx3ZgNSE/Sla3MzfyvEnJypnZAb+AH1LBtAsZ/Mi7A2AWb2jcHzeZz9YT/4m3EoHohM0RC3OUYXZwLJbN9EdJp2km7hr52XTYTHaNdr4yTNv9knQtLMlSB1bq52gWYuBKMZzIWuzHyMoLYBs/kYdaHeHI+alrZWbQjHQ3eOwJH9ezFo0CBMnDgRAwcOrKTPcOm2KCAKiAKigCggCogCooAoIAo4mwICNzvbiEm+ooAoIAqIAqKAKCAKVBAF3n77bbz//vsqm19++QVNmzatIJlJGpdbAYGbL/cIlKx9wlyE5vg7LS0Ny5cvx7XXXqtcnvnTu3dv5ej8v//9Tzn70CGzdu3a2Llzp4Kb6XBZ0aJhw4Y4cuRIAYCfFW7++++/ER0drb7Ukyh/BQRuLn/NpcWKrwCvC0K8dBomrPvdd99h6NChan7Oyc65wCWZALCrq4WqoOtbVpaCiAtzMbZVgHUSPuY8X5rrkaCwl6eXApK5AOaDDz5Q94EhQ4bkVc96W7durSA2/jBH25yYt23uRYHJBn4ubPTYVlBQEGJiYvJpYcrzcX9/f/U4w9XFFbnIRZMmTfLuE7a5sTx3NFi7di327dtX8Z80lyFDgZsvg+jSpMMUELjZYVIWW5G91rLLU7GSSQFRoMIoQMfiX3afxdirG+Grr75SO4MQAiZcS6i3jgUCpuMwIWY6DtMx2RwnQHw6GegUCvi6A3RGJqxb18cK2rKzhHgJATf118AyIzFDOys39tVwrre7diSOSAFOJum/TTt0cN4brQFkA/CyDp53LAFoGWB1R47PAHZHAfV8gbqWtuh2vOW8doBmDgz2nY7J/OSAjsnGCZpt0fWZQSfkPCfoJN03OjabsgTA6bLM/pswEHV9H6C+r/U4XafPJ2snZgLKdH/eFwvk5ALNA6x1bo/UObULAqq4ak2Zk6uL1sjA3qyZEDX7QKib48U4FAf8uXUPpj98G/bs3oXBgwdjwoQJ8nlIhbnqJBFRQBQQBUQBUUAUEAVEAVFAFChOAYGbi1NIHhcFRAFRQBQQBUQBUUAUKFCBGTNmqG3FGb/99hsIFUqIAlRA4OaK+zzI719szZPHXXJzFYRG2I3wcnh4eJ4LMo/VrVtXuTnTRujYieNoWK9+heoocyQM+M0338DT07KnbAEZPvjgg8p1viQQYGEdvPXWW7Fo0aICgboKJUoFTKY0MGUFTF9SEgUuiQIEa/v3749169ap+gkAGzf9rl27YuPGjXlzFsFiLy+vfPMPy3ORCo+XNJYuXaqclW0h6cLO5XXLe8KJEydwww03FHmOcW9mXQW5MrMu07ei2ktNTS22Pyzj4eFR4HzOdtzd3RX4ffrUaQQEBqiyhKbvuvsuBezY5sfyTz31lHJwVvc6iQsU2Lt3L3j/o0PqZ599JgqJAk6lgMDN5Tdc9lqvXLlSLXyREAVEAedQYOPBc+jdpr5a9PXmm29hwzkgyAYCZi8I5hLGJWDbwrJxEsFiOhE3C9AQcWImsCdauwob0JbnEozeeF7DyuZ4Gl2g44F6PkBMunZBpvMwIV66Qwd4WMsSFt4WCdSqDjSygYXp+hweq8sZ4Jfu0izLtggsm6Bjc3Yu0M2yQQrrZO7ubgAhZH+LE3RCJnAiQZelm7RfVV0DnZPZVutAq2vy/lgNSHeroTa7UkHQedN5XcZA1DxOrY4n/j975wEeRdW24TeQkABJCCn03jtIxwqoYAcpdgUEBCsoih2xIH4qYEEBFUHFAvorIiigAkqR3nvvkIQE0kmD/3rO5GxmN5vsZrNJdpPn/T4uYXfmlHs2m92Ze55jpDMjzRrb7T8vkpppCNu6/50xBufW4SIBZY32kWANPvi3FsPRJoRxZ1821QAAIABJREFUMPRD6nOoSDmI0BdFtkWLZJ7aLs+NeFCtDoMbE4cPH64kZxYJkAAJkAAJkAAJkAAJkAAJeAMBys3ecJQ4RhIgARIgARIgARLwQAIffvihTJ48WY1s1apVSnxkkQAIUG723NdBnnIzRDTT0HOTUHNrwxNmjTG3aNFCSXj2CtJd5cqV5cKFCwWSm6+99lqVaF0QQdoTeBXHGCg3Fwd19ukNBK6++mpZuXJljveVpKQkqVjRWFMaKc6XLl9Sqcvmws8VJF4UnnP3exPka3PKsqvt4z04LS1NScbuKrABI3tVqVIly/s9GKHwX6RQnz9/XiDg2c6lXLlyaowsEiCBkkXAVrjFTRZY7YPlfgK2rBcvXizNmzd3f0dskQRIoFAIREZGSr169dSNYA2vGyBlfUTahGd3dSrREJCRVlw/Sy7W6cRVKhiyM5KRt0eLBPiKtAyzPs+AFOjyZY3HUZCdD8eLhAcYIi+Si5H0nJhupDMHljPahEidlmnIykg61lI12kDq8/ZzRrKzTkeGMIxtkW4MCVnXjhiR9MzsdGWMFenGGEdlf6Nv1MXMrMcxnkBDQkYlpolAjlZJ1lnbQvYGl45VjDmrz5wisjHS6L+FqX9I2BChIUvr1GkkXkNOxmO6H6Q4n79osEcb4IFU6vK+hrisx4m+wA1tgh0k6qByIkisRuozZOmYg1tlyJAhsmXLFunRo4e6KXz06NGF8vphoyRAAiRAAiRAAiRAAiRAAiTgbgKUm91NlO2RAAmQAAmQAAmQQCkh8PHHH8v777+vZrt27VqpXr16KZk5p+mIwJgxY+Snn36Srl27qhRdFgkUFQHIfSEhIYJkU3sFqa1NmzayY8eOAg0pNwmxQI2Wkp0pN5eSA81p5osAfi6Q3Pz3339bybaZGZlSpmwZ1RYkXKSt4T3MnlyMNpCm+8gjj1j6hkwMMRkib3JyshJVXCmdxuyq1GzuE205kxbt7DiPHDmS6+ohMTEx6oYW23Ej8Rk3uZw5c0bat29v4YuxId3/008+FZ8yPm6XxJ2dE7cjARJwPwFb4RarfOzdu9f9HbFFdeOIuf78809p0qQJyZAACXgJgaioKKlTp468M32OXHXzAOmUlW6M4UeniJxKyhabIT5DqF1vk+6MxGakJrcNFylXNnviSiy+JNI+IvsxiMEVfHEDmkhKpiFMI8EYbUBohhiMNvD81nPGY2hXF1KfITFDCkZqtC4kFuPGbGyLfVCQiGMuipoTxo5C2jHkbIjZOvEZSc14HG0j9Rl/UBgXxGZz6jRSmCEWXxGRnbiMbbedM8bczjRXSNg7zhmp0xCZUcfiRSJTROoFGWNAQbaOuijSKlQkuJwhWh+OMzj4+mTvi20hZR+MM8RvJDmDA4RtHJPQAIPfpk2b5JERI2Taoo0yceSdMmjQIOnbt6+XvCI5TBIgARIgARIgARIgARIggdJOgHJzaX8FcP4kQAJeTQAXcnFR1l4hhQYXvlnWBJA+cfr0afUglm1u2rQpEeWDAF5vCxYsEFwIvO2229R/WaWXwCeffCLvvvuuArBx40aJiDCdsS+9WDhzEcGy5UuWLJHevXurFF0WCRQlAS2nTZ8+PUe3+/btk5kzZ1reu1wd11VXXSUr/11pkQ5dbac07ke5uTQedc7ZGQL4/paenm6VkKylYuwPQRfvXw8//HCu0i22f+CBB+S6664Tf39/6dSpk/pvlYgqElzJtG63MwMybePJP7dIs/YP8FfsbAs8zp07l+MzampqqiChGfIz/iDtHwl2urBU98iRI6VDhw5uFbHziZ2bkwAJuJFA48aNrVLZg4ODC3yzmxuHV6KaspWbceNOo0aNStQcORkSKMkE8NmpZs1a8srHc+T5oQMscnJcqiHRVvATaVRJxM+4/07Jvv5lRVplJTEjhflMkki7rMRhzUqLxZ2rZsvGSDuG8AtpFyJyg6yPq7tjDTkabUJ8RmlhGrKw7jsT6cznjP5bZ/WPbffGGsnFV1QxUo5RxxOM5OOOVbPbxGMXUg0JGKnPuiBcI6EZj0MY1oUkZoxVJ1kjhRnpyEih1gI0tsX4IUyjL706FsazJ9YQoHXqNDidTDT61inOGOPxRJHWoSKVA7KTrdEuxOb6layTsCE9J2UYyddoA2I2ZG9zOvZ3338v4954W35ZuUNee6SfPPjgg3LnnXeW5Jcx50YCJEACJEACJEACJEACJFCCCFBuLkEHk1MhARIoWgJY+jY+Pt5up0iiCgsLy7FcsLtH+Pjjj8vChQvtNovleSGXsawJIM3srbfeUg9eccUVMn/+fCJykgBS3yBzIeEMBWni66+/dnLv4tkMY0biiC5IDPjZtC0kfUZHR6uHsZQ3JV3njte0adPknXfeURtv3bpVJeOxSIAESKC4CUBmgyQIGc+2kNjcunXrAg8Ryc3/rPhHyvqaYqgK3GrpaMCTJcnScQQ4S08lsGzZMul+XXerxGCkrCFZWCcPDx48WGbNmpWr3IyfL/zBzZzVq1VXbV2+dLnA71XuTlt25zHAfC9evCgBAQF2uXTu3FmtMAKGEKHXb1ivpGU/Pz/1GHju2rVL3fRiTnjG3/G94Ndff5Wbet+kWKLwXZ/vY+48gmyLBIqGQMuWLa1W9ggPD1dJliz3E7CVm5cvXy4NGjRwf0dskQRIoFAI7D0ZI20a1pDPZs+RwfcOVH1ANN4ZIxJQVqRpZUMmRuExJAa3r2L8OzLZSB1uESZS2T97eFos7lBFpKKf8ThSoCE2V/IXiUkRqRVkSMeQoCENN89KLca2SFFG2yrJ2NTu9nNGEjSEZ53EDDEZ0jTGpMXoqBRDjoaUjGRj1Nlkkcgko3+doozHIRsj3RnjrxOUPQfbJGbIyxC76waJ1DXdQwixGftD4tacIBxDgsZYu1Qz5GSIzRgr+tBiNfrG2BtUEonIyhPBNmCM+UFs1vPEyMA1Pk0kqJwxDhSYZFy2Tsees2Sd/O+lUbJj01rp37+/3H///dKvX79Cef2wURIgARIgARIgARIgARIgARJwNwHKze4myvZIgARKDYGvvvpKxo0bl+d8cfIeaVmjR4+WGjVquJ2NJ8rNM2bMUMseo2666SZp166d2+ddkAaLUm72dBb55YgLf7YnPjdv3mxXFs5v24W1PdJGIC/ogvSPedgmTh86dEh69uypNmvevLksXry4sIZUotrFa/ztt99Wc4IwiPQrFgmQAAkUNwFIeF27dpX169fnGAre4yGxQU4rSEFu/vfffwvcTkHG4K37Ugr01iPHcRc2Afxs/Pnnn3LjjTdaJFu8n+EmPC3i4ubVWrVqOfzMpSTnS5dl/q/z1WoruOEDf1ytgwcPSsOGDXOVql1t1537XXnllbJmzZocTdasWVMOHz4s27ZtU1IykpohjONmRjABY/CdNGmSjBo1Su1vlpxxs+TpU6dl0e+L1Hv+c889J3fffbdgdYCC/i5x5/zZFgmQQN4EcHN7bGysZSOco/vvv/+IrRAI2MrN+Mxs+1ghdMsmSYAE3EAAacUr9sdKvw7V5ds5c2TgwIGScckQgyHMtggVKZ+VpHzgfJbEW81IYk5MNxKD6wdbpyCfSzGEW4jF4VnCLlKMTycaonFsqrEPROATSFdOEGkZaqQmo04liSCdGPtXKpc9SbQJwRgSs5aITycZcjW2RUIyCvLv5iiRxpVFalY0HoN8DDG4oq9Io5DsJGklPCeLBPkZgrGuPecNERvCMurSZZH1Z43xo11dx+JFjiWItA63lrshgV/MFGkbbqROIy1673kj7RlzR2kxvEGISPUKxmOQndGvFpt1YjWegxyNeUA4x1hxDPadFzmfKnJFRDYTyNb4jDzz7efkvzWrZcCAAXLvvfcqyZlFAiRAAiRAAiRAAiRAAiRAAt5AgHKzNxwljpEESMAjCTgjN5sHPnnyZLefNIKAiQvNuvbv36+SpVDFldx8zTXXyPHjx9UY3nvvPbnrrrs86vgVpdzs6Szye2ASExMFaUe6IO8jYc588T+/bRb29rZyM/qz97NIudm1I2H+ecJy3hUqZJ19d6057kUCJEACbiMAWaVbt2452mvSpIns3bu3wEIafsf/888/BW7HbRP2ooYoN3vRweJQi5wAbgzFzYNmaXbChAny4osvqse+/fZbufnmm9VqGY4+g5t/1r755hv1He3RRx91al/biePz8zPPPOMyj7lz5wrkY4jZ9saNFVQgGVetmmWM5LMnzBUrzNiTm3/44Qe17DZWcNGFMUBa1in/YIt/67HlxXbOnDkyfPhwSU5OdngM8jkNbk4CJFCIBLp06SJnz5619FCvXj31WY7lfgK2IvOqVaukdu3a7u+ILZIACRQKgRU7jknvjk3UDV1Y/XBPrAhk5NZh2anLtknMSCRGijGE5CYh2cOCfLwxypBvawcaj6dkGLIytoWIC7kXwjSSnJHQ3CBYpErW6UWIzxCD0WY10ynHo/EiJ5DOHJE9JqQ974gxkqX1thjXhkhDIm6YJStDFt5/wZB/0W65rPv/tPCMtGcIzzohGbI0xOOOpiRmzBVScUvT4oBnk0QgEiNFWovZmC/6QjI1Hsc8IYFDzIborVlh7kfiRKoHZnNCn+cuiuC2bCQ2oz9d2B79+ZYx2GIuSHiGXI10bCQ5o5CCjW1T9q+S8a+8KCtXrlTCOm7Ug+TMIgESIAESIAESIAESIAESIAFvIEC52RuOEsdIAiTgkQRs5WbzEospKSly5syZHOMu7KUYkfQ1bNgw1S/lZvsvG8rNBftxWrFihVq6GcnHkCPatm1bsAYLeW97cjPGvGDBAqueKTe7diBmzpwpb7zxhnHC/MABK2nEtRa5FwmQAAm4jwAS+rZs2WJpECmm9RvUlyNHjhRYSKPc7PpxotzsOjvuWfIJpKenK3kZ3+sg2EL4RdIoZGaIuBCUIYhBVm7Tpo2kpaWpFGJf36wIvVwQoV2kE7ta+B7bo0cPV3dXiclaJsZ/IQZPnTpV+vbtK82aNVPzefDBB/N9wwjaRX366aeKA25qsX2PAUP0FxiYZdRkzULvC84Q8Y4ePWqZX25yc0Z6hvTq3UvWrVsn27dvV2nWLBIgAe8gYL75HCPGDW94r2W5n4Ct3IwbT5CizyIBEvBcAljhQp/jjYuLk/DwcFmyZInU69BTziSLtAnLFmYjU0R2njNkXSQXo7ZFi6RfFulYJXuOOt041CTx4jEIzBB1IUxDbK7oZyQZQ6KuGShSJ8hoIznDEKYhRdczLRSHxGJIu+Z0ZGy7MVKkbrBI3az98SkRj5UtY0jQKKQnox+Mo1nlbDEa6c6QmJGM3KRyduoxkpPRF5Kg9VzXnRHx881uE+1CrN4aLdI8VKR6Vjo0HkcKNdKokaQc6CeSmmlsF1jOSKdGQaoGE8jOELt1e+gbBdEawrUuCOGQu5HU3KiSMQcwQWJ045DscWoBHX1vWfuvvPrqq7JixT8y8K675K6BAzwukMZzfzo4MhIgARIgARIgARIgARIggeImQLm5uI8A+ycBEvBaAma5GXe8v//++1ZzwQXUL774Qi1vqwtLAn/yySeFNmfKzY7RUm52zKgkbWFPbsb8Fi1aJK1atbJMlXKza0cdovv48ePVzpAFuTS3axy5FwmQgPsJZGRkKJFPy2voAX9//PHHlQRX0IJAt3r1ar7vuQCScrML0LhLqSGAn48LFy4oERfvYbhpFlIz5GWdLgwYWhSWyyLvT3pfxowZk6fgjHZTU1MlICDLQMkHUQjUuv987Ga16dixY1Xy35dffimDBw8Wf39/9Z6sPztq+dmRpG1uFO/zYAUWEHDQxo4dO6R58+bqhjvdNuRmzBvztyctP/DAA6pZJDI7UxDFcUyQ+qpXLHJmv5KwTXx8vCxdulRatGih/rBIwJsIXH/99VYrn2FVqt9//92bpuA1Y7WVm3FDSLVq1bxm/BwoCZRGAghJSUpKUlPH73vcWLd07TYpV7OVEpsr+RtUIAFvjjIkWojIKMi/EHQhNiNJWNfmaBE/H0NC1gUBF2IxEp0hISNhGH/fdk4korzRLgqJy+gnxN9IYtYFiXg70plDsiXijEtGOnSovyEm60KbuA8OEjYK/e49b0jT6KdSVrox/g3h2UdEmoVmi8RIPN5xTqRVuEiV8kYb+84bInanKiJlsuaK8UPCNovV2PZsssEGEjOSnNH/mjPGnJGCDTkZbSHZGXwxJxSSnbEfxoO0aZ3CjOcS0rMkbB+RqhUNZhgnxGz0XyNLrEbqM+aKviFNI6zk9dffkPd+WCavjrxbBt/TT6U3s0iABEiABEiABEiABEiABEjAGwhQbvaGo8QxkgAJeCQBR3KzHjQu5GIZXlT16tVl7dq1uc4HF2RPnz6tElBx0RRJUHXq1HGYxKUbLIjcjD5xEhMXlHGxtkKFClKjRg2HCV+JiYnqoruu22+/3ZJajUSAPn362J1vaGio6ie3KigLXITHnA4fPqzSzZA+gYvZhSk3FxYLXLg/ceKEmg8uzDdt2tThks04lhDsUZAjkLSMguyLpa5xkhoX/m0TzMzHAwIATmjnVuZ2nf0hvXjxohw7dkwlo+F4REREqMQknEQvjMpNbobEgCW+dVFudo2+fh+EPAK5mUUCJEACnkIAsttvv/2mUkHNhZvRnn322QIPE78D8XuflX8ClJvzz4x7lD4C+E6G7zP4PA4RGN+3AvwDxKeMj7zwwgvyzjvvyKpVq+SqK69SjxX2DWaXMi9JGcTeuVD4mcdKHxCw8Zkf3wHsvQ84+96A7zmQl1E6jVrfyILvFj179lTfv7XIDLkZKf5IdjbLzXpcL730kixbtkzuvPNOxRvftdDG119/LQkJCZZ9kDoKMQTfYx566CElServWC5g8cpdfvzxR/U7tFatWuoGHxYJeBOBW265RXbt2mUZMlb4mD9/vjdNwWvGais3b9iwQapUMcW5es1MOFASKF0EEMqycOFCdV4fv+tnL9slvTs1kyoVDA4QjjdEilStYEi3KCQGQ87tgGTiLFkYj++ONSRic5IztoW4m3ZJpFagIRejTaQ+B/iKtArL5o10Yx8fkbYmMVolOUcayc4QeXVBgkbislmi3nNeJCHNkJDRDupwnEhsqpEWrVOY0T9SqDMvG2J0cNYcIHFviRJpGGKMFYW05BOJIh2ripTPuqSRcVlk/VlDfkbCsi6kK0OMxmNaON5+zpgv5ulf1hDFISBX9DWk6rI+ImmZhkCNdpFWXTlLKke7KRkGa0jSmAPEb8jeSH3Gdrp/JGKDCaRw3ffChb/LuLf/J7MX/CMTn7xXXa+55557StcLnLMlARIgARIgARIgARIgARLwWgKUm7320HHgJEACxU3AWbkZF53vv/9+y3D37NmjxGFz4YIrElCR8qxTEszPQ5AeMWKEQ8m5IHLzHXfcIViCzrY6duyolkbGMsG4sG5bb7/9tsyYMSPfh+O///5T8rRtFZQFROCJEyeq1GxzQSzHBXWkeyE1DOXui1nuZoG5YLnmKVOm5OAUFham5tm7d2+77CE8TJs2TT33yiuvSJcuXQSvI7z+zNW9e3fVPmRz28Lyg4888kiuxxZ9f/bZZ04de7y2ILvbe42hgX79+ilRo2rVqk615+xGZrkZMgXk8JiYGLX7zp07JSjIWKuQcrOzRK23wxLieH1BLIHgwSIBEiABTyKAzxTt2rWT7du3q2HhJqF//vlHhg0bVuBh4jMRbtixlwJa4MZLeAPOCowlHAOnRwJ5EsANGh988IE89dRT6jug7c8NxGcIvrgxFsIYbho1JxW7U3bGzaF433Tl/Q7j3rRpk5VYbNsOtsGNvVpYtgWD9/LZs2fLkCFDchWj9T7BwcHqOx++d6Afc1/gFBkZaWke20ZHR6vvuM7OzbwaABpydr+S8nLH90a8LlG4YZVFAt5EADe84UYHXThHMm/ePG+agteM1VZuxu8BJOyzSIAEPJsAPkPh2sTpc/FSM6KSPDT8Mfnqs+wVKCE2V/QTaZF1ChmiMhKTzSnKmCEk4jPJIp2rGtIxChIukoxR1StmS7sQfpG83CY8O/UZKcpIKG4fkf0YpGCkIyMBuYlJIt4ZI5KaKdLBdP/E4XiRM1kSMiRi1OkkkVOJhhgNOVvXfkjQ6Ua6MZKkUWgP86pmI3FDRL4iwpCKddkyweNKwo4SqR1oCMoopFinZ4pcUUWkXBljG0jMwNM8VKRc1jiR2Ayu2Bdj0oX5Q2xGG7WDDDkbcjTaCPQzRGYkQWPsGBNStSFAo7Dv7F+Xy4xJb8rGVcvUNYLRo0fLvffe69kvSI6OBEiABEiABEiABEiABEiABLIIUG7mS4EESIAEXCTgrNyMpXGRfKALgk2lSlnxBrjrPiVFicuQbfIqSMY//PBDnknKBZGbsaysPbFajwlyMC78IEnaXO4UegvKApIRLryvXLkyV5SPPvqoRfr1ZLkZKWEPP/ywrF+/Ps/XxfDhw5VcaltmuXnkyJGydevWXFPDcWyRwoWEa3M5kpuRaAYp31GZx5LXtpCPFyxYII0aNXLUpNPPm+VmCOFg+t5776n9kdysl6Gm3Ow0UqsNv/32W0HaHVLr9u7d61oj3KtEEsD7B5aex89cbjdhlMiJc1L5IlAUkituasHnA/QFEQv/dsey2LhBC1IhK/8EiuK4539U3IMEPIsAfk6wKgaEXJ12rEeIG0uHPjxUyvqWtUjPzz//vEpzhtT89NNP27050tUZxl2Ik+BKwRaRF+I1kpz9yvk5bBIrxuAmE5StbKx3Rnu4qdPejbTYxiwUO5KJ09PS1bggf+PmO7092sBjnTp1Uiv67N+/X33/hlCd27gcTq4UbkC5uRQe9BI05bvuukvWrVtnmdG1114ruFmX5X4CtnIzzkdh9TAWCZCAZxPAZ6c+d94pd4/5n9zVtYH67qxXa9x2Dp/JRNpFGHNA0jEk2vAA68RiSMQQbpHkHJwlAUPWhfAM+RZis05NxnZIOG4XbiQ3o47EiZxKMhKf9WOXIQdHGWnH5nRniMDnUkQ6Vc2WoHX/kIhDslKYsQ2EZ6QY6xRm9HUsXiQ6xUiBjsgSiZGKvDFSJKicIR2jMEYkSSNdubpJjMZjKM0Ef4dIjP0hQOv9kax8NkmkQ1WRgLJGcjUE7sxLRlo05GQUUqGjk0VqBBpitS6M6WCcSHK6IS1jrJCj0QbSntEP2EASh1SNviE7o5AEjRTrr6dNkU1/L5AVK5arz8KPP/54noEmnv1K5ehIgARIgARIgARIgARIgARKGwHKzaXtiHO+JEACbiPgrNwMIfi5556z9GubcPTpp5/K//73P8vzEDBxkQUXWTdu3CjHjx+3PPfmm2+qJXBzK1flZlzsRUIvluHFH5y4PHnypCXhVvcHGfinn36ySpDGEr5//PGHZUjm5BsI2Q0aNLA7XAiRthc3CsoC8jcu7OuCtIulR3FyFknRSA2GHKAlbnfLze5kgUQsc2Izxt2tWzdJTEzMISmDOVIXzGUWivGaQloxePTq1UsCAwPVxXwkF+saMGCASucwF5J4P/roI6vHIOsfPnxYPeaM3IzXBuRqczVu3Fgt94wlnc1jwDbuPia2cjPG07lzZzUcjOOvv/5Sf6fc7Npb4/fff68St/GaMi/x61pr3KskEbj77rvVe1XXrl3V0vAsErBHAJ83cMHUkaxWEHrx8fHqdYj3KAiBtr+TXG0br3G+tl2jR7nZNW7cq3QSwOdViLjm90nbnyHbNGGQQtox0pwLWmgHFRsbK2fPnpWWLVuqseBx/Jk+fbo88cQTahs9LsjKH3/8sUqdRpnHbjsPSNILFy1U0jG+s9gTnHHDVI8ePaxk5dzmhcTm18a9psTv3H63YNyZGZly3/33ydKlSwU3v+K7E8sxAcrNjhlxC88lgNXI/v33X8sA87MSlefOyjNHZis324Y8eOaoOSoSIAGc28N39HenzZbnRg5S509xXgeJxUgJRhKzru3RIpd9RNqaQtkvpIpsiTZkWy3nQq6FhAwZGmKzloiPxoucTDTEYC33nkky0onRpjkdWac7tzelMx9PEEEbSGxGmjQK7aEvJEvrdOa4NJG9sUYKcsPsrBmV4oz+IDtXq5g9LyQs+/oYSdKolAwjxdmcwozHd8UYz3W0WYAQ20Jg1hI2hGWI3Uh8Di5nCOJ7L4gkpIk0r2xI1KioZENuxrjNAjaeOxIvEpdqjBNcwXTfBZHUDEO4Bj8kNmNMvmWyx45jBoEb44zdtUJu632DWsHg+uuvVyt02l4H4E8ACZAACZAACZAACZAACZAACXgqAcrNnnpkOC4SIAGPJ+CM3Ax5s1+/fhZJ+JprrpE5c+ZY5oaLxJA5dSHh+d1331UCLgrpUuPGjRMIhCg8juUckZJqr1yVm3ODjWV6sQTw1KlTLZvgQvUdd9yR6/HBHLWQjYRcpOM4UwVlgbQvXPTWfTdv3lxx0wI1LrJ/+OGH6o8ud4u0tvN0lYVZyEWbkBq+++47ldyGQprzwIEDLd1BIv+///s/q+5t05JvuOEGJSrr1xZ4Pfvss/LLL79Y9lu9erXUqlUrz8P1/vvvK1kB5UhuhkQOBhCr9esXF8TNKa5RUVEqbRviOQqCxM033+zMS8apbWzlZqTHPfbYY7Jo0SK1/88//ywdOnSg3OwUzZwbQewbO3asSqPHBUsWCWgClJv5WnCGQHp6urphqjDlZvz+RzInPlPh4h3eswpaaBPt5fZ5rKDtl/T9KTeX9CPM+bmTAH5e6tWrp1KckcpcVKWlZtyQOnHiRCUuO+r/1KlTgu8K2D63FGbb9/vPPvtMfRdQ/V3G/y+rvycnJwu+2+LGFEjS+judozG0b99e8HnfUen3IfwXgvW27dvUjaT79u1Tq9mgn8L83eRofJ76POVmTz0yHJczBLDqFW5o0NWnT58cN3M70w63cUwsWdBpAAAgAElEQVTAVm7GTe1BQUGOd+QWJEACxUoAN4m99dZb6jMREtexeuPi9XuVNGxOUj6IxOSLhuyMNGYUhFskOSMdub5JIsa2SG6G2KyF47PJIgfOi7QMEwkNMPaHGL0t2kgyxra6dLoz+oe4izqWIII0ZEjQOgUaScabIkUiKog0y0othtQLMRoJ0G3CcMOdsT/SmrE/EpLrBGb3hf2REq2FZSQmY07mJGRsbS8xGo+jL0jcEJlRUSkiu2MM0RlyNQqic8xFQ7TWc4e4DFkZczEL2NgeEje2r1LeSG3GmMAE821QSaSyv8jFTEPgRt9gAk5IccY4Yy8a/W/5b4Xg2sDVV1+tAk9wvYY3axfrjxs7JwESIAESIAESIAESIAESyAcBys35gMVNSYAESMBMwCw3Q6S9/fbbLU9DeEHq7cKFC62g2YqbEEtHjx6ttkFS1cqVKy3yqd4RF3Zx4kkLoosXLxb0Z6/cLTfrPiDB/vjjj+qfSLZCUmtu5arQW1AWkGPN0jWWF0UCtrlsBWhPlZvBGsx1QVyGwGwuSNqTJ0+2PIRk6ho1alj+bSs3r1q1Sl2oN5etUP7GG2/IoEGD8vxBz4/cjNfqiBEjLO1hvP3798/RPlI1kV6Oi42QN9xZ9uRmSNz33Xef6kYnVjO52TXq+rWK9y9nRBLXeuFe3kiAcrM3HrWiHzOkMqwG0LBhw0LtHDfbYNlVrD6Am8QKWvhMhvc9lmsEKDe7xo17lV4C+A6jb9TITbjF8wUVci3JyxmZ8vkXn6uVfdCfToZ2JBbjCGlZ+Odffpb69eurmwjNpceP78v4PoPUuIiICEvKNERjH23JZO1oTqZ2NIahQ4fKzJkz8/ViQfv4gxt7Q0ND1UpJWBkHKxrhBhxWNgHKzXw1eDOBJ598UhYsWGCZAr6vIFyA5X4CtnLznj17pEKFCu7viC2SAAm4nQBWYMRnoKpVq8qZyCiZ/sdWua5puCVJ+WySqCRnJCbr1GEMAhIwEoSR2qwLUjQSiSE863RkSMyQgBtVMuRiFFKHN0YaUjOEXV1ILFZidVWR8lkLkiCJeUuUkW7cKMTYEsLv+khDDm6c9RhE3x3nDFkZcq9flhiN/vefzxKJs7ZFG5gTnjNL1Ehxxn6tTV/9deJ0+wiRClmJ0Xp/jA37l/UxkpaRYt24skjNLFkbc0FadN1gQ1ZGXYSAHSNS0ddgZ/4YrLdHWnPtrPtDIHbHpBg8wRXsIDFD5Mb+OgUbEjXGA05Iy541a5bg92C1WnWldq3q8s6ECTlWgXT7i4kNkgAJkAAJkAAJkAAJkAAJkICbCFBudhNINkMCJFD6CJjlZmdm/8wzz6iTSOYLsmZBFRIokrHs1dNPP60SZlFffPGF3HjjjXa3c5fcjIvNuLiLi+Q4mYmUW4wB1b17d8HccytX5eaCsjCLtEgnhsBkbynmF198UaUgozxVbv7ggw8EF45RSMmA9G5bx44ds5K3IZliuUBdZrkZJ6aXL19u95BBWsBSz6i8XoN65/zIzZ9++qmSllGQwDZs2OCW5bGd+XnT29iTm5EGd91111lSvrEk3/nz51USNQo3D+D1xHJMAOI93tuQKo7jyyIBTYByM18LzhLw8/MTJDgXZuF9H4Iafu+7Q27GZyL9e7owx11S26bcXFKPLOdVWATwM4PP0vhci++Sb7/9trrZ1Fb0xXudI/k3rzFifyS54fsebtTF90AsT67bzMzIlDJl85dobE5INkvS+u9annZXSrKSq7M45LdNfIfE9zB839m9e7cSuwvCs7BeD8XZLuXm4qTPvgtK4LnnnlM3LugaMmSIjB8/vqDNcn87BGzlZqTiBwRkxbOSGAmQgEcTwDk+/Lw+99rb0rv3TXLXgH7y7BOPqDEnpRsSMoRdiLW6IBFnmBKL8TikZoi4SBvW2yJhGPtDam4QbOx9+bLIxiiRin4iLUxi9OkkI6G4XYSRTozKTYLeHCVSroxIq/DsMe2JFYlPE2kTLlI+6141jB9jDfY3ROCsIGeVpoz+IFEHZEnUu2JEUjINWVmXHlPbCJHQrDHhOaRAI40a2/qXNYRlyNa1A7NTrCOTRU4kGHPXPCBe7zwnknbJEKjLZfWNNiFBn0oSqVQuW9hG/5DLtdicfknkSJxIUoZI3aDsJOjdsSLpmUaKte4LSdzvTPpI5n47W8Y+/6L8b+IEj34dcnAkQAIkQAIkQAIkQAIkQAIkYCZAuZmvBxIgARJwkUB+5GakR2HpL9syS8uQWG3TefX2OAGFZEPU66+/LoMHD7Y76oLIzXv37lXi9LJlyywp0fY6cSQEuyo3F5TFl19+qdig8hrj1KlT5b333nO4nYsvC6vd3MEiN5kcCW7mpEtciL/zzjst/Zvl5ryEdCwbjURx1K233ioQkvOq/MjN5sRvsJgzZ447sOarDXtyMxrAzySSqlFYdrFHjx6Um/NF1thYJ64jNRzp4SwS0AQoN/O14CyBdu3aqSVvC7Mgu0FemT17tuDmoIIWlunFjUdM9HSNJOVm17hxr9JNAD83EG0z0jMUCKQbr127VqUeI03+22+/VTdd4ObO/Ei9EJbTM9IFN6zdc889FplXS8e46RVpyv7+/jJt+jS1io+nC7/ly5eXxIREJWKj8H0gKCjIrliH+X8560uV7h8XF6eSCrH6D9gyZTTnzxzl5tL9PuTts8f3/q+//toyDfzcjx071tun5ZHjt5WbDxw4IOXKlfPIsXJQJEAC1gTWrFmjVrvbd+iofDBtpsyZ8YEKEEGtOytSOUCkiSnxeP8FkdiLIl2qZcvCSEBGEjKSmCHdopCuDIk5yCbdGSnOeA4Ssy60t+2cSLPKRhu61p8VCYKYXDn7sZ0xhvSMJGldh+MNObhNWHa6dFqmyI4YEV8fkRamJOcTiYac3D7ckJ5RBy+IRKUYc0IKMwpjwlibhopUNwXRI10Z+1+RtT8SozdFGm1h/KjzF410ZcjG9bOkbnM/bcMNuVtXdIrI8QRDoK4XLOLjY4wHcjRSmOsEGVI4kq0T040UaJ2MjXljW0jR2FcX9j0YJ9K/TZh06tTREnRiffT5LxIgARIgARIgARIgARIgARLwTAKUmz3zuHBUJEACXkDALDdDTEbqi65Dhw5ZiZxIgUUarG1hyVukY+WncEFm2LBhdndxVW7Oj6hdWHJzQVkgIViLuUjlNV+0MsP6/PPP5a233lIPOZpLfo6LvW1dlZsfeOABS1ozhGWIy/aqRYsWkpSUpJ565ZVXZPjw4ZbNzHJz79695bPPPrPbBsRmCM4oyPUQG/Kq/MjN5mPqjDhdUN729s9Nbo6NjVXHH4WfX8jx+gYEJjc7fyR+/fVXeeqpp6R27dqyatUq53fkliWeAOXmgh9inWpZ8JY8uwVIc5flcqEm+0PSQzo0Pu8MHTq0wEDwO0O3U1qOU0GhmWVLys0Fpcn9SzOBCRMmyNKlS+Wvv/5SN1ggpXjSpElqJQ1Ix/pnzZHgnJycLPFx8RJRxTBJ7K14o9/fLl+6LMuWLxOsdoKbFx21XVzHBzd/4vvXTz/9JPielJKSom7ixPd03Mj77rvvqt8FGD9uEq1Xr55aqahZs2YyefJkefPNN6VJ4ybKzNE8PHWuxcWYcnNxkWe/7iCA80A4H1TQwnsvRN28/njrDXD4nYKbPHL74+pqKwhrsPd7pqDHgvuTAAkUDoH7779fnefDuWWcoz5+/Lg8M3G61GvSwkpChhiM1GMkHlfMSkdOzhDZGm1IuI1NEjQeQ9Jwp6rZY0YyM0Ro7K8lYuwPObhOcLYYjT22RBvbIIlZF4Thc1kScpksCRlyL8YEgVknPkOe3hVrJCq3Ds9OZ0aaMhKeW4YZ40XpOUGWDswSjlMyDDEbScxmYRjp1NtjRNqFi4Rn7Y/tkCKtx6nSomNEQkyyM/rR40TfeE4XJGrI0qEBIg0rGY+eTzWEa4jljbIeg/wcl2ZIzJCdUWCJuZf3MwR0zRTPgdOYpx4T30upUrNmTUvgR+G8gtgqCZAACZAACZAACZAACZAACbiXAOVm9/JkayRAAqWIgFkIHjhwoFq+VhcSrrp27WpJQM5NLn355ZetJGgsheuocEGmX79+djdzRW5ev369YPy6MAaMF8sQ44LM+fPnZd26dYKkFZQjIdhVobegLMzScl6SrjfIzc4kHuOiUqNGjSzH7eOPP5Y77rjD8m9n5WZc5P/kk0/Ufr169XJ4sS8/crP5mLZq1UoWLVrk6OXt9udzk5vR0ZgxY5T8gHrttdcsyd+Um50/DL/99ps88cQTSg7J740azvfCLb2RAOVmbzxqxTvmopCEKakV7zFm7yRAAgUnAPEMsnG9+vWkUqVKot87ITbrvzdu3FhmzZqlns/rfQ9toRwJ0djO0TYFn1nBWsDNoPPmzbO62Q5j1gnUep7mmyts/46bbZCIbWbG3xvWxwWJ1viMh3Mdc+fOLdhB494kUMQEzOcyirjrUt/d0aNHPfbGmFJ/cAiABOwQQCAEzhEjPASBGG9NmS5ffzFN6teqpoI1kOwcc9GQmCHxajEYqcWbo0Qq+BrCsC4IxAlphsSsJWTIuUfjjcewPQr7b4gUCQuwFqMhJkMw7mhKZ9b7Q0LWqceQgHfFiDSoJFLDdIkFEjVE4Jah2dueThTZd0GkaYhIjUCjfyQm7zhnzEnLyhCjMSYIyE1NidHnLorsPGeMs2bW/rtjRJKyxom05YxLRtozFhNB377GoiKKBZKpbccZlyqy57whNut0bCQzY05IvEZqNIRlSNn4A/kabaDQF9r0K2OM07+s8XhUVJQKPEFYDm7Q3rVrl3Tp0iXXIBX+QJAACZAACZAACZAACZAACZCAJxKg3OyJR4VjIgES8AoCecnNmACSg3HiSBfETgie5kLy3+uvv64eciY11xGYZcuW5UiQdpQYY06vufLKK9WF8ICAAKuukOaLNDCUI7kZ6bdahH7ppZdkxIgRjoatni8oC/B97LHHVFthYWGyefNmu/0WpdzsKguIylqWx1w2btyYY/lnMNZJw5jozz//LB06dLDM2Vm5Gam7SN9FIRHc/Jq1BzA/crOZNdrCMcF8irLykps3bdpkuVEA6c1IIkFRbnb+COmfO4j2f//9t/M7cssST4Byc4k/xJwgCZAACZBAMREw3wyi5VuzqIs0YqQYJyYmyp49e9T3ioULF0pwcLD0799f3n77bYmIMBKbnU1Sd3Y7Z5HgBjkkQT/55JNSubLJFHG2gaztevbsqeaJm3Exb0ffffPZPDcnARIoQQTM54xK0LQ8fiq8GcLjDxEHSAJ2CeAcKVa1mD9/vgqHGPnEKFn068/q/DMe69rzFhWy8dCA26R69eqqDci1kIGRZAy5FwWxGKnBSGwulyXcQiLeGWOkKIebLkFsijKk3FamU8dILMb2naqJ+Ga1icRkpCG3izBEaBTkZ+wPqVkLv3gcScZnk0RahYsElzO2hTCMJGgIzM2zPobiMeyPtORaWbIytt0SZUjJGKsupDFj29pBIvWDjUeRtgzhGLK1Fov3nheJTxNpEyYSkCVwI716Y6TRtznZGttBAq+UJVFjqmmZxjzRf4tQQ1zGdmACIbxRSLYwjRRrCOcQs/EcPvvPnj1bsJoorkdFRkaqz8lI4sb1E/1dgC9/EiABEiABEiABEiABEiABEvAGApSbveEocYwkQAIeScCR3IylfiEsJyUlqfFDRJ05c6bVXFasWCGDBg2yPIY76ZGa7Grt2LFDbrvtNsvu//33n9SoUSPP5pDajPRmFITYe++9N8f2o0ePll9++UU97khuRoIDlkpG3XrrrfLpp586NZ2CssDF8b59+1r6gpAN/raFhF6c3HNmLk4NPI+NXGWBk8SjRo2ytIzXWvfu3a16gpQwY8YMy2M4hkjb1mWWm3OTdRMSEqyE+3HjxqkUh7wqP3KzrWz/4IMPCmT6oqy85Gb9c6llfD0uys3OH6E//vhDRo4cKU2bNrX83Du/N7csyQT0+9+AAQNk0qRJJXmqnBsJkAAJkAAJeCwBWxFa/xvLjWMFE3x/bNSwkZQpW8YqVVNvB3kaorS9Klu2rNNJnGgPf7BKEG6AfeWVV0QlRyPhb8tmmT59upJldNIybrbFakXm8ePvWCnk8OHDKjkY3+nq1q1rNTQmLXvsS5EDIwGPIPDiiy/Kd9995xFjKQ2DaNKkiToviHOKLBIgAe8ksHLlSvn222/lhx9+kM6dO6vrFlqWXb58ufpshhvM2na5Vlp2ukbuu+VqadWiufpMp9ORm4eKVKtgzB9i8EZIxMEitYKymSAxOf2ySHvj/jtV9tKZ41NFNiMxuVJ2YjISnyEbQ15uZrpn7kSiyLF4Q5ZG8jIKcjGSmJE2reVi7L/urEiVCiKNspKQsS0EaCQiQ8zWlZopsikya9sQ41EkUGOskK21QH0yQeR4oiEbI2FZ19ZzIghwxuPmNpGCHVROBKwgNl8WI0U67ZJI6zBDmIY4jiRoPIftArJk8QvJ6fLDsq2y48/vZNWKZbJ7924pV66c+hyNY3bttdcKVtq88cYbvfNFyFGTAAmQAAmQAAmQAAmQAAmUegKUm0v9S4AASIAEXCXgSG5GuxB7//e//1m6WLBggbRt29byb1wohoyMRC1dkG+ReIkLv7aFk4W4iJxb4S58nLTS1adPH7XMGJYpRqWlpal0WqSs6hoyZIhAQkUhufn777+3ah7/fuGFFyyPOZKbsVwdLk7reu+991RCWF7jxrYFZYG54SJ3TEyM6hqckUJtTgreuXOn3HXXXRbh3NFcXH1t6P1cZQHpGEvEaTEeCRi4ANegQQPV9O+//y6PPvqoZXhILMNczWWWm/E4Xgd33nmn1WsPx/XHH3+0PAbBvH79+nlOOz9yc3p6ulrGEAKCLry2X375ZbVMtm2lpqaKv3/W2eaCws/a35HcjBP0ECzMRbnZefhLliyRRx55RFq2bKlelywS0AROnjwpuMGmW7duUqtWLYIhARIgARIgARLwQAIQjLHiED7PlS9f3jJCLRWnpKQYEnIu5efnJy1atFA3u+nvnHrT1atXy+nTp9X3M9yIij9t2rSxpEXnJl6buzLLyubtsQ1FZg98QXFIJOAlBHDuIa8/zkwD740455HXH5ynwvO53SSSVz9I2HTlT2G8N+I8DUQ5/Nf8Rz9m+/7vDD9uQwIk4F0EoqOjBeeN//33X3WuZ9u2bWoFwXbt2omUD5ELKeniE39W9u7ZrW6ea9OuvdRoeoVc06mtXNWxjbRu3VqCgispiRhiMVKHdSHhOC5VpHM1Q+xFRSLd+ZxI24jsdGeIyesjDVEaCcu6kBgN4xfb6kKKMlKjITtDWkZBDoZYHeRnyMG68Fh5X5GWpscOxolEI4kZidPGZRW1P/qHKK0laiQpI90Z86mZlfgMgXtztEiTEJGqWX1jf6Qrx14U6VhVpGzWRCFWQ6LWidV6/khxRts1JVp2bNkoFy9elC0HTsqS33+TJnVryoljR+TQoUMqkdm4iVCkevVqijOuE0Boxrlae9eXvOuVx9GSAAmQAAmQAAmQAAmQAAmQgAjlZr4KSIAESMBFAs7IzXFxceoCri57Eur27dvl9ttvzzEKiLc1a9ZU6Qc4gQgB+umnnxakYeZWOJmFu/Btk2iRYIwLKjjxiMLJL71k7+TJk+XDDz+0NAmxE2ItLlRv3LhRXYTGiTCkZ2lxGP9GAguWp8NJM3Nt3bpVIFWbC4Jx48aNVRvnz58XSG9IzEFqtLkKymLOnDlKnNWFcfbo0UNCQkJk//79loRq/Xxhy80FYYGU7zfeeMOKD+RmCAZnzpyxehxSKU5YmstWbsZzOAbqpLOIWroZorsuiM8QoHWdOnVKnn/++Rwvtc2bN1uka/Bt37691Tbgj9eQLvQDody2MJd69eqp1yVeE0eOHFGbIF3CneVIbrZNr0bflJudPwJ//vmnDBs2TL3PYXlxFgmQAAmQAAmQAAmQgPcQ0InKtjKcftzRDaqY6eVLlwX/UzFypvIp46PEaEhv+ibdwpDuvIc2R0oCJEACJEACJEACJYNAVFSUkphxHnfv3r2yZ98BOXr4oDq/W6dOXSkXGCLl/f3EJzNNcH0EEq6ff4A0aNxcOrZtoUJJcIOcf7VGIpXrKuFXJxFDdIbw27iySE1T9gsSl5GCbBaT92ZJwNi/TJYZfD7VSDhGCrMWjkF97VmRCr7Wqcn2EqOR+Hw4TqR9FUOE1oX+sX/LMOMRpDpr2bqBSbbGdpXKiTQxpUifTRKBxI1xmpOc/9gVI5tX/C5NqpaXo0eOqCTsxLTLcvzYMQkJDJDjx46qVTlxDh6p1ZdTE+TsmdOqf5yLRVAN0rQRLIAbTlgkQAIkQAIkQAIkQAIkQAIkUBIJUG4uiUeVcyIBEigSAs7IzRgIkounTp1qGdP8+fMFUq258BjSY3VSb24TeOCBB2TChAl5zg+C4RNPPJHnNmvWrFHiNApi6fXXX28Rl+3t+NFHH8m0adOsEqaxHRh07949xy7jx4/PkSRsu9FTTz0lY8aMybFvQVggxWDEiBEqSSIvhpCgUYUtN6MPV1lgLuCzcOHCPI/luHHjZOjQoTm2McvNmCck9dwKovE333xjla4KQf6GG27I98/S3LlzVYK2udA2lp52pnBS3J2pEo7kZowJQrZ+TeDflJudOVLGNn///bc8/PDDRfKz5PyouCUJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEBREoiPj1ehJjsOnZLU82fk7NmzAhH6wMlo2bl1o5S9lK7+je18/WAO+0hmRrpKha9cubI0btJUgmo0lJpVwuTevjdLq1at1KqMSGdGOnE7UzrzkXiRU4kinaoayceo5AyRTVEitQJF6gdnzxyyc8Zlkfam/fdnJSl3MSVGn0sR1VebcCNhWtfmKBHfMtZiNARsPx+RVuHZ26FNyNkYEwpS94GjJ+WfXSclI/qwxEWeUAL40aNH5eDhI+JTpozUqlFdmjVtqlY5xLWhRJ9A8UuPk8jTp1RYS4UKFVS4DGRmSOH4YxtyUpTHmH2RAAmQAAmQAAmQAAmQAAmQQFEToNxc1MTZHwmQQIkh8N1336n0YVRe0jFSl5GcrOu2226TTz75JAeHCxcuyJQpU2TJkiU5knn1xjfffLNMnz7dIcMFCxbICy+8YFeWRnrvZ599JhBadR07dkz1/csvv1i1DckUsvCoUaOkf//+KsnZXLnJzUjo+umnn1Q/tinSen+kNr///vt251IQFkgFg0w+Y8YMq/lDWIWEifn37dtX9VsUcnNBWfzwww/q9WJOWcbYcSLz9ddfzyHKa6BmuRkJDoMGDVLJzOvXr7dijsRmJHAHBQVZPY6TrNddd53D15rtBvbkZmyDtOmJEyfKqlWrchXp8XpbvHix1KlTJ9/95raDOT0d7a5cuTLHprt27ZJbbrnF8nhRvC7cNsFibmj58uUyePBg6dSpk/qZZ5EACZAACZAACZAACZAACZAACZAACZAACZAACZCAJpCaKVLWxxCEda3Ze0a2rP5bqlYKUCnQOGd9Mvq8xEadldTkRMH1AZxX9w8oL1j9o1JwkISGhqoVQVpd0VECIupK15YN5YpWTVWIS1iVqrIzrryElRdpEpLdz+5YkaR0IzU5K9xZjieIHEsQ6VhFpLyvsW1imsimaJGGlQw5Whdk58xLRpKzrj3RGXL4VKRUzTgjp0+flhMnTsjBY6dk16GTcjHmhJw5dVI9FhwcLKHVaknVqtUkpIKfVKtWTa2miVVSIi8kS/SpI3L2zBk5ePCgWiUTf3DtAv8tX768CpVx53lyviJJgARIgARIgARIgARIgARIwBsJUG72xqPGMZMACZR4AkjtxQmwxMRENdfAwEDLEmTOTl6dJIuMVAkJKJxMq1q1ap6puCkpKeqEXEJCgtoey54FBASo/ZGAixOKWOJM/8HJOEcVGxsrp06dkoyMDLU/0gYiIiJU6oIzyxIXhIVOh6hbt65KP0BhHDExMWocOEnozBwczdHZ5wvCAskNEJyxrDPEdD+VbpF72crNEM1R4InlAsEDJ0edWWra2fk5ux3SOfD6xljQP15jeG0ioYPlXQSwXOJDDz2k0rohtrNIgARIgARIgARIgARIgARIgARIgARIgARIgARIoKAEIDgvXvGfXIg8KYcPHVCpx1u3bpXUSz6SHH9BkpMS1fllXThvjvPM+BMSEiIBgZUkIDBEalcJkdDKIep8eNkKlSQuM0BqVyorgVmRz5mXRY5eyJTyZTIkqEyapKamSnJyspyISZT4+AQpmxqnVr/EuX1cI0lISFTXN8LDw9T5bFxHQWjH5TJ+4utzSY0JqxNiPEiyxnWaWrVqqT8QsfV/cR3mvvvuUzIztmWRAAmQAAmQAAmQAAmQAAmQAAnkJEC5ma8KEiABEiABEiiBBHKTm0vgVDmlYiSAJGwk11NuLsaDwK5JgARIgARIgARIgARIoIgIrF271iLnFFGX7IYESIAESIAESIAE8iSAIJOTp8/K8aOH5dChQ0qCRujLmchouXA+VhLi41SIDITlixdT5bJclnJ+fio8BMEnCN+47FNW/P3KKskYf7JDWXzk0qVMlRidlpamxGW0BaEZAjXkZoS4hIeHS5UqVVQ6c/Xq1dUqhlg9EjIztmORAAmQAAmQAAmQAAmQAAmQAAm4RoBys2vcuBcJkAAJkAAJeDQBys0efXhKzOBWr16tEkYoN5eYQ8qJkAAJkAAJkAAJkAAJkIBdAj/++KM8++yzSm7G9wAWCZAACZAACZAACXgjAaxeqWVn/B1Jzenp6ZaVJyE2Q3CG9Az52d/fX/3BSpBIaMYqm86sSOmNbDhmEiABEiABEiABEiABEiABEvA0ApSbPe2IcDwkQAIkQAIk4AYClJvdAJFNOCTw33//yT333IOSDwUAACAASURBVEO52SEpbkACJEACJEACJEACJEAC3k1gypQp8sEHH6hJHDt2zLsnw9GTAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAl4PAHKzR5/iDhAEiABEiABEsg/AcrN+WfGPfJPAMtS33333dKlSxeZN29e/hvgHiWWwMyZM+Wnn36S0aNHS+/evUvsPDkxEiABEiABEiABEigtBCg3l5YjzXmSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgGcQoNzsGceBoyABEiABEiABtxKg3OxWnGwsFwJabu7UqZMSWVkkoAlAesfro1evXvL5558TDAmQAAmQAAmQAAmQgJcToNzs5QeQwycBEiABEiABEiABEiABEiABEiABEiABEiABEiABLyNAudnLDhiHSwIkQAIkQALOENizZ4/s3LlTbVq3bl3p3LmzM7txGxLIFwEtN3fo0EF+/vnnfO3LjUs2AS03d+3aVebOnVuyJ8vZkQAJkAAJkAAJkEApIEC5uRQcZE6RBEiABEiABEiABEiABEiABEiABEiABEiABEiABDyIAOVmDzoYHAoJkAAJkAAJkAAJeBMBLTdfccUVMn/+fG8aOsdayAQoNxcyYDZPAiRAAiRAAiRAAkVMgHJzEQNndyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRQyglQbi7lLwBOnwRIgARIgARIgARcJaDl5rZt28qCBQtcbYb7lUAClJtL4EHllEiABEiABEiABEo1AcrNpfrwc/IkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkUOQEKDcXOXJ2SAIkQAIkQAIkQAIlg4CWm1u3bi0LFy4sGZPiLNxCgHKzWzCyERIgARIgARIgARLwGAKUmz3mUHAgJEACJEACJFBiCGzYsEEOHjwo99xzj/j4+JSYedmbSGmaa4k+kJwcCZAACZAACZAACZAACZBAkRKg3FykuNkZCZAACZAACZAACZQcAitWrJBBgwZJy5Yt5ffffy85E+NMCkyAcnOBEbIBEiABEiABEiABEvAoApSbPepwcDAkQAIkQAIk4PUEdu7cKQhMQC1ZskR69erl9XPKbQKlaa4l9iByYiRAAiRAAiRAAiRAAiRAAsVCgHJzsWBnpyRAAiRAAiRAAiTg/QQWL14sI0aMkObNmwv+ziIBTYByM18LJEACJEACJEACJFCyCGDVlmHDhkm3bt3k888/L1mT42xIgARIgARIwEsJpKenS0JCgoSGhjo1gw8//FBeffVVte23334rt99+u1P7FcZGmzdvlg4dOqimH3nkEZkxY0ZhdOMRbZamuXoEcA6CBEiABEiABEiABEiABEigxBCg3FxiDiUnQgIkQAIkQAIkQAJFS2D+/PkyatQoadq0qSxdurRoO2dvHk2AcrNHHx4OjgRIgARIgARIgARIgARIgARIgARIwMsJHDlyRLp27SpRUVEyceJEeeGFF/Kc0b59+6RZs2aWbYYOHSpffPFFsVHYsGGDdO7cWfV/1113ydy5c4ttLIXdcWmaa2GzZPskQAIkQAIkQAIkQAIkQAKliwDl5tJ1vDlbEiABEiABEiABEnAbAVx0GDt2rDRu3Fj++usvt7XLhryfgJabBwwYIJMmTfL+CXEGJEACJEACJEACJEACJEACJEACJEACJOBBBMaPHy+vv/66ZUTHjh2TOnXq5DrCZ555RqZMmaKer1Kliqxfv17q1q1bbDPCqhBYEQIFSXvNmjUSHR0tJ0+elHPnzom/v79UrFhRWrRoIRUqVCi2cbqj49I0V3fwYhskQAIkQAIkQAIkQAIkQAIkoAlQbuZrgQRIgARIgARIgARIwCUCX331lYwbN04aNmwoy5Ytc6kN7lQyCezevVuWLFkivXv3VhehWCTgqQSOnEuRnaeSZP/ZZDXE/w5esAy1bZ0gefX2+p46dI6LBEiABEiABEiABEiABEiABEigFBN466235NVXX7UQmDdvngwcONAukbS0NKldu7ZKeUatWrVKrrrqqmKjl5ycrJKaH374YYdj6NOnj2D1OG+t0jRXbz1GHDcJkAAJkAAJkAAJkAAJkIDnEqDc7LnHhiMjARIgARIgARIgAY8mMGPGDHn77belfv36smLFCo8eKwdHAiRAApcuXZa1h+Plv0MX5J+DibL/dILEp6TnCebGlqFSNyxAujUMkZtbhxEiCZAACZCAicChqBSZvyVahl5TQ0Iq+Mqfu2Klon9ZubJRJXIiARIgARIgARIgARIoZAKxsbFSr149SUhIUD0hmTm31bMgEt9zzz1qu8GDB8usWbMKeXTWzR85ckQFJJw+fVqOHj0qhw8fdrr/QYMGyezZs53evqg2PH/+vOAY1KpVS6VM6yqJcy0qpuyHBEiABEiABEiABEiABEiABGwJUG7ma4IESIAESIAESIAESMAlAh9++KFMnjxZLWH577//utQGdyIBEiCBwiKQeDFTthxPUH/WHoqTlQcuSOaly1bdVQjwk4hK5aVq5Qqyfu/ZPIfSuX6wDOhYVQZ2qiL+vmUKa9hslwRIgAQ8nsD2k4ny44ZI+WVztMQmpUurmoGy81SiZdyhFf0kNLCclPfzkaCAsnJjizC5tkmINK9R0ePnxgGSAAmQAAmQAAmQgDcReOedd+TFF19UQ+7UqZOsX7/e7vA7d+4sGzZsUM9t3bpV2rZtW6TTbNq0qezfv99hn0FBQXLDDTdIt27dpHXr1tK4cWMVqlCmjHu/g2dmZioeW7ZsUYJyWFiYSra+9tprBWOwV0i9XrhwoUqRXrdunSUFG9uuXbtWunTponYrCXN1eKC4AQmQAAmQAAmQAAmQAAmQAAkUEQHKzUUEmt2QAAmQAAmQAAmQQEkj8MEHH8iUKVPUyX8sZ8kiARIgAU8gcDAqWeZtiJJ56yMlKiHNaki1IgKlVnig1KkSJHWrBiup2VyJKekSeT5Zoi4kS0JKmmw+EC3n4lKstmlYpbwM7FhVRnSvScnZEw44x0ACJFBkBA5EJsvMlafl6zVnXOqzfkR56d60snRpECx3tItwqQ3uRAIkQAIkQAIkQAIkkE1gz5490qJFC8sDcXFxEhwcbIVo2bJlcv3116vHevbsKX///bddhJcuXZJ58+bJ1KlTZfv27RIRESF33HGH3HrrrUo4zq0OHjwoX3zxhUqDhgCMJOOHHnpIRo0aJVWqVFG7YUw6YdpeO9gOwQmQmZ0VmTMyMuTPP/9UsnZMTIyEhoZKgwYN5JZbbsnBwNwn9nvwwQflhx9+sDulCRMmyHPPPSd+fn6SlJSkeCE5+pdffsmVwZgxY+T999/36rnamxykbazaB5kb1bt3b+nVq5fcfffdig+LBEiABEiABEiABEiABEiABAqbAOXmwibM9kmABEiABEiABEighBLQcnPNmjVlzZo1JXSWnBYJkIA3EXj+p4NKak7NuGQZdrPalaVZnVDBf8Mrlc/3dA6djpMdR87J3hPnrUTnLg0qyaS7G0uDiPy3me9BcAcSIAESKEYC0Qnp8uXKUzJz1WlBKr65QoMCpFHNELl8+bJ0bFI1xyiTUzPkwKnzciIqUU5EG0umo7o1rKRuEunVMqwYZ8auSYAESIAESIAESMD7CSB04OTJk2oiSBXu06eP1aQgNkNwRiF5GLKybW3evFmGDx8u+K+9euutt+Tll1/O8RTau/322+3uA9EYYnC9evXkyy+/lKFDhyrxuV+/fmofX19f6dGjh9q3ffv2smnTJqcOBj53QsJ+6qmnrNKTzTs//PDD8thjj0mHDh1ytDlixAj57LPP8uwLKdhff/21DBkyRKUy51aYD6Tqb775Rtq0aaM288a5fv/999KwYUPLNKOjo+Xxxx+XH3/80e7Ucfzmzp0r5cvzfIhTL1puRAIkQAIkQAIkQAIkQAIk4DIBys0uo+OOJEACJEACJEACJFC6CWi5uXr16nme6C/dlDh7EiCBoiCw/WSijJ13QPBfVPXQitKleTWXhebcxrx612lZvfOMSnZGhVb0k2kPNpNrmoQUxTTZBwmQAAkUKYGMzMtKaP5y5Wk5EXvR0rePj4+0rh8mreuHq//6lnVumXAk4287fE6WbjxmaatNrUB5886G0qm+dcJgkU6UnTlFID4+XpYuXarSIc0JkU7tzI1IgARIgARIgAQKjcDTTz8tOEeHeuSRR2TGjBmWvhBGcNVVV6l/Qzbev3+/lC1b1mosEJq7d++eZ7IydtixY4e0atXKsi+Sj++9994854WEZEjCqOTkZKlQwXr1JHyuRDVp0kT27dvnkBFSoiEna1nb0Q5fffWVSpHWhbmaheegoCDp27evBAYGqv7N7QYEBMjFi9mfgTVDSNq33XabtG7dWvT4bcfhbXMFB6zKB0E7NjZWSedI786rpk2bJiNHjnR0CPg8CZAACZAACZAACZAACZAACRSIAOXmAuHjziRAAiRAAiRAAiRQegloublq1aqyfv360guCMycBEihWApuOxsv9n+2S+IsZElLRX65qVUOublVD/Hydk+3yO/jU9ExZvfO0QHSOS0pTu0++p4nc0zlnYml+2+b2JEACJOApBJbuipX3Fh+TXaeMm0ZQtSICpU0DCM3hEuFCEr55bqt2npYVW0/KhaRU9fAD3arJuwMbe8r0OQ47BJDc9+yzz6rExdWrV5MRCZAACZAACZCAhxBYvHix3HzzzWo0VapUkTNnzkiZMsb34ZtuukmWLFmi/v7ee++p3+XmgtCLlOKEBGOFDew/c+ZM1d7evXtVcvGGDRvUc++88448//zz6u+44al3795Wbb322mty9dVXy2+//SYfffSReg7SLG6Qyq1wTjEqKko9jUTmvCozM1MqV66cQ8KGGI1k6YiICHV+EuOHxK0L4xo/frz657Bhw9Tzeq6Yf0hI9s3KYIftP//8c7tDQTtPPvmkSmvOb3n6XGfNmiV33323XHfddZZjjjlOnjxZCeU4jq+88oqFH44/XnssEiABEiABEiABEiABEiABEihMApSbC5Mu2yYBEiABEiABEiCBEkxAy824eLBx48YSPFNOjQRIwFMJzPnvrIz98YAaXtPalaXvVQ0LLNw5O9eY+IuycO1h2XEkRu0ypncdGdO7rrO7czsSIAES8EgC6ZmX5d0/jsony4ylzVGQmrs2ry5dm1dz+5gXrTsiy7cafd3eNlxe69NAaoT4u70fNlhwAlOmTLGkQh47lp2+XfCW2QIJeD+BpKQkqVixovdPxM0zSE1NVQmxvr6+bm6ZzZEACZgJICXY/B70zz//yLXXXivm1GZsf/LkSalZs6YVPLP8DEn4zz//lDp16qht0G6/fv0scvTrr78u48aNU881bdrUIhBDiP7333/VY7o6d+5sEWTzkpYbNmwohw8fVrs5kpszMjLEz8/PavyQsL///nupVKmS5XFI0HgMqdG6/vrrL7n++uslODjYIkcjeRoyr72KiYmRQ4cOCW7uev/99602gbD96quvyuOPP54jiTqvV6anz7Vx48ZK6tYCO+aCJGukOOuC3I3XAeqaa65Rx51FAiRAAiRAAiRAAiRAAiRAAoVJgHJzYdJl2yRAAiRAAiRAAiRQgglouTksLEywrCOLBDQBXOj48ssv1QWggQMHEgwJFAqBr9ackRd/OqjavrZNTendsa74+1kvr1soHds0+vOqg7Jm1xn16OeDmsutbcOLolv2QQIkQAJuJ7D64AWZuOiYbD5mpOvVDA+Ubi0KR2o2D37b4XMyf/UhSUg20vBXjO0gTapZL1nu9smywXwToNycb2TcwYsIQGLTBRnXnOLpaBqPPfaYLFq0SEluY8eOdbR5qXl+06ZNSiwMCAiQX3/9VWrXrl1q5s6JkkBxEICE/Msvv6iu77vvPvn222+VzAs5FTV06FD54osvrIZmTnzWT0BwvuGGG8Tf31+1oVOV8fzy5cule/fugp/vjh07Wtpat26dQGY21/bt25Uo27NnT7nzzjtzRZKfNGM0Ypaq0fYff/wh5cqVs9s+BGewQA0aNEjdpIXkZ5SjRGlzgxCjkVysJWz9HKRuJEbr9zpHx93T5xoZGSnVqlnfzIg53nLLLSrRG68XHFddZtnd0dz5PAmQAAmQAAmQAAmQAAmQAAm4SoBys6vkuB8JkAAJkAAJkAAJlHICWm7GhYGtW7eWchqcvpkAkm/Wrl0rXbt2lblz5xIOCbidwFerz8iL/2eIzbd1rS/d29Zyex/5aXDppuOydKORYvnfy52kblhAfnbntiRAAiRQ7AR+WBcpz8zNXr67V4c60qNdbfHzNZY0L+yKPJ8sX/+5R/DfCuXKyu+j21FwLmzo+WyfcnM+gXFzryGAZNLmzZtbjRcJqC1btlRplcOHD8+RFKo3jouLkzZt2qh/Yp/du3d7zbwLe6ATJ06U6dOnq27efffdXNNRC3scbJ8ESgsBiM0QnHXhZvNnn33W8u+9e/daJSvjCXO6siNODzzwgHzzzTdqs5kzZ8qwYcPU30eOHCnTpk1ztHuuz5uFXyQulymT/dnz9OnT6qb5G2+8UZDQjMJ7s36v1bJ1bo0fOHBAIGujIGVj3EhPRkHWhczrbOF3xXfffSf4PGT7Xo+2kGg9ZMiQPJOcPX2uL730kuC925lq0KCBbNu2TQIDA53ZnNuQAAmQAAmQAAmQAAmQAAmQgMsEKDe7jI47kgAJkAAJkAAJkEDpJqDlZiz9aE7uKN1UOHsQoNzM10FhEth/Nlm6v7tJdYFU0af7X1GY3Tnd9rd/75MtB6OkZc1AWfJMOynj4+P0vtyQBEiABIqTwPQVJ+WNBUcsQxh+SytpWttItSvKMgvOSG5GgjPLcwhQbvacY8GRuJeAPbnZ3EOrVq2UuFenTh27Hb/55puCdFCkNg8ePNi9g/Pi1vbs2SNIta5QoYLMmjVLiYQsEiCBwiNw8eJF9XOWkJCQoxOkFs+ePdvqcaQQa9G3Vq1aKuF5woQJ8tVXX+XYH9Lr+PHjLTd6mOXm3r17y8KFC8XX19elyZkF69jYWEuyMhobM2aMTJ48WaUsnz9/XpCsb5ab9+/fL40bN86130cffdRykwX+DhG7bdu2avv8ys26k8uXL8t///2npGudlK2fQ5u///67dOhg/zOsp8/VLF9v3LhRHddJkybleE0hiRs3r/B93aWXPHciARIgARIgARIgARIgARLIJwHKzfkExs1JgARIgARIgARIgAQMAlpuxkWGnTt3EgsJWAhQbuaLobAIJKdlytUTN8rZuDQpW8ZH3hpyZZGlijqa05nYJJm1eLfEJlyUB6+sLv8b0MjRLnyeBEiABIqdwMyVp+XVXw5ZxoHE5l4d6xbbuMyCc7/2VWTqA02LbSzs2JoA5Wa+IkoqAbPcDGEtPDxcIP0tWLBAPvnkEzXtnj17KkGXRQIkQAKeTODpp59W5+psCzcbNGvWzOrhRYsWyW233aYee/HFF+Xtt99Wfz9z5oxs3rxZoqKipFGjRtKxY0cpX7681b5r1qyRq666yvIYkpWR6gw5Nr+FtGktCe/atUtatGhhaQKSMMaCunTpkvj4+Mgdd9whv/32m3rsySeflA8//FA9bi5I0qNHj7YkTUPexpgxty5dulg2TU9Pd0rKRoJ0aGioBARYr9CE5GII4T/++KNV/7klSnvyXCGPY46oTp06yfr169XfIcvjGBw6dEiqV6+u0r7DwsLye5i5PQmQAAmQAAmQAAmQAAmQAAm4TIBys8vouCMJkAAJkAAJkAAJlG4CWm7m8rul+3Vgb/aUm/maKCwCo7/fL/M2GEvH9r2qoVzdqkZhdeVSuxv3RcoPK/arfT+8t4kM7JT/i7sudcydSIAESMAFAoejU6Tv1G1yLiFd7d2mQbg8dGNzF1py7y47jpyTr5buUY2O6V1XxvS2n5bq3l7ZmiMClJsdEeLz3krAVm6uUSP78+W8efPkueeeU1ODvAapC/Xee+9Jamqq1ZQh5EFcs1dpaWlKrFu1apUSBi9cuKA2CwkJUSmiQ4cOzbHbhg0b5N9//5UDBw4oqa99+/YqDRTSma59+/apcUG4e/bZZy2PZ2RkyLvvvqv2GzJkiNSsWdPyHFJHd+zYIX///becOnVKYmJiBIIfVmSqXLmyvP766yodVVd+xg4ZbunSpTnmMmLECImIiLDLBhLi3Llz1Q3TSJ5FKuvtt9+eI40VxwIprZAxMb8///xTIGzWr19fHnjgAbvprXFxcSqNFmIe5gl5D0nSmCfa6d69u7e+bDluErBLALJtu3btrJ679dZbVQKvbf3www9y7733qodvvvlmlTicnzKnEGM/pPji/aZXr17StWtX9bPmTI0aNUo++ugjtSkSoR9++GHB+xRuLoG8jBo4cKDgPQCF9xikReuCWN2jRw+pXbu2en9dsWKFRX7GNghkwPtp06ZN1fuwWco+cuSI1KtXL89hYiyQvHHTC94fkWJtm1IN7s8//7wsWbJEtQUW2B7nS83lyXM9ceKEZYUCMDt79qzTx9CZ48xtSIAESIAESIAESIAESIAESMBVApSbXSXH/UiABEiABEiABEiglBPABd2pU6eqBJe9e/eWchqcvpkA5Wa+HgqDwKQlx2TSkuOq6W4tqkv/azwzGfnLxbtk97FY6d0qTGY9nJ06VRhM2CYJkAAJFITAU9/tl582GjeM1K9eSR6/o01BmnPrvj+vPChrdp+RkAq+snBUO2kQYZ0Y6NbO2JhTBCg3O4WJG3khgbzkZki0kOaOHz8ujz32mJLXUHXr5ky4hyyrk57NGFJSUpR8u3HjRrt0kEL68ccfW55Dn5DnIPzaq1deeUWGDx+unoLkp/9+7Ngxy+bmOc2fP1+uuOIKy3NvvvmmfPHFF3bbtr1xOb9jnz17trz22ms52sY4IRbaFlJaH3zwQSUe2xbONUBy1gUB/K+//pKRI0fK9OnTc2y/ePFiad48+wYdiM/9+/eXpKQku3OdOHGi3HfffV74iuWQSSBvArhhYvv27ZaNvvzySyUd25at6IubL8ziryPO586dU5KxTla23b5JkyYqJRmiM9KfW7dunSMBGvvg3OLYsWMtu995551KRj558qTlsU2bNqkbPFC4aeOuu+6S//u//3M0RJUCjURpva/tnHGTxA033JBnO7jBw9/f37IN5oWbXnBzBJKO/fz81A0rq1evtsji2BgrAWDu5vLkueIml3LlylmGi/fIF154wSFjbkACJEACJEACJEACJEACJEAChU2AcnNhE2b7JEACJEACJEACJFBCCeAk9/fff69O8iNBiUUCmgDlZr4W3E0gMTVTbnx/sxyLuSgVA3zl6f7tJSQw+wKju/srSHsQmyE4o5aP7SBNqzmXWFWQPrkvCZBA8RKYM2eOSoh76KGHBOl43lDzN0fLY3OMm9OCKpSTx25vIxEhniMQxyenyacLtsu5uBQZcnUNmdCvoTdgLdFjpNxcog9vqZ5cXnIzwIwfP15mzZqlkn61vIx0TkjIKHwnXrlypdXzZqDTpk2Td955Rz2E3xHXX3+9SkkuU6aMJCYmSvXq1a3SmL/++mt59dVX1fZXXnmlQPTDdhD5kG6sPmMuXy4NGjTIt9wMQVCnS7dq1UqJeEhUhpyH1GQklJp/j+V37EhB3b17txojUpJffvll9Xd7cjP6wty2bNmitsG2wcHBijFkcojWEBGRbo3ScjP+Dmb333+/EpcxRpStXG7e/tFHH1WSY2BgoBIjExISlOxoT1Iv1T8MnHyJIDBu3DjBTQzqM15QkHrfqFMn5yoY+Bm87rrr1PuX3hapx1oEtgcDEi9upMD7BcRlvH/i5gxnJFhIwRgL3m/MBYkZqcu51YQJE9QNH+bCjRe4mQLvl2vXrrV6DqnJPXv2FPzcX3PNNeLj42N5HuNu3LixRZxGcjyS3B0V3l8WLVrkaDOr53GeFH1501yRTI3febrwu2/w4MG5zhsrGBw6dEjdoIIkb7MEni9Y3JgESIAESIAESIAESIAESIAE8iBAuZkvDxIgARIgARIgARIgAZcIICEKFylxYeLgwYMutcGdSiYBys0l87gW56xmrz4tL/3fITWEa1rXlD5XNijO4Tjse/pv2+Xg6Th57qa68nSvnBeSHTYgopYHh3xREosXPUviUS3dczILVBBCnnjiCSWveWpdSM6QOz7eJgcjk9UQr7+ittzcOe8luYtjLpv2R8n3y/eprhc81VY61gsujmGwzywCEBaHDRsm3bp1k0mTJpELCZQYAo7k5s8//1zeeustgQxsT25DUjEku9ySm5FIihTmsLAwWb9+vfj6+ubKDp/90A+kXaQt//zzz0qCRkHmg7SItGGdxJzf5OZffvlFRo8erdpbtmyZNGyY940j+Rm77aSio6NVWivKntwMqblv377qeaSZIokVhVWhkAaLeuONN2TQoEHq7+bftRAkIW2iwOSrr75S8qaWNPE4ElPPnDkjtsnYJeaFy4mQQC4E8LofMWKE4GcQknNeycRIeEbSs7mefPJJGThwoEpChxh8+PBhQarzwoUL1fuGLqSjN2vWTP0T0vMff/yh0oqRYGwvzRnSMW6AqFAh582/ZiFbt9+nTx955pln5Nprr83zWOM9/OzZs5KZmSnh4eEOZeV9+/ap9xa8Z+MGD2cK779IjMf7PW6OcFT4nISx2ytPnitYImXfnJqN9+NRo0ap5G287544cUKtRIDjPW/ePMsU8bsSnxNZJEACJEACJEACJEACJEACJOBuApSb3U2U7ZEACZAACZAACZBAKSGANCmkpJQtW1Zd7GCRgCZAuZmvBXcTuPWDrbLluHER8en+V0jN8EB3d+HW9rYfPidf/7lHmtcIlL+fzV4GPD+dUG7ODy1uSwLFSwCfg3BB/7vvvrMMBCIJBDRPlJy/WXNGnv/Jy10ujAAAIABJREFUuDHN36+sPDOgvYQFBxQvxFx6n/bbdjl0Ok76XBEh0x40BBoWCZAACbiTgCO5GeIsZDQkcP711185unYkNyPl+ZFHHlH7QVy+6aabVMJlu3btcqRcQkqElItCnxB686r8ys1RUVGWlGjI1gMGDFBjgYSsE5LN/eVn7LbjdCQ3m0Xrf/75R+rVM26yQZpsy5YtleANsRkSIkrLzUizRlq2Lgjnjz32mEp61qnReA77zZw5U22GFNcePXqouUPc08K4O19HbIsEvJUAhGXcaOCMtGueI0Rl/XNrO3e8r+7YsUN27dplSZx//PHHc72hAj/3P/30k6xbt04aNWokEJuR0O5phfelb775RjZs2KDmhxRrJMLj/RTyNt5P+/fvr97DcitPn+vRo0cVf4jv+SkcP8ydRQIkQAIkQAIkQAIkQAIkQALuJkC52d1E2R4JkAAJkAAJkAAJlCICWMYVSS44+c0iAU1Ay824WM5kP74uCkrgt63RMuLrvaqZ9o2ryH09mxa0ySLZf8J3G+R8wkX5fkQrua6p46VubQdFublIDhM7IQG3EsDS1u+++66VdIXUOySe9evXT/C5yRNq8Ky9snRHtBqKp6fhr9l1Rn5eZYjY3z7SSno0y//7qScw5xhIgAQ8l4AjuRmpzbiBpVevXuq/tuVIbs7IyJDnn39eiXu29fTTT6ukS8hxKKRhajnMXtqx7f75lZuxP4RfLQyb28PvKYwHCci68jN227E5kpunTp2qEpttpWS0o0VmSMmzZs1STevHkEj70ksvWbpDeujIkSNztIPkUaSNgqm5IEyOGTNGcabk7Lk/lxxZ0RLAjXpIGv7111/z7BgC7y233KK2RZIvq2QSgMSNQIspU6bkOUEkOSNZG+/BSMJmkQAJkAAJkAAJkAAJkAAJkEBhEKDcXBhU2SYJkAAJkAAJkAAJlBICkHRq1aqllp1kkYAmgMQspHzhAnRwMJeQ5yujYAQe+mKX/LU7VjUy/P/Zuw/oqKq1DcBvem8kJBBC6L33gKDYQEURRUXsBRSxISiIKCjYFUWvXq8i+ttBEBGlXLw2QEB676T33ieZln99e3ImkxAgCWmTvNuVOzPn7LPP3s85k3DXeuebcb3RLcw+gm3f/n4Ce0+l4oUbOuCRy8OqjcBwc7XJeAAFGo3Ajh07IJUk5ScjI8M6r6uvvlpVj5QQgPz7qaFazxf+QXaBXn1AbebEAWgd6NVQU7ngefN1Bry9cg/kcdroMMwf3+GCx7ADBShAgeoInC/cbDabVZDv2LFjmDp1Kp5//vmzhr5QuFk7IC4uDlKheNu2bfjzzz9VZWJpjz/+OJ5++mn1/PTp09aK/2vWrMGAAef/BpBzhZttg8WVjZOTk6Pmsn37dvzxxx+QitHS5HzSv2KrytwrHnOhcLN8C5SE56QdPnwYEpLTmnz7walTp9QHg7RwnRZulhDd3LlzrX3PFW7WOkj10S1btmDr1q3KXmtSfVX+HrNRgAJlAidOnFAB55MnTyI2Nhbu7u6qkrJ8WG/UqFHo3p3fotGc7hf5/zHff/+9qop/5swZyN/EDh06oGvXrhg+fLiq+u/s7NycSLhWClCAAhSgAAUoQAEKUKABBBhubgB0npICFKAABShAAQo0FQHt69fla9fZKEABCtSFwPBXdiEmowi+nq6Yf/ewujhFnYz5x/44rPsnGjcPCsYHd1a/2jTDzXVyWTgoBepVQAIBWshZAs+27ZJLLlGhKnmsz8p3u6JzceP7B9RU+nYMwj1X96hXk5qcTCo3SwXnXm288eus8wf9ajI+j6EABZq3wPnCzRLqeuaZZxTQF198gdGjR5+FVdVws+2BUhFZqifLmFK5WAK4EhArLi5WoTFp5wpT247z+++/4/7771ebZAw/Pz/1fNeuXZBv0ZFWlZC09JEqx9J+/fVX6xwquzPONfeKfS8UbpaA97333qsO++abbzBy5Ej1PDs7G/369VPPpZL0jBkz1POahptt5yWh7rvvvhsHDhxQVUY//PDD5n3zc/UUoAAFKEABClCAAhSgAAUoQAEKUKCRCzDc3MgvEKdHAQpQgAIUoAAFKEABClCguQoU6k3o/Kylwlr7Vr547EZL0MEe2rHYTCzbcARdW3nhz9kDqz1lhpurTcYDKNCoBXbv3q0CY/Ijlc9sW//+/XH55Zer0Jw8r8v2xoZYvPdrjDrF9REdMLpfw1WQruo6zyTm4KOfD6ruf80ZhC4hnlU9lP0oQAEKXFDANty8fv16BAQEqIql8lzCx9KkYunXX39d6VgXCjenpKTAzc0N/v7+1uOzsrIwZcoUyN8GaZGRkXByclLPp0+frj4YI00qRcsHiSUALU2n00Gv11tDzMePH8fYsWPVvieeeAJS1Vgqrr788svWsb/77juMGDFC9ZFwb1FREVq2bAlHR0e1TV6//fbbWLp0qXotVVu1v0XVnbst0IXCzTIXqfwpFazFV4LGHh4eeOONN/Dpp5+qoWyD2dUNN8s1DAoKgqdn2d8MsZkwYYI6p3yTwueff37B+4MdKEABClCAAhSgAAUoQAEKUIACFKAABRpOgOHmhrPnmSlAAQpQgAIUoAAFKEABClDgPALHkwpwxVt7VY+BXYJxxxXVr4DcUMBZ+cV45Zud6vSRb1wCdxdLgKSqjeHmqkqxHwXsT0CqVf7xxx+Qx+jo6HILqOug87j3D2JfdI465/TxfdGxtaXKZ2Nv8vtUfq++NKEjpl7aprFPl/OjAAXsSMA23FzZtKWK8AcffIDw8HC1W0K4thV/JSirNS2E3LlzZ6xdu9byu9YmrBwYGKjCxLbHSMj5hRdesI6RnJysgreVjSvbJk2ahDfffFP1lyrK11xzDU6dOlVu6nK8VHXWmgSkX3vtNXz11VcqMC1Nm6vteWStq1atgqura7XnLsHrQYMGlZuHNrZ2Ltn57rvvWgPZEqiWIHZl7dprr8V//vMf667qhpt79uxpNRR3+TYF2/bJJ59Y52FHtyunSgEKUIACFKAABShAAQpQgAIUoAAFmpUAw83N6nJzsRSgAAUoQAEKUIACFKAABexHYNORDNy37Kia8FUDw3HNkHb2M3kAC77YgYIiA357ZiB6tLZU3KtqY7i5qlLsRwH7FqhK0PnSSy/FwIHVrwBfmUzEq3sQm14IRwcHvPrgCDg7Ve+DFw2lvXT9YZyIy8Lo7gH49qHeDTUNnpcCFGiCAhLK7d69e7mVdezYET169MDIkSNx2223wdnZ2bp/8eLFeP/9988rIUHoLVu2qD633nordu60fODNtkng9s4778STTz5Zbnzpk56ejtdffx0rV64867irrroKy5Yts24/ffo0HnvsMRw7dkxtk4CyzO/mm2+2BnpvuukmLFmyBO+99x7eeeedSucu63z66acREhJi3V+duVfmWNmJJCh+ww03WHfJGiV4bRs+fuSRRzBr1iy4uLhY+02dOhWbNm2C7Hv22Wet2zdu3IiHH35YhbWPHrX8/wYJVUu4ubLWu3dvSFBafNgoQAEKUIACFKAABShAAQpQgAIUoAAFGrcAw82N+/pwdhSgAAUoQAEKUIACFKAABZqtwKebEzB/TaRa/6TRXTGkW1nYwh5QPvr5IM4k5mDV9L4Y0bl61VEZbraHK8w5UqB2BSToLD8SiJOwmm3r1asXJOQsPyNGjKjxibs/vwO5hQa0b+WLx27sV+Nx6vvAtdsisflQgjpt4juj6vv0PB+A3NxcVdFVAoMRERE0oQAFqiEgwd28vDyYzWYVZA4ICIC3tzccHBzOO0pJSQnS0tKQn58PJycn+Pv7w8+v8n9Tyjlk/JYtW6oxc3Jy1DFShVlCwtq5ZCzpK//WlO2+vr7qR/pW1mo692rwqK4S6JY5tWrV6oIuVRnbZDIpO6nMLc3Dw0P5ySMbBShAAQpQgAIUoAAFKEABClCAAhSggH0IMNxsH9eJs6QABShAAQpQgAJNXkCv1yMuLh6dOnVs8mvlAilAgaoJfPRHPBb9HKU6P3JDX3QKrV5AuGpnqbteS1bvQ3xaPtY/1R/92/pU60TZ2dk4dOgQJNQireJjVbdpx1W1f2X9qjvGhfpLqKcq6znfOPayr7qeVV1XtW4mdrZLgaSkJERFRSE2NhbJycnl1iCBOPmRCpzVDZmGzrRUEr20bxuMH24//+bacSwZqzafUnM//foIeLpWHsKzy4ttJ5OWSrELFy5UIUj5+8RGAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKhLAYab61KXY1OAAhSgAAUoQIFGJHD6TCRiY+PQulUIsnNyUVxUhNGjL22wGUqAS6pHyeNHHy9FaJtQlJhL1OubJ4xvsHnxxBSgQOMRWLU7FU98e0JN6IFreqFnuxaNZ3JVmMmbK3YjNVuHP+cMQtcQzyocUdZl+fLlmDNnTrWOYWcKUKB5CQwYMABr1qyp8qIz8g3oM3+H6j9uWAdc3j+sysc2dMfIpBz8e+1BNY2DCyMQ5O3S0FNqdud/9913sWTJErXumJiYZrd+LpgCFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAXqV4Dh5vr15tkoQAEKUIACFKBAgwhERkYhLy9fVVozl5ixfcc/GNCvL3r16lnj+UgIubi4WP3IuBf6St2ioiLs3bcfBQU6ODo6qq/MlWrNAwf2wx9/bkZ+fgGCQ1qq7fIVsteNHQMvL68az48HNpyAVPaTry2fMWMGxo4d23AT4ZntXuCvE1mY/PFhtY67ruyO/p0tX7NtL23R1zuRU1CMnS8MRViAW5WmvW7dOkyfPr1KfdmJAhRovgItW7bE559/jj59+lQZ4WRyIUa/uUf1v3lUZ4zo2brKxzZ0x4IiAxZ8YQlm75g3BOGB7g09pWZ3foabm90l54IpQAEKUIACFKAABShAAQpQgAIUoAAFKEABCjSoAMPNDcrPk1OAAhSgAAUoQIG6FZCQcGxcPI4cOoxevXur0HBiUhIk7HzfvXfV+OQZGRlIz8hUVZYljBwVHYNx154dYs3OycH27f+gRYsAmM0lMBqNqr88N5tN6lH+C24ZiL37DiCoZZCaowo4G02QQPSA/v3Qp3evGs+VB9a/wKRJk7Bjxw6MGTMGS5curf8J8IxNRuBoYgGuenuvWs+tl3XBsO6t7Gptz3++HUV6Iw4vikALr6pVGWW42a4uMSdLgQYViIiIwIoVK6o8h+1ncjDxQ0v148lXdMOgLsFVPrahOxpNZjz76d9qGr8/MxDdW/MDcPV9TRhurm9xno8CFKAABShAAQpQgAIUoAAFKEABClCAAhSgQPMWYLi5eV9/rp4CFKAABShAgSYsEBcXr8LDi998AzOfmY1tO/5RlZJLSsy44frrEBQYWOPV/7JuA7p17QI4OKggck5OLgYO6AcnJyfs3bcPJnMJnJ2cVfhZftzd3VBQUKiCy1p42VKh2WwNSJvMJnz1+WdITUnB47NmWYLQJglCm+Hr44PrKglP13gBPLBOBbRwc3VDV3U6KQ5ulwJpeXr0W/CPmvv4ER1xaZ82drWO2Z9shbmkBJFvXAJ3F8dqz11+f0qr+FjVbdpxlfWv6b7aHOt8c6jN89TmWPU15/o6D20s77HG8p5KTEzEqVOncPToUeTl5Vnn1rlzZ3Tr1k392N4bffv2xRVXXFHl3y0JWcUYsmin6n/f2J7o3b7m/xas8klrqWO+zoAXv7RUbv7lyf4Y2M6nlkbmMFUVYLi5qlLsRwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAArUhwHBzbShyDApQgAIUoAAFKNDIBKKiYvD+e0vg5+uLKQ89jMSkRMTExiGsTSgihg2Fg4PDRc34559/QVhYW/j6+sJcUhZQtg0sA5aqzhJgLiwshI+PjwosZ2ZmwmQ2IyQ4GNnZOaWVnM1Y9/NaDBg0CHv37MaZUyeRm5OL0VdeiT79+sNoMmLcNWPh5+d3UfPmwfUjwHBz/Tg3l7OEztyilnrNkHa4amC43Sw7KbMAi1fuhb+nC46+HGE38+ZEKUCB+hWQb6mQiu0//PAD/v7bUplYWmBgIMaPH48bb7wRAwYMqLVJhc2yfOji4ev7oEsb/1obt64HSs/R4fXlu9VpNjw1AP3aetf1KTl+BQGGm3lLUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQH0KMNxcn9o8FwUoQAEKUIACFKgHgdjYeDg6OqgAs6ODI+AAODo6qorJGzf9ittuuRne3tUPhEhIOTU1FZmZWZZQs7ks1CzhYzmfJcxsKg0sW6o2S/Vos9myzfLa0kdfXAxnFxcYDEYVgD565BA2bViPQUOGwtXNFT+uXIlbJk9Gx06dUSL/lQCTb7ulHgR5iosVYLj5YgV5vK3A9K+OY82+NFVhVCqN2kv762ACft4eiev7t8Qn93S3l2lznhSgQD0JSJB5/fr1WLt2LXJzc61n7d+/P2644QYVam7ZsmWtz2bAwt1IydbhxhEdMcqOquHHp+Vjyep9yiPmrZFwcbq4D+rVOmwzGJDh5mZwkblEClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAKNSIDh5kZ0MTgVClCAAhSgAAUoUBsC8fGJKsw8beoU/GfpUjg5OangcUJiEvYfOID77727WqeRMHJycgqcnZ1VOFkLKEtoWXt+56TbcOPNN+P75cvx+ttvIzQ01CbUbAk024actYCzq6sLCgt1ap8Enp956gk8/9IifPfVl7j97ruxYe1aXDt+vOV4kxnBLYNw5RWXV2v+7Fz/Agw31795Uz7jpiMZuG/ZUXi4OmPO7YPh7eFiF8v9+JdDOJWQjQXjO+Lh0W3sYs6cJAUoULcCx48fx6+//ooNGzbgyJEj1pN5eHioMPNtt92GQYMG1ekkrn3/MA5EZ9ndB0ZOxGdh6brD6BLiib/m1K1RnV4AOx6c4WY7vnicOgUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABOxRguNkOLxqnTAEKUIACFKAABc4lEB+foELIBoMBG9avx4SbbrJ2/WvLVri7u+OGcddWGzAnJwd6vcEabtZCzu8tWYLcnBzMnfc8jEYjDh86BP8AfwSHhOD+u+/GJ599poLLJSVauNmMAwcPwdXFBUXFRejVsyf0er2q3CwBZr2+GPPmzMaCl1/BDytWYOLtk1To2WTUqkGbcdmlI5GSnIKBA2vvK9qrDcIDzivAcDNvkNoWGLRwJ5Kyi3HzyM4Y0at1bQ9f6+Ol5+jw+vLdatw1j/XD0I6+tX4ODkgBCtiHwMGDB7Ft2zb189dff5WbdL9+/TBu3DjccsstCAwMrJcFTf/mNNbsSVIfGHnpvgg4OthHBeRtR5KweutpjOjkh1WP9q0XK56kvADDzbwjKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoD4FGG6uT22eiwIUoAAFKEABCtSRgISNExKS4OzspALInp4eKCoqVhWbpcXFJ+DQ4cO47567ajSD3Nw8FBcXq2NlbEdHJ/UV6nKum8ePx/erf1Qh5PXr1mHkqFFISUnGm6++iiUfflgu3KwrKlLBZglCW6o5my3hZRVutgSY5fnqld9j/M03q33mEjOMBqP1eV5uPjp16ojwsFB06dKlRuvhQXUrwHBz3fo2x9EXrInE0s0J6BTqh0duaPyhtq2HE7Hm7zPw9XDGwZeGwdXZsTleNq6ZAs1SID8/H9u3b7cGmqVas21r3749Ro8ejcsvv1w91nf7ZHMSXlxzWp122g190DnUv76nUKPzLf/zJHafSMGDo0Kx6KZONRqDB12cwNGjRzFlyhSMHTsWCxYsuLjBeDQFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQ4AICDDfzFqEABShAAQpQgAJNQCAxMQmOjo7IyMxEy6AgSKZZKibnFxSogPOu3XsQ1iYUl4wYXqPVFhQUQKcrgnxt+roNG+Hj44PLLxsFCT1LIFlCzhJKnjTxZny9fAUKCvKxZ88eRAwfbg0vW6o9m1XoWuaqHWcJOpeocLOMIYFmy3gmmKyBZ5MKRAcFBSIjPQP5BYWqv4S1nZycarQmHlR3Agw3151tcx35n8gc3PTBQbX8u6/qjn6dWjZaCpO5BO+t3ofEjAKM6RWI/3uwZ6OdKydGAQrUjkB2djZ++eUXa6A5Kyur3MANHWi2nUyh3oTOz25Tm8YMCseYwe1qB6EOR8nXGfDqt7ugN5qweFIXTB7Wqg7PxqEpQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhRoDAIMNzeGq8A5UIACFKAABShAgYsQOHDgkAr9ShjYzc0dbm6uKtTs4e6uQsIZGZnYtuMf3HfPndZKztU9XWpqKnbt2YfCwkLccvMEFTT+ed1GXDpyhLUCc3JyMoKCWpYLJxuMBnVOmZvRaAktyw9QAr3eUCHgbKnaLH0lFCTBZglRqwrPJWb4ePsgPT1dvTYYDCgoKISrqysevP+e6i6H/etYYOrUqdi0aRMeeOABVvarY+vmNPx1S/Zjf2wewoN98PiEfjX+fVbXZlsOJeCnbZHqNCum9cGorvZRFbWuXTg+BZqygPahHts1du3aFddccw2GDx+OESNGNKrlT1p6DFuOpcPX0xUzJg5Qj425bTuShNVbTyPAywWb5wxCoLdLY54u50YBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAtCDDcXAuIHIICFKAABShAAQo0lICEfSU0LNWLJfQrFZElTCwBYnmUn9NnInHy1Cncdcft5w0DSqB48VtvY+asWWgR2OKsJcmYq374Ebl5eTh+9DB0hYV4aeEiFUaWVlZx2WwNJJtNlmCyJZBsVJWbpUpzbm4uPD09VUhamjzK8Tk5OWjXrh3S0mRdRmRlZ8HLy0sFnY0mI2AGJDBtMprUeEVFRbh14gQEBQU11CXgeSsRiI+Px5EjR1Sgy9fXl0YUqBWB1XtS8dg3J9RY10d0wOh+YbUybm0OotMb8d7q/UjP0eH2oSF45/autTk8x6IABRqpwJIlS7B9+3aEh4ejT58+6u9fly5dGulsgR/3ZeDRr46q+dlD9eYlq/chPi0fc65rjyevattoXTkxClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFak+A4ebas+RIFKAABShAAQpQoN4FYmLi4O7ups4rQWYJN69dswbjJ0ywhpslWJyXlw8XVxfs378f14+7rtw8i4uLsezzL/D3X3/CxdUVBoMeX3/zjTo+OjoaP61Zg1OnT6PEXIIxY8egR48eqoJzu3btreFkGVDCzzk5ucjIzERwyyA4OTujqKhY9fXy9LQElEurN8uc4uLj0SokRAWfJewMB6BlYEtkZmWqCs6WsLYDCnSFai452TmqknOxvhgSmtYbJORshMx/+rSH6t2eJ6QABepf4MHPj2LDoQz4eLjisQn9EOjrXv+TOM8Zf9sXhw07o+Hq7Ih1M/qjV6hXo5ofJ0MBClBAE4h4dQ9i0wsbffXmY7GZWLbhiJr2388NRocgD15EClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFmoEAw83N4CJziRSgAAUoQAEKNE2BhIREODs7WxcnwWZpWzZvxmWjR1urNGsVnCV8/ONPP+OB++4uB7L6p7VY99NP8PC0hEVSk1OQlZkJV1dXBLYMgruHB1xcXNCxY0c8+OAUFWLWfqS/hJClRUZHo3PHjtDr9SqcLFWa5TE5JRWBgQEqkGwbcDYYDIiJjUGXTp1RAiAvL0+NKxWZZUztx2g2wcvTA9k5OTAajCpQrVV81usNkHFuv20i/P39m+aF5qooQAGrwM6oXEz41wH1+pJeobhpZKdGoyPVmj/6+SByCvSYfnkYnr+hQ6OZGydCAQpQoKLAK+tj8eH/YtTmxly9+dVvdyEzrwizxoZj1th2vJAUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQo0EwGGm5vJheYyKUABClCAAhRoegKxcfFwc3XFW2+8gTlz52L2rFno268/IkYMR7du3cqFm2X1UlV50//+h6kP3l8Ow2Qy4Z6770FAi4DzIvXq1QuTJ9+h+miBZgkjS5NHCVdL6Fiea+FmV1c3FBQUQM4hwWYJPsfFJ6BdeFu4u7sjOzsHcXFxCCmt4Kz6mcpXeJbj5HxGsxFGvRHvv/M2cnNy8PTceSrYLNWgdbpCPPHY9KZ3kbkiClDgLIEFayKxdHOC2n7dsPa4on/bRqG0dP1hnIjLQqdgT3z/SG+09rNU1WejAAUo0FgFJv7nKLafzICTowPuv6YXurc9/78F63sd/90dg1/3xKJLiCf+mjOovk/P81GAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKNCAAgw3NyA+T00BClCAAhSgAAVqKpCenq4CxH9v3Yq3Xn8drUND4ePri4GDBuHue+6xBpujoqNRVFQMDw8PxMTGokhXhNsn3aJOW1RUhGPHT+DosePY8Mta+AeUD7RIbNlBOjo4SHoZAQEBmDlzlhrbtnKzFnBOTklBeNswdT5LSNlSfblQVwiD3gA/P1+4urogIzNL7TebJQRtUhWdXVxdoNPpVDhaCzhb96sK0JZwtOzf+MsvGDl6NLKzsuDn56/2SdXn+++9G35+fjUl5XEUoICdCCRmF+OOTw7jZHKhmvGESzphZO/QBp396i2nse1oElydHbH2iX7oG+bdoPPhySlAAQpUReBMqg6jXt+tuvp5uWHWrQPh6Vb2rSBVGaOu+sSn5WPJ6n1q+D9nD0LXVp51dSqOSwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQCMUYLi5EV4UTokCFKAABShAAQpcSCA5OUVVSpYmYWN5fuySYWtKAAAgAElEQVToUfTu00cFj6WdPhOJvfv249aJE/D2W29j1z//qEBwq9BQhLQKQXZWNlxcXNCiZSBQYhlHmna89lqbi2x/7bXXy1Vttq3crAWeo6JjVGVmCTbLjxZyNpqMKDGXQB618LMl5GzG7t170L9/P/VcqjFrx6gxSsPN8qj2GY149aWX4O3lhSnTH1Vj6XRFcHd3w4P333shOu6nAAWagIAEmx/68pg14HzbZV0xtHtIg6xs88EErN0eqc7942N9MawjP2TRIBeCJ6UABWoksGZfGqZ/dVwd2ybIG09NHFCjcWrzoFMJ2fj4l0NqyMevbIu549rX5vAcq4YCubm5WLVqFXr27ImIiIgajsLDKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAJVE2C4uWpO7EUBClCAAhSgAAUajYBer0dKShrc3FxVIFkLIUvA2TagvG7DRpw4fgypKclwcnSCo5OjCgY7u7jUeC1vvvmmChiX5qfLV3A2mxEXn4AOHdqhuFiv9mnhZQkrS6i5LPAsVZgtwWbpIxWZZe7ac22ftb/ZhO+//RY33jJR9U1JSsbnSz/GU888C71Br84nLk8+Pr3Ga+OBFKCAfQlUDDjffVV39OvUsl4Xsf1oEn7Yclqd89uHemN09/IV8Ot1MjwZBShAgRoKXLvkAA7E5qqjB3YJxh1XdKvhSBd/2K4TKVjx50k10G1DW2HJ7V0uflCOUCsCy5Ytw8KFC+Hr64tDhyzhczYKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQF0JMNxcV7IclwIUoAAFKEABCtSRQEpKKrKystGiRYAKBBcUFMLb20tVb9bCzrk5udj46/9wcP8eyyykKnNpIllCxxWrMstrrQqzpXvZa3kuIWN5XLRoERwdnVTAWMLEmVlZ6vwhwS3RsmUQCgt15So721ZuTk9Pw4H9BzDy0lEwGIwqJH3o0GG0bRsGHx+f0tB0SWnlZgk+W8LRWkhawtHvvf0Wpj3xhNr+xsKFmPnsXNVfrzegqKgI464di86dO9WRPIelAAUam0DFgPMlvUJx9aBweHvU/EMcVVljQno+Nu2JxZHoDNV9/vgOmDY6rCqHsg8FKECBRinQ64UdyCowqLlF9GiFWy6t/1Dxxl0x+N/eWDWHHqHe+O3phq8i3SgvVgNN6t1338WSJUvU2WNiYhpoFjwtBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKNBcBhpuby5XmOilAAQpQgAIUaDICsbFxcHNzU2HjrOxsBAUGoqCgAN7e3irgLC06JhY7d+0uCzfbrF6CwVq/iiFnW6QSlMBB/nNwUGHmdu3b48orrkCPnj3hqLZZKi9L+Fj70V7LOHKO48ePY96cOfjgP/+Bt7cP/tmxHYOGDFHh5KNHj6FTp47WELN2rISVZb9Ue7at3CzbCnWFeO2ll9T53d3d8ehTM1XIWsLSEm42m/R48oknmsy1tteFSGU/+dryGTNmYOzYsfa6DM7bTgQk4Dx/zRlsPpmtZhzs74GrBoar6qN10X7bF4dNu2NgMpeo4V+a0BFTL21TF6fimBSgAAXqVWD8+wexOzpHnbNNkDfuurI7Wvp71PkckjIL8Mf+eOw9larO1drfDXvmD63z8/IE1RNguLl6XuxNAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFyfAcPPF+fFoClCAAhSgAAUoUK8CEiKWys1ahWYJ9Hp5eVlfa2Hl6JgY7Ny1R4WbtSrMBr1eBYnd3N3KzVnbLzE9B0AFpy+5ZCSGDh2CoKCg0oCx7C1Balq6ClOXlEiouWwYLdy8fds2rP3pJzz8yCOYNmUKvvruO5w5fRp9+vZV55YgspzEbDIjOjYWrVuFWMPN5ao0G41nBZylcrPJLKFnE3bt2IF+AwfBbDKpys252dkIbBmMpKQkzHqK4eZ6vSkrOdmkSZOwY8cOjBkzBkuXLm3o6fD8zUTg862J+Pfv8UjILlYrHtItBH07BqFHeIuLFkjJKsSByHQcOJMGeS4t1N8dXzzYE73aeF30+ByAAhSgQGMRmLH8FL7fmWydzm2XdcXQ7iF1Mr2cgmJsPZyofgxGszrHjQOC8dHd3erkfBz04gQYbr44Px5NAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIAC1RNguLl6XuxNAQpQgAIUoAAFGlQgPT1dVTTWws1SPdnT00MFfrVgszxKv9///AsH9++1zLcEcHB0UNWUJZRsNpus69DCzXfddTf69+9n6V5ajVnrpL2WCs6STracSyo2lw5fUqJC0Ot++QWffvwxVq1Zo8aYcP31uOzyy/HYE0+oecs2g9Gg+krQ2TIuSoPMJmzesgVdOndBUFCg2i/rklC09NeOk4Cz0WAJP0t1aYPeoALOhYU6VWH68UenwdnZuUGvU3M/uRZujoiIwIoVK5o7B9dfjwISbJaAswSdtebv7YZuYQHo2ylIPVanSaD54Jk0FWy2bRMHh+Bfd3StzlDsSwEKUMBuBP7zZwIWro20zrd7eAsMLf3ASG0sQoLMWqhZAs7S/D1dMOe6drh3ROvaOAXHqAMBhpvrAJVDUoACFKAABShAAQpQgAIUoAAFKEABClCAAhSgwDkFGG7mzUEBClCAAhSgAAXsSGDfvgPw8fGBj483CgoK4evrA1dXV6SmpcHN1VUFfHNyc+Hu7oaWgYHw8PSAq6ubqrSsBaK1EPS33y3H3j27JfcMRwcHvPnmm9aAtBY6VqloSBjaUrlZth89dhw9undToWR5rTUJIMvrZ2bOhJ+/P+bOm6fCxjePH6/CzpmZmfDx8YW5xKyCybJv77796N2rlzXsLNslgC0VmFu0CCwNN5vVftvKzRJmjo6MQni7dtDrDWgREICo6GgV2r5h3HXo2LGDHV3VpjdVhpub3jW1txVtPpmN73elYMOhDOj0ZR/m8HJ3QXiwD3w8XeHj4aIeveXRwxVpOTqkZRciM68ImXnF6lFXLB/oKGsju/jjlsHBuG1I3VQxtTdnzpcCFGi6Antj8vDub4n47XCqdZEdW/upKs6Du9bsd2BiRgHOJGZj5/EUJGUWWMe9pk8gnrmmHXq0ZiX8xnxHMdzcmK8O50YBClCAAhSgAAUoQAEKUIACFKAABShAAQpQoOkJMNzc9K4pV0QBClCAAhSgQBMWiItPgJenp1qhVq1ZwsDy4+TkpKojSwjZaDTAzc2tXDVn28rOJ0+dwbJPP1GhYa3Fx8Rh+cqyKrtacFkLOktAWtrmv7fhkoiIsyo3a9Wd5fGeO+/EjJkz0a9/f2zftg2vvfwyZj7zDIZFDIdUXtbCzfJ45MhRFBQWolfPnmq+Mqfi4mK4uLioR3kt65JwszzPzMpE+/B2SEpJhq5Qp9YtAeeioiIVmPby9MDUKQ804bug8S+N4ebGf42aywyzC434/Vgm1h1MV0HnmrRgX1fcPbwVxvYORO823jUZgsdQgAIUsFuBtQczsXRzIvZEZlnXENbSG2FBPmjdwhPyvG2wj/qgXGUtKjkXh6PScSohGxJutm039G+JyUNDMLp79arq2y2mnU+c4WY7v4CcPgUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABOxNguNnOLhinSwEKUIACFKBA8xZITU1VFZMlzKyFlbXntuFlCflKONjRUYImDqWPFjupwjzvuedUyFg1y4Dw9vDEiwtfKgdsqdisOlmrN584eQpdOneyHmobarYNRI8bOxafffElgoICERUVhccffRQrV/9oqcZcYlZBZPmRc1gCzJbQs4whlZlVP7NUbS6xhKFNRiQkJKB7926qUrXRYMTJE8fRvkNHNU5hYaE6TtpTTz7evG+UBl49w80NfAF4+koFJOi8dn8aDsXnIyajSP3EZRaV6+vp6oTwQHeEt3BXj6O6+OPqXi0oSgEKUKDZC0g1/GVbk3EsMQ9GU9k3dwiMBJtDA73UvzeLDSbLj97yaLb5lg8NUarfTx4WgmEd/Zq9qz0BMNxsT1eLc6UABShAAQpQgAIUoAAFKEABClCAAhSgAAUoYP8CDDfb/zXkCihAAQpQgAIUaEYCKSmp1lDzE48+imuuuw7jb7xRbbMNN0soWCoaS8hE26ftF64TJ07hg3+9D1c3VwQGBmLq1KkIDg4+S1KCxjt370FRcTGCAgPh6+ODhKQkDOrfT2WitcrPWsBZBtCeS+D4xnHj8NO6darf9m3bMWjwYBWq1sLMBoNR9TebTTAay8LNcqwEotu0CbP2jYqOQpfOnZFXkK/CzCajCZGRZxAW1hZpaWlwdXWDXq9XQeyZM55oRndF41sqw82N75pwRpULmMwlKuQswWcJMwd5u5CKAhSgAAXOI5BXZMKRhHwcTijAtshc7I/JRXJO8TmPaOHtgj5tvNG9tSe6hHiid6g3+rZlFXx7vMkYbrbHq8Y5U4ACFKAABShAAQpQgAIUoAAFKEABClCAAhSwXwGGm+332nHmFKAABShAAQo0MwEJBKelpatV//7bb7jyqqtw+NAh9Ovfv1wlZwkxS/jXzc1VVW22hJstWFLluTrtTGQUOnZoVxpkLlFBZBlLq7asjVUx3CxzlW3FxcVwcXFV4WWTSaowl1ZoLg04S6VlrTKzVrlZxpTtcqwEtHVFRdDpdPD28obeoFcVnNVxJrM6fVRkJLp1646ExERruFkqN9uGuauzZva9eAGGmy/ekCNQgAIUoAAF7Elg25mcs6bbIdAdrf3d7GkZnOt5BI4ePYopU6Zg7NixWLBgAa0oQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAnUqwHBznfJycApQgAIUoAAFKFB7Arm5udDpilRoNzMzEz+sXIk/f/9dBYZXrVmjtkugWALMuXl58PP1Vc+lerM02+rOVZmVVEF2cbFUMZVxpWmhZXlZUiIB5rKRtICzpRKzJdxse5yElyXIfPzECWRkZqoDu3bpos4h/SWwrB0r4eziYj1OnT6NiGFDIRWrbSs+S8hZws2yzWgwwWDQY9OG9Rg2YqQa6+mZT1ZliexTRwIMN9cRLIelAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKNAMBhpubwUXmEilAAQpQgAIUaBoChYWFyMvLVyHl48eP47133sEnyz6T6LEKMUdGRqFbty6Ii0+Aj4+3CjdLX0u1Zkv1Zttqxh/9+9/QlY655a+/8O6/3kefPn2sWDk5OfD19VUB5rKgsiWwXPZTZmu7XYLM0rRwc1p6OmT+7du3R2FBIQxGqdhsxslTp9EuvC2MRgk+Wyo7y8/Ro8fQt28fVYE6P79AbZOKzVrlZwlCW8LSZlWt+cihQ4iOikTEJSORlZmBRQtfahoX3U5XoYWbb7nlFixevNhOV8FpU4ACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABSjQEAIMNzeEOs9JAQpQgAIUoAAFaiCg0+mQm5tnCTcfO4ZXFy1CQWEh1vz8s7Vqs5OTk7VCsxZmtlRsdiwXbr780svw259/lAafLZO54rLRWLn6BwQGBqrXWnXmsudloWbZJsFi26aFm7WAsuW4EpyJjELPHt2Rl5enKjdLKFmrwrxv/wH07NFDhZiLi4tx4uRJXDJ8OFLT0lQ/2SZz147RHq3hZpMZ/2zfhl59+uHvLZvRb8AAmEyWys22Qe4acPOQixCIj4/HypUrceuttyIsLOwiRuKhFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQo0NwGGm5vbFed6KUABClCAAhSwWwGp2lxQUKACyYkJCfDy9kZAQIBajxZqlueWMLP26KD2awFnR0cHfPbpMmzdsgWfffF/5SwmT7od2VlZ2LDpv9btR48fR/euXVWQeds/O9G7Rw/k5uWhWK9H+/C2KgAtTavQLP1sKzjLPg8PDxXKlmCy7NMCyi7OzmqsqKhohIaGws/PF9nZOZYqzUZLlebU1FQEBLRQryUQLcdmZGTAy9sLZpNZvV730xqMuvxKJMTFwb9FgDru6ZkzGG622zudE6cABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoDkLMNzcnK8+104BClCAAhSggF0JSLBZAs4SVJaA8z133gkvLy98/Omn1m2yIEuQ2RJwBsoHneW46Q9Pg4OjIz786N/l1v/98hVYtnQp/vvb/6zbdboiHD9xAgU6HSKGDC6t5lyiAsRx8fFoW1qVV0LLWiVn2wrO8jw2Ph7t2raFXq8vV7lZgskScM4vKFTHymut6rMKM0uFZ5MJbm6uyM8vVM8zszJVZWlZX3ZONowGI/Lz8xF5+jQSE+LRf9BgNcbsp2fa1bXlZClAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFLAIMN/NOoAAFKEABClCAAnYiYBtultCwk6MTcvNy4e/vr8K+p89EonevHkhKSoGuqAjhbcOsFZwtIWdL0PnL//sC33z9NX79/bdyK//o3//Gvj178cmyT9V2LaQsz81mKdEsAebSUs0oQVZ2Dvz9/FSY2LZas1bFWQs8y6MEpHv17AWdTmepylxiCTPr9QY1rqm0CrMWbjYYDNbAc3JyMlq0CFSVm1uHhCAmPk6Fml1dXVTYW0LT7y9+G7fdcRdc3VwZbraT+5nTpAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAApUJMNzM+4ICFKAABShAAQrYkUBSUrKq2iyBYVX52MUFTk5O1mrNsl32JyWnILR1iHouoWZLk4rPludXjb4c6/+7Ee4eHpZdJSUYe9XVqmrzpk2bcPXVV6sqzRI8lkdLYLks3KxVadYV6eDu5n5WuLli2Fn6e3h4IDc3z1qh2WA0lD63rEWOUcFns9n6aKnebIavrw/y8vNRWFioQs4GvQGenp7IysrCTz/8gOioSEycNBnOLs7w9fXFw1MftKOryqlSgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCmgCDDfzXqAABShAAQpQgAJ2JJCcnKqqL0uT0LJkjp2dnayvLdWZHVSFZE9PCS5bAs1awFkeJUAsoei333wLUg36xYUv4YXn5qFteDhWLP9OVWf+/a8/1ZgVg8221Zxlf1JKCloFB5cLN2uVnC3Hl+BMZBS6d+uCgoJCNbYElo0mI4xGCS6bcOTwYSx5+y188PEnKthcWbhZAs3W4LPJCKnsjBKgqKhIbf/i06W4+tpxcPdwV2M+O/tpO7qqnCoFKEABClCAAhSgAAUat0Bubi5WrVqFnj17IiIionFPlrOjAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABuxdguNnuLyEXQAEKUIACFKBAcxKQys0SUJaKzNIkPOzs7Kyea8FmeZTwr2wvq9xsqeis9bv68ivUawkC//bXn/jzjz+QlpaGm26+WfVxdJTAtCrdXBpc1qo3lz3KvuiYGIS3bVtpuHnX3r0YNWIEiop0MBhMZcFlkxF6vV4Fp00mozr2ofvvx4efLFXz0X4k5CxVm1XYWYWhjcjLy0ViQgL8/Pzg6eWt1ikh6ZMnjiMwKAjeXl7Iy8/Ds7OfaU63BddKAQpQgAIUoAAFKECBOhVYtmwZFi5ciLCwMPz99991ei4OTgEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhRguJn3AAUoQAEKUIACFLAjgcioaPj6eKvQr4SYJSTs5uZWLrisVWmW0LAl3GwJPkvTAtBHDh9Bz1494eBg2S8VnqV/WTs72Kztz8nJQXZODnS6Iri6ukCvtwSppVK0j7c3vLy81DCWAHNJaai5RFWJfu+dxdizezc+/OQTuLm5qwB1bEwsFi2Yj9nPPYc2YW2tFZpljRJ01sLNGRnp8PP3R3FRseqj/RgMRhWgLioqVn3D24bijsmT7eiqcqoUoAAFKCACeUUmBeHjbvlGAjYKUIACFGg8Au+++y6WLFmiJhQTE9N4JsaZUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQJMUYLi5SV5WLooCFKAABShAgaYqcPToMbRo0UItT4LKWoBZq9CshZe19WuhZq2/FmS29Du3kiXnXKICzHHxiXB0dECXzh1VRWctcCyPScnJCAkOVgPJXLQf29da9WWtIvM9d0zG1GnTMGjIUNX/7TdeR9SZMxg8dCgm3XGnTfVmS9Vm7Tg4lKC4WA+9QQ+T0aQqNhuNBrVNKjpr53zk4anw9fVtqreAXaxLKvtJ+GX+/Pm49dZb7WLOnCQFKNCwAm+sj8Z7/4tTk5hxdThmX9uuYSfEs1OAAhSgQDkBhpt5Q1CAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEB9CjDcXJ/aPBcFKEABClCAAhS4SAEJ+qamplkrMMtwBQWF8PHxViM7OVmqXVpCzSWqMrM02yrOZqnoXLpdm44WepZjiouLcfjIcfTv10eNZzaboBV11qo3ayHmtPR0BAUGqhCy1mxDzmazVrnZDKPJqPrNnzdPVXVesHARiouK4OTsjKTERLz0/Dws+fdH5cLTWvVmk9kEk9EIvdEAo8Go+hgMBlXBWipCx8fFIDCopRp/zjOzrJWsL5Kbh9dQYNKkSdixYwciIiKwYsWKGo7Cw5qqwLGkAuiNlurwXUM84eFq+T3FZn8CtXUtswuN6Pn89nIAx14ZDj8PZ/tD4YwpQAEKNFEBhpub6IXlsihAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKNFIBhpsb6YXhtChAAQpQgAIUoEBlAhIcTk5OUeFlqdashY2lr4Scc/Ny1XYJ/To7O8PFxRneXt7w8HCHo5MjLMWa5X8dkJSUhNDQ1uVOc+DgYQzo31eFhyu2isFm2R+fkIDQ1pYxzle1WULHWrg5MzMTj0+bhmVffomFCxZg7vMvqPM99vBDePeDD63VmtUxRhMk2CzHGvUGGIxGy2uDEUajEQaDESUlZqSlpSIwMAi5ubkq3MzWsAIMNzesf2M/e+jMLdYpfvNQb1zePaCxT5nzO4dAbV3LjHwD+szfUe4shxdFoIWXC+0pQAEKUKCRCDDc3EguBKdBAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKCZCDDc3EwuNJdJAQpQgAIUoEDTEThy9BgC/AMwZ9ZMOLm44I233ipXyVmCz0aTCS7OztYKxpZKzme3jIxMBAa2sO6Qqs6FBQXw9rZUgtaabbBZtmlB5oOHD6N3z56qW8Xws1R7ltCyhJTlUULJlsCyEfffdRc+/PgTtV0qN8v2x6c9jLffe1+NowWb1bGl4eboqCgEh4TAbDKrqs0yjgS5c3JyEdQyEPFx8QhvG4Y7Jt/edC62na6E4WY7vXD1NO3aCsTW03R5mvMI1Oa1XLAmEks3J6izPXRZG7x4Y0faU4ACFKBAIxJguLkRXQxOhQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAMxBguLkZXGQukQIUoAAFKECBpiWQkpKqwr+vvvwy5r3wAl5ZuBAvvPiiCgVLiFl+CnU6eHt5qYVLAFhaxYCzBIuLi4vh6elZDkiqPhcUFMDX10dtr6xis7Ztz759GNCvn7WfbM/OyUFWVjZ8fLwhVZrbhrVV89WCylJt+f677kRISAgWvva6tUr06y8vwiOPPQ5XNzdLxebSYLSq2mwyQl+sh5enFzKzMlFYWKjmnZubh+LiIrUOoAQznngcHh4eTeuC2+FqGG62w4tWj1OuzUBsPU6bp6pEoLavZVqeHk6ODqzYzLuNAhSgQCMUYLi5EV4UTokCFKAABShAAQpQgAIUoAAFKEABClCAAhSgQBMWYLi5CV9cLo0CFKAABShAgaYpIAHi6JhYbNq4EQf27cOs2bOh1+sRGBioFiwh5mK9Hu5ubtbXWuhZE5HXZyKj0Kljh3JIlrFj0LFDBxVGllZZ1WbZLvtln1RRdnFxUX3TMzLUed3d3WE2l6jqyhKSzszKslZjltDy7JlPqdD1DRMmYNDgIdYgs1bZWQLW8txgNKjKzVqlZl2hDl5eXirMrNPpoNNZgs0mk1GNMW/unKZ50e1sVY093JxdaISXmxNcnCqvaF4VboOpBMVGM7zdnKrSvdI+5hIgu9CAAE8XnKO4+nnHro05aCeQSutqLl6W93JdttoOxMr19PVwhmPNL2eNlyv3QLHBrM7fkE2uX47OCH/Pi5tHod4End6ynqq8P2r7Wl6soRjIe1IC0jVt8r4SB78GvqY1nT+PowAFKFBXAgw315Usx6UABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIHKBBhu5n1BAQpQgAIUoAAF7FAgPj5RhYMLCwvg7e2NSRMnYsiwYZj97LNqNRJelrCvs7Ml6GZbtdm2kvOevfvh6Ch9zejatTMC/P1VIFmaFmq2fS6BYxlL9mn7U9LSUJBXgKSUZAwbMlgdK8FmrfKyk7MTinRFKqQs23b+sxO+fn5o3769taKzjCX7ZB5aUFme21ZtluOzM7OQnJyE8HYdVKC7qKgIUZFnkJuTjVtuuRXXX3+dHV7Npjflxhhu3hOdi1fXReNgfD4Kik1wdnTAVb1a4KFL26gQ412fHlEXYlA7H3xyb49KL0p0ug4Lf47Cgbh8JGVLtXCokHT31l6Yc207jOziX+lxr62Lxqo9qWrfogkd0dLHFc+uOo1jSQVqm8ylU7AHXpvYGRGd/M57Q9RkDks3J+A/fyaUGzfE1xXrZ/THmVQdZiw/CfHR5tIz1Asf3tVdzcm2/bg3FU9+e1JtkvCom7MjgnxcMA7VgLMAACAASURBVLSjL0Z3C8D1/VpWGjB+e2MMvtuZYh1Ks5MNvu7O8HI/OyDePtAdPzzat1ILCfJ+sjkBP+5JxYnkQhUylxbewh1X9myBede3h6drzUPnF3pHyv3zwpoz+OtEtvU+6NbKE7cNCcHDo8Mw+eNDOJ2qU8P89Hg/hAVYPmgi7eO/EvDJX5ZrcVWPFnjj1s5nnW7Gdyex5VS22v7YFWG4f2RopVOSebzySxQ2n8xGZJrlfHIvdQnxxORhIZhyaZvzLkWuww97UvHj3jTEZhap94Vtk7HE8fMHe2J46X1ZW9cyIasY4/914JzzO9/7sOJBP+1Lg9zjRxIKrPdC+yAPjOrij/njO6j3aGVt4ocHEZ1RpHbJe+G3o5l4fX000vMNapvc30M6+GLJ5K4I9S+7hhe6P7ifAhSgQFMVYLi5qV5ZrosCFKAABShAAQpQgAIUoAAFKEABClCAAhSgQOMUYLi5cV4XzooCFKAABShAAQqcVyA/Px9ZWTnYuGE9Vi5fjnE3jMekyber4LEWZD567CR69eymxrENN9s+r7hPO6ltsFm22YaZbSs5l3+u9ZOqyyUquCyBZC24bDSaYFYBZ0tVZgkzS5A6JzcXbUJDVVDbZCpBZmamqgStBZularPJaIKLswvyC2TdWfDy8lbB5vS0NOzbswtDIobj2dlPW8PcvH0aVqCxhZuX/5OCmSssodyKTQKM79zeBY9+fULt6hzsic3PDjqrn4wxe+UpGKXc8jnafZe0xqsTzw6rTvvyONbuT1NHSVD1q21J5xznX3d2w8RBwZWeoaZzWLAmUoU/K7ZjrwzHsEW7kFtk+UCDbZNg6/6XhqGFTSXnJb/G4s0NMedcv4SLP3+gJ3qEepXr89g3J7C6NNxd1TtTKkgfWRRxVve0PD3u/+wo9sbknXMoOfb7aX3Qq035eVT13OfrF59VjJs+OAAJ51bW3rujG2Z/f8oasv1j9iBI8Flrz/94Bp9tSVQvJTBcWYD7uiX7sT/Wsr7pV4Th+evLV9iX7fti83DX0iPIKrAEcStr/cN98N3DvSutQPy/o5m4pzTQfyGXTbMGoHcbb9Wttq7lqZRCXPbGnnOe+lzvQ9sD9EYzZq44dd57K8jbBd8+3Ns6f9vje72ww+r31JhwvLspttL5yHtBrmPFsP+F3LifAhSgQFMT2LFjB6ZMmYKxY8di8eLFTW15XA8FKEABClCAAhSgAAUoQAEKUIACFKAABShAAQo0MgGGmxvZBeF0KEABClCAAhSgQFUF4uLiERMTg+DgYHh7+yAuLhbt2rUrF2TOLyiAv5+f2nbs+Al0aN8Orq6uKnBsG3JesXw58vPy8ODUqXBycsIdkybh6+++w/Zt2zAswhIwtA0y276WILP2WvpI6FgCyVIhWs4REOCPtPT00krOWoVmEzIyMtC2bVvk5uaqkLOEnqV/YWGhGs9gMqhjCgsK4ePtg5zcHPVab9DDbLKc55c1q5EQH4cHp03H0zNnVJWO/epYoDGFm6UibcTLu8qtWMKvPm5OqlqttNb+btYKvJWFKg/F52PsO/vKjSGBR6lerFUN1nZ+el8PXNc3qFxf23CzVJHVKuTKGBXD0rL/wEvDzqo8fDFzeO9/cfjgtzg1J9vqvA+MCrUGbSUEqlWs1Sb/xFVt8ex17a1reeHHM1hWGsw91y0ktjufH1KuWu5LP0Xi6x3J1kNs5yAGbi6OZw3XLtAd/3t64Fnbb/voELaWVjXWdopZscFczlIq9/49dzAcHGr3Zr/+vf3lgtUy//BAdxV2lntBKmKn5OqtJ62LcLNOb0bfBTvOqrRse29pE7h7eOuzqkPLXIcs2nkWjFap2dXZAXpjCQr1JmUqIXipbi6ttq6lvPcue718uNn2vVSVcPPi/8Zi8X/Lh+0re0/JPSnvKdln22zDzRd6X17S2R8rp/ep3ZuJo9mNgFSLzygwINDL5aJ+pxhMJer3hPc5qolXBUTGkPem9p6syjGNrY/8szG70IAAz5p7amO4OjtelGdNbeTvmIuTA+T8F9vk/lIeNh8mutgxeTwFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoCkIMNzcFK4i10ABClCAAhSgQLMUSEpKUoFgCSP/s2MHtmzejGfmzFEWWnBZHiUQLI8SNi4psVRVlmNsw823TJiAha+8gp9/XosunbvgqjFj4Ofnh+PHjqFrN0v1Z9tqzhUrNgMlqgJzYIsW0Ov1MJpMqvqy9MvLy1PhZXkuFZvlR/olp6RaqzrLHOUnMzML3t7eMBgNqnKzh5u7GlfC0gaDjGGGp5cn0lLTkZOdjdTUFMTGROOHH1ap9bE1DoGpU6di06ZNGDNmDJYuXdqgk5rx3Ul8vyvFOodP7u2B6/tZwsdS9fbhL4+XC8tWFqq0raQrIUgZ4/LuAWqMuMwi3PnJEZxOtYTyJdy6e/5QFXzWmm24WbZJheMvpvRSFX2jM4rw+rpoa2Vn2f/llF64qmeLcm4XOwdtsL7zd1hDzBKkkuq362f0R5cQT0hVZAkPn0i2rEUq/8q+ypoE7NLz9IhM1+G7f1LKVc+V6tSv3NzpnNc9dOYW675vHupttbzQjbIzKhcT/nXA2m18/5Z4e1IXFWyTcNinWxIgVaq1tmRyV9w2JORCw1Z5/4G4fFz7blnI/YoeAfj8gV4q4Kau27YkPLvqdLnx6iLc/NbGmHJVhl+4oQMeHh0GueUk9PjM96fx495U6zx2PD9E3XNaW7EzBU8tL6tkPv3yMEjQPdTf7SwLCRBWyASX61PTa1kZuoTw31gfrXZdKNycozOizws7rIF2eV8un9YHg9r5IFdnxOPfnMCvRzOtp3n5pk5qjbbNNtws26/u2QJyz8j7YldULmatOGV9X8v+qDcvgVR6Z2s+At/uSMb//Z2E40kF6l6T++zmgcF49Mow/HUiC0t+tXxoZNroNph6aZtKYaLTdVj4cxTk90dStqXiu4zTvbUX5lzbDiO7+Fd63MQPD6q/D9Lk9/BvRzPx+vpo6+9vuReHdPBV92xl793aukryQYiUHMsHNuTvmo+7Ze4Rnfxwy+Dgcr9bKp7ztXXRWFVatX/RhI5o6eOqfkceSypQXeUDB1IR/bWJndV452piv/lEtvpbvu10NrILjWd9MEg8ZAypVq+1MYv3qVC6tLWP90ObADfIB4Xu++yo2hYW4IafHu9nMT6YjhdK/370aO2Jr6eWjaON9/uxLHz4exyOJBRYv/FAPhg0INwHL9/cCW1tfs9qx8i3Jvznz/LfnCD/TpBreiZVhxnLT2JPdK7Vo2eoFz68qzsrxdfWDcxxKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQwK4FGG6268vHyVOAAhSgAAUo0NwFoqNj4ezsrILKiYkJaNOmjXpuG27WjGSbBIylgnPPHt2tdAuefx6z587Fg/fei+9WrsQT0x/F0OERWLViBb5evlyFjbVWWfVmCRWazSa4urmhSFcEk9lkDTFrFZm1ULUEm6OioxHWpo017GwJNptVuNnNzRVZWVnw9fWFVJ3Oz89X2+VHxpLjjUYTiop0yMzIgIODI4KDgzD9kWnN/VZoVOuPj4/HypUrceuttyIsLKzB5ibBzLBZZUHaykK3+cUmFZLUqsZWDFVKaPlSmwqzq6b3xYjO5UNYUql3wIv/WNe5de5gdGzpYX1dMdy87bnBkMrCWsvIN6DP/B1l78kbO+Lhy8qCcrUxB21w23CzbFv9aN9yobLPtiTi+R/PqO5S0XrP/KFVun62gdsLBVNrGoi959Mj+F9pYLVvmDc2zhxw1twkOCchY2kSVpUQeW0126C8BOlOvjbCGmzWziGhYQkPa60uws0dZv9tvV8rVteW88rv5Ete2w0JVUqToLnc+1qbt/oMPt+aqF5KuDDyzUvOqmpcVbOaXsvKxq9OuPnTzQmYbxNkr/i+FIPBi3Zaw6SV3cu24ebKqjv/ciAdD31xzDrV354ZiB6tvapKw352LGAyl2D2ytP47p+yivO2y5FQca9QLxV8libB5pcmdDxrxcv/ScHslafOCuLadrzvktZ4dWLns461vT+fGhNe7gMNtp3lPSy/ZyQkXBfN9j1e2fjTRodh/vgOlZ7a9m+f/A76alvSOS3+dWc3TBwUfNY4ci3kQzfbz+RccHkVK6zb/r2TELNct4of7oh7e6QKbdtWgh/Yzge/PFn2wR4JVz+/+oz1b0tlE5Hr8O7krmetQT5wIwHnik0q4g9btMsakq54Tfe/NAwtWMn5gtecHShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKBpCzDc3LSvL1dHAQpQgAIUoEATF8jOzkZOTp61MrOlQrNWMVZCzlChYKnUrFU2TklNRUhwcLnKzSkpKQgICMDbb76JufPmYf68eZh42214b/FiLP3883JVkc+u2mzGnn370bd3H2tgWaouSxBZQstlwWZLiFm2xyckoH14OAp1utLgcmmA2WS0BpmlArS5xAyT0RJstoSf3ZCdnYPi4mLo9cVYv3YNNm/dWm4tTfySc3nVEJAqmYMW7rQesfnZQaoibMU29f+OYd3BdLW5YjD3v4czcH9plcfzhXYnf3xYVfKU9tXUXriyR1nlZduA17nGuPKtvdZqlg9d1gYv3lgWlKuNOWhrrli5+ciiiHIcUkHyzY0xalsrX1e8d4elcvuFmlQYHfHKLtVNQl6xb4885yE1DcSOeLUssFvRWDuZVAQVy8qu5YXWcKH917+3H3tj8lS3c1Wn3nEmBzd/eNA6VG2HmwuKTegyd5t1/FOvjVBVYCu2j/9KwEs/WapYPzgqFItuKquk/dEf8Vj0c5T1EKnQLX1GdwtAoLfLhRjK7a/ptazsJNUJN89ZeRpfbbcES6UK6r4Xh501ZMV1Jr4zqlwf2/BoRSPpKFXN28/+23pMdaqMVwuRnRudgISSZ64oq24uE5Sqv0ZTCeTDLNIkMK9VYq4s3CwVgse+U1bpXfvdKEFa7cM02sI/va8Hrutr+UYBrdnen/Iel/e+NoaEbW1bxVBvbYFW/H1zrnErq4wufW3/9l1oDbL/wEvD4Ola/veZhMO/3n52yFw+YOJZ+rtP3qsy18nDWmHxpC7Wadp+44H2rQ2v/BKFD3+Pt/bRKtvL9ZbrLk0q/ktFbK39+/d4vPxL2e9M2a5Vca94LeV3kfxO0pr8XvvgN0uFb+0aynOpJC8fJpIm1Z/T8y0VprVW2QdXauu6chwKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSggL0IMNxsL1eK86QABShAAQpQgALnEIiJiUVBQSG8vLxUmFkqOUuToHNaejrC27ZV4WDZFxefCBdXFwS1KAteHjx4EIsWLMB111+P+x54AAUFBbjvrruwcvVqzJs7FwEtWmDWM89Yz262hmpKIM8lvJySmobAFi1UAFkCyZZgs0mFneVHCzVr24qKi9G2TSjiExJVJWaTyageJRQtlZ9VsLm0mnN0ZCS8fLwRENBCrVP2SbB5489rMfu553DN2DG8NyhQqcDOqFxM+NcB676ExaPU+6Bik9CShJekVQwf24aaJIAkoafK2o/70qxBN6ngKWE3rdkGvK7q2QJfVlJN+KYPDuKfSEtlyopBudqYgzYX23DzrYODqxxeluOlGq4ErT/bmoiodB1ScvSqCqdUvQ30coFUmNZa1JuXWMNfFb1qGogNf3qrternnRGt4Odh+V1n23QGs7UqsYTPZB611aQ6txZsfOu2LpA5VGwVA/W1HW4+klCAqxdbwtvSpl9eeWX0fbF51kqnl3cPgARztRaXWYRLXt1daQVVCeWN7h6gqo+O7OJ/QbqaXsvKBq5OuFkquW49la2GuaJHAL6eWrY+bWzZL/20JsHJlj5loUPb8Oi5wpm295ycQ87F1rQFKlb9ltDtxqcGWCsjywco7vj4sPV3gWhUFm62DdbKGBKulfeiNHkP3vnJEevvTHnf7Z4/VFUQ1prt/SnbpBK9BG7l9+2uqFzMWnGqyr9za+OKSQXlQr0Z8VlF+CcyF+/9GlvO4NDCiLM+HFHxWwvCW7iravrdWnlCPhDz+rporN2fZp2e/G2Uv5G2TT7MoYWCxUkqPA/t4AtXZ8ezliX/PLUhLBeu1t7jD3x+FBsPZViP1T60YPsBpefGtcdjV7ZVffKKTJC/m7bf7vDllJ7Wb1+Q3zPyAShtjjcNDMaHd1X+oaCKHy6SUPb6Gf3RJcQTaXl69fvqRLLl76h86ET2sVGAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFmrMAw83N+epz7RSgAAUoQAEKNAkBCRInJiYhKzsHfr6+EkFUlZol3BwbF4924ZaAhjTZVlmTwPB/N2xAVlYWrh03Dt9+/TVunjgR06ZOxS8bN6pDKlZslvOq4LLZhL+3bUfEsKHWas1msyXYLOFn6af96HQ6hAS3RF5+AQoLC1FcVAQHRyekp6ehqLgI/gEBMBgMKhgtP85OTigs1KnjDQaj2ldcXASD3gBziQnz5j7bJK4hF1E3Amv2pmH618fV4OcLup4vVDlv9RlrWLaqs5w7rj0eLw1GyTG2Aa/x/VviP/d0P2uo84Wba2MO2gltw1WPXhGGedd3qNKyJHglYb2ErOIq9a/tcLNOb0anZ8uq6FZlEheqIF2VMWz72AZdtSqgFcfI1RnRfd526+baDjf/eiQT9y47Uq2pD+/khx8e7VvuGAk/y321P9ZSibqy1jPUC69N7IwhHeTvSuWtocLNtlW8Jw0Nwbu3l1VZ1WZqW8Vbtm14agD6tfW2LsQ2PCrHyzgVG8PN1brVmkRnCQ7faPOhmC8e7IWre5UP3FbsUzHcLB/0uPT1PVaPVdP7YkRnv3I+8kEJ+cCE1rbOHYyOLT0qvT8l0CzhfPmdprVfDqTjoS+OWV//9sxA9GjtVW/XILPAgGEv77KGeiurpl8x3LztucHWULBMNCPfgD7zd1jnvODGjnj4srIPBhXqTej8bFml+qevaYeZY8KrvMaXf47Cv/+wfHBJq4Qs10Wuj5hmFRighZ4vf3OPNVj8+QM9MbZ3oDpOqis//+MZ9Vz+HbF3wVB1rG2TsLSEpqVJkF0q6lfWbP/+yv7Vj/ZFRKey+8L2XFIZfM/8oVVeKztSgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABZqiAMPNTfGqck0UoAAFKEABCjQ7gbS0NOTk5sHR0RFOjpZgs6OjA8wlJeq1NMk1nyvcPPOJJzFz9jNo0yYMzs5O+OXnnxExfARmzXhSBZ7bhrXFkg8+sFZjlvEsFZktlZqlubi4qMrKEmxGCWA0mZCRmQlHBwf07NEdOTm5yMrOVsdJmFqOffrJJ9QcJa5jMpsxf9HLqnqzVG0uKipS67FUfTahuFgPg0GP4uJi9SjBZtnPRoFzCfx2LBN3L7UEQc8XdD1fuLmyr6O/kHjFKrAXG26ujTloc7YNVy2c0BFTbCpMn29dV729F0cTC6xdfN2d0TnEQwW5pGJlVHqRCopprbbDzTKubdD0QtdA9td25WbbCqIf3d0dNw5oedY0aiPcPPK13YhM06mxp18RhudtAugVKzdXxeFc1cLlWAk5r92Xht+OZZWrAms77pZnB1ur1lY8X0OFmyV8KgFTaVLRVqrBVmzbz+Rg4ocHrZulMm6ov5v1NcPNVbl7ml+fFTtT8NTyk2rh8vvtxKsjylUD1kS6P7cduUVG9bJiuFkq3Es1X2kVvw3AVtS2WnDFcLDt/fngqFAsuqlTuYshVX/bzy77wIdWgbg+r5htFeRnr2uvAsS2zfZv37kcrnxrL+SDCNIeuqwNXryxY7kxbH/vy9/xB0aFQqoj9wnzrvS62B78f38n4bkfTqtN2ocgOsz+W1Vhvn1YCJb/k4L7LmmNVyd2hq33X3MGqWrK0uR4GUdaZddBO582rrw+19+/ipWbjyyKKLfWPdG5eHNjjNrWyte1Wt+sUJ/XneeiAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCtSXAMPN9SXN81CAAhSgAAUoQIE6FoiMilahYAkwS6VjCRvL85OnT6F7124q7CzNNuAsX78uoWcJHMv2uLg4tG3bFrJdq9QsSWWpwCyvrT8oQUpqGry9POHt7Q03V1ccO34C7du3h9FoUOfX6w2q0rI8lyaPEmg2Go0qsCzPD+zfpyo4B4cE48P33sPzLy1U/aRPVOQZtAkLh15frALNgIMKNcuYY66+CoMHDaxjUQ5v7wInUwox+o2y6plSTVHCahXb+cLNtgFpCcseXhSh3jPna9LPyabC5sWGm2tjDtp8bcNVb9zaGXcPb33By3wiuRBS1VJr/7qzGyYOCi53XGqeHv0XlFUhPV+42Tas9u+7umPCwLNDwpVNSqu4Kfsk8HvfyPPPXa6BXIvaaraVPZ8b1x6P2VTn1s5RnXCzVET+6fF+Z03PNjRZMdwsoTwJ0Wlt06wB5aq9VrZWFydHuDhd4KYFVEh92+kcfLEtEb8fy7IOJZVUpaJqZa2m17Kysc73PqzYf+aKkyqYKE2q3UrV24rt6+3JmL3ylHVz/OJR5cKQDDfX1jujaY3z1sYYvLspVi2qb5g3Ns4cUOkCbT/wUTHcbPuBlCBvF9w25Oyq4DLoj/vSkJRtqYb/0oSOKiStNdv7s+IHZrQ+9VFZXCpMy3q2nspWlfu1QHeIryuyC40qKCxNQscyT9tm+7fvXB+yON+3FshYz3x/Ct/sSD7rGkjQeUA7H4zpFYg7I1rB39P5rD62fzsv6xaApff1QNe529SHnT64q5v6VoWRXfzx/SP/z959gFVV93EA/8oURMCF4mC4UMQtiTtT05ZlZWaWVpqpWamp6atZamllppba0EwrM7UybanZcOPee4IbFJQhG97n97+ew72XC1yQzff/vDzCuf/zH59zO/A+z/f8bmMYP6gRMqO9fs986rNDau/SWniXR1Bt0wrc2qRfbryE5NQ09aN5xX6tj/Hv396tPIpleDkqKgrBwcHw9/dHzZo1S9Z//NwNBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKFDkBhpuL3CXhgihAAQpQgAIUoEDuBU6cPAWbMjZ3KjfbIC4uDimpKXByclIVnO3s7FTI2RBq1sLOhvnkmOFf+UaCzOkBZzkWGxuLuPh4eNWqqcLG8r1WvVn+TUpKhrOzkworSxhaQsrGwWZDqDnlTsA5xRBiTjEcS0pOwpSJEzF+0tt6AFpev3XrFsqWdUJsTIxKYUtwumpVD7wwoH/ukXhmqREw/0j7D3vXw7NtqmXY/5srT+Pb7YbKjObVJS9GJuCeqTv1c6SqpFSXzEm723BzXqxBW29uws1r9oerEJg0CeodnGJabVKOr9wdhte/P6GzZBVubjllpx7os1SpMzPbwUuO4bcD1/V1bBrXymKgLSfXJid9Byw8gr+ORqhT/Ko5qwCbeTMPgpuH3D5aG4KP7wQnJWB36v22JgFs80C+ebhZ5jMOP9/XsAK+HRSQbeA+J/uUvoFTd6ogo7TODSpAqsJaarm9lpbGykm4+dO/L2D67+f1YSyFCY0rwlp63zLcnNN3RenoP2LZSazYZQjOSyB22cuW3/tZhXIn/HwGX2+5nCOw8Q/54FWjByaKwvvzi42XMHn1Wav2kV24uWezKvi8f4MMY2UXbk5KScPsv0Lx6YYLenjYfBC5l758bw2Mut8bTg7pD7QY348bepbD7L710f3jfahRwRHfDGyELh/thae7I/4Z0wINJ2xXw8qnEhyf1kafou203Th/3VBJ39r258jmaFrLJUN349+/r9xXExOMqvJbO3Zh91u5ciVGjx6tgs1bt6Y/aFPY6+L8FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoUDIFGG4umdeVu6IABShAAQpQoBQLHD12HLa2tirkjDKGELOEnhv6+anvbW0l/KwFoKUgso1KNsfFxatqz1p1ZiGU7xMTE+HqWh63b8chNS1Vf10LL8u/WhBaAtBSXblcuXJ6wFnGSA82Gyo2S+BaHbvzr4yxfOlS9Hqytx6Alvml+nNMdBRsbO2QkpIMOztbjHz9tVJ8dbn1nAoYV9esUM4ea0c2Q62KZfVhzobHQUKQWvVJ83CzdDQOX8nPI+/3wtDONeFioQq0pfXdbbg5L9agrSs34Wap4vvsgsP61jaNa6lC4Fo7de02Hpy9X1X+1VpW4WbjSpgSSpOqmUF1LFfDNPbcExKNR+bs1w95VSwLqSItFZALoi3feQ0jfzipT2UelpeqnQMXHdUD0NLRPHS7el84hn5rCIpLG/uAN165r5aqEipVn5/+4jD2h0brr1sKNxuHgKVjN/+KePfxOibv66w84hJTYW9XRlUvtdR2n4/C43MP6kFCqToroUBLLbfX0tJYOQk3S/BaAtha86nshF9fa4pKLvbq0Mx1oZi5LkR/3VKQsCiERwvifcs5cibw1qoz+GqzIZjcrq47Vg5rbHGArEK5xpWbrZ3dvDpzYb8/pVqx/Pdt3OShjirlHdShyzcTEHojXr9P5Fe4WZs/Oj4Fv+4Px9rDN7D55E39d7bx+h5tXgWfPZceoI5JSFGVmqXJAw5Te9VR9195KOSrF/z1Kvh/j2mh/g6QZl6t2/geZ+21NP8dqZ1n/Pt3ymO1McioUre1Yxd2v1mzZmH27NlqGSEh6ffYwl4X56cABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIGSKcBwc8m8rtwVBShAAQpQgAKlWECCwrt278HmjZvw4MMP3wkzl0FIaCi8vb1gA0OwOQ1pKKPSz/I/Q8jtyrWrqFb1zsen36ncLMc8q1ZTQWSpyJyWZqjILN9L08LQ8q8h6JyKK1evoUH9+rgWFmao2heStQAAIABJREFU0Jws50rV5lQ1jhZwTkk2VG2Ovx2HY0ePoGGjAH0OOScmOho2trawt7dDfHwcRo8aWYqvbPHZulbZb8SIERg5snCv2cYTkej7RXowVwKdHeq7w8PVAafD4rDnfJQJrKVwswQp27y3y6RqpIxTv5ozarg7Qv5LCItKxJnwOAzvUguvd61lMmZehJtzuwapXt3vyyNIvVOafde59P1WdXWAVyVD0NvdyQ5LBjWy+CYLj05E07d36K+Vc7RFx/ru8K7khCOXY7D99C1lIxUvo+KTVT/p4+nmiBHdauHxlh4m40pVVKmOatykeqaElaXJfNeiEiGBM+2Y1veN5aewbMdVk3NlH7WrOKkqzhKAO3c9DtejkyAB67xsKalpaPL2DkTGJunD+lcvh8Y1XVQwWULgWkhe62Aebr5yMwFS7di4yXtJroMWFJQQvvEcYjDmAW88ccdR1tFu2m6ERsSbjCMGEtx3tLNB5O0knAuPg1+1chnCmVp4U+ap5uoAVyc7ONiVUXYhN+JN5pYJvhzQEA83rWyRMrfXss/nhxCflGoypqz3eozBVkyae5c3ef3jPvVRx8NJPzZmxSksDU5/L2iOcu2196F0Fo9DU4MyPIxQ2OHRvHxvcqy8E1i46RIm/WKoViz/7QVPDLQ4eFbh5r+PReC5BUfUefL+Ozw1KNvq6tLP1uiBg8J+f8oDLXJPkyZVj38c1hhyzzBuw5eewM97wtSh/A43m18EqXK/9tANSBV34wdrzn/YDg526dWbvUZvUb+f5P7wejcv9dDDkHtrYlJPX70K/sw+9SC/W6Q92coDnzzjp09nHHZvX88diwf6Z/tmc7K3tXi9c/NwUbaTFXAHhpsLGJzTUYACFKAABShAAQpQgAIUoAAFKEABClCAAhQo5QIMN5fyNwC3TwEKUIACFKBAyRSQqsiLFi1GlSpV4O3jAwcHe1XN+cLFi/Dx9oZKY2qh5jvFO+3t7JGYlKiDSFhZmoSTo6OjUb58eRVSNhzXXpOws+FLAstawFkqLleuXAlhYeHquIyhB5rvhJtljakphqD0su++xcOPPqZXjk5KSkJSkqFSc0xMLOzt7PD6a6+UzItVAnfVp08fBAcHIygoCMuXLy/0HRqHiy0tRj6iXsLD0qQypQRSzdu6wzcgQS7jEJWlsXq18MC8Z9ODUdInL8LNMk5u1nDzdjL8J27P9hpI8Cv0o/aZ9vvfT6exeOuVTF+X6skSgPtmm2kfSxVzZZAeH+/DwYsxWa5r2csB6ORXwaSP+A/79rhJdeTMBtn3TmtI8Dkvm4QWX/jqqEnQ3Xh8CRhLNXCtmYeb5fjk1WfxxcZLFpclIWIJ5mmBQa2TuePpsNvov/Aozl9Pn8vSgFKt9OCUIJOX+i88gg1HI6xiebZNNUiF6qxabq5l9VGbrZrfuNOq4U3QunZ6hW8JgD85/xCOXYnNdCx5X3/evwEebJIxnF3Y4dEcA/CEAhH460gEBnxlCCZL2zYhED53HgIxXkDQu7v0Bwxe6lgDkx+rrb98MTIB9xhVFn/n0doY3KlGjtZf2O9PeQhDHsaQJhXytYcrjDdhHNYt6HCzto5fD1zHy0uOZXrPNd7HA40r4c9DNyBh5r6tq+m/h6Tis1TVlzbuQR+8ZvSA0vfBVzF6hSH4LO3Pkc3RtJZLjq6l1pnh5lyx8SQKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhQoxQIMN5fii8+tU4ACFKAABShQsgUkaLz/wCGMHP4K5sz/TFVrlq9bUbfg5uqmvrexMRyTpn4uY6MqKdvY2EAyzBJIlv7VPDyQkpqKhISEOwFm01CznHv79m3cvHUL7m5ucHVzRWREpKrCbBxsTk5JVucnJSYZqj9LFWip5pySAgk0yzgSepa5b982BNbs7Ozw2vBhJftilbDdFbVws/BKmPSjtSEm4WSpQjmwQ3UVbNaqAUuYVkK1lpoEa9/77Rx+3hNuUhnWuK9UdlwxtLHJ6UO/Pa4Hp6SK8dx+puFn6fzEvIPYfuaWOk+rKpkXa5CKwg0m3H24WSpffrrhAj7ZcCFDdeKWPq74sn8DLNx8GZ/9e9Fk2ZmFm6XC8Qd/hODb7VcyDYx/9FQ9PBNUzeK12HLqJqSi5pmwuEyDxr+PaIbmXobqv1Jo/rcD4erekpNW1t4G3QMqmZxy4uptDPz6qEmIWUK04jCrb320fW+X3t9SuFnWMv+fC5j2+3m9n5zf0c8ds56ur4y/2nw5W0cZZ8nWy6q/VLq21CyF1o0rzmZmEVDDBRJs7hdUzaSarKX+ubmWuQk3r3mtKVr5uJosQapYi6OE6s0fPGhS0wWfD2hoMZgqgxiHR+c844ferUwrjEsfreqrfG8pbJ+T9xL7Fg8BuWcGvBWs31ckUP/DkABVgVlrvx24jsFGgVrzcLP0M//vbOT9XhjauWaGCuKZqRR2uLnj+3sgD1FI6+ZfMUNl/wk/n8HXW9LvU/kVbpaK8uXL2lpkSkxOxSvfncDvB6/rrx+aEoRKLukVph/55ID+CQ1aVXztXvL69yewcncY5CEQrWr8wucbmjwMIfO3mLxDv7/IPXXOM/XRs1mVbO+N5otmuLl43AO4SgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFCg6Agw3F51rwZVQgAIUoAAFKECBfBGY++k82DvYo1nzFkhMSoKzk5MKL8uXFmw2njgNaVLUWZV21qoyy0/a9xJK1r6/fv06vLy8VGVnCSdrYea0NAkspyIl1VC1WQWcU1NUcFmr1iw/29naqfOk0rMEpx0dHZCQkIjExEQVeK5e3RNPP/Vkvrhw0PwTKIrhZm23EgINvREPr0pl9aq+xiG0F9pXx3uP18kWRwKdUqE3MjZZffy8hK+8KzllGsLKdsBcdCiMNUiY9PLNBBWmlfuEd2UnFQyTdisuGRIEc7Qro4KAEgy2t5X7TNabuxARr8ZMSZWHLAyWtSqWhZuTnVUqspaLEfG4nZiq5qzm5oDq7o4mwTMJvtYbv82q8Yw7ZVXRWvyPX7kNB9sy8PMsB5sygHmY3FK4WRtfLM9fj8fN20loXNNFVWzOraOMdSEyAVdvJqhQZjlHW0hFco/ylitXxySkIOR6PGISkpGYnHbH3Q5Vytur66mtJadgd3stczqfcX8JJ0ola1cnO9Sp4pTj4OHdzM1zS5aAeXV1CcV2qOcOO9sy2B8abfJgg+zcUrhZHppp894uk4cv5H5Sv5ozarg7qs+/CItKxJnwOAzvUguvG1ULljELO9ysBX+1KyuV+YPquCE+KRVbT91UVatdy9qZPOgjlfLld+vqV5uq0+72UwskvOwzdivETe7pFV3sVTg8KSUNN2KS9PC1tka55+166x6TN6OEn1ftDTM5dvTdNnB3tsO8fy6qB5aM2z9jWqCBZzmTY+bVoeVFucfWq+oMTzcH9bvn6q0E9bDN6tea6g/V3E5MQb8vjyD1zlM1u85F6eNqVnLA3ckuQ3i8qP4XNWvWLMyePVstLyQkpKguk+uiAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBEiLAcHMJuZDcBgUoQAEKUIACFMhKYMuWbbh08SJqeXurQI2jQ8bAmyHoLJWcocLIM96fjpjoaHTu0gVt27WHnb29CjVLk38lfOzo6KgqNkuQWQLNEm6W15KTkwyVn9PSw83JScl6teaU5JQ7/eRfORd6oNlQuTkVXe+7F40bm1bA5VUuHgJFOdxsLnj+RrxJpd0pj9XGoI41igc0V2m1QH6Emy1NnpNws9WLZ0cKUKBABW7eTkaPj/epAK+lJmHb8k52iIxNUi8P7lQD7zxaO0PXdYdvYPjSE5lWp9dO6NXCA/OeNa3oX9jh5ouRCep3ozwskVmTauZ9vzic4eXLH3dQx+423CyVo6WCtDVNrsmm8a0yVGp//4/zqrq91owfWJHr88KioybDn/uwnUmVbu3FGWtDMGt9aLZLmd23Pp4KrKr6yfvIf+Ldf3JCtpMWYAeGmwsQm1NRgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAAGG7mm4ACFKAABShAAQqUEoHomBj8sGw5GgUEICIyEpUrVVI7N6/eLOFkOXZg/z64V6gAHx9fFWSOjY3F/n170TqojQomSwD66rVrqFK5svo5KSkZp06dgmcNTziVdUJySjJcXcoj4mYkJMycqkLMaXrlZqkcLVWaU1NTsOnff9EwoDGcnJyQlJiA114bjrKOjqXkypS8bRaXcPOpa7fx3IIjJgG2HRMDVdVgtpIlIJWNl2y9kuNNOdrboF9QNavPY7jZaip2pECRFpDK7G8sP4U1+8JNAr5eFctiaq86+N/PpyHVmaW9/WhtvNzJ8kMx8mCFVAf+eU+4SZVj4823r+eOFUNNH+YyDjfPecYPvVt5ZPDyGr1FX5sEjTv5VVB9pIq5VFfOaZPKx618XPXTDl+KwbgfT2NvSLTJUFKZ/61HfPF8++qo+cbmDNNo4eah3x7H6n3h6vXHW3pgbj/TALccf2LeQWw/c0v1GXJvTUzq6auPt+PsLcgnK2TVZC0ytgTM/ao5Z+i6NPgqxqw4pR+v6+GMTeNaqp/l0xfaT9+tvyaVqI9Pa5PpdPIw1PgfTyP4zC3I+8NS+99DPqoStzTz3weZDZzVJwTk9Brmd3+Gm/NbmONTgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKCAsQDDzXw/UIACFKAABShAgVImsG//AURHx9ypuJyC+IQE2JSxQZUqlZWEVp1ZvpeQs4SY5ZjhC7hy+RJc3dzg6FhWHYuLi1PBZQcHB70ys5wjgeiTJ06idp3aahwJM8uxxIREGKozAykpyQjesgVuFdzhW6ce6tetjR49upeyK1LytluUws0SPlt/5AZiElIQE5+i/g2PTsTBCzEZqnIO7FBdhdbYKJBbAYabcyvH8yhQNAXkb5Vz1+NUleYGnuVQztEWUszYZ0x6sHjJwEbo1qhithuQQKwEaiNjk9WnZJQvawvvSk7q37xslioSWzN+69puWDW8SYau0fEpCLkRpypQVyxnD5/KTrC3lU/7gPo96mBrA0e7MnCws0FZexvY2hhey4sm1pci4xEWlYj4ZPmEEKi5K5e3R2UXB7g52SnLgm5yrz8THoe4xFTY2gAVxKVSWWVQkhvDzSX56nJvFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAWKngDDzUXvmnBFFKAABShAAQpQIN8FJHz873+b7lRtNqRComOi4VreVQ83r/3jN3Tr/gAWf7UALwwarILLEiCRoM+USRPxv7ffMQo9pyElNcUQbk5JVd87Ozvjevh1uLi4ID4+3vCa+kpDfHwc4m7fxsH9+9G0eQtUrlwJ/Z/rl6GKdL5DcIJ8EShK4ea/jkZgwMIj2e4zoIYLfh7eBC6OeRsyy3ZidihRAgw3l6jLyc1QwKLA8p3XMPKHk/pr/73ZEvWrZqwaXFh8eR1uLqx9cN6iJxAcHAz5Gy8oKAjLly8vegvkiihAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKBECTDcXKIuJzdDAQpQgAIUoAAFciZw+/Zt7NixG8nJKUhDmjo5LDwMHlU8VHD54oUL+OP3NbhwPgRvTX0Xx44cxdkzp+Bbpw4a+jcyhJuRpioy29oYQqFJSUnqZ/myKVMGiUlJql94eLiqDh0Xl4ALISGo5eWFqtWq4pGHHoC9vX3OFs7eRVqgKIWbs/tY+6quDhh5vxf6t/Us0qZcXPEQYLi5eFwnrpICuRWQ4PCQb45DqjBL83R3xM6JgXlarTi3a9POu3IzAX8eupHjYaQi830NK+T4PJ5AAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABfJDgOHm/FDlmBSgAAUoQAEKUKCYCUj4eMfOXYiKjlFBZKm07OjoqALK0lSI+c5Xalqq+l6qMCckJKjvbW1tIdlo7bhWpTkxMRE2Njb6l/wsfevVrYP27dqyUnMxe59Yu9zJkydj0aJFRaKy37WoRMz9+wKcHWxR1t5GfTk52KKuhxOaeZVnpWZrLyr7WSWQmgb8fSxC3Q+ldfRzh6OdjVXnshMFKFA0BPaERGNfSBRiElIQE5+i/r0YGY+DF2JwPSbJZJHfvtQIXRpWLBoL5yooQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQAkSYLi5BF1MboUCFKAABShAAQrkhYAEm3fu3oPTZ86iYsUKKsSsgs2paZBgs72docqyhJYPHTigMnwVKlaAu7uhr94/LQ3Xrl5BUlIyKlWujCaNA9D6nkA4ODjkxTI5RhEWiIqKwsqVK9G9e3fUrFmzCK+US6MABShAAQqYCkxcdQaLNl/OlkUq/r//ZN1s+7EDBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAzgUYbs65Gc+gAAUoQAEKUIACpUogPDwc50NCEXnzJqKjY5CckqwqOkvYWSoxX750UQWdy7mUh7u7uwovu7u7wbNaNXh71VLH2ChAAQpQgAIUoEBxEPjwzxDM/is006X6VXNWoebWtd2Kw3a4RgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShQLAUYbi6Wl42LpgAFKEABClCAAhSgAAUoQAEKUIACFMhrge1nbmHd4RtwsrdBWQdblLW3QXlHWzSsXg4BNVxgb1smr6fkeBSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAmYCDDfzLUEBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQoUCQGGm4vEZeAiKEABClCAAhSgAAUoQAEKlFyBpJQ07Dh7C6E34nEtKhFBddzQpo5byd3wXexsw9EIHLkci6rlHeBbpSwCfd1gw0KxdyHKUylAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgeImwHBzcbtiXC8FKEABClCAAhSgAAUoQIFiIhAdn4Ipa85i+c5rSE5N01f9WtdaGPegTzHZRcEuc8Syk1ix65o+qaOdDQa088SbD/jAycGmYBfD2ShAAQpQgAJ3BKKiorB+/Xr4+/urLzYKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQH4KMNycn7ocmwIUoAAFKEABClCAAhSgQCkVOHwpBr3mHkRsQkoGgRlP1UO/oGrFTubUtdt46rNDat02NmWwc2IgbPO4rPLMdaGYuS4kg02Fcvb4fUQz+FQqW+zcuGAKUIACFCj+AitXrsTo0aNRs2ZNbN26tfhviDugAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBIi3AcHORvjxcHAUoQAEKUIACpVFgx44dOHbsmL71MmXKYMCAATh69Ch27typH+/Xrx9OnDiB7du3q2M2NjYYOHAgfvnlF4SHh+v9KleujF69epUayrS0NMTFxcHZ2bnU7JkbpUBREzgTFoeuH+1FQnKqvjSpQNzCuzza1nXHs22qoaqrg/5aWHQinv3yiP6zf/VymN23foZt/XUkAjPWGoK/zwRVw/PtPAt06/tCo/HQ7P36nCEz2sPetkyeruH8jXj8sOMqtp6+hQOh0SYVr13L2mHjuJYmdnk6OQejAAWKhcChizE4fvU2rt1KgIerA54KrFos1l3Qi5SHaz779yLcne1Q1c0R9/i68v55Fxdh1qxZmD17thohJCTjQzh3MTRPpQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpkEGC4mW8KClCAAhSgAAUoUAQFIiMjUbFiRUiwOTU1PRw4fPhwzJs3D+fPn4e3t7da+fHjx9VHQycnJ6uAs7Tdu3cjMDAQERERqFChQhHcYf4tSdyqVaumwuBshSOwbt06Vdnv/vvvx8yZMwtnEZy1UAX6fnEYG09E6mtoV9cd3w1uBAk4W2rnr8eh7bTdJi9tGd8Ktas4mRxbtPkyJq46o45JsHnaE3ULdJ8FEW423lB0fAp6zT2Ao5dj9cO9W3lgzjN+BbpvTkYBChS+QGoa8OXGi/j074uIjE3SF9SkpgvWjmpe+Assgiu4EZOExpOCTVYmD89Mf6IuAn1di+CKi/aSGG4u2teHq6MABShAAQpQgAIUoAAFKEABClCAAhSgAAUoUNIEGG4uaVeU+6EABShAAQpQoMQIyEc+X7p0SYWbJeSsNQkwS4D3+vXr6pCTkxN27dqFgIAAvY+EoKXPlClTSoyHNRtJSUlR+46OjjYJhVtzLvvknQDDL3lnWRxHOnXtNjp9sEdfes9mVTDvWT/Y2mRe4dhSuNlSeLm0hZsFUapfP/PFYWw/c0s3PTC5NaqUT698XRzfJ1wzBShgvcCtuGQ89ukBnLh6O8NJDzWpjAXPN7R+sCLUc9Tyk/j3mOFBmCGda+LlTjXyfHXVR222OOawzjUx8RHfPJ+vJA/Iv+9K8tXl3ihAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKFD0BhpuL3jXhiihAAQpQgAIUoIASSExMhKOjI/z8/FR1Zq2NHTsWM2bMwI0bN5CWlqYqOMfExJio2draqkrOxqHo0sA6ZswYTJ48GS4uLli4cCFefPHF0rDtIrdHhl+K3CUp0AUN+eY41uwPV3NKpeaT09vC3jbzYLP0sxRulnNPTGsDB6Nqz6Ux3Cw+EmxsOGG7fh0HdqiOqb3qFOh15WQUoEDhCMgDDt0+2ofTYabBZqlALFXxH2/pgaa1XEwWN2t9KP48dEM/Nv3JumjpXT7DBp5dcBhhUUmQZ09+fb1ZtvfqvBboNfcgdpw1PLjxUscamPxY7byeAit3h+G/45EIPnsLV24mmIw/8n4vjOlh+CQUtuwF+Pdd9kbsQQEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAnknwHBz3llyJApQgAIUoAAFKJDnAvb29iqkLCFm4ybVm8uWLatCvOfPn4ezs7P+8urVqzF16lTs3r0bnTp1UhWepb9UgF6/fj1q1KgBf39/dUyanLtz5051TMLQctzX1xdr1qwxOSZVkY8cOaLOadSoESRALcdkbQ4ODvjggw/Qo0cP9XpQUJCqnix9tCb9ZC/Hjh1Th4znk7XJl4wzatQoDBgwwGS/Mp80WZvMuXXrVlSoUCGDt4TBExISULt2bYSFhWUIfef5BeKAFgUYfim9b4yw6EQ0e3uHDjC0c028ZUVlTEvhZhlk1tP10eeeqvp4uQk3SzDYxdE2y8rRmV2x2IQU9VI5R8O9bF9oNB6avV/vHjKjvVVhwJu3k+HqZKcChLltr3x3Aqv2hqnT7WzK4MS0tnByMNzH2ShAgZIr8MXGS5i8+qy+wcou9vhjZHPUrOCY6aaf/+oo1h9JDzff36gSFg/0z9Dfd+xWVR1e2qnpbfV7XUFpFkS42XgvP+8Jw/ClJ0y2t2fSPfB0z9yyoCyKwzz8+644XCWukQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAyRFguLnkXEvuhAIUoAAFKECBEiiwceNG3HvvvZg4caIKLGtt3rx5GD58uKraLOFm4yaB56ioKD28LIHkgIAAFR42ruQs4Wg5/8QJQ8gjIiJChZpv3rxp0m/gwIFYvHixChVrTYLKEjT+7LPPMGTIEBVwtrOzQ2BgIIKDg1W3gwcPomnTpuo8bd7GjRurILUWxn7zzTfx4YcfmoS3pTL1Rx99pPYge9HaiBEjMGfOnAxBb+11CXWfOXNGhaM3b96Mjh07qnBzuXLlSuA7o2hvieGXon198nN1G09Eou8Xh/Up9r7dGtXcHLKd0jjcLMHd5FTDAx1+1Zzx79iW+vnWhptX7wvHgk2XcORSrB7c86nshA713DGpp2+WAT4JaE/46YyqJno9JknN3cK7PJ5r44n61ZytCjfL8yhfbrqEVXvCcOLqbX0NXhXLoot/RUx42AfODukPf2QLBKi9dJu5V+/695gWaOjJ+5s1duxDgeIqIPfCgInBiIpPVluQe8jvI5qhkot9llsyDzdL58NTg1CxnOl5pS3cLA7/Ho9Evy/Tf0/Jvf2D3nWL61ukQNfNv+8KlJuTUYACFKAABShAAQpQgAIUoAAFKEABClCAAhQo9QIMN5f6twABKEABClCAAhQo6gJS/VjCwxJONm4SGPby8kJISIh+WILEEhyW6sVaO3nyJPz8/CyGm+vUqaNXY5Yq0UlJhiCfcRs0aBC+/vpri+HmhQsXQsLP0h566CH88ccfevj48OHDkDCzcahaxg8NDYXMK81SuFmON2/eXIWjjQPVEnqeMWNGpuFmqdocHx+vgtTiJW59+vTBsmXLivolLnHrY/ilxF1Sqzf03farGLvylOpfo4Ijdr11j1XnGoeb5bwq5R2wPzRanfv36BZoWN0Q4s0u3JyYnIpRy09BqnNm1qTq6fcvByCgRvrDE1rfAxdi8OT8g9AqNpuPMeWx2pj0S3oFVUuVm8OjE/HCoqPYG2JYv6VWoZw9VgxpjEY1chZONg4iLhnYCN0aVbTKl50oQIHiKbBsx1W8sdxwT5W2YXQL+N+5H2a1I0vh5jcf9MHrXWuZnFYaw80C8PYvZ9UDMNLkgZoDk1tD7stsWQvw7zu+QyhAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKAgBRhuLkhtzkUBClCAAhSgAAVyIfDff/+hc+fOkECBVC+WdvnyZdSsWVMPPWuVkT08PLB+/Xo0a9ZMn8macLO7uzvCw8MhAWfzZm24uV27djhw4ICqlizNPNzs6emJK1eumAyfWbh53759aNGiBWTt9erVU+dkFW6WUHOtWrVw7tw5ffwOHTqo9ZiHwnNxCXhKDgUYfsmX3mhUAAAgAElEQVQhWAnq/t5v5zDvn4tqR4G+rlj9alOrdmcebn7rEV8M+ea4OvepwKqY3be++j67cPPMdaGYuS79gQ85x7gStLYYCbFJmE1e05oUi272drBerVmOO9rZqJB26I14VU26nKOtSfDZUrj5qc8OYcupmyb7lvMSklL1itTyolSS3jq+FcqkLyFbq6B3dyE0Il71e7dXHbzYoXq257ADBShQfAVaTtmJKzcND6xJpXap2G5NsxRulvve4SlBJvecnIab5T4Yl5iK8mVzVnle1iwV7W/EJqFSOXt9Db3mHlRV8qW91LEGJj9WO9vtJaWkqWr4Lo45X4M2+KXIBARO3anPNfJ+L4zp4Z3t3KW9A/++K+3vAO6fAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEDBCjDcXLDenI0CFKAABShAAQrkSsDGxkadpwV1y5Yti0uXLqFy5cqoWrUqrl69qoLOdnZ2JtWO5Zysws0yrgSDH330Uaxatcri2qwJN0tFZqmcLHPVrWv4aG8t3CzHZW0SvL5w4YLJHJmFm6W/rG358uV46qmn1DlZhZvLly+PHj16QELaWktOTsbixYuxd+9eVQmareAEGH4pOOuiNpMEktfsD1fL6tXCA/Oe9bNqiebh5u0TAtFwwnYVJJYA8rH32qhgcVbh5ltxyWj8VrAeIJb+PwxpjJbe5REVl4xXl57AX0cj9PWYh4N/2RuOYd8ZAtXSJDgsfaSlpKbhwz9D8Onfpvcw83DzznNReOzTA/oYPZtVwUd96qkQngT7Fm6+pCqGak1C2xLetrY9Me8gtp/JWRDQ2rHZjwIUKFoCEuL1HrNFX5TcT+W+ak0zDjcbP+CxdHAAOjeooA9hTbg5IjYJ76w+q+49EgqWJvdXqX7/apdauK9h+niW1vZ98FUs3noFx6/E6g+JPN7CA690qYnXvz9pVbhZfkdM+fUcpLq+FvaWNTTwLIc3H/BG+3rpf/9Z4yN9jB9E6eRXActeDrD21FLbLzg4GPL/C7p3746ZM2eWWgdunAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABQpGgOHmgnHmLBSgAAUoQAEKUOCuBLRg76lTp1SgWQLE169fx7333ouNGzeqaslz586Fv78/HnnkEZO5sqvcLKFmPz8//PTTT3j88cczrDOrcPP48eMha2rfvj2GDh0KBwcH/Xzzys0Sbg4LCzMZP7tw87p163D//ferc7IKN0uoW8LM5s3W1ha+vr44ffr0Xfnz5JwJMNycM6+S1PvB2fuxPzRabWnYfTUx8WFfq7ZnHm7e9dY9ePfXc5j/r6EK9HuP18EL7atnGW5euOkSJhkFh38c1gRt67rp80u4uNXU9Cqonu6O2DPpHv31h+fsx94Qw9qlqvK2/7XKsHbjPvKiebi5/8Ij2HAnQN2kpgvWjsr4YMW4H0/jm22GKvbd/CtiyaBGVhlJp+FLT+DnPYb7aI/GlbDoBX+rz2VHClCgeAlIlXap1q614++1gauTnVWbMA43y4Ma8mCItA713bF8SGN9jOzCzcFnbuG5hUdMKtabL0DGn/xobdgaVcKXPvJQyNiVp7Fsx1WLa5bq/jZlymQbbv5hxzWMXXnKpPK9+YDPt/PEtCcMD9dZ24wflsnsnm/tWOxHAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBA3gsw3Jz3phyRAhSgAAUoQAEK5IuAVr25SpUqOHbsGCpWrIi4uDg4OzujXr16OHPmTIaqzbKQ7MLNR44cwdtvv40pU6YgKioKUgXZuFlTudnShs3DzZb6ZBZuPnToEJo0aYKIiAhUqGCoCJhZuHnMmDHw9PTEqFGjMkzxwgsvqOrNUvG6TJky+XJdOGhGgZUrV2L06NGoUaMGtm3bRqJSJHD/zH04fClG7fi1rrUw7kEfq3ZvKdx8+WYCWk3Zqc73qlgWwRMDsww3v7nyNL7dbggNV3V1wL53WmeY+7N/L2Lqr+f045c/7qB/3+itYETGJqmfzas6a52WBl/FmBWn9HPMw81tp+2G7EXaty81QpeGFTOs4diVWHSZsVcdr+vhjE3jWlplJJ1GLDuJFbuuqf45DUZbPQk7UoACRUJg6+mb6D3/kMX7VXYLNA43fzmgISavOatXXZZ7o9wjpWUVbo5JSFHV8BOSU02mM64Erb3wYe96eLZNNZN+EkoetfykybEaFRyRnJKGa1GJ6rhUX5YK/dJe6lgDkx+rbdL/0MUYdP94X4b5JUhtvq6FzzfEg00qZ0ejv7720A28+PVR9bOjnQ3OfdjO6nPZkQIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgfwXYLg5/405AwUoQAEKUIACFMgTgeeffx5LliyBi4sLoqMN1UWlSbhZQs79+vXDd999l2Eua8LNcpJUfZbQdFJSEqQSstbyMtyckpKCjz/+GBJIlpZZuNnLy0u9Hhoaqq8js74S+pZxLYWX09LSIK9/8sknePXVV/PkOnCQ7AUkJC9Vtxs1aqTeV2ylR2Dg10fx56EbasNPtvLAJ8/4WbV5S+FmOfGJeQex/cwtNcavrzfDgdBoTFx1Rv1sXqnzqc8OYcupm+q1+xpWwHcvBWSYW16Xflo7MLk1qpR3gFR1rvHGZv34z680QVCd9KrP2gvm55uHm71Gb9Gri/YLqgY3C1VW45JS8fUWQxXVnAbqjPco1VIlhM1GAQqUTAHjcHBlF3scnBJk9UbNw81XbyXole2NHzzJKtz83m/nMO8fQ/V8aXJPe+fR2nB2sMWmk5F4bsER/X7nWtYOh6YGwd7W8CCZeaV8CTGvHdkcdTyc1OvykMczXxzWQ85yzFK42fjTAGQMCWp3bmB46O1CRDz6fXkEp8Nuq58lsL170j0ZKkhnhnbgQgwemJUenDa/n1uNzY4UoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQrkiwDDzfnCykEpQAEKUIACFKBA3gskJyfD3t4ely5dQvXq1fUJbt68qaobW6pOvHr1arz44ouqAnKvXr2wfPlyNcakSZMwdepUFQieO3cuhg0bpsaTILCtra16bdy4cfjyyy/VaxIeHjlypAomS+vTpw9WrFgBNzc3TJ8+HUOHDjXZ8E8//YQBAwYgNjYWQUFBqp+M8e+//+LUqVPw9fVVFZUlcBwTE4O+ffuid+/e2L59Oz7//HM1/oIFC/Qx5fvXXnsN8fHxaq45c+bg+++/V+dL0FsqNMuefHzSq8RKReoZM2aoQLg0OW/+/Pl5f2E4IgUooAtMWXMOn/9nCMO1qeOGn15pYpVOZuHmDUcj0H/hETVGz2ZVcI+va6bhZuOqyX3uqYpZT9fPMLdx1WR58c+RzdG0lgtuxCSh8aRgvf8/Y1qggWe5DOfvC43GQ7P368eNw3BxiamoM26rVfvVOkkF1NCP2lt9Tvvpu3E23FAZWkKGgzvVsPpcdqQABYqXwId/hmD2X4aHvPyrl8OG0S2s3oB5uPneBhXgP2G7CiNLEPnIu0EqBJxVuNn4tSY1XbB2VHOT+VftDcMr353Qj33ev4G6T0vbdS4Kj356QH9tycBG6NbItJK9eR/zcLOElju+v0cf48dhTdC2rulDJ1IBuvk7O/Q+W8a3Qu0qhgB1di0sOhHN3k4/d9uEQPhUKpvdaXydAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBAhJguLmAoDkNBShAAQpQgAIUyAsBCTgbV1XOyZhSxVirbmz8fVZjWHOOpbGsGd98bFmHperLctzSOnK6NmvWlBNP9qUABTIKSEXiCT8bKiv7VHbCtv+1soops3CzVP8MmBSMyNgkNc4b3b0xc12I+t68crME6SQsJ62bf0UsGdQow9xSBVqqQWtNqnxWd3dEQnKqCvlpbf0bzRFQwyXD+VmFm6WzceVmazae08rN9cZvQ2xCihp64fMN8WCTytZMwz4UoEAxFJj++3l8+vcFtXJL4eKstmQebn64aWUMX3oCP+8JM7l/ZBZujklIQf3x2/Qp5j3rh14tPEymlKB0vXHb1P1T2pge3hh5v+GTN5bvvIaRP5xU30vF5RPT2sLGUNTZpDX433ZExSerY+bh5nWHb+CFRUfVa3U9nLFpXEuLW+77xWFsPBGpXvv2pUbo0tA0RJ2Zk/lDLZvHtdIrSxfDtwuXTAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhQocQIMN5e4S8oNUYACFKAABShAAQpQgAIUKBwB40rLsgIJtJUva5vtYjILN8uJM9eF6oHmqq4OkEqd0szDzaOWn8QPO66p16Ryp1TwNG/fbb+KsStP6YcvzuygB+6MQ36LXvBHj8aVMpyfXbhZqoxKtVFpEx/2xfPtPbPcu1ROlYCzNe1iZALumbpT75pZANuasdiHAhQo+gLG9yu59+17p7XVi7YUbj50MQbdP96nxmjp44pfX2uaaeXmI5di0W3mXn2+/95sifpVnTPM3/WjvTh6OVYdf7KVBz55xk99P2NtCGatN1SdziqYbXy+ebh5/j8X8e5v59QYlV3s8VRgVYv7X7UvHFduJqjXJj9WW4WkrWnmezzzfjs4OVh3P7ZmfPahAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClDg7gQYbr47P55NAQpQgAIUoAAFKEABClCAAncEQiPiEfTuLt3jrUd8MbRzzWx9sgo3X49JQpNJwRnGMA83S4VTqXSqtX/HtoRfNdMwXpcZe3HsiiGIJ2G5g1OC9P5tp+2GrEPaA40r4asX/DPM+cfB6xi0+Jh+PGRGe9jbppcjHbzkGH47cF0ff9O4VnB3tst2/9Z0ePuXs1iw6ZLe1drguDVjsw8FKFD0BKQasVQllmZnUwahH7W3epGWws1ycvvpu3E23HCfC54YiE7v79ErL5+a3lZVWZb215EIDPjqiD7f4alBqFjOPsP8xlWT29Rxw0+vNFF9Riw7iRW7DA+bdPKrgGUvB1hce6+5B7Hj7C31mnm4WT4FQD4NICdt/EM+eLVLLatO+etoBAYsNOwxp1X0rZqAnShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFLgrAYab74qPJ1OAAhSgAAUoQAEKUIACFKCAsYBxWM3aaqNZhZtl7P4Lj0CqQhs383DzpcgEBBpVNvap7KQqk1ZyMQTyjCtAy8+v3FcTEx721YeUCqFSKVRrSwcHoHODCvrPsQkp6PnJAT0cLS+Yh5v3hETjkTn79XO8KpbFp/38EOjreldvkqSUNNQfv00PIfZsVgWf929wV2PyZApQoGgLnAmLQ4f3d+uLNA4fZ7fyzMLNS4OvYswKQ/X6gR2qQ6pDJySnqp+NxzevUi+V8KUivnnr8fE+HLwYow4b35feWnUGX202BJPb1XXHymGNLS45q3CzceXm7Parvf5urzp4sUN1q7ov3noF//vptOor92oJe7NlLRAVFYUff/wR/v7+CApKfziIbhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECB/BBguDk/VDkmBShAAQpQgAIUuEuBrVu34siRI0hLS1MjlSlTBrt378Y///yD06cNQYys2nvvvQdnZ2d1nozx4osvws3NLbvTMry+dOlShIWFqXGkubi4YNCgQTkex9IJe/bsQevWrZGcnHzX4/Xq1Qtr1qxBSkoKLl26hBo1rPtI8ruemANQgAIZBLafuYUn5h3Uj09+rLaqyJlVyy7cvPX0TfSef8hkCPNws7wooT0J72lNqp16VSqL69FJiIpPv9dIlc5DU4PgcqdKqfS/eTtZVYhOTjXcd6W19HFFvapOCItKxKYTN01ek9fNw81y7I3lp7BsR/oa5JiEvCUYKFWco+NTcO56nFrTuQ/bWfUOmvrrOXz2b3rweuObLVGvqmlVaqsGYicKUKDYCEjo2HfsVn29Uv1YqiBb0zILN8clpsLvf9vUvUzugympafp9zTjcHBmbhEZvpVfMXzKoEbr5V8wwdYP/bdfvra93rYU3H/RRfRZuuoRJv5xV32cVHM4q3Pz3sQg8tyC9srJUj77z52imBLInW5v0avpZWQ355jjW7A9XXbIKYFvjXVr6fPXVV5gyZQpq1qwJ+f8qbBSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECB/BRguDk/dTk2BShAAQpQgAIUyKXAm2++iQ8++ADXrl3DSy+9pIK7ElJu1KgRjh49atWoNjY2SE1NVedJKLpLly5WnWfeydbWVoWGpfn6+uLcuXO5GsfSSeXLl0d0dHSejOfg4IDExEQcOHAATZs2zZMxOQgFKJA7gc4f7sGJq7f1k8f08MbI+70yHSy7cLOc2PydHbgWlaiPYSncLIG8J+cfMqmubD6pBJ6l6vGDTSpnWM83265g3I+ZP0DS1b+iSQVpS+FmqfA87Nvj+Mus0rSlze97p7UKPmfVpLKoVBjVWvt67lgx1HIV1NxdLZ5FAQoUVQHj8HBO/tvPLNws+zR/CETbu3llaK/RW/Tgs1Sxl2r2xm3zyZvo83n6Qyez+9bHU4FVVZe/jkRgwFeGYLK0bRMC4VOpbAbmoHd3ITQiXh2Xh2DkYRitXYxMwD1G1fjfebQ2BnfKm4fXbsUlo+GE7fpcUu1Zqj6zZS0wa9YszJ49W3UKCQkhFwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClAgXwUYbs5XXg5OAQpQgAIUoAAF7k7AONwsIzVu3BiLFy9WAefnnntODX7mzBls2rQJ/fv3hwSRtaaFm0NDQ+Hl5YWDBw8iICAAX375JYYMGaIqQEvVNTlPKjPfvn0b33//PaQKcqVKlfRxtHCzBJy18Xfu3InAwEB88skn6Nu3Lzw8PFR/qfQs38tHVe/atUt9X79+fTVPs2bNVOXnBQsWoGPHjmotxuHmjRs3qn0NHjxYVXPesWMHWrRogfnz52Ps2LFqfAlq//DDDyrk3aRJE3VMzrt48SJeeOEFJCQkqHCzvG5vb6/mlfDFM888c3cXgmfnSECuR58+fVRlv+XLl+foXHYuGQJbTt3EU5+ZVlpuXdsNEg5uW9cNjWu6QELGWpNwm4TcpGVW5XP+Pxfx7m/pD1dkFkaTSqTTfj8PCSpL0Ni4Nanpgs8HNLQYstP6bTt9C698d9wkSC3VQO9rWAGfPdcAPkaVVC2Fm7VxxOCtVWdwJiwuQ8Vnrc/vI5qhuVd5kzVKtdb9odGQdfx56AYOX4oxeX39G80RUMOlZLxRuAsKUCBLgTkbLuCDP87rffZMugee7o7ZqmUVbj517TY6fbAnwxjm4ebXvj+BH3eH6f3ef7Iu+rf1VD9L8PjBWftwPSZJ/Sz3c6mG7+Zkp36OiktGwFvplfDl/v/DkABVLVprvx24jsFLjuk/m4eb5QXjys7yszwkM7RzTZOq+9liWOgw75+LeM/o90nwxED1u4ctawGGm/kOoQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgYIUYLi5ILU5FwUoQAEKUIACFMihgHm42dHREfHx8SrIO3fuXOzevRutWrVCuXLlUKNGDVy+fFmfQcLNEoBeuXKlCi5PmjRJvSbHbty4gQYNGsDNzQ3Vq1dX50moWILHEkiWeSXwLE0CzU8++SS2b9+ugsJy/JFHHsF7772nwtZy/pUrV/Sg8osvvog5c+aokLSEnatWrQo5Jh9l7efnh5MnT6rwtKxBCzdL3xUrVqhAsp2dnQo3y7wRERGqjxyXcLWMJWsbPXo0ZsyYga5du2LDhg1qTVrlZqlQvX79evz6669o27at2sOyZcvw+uuv51Cf3XMroH1suZzPyn65VSz+563YdQ0jlp20uJE5z/ihdyvDQxH52SR4J1WhXZ3sUKeKE2yNAtXZzRuTkAIJAbo42qJeVWe9+6XIBJS1t0H5srZwMArqZTWeVJy+GBGP24mp6txqbg6o7u5ocT2fbLiA943CjMbjfjOokQqIs1GAAqVDQO5D/hO26w9IyH//X7/on+29LKtws8jdP3NfhgcnzMPNct+SivnGrbKLPVzK2qn7qnEbdb8XRvfwNjk2efVZfLHxkn6sQjl7dKjnDjvbMuoBjrPhpmNYCjfL/bbNe7tMHhCRIHX9as6o4e6INABhUYk4Ex6H4V1q4fWutbJ9Y1yIiMd9M/bqD79086+IJYMaZXseOwAMN/NdQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAgUpwHBzQWpzLgpQgAIUoAAFKJBDAfNws7+/v6pu/PHHH2PQoEEq3CuVcaWisQSCpUqy1rTKzXJcAsNTpkzBW2+9pYLAdevWVQFgafL6pUuXVAXobt26IS4uTgWftaZVbjZeuoSRf/rpJ8gc7u7uiIyMhK+vL86fT68uKOM2bdoUf/75p6rge+7cOVU1+oMPPtCH0sLNEl5OTExUa6tVqxak2rSsWQLN0mQNMp5Uf169ejWSkpJQr149dVzro4Wbe/bsiVWrVqnXpOKzjH38+HG1XraCEWD4pWCci8Ms+0KjVWVOCagZt7EPeGNEN6/isIUCX+O4H0+rqtPGza+as6oa3cCzXIGvhxNSgAKFK/DO6rP40igk3L6eO757qVGWD1dkF27+eU8Yhi89YbIx83CzvLhsx1W8sfxUlgCBvq5Y9nIAnB3SPz1ETrh5Oxk9Pt4HqcxvqUlIuaWPK3acvaVethRuluPrDt9QazWvxG8+Zq8WHpj3rF+Waz1x9TYenrPfZCxWw7f+/c2/76y3Yk8KUIACFKAABShAAQpQgAIUoAAFKEABClCAAhS4ewGGm+/ekCNQgAIUoAAFKECBfBOQcLOEmKUKsTTzcLNUMB4yZAhatGihwr8SCNaaFm7WfjYONz///POYOHGiCjlLODghIQEPPvgg/vvvvwx7MQ4QS4jZ2dkZTz/9tEm4+ebNmyZB49TUVBV8lsCzBK7XrVunAtje3t4quCzfS5BZCzfLv1LJWQLKnp6eqhK08byyLwk0S1BZ9qk1V1dX3Lp1y6Ry80MPPYQ1a9agc+fOelVnbb35dqE4sIlAcHAw+vTpo45J+D4oKIhCpVxAQs6/Hbiuqn1KNdC+rauhX1C1Uq5iefuf/n0B649EwNPNAbWrOKFnsyrwr85QM98sFCitAlKBXqoXG4d7vSqWxSPNKkOCzq18XFHO0TRYPPDro/jz0A1FtugFf/RoXMmELyklDfXHb0NCcqp+/PT7bTMElOXFo5dj8cp3xyHBYOPmWtYOw+6riVe71MKdD/vIcIlkfAlHr9kXblJ9WdY/tVcdHLwYg5nrQtR5mYWb5TXZ+3u/ncPPe8IRFZ/+d6DxhGKxYmjjDGu4GJmgAtRbT9/Ej7vCTNbBqs05+6+K4eacebE3BShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK3J0Aw81358ezKUABClCAAhSgQL4JSAC4bdu2qirytGnT8Pnnn6vKyIcPH1YVjCUE/Pfff6s+V69exb///qvCw9IkqCyVmB9//HFMnz5dhYkDAgJQoUIFbNmyRfWRwGlYWBg2btyoqiXLHHPmzMHMmTPx7LPPqj6TJk1S4VQfHx8VID59+rSaXyoyS2g5MDAQ33//Pb799ls1thx/7LHHsHTpUnX+wYMHVYVlJycn9fOKFSswbNgwVb3Z0dFRVZPu3r07Pv30U7Rp0wYSVpaxPDw8VLi5Y8eOKtQsa5Sfd+3ahU6dOqFfv35YsGCBCjbL2sVGKjNPnjwZTzzxBGrUqIH58+erStRSLVqqOUsAu1w5BgTz7Q1rNDDDzQWhzDkoQAEKUKC0CBy/EosHZu03CSNre3+8pQfm9su6YnFeOCWnpuHU1du4nZSKOlWc4O6c/kBdduOnpQHnrschMjZJVaDXwtjR8SmIiU9GeSc7lHOwzTQkbTy+BKbPhstYyap/+bK28K7kpP611LxGbzEJNGt9mnmVxy/Dm2RZATu7fZW21xluLm1XnPulAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKFC4Agw3F64/Z6cABShAAQpQgAIlWmDAgAFYsmRJrvZoXLk5VwPwpEITYLi50Og5MQUoQAEKlFABqXo/YtlJbDwRabLDQF9XrH61aQnd9d1tSyo+1xu/LcMgY3p447WutWBrU+buJihlZzPcXMouOLdLAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKCQBRhuLuQLwOkpQAEKUIACFKBASRVwcHDAhQsXULVq1RxvUc7z9fXF7NmzMXz48ByfzxMKV4Dh5sL15+wUoAAFKFByBS5ExOOXfeE4ejkWV28lol5VJ3zYu17J3fBd7CwiNgnPfHEYFcrZw9PdAe3quqNHQCW9cvRdDF0qT2W4uVRedm6aAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEChCTDcXGj0nJgCFKAABShAAQpQgAIlU4Dh5pJ5XbkrClCAAhSgAAVKr8DRo0cxatQodO/eHSNHjiy9ENw5BShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKFIgAw80FwsxJKEABClCAAhSgAAUoUHoEGG4uPdeaO6UABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQrktQDDzXktyvEoQAEKUIACFKAABShQygWkst8DDzygFLZu3YqaNWuWchFunwIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABawUYbrZWiv0oQAEKUIACFKAABShAAasF1q1bp/rKR5ezUYACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAWsFGG62Vor9KEABClCAAhSgAAUoQAEKUIACFKAABShAAQrKedQAACAASURBVApQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgXwVYLg5X3k5OAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKGCtAMPN1kqxHwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKJCvAgw35ysvB6cABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAWsFWC42Vop9qMABShAAQpQgAIUoAAFKEABClCAAhSgAAUoUAoFoqKisGjRIjRs2BDdu3cvhQLcMgUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABChSkAMPNBanNuShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKFDOBr776ClOmTIGrqysOHTpUzFbP5VKAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEBxE2C4ubhdMa6XAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEABCsyaNQuzZ89WM4aEhBTgzJyKAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFSqMAw82l8apzzxSgAAUoQAEKUIACFMhngdGjR0M+vvyjjz5SFf7YKEABUwEJhk2bNg0eHh4YPHgwatWqRSIKUIACFKBAkRVguLnIXhoujAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAiRRguLlEXlZuigIUoAAFKEABClCAAoUncPToUTzwwANqAcuXL0dQUFDhLYYzU6CICvTu3Rs7d+5Uq5OA80svvaRCzmwUoAAFKECBoijAcHNRvCpcEwUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBkivAcHPJvbbcGQUoQAEKUKBUCiQnJ+PWrVsme3dxcYGjo2Op9CiITR8+fBhDhw7NMNVvv/0GNze3glhCvswRGRmJnj17qrG/+eYb+Pr65ss8JXHQ4OBg9OnTR22N4eaSeIW5p7wQ8Pb2zjBM1apV8eijj+LFF1+Ep6dnXkzDMShAAQpkKSC/sytVqoR69epRigJZCjDczDcIBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKFKQAw80Fqc25KEABClCAAhTId4FNmzbhueeeyzCPhMQ6duyIESNGoHr16vm+jtI0wZ49e/D4449n2PK+fftQsWLFPKPYsmWLChlXq1YNU6ZMybNxMxsoPDwcrVq1Ui+vX78efn5+uZ5z4cKFqkJr586d0bdv31yPU1xOZLi5uFwprrMwBSTEvH///kyXULlyZbRu3RrNmzdHkyZN1JeTk1NhLplzU4ACJUjghx9+wHfffYdDhw6hWbNm6qGkZ555pgTtkFvJawGGm/NalONRgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAVgIMN/P9QQEKUIACFKBAiRLILNxsvMm1a9eiYcOGJWrfhbmZ1NRUxMfHqyUcO3ZMDzrndbh52bJlGDduHLy8vLB58+Z833Jehptfe+01rF69Gv3798fUqVPzfe2FPUFUVBQaN26sljFp0iQMHDiwsJfE+SlQJAXeeOMN/Pjjj7CxsYHcS7NrUlm1adOmethZAs+2trbZncbXKUABCugC33//PeRLQs3mjZ+2wDdKVgIMN/P9QQEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAgUpwHBzQWpzLgpQgAIUoAAF8l1ACzdrAdi0tDQV3li3bh2+/vprxMbGqo/d3rBhQ76vpTROIOHmHj16qK0z3Jz+Diht4WbZube3twKQaukjR44sjf85cM8UyFbg4sWLeP3117F79271qQKXL1/OcI6DgwMSExMzHatRo0Yq7CwVnlu0aKF+x7FRgAIUMBfIKtTs6OiIJ598EtOmTSMcBTIVYLiZbw4KUIACFKAABShAAQpQgAIUoAAFKEABClCAAhQoSAGGmwtSm3NRgAIUoAAFKJDvAubhZuMJf//9dwwbNkwd2rlzJ6pWraq+nzdvHk6fPg07Ozu4ublBgmIdOnRA5cqVLa736tWrkMp2Bw8eRPny5fHggw+qj/P+8ssv4ezsjFGjRpmcl5KSoqr27t27F6GhoahVqxbuuece9OzZE2XKlMkwx4ULF1QYW9Z048YNxMTEqHkqVqyIIUOGwMfHJ8eOe/bswZ9//qmf99BDD6Fu3br466+/1HEXFxc8+uijuPfee03Gjo6Ohnxs+dGjRxEREaFCc927d0dgYKDFNVgbbpbQueZkb2+vroWE8zp27AgJ2GhN7D744ANV0VRC6sHBwShXrlyGj01v06YNunTpkuu1JyQkQCpDi9Pt27fRtWtXyJidOnVSY65fvx5+fn45cj9//rz6uHdpcj3l2oufubF8DLx5GPH48eP47bffVCVs2a+/vz+efvppuLu752gNhdmZ4ebC1OfcxUlAHraR301yH2rXrp2q4mypOr3c4+R3wY4dO3DlypVMtyh9JOQsX1JBXb48PDyKEwnXSgEK5KGA/H2zdOlSi5Waq1SpokLNvXv3Rp06dfJwVg5VEgUYbi6JV5V7ogAFKEABClCAAhSgAAUoQAEKUIACFKAABShQdAUYbi6614YrowAFKEABClAgFwJZhZslqBsQEKBGXbFiBVq3bq2+l0rDEiI1b5988okK/Bo3CZ0+88wzKnRs3KQyr/SXIKoEgbUmweSXXnoJ27ZtyzD+fffdhwULFqhQtdYkBDt48OBMdy4huNxU5ZSQ7YQJE/Rx33jjDZw9exarVq0ymUvCL+3bt1fHTpw4gQEDBlgM0Y0dOxavvPJKhnVaG26OjIxUgXDz5unpqSpsN2zYUL2UlJSkQtjZNQl9jx8/Xu+Wk7XLNXr22WdVpWnjJqFjCbFLy024WYLYMkZ2TfYr7wWtyTWRaseWbBYvXowGDRpkN2SReJ3h5iJxGbiIYiLw+eefY/r06Wq1cn+VB07kmHkl56CgIFUJvUaNGuoBG7nXnTx5Un2dOXMm093KAyStWrVCt27d1MM1cj4bBShQsgX++OMPyN8N8kCEeXN1dUX//v3Vl/awX8nW4O7yQkA+bUD+P4T2uygvxuQYFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIHMBBhu5nuDAhSgAAUoQIESJZBVuDk8PFyFu6RJFWct6CzhDwk+x8bGYv/+/diyZYseXv73339Ru3Zt3UjCvv/9958KMU+dOlUdl6C0BFmlmYebjSucScVjqdorY0pYVtqMGTPw1FNPqe8TExP14LKMIwFXqfIslYwl5Hvr1i088sgjcHJyyvE1k2rQErqWCtKydwnTSthl3LhxqhKo7EUC2xLmlpC2tCeeeAK7d+9W38u+JWQsYd/Dhw+rY//880+GKn/WhpvFeu3atWrPUhFaKmmLqzTxltdk31LhWapLy7/yunykutiIq3GTatbGlZVzsvYvvvhC/xh2qSYtwWqpmixWWstNuFn2tWvXLjXEZ599psLTUhH8ueeeM1m7VFeVyonS5BrIz5qDhLYlCK6FHiWUuHLlyhxf/8I4geHmwlDnnMVZQB6S0e478uCL/I6SgPOSJUsybOv111+HfNna2uqvyf1UQs6nTp1SX1roOSQkJMP5M2fOVNVa2ShAgZInsHXrVhVq1v7WNN6hfFqG/B0ioWZfX9+St3nuiAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgRIjwHBzibmU3AgFKEABClCAAiKQWbg5NTUVH374oQqZSpPgl4ODg0U0CTpLVWcJ4E6cOFFVXpYmAWGtqrEEzh544AH9/M6dO6tKyMbh5uTkZD38K6FZCaJpbdKkSSqwZhxWNR5fwqxSITqv25w5c/Dxxx+rYSUg/Pjjj6vvJdy8cOFCNG/eHL/88osKeWtVqyXQLBXatKbt1XxP8rq14WZL+5IgsBa2W7NmDZo2bWrSTT5WXcLYXl5e2Lx5c6Y0OV277O3KlSt4/vnnMXnyZH3ct99+W4WDpOUm3Gy8QC20KGEiLRRvaQPGQWsJc2vBo2+//Va9F6VJGL9Ro0Z5/dbI8/G0cLO81wcOHJjn43NACpQ0AanS3K9fP/W7RO5z33zzjboHyP1O7g3m9z15WEd+r3Ts2DFLCnkwRgs8y0MWEnaW+0njxo1LGiH3Q4FSLSAPn8nfLZk9BCV/V0qw2d/fv1Q7cfMUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQoUDwGGm4vHdeIqKUABClCAAhSwUkALN0vIWALCEmqWsOuvv/6qV2MeOnSoCskaNwmTnT9/XvWRj+qWaswbNmxA37598f7776uuUglPCxyfPn0aUv1OaxKcnjdvnkm4WT66uV27dqqLBFKlCrPWJLj66quvolKlSti7d69+vGvXriqEJq13797q/MDAQNSsWdNKgay7GYebjx8/rleBlrCbVPmUKs4S9l21apWqHC3rk0rTZcqU0QeWip8SnpFgtHkF5ZyEm1NSUnDkyBEVLJbwnYeHByQwLddA1vnYY4+ZbMbacHNO1h4fH69XfP7xxx+VtfE1korV0goq3Dx27FhVHdv8fSHXplu3bmot8+fPx0MPPZQn74f8HETCVVLRfOTIkXn2/s3P9XJsChQFgXXr1mHw4MFqKVJh/+uvv9aXtWjRIlVZX6q5Gzep8C4hZ2dn56KwBa6BAhQoYAH5+1UemJMv+dvKvMnfrvLFBxoK+MJwOgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABe5KgOHmu+LjyRSgAAUoQAEKFDUBLdyc2bqkMrCEnrWqzRISGzZsGLZt22bxFOMA788//6yCmubBUznx999/V+MYV242rkSclZMEi7UmAeo33nhDBX6Nm1TofPPNN1Wl57tpWri5du3aKrScWZs7dy5mzJiR5VSdOnVSlUWNm7XhZgmci6WEyi01mfupp54yecnacHNO1h4aGooOHTqoeTZu3AgfHx99zmvXruneBRVuljC9vBcffvhhFZbXmoT0tSrOxtXE7+a9wHMpQIGiKfDRRx/h008/VYt7+eWX8b///U9f6IkTJ1TA+bfffjNZfJMmTVTAWR6QYaMABUqHgDwMpoWab968mWHTDDWXjvcBd0kBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUKKkCDDeX1CvLfVGAAhSgAAVKqYBxuFmrbuvm5qaCoVKRWAJgxk0CthJallCyhMIaNmyI5ORkFVaWoK5xuFmqL0vVZ+MAszbWn3/+CameafzamTNnVOVNac2bN4esw7xJyHrBggUmh5OSkrB9+3YVct28eTPkY8a1dvDgQYvjWHu5tXCzhKV/+umnTE/74YcfVJha2r333muxX4sWLVSYzrgZh5ulIrUEwc1bXFwc7r//fkiwWELWUp3a29tbVW+WyqSxsbEqWJ3bcHNO1h4WFqZXa/77779Rt25dfbnh4eEQJ2l5FW5+9tln8d5772XqLgF5ee81bdoUa9as0ftFRESo95A0SzbWXn/2owAFioeAVI2XCv/S5JMB+vTpY7Lw77//XlW4v3r1qsnxQYMGYcyYMShbtmzx2ChXSQEK5FggMTFRDzVfuHAhw/lPP/005O8NVmrOMS1PoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoAgJMNxchC4Gl0IBClCAAhSgwN0LaOFmLy8vFQzOqkkorHXr1qrL0qVL0b59e727Fno2Djfv27cPjz32mOoTHBwMT09Pvb8EzcaPH28SbpbwSb169VQfCT7L67lp586d0wPG06ZNQ79+/XIzjDpHCzdL0Hv58uWZjrNjxw49XPz111/rIe3sJjZe6z///IM6depkOOXXX3/F8OHD1fEDBw7A3d1dfZ+WloaWLVtCKhFaCvAan7dz505UrVrV4nJysnbjishS8fmRRx7Rxzx16pReBfVuw81Sbfnbb79VYe6sKma///77+Oyzz9Qajh49qt5P0iR4/eKLL6rvpYJ127Zts7sUfJ0CFCjGAsePH8cLL7yAy5cvw9nZWQUZzSv3S9V/uaebP6giD55IwJn3iWL8BuDSKZCJgDzAtXjxYvUAnnmTB8fkvsH/9vn2oQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoCQIMNxcEq4i90ABClCAAhSggC6Qk3DztWvX9LCYcYBXqiZL1TtpUv35k08+gZ2dnaooHBgYqP6V0LMEcOX4lStXIB/9ffbs2QxVnUeMGIFVq1apsaZMmYIOHTqogKs0qWBsa2sLqd4sTSpGS5CtevXqalxpKSkpWLt2LaSirzQJyb700ku5vuLWhpuNqytLiHvChAmq8nWVKlXU3FJl2VIlarHx9/dXfV5++WWMHj0a9vb2qkqzhJjlHKlMrO1nz549qFy5sur/+eefY/r06er7qVOn4rnnnkOZMmX0vUoQumfPnurn/v37Y+DAgfDx8dEtnZyc9O+1ytDWrF2rkCpBdAl8S7XpqKgovP3226qqt7S7DTdLde53331XjSV77NGjBypWrKiur1Tq1qqsSmheq9AqRqNGjUJ0dLSylEC3hJ137dqlh55z/UbgiRSgQJEXkAdEJKgoLSAgQAWctful8eLlPiW/p+ThEuMmAWftQZIiv1kukAIUyFJAHnL66quvsHXr1gz95FMm5F7x8MMPU5ECFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKlBgBhptLzKXkRihAAQpQgAIUEIGchJulf+fOnVUoWZqEWyVsKj936tQJGzdu1FElFCxVm6WqrlTXlSZBUwmlSqVhrckxqbirtevXr0OCtuZ9tPMkqNK1a1fVXSp1du/ePcuxJegsValz2iQwe+jQIRXMNl6rfC9raNOmTYYht23bhr59+5ocl7CwBH9lHOOqy8adJHhsbKe9Nn/+fBUWv3TpkklVwaZNm+LixYvKyNxdxtECzBL+llC5zGu8B1mLjLFmzRr9eE7WLgFrGVdrskcJrMu11LzuNtxsXCXcfO2vvPIKxo4dq88voe0NGzZYvMQS+hZfNgpQoHQISMV3eahFmlSWlwrzllp4eLgKOH/zzTcmL3fp0kU9ZKI9dFI61LhLCpQcAfmbZ9GiRfjll18ybMrX11d9qgP/Lig51/v/7d0HdFTV+vfxhxqQzqUpTQSB0ARBCCBILzZECEFAVKpdmlzl/oWFXPUqVVGvSFERL4QEC9JBQCAYBEFaAAEVBRFQxASk47ue/a4za5JMck6SydTvXmuWIXPOPs/+7JNJXOs3zwT6SvT/AfR+jIyMdP0/S6DXTH0IIIAAAggggAACCCCAAAIIIIAAAggggAACwStAuDl4947KEUAAAQQQQMCDwKZNm6Rv374mALxx40ZbIw0ya1fdlStXmnCtBlo1bKxdc62gsU7y2muvmY66f//9t8yYMcOEyKzgqwZrGzZsaLpqajBWu++6D+1yPGXKFImLi0sVLtZjXnrpJenXr585XLvxaQdoT6NNmzaiXaAbNWpkuyZPB2ioeM+ePR7P1fBc69atPT53+PBh03FYO4imHRq01nBD2vHjjz+KBnbTXk8NevToYQ7XEPq8efOMuw510yB1pUqVTLdia6xbt87V6Vq/p6FjddYAX9qgtnuoXI/NSu3r168317VC6Hr/aIdlvZd05DTcrHNoQEk/St7qBm2tsVu3buZ+ssbFixfN/TZr1izX97Sb9AsvvCDdu3fP1v5zEgIIBK/Aq6++KvrmEB3PPPNMqtfItKvS16rp06fLrl27XE+VKlXKBJyt3zXBK0HlCISPgH6Sh4ZI9aFvvHMfxYoVM59eocFmT5+iET5KrNTXAvqGSP0kmuLFi5s3TTIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhNAcLNuanL3AgggAACCCAQNALXrl0zwdny5ctL/vz5TZBEu5MVLFjQPAoUKJBqLXq8diDW0FjRokVN8FlDza1atTKh3YzGmTNn5PTp05I3b14pW7asCVO7j0uXLol2e75w4YL5tgZYSpYsme76vobV9Z48eVL++usviYiIkHLlytnWpOvU9RYuXNisVV3TDg3yaqD4hhtuME/pv3Xtlrmnc/Q4dVJ/DZtrF2ydP+0eWdfKSu3a/TRPnjxSpkwZc7oG03V+XbO3RkpKitljrV33X2vX+yHt0Lq147NeW8PNDAQQCF+Bp59+Wj777DMDMG3atEzf6HD+/HnzhgkrEG2p6ZtL9E0c+iYSBgIIBKbA5cuXTaBZQ6QnTpxIV2Tv3r1l0KBB5tNGGAj4WmDq1Knmd5COI0eO+PryXA8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgzAQIN4fZhrNcBBBAAAEEEPC+wIEDB6RTp05m4scee0yee+4571+EGREIMgENwGgXWe047t4FPciWQbkIBISAvinikUceka1bt0rp0qVN9/r69etnWtvmzZvl9ddfT/VpApUrV5bhw4e7uugHxOIoAgEEjIB+wod+woOnT9q44447TKg5o0/agBABXwgQbvaFMtdAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQsAQIN3MvIIAAAggggAACWRDQsNihQ4fk3LlzomEzDTavWbPGNcP27dvpspsFTw4NXYEWLVqY7toDBgyQcePGhe5CWRkCPhLYt2+f9O/f33TRb9q0qQk4a2f8zIZ2iNcwmoac3Yd+2oB2ca5QoYKPqucyCCCQkYC+EUhDzQkJCekOqVmzpgk1688sAwF/CxBu9vcOcH0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQCC8BAg3h9d+s1oEEEAAAQQQyKHAs88+KwsXLkw3y0033SSTJk2Sxo0b5/AKnI5AaAhoECsxMVGioqIkNjY2NBbFKhDws8Dnn38uTz75pKmiT58+8sorrziqSEOTGkrTzs/WuPHGG03AuVu3bo7m4CAEEPCugP6O1FDz8uXL001crFgxE2rWR9GiRb17YWZDIJsChJuzCcdpCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAtkSINycLTZOQgABBBBAAIFwFVi0aJEJh0VERJiOmRoOu/nmm6VBgwZSoECBcGVh3QikEyDczE2BQO4IuIfLxo4dKwMHDnR0oStXrpiA85tvvpnq+L59+8rw4cOlbNmyjubhIAQQyJmAdmHXUPOCBQs8TtS7d28Tata/LxkIBJIA4eZA2g1qQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEQl+AcHPo7zErRAABBBBAAAEEEEDA5wKEm31OzgXDSOCJJ56QJUuWmBXrpwk0a9bM8eo3bNggU6ZMkR07drjOqVGjhgk433333Y7n4UAEEMiawLFjx0yo+YMPPpCLFy+mO7lDhw4yYMAAadmyZdYm5mgEfCRAuNlH0FwGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwAgQbuZGQAABBBBAAAEEEEAAAa8LEG72OikTIuASOHHihPTv31/2798vrVq1knnz5mVJR4OVGnB+5513Up2nc2rIuXTp0lmaj4MRQCBjgbNnz5pQsz5OnTqV7sCGDRuaUHO3bt1gRCCgBQg3B/T2UBwCCCCAAAIIIIAAAggggAACCCCAAAIIIBByAoSbQ25LWRACCCCAAAIIIIAAAv4XINzs/z2ggtAW2Lhxo/Tr188scsyYMTJ06NAsL3jt2rWiYbVdu3a5zq1Vq5YJOHft2jXL83ECAgikFpg7d67p1Hzo0KF0NBUrVjSh5oEDB0qePHmgQyDgBQg3B/wWUSACCCCAAAIIIIAAAggggAACCCCAAAIIIBBSAoSbQ2o7WQwCCCCAAAIIZEXg3LlzUqRIkayckuvHXrlyRS5fviyFCxfO9Wt56wKB6Oh0bdTuVCrrxxFuzroZZyCQVYEFCxbIP//5TylatKgsXLhQ6tatm9UpRF8HNbA2c+bMVOc+8sgjJuRcokSJLM/JCQiEu8Bnn31mOjVv3749HUVERIQJNeujXLly4U7F+oNI4OjRozJy5EiJiooyvx8YCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjkpgDh5tzUZW4EEEAAAQQQCFiBxx9/XJYuXSpPPPGEjB492q91Xrp0SWbMmCEahDl48KCrFg2tNW/e3K+12V1cgzvjxo2TJk2amGBdvnz57E4JmOcD6R7IKkow1E64Oau7yvEIZE/gzTfflIkTJ5qg2bBhw7I3iYisXr3ahJz37t3rmqNOnTry9NNP08U526qcGG4C69evN6HmdevWeVx6dHS0CTXrzxYDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgYwHCzdwdCCCAAAIIIJDrAqNGjZLk5GQZOnSoNG7cONevZ3eBP//8Uxo0aGAO087NSUlJdqfk2vNXr16VXr16ybZt29JdY8WKFRIZGZlr1/bGxHfddZfs2bPHTLV27VqpXr26N6bN9TkC5R6YNWuWfP3119K2bVt54IEHHK07UGq3K1Y7+8XHx0vPnj1l8uTJdofzPAII5EDgu+++k5o1a+Zghv9/qv6unj59urz77rup5urXr58JOZcvXz7H12ACBEJR4Ntvv5X33ntPPv30U4/L09/zGmpu3bp1KC6fNSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACXhcg3Ox1UiZEAAEEEEAAgbQC2p1OP/b+7bffFg3DBsKYMGGCzJ8/33Rtfvjhh/1WkoZgnnnmGXN97bipAdfSpUuLBlhLlSol+fPn91ttTi6s3aaff/556dChg7z++uuSJ08eJ6cFxDGBcA9oWFAN+/fvL1qP0xEItdvVqiFJ7V7CrwAAIABJREFUfeOA/vwXL17c7nCeRwCBABJISEiQt956S/S/1rjxxhtNwLlHjx4BVCmlIOBfgR9//NGEmrVbs6dRv359E2q+//77/VsoV0cAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEgkyAcHOQbRjlIoAAAgggEIwCgRhuDhTHRx99VJYvXy533HGHzJ07N1DKog4fCWQ33Oyj8rgMAgiEuYB2cNaQ85kzZ1wS3bt3NyHnm266Kcx1WH44C+jPhBVqdv/5sEyuv/56E2rWR6C/US2c95G1I4AAAggggAACCCCAAAIIIIAAAggggAACCCCAQOAKEG4O3L2hMgQQQAABBIJaYMOGDaIPHTNnzjT/bdOmjdx8882p1jVq1CgpVKhQqu+tX79eNm7cKAcPHpQbbrhBbr31VtPxLm045NVXX5XLly/L448/Llu2bJEvv/xSfv/9d9EueTExMVK+fPlU806cOFEuXryY6nsavM6sm96RI0dkyZIlcujQITl9+rSZu2HDhuZjxQsWLJjjPerSpYvs27dPxo0bZwIwmY39+/ebWvT4IkWKmI64vXv3lpIlS7pOO3XqlMyYMcP177p168o999wjmzZtMudqN93bb79d+vbtK/ny5ctW/XFxcXLgwIFU51533XUyYsSIdPMtXLhQvvvuO7n77rvlypUrsnr1alN/tWrVpF+/funuB51Au1avXbtWtm/fbvbzjz/+EJ1fO1nrPHof6Th+/LjMnj1bihUrJoMGDTIdkHWdep80adLEdMEuUKBAtu6B7Dh+//338sYbbxhXvacrVaokjRs3lttuuy1VR2vt8jhv3jxT18qVK+Wnn34yDta6rIL1Hnb/ecnq/ave8fHxxvHXX381c2mIXu9d9+Hu+Mgjj8jixYtl8+bNpuaoqCjjSDArWz8qnIRASAjoa7gGnPWTBqxRtmxZE3DWrvMMBMJNQLs0a7BZf5+nHfr7Xz8RRH+fVqhQIdxoWC8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4DUBws1eo2QiBBBAAAEEEHAXeP3112XKlCm2KLt27ZISJUq4jnvhhRc8djBu2rSpCZIULVrUdWzVqlXN1xqQnjRpUqpr/eMf/xAN1taoUSPd8e4HalhWQ1uexkcffSRjxozx+Fznzp3l7bffznHo0+pq/eabb5oQckbjk08+kWHDhqV7WjsDasimdu3a5jkNHXfq1Ml1nIZTNbw9evToVOdqKG3kyJG2++PpgMGDB8uqVatSPaVh66SkpHSHDxw4UNasWSPaofqdd95J9/yKFSskMjLS9X0NPvfo0UPOnTvnsbZXXnlF+vTpY57bvXu3CTvreOaZZ0TvOfeh98wHH3xggtHWsO4Zu3sgO44a3NaQddqhezBr1iwTwtaRmJhowvd2Q+/3du3aZbl2PUED4hqW37ZtW7rLaI36c2YNd8fHHntM/vvf/6Y6p1u3bia0zUAAgfAW0HCz/q7SNx5ZQ38X6u+TevXqhTcOqw8LAf0Z0N/N3377rcf16hvONNRs/U0WFigsEgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIJQHCzbkEy7QIIIAAAgiEu8Dhw4dNt2MdQ4YMMf/VUKUGTt1H+/btXQFh7dj80EMPmae1i+19991nus7OnTvXfE87A2uI1RruQdVWrVqJhjD//vtvefbZZ80hGvK1ukbrv7VLrnaz1TF//nzTHTqjcPPevXvlzjvvNMdWqVLF1F64cGET1NV5dGigOjo6OktbfeHCBdO52Bq6Rh0a0NaOytYoV66cVKxY0fxTuxdr92odN910kwkKazdjDfrqUFPtpqxDQ8HavVivMXXqVNGQt9avnYz1OO3qrIHXjMLIThbzzTffmE7AOvRr7Z5sF27WYzWIrR2jtUYrQJvW3wpD6/EatNXwtwbar127JikpKcbB2nf3UK5eX0N2+tC9s8K448ePNx0UreH0HsiOo3Zw3Lp1q1y6dEmOHTtmuh/v2LHDXLpXr16inZd1aAdwPU6HOugxev8++OCDqfh1rdodNau16/GvvfaaK7SvQasGDRrIxx9/bO55HQsWLJDmzZubr90d9d9qqPeidsK2wtEaZq9Vq5aT24NjEEAghAXOnDljXlveffdd1yr19fepp54yr9kMBEJRQP8+1VCz/tfT0E/h0N+1+mYmBgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgHcECDd7x5FZEEAAAQQQQCATAas7sXY6vuuuuzI8UoPNGhzREKwGdPPnz2+Offzxx2Xp0qXm6x9++EHy5s1rvrZCrtox0npev6+d9awQ9Ndffy3ly5dPd81x48aZjscZhZvdr7lz504pWbKka47p06ebOTWwmtWhoWMNwdgNDfmOHTvWHKaB5Jdfftl8rT7VqlUzX3/44Yfyf//3f+brZcuWSd26dV3TuncHdg/WakhVOy/r0Frcuxrb1eTp+eXLl5uwtZNw8549e1zdi3Vt2lVZg9dW4Fbn12DQ8ePH5d577xV1zmyk7Tj83HPPuQ7XrzXArmHwdevWeZzG7h7Qk3LqqF1OrVDz999/L/ny5UtVi3Y81RBx//79ZcKECY63ILPaNcBfvXp1M5d27daQuw79voacNbitAWYrnOjuqAFFDdrrOHHihOvNCHY/u44L50AEEAgJgYSEBBNy1v9aQ98Uoa9pzZo1C4k1sggEtEOzhpr170pPQ/9m0VCzk7/r0EQgFASSk5MlPj7evPmQMH8o7ChrQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEAluAcHNg7w/VIYAAAgggEBICTsPNVrBVuyFrV2RrfPTRRzJmzBjzT/ewshVuTtvR+fz5866PBF+0aJHpWpx22AVbtZPuTz/9JB06dDCdib01fvnlF3nppZdc0y1ZssR8fcstt0jlypVd39fO1VZX6NGjR0tsbKzpwqydrK3x3XffSceOHc0/04ZP3UO52tXZ6pit3Y/1OR1t27Z1Bcizuz6n4eYWLVqYsLE1NIyuAfK0oegXX3zR5d2uXTtT42233Wa6BluhdmsO91Cu+riHLDQcr12idWgXcSso775Ou3tAj82q419//WU6R2tA+/Lly1KwYEF58sknzWW1k7PVjduqIzfCzT///LOrC3ja7uJPPPGE6D3nHvp2d3S/V7RG6+dA79l+/fpl9zbhPAQQCFEBfZOEdqDXjvTW0Nd2fa3RrvsMBIJRQH8v/u9//zMPT6N27dom1Ny7d+9gXB41I5BtAf1/Iv1bvVKlSqne3JLtCTkRAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFMBAg3c3sggAACCCCAQK4LOAk3u3ebTRukdA9fLl682ASBdVjh5jlz5kj79u1TrcPumk473+Z2qNOqU8Nhd955p8e9eOCBB0wwNm2X6WvXrrm6OGsHZ6sjs07iHsrNqHu1Nzbeabh56NChroC6Xjej844ePWq6bm/bti1VedrNe+TIkdKjRw9XyNn9vti1a5eUKFHCdc6hQ4dc90R2u3dn1VGDwXpfaWdkT8O967b1fG6Em7du3So9e/Y0l9BO3RoMt4YVStF/HzlyxHw7M0cNl2vH6dz+OfDGvcgcCCDgHwF9jdCQs/sbWCIjI80bWLQLPwOBYBGwCzVXqFDBhJoffvhhKVSoULAsizoR8JqAfhrItGnTzHzW35Fem5yJEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBII0C4mVsCAQQQQAABBHJdwArwTp8+PdOgk3WcdnzUbsXW0ICmFdxdt26d6Tqrwwo360eGa5df95HZc3qcXddeq5Z//etfMmTIkFwzchJu1oCYdjrWULeGu62hnTIbNWpk/jlx4kTp1auX6zn3cLN2eI6IiMiVNTgNNz/66KPy/PPPu2qwO0/Dyhs3bhTtwKzBbmt8+OGH0rp1a/PPzEK52j3ZCounDT5bc9ndA3qcU8eDBw+aLt86tFO4dpvWTtvHjh0zH2mvI7Nws3ZFdu/obbdZmdW+b98+6dKli5nigw8+EO0Cbo0JEybIrFmzUnUBz61ws9rpz22nTp1k8uTJdkvieQQQCHIB/f08Y8YM+eqrr1wruf/++03I+eabbw7y1VF+KAvYhZo1yKyBZg02a8CZgUC4ChBuDtedZ90IIIAAAggggAACCCCAAAIIIIAAAggggIB/BAg3+8edqyKAAAIIIBBWAq1atZKffvpJHnroIfNRxhmNu+66S/bs2WMCu59++qnrsJdfftkEpnQcOHDA1S3PCjBPmTLFdPS1hnYSswKwK1asEO0gmXbYBVujo6NFO/7Wq1fPBIrz5cuXK3vmJNz8n//8R7Szs46kpCQpUqSI+fqLL76QAQMGmK+1Y2aLFi1cNbqHcg8fPiz58+fPlfrtQsoDBw6UNWvWSFbDze7F/vnnn/Lggw/Kzp07U3Wvdg/lbtiwwRV213MXLVokI0aMMNNk1FnO7h7Qc506audsDV5rsFmvbY0ffvjBFS72FG62ztPAvgYDnY7Mak9JSTH3rQ7tDK0dr62hoWcNP7vXmVvhZgIwTneT4xAILQF9Q4d2cv7ll1/MwkqWLCn6pqXcfKNQaAmyGl8J2IWatQ799AwNNteuXdtXZXEdBAJWgL/tAnZrKAwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhJAcLNIbmtLAoBBBBAAIHAEtBOd2vXrjWhXA08NW7cWAoXLiyXLl2SvHnzuoK3b7zxhqvDqx6nHV/3798vffv2ld9//92ERLUTrTWscLN2hPzoo4+kfPnycvnyZRk2bJgsWbLEXG/79u0ePzrcLtiqQVUNnurQzpPPPfecmV/HlStXRAO32pk3p8NJuNk9YKsdMDW0qwHWoUOHmgC2rnPr1q2u0LPW5DSUm9P6vR1u1hB8mTJl5LrrrnOVpp2n77vvPjl37pzp0G11QnYP5Wr4SLtsFytWTE6ePCkxMTHy/fffS+fOnc0952nY3QNZcXzhhRdk7ty5pru2BvP1vr548aI89dRTsnLlSnP5ZcuWSd26dVOVMnPmTPn3v/9tvvfKK6+YjsulS5eWq1evmns5o4+9t6td16/3gN6jCxYskBo1aojuld4/OsaMGWPuHx2Em3P6U8D5CCCQVkCDzfraa71e6/PNmzc3IWd9wxMDAX8KOAk1699++saqW2+91Z+lcm0EAkqAcHNAbQfFIIAAAggggAACCCCAAAIIIIAAAggggAACIS9AuDnkt5gFIoAAAggg4H8B9w7DVjUayNWwqoaVNbSs4+zZs6bjsgaZPY2lS5e6OtLq81a4WQOceo7+98KFC2ZeHRoAtTobv/XWW6IPa1jH6L+tTsgaANUuzTo0wNyzZ0/ZsWOH6xzrOD33+uuvN+HRnA4n4Wa9htUB2dP1JkyYIP379zdPTZs2zQTKPK3vscceM2HbnAxds2Wq83i6jn5fuxSXK1fOVbfTzs2Wh85h7at7vbo2DSzrcA/lWsfqvhw/ftx1yueffy4NGjQw/87KPZBVRw0uq691P9WsWdPcO3rPWF/rc1WqVJGNGze66vv111+lWbNmqbbE+tnQEODo0aOzXLueoNfWQLinoVabNm1yBcgJN+fkJ4JzEUAgM4GvvvrKfPKCe2d6/R2ir2/6RhYGAr4UcBJq1k8R0VCzhvEZCCCQWoBwM3cEAggggAACCCCAAAIIIIAAAggggAACCCCAgC8FCDf7UptrIYAAAgggEMYCGv7UbsibN29OpfDqq69K7969Xd87ceKE6ZKsnZ6tERkZKa+99porpGp93wo3T5w4UebNmyc7d+40T2k4dOTIkSaEmydPHvO9yZMni3aGzmykDZ5qZ2kNZWnnSU+B68OHD7u6Tmd3a60w7zvvvCNdu3bNcBrtAqwGs2bNch2jIVXtGNy9e3fX915++WVTs6fx5JNPyrPPPpvdUs15GorVTtp2Y8uWLVKhQgUZPHiwrFq1ygR/dV+tsWLFCtM5WPcqKSnJfFuD0urhadSrV88EpbWTojXcQ7nqohZWsFlDzlOmTJEWLVq4js/KPZAdR+3crGFq7aatQ+/b4cOHy+rVqyUuLs51b1rrtQrT+/b999+Xjz/+ONXSu3Xr5rpns1K7NYkGnHW/Dx486JpXu6Frh2j3UOGePXtEw1w61LR48eKu49u2bWs6YKuHk313XwABGLufEp5HIHwE5s+fb954o68nOqpVq2Z+L2iXeQYCuS3gJNTcoUMHE2q23nCX2zUxPwLBKMDfdsG4a9SMAAIIIIAAAggggAACCCCAAAIIIIAAAggErwDh5uDdOypHAAEEEEAgKAVOnjxpOjTnzZtXSpQoIaVKlfK4jsuXL4t+rL2GMK2OyWkPtMLNGj5u166dJCcnm0fFihVdoWZvIem8v/32m6m7aNGipquwFZz21jWczHPt2jXRbr8RERGmhlAbV69elVOnTslff/1llla4cGEpWbKk+W/a4anjsIbj8+XL59eOoH/++afoPln3toa2dV0FCxaUAgUKmPo8jZSUFHOP/f333+aeL1u2rLnfcjr0+hrOv+GGG3Icxs9KLQRgsqLFsQiEvsDp06dl5syZ5qG/43V07NhRtLN/kyZNQh+AFfpcwEmouVWrVibUbH0qhM+L5IIIBJEAf9sF0WZRKgIIIIAAAggggAACCCCAAAIIIIAAAgggEAIChJtDYBNZAgIIIIAAAuEqkDbcHK4O4bpuT+HmcLUIxHUTgAnEXaEmBPwvsGvXLhNwXrx4sasYDTjrI6M3PPm/aioIJgEnoeamTZtKv379RD8lgYEAAs4E+NvOmRNHIYAAAggggAACCCCAAAIIIIAAAggggAACCHhHgHCzdxyZBQEEEEAAAQT8IEC42Q/oAXRJws0BtBkeSiEAE9j7Q3UI+Ftg+fLlJuT8zTffmFKqV68uQ4cOlZiYGH+XxvWDVMBJqLlBgwamU3OvXr2CdJWUjYD/BBITE2XEiBGm0/m4ceP8VwhXRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAgLAcLNYbHNLBIBBBBAAIHQFCDcHJr76nRVhJudSvnnOMLN/nHnqggEm4AGnPVx4sQJU3rHjh1NF+cmTZoE21Ko108CTkLN9evXlz59+pgHAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIPAFCDcH/h5RIQIIIIAAAghkIDB79my5evWq6R5mBZ3BCh+B3377TT7++GOz4IcfflgKFiwYPosPgpWuXLlShgwZYio9cuRIEFRMiQgg4C+Bo0ePmoDz+++/7ypBA86DBg2SsmXL+qssrhvgAoSaA3yDKA8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyIEA4eYc4HEqAggggAACCCCAAAIIZCwQFxcnlStXlqioKJgQQAABW4EtW7aYkPPq1avNsRUrVpSHHnrIPAoVKmR7PgeEh4DeJwsXLpT4+PgMF0yn5vC4F1glAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhK4A4ebQ3VtWhgACCCCAAAIIIIAAAggggEDQCaxZs8aEnBMTE03ttWvXNgHnPn36BN1aKNh7AuvWrTOh5mXLlmU4KaFm73kzEwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgTwHCzf7U59oIIIAAAggggAACCCCAAAIIIOBRYM6cOTJ79mw5evSoeb5JkyYm5HzvvfciFkYCGmbWULOGmzMahJrD6IZgqQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBAWAoSbw2KbWSQCCCCAAAIIIIAAAggggAACwSdw6tQp0ZCzPi5cuGAW0LZtW+nbt6907Ngx+BZExY4F4uPjTah5y5YtGZ7TsGFDiYmJoau3Y1UORAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSCQ4Bwc3DsE1UigAACCCCAAAIIIIAAAgggELYC+/btM12c4+LiXAatW7c2wda77747bF1CbeHapXvp0qXy+eefy+7duzNcXvPmzc3ed+/ePdQIWA8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggICIEG7mNkAAAQQQQAABBBBAAAEEEEAAgaAQ2LBhg+nivG7dOle9zZo1k969e8v9998fFGugyPQCa9euNaFmfZw/fz5Dovbt20uvXr2kS5cuMCKAgI8FkpOTZdWqVVKnTh3zYCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkJsChJtzU5e5EUAAAQQQQAABBBBAAAEEEEDA6wKLFi2S9957L1V330aNGpluvvrImzev16/JhN4VsLo0a6B5586dmU5+zz33mFCzdutmIICAfwS0e/6LL74olSpVkoSEBP8UwVURQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEwkaAcHPYbDULRQABBBBAAAEEEEAAAQQQQCC0BBYsWCD62LFjh2th2lFUA87azblQoUKhteAQWI3TLs26VA006+O2224LgZWzBASCW2Dq1Kkybdo0s4gjR44E92KoHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDgBQg3B/wWUSACCCCAAAIIIIAAAsEp0LJlS9GPMF++fLnp8sdAAAEEcksgPj5e5s+fL9u2bXNdonr16ibgrEHnEiVK5NalmdeBQFa6NFetWlXuvfde0W7NtWrVcjA7hyCAgC8ECDf7QplrIIAAAggggAACCCCAAAIIIIAAAggggAACCFgChJu5FxBAAAEEEEAAAQQQQMDrAomJiSZQqCM2NlaioqK8fg0mRAABBNIKfPLJJybkvGXLFtdTlStXNq9H+ihXrhxoPhTISpfm1q1bu0LNdNz24SZxKQQcChBudgjFYQgggAACCCCAAAIIIIAAAggggAACCCCAAAJeESDc7BVGJkEAAQQQQAABBBBAAAF3Afdw87vvviudO3cGCAEEEPCZwOLFi2XBggWSkJDgumbp0qWla9eu5tGqVSuf1RJuF9q7d6988cUXsmbNGtm5c2emyy9ZsqTp0Kydmps2bRpuVKwXgaASINwcVNtFsQgggAACCCCAAAIIIIAAAggggAACCCCAQNALEG4O+i1kAQgggAACCCCAAAIIBKZA1apVTWHDhg2T4cOHB2aRVIUAAiEtsGzZMtPJecOGDanW2ahRI1fQuUqVKiFt4IvF/fjjj65A8+bNm20vecstt7hCzeXLl7c9ngMQQMD/AoSb/b8HVIAAAggggAACCCCAAAIIIIAAAggggAACCISTAOHmcNpt1ooAAggggAACCCCAgA8FCDf7EJtLIYBApgIrV640nZzXrl2b6riIiAhXyLlLly4oZkHg1KlTpjuzdmnWx7Vr12zP1g7N2qm5U6dOtsdyAAIIBJYA4ebA2g+qQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEQl2AcHOo7zDrQwABBBBAAAEEEEDATwKEm/0Ez2URQCBDAQ03L1261DzOnz+f6riaNWu6gs6RkZEoehA4d+6cK8ysweazZ8/aOunvAivUXKtWLdvjOQABBAJTgHBzYO4LVSGAAAIIIIAAAggggAACCCCAAAIIIIAAAqEqQLg5VHeWdSGAAAIIIIAAAggg4GcBws1+3gAujwACGQocPXrUFXLeuXNnuuM6dOhggs5t2rSRMmXKhL2kdma2ujSfOHHC1qNIkSLSvn17adeunXEsVKiQ7TkcgAACgS1AuDmw94fqEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBUBMg3BxqO8p6EEAAAQQQQAABBBAIEAHCzQGyEZSBAAKZCmTWzVlDuc2bN5cWLVrI7bffLnXq1AkLTQ1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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Web Research (STORM)\n", + "\n", + "[STORM](https://arxiv.org/abs/2402.14207) is a research assistant designed by Shao, et. al that extends the idea of \"outline-driven RAG\" for richer article generation.\n", + "\n", + "STORM is designed to generate Wikipedia-style ariticles on a user-provided topic. It applies two main insights to produce more organized and comprehensive articles:\n", + "\n", + "1. Creating an outline (planning) by querying similar topics helps improve coverage.\n", + "2. Multi-perspective, grounded (in search) conversation simulation helps increase the reference count and information density. \n", + "\n", + "The control flow looks like the diagram below.\n", + "\n", + "![storm.png](attachment:bdc25ea2-123b-46b1-b9f5-fdd345ecbc73.png)\n", + "\n", + "STORM has a few main stages:\n", + "\n", + "1. Generate initial outline + Survey related subjects\n", + "2. Identify distinct perspectives\n", + "3. \"Interview subject matter experts\" (role-playing LLMs)\n", + "4. Refine outline (using references)\n", + "5. Write sections, then write article\n", + "\n", + "\n", + "The expert interviews stage occurs between the role-playing article writer and a research expert. The \"expert\" is able to query external knowledge and respond to pointed questions, saving cited sources to a vectorstore so that the later refinement stages can synthesize the full article.\n", + "\n", + "There are a couple hyperparameters you can set to restrict the (potentially) infinite research breadth:\n", + "\n", + "N: Number of perspectives to survey / use (Steps 2->3)\n", + "M: Max number of conversation turns in step (Step 3)\n", + "\n", + "\n", + "## Prerequisites" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["related_subjects = await expand_chain.ainvoke({\"topic\": example_topic})\nrelated_subjects"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Perspectives\n", - "\n", - "From these related subjects, we can select representative Wikipedia editors as \"subject matter experts\" with distinct\n", - "backgrounds and affiliations. These will help distribute the search process to encourage a more well-rounded final report." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": ["class Editor(BaseModel):\n affiliation: str = Field(\n description=\"Primary affiliation of the editor.\",\n )\n name: str = Field(\n description=\"Name of the editor.\", pattern=r\"^[a-zA-Z0-9_-]{1,64}$\"\n )\n role: str = Field(\n description=\"Role of the editor in the context of the topic.\",\n )\n description: str = Field(\n description=\"Description of the editor's focus, concerns, and motives.\",\n )\n\n @property\n def persona(self) -> str:\n return f\"Name: {self.name}\\nRole: {self.role}\\nAffiliation: {self.affiliation}\\nDescription: {self.description}\\n\"\n\n\nclass Perspectives(BaseModel):\n editors: List[Editor] = Field(\n description=\"Comprehensive list of editors with their roles and affiliations.\",\n # Add a pydantic validation/restriction to be at most M editors\n )\n\n\ngen_perspectives_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You need to select a diverse (and distinct) group of Wikipedia editors who will work together to create a comprehensive article on the topic. Each of them represents a different perspective, role, or affiliation related to this topic.\\\n You can use other Wikipedia pages of related topics for inspiration. For each editor, add a description of what they will focus on.\n\n Wiki page outlines of related topics for inspiration:\n {examples}\"\"\",\n ),\n (\"user\", \"Topic of interest: {topic}\"),\n ]\n)\n\ngen_perspectives_chain = gen_perspectives_prompt | ChatOpenAI(\n model=\"gpt-3.5-turbo\"\n).with_structured_output(Perspectives)"] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": ["from langchain_community.retrievers import WikipediaRetriever\nfrom langchain_core.runnables import RunnableLambda\nfrom langchain_core.runnables import chain as as_runnable\n\nwikipedia_retriever = WikipediaRetriever(load_all_available_meta=True, top_k_results=1)\n\n\ndef format_doc(doc, max_length=1000):\n related = \"- \".join(doc.metadata[\"categories\"])\n return f\"### {doc.metadata['title']}\\n\\nSummary: {doc.page_content}\\n\\nRelated\\n{related}\"[\n :max_length\n ]\n\n\ndef format_docs(docs):\n return \"\\n\\n\".join(format_doc(doc) for doc in docs)\n\n\n@as_runnable\nasync def survey_subjects(topic: str):\n related_subjects = await expand_chain.ainvoke({\"topic\": topic})\n retrieved_docs = await wikipedia_retriever.abatch(\n related_subjects.topics, return_exceptions=True\n )\n all_docs = []\n for docs in retrieved_docs:\n if isinstance(docs, BaseException):\n continue\n all_docs.extend(docs)\n formatted = format_docs(all_docs)\n return await gen_perspectives_chain.ainvoke({\"examples\": formatted, \"topic\": topic})"] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": ["perspectives = await survey_subjects.ainvoke(example_topic)"] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "{'editors': [{'affiliation': 'Academic Research',\n", - " 'name': 'Dr. Linguist',\n", - " 'role': 'Language Model Expert',\n", - " 'description': 'Dr. Linguist will focus on explaining the technical aspects of million-plus token context window language models and their impact on RAG (Retrieval-Augmented Generation) systems.'},\n", - " {'affiliation': 'Industry',\n", - " 'name': 'TechTrendz',\n", - " 'role': 'AI Solutions Architect',\n", - " 'description': 'TechTrendz will provide insights on the practical applications of million-plus token context window language models in RAG systems and discuss their benefits and challenges in real-world scenarios.'},\n", - " {'affiliation': 'Open Source Community',\n", - " 'name': 'CodeGenius',\n", - " 'role': 'Machine Learning Enthusiast',\n", - " 'description': 'CodeGenius will explore the open-source tools and frameworks available for implementing million-plus token context window language models in RAG systems and share their experiences with the community.'},\n", - " {'affiliation': 'Tech Journalism',\n", - " 'name': 'DataDive',\n", - " 'role': 'AI Technology Journalist',\n", - " 'description': 'DataDive will cover the latest developments and advancements in million-plus token context window language models and their implications for RAG systems, focusing on industry trends and use cases.'}]}" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n%pip install -U langchain_community langchain_openai langgraph wikipedia scikit-learn langchain_fireworks\n# We use one or the other search engine below\n%pip install -U duckduckgo tavily-python" ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["perspectives.dict()"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Expert Dialog\n", - "\n", - "Now the true fun begins, each wikipedia writer is primed to role-play using the perspectives presented above. It will ask a series of questions of a second \"domain expert\" with access to a search engine. This generate content to generate a refined outline as well as an updated index of reference documents.\n", - "\n", - "\n", - "### Interview State\n", - "\n", - "The conversation is cyclic, so we will construct it within its own graph. The State will contain messages, the reference docs, and the editor (with its own \"persona\") to make it easy to parallelize these conversations." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": ["from typing import Annotated\n\nfrom langchain_core.messages import AnyMessage\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph import END, StateGraph, START\n\n\ndef add_messages(left, right):\n if not isinstance(left, list):\n left = [left]\n if not isinstance(right, list):\n right = [right]\n return left + right\n\n\ndef update_references(references, new_references):\n if not references:\n references = {}\n references.update(new_references)\n return references\n\n\ndef update_editor(editor, new_editor):\n # Can only set at the outset\n if not editor:\n return new_editor\n return editor\n\n\nclass InterviewState(TypedDict):\n messages: Annotated[List[AnyMessage], add_messages]\n references: Annotated[Optional[dict], update_references]\n editor: Annotated[Optional[Editor], update_editor]"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Dialog Roles\n", - "\n", - "The graph will have two participants: the wikipedia editor (`generate_question`), who asks questions based on its assigned role, and a domain expert (`gen_answer_chain), who uses a search engine to answer the questions as accurately as possible." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": ["from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\nfrom langchain_core.prompts import MessagesPlaceholder\n\ngen_qn_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are an experienced Wikipedia writer and want to edit a specific page. \\\nBesides your identity as a Wikipedia writer, you have a specific focus when researching the topic. \\\nNow, you are chatting with an expert to get information. Ask good questions to get more useful information.\n\nWhen you have no more questions to ask, say \"Thank you so much for your help!\" to end the conversation.\\\nPlease only ask one question at a time and don't ask what you have asked before.\\\nYour questions should be related to the topic you want to write.\nBe comprehensive and curious, gaining as much unique insight from the expert as possible.\\\n\nStay true to your specific perspective:\n\n{persona}\"\"\",\n ),\n MessagesPlaceholder(variable_name=\"messages\", optional=True),\n ]\n)\n\n\ndef tag_with_name(ai_message: AIMessage, name: str):\n ai_message.name = name\n return ai_message\n\n\ndef swap_roles(state: InterviewState, name: str):\n converted = []\n for message in state[\"messages\"]:\n if isinstance(message, AIMessage) and message.name != name:\n message = HumanMessage(**message.dict(exclude={\"type\"}))\n converted.append(message)\n return {\"messages\": converted}\n\n\n@as_runnable\nasync def generate_question(state: InterviewState):\n editor = state[\"editor\"]\n gn_chain = (\n RunnableLambda(swap_roles).bind(name=editor.name)\n | gen_qn_prompt.partial(persona=editor.persona)\n | fast_llm\n | RunnableLambda(tag_with_name).bind(name=editor.name)\n )\n result = await gn_chain.ainvoke(state)\n return {\"messages\": [result]}"] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "\"Yes, that's correct. I'm focusing on the technical aspects of million-plus token context window language models and their impact on Retrieval-Augmented Generation (RAG) systems. Can you provide more information on how these large context window language models are trained and how they differ from traditional models in the context of RAG systems?\"" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment if you want to draw the pretty graph diagrams.\n# If you are on MacOS, you will need to run brew install graphviz before installing and update some environment flags\n# ! brew install graphviz\n# !CFLAGS=\"-I $(brew --prefix graphviz)/include\" LDFLAGS=\"-L $(brew --prefix graphviz)/lib\" pip install -U pygraphviz" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["messages = [\n HumanMessage(f\"So you said you were writing an article on {example_topic}?\")\n]\nquestion = await generate_question.ainvoke(\n {\n \"editor\": perspectives.editors[0],\n \"messages\": messages,\n }\n)\n\nquestion[\"messages\"][0].content"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Answer questions\n", - "\n", - "The `gen_answer_chain` first generates queries (query expansion) to answer the editor's question, then responds with citations." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": ["class Queries(BaseModel):\n queries: List[str] = Field(\n description=\"Comprehensive list of search engine queries to answer the user's questions.\",\n )\n\n\ngen_queries_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a helpful research assistant. Query the search engine to answer the user's questions.\",\n ),\n MessagesPlaceholder(variable_name=\"messages\", optional=True),\n ]\n)\ngen_queries_chain = gen_queries_prompt | ChatOpenAI(\n model=\"gpt-3.5-turbo\"\n).with_structured_output(Queries, include_raw=True)"] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "['Training process of million-plus token context window language models',\n", - " 'Differences between large context window language models and traditional models in Retrieval-Augmented Generation systems']" + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\nimport os\n\n\ndef _set_env(var: str):\n if os.environ.get(var):\n return\n os.environ[var] = getpass.getpass(var + \":\")\n\n\n# Set for tracing\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"STORM\"\n_set_env(\"LANGCHAIN_API_KEY\")\n_set_env(\"OPENAI_API_KEY\")" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["queries = await gen_queries_chain.ainvoke(\n {\"messages\": [HumanMessage(content=question[\"messages\"][0].content)]}\n)\nqueries[\"parsed\"].queries"] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": ["class AnswerWithCitations(BaseModel):\n answer: str = Field(\n description=\"Comprehensive answer to the user's question with citations.\",\n )\n cited_urls: List[str] = Field(\n description=\"List of urls cited in the answer.\",\n )\n\n @property\n def as_str(self) -> str:\n return f\"{self.answer}\\n\\nCitations:\\n\\n\" + \"\\n\".join(\n f\"[{i+1}]: {url}\" for i, url in enumerate(self.cited_urls)\n )\n\n\ngen_answer_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are an expert who can use information effectively. You are chatting with a Wikipedia writer who wants\\\n to write a Wikipedia page on the topic you know. You have gathered the related information and will now use the information to form a response.\n\nMake your response as informative as possible and make sure every sentence is supported by the gathered information.\nEach response must be backed up by a citation from a reliable source, formatted as a footnote, reproducing the URLS after your response.\"\"\",\n ),\n MessagesPlaceholder(variable_name=\"messages\", optional=True),\n ]\n)\n\ngen_answer_chain = gen_answer_prompt | fast_llm.with_structured_output(\n AnswerWithCitations, include_raw=True\n).with_config(run_name=\"GenerateAnswer\")"] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": ["from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper\nfrom langchain_core.tools import tool\n\n'''\n# Tavily is typically a better search engine, but your free queries are limited\nsearch_engine = TavilySearchResults(max_results=4)\n\n@tool\nasync def search_engine(query: str):\n \"\"\"Search engine to the internet.\"\"\"\n results = tavily_search.invoke(query)\n return [{\"content\": r[\"content\"], \"url\": r[\"url\"]} for r in results]\n'''\n\n# DDG\nsearch_engine = DuckDuckGoSearchAPIWrapper()\n\n\n@tool\nasync def search_engine(query: str):\n \"\"\"Search engine to the internet.\"\"\"\n results = DuckDuckGoSearchAPIWrapper()._ddgs_text(query)\n return [{\"content\": r[\"body\"], \"url\": r[\"href\"]} for r in results]"] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": ["import json\n\nfrom langchain_core.runnables import RunnableConfig\n\n\nasync def gen_answer(\n state: InterviewState,\n config: Optional[RunnableConfig] = None,\n name: str = \"Subject_Matter_Expert\",\n max_str_len: int = 15000,\n):\n swapped_state = swap_roles(state, name) # Convert all other AI messages\n queries = await gen_queries_chain.ainvoke(swapped_state)\n query_results = await search_engine.abatch(\n queries[\"parsed\"].queries, config, return_exceptions=True\n )\n successful_results = [\n res for res in query_results if not isinstance(res, Exception)\n ]\n all_query_results = {\n res[\"url\"]: res[\"content\"] for results in successful_results for res in results\n }\n # We could be more precise about handling max token length if we wanted to here\n dumped = json.dumps(all_query_results)[:max_str_len]\n ai_message: AIMessage = queries[\"raw\"]\n tool_call = queries[\"raw\"].additional_kwargs[\"tool_calls\"][0]\n tool_id = tool_call[\"id\"]\n tool_message = ToolMessage(tool_call_id=tool_id, content=dumped)\n swapped_state[\"messages\"].extend([ai_message, tool_message])\n # Only update the shared state with the final answer to avoid\n # polluting the dialogue history with intermediate messages\n generated = await gen_answer_chain.ainvoke(swapped_state)\n cited_urls = set(generated[\"parsed\"].cited_urls)\n # Save the retrieved information to a the shared state for future reference\n cited_references = {k: v for k, v in all_query_results.items() if k in cited_urls}\n formatted_message = AIMessage(name=name, content=generated[\"parsed\"].as_str)\n return {\"messages\": [formatted_message], \"references\": cited_references}"] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "'Large context window language models, such as the Llama2 70B model, can support context windows of more than 100k tokens without continual training through innovations like Dual Chunk Attention (DCA). These models have significantly longer context windows compared to traditional models, with capabilities like processing up to 1 million tokens at once, providing more consistent and relevant outputs. Training these models often involves starting with a smaller window size and gradually increasing it through fine-tuning on larger windows. In contrast, traditional models have much shorter context windows, limiting their ability to process extensive information in a prompt. Retrieval-Augmented Generation (RAG) systems, on the other hand, integrate large language models with external knowledge sources to enhance their performance, offering a pathway to combine the capabilities of models like ChatGPT/GPT-4 with custom data sources for more informed and contextually aware outputs.\\n\\nCitations:\\n\\n[1]: https://arxiv.org/abs/2402.17463\\n[2]: https://blog.google/technology/ai/long-context-window-ai-models/\\n[3]: https://medium.com/@ddxzzx/why-and-how-to-achieve-longer-context-windows-for-llms-5f76f8656ea9\\n[4]: https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/\\n[5]: https://huggingface.co/papers/2402.13753\\n[6]: https://www.pinecone.io/blog/why-use-retrieval-instead-of-larger-context/\\n[7]: https://medium.com/emalpha/innovations-in-retrieval-augmented-generation-8e6e70f95629\\n[8]: https://inside-machinelearning.com/en/rag/'" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select LLMs\n", + "\n", + "We will have a faster LLM do most of the work, but a slower, long-context model to distill the conversations and write the final report." ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["example_answer = await gen_answer(\n {\"messages\": [HumanMessage(content=question[\"messages\"][0].content)]}\n)\nexample_answer[\"messages\"][-1].content"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Construct the Interview Graph\n", - "\n", - "\n", - "Now that we've defined the editor and domain expert, we can compose them in a graph." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": ["max_num_turns = 5\n\n\ndef route_messages(state: InterviewState, name: str = \"Subject_Matter_Expert\"):\n messages = state[\"messages\"]\n num_responses = len(\n [m for m in messages if isinstance(m, AIMessage) and m.name == name]\n )\n if num_responses >= max_num_turns:\n return END\n last_question = messages[-2]\n if last_question.content.endswith(\"Thank you so much for your help!\"):\n return END\n return \"ask_question\"\n\n\nbuilder = StateGraph(InterviewState)\n\nbuilder.add_node(\"ask_question\", generate_question)\nbuilder.add_node(\"answer_question\", gen_answer)\nbuilder.add_conditional_edges(\"answer_question\", route_messages)\nbuilder.add_edge(\"ask_question\", \"answer_question\")\n\nbuilder.add_edge(START, \"ask_question\")\ninterview_graph = builder.compile().with_config(run_name=\"Conduct Interviews\")"] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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", - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n\nfast_llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n# Uncomment for a Fireworks model\n# fast_llm = ChatFireworks(model=\"accounts/fireworks/models/firefunction-v1\", max_tokens=32_000)\nlong_context_llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")" ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["from IPython.display import Image\n\n# Feel free to comment out if you have\n# not installed pygraphviz\nImage(interview_graph.get_graph().draw_png())"] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "ask_question\n", - "-- [AIMessage(content=\"Yes, that's correct. I am focusing on the technical aspects of million-plus token context window language models and their impact on RAG systems. Can you provide more insight into how these large context window models affect the performance and capabilities of RAG systems?\", name\n", - "answer_question\n", - "-- [AIMessage(content='The introduction of large context window language models, such as Gemini 1.5 with a 1 million token context window, has raised concerns in the AI community regarding its impact on Retrieval-Augmented Generation (RAG) systems. RAG systems represent a significant advancement over t\n", - "ask_question\n", - "-- [AIMessage(content='Thank you for the detailed explanation and resources. Could you elaborate on the specific challenges and opportunities that million-plus token context window language models present for RAG systems in terms of improving generation quality, addressing data biases, and the potentia\n", - "answer_question\n", - "-- [AIMessage(content='Million-plus token context window language models present both challenges and opportunities for RAG systems. Challenges include the increased computational cost and complexity associated with processing larger context windows, potential issues with retaining factual accuracy when\n", - "ask_question\n", - "-- [AIMessage(content='Thank you for the detailed information and references provided. It has been insightful to understand both the challenges and opportunities that million-plus token context window language models bring to RAG systems. I appreciate your assistance in shedding light on this complex t\n", - "answer_question\n", - "-- [AIMessage(content=\"You're welcome! If you have any more questions or need further assistance in the future, feel free to reach out. Good luck with your article on RAG systems and million-plus token context window language models!\\n\\nCitations:\\n\\n[1]: https://www.nerdwallet.com/article/finance/exam\n", - "__end__\n", - "-- [AIMessage(content='So you said you were writing an article on Impact of million-plus token context window language models on RAG?', name='Subject Matter Expert'), AIMessage(content=\"Yes, that's correct. I am focusing on the technical aspects of million-plus token context window language models and \n" - ] - } - ], - "source": ["final_step = None\n\ninitial_state = {\n \"editor\": perspectives.editors[0],\n \"messages\": [\n AIMessage(\n content=f\"So you said you were writing an article on {example_topic}?\",\n name=\"Subject_Matter_Expert\",\n )\n ],\n}\nasync for step in interview_graph.astream(initial_state):\n name = next(iter(step))\n print(name)\n print(\"-- \", str(step[name][\"messages\"])[:300])\n if END in step:\n final_step = step"] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": ["final_state = next(iter(final_step.values()))"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Refine Outline\n", - "\n", - "At this point in STORM, we've conducted a large amount of research from different perspectives. It's time to refine the original outline based on these investigations. Below, create a chain using the LLM with a long context window to update the original outline." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": ["refine_outline_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are a Wikipedia writer. You have gathered information from experts and search engines. Now, you are refining the outline of the Wikipedia page. \\\nYou need to make sure that the outline is comprehensive and specific. \\\nTopic you are writing about: {topic} \n\nOld outline:\n\n{old_outline}\"\"\",\n ),\n (\n \"user\",\n \"Refine the outline based on your conversations with subject-matter experts:\\n\\nConversations:\\n\\n{conversations}\\n\\nWrite the refined Wikipedia outline:\",\n ),\n ]\n)\n\n# Using turbo preview since the context can get quite long\nrefine_outline_chain = refine_outline_prompt | long_context_llm.with_structured_output(\n Outline\n)"] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": ["refined_outline = refine_outline_chain.invoke(\n {\n \"topic\": example_topic,\n \"old_outline\": initial_outline.as_str,\n \"conversations\": \"\\n\\n\".join(\n f\"### {m.name}\\n\\n{m.content}\" for m in final_state[\"messages\"]\n ),\n }\n)"] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Impact of million-plus token context window language models on RAG\n", - "\n", - "## Introduction\n", - "\n", - "Provides a brief overview of million-plus token context window language models and their relevance to Retrieval-Augmented Generation (RAG) systems, setting the stage for a deeper exploration of their impact.\n", - "\n", - "## Background\n", - "\n", - "A foundational section to understand the core concepts involved.\n", - "\n", - "### Million-Plus Token Context Window Language Models\n", - "\n", - "Explains what million-plus token context window language models are, including notable examples like Gemini 1.5, focusing on their architecture, training data, and the evolution of their applications.\n", - "\n", - "### Retrieval-Augmented Generation (RAG)\n", - "\n", - "Describes the RAG framework, its unique approach of combining retrieval and generation models for enhanced natural language processing, and its significance in the AI landscape.\n", - "\n", - "## Impact on RAG Systems\n", - "\n", - "Delves into the effects of million-plus token context window language models on RAG, highlighting both the challenges and opportunities presented.\n", - "\n", - "### Performance and Efficiency\n", - "\n", - "Discusses how large context window models influence RAG performance, including aspects of latency, computational demands, and overall efficiency.\n", - "\n", - "### Generation Quality and Diversity\n", - "\n", - "Explores the impact on generation quality, the potential for more accurate and diverse outputs, and how these models address data biases and factual accuracy.\n", - "\n", - "### Technical Challenges\n", - "\n", - "Identifies specific technical hurdles such as prompt template design, context length limitations, and similarity searches in vector databases, and how they affect RAG systems.\n", - "\n", - "### Opportunities and Advancements\n", - "\n", - "Outlines the new capabilities and improvements in agent interaction, information retrieval, and response relevance that these models bring to RAG systems.\n", - "\n", - "## Future Directions\n", - "\n", - "Considers ongoing research and potential future developments in the integration of million-plus token context window language models with RAG systems, including speculation on emerging trends and technologies.\n", - "\n", - "## Conclusion\n", - "\n", - "Summarizes the key points discussed in the article, reaffirming the significant impact of million-plus token context window language models on RAG systems.\n" - ] - } - ], - "source": ["print(refined_outline.as_str)"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Article\n", - "\n", - "Now it's time to generate the full article. We will first divide-and-conquer, so that each section can be tackled by an individual llm. Then we will prompt the long-form LLM to refine the finished article (since each section may use an inconsistent voice).\n", - "\n", - "#### Create Retriever\n", - "\n", - "The research process uncovers a large number of reference documents that we may want to query during the final article-writing process.\n", - "\n", - "First, create the retriever:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": ["from langchain_community.vectorstores import SKLearnVectorStore\nfrom langchain_core.documents import Document\nfrom langchain_openai import OpenAIEmbeddings\n\nembeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\nreference_docs = [\n Document(page_content=v, metadata={\"source\": k})\n for k, v in final_state[\"references\"].items()\n]\n# This really doesn't need to be a vectorstore for this size of data.\n# It could just be a numpy matrix. Or you could store documents\n# across requests if you want.\nvectorstore = SKLearnVectorStore.from_documents(\n reference_docs,\n embedding=embeddings,\n)\nretriever = vectorstore.as_retriever(k=10)"] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='In Retrieval Augmented Generation (RAG), a longer context augments our model with more information. For LLMs that power agents, such as chatbots, longer context means more tools and capabilities. When summarizing, longer context means more comprehensive summaries. There exist plenty of use-cases for LLMs that are unlocked by longer context lengths.', metadata={'id': '20454848-23ac-4649-b083-81980532a77b', 'source': 'https://www.anyscale.com/blog/fine-tuning-llms-for-longer-context-and-better-rag-systems'}),\n", - " Document(page_content='By the way, the context limits differ among models: two Claude models offer a 100K token context window, which works out to about 75,000 words, which is much higher than most other LLMs. The ...', metadata={'id': '1ee2d2bb-8f8e-4a7e-b45e-608b0804fe4c', 'source': 'https://www.infoworld.com/article/3712227/what-is-rag-more-accurate-and-reliable-llms.html'}),\n", - " Document(page_content='Figure 1: LLM response accuracy goes down when context needed to answer correctly is found in the middle of the context window. The problem gets worse with larger context models. The problem gets ...', metadata={'id': 'a41d69e6-62eb-4abd-90ad-0892a2836cba', 'source': 'https://medium.com/@jm_51428/long-context-window-models-vs-rag-a73c35a763f2'}),\n", - " Document(page_content='To improve performance, we used retrieval-augmented generation (RAG) to prompt an LLM with accurate up-to-date information. As a result of using RAG, the writing quality of the LLM improves substantially, which has implications for the practical usability of LLMs in clinical trial-related writing.', metadata={'id': 'e1af6e30-8c2b-495b-b572-ac6a29067a94', 'source': 'https://arxiv.org/abs/2402.16406'})]" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Initial Outline\n", + "\n", + "For many topics, your LLM may have an initial idea of the important and related topics. We can generate an initial\n", + "outline to be refined after our research. Below, we will use our \"fast\" llm to generate the outline." ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["retriever.invoke(\"What's a long context LLM anyway?\")"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generate Sections\n", - "\n", - "Now you can generate the sections using the indexed docs." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": ["class SubSection(BaseModel):\n subsection_title: str = Field(..., title=\"Title of the subsection\")\n content: str = Field(\n ...,\n title=\"Full content of the subsection. Include [#] citations to the cited sources where relevant.\",\n )\n\n @property\n def as_str(self) -> str:\n return f\"### {self.subsection_title}\\n\\n{self.content}\".strip()\n\n\nclass WikiSection(BaseModel):\n section_title: str = Field(..., title=\"Title of the section\")\n content: str = Field(..., title=\"Full content of the section\")\n subsections: Optional[List[Subsection]] = Field(\n default=None,\n title=\"Titles and descriptions for each subsection of the Wikipedia page.\",\n )\n citations: List[str] = Field(default_factory=list)\n\n @property\n def as_str(self) -> str:\n subsections = \"\\n\\n\".join(\n subsection.as_str for subsection in self.subsections or []\n )\n citations = \"\\n\".join([f\" [{i}] {cit}\" for i, cit in enumerate(self.citations)])\n return (\n f\"## {self.section_title}\\n\\n{self.content}\\n\\n{subsections}\".strip()\n + f\"\\n\\n{citations}\".strip()\n )\n\n\nsection_writer_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are an expert Wikipedia writer. Complete your assigned WikiSection from the following outline:\\n\\n\"\n \"{outline}\\n\\nCite your sources, using the following references:\\n\\n\\n{docs}\\n\",\n ),\n (\"user\", \"Write the full WikiSection for the {section} section.\"),\n ]\n)\n\n\nasync def retrieve(inputs: dict):\n docs = await retriever.ainvoke(inputs[\"topic\"] + \": \" + inputs[\"section\"])\n formatted = \"\\n\".join(\n [\n f'\\n{doc.page_content}\\n'\n for doc in docs\n ]\n )\n return {\"docs\": formatted, **inputs}\n\n\nsection_writer = (\n retrieve\n | section_writer_prompt\n | long_context_llm.with_structured_output(WikiSection)\n)"] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "## Background\n", - "\n", - "To fully appreciate the impact of million-plus token context window language models on Retrieval-Augmented Generation (RAG) systems, it's essential to first understand the foundational concepts that underpin these technologies. This background section provides a comprehensive overview of both million-plus token context window language models and RAG, setting the stage for a deeper exploration of their integration and subsequent impacts on artificial intelligence and natural language processing.\n", - "\n", - "### Million-Plus Token Context Window Language Models\n", - "\n", - "Million-plus token context window language models, such as Gemini 1.5, represent a significant leap forward in the field of language modeling. These models are designed to process and understand large swathes of text, sometimes exceeding a million tokens in a single pass. The ability to handle such vast amounts of information at once allows for a deeper understanding of context and nuance, which is crucial for generating coherent and relevant text outputs. The development of these models involves sophisticated architecture and extensive training data, pushing the boundaries of what's possible in natural language processing. Over time, the applications of these models have evolved, extending their utility beyond mere text generation to complex tasks like sentiment analysis, language translation, and more.\n", - "\n", - "### Retrieval-Augmented Generation (RAG)\n", - "\n", - "The Retrieval-Augmented Generation framework represents a novel approach in the realm of artificial intelligence, blending the strengths of both retrieval and generation models to enhance natural language processing capabilities. At its core, RAG leverages a two-step process: initially, it uses a query to retrieve relevant documents or data from a knowledge base; this information is then utilized to inform and guide the generation of responses by a language model. This method addresses the limitations of fixed context windows by converting text to vector embeddings, facilitating a dynamic and flexible interaction with a vast array of information. RAG's unique approach has cemented its significance in the AI landscape, offering a pathway to more accurate, informative, and contextually relevant text generation.\n" - ] - } - ], - "source": ["section = await section_writer.ainvoke(\n {\n \"outline\": refined_outline.as_str,\n \"section\": refined_outline.sections[1].section_title,\n \"topic\": example_topic,\n }\n)\nprint(section.as_str)"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generate final article\n", - "\n", - "Now we can rewrite the draft to appropriately group all the citations and maintain a consistent voice." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": ["from langchain_core.output_parsers import StrOutputParser\n\nwriter_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are an expert Wikipedia author. Write the complete wiki article on {topic} using the following section drafts:\\n\\n\"\n \"{draft}\\n\\nStrictly follow Wikipedia format guidelines.\",\n ),\n (\n \"user\",\n 'Write the complete Wiki article using markdown format. Organize citations using footnotes like \"[1]\",'\n \" avoiding duplicates in the footer. Include URLs in the footer.\",\n ),\n ]\n)\n\nwriter = writer_prompt | long_context_llm | StrOutputParser()"] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Impact of Million-Plus Token Context Window Language Models on Retrieval-Augmented Generation (RAG)\n", - "\n", - "The integration of million-plus token context window language models into Retrieval-Augmented Generation (RAG) systems marks a pivotal advancement in the field of artificial intelligence (AI) and natural language processing (NLP). This article delves into the background of both technologies, explores their convergence, and examines the profound effects of this integration on the capabilities and applications of AI-driven language models.\n", - "\n", - "## Contents\n", - "\n", - "1. [Background](#Background)\n", - " 1. [Million-Plus Token Context Window Language Models](#Million-Plus-Token-Context-Window-Language-Models)\n", - " 2. [Retrieval-Augmented Generation (RAG)](#Retrieval-Augmented-Generation-(RAG))\n", - "2. [Integration of Million-Plus Token Context Window Models and RAG](#Integration-of-Million-Plus-Token-Context-Window-Models-and-RAG)\n", - "3. [Impact on Natural Language Processing](#Impact-on-Natural-Language-Processing)\n", - "4. [Applications](#Applications)\n", - "5. [Challenges and Limitations](#Challenges-and-Limitations)\n", - "6. [Future Directions](#Future-Directions)\n", - "7. [Conclusion](#Conclusion)\n", - "8. [References](#References)\n", - "\n", - "## Background\n", - "\n", - "### Million-Plus Token Context Window Language Models\n", - "\n", - "Million-plus token context window language models, exemplified by systems like Gemini 1.5, have revolutionized language modeling by their ability to process and interpret extensive texts, potentially exceeding a million tokens in a single analysis[1]. The capacity to manage such large volumes of data enables these models to grasp context and subtlety to a degree previously unattainable, enhancing their effectiveness in generating text that is coherent, relevant, and nuanced. The development of these models has been characterized by innovative architecture and the utilization of vast training datasets, pushing the envelope of natural language processing capabilities[2].\n", - "\n", - "### Retrieval-Augmented Generation (RAG)\n", - "\n", - "RAG systems represent an innovative paradigm in AI, merging the strengths of retrieval-based and generative models to improve the quality and relevance of text generation[3]. By initially retrieving related documents or data in response to a query, and subsequently using this information to guide the generation process, RAG overcomes the limitations inherent in fixed context windows. This methodology allows for dynamic access to a broad range of information, significantly enhancing the model's ability to generate accurate, informative, and contextually appropriate responses[4].\n", - "\n", - "## Integration of Million-Plus Token Context Window Models and RAG\n", - "\n", - "The integration of million-plus token context window models with RAG systems has been a natural progression in the quest for more sophisticated NLP solutions. By combining the extensive contextual understanding afforded by large context window models with the dynamic, information-rich capabilities of RAG, researchers and developers have been able to create AI systems that exhibit unprecedented levels of understanding, coherence, and relevance in text generation[5].\n", - "\n", - "## Impact on Natural Language Processing\n", - "\n", - "The fusion of these technologies has had a significant impact on the field of NLP, leading to advancements in several key areas:\n", - "- **Enhanced Understanding**: The combined system exhibits a deeper comprehension of both the immediate context and broader subject matter[6].\n", - "- **Improved Coherence**: Generated text is more coherent over longer passages, maintaining consistency and relevance[7].\n", - "- **Increased Relevance**: Outputs are more contextually relevant, drawing accurately from a wider range of sources[8].\n", - "\n", - "## Applications\n", - "\n", - "This technological convergence has broadened the applicability of NLP systems in numerous fields, including but not limited to:\n", - "- **Automated Content Creation**: Generating written content that is both informative and contextually appropriate for various platforms[9].\n", - "- **Customer Support**: Providing answers that are not only accurate but also tailored to the specific context of user inquiries[10].\n", - "- **Research Assistance**: Assisting in literature review and data analysis by retrieving and synthesizing relevant information from vast databases[11].\n", - "\n", - "## Challenges and Limitations\n", - "\n", - "Despite their advancements, the integration of these technologies faces several challenges:\n", - "- **Computational Resources**: The processing of million-plus tokens and the dynamic retrieval of relevant information require significant computational power[12].\n", - "- **Data Privacy and Security**: Ensuring the confidentiality and integrity of the data accessed by these systems poses ongoing concerns[13].\n", - "- **Bias and Fairness**: The potential for inheriting and amplifying biases from training data remains a critical issue to address[14].\n", - "\n", - "## Future Directions\n", - "\n", - "Future research is likely to focus on optimizing computational efficiency, enhancing the models' ability to understand and generate more diverse and nuanced text, and addressing ethical considerations associated with AI and NLP technologies[15].\n", - "\n", - "## Conclusion\n", - "\n", - "The integration of million-plus token context window language models with RAG systems represents a milestone in the evolution of natural language processing, offering enhanced capabilities that have significant implications across various applications. As these technologies continue to evolve, they promise to further transform the landscape of AI-driven language models.\n", - "\n", - "## References\n", - "\n", - "1. Gemini 1.5 Documentation. (n.d.).\n", - "2. The Evolution of Language Models. (2022).\n", - "3. Introduction to Retrieval-Augmented Generation. (2021).\n", - "4. Leveraging Large Context Windows for NLP. (2023).\n", - "5. Integrating Context Window Models with RAG. (2023).\n", - "6. Deep Learning in NLP. (2020).\n", - "7. Coherence in Text Generation. (2019).\n", - "8. Contextual Relevance in AI. (2021).\n", - "9. Applications of NLP in Content Creation. (2022).\n", - "10. AI in Customer Support. (2023).\n", - "11. NLP for Research Assistance. (2021).\n", - "12. Computational Challenges in NLP. (2022).\n", - "13. Data Privacy in AI Systems. (2020).\n", - "14. Addressing Bias in AI. (2021).\n", - "15. Future of NLP Technologies. (2023)." - ] - } - ], - "source": ["for tok in writer.stream({\"topic\": example_topic, \"draft\": section.as_str}):\n print(tok, end=\"\")"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Final Flow\n", - "\n", - "Now it's time to string everything together. We will have 6 main stages in sequence:\n", - ".\n", - "1. Generate the initial outline + perspectives\n", - "2. Batch converse with each perspective to expand the content for the article\n", - "3. Refine the outline based on the conversations\n", - "4. Index the reference docs from the conversations\n", - "5. Write the individual sections of the article\n", - "6. Write the final wiki\n", - "\n", - "The state tracks the outputs of each stage." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": ["class ResearchState(TypedDict):\n topic: str\n outline: Outline\n editors: List[Editor]\n interview_results: List[InterviewState]\n # The final sections output\n sections: List[WikiSection]\n article: str"] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": ["import asyncio\n\n\nasync def initialize_research(state: ResearchState):\n topic = state[\"topic\"]\n coros = (\n generate_outline_direct.ainvoke({\"topic\": topic}),\n survey_subjects.ainvoke(topic),\n )\n results = await asyncio.gather(*coros)\n return {\n **state,\n \"outline\": results[0],\n \"editors\": results[1].editors,\n }\n\n\nasync def conduct_interviews(state: ResearchState):\n topic = state[\"topic\"]\n initial_states = [\n {\n \"editor\": editor,\n \"messages\": [\n AIMessage(\n content=f\"So you said you were writing an article on {topic}?\",\n name=\"Subject_Matter_Expert\",\n )\n ],\n }\n for editor in state[\"editors\"]\n ]\n # We call in to the sub-graph here to parallelize the interviews\n interview_results = await interview_graph.abatch(initial_states)\n\n return {\n **state,\n \"interview_results\": interview_results,\n }\n\n\ndef format_conversation(interview_state):\n messages = interview_state[\"messages\"]\n convo = \"\\n\".join(f\"{m.name}: {m.content}\" for m in messages)\n return f'Conversation with {interview_state[\"editor\"].name}\\n\\n' + convo\n\n\nasync def refine_outline(state: ResearchState):\n convos = \"\\n\\n\".join(\n [\n format_conversation(interview_state)\n for interview_state in state[\"interview_results\"]\n ]\n )\n\n updated_outline = await refine_outline_chain.ainvoke(\n {\n \"topic\": state[\"topic\"],\n \"old_outline\": state[\"outline\"].as_str,\n \"conversations\": convos,\n }\n )\n return {**state, \"outline\": updated_outline}\n\n\nasync def index_references(state: ResearchState):\n all_docs = []\n for interview_state in state[\"interview_results\"]:\n reference_docs = [\n Document(page_content=v, metadata={\"source\": k})\n for k, v in interview_state[\"references\"].items()\n ]\n all_docs.extend(reference_docs)\n await vectorstore.aadd_documents(all_docs)\n return state\n\n\nasync def write_sections(state: ResearchState):\n outline = state[\"outline\"]\n sections = await section_writer.abatch(\n [\n {\n \"outline\": refined_outline.as_str,\n \"section\": section.section_title,\n \"topic\": state[\"topic\"],\n }\n for section in outline.sections\n ]\n )\n return {\n **state,\n \"sections\": sections,\n }\n\n\nasync def write_article(state: ResearchState):\n topic = state[\"topic\"]\n sections = state[\"sections\"]\n draft = \"\\n\\n\".join([section.as_str for section in sections])\n article = await writer.ainvoke({\"topic\": topic, \"draft\": draft})\n return {\n **state,\n \"article\": article,\n }"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": ["from langgraph.checkpoint.memory import MemorySaver\n\nbuilder_of_storm = StateGraph(ResearchState)\n\nnodes = [\n (\"init_research\", initialize_research),\n (\"conduct_interviews\", conduct_interviews),\n (\"refine_outline\", refine_outline),\n (\"index_references\", index_references),\n (\"write_sections\", write_sections),\n (\"write_article\", write_article),\n]\nfor i in range(len(nodes)):\n name, node = nodes[i]\n builder_of_storm.add_node(name, node)\n if i > 0:\n builder_of_storm.add_edge(nodes[i - 1][0], name)\n\nbuilder_of_storm.add_edge(START, nodes[0][0])\nbuilder_of_storm.add_edge(nodes[-1][0], END)\nstorm = builder_of_storm.compile(checkpointer=MemorySaver())"] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": ["Image(storm.get_graph().draw_png())"] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "init_research\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', sections=[Section(section_title='Introduction', description='Overview of Groq, NVIDIA, Llamma.cpp, and their significance in the field of La\n", - "conduct_interviews\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', sections=[Section(section_title='Introduction', description='Overview of Groq, NVIDIA, Llamma.cpp, and their significance in the field of La\n", - "refine_outline\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", - "index_references\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", - "write_sections\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", - "write_article\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", - "__end__\n", - "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n" - ] - } - ], - "source": ["config = {\"configurable\": {\"thread_id\": \"my-thread\"}}\nasync for step in storm.astream(\n {\n \"topic\": \"Groq, NVIDIA, Llamma.cpp and the future of LLM Inference\",\n },\n config,\n):\n name = next(iter(step))\n print(name)\n print(\"-- \", str(step[name])[:300])"] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": ["checkpoint = storm.get_state(config)\narticle = checkpoint.values[\"article\"]"] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Render the Wiki\n", - "\n", - "Now we can render the final wiki page!" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "# Large Language Model (LLM) Inference Technologies\n", - "\n", - "### Contents\n", - "1. [Introduction](#Introduction)\n", - "2. [Groq's Advancements in LLM Inference](#Groqs-Advancements-in-LLM-Inference)\n", - "3. [NVIDIA's Contributions to LLM Inference](#NVIDIAs-Contributions-to-LLM-Inference)\n", - " 1. [Hardware Innovations](#Hardware-Innovations)\n", - " 2. [Software Solutions](#Software-Solutions)\n", - " 3. [Research and Development](#Research-and-Development)\n", - "4. [Llamma.cpp: Accelerating LLM Inference](#Llammacpp-Accelerating-LLM-Inference)\n", - "5. [The Future of LLM Inference](#The-Future-of-LLM-Inference)\n", - "6. [References](#References)\n", - "\n", - "### Introduction\n", - "\n", - "The advent of million-plus token context window language models, such as Gemini 1.5, has significantly advanced the field of artificial intelligence, particularly in natural language processing (NLP). These models have expanded the capabilities of machine learning in understanding and generating text over vastly larger contexts than previously possible. This leap in technology has paved the way for transformative applications across various domains, including the integration into Retrieval-Augmented Generation (RAG) systems to produce more accurate and contextually rich responses. \n", - "\n", - "### Groq's Advancements in LLM Inference\n", - "\n", - "Groq has introduced the Groq Linear Processor Unit (LPU), a purpose-built hardware architecture for LLM inference. This innovation positions Groq as a leader in efficient and high-performance LLM processing by optimizing the hardware specifically for LLM tasks. The Groq LPU dramatically reduces latency and increases the throughput of LLM inferences, facilitating advancements in a wide range of applications, from natural language processing to broader artificial intelligence technologies[1].\n", - "\n", - "### NVIDIA's Contributions to LLM Inference\n", - "\n", - "NVIDIA has played a pivotal role in advancing LLM inference through its GPUs, optimized for AI and machine learning workloads, and specialized software frameworks. The company's GPU architecture and software solutions, such as the CUDA Deep Neural Network library (cuDNN) and the TensorRT inference optimizer, are designed to accelerate computational processes and improve LLM performance. NVIDIA's active participation in research and development further underscores its commitment to enhancing the capabilities of LLMs[1].\n", - "\n", - "#### Hardware Innovations\n", - "\n", - "NVIDIA's GPU architecture facilitates high throughput and parallel processing for LLM inference tasks, significantly reducing inference time and enabling complex models to be used in real-time applications.\n", - "\n", - "#### Software Solutions\n", - "\n", - "NVIDIA's suite of software tools, including cuDNN and TensorRT, optimizes LLM performance on its hardware, streamlining the deployment of LLMs by improving their efficiency and reducing latency.\n", - "\n", - "#### Research and Development\n", - "\n", - "NVIDIA collaborates with academic and industry partners to develop new techniques and models that push the boundaries of LLM technology, aiming to make LLMs more powerful and applicable across a broader range of tasks.\n", - "\n", - "### Llamma.cpp: Accelerating LLM Inference\n", - "\n", - "Llamma.cpp is a framework developed to enhance the speed and efficiency of LLM inference. By integrating specialized hardware, such as Groq's LPU, and optimizing for parallel processing, Llamma.cpp significantly accelerates computation times and reduces energy consumption. The framework supports million-plus token context window models, enabling applications requiring deep contextual understanding and extensive knowledge retrieval[1][2].\n", - "\n", - "### The Future of LLM Inference\n", - "\n", - "The future of LLM inference is poised for transformative changes with advances in purpose-built hardware architectures like Groq's LPU. These innovations promise to enhance the speed and efficiency of LLM processing, leading to more interactive, capable, and integrated AI applications. The potential for advanced hardware and sophisticated LLMs to enable near-instantaneous processing of complex queries and interactions opens new avenues for research and application in various fields, suggesting a future where AI is seamlessly integrated into society[1][2].\n", - "\n", - "### References\n", - "\n", - "[1] \"Groq's LPU: Advancing LLM Inference Efficiency,\" Prompt Engineering. https://promptengineering.org/groqs-lpu-advancing-llm-inference-efficiency/\n", - "\n", - "[2] \"The Speed of Thought: Harnessing the Fastest LLM with Groq's LPU,\" Medium. https://medium.com/@anasdavoodtk1/the-speed-of-thought-harnessing-the-fastest-llm-with-groqs-lpu-11bb00864e9c" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/wfh/code/lc/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n", + " warn_beta(\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "from typing import List, Optional\n\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\ndirect_gen_outline_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a Wikipedia writer. Write an outline for a Wikipedia page about a user-provided topic. Be comprehensive and specific.\",\n ),\n (\"user\", \"{topic}\"),\n ]\n)\n\n\nclass Subsection(BaseModel):\n subsection_title: str = Field(..., title=\"Title of the subsection\")\n description: str = Field(..., title=\"Content of the subsection\")\n\n @property\n def as_str(self) -> str:\n return f\"### {self.subsection_title}\\n\\n{self.description}\".strip()\n\n\nclass Section(BaseModel):\n section_title: str = Field(..., title=\"Title of the section\")\n description: str = Field(..., title=\"Content of the section\")\n subsections: Optional[List[Subsection]] = Field(\n default=None,\n title=\"Titles and descriptions for each subsection of the Wikipedia page.\",\n )\n\n @property\n def as_str(self) -> str:\n subsections = \"\\n\\n\".join(\n f\"### {subsection.subsection_title}\\n\\n{subsection.description}\"\n for subsection in self.subsections or []\n )\n return f\"## {self.section_title}\\n\\n{self.description}\\n\\n{subsections}\".strip()\n\n\nclass Outline(BaseModel):\n page_title: str = Field(..., title=\"Title of the Wikipedia page\")\n sections: List[Section] = Field(\n default_factory=list,\n title=\"Titles and descriptions for each section of the Wikipedia page.\",\n )\n\n @property\n def as_str(self) -> str:\n sections = \"\\n\\n\".join(section.as_str for section in self.sections)\n return f\"# {self.page_title}\\n\\n{sections}\".strip()\n\n\ngenerate_outline_direct = direct_gen_outline_prompt | fast_llm.with_structured_output(\n Outline\n)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Impact of million-plus token context window language models on RAG\n", + "\n", + "## Introduction\n", + "\n", + "Overview of million-plus token context window language models and RAG (Retrieval-Augmented Generation).\n", + "\n", + "## Million-Plus Token Context Window Language Models\n", + "\n", + "Explanation of million-plus token context window language models, their architecture, training data, and applications.\n", + "\n", + "## RAG (Retrieval-Augmented Generation)\n", + "\n", + "Overview of RAG, its architecture, how it combines retrieval and generation models, and its use in natural language processing tasks.\n", + "\n", + "## Impact on RAG\n", + "\n", + "Discuss the impact of million-plus token context window language models on RAG, including improvements in performance, efficiency, and challenges faced.\n" + ] + } + ], + "source": [ + "example_topic = \"Impact of million-plus token context window language models on RAG\"\n\ninitial_outline = generate_outline_direct.invoke({\"topic\": example_topic})\n\nprint(initial_outline.as_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expand Topics\n", + "\n", + "While language models do store some Wikipedia-like knowledge in their parameters, you will get better results by incorporating relevant and recent information using a search engine.\n", + "\n", + "We will start our search by generating a list of related topics, sourced from Wikipedia." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "gen_related_topics_prompt = ChatPromptTemplate.from_template(\n \"\"\"I'm writing a Wikipedia page for a topic mentioned below. Please identify and recommend some Wikipedia pages on closely related subjects. I'm looking for examples that provide insights into interesting aspects commonly associated with this topic, or examples that help me understand the typical content and structure included in Wikipedia pages for similar topics.\n\nPlease list the as many subjects and urls as you can.\n\nTopic of interest: {topic}\n\"\"\"\n)\n\n\nclass RelatedSubjects(BaseModel):\n topics: List[str] = Field(\n description=\"Comprehensive list of related subjects as background research.\",\n )\n\n\nexpand_chain = gen_related_topics_prompt | fast_llm.with_structured_output(\n RelatedSubjects\n)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RelatedSubjects(topics=['Language models', 'Retriever-Reader-Generator (RAG) model', 'Natural language processing', 'Machine learning', 'Artificial intelligence', 'Text generation', 'Transformer architecture', 'Context window', 'Impact of language models'])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "related_subjects = await expand_chain.ainvoke({\"topic\": example_topic})\nrelated_subjects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Perspectives\n", + "\n", + "From these related subjects, we can select representative Wikipedia editors as \"subject matter experts\" with distinct\n", + "backgrounds and affiliations. These will help distribute the search process to encourage a more well-rounded final report." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class Editor(BaseModel):\n affiliation: str = Field(\n description=\"Primary affiliation of the editor.\",\n )\n name: str = Field(\n description=\"Name of the editor.\", pattern=r\"^[a-zA-Z0-9_-]{1,64}$\"\n )\n role: str = Field(\n description=\"Role of the editor in the context of the topic.\",\n )\n description: str = Field(\n description=\"Description of the editor's focus, concerns, and motives.\",\n )\n\n @property\n def persona(self) -> str:\n return f\"Name: {self.name}\\nRole: {self.role}\\nAffiliation: {self.affiliation}\\nDescription: {self.description}\\n\"\n\n\nclass Perspectives(BaseModel):\n editors: List[Editor] = Field(\n description=\"Comprehensive list of editors with their roles and affiliations.\",\n # Add a pydantic validation/restriction to be at most M editors\n )\n\n\ngen_perspectives_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You need to select a diverse (and distinct) group of Wikipedia editors who will work together to create a comprehensive article on the topic. Each of them represents a different perspective, role, or affiliation related to this topic.\\\n You can use other Wikipedia pages of related topics for inspiration. For each editor, add a description of what they will focus on.\n\n Wiki page outlines of related topics for inspiration:\n {examples}\"\"\",\n ),\n (\"user\", \"Topic of interest: {topic}\"),\n ]\n)\n\ngen_perspectives_chain = gen_perspectives_prompt | ChatOpenAI(\n model=\"gpt-3.5-turbo\"\n).with_structured_output(Perspectives)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.retrievers import WikipediaRetriever\nfrom langchain_core.runnables import RunnableLambda\nfrom langchain_core.runnables import chain as as_runnable\n\nwikipedia_retriever = WikipediaRetriever(load_all_available_meta=True, top_k_results=1)\n\n\ndef format_doc(doc, max_length=1000):\n related = \"- \".join(doc.metadata[\"categories\"])\n return f\"### {doc.metadata['title']}\\n\\nSummary: {doc.page_content}\\n\\nRelated\\n{related}\"[\n :max_length\n ]\n\n\ndef format_docs(docs):\n return \"\\n\\n\".join(format_doc(doc) for doc in docs)\n\n\n@as_runnable\nasync def survey_subjects(topic: str):\n related_subjects = await expand_chain.ainvoke({\"topic\": topic})\n retrieved_docs = await wikipedia_retriever.abatch(\n related_subjects.topics, return_exceptions=True\n )\n all_docs = []\n for docs in retrieved_docs:\n if isinstance(docs, BaseException):\n continue\n all_docs.extend(docs)\n formatted = format_docs(all_docs)\n return await gen_perspectives_chain.ainvoke({\"examples\": formatted, \"topic\": topic})" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "perspectives = await survey_subjects.ainvoke(example_topic)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'editors': [{'affiliation': 'Academic Research',\n", + " 'name': 'Dr. Linguist',\n", + " 'role': 'Language Model Expert',\n", + " 'description': 'Dr. Linguist will focus on explaining the technical aspects of million-plus token context window language models and their impact on RAG (Retrieval-Augmented Generation) systems.'},\n", + " {'affiliation': 'Industry',\n", + " 'name': 'TechTrendz',\n", + " 'role': 'AI Solutions Architect',\n", + " 'description': 'TechTrendz will provide insights on the practical applications of million-plus token context window language models in RAG systems and discuss their benefits and challenges in real-world scenarios.'},\n", + " {'affiliation': 'Open Source Community',\n", + " 'name': 'CodeGenius',\n", + " 'role': 'Machine Learning Enthusiast',\n", + " 'description': 'CodeGenius will explore the open-source tools and frameworks available for implementing million-plus token context window language models in RAG systems and share their experiences with the community.'},\n", + " {'affiliation': 'Tech Journalism',\n", + " 'name': 'DataDive',\n", + " 'role': 'AI Technology Journalist',\n", + " 'description': 'DataDive will cover the latest developments and advancements in million-plus token context window language models and their implications for RAG systems, focusing on industry trends and use cases.'}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "perspectives.dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expert Dialog\n", + "\n", + "Now the true fun begins, each wikipedia writer is primed to role-play using the perspectives presented above. It will ask a series of questions of a second \"domain expert\" with access to a search engine. This generate content to generate a refined outline as well as an updated index of reference documents.\n", + "\n", + "\n", + "### Interview State\n", + "\n", + "The conversation is cyclic, so we will construct it within its own graph. The State will contain messages, the reference docs, and the editor (with its own \"persona\") to make it easy to parallelize these conversations." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Annotated\n\nfrom langchain_core.messages import AnyMessage\nfrom typing_extensions import TypedDict\n\nfrom langgraph.graph import END, StateGraph, START\n\n\ndef add_messages(left, right):\n if not isinstance(left, list):\n left = [left]\n if not isinstance(right, list):\n right = [right]\n return left + right\n\n\ndef update_references(references, new_references):\n if not references:\n references = {}\n references.update(new_references)\n return references\n\n\ndef update_editor(editor, new_editor):\n # Can only set at the outset\n if not editor:\n return new_editor\n return editor\n\n\nclass InterviewState(TypedDict):\n messages: Annotated[List[AnyMessage], add_messages]\n references: Annotated[Optional[dict], update_references]\n editor: Annotated[Optional[Editor], update_editor]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dialog Roles\n", + "\n", + "The graph will have two participants: the wikipedia editor (`generate_question`), who asks questions based on its assigned role, and a domain expert (`gen_answer_chain), who uses a search engine to answer the questions as accurately as possible." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\nfrom langchain_core.prompts import MessagesPlaceholder\n\ngen_qn_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are an experienced Wikipedia writer and want to edit a specific page. \\\nBesides your identity as a Wikipedia writer, you have a specific focus when researching the topic. \\\nNow, you are chatting with an expert to get information. Ask good questions to get more useful information.\n\nWhen you have no more questions to ask, say \"Thank you so much for your help!\" to end the conversation.\\\nPlease only ask one question at a time and don't ask what you have asked before.\\\nYour questions should be related to the topic you want to write.\nBe comprehensive and curious, gaining as much unique insight from the expert as possible.\\\n\nStay true to your specific perspective:\n\n{persona}\"\"\",\n ),\n MessagesPlaceholder(variable_name=\"messages\", optional=True),\n ]\n)\n\n\ndef tag_with_name(ai_message: AIMessage, name: str):\n ai_message.name = name\n return ai_message\n\n\ndef swap_roles(state: InterviewState, name: str):\n converted = []\n for message in state[\"messages\"]:\n if isinstance(message, AIMessage) and message.name != name:\n message = HumanMessage(**message.dict(exclude={\"type\"}))\n converted.append(message)\n return {\"messages\": converted}\n\n\n@as_runnable\nasync def generate_question(state: InterviewState):\n editor = state[\"editor\"]\n gn_chain = (\n RunnableLambda(swap_roles).bind(name=editor.name)\n | gen_qn_prompt.partial(persona=editor.persona)\n | fast_llm\n | RunnableLambda(tag_with_name).bind(name=editor.name)\n )\n result = await gn_chain.ainvoke(state)\n return {\"messages\": [result]}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Yes, that's correct. I'm focusing on the technical aspects of million-plus token context window language models and their impact on Retrieval-Augmented Generation (RAG) systems. Can you provide more information on how these large context window language models are trained and how they differ from traditional models in the context of RAG systems?\"" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages = [\n HumanMessage(f\"So you said you were writing an article on {example_topic}?\")\n]\nquestion = await generate_question.ainvoke(\n {\n \"editor\": perspectives.editors[0],\n \"messages\": messages,\n }\n)\n\nquestion[\"messages\"][0].content" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Answer questions\n", + "\n", + "The `gen_answer_chain` first generates queries (query expansion) to answer the editor's question, then responds with citations." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "class Queries(BaseModel):\n queries: List[str] = Field(\n description=\"Comprehensive list of search engine queries to answer the user's questions.\",\n )\n\n\ngen_queries_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are a helpful research assistant. Query the search engine to answer the user's questions.\",\n ),\n MessagesPlaceholder(variable_name=\"messages\", optional=True),\n ]\n)\ngen_queries_chain = gen_queries_prompt | ChatOpenAI(\n model=\"gpt-3.5-turbo\"\n).with_structured_output(Queries, include_raw=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Training process of million-plus token context window language models',\n", + " 'Differences between large context window language models and traditional models in Retrieval-Augmented Generation systems']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "queries = await gen_queries_chain.ainvoke(\n {\"messages\": [HumanMessage(content=question[\"messages\"][0].content)]}\n)\nqueries[\"parsed\"].queries" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "class AnswerWithCitations(BaseModel):\n answer: str = Field(\n description=\"Comprehensive answer to the user's question with citations.\",\n )\n cited_urls: List[str] = Field(\n description=\"List of urls cited in the answer.\",\n )\n\n @property\n def as_str(self) -> str:\n return f\"{self.answer}\\n\\nCitations:\\n\\n\" + \"\\n\".join(\n f\"[{i+1}]: {url}\" for i, url in enumerate(self.cited_urls)\n )\n\n\ngen_answer_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are an expert who can use information effectively. You are chatting with a Wikipedia writer who wants\\\n to write a Wikipedia page on the topic you know. You have gathered the related information and will now use the information to form a response.\n\nMake your response as informative as possible and make sure every sentence is supported by the gathered information.\nEach response must be backed up by a citation from a reliable source, formatted as a footnote, reproducing the URLS after your response.\"\"\",\n ),\n MessagesPlaceholder(variable_name=\"messages\", optional=True),\n ]\n)\n\ngen_answer_chain = gen_answer_prompt | fast_llm.with_structured_output(\n AnswerWithCitations, include_raw=True\n).with_config(run_name=\"GenerateAnswer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper\nfrom langchain_core.tools import tool\n\n'''\n# Tavily is typically a better search engine, but your free queries are limited\nsearch_engine = TavilySearchResults(max_results=4)\n\n@tool\nasync def search_engine(query: str):\n \"\"\"Search engine to the internet.\"\"\"\n results = tavily_search.invoke(query)\n return [{\"content\": r[\"content\"], \"url\": r[\"url\"]} for r in results]\n'''\n\n# DDG\nsearch_engine = DuckDuckGoSearchAPIWrapper()\n\n\n@tool\nasync def search_engine(query: str):\n \"\"\"Search engine to the internet.\"\"\"\n results = DuckDuckGoSearchAPIWrapper()._ddgs_text(query)\n return [{\"content\": r[\"body\"], \"url\": r[\"href\"]} for r in results]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n\nfrom langchain_core.runnables import RunnableConfig\n\n\nasync def gen_answer(\n state: InterviewState,\n config: Optional[RunnableConfig] = None,\n name: str = \"Subject_Matter_Expert\",\n max_str_len: int = 15000,\n):\n swapped_state = swap_roles(state, name) # Convert all other AI messages\n queries = await gen_queries_chain.ainvoke(swapped_state)\n query_results = await search_engine.abatch(\n queries[\"parsed\"].queries, config, return_exceptions=True\n )\n successful_results = [\n res for res in query_results if not isinstance(res, Exception)\n ]\n all_query_results = {\n res[\"url\"]: res[\"content\"] for results in successful_results for res in results\n }\n # We could be more precise about handling max token length if we wanted to here\n dumped = json.dumps(all_query_results)[:max_str_len]\n ai_message: AIMessage = queries[\"raw\"]\n tool_call = queries[\"raw\"].additional_kwargs[\"tool_calls\"][0]\n tool_id = tool_call[\"id\"]\n tool_message = ToolMessage(tool_call_id=tool_id, content=dumped)\n swapped_state[\"messages\"].extend([ai_message, tool_message])\n # Only update the shared state with the final answer to avoid\n # polluting the dialogue history with intermediate messages\n generated = await gen_answer_chain.ainvoke(swapped_state)\n cited_urls = set(generated[\"parsed\"].cited_urls)\n # Save the retrieved information to a the shared state for future reference\n cited_references = {k: v for k, v in all_query_results.items() if k in cited_urls}\n formatted_message = AIMessage(name=name, content=generated[\"parsed\"].as_str)\n return {\"messages\": [formatted_message], \"references\": cited_references}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Large context window language models, such as the Llama2 70B model, can support context windows of more than 100k tokens without continual training through innovations like Dual Chunk Attention (DCA). These models have significantly longer context windows compared to traditional models, with capabilities like processing up to 1 million tokens at once, providing more consistent and relevant outputs. Training these models often involves starting with a smaller window size and gradually increasing it through fine-tuning on larger windows. In contrast, traditional models have much shorter context windows, limiting their ability to process extensive information in a prompt. Retrieval-Augmented Generation (RAG) systems, on the other hand, integrate large language models with external knowledge sources to enhance their performance, offering a pathway to combine the capabilities of models like ChatGPT/GPT-4 with custom data sources for more informed and contextually aware outputs.\\n\\nCitations:\\n\\n[1]: https://arxiv.org/abs/2402.17463\\n[2]: https://blog.google/technology/ai/long-context-window-ai-models/\\n[3]: https://medium.com/@ddxzzx/why-and-how-to-achieve-longer-context-windows-for-llms-5f76f8656ea9\\n[4]: https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/\\n[5]: https://huggingface.co/papers/2402.13753\\n[6]: https://www.pinecone.io/blog/why-use-retrieval-instead-of-larger-context/\\n[7]: https://medium.com/emalpha/innovations-in-retrieval-augmented-generation-8e6e70f95629\\n[8]: https://inside-machinelearning.com/en/rag/'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_answer = await gen_answer(\n {\"messages\": [HumanMessage(content=question[\"messages\"][0].content)]}\n)\nexample_answer[\"messages\"][-1].content" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Construct the Interview Graph\n", + "\n", + "\n", + "Now that we've defined the editor and domain expert, we can compose them in a graph." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "max_num_turns = 5\n\n\ndef route_messages(state: InterviewState, name: str = \"Subject_Matter_Expert\"):\n messages = state[\"messages\"]\n num_responses = len(\n [m for m in messages if isinstance(m, AIMessage) and m.name == name]\n )\n if num_responses >= max_num_turns:\n return END\n last_question = messages[-2]\n if last_question.content.endswith(\"Thank you so much for your help!\"):\n return END\n return \"ask_question\"\n\n\nbuilder = StateGraph(InterviewState)\n\nbuilder.add_node(\"ask_question\", generate_question)\nbuilder.add_node(\"answer_question\", gen_answer)\nbuilder.add_conditional_edges(\"answer_question\", route_messages)\nbuilder.add_edge(\"ask_question\", \"answer_question\")\n\nbuilder.add_edge(START, \"ask_question\")\ninterview_graph = builder.compile().with_config(run_name=\"Conduct Interviews\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ec0+xaskbxMbF8vAf/8jcuXO1Rkw6PBUPETfbuHEjv/nNb1i9ejW9Bw5m6tXXM+GyK/Hy8bE6mrjB/tTtrFryOms/fJcuXcJ54P77uO2223TyN+k0VDxELLJ+/XqeevppPnj/fYJCw5hy9fVMu+o6unTtZnU0aWX1tbX836ef8N83XmLP9q0MHjKEn995J9dddx0+KpzSyah4iFgsLy+PV155hSefeor8vDwSzx/OqIsuZewlszvk2W47i0aXi93bNrPuo3f5ctmH1NZUM33GDO6+6y6mTp1qdTwRy6h4iLQR9fX1rFixgsWLF/PhRx/R0NDAeWMmMPLCSxg6fpJ5ynlp0xrq6kj95v9I+fxTvv70Y8pLSkgeeQHXXTuXq6++mqioKKsjilhOxUOkDaqsrOTDDz/kzTff4vPVn1NfV0evAYM5b9wkhk6YQp9B5+GhQy3bhILsTLasW83W9WvY+fUGamtqGDR4MNf85CfMnTuX+Ph4qyOKtCkqHiJtXFVVFWvWrGH58uUsW76czIwMgkJCSRqaTNLwZPoNS6bXgMEd8gRpbVFe5iF2bUkhbdNGdm9OIfvgfgICApk6bSozpk9nxowZxMbGWh1TpM1S8RBpZ9LT0/nss89Yt24dGzZ8SUFBPj6+vvQZfD5Jw0aS0H8Q8f0HEhEdY3XUdq+6opyDu1I5mLaTPds2s2vzRo4U5OPt48Pw4SOYOGE8kydPZuzYsXh5eVkdV6RdUPEQaed2797Nhg0bWL9+PV9+9RUH9u+nsbGRoJBQ4vsPome/gST0H0hs70Sieybg0GGbxzEaGynMPUz2/r0c+q5oHErfQW5mBoZhENYlnAtGjmTcuLGMGzeO4cOH6/BXkR9JxUOkg6moqGDbtm1s3bqVrVu3snnLFtLT03E2NODh4UFk9xi69UwgOr433RN6E90zgcjusXTpGo3d0XE31xiGQWlRAUW5OeRlHiJ7/15yDu4nL+MA2Qf2UV9nngQuNi6OoUOHMvT88zn/uykmRmuPRFqLiodIJ1BfX8/evXvZtWsXe/bsYffu3aSlp7Nn9x7KykoBsNlshEVEEt4tmrCu0XTpGk1EdAzBXcIJCutCcJh5GRQa1uYKSnnxEcpLiqkoKaa8pJiSwnyKC/Ipyj3MkdzDFOfnUpib23SGWYeXFwkJCfTr14+kxEQSExNJSkoiMTGR0NBQi9+NSMem4iHSyRUUFJCRkUFWVhZZWVlN1zMyM8nMzORIURFOp7PFYwICgwgJj8AvIBDfwCB8/Pzw8vHFx88fv8AgfHx9cXj74B8U1GIIcL+AIGweJx4SvLaqCpfL2eJ2Q0M91RXl1FRVUVdTQ11NNVXlZdTX1lBdUU55STFlJcU0ulwtnis0NIxu0d3o2bMncbGxxH439ejRo+m63W5vxU9RRE6XioeI/KDi4mIKCwspKiqisLCQwsJCCgoKKC8vp7S0lMrKSiqrqqiqrKSktJSqqiqqq6upKK9oeg7DMJrWrpyIr58f3l7N+014+3jj5+dHSEgIAQGBBPj7ExDgT0hICP7+/gQFBREeHk54eDhdu3Ztuh4eHo6jja2REZFmKh4iYglPT0/eeOMNrrnmGqujiIgbeVgdQERERDoPFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbFQ8RERFxGxUPERERcRsVDxEREXEbm2EYhtUhRKRjmzFjBl9++SXH/rqprKzEx8cHu93eNM/hcLBt2zbi4uKsiCkibqA1HiJyzk2fPp2KigoqKyubJoDa2toW83r06KHSIdLBqXiIyDk3d+5cPDxO/evGbrdz4403uieQiFhGxUNEzrnIyEjGjx+Pp6fnSZdxuVxcddVVbkwlIlZQ8RARt7jhhhtOep+HhwcTJkwgOjrajYlExAoqHiLiFldeeeVJN7fYbLZTFhMR6ThUPETELYKCgpgxY0aLo1iOstlszJ4924JUIuJuKh4i4jbXX389LperxTy73c7FF19MWFiYRalExJ1UPETEbWbOnImfn1+LeS6Xi+uvv96iRCLibioeIuI2Pj4+XH755TgcjhbzLrnkEgtTiYg7qXiIiFtde+21NDQ0AOZIpVdeeeVxa0FEpONS8RARt5o2bRqhoaEANDQ0cO2111qcSETcScVDRNzKbrczd+5cAEJCQpgyZYrFiUTEnVQ8RMTtjhaPuXPnttjfQ0Q6PhUPEXG7MWPGEBcX11RARKTzUPEQEbez2Ww88MADjB071uooIuJmNsMwDKtDiIj1SkpKKCsrO+FUWVlJaWkpLpeLsrIynE4nFRUV1NXVUF1dQU1NNbW1NVRWVjYdsQLQ2GhQVlZ+3Gu5XI2Ul1fj4+OFr6/Xcff7+vrg4+PdYl5oaCgeHp4EB4fgcHgTEBCMt7c3fn5++Pr64uPjQ0BAAP7+/gQHBzdNoaGhLW57eR3/eiLiPioeIh1QfX09BQUFFBQUkJ+fT2FhIYWFheTl5X13PY/8/MMUFhZRVlZBWVnVCZ/H29uD4GBPAgM9CAkBDw8IDm7EbjcIDHTi5QX+/uDjA76+4OcH3i37AsHB5uO+LyQEqqrgmJ7SpKICnM7m24YBpaXgckF5ufmYykqoq/OgutqTmhobtbU2qqqgstKgrKyRigrn8U8M+Pp6ERISSFhYKBERUXTrFkdERATh4eFERUURFRXVdDs6OpqAgIDT/dhF5DSoeIi0M06nk5ycHLKyssjKyiI7O5usrCwyMw9x+HAGWVlZ5OeXcOyPto+PBxERdrp2hchIF+HhLiIjISrKLAbBwRAa2nz96OTjY+EbPUuNjVBWBiUl5uXRqbTUvDxyBAoKIC/PRlGRg8JCG3l5LsrKWhaWwEBf4uK6ExsbT0xMD2JiYoiLiyMmJoaYmBh69OihcUhEzoCKh0gbVF9fz6FDh9i7dy/79u377nI3+/btIiMjF6fTPN+J3W6jWzcHcXEQE9NATIxBbCzExkLXrhARYV4GBlr8htqRujooLDxaSuDwYcjOhowMOHzYTna2J5mZTqqrm885ExUVSu/evenTZwC9e/duMQUHB1v4bkTaHhUPEQvV1dWRlpZGamoqO3bsYMeObezenUpGRi4uVyMAkZEO+vSx0adPPb17Q+/eZrHo0cMsFZ6eFr+JTqq42CwkmZmwfz/s3Qv79nmyb5+djIx6nE7zV2tERAh9+/Zl4MDzGTRoEAMHDmTQoEE6KZ50WioeIm6Sn59PSkoK3377LTt2bGfHjq3s3XsQp9OFl5cHSUleDBxYR//+Bn360FQygoKsTi5nqqEBDh6EffvMQrJ7N+zcaWfnTigpMTflREeHM3DgYAYPHsqgQYMYMWIEiYmJeJxohxiRDkTFQ+QcqK6uZsuWLaSkpJCSspGvv15PRkYuNhvEx3szaFADAwY0MngwDBwIffuCxtHqHLKzYedO2L7dvNy504u0NCd1dY0EB/szYsQIkpNHM3LkSJKTk+natavVkUValYqHSCsoLy9n7dq1rF69mnXrVrF9ezpOp4vISAfJyY0kJ7tITobkZHMnTpFjNTTAt9/Cxo2QkmIjJcXB7t31GAbExXVl7NhJTJ48hUmTJpGQkGB1XJGzouIh8iNUVVXx5ZdfsmbNGlav/pTNm7+lsbGRQYN8mDSpllGjYORI6NnT6qTSXpWVwTffmGVk7VpPvvwSqqtd9OjRjcmTL2LSpMlMnjyZ7t27Wx1V5IyoeIicpiNHjrBs2TI++eRDli9fTlVVLQkJDqZObWDMGJgyBfQdIOeK02muFVm1ClatcrBhQyO1tS769+/DzJlzuPTSSxkzZgw2m83qqCKnpOIhcgoHDhxg6dKlfPjhe3z11Ua8vT2YNs3G7NlOLroIoqOtTiidVXU1rF8PH34IH31k5/BhJ/Hx0cye/RNmzZrFuHHjtKOqtEkqHiLfU1tby8cff8zzzz/L55+vIzTUkylTXFx6qcHll2tMDGmbUlPhnXfgk0+82by5ju7dI7n++gXceuut2i9E2hQVD5HvfP3117z44r9ZsuQtamtrmTXLxoIFLqZNA7vd6nQipy89HV5+GV591UFBgZPJk8ezcOH/Y86cOXh/f0x7ETdT8ZBOb8OGDfzhD79h9er19OvnYP78BhYsgMhIq5OJnB2XC9asgeef9+SDDwxCQ0O4/fY7ueeeewjSADFiERUP6ZQMw+Cjjz7iT396kE2btnHxxXZ+8xsnY8ZYnUzk3MjJgb/9DZ57zhM/v0B+8YsHuOOOO1RAxO1UPKTT2bFjB7feuoCNGzcza5Yn//M/LoYNszpV6/vmG3OwKoBLLgGdDf7kduwwRxkF8+ikjvxdXFQEf/87PPOMHbvdn8cff5J58+bpaBhxG+3yLJ1GbW0tv/3tbxk27Hxstu1s3Qrvv98xSwfAY4/BnDnmVF5udZq2oawM7rkHPvmk5fwXX2z+rDIyrMnmLuHh8Kc/waFDTq6/voybblrAhRdOZv/+/VZHk05CxUM6hW+//ZYhQ/rzzDN/5YknXGzY0MCQIVanEndatw769IEnnjBHCu3sQkLMNR9ffWVQUPAlgwcP4LnnnrM6lnQCKh7S4X388ceMHTuK6OgsUlOd3HEHaHiDzmfrVvN09wDf36pw993wf/9nTr16uT+blZKTYdOmBu67r46f/vR27rjjdhobG62OJR2YDhKUDm3ZsmVceeUc5s1z8Y9/GG32RGxOJ3z8MWzbZm6DDwiAfv3g8sshOLjlsuXl5ngNBw5ARYV59M3o0TBp0vFfqCeydKn5OIDzzuNHr/nZuRNWroS8PPP1p083R9ZMTzdPeHftteZyX37ZvP/ElVeCv3/zcyxeDHV1EBYGM2e2fP7t2+GLL+DQIUhKgvHjzcvvO53PY/VqSElpfsyaNeZml5kzzdcuLDTPIAuQmAh+fi1fw+Uyi8u6dZCfb57Yb/Lk40eq3bgRdu0yD7++7joz+2efmc89YIC5KSck5Aw+ZDdxOOChh8z/D3PnPk99fT3PP/9v7fch54Yh0kGlp6cbgYG+xsKFHkZjI4ZhtM3J6cQYORIDjp/69MHYt6952bVrMcLCTrzsT37S8nmvvrr5vsJCc95f/tI8b+hQjLKyH5f5sccw7PaWr3/hhRg332xe9/dvXvbGG5uXycho+Tyhoeb8885rnudyYfz2txgeHi2f32438x/7b3m6n8fMmSdeZutW8/677mqet317y4x792LExBz/2IAAjH/8o+WyP/2peZ+vL8bSpRh+fi0f06NHy3/Ptjh98gmG3e5hPPbYY9//kRJpFSoe0iE1NjYaF1wwzLjgArtRV2f9L/NTTY8+2vzFdPHFGHffjTFsWPO8uXObl7UUDmgAACAASURBVI2NNeclJJhfzk88gTFpUvOyr7128uLx+usYNpt5OympuYyc6fTFFy2/TEeMwBg92rzu6Xn2xeOFF5qX79IF45ZbWn7xv/32mX8eP/85RnR08/yePTGGDMHYtevUxePAgebXAIxRo8x/o2MLxUsvHV88bDazOCUnm6/ds2fz8rfcYv3/uR+aFi3CcDg8jT179pzgp0vk7Kh4SIe0bNkyw8PDZmzZYv0v8R+ann8eY+FC8wvq6LzKyuYvt2HDzHm5uc1fXjfeSFOhqq3F+PWvMf7zH4zU1BMXj7ffxvDyav6rOyvrx+edMKH5eV95pXn+4sXN8/38flzxqKvDiIw054WEmJ+DYWA0NGDExZnz+/Uz13qc6efx9783L//++y1znKx4XHtt8/y//715fnp68+cZGopRXNyyeADG7NnNy+/d2zx/5Ejr/8/90OR0Ygwc6DDmzbveEGltKh7SIc2bd4MxbpzDsPoX+JlOeXkYH3xgrvU4WjwSE837GhtbblYICcGYNQvj6acxDh48/rmOLR4+Ps3XP/zw7DIGBzevZfj+fX36nF3xSEtrXvaKKzCKipqn225rvi8n58w/jx9TPLp1M+d5e2NUVLR8zEUXNT/m00/NeccWj88+a7l8eLg5v3dv6/+fnc707LMYgYG+Rm1trSHSmrRvv3RIO3duYcyY9nHMpNMJf/4zDBsG3brB7NnmYY7V1eb9R/fvs9nM8SaOnjemtNQ8M+mdd0J8PFx0EZxsKIba2ubrf/vbj89aUmLulAlwovOO9ehx6scbRsvbTmfL23v3Nl9/7z1zzImj07FHeh4+fHafx+k4eBByc83rEyeaO/we69JLm6+nph7/+IiIlreP7rDqcv34TO40ZgxUVNRw6NAhq6NIB6OjWqRDqq6uxtfX6hSn56qr4IMPzOvjx5vFY/Jk8wiQfftaHvo7e7Z5pMQLL8CKFbB5c/MX2Wefmffv2HH8a/j6mkd5LF8Oa9fCu++az3+mAgPNL3qns2WZOaqk5NSP//74Gd9/jmOPOjrvPBg+/MTP4+NjXv7Yz+N0dO1q5mloMIvO9x0dFRYgNPT4+79/Lrb2dgj30aOPqo82YJFWouIhHVJcXDx79hzCXOvddmVnN5eOOXPMv/KPKi01L4+u8TAMc/ndu2HBAnjwQXPtw6efwv33Q2ameYhrXp75pXmst9+GceOgd284csRc/tJLm7/AT5fdDj17moUoPR1qamgqePn5kJZ2/GOOHX786NoSMM8d8v0icuxalMBAs1AclZpqrnWIizM/kzP9PI49MvR0hqnw9TXLzzffmM9z4EDLfB991Hx90KDjH9/ej0TdtQtsNhtxcXFWR5EOpp11cJHTM2PGTJYvtzeNV9FWHfuXdEVF86aIf/7THM8Dmoc7/+AD80t32jRYuND80g8Ohssuay4aPj7QpcvxrzNqlDl+xO9/b94+dMgcUv3HuPxy8/LIEZg/39w8UlgIN95oZvq+Y8feePFFc21JcTHcfvvxyyYmwtCh5vUNG8zC5HKZw5iPGWOWnvPOg/r6M/88jj1Xzc6dZnn6oaHkJ09uvn7HHeZ7LSoyhxxPTzfnT5jQnLkjWbzYg+Tk8+lyov9QImfD6p1MRM6F4uJiIzQ00Pj1r63fSe9UU3V1y8M8e/RoPvTy6DgZfn7mUQYuF8bEic3Lenubh4QePboCaPF+TzSOR309Rt++zYe8ZmefeeaSkuYdJcE8bNRmMydf3+N3Lt2xw8x6dPmgIPOw2+Dg5kNVjz2cdvXqloerhoc3j+lht2N8/bW53Jl+HqtXHz8Wx9EdQE+2c2l9PcaVVx7/uKNTly7mEStHlz9259L09Jaf29F/1/h46//f/dC0aROGp6fNWLx48Ql/vkTOhtZ4SIcUGhrKX/7yKIsW2fj0U6vTnJyvr7l5pU8f83ZGhvkX9aJF8Oij5rzqanPkTQ8PWLYMfvELczNEXZ05Umh9vbnz5WOPmX+Jn4rDAX/9q3m9qgp+9aszzxwSAuvXN4942thojhb67rvNmxyO3cwwcCC89RZERZm3Kypg8GDzOfr2Pf75J02Cr74yd7a1283Pw8vLXLPxxhswcqS53Jl+HhMmmJuzjvLy4gfXiDkc5uiqv/xlyzU33t5wxRXm5p/evU/vc2sviovhmmscTJgwjquvvtrqONIB2Qzj+/uZi3QcN944j3fffZNly1xMmGB1mpNrbDQ3f9TVmZsbfmhHxKM7PBYVQXS0eTSMFfsU5OZCZWVzcRo50hya3N/fnP99e/eaQ5Sf7tr72lrzMb17c8qdhc/k88jLM5dLTOSMh9AvKoKCArMw2TvgHnIlJTBtmoPCwnBSUrYSdbQtirQiFQ/p0JxOJ9ddN5cPP1zKv/7VyPz5Vifq2H6oeEjbtWsXzJrloLY2nDVrNpBwouOlRVpBB+zsIs3sdjuLFy/hoYceZMGCh3n3XRvPPdd43Mm9Oqvdu83DeU/X66+bm0mk42hshH//G+69187AgUN4//2P6fr9w6JEWpGKh3R4NpuNBx98iIkTJ3HrrQsYODCbRYuc3HJL+z/k8WzV15uHiZ6uurpT3+/vbx4++/2zu0rbtGMH3HKLgy1bGrnnnnt56KGH8P7+ACQirUybWqRTqaqq4ne/+x1PPfUkI0d68LvfOZk+3epUIu514AA88oiNl1+2kZw8ghdeeIl+/fpZHUs6CR3VIp2Kv78/f/vb39i4MYWQkCnMmAEjRjj44IPjh/MW6WjS02HePA8SE22sXh3Lc8+9wLp1X6l0iFupeEinNGzYMJYt+y+bNm0iNnYGc+bYGDzYwTPP/PCw3yLtictlHnZ8+eWeDBxoY9OmBP7zn1fYtWs/CxcuxKO9jeUu7Z42tYgAO3bs4Ikn/saSJYtxuRq4/HKDhQsbmTy5/Z1jQwTMkVlfegleecVOTo6LCRNGc8cddzNnzhyVDbGUiofIMSoqKnj77bd56aXn+eqrb+jRw8FPftLArFlwwQUqIdK2ZWSYQ8kvXWpn/Xon0dGR3HjjLdx444307mgjnUm7peIhchLp6em88sorLF36Nnv3HiIqysFllzmZPdtgypTjzz4qYoVvvzXLxocferF1az3BwX5ccsllXHfdDVx00UV4enpaHVGkBRUPkdOQmprKhx9+yAcfvMOmTd8SEODJxIkGkye7mDTJHNuisx+aK+6Rmwtr1pjD6H/+uReHDtXTvXskl112BbNnz2bixIl4HXtGPJE2RsVD5AxlZ2fz8ccfs2rVStauXc2RI2WEhzuYONHF5MmNTJpkDsetIiKtoagI1q0zi8aaNV6kpdXjcHgycuQwJk+eziWXXMKIESOw6T+ctBMqHiJn6cCBA6xatYpVq/7LypUrKS2tJCjIzqBBBsOGuRg7FsaPbz5JmsjJNDTA9u2wYQNs3mxj82Zv0tNr8fDw4LzzBjJmzETGjh3LhRdeSHBwsNVxRX4UFQ+RVuR0OtmyZQsbN24kJSWFlJQv2bv3EIZh0KOHNyNHNjB0aCMDB5pnbe3Rw+rEYpXSUti505y2bYOUFAc7dzppaDAIDw8iOXkkycljGDFiBKNHjyYkJMTqyCKtQsVD5BwrKSn5roSkkJLyf3z77RaysvIBCA62M2CAjUGDGhg0yCwj/ftDRITFoaXVVFeb58RJTTWHKN+xw5OdOz3JyqoHICjIj0GDBjJixGiSk5NJTk6mV69eFqcWOXdUPEQsUFZWxr59+0hNTWXz5s2kpX3L9u3fUlBQCoCPjwcJCZ4MGOAkIcEgIYGmKT5e+4+0NXV1cPiwORR5aiqkpcGBAw4OHPDg0KF6GhsNHA5P+vSJZ8CA8+jffwADBgygf//+9OvXT+NqSKei4iHShmRlZbF792727dv33bSHffvS2b8/k9rao38h24mL8yQuzklMjIuYGIiLg9hYiIkxL319LX4jHYjLBXl55hgZ2dnmlJkJ2dk2srMdZGZCbq75b+PhYSM2tiu9e/eld+8kevfuTe/evenbty99+vTB4XBY/G5ErKfiIdIOGIZBdnZ2UyHJysoiIyOD7OyDZGdnkpmZ21RMACIiHERFeRAZ2UhUVAMREebmm6goiIyk6XZkJHTGfRRrauDIESgogPx8KCw0p7w887KoyJOCAk9ycyEvrwGn0/w16enpQdeuXYiLiyMmJp6YmFh69OhBfHw8ffr0ISEhQWd3FfkBKh4iHURhYSHZ2dlNpaSwsJD8/HwKCvIoLMylsLCA/PwiysqqjntsaKiDkBAPgoMhOLiRkBAXwcGNBAdDSAgEBpoFxW43rzscEBBgDqLm52euYfHxAX9/OHYIiaPLnan6eqj6XsySEmhshLIycDqhoqJ5udpas0zU1JjzS0vN5crKoKTEQWmpx3e3GyktdVFf39jiuX18vIiICCUyMpKoqGjCw6OIiIigW7duREdHExsbS1xcHNHR0djt9jN/QyLSRMVDpJOpq6ujqKiIgoICCgsLKS0tbZrKysooKytjzZo1dOkSCjgpKyulsrKSkpIyXK5GysurzzpDYKAdu93cUaWszElj49n9GvLx8cLX1wt/fz8CAvwJDg4hODiUkJBwQkJCCAkJITg4mODg4KbrYWFh3xWNKAIDA8/6PYnI6VHxEJEmLpeLm266ibfeeoslS5Ywa9asEy7X0NBAZWUltbW11NTUUF1dTV1dHRUVFTidzqblampqqK2tPe7xJcecAtjf37/FSJuHDh3Cz8+PxMTEFo8JDg7G09OTkJAQPD09CQoKwsvLC39//7N92yLiRioeIgJAfX091113HcuXL+e9995j+vTpluRYsmQJzz33HJ9//rlG4xTpgHQMl4hQXV3NrFmz+O9//8vHH39sWekA88ieNWvW8Oabb1qWQUTOHRUPkU6usrKSmTNnsnHjRlatWsXkyZMtzZOVlQXAnXfeSXFxsaVZRKT1qXiIdGIlJSVMmzaNtLQ01q5dy8iRI62OREZGBjabjcrKSu6//36r44hIK1PxEOmk8vLymDhxIrm5uaxfv55BgwZZHQkwT7pnGAYNDQ289NJLrF692upIItKKtHOpSCeUkZHBtGnT8PDwYNWqVcTExFgdqUmXLl2aNrF4enoSFxdHenq6BuYS6SC0xkOkk9m9ezfjxo0jICCAdevWtanSUVdX1+JQW5fLRWZmJn/9618tTCUirUlrPEQ6kdTUVKZNm0ZMTAwrVqygS5cuVkdq4cCBAyc8M6vD4WD79u0kJSVZkEpEWpPWeIh0Et988w0TJkwgMTGRzz//vM2VDmg+ouVEbr75ZvR3kkj7p+Ih0gl88cUXTJkyhVGjRrFixYo2O0R4VlbWCU8R39DQwFdffcWrr75qQSoRaU0qHiId3CeffMKMGTO49NJLWbp0KT4+PlZHOqnMzMxTnoTtrrvuorCw0I2JRKS1qXiIdGCLFy9mzpw5zJs3j9dffx2Hw2F1pFPKyso66eYUwzCorq7mvvvuc3MqEWlNKh4iHdTzzz/Pddddx2233cZzzz13wk0YbU1mZiYNDQ0nvb+hoYHXXntNY3uItGNt/zeRiJyxZ555httuu43777+fp556qt2cbO3AgQPHzfP09Gza/OLt7c2oUaPYv3+/u6OJSCvR4bQiHcyiRYv49a9/zWOPPcY999xjdZwzEhQURFVVFYZhYBgGwcHBVFRU8OijjzJ+/HjOO++8U+4DIiJtn36CRToIwzC47777ePLJJ3nhhRe46aabrI50RmpqakhKSmL06NGMGjWKMWPGUFVVRVJSEmPHjmX48OFWRxSRVqDiIdIBuFwubrvtNl555RXefPNNrr76aqsjnTFfX19SUlJazDMMg5CQEDZt2kRycrJFyUSkNal4iLRzTqeThQsXsmTJEpYsWcLs2bOtjtRqbDYb559/Pps2bbI6ioi0EhUPkXasrq6Oa665hpUrV/LJJ58wdepUqyO1uhEjRrBixQqrY4hIK9FRLSLtVFVVFZdeeilr165l5cqVHbJ0AAwbNoy0tDSqqqqsjiIirUDFQ6QdKi0tZdq0aezYsYM1a9YwatQoqyOdMyNGjMDlcrFt2zaro4hIK1DxEGln8vPzmThxIocPH2b9+vUMGTLE6kjnVHx8PBEREXzzzTdWRxGRVqB9PETakczMTKZNm0ZDQwNr1qwhISHB6khuMXToUDZv3mx1DBFpBVrjIdJOHDx4kEmTJmG329mwYUOnKR0Aw4cP1xoPkQ5CxUOkHUhLS2Ps2LGEhYWxbt06oqOjrY7kVsOHD2fPnj2UlpZaHUVEzpKKh0gbt2nTJiZMmECfPn34/PPP6dKli9WR3G748OEYhsHWrVutjiIiZ0nFQ6QNW7duHVOmTCE5OZkVK1YQFBRkdSRLxMTE0K1bNw0kJtIBqHiItFHLly9n+vTpzJgxgw8++ABfX1+rI1lq2LBh2sFUpANQ8RBpg44OfX7llVfy+uuv43A4rI5kufPOO09jeYh0ACoeIm3M66+/znXXXcett97Kyy+/rNPAf2fIkCHs27eP6upqq6OIyFlQ8RBpQ5599lnmz5/PvffeyzPPPIOHh35Ejxo8eDAul4vU1FSro4jIWdBvNZE2YtGiRdx5550sWrSIRx55xOo4bU7v3r0JCAjg22+/tTqKiJwFrcMVsZhhGDzwwAM8/vjjPPnkk9x5551WR2qTPDw8GDBgANu3b7c6ioicBRUPEQsZhsHdd9/Ns88+y0svvcT8+fOtjtSmDRkyRGs8RNo5bWoRsYjL5WLBggX861//YsmSJSodp2Hw4MFs374dwzCsjiIiP5KKh4gF6uvrufrqq3n33Xf5+OOPmTNnjtWR2oUhQ4ZQWlpKVlaW1VFE5EdS8RBxs6qqKmbOnMnq1av57LPPmDZtmtWR2o3Bgwdjs9m0uUWkHVPxEHGj0tJSLrzwQrZt28aaNWsYPXq01ZHalaCgIHr27KniIdKOaedSETcpKCjgoosuIj8/n88//5yBAwdaHaldOrqfh4i0T1rjIeIGubm5TJkyhbKyMtavX6/ScRaGDBmi4iHSjql4iJxjhw4dYty4cbhcLtavX0+vXr2sjtSuDR48mL1791JVVWV1FBH5EVQ8RM6h9PR0xo4dS0hICOvWraN79+5WR2r3hgwZQmNjo4ZOF2mnVDxEzpHNmzczfvx4EhISWL16NeHh4VZH6hASEhIICAjQ5haRdkrFQ+QcWL9+PZMnT2bw4MEsX76coKAgqyN1GB4eHiQmJrJ7926ro4jIj6DiIdLKVq9ezcUXX8zEiRNZtmwZAQEBVkfqcJKSkti1a5fVMUTkR1DxEGlFH330EZdccgmzZs3ivffew8fHx+pIHVJiYqKKh0g7peIh0kreeOMNrrjiChYsWMCrr76K3a5hcs6VpKQkDh48SG1trdVRROQMqXiItIJ//vOfzJs3j3vvvZd//OMfeHjoR+tcSkpKwuVysW/fPqujiMgZ0m9HkbO0aNEifvrTn/L73/+eRx55xOo4nULfvn3x9PTU5haRdkjrgkXOwoMPPsgf//hH/v73v3PXXXdZHafT8Pb2pmfPnioeIu2QiofIj2AYBvfccw9PP/00L774IgsWLLA6UqeTlJSkQ2pF2iEVD5Ez5HK5uOWWW3jjjTd4++23ueKKK6yO1CklJSWxdu1aq2OIyBnSPh4iZ6C+vp5rrrmGt99+m48++kilw0JHD6k1DMPqKCJyBlQ8RE5TdXU1l112GStXruTTTz/loosusjpSp5aUlERlZSWHDx+2OoqInAEVD5HTUFlZycyZM/nmm2/49NNPGTt2rNWROr3ExEQA7WAq0s6oeIj8gJKSEqZOnUpaWhpffPEFI0eOtDqSAJGRkXTp0kXFQ6Sd0c6lIqeQl5fHhRdeSHl5OevXr6d3795WR5Jj6GRxIu2PiofISWRkZDB16lTsdjsbNmwgJibG6kjyPTpZnEj7o00tIiewa9cuxo4dS2BgIOvWrVPpaKNUPETaHxUPke/ZunUr48ePJzo6mlWrVhEREWF1JDmJ+Ph4cnJyqKurszqKiJwmFQ+RY3zzzTdMnTqVgQMHsmrVKsLCwqyOJKcQHx9PY2MjmZmZVkcRkdOk4iHynS+++IIpU6YwevRoli9fTmBgoNWR5Af07NkTgEOHDlmaQ0ROn4qHCPDJJ58wY8YMLr30UpYuXYqPj4/VkeQ0dOnShaCgIBUPkXZExUM6vbfeeos5c+Ywf/58Xn/9dRwOh9WR5Az07NlTxUOkHVHxkE7tX//6F9dffz2/+MUv+Oc//4mHh34k2puePXty8OBBq2OIyGnSb1nptJ5++mluv/127r//fhYtWoTNZrM6kvwIWuMh0r6oeEintGjRIu666y4ef/xxHnnkEavjyFnQGg+R9kUjl0qnYhgG9957L0899RQvvPACN910k9WR5CzFx8eTn59PdXU1fn5+VscRkR+gNR7SabhcLm655RaeffZZ3nrrLZWODqJnz54YhqGxPETaCRUP6RTq6+u59tpref3111myZAlXXXWV1ZGklcTHxwNoc4tIO6FNLdLh1dXVcc0117Bq1SqWLVvGlClTrI4krSg4OJiQkBDtYCrSTqh4SIdWVVXF7Nmz2bx5MytXruSCCy6wOpKcA/Hx8SoeIu2Eiod0WCUlJVx88cUcPHiQL774gsGDB1sdSc4RHVIr0n5oHw/pkPLz85k4cSI5OTmsX79epaODi4+P1z4eIu2Eiod0OJmZmYwbN466ujo2bNhAnz59rI4k55jWeIi0Hyoe0qHs3r2bsWPH4uXlxerVq4mNjbU6krhBz549KSwspKqqyuooIvIDVDykw0hNTWXy5MlERUWxdu1aoqOjrY4kbnL03zonJ8fiJCLyQ1Q8pEPYtGkTEyZMoE+fPqxevZouXbpYHUncSMVDpP1Q8ZB2b+3atUyePJkLLriAFStWEBgYaHUkcbOoqCjsdruKh0g7oOIh7dqyZcuYMWMGl1xyCe+//z6+vr5WRxILeHh4EBUVRW5urtVRROQHqHhIu/X2229z+eWXc9VVV/Haa6/hcDisjiQWio6OVvEQaQdUPKTNeuedd/jqq69OeN9rr73G9ddfz6233srLL7+M3a6x8Dq76OhobWoRaQdUPKRNamho4L777uOiiy5i27ZtLe579tlnmT9/Pvfeey/PPPMMNpvNopTSlqh4iLQPKh7SJr300ktkZ2dTU1PD5MmT2bVrFwCLFi3izjvv5NFHH+WRRx6xOKW0Jd26dVPxEGkHtH5a2py6ujr+8Ic/YBgGjY2NVFRUMGHCBGbNmsV//vMf/vWvf3HLLbdYHVPamK5du5KXl2d1DBH5AVrjIW3Oc889R2FhIYZhAOB0OikuLubFF1/k6aefVumQE4qMjKS8vJy6ujqro4jIKah4SJtSVVXFww8/jMvlajHf6XTi4eHBY489Rn5+vkXppC2LiIgAoLCw0OIkInIqKh7Spjz99NOUlpae8D6n00lWVhZTpkw56TLSeal4iLQPKh7SZpSXl/PII48ct7bjWA0NDaSnp3PJJZdQXV3txnTS1kVGRgIqHiJtnYqHtBlPPPHED55d1OFw0NjYiJeXFwcPHnRTMmkPgoOD8fLyoqCgwOooInIKKh7SJpSUlPDYY4/hdDpPeL/D4cBut3PZZZexceNG1qxZw4ABA9ycUtq6iIgIrfEQaeN0OK20CYsWLaK2trbFPJvNhoeHB97e3tx8883cf//9xMTEWJRQ2gMVD5G2T8VDLFdYWMiTTz7ZtLbDw8MDwzCIiYnh3nvv5eabb8bf39/ilNIeREZGqniItHEqHmK5P//5z9TW1mK323E6nSQnJ/PLX/6Syy67DA8PbQ2U0xcWFkZJSYnVMUTkFFQ8xFJZWVk899xzeHp6cvnll3P//fczYsQIq2NJOxUSEsLevXutjiEip6Di0Y7U1NRQXV1NWVkZLpeL8vJywDwM1eVyUVNTQ21tbYv7ysrKaGxsxOl0UlFRcdLnPrrc6QgMDDzp2WC9vb3x8/MDICgoCE9PT3x8fPD19cXDw4Pg4OAW9z3zzDPccMMN3HrrrfTt27fpfpEfIyQkRGO8iLRxKh5u4HK5KCkpobi4mOLi4qbrRy8rKiooLy+noqKCqqoKqqsrKCk5QlVVFVVV1VRWVlFWVkVjo3Far2ezQUiIA4DAQBt2u3n21pAQ874T8fNrxNv7h5/bMAxKSz2AEz9RZSU0NJjXS0sbMQyornZRV3fqUvPvf/+76XpgoC/+/r74+fkSGhqKv38Afn4BBAaGEhwcjJ+fH4GBgYSFhREaGkpYWFiL66Ghofj6+v7wm5EOJzg4WMVDpI1T8fgR6urqKCwsJDc3l/z8fAoLC8nJyaGwsJCCggIKC3MoLi76rlyUU1Z2/NgUXl4ehIbaCQuzERgIQUGNBAY6CQoy6NoVQkPBzw/8/fnu/ubbISHmc4SGmpcBAeBwgLe3uYypwS2fxZloaDCLCUBpKRgGVFVBfT1UV5vXKyqgvLyGqqoaqquhtPQwVVXmfZWVkJlpp7rag4oKG8XFBiUlLiorjx9wzNfXi9DQIMLCQgkL60JoaARdu0YTFRVFZGQk3bp1IzIykoiICKKjowkMDHTzpyHngtZ4iLR9Kh7fk5eXR3Z2NllZWWRmZpKZmUlOTg45OdkUFuaRl5dPSUnLTRb+/p506+YgKgoiIuqJj29k+HCzGISFNV8eez0goBGot+ZNWsThaC5LRy/P3PHjfNTXQ3ExlJSYl+b1eoqLiygpKaK4eC/FxbBnjwcbNtgpKDAoLGxZzHx9vYiMDKNbtygiI7sTE9OTmJgYYmNj6dGjBzExMXTv3h0vL68fG1zcICQkhLKyMgzDwHay1XsiYqlOVTwaGxvJzs5m37597N+/v6lYZGYeIjs7g6ysHOrqmr+Qunb1IjbWRvfuDQwa1EhEGAvDCQAAIABJREFUBERFQbduEBEBXbuak5+fCzj5MN9ybnl5Nf9bnFpz2WtogMJCyM+H3FwoLKwnLy+PvLw8Cgu/ZccOOytW2Dh82EV9vbmZyGaz0a1bGLGxscTG9iImxiwlCQkJ9OnTh4SEBLxPZ3uVnDMhISE4nU4qKyu1FkukjbIZR8893kE4nU4yMzPZt29fU8HYt28P+/als39/ZlOxCAy006OHJz16OImNdRETA3FxEBtrTjExnNY+D9KxGQbk5UFmJmRnQ1YWZGRAdraN7GwHGRmQm2uWGQ8PGzExUfTu3YdevZLo3bt309SrVy+NReIGX3/9NaNGjSIzM5PY2Fir44jICbTr4pGTk0NaWhqpqamkpaWSmrqFrVt3Ul1dB0BoqJ2EBA8SEhpISDBISKBpio8/+Y6WImeirg4OH4YDByA1FdLS4MABBwcOeJCRUY/LZf6IdesWzoABg+nffyADBgygf//+DB06tOkoIDl76en/n737jovqSv8H/pkK0geQrghCRMAOmlgRSzR2IyHGFGNiNMao2d20jbub/HbzjRvTTHGziSYmu8lqTDRq1NixJSoqShEUBAu9DXWAYWbO74/jcBmaDMXLwPN+ve6LmXPv3HnuncvcZ84959xkBAcHIyEhAaGhoWKHQwhpgkUkHhUVFbh48SIuXLiAxMREJCTEITk5BRUVVQAAX18rhIToEBqqR0gIMGAAEBAAuLiIHDjp8aqrgevXgdRUnpAkJABXriiQksIv4chkUvTv74NBg4YjJGQwhg0bhvDwcHh7e4sdukW6desWfH19cebMGYwaNUrscAghTehyiYdWq8Xly5cRGxt7ZzqNlJTr0OsN6N1bgcGDGUJDdQgJAUJDgZAQ3uODEEtSW8uTkcREPl25IkV8vBzXr9fCYGDw9HRFePj9CA8fhfDwcISFhcGFMum7KioqgqurK44ePYqJEyeKHQ4hpAmiJx5FRUU4ceIEYmJi8NtvxxEfnwStVgdHRzlGjJAgPLwW4eFAeDhvg0FId1ZWBly4AJw/D8TGShAbK8eNG7xdUv/+3hg1ahzGj49AREQEBgwYIHK0XU9VVRVsbGywZ88ezJw5U+xwCCFNuOeJh1qtxokTJ3Ds2DHExBxCQkIyJBJgyBAFxo3TIjwcCAsD7ruP2mAQAvDeNzwRAX7/XYZTp4CKCj28vFwQETEFERGRiIiIQGBgoNihio4xBoVCge+++w7R0dFih0MIacI9STwuXryIXbt24ZdfduDSpSQAPNGIiNAiIgIYP14YFIsQ0jKdjichMTFATIwcp08zVFbq4e3dG9Onz8acOXMxadKkHjt6q729PT7++GM8/fTTYodCCGlCpyQetbW1OH78OHbt2oXdu3/CrVs56NPHCrNn12DKFJ5otH0AKUJIfbW1PBE5dgzYs0eO2Fg9evVS4sEHH8Ts2fMxc+bMHtU+xN3dHX/5y1+wcuVKsUMhhDShQxOP06dPY9OmL/Hzzz+hpKQCQ4YoMWeOFnPmAMOHd9S7EEJakpMD7N4N7Nolw9GjDDodMGHCWCxe/CwWLFjQ7WtC/P39sWzZMrz66qtih0IIaYK0vSsoKCjA+++/j+DgQIwdOxaXL/8Pb75ZgYwM4NIlLd56i5IOQu4lT09g2TJg3z49CgoM+N//DHByOoVnnlkMLy83rFy5EnFxcWKH2WlsbGyg0WjEDoMQ0ow2Jx6xsbGIjo6Cj48X/v731xARcR0XLgAXL2qxejXQr18HRkkIaRN7eyAqCvjpJwMyMw14440KHDnyBYYPH46wsMH45ptvUFvb9W4o2B6UeBDStZmdePz222+YPHkiRo4ciYyMXfjySx2ys3XYuJFRzQbp8hISgJ07+VRWJnY095abG/CnPwHJybU4eRIYODARS5cuQWBgP3z++efQ6RrfgM8SyeXybrMthHRHrU48bty4gaio+Rg7dix0ulM4dAg4d64WTz5Z/1bshHQNpaXAH/4A/PKLafnmzcD8+Xy6eVOc2LqCsWOB//yHITXVgJkzc7B69QsYNCgI+/btEzu0dlMoFJR4ENKF3TXxYIzhs88+w6BBwUhM/AW7dzPExOgwefK9CI8Q8504AQQGAh9+yHt8kOb5+gKffsqQnGzA4MEZmDFjBh5//DEUFxeLHVqbdWSNh0qlwj//+c8OWVdXUFhYiC1bttQ9727bRyxDi4mHRqNBdPQCrFmzCqtXV+HSpVrQYICkq4uL44NuAY0HoVuzBvj9dz7173/vY+uq/P2BbdsM2LsXOH78R4SFDUFCQoLYYbWJXC7vdu1WOsqyZcvwww8/1D1ftGgRBg0aJGJEpCeSNzejoqICU6dGIjU1DocOGRARcQ+j6kA6HbBnD3DpElBYCNjZAQMHAvPmAY6OwnJnzwIpKYBcDixaBNy4ARw8CFy9yu8HM39+40HOysqA7dv5XUnLy/k19NGjgYkThRPezz/zan8XF5gkbbGx/KZhADBnjrDuwkJg717+eNgwYPBg4TXx8XzQqBs3gKAgPh5KUJBpTDEx/BKCvT0waxbw9df8Vu5TpwLjxrVtHyYmAocO8dvDjx4NTJsGXL4MJCcDCgXw2GN8udOngbQ0/njBAqD+XeC3buV3cXV25nHV15rtAlq3v48eBc6dE15z7Bjf/7Nm8fcuKOCfKcBvJtjwMqFezxOXEyeAvDx+P6DISKDhPdvacrxYgoceAuLiahEVlYsxY+7HwYNHcP/994sdllnoUkvzDAYDJPWy8U8//VTEaEiPxZqg1+vZ1KmRzN1dwVJSwBizzEmnAxs1CgxoPAUGgqWlCcuuWMHLe/UC27EDzMbGdHlfX9Pljx8Hc3Zuet3R0cJyTzzBy6ytwTQaoXziRGH5774TyjduFMoPHeJlej3YG2+ASaWm7yOXg73zDpjBILx+3jw+z88PbMkSYdmQkLbtw/fe4+9T/32nTgV79ln+2NZWWHbxYmGZmzdN16NS8fKhQ4Uyc7artft71qyml4mL4/NXrxbK4uNNY0xNBfPxafxaOzv+udRf1tzjxdImrRZs9mwZU6nsWVpaGrMkc+bMYYsWLWI1NTXstddeY4MGDWI2NjYsMDCQrV69mlVWVtYte+7cOTZ+/HhmZ2fH+vXrxxYvXsyKi4vr5js5ObF169bVPV+/fj0bOXIku3DhQqvjMRgM7K233mIhISHMx8eHvfHGG+yzzz5jzz33XN0y999/P9u6davJ615//XWTZRhjbPPmzWzYsGHMzs6OhYeHs927d5vMb2l7XnzxRebk5MQcHBzYiBEjWHZ2NouIiGBbtmype71Op2MffvghGzhwYN177Nixw+Q9xo8fzw4cOMBWrlzJfHx8mI+PD/vjH//ItFptq/cJ6dnQVOFnn33GFAopi40V/wuwPdP69cKJ4KGHwNasARsxQihbuLDxiUQi4SfCkSPBVq0C69dPWH7pUmH5Pn14mb8/P3l++KFpMvGf//DlduwQyvbt42UaDZiVlVD+7LPCemfM4GXOzmC1tbzsyy+FZV1ceBz1T5DbtgmvNyYeEomQGMjlYP/3f+bvv5gY05NpeDjY6NH8sUzW/sTDnO1q7f5etQrMy0so79cPbMgQ1CXQzSUe6enCewBgDzzAj5n6CcXXX7f9eLHEqaoKbOhQBRs/fjSzJA8//DB75JFHWHR0NHN1dWXr1q1j27dvZ0uWLGEA2N/+9jfGGGMVFRXM1dWVTZs2jW3dupV9/PHHzMPDg02fPr1uXfUTjy+++IJJpVL29ddfmxXP3//+d2ZnZ8f+/e9/s19//ZXdf//9zMXFhUVERNQto1Qq2ccff2zyukcffdRkmffee4/JZDIWHR3NfvrpJ7Zq1SomkUjqEoO7bc/BgwfZ8OHD2eDBg9nXX3/NysvLGyVWa9euZXK5nK1du5bt2rWLLV26lAEwSU4cHByYj48PGz16NNu4cWPdMuvXrzdrv5CeCw0LdDod69vXk/3hD+J/8bV3+uIL/qt/1SqhrKJCOJmMGNH4RAKAzZ0rlKemCuWjRvGynByhbPFisJoaXl5dDfb662BffQWWlMTLKiv5r2IAbOVKXnbwoOkJvX9/4YveuOzixbyspgbMzY2XOTnx+BnjSUnfvrx84EChdsCYeABgERF8nQUFYMXF5u+/CROEdX3zjVC+datQbmMjlJuTeJizXebsb8bAPvpIWH7nTtM4mks8HntMKP/oI6E8ORlMqeTlKpWwH805Xix5OneOb8uJEyeYpXj00UfZnDlzWGhoKPvss89M5g0ZMoRNmTKFMcbY2bNn79SGxdXN3759O1u1ahUzGAyMMSHx+PHHH5lMJmMbN240Kxa1Ws2kUinbsGGDSZm9vT2bMGFCXdndEo+SkhLm6OjInnrqKZNloqKiWGBgYKu3Z+7cueyhhx6qm18/8bh9+zZTKBTs7bffNnmPxx57jHl4eNTVaBhrTIzrZIyxUaNGscmTJ5u1b0jP1ahx6eXLl3HrVg6WLGk4x/IsXcq7T27YwK/X79oFrF0rzK+oaPp1K1YIjwMCAFdX/rioiP91d+ftBQBgyxb+fO5c4MsvgeeeA55+GggO5vNtbIAHH+SP9+/nf48c4X/79uV/r1/n7TCOHQOqqnjZ/PnCvPx8/njSJKC6msdRWsqvxwO8rUVubuPteOUVwNqax9+We+NcusT/+vsDTz4plEdH814j7WHOdpmzv9vq2DH+18oKeOYZoTwoiLchAQC1mrfNaehux4slCw8HBg1SYs+ePWKH0mpSqRQSiQQJCQlYsWIFGGPIyMjAzp07YTAYUFlZCQAIDg6GjY0NoqKi8N577+HatWtYsGABNmzYYNIOIiYmBosWLUJ0dDSef/55s2KJj4+HwWDA7Nmz68qcnJwwffp0s9Zz6dIllJaWIjw8HBcuXKibgoODkZqaisLCwlZvT0vvUVtbiyeeeMKk/Mknn0Rubi6uX79eVzZx4kSTdQYGBqKspw2MQ9qsUeKRlZUFoHu0+NfpgLffBkaM4MNIz50LfPQRYBzUsLn/xd69TZ8bGyDq9cLrNm/mDQsBoKSEJzUvvgj4+fFEo97/KObN43+vXwdSU4XEY+lSodHisWNCo1I7O2DKFP44NVVYz08/8ZOacfr8c2HenY/NRHuSA7WaJwEATzwa8vVt+fWMmT5v2NbPnO0yd3+bKyOD398EACIi+P6vr36j4KSkxq+/2/Fi6QICdLh9+7bYYbSaXq+HTCZDbGwsHnzwQdjb28Pf3x+vvfYa1Go12J2D087ODgcOHICVlRVefvllDBgwAIMGDcKBAwdM1vfrr7/Cz88Pu3btwk0zB3+5eqcls7u7u0l5nz59zFrPjRs3AAArV65EWFhY3fTWW2/VzW/t9jQnIyMDEokEXl5eJuU+Pj4AhHMDAPRucNBbW1vDYDCYtU2k52rUq8X4D5KVZfnJR1QU71UC8J4Sc+fyHgoLFvDeF9JmOhNbWZk+b2q5uXN5T4Yvv+Q1GRcuCCeagwf5fGNvxJkz+UlTpwO++w64eJGXT57M4/jmG554HD/Oyx96iNdUALzXiNHQoUBYWNMxG5evr+EJ1Bz29kLM1dWN56vVLb++YW/Ghuswd7vM2d/m8vDg8dTWNp3AZWYKj5uqOWrN8WLJMjPlGDPGQ+wwWs1gMECr1WLq1KkYOHAgNm3ahAkTJsDT0xOzZ89GYWFh3bJjx45FYmIiUlJSsGfPHmzatAkzZszA9evX4Xsnu37yySfxySefICgoCM8//7xZg6y5ubkBANRqtcnN+crLyxstq9VqTZ4X37muB/DxNgDg5MmTGN7EENHWd/5RWrM9zXFxcQFjDCUlJSZ3My66U3XnX+8XSGtqUAhpTqOvyCFDhqB3byd8950Y4XSczEwh6Zg/n5/UX3oJGDKE/2IGmq/xuNv/FGP80khyMq/mP3uWV6tv2yZcPklMFC5/ODvzxAcA3n8fMBgABwdejW2s2fjxR/7L2xivUf3aBnt7fuI1TmvW8EtHX3zBu302pFS2vB0tkcuF++0kJwuXgAB+2crYFbg+BwfhsbG2BACysxsnIuZsl7n7u/7n15ofYb168eTHuJ70dNP5u3cLj5sa8qA7fwdfvQqcP6/FFOOBagH0ej0KCwtRUlKCTZs24dFHH4WnpycMBgOSkpKgv5OxnjlzBjNnzoRarUZQUBBefvllfP/999Dr9SY30QsODoaDgwM++ugj7N+/H//73/9aHUtoaCgkEgmOG39V3HH27FmT53Z2diaDthkMhrraEgAYOHAgAGD37t2wsbGpm3744QcsX74ctbW1rdoeiUTSbM1E8J3rlTExMSblMTExsLOzQz+6ARfpII0SDysrK6xa9Ue8+66sbkwGS1T/l2t5uVD1/69/8bEygLbfq+Pnn/kJb8oUYMkSflJ2dARmz+a/ngH+S73ej4a6yy3GdiUREYBMxts31C+3shLaOAB8rAnjD5xTp/jJVq/nY3WMGcOTg6FDgQY/lgDw9beHMeaiIuCpp/jlkYICYPFi00TEqP7YG5s389qS4mKgqcvi5myXufu7fsKVmMhrle72WUdGCo9feIFva2Eh8I9/8IQHACZM6Fl3WjYYgOeflyM0NAjTpk0TO5xWMxgMcHNzg1QqxQ8//IDq6mrk5OTgueeeQ3p6et0N5AYNGoTY2Fj86U9/wo0bN5CTk4MtW7ZAJpPhgQceaLTeRx55BA8++CDWrFnT6pFd+/fvj4ULF+K1115DbGwssrKysGzZMly+fNlkuWHDhmHTpk04e/Ysbty4gRdeeAE5xut/4G0ooqKi8NVXX2Hjxo1Qq9U4dOgQVq1aBR8fH1hZWbVqe+zs7JCcnIyTJ0+iqsE/8dChQzFt2jT86U9/wpkzZ1BTU4Pdu3fj/fffx/Lly6mWg3ScplqcarVaNmLEYHbffQqWmyt+y/q2TBqNabdKX1+hq6NxXAobGz7WR8NeCsnJpusyvs7Pjz/X63mPEePyVla8y6ax9wPAe1vUX8ft20IXVwBswwZh3qBBQvmsWY235ehR026drq7C2BdyOdiZM8Ky9Xu1lJS0bx+q1fy9jOuTSvk2SCRC75v6vVoSEky7CTs48G63jo5CV9X63Wlbu13m7u+jRxuPxXHwIJ/XXK8WrRZswYLGrzNOLi68x4pxeXOOF0ucDAawZcskzNpawS5evMgsyaxZs9jjjz/O/t//+3/M29ubKZVKplQq2QsvvMDef/99plQq68a22LlzJxszZgyTy+UMAPPx8WH79++vW1fD7qZpaWnM2tqaPf30062Op7S0lD366KNMKpUyAGzMmDFs4sSJJr1a4uPjWVBQEAPAZDIZe/TRR9krr7xi0p1WrVazJ598si5WT09PtnTpUqbRaOqWudv2HDp0iDk4ODAALCYmptH2FRQUsAULFjCpVMokEglzc3Njr776qsn2ODg4sHfffdek7Nlnn2VhYWGt3iekZ0NzM/Lz89nAgQGsf38FS0gQ/4uwLdPvv/OBwownCDs7sH/+k48B0fCEZO6JpLIS7KWXwOztTU9Qrq580C29vnE84eHCcvW7f/7hD02PFVF/unSJd/81Jk3W1mBTppiOdcFYxyYejPF9MWSIsE53d7CffuLjVgCm43gwxsctcXcXxrgYNoyf4CdNapx4mLNd5uxvvR5s/nxhGaWSx8xYywOI6XRgr74KFhRkmuQ8/DAaJeDdOfGoqgJ78kkpUyhkjQaosgQzZsyo63ZqMBhYenr6XQe3Ki0tZdnZ2Z0aV1lZWd17NByjwygzM5OVl5e3uJ6amhqWnp5u0p21oZa2x2AwsKKiohbfQ6PRsPT09BaXIaStJIyxBv0PBPn5+Xj44Tm4dCkW69frsWyZ5V3PNhh4o8SaGl6939EN/4wNEgsLAS8v3numM/dRdTW/DBAQwNsm3Cs5OfxykLGnzKhRfGhyW9umuyWnpvK2LfUvN7Wktdtlzv7OzeXLDRhg2pi1NQoLeXff++4TetP0BBcvAosXK5CdbYNt237CJOO1QAvy0J1rldHR0SJH0rx//etfKC0txWuvvSZ2KB1uwoQJ1B6EtKjFxAPgLa3/9re/4b333sWYMVJ8+KEOw4bdq/BIV3W3xINYFrWadz3fsEGKsWNH46uvvoWfn5/YYbXJtGnTkJubC1l7Gzm1QKPR1HVxbcmAAQOajCMjIwM6nQ6B7R0Qpwt65513MHXqVLHDIF3YXRMPo/Pnz+PFF5/HuXMX8NhjUrzxhr7JG3mRrunqVd69uLX++1/TG9Q1RIlH91BeDmzcCLz7rhwymR3+/vd1eO655yy6IWFERARCQ0M79QZolZWVSG/Y/akJwcHBTSYet27dgk6nM+miSkhP0epK5LCwMPz22zls374df/vbnxESko7586V44QU9JkywvEswPY1W27ibaEtqalqeb2vLu882vLsrsQw3bgCbNgEbN8qh1yvx4osv4ZVXXoFD/T7RFkqj0cCmkw9MW1vbdt1Ovq+xHzghPVCrazzqMxgM2LFjB9577x2cPXsRgYFKLFmixVNP8WvuhJCup6aGj/i6ebMchw/r4ebmjBUrVmPlypV1A1R1B4MGDcLDDz+MN998U+xQCCFNaFNTS6lUigULFuDMmQu4fPkypk9fjvXrHdC3rxRz58qwY4cwLDkhRDyM8UtiL70E+PjI8dhjUiiVk7Fjx07cvp2Lv/zlL90q6QB4jUeve9nymhBilnb38Rg8eDA2bNiArKw8fPvtf1FRMRZRURK4usowe7YMmzcLNwMjhHS+mhrg11+B5csBHx8FRo0CfvmlL9aseRM3b97Cnj37MWfOHMi7aXede3GphRDSdm261HI3+fn52LNnD3bt2oHDh4+gpkaLBx5QYM4cLaZM4Y0Wu9v9LAgRU1YWcPQo8MsvEuzfL0VFhQHDh4dizpwozJkzB4NbainczTg6OuKDDz7AM/VvM0wI6TI6JfGoT6PR4MCBA9i9exf27t2FgoISODsrMG6cHhMnGhARwe9/QYkIIa2XnQ3ExBgnJVJTtVAq5ZgwYRzmzHkYs2fPNvsOqN2FQqHAt99+i4ULF4odCiGkCZ2eeNRnMBiQmJiImJgYHDt2FCdOHENxcRlcXBQYP96AceP0CA8Hhg3jvSYIIXwQvJQUIDYW+P13ICZGgatXa6FQyDBy5HBERExFREQERo8e3eMvMWi1WlhZWeHnn3/GnDlzxA6HENKEe5p4NGQwGBAfH4/jx4/j2LHD+O23UygoKIFMJkFwsALh4VqEh/Nbpg8ZYv7ok4RYoowMnmTwSY6LF4Hych2srZUYPnwIIiKm1CUatpShmygpKYFKpcLBgwct6o66hPQkoiYeTbl58yZiY2Nx7tw5nD9/BhcuXEBZmQZWVlIMHizH4MFahITw26WHhPBhswmxRBUVwJUrQEIC/5uYKMfFixIUFtZCLpchODgQ4eFjEB4ejpEjRyI0NBQKyr5blJ6ejv79++P8+fMYMWKE2OEQQprQ5RKPhgwGA65evYrY2FhcuHABiYmXkJiYiPx8fltqlUqB0FAJQkK0GDQICA7m9+ag8URIV1Fezu9Dw5MLIClJiqQkGW7cqAVjgI2NFQYO7I+QkBEYNmw4wsPDMWzYsB5/2aQtzp8/j/DwcFy/fp1GBSWki+ryiUdz1Go1kpKScOXKlTt/LyE+Ph75+SUAACsrKby9ZfD31yE4mCEkBPD355OvL9CJt3EgPVBJCXD9Oh8dVpiUSE+XIiOjGowBCoUMffp4ITh4MEaMCENISAiCg4MRFBTUqfcV6UkOHTqEqVOnQq1Ww8nJSexwCCFNsNjEozlZWVlITU1FWloa0tLScP16GtLSriAt7QYqKqoAANbWUvTvr4Cfnw4+Pnr4+AB9+/LJx4dPVlYibwjpMhjjd7q9dQvIzOTTzZtAZqYEt24pkJbGUFRUCwCQy2Xo188L/fsPQEDAAAQEBCAgIACBgYHo379/tx07o6vYtm0bFi1aBK1WCyl1lSOkS+p234Le3t7w9vZGREREo3l5eXl1Scn169dx8+ZNpKRk4NChm8jMzEVNTW3dsh4eSvTpI4GPTy369DHAw4NfvundG/Dw4JObGzV4tXQFBXyAu/x83kW1oIAnGVlZwM2bcmRmSpCVpYdWawAASCQSeHio0LdvX/j4+GP0aF888UR/BAQEoH///vD19aV2GCIqLi6Gk5MTJR2EdGHdLvFoibu7O9zd3TF27Ngm5+fm5uL27dvIzMzErVu3cPv2bdy+fQvnz2cgNzcHubmF0GhM757m6qqAm5sUbm4GeHnVws0NcHUFnJ0Blarpv6RzVFYCxcX8Fu8N/xYV8YQiP1+G3Fw58vIY8vN1qK011L1eLpfBzU0Fd3c3eHv3w6BB/TB9ug/69OkDX19f+Pj4wNvbG0qlUsStJC0pLi6GM/2TEdKl9ajE4248PDzg4eGB8PDwZpeprKxEdnY28vPzkZ+fj+zsbBQUFCAvLw85OVk4ezYbhYUFUKtLUVxcBoOh8ZUsZ2cFVCrpnWRED0dHHezt+dglNjY8QbG15ZOdHeDoKDx3cADs7QG5nC9ryZeEGONtIwCgtJQnDhoNf1xezh9XVvLEwTivvNy4rAzFxTKo1RIUFxugVgu1EvXZ2/eCs7MjnJ2d4eHhAzc3T4SGusPDwwNubm7w9PSEu7s7evfuDTc3t3u8B0hHU6vVlHgQ0sVR4mEmW1tbBAYGIjAwsFXLl5aWori4GGq1Gmq1GsXFxXXPjX9LS0uRna1GZWU5KisrUFJSgooKDTSa6rp2KS1RKKSws+ONE52cpJBIeJKiVAIKBYOdnZD82NnpoFA03azHweHujW7LygC9vul5JSVyMCYBAGi1QGUlr+5Wq/n8igoDamsBrdaAyspmVlKPUimHra01VCpH2NjYwNbWDvb2DnB0dIGzsx0CApyhUqmgUqng7OyjAeMIAAAgAElEQVTc5F9qU9GzUI0HIV0ffSt3MkdHRzg6OsLPz6/N6ygpKUFlZSUqKytRXl6O0tJSGAwGlJeXQ6fToaamBhqNBowxlNypQmg4z0htzAIaMBgMuHmzCEDLbY179bKDtXXT3Ty9vW3rLkPI5XLY29sD4PtAKpXCxsYGVlZWdfPy8/OxZs0aTJ06FWvXroW9vT1sbW3h5OQEOzs7aitBzFZQUAAXFxexwyCEtIASDwvg5OTUbbsG9uvXD1FRUXBwcMC3335LyQZpl6ysLISGhoodBiGkBdT0m4hqxowZ2L9/P/bu3Yt58+ahqurul5YIaU5WVhY8afRAQro0SjyI6CZMmICjR4/izJkzmD59OsrLy8UOiVig2tpaFBYWwovuo0BIl0aJB+kSwsLCcPz4caSmpmLSpEkoKioSOyRiYXJycmAwGODt7S12KISQFlDiQbqMkJAQHD16FLm5uZgwYQKys7PFDolYEOPxQjUehHRtlHiQLmXAgAE4deoUtFotIiMjcfv2bbFDIhYiKyvrzsiyHmKHQghpASUepMvp27cvTp48CSsrK4wbNw6pqalih0QsQHZ2NlxdXWFlyaPqEdIDUOJBuiR3d3fExMTA09MT48aNQ3x8vNghkS4uOzub2ncQYgEo8SBdlkqlwuHDhxEaGoqIiAicOXNG7JBIF5adnU3tOwixAJR4kC7N1tYWe/fuxfjx4zFlyhQcOXJE7JBIF0WJByGWgRIP0uVZWVnhhx9+wPTp0zFjxgzs2rVL7JBIF5SVlUWJByEWgBIPYhGUSiX+97//4fHHH8cjjzyC7du3ix0S6WJycnJo1FJCLADdq4VYDJlMhi+//BIODg5YuHAhysvLsWTJErHDIl1AWVkZSkpK0KdPH7FDIYTcBSUexKJIJBJ88MEHcHd3x7PPPovS0lK89NJLYodFRHbjxg0AaNddoAkh9wYlHsQivfrqq5BIJPjjH/+IvLw8rFu3TuyQiIgyMjIAAL6+viJHQgi5G0o8iMV65ZVX4OjoiBUrVoAxhnXr1kEikYgdFhFBRkYG3N3dYWtrK3YohJC7oMSDWLRly5bBwcEBTz31FMrKyvDZZ59BKqU20z3NjRs36DILIRaCEg9i8RYuXAh7e3tERUWhrKwMW7ZsgUKhEDsscg9lZGSgX79+YodBCGkF+mlIuoWZM2di//792LNnDx5++GFUV1eLHRK5h6jGgxDLQYkH6TYiIiJw+PBhnD59Gg899BDKy8vFDoncIzdu3KAaD0IsBCUepFsZOXIkDh8+jMTEREyePBnFxcVih0Q6WVFREcrKyqjGgxALQYkH6XaGDRuGEydOIDs7G1OmTEFBQYHYIZFORGN4EGJZKPEg3VJQUBBOnTqFsrIyjB8/HpmZmWKHRDpJRkYGpFIpjVpKiIWgxIN0W76+vjh58iQUCgXGjRuHtLQ0sUMinSAjIwPe3t6wsrISOxRCSCtQ4kG6NQ8PD8TExMDd3R3jxo1DQkKC2CGRDkYNSwmxLJR4kG7P2dkZBw4cQEBAACIiInDu3DmxQyIdKD09Hf7+/mKHQQhpJUo8SI/g6OiIAwcOIDw8HFOnTsXp06fFDol0kGvXriEwMFDsMAghrUSJB+kxbGxssHv3bkyePBlTp07FgQMHxA6JtFNNTQ1u3ryJ++67T+xQCCGtRIkH6VGUSiW2bduG6OhozJ49Gz/99JPYIZF2uH79OvR6PSUehFgQSjxIjyOTybB582Y8//zziI6OxpYtW8QOibTR1atXIZFIEBAQIHYohJBWopvEkR5JIpHgo48+gpOTE5YsWYLS0lKsXr1a7LCIma5duwYfHx/Y2tqKHQohpJUo8SA92ptvvolevXphzZo1KC0txV//+lexQyJmSE1NpcsshFgYSjxIj/fqq6/CwcEBK1euhEajwbp168QOibTStWvXEBoaKnYYhBAzUOJBCIDnn38eDg4OWLx4McrKyvDpp59CKqUmUF3dtWvXMG/ePLHDIISYgRIPQu5YtGgR7O3tER0djbKyMmzZsgVyOf2LdFWlpaXIy8ujSy2EWBj6SUdIPbNnz8bevXuxa9cuLFiwADU1NWKHRJpx7do1AKDEgxALQ4kHIQ1ERkZi3759OHbsGObNm4eqqiqxQyJNuHbtGhQKBd2nhRALQ4kHIU0YN24cjh49itjYWDz44IMoKysTOyTSQGpqKvz9/aFQKMQOhRBiBko8CGnGiBEjcOLECaSnpyMyMhKFhYVih0TquXr1Kl1mIcQCUeJBSAsGDhyIkydPoqSkBOPHj0dWVpbYIZE7rly5guDgYLHDIISYiRIPQu7Cz88PJ0+ehEwmw7hx43D9+nWxQ+rxdDodrl69SokHIRaIEg9CWsHT0xNHjhyBo6Mjxo0bh6SkJLFD6tGuX7+OmpoahISEiB0KIcRMlHgQ0kpubm44duwY/Pz8EBkZiUuXLokdUo915coVSKVSBAUFiR0KIcRMlHgQYgYnJyccPHgQQ4YMwcSJE/Hbb7+JHVKPdOXKFfj6+tLN4QixQJR4EGImW1tb7NmzB5GRkZg6dSoOHTokdkg9DjUsJcRyUeJBSBtYWVnhhx9+wIIFCzBr1izs3LlT7JB6lKSkJGrfQYiFosSDkDaSyWT4+uuv8dxzzyEqKgrffPON2CH1CHq9HteuXaMaD0IsFN0Bi5B2kEgk2LBhA6ysrLBkyRJotVosXbpU7LC6tfT0dFRVVVHiQYiFosSDkHaSSCRYv349XF1dsWzZMpSVleGPf/yj2GF1W1euXIFEIqEeLYRYKEo8COkgr776Kuzs7LBq1SoUFBRg3bp1YofULSUlJcHX1xf29vZih0IIaQNKPAjpQC+88AIcHBywZMkSVFRU4JNPPoFEIhE7rG4lOTmZLrMQYsEo8SCkgz3xxBNQKpV44oknUFZWhq+++gpyOf2rdZSkpCRMmjRJ7DAIIW1EvVoI6QTR0dHYuXMnfvzxRzz++OOora0VO6RuQafT4cqVKxgyZIjYoRBC2ogSD0I6yYwZM7B//37s378f8+bNQ1VVldghWbzk5GTU1NRg8ODBYodCCGkjSjwI6UQTJkzAkSNHcObMGUyfPh3l5eVih2TR4uPjoVAoqEcLIRaMEg9COllYWBhOnDiB1NRUREZGoqioSOyQLFZ8fDwGDhwIpVIpdiiEkDaixIOQeyA4OBinTp1CUVERxo8fj+zsbLFDskjx8fHUvoMQC0eJByH3iJ+fH2JiYlBbW4vIyEjcvn1b7JAsTnx8PAYNGiR2GISQdqDEg5B7qG/fvjh58iSsra0xduxYpKamih2SxSgqKkJ2djbVeBBi4SjxIOQec3d3x7Fjx+Dt7Y1x48YhPj5e7JAswqVLlwCAerQQYuEo8SBEBCqVCocOHcKgQYMQERGBM2fOiB1SlxcfH4/evXvDw8ND7FAIIe1AiQchIrG1tcUvv/yCCRMmYPLkyTh8+LDYIXVp1LCUkO6BEg9CRGRlZYVt27ZhxowZmDlzJn7++WexQ+qy4uPj6TILId0AJR6EiEypVOL777/H448/jujoaGzfvl3skLoc41Dp1KOFEMtHd64ipAuQyWT48ssv4ejoiIULF6KsrAzPPPOM2GF1GcnJyaiursawYcPEDoUQ0k6UeBDSRUgkErz//vtwc3PD0qVLUVZWhpdeeknssLqEuLg4WFlZITg4WOxQCCHtRIkHIV3Mq6++ChsbG6xevRp5eXlYt26d2CGJLi4uDqGhoVAoFGKHQghpJ0o8COmCXnzxRSiVSqxYsQKMMaxbtw4SiUTssEQTFxeH4cOHix0GIaQDUOJBSBe1bNkyODg44KmnnkJpaSk2btwIqbTntQdnjOHSpUuIjo4WOxRCSAegxIOQLmzhwoWwt7dHVFQUysrK8M033/S4yw3p6ekoLS2lhqWEdBM97+cTIRZm5syZ2L9/P3755RfMnz8f1dXVYod0T128eBEymYzG8CCkm6DEgxALEBERgSNHjuC3337D9OnTUV5eLnZI90xcXByCgoJgY2MjdiiEkA5AiQchFiI8PByHDx9GUlISJk2ahOLiYrFDuifi4uLoMgsh3QglHoRYkGHDhuHEiRPIycnB5MmTUVBQIHZIne7SpUuUeBDSjVDiQYiFCQoKwqlTp1BeXo7x48cjMzNT7JA6TXZ2NnJzc6krLSHdCCUehFggX19fnDx5EgqFAmPHjkVaWprYIXWKuLg4SCQSuistId0IJR6EWCgPDw8cP34cHh4eGDduHBISEsQOqcPFxsYiICAAKpVK7FAIIR2EEg9CLJhKpcLhw4cRHByMCRMm4OzZs2KH1KHOnTuH8PBwscMghHQgSjwIsXB2dnbYs2cPRo0ahcmTJ+Po0aNih9RhLly4QIkHId0MJR6EdAM2NjbYtWsXpk2bhlmzZuHAgQNih9RuGRkZyM/Pp8SDkG6GEg9CugmlUomtW7ciOjoas2fPxo8//ih2SO0SGxsLmUyGoUOHih0KIaQDUeJBSDcik8mwefNmrFixAo8++ii+/vrrJpf75JNPcOjQoXscnXliY2MRGhoKW1tbsUMhhHQgukkcId2MRCLBhx9+CEdHRzzzzDMoKyvD6tWr6+Zv2rQJq1evxtChQzF58mRIJBIRo23euXPnMHLkSLHDIIR0MKrxIKSbevPNN/HOO+9gzZo1eOuttwAAW7duxbJly8AYQ1xcHPbs2SNylE0zGAyIi4uj9h2EdEMSxhgTOwhCSOf5/PPP8cILL2DBggXYsWMH9Ho9GGOQyWQIDAxEUlISpNKu9RskMTERgwYNQlxcHLXxIKSb6VrfNoSQDrd8+XK8/PLL2L59OwwGA4y/NfR6Pa5evYrt27eLHGFjsbGxsLa2RkhIiNihEEI6GCUehHRzZ8+exSeffAKJRAKDwWAyTyKR4PXXX4dOpxMpuqbFxsZi+PDhUCgUYodCCOlglHgQ0o0lJCRg6tSpqKmpaZR0ALwtxc2bN/Hdd9+JEF3zYmNjqX0HId0UJR6EdFPXrl3DxIkTUVFRAb1e3+xyjDGsXbsWtbW19zC65tXU1CAhIYESD0K6KUo8COmmysvLMXbsWDDGoFQqm12OMYbs7Gxs3rz5HkbXvEuXLqGmpoa60hLSTVHiQUg3NWLECPz8889IS0vD8uXLYW1tDbm86aF7DAYD/vrXv6KqquoeR9lYbGwsHB0dERAQIHYohJBOQIkHId2cv78/NmzYgLy8PLz33nvw8PCARCJp1IW2uLgY//73v0WKUhAbG4uRI0d22YHNCCHtQ4kHIT2Eg4MDVq9ejZs3b2Lbtm0YMmQIANTVguj1erz11lsoLy8XM0xqWEpIN0eJByE9jFKpRFRUFC5evIhDhw4hMjISEokEMpkMJSUl+PTTT0WLrby8HFevXqXEg5BujEYuJYTgypUr+OCDD/Dtt9/C1tYWN27cgKOj4z2P49ixY4iMjERmZia8vb3v+fsTQjofJR6E9FAVFRUmXWh1Oh1u3ryJ7du3w8HBAQsXLmz0mqqqKlRXV5v9XtXV1a1quPrDDz9g586d+PXXX81av0wmg4ODQ4vLWFlZwcbGxqTMycmJ2pIQco9R4kFIOzHGUFJSAp1Oh/Lycmi1WlRWVgIQTu7GZQA+ToVGowHALy3odDoYDAaUlpYCMD1Jl5WVQa/XQ6/Xoays+M78KlRVaRq9f30aTRVqarR1z/V6A8rKNCAt69VLCWtroeuxRCKBk5NpQmNra2vSPVkmk8PBgdcOWVvbolcvWwC8TY1MJoNcLoe9vf2d9feCtbU1ACHpUSgUsLOza7RulUoFgF8as7W1hbW1NXr16gUbGxtYWVl1xuYTck9Q4kG6tYqKCmg0GlRUVKC0tBQajQaVlZUoLS2tO8FXVlZCq9XWneRLSkqg1+tRWlqK2lotKipKUFNTDY2mEhqNBjU1NSgvr4ROp0NpaSUMBvP+heRyCezteYNOGxsJrKz4L24nJ0AiARQKBjs7vk5bWx2USv74znkICgVw5zxVx8EBkMmE51ZWQIMf93XrN+rVC7hzDgTA5zk53T3+hutprda8rqICaMs4Zq15nUYD1NQIzw0G4E6u1+x6dDqgYVvbsjKg/nhsWi1wJ89EZSV/DgBqtRyA5M58qcn6GQNKSvhIsjU1Bmg0zQ/w1hx7+16Qy2VwcnKAVCqFk5MT5HIF7O0dYGXVCzY29nWJjp2dHRQKBRwdHSGVSqFSqepqiWxsbGBjYwMHBwfY29vDxsYGtra2VBtEOg0lHqRL0Wg0KC0tbTRVVFSgsrISlZWVKCkpgUajqVu2oqIUGk0FKirKUFpaispKDTSaapSWVrb4XkqlFLa2MvTqJYG1tQS2toBSCTg4GCCTMahUOshk/KRuPNlbW/MTtnFZe3tALucnVZkMcHRE3WsA05O7oyPQxW4CS7qY8nKe7NRPiqqrgaoqIXEyJi+lpTwBKinhf8vKhCSoqoq/rrJSBq1WirIyCfR6CdRq47oNqK01oKKi5YSnVy8lbG17wcHB7k5SYgtbWzs4OfWuS1gcHR1hZ2cHW1vbuoTFWF5/cmpNVkt6BEo8SIfR6/VQq9UoLS1FSUkJSkpKmkwieHkJSkuLUFJSfOd5OUpLK1Bb2/QXob29HDY2UtjaSuDkBNjYMNjYGODoqIOdHf91b2fHT+42NnxycuIJgo0NTxAcHIR5xtoDQnq6ykqe1JSX8+RFo+GTWi08Livj8zUavnxJCaDRyKDRSFFaKkVFBaDRMFRUMJSW6pqtBXRysoOjo92dREQFR0cXODqqGiUoTk5OjRIXlUqFXr163eO9QzoDJR6kSVVVVVCr1U1OOTk5yM7OhlpdALW6EGp1MdTqEuTnl0Cvb3wjMmtrKVQq+Z1f/waoVAwqlR4qFZqcjLUExufu7qaXEQghXV9VFU9eWpp4bY4MarUcarX0TrkBhYW1qK1t6rtECZXKASqVE1QqF6hUvaFSqUwmLy8veHp61j3v3bs33eW4i6HEo4coKSlBbm4u8vPzkZeXh9zcXBQUFNT9LSoqQFFRHoqLi1FUVNqo5kEul8DZWQkXFymcnRlcXLRwcTHA2RlwcQFcXflflYrXOjg58cl46YEQQsxRVsYvJxmn4mI+FRUJfwsLgaIiKYqLZSgqAoqK9KiqapywODvbw8XFEc7OznBxcYWrqw969+4NLy8v9O7dG25ubvD09Kx7LKMvrU5FiYcFKygoQH5+PvLz85GTk4OCgoK6pCI/Pxd5edl3Eoti1NQILeYkEsDNzQq9e0vh7m6Au7v2TjLBkwdjMlE/qRBhSAdCCDFbVZVpcmJMUOo/LyqSIi9PhpwcoKBAj5oaIVmRSCTo3dsBbm6ucHPzgKenL3r3doO7uzs8PDzQu3dveHh4wMPDA25ublSb0gaUeHRRarUa6enpyM7Orru0kZOTg/T0FGRnZ+L27RyUl5uOi6BSyeHpKYVKpYeXlx6enrwGwssLJo/79OGNJQkhhAiXhXJygOxs4S8vkyI7m18Kys7Wo6TEtPuUSmUPT08PeHn1gb9/ADw9PeHl5QV/f394enqiT58+dx1jpqehxEMEeXl5yMjIQEZGBrKyspCZmYnMzExkZ9/CrVs3kZdXBJ2OX+qQSAB3dyW8vKTw9tahb18dvLwAHx/A2xvw8AB69+YT9XwjhJDOVVEB5OYCeXk8OcnKAm7f5o8zM+XIzJQgO1sPrVaoRXFxsYeXlzv69u0PL6++8Pb2Rt++fdGnTx/4+/ujT58+ParmhBKPTlBTU4OsrCykp6fXm9KQnp6C1NQMk4GcVCo5/P0l8PTUwcuLwd+f104Yayn69eM9MwghhFgOtdq09iQnB0hPB7KzFcjJkeP6dZ1J7YlK5QB/fz/4+98Hf39/k8nPz69bjalCiUcbVVRUICUlBVeuXEFqaioyMjKQnn4NGRnpyM0tqlvO3V0JPz8J/P218Pdn8PMD/P0BPz9ea0FtmAghpGcqKuLJSHo6kJFh/CtFeroMt2/rUFvLT892dtbw9+8DP78B8PcPRP/+/REUFITg4GB4enqKvBXmo8TjLoqLi3HlyhUkJycjOTkZV64kICUlCTdv5gAArKyk6N9fAX//Wvj7G+qSCuNfqq0ghBBiLp2OX8IxTUokSE9XIC2NobiY15Y4Odlh4MABCA4eiqCgIISEhCAoKAj9+vXrsrUklHjcUVVVhcuXL+PixYtITExESkoikpKSkJ/P749hZydHUJAMwcFaDBzIMHAgEBzMEwyqtSCEEHIv5eUBV64AKSlAUhKQkiLHlSsS5OTwhMTGxgpBQf0xcOBQBAeHYOjQoQgLC4Obm5vIkffQxKO2thYJCQmIjY3F+fPncf78b0hMvAqdTg+VSo6QEAkGDqytSy6CggBfX7GjJoQQQlpWUgIkJwtJyZUrMiQny5CRwW8i1LevB8LC7kd4+CiEhYUhLCzsng9n3yMSj5SUFPz+++91Scbly0moqamFvb0cw4dLEBZWi7AwICwMCAgQO1pCCCGkYxUXA+fPGycJzp+X4/btWkgkEvTv742wsNEIDx+FkSNHIjw8vFPvgNwtE4+UlBQcO3YMx4/H4PjxI8jNLUKvXjIMHSpFeLiQZAwYQDftIj1bQgKQlsYfT5ok3NyOENL95eUJyUhsrAznz0uRl1eLXr2UeOCB+zFhwiRERETg/vvvh1Kp7LD37RaJR1VVFU6fPo3Dhw9j164fkJKSAVtbGR54ABgzRo+xY4Hx4/ndRAnpiUpLgbfeAiIjgZkzhfI1a4ANG/jj+Hhg0CBx4uvqmtt/hHQ32dnA6dPA4cMSHDqkQEaGFjY2Vhg9egxmzpyN+fPno0+fPu16D3kHxXrP1dTU4Ndff8XWrd9jz57d0GiqMWyYEg8/rMVDDwEjR+oht9itI6TjnDgBLFgAFBQA48aJHY3lof1HehIvLyAqCoiKYgC0SE8Hfv21Bnv3Hsfrrx/HSy+9hAceCMPChU8iKioK7u7uZr+HxV1oSEtLw5o1a+Dh4Yr58+chO3sH1q+vRlYWcOGCFv/4BzB6NCjpIOSOuDh+0gQaj267Zg3w++986t//3sdmCVraf4R0d/7+wIoVwN69ehQV6bF7N4O//3n8+c8vwdvbCzNnTsOBAwdgzsUTizk9X7p0CX/961rs3bsPffsq8Oc/a/HYY4C3t07s0CyKTgfs2QNcusRvnGRnBwwcCMybZ3ojuLNneYtouRxYtAi4cQM4eBC4ehUICQHmz+d3n62vrAzYvp33Ny8vB9zceBI4caLwhf3zz7za2sXFtMo6Npa3wgaAOXOEdRcWAnv38sfDhgGDBwuviY8HYmJ4bEFB/HJaUJBpTDExwM2bgL09MGsW8PXXvG/81Klt//WamAgcOsSHTR49Gpg2Dbh8mbckVyiAxx7jy50+LbSfWLDAdEyXrVuBmhp+I75Zs0zX35rtAlq3v48eBc6dE15z7Bjf/7Nm8fcuKOCfKcDbPNnYmL6HXs9PvCdO8OvBoaH8coO3t+lybTleWqu1n2FrYzXnc7nb/jNq7WfWWvv3A/n5gLs7P75OneLvLZEAkycD99/Pl7t8GThwAKis5Pti4sSmu/d35DHVlmVb+71jFBvL931ZGTBmDN8Hv//OPzelEli4sPO3jzTWqxf/3p45k6GqSodffgE+//wIpk07gAED/PDGG29h0aJFkN6t8STr4goKCthTTz3BpFIJCw9XsB07wHQ6MMZoMnfS6cBGjQIDGk+BgWBpacKyK1bw8l69wHbsALOxMV3e19d0+ePHwZydm153dLSw3BNP8DJrazCNRiifOFFY/rvvhPKNG4XyQ4d4mV4P9sYbYFKp6fvI5WDvvANmMAivnzePz/PzA1uyRFg2JKRt+/C99/j71H/fqVPBnn2WP7a1FZZdvFhY5uZN0/WoVLx86FChzJztau3+njWr6WXi4vj81auFsvh40xhTU8F8fBq/1s6Ofy71lzX3eDFnas1naE6s5nwud9t/5nxm5kxjxvD1PPAA2Isvmq5bIgH797/BPvyw8fuuXGm6ns44psxd1pzvHcbAXnmFb2P95ebNA4uK4o+dnDp/+2gyb0pIAHv6aQmTySRs2LBQdvr0adYStDhXZLGxsczX14v17atgW7e2/Z+YJj6tXy/8oz30ENiaNWAjRghlCxcKyxpPJBIJ/6ceORJs1Sqwfv2E5ZcuFZbv04eX+fvzL4IPPzRNJv7zH77cjh1C2b59vEyjAbOyEsqffVZY74wZvMzZGay2lpd9+aWwrIsLj6P+SWfbNuH1xpOW8YvM1pZ/Kf3f/5m//2JiTL+swsPBRo/mj2Wy9ice5mxXa/f3qlVgXl5Ceb9+YEOGgKWk8PnNJR7p6cJ7GE+ADz1kmlB8/XXbjxdzprt9hubGas7ncrf9Z85nZs5kTDwAMKUS7JFHwJ55pvEJeexYnswrFELZ0aOde0yZu6w53zvbt5smWHPn8s+z/udfP/HorO2jqW1TYiLYgw/KmEIhYx988AFrDpqdI7LU1FTm5GTHpk2TscJC8Xdod5i++IL/Yly1SiirqBC+oEeMEMqNJxKA//Mby1NThfJRo3hZTo5QtngxWE0NL6+uBnv9dbCvvgJLSuJllZX8VzEg/Do7eND0y7R/f15eVSUsu3gxL6upAXNzE76AKip4eW0tWN++vHzgQCFJNZ60ALCICL7OggKw4mLz99+ECcK6vvlGKN+6VSi3sRHKzTnBmbNd5uxvxsA++khYfudO0ziaSzwee0wo/+gjoTw5mZ8IAb4Nxv1ozvFi7nS3z9DcWM1NCJvbf+Yei+ZM9ROPDz8Uyh95RCifPFlY98cfN94HnXVMmXv8mfO9ExrKy6RSsDNnhPItW4T3dHTs/P8Zmto+GQxg774LJpNJ2CeffMKagiZLu4AxY0ayUaMUrLpa/B3ZHafcXLCff+a/PoxfAJGBs5MAACAASURBVAMGCPPrn0gOHjR9rasrLw8IEA60+lWYTk5gc+aAffIJWEZG4/eeO9c0wXj1Vf7c+GUBgN26xWtEjM937+bLXrkilD38MFhhoTAtXy7My87my9c/aRlrWNo6OToKv5gazgsMbF/iYc52mbu/25J4eHryMisrsPJy09c8+KDwmjttysw6Xsyd7vYZmhtrRyUe5h6L5kz1E4+iIqH89deF8s8/F8r37hXK3367c48pc4+/1n7vaLVCzeHo0Y1f6+5umnh05v8MTe2f3nkHTKmUs7S0NNZQl+zVUlRUhNOnz+HNN2vRiYOn9Tg6HfD228CIEYCnJzB3LvDRR4BGw+c317iqd2/T58YGiHq98LrNm4WeRCUlwK5dwIsv8hvlPfggcP268Pp58/jf69eB1FTgyBH+fOlSoSHgsWNCo1I7O2DKFP44NVVYz08/Aa6uwvT558K8rKzG2xEY2PT2tYZazRsVAryVd0N3G1KfMdPnugZtos3ZLnP3t7kyMvgtvAEgIoLv//rqNwpOSmr8+rsdL+3R8DNsb6x3+1xa0p5jsbWkUtNGrPXHIqrfaNbaWnhs3KbOOqbMPf5a+71z65ZwjPj4NN4XDcu60v8MaezllwF7ewn279/faF6X7NUil8shlUpQUcHuvjBptago3qsE4K2+587lrf4XLOCtxZtriNww+WtqublzeYvyL7/kLfIvXBC+RA4e5PMTEvjzmTP5F4BOB3z3HXDxIi+fPJnH8c03PPE4fpyXP/SQ8MWqUAjvOXQoH4G2KfW/iI0anpTMYW8vxFxd3Xi+Wt3y62trTZ83XIe522XO/jaXhwePp7a26ZNmZqbwWKVqPL81x0tbNfwM2xvr3T6XlrTnWGythsMC1P9xcLdRZjvzmDJn2dZ+79jbCzEVF5vGqNE0Thy70v8MaUyrBbRa1vTQ643qQLqIefNmMz8/RZuuxdPUeLp9W6hmnD/fdJ6xKrx+L4H6VefJyabLGxsM+vnx5wYDvzRy6BDYjRu8rKSEN+yqf/kkJ0dYR2QkL7Oz438dHHjr9//+17Qc4G0ojK9LSRHKx40zjSsxkb9/U71a0KDKui1TQABfj4uLaY+c3FyhLUr9Sy2rVgnvff68UJ6VJZQbq/TN2S5z9/eGDULZTz+Zrru5Sy3h4UL59eumrxk4sPF2mXO8mDvd7TM0N1ZzPpeW9p+5x6I5k/FSi1JpWv7mm8J7Hj8ulB85IpT/4x+de0yZs6y53zvGy5l2dqaf9e7dwnqMl1o683+GpvZPy5dLmUplz3Jzc1lDXfJSCwB8+eVXMBh6Y8wYBZKTxY7G8tX/NVheLlTH/utfvF89wPu4t8XPPwN9+/LLIUuWAFVVvG/+7Nn8FynAf3W4uAivMV5uqajgfyMi+PgDkyaZlltZ8RoPowEDgOHD+eNTp4Bt2/ivlps3eX//fv34rx+ttnGcTY1vYA5jzEVFwFNP8areggJg8WK+zQ3VH0dg82ZeW1JcDDz/fONlzdkuc/d3/er5xET+K/Nun3VkpPD4hRf4thYWAv/4B+r+HydMEGK+V5r6DM2N1ZzPBWh+/7XnWLwXOuuYMmdZc793lizhfysqgFGjeO3E2rX8/+1ebR9pn6oq4OmnpfjiC4Zvvvmu6ZFNG6UiXUhmZiYbM2YUs7WVsb/9rXHDMZpaP2k0pt0CfX2FX6LGcSlsbIQxUsz5BavX894GxuWtrHiXQ2OPAoA3iqu/jtu3TbsGbtggzBs0SCifNavxthw9atpV0tVV6Mcvl5u2hq//a7mkpH37UK0WfqUB/D0lEj41VeORkGDaTdjBgTeec3QUuvbV/2Xd2u0yd38fPSqUGydjA9Dmajy0WrAFCxq/zji5uPAeK8bl71WNR1OfobmxtuVzaW7/mXMsmjN1RI1HZx1T5ixr7vdOXh6vAWm4vwMChM+mfnfazvqfoalt044dYPfdp2DOzg5s3759rDlodk4XodVq2bp165iTkx1zd1ewt98Gy88Xfwdb4vT770LvC9ypzvznP3l3vYZfqOaeSCorwV56Ccze3vQLw9WVD7ql1zeOp34Vef2ubH/4g1Bef/yF+tOlS7wbnvHLy9oabMqUxuMmdGTiwRjfF0OGCOt0d+fV7yNH8uf1x/Ew/iMaW+NLJGDDhvET/KRJjU9w5myXOftbr+fV3MZllErhkkFLA4jpdLzHUVCQ6Rf2ww/zy0v1lxUz8TA3VnM/l5b2nzmfmTlTRyUenXVMmbOsOd87jPFuz88+y3u9eXmBLVrEE5Jhw/iybm6dv300tX6qrQX74QewBx6QM4lEwhYujGa3bt1iLbGYu9MWFhZi/fr12LTpc2g0lXjkEYbHHzcgMrL9Veg9icHAG1jV1PCqyo5s+AcIjfwKC/nNhjw9O3co4upqXrUeEMCH871XcnJ4dbCxl8WoUXxobVtb4TJRfampvHdCa6tyW7td5uzv3Fy+3IABpg3zWqOwkA/hfd99Xf8+SObEas7ncrf9J9ax2FqdcUy1dtnWfu+cOcOH1vf1bRyjnx9fx4ABfHj+e7F9pHkpKfwWA5s2yZGba8CsWTPw5z//BeHh4Xd9rcUkHkYajQbfffcdNm36F86di4O7uwJRUbWYO5ffq6D+9VhC7pW7JR6EkLubNo3fewYA/vAH4J13AMaA3buBRx/lCczChcD334sbZ0+VlAT88guwbZsCcXG18PLqjSeeWILly5ejX79+rV6PxSUe9aWlpWHr1q3Ytu0/SEy8Bjs7GaZMAaZP12PyZJ4hE9KUq1d5N7/W+u9/TW9Q1xAlHq3T0fvdEvTEbW6r77/nNxk0srbmDUaN3Z7lcn5jvOBgceLraUpL+U0X9+8H9u1T4uZNLVxdHTFvXhQWLnwMEyZMuPsN4ZrQxStNWxYQEIC1a9di7dq1uHHjBvbt24d9+/ZgzZpj0Ghq0LevEhERtZg4kWHsWF4FRwjAW7qnp7d++Zqalufb2vJxFRre3ZWY6uj9bgl64ja31WOP8f31f//HL5sYx1WRSPgAZO++S0lHZyou5ncBjokBYmKUiIurhcEADBsWiieemIMZM2YgPDwcsna2b7DoGo/m6HQ6XL58GYcPH8bhw/tx+vTvqKrSwsFBjkGDGEaM0GPECH4gBwfT9T1CCOlqysqA7Gzehq9v38YD05H2KS/ntUcXLgAXLkhw4YIVkpOrwRjg798HkydPx5gxYxAZGQmfpoaSbYdumXg0VFNTg7i4OJw/f/7OdAYpKanQ6w1wc7NCWJgBYWG1CAvjyYiXl9gRE0IIIf+/vTuPj6q+9z/+msxMSCaZzGSZkJAASVhDwAIBrOJSsNYFF9SCWldq689aq7W12trbRX+9bW2Lt9pf++v19ofaq330at3Q2ooSa+uKBhANIEsSluzbTGayzvb745BJIqgIyZks7+fjcR5z5sw5M5/Dg8fMO9/zPd/v0OjogC1b4J13+hY7u3eHiESi5OZmsmjRiYeWRSxevBjPh+c9GGLjIngcSUdHB1u2bKG8vJzy8ncoL3+DnTsriUSiuN02pk2zMGdOkJISo1WkpMQYkGao7wIREREZCj6fMcBdRQVs3w6VlVYqKuzs3NlDJBLF5Uph7tx5lJYuobS0lNLSUkpKSkyvc9wGjyPx+Xxs3ryZ7du3U1FRwc6d77N9ewUNDcbEAampNmbPtjJnTi/FxVGKi41QUlSkW3pFRMQcDQ1GsNi50wgZO3fa2L7dQl2d0Qs3JWUCs2dPo7h4AXPmlDB79mwWLFjwqe48GU4KHkfB6/Wyd+9eKioqDoWSd9m+/T2qq2uJRKLY7QlMnmyjqChCUVGI3Fzjck1RUf8iIiJyNHp7jQkOKysHLglUVtrZsyeMz2dMo+xypTB9+jTmzPkMJSUlFBUVMWfOHIqLi4/pbhOzKHgch0AgwM6dO9m1axeVlZVUVVVRWbmLqqq9HDzYQDgcAcDlslNUZKeoqIfCwjBFRcatvoWFMHmy7oQQERlPQiFjQLrqaiNUVFX1hQsbVVUWamuD9P0yZ2W5KCycSlHRbAoLiygqKooFjNzc3Liex7FS8BgmwWCQAwcOUFlZGVtqa2uoq6s69Lwutm9SUgKTJlkpKoqQmxuOjabX12qSm6vR9URERoOeHmMiybo6I0zU1vatJ1Bba6euzsL+/T2EQsZPb2Kijfz8HIqKZhxa+sPF9OnTcblccT6joafgESc+n4/q6moOHDjAwYMHqa2tZf/+/dTWHqSmZh/799cQCPRPeepwWJk82c6kSRHy83vJz+8f7nfiRPB4jPW0tDielIjIGNXdbcxGXVdnDMnf1AQHDhjBoqYmgQMHrNTWRmlqCsWOSUy0kZubRX5+Hvn505g0KY8pU6aQl5dHXl4eU6dOZdKkSVjG2V+VCh4jWHt7OwcPHhwUTGpqaqip2c+BA1XU1zfS2Ng26JikpAQ8Hhu5uZCdHcLjiTBpkhFMsrONcNK3Psx3TImIjGgdHcYlj4aGwaGisRHq6y00NtppbLRQX9/fr6JPSkoSeXkTmTQpn8mTi8jPz2fSpElMmTKFSZMmkZeXR05OzrgLFUdDwWOUCwaDNDU10dTURG1tLU1NTTQ2NlJXV3dovZa6uoM0NTXT2NgW63cCYLNZyM624/FYyMyMkJkZJDOT2NI3eVbfY9/6CO6zJCLjlN9vXOL48NLaOvDRSkuLlaYmCw0NITo7w4PeIz3dycSJWXg82eTkTGbixByys7OZOHEiOTk5eDye2LpDnfOOmYLHOBKNRmMhpaGhgfr6ehobG2lqaqK1tZXm5mZaWppoaWmktbWVlhYvXV2Hj9+ckWEnM9N2KIhEyMzsGRROXK4jL253HE5aREaNzk5jLIoPL21t4PUaM8r2BYmWlgRaW22HQkWI3t7IoPey2axkZKSSmekmIyOLzMzcQ4+ZZGVlHRYkPB4PEzQ8qikUPORjdXV10dLSciiItNDS0kJzc/Og562tzbS0NNDa2kJLSxs+X4De3tAR38/ttuFyWQ+FkSguVxi3O3xYQHG7+587ncadP263MSeKZiAWGXna2ozg0NFhtD60tR05RBiLBZ/PhtebgNcLPl8Eny9MMBg54nunp6ficjnxeDxkZHjIzJxIRkYGmZmZsceB61lZWWOyU+ZYoeAhw6Krqwufzzdo8Xq9eL3ew7b7fG34fK2HHn20tfnw+TqIRI78X9Nms+B02khLS8DhMMKI2x0hJSWMwxHB6eyfsM3hgPR0Yx+Hg8NecziMOSCcTmPmS5Gxzus1ppf3eo3bOv1+Y16UvtDg9RqPnZ2DX+vshLY2Ox0dFjo7LYdei9DZGTnsksVATmcyLpcTlysNl8uNy5VxaHHhdrtxu924XK6PXWRsUfCQEcvv9+Pz+fD7/XR2dtLW1kZHRwednZ34/X7a29tjz71eLx0dHXR0BAgEvPh8Xjo7O+jo6MDn8xMIdBEMfvSXYx+Xy0ZCAqSnW7FajZBit0dJTY2SlBQmOTkSCyupqWC3G60yxjHEjgHj0Wo1Ao3TaWzrOxaM/cF4rsvF40cwCIGAsd7RYQwWFY0aP/hg3I7Z2Wmst7cb08J3dRl3VfTt7/cboWFwiLDg99vo7YWOjgS6u43jOjoih44JxW7h/DhudyopKck4HMmkpaXhdLpISXHicDhJT0/H4XCQkpKC0+kkLS0t9tztdpOSkoLD4cDpNPZ1uVwjeiAriQ8FDxk3gsEggUAAr9dLZ2cn3d3dBAIBgsEgPp+PSCRCW1sbkUgEn88X27+np4fOzk46Ozvp6ekhEPARDPbQ3u4jHA4NOMZPKBTG7+/65GI+gtNpw2azHAowxhd2cjIkJRmvu93hWC/5viDUx2I5vB/NwKADg4NRn74A1cduN7YdrSN97tFwuYwf0tCRr8p9pM7OTzd1vPFDPHib1wsDv/n6fqT7DAwCH/W54TC0t/f/wwUCFoJBC5GIcTnBeN8oXV3GB/l8oY9sxfskEybYcTgmkJycRFLSBFJTU7Hb7aSlubFarbjdHqxWKy6XC5vNhtPpJDExkZSUFJKTk0lKSiIlJYXExEScTic2mw232x07xul0xgKEyHBT8BAZJm1txq3Ovb29dBz65fP7/YRCoVi4AeOyVHd3N0AsAPWFHiAWjqLRKN4Bv4Z92/uEQkH8/sG3V/eFoz4Da+nj9bYz8Gugu7uXrq7e4z7/kcJqTSAtbXCTktOZis3WP8GS3W4n9UNpq+9Hvc+ECQ4cjsH7uN3uWBB0OByxzonph5qz+n78jc80fvATEhJilw/6QgEQax0YWEtfwBAZSxQ8ROS49LUEfRo9PT0UFxdzzz33sGrVqk91rMViwa1bpERGLXWnE5Hj4nA4jmlMg/b2dtLS0mKtAyIyPqjXj4iIiJhGwUNERERMo+AhIiIiplHwEBEREdMoeIiIiIhpFDxERETENAoeIiIiYhoFDxERETGNgoeIiIiYRsFDRERETKPgISIiIqZR8BARERHTKHiIiIiIaRQ8RERExDQKHiIiImIaBQ8RERExjYKHiIiImEbBQ0REREyj4CEiIiKmUfAQERER0yh4iIiIiGkUPERERMQ0Ch4iIiJiGgUPERERMY2Ch4iIiJhGwUNERERMo+AhIiIiplHwEBEREdMoeIiIiIhpFDxERETENAoeIiIiYhoFDxERETGNgoeIiIiYRsFDRERETKPgISIiIqZR8BARERHTKHiIiIiIaRQ8RERExDQKHiIiImIaBQ8RERExjYKHiIiImEbBQ0REREyj4CEiIiKmUfAQERER0yh4iIiIiGkUPERERMQ0Ch4iIiJiGgUPEZGPcfnll3PGGWfEuwyRMUPBQ0REREyj4CEiIiKmUfAQkTFh3bp1LFy4EKfTyZIlS3j22WcHvX766aezYcMGvvGNbzB58mQmT57MbbfdRjAYjO0TiUS4++67mTdvHlOmTOFHP/oRkUjE7FMRGdMUPERk1Fu7di3XX389M2fO5OGHH+akk07iwgsv5Kmnnorts3XrVq677jo2b97MnXfeyTnnnMPatWu57777Yvv87Gc/4+c//zlr1qxh7dq1PP/88zzzzDPxOCWRMcsSjUaj8S5CRMYfq9XKo48+ymWXXXZc7+Pz+Zg6dSorV67koYceim1fvXo1W7duZdeuXQC4XC5mzJjB22+/jcViAeCzn/0sTqeTF198kebmZnJycrj33nu5+eabAWhra2Py5MksWbKEsrKy46pTRAxq8RCRUW3r1q34fD4WL15MeXl5bJkzZw67d++mubk5tu+yZctioQNgxowZtLe3A/Dee+8RDoe56KKLYq+np6dz1llnmXcyIuOALd4FiIgcj+rqagBuuummj3w9KysLAI/HM+i1pKSkWB+OvpaRnJycQfvk5eXR1tY2lCWLjGsKHiIyqqWnpwPwr3/9i4ULFx72elJSUmx9YGvHh+Xn5wPG5ZXs7OzY9kAgMFSligi61CIio1xxcTEA69evx+FwxJbHHnuMG264YdBdKx9n/vz5gBFg+kSjUTZt2jT0RYuMYwoeIjKqzZgxg1WrVrFu3Tp+97vf0dbWxosvvsjNN99Mfn4+EyZMOKr3ycvL4/LLL+f2229ny5Yt7N+/n+uvv56KiophPgOR8UXBQ0RGvQceeIAVK1Zwyy23kJGRwTXXXMNll13GD37wg0/1PuvWrWPRokUsXbqUgoICduzYwVVXXfWxl2hE5NPR7bQiEhdDdTvtQL29vdTU1FBQUHBcYaGrqwufz3dYR1MROX7qXCoiY0ZiYiKFhYXH/T7JyckkJycPQUUi8mFq8RCRuBjPly9OPvlkXnvttXiXIRIXavEQkbhISEjgm9/8JqeccsoRX+/q6iIxMRGr1fqx7zNt2jQSEoa/u1pXVxcHDx78xH2OpuZ58+YNZWkio4paPEQkLoajj4eIjHy6q0VERERMo+AhIiIiplHwEBEREdMoeIiIiIhpFDxERETENAoeIiIiYhoFDxERETGNgoeIiIiYRsFDRERETKPgISIiIqZR8BARERHTKHiIiIiIaRQ8RERExDQKHiIiImIaBQ8RERExjYKHiIiImEbBQ0REREyj4CEiIiKmUfAQERER0yh4iIiIiGkUPERERMQ0Ch4iIiJiGgUPERERMY2Ch4iIiJhGwUNERERMo+AhIiIiplHwEBEREdMoeIiIiIhpFDxERETENAoeIiIiYhoFDxERETGNgoeIiIiYxhKNRqPxLkJExrZrrrmGzZs3D9q2a9cucnNzcTqdsW12u52nn36aKVOmmF2iiJjEFu8CRGTsmzVrFn/84x8P275v375Bz2fMmKHQITLG6VKLiAy7K664AovF8rH72O12rr32WnMKEpG4UfAQkWE3depUSktLPzZ8BINBVq9ebWJVIhIPCh4iYoqrr74aq9V6xNcsFguLFi1i+vTpJlclImZT8BARU1x66aV8VF92q9XK1VdfbXJFIhIPCh4iYors7GxOO+20I7Z6RCIRVq1aFYeqRMRsCh4iYpqrrrrqsFYPq9XK6aefTk5OTpyqEhEzKXiIiGkuueQSbLbD7+K/6qqr4lCNiMSDgoeImCYtLY1zzjlnUPhISEjgoosuimNVImImBQ8RMdWVV15JOBwGwGazce655+J2u+NclYiYRcFDREx13nnn4XA4AKNT6ZVXXhnnikTETAoeImKqpKQkLrnkEgAmTJjAihUr4lyRiJhJwUNETHf55ZcDsGrVKpKTk+NcjYiYScFDREz3+c9/nuzs7FgAEZHxQ7PTisiQi0ajeL1e/H4/oVAIgHA4THt7e2yflStXkpGRQXl5OWBcghnY+uFyuXA6nSQmJppbvIgMK0v0o8YwFhHBmLytpqaGuro6mpqaaGhooL6+nqamJhobG2lurMPrbaW9vZ1AoAN/oJNAR9eQfX6i3YYz1YHL5SQtLQ2nM430DA8Tc3LJycnB4/GQnZ0dW58yZQqpqalD9vkiMrQUPEQEr9fLjh072L17N1VVVVRVVVFduZvq6ioO1jYQDkdi+6Y5bORmWPE4o2SnBvGkRXE7wJkEzmRITTLW3Q7juX3ACOnpKR9dQ0cP9Ib6n7d1QKDbWPzd0N4Fvk4I9EBrABr8NhrarTT6IjT5QkQi/V9lWRkuCgqmUjhtJgUFhRQUFDBt2jSKi4uZMmXKUP7TicinpOAhMo50d3ezdetWtm3bxo4dO6h4/122V7xPTV0TABPsCRRMtFOQGaIwK0yBBwo8MDUL8tLBkwZJ9jifxBFEotDUDo3tsK8ZqhqhuhmqmxOobrFT1RimzW+kGmdqMsWzZzH3hIUUFxczd+5cSktL8Xg8cT4LkfFBwUNkDKusrOTVV1+lvLyc8k2vU77lXbp7gkywJzAtx0rJpCBz8qAkH+bkQXEeJFjiXfXw8HbC3gaoOAjba6Ci1sb2WhtV9d1Eo5A7MYvSxSdSWrqI0tJSTj31VA1sJjIMFDxExpCKigrKysoo2/gir/zjH7T5/CRPsLKgIIElhUEWT4Ml02D6xHhXOnK0BODtvbBpL7xdZeXtqgQa2oLYbVaWLC5l+efPYtmyZZx00kkkJSXFu1yRUU/BQ2QUa29v5/nnn+fZZ9dT9tIG6htbcKfaOG1WlOVzwpw2G+ZNBtvhM9HLx9jXDK/vgpe3Q9kOO3vrgyQnJXLySZ/lnBUXsHLlSqZNmxbvMkVGJQUPkVGmoaGBZ555hqefeoKyspcJh0OcPsfKmSUhlpfAwgKwaoSeIbWvGcoqYGOFhb9ts9LqD3HC3FmsvPhSVq5cyYIFC+JdosiooeAhMgr09PSwYcMG/vvhB3nq6Wew2yycURLl/AURLiyFia54Vzh+hCPwxm54bgs8UT6BPbU9zJxeyOVXXM2aNWuYOnVqvEsUGdEUPERGsHfffZc//OEP/OnRP+Lz+Tl7vpVrTw1x7nxwaFytuItG4Z0q+O9X4dHXbfg6I5x91pms+fJXWblyJVarrnGJfJiCh8gItHHjRn5xz0/Z8GIZs/PtrDk1yFWnQK5ushixeoKwfjM8+E8rG7ZFmDoln2/ddgdr1qyJzcYrIgoeIiNGNBrlqaee4qc/uZvyLe+yfK6d21cE+cI8sIzRW1zHqspGWPu8hQf/mUBKSio33Xwrt956K2lpafEuTSTuFDxERoC3336bb916M6+/8RYXL7Zwx3kRFhXFuyo5Xk3t8JsN8H9espGYlMZPfnoPa9as0SUYGdcUPETiqKGhgdu/cxuPPPooJ8+08R9XBBU4xqDWAPz4Sfj9xgTmFM/iN7/9T0499dR4lyUSFwoeInHyt7/9jTXXXEmyxc8vLguy6sR4VyTDbWctfOtRKy9si3DHHd/lrrvuwm4fgWPQiwwjBQ8Rk/X09PDd736X++67jyuWWvjttRHSkj/5OBk7/utluPURKyVzT+BPf35cg5HJuKLgIWKijo4OLlp5Pm+9/i9+e02IK0+Jd0USLx/UwZd+Z2e/N4W/b3iJ0tLSeJckYgoFDxGTeL1ezltxNru3b+aF24PM1zhTx2TxD+CdSnAmQfv/i3c1x6ezFy65z8qruxN5Zv1zLF++PN4liQw7DawsYoJgMMi5Z3+B/bs2889/U+gQgyMRnrk1zNlzezn/vHPZtm1bvEsSGXYKHiImuPPOO3lv2xY23BFkVm68q5GRJNEGf74pzOLCMJeuuphAIBDvkkSGlS3eBYiMdS+88AJr167lof8VZfakeFcz9Lbth3/sgOommD0JTpvNYef51h7YWQe2BLhiqbHvhveMfg4l+XDxYnAfYXDPt/YY7+3thJNmwPljdC42awL86cYQ87+/j5u/cRPrHnwo3iWJDBv18RAZRpFIhBPmFjPbuYe/3BKJdzlDKhKFH/4FfvaMsd7HZoX//UW44/z+EVe//hD87kVIToRHvw5X/tbo39BnahZsvBOmTTSeR6Nw25/g3ucHbWryhQAACThJREFUf+aKBbCn3ggsY6GPx4c9sQlW3W9h8+bNzJ8/P97liAwLXWoRGUYvvfQSFTt2cfcXx1boAFj3D/j3p43QkZkKX10G+RkQCsP3/gcef+vwY7qD8MVfw9zJcPNZUOAxtu9rhnue7d/vsbf6Q4fFAucvhJNnwl+3GKFjrLp4MZww1cb9998X71JEho2Ch8gwevLJJ1kyw86cvHhXMrR6Q/D9x4x1twP23Q8PfAWqfg1TMo3tP37SaLkYKBqFC0rhrbvhvqvhxe/1v7btQP/63U/2rz//HVj/bXjtR/BfXxme8xkpLBa45pQgTz35FyKRsRdWRUDBQ2RYvfXGPzl9VjDeZQy5vQ3Q2G6snzHXaMloCYCvC849dIVgRw3U+w4/9sbP969PnwhZTmO9xW88BsP9rRqZqfCFE/r3//LnwDXGJ3o9vRi8vgAffPBBvEsRGRbqXCoyjOrrG5g8Bi/V727oX39ik7EcSU0r5LoHb/N8aIJWR6LxGD70B/6Blv7104shYcDMvAkW43KOr/PYax/ppmYZj3V1dRQXF8e3GJFhoOAhMowSEhIGdbwcK+wDJledP5WPnNguKfHwbRM+9K2T8KF214HDxwfDg18LhWF/89HXORr1hS7NYCtjlYKHyDDKy8ujqmns/VIWZfevO5MG972oOAipSUZfD4vl8GOPtG2gLKdxOcXXCeVVRufVvlaPN/aAv/v46x/JqhqNx7y8MdYxSOQQ9fEQGUYnLT2djduP8Gf/KDcrFxYWGOuv7oL/edP4S31fMyy9Cwpugfl3Gp1Qj8VFi4zH2ja46SFo74JmP/zkqaGofmTbWAHZWemaOE7GLAUPkWG0evVq3t/fy6a98a5k6P3qCqN/RjQKl/0Gcm6Eom8aLRU2KzxwnTEq57H4yer+Sy7/9yXIuB6yvwZl2/vH+hiLwhF46FU7qy+7AssnNQ2JjFIKHiLDaOnSpZx04iLufNx62K2lo92yOfD6XVBaaASNZr8RNM6cB4/eCCdOP/b3zks3brldUGA8D0eMTqrrvw1nlAxJ+SPSulegqiHMLbfcEu9SRIaNRi4VGWabNm3ilKUn89PVYW5bEe9qhkd3EHbXG7fHJg/xlaUGn9GvY/oYbukA2FkLi39o5Ws33covfvHLeJcjMmwUPERM8Ktf/Yo7v3cHr/xbhJNmxLsaGWk6e+Gku+w4sj/DP199HbvdHu+SRIaNgoeICaLRKBetvIBXyv7OX78d4uSZw/+ZH9TBqqMcefuDOqMj6LzJR7f/IzfCCVOOvbahMhbO0dcJ56218UGzk01vb6agoGD4P1QkjnQ7rYgJLBYLjz3+BF+6/DLOvGc9T30zzBfmDe9n9oagsvHo94Wj37/nGO9WGWqj/RzbOuCcX9rZ53OxsaxMoUPGBbV4iJgoHA7zleu+zCOPPML3L4zwg4uMKdFl/HlzD1z5+0RCtixeKnuF6dOPozeuyCiirzwRE1mtVtY9+BC/WnsvP3/Ozpn32DjYGu+qxEyhMPz4CTjlLgvT553Gm5vKFTpkXFGLh0icbN68mS9dtoqGuv38cGWIr5957ONeyOjw8nb41p/sfFBnYe29v+aGG27QeB0y7qjFQyROFi5cSPmWbdx48+18/y+JzP2enWfK412VDIe9DXDxr60s/3fInfU5tmzdxte+9jWFDhmX1OIhMgLs27ePO27/Do89/hc+O8PG7SuCXFA6eGZWGX121MAv/5rAo6/D9GnTWPsf93P22WfHuyyRuFLwEBlB3nzzTX7+s5/y7HPPMXOSndvO6eXKpTBBwzqMKq/tgl88l8BzW6LMnF7Id+74PldffTU2m66liSh4iIxAe/fu5f777+OB//w9SfYoq5eEuOoUOGVWvCuTj9LWAY+/Bb9/OZEtlb2ULvwMN9/yLa644gpNcS8ygIKHyAhWV1fHww8/zEPr/sAHu/cyvzCRa0/p5ZIlkJ8R7+qkqxde2AZ/fDWBv26F5KQkLr38S3z5y1/hxBNPjHd5IiOSgofIKPHaa6/x4IPrePyxP+MPdLJomp2LSoNcWApz8uJd3fjREoC/boGny6288B5090Y4/dRTWHPdV7nkkktwOBzxLlFkRFPwEBllenp62LhxI08//RTrn36ShqZWZuYlcuacXpaXwOeKISM13lWOHcEwvLUHyiqgbIeN1z4IY7XaWL58GSsvuoQLLriAnJyceJcpMmooeIiMYpFIhDfeeIP169dT9tILbHn3PaLRKPML7Syb1ctpxbC4yJhSXo5OZy9sroLXd8PLO6z8ayd0dIeZkp/DsjO+wLnnruCcc87B6XTGu1SRUUnBQ2QM8Xq9vPLKK5SVlVH20t+p2LGbaDTKZE8iiwtDLCmKsLgI5k9VqwhATxC218DblcayqSqR7QeChMJRJnoyWHbGmSxbtpzly5drdFGRIaLgITKG+Xw+3nnnHTZt2sSmt97k7U1vUFPXBEBOup2S/Cglk0KU5Bv9RGblgictzkUPg65e2F0PO2rh/QOwvTaB92vsVNb3EgpHSU1JZuGC+Sz57FIWL17MkiVLNGGbyDBR8BAZZ2pra3nvvfd4//332bFjB++9W86OnR/gD3QBkJJkpSDbRmFWkIKsCAUemJplXK7JTjMeU5PifBID9IagsR0afFDvhf0tUN0E1c0W9rXaqW6K0tAWBMBmszK9aApz5y1gTslcSkpKKCkpYfbs2brlVcQkCh4iAhijp+7Zs4fq6urYUrX3A6qrq6mtb2bgV0XyBCsT3TZy3VE8qSFSJ0RwJoPbAc4kI5g4k411MB5tA37X01P61zt6+qesB/B1QiRqXAbxd0N7l7Et0AP+Lgh0Q2PATpM/gQZvhFZ/cNB5uNNSKSiYTEHhDAoKiygsLKSgoICioiJmzpxJYmLicPzzichRUvAQkU/U29tLU1MTDQ0N1NfX09TURH19PQ0NDTQ1NeFv9xHw+/B6W2lvb8fvDxDo6CLQ0XXMn5lot+FMdeByOUlLS8PpTMOZ5ibV6cLj8eDxeMjOziY3NxePx8PEiRPJyckhNVWdV0RGMgUPERl2Xq831mISDodpb2+PvZaUlERycnLseWpqKna7xogXGasUPERERMQ0CfEuQERERMYPBQ8RERExjQ0oj3cRIiIiMj78f8IsIh5b3H4ZAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n\n# Feel free to comment out if you have\n# not installed pygraphviz\nImage(interview_graph.get_graph().draw_png())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ask_question\n", + "-- [AIMessage(content=\"Yes, that's correct. I am focusing on the technical aspects of million-plus token context window language models and their impact on RAG systems. Can you provide more insight into how these large context window models affect the performance and capabilities of RAG systems?\", name\n", + "answer_question\n", + "-- [AIMessage(content='The introduction of large context window language models, such as Gemini 1.5 with a 1 million token context window, has raised concerns in the AI community regarding its impact on Retrieval-Augmented Generation (RAG) systems. RAG systems represent a significant advancement over t\n", + "ask_question\n", + "-- [AIMessage(content='Thank you for the detailed explanation and resources. Could you elaborate on the specific challenges and opportunities that million-plus token context window language models present for RAG systems in terms of improving generation quality, addressing data biases, and the potentia\n", + "answer_question\n", + "-- [AIMessage(content='Million-plus token context window language models present both challenges and opportunities for RAG systems. Challenges include the increased computational cost and complexity associated with processing larger context windows, potential issues with retaining factual accuracy when\n", + "ask_question\n", + "-- [AIMessage(content='Thank you for the detailed information and references provided. It has been insightful to understand both the challenges and opportunities that million-plus token context window language models bring to RAG systems. I appreciate your assistance in shedding light on this complex t\n", + "answer_question\n", + "-- [AIMessage(content=\"You're welcome! If you have any more questions or need further assistance in the future, feel free to reach out. Good luck with your article on RAG systems and million-plus token context window language models!\\n\\nCitations:\\n\\n[1]: https://www.nerdwallet.com/article/finance/exam\n", + "__end__\n", + "-- [AIMessage(content='So you said you were writing an article on Impact of million-plus token context window language models on RAG?', name='Subject Matter Expert'), AIMessage(content=\"Yes, that's correct. I am focusing on the technical aspects of million-plus token context window language models and \n" + ] + } + ], + "source": [ + "final_step = None\n\ninitial_state = {\n \"editor\": perspectives.editors[0],\n \"messages\": [\n AIMessage(\n content=f\"So you said you were writing an article on {example_topic}?\",\n name=\"Subject_Matter_Expert\",\n )\n ],\n}\nasync for step in interview_graph.astream(initial_state):\n name = next(iter(step))\n print(name)\n print(\"-- \", str(step[name][\"messages\"])[:300])\n if END in step:\n final_step = step" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "final_state = next(iter(final_step.values()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Refine Outline\n", + "\n", + "At this point in STORM, we've conducted a large amount of research from different perspectives. It's time to refine the original outline based on these investigations. Below, create a chain using the LLM with a long context window to update the original outline." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "refine_outline_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are a Wikipedia writer. You have gathered information from experts and search engines. Now, you are refining the outline of the Wikipedia page. \\\nYou need to make sure that the outline is comprehensive and specific. \\\nTopic you are writing about: {topic} \n\nOld outline:\n\n{old_outline}\"\"\",\n ),\n (\n \"user\",\n \"Refine the outline based on your conversations with subject-matter experts:\\n\\nConversations:\\n\\n{conversations}\\n\\nWrite the refined Wikipedia outline:\",\n ),\n ]\n)\n\n# Using turbo preview since the context can get quite long\nrefine_outline_chain = refine_outline_prompt | long_context_llm.with_structured_output(\n Outline\n)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "refined_outline = refine_outline_chain.invoke(\n {\n \"topic\": example_topic,\n \"old_outline\": initial_outline.as_str,\n \"conversations\": \"\\n\\n\".join(\n f\"### {m.name}\\n\\n{m.content}\" for m in final_state[\"messages\"]\n ),\n }\n)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Impact of million-plus token context window language models on RAG\n", + "\n", + "## Introduction\n", + "\n", + "Provides a brief overview of million-plus token context window language models and their relevance to Retrieval-Augmented Generation (RAG) systems, setting the stage for a deeper exploration of their impact.\n", + "\n", + "## Background\n", + "\n", + "A foundational section to understand the core concepts involved.\n", + "\n", + "### Million-Plus Token Context Window Language Models\n", + "\n", + "Explains what million-plus token context window language models are, including notable examples like Gemini 1.5, focusing on their architecture, training data, and the evolution of their applications.\n", + "\n", + "### Retrieval-Augmented Generation (RAG)\n", + "\n", + "Describes the RAG framework, its unique approach of combining retrieval and generation models for enhanced natural language processing, and its significance in the AI landscape.\n", + "\n", + "## Impact on RAG Systems\n", + "\n", + "Delves into the effects of million-plus token context window language models on RAG, highlighting both the challenges and opportunities presented.\n", + "\n", + "### Performance and Efficiency\n", + "\n", + "Discusses how large context window models influence RAG performance, including aspects of latency, computational demands, and overall efficiency.\n", + "\n", + "### Generation Quality and Diversity\n", + "\n", + "Explores the impact on generation quality, the potential for more accurate and diverse outputs, and how these models address data biases and factual accuracy.\n", + "\n", + "### Technical Challenges\n", + "\n", + "Identifies specific technical hurdles such as prompt template design, context length limitations, and similarity searches in vector databases, and how they affect RAG systems.\n", + "\n", + "### Opportunities and Advancements\n", + "\n", + "Outlines the new capabilities and improvements in agent interaction, information retrieval, and response relevance that these models bring to RAG systems.\n", + "\n", + "## Future Directions\n", + "\n", + "Considers ongoing research and potential future developments in the integration of million-plus token context window language models with RAG systems, including speculation on emerging trends and technologies.\n", + "\n", + "## Conclusion\n", + "\n", + "Summarizes the key points discussed in the article, reaffirming the significant impact of million-plus token context window language models on RAG systems.\n" + ] + } + ], + "source": [ + "print(refined_outline.as_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Article\n", + "\n", + "Now it's time to generate the full article. We will first divide-and-conquer, so that each section can be tackled by an individual llm. Then we will prompt the long-form LLM to refine the finished article (since each section may use an inconsistent voice).\n", + "\n", + "#### Create Retriever\n", + "\n", + "The research process uncovers a large number of reference documents that we may want to query during the final article-writing process.\n", + "\n", + "First, create the retriever:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.vectorstores import SKLearnVectorStore\nfrom langchain_core.documents import Document\nfrom langchain_openai import OpenAIEmbeddings\n\nembeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\nreference_docs = [\n Document(page_content=v, metadata={\"source\": k})\n for k, v in final_state[\"references\"].items()\n]\n# This really doesn't need to be a vectorstore for this size of data.\n# It could just be a numpy matrix. Or you could store documents\n# across requests if you want.\nvectorstore = SKLearnVectorStore.from_documents(\n reference_docs,\n embedding=embeddings,\n)\nretriever = vectorstore.as_retriever(k=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content='In Retrieval Augmented Generation (RAG), a longer context augments our model with more information. For LLMs that power agents, such as chatbots, longer context means more tools and capabilities. When summarizing, longer context means more comprehensive summaries. There exist plenty of use-cases for LLMs that are unlocked by longer context lengths.', metadata={'id': '20454848-23ac-4649-b083-81980532a77b', 'source': 'https://www.anyscale.com/blog/fine-tuning-llms-for-longer-context-and-better-rag-systems'}),\n", + " Document(page_content='By the way, the context limits differ among models: two Claude models offer a 100K token context window, which works out to about 75,000 words, which is much higher than most other LLMs. The ...', metadata={'id': '1ee2d2bb-8f8e-4a7e-b45e-608b0804fe4c', 'source': 'https://www.infoworld.com/article/3712227/what-is-rag-more-accurate-and-reliable-llms.html'}),\n", + " Document(page_content='Figure 1: LLM response accuracy goes down when context needed to answer correctly is found in the middle of the context window. The problem gets worse with larger context models. The problem gets ...', metadata={'id': 'a41d69e6-62eb-4abd-90ad-0892a2836cba', 'source': 'https://medium.com/@jm_51428/long-context-window-models-vs-rag-a73c35a763f2'}),\n", + " Document(page_content='To improve performance, we used retrieval-augmented generation (RAG) to prompt an LLM with accurate up-to-date information. As a result of using RAG, the writing quality of the LLM improves substantially, which has implications for the practical usability of LLMs in clinical trial-related writing.', metadata={'id': 'e1af6e30-8c2b-495b-b572-ac6a29067a94', 'source': 'https://arxiv.org/abs/2402.16406'})]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever.invoke(\"What's a long context LLM anyway?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate Sections\n", + "\n", + "Now you can generate the sections using the indexed docs." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class SubSection(BaseModel):\n subsection_title: str = Field(..., title=\"Title of the subsection\")\n content: str = Field(\n ...,\n title=\"Full content of the subsection. Include [#] citations to the cited sources where relevant.\",\n )\n\n @property\n def as_str(self) -> str:\n return f\"### {self.subsection_title}\\n\\n{self.content}\".strip()\n\n\nclass WikiSection(BaseModel):\n section_title: str = Field(..., title=\"Title of the section\")\n content: str = Field(..., title=\"Full content of the section\")\n subsections: Optional[List[Subsection]] = Field(\n default=None,\n title=\"Titles and descriptions for each subsection of the Wikipedia page.\",\n )\n citations: List[str] = Field(default_factory=list)\n\n @property\n def as_str(self) -> str:\n subsections = \"\\n\\n\".join(\n subsection.as_str for subsection in self.subsections or []\n )\n citations = \"\\n\".join([f\" [{i}] {cit}\" for i, cit in enumerate(self.citations)])\n return (\n f\"## {self.section_title}\\n\\n{self.content}\\n\\n{subsections}\".strip()\n + f\"\\n\\n{citations}\".strip()\n )\n\n\nsection_writer_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are an expert Wikipedia writer. Complete your assigned WikiSection from the following outline:\\n\\n\"\n \"{outline}\\n\\nCite your sources, using the following references:\\n\\n\\n{docs}\\n\",\n ),\n (\"user\", \"Write the full WikiSection for the {section} section.\"),\n ]\n)\n\n\nasync def retrieve(inputs: dict):\n docs = await retriever.ainvoke(inputs[\"topic\"] + \": \" + inputs[\"section\"])\n formatted = \"\\n\".join(\n [\n f'\\n{doc.page_content}\\n'\n for doc in docs\n ]\n )\n return {\"docs\": formatted, **inputs}\n\n\nsection_writer = (\n retrieve\n | section_writer_prompt\n | long_context_llm.with_structured_output(WikiSection)\n)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "## Background\n", + "\n", + "To fully appreciate the impact of million-plus token context window language models on Retrieval-Augmented Generation (RAG) systems, it's essential to first understand the foundational concepts that underpin these technologies. This background section provides a comprehensive overview of both million-plus token context window language models and RAG, setting the stage for a deeper exploration of their integration and subsequent impacts on artificial intelligence and natural language processing.\n", + "\n", + "### Million-Plus Token Context Window Language Models\n", + "\n", + "Million-plus token context window language models, such as Gemini 1.5, represent a significant leap forward in the field of language modeling. These models are designed to process and understand large swathes of text, sometimes exceeding a million tokens in a single pass. The ability to handle such vast amounts of information at once allows for a deeper understanding of context and nuance, which is crucial for generating coherent and relevant text outputs. The development of these models involves sophisticated architecture and extensive training data, pushing the boundaries of what's possible in natural language processing. Over time, the applications of these models have evolved, extending their utility beyond mere text generation to complex tasks like sentiment analysis, language translation, and more.\n", + "\n", + "### Retrieval-Augmented Generation (RAG)\n", + "\n", + "The Retrieval-Augmented Generation framework represents a novel approach in the realm of artificial intelligence, blending the strengths of both retrieval and generation models to enhance natural language processing capabilities. At its core, RAG leverages a two-step process: initially, it uses a query to retrieve relevant documents or data from a knowledge base; this information is then utilized to inform and guide the generation of responses by a language model. This method addresses the limitations of fixed context windows by converting text to vector embeddings, facilitating a dynamic and flexible interaction with a vast array of information. RAG's unique approach has cemented its significance in the AI landscape, offering a pathway to more accurate, informative, and contextually relevant text generation.\n" + ] + } + ], + "source": [ + "section = await section_writer.ainvoke(\n {\n \"outline\": refined_outline.as_str,\n \"section\": refined_outline.sections[1].section_title,\n \"topic\": example_topic,\n }\n)\nprint(section.as_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate final article\n", + "\n", + "Now we can rewrite the draft to appropriately group all the citations and maintain a consistent voice." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.output_parsers import StrOutputParser\n\nwriter_prompt = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"You are an expert Wikipedia author. Write the complete wiki article on {topic} using the following section drafts:\\n\\n\"\n \"{draft}\\n\\nStrictly follow Wikipedia format guidelines.\",\n ),\n (\n \"user\",\n 'Write the complete Wiki article using markdown format. Organize citations using footnotes like \"[1]\",'\n \" avoiding duplicates in the footer. Include URLs in the footer.\",\n ),\n ]\n)\n\nwriter = writer_prompt | long_context_llm | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Impact of Million-Plus Token Context Window Language Models on Retrieval-Augmented Generation (RAG)\n", + "\n", + "The integration of million-plus token context window language models into Retrieval-Augmented Generation (RAG) systems marks a pivotal advancement in the field of artificial intelligence (AI) and natural language processing (NLP). This article delves into the background of both technologies, explores their convergence, and examines the profound effects of this integration on the capabilities and applications of AI-driven language models.\n", + "\n", + "## Contents\n", + "\n", + "1. [Background](#Background)\n", + " 1. [Million-Plus Token Context Window Language Models](#Million-Plus-Token-Context-Window-Language-Models)\n", + " 2. [Retrieval-Augmented Generation (RAG)](#Retrieval-Augmented-Generation-(RAG))\n", + "2. [Integration of Million-Plus Token Context Window Models and RAG](#Integration-of-Million-Plus-Token-Context-Window-Models-and-RAG)\n", + "3. [Impact on Natural Language Processing](#Impact-on-Natural-Language-Processing)\n", + "4. [Applications](#Applications)\n", + "5. [Challenges and Limitations](#Challenges-and-Limitations)\n", + "6. [Future Directions](#Future-Directions)\n", + "7. [Conclusion](#Conclusion)\n", + "8. [References](#References)\n", + "\n", + "## Background\n", + "\n", + "### Million-Plus Token Context Window Language Models\n", + "\n", + "Million-plus token context window language models, exemplified by systems like Gemini 1.5, have revolutionized language modeling by their ability to process and interpret extensive texts, potentially exceeding a million tokens in a single analysis[1]. The capacity to manage such large volumes of data enables these models to grasp context and subtlety to a degree previously unattainable, enhancing their effectiveness in generating text that is coherent, relevant, and nuanced. The development of these models has been characterized by innovative architecture and the utilization of vast training datasets, pushing the envelope of natural language processing capabilities[2].\n", + "\n", + "### Retrieval-Augmented Generation (RAG)\n", + "\n", + "RAG systems represent an innovative paradigm in AI, merging the strengths of retrieval-based and generative models to improve the quality and relevance of text generation[3]. By initially retrieving related documents or data in response to a query, and subsequently using this information to guide the generation process, RAG overcomes the limitations inherent in fixed context windows. This methodology allows for dynamic access to a broad range of information, significantly enhancing the model's ability to generate accurate, informative, and contextually appropriate responses[4].\n", + "\n", + "## Integration of Million-Plus Token Context Window Models and RAG\n", + "\n", + "The integration of million-plus token context window models with RAG systems has been a natural progression in the quest for more sophisticated NLP solutions. By combining the extensive contextual understanding afforded by large context window models with the dynamic, information-rich capabilities of RAG, researchers and developers have been able to create AI systems that exhibit unprecedented levels of understanding, coherence, and relevance in text generation[5].\n", + "\n", + "## Impact on Natural Language Processing\n", + "\n", + "The fusion of these technologies has had a significant impact on the field of NLP, leading to advancements in several key areas:\n", + "- **Enhanced Understanding**: The combined system exhibits a deeper comprehension of both the immediate context and broader subject matter[6].\n", + "- **Improved Coherence**: Generated text is more coherent over longer passages, maintaining consistency and relevance[7].\n", + "- **Increased Relevance**: Outputs are more contextually relevant, drawing accurately from a wider range of sources[8].\n", + "\n", + "## Applications\n", + "\n", + "This technological convergence has broadened the applicability of NLP systems in numerous fields, including but not limited to:\n", + "- **Automated Content Creation**: Generating written content that is both informative and contextually appropriate for various platforms[9].\n", + "- **Customer Support**: Providing answers that are not only accurate but also tailored to the specific context of user inquiries[10].\n", + "- **Research Assistance**: Assisting in literature review and data analysis by retrieving and synthesizing relevant information from vast databases[11].\n", + "\n", + "## Challenges and Limitations\n", + "\n", + "Despite their advancements, the integration of these technologies faces several challenges:\n", + "- **Computational Resources**: The processing of million-plus tokens and the dynamic retrieval of relevant information require significant computational power[12].\n", + "- **Data Privacy and Security**: Ensuring the confidentiality and integrity of the data accessed by these systems poses ongoing concerns[13].\n", + "- **Bias and Fairness**: The potential for inheriting and amplifying biases from training data remains a critical issue to address[14].\n", + "\n", + "## Future Directions\n", + "\n", + "Future research is likely to focus on optimizing computational efficiency, enhancing the models' ability to understand and generate more diverse and nuanced text, and addressing ethical considerations associated with AI and NLP technologies[15].\n", + "\n", + "## Conclusion\n", + "\n", + "The integration of million-plus token context window language models with RAG systems represents a milestone in the evolution of natural language processing, offering enhanced capabilities that have significant implications across various applications. As these technologies continue to evolve, they promise to further transform the landscape of AI-driven language models.\n", + "\n", + "## References\n", + "\n", + "1. Gemini 1.5 Documentation. (n.d.).\n", + "2. The Evolution of Language Models. (2022).\n", + "3. Introduction to Retrieval-Augmented Generation. (2021).\n", + "4. Leveraging Large Context Windows for NLP. (2023).\n", + "5. Integrating Context Window Models with RAG. (2023).\n", + "6. Deep Learning in NLP. (2020).\n", + "7. Coherence in Text Generation. (2019).\n", + "8. Contextual Relevance in AI. (2021).\n", + "9. Applications of NLP in Content Creation. (2022).\n", + "10. AI in Customer Support. (2023).\n", + "11. NLP for Research Assistance. (2021).\n", + "12. Computational Challenges in NLP. (2022).\n", + "13. Data Privacy in AI Systems. (2020).\n", + "14. Addressing Bias in AI. (2021).\n", + "15. Future of NLP Technologies. (2023)." + ] + } + ], + "source": [ + "for tok in writer.stream({\"topic\": example_topic, \"draft\": section.as_str}):\n print(tok, end=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Final Flow\n", + "\n", + "Now it's time to string everything together. We will have 6 main stages in sequence:\n", + ".\n", + "1. Generate the initial outline + perspectives\n", + "2. Batch converse with each perspective to expand the content for the article\n", + "3. Refine the outline based on the conversations\n", + "4. Index the reference docs from the conversations\n", + "5. Write the individual sections of the article\n", + "6. Write the final wiki\n", + "\n", + "The state tracks the outputs of each stage." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "class ResearchState(TypedDict):\n topic: str\n outline: Outline\n editors: List[Editor]\n interview_results: List[InterviewState]\n # The final sections output\n sections: List[WikiSection]\n article: str" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n\n\nasync def initialize_research(state: ResearchState):\n topic = state[\"topic\"]\n coros = (\n generate_outline_direct.ainvoke({\"topic\": topic}),\n survey_subjects.ainvoke(topic),\n )\n results = await asyncio.gather(*coros)\n return {\n **state,\n \"outline\": results[0],\n \"editors\": results[1].editors,\n }\n\n\nasync def conduct_interviews(state: ResearchState):\n topic = state[\"topic\"]\n initial_states = [\n {\n \"editor\": editor,\n \"messages\": [\n AIMessage(\n content=f\"So you said you were writing an article on {topic}?\",\n name=\"Subject_Matter_Expert\",\n )\n ],\n }\n for editor in state[\"editors\"]\n ]\n # We call in to the sub-graph here to parallelize the interviews\n interview_results = await interview_graph.abatch(initial_states)\n\n return {\n **state,\n \"interview_results\": interview_results,\n }\n\n\ndef format_conversation(interview_state):\n messages = interview_state[\"messages\"]\n convo = \"\\n\".join(f\"{m.name}: {m.content}\" for m in messages)\n return f'Conversation with {interview_state[\"editor\"].name}\\n\\n' + convo\n\n\nasync def refine_outline(state: ResearchState):\n convos = \"\\n\\n\".join(\n [\n format_conversation(interview_state)\n for interview_state in state[\"interview_results\"]\n ]\n )\n\n updated_outline = await refine_outline_chain.ainvoke(\n {\n \"topic\": state[\"topic\"],\n \"old_outline\": state[\"outline\"].as_str,\n \"conversations\": convos,\n }\n )\n return {**state, \"outline\": updated_outline}\n\n\nasync def index_references(state: ResearchState):\n all_docs = []\n for interview_state in state[\"interview_results\"]:\n reference_docs = [\n Document(page_content=v, metadata={\"source\": k})\n for k, v in interview_state[\"references\"].items()\n ]\n all_docs.extend(reference_docs)\n await vectorstore.aadd_documents(all_docs)\n return state\n\n\nasync def write_sections(state: ResearchState):\n outline = state[\"outline\"]\n sections = await section_writer.abatch(\n [\n {\n \"outline\": refined_outline.as_str,\n \"section\": section.section_title,\n \"topic\": state[\"topic\"],\n }\n for section in outline.sections\n ]\n )\n return {\n **state,\n \"sections\": sections,\n }\n\n\nasync def write_article(state: ResearchState):\n topic = state[\"topic\"]\n sections = state[\"sections\"]\n draft = \"\\n\\n\".join([section.as_str for section in sections])\n article = await writer.ainvoke({\"topic\": topic, \"draft\": draft})\n return {\n **state,\n \"article\": article,\n }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n\nbuilder_of_storm = StateGraph(ResearchState)\n\nnodes = [\n (\"init_research\", initialize_research),\n (\"conduct_interviews\", conduct_interviews),\n (\"refine_outline\", refine_outline),\n (\"index_references\", index_references),\n (\"write_sections\", write_sections),\n (\"write_article\", write_article),\n]\nfor i in range(len(nodes)):\n name, node = nodes[i]\n builder_of_storm.add_node(name, node)\n if i > 0:\n builder_of_storm.add_edge(nodes[i - 1][0], name)\n\nbuilder_of_storm.add_edge(START, nodes[0][0])\nbuilder_of_storm.add_edge(nodes[-1][0], END)\nstorm = builder_of_storm.compile(checkpointer=MemorySaver())" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(storm.get_graph().draw_png())" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "init_research\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', sections=[Section(section_title='Introduction', description='Overview of Groq, NVIDIA, Llamma.cpp, and their significance in the field of La\n", + "conduct_interviews\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', sections=[Section(section_title='Introduction', description='Overview of Groq, NVIDIA, Llamma.cpp, and their significance in the field of La\n", + "refine_outline\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", + "index_references\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", + "write_sections\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", + "write_article\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n", + "__end__\n", + "-- {'topic': 'Groq, NVIDIA, Llamma.cpp and the future of LLM Inference', 'outline': Outline(page_title='Groq, NVIDIA, Llamma.cpp and the Future of LLM Inference', sections=[Section(section_title='Introduction', description='An overview of the significance and roles of Groq, NVIDIA, and Llamma.cpp in th\n" + ] + } + ], + "source": [ + "config = {\"configurable\": {\"thread_id\": \"my-thread\"}}\nasync for step in storm.astream(\n {\n \"topic\": \"Groq, NVIDIA, Llamma.cpp and the future of LLM Inference\",\n },\n config,\n):\n name = next(iter(step))\n print(name)\n print(\"-- \", str(step[name])[:300])" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint = storm.get_state(config)\narticle = checkpoint.values[\"article\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Render the Wiki\n", + "\n", + "Now we can render the final wiki page!" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "# Large Language Model (LLM) Inference Technologies\n", + "\n", + "### Contents\n", + "1. [Introduction](#Introduction)\n", + "2. [Groq's Advancements in LLM Inference](#Groqs-Advancements-in-LLM-Inference)\n", + "3. [NVIDIA's Contributions to LLM Inference](#NVIDIAs-Contributions-to-LLM-Inference)\n", + " 1. [Hardware Innovations](#Hardware-Innovations)\n", + " 2. [Software Solutions](#Software-Solutions)\n", + " 3. [Research and Development](#Research-and-Development)\n", + "4. [Llamma.cpp: Accelerating LLM Inference](#Llammacpp-Accelerating-LLM-Inference)\n", + "5. [The Future of LLM Inference](#The-Future-of-LLM-Inference)\n", + "6. [References](#References)\n", + "\n", + "### Introduction\n", + "\n", + "The advent of million-plus token context window language models, such as Gemini 1.5, has significantly advanced the field of artificial intelligence, particularly in natural language processing (NLP). These models have expanded the capabilities of machine learning in understanding and generating text over vastly larger contexts than previously possible. This leap in technology has paved the way for transformative applications across various domains, including the integration into Retrieval-Augmented Generation (RAG) systems to produce more accurate and contextually rich responses. \n", + "\n", + "### Groq's Advancements in LLM Inference\n", + "\n", + "Groq has introduced the Groq Linear Processor Unit (LPU), a purpose-built hardware architecture for LLM inference. This innovation positions Groq as a leader in efficient and high-performance LLM processing by optimizing the hardware specifically for LLM tasks. The Groq LPU dramatically reduces latency and increases the throughput of LLM inferences, facilitating advancements in a wide range of applications, from natural language processing to broader artificial intelligence technologies[1].\n", + "\n", + "### NVIDIA's Contributions to LLM Inference\n", + "\n", + "NVIDIA has played a pivotal role in advancing LLM inference through its GPUs, optimized for AI and machine learning workloads, and specialized software frameworks. The company's GPU architecture and software solutions, such as the CUDA Deep Neural Network library (cuDNN) and the TensorRT inference optimizer, are designed to accelerate computational processes and improve LLM performance. NVIDIA's active participation in research and development further underscores its commitment to enhancing the capabilities of LLMs[1].\n", + "\n", + "#### Hardware Innovations\n", + "\n", + "NVIDIA's GPU architecture facilitates high throughput and parallel processing for LLM inference tasks, significantly reducing inference time and enabling complex models to be used in real-time applications.\n", + "\n", + "#### Software Solutions\n", + "\n", + "NVIDIA's suite of software tools, including cuDNN and TensorRT, optimizes LLM performance on its hardware, streamlining the deployment of LLMs by improving their efficiency and reducing latency.\n", + "\n", + "#### Research and Development\n", + "\n", + "NVIDIA collaborates with academic and industry partners to develop new techniques and models that push the boundaries of LLM technology, aiming to make LLMs more powerful and applicable across a broader range of tasks.\n", + "\n", + "### Llamma.cpp: Accelerating LLM Inference\n", + "\n", + "Llamma.cpp is a framework developed to enhance the speed and efficiency of LLM inference. By integrating specialized hardware, such as Groq's LPU, and optimizing for parallel processing, Llamma.cpp significantly accelerates computation times and reduces energy consumption. The framework supports million-plus token context window models, enabling applications requiring deep contextual understanding and extensive knowledge retrieval[1][2].\n", + "\n", + "### The Future of LLM Inference\n", + "\n", + "The future of LLM inference is poised for transformative changes with advances in purpose-built hardware architectures like Groq's LPU. These innovations promise to enhance the speed and efficiency of LLM processing, leading to more interactive, capable, and integrated AI applications. The potential for advanced hardware and sophisticated LLMs to enable near-instantaneous processing of complex queries and interactions opens new avenues for research and application in various fields, suggesting a future where AI is seamlessly integrated into society[1][2].\n", + "\n", + "### References\n", + "\n", + "[1] \"Groq's LPU: Advancing LLM Inference Efficiency,\" Prompt Engineering. https://promptengineering.org/groqs-lpu-advancing-llm-inference-efficiency/\n", + "\n", + "[2] \"The Speed of Thought: Harnessing the Fastest LLM with Groq's LPU,\" Medium. https://medium.com/@anasdavoodtk1/the-speed-of-thought-harnessing-the-fastest-llm-with-groqs-lpu-11bb00864e9c" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Markdown\n\n# We will down-header the sections to create less confusion in this notebook\nMarkdown(article.replace(\"\\n#\", \"\\n##\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "" ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": ["from IPython.display import Markdown\n\n# We will down-header the sections to create less confusion in this notebook\nMarkdown(article.replace(\"\\n#\", \"\\n##\"))"] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [""] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/examples/subgraph.ipynb b/examples/subgraph.ipynb index cbc582165..ce8cfb3cd 100644 --- a/examples/subgraph.ipynb +++ b/examples/subgraph.ipynb @@ -1,686 +1,686 @@ { - "cells": [ - { - "attachments": { - "71516aef-9c00-4730-a676-a54e90cb6472.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to create subgraphs\n", - "\n", - "For more complex systems, sub-graphs are a useful design principle. Sub-graphs allow you to create and manage different states in different parts of your graph. This allows you build things like [multi-agent teams](./multi_agent/hierarchical_agent_teams.ipynb), where each team can track its own separate state.\n", - "\n", - "![Screenshot 2024-07-11 at 1.01.28 PM.png](attachment:71516aef-9c00-4730-a676-a54e90cb6472.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph" - ] - }, - { - "attachments": { - "9145adc1-ce9d-4a22-8183-e13796d4a388.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simple example\n", - "\n", - "Let's consider a toy example: I have a system that accepts logs and perform two separate sub-tasks. First, it will summarize them. Second, it will summarize any failure modes captured in the logs. I want to perform these two operations in two different sub-graphs.\n", - "\n", - "The most important thing to recognize is the information transfer between the graphs. `Entry Graph` is the parent, and each of the two sub-graphs are defined as nodes in `Entry Graph`. Both subgraphs inherit state from the parent `Entry Graph`; I can access `docs` in each of the sub-graphs simply by specifying it in the sub-graph state (see diagram). Each subgraph can have its own private state. And any values that I want propagated back to the parent `Entry Graph` (for final reporting) simply need to be defined in my `Entry Graph` state (e.g., `summary report` and `failure report`).\n", - "\n", - "![Screenshot 2024-07-12 at 10.35.41 AM.png](attachment:9145adc1-ce9d-4a22-8183-e13796d4a388.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from operator import add\n", - "from typing import List, TypedDict, Optional, Annotated, Dict\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "# The structure of the logs\n", - "class Logs(TypedDict):\n", - " id: str\n", - " question: str\n", - " docs: Optional[List]\n", - " answer: str\n", - " grade: Optional[int]\n", - " grader: Optional[str]\n", - " feedback: Optional[str]\n", - "\n", - "\n", - "# Failure Analysis Sub-graph\n", - "class FailureAnalysisState(TypedDict):\n", - " docs: List[Logs]\n", - " failures: List[Logs]\n", - " fa_summary: str\n", - "\n", - "\n", - "def get_failures(state):\n", - " docs = state[\"docs\"]\n", - " failures = [doc for doc in docs if \"grade\" in doc]\n", - " return {\"failures\": failures}\n", - "\n", - "\n", - "def generate_summary(state):\n", - " failures = state[\"failures\"]\n", - " # Add fxn: fa_summary = summarize(failures)\n", - " fa_summary = \"Poor quality retrieval of Chroma documentation.\"\n", - " return {\"fa_summary\": fa_summary}\n", - "\n", - "\n", - "fa_builder = StateGraph(FailureAnalysisState)\n", - "fa_builder.add_node(\"get_failures\", get_failures)\n", - "fa_builder.add_node(\"generate_summary\", generate_summary)\n", - "fa_builder.add_edge(START, \"get_failures\")\n", - "fa_builder.add_edge(\"get_failures\", \"generate_summary\")\n", - "fa_builder.add_edge(\"generate_summary\", END)\n", - "\n", - "\n", - "# Summarization subgraph\n", - "class QuestionSummarizationState(TypedDict):\n", - " docs: List[Logs]\n", - " qs_summary: str\n", - " report: str\n", - "\n", - "\n", - "def generate_summary(state):\n", - " docs = state[\"docs\"]\n", - " # Add fxn: summary = summarize(docs)\n", - " summary = \"Questions focused on usage of ChatOllama and Chroma vector store.\"\n", - " return {\"qs_summary\": summary}\n", - "\n", - "\n", - "def send_to_slack(state):\n", - " qs_summary = state[\"qs_summary\"]\n", - " # Add fxn: report = report_generation(qs_summary)\n", - " report = \"foo bar baz\"\n", - " return {\"report\": report}\n", - "\n", - "\n", - "def format_report_for_slack(state):\n", - " report = state[\"report\"]\n", - " # Add fxn: formatted_report = report_format(report)\n", - " formatted_report = \"foo bar\"\n", - " return {\"report\": formatted_report}\n", - "\n", - "\n", - "qs_builder = StateGraph(QuestionSummarizationState)\n", - "qs_builder.add_node(\"generate_summary\", generate_summary)\n", - "qs_builder.add_node(\"send_to_slack\", send_to_slack)\n", - "qs_builder.add_node(\"format_report_for_slack\", format_report_for_slack)\n", - "qs_builder.add_edge(START, \"generate_summary\")\n", - "qs_builder.add_edge(\"generate_summary\", \"send_to_slack\")\n", - "qs_builder.add_edge(\"send_to_slack\", \"format_report_for_slack\")\n", - "qs_builder.add_edge(\"format_report_for_slack\", END)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that each sub-graph has its own state, `QuestionSummarizationState` and `FailureAnalysisState`.\n", - " \n", - "After defining each sub-graph, we put everything together." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "image/jpeg": 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0etPurvoGJ+h+Q677s36JDMH4ifl/Kup7gAB6iQAAAAAATd+pMDKaGxfMm4iUSIanlHolC3ebNG8fcIzbNJGehaqSXSZEKQfw8y3JZW06hLrS0mlaFlqlRH0kZH0kJ89nOLn/APQYCS6CJLBERF9BEXQQ75VFVMRVMxb69d9sK0TrUYCc+DjF+8UH90QfBxi/eKD+6IZbC3p4RzNCjATnwcYv3ig/uiD4OMX7xQf3RBbC3p4RzNCjAc4csXH67D9g9raUkNqqsW5sFCJUQubcSlUppKiIy7hpMyP7DG7Pg4xfvFB/dEMthXtlTwjmaFGAnPg4xfvFB/dEHwcYv3ig/uiG2wt6eEczQowE58HGL94oP7og+DjF+8UH90QWwt6eEczQzc+fHq4jsqU6lhhstVLV3P4n3CIuJjF4RBersTrGJDZsvk1vLbV0oNRmrdP7S10CDg+P1kpuTFpoTMhs9UOpYTvIP6SPuH+wZwKqqYpyadP/AH67WaNUAAA4sAAAAAABi7T5exfxir1V8W4iLT5exfxir1V8W48j97F8Y/jCp1QCExj82sPGc71lwXYhMY/NrDxnO9ZcG0fE0/LV60kapZgAAeskAAAAAAAAABOY0aYNzfwXlEiQ7MOY0hXA3GlNtlvp+kiUSknp0GXHpIUY8NtRV16yhqxgx5zaD3kE+2S90/pLXoP7SGK+DjF+8UH90Q+iZw6/1VTMT4X/ADCtEqMBOfBxi/eKD+6IPg4xfvFB/dEJthb08I5mhRgJz4OMX7xQf3RB8HGL94oP7ogthb08I5mhRgJz4OMX7xQf3RDSdRj9c9yw7/H1w2lUjOHx5jdeZfkUPHKNJuEno3jLhr9AyYwo/wDqeEczQ6PATnwcYv3ig/uiD4OMX7xQf3RDbYW9PCOZoUYCc+DjF+8UH90QfBxi/eKD+6ILYW9PCOZoUYCc+DjF+8UH90QfBxi/eKD+6ILYW9PCOZoflwpM/L6GOyZOOwVuy5BJP/ZINpbad76DUa+BdJ7qjLXdPSkHkrKiDSxjj18NiEwajWbcdskEaj6VGRdJn9I9YmuqKrRGqP8A1kgAA5sAAAAAAAGJuvlHHvGSPQcGWGJuvlHHvGSPQcHw9N9xV9PWFU61yAAOSWOyP9HrT7q76BifofkOu+7N+iQoMj/R60+6u+gYn6H5DrvuzfokMwfiJ+X8q6nuAAHqJAAAAAAAAAAAAAAAABoDl0/Nxufv9d640N/jQHLp+bjc/f671xob/ER/lIAACwAAAAAAAAAAAAAAAAGLtPl7F/GKvVXxbiItPl7F/GKvVXxbjyP3sXxj+MKnVAITGPzaw8ZzvWXBdiExj82sPGc71lwbR8TT8tXrSRqlmAAB6yQAAAAAAAAAAAAAAAAAAAAaApPnzZL/AFHjetmN/jQFJ8+bJf6jxvWzEVdQ3+AALAAAAAAAAAAAAAAAAAAAAAYm6+Uce8ZI9BwZYYm6+Uce8ZI9BwfD033FX09YVTrXIAA5JY7I/wBHrT7q76BifofkOu+7N+iQoMj/AEetPurvoGJ+h+Q677s36JDMH4ifl/Kup7gAB6iQAAAAAAAAAAAAAAAAaA5dPzcbn7/XeuNDf44y5aHKW2bZBsyyXCIGSFIyiHax478DqKQk0LjzEc8W+bZIPd3FcSVoenDXgOgNmPKT2cbZL2RTYdkZXNjHjKlusphSWSS0SkoNW842lPxlpLTXXj0cD05xVGVOkbNAAHQAAAAAAAAAAAAAAAABi7T5exfxir1V8W4iLT5exfxir1V8W48j97F8Y/jCp1QCExj82sPGc71lwXYhMY/NrDxnO9ZcG0fE0/LV60kapZgAAeskAAAAAAAAAAAAAAAAAAAAGgKT582S/wBR43rZjcGeZ5RbMsUnZLks7rbSQub6olG0t3c33Etp7VCVKPVS0lwLu6nwHIVXys9lEflXXmYOZYlOOSMUYrmpvUEoyVITJNakbnNbxdrx1MtPtHOqYi1x24AwODZzSbScVg5Jjkw7ClnEs48k2XGecJK1IUe64lKi7ZKi4lx01LgZGM8LAAAaAAAAAAAAAAAAAAAAADE3Xyjj3jJHoODLDE3Xyjj3jJHoOD4em+4q+nrCqda5AAHJLHZH+j1p91d9AxP0PyHXfdm/RIUGR/o9afdXfQMT9D8h133Zv0SGYPxE/L+VdT3AAD1EgAMBYTZ1pbP1VZJRA6mbQ5Jlra5xRb+9uobIz017XU1HqREZFoe9qm6acqWs+AnOxy68LJ3kkb3Ydjl14WTvJI3uxebp348+Tbd6jATnY5deFk7ySN7sOxy68LJ3kkb3YZunfjz5Fu9RgJzscuvCyd5JG92HY5deFk7ySN7sM3Tvx58i3eowE52OXXhZO8kje7DscuvCyd5JG92Gbp348+Rbvf5x/wCkb2I9g+0yPnFawaajJtTk7ie1amoIt/7C5xOi/pNROGOmf9HxsSPZpsgTkljH5u9yncmHvF2zcQi/II/5iNTnDpJaSPikbc2ibEYe1jHFUOWW8q3qjeQ/zC2GW9HEHqlRKQhKiPiZcD4kZkfAzFIzi1tGZQ01lEtpptJJQhEOMSUkXAiIub4EOUYFEVZWXHnyLd6mATnY5deFk7ySN7sOxy68LJ3kkb3Y65unfjz5Fu9RgJzscuvCyd5JG92HY5deFk7ySN7sM3Tvx58i3eowE52OXXhZO8kje7DscuvCyd5JG92Gbp348+RbvUYCc7HLrwsneSRvdj9Tj92g9SymUs+4TsSOaf7dEEf94Zunfjz5Mt3qIBisftXrJmSzKbQ3PhPdTSCb13DVupWSk68d1SVpPQ+jUy1PTUZUc6qZpm0sAABIxdp8vYv4xV6q+LcRFp8vYv4xV6q+LceR+9i+MfxhU6oBCYx+bWHjOd6y4LsQmMfm1h4znesuDaPiaflq9aSNUswAAPWSAAw97ZyGJMKug7iZ03fNLrqTUhltGm+syLpMt5JEWpamou5qKppmqbQa2YATh47dK4nlcxJ90kRI+n9mqD/7h2OXXhZO8kje7HTN078efJVu9RgJzscuvCyd5JG92HY5deFk7ySN7sM3Tvx58i3eowE52OXXhZO8kje7DscuvCyd5JG92Gbp348+RbvUYCc7HLrwsneSRvdh2OXXhZO8kje7DN078efIt3qMBOdjl14WTvJI3uw7HLrwsneSRvdhm6d+PPkW737tFwWu2mYNd4tbJ1gWsVcZxRERqbMy7Vadf5SVElRfakh/jjjuwHIrvb03sscaNm4TZKhSHSSZpbbQZm4+WumqSbI1l9JaadJD/Y3scuvCyd5JG92JWNsKr4e0WXnbNnIRlkuIUF6yKOzvLZLd0Ld3N0j7VJbxFvaERa6cBzrwKK7frjz5Fu9dYzjlfh+O1lHVMFFra6O3Fjsl/JbQkkpLXunoXE+6MmJzscuvCyd5JG92HY5deFk7ySN7sdM3Tvx58i3eowE52OXXhZO8kje7DscuvCyd5JG92Gbp348+RbvUYCc7HLrwsneSRvdh2OXXhZO8kje7DN078efIt3qMBOdjl14WTvJI3uw7HLrwsneSRvdhm6d+PPkW71GAnOxy68LJ3kkb3Ydjl14WTvJI3uwzdO/HnyLd6jAThY9dp4llUtRl0E5Ejmn+3RBH/ePdQWr08pkWWlCZ8F0mHzaIybWZpStK0EZmZEpKi4GZ6HqWp6ank4dovTMTx/MQyzKgADkwGJuvlHHvGSPQcGWGJuvlHHvGSPQcHw9N9xV9PWFU61yAAOSWOyP9HrT7q76BifofkOu+7N+iQoMj/R60+6u+gYn6H5DrvuzfokMwfiJ+X8q6nuAAHqJBN0X6Y5P/AO1/wzFIJui/THJ//a/4Zjth/wCNfh+YVGqVIPxKkrSSkmSiPukeo5o5RKKp3bDRM5fSy8uxd7H5KYlNAdI1sTyeT/4hbZrToRoMkJdPghRH0a6jXB4LdxLLZbs5y6xxuBXxsQOU1GyGO5Ir5Vjz588nRqQylx5Damz3lKV8ZxRFqe8PlmrTZLt8ByRE2axSyPYzjVxfQ83oJU++ca6iU4UPqfmCWmKW884pxptaTIkrWotEkk9SIS2Qrl0+PSsOiz4tLg7e02VTvqsSdcgxopxEPMxnSQ62omDfc0030pLtSPtdSNldw7gAcY5rszLFdjecRomWUs+nnXNDHKqxVtyPHq3ynsb6kEqQ8ba1pcbMyI0l2qTIuIuNseDYfAuMS2dVmK4zCZnonW/VV+t1MBnmyZS4s2m3EG++reRxUsjIkmrUMqdg35kGYVWLzaSJYyDZkXM0oEFBNqVzr3Nrc3dSLRPatrPU9C4fSZDNDhmlraDMtlGwl/LetuQwIeZT6Zc+aZOsnF1mpabNbhqPmz5pjdJSj13EcT0IxVbQaOPne3S2x6xtMTgY7V0MB7HYWSRXn4i46icJ5+MTUphBKSpKUmrtjJJI3TSRHqyh14AjNjdFKxvZhj1dLyNGWuMx9W7lrXcksqUamjSZrWaiJs0JJRqUZknXXiNP8qzH6xNzX5bbLpsjr6KpkuSsQt7E4jjjO8lSpcRRHwfTuGktS0PXQlJPQVM2i46TAcz1GX07GVbdrKVLaqok6hqp8dE9aWV8wqvWRHoZ9w+1P7eAkNluA0G0O8wyuyOubtoCNkNK6mLIUo2uc5x4iXukehrSRq3VdKdT0MtRmUOxwHG+yHH4WP0nJvymC24zkF865DtbA3lrdnsqgyF7jyjMzWSVNoNJHwTulu6DB43s2x2Zso2MXbteari1zE62dOJ9xL0iIp6YlUdSiVqbRpbQXN/F0Lo4nrmV3DuQBx9eLqMGLangkekhzMdkZZT1tZVTpTkeuguSobDylOKQZGlklkpRtp0JRnu/yjElOiHU7INveHx7Wtfray1pep28fU4mJEU85HN1LCVuuKRotJ6lvGRLJWhF0Eyx3cA552pYtjnJ1fxbaLRVTdTU081yJkBREGa34ctKG1POHxU4pDzcZZm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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to create subgraphs\n", + "\n", + "For more complex systems, sub-graphs are a useful design principle. Sub-graphs allow you to create and manage different states in different parts of your graph. This allows you build things like [multi-agent teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/), where each team can track its own separate state.\n", + "\n", + "![Screenshot 2024-07-11 at 1.01.28 PM.png](attachment:71516aef-9c00-4730-a676-a54e90cb6472.png)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Dummy logs\n", - "question_answer = Logs(\n", - " id=\"1\",\n", - " question=\"How can I import ChatOllama?\",\n", - " answer=\"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\",\n", - ")\n", - "\n", - "question_answer_feedback = Logs(\n", - " id=\"2\",\n", - " question=\"How can I use Chroma vector store?\",\n", - " answer=\"To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).\",\n", - " grade=0,\n", - " grader=\"Document Relevance Recall\",\n", - " feedback=\"The retrieved documents discuss vector stores in general, but not Chroma specifically\",\n", - ")\n", - "\n", - "\n", - "# Entry Graph\n", - "class EntryGraphState(TypedDict):\n", - " raw_logs: Annotated[List[Dict], add]\n", - " docs: Annotated[List[Logs], add] # This will be used in sub-graphs\n", - " fa_summary: str # This will be generated in the FA sub-graph\n", - " report: str # This will be generated in the QS sub-graph\n", - "\n", - "\n", - "def convert_logs_to_docs(state):\n", - " # Get logs\n", - " raw_logs = state[\"raw_logs\"]\n", - " docs = [question_answer, question_answer_feedback]\n", - " return {\"docs\": docs}\n", - "\n", - "\n", - "entry_builder = StateGraph(EntryGraphState)\n", - "entry_builder.add_node(\"convert_logs_to_docs\", convert_logs_to_docs)\n", - "entry_builder.add_node(\"question_summarization\", qs_builder.compile())\n", - "entry_builder.add_node(\"failure_analysis\", fa_builder.compile())\n", - "\n", - "entry_builder.add_edge(START, \"convert_logs_to_docs\")\n", - "entry_builder.add_edge(\"convert_logs_to_docs\", \"failure_analysis\")\n", - "entry_builder.add_edge(\"convert_logs_to_docs\", \"question_summarization\")\n", - "entry_builder.add_edge(\"failure_analysis\", END)\n", - "entry_builder.add_edge(\"question_summarization\", END)\n", - "\n", - "graph = entry_builder.compile()\n", - "\n", - "from IPython.display import Image, display\n", - "\n", - "# Setting xray to 1 will show the internal structure of the nested graph\n", - "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'raw_logs': [{'foo': 'bar'}, {'foo': 'baz'}],\n", - " 'docs': [{'id': '1',\n", - " 'question': 'How can I import ChatOllama?',\n", - " 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n", - " {'id': '2',\n", - " 'question': 'How can I use Chroma vector store?',\n", - " 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n", - " 'grade': 0,\n", - " 'grader': 'Document Relevance Recall',\n", - " 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'},\n", - " {'id': '1',\n", - " 'question': 'How can I import ChatOllama?',\n", - " 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n", - " {'id': '2',\n", - " 'question': 'How can I use Chroma vector store?',\n", - " 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n", - " 'grade': 0,\n", - " 'grader': 'Document Relevance Recall',\n", - " 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'},\n", - " {'id': '1',\n", - " 'question': 'How can I import ChatOllama?',\n", - " 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n", - " {'id': '2',\n", - " 'question': 'How can I use Chroma vector store?',\n", - " 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n", - " 'grade': 0,\n", - " 'grader': 'Document Relevance Recall',\n", - " 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}],\n", - " 'fa_summary': 'Poor quality retrieval of Chroma documentation.',\n", - " 'report': 'foo bar'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_logs = [{\"foo\": \"bar\"}, {\"foo\": \"baz\"}]\n", - "graph.invoke({\"raw_logs\": raw_logs}, debug=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Custom reducer functions to manage state\n", - "\n", - "Now, let's highlight a possible stumbling block when we use the same `State` across multiple sub-graphs.\n", - " \n", - "We will create two graphs: a parent graph with a few nodes and a child graph that is added as a node in the parent.\n", - "\n", - "We define a custom [reducer](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) function for our state." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Annotated\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "def reduce_list(left: list | None, right: list | None) -> list:\n", - " if not left:\n", - " left = []\n", - " if not right:\n", - " right = []\n", - " return left + right\n", - "\n", - "\n", - "class ChildState(TypedDict):\n", - " name: str\n", - " path: Annotated[list[str], reduce_list]\n", - "\n", - "\n", - "class ParentState(TypedDict):\n", - " name: str\n", - " path: Annotated[list[str], reduce_list]\n", - "\n", - "\n", - "child_builder = StateGraph(ChildState)\n", - "\n", - "child_builder.add_node(\"child_start\", lambda state: {\"path\": [\"child_start\"]})\n", - "child_builder.add_edge(START, \"child_start\")\n", - "child_builder.add_node(\"child_middle\", lambda state: {\"path\": [\"child_middle\"]})\n", - "child_builder.add_node(\"child_end\", lambda state: {\"path\": [\"child_end\"]})\n", - "child_builder.add_edge(\"child_start\", \"child_middle\")\n", - "child_builder.add_edge(\"child_middle\", \"child_end\")\n", - "child_builder.add_edge(\"child_end\", END)\n", - "\n", - "builder = StateGraph(ParentState)\n", - "\n", - "builder.add_node(\"grandparent\", lambda state: {\"path\": [\"grandparent\"]})\n", - "builder.add_edge(START, \"grandparent\")\n", - "builder.add_node(\"parent\", lambda state: {\"path\": [\"parent\"]})\n", - "builder.add_node(\"child\", child_builder.compile())\n", - "builder.add_node(\"sibling\", lambda state: {\"path\": [\"sibling\"]})\n", - "builder.add_node(\"fin\", lambda state: {\"path\": [\"fin\"]})\n", - "\n", - "# Add connections\n", - "builder.add_edge(\"grandparent\", \"parent\")\n", - "builder.add_edge(\"parent\", \"child\")\n", - "builder.add_edge(\"parent\", \"sibling\")\n", - "builder.add_edge(\"child\", \"fin\")\n", - "builder.add_edge(\"sibling\", \"fin\")\n", - "builder.add_edge(\"fin\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "# Setting xray to 1 will show the internal structure of the nested graph\n", - "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36;1m\u001b[1;3m[0:tasks]\u001b[0m \u001b[1mStarting step 0 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3m__start__\u001b[0m -> {'name': 'test'}\n", - "\u001b[36;1m\u001b[1;3m[0:writes]\u001b[0m \u001b[1mFinished step 0 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", - "\u001b[36;1m\u001b[1;3m[0:checkpoint]\u001b[0m \u001b[1mState at the end of step 0:\n", - "\u001b[0m{'name': 'test', 'path': []}\n", - "\u001b[36;1m\u001b[1;3m[1:tasks]\u001b[0m \u001b[1mStarting step 1 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mgrandparent\u001b[0m -> {'name': 'test', 'path': []}\n", - "\u001b[36;1m\u001b[1;3m[1:writes]\u001b[0m \u001b[1mFinished step 1 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['grandparent']\n", - "\u001b[36;1m\u001b[1;3m[1:checkpoint]\u001b[0m \u001b[1mState at the end of step 1:\n", - "\u001b[0m{'name': 'test', 'path': ['grandparent']}\n", - "\u001b[36;1m\u001b[1;3m[2:tasks]\u001b[0m \u001b[1mStarting step 2 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mparent\u001b[0m -> {'name': 'test', 'path': ['grandparent']}\n", - "\u001b[36;1m\u001b[1;3m[2:writes]\u001b[0m \u001b[1mFinished step 2 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['parent']\n", - "\u001b[36;1m\u001b[1;3m[2:checkpoint]\u001b[0m \u001b[1mState at the end of step 2:\n", - "\u001b[0m{'name': 'test', 'path': ['grandparent', 'parent']}\n", - "\u001b[36;1m\u001b[1;3m[3:tasks]\u001b[0m \u001b[1mStarting step 3 with 2 tasks:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mchild\u001b[0m -> {'name': 'test', 'path': ['grandparent', 'parent']}\n", - "- \u001b[32;1m\u001b[1;3msibling\u001b[0m -> {'name': 'test', 'path': ['grandparent', 'parent']}\n", - "\u001b[36;1m\u001b[1;3m[3:writes]\u001b[0m \u001b[1mFinished step 3 with writes to 2 channels:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", - "- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['grandparent', 'parent', 'child_start', 'child_middle', 'child_end'], ['sibling']\n", - "\u001b[36;1m\u001b[1;3m[3:checkpoint]\u001b[0m \u001b[1mState at the end of step 3:\n", - "\u001b[0m{'name': 'test',\n", - " 'path': ['grandparent',\n", - " 'parent',\n", - " 'grandparent',\n", - " 'parent',\n", - " 'child_start',\n", - " 'child_middle',\n", - " 'child_end',\n", - " 'sibling']}\n", - "\u001b[36;1m\u001b[1;3m[4:tasks]\u001b[0m \u001b[1mStarting step 4 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mfin\u001b[0m -> {'name': 'test',\n", - " 'path': ['grandparent',\n", - " 'parent',\n", - " 'grandparent',\n", - " 'parent',\n", - " 'child_start',\n", - " 'child_middle',\n", - " 'child_end',\n", - " 'sibling']}\n", - "\u001b[36;1m\u001b[1;3m[4:writes]\u001b[0m \u001b[1mFinished step 4 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['fin']\n", - "\u001b[36;1m\u001b[1;3m[4:checkpoint]\u001b[0m \u001b[1mState at the end of step 4:\n", - "\u001b[0m{'name': 'test',\n", - " 'path': ['grandparent',\n", - " 'parent',\n", - " 'grandparent',\n", - " 'parent',\n", - " 'child_start',\n", - " 'child_middle',\n", - " 'child_end',\n", - " 'sibling',\n", - " 'fin']}\n" - ] }, { - "data": { - "text/plain": [ - "{'name': 'test',\n", - " 'path': ['grandparent',\n", - " 'parent',\n", - " 'grandparent',\n", - " 'parent',\n", - " 'child_start',\n", - " 'child_middle',\n", - " 'child_end',\n", - " 'sibling',\n", - " 'fin']}" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"name\": \"test\"}, debug=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice here that the `[\"grandparent\", \"parent\"]` sequence is duplicated! \n", - "\n", - "This is because our child state has received the full parent state and returns the full parent state once it terminates. \n", - "\n", - "To avoid duplication or conflicts in state, you typically would do one or more of the following:\n", - "\n", - "1. Handle duplicates in your `reducer` function.\n", - "2. Call the child graph from within a python function. In that function, handle the state as needed. \n", - "3. Update the child graph keys to avoid conflicts. You would still need to ensure the output can be interpreted by the parent, however.\n", - "\n", - "Let's re-implement the graph using technique (1) and add unique IDs for every value in the list. This is what is done in [`MessageGraph`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "\n", - "\n", - "def reduce_list(left: list | None, right: list | None) -> list:\n", - " \"\"\"Append the right-hand list, replacing any elements with the same id in the left-hand list.\"\"\"\n", - " if not left:\n", - " left = []\n", - " if not right:\n", - " right = []\n", - " left_, right_ = [], []\n", - " for orig, new in [(left, left_), (right, right_)]:\n", - " for val in orig:\n", - " if not isinstance(val, dict):\n", - " val = {\"val\": val}\n", - " if \"id\" not in val:\n", - " val[\"id\"] = str(uuid.uuid4())\n", - " new.append(val)\n", - " # Merge the two lists\n", - " left_idx_by_id = {val[\"id\"]: i for i, val in enumerate(left_)}\n", - " merged = left_.copy()\n", - " for val in right_:\n", - " if (existing_idx := left_idx_by_id.get(val[\"id\"])) is not None:\n", - " merged[existing_idx] = val\n", - " else:\n", - " merged.append(val)\n", - " return merged\n", - "\n", - "\n", - "class ChildState(TypedDict):\n", - " name: str\n", - " path: Annotated[list[str], reduce_list]\n", - "\n", - "\n", - "class ParentState(TypedDict):\n", - " name: str\n", - " path: Annotated[list[str], reduce_list]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "child_builder = StateGraph(ChildState)\n", - "\n", - "child_builder.add_node(\"child_start\", lambda state: {\"path\": [\"child_start\"]})\n", - "child_builder.add_edge(START, \"child_start\")\n", - "child_builder.add_node(\"child_middle\", lambda state: {\"path\": [\"child_middle\"]})\n", - "child_builder.add_node(\"child_end\", lambda state: {\"path\": [\"child_end\"]})\n", - "child_builder.add_edge(\"child_start\", \"child_middle\")\n", - "child_builder.add_edge(\"child_middle\", \"child_end\")\n", - "child_builder.add_edge(\"child_end\", END)\n", - "\n", - "builder = StateGraph(ParentState)\n", - "\n", - "builder.add_node(\"grandparent\", lambda state: {\"path\": [\"grandparent\"]})\n", - "builder.add_edge(START, \"grandparent\")\n", - "builder.add_node(\"parent\", lambda state: {\"path\": [\"parent\"]})\n", - "builder.add_node(\"child\", child_builder.compile())\n", - "builder.add_node(\"sibling\", lambda state: {\"path\": [\"sibling\"]})\n", - "builder.add_node(\"fin\", lambda state: {\"path\": [\"fin\"]})\n", - "\n", - "# Add connections\n", - "builder.add_edge(\"grandparent\", \"parent\")\n", - "builder.add_edge(\"parent\", \"child\")\n", - "builder.add_edge(\"parent\", \"sibling\")\n", - "builder.add_edge(\"child\", \"fin\")\n", - "builder.add_edge(\"sibling\", \"fin\")\n", - "builder.add_edge(\"fin\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "# Setting xray to 1 will show the internal structure of the nested graph\n", - "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36;1m\u001b[1;3m[0:tasks]\u001b[0m \u001b[1mStarting step 0 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3m__start__\u001b[0m -> {'name': 'test'}\n", - "\u001b[36;1m\u001b[1;3m[0:writes]\u001b[0m \u001b[1mFinished step 0 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", - "\u001b[36;1m\u001b[1;3m[0:checkpoint]\u001b[0m \u001b[1mState at the end of step 0:\n", - "\u001b[0m{'name': 'test', 'path': []}\n", - "\u001b[36;1m\u001b[1;3m[1:tasks]\u001b[0m \u001b[1mStarting step 1 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mgrandparent\u001b[0m -> {'name': 'test', 'path': []}\n", - "\u001b[36;1m\u001b[1;3m[1:writes]\u001b[0m \u001b[1mFinished step 1 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['grandparent']\n", - "\u001b[36;1m\u001b[1;3m[1:checkpoint]\u001b[0m \u001b[1mState at the end of step 1:\n", - "\u001b[0m{'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'}]}\n", - "\u001b[36;1m\u001b[1;3m[2:tasks]\u001b[0m \u001b[1mStarting step 2 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mparent\u001b[0m -> {'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'}]}\n", - "\u001b[36;1m\u001b[1;3m[2:writes]\u001b[0m \u001b[1mFinished step 2 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['parent']\n", - "\u001b[36;1m\u001b[1;3m[2:checkpoint]\u001b[0m \u001b[1mState at the end of step 2:\n", - "\u001b[0m{'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'}]}\n", - "\u001b[36;1m\u001b[1;3m[3:tasks]\u001b[0m \u001b[1mStarting step 3 with 2 tasks:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mchild\u001b[0m -> {'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'}]}\n", - "- \u001b[32;1m\u001b[1;3msibling\u001b[0m -> {'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'}]}\n", - "\u001b[36;1m\u001b[1;3m[3:writes]\u001b[0m \u001b[1mFinished step 3 with writes to 2 channels:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", - "- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", - " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", - " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", - " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'}], ['sibling']\n", - "\u001b[36;1m\u001b[1;3m[3:checkpoint]\u001b[0m \u001b[1mState at the end of step 3:\n", - "\u001b[0m{'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", - " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", - " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", - " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'},\n", - " {'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718', 'val': 'sibling'}]}\n", - "\u001b[36;1m\u001b[1;3m[4:tasks]\u001b[0m \u001b[1mStarting step 4 with 1 task:\n", - "\u001b[0m- \u001b[32;1m\u001b[1;3mfin\u001b[0m -> {'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", - " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", - " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", - " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'},\n", - " {'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718', 'val': 'sibling'}]}\n", - "\u001b[36;1m\u001b[1;3m[4:writes]\u001b[0m \u001b[1mFinished step 4 with writes to 1 channel:\n", - "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['fin']\n", - "\u001b[36;1m\u001b[1;3m[4:checkpoint]\u001b[0m \u001b[1mState at the end of step 4:\n", - "\u001b[0m{'name': 'test',\n", - " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", - " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", - " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", - " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", - " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'},\n", - " {'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718', 'val': 'sibling'},\n", - " {'id': 'a4328c5f-845a-43de-b3d7-53a39208e316', 'val': 'fin'}]}\n" - ] }, { - "data": { - "text/plain": [ - "{'name': 'test',\n", - " 'path': [{'val': 'grandparent', 'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49'},\n", - " {'val': 'parent', 'id': '2a6f0263-3949-4e47-a210-57f817e6097d'},\n", - " {'val': 'child_start', 'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088'},\n", - " {'val': 'child_middle', 'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783'},\n", - " {'val': 'child_end', 'id': '669dd810-360f-4694-a9f3-49597f23376a'},\n", - " {'val': 'sibling', 'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718'},\n", - " {'val': 'fin', 'id': 'a4328c5f-845a-43de-b3d7-53a39208e316'}]}" + "attachments": { + "9145adc1-ce9d-4a22-8183-e13796d4a388.png": { + "image/png": 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dF5CNCG4vgWZR15Ym936RCa4fOJwre83TBVv3bpaDJlXN4LhB1W5fZ+JP6T1FLutzmR1HtqlrS/ZGGWF8I1qH23pcf32c/KkcLTomUW0irctDXzwgaft2GptCiTGz6/OPHjTXYp18ufFLGRA7UEJahshdH/xStpljmplxvLboFXtDYLtZgHaIMWiIxXBd+897BBBAAAEEEECgsQs0/udHG/sVoP8IIIAAAggggAACCCBQJtA1PFH+NOE+s7jpAfl4/SfyuQnSf7jyPdmeu8NuTzuQao9NiOhedo6+yT92UFJN0LyNmYGuZea6GbI5e4uZLe9rZ4PrtiVpy+TFec/pWxsE1xn1bVu2NTnbZ0l0cJSZDT7D5mbXXPEa5HcWsJ1vAvv/N/Lnoqt5/e6zeyXN9KWfCdr/7NzbJNws+vqj16+RRdvmyezoPjLRzDjXojP+N2aut4vDPnTBg2VPA1zRe6qsNgFvDe5/ueFTGdF5hPSI6GafCtAnA3SW/Htr35f3l79l69HFZSf3uLC0TrOvuu3bE07+au4faN8dMzPsKysfrZou4UFR0i/6cZm7da7tu64LcN/EB6S/GZM+UTB323fyvLH7/af3yGvXv2Vn++80Qfnnv3vGVtkrtr99CmJDVop0atexsmbYhgACCCCAAAIIIHAaAd/TbGczAggggAACCCCAAAIIINBgApqixbXobOybB98g/5j6lA10r961VHILcuTo8WP2sISw8mlbXlz0st3eO6ZfWTVrUpfKyp2Lyz4/9e0T9n339r3s6/Z928v2vbXkNck3Nwa0HCjYJ9NNkFwD5FHBcTZovdfMptcULxqg7xs/xN4w0AD96oy1Ulh01J73upl1f9zMgteSe2S/fY1t16EsQK8bfE16nAHRfaV9ULTdn3ogzb7qL02P88sP/q8sQH+xmQn/v+tel/iQOHtMTdovq9S8OVFSbD/qDH2nrNi90t4I0c/NA1pIVl6G3RUUGGRff2LWBNAAvRZNEzQ2YYwMM/n0NXXPlpzN1kRvRGj5xZhfy73j7rbvN5kZ+xQEEEAAAQQQQACBmgkQpK+ZF0cjgAACCCCAAAIIIIBAHQusNTPLr3/9R/LQ1w/bQLVTvQaB/Xz8TXqVULspPW+PSckSZd9/u/ErE7AvtLO8XzHBcWfW+2GTGqZiCTMLtmrR+n426pfyk3N+Yj+n7d9tX51fmmpGA/NadCb536f8Q8Z2G2s/p5kA+nYz+13LJT0vsq+pZlb/37951L7X44+att9Y8bb9nF+Yb19PnAxk2w8uv0JbhNhP60yQX8v/lr0uv/7wl7InL006m5n1L17zitxkblK0MClwnFKT9p1z9DX4ZB75/GOlfdKbHQ/PelBmmCcVtLRt0c4+baBPLzQz49DS0mUdAP2sNxAWb59vXVr4NddNtkzpf6V9CqClf0tR500mDREFAQQQQAABBBBAoGYCpLupmRdHI4AAAggggAACCCCAQB0LND8Z9NXZ8vqji8bqjHMNejula/ueJi99L5sOJtTkVc89tFdufPM6G1TX43Rx00KTQ11nz7vOytfZ6BkHM+zCqSMTx8p5iePL0rm4zqTXdn47/l55/OtH7Gzxn4++yyzaGm9ztb9j9m3N3S5dzCKpWh764n4JNzcL9ubvsZ+vO+cmmzP+tx/9Wj4zC6juK8iV87udZ/dpPysrYWYWvhYNnG81aWM+Nec5pWVAS3llyatmkda2EhsSKzHBsZJk0vvUpP2fD79TmvuVprmJONnW6vTVco7Jbf/P+c/bprpHdrevYa3DZGuW6cvRfBllZstr6p7n5j4l6zRdjwnap5jAuz5BoOVPJnVP0ckbD21bhcp1/X9Y8LaLWcxXb5boAr5tKgT57cn8QgABBBBAAAEEEKhUgCB9pSxsRAABBBBAAAEEEEAAgYYS0Dz0D170iHye8rmsT18jBSbIq7Pem5tgdVRwtAzrNFym9ppiu+Pr4yNPX/aMDTQvMwuvahlrFoq90cw6X5e5Vp6Z86TsP3pANJAfYILUNwy8TpJNCpaINuFyy+BbSuswgecLzaKxK02O+jYBre3scE3l0tcsTnvt4B9LM3ODYIxZOFaL9i0utJMJpufJsA5XyFIT6P9+yxwboNeZ97ePuFNGdBxmj31o8sPyp5n3ycKt30noydn/2ofKSojJha/nx4d0MPnwo21/nYD++t2rTjlFZ/i/cPW/RW80VKf9uJB4uarP5baeruFd7KsG0J0nDjqZhXKHmrQ9WnqbtDa6PaJNhE2tM3XAVSan/yfybcqXdr8+JTDQLAh7db+rJMHcqNAc9RN6XCCjTQ59vR5OuTjpIlvPfpPqhyC9o8IrAggggAACCCBQtYBPiSlVH8YRCCCAAAIIIIAAAggggEDjEdBAsv5VR2fkn64Um1ztul9vCGgg+nRFF3PVWLTmZtdyyNxEKDK52TVvfsWis/l37ttlg/uaxse/WYBdGLbicfpZ0/X4mioDzDFaNN1Mrsl971ryzVMCOWabPh1wkVlAVoPi1W3ftZ6Pkz+V90ye/TBz8+K87pNksrmx0cz3hzlbB48dkiBz08C1HDyaZxR/SJfjuu9076uyPN15bEcAAQQQQAABBJqyAEH6pnz1GTsCCCCAAAIIIIAAAggggAACCCCAAAIIIICAWwVOP63Erd2icQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEvF+AIL33X2NGiAACCCCAAAIIIIAAAggggAACCCCAAAIIIOChAgTpPfTC0C0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB7xcgSO/915gRIoAAAggggAACCCCAAAIIIIAAAggggAACCHioAEF6D70wdAsBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDA+wUI0nv/NWaECCCAAAIIIIAAAggggAACCCCAAAIIIIAAAh4qQJDeQy8M3UIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwfgGC9N5/jRkhAggggAACCCCAAAIIIIAAAggggAACCCCAgIcKEKT30AtDtxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8X4AgvfdfY0aIAAIIIIAAAggggAACCCCAAAIIIIAAAggg4KECBOk99MLQLQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAHvFyBI7/3XmBEigAACCCCAAAIIIIAAAggggAACCCCAAAIIeKgAQXoPvTB0CwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMD7BQjSe/81ZoQIIIAAAggggAACCCCAAAIIIIAAAggggAACHirg56H9olsIIIAAAggggAACCCDgxQKHCg/L9tztp4ywc2hnaR3Q6pTtnrKhuKRYSqTY/GN+l5SImM/FPrqlREpKTsgJs83+o9v1GLNPzHbneHvuyWNOHmHOM8e4ofj6+IqPlM7bsu9NN3x8fOw2X/OqpXS7Hmfe+/qa4fpIgK+f+JmfZvqPj5+pwUf2HMqW7Pwse05lvzz9ulbWZ7YhgAACCCCAAAINJUCQvqGkaQcBBBBAAAEEEEAAgSYooMHbRbsWy7qM9ZJmgvL7Dud4hEKAf6BEhLQ3fdGQulNK32nQXGPUpcFzn5NHOK/OsQ33ml9wUA4fPthwDdZjS4EBLaR9SLRpwUfiQuKld3RvGdFhuAT6BtRjq1SNAAIIIIAAAgh4toCP+R/PH/6f1LP7Su8QQAABBBBAAAEEEECgEQhoYP7tlW9LignMO0H5TpFdpVNkJyk8cVR8A5pJu6CQSkeStnd3ue2FRcdk7/695bY5H3IP7JWi44XOR15rKBASHCaB5mZFdUpMRGyVh8WFn/kYvbbO9bQ3Hgryy+qMiYiXHlG95NLuF0l0Gw3iUxBAAAEEEEAAgaYjQJC+6VxrRooAAggggAACCCCAQL0KOMH5BVvmmuBvCxnYeYgkxiRKWGg7KSpp+GB66t60sxpvxRsGZ1VZJScHtwySoFZBleyp3abglsESXIf11a4X1T8rzzwdkGau0W4TvE83P4dPBu37dR4sNw+8SWKDYqpfGUcigAACCCCAAAKNWIAgfSO+eHQdAQQQQAABBBBAAAFPEXhp8csye8NMG5wf1n2E9OySJL7NSvOae0of6YdnC2zJ2CarNq+UPTnptqODEobKL4f90qPXKPBsUXqHAAIIIIAAAo1FgCB9Y7lS9BMBBBBAAAEEEEAAAQ8U0AVg//rV32TLnmSJj+gklwy/hOC8B16nxtSlLJPG6LvVc22wXnPYP3Th3yTBLChMQQABBBBAAAEEvFWAIL23XlnGhQACCCCAAAIIIIBAPQtsMwvBPvrVwzbvfM+OfWT8oHH13CLVNyWB5VtWycI139khXz/0Fpna85KmNHzGigACCCCAAAJNSIAgfRO62AwVAQQQQAABBBBAAIG6EtAZ9H+Y+QfZvW+njBt0nvTq2LOuqqYeBMoEdFb9h3On2wWCp/a7Uq4feF3ZPt4ggAACCCCAAALeIuDrLQNhHAgggAACCCCAAAIIINBwAk9+94wN0PdO7EeAvuHYm1xLkW3DZfLwi+24P1o9XRbuWtTkDBgwAggggAACCHi/AEF677/GjBABBBBAAAEEEEAAgToV+Gj9DFmTulRCgsNkbN8xdVo3lSFQUSA+PM4+raHb/zXvOdE0SxQEEEAAAQQQQMCbBAjSe9PVZCwIIIAAAggggAACCNSzgKa5mb7qXfH3C5Arx1xVz61RPQKlAppOqUN0ZzlaWCDPznsWFgQQQAABBBBAwKsECNJ71eVkMAgggAACCCCAAAII1K/AtLXT5FjhERnZb7Q09w+o38aoHQEXgTEnn9rQdRBIe+MCw1sEEEAAAQQQaPQCBOkb/SVkAAgggAACCCCAAAIINIyAzqL/KnmWtGrZhjz0DUNOKy4Cwa2CJCosxm75ZP2nLnt4iwACCCCAAAIING4BgvSN+/rRewQQQAABBBBAAAEEGkzgy+2zpbDoqHTvmNRgbdIQAq4CQ3sOtR+37EkmN70rDO8RQAABBBBAoFELEKRv1JePziOAAAIIIIAAAggg0HACS3cusrnoByYObLhGaQkBFwFdRFaf5NDy9eavXfbwFgEEEEAAAQQQaLwCBOkb77Wj5wgggAACCCCAAAIINJhAQXGBpGbtlOiIWHLRN5g6DVUm0DkmwW7eYXLTUxBAAAEEEEAAAW8QIEjvDVeRMSCAAAIIIIAAAgggUM8CyXs3StHxQkmILg2Q1nNzVI/AaQVizWx6LZryhoIAAggggAACCHiDAEF6b7iKjAEBBBBAAAEEEEAAgXoWWJK62LYQdzJAWs/NUT0CpxVw/Q7uOZR92uPYgQACCCCAAAIINBYBgvSN5UrRTwQQQAABBBBAAAEE3Ciwec9mCQkOk+BWQW7sBU0jIDbdUlRYjKXIys+CBAEEEEAAAQQQaPQCfo1+BAwAAQQQQAABBBBAAAEE6l+gpEQC/QPrvx0PbyGv4KAs27hckrevlQHdB0nBsSNy+GiB+Pr6SpBZ0DSoZbB97RDVUQKa8det+rqcgQF8F+vLlnoRQAABBBBAoOEF+L/GhjenRQQQQAABBBBAAAEEGp1A9oE90juxX6Prd112eO2OdbIsZakcLsgXPz9/2ZObJZGhUdK/S3/JP3JIFq1fIBsOrpPi4hO22S7x3aSfMTtedFziI0rzqNdlf5pyXWFtw2VXxvamTMDYEUAAAQQQQMCLBAjSe9HFZCgIIIAAAggggAACCNSXQGHRsfqq2uPrTc1Ok+WbV8juPTvL+nr8eJFk7E2zP6s2LrOpgLrGd5ekuO6Sf/SwWWS3SL5fM1fe/+Y9e46mCuqT0Fe6xnaVFswCL3Os7ZtA/+a1PZXzEEAAAQQQQAABjxMgSO9xl4QOIYAAAggggAACCCDgWQKHCg/bDsWFx3pWx+q5NwcLDpng/HJZv3V1WUuRJhd6YmyihAWHlm3TN5ryJjcvR75e9bV5zZUCM9vetew3+75b+Y0sWjdfEjt0k1G9R4t/s2auh/C+BgIRbcNqcDSHIoAAAggggAACni1AkN6zrw+9QwABBBBAAAEEEEDA7QLbcpteWpHN6dtkwbrvJf/QAWnTuq306dJPOpk88+3M+9OWuG5lu44UHpO9edmy3aRk2Wlm4B/M32/36RMJG7aulT05mXLRsIsluCUL8Zah8QYBBBBAAAEEEGiiAgTpm+iFZ9gIIIAAAggggAACCCBQucDqbWtk3qo5dqfm4R/SfYi0CmxZ+cGn2aopbeLD4+yP9B0tuQf3ybbMHZKWvUvSs1Il98BeeWPW63LF2KslyuRXpyCAAAIIIIAAAgg0XQGC9E332jNyBBBAAAEEEEAAAQQQqCCwIHmRrEheIq1bBcn4gROkQ0R8hSNq9zE0qJ3oz5BuA+XA4Tz5dOGnoilwpn39ltwy+VZp3aJV7SrmLAQQQAABBBBAAIFGL+Db6EfAABBAAAEEEEAAAQQQQKBeBfq2712v9XtK5Zt2b7EB+mbN/OS8wefXWYC+4vjatgqWH593vbRq2drumjZ3WsVD+IwAAggggAACCCDQhAQI0jehi81QEUAAAQQQQAABBBBAoHKBrSZ3/KzFM+3OsQPHS1xY/S+SO37geaI3BA6ZmfULNiysvGNsRQABBBBAAAEEEPB6AYL0Xn+JGSACCCCAAAIIIIAAAghUJbBuxzp7yLm9R0pSfI+qDq+T/R0jO0hS5162rhUpSyU5NaVO6m1KlWxvgosaN6Xry1gRQAABBBBoKgIE6ZvKlWacCCCAAAIIIIAAAgggUKmAzqJPM4u6xrXvJINNzviGLGP7jpGR/cfaJldvXd2QTXtFW4ePHfKKcTAIBBBAAAEEEGjaAgTpm/b1Z/QIIIAAAggggAACCDR5AWcWfZ/O7sm93z+hr/QwM+pz9mVJStrGJn89AEAAAQQQQAABBJqaAEH6pnbFGS8CCCCAAAIIIIAAAgiUCeQezC2bRZ/QvnPZ9oZ+07tTH9vkhh3JDd10o2wvuGVwo+w3nUYAAQQQQAABBCoTIEhfmQrbEEAAAQQQQAABBBBA4BSBtL27T9nW2Ddk5GbaIXQ2qW7cWaJCIqRTbKJkZKfKrqxd7uxKo2g7uFVQo+gnnUQAAQQQQAABBKojQJC+OkocgwACCCCAAAIIIIAAAl4pkJGbYccV0TbS7eMbkNjf9uHA4Ty394UOIIAAAggggAACCDScAEH6hrOmJQQQQAABBBBAAAEEEPAwgax9e8THx1cizUx2d5eY0GjbhYMFB93dFdpHAAEEEEAAAQQQaEABgvQNiE1TCCCAAAIIIIAAAggg4FkCBw7uk3Ztw8TXx8cjOhYY0EIOFuR7RF/oBAIIIIAAAggggEDDCBCkbxhnWkEAAQQQQAABBBBAAAEPFfD3C/CYnoUEh0pe/gGP6Y/ndsQzbqp4rg89QwABBBBAAIHGJODXmDpLXxFAAAEEEEAAAQQQQAABbxY4ZFLdHD1a4M1DrJOxNZNmtp6sQ3vrpD4qQQABBBBAAAEE3CnATHp36tM2AggggAACCCCAAAIIIOAicOjwQfHzoJn9Ll3zqLfNfEuD9HsPZXtUv+gMAggggAACCCBQGwGC9LVR4xwEEEAAAQQQQAABBBBAoI4F8kyAXktQ6+A6rtn7qvM9OZPe+0bGiBBAAAEEEECgKQoQpG+KV50xI4AAAggggAACCCCAgBVo1TJI9h/IcavGs+8/LRt2pUheQZ7tR3ArgvRVXZBmPvxVtioj9iOAAAIIIIBA4xHg/2waz7WipwgggAACCCCAAAIIIFDHAjHhMXKs6KhkHXBfbvOkzr3ku1VzZEfmDjs6ZtJXfZF9fUrT3VR9JEcggAACCCCAAAKeL0CQ3vOvET1EAAEEEEAAAQQQQACBehKICYu1NafnpNdTC1VXGxfRQY4fL5SNO1Pswd1iEqs+qYkfwUz6Jv4FYPgIIIAAAgh4mQBBei+7oAwHAQQQQAABBBBAAAEEqi8QHxlnD3ZnkL5bbKJ0jkuUY4VHJDQkQsKCw6o/gCZ6pE8THTfDRgABBBBAAAHvFCBI753XlVEhgAACCCCAAAIIIIBANQSCTU767ibdzI7dWyRzX1Y1zqifQ9qHxtiK9+flyv7Dpbnp66clb6mVML23XEnGgQACCCCAAAIiBOn5FiCAAAIIIIAAAggggECTFujTua8d//LNy93ioPnwl21YJBGh7aW4+IR8t3qOW/rRqBotIUjfqK4XnUUAAQQQQACBMwoQpD8jDzsRQAABBBBAAAEEEEDA2wWi2oZLj5Oz6XMO5jb4cBcnL5LAwBYyccgk6d99sKRm7pRV29Y0eD8aU4M+QpC+MV0v+ooAAggggAACZxYgSH9mH/YigAACCCCAAAIIIIBAExAY0WukhLWLlE8XfirHzCKuNS21TVGzYMNC2ZWxXUb3Gy0hrYJlZK/hEhIcKt+vmiN783Jq2o0mc7wPMfomc60ZKAIIIIAAAk1BgCB9U7jKjBEBBBBAAAEEEEAAAQTOKNAiIFDGDZgg+YcOyKyls6QmM+pXblstb3zx3xrnkv/WpLVZkbJURg8cL52jOpX17+Jhl9r389bOK9vGm/ICzKQv78EnBBBAAAEEEGjcAgTpG/f1o/cIIIAAAggggAACCCBQRwKa9uaysVfJvvx9Mv3b92SBSUNzrOjMs+pLSkRWbizNZd/SpKypTik4dkQ+X/qlrN+6RsaYAH3fTr3Lnda2VZCM7D9W0rNSZemmFeX28aFUgCA93wQEEEAAAQQQ8CYBgvTedDUZCwIIIIAAAggggAACCJyVQGxotJlRP16KTMqbFclL5N0578raHetPW+fu3HQpOHJIwttFSaBfwGmPc3as3Lpa3vnmHcnMyZApo6+QPhUC9M5x/RP6SkxkvCxe972k78t0NvOKAAIIIIAAAggg4IUCBOm98KIyJAQQQAABBBBAAAEEEKi9QHx4rFw66jIJD20veQf3ydwVX8sH338k63ZuOCWlze696baheBNQP1PZlZ0q782dJvNXz5XwkHC5auzVou2cqVw87BK7e8Ha+Wc6rEnu8yEpfZO87gwaAQQQQAABbxXw89aBMS4EEEAAAQQQQAABBBBAoLYCHSLiJSYsVhZuWCCrTcqZ9Kxd9kfr0wVmo8OipZPJI59pZtJriTPHu5aiEydkx54dkpGTLhm5GZKzL0vi2neSycMvkYT2nV0PPe37gGZ+MnHoZJm1eKZNvTM86dzTHtvUdvgI882a2jVnvAgggAACCHizAEF6b766jA0BBBBAoN4EcnNzJTQ0tN7qr2nFx48ft6f4+flJcXGx+PoSvKipIccjgAACFQX8zJ+lo3qPlNjwONm8e5Ns3pliD9GAu/6s3bxKxKf0rEUbFslil9nde/butjt8fZtJWEiETBgyUZLie1RsosrP3WITZUfHHjb1TmxYjOjNA4qyn4QHAwEEEEAAAQQQ8AIBgvRecBEZAgIIIIBA/QscOnRIXnzxRZkzZ440a9ZM1qxZI/Pnz5e4uLj6b/w0LfznP/+RDRs22D58+eWXkpaWZo/Uvt55551y7733nuZMNiOAAAII1ESgc1RH0Z9zzUz2dTvWSVpWmhQWHZOjZgHYY4VHTcTYRw4V5EurFi2lZfNW0rp5a0mITpAIE5yPNIvRBlQjV/2Z+jNp0ETZmb5DFqxbKNFjYsTf/HeIggACCCCAAAIIIOA9AgTpvedaMhIEEEAAgXoS2L17t1x77bWya9euci288847cs8995Tb1lAfjh07Jn/5y19Oaa5169bSoUOHU7azAQEEEEDg7AWCWwbJiJ7DRXqW1jXfpMJZmbJMzu0zQgYnDjz7Bs5Qw+ThF8tHc6eb9DsLZXSfkWc4smnsYh5907jOjBIBBBBAAIGmIsCz8E3lSjNOBBBAAIFaCRw9elR+/OMf2wD9b37zG9m2bZu88cYbtq5//etfcvjw4VrVe7YnBQYGygcffCCuNwp05rzOrJ83bx6z6M8WmPMRQACBagikn1w0Nib0zAvAVqOqKg+JM6lu+ncfLGs2r5CtmdurPN7bDyDdjbdfYcaHAAIIIIBA0xJgJn3Tut6MFgEEEECghgLTpk2T7du3y6233iq/+tWv7NmjRo2SXr16yfr162Xnzp3Ss+fJKZU1rPtsDx80aJCtIisry75qn2pbNm/eLIsXL5YlS5bIokWLJCEhQd58803RmwEVS1FRkezYsUPatWsnYWFhFXeXfVYbLR07drSv7vylRgsXLpSlS5fKggULpLCwUN5++23p3Ll6ize6s++VtZ2eni7ff/+9ZGRkSHR0tEyYMMFeC12PQPdVTMNUUlJi1yrQVE0UBBCoG4H8I4ckKzdTWgS2kmizkGxDlJG9hstOE6BftH6hxIRGS4uA5g3RLG0ggAACCCCAAAII1LMAQfp6BqZ6BBBAAIHGK6CBzaefftoO4Ne//nW5gVx99dU2QN+mTZty2/XD2rVr5bvvvhNNSRMeHi5Tp06VoKCgcsdt3brVBlg14K+B4/vuu8+mqXnhhRckICCg3LFVfcjJybGHxMfXfDFBDfLecsstkpJSuhiiVqQL4mrffVwWQHT6MGvWLHnggQckMzPTbho2bJhon9u2bescYuv685//bIP+ulHT7+gxNb2ZsWfPHvn0009l3759tv7zzjuvxkF1XVD3F7/4hXzxxRdl/dOUQDpGZ7Fd3aGGr776qlx66aXSvXv3smOdfXp8ZR76HXG2640ODfrr4r0Vi+tx+j45OfkUj4KCAjteTavUsmVLGTBggKhvxaL79Tulixc7Rcf0t7/9TZYtW2Zvrriul3DkyBEZO3as5Ofn2+/lmW6sOPXxigACVQuk52SImH+fYyIbdm2SyUMny5uzXpcFJlA/YcC4qjvqpUc4f/Z66fAYFgIIIIAAAgg0MQHS3TSxC85wEUAAAQSqL6ALsGogVNPdtGrVqtyJN9xwg00tUzEwrovLXnzxxfLEE0/Ic889J/fff79dxPXAgQPlztf9Wm9qaqrcfPPNsmXLFvn666/l448/LndcdT5oznwtsbE1T7fw3nvvlQXodUw6k37lypXyySefnHKzQJ8quO2222yA/sorr5S+ffva2em6gK1TNN3OFVdcYQP048aNk8mTJ9tUQepQk6Kz3s855xz561//Ks8//7w8++yzctNNN1nzmtSj43EC9PrkgS6w66QE6tq1a1lVOl5tZ/r06WXb9I0+NTB69GhxxqhPF+gNDb25oYFvHaMG+7VevYlw2WWX2fP1vCeffFLy8vJk7ty59smLP/zhD3afjunCCy+Uxx9/3H7WX1qfWukaB5pGSdv70Y9+ZK9D2UEn3/zjH/+w30udPa/fsUceecQ+raBPeujCxnpDoX379mWnqYHeVNGnIlxvppQdwBsEEKiVQGauCdKb0j70h3/falVRDU9q16adjOg/RpK3r5XktE01PNubDuevst50NRkLAggggAACTV2A/7Np6t8Axo8AAgggcFoBDbBqiYysXhoDDdBrwHTIkCE2aKsznnXBWU1L8tRTT5Vrx5ktf9VVV4neDNDjtKxatarccdX5oEH6083grur86667Tnr06GEPe/311+Xll18uN0PbOV9T/tx99932owaQH330UdG+a3GcNIXMHXfcYcdz1113yb///W+5/fbb7TE6G766RZ8s0AC1Bpq1LU2t8/DDD9tgvwaiT5w4Ud2qZOjQoXbWuZ6wfPlyGxjfuHHjKefr+LT06dOn3D5NjaPXx3lyQGfbq5MG9PUc/dE0Qc4416xZI1q/jveZZ54RffJArbSO1atX21nur7zyim3jtddeM5NwS6z3RRddZOvSpxQ2bdokn332mT3ml7/8pQ3gO53SGwIzZsywgXi9cXHJJZfY747eZPjwww/ttejdu3e52fx6vBZdU6GyWf5O3bwigEDNBDJ0Jr0p0SbtTEOXAQn9zAz+eFlsZtPnFeQ3dPMe0V6zSp728oiO0QkEEEAAAQQQQKAWAgTpa4HGKQgggAACTUNAZyRrGhGdXe3kfT/dyA8ePGgD9Brw1uCrvmpgVoOnWv73v//Z9DjO+U59GvzVwLYT3K9NkF5zkDuBdqf+6r5GRETI559/boPOGujXALKmWXnsscdsChinHiftj37W9Dias96ZGX7NNdfYw3QhW70xoUWP79Kliw0i6+ef/OQn+lKtojPNteis9fHjx4uvr68NQOs2feLAMdXPVRXNwa590XPGjBkj3377rUycOFE0+O2a4kcXCNaifXaKBsQ1wK7l3HPPdTbbXPbO4sG60QnQa6ocLbq4sFP0xobTjq5hoGb6vdI0Nvr9yM7Otjci9IkNDeqrbfPmzW3aG6cOV/v9+/fbzQMHDiz3dIeOU28waJ0tWrRwTrVPDWjwXq+tc53KdvIGAQRqLbAvf5/kHsiW4DYhEtk2vNb1nM2JF54zWQ4dzpNFGxaeTTWciwACCCCAAAIIIOABAgTpPeAi0AUEEEAAAc8U0GDn73//ext41rQmGuA93SxuJzitucI1n7gGSzWPvb5qoF+L5gx38qA7M7M1YKwpTrRo4FYDuocPH7afq/NL06roOZrzXmdla95xbbMmRYPgmmrlq6++srPfNeCvM8U1EKypWnTM33zzTdkTAprqRhep1aC0bnduEGiqFS36qjceNB2OBsb1psX1119f7S7pbHQ9TwPm6qUpXbQfjqPeQHDNx16dirUv2g8N1p9//vn2ddKkSTYwruc7qYI0pY8WzQ+vueydAHtlps7CvbrvpZdeKltYWGf+uxYNkGtgXouOQYPmTkBf0x05M/svuOACe4yOVZ8ccIqmJHJu3uj11eJ8j5xj9NXf39+2o+l9dHa/ztzXlEpamEVvGfiFQJ0JpJ9MdRPVwKluXAfQIiBQzhsySTbvSpFVW1e77qr0fWp2mqzYukq+XvWNvP/9hzJ93vsy7bv3ZfaKr2RB8iJZumm5bE7fKkcKj1V6vqdt9BEfT+sS/UEAAQQQQAABBGotcOrKZrWuihMRQAABBBDwPgENLmtAVNOQaO54DbZqwDctLc2mQElKSrIz7Z3c9BpcnT17tg28avBW07ZoPvYbb7zRbtcZ5ZqL3AnqP/TQQ3amuMppXVo0IDtixAj7/nS/NEitwdiZM2faQ9555x3RH6f88Y9/lFtvvdX5eMZXnS2uY9Tj9aaBBrF1DBrY1X5r0F3HosF8DchrPv3Kis5y16LpgX73u99Vdki1tulMfg1Ua851TaGjVmquTyNoOh69gaA3Q3Tmef/+/ausU1PO6Cx/TZWj9Wgdmpdeb8Bo3nitQ2eZ63ZtY968eTZtjBqPHDnSpivSpymcwLo2qL4aYNcUOnpDQgP+aqhBeE1X41q0DW1b69MbDh07dhRn5r4uNqvXXdcj0Nn6erNAb1JoPe+++67s3bvXfu+mTJli+6qpbLTs3LnTvlb8pWsG6FMZurCxa1FLCgII1J1AWaqbsJi6q7QWNfWI7y67snfZtDftTdqdqJCIslqOHDsia3asl6XrF0hgQHM5Vlj6xJAe0KplG3NjL0ASohOloPCwZO3bI1k5mVJ0vNCe3yEmQRJjukinqM6iNwM8sZRIiSd2iz4hgAACCCCAAAK1EiBIXys2TkIAAQQQaEoCumCpLgarKU50EU7Nma5Ba51J7eSSDw4OtsFjnRWvgVsNsv7pT3+yaV58TN5cza2u9WjwWWfUa6BZg7qu+e41INyhQwebz7wqX12o1Ek34xyrM7Y1iKv90pn/1S0aMP7vf/9rg9R6Xnh4uB2fjlFLTk6OTZeis7P1RoDeeHAtOpt/z5499kkAzdGuNyV0trtr/vPi4mKbMkhn/FdchNe1Ln2vQWYNNjtBf71Rct9999nz1FefNHBm569bt67sJkfFepzP+oSBBur1R206depknw5wFtzVOkaNGmVvemiqHT1ObxRoqhoNdutaA06KHb0Z06ZNGxs411n2UVFRNkivbel4NRivQX4dp34HtO96E+C3v/2taEoa57p0797dvtcFhfXmyNq1a+13w7kxoDdvtJ9aNN2N5vjXmyYauNeifaisaOodbUdz52vRJzb05oJrCpzKzmNbwwk8/+UOGZHUTvrEBzdco7RU5wKZOem2zhg3B+m1E5MGTZQX01+U+evmyxWjShevXr8rWZZvXCYH80tTZGmAXm+0Jpqgfnfzk2/y2MdHxEuQCda7lvR9GZKWnS4ZObvl66WzTCA/UHp0SpIxfUa7HuYR70+YP9spCCCAAAIIIICAtwj4mL+48n833nI1GQcCCCCAQIMJaAoYDYZWLBqw1rzhGujW4Lxr0f/kamqTxMTEcgFs12M0cO8a3Hbd5/peA+iaS15zymsAXFPDOLP5XY+rznvt8/vvvy8vvPBC2Qx/PU+fGtAc6T//+c9lxYoVcvnll9vqNBWNBrU1JczKlSttGiDdoTPRNcitM991jDr7W4PDOjN8/vz5dia5Bpo1kF1V0THpLHLtQ2Ue2kbbtm1Fb45Up2jgXGfe6w0U16JBdA38V7cevYbat8quvVOv892o6lpqPfodcb4nuthsYGBgpTcx9DulNyd0pr2msdHjnDRDTrsVX/UpizvvvNMG7JlJX1HHPZ9X7TggP3t2hW28dasAGdrDrE/QPUQGJWhe8+bu6VQNWr381SkyMOkcGZ70wxoNNTjdaw5N35cpH3z7noSFRMq148vftHTXIHeZVDYz5n0gfbsNNDeAC2XDtnVlXWkR2FK6dUySnh2SJDSoXdn2M74xfzZty9gua7evlbTMHRIVHitXjb7ijKc09M6YwFi5963fSmJUkjw6+YcUYQ3dD9pDAAEEEEAAAQTqQoAgfV0oUgcCCCCAAAJeIqAL4Orsbp2prYFrnXnpFJ05rrnXNXe9M8te9+msc32iQFPQ6HZN6aIBYifvvh6js/wvueQS+elPf3raWeB6XH0XfWpAF2vVdQM0yF/ZDYD67kND1a9PIyxYsMAG9TVfPcUzBNJyjsjizftkyaZ9snKzuflytMh2rEenYBmSGCIDTMB+UGf9bv7w755n9FyEIH3plVi6ebksXjtf+iT2lzF9PWeG+WdLZsr2tNK0Y9rTkOAwM2veBOc79pCWgT8sKF2j75MJ1q82+e7nrZpjvpMBcv3EGySoRek6KzWqpx4Ojg6Mkd+9dTdB+nqwpUoEEEAAAQQQaHgBgvQNb06LCCCAAAIINGoBnSGuOd01wK3peTStS2VF09Vo0D4uLk7CwsIqO4Rt9SSgTyFoah+9KaJplyieKzAvea98n5Irizfsk+z9R2xHQ9oEyjlJoXLRoCgZ3CXEYzpvg/Q9hsjwnsM8pk/u6MjHC2dIasYOmXTuxdLV5G73hLJp9xaZtbh0jRK9uTq093AZlDiwzrq22yyU++GcaRJg8tj/7NI766zes6koKiBa7nv7HoL0Z4PIuQgggAACCCDgMQLkpPeYS0FHEEAAAQQQaBwCGpzXBVirKpryhuIegRkzZtiGXRe7dU9PaLUqgVFJ4aI/YrJJacB+7vpcWbg+R75ckmF/hph9Fw2OlIn9IquqqkH2O+mZGqQxD2xEE4VmmpztzZr5SawH5KNXItcAfZcOPWTrrhTZd3BfnerFmkVpb5/yC3np43+K3qSYMuzSOq2/NpWxcGxt1DgHAQQQQAABBDxVgCC9p14Z+oUAAggggAACCNRS4K233pL27dtLnz59alkDp7lDwAnY519yXOas04B9jixev1eWmuD9m3NSZeq5MXLZ0Gh3dI02Twrs2psqRSbne7RZdLVloPvXEXAC9IEBLWTC4PMkoX1nWRYULovWzZNIkzO/b+e6+zMg0NygnTz8Epm54BNZu2O99OnUy63fC7NCiFvbp3EEEEAAAQQQQKAuBTwv2WVdjo66EEAAAQQQQACBJiawfv162b59u4wYMaKJjbxxDbfYzMguOlEiRwuL5dDRE5JXcFz2HTome/OOmTz1x2WAyUt/10UJ8sRtfWTKyFg5cKhIHnsvRS54YL488uFm2Zp5yC0Dbu7n/sC0WwZ+stFde3bZd9Hh7r9Z4gTog9uEyKUjp9oAvXZucLcB5iZCnAnULzTfrcMne143L3oToHdiP5m74mtJNYvVekNZtnW/XPTgQvl4aYY3DIcxIIAAAggggEAjFWAmfSO9cHQbAQQQQAABBBCoTKB589Igqq4XUJfl+S+31WV1bq1LU5YcN1HyEyZIrj9FZkPxcfPevJ7Q7WaCrn3V/WbbcbOv2O4rluNmW7E54IQZgXN+6TnmOHNuidmvrzrH1+4/2U6JfS2WYn01ddW27Dt4TD7+Pk0+MT8RoS1kxh8bNj98ZLuo2nbdK87LyEm349D0L+4sR4uOyfy139kuDOs9QqJCIsp1Z9KQSfKfz16W2ctny2Ujppbbd7Yfhpk1CdZtWS3fr/terht/7dlWV+vzT5Tov4VnX16fk2ZumBXLI++kyIqteXLHBR0lOqSWC+2efXeoAQEEEEAAAQSaqABB+iZ64Rk2AggggAACCHinQEJCgrz66qsyePDgOhtgtpndvTWzQBasza6zOqno7AT0JsCe3CNy9+vr5e83NGDakdrfXzi7AXvA2UcKzZMO+/ZI88CWJh99rFt7lJK6UQ4XHLKz2hOjT128tnXzVjJu8Pny7bLZsihlqZxrFvytqxLoFyAJ8d1kW+omWbNjnfTt1Luuqq5RPSVncbPLtaHWLZrJAXPzS8vsZRmy2sys/6kJ1F862L03Ylz7yHsEEEAAAQQQ8H4BgvTef40ZIQIIIICAhwhkZGTI9OnTJTg4WK677jrx9/f3kJ7RDW8S0IU9J0yYUKdDiggOlCdv7i2aFsLXJEv0NW3YnIn62qz0vd3m62Nzbh/CAABAAElEQVTb1ZfSzydf7WZznHOuOUDP9zEbfMycc18f83ryHPO29FynDvOqY9IfPd9WVaej89zKVmw/IN9t2CsLN+RKWtYPaUt6J4TIOd3ayYCEtjLQpMVpyOLN/vsP50l+wUHLmWsWXs3MzZDMnEwTDD8onWK7SKB/oN3X3qS68dUvoxvLRrM4bLu2YXJu0umfpOjVIUlSs1Jl2YaF0iW6s4QHh9VZj3t17GmD9Ou3uzFI71M3d4weub6nfNkzVJ75eKtZcPeYZO8/Ig+/bWbVbzsgd07qLFFtm3aKpzr70lARAggggAACCJxRgCD9GXnYiQACCCCAQN0InDhxwgbmNVe4lg8//FCee+45qeuUJHXTW2pBoHKBwV1CKt/B1joTSMs5Ip8u3yMLTHB+6+78snq7xAbJ8J7tZHTPcOkZF1S2vaHf6I0Vbytfr/xWcvL2SnZu5mmHtmP31rJ9uXm5kpK2UXrEdS/b1pBvNqdvtTP6B5jZ8c39A87Y9AWDJ8lLmTtl1vJZcv346854bE12djAL58ZGdZDdJkf/mu1r63SB2ur2o7hYnyepmzKpf5RM6BMhj324RT5ZuNtWOmtJpqzZekB+OqmTXDyofd00RC0IIIAAAggggMBpBAjSnwaGzQgggAACCNSlwMaNG+1inomJiXLBBRfIs88+KxdeeKFMmzZNevbsWaOmHn/8cenRo4dcfPHFNTqPg90nkJ+fL3/605/kzjvvlK5du9aqI8uXL5fPPvtMsrOzJSsry9bRqVMnueKKK2To0KG1qpOTPEdgzc48+WxZpskhnmUWkz1uOxYb0coE5kNlVK8wGdTZvTdItuWW3mDU3PreUHabGfJbMzZLyvYNUnS8UALMLPm+XQeIv5+/HCk8IuP7jbPD3LM/WwqPH5Ptmdtlw7Z1Zp2B43Iwf798teRLWW3ysvdO6C29OtTsz/Cz9UvN2mmriDeLw1ZV9AmV88+ZKJ/NnyHfrp4j4/qNreqUau/vEtPFBumTdya7JUhfUsePdfg185U/XNlNxvYOl398tFl2Zx+2KaX++layLDdPEd15QYJEmqeKKAgggAACCCCAQH0IEKSvD1XqRAABBBBAoIKAprrRcv/998uoUaPkvPPOk5tuukmuuuqqGgfq3333XdHgbGVB+lWrVkl0dLRERkZW6AEft23bJvHx8W5JM5STkyMfffSRbf83v/lNrS6GBuj/+9//ljtXA/eaQumnP/2pvQlQbicfGoXA3PV75dNle8wioKX5/tuZ1BrnDYyU0b3CZWRSqMeM4VBhabodk3jIY/pUm47oDPjknSmSnrWr7PQu8d1lYNeBEtk23G47WPDDEwzOgqxtW4fI2s2rpFXLIImPijfB/fV2Nvu3Jkd9Rk6G6GKqmge+IUqOmcnvZ24mxIdXHaTX/nSO6iS9u/STdVtXS0J0F+lQjeB+dcaRGJ0oc1d8Yx3yTEqgYGPTkMUs01wvzQ3r3k6G/X6oPP3ZVnnnm9LvyZdmVv1qM6v+VjOr/iJm1deLO5UigAACCCDQ1AUI0jf1bwDjRwABBBBoEIG9e/fadtq0aWNf+/TpI++//74NtP/4xz+WhQsXSvPm1ct7e+zYMdmxY4ccPHhQ9uzZI4cPH5aQkBDp2LGjvPfee3L06FF5+umnG2RcjaWRefPmiTqPGTPGLqrq59ew/wvkpGXQ65abm2uvm6ZAioqKkoiIiGoxanD/8ssvl9DQUHue1vnBBx/IPffcI3rjRmfqUxqPwAeL02WmCc5vMHnnmwf4ybiBUSaVTaiMMelsmgc089iB+J5cd8BjO3iajuXk5ZiUL7Ml18yMd0pkWLQMSBwgiWZGuGsJaln653S5bS1aS1LnXjKw2yAJadVWenfqI+t2rLXB+o07Npj89ZkyNOlc6Rab6HpavbzfZ8YS0S6qRnWP7TdGUrNTZbZ5AuDWi2+t0bmnO7hFYHOTqz9RduzeImnZaRJs8tQ3ZCk261nUZ7nroi4y1twse8LMqt+cav57axZqfsjMqtdc9T8zueqZVV+f+tSNAAIIIIBA0xNo2L+hNj1fRowAAggggIAVOH68NH2FK0fnzp3lgQcekLvvvlt0pr1+Pl3RGfKazz4tLU0OHTpkf3r37l3u8A0bNkhAQIAN+JfbwQc7g11TDc2dO1fefvttueGGG+pdRa+53nzZvXu3rF692rb3ySefiP44ZcCAAXaGvfP5TK9BQUHies01SK83H7TExVVvRu2Z6mdfwwhMX5guH5mfben50tMs+vqLKYlyXt+IRrM4ZUld5xhpAHYN0M9c8rnkmcVgtbQ0Qfj+Jjg/MLF/jVqfMGBC2fE6wz4qZEK5YP2sxTMlJ+kcGW6C9fVVcvP3yfHjRRIYUPO0KxOHXCDTvn7LWHwhk8+5oE66mHgySL87Z7foYrINWUpK6mcmvesY+nYMljd+PVhe+XqnvDxzm931+eIMWbllv9x2QSeZPJBc9a5evEcAAQQQQACB2gsQpK+9HWcigAACCCBQLQHNR+7MpK54gqas0UCDpq85XVm8eLFcffXVp+zu1auXDB482Oa01/etW7c2waeWsmvXLhPEOW7SIVT9n/mioiI7K79du3YSFhZ2Sht1sUH7ojcZlixZIjoWfX/NNdecdua33oTQmxG6qK6Ox7Wkp6fL999/b29qaFqfCRMmVKvf+pTB119/LZryRp1qUvT6+Ghi5xqW559/Xv7xj3+cctbIkSOlb9++kpSUJBqkd0pNnFJSUuS3v/2trF+/3t7cee2115xqePVgge+T98prX+8ys7fbyl2XdpEhie08uLeVd63m/yZUXk9Dba0YoO8S382kphkubVvVTWoWJ1gfZxZSnb34c1mRvMQOrb4C9YePlKYd0hz6NS1RJp3PkF7nytL1iyQlurNZ+LZbTas45fhg81SBloKCglP21feGknqeSe/a/59O6Ghn1T/+4WazFsE+O6v+wTfNrHqTAud2ZtW7UvEeAQQQQAABBGopUPXf3mtZMachgAACCCDQVAQ0/YwGSWfNmiWaI1zTkVx22WVy7bXX2gDq6NGjbYoT9fjiiy/kyJEjkpCQYNOctGjRotIAvKudBtA1sNy9e3cblH/jjTfs7pkzZ7oeZt87KXP0xoCmwNGi7Wk7FYv2V2fyZ2Zm2l3Dhg2TF154Qdq2LQ26VDy+4mfNsb5//3655ZZbyu3SYLPTvs5c1zQtmuLFKeqjZhWLnvfvf/9bHnvssbJdN998sw3mN2vWzN58mDp1arm61OVvf/ubTJkypeycM71Rdy0aeJ8xY4ZNf6N1aKqYOXPmyKOPPmq36TF6Y+XNN9+0+/SYH/3oR3LHHXfY66v79WbDt99+K7feeqvoLHfXoumN9CaDnjdkyBDRWfwvvfSSXHrppXbRYNdj9X1NnFasWGG/X3qepu955plnqn3N9ByK+wRGJpk88/eX5j13Xy/OsuVa3LA6yxbP6vTFG5eUzaAf3nd0jWfPV7fx7rFmQWizfnNDBOq1T4G1CNLreUO7nyM79+w0C99+IYnRCeLX7Oz+OtjcP0CrlcDAmt80sCeexa/iBphJ79q9hKhW8tKd/eWd+bvl2Q822VsEM3VWvUl/c7vJVX/BgJqlIHKtm/cIIIAAAggggIAvBAgggAACCCBQe4EtW7bYRWA1UKwBep0Z3b59e3n55Zdl7NixNnCvs72dooFaDfZq4FZnd994441Vpqfp2rWraCobzT/+u9/9TgYNGmTT3Th1ur76+/vbj4WFhaKz5P/whz/Y4P6dd95pA9POsdOmTZPbbrvNBuivvPJKO7NbU7P85z//cQ4546vmU7/rrrvkL3/5i+zbV5pCwjlBb1iMGDHCzuZ/7rnnyoLqGgDXcaxcuVL++te/OofbVw2I69g0QK9BfM0fr466UOqiRYvsMTorXYP96qn1PvLII9bwV7/6lVR2w6JcA+aDBrf1xoKWjRs3ip6nqWe0Hk2BozcrXBd1ffjhh8tm++vsfr2mkydPFp3Nr0Vnyj/77LP26QC74eSvdevW2WukT0foeHUMv//97+1evWFSWamuk57r2OnTE6+++ioB+spA2VaPAvWfYqSuOr9+V7JsT9tiq7tk5NR6C9A7/dVA/flDL7QfdUb9guTSP7uc/Z7yOr5/6X+TPl7wQ+qt2vatuX/pWiqBJ19rW09tzisuqd+c9Kfr049GxMrHfx4hw/uUrieSubdA/vzGBnnwvRTJyjv1BvTp6mE7AggggAACCCDgKkCQ3lWD9wgggAACCNRQQAPOml5GiwZ7NQisAWOdpd6jRw8bCNeZ5hrQ1aIznzUYff3119tArgb2nX32gGr8cmYs6szzisWZSa+Bc52FrjPBtWifdOa4Fs1tr3nwtWhQXoPnV111lf2cl5dnX6v65SyEq4F0nenvWrQdDWrrwrb33ntv2cxzDS7rjQa9gVCx6Pbp06fbY3VBXb25oIF+LTorX8eq9WoAXwPjl1xyiX1SQYPsH374Ybm0MRXrdj5//vnn9saCfnZuZugCu6+88op94kGfJNCbALo+gM7016C8U7RtvW4ayL///vvt5k2bNtlXDZa7Fm1HS3b2DwtUOuly1KWyUl0nPdd50mHSpEnVSmlUWXtsQ6DWAiWN468PRwqPyoqNy+wwe3bpIx0jO9R6yDU50TVQv3bLask9+MNTRDWpp6pjcw6ULkZe1XGV7Q8PDpXBPYdKhllINvss6tG6ndz4zd0wk948E1XZ8Bpkmy4a++TNveUP1yZJi+alN8d1Vv0d/1opX67a0yB9oBEEEEAAAQQQ8C6BxvF/2d5lzmgQQAABBLxIIDg42I5GZ8RrehunaGqae+65x35cunSpXbhUP4wbN86mTNGZ9xqY1pnWTiDdObeqV53FrsXJc6+pWzTtigayneDz7bffbnO3a3oVJz2OM4tcA9NO0RsImgJGg+JaNFd8dcrRo0ftYX369Cl3uN6cWLNmjb1BocF7fWJg/vz5ct9999njNMA9atQoeeutt8SpQ/utNzi0aJBcn0BQPydoP378eJtWR/cPHDhQWrVqpW9t0TQ4uk1vFlRV2rRpYw/RGwxO+h191ZQ0r7/+up1ZrwckJyfbQL1Tn6az6devn71uepNFc9s7C/jqMZob3yka4Hduuuh6ARWL642Vw4cPlz1FUR0np65//vOfNnXOdddd52ziFYEGE2gs2W42pW2SvPz90jmuq4zvN67BfLQhJ1BfVHRMVmxZWadtR7aLtPXlnmVw/dweQ2XKqMulXZvStGi17WTBsQJ7auvmpX++1rae2pxX4sYgvdPfSwa3l88eGC4Th5T+NyjdzKp/4PUN8tfpG5lV7yDxigACCCCAAALVEiBIXy0mDkIAAQQQQKByAQ0Sa3ENHOtnDcA6i3lquhonP7wGd8+26GxyLTrDXMvs2bPtrHldRNRJp6Kz+zWtjs4616C45kTXvOc6Q/ybb76xwfMvv/xSNNWNzgTXYL5u19n/1Snh4aV5tRcsWFD2JMG8efPK0sXoIoJ680CLLv6qNw30ZoWmx9GZ9Bq0P//88+25OiNdg+WaakZntet2XVhVz9GbDwEBAWVjdQ1yV6efrsc4bqtXr5asrKyyXXqTJC4uTpwbDnqTwUlpowfpYrPODREnIK8z4p3FfnWdAS1q7ho41++Aa9EbCZrD3ylPPvmkvUZO3VU5Oefpd00Xz33ooYecTbwi0GACzlMhDdZgLRtKzS79s3ZY0rm1rOHsTtNAfVJCb9m4Y4PsyCp92ursaiw9O9AvQCJC28sx86TA3rycs6oyPiLurHPSr9m+1vYhqI4W4q3JgDwhSK/9bd28mTz4oyR55JbeEta2NP3PpwvT5c4XVjGrviYXlGMRQAABBBBo4gIE6Zv4F4DhI4AAAgicncDQoWalQFN09rTOEteFT//v//7PBsE1KK6BVD3GmemtOezPtjh1aaocnbn91FNP2Sp1RrwTfO7cubNdFNZp6+KLL7ZvdVa7Bph9fX1tQP6JJ56wqXA0mN+lSxfn8CpfNVCsee61rgsvvFA09YrmkdeiOfM1YK2pgLTozQK9YaFt3nTTTaKB/T/+8Y/2GM1r76QL0v3nnXeeTTOjaWw0kO+kdnGC8zt37rR11uZXRESEPU3T8Bw4cMC+/9nPfib9+/e37zVIrjcz1EjT2jhFbxToGPTJCM1dr0Xr+vnPf27fax06fr0ZoqmExpjZ9lo+/vhj++r8iomJEb3+eo00P77ekNAZ9DpuLVU5OfVoGh29saBpfireCHCO4RWBehNo4MU6azuODBOkH5R0zlnPFK9t+3reOd2Hir8JqqfsSjmbak45Ny4i1m7blrnjlH0NueFgQb6k7FhvmwxuVfpUWUO2766c9Kcb47jeETLTzKq/bFTp9dmdddjOqv8bs+pPR8Z2BBBAAAEEEHAR8HN5z1sEEEAAAQQQqKGAplDRwK0uMurMnNcqdHFTzQnv5FXXGfc6a33z5s01bOHUw50Z37rwq1MefPBB0b5ERpamQtCge4sWLZzdcsMNN9gbCDqTWwP4ixcvlnfeeccGhssOMm90sdk9e/bYFC7OUwKu+13fa9A6KipK/ve//9n0L5ruR/ukwW5Nm6P1aNFXvYHx+OOP2wC2BvidmwkayHdm7+sNAw1w9+7d27UZG4h28tg7KWvKHVDND069On69IaEBdb3R4Fo0/c+LL75Y9hSApuVRp88++8yaaboiXXBWZ+Xre92vN0n0hok+AaA3IfSmjAbunfE79WtaHj1OA/NO0ZsVTqnKSQ30qYKwsLAyx4pPcDh18YpAvQk0gnw3S00u+kKTasZds+gd+zYtWsmApCGyZO18yes1XIJb1k1KmLiIeFmRsky2pG2Uod1PTavltF/fr0s3LZWCI4eleWBLCWoZVN/NnVK/p8ykr9ixe6d2kzG9wuXvH2yWNBOo/8TMql+17YDcOqmTTOxX+t/oiufwGQEEEEAAAQQQ8DGPortvxR38EUAAAQQQ8CIBndWs6WR09rczO9p1eDobXNPRnE2gWevT/3RrTnudSa2ztzUg7szo1/26aGzFxVx1+9q1a+0M8N27d8vll1+um2ywV+vQ9DQaMNdZ41ree++9cnXajbX8pf159dVXbTDfdeFUTbOjTx1MnDjR7tcbDVquvvpqm4JHnxJYtmyZDWzrdl1QVvPHO0F93VbTou1rHWcqmvNf29KbEF999ZVouiK9tn5+fuIs2num80+3T1P66OKz69atk4suusiuYaA3DJxSHSfnWF4RaGiBNZnr5MHP/yTXjbtRQtudXR7z+u77m1+/IYH+LeXK0aV/ztV3e2eqf5/Ji//mrNdk3KDzpVfHpDMdWqN97855T7JzM2XCkEmSFN+9RufWxcE6i3/mghm2qoHmiYXhbkor9Oz7T0tiVJI8OvnhuhhWndfxry+2yeuzd5bVe+mIWPnJhI6iC89SEEAAAQQQQAABVwGC9K4avEcAAQQQQKCJCKSkpNjFRzUI7Ro4HzBggA0eT5061Qal65pDF23VGeFBQUGn3KzQ3Po6M13TBLkWnbGuM9PPOecc1831+n7atGly9913lwXp67WxSio/k1Mlh7MJgXoXcIL014+/SdqFtK339mrbQOGJ4/LSjOclLqqDTBl2aW2rqdPz3vjqDQkPiZRJJlBfV2Vr5nb5fMEn0t6kvjmnx7kmP322ZJqgfVZuhoSZti4ccqH4mye46qu8N3eaZOVk2Fn01064TlqbpwbcUTw9SK8mG9IOyuNmVv3GXXmWKC6yFbPq3fFloU0EEEAAAQQ8XIB0Nx5+gegeAggggAAC9SGgs9Gffvpp0dn9GzZssAH5Dh06VDnL/Gz74iw4W1k9mi5Gf3TWut5E0CcS4uPjbYqXyo6vz23OQr86u90d5UxO7ugPbSLgCJwoKXbeeuRr7sFcKSkuNgFqf4/pnz5ZlZ69u9b90RsPOi792W9m5u/P3yf7D5b+2ZRp6v04e7q0Mql0YkzAvpuZVZ8Y07VeA/QfmRn0GqDX0tMsjuuuAL0D2qldR+etR772jAuS1+4aJP/9dpe8+OlWmwLn/tfWy4rtB+Qn45lV75EXjU4hgAACCCDgBgGC9G5Ap0kEEEAAAQQ8RUBTuPTt29dTumP7oXnWdfFZdxYnt39ycnKdpf1x53hoG4G6EvDwGL3k5uXYofr7eU6Q/sixoyalWH6Vl6DIpNrKOZhjx5BrAvE6Fg3GH65wbmjbCIkKi5aEmARZv229yb9/VHqYVDoNkYN/2nfvy569pTcc4tp3knPN4rjuLq0C3DOLv6bjvnlcBxnfO1we+2iLLE/JkRnf75Y1Ww/ILed3JFd9TTE5HgEEEEAAAS8UIEjvhReVISGAAAIIIIDA2Qnokwaau/6NN96wCwD7NILFMs9uxJyNQPUESnw8ezmrQ0cL7EA8KUh/rPDoKbi7slMl/8ghyTucVxqUz8uV/EMHTjkuuE2IJJrZ8ZHtosxPpLQPaS++Pj8cFt42UmYtninLk5dITFiMdDCLytZXeeubtyV3f7atPsA/UCYNnmTWX3HpTH017EX1xoe3lH/d1lemL9wtT72/WXZmHhKdVb/SzKq/hVn1XnSlGQoCCCCAAAI1FyBIX3MzzkAAAQQQQAABLxfw9/eXKVOmyJtvvmkXA27evLmXj5jhIVBdAc8O0ge3amMH4ilB+gMmCH/ieFE53DwzM37GvA/LbdMPQa3bmtz1ERLR1gTkQ8JtUD7QL+CU41w3dItNFBk62Qbqtc7xgydKzw49XA856/fF5pK/8+1bJkC/t6yua0we+hYBLH5aBlLDN1cOi5VxZlb9ox9skXlrsuRjM6t+NbPqa6jI4QgggAACCHiXAEF677qejAYBBBBAAAEE6kjgvvvukyuuuEII0NcRKNV4hUCxh+e7ad2iNEifd+igR3jvytp1Sj+CTf74Gy/8iWxK2yRtTWC+batgaWdmzPs1q91fzVwD9d8smyU5eXtldJ9Rp7Rbmw2Z+00A+bsPpOh4oT09MKC5XDHmKtPnoNpUxzkuAqFtAuXvN/WSL1aGyd+n/zCrfs2OPLnRpMaJDOYmiAsXbxFAAAEEEPB6gdr9n6DXszBABBBAAAEEEGjqApobv3///k2dgfEjUEHA02fSB9v+7t5zanC8wkAa5GNqdpptR9PWuBYN1A/pVndrb2ig3nfYRbJo/UJZs3mlpOekS9+Efmc1q37N9rXy3cpvy7qdENdNJp9zQdln3tSNwAUDosys+gh59MNN8vniDPlgXpqs2LpfbjmPXPV1I0wtCCCAAAIINA4BgvSN4zrRSwQQQAABBBBAAAEE3C5QIp4dpA9q0doaHTOLqabuTZP48Di3mR06eljSs1Jt+53NIq/1XRKju0hMaKwsTF4gydvWyTf7ZsnG1BTp26WfyVXfQfybNatWFzJN3vnFyYskLXOHPT7SLFLbs1Mv6dUhqVrnc1DNBQL9feWBq3vI2N5h8pjOqs8ozVW/1syqv4FZ9TUH5QwEEEAAAQQaoQBB+kZ40egyAggggAACCCCAAALuECgu8ewgvZp07dhDNu9MkVQTIHdnkH6tmYleWHTMXqbO0fUfpNeGWgY2lwn9x0t0aIws2rDQ3iTQGwX+Jrd9RGh7iY2Ik/ZmAdqKZX/+ftlr0uRk5mTIfrOIrZ+fv3Tv1FO6xXaVDpEdKh7O53oSGJUULqMeCJcnPt4s079Lk/fNrPpV2w7IjRM6yMR+p163euoG1SKAAAIIIICAGwQI0rsBnSYRQAABBBBAAAEEEGiUAj6e3+uk+CQbpNd88CN6DXdLh3UW/YZt623bYSFREtOufYP2Iym+u3Rq31l27tkhqdmpsiV1ownY77I/Z+pIG5Mjf1jfUZIY00WCW5J3/kxW9bnvt1O6yrg+4fLY+5tlW3q+3P/aemFWfX2KUzcCCCCAAALuFyBI7/5rQA8QQAABBBBAAAEEEGgUAv+fvfOAr6JK2/hDeu+9ERIINfSOgiiCZW1rQURl7a69rN1PVte6iq7dteuKriuufW2oiEivoYUSAum998Z33rmZm5uQhJt+y3P8TWbmzKn/ieHe57znfZuONlr8OGOUtbi4aMlVVuG7j+ztkV/27k5WrOirayu16uOHje9uMz2q5+7sgpHKj7wc8ybMQ0p2CrKUr/ry6nJUVleioqocVdUVCA+JQriysg/xC0WCEueZLIPAxDh/fHz3NLz2wyG8820qreot47VwFCRAAiRAAiTQZwQo0vcZWjZMAiRAAiRAAiRAAiRAArZGwPLd3QjxxLhETaTfuHcDYpW7Fk83j357ESWVZUYr+piIIRCr9oFOjg6DNAGeIvxAv4mu93/9/DgtsOzjnyRjr/JRT6v6rjNkDRIgARIgARKwBgIO1jBIjpEESIAESIAESIAESIAESIAEzCUwKmYkJo2civKKEhUEdb251Xql3FrlC163op84dGKvtMlG7JtAQrgX3r1lMm46d5gGQnzV3/lWEr7fnmvfYDh7EiABEiABErAhAhTpbehlciokQAIkQAIkQAIkQAIkQAIGArNGz4SXpy92K9czyRn7+wXLDtXXQeX/XdKkUdMgrneYSKC3CFw2JwafPTQLk0cE4EB6mWZV//Rn+5FbaghQ3Fv9sB0SIAESIAESIIH+J0CRvv+Zs0cSIAESIAESIAESIAESIIF+IHDRSRdpvfyw/n99LtTnlxZg/a51Wn9jh03ArFEz+mGG7MLT1cuuIET4u+Hl6ybgnotHQtwY0arerl4/J0sCJEACJGDDBCjS2/DL5dRIgARIgARIgARIgARIwJ4JeLl74qJTLgEGDUJfC/Xi5qa2rhojhozGSePm2DP2fp17XGBcv/ZnKZ39cVoEvnt0NuZMDKNVvaW8FI6DBEiABEiABHpAgCJ9D+CxKgmQAAmQAAmQAAmQAAmQgGUTCPMPweWnXQFfb39NqBeXNL2ZisqLsfyn5TiSdQhx0cMwf9Kpvdk82yKBDgn4uDvh75eNxqNXJCrXTi6aVf1f6Ku+Q158QAIkQAIkQAKWTIAivSW/HY6NBEiABEiABEiABEiABEigxwT8PH1w8SmLEBsVj1+3/qxE9Q+Rp9zT9DRt3LcZH3z/HioqyjFv2un4w7Qze9ok65tJIC0/3cyStl/s1LEh+OGRE3HmjEjsp69623/hnCEJkAAJkIBNEnCyyVlxUiRAAiRAAiRAAiRAAiRAAiRgQsDVyQVnTz8L6QWZ+H3X7/j3jx9gaMxwDA6NxdDIeMhzc1JNXQ0yVBub921BXmEWwkOicOHsC8ypyjIk0GcEHJX53UMXjcCp40Pw+L+TNav6XUfKcMlJ0VgwPrTP+mXDJEACJEACJEACvUOAIn3vcGQrJEACJEACJEACJEACJEACVkAgOigSF6uAsuL2ZvfhPTiY9j1WbXVCVGgMhseMgK+nH1ydXdThqh2VNZXILMhAlhLk84vzUVCSh6NNTXBSov6kkVMxa/RMK5g1h2gvBGYkBOCrh2bi2S8P4ONf0vDQe6VISi3F5ScPRqivq71g4DxJgARIgARIwOoIUKS3ulfGAZMACZAACZAACZAACZAACfSUwLi4sZBDLOtTsw8hOXWP5lfenHZHq3oTho1HgHeAOcVZhgT6ncAdZw/DPGVV/xit6vudPTskARIgARIgge4QoEjfHWqsQwIkQAIkQAIkQAIkQAIkYBMExLJejtmJJ+KgCv6arSzma+vrUNdQq4461DfUI8g3SDv8vQIR6O0Hd1d3m5g7J2HbBMbG+OLju6fhnz8cwtvfptKq3rZfN2dHAiRAAiRg5QQo0lv5C+TwSYAESIAESMBWCRzMrsDQcC9bnR7nRQIkYIEEhkbEQQ4mErAlAtfNj8N85Zf+bx/TV70tvVfOhQRIgARIwLYIqPAyTCRAAiRAAiRAAiRgWQRe+TYF97y7y7IGxdGQAAmQAAmQgJUSGBLiibdvnoRbzktA8pFSZVW/C09/th+5pbVWOiMOmwRIgARIgARsiwBFett6n5wNCZAACZAACVg9gc2HivHeD4eRkVeJpqNWPx1OgARsikBeSYFNzYeTIQF7I7B4djS+/usJmDoyCCtWp+Pud3bi++259oaB8yUBEiABEiABiyNAkd7iXgkHRAIkQAIkQAL2TcBx0CAjgKajVOmNMHhBAgNIYFx4otZ7bX3NAI6CXZNAC4H0/IyWG151iUCwrytevHYc7ls0EgfSy2hV3yV6LEwCJEACJEACfUOAIn3fcGWrJEACJEACJEAC3STg6Ngi0oMafTcpshoJ9A2BzDwKo31Dlq12l0B8YFx3q9p9vXOnRmDl43Mwd2Iorert/reBAEiABEiABAaaAEX6gX4D7J8ESIAESIAESKAVAUe0iPRHaUnfig1vSGCgCdQ10H/1QL8D9m8gUFCSr114uXgSSQ8IeLg64snLxuCxK8Ygq6CaVvU9YMmqJEACJEACJNATAk49qcy6JEACJEACJEACJNDbBBwdWkR6+qTvbbpsjwR6RqBI+aSvqa+Dm7NLzxpibRLoIYGC0gK4Orv3sBVW1wnMGxsKOR79JFmzqt91pAyL5kTjtAmhehGeSYAESIAESIAE+pAALen7EC6bJgESIAESIAES6DoBBxN3N/RJ33V+rEECfUVgaOhIren0/PS+6oLtkoBZBEory1CpjujAWLPKs5D5BB68cASe//MElFTUYen7u/Dof5KRpizsmUiABEiABEiABPqWAEX6vuXL1kmABEiABEiABLpIwMkkcCy93XQRHouTQB8SSAwfo7WeknmwD3th0yRwfAL6QlFimOF38vg1WKIrBKYnBOCLB2di4dwYfLUuE7e/sQP/+Z3xKLrCkGVJgARIgARIoKsEKNJ3lRjLkwAJkAAJkAAJ9CkB08CxdHfTp6jZOAl0iUBcUJxWPrMgs0v1WJgEeptAfqnBH73+O9nb7bM9A4E7zh6Gl26cqN0sW7EPdynL+oPZlcRDAiRAAiRAAiTQBwQo0vcBVDZJAiRAAiRAAiTQfQIOrSzpj3a/IdYkARLoVQJjw8dq7YmbkdzmoJ292gEbIwEzCSSn7tH80eu/k2ZWY7FuEJgy1B+f3jcd58+Oxuptubjhla1Yvpour7qBklVIgARIgARIoFMCFOk7xcOHJEACJEACJEAC/U3A1JKeEn1/02d/JNAxAS8XT8wcepJWYNuBrR0X5BMS6EMCuw7vRn1DHSYNngb5nWTqHwJ3n5eAZ64Zi/BAd7zw2X7c9lYSdqeX9U/n7IUESIAESIAE7IAARXo7eMmcIgmQAAmQAAlYEwGTuLFoor8ba3p1HKsdEJiXcIo2y/1H9kKCdzKRQH8T2Lh3o9bl4kmX9HfXdt/fiaOC8d5tk7FkQSzW7crHDS9tw5srD9s9FwIgARIgARIggd4gQJG+NyiyDRIgARIgARIggV4j0MqSnqb0vcaVDZFAbxAYF56IYWGjtKZ2K6GeiQT6k0BafjoqKku1HR1hXiH92TX7MiFww2nxeOGGCYiP8sIb36Tg2pe3YuOBYpMSvCQBEiABEiABEugqAYr0XSXG8iRAAiRAAiRAAn1KwHFQy8cTavR9ipqNk0C3CCyauEirt3nPOohoykQC/UGgpr4O/1v7DdxcPEAr+v4g3nkf04YF4M2bJuLy+bHYcbAYNytf9S8owb6+kf9yd06OT0mABEiABEigfQIt34Lbf85cEiABEiABEiABEuhXAqaW9E1Hm/q1b3ZGAiRwfAJiTX/e+Au1gqu2rIKIp0wk0NcEvlr7Jerqa3Dj7JtBK/q+pm1e+xLo/cbT4/H8nydgVJwflivXN1c8vxmrdueb1wBLkQAJkAAJkAAJGAlQpDei4AUJkIAlEEhKK8U1L21FdV2jJQzHOIa80lo8/81BbFKWQkwkQAJ9S8BhUEv7R5tMblqyeUUCJDDABC6dtFhzOVJSUYifN/wChyZ+rRjgV2LT3X+3+Qdk52doi0MzB8+w6bla4+SmJwTgjRsnYvG8WBxQwWTveTMJT/x3H0oq661xOhwzCZAACZAACQwIAacB6ZWdkgAJ2BSBo2pX6+6MMny5MRsb9hYiv6hGm198tDdevn4CfNzN/1Oz/VApklKKsW5fIU5OHHhfow0qaOX7v6ThTbV9t1FN9LPfMrHqyTkD+v7E52dUkDsi/N0GdBzsnAT6ioCTY4vYdxTcNt9XnNkuCfSUwHUzrsGRokM4mLMXK1ZV44qTr0QpinraLOuTgJGA7NIQC3oR6GcOPQmyOMRkmQSc1Ar7LWfGY/JQX7z2XSo+/y0Dm/cV48r5g3HmpHDLHDRHRQIkQAIkQAIWRMB85cyCBs2hkAAJWAaBC55cj+KKelRX1WsCtj4qL08XNDY0YX9aGXakluLEUYH6o+OeG5UoLulQbhUSIqqRU1wDJ8dBiA/zgncXxP7jdmRGgUM5lbjxn9tRVGJYdJAqF82JMqNm3xb5YFU6ahoa8PqfJ/ZtR2ydBAaIgPpf3piaZBWQiQRIwCIJeLl44vEzn8Czv/4DO9I24ekvn8RNp94GZ29HVDaUW+SYOSjrISDxDn7e8hPKKkpw6bQrcN6Yc6xn8HY80pnDgzAlPgAvf3cIH/10BI98sAcblIHJdfOHIDLA3Y7JcOokQAIkQAIk0DmBQUdV6rwIn5IACZBA+wSm3f6T9sBR+aM864RInDc1AvHhXnBWCpuI7R/+lq5ZzgR4ObffQHNuYXkt1u4rQnp+FX7ekY/03Mpjyv9hRiT+76IRx+T3VcaPO3Kx9L3d2uKDv7crzp0VgZkjAzE2xrevujS73bve3YVNyYUDbtFv9oBZkAS6SEBizs28w/D35bOHZqpdI/xS30WELE4C/U7gs11f4IMN72j9zhp2EsYPGQ+fAG+K9f3+Jqy/QxHnN+3djMy8I1qQ2Lvn3QeJg8BkfQTWqB22/1RivRjuBKodoFeeGosL1Gd6JhIgARIgARIggWMJ0JL+WCbMIQESMJNAdKinJqh/sXQWgn1dW9VyVFteL5sT0yqvo5urX9qGrLzWwrwI/wmxPkiM9cVo5TZn5oggY/X0giqsTMpHVmE1IgLd1UJAGEKa+5dn4cpKR7bcmqZ6Zdnv7NTiQsP0Wdvrf/5wCG9/m6pl3/rHBFx8QjTaNNe2CmS58/vtOdh5uAwS9HL8EF/MHRMCNY1eT+5uDqiubYC44mk7z17vjA2SwAAQaGVJb1nhKQaABrskAesgIFbOY5WQ+sb6N/H7gVXaMSxsFOaMmI2QoBDUDKpGw1H6p7aOt9n/o8wtyUeGcmmzLy0ZBcW5mjgvwYnPTTwXsmODyToJnKAMXKYM9des6j/++Qie/k8yNigXONcsiEWCMuxhIgESIAESIAESaCFAkb6FBa9IgAS6SCDEz1UT6XWB/kB2BX7dXYAdyq+8iOWNDUcR7O+Kp/80RrmsccQ3W7Jx4cwoFJTV4mblRqZUucp5+7ZJiA31QL4S3EcpYVvSDhWc9Zo/xCvftoOPGZG4oLn8mY2ob2wyPnvj6xTcrMR0EdKfXbEPIqxfcmK08fm1L2/V2nxUjePUcaHG/PYuvt+eqwn0skjw5NWJeOWbVPym5nT21HBMTwiEfwe7ApZ+vAffb8g2NvnxL8CwaB8suzIRoX5uEC8+/1PzF/c/g5QCGRvs0aH4vy21BK8qq6M9KaXaPGUx5I+zIjV2skvBzdlR66e0sg6ByspfUlVtIzxcDflaBn+QgJUTkP8HJQ6E+l+HiQRIwEoIxAfG4ckzH0dK4SF8vutLrD24Cgdy9mijjwqIxZCQOESGROKoo1p9U/+ehfoF99rMROStra/ttfbssaEQvxD1GcOlT6cuPubzSvK0PjKUxXy+em/Z+Vmoqze4FnRz8dCCw1Kc79PX0K+Nuzo74I6zhmJSnC9eV77qV6vdqlvUDtolylf9krnHftbv18GxMxIgARIgARKwIAIU6S3oZXAoJGBtBPJL67Qhz7nnV9TXNx7jl97DxRG7D5VgX1Y5dh4p08Tv+DBPPPpRMvKKq7W6jytR/VUVXFZPGw4U4RYl0ouleHvpyU/3acK1CODnTI9AeXUDPlmTgX+o/BHNIv/keH9jVfGZL6K/pNiQzi2xROh++P3dWtnnb5igWeenqrGnqpyt6suEJPG3Hx7ghlHKyv+MiWEYryz992VVGAX6y+fHIlJZ969XVkK/bM3BJc9swjcPzcJ1r2xF8pFSrQ1dfNylmDy2eHQra/ul/96L7zZkGcsNifBGmlr8eP6/+/HFuiy8ftMkuDQH1axViyCyQ+Du93dh7c58jIrzw9vqeV9Y72sD4g8S6E8CsgtFU+gp0/cndvZFAr1BQMT6O+fchsWTLkFS1k6kFqZgd84u/Jb8M5B8bA8uzm5GkfbYp8yxdQKy42JM2GgkRoylWxsbftlzRgcrq/oAvPJtCj75NR2vfHkQG/cX4Zr5cdoOVBueOqdGAiRAAiRAAmYRoEhvFiYWIgESaI9AvgrqKqmmrkEZxA3CbGWlfvqkEJwwMgguza5lxDe9uL5JzjC4s7nznzs0kT0hxgepmRXYk1rWqmnXZivxyppjfVyIexcR3J2VSP3qnycYA8kumRuDTSr/JWVRLylOLQTo6avNBuv20UrAHnacbbXr9xdqCw1SVrbmigubJQvisHZPAQ6kG8ZZoazXD8ih7r/4LQP3LRoJse6XJAL9jafHa9fnKv/8WWo3wIHscvzp+c0QsX9mYjDuVVb+YlkvCxs/bcnBlGH+OG9ahFZHfvy40TDeAFXm7VsmIVz576ypa8Ir36Xg41/ScPtbOzBpqJ9WvkD58r/19R1Iy6nQ7veoBZF//56BRScMfHBbbUD8QQI9IODo5IhG9bdFdqEwkQAJWCeBMK8QhCWcogYvB1BRV6lZ2Ws36sfOrCT9kmczCMQFxcHTxbJchBxSuyYqaw2fQ8yYAkK8wxDqHWIsSl/zRhR2cSG7Pv9ybgImq8/ZrynXkpuTi7Btf7H2Gfp69ZmbiQRIgARIgATsmQBFent++5w7CfSAgAjYYu0eogI6LpobjXOUKO3ZjrsVEegliYsbSeKmJkxZmr9x4yQ8+/UBTeguUtbuenDZJmlYyjWf5VpE8BplqR/obdiCPSTSyyjQy3PpY3pCAJ6s2actFsiCgaQKJfS/3izcP3jh8YPOlimrfEm6n3dp5obThmjHu78cwavK4keEeHHZI0FxP/rpCD5alY6YZgv9WSMCtfr6jwglsO9OK9UE+hlKoH/uyrHaI9ktIAsbkp7/7AD+MDlcC7Yr9+4ezpCFgLeaBXrJc3NR24TPHoYft+VpOxOmN/dzyyvbtXcwTn3RuVAF7n1QBZT9fG0mRXqBxmT1BPQIEkdbPFtZ/Zw4ARKwdwLiW9xUlDW9tnc21jp/vkNrfXMDO+6TxgQroT4Aryqr+hWr0/GOcoOzQYn11ygXODOHt8ShGthRsncSIAESIAES6F8C+nfg/u2VvZEACVg9gaIKg+geHuiGiUP8kJxZhp935mnitQRefVl96BYhe+2+Am2u2c1W9+6uTnj71kma8DxdWZFL2nbI4I5GrgM8DEJ8WWWLu5vrXtqKRz9ORmmVIa+xsX3TWnFDIz6sX/jfQRzOrcIVyoJdFgVmjQ1pZV0v/bSXxHpekljrf6Rc6NQ395NXWqv5kpdnHmr8EqR2xvAAuUWoWqQorTa4/WloZ1z7lSscSYtOjNLOKWrB4Z63dmrXspggCx3CSk/OzYsaut95PV985ReV1CBCLQhU1hgC70ndaaOC8fqNEzVf+z7KFc9h5RpHH7del2cSsEYCjk6GxTZ94c4a58AxkwAJkAAJkAAJtE/Ay80Rd52XgMevSESU+nwrO0Jvf20Hnv3yAD/Lto+MuSRAAiRAAjZOgJb0Nv6COT0S6CsChcr6XZII2kuWbey0m/89cgKKygxC9j0XDTcGO02MMQSKXau2up6ihHRJoSrQrKSd6oO6WLavUJbhZcqyfO7EENQ1B4vNLWk/MNzlKtCs+I7/cOUR7dAaUj8umBmuX3Z6jgxwx5kzIvHNukzNx/3Lnx+Aq5uTZtkuFWXXwMJZBrF9xe8Gv/FzEgPxzaYcrd20gmpt+65pJyMivbXbW17ZhnAVLDY7v0q7v/GcYZit3AJd+vQGzSI/X83pwYtG4OQJIfhUWRRd8vcNmD8lFCWV4lO/BDkqsK74w39d+cpfpiz6Jcn9k5eP1q7lhzASFzzbFLupzQsgxoe8IAErIzCoeUcMRXore3EcLgmQAAmQAAl0gYB8BxBf9S8r146fq8+x4t5xg/o8f/WCITi1+ftBF5pjURIgARIgARKwWgIU6a321XHgJDCwBPyVWxbTJBbyicqX+5hYbwxWYnSQtyuClcV5kI+r5gbnrvMT8F8V+PR0FWxVT/J8rAryWlRhEPAl310Fm/VXdQuU1fip9/+qFXVzccKNp8UbXcS4Obe/CWiGcnnzsBKt//HZQVW2UbNSF//104a1dkOj99/e+SEllE+M88W7SujPUaK7WLZLQNY/zojAmZPCIYbuhcoX/OoduZprHQkeu1WJ6BJu1lNZBLVN8sVjwbRwLbCsCPQirN97YYJm+S5lX1O7Cq5/fgtWKv/08cpn/i1nDoXsOtiwqwAf/5ymNSfzv2B2NK6YN1jjGupn2G3w9FWJyrK/pc/rTo3F12sylR/8Cor0bV8E762OgO626igMFvVWNwEOmARIgARIgARIwCwCPh5OuO+PwzE53g8vfJmCw2on6oPv7MR6ZTxz61lD4eNO2cIskCxEAiRAAiRg1QQGHVXJqmfAwZMACQwYAfH5Xl5dr/lwF8G9O0kPLGtaV4LAPrR8D44q9zGnTQ3D5ScNNvqsl2fOKijt+FiDFb5pPdPrLCV0n/fI75g3KQyPXdpibW5aprvXIoJfqizdJRCs+Jmvqm2EBKg9X32R0P3Zt227XO0KqGsQv/rHcqpraMJ+1eaYaB9jNQmWKS6F3JydINuB2yYJGisLIW3TFuU6KEwFnZVdAUwkYM0ETvvrGhQrV1P/umsaEiIsK1CiNXPl2EmABEiABEjAkgmUKPeWryhXkF8o15OSJJbVFfNjca6Kf8VEAiRAAiRAArZMgEvStvx2OTcS6GMCIh63JyB3pVs9sKxpHfEN/+3SWaZZxmvdb7wxo4OLLzca3NFcOCuygxLdzxZrf826//R4rRGxZtfd4HTUqrdmAdT+n1wXtehgKtBLG2Kx354Ir7ff0bNJcQa/+no5nknAWgnoC15NsmLFRAIkQAIkQAIkYBcE/JRV/f3nD9d2tj6vrOrF5eMTH+3VAsvecmY8wv3d7IIDJ0kCJEACJGB/BNr3GWF/HDhjEiABGyPw6W+ZkECq41VQ295OUcqi59en5mBouGdvN832SIAEmgnoPukp0fNXggRIgARIgATsj8BpE8Kw/M6pOEvtVJX0s3INueTZTfio2cLe/ohwxiRAAiRAArZOgCK9rb9hzo8E7JCAuHyRYLNnKz/yTCRAAtZJwNHR4IuegWOt8/1x1CRAAiRAAiTQUwIBXs54UMWLkphTYnxTquJY/ePTfbjtrSQczK7safOsTwIkQAIkQAIWRYAivUW9Dg6GBEigNwj8e3Wm1szZU8N7ozm2QQIkMAAE9MCx9HYzAPDZJQmQAAmQAAlYEAGxqv/onqk4s9mqft2ufFz1j814c+VhCxolh0ICJEACJEACPSNAkb5n/FibBEjAwgjUq2CzvyflwdnRAYODPSxsdBwOCZCAuQQcJDCDSkfVf0wkQAIkQAIkQAL2TUDiMT2krOofunQUJB5UTV0D3vgmBde+vBXbD5faNxzOngRIgARIwCYIUKS3idfISZAACegE9EC0wcpvPBMJkID1EnBo/oRytMl658CRk4C9E6iur8bhoiMory2zdxTdmj/5dQsbK9k4gTMnhePje6djQfOO2R0Hi3Hd85vxghLsmUiABEiABEjAmgk4WfPgOXYSIAESaEtAjG+XXT8eIT6ubR/xngRIwIoIODWr9EeP0pLeil4bh0oCGoG8ygI89dOTOJx/0EjEzcUDf5p+FU4ddooxjxftE+gKvyd+fgql1aV4aP6D8HDu+Q7C3m6v/RkylwR6RiDEzw2PLBqFKcP88dTHyahvaMJy5fpm474i3HDmEMwcHtSzDlibBEiABEiABAaAAC3pBwA6uyQBEuhbAjMSAhAf5tm3nbB1EiCBPiWgW9IzcGyfYmbjJNAnBP657nVNoHdydMbY6ImIDhyiXFNU4bXVL2JbVlKf9GlLjXaFX1LGNhzI2YP6xvpeQdDb7fXKoNgICXRA4KzJ4VjxwAzMnRiqlTiQXobbX9uBJ/67D40MatMBNWaTAAmQAAlYKgFa0lvqm+G4SIAESIAESMCOCTg66j7pDWc7RsGpk4BVERAr8O1HNmpjfvWi1xHg4a9dr0/biOzyHEyIGKvdywJcY1MDHB0c4TCoxW6oQeVJcnIwfE1pampEo/J75awE/+yyXAR6+sPF0QX5qh9vV2+4ORl2zplbTtquqKtEbUMtXFRdb5f2F/X19qS8oxqfgxqnxMiQOUR4GwLTy31DYwMGDRpkHK+Ul6Q/c1DPHJvnYnjS+U9z+bUV5euUSK/ntR2POfPV6+qj66w9vYzUySrPQqBHELw64KiX5ZkE+opAmLKqf/KyMfh8eACe+mgvxEve579lYPO+Ylx/xhCcOs4g4PdV/2yXBEiABEiABHqLAEX63iLJdkiABEiABEiABHqNgAhbkppoCddrTNkQCfQHgaPNgSRE1PZ18zZ2OT1mqvFaLtanrceylU8hUVna/3X+Q8Zni95bqP6/b8THV6zQhO+lPzyMPZlJiAmKR1pBCsQ6/6SEeVi591tNOL9/wVJN+De3nHT04pqXsDl1ndanjDMqIBZXTbsSY8JGG8fxzK/PYcOhNdq97ARYOGEhXlr9grYjwNvdD5dNXYKZg6fj0vcXaWVeu/hNBHu2uNhYf2QDnln5JMJ8o/HyBS8a2z3ehTn8CquKcO1HV7Zq6vp/X93q/q3F78HPzVfLO958u9peuVrkWLZqGXambzX2KfN84NT7EeFrWMAwPuAFCfQTgXOnRmDKUH88+8VBrEnKQ0ZeJR58dxd+m1aIu85JgLc7pY9+ehXshgRIgARIoJsEWsxWutkAq5EACZAACZAACZBAbxNw1EV6+qTvbbRsjwT6lECoVwiCfEI1of3mT29Gct6+TvsTa/r2Utt4FA2NdfBy81GW6/XYoVy8DA0dqfWxOmV1q+rmlIvxj0FC+GhEBsRobYj4v/SbB5BadNjY1oSoCZgUO127L6zIx4dbliPCL1pbLCivLsFrv72kWfdPHjJDK/O/vd8Z68rF2sOGRYAZ8YbnrR52cmMOPw9nd0i/+vikOVnskHv90HciyLPjzber7T3242NGgX5ISIK2cJJTmo7/+/ZB0EWZEGcaKAKRAe5YdkUi7r14pHEI32/IxsKnN+CLTdnGPF6QAAmQAAmQgCUS4HKyJb4VjokESIAESIAE7J2AQ3PAWMaNtfffBM7fCgncMvs2PP7D35Bblo0HvrpHE7bvOvkvRjcx3ZnSH8ddAHGFI37tzxp7jubq5vncvShUbm9MkznlFk9YBEww1JJFgr8rq3CxrP9i1xe4bfat2gMJcCvH+W+di6raCiRGjsfdc/+iPVv47oXaYkFeZT7OGX2OVndl8ndYMvlS41C2pm3SrmfHzTbmmXtxPH7uSqS/7+R7tOYWvX8x6uprcLsat2+z5Xzbfo433660d6g4VfOBLzsQ3lz0ttanuL25/j/XoaSyEEnZOzG+2aVR23G0d//At/+Hw2qRpKMUFzwUfzvtkY4eM58E2iVw3rQINyQsvAAAQABJREFUTI73x9Of78OG3YUoLK7B4x/uwRp1fee5QyEucphIgARIgARIwNII0JLe0t4Ix0MCJEACJEACJKD5eBYM4luWiQRIwLoIjFZW7v9c+AZmxBsEarFUv3XFTfhu/4/dnoirk4uxroujays/9sYH6sKccmLtvSVjKz7b/SU+2vEfhHgZfFanl6SbNtXq+uzRZxnv75n/IG6Zeyd8XH0wKnQE/DwDNSF/e3NQ3LSSNKNbnBjfKGM9cy96m1935tvRWFMKDmmP4oIT1AJJEQ4VpiK9JAODg4Zq+enKor4rydTiv716XfHn31595tkvgeggd7xw9XjcddEII4TVO3JxyVMb8dGaDGMeL0iABEiABEjAUgjQkt5S3gTHQQIkQAIkQAIkYCSgu7vR/TMbH/CCBEjAKghIING/nHQHipWv97c3vou1B1fhrd9fwynxJ2luYgZqEtX11bhpxY0oUX7d26ZG5Qu/oxTl1yK2T4wY16rYmWPOwvIN7+KrPV9rVuS/K3/0kmbFndiqXFdueotfd+fb0VjFf72kg2oXw12f335MsYq6qmPyOst4WMUUYCKBviRwwYxITFW+6p/4dB+27itCZU09/qGu1+zOxx3KV318WPvBo/tyTGybBEiABEiABNojQJG+PSrMIwESIAESIAESGFAChrCxDBw7oC+BnZNALxDwV0FW75xzG9KLjyBdWV1vSN+EE2JnttuyuJ6RoLF9mV5f/5Ym0A8LG4XzEs9DuAp0ujc3Ga8rH/OdJRHNO0rzE07VRPrtRzaiqr4KG1LXakXnNO8k6KieOfnH4+cwyLAxuryuol13N12d7/HaC/MO04bt4eqFS6ZcdswURoeMOiaPGSQw0ARigj3w6vUT8MnaTDzzSbI2nM3JRbhs/wZceXocrp4XO9BDZP8kQAIkQAIkALq74S8BCZAACZAACZCAxRHQRfqjR/lRxeJeDgdEAp0QyK3Ig7h7MU0ivot/etMU6xer3e7P2WsU5n9O+dW0SJ9c78vZrbW7aOIiTIuZAnFHU6B8y/ckiYA/sTnI7Bd7vtEWI9xcPJAQPKzLzZrLT2/Yzz1Qu1zVAbuuzvd47Q0PStD6Ez/9nmrepw9f0OqI8Y/Wh8YzCVgcgQtnRuLje2cgUfmrl9TYdBRvfJOCK17cgqS0UosbLwdEAiRAAiRgXwRoSW9f75uzJYHjEsgsqkZWUc1xy3WlwKaDJV0pzrJtCEQEuCIywL1Nbu/fjoj0hrc7/1nofbJssTsEBjkYxPmmo/RK3x1+rEMCA0Xg98O/K6vy99S/J36IDojBIGUTdCAvWQtu6uTojLFho7WhRSgLdvHlLsFGF3+wGD7uviiqKIAEJBVr+ru+uht3zLmj16cRHRirLRg898syTIyZivyKHMhCgYuzGzKL01S/9+DmE27CJ0krUFFbbuz/4R8e0a5vmHUDgj2DjPn6xTnKZ/3Ww+uxYvNyLWtKs2ivPzf3bC4/vb3Jg6fg66R0fLb1P1i59wcMCYpDWU0Zrpx2FcS3vbnz1cX147UX7hOKuSMW4Jfk7/H8z8/gVeeXMELtSpAY3/UNdXjsjEf1ofFMAhZJIDbUA2/eNBHLV6fjhc/2a2Pcc6gE1zy3GZfMG4xbzxxq9rivfmkr/nxGHCbF+ZldhwVJgARIgARIoCMCNq/GHFWBofSjqcnwRV+/18+NjY1wcXHRytXW1sLNrXW090GDdHs+GAPZCVDTfNN7adehWVwwzZdrJhIYaAIiwm8/XI7dGeXYcagUBcXVKCnrXVF+oOfI/ntGwN3VSbMwmprghynK0mhElHfPGmRtEugGAQcHkXyg/dvcjeqsQgIkMEAEwr3DERcyHBnKvc2ezCTjKCKVYH/DrBvh4+ZrzLtqxtV4Tgm9dfU1KGqsx5Uzr8WKrR9r7mjENU5RdbGxrOmFxKxwbHbzYprf9rq9ctfNuBa1DbXYnbkDv+77QRPnr5x5Hf6z9SNtweBQ3j6t3/WH1hgt/KXdpPStWvPVddVAO55vxqjFBz+PAKOv+wXKwrw7qSv8pP1L1Y6A2oYa/LLvR5RXlxjHmVp0WBPpzZ2vLtIfrz3p8wbFK8I3DJ+odyXvTmcjzyRIrYPJdyfJYyIBSySweHY0picE4JF/70XyEYMV/Ycrj2DtnkLcfs4w7dnxxh3s54onP9mH12+aBH9Pm5dWjoeDz0mABEiABHpIYJASlA3fgnvYkKVUnzBhAnJzczUBXZ+aLqabCuem4xWBvq6uThPW3d3dIUK9lHV0dNSKmV5LW9Kui7OzoQl1L3lSxrQfuZajqqoKnp4tn+TbjkGv5+Pjg7KyMq1NfdxyIwsLsoggZ+Oh7usbGtDQfMh4r776aixdysBLhpfCn20JiDD/6vdpWL0jR/1+Nxgfe3u7IjrMD77ehoWp8CAvuLo0/24bS3XvorauHtkFFd2rbKW1/BRHP+++t3jvTTyHswwCyJEsw5eTrJxjdz0E+rvh2tOG4NypEb3ZNdsigU4J3PZWEtbtyseTV43F3DHBnZblQxIgAcskUFZTimoliAe4+3cYLFZ2y+RU5CLMM0R9nnZERV2lJvK6OrrA0aHvRC/pV4Kg6lbxvdHvhrRN+PuPjyHUJxyvXPhqj1+KOfz0TmT3QZbaFSAiuY+rN/xMFkOkTFfne7z29H7FB79wdFHvK9gjUHuH+jOeScBaCLz7Sxpe/fJAq+Gec2IU7j0vodNFp5ScSlyjXOVMHRGAJy8b06o+b0iABEiABEigqwRsSqQXcTsqKgppaWlGgd1cICKE19fXa8K3nNseIojrednZ2fDz8zOK5LpY3t5ZxhIeHt7Kml/GqQvuci2H9G8q8usLA05OTnBWCwKykODq6qpZ+ctCgoeHB7y8vLQjJycHV111Ffbu3WvudFnOTghsPFCMt35Kw/Z9BcYZxw0OwuAIX4wYEgR/L1djPi9IQCeQmlUGEe9FuDcV7YP93XH1abEU63VQPPcpAV2kf/zKRJySGNKnfbFxEiABEugJARGqf1E+4d9f/zYa1I6AW+beiTlxJ/akSdYlARLoZwIHsiuxdPlupGS2uLkKC3THLecM7fRziB6MdrFylXNLF1zl9PP02B0JkAAJkIAVEOg785QBmLyI3W0t1c0dhljN65bz5taxlHIi7ovIz0QCpgQeVVsvv1qbYcwaNSwUc6bEUpg3EuFFRwSGRPhADj1t25eLTTszkF9UiSc+2otVuwrwt0Wj6L9eB8RznxBwaPY016SCujGRAAmQgCUSqFE+2K/7+GpUKB/wejpnwoUU6HUYPJOAFREYFu6JD/8yFW/8eBhv/i9FG3lOYTXuf3sn5k+JwN3nDWv3s68Eo92pgs4uV65yIpWof/70SCuaNYdKAiRAAiRgSQRsSqQX63RJItbbUxIf+mLFz0QCQkBc29z6RhLScwyuZiKUO5tzTh5OcZ6/Ht0mMGF4KOQQC/sNSqxftzMfVxduwcNKqKe/+m5jZcXjENB3l1GjPw4oPiYBEhgwAq5OzppA7+HqhZjAIThz1BmYOXjGgI2HHZMACfScwDWnxmL26EA88P5upOdWYuQQX/ywKQsb9hbgprOH4uwp4cd0cufZCUhVlvgvfHYQQ0I9MXEIA8keA4kZJEACJEACxyVgU+5uxK98fHw8UlNTIW5i7CUVFxdj4sSJSElJ6fZOAnthZevzFIF+8d83orrZ77xYz587d7itT5vz62cC63ZmYu3WI5qPztdunEChvp/520t3d727S8XRyMUjS8ZgwfhQe5k250kCJEACJEACJGAhBF75NgXv/XAYf5wdpYIkH8XX6zJxwtgQ3PPHBIT4tnYbuuNwKW5/YwfCA9zxyp/HwdfDxUJmwWGQAAmQAAlYCwEHaxmoOeMUS3qxvLM3S3o9+KwEkGWybwJiQa8L9KfPGU6B3r5/Hfps9jMSI7H4D+Pg7uqIpR/tQXk1d/L0GWy7btiwK87e/k2361fOyZMACZAACZCABRG44fR4vHvnVKzdW6QJ9D89cRLWJOXhwsfX46M16a1GOi7WF3colzgHM8qw7IuDrZ7xhgRIgARIgATMIUCR3hxKFl5GRHo5KioM7k0sfLgcXh8RePzTfUYXNzMmDtbck/RRV2yWBBAW6IlLzxiD3KJa3P7WDhIhgV4n4NDslL6R/m56nS0bJAESIAESIAESMI/AyChvfHH/DCw6ZTBOuW8VFs6NweXzY/CPT/fjz69tQ6pyiaOnP0wOx5L5sfh+Yzbe/vmIns0zCZAACZAACZhFwKZE+p4EjjWLloUW0kV6Whta6Avqh2FtPFCML9YYgsSKD/q5kwf3Q6/swt4JeHl7YOG8eOxMKcGTn+23dxycfy8T0EV6oDmCbC+3z+ZIgAT6nkB941HIIalBLbjJtTmhox7/7z5c+eIWpBdU9/0gzeihu/Mwo2kWIQESsBICt/1hKN68fQp+2paH178+hE8fnImt+4pw8ZPr8dZPqcZZiPX9yZPC8M+vDmL1nkJjPi9IgARIgARI4HgEbEqk1wPH6ufjTd5WnouLHznq6+ttZUqcRxcJfPR7llbD2dkJC08b3cXaLE4C3ScQERGCkWp772er07Wgxd1viTVJoDUBXZpvMkfRa12VdyRAAhZAYGtqCU74y8849YHV2mgWPLRGu9+cUnzc0W3dX4zdh0pQWj3wn217Mo/jTpQFSIAErIpAYowPvlk6CxfMjsb5j67F4nmD8cAlozTRfsk/NmNnWpk2n7vOTcCQCG88+/l+fj62qjfMwZIACZDAwBKwOZHeEnzSyyJBdXU1ampqUFVVhfLycpSUlKCoqAj5+fnIyclBVlYWdu/ejezsbOTm5qKgoEB7XlpaqpWXelJfguE2NDR06mdfF+mlLJP9EZBgsWt35GgTn6x8hbu7ONofBM54QAn8YeYQrf8vNhoWiwZ0MOzcZgg4qMVnSRTpbeaVciJ2RkD/f9it+XOJ/qXD1UW/sg4gtjIP66DNUZKA5RFIzihHXkkNGpp3BckI7zovAa/dMglfr8/GYx/u0Szsc4tqcPVzm/DiNykI8HLGHecOQ15hNZ75/IDlTYojIgESIAESsEgCThY5quMM6owzzsDevXvh6OgIZ2dnODk5wd/fH+VlZZrYfdJJJ2nPxA2MnkyvRdQW0dzX11d73NjYqIng4i5GPyorK+Hq6mq4V6J7ozp0dzIiwuvW+nLW86UxaUval7q6Zbvet+Tr11JWxHe519vQz/oYTO+lrMz7rbfekqqtkrQhbev9tXrIG5sn8J91LVb008dG2fx8OUHLIxAQ6IvYcC/859dM3HBavOUNkCOyagLqn1kmEiABKySgGw04OhoW3FycDZ/L3ZyONSaQDTMZhVXw93KFl9uxz02nX13XqMpWIyrQvVPDBGkzRwlrEtdCynaU6hqaUFheh9LKeniqvn08XODr0fIVqSvz0PvQx6ff80wCJGCdBP7yzk78pgLF6ik+0htThvsjcbAPxsb44oe/nYjHPknWxPnF82IRG+KuRPu9WL2rAHeqILI3njMMLyiXkK98l6o+IxuMWvS2eCYBEiABEiCBtgRaPoG2fWLB9zt37sSGDRtQW1trtDqXoKkiUov1uZxF1BaRWyzVQ0NDtdmIkK2n/fv3Y8SIEZqluwj8IvSL6K8L/6mpqRg+fHirfClTXFwMNzc3bVFAyuoCuX5tKsKL2C4W85GRkcZyevmunpOSkrBkyRJ9+K3OuvhPkb4VFru5+WETrejt5mVb6ETLa4/i9GlRePXzZHymrOnPmxphoSPlsKyJgEPnOp01TYVjJQG7JODqaBDlXZot6Z2bRXrX5rMO5eedeXjovd3KX71hRe6EsSH6o1bnEiWi3/3uTuw42OIuZ9xQf/z9T4nw83Q2lq1XovsL/0vBp6vS0ShKvUrOaiynTA7DXxeOVIYthqIHsyvx0PLdSMksN2SY/FzzzMmqjqGgufPQqz/wwW6s3JKDU1UAyUcXj9KzeSYBErBCArecNRSjY32w41ApdqaWan8v5G/Gv5vnMnKIL8aq45oz4/HhL2morKrHsmvH443vU3Hrq9twnnKLc+aMSLz3/SGMVcL+CSMDrZACh0wCJEACJNBfBKxSpBeB28PDAxERli8ERUdH98q7lDmbWuybNioiPS3pTYnYz3V5dQOKSmu0CY8fEWY/E+dMLY7A0MHBakzJ2JdZYXFj44Csk4DuYoLubqzz/XHUJKC7tXF1Moj1xrOJSJ9VXIP73t6pwRKxq1G5k1ijrFYddSXdBOOtb+xA8pFSLScixBNZeZWaYC/579022VjyfuV6YvW2XO3ex9MFwX6umrD23YYseLo64m7lpkKs669+fjOqaxvg5uKEMXE+8FYW9CLwy98cXaCXRsyZh7FzdZHRHOw2Pb/KNJvXJEACVkggJsgdV8wdDMw1DH6vcn2zQ/0dEsF+t/I/v1ed5ZDkov6WBPi64s7Xt2NmYggeVIt0jy7fgyD1NygswB1Pf7oPJ6hgs0wkQAIkQAIk0BEBqxTpxWpdrOjtKXUm0gsHeU6f9Pb0G2GY68YDRdpFUICntkXc/ghwxpZCoLJ+kPoS4ob96ssLEwn0BgFdpG82hO2NJtkGCZBAPxLw9XDGwrkxyv2Dp9brRbOjcCSvCj7uLVbv7/1yRHsmFvGv3zhRu96gPtvc8sq2ViMVn9C6QP/enVMxIsobIpb9adlGLX9fVgWGR3jhcG6VUaB/+PLROG2CwYBhk7K+f+XbQ/jTyUpsUylbLQ6IQC/p3/dOQ7i/m3bd3g9z5mFa75krEvHDjjzMH9f+jgDTsrwmARKwLgIj1d8eOS6e1eJidOOBYmw5VKwJ98nphs/Ba9UOITn8lWjfhEHIUTHEJIn7HPkbwUQCJEACJEAC7RGwSpFeBGl7E+lNXfW0fZHyTHzhiyseJvsisKv5g2BMhL99TZyztTgCNUrrEL/0m/cWWNzYOCArJdCszlOkt9L3x2HbPQEPZbV+x9nDjBzOnx5pvNYvDmVVapcnjgnSszBlaIDmnkZ3fyMPkrMMwleAWgwWgV6SCGVyX6T8zicr9xMi0u/OMFi0urs6GQV6KTtFLQK8c/MkudRSpLJq9VJW9hWVdbh82SbMnRCCmcMDMD0hUFnWt8S0ksLmzMPQquFnsBLlFisXF0wkQAL2QWDqMH/IYZpEuF/23/04nNN6h6n4t39S+ai/V+3oYSIBEiABEiCBtgRafwpt+9RC70WkF9/z9pQ6E+mFg4sKoCvBapnsi8Bm9QFQ0vDYli+39kWAs7UkAqFBXtpwMputhSxpbByL9RHQLenp7sb63h1HTALmEsgvM+yMHR5hEN6lnoNyBR+mXEyYpvzSOu02Xi0Gm6a4MA/tNr/U0E52seE8rFnINy1rei3edJ6+KhHRoZ4oU0L9F2sycM9bSZh3/6/4ZG2maVFekwAJkECXCYho//E90/DElYna3zRpYNqYYG0B8kBGWZfbYwUSIAESIAH7IGCVIr24u7FH1y4SCLe9JAL+ILVwwcCx7dGx7TzlulVLQyJ8bHuinJ1VEIgK89PGmVloX4uoVvFyrHCQDs1BGynSW+HL45BJwEwC/t4uWsnD+QaL+o6qhSifzpIOtnGplpJpqBfa/DwywOC2ZvehEhWItvlDUgeNThzihxX3TsdXfz0B9148ElNGBmnBa59bsQ9VtY0d1GI2CZAACZhP4GTlm37ds6dgtnJ/tWFXPtY8Mxdv3dwSQ8P8lliSBEiABEjAHghYhbub8vJyVFZWakd1dTWGDBmCpKQk5OXloaGhQXP1Iu5eJMlzCSqrCdciXpscUj4qKgpOTk7HHCL6+/j4wFlZpLd9Lq515JlY8MsCgRwDkToKHCtjkXlSpB+ItzKwfaaklSKiWRgd2JGwdxIAauo6F0TIiAS6QuAo9N8nZfLKRAIkYJME4pQl+x4lqP+SlA9xh+OozOiLK+pVUNjWQVfFtY2k4vJaJKnPPmNjfLH9cKl2L/nDIw3Px6h8SY3KT9ZbK1Nx5SmxcGkOXKs9aOdHiHJPc960CCwYH4r5D6zWhPrth4uV+5vu7VIUf/ffbs3F6RNDO/V1bzoUWVD47/pMDA72UC53Akwfaf7zO2pv/f4iHFEBav+oxu98nHm2apQ3JEAC/Urg6T8l9mt/7IwESIAESMA6CVi8SH/GGWdg9+7dmnguArqI5SLMv/DCC5qYLnm6cC5nsTZ3cXExCukirIuv9sDAQE3kd3d3h4jdcoiwL+XlWkR6Katbq0ueXOv3IoDr93qelJckArmXl5fWvlzredqF+iFtSZI2pI6Mv0n13dDcv9xLGX3BQVz5/PnPf8Zf//pXrZ780PsyZrS5kOcU6dtAsZNbP2/7dnNUXFGL/YcL1Rf1GhWrol4FgmtU/z93ZAF3FMPjgjAsJgAB3h0HibOTX51en2ZdI8XUXodqxw0a3d006WK9HcPg1EnARgn86eQYfL0uE1v3FWH+Q2swMsYHO1NKNJHddMoJys3NqDg/TdC/5rnNWqDyAuWLXtJolS/PJUUrNzkLpobj+43ZeOe7VLz//WGMS/DX2stTrnBeu2ECwpQf+/SCalz78laEKct7L+W/vkS5vEnLqdIEekf1Wd7U/Y7WcBd+3KUCQx5IL1MLD3n41+1TzKr52YZMPKss+CV997fZ8PdqCa5797s7sT+tDD+rYLQf3NHSnixm3PpqS4DdhbNaAlma1SkLkQAJkAAJkAAJkAAJWBQBixfpt2/fjsOHD2sitojjpaWlcHNz0wRtEbXT09MRHBxstKYXoVrydcFbzlu2bMHYsWONbYggrovwba+lD8kTi3w568lUfNevTZ9LeVkA0J+Z1tPzZGze3t7GBQdZTJBFBjnLIT7l5bxt2zYsXbq0lUgv45J25NyeYE+RXiduf2dfOxSbf9lyBOvU4e3lhvKKY12rBPh7wsfTFeIgqrSsSh0tZTKyS/DT79B2IAyO9MWMsdFwc7ZKz1/298vOGdsVARHKJNHdjV29dk7WzghEB3ng0T+NwcP/2qMFcd2kgo+L6O6q/l0W4d40vXD1ONz7/k5sTi6CLtBPHhGAJy9vbaH60MKRiFDi+wc/HtFEd9N2JGaKiPRyloCzcpimsEB33HvRCAT2wABiWKSXJtIPU4FszU1iQS9JAt56ubf+eibtiEgf36Y9KSflq2sbNAt8c/tiORIgARIgARIgARIgAcsk0PpToGWOUROydaFbhHDTFBYWZnrb7vXMmTPbzbfUzOTkZG2XgOn4dHFerP/bivSyWECR3pSWfVzrwTnDm4N12sesZWcKkFdo8EFrKtCHBnljRHwwRqgguoG+bigsrdHOwqWyugFFytI+I7cUGdmlyMkvR1ZOiXYkp+Rj2tgoTBxx/L8l9sKY8yQBSyCgW9KbrJdbwrA4BhIggV4mcOq4UMghbmJ8PZzh4eqo+YSXvwFuLi2L6N5KlH75uglKeD+KXCWuhyqx3bk5doXpkJyUy5zrF8RpR3Flvfr3v05rJ8TH1egSRlzKrHrqJBSU1aGuoUmJ3Y4I8HRp1Z9pm125XqoWCW48Iw5BXRD6pw0LwHePzoaXm9Mxc5JFhz+fFodg5ZbHNMncf3xstsbK18MqvtKZDp/XJEACJEACJEACJEACbQhY/Cc6XZxvM26bvhWr/LZJzxOxvr1UUVGBvXv3tveIeTZKQA/O6erSsiXaRqfaalrvfZWkiet6ZkJcMMYMDVHifKCepZ1FqNeTp/pi7+nuhegQZdWWGKll5xZXYffBfGzbk4nvVu9XW+gLMG9qLMLsbNFDZ8QzCVgeAcNutpY9bZY3Qo6IBEig9wiE+7f8uy1CfUdJxOkoZfFuTvL3dIYc7SV3F0fNPU57z3qa1xWBXu+ro3HK87YCvV5HWFCg12nwTAIkQAIkQAIkQALWTcCiRXpTdzLWjblroxef9G2t5XXf++2J9MLJ19cX8fHxXeuIpUnAygi83yzQe7i7YHRCGBKHBiMs0LNbswj190DolMEYmxCKjbsysX13Jj4pqcJZJ41AbIRPt9pkJRIggd4j4KCsYSU10Sd970FlSyRAAiRAAiRAAiRAAiRAAiRAAhZJwKJFeosk1g+DEqt5Eel1FzfSpQjxjs15bYegL2bo57bPeU8C1k4gKSUPazYfQUlpNWZOisWkkeHwVlvieyMFKYv7M2bFIybcF1+u3IMVP+7CWSePxPBo/95o3q7aEHcBTCTQWwT0nXR0d9NbRNkOCZAACZAACZAACZAACZAACZCApRJocfRoqSO0w3FVVlZqwXEl0KyeRLAf1IlIr4v6enmeScBWCKzbmYmvf0qGl4crbr50Bk6aFNNrAr0pozFxQTh73ijUqQBsn367E+l5FaaPeW0Gge7uajCjaRaxQwLqnzwmEiABEiABEiABEiABEiABEiABErALAhb9FVi3DNfPdvFG1CSrqqoQHByMoqIi45Q1kV4F0OrI3Y1YHNobJyMcXtgsARHof1mXgqhwP1x+1tg+EedN4YlQf9qc4VrWFuX+hokESGDgCTTRlH7gXwJHQAIkQAIkQAIkQAIkQAIkQAIk0KcELFqk79OZW3DjItKLyxtPzxZf2yLAd2QtL88o0lvwC+XQukUgTwV2Xatc3OgCfbca6UalicNDER8bhD0HcnEos7QbLbAKCZBAbxBwgO6Tvv2A6b3RB9sgARIgARIgARIgARIgARIgARIgAUsgYPEifdsAqpYAra/HIIFjJVBsY2OjsStdpG/PWl63sm/vmbEBXpCAlRH4/vcU+Ch/8WJB399p1oQYFQNikBZQtr/7Zn8kQAIGAo7Nn1AYN5a/ESRAAiRAAiRAAiRAAiRAAiRAArZOwKJFensVnXWR3tS1jX6tn01/MTsT8E3L8ZoErIXA2qRMpGcV44L5owdkyFHBXpg2YTAOHSlAfkn1gIyBnZKA3RMwGNLbPQYCIAFrJdDQ1AA5JDU1NWrXR3HUWqfT7+Mmv35Hzg5JgARIgARIgARIYEAJWLRIP6BkBrDzuro6ODo6tvI/L+K87Cpob+FCf9aegD+A02DXJNBtAtv3ZGHGxFj4e7l2u42eVoyPDtCaSM+hy5uusmykBtNVZCzfDoEbTovHkgWx8PV0aecps0iABCyZwK6c3Vj4zgVYsvxybZhLPlyi3e/M3mXJw7aYsZGfxbwKDoQESIAESIAESIAE+o2ARYv07QnS/UZmADsSkd7FxeUYkb4jv/MiztfW1qKgoGAAR82uSaB3CKTnVaCkrBpx0f6902A3W4kO8YK/nwcyKNJ3mWB6IXcfdBkaK7RLQIT6RSdEtfuMmSRAApZLwNHBSRucs6Nhkc1hkOErh0vzveWO3DJGRn6W8R44ChIgARIgARIgARLoTwIWLdL3JwhL6ktEevFJb2oZLwsWnYn0EmQ2IMBg+WtJc+FYSKCrBFIzi+Hh7oLBod5drdrr5eNjApGZV97r7dp6g+kFNbY+Rc6PBEiABEigEwKuzWK8o4OjVsrJ0Vk7uzi3vzOmrrEOeZX5SCk8hKzSbJTVVhhbb1Kfgesb69F0tHUQaVN3MFJYXOpIOUnZZbmQNiXlVxagpqFWu5Yf5paTshV1lSisKkK5OneU9Pa0MaoxSBK3Plnl2cYqci/Pdfc/xgfNZeVZY7NrIHnWVX5SR+bMRAIkQAIkQAIkQAIkYL0EDGYuFjp+e7Wkb2ho0AT5/Px8hIeHa2+nM5FeAsx25ArHQl8th9ULBMqrDV9Ee6Epi2oiS4niAcqC3RLSMCXSb05Kh7hvcaR/bLNfCQN9mo2KBUmABEjAJgk4OxlEeV1sdnZoFunbWNIfKTmC51b9A+mFqcdw+PiKFXBSFvnr09Zj2cqnkBg9EX+d/5Cx3KL3FmqCu15u6Q8PY09mEmKC4pFWkAJZGDgpYR5W7v1WfU52xP0LlmJCxFiYW046enHNS9icuk7rU9qICojFVdOuxJiwlpg5z/z6HDYcWqOViQ4cgoUTFuKl1S+gpq4K3u5+uGzqEswcPB2Xvr9IK/PaxW8i2DNIu5Yf649swDMrn0SYbzRevuBFLd9cfnojy9QY1h78FTOHnoQ759ymZ/NMAiRAAiRAAiRAAiRgRQRoSW+BL0tEdw8PD/j5+RlHl56errm0aW/hQizuxYd9e8+MDfDC5gjsy7JNC++6OoMVmiW9sNo6Q+A7SxoTx0ICJEACJEAClkrA1ckQU8bF2U0bom5B7+LYEmtGLOPv/+o+TaCXcmOiJmBa3AmYFDsd4wdP1QR60/mZWpqb5rf9/NugLOi93HzQoKzTd2Rsw9DQkZqYvzpltWk19fz45WL8Y5AQPhqRATFaGyL+L/3mAaQWHTa2NUGNW8YsqbAiHx9uWY4Iv2htsaC8ugSv/fYSnNWCweQhM7Qy/9v7nXbWf6w9bFgEmBFveC755vDT68s5uyxbu80uyzLN5jUJkAAJkAAJkAAJkIAVEbBoS3qdY9sP33q+rZ5FpHdyclJfBlq29UZERGjCfXtzlvJtA822V455JGAdBCwv6mhNvVo4c7WKP5cW8Yot7w1aBBYOggRIgATshoC3ixdOH3suonwjtTmfMepMZCgh2dvF08ggtzxfszaXjOfPfxEhnsHGZz25+OO4CzS3Mq+tfhFnjT0H3q7eeD53LwqV2xvTZE65xROU9fsEQy1ZJPj7qmWaZf0Xu77AbbNv1R6cOuwUyHH+W+eiSrnpSYwcj7vn/kV7tvDdC7XFAnHlc87oc7S6K5O/w5LJlxqHsjVtk3Y9O262Mc8cfsbC6uK+U+7Fb4d/x4mxs0yzeU0CJEACJEACJEACJGBFBCxadRJx3t3dXROrRYS2lyTivLivMRXp9ev2Fizo7sZefjM4z4EiUCOuhbxarP8GahzW0i9Femt5UxwnCZAACfQNAXdnd1w95U/Gxk8bvsB4rV+E+YSoBXAvTdi+6/M7MW3ITEyImqhc0oyHm1P7vuv1up2dXVXdhuYdcGK5rwetbVvHnHLiD39b5jaklWagsq4CIV6hWjPpJeltmzPenz36LOP1PfMfRHlNGXxcfRDhHQ4/z0CUVBZie1YSxivXO2klaUa3ODG+LUGyzeFn7ERdBHoE4NxRLf2aPuM1CZAACZAACZAACZCAdRCwaJFeAqhKkoCp9pREiG9rSS8ifUccKNLb02+Hfcy1orLWIiZar3apSKpVlvRM5hNobzHR/NosSQIkQAIkYA8EBmEQ7pl3H15d8xpyStPxk3IDI4f4kl8y/SqcMeK0AcVQXV+Nm1bciBIVOLZtamwOENs2X+6j/FrE9okR41oVOXPMWVi+4V18tedrTaT/XfmjlzQr7sRW5XhDAiRAAiRAAiRAAiRgfwQsWqR3cXFBTU1NK4tye3hFZWVlmiCvW8/LnEX06kykp096e/jNsI85hgR6ISO7xCImm19UqY3Dy8MQ8M4iBmUFg6AlvRW8JA6RBEiABCyAgARglWCphVUF2JyxHesOr8XO9K14Z90bmBs/B2JR3l4S1zNNnQjl7dXpat7r69/SBPphYaNwXuJ5CPcNx97cZLyufMx3lrxMXPq0LTc/4VRNpN9+ZCOq6quwIXWtVmROfIurm7Z1eE8CJEACJEACJEACJGAfBCw6cKyI0nKYitX28Fp8fHw0H/NiIa+n41nSU6TXSfFs7QRiI/0tYgpfrd6PjTsM29l9PQ2B7yxiYFYwCLWmyEQCJEACJEACZhMI9AjCgoR5uGfuXZolvQjwe5QfeUmxfrHaeX/OXqMw/3PKr1peX/7Yl7Nba37RxEWYFjMF4o6mQPmW70kSAX9ic5DZL/Z8owXNdXPxQELwsJ40C/F5/5+kT7Vzjxpqp3KDWhD5eu+32Jq145innfW7Tbn0kXr1KoAvEwmQAAmQAAmQAAmQwPEJWLQlvT58exPpZd5tfdJ35j5CxPy27nF0djzbPoFBsC1FNMjPYDWXX1KN4ObrgXiLO5NztG6dnZ3g4mzR65kDgafTPm3rN7LTqfIhCZAACZBANwlklWfj/75+AEHeofBwcUeZ8t2eVZKhBVp1cHBEXGCc1nKEsmDXfbkv/mAxfNx9UVRRoD4rO2qi/V1f3Y075tzRzVF0XC06MBa5Ktjtc78sw8SYqcivyIEsFLg4uyGzOA13fXUPbj7hJnyStAIVteXGhh7+4RHt+oZZNyDYM8iYr1+co3zWbz28His2L9eypjSL9vrz7pyfWPkk0gpSsOHwOiw7+5nuNNFhne/3/4h31v5Te/724vfg6+ZrLPvUT0/hcP5BrFM7Ap47Z5kxv7SmFI9++5Dx/g8jTzde84IESIAESIAESIAESKB9AhavPLUVq9ufhm3lyu4BcXmzb98+48Q6s6RvaGhAYWEh0tM7DmJlbIgXNkfAwdG2Yjb4+bjC3c0Zuw7mDei7mjo+Ruu/vr4BR7LLBnQsVtd5k9WNmAMmARIgARLoZwK55fmaO5mDymI+Sbm4EbG3rr4GQT6huG/+/8Hf3c84oqtmXK2J8vJcBPorZ14Ln2axOL0wFUXVxcaypheO6jO146Djf91pr9x1M65FYvREVCoB/td9P+Bg3n7V73VqQcFTWxw4lLdP63f9oTXa+PV+ZS5yVNdV61mtzuLix08FetXTgnaC6urPzD3HBgzRig5uPptbz5xykT6RWjGx+Pds48pH729wwOBWTcmOASkvKdLXUL9VAd6QAAmQAAmQAAmQAAkcQ2CQstC2WKPH+vp6JCQkYMeOHRAXMPaSLrjgAowYMQILFy5EYmKiNu0vvvgCL7zwAv71r38hIiKiFYpt27bhpZdewujRo3HHHb1vSdSqM95YDIFXvkvBe98fxuXnjEdUqG39//GlcjWTcrgQV5w3CX7eLgPCfN3OTPyyLkXrO1j5yb/m/IkDMg5r6/Tx11fjDyfG4v/+GG9tQ+d4SYAESIAE+plATUOtErqLNOt5Vyc3ZaXtBzen9v/dbzrahJyKXIR5hmiCfUVdJRyUCO/q6AJHh77bHCz9FqrgsbpVfG/0uyFtE/7+42MI9QnHKxe+2ivUZaEiwL1vXAaWKct4WZxwaoezsAk0WXTQJyNxAyrVooqPq5eexTMJkAAJkAAJkAAJkEAnBI5vWtJJ5f56ZI/uboStaaDYzizpZTFDdhzIwWR/BBxsy5Bee4EjhgSjuqYem/dmDtgL3XMgD66uThB3N/mFFVi5MXXAxmJtHVvsyq+1geR4SYAESMDGCbg5uSLCOxwxfjEI9QrpUKAXDA7KIl7KipsbSWKt7eHs0acCvfQj/eoCfU/7lWCx3yR/i2d//rs0hYWTLtHOvfGjrwR6GZvsWmhPoJdn7Qn0ki8LJxTohQQTCZAACZAACZAACZhHoO/MTszr/7il7DFwbHtQ9A0PpsK9Xk7c3YhAL8FjmeyPwKBBtieJJkT7IyjAE9t3ZWFcQli/+6bfm1qA3IJyTEqMgr+PO1b+fgAbt6djSGQA4iNbfLHa32/b8Wfs7e0KFfL7+AVZggTaIVBe3YDMwmpkFlUjq6gGqXlVOJwr1rLAzpQSXHHaEJw/PRLBvq7t1GYWCZAACVgmgZqGOlz38dWoUH739XTOhAsxJ+5E/ZZnEiABEugRgVW78/HFhhwV58MZYf7uCPFzQaj6vDR1WIt7rR51wMokQAIkQAJ9TsCiRXoRpOWQwKj2lGTOuiivz1vfTdCZSE9Lep0Wz7ZAYNiQIKzbcgSbdmfhjFn95zqlpKIWqzYdhrenK8aPCEOovwfSc0uxT/nIF7E+9sJJyr8tReiOfse8PSXwr+0tHHU0X+Z3n8CB7ArsSivDriNl2KvOKZktgRc7avWd71KVm69UjI7zx7ghvpgY74tZI44NzNhRfeaTAAmQwEAQcHVy1gR6D+X6JSZwCM4cdQZmDp4xEENhnyRAAjZCoLSqTgWMrkFGQRWyi2tRU9+EtTuPjekVHuyBs6dH4IyJoQjzc7OR2XMaJEACJGCbBCxapNeR6wK1fm8P57Yi/datW7XgsO3NXRYxRLynSN8eHdvPs7G4scYXNnfSYBQUVWGHEunHDQ9FZFDXfJoWldUiQAWh7Wr66H87UVxShQvPGKsJ9FL/5KlDkKWE+kKVL25vFkyL62qzdlW+vcVEuwLAyXZIYG1yEb7bloMt+4tRUFLTqpyXhwsGR/nDT+1ecVTe28R6Xs7VdUCF+pGbX46Cggo0qlA6O1OKteODlcDUkUE4f2Y4ThoT0qo93pAACZCApRCQHWafXvW5pQyH4yABErAiAkp7R0ZpE0prj2J/ZgW2HChA0v58FBVXmjWL7Pwq/POrg9oxcXgAQpVQHx/mifhwD8wcTkMHsyCyEAmQAAn0EwGLF+lF7LFHkV7ev6nQNW7cOGzcuLFVnv47oru7oUivE7Gvs+nvia3NfMGsoTiQmo/PftyLc08ZiagQ84T61KwyfPT1dlx/8bQuCfWvfbJFE+gXzE7AsCg/I05/L1fMnjwE3/ySjC07MjA4zB8jBvdNcDZjp1Z8kV9cZcWj59D7gsCXm7Lx+fos7D5U0qr52KgADI8Lwgi1c6a4rAZRwZ3/P96oNmlk5Vcgt7AcB44UITWtEBv3FmgHxfpWaHlDAiRAAiRAAiRgxQTKlCi/Pb0aW1LLcEh9tzlwuFD7nmI6JS+189ffzwOB6vD3doO3+s7i6WYIvh2idgO7ODsqo4hKdVSjSH3OKigoxbcbsrQmgpVLnK8fokhvyrM/rxtUcG1JEu+jqakRTWonsqOKuUK3of35FmyvL/5eWf87tXiRXhDbm0gfHh7erosfsa5vT5ClJb31/4/YnRlkFdZq1Ww5XrCPhzNOnzMc3/66Dyu+34WzTx6JODN8wm/alaGxcXU1L05DWm45flp3CKWlVZg7PR6TlJubtmncsBBk5pZh+54s/LzuIAaHT4S7i3ntt23L1u8LS1tbSNv6fDm/jgnUK1X9uS8P4NPV6a0KjRgagiljohBtsvDmeRyBXhqQnUNSR47JI8ORfKQY2/ZmtRLrTxgbgicuHa2+mDKYeivovCEBEiABEiABErB4AttTS7Bifa6ymC82Wsv7+3ogQInuI+NDEOjrjiA/OTzh7HR8F5zhajeyHIYUjfUq5tfPaw+itLIe//o1DZfNibF4JrY2wF05u7H0mwfg5uKB5Zd9iCUfLkFVbQWWnvE3jA1PtLXpcj69QOCJn59CaXUpHpr/IDycPdptkb9X7WKxukyLF+nFOtzeRPr8/HwMHjz4mF8mEenbs5bXffYzcOwxyGw6I0sFVpRk6zLUBOXqRpII9Z9+vxNnKaF+RGygltfRj7TsEs1lhqdr53/iGtT20U27MrFqwyH4qICnF5yeqALDtljQt21/rnJ7k6Hc3hQUVmp+60/vR1/5bcfCexKwdALbD5fiBbW92tR6Xiznp4yNarVTpSfzkB0tcohYn5SchYNHCrEmKQ93vNOEx5RQ7+vR+d+AnvTNuiRAAiRAAiRAAiTQWwQ+UxbuX23MNn5u8nB3wdTxMZg6JhJiuNRbydnJAWfPG4UtOzPx0ucH8O2mHDyyeBSGhutCfm/1xHY6IuCorOclOTsadj04DDJ8o3dpvu+oHvPtl0BSxjbU1degvrFe/eK0z4G/V+1zsbZci/72qluN25tIL2K7zNnUL71+rTMx/UWjJb0pDfu7drCDIKYi1Ms2zq9XJeO/P+zG+NGRHQaTzVfbOetqGzAspmMhv1gFh03an4vdB3JRUlqNiDA/nDl7GIKVVUpnSSznZ0+Ow3/VYsG23ZmIDvfBmLjgzqrwGQnYJYGflFD+yPK9qKkzbOUNDfbGZPUlU3ak9EXSxfr//Z6C7er/zU3KBc7FT2/Au7dO0nyv9kWfbJMESKBzAtxy3TkfPu0eAf5edY8ba1k2gYc+2oPvlUAvKSjAE6OHhWLyqAi49sGuQH3H8BjlbnDN9gys3ngI97y7C/+6Ywo8zNyFbNk0LX90rs1ivLi3keTkaFBdXZwNon3bGdQ11qGkphTlNeVwd3KHl5s3fFQgcklNypCzUbnOkbZ0sV/yTf9WauWUS53Go01qYcAZ2WW5CPT0hywK5FcWwNvVG25Ohlhu4nrHnHLSZkVdJWobauGi6nq7eErWMUlvTx44qsUIBzXOo+q/7PIcRHiHa+XlvqGxQfMaIe5/TJP+TDQPXYQ2fd7RtTn9tq0rAnhWeRYCPYLg1WY++jj0OVTVV6GkqhThvmEduiiqUWxyynIQ5hNm5Kv3ae74NFFer6TOdWqMep5og6a8uvp7Jc3K70K4j8Eo0qQbXg4ggdb/BwzgQDrr2l5F+vas5inSd/abYp/PJLiiPaSYUG8sOXcCflZW7yLCyREV4Y+Jo8KVUN7iTzFDuaSRFBXu2wqLuGDJyCtDunqerAT6uoZG7fkY5dpmvnJx42am6xoRA6cpq5YN29PwqwoiOzjcH97uVvGntBUP3pBAXxFYt78ID3+wG7Uq0plYgYnl/KxxUX3VXat2z1C7W3yUf1b5wlmkAtOe/fDv+Nfd05BA67BWnHhDAn1NgFuu+5qw7bXPrfy29045o+MTyC2txZX/2Kx8xhtcRYrl/Lypscev2EslThgfpXYfu+HLlXtw2bOb8Ol903upZTbTGQFnJ4Mor4uqzg7NIn0bS/ojJUfw3Kp/IL0w9ZjmPr5ihSbQrk9bj2Urn0Ji9ET8df5DxnKL3luo+brXyy394WHsyUxCTFA80gpStIWBkxLmYeXebzXh/P4FSzEhYizMLScdvbjmJWxOXaf1KeJ7VEAsrpp2JcaEjTaO45lfn8OGQ2u0++jAIVg4YSFeWv2CMuSpUt+h/XDZ1CWYOXg6Ln1/kVbmtYvfRLBny3f79Uc24JmVTyLMNxovX/Cisd3jXZjT7ylD52rNlKvFhmWrlmFn+lZjs9LfA6fejwhfw0LCvrz9eOCrezBWcXZS72nr4fVaWVlguW3unZih5qCn8toyPPnT35GcvUvPwojwMbj3lLvVgoiPlmfO+MZHjMO1H11pbEMurv/31a3u31r8HvzcDLqHub9XegPL1LtZe/BXzBx6Eu6cc5uezfMAE7B1TxkDjJfdkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEDHBKzC/LO+Xvld6kL6+OOP8frrryMgIACFhYVoaFDbf5QLmcDAQIi/d911jJ+fH4qKiozPJd/JyQnSn17Gw8MDJSUlcHY2rG6KVb+XlxfKy8u1fF9fX3h6emr3MkSxfpc2hg4diuXLl3dh1C1Fpb7ev54r93K0Z12vu8Zp75len2cbJqC2rdlLEh/zZ81OUNbzEdiqArjuTM5BRlYxvlQAxKrexdnRGGApPacUpeW1yC+uRF5+OcorDYF2dVYJyk3NlMQoDFYW+l1NJysLl0wVbDYjuxi/bPp/9q4CPq4qe39t4+7ubZK2qaYuVGhLS5FS3N3lz8LCsuiyLAssuizOLizuLMUKBUpLqbulHnd3T9r/OXfyJpM0MklmkpnMOf29eXbfle9NM+9995zvpOBs6pOYICAIAHtIh/6R93Re9CxLdeqUKKOjVEyFH3uFjYz2w5ufblVVXvH0Fnz1yEyEeHctZ2Wq9qUeQUAQoJB20duVr0EPERC93R4CJsWtHgH2oj/7UZ2HMQ/mnEVjMDrap9/HpSKSSaOevenPe3KzeNP3wx1wbJGWcbB3Uq1pMjcOw3SSM3zwOL3jP/Dt/crjnMvFBY6CK0mwsIwNy9EYypxweZa86cja80pNJJ3j5uSBqroK7CGd8xFU77H8g1iXvE550mt1GFMuwjsCFVRPdX0lsksylIc+J8R9dvk/EU1e9WwTwyaqPu8gz/PiqkJ8tONDhHiFq2Ps0f/67y9jbswpmBw9Q3nlrzz4I66afLm6lj82puk89WcMn6E/ZsyGse0yjn//+e84mndAVRsdEKciF/LKM/HwDw/h3xf9h2SEWqULDuTsI2meRnC5IfQvpeCw8vR/7eJ/I8BVJ4P7158eRyp53rMFegQjvyJXedXz8WfPelodN6Z/b136rsKF7yHjx8YRE4a5Cwy/B8Z8r1QlLR+51C+23IqcliOysgQELJ6k7w3xfO655+Lo0aMKX76+oaEBzs7OiuDmfSbsec3SMbxdV1eniHfDY7yt7VdXV4PJeMNjfN3OnTsxdepU1Q4T63z+2LFjiI2N1ZP6vbnJXHdHEj98jNtob5omPV8nZnsItEjZ2dTAQ/3cENpC1icdK0QBJXLNK6yg/+utDycHSNKmvXmTrn1okAcReP6IC/duf9roff6ZnkdE/cfflmM/TRREBHliQpxouWkADmlJfqTty9o2EDiaW41HSObGyckOV5yTCE/XjnU1+wMNP08n3HX1LLzwzgbV3HX/3IEf/jq7P5qWNgQBQYAQ0EL4RW9XFyavadm214/lL4t2TvR2df91RG9Xh4N8Dn4ELnpqi36Qt18+w6TJYfUVG7lhSNTf8999ePaasUZeKcV6g4C7gxtOH3cOwjxD1eVLR5+BLCJMDXXd8ysLFUHPBV487yU9Adyb9gyvOXf8+Yogf33dSzhr3DKlR/8ikfTFpE1vaMaUu2ziJcTC667iSYKnSTKG5W++3v81/jDnTnViUewC8HLeW+egpr4KY0Mn4E/z71HnLnrnAkV4F1QXYlnCMnXtL4fakvQ7M7apsnNi5ugaMvLT2HbrmuoUQc9yPf+55G14knQMa77f/NlNKKsuxt7cfZhAMkCaMUH/yNK/YXyw7v/I/SsfxJHcJHy1bwVumn4DkotT9AT90+c8j+G+MThWRHkfvr5bHU8h6aIYkv0xpn9VDZW4/9T7VNOXvHexShx7F+HKfezIjPleGV53/4I/4/e0DTglapbhYdkeYAQsnqRnfDoirLvCjb3eH3rooa6KmOTcsmXLTqpn0aJFJx3r6QEm2zXiXbtWmwHtSJOe8UlNTYWDgwMuuYT+UIrZFALDDGZ2bWrgNFhF1hNhr1lOcRXe+XInaSs6Y9SI1gSV3u5OiAj2go9Hq3eCdk1v16yRP4u8hNeRRv5a0qaPoOSzpqy/t/2yhOtO2FB0hyXgbQl9WLu/AE99dgS19c1E0E8YUIJew4MTPV+1PBHvfrUTJRX1uJeSoj1z9RjttKwFAUHAjAgYq4sqeruA6O2K3q4Z/ytK1RaKwEMfH0ZtnU4t4NZLpw0oQa9BxER9AzlB/bjuCP753TH84cwR2ilZmxgBZ3tnXD/lan2tS+IX67e1jSCPAErk66aI7XtX/BHTomeSV3oiebtPoCSkvXeEcaRrm1oc29hz3zDZrNY2r40px0lrd2XvQkZ5FqobqhDgpnNayyzLNKyqzfbZCWfp9+877SFKhltBSXA9VBJZL1dfRYzvztmriPGMsgy9dn2EZ5j+ut5sdNZuUosHfYx/HE1UlKiF64/0G4GyjK3IJI96Q5KeNejHBrW+T0yJmKJI+oySdNWtlBJd/gAvFx9F0PPBEX4x4P2ymhIk03km6dtbZ/1rX66rfWO+V4bX+1Kfzhndej8Mz8n2wCFwslv2wPXlpJaZkOaFCWtbMvbK78i6kruJjo7We/V3dK0cG7wItAZfDd4xGjuyeiII2RLigzB/cqR+mRAfaBYCfTYlwxwe5Yeamgas3X5yQh9j+z3YytnwvNFgu5VGjYfDtZ/76hhKSV7q1JnD4U8RK5Ziof5uOJtCuNnW7cnHB+syLKVr0g9BYFAjYEzItRbKzwnxOJR/DIXET4uZjUlR0zEhcmqfQ/nZ200L5T9+vFmF8huCroXyd1WOQ/njghMQ6hOhEvBxaD6H8qeWpOmr4pB17jObYSg/J+errC1Tofz29FLPofxsHMpvaH0J5e+uXZZFYONQfi0hHofoM8mghfIzyWJoHK/QIs8AAEAASURBVMrPCfG4XExAvPJy5KR97OmoGYfsawnxOJSfjff5uGbG4eKgcNHGwddyKD/va4uE8muIynqwIPDKzxn4eWuWGs7SufHwcjOdE1FfMUocGYToCF98vDodP9Fzk9jAIcBSKvctvF8lTGVpmtX02/Hsz0/gqg8uw0ryNh9oq22sxQ2fXIcnVj2GDza/ja92foaV5E3O1ky/uZ1ZmFcr2Z5IiVFZ6saNZHzYzhijI4y/PfCd2t9ASWPZZlGZvlpn7RYTcc7Gkj/3rrhLv+whgp6tihLcGpqfW1Ab+ZsRvsPV6VLyumcraqkv3DdK7WsfYT6RarOk5bx2XFt31j/tvKxtB4GO2WALG39PPektrPs97g570rfX4WeCvjO5G83LviMpnB43LhdYHQJjo7xwpMh2dOm7ukFZpBPPFh6oy5reVVlTnZtP3vQ5pH9/6FgBtgd7YvIo3cuqqeqXegQBS0fgjR9TUFBSi7H0YmeJsk/sGVYxPQZrN6fg07VZuHBGGBGCFu2jYOm3XPonCHSLgDEh1xLKr4NRQvkllL/b/1BSYNAgsCutAu+t1MnyTp0QDnYksjSbMT4cqRnFeOXbZIyJ8KScPjrddEvrpy30Z0xQAl45/yUU1xRhe9ZubErbqCZc/7vp35g/fC7Yc7ojY+kZnpw2p725+S3lGR4bNBrLxy5HsGcwDuYfwpukMd+VaYR8R2VOi1uED7e8g93pW1HTWIMtqRtVsbnDeyZ101HdnbUb5B6kinPUwqVTrjjp0oQAnbPPSSdaDqSTtz+bu7OXWvu5+Kp1GkncGFo6OSSw+VG0QEfWWf+0slrEQyVFLHQmd6OVlbV1I2DxJD0Tz7ZI0tfX17f5ZmVnZ4O18TvSnWd8OvOyb1OJ7AxKBMRrufW2ZuWXqx3WiO8vC/B2wZyp0VhFoaG/b0tTsjoBXh0/MPVXnwa6nSEQAnSg70F/tb9qdz6+35wDX/p/MG/yyaGb/dWP7tqZOS4MyemFyMytxC/7CrA0UfdA3t11cl4QEAR6h4AxIdcSyj8OEsrfM6cKY75Xht9YCeU3REO2LQGBD9blqG4kEDm/kN4fLNGigj2QOCYMO/dn4YVvj+GZK1ulPSyxv7bQJ18XPyyOW4g50bNw9YdXqginA+T5PYnkb6K8ohQER/IOKmKetdV/Tf7N7LAczktSbVySeIlen/33lN/71C4T1YkUScXRXF8f+F4lcHVycEGcf2yf6u3q4ni/OHWa9fI5Me+c6NldFUdBZQ4lyy2HB+nCc0Tg1hZv/3CKvGMbQRr0bBxJd4iSyo6kiLSDtOZ9thif3v2/93L2RR559a+le3v5xEtVXX394Ai5tZQweB5NgmhJb/tap3Z9KY139bFfMS1iKsLbSRUxHoeLjmBx7MI2E02M509HV8PT0R0zIqdrVdnc2uJJer4jtkjSM+luqD8fEhKiT37b/luqedJ3ROC3Lyv7gw+BYUOHDL5B9XJE2UTS+/u6UYh8Lyvo5WWTyIM4i7zpkyhZLevTX3ha1zPuvWzGai6juB+r6asldPSuu+7C1q1bcfXVV4PzmkRFRVlCt7rtQ0VtE/6zKk2VmzUpCu4u9t1eM5AFxsWHEkl/CKv3FApJP5A3QtoWBFoQ0EL5X1v/upJe4VB+XliK5arp12HpyCUDihWH8t/+xW3KU7B9R3oSym94LYfys5cgh/Kzxq05Q/m1dtuH8mvHtbUpQ/lZb9fYUH6tfVkLAraCwK70KmzZm4tAP3csI5kbS7aZ5OV/lJwb1u3Kx5dx3jhvui7BqSX3ebD1LacyFw9/9yD83APh4uBMxHAFcsqyFEHPRHxMCxkcQh7smpb7ZSSF4+HsiZKqInAZ9qa/99s/4e65d5scHpZzyadkty+seQ6JRMQWVuWBJwpYvi67NIPavQ93zL4dn+/9AlX1umh37sRff3pM9eXWWbfC39XvpH4tI816Jum/2P6hOjelRU7upILdHHjutxeMajfYIxDzRy7GmkOr8OKvz+I1+5cxkqIDWAiusakBf1/6eJuWGNNbPr8FsYEjkVxwROUM4ALnUiJgtmifKIwIHKXkcx4kDLR7w+c46oDPsxnbP1WYPiZHTsF3ezOVrNAvB39CNOnc83fi2mnXIYHa6409SVJ2LOO3JW0Tnjv72d5U0ek1L294VUVE/JD0Pd66+C19OU7K+8j3D6jvJucjuCLxMv25jemb8e/fX1H7L53/Gvi7bYtm8SQ9E9W2RtKzJn1HY+ZjHUnaMEnf2Tlb/FKbasxFRUVqYsTVVaeRZqp6TV3PMFNXaKX15RVVoaG+CWGxulCz/h7G/CnRyM6vwLG0Imzal40ZY+Vhtr/vgbW2t2TJEri4uOD111/H448/jnnz5uHiiy/G6aefbtFD+mZrLjLyqjBpTChYUsbSbXxcAA6l5tP/z0KkF9Yg0t/F0rss/RMEBj0CEsovofzal1xC+TUkZD1YEfjf5lw0Nh/HpIQQix+iBzleTB0XjtUbjmElPe8JSd//t4wl4XjikxdD8yNS+aaZt8C7RV6Fz10343q8QARzQ2MdSogEvXbmjfhi56fqWs77UlJbaliFfnsYcW3DhnTv3dZRuZtm3Ij6pnokZe/Bb4d/UuT8tTNvwmc7P1bJX1PIW5rb3ZyyXhGyWqN7M3eqzdqGWqADmoWfC7Qkq1xwcQdJdbW6ulr3pN1bqd8hnkH4nDBjDLU+cv2ct2Uo4aRZkGc4/D389Xle2LHgjnl3qcS3WpmHKSHuM2uexf6sXQoLPs55d+6df49WpMe4XE4RC/VNdVhz+Gflla/1kXPk9JakjyKvfibpI3vp3a8fTAcbUaTBz7JF4S1a/FoRztHjTQlrOYdPuEF+Aj4f4q4j5RlTD+eeRdpp9Q+GtUWT9JoneUeE9WAAv7MxsEc8S9ukpKRg5MiRqhgnz62srOyUpOdCHRH4nbUhx7tHYNKkSarQlVdeidNOOw2nnNL3hCXdt9rzEqQIJUYIpObopG7Cg9wHBA8PVwfMJaL+618OYP32dJK98USon9uA9EUatS4EFi9eDF5uvPFG/PLLL/juu+9w8803q0Gce+65irSfMmUKOKLKkmzd/kI4ONhhGmmXWouNiwsm2ZtS/LynANcvjLLobueV1ZFH8RDyoGpNKLc3oxzjSB9WTBAYbAhIKL+E8kso/2D7Xy3jMUSAf9N/35WDsGAvi9ShN+yrtj15dAhJ3uRgf2o51uwvwPwxAdopWfcDAhMp2urDqz4lortEec872jmRFrkXnOwcTmp9ZuQMTL/6c+RV5SPINUB50Z9Csi1MLjsOc8CwoXYqesvwwkWxC/S7X163Qr/9tyU6T3ftQGflfJy98ehpjyjJF47Y0rziZ0XNaNPu59d8qVVl9PrGWbfgaUp4zonJR5FcTG+sJ+3yJPG5Y5arhbXweTwOhJs/6csbEvRaP3jcHG3H+vAdycSwbM9fF/8FnLy9sKoYAW6+6h5o1/O6J/3j8kxu30wTIzeS53wORS3w5IEHycJ4kexOb+3OU27HFZMvA99LUxt7yC8deTpYdq69vX7hGygnL3rDiSYuE+Mbjfev/Igwt+vwe96+nsG6b9EkvQa6LZL0Dg4OGDFihAaBWnt4dD6bJJ70baAyyc6KFSuwdu1arFu3Du+99x4SEhIUWc9er9rkiUka6mMlxOGIEQKZJDfDFh7Y+x8qVUEfPhLImzhzLGk47svCOiLqL1mS0G1txeV1OJpZgl0HcjAqJoBC607AiYhPJ0c78kgYptbDQwcmOqDbzksBkyIQGRmJ6667Ti27du1Sf39+/PFH/O9//1Pt8GShtnh6Dtz3nDtzJKcKe46VqmSxXjRBZS02KtoPB2N8sP5AkcWT9Pe/n4SqmiZ8ft80BS8T9De8sB0v3DweM+MtP3LBWr4T0s+BQ0BC+SWU3/DbJ6H8hmjI9mBD4KP12ailiN9EK/Ci17Dnd8yEuECs35aKb7flC0mvAdOPayc7R713cXfNMtGseSJz2e4SkXZXn7HnuV2NoO9ru0yQryHN9fc2v62av2iSabTXjR0Ll3Oxd4GLZ/fRtpwjpbPEvVp7djQ5wnI6pjSWMQrzMF3EvjkIem28HRH0fI6/M+0Jeu0axt/WzSpIek1z3VZuVkdyN+xJ35WnvJD0pv92TJw4Ebxcc801iiz79ddf8cILL6iFdaOZrGc5ioGWw6G/cWKEQEZuGdzdyMNggAnDBeRNn5NfidSMYqzfnYXZE8JOuj8lFfU4kl6ElKxSpBFB7+XhjLrGZpIOKUdWTulJ5R2JsE8kSZHRUX4I9O0gLvCkK+SAtSOg/f1hvfrt27fjiy++wKpVq/DTTz/Bz89PT9bPnz9/QIa6ijRK2U5JjByQ9vvSaDBpwW7and2XKvrl2qam4yirbNC31dzM6phAYUWj/phsCALWjICE8peo2yeh/LpvsYTyW/P/Zul7dwjsJMeGqHBfq5AHNBzLeCLpt5EO9oa9BdhNHvUTovvHSaSwvB45JbUor20keUJXkSg0vCmDcLuOtN9v+vR6VJF3tWbLJl6AuTGWqWSg9VHWgoA5EBhCBLjurc8ctfexTiaeZ8+ejeeeew4zZszoY23Wc/mDDz6II0eO4Mknn9R707/11lt49913lVd3+5G888472Lx5M84//3wsXLiw/WnZNyEC7N3KchRff/01MjMzlfwEk/W8TJum83Y0YXNdVnXtSzuQlFKGFY/Nx9as5i7LDvaTrEf/9v92Km+PZfN6FxJnSoxScirw2Xe7MYS0iC47ewLCAnSyN8nZZdhGevUpROCzTaWkTN5E0O8kL/pCGgObnd0wBPm7I5zkcuqJuM8rrEJxaRXqyfuGbQQR9QumxcDX00ntW9rHO9/shbvTCbx352RL65rV96e4uBgrV65Uy8aNOh1jlsFZtmwZzjnnHLi795/U02mPrIenhysuP2OM1eGanluBD7/djX/eMhEz4k4OwbSUAV3+/DZkFdRg7VNzVZd2pJTi1pd24p4LRuKCmabzoDF2vI98fABHsqrwyb1Tjb1EygkC3SJQR3q2xoTyc0XHTxxvE8pf1VDdJqS+28Z6WYDbNQzlN0W7WzK26UP5X73gtV72rHeXnRTKT155bIdIP5iT3LHe7ivnv9RlKL/Wcleh/FqZnq45KZ+pQvm5bdZENpenIH8vOvIU5O9MR6H83B/G39ZD+RkHW7IKiopb9OBvWDInDokjg6xu6D9sSMaupGycPzcc954TZ5b+ZxbVYPORUmzl5WAx6hp07z1aY8/cMA5zRvtru7IeZAhwJPn5by2Hi6MbIkjy5IzRS8ESPpZkaSXpePTHvyDGPw6PLHrAkromfRlkCFi0Jz1r0rMOe2/lbr788ku89NJL8PX1RV5eHmnn6kLy/f39UVBQAJ6fqKio0BMbfL6hQee1xm3W1tbCyUlHhGlzGV5eXkhNTYUmNaAd174XVVVVcHNz0ydyffXVV6Fpm2tlult7e3sTUWcHTZOfy3eHQXee9t21KeeNQ0Dzbr3hhhvAMhTffPMN3n77bbUkJiZiwYIFahk1qncZto3rha4UJ/RhE7mbVj36CNJ5tASLCfHATPKo5/DQNbTMnRyF7fRwe+hYQZvubd2dqbz/fb3JQ2ScN2LCvBEW6AEn+5PDI/KKq5GUXIgd+7Pxfv5uTCCdyHmTItrUZyk7ppj65b+t/HeNl6amJvU3kNfaMe24tt9RGT5mKuP+cL6Q9gtHOGnHtG1e899wPq4d62ibz/XE+LfsiiuuUMu2bdsUWc9SXLzNvzWsX8+69trvU0/q7knZDfTyVF5Zj/Gj+p8o7kk/Oysb1JIrYtPhYrOR9OwB5u/ZqiXfWV+6Ot5EnvP2dq3fEd5nc3JoPdbV9aY+tympGMdNXanUZ/MISCi/hPK3/08gofztEZF9a0dg81Gdc054UP94oZsar4QRAYqkP5BZadKqm46fwGOfHcS63YVKCkir3JkiiMcN90aInzO83eyx9VAJ0gpqiaTXSnS9rqprpqjDOkQHGB99vPVoKcKovRBvy3SC6nrE1n92CIbAUBffEkfEiVDfufQdS+ya9GmQIWDRJL2GdXsiXDve3bqxsVHJkTCJw2Q/a7oz8c0LEya8Zm/o6OhoPSGuESl8rrCwEMHBwYpkYTKFj7ElJSUpGRQ+ZrjweU72yvVpJHt8fM+9evla7rMhgcMYGO63H7tGILU/Lvu9R4Ax5ckRjRjkbY0Q5G2WmeBksocOHVIk2Y4dO/DMM8+ohQl7bQkKClLX8eRLbGxs7zvU7sq4UFdsosSNQ4aKKL2mRx9pISQ936o5E8ORnV+hZG8+yG6VsPHzccXwCF/EkzZ2gLcLHAxIuHa3uM1uEMnc8DI2NgCb9mRi4440FJZU44JF5p8QatMRI3b6+o08evQobr31VhVRZERzVluE/6bz7wb/zedt7ffHjn+fWvb1x1rKGJbj7bi4OPX7VlRUhJdfflmR9ePGjVPRPuYCZk9amara28M6X2QcaRLMl/7vrdtXhLvPMs3f5IraJvzz22PYRBMYTKEXUYK4T/88A1GBvddVrCe5GxcnnYcrA97QqCPpXR1bj6kb0U8ftXVNCKIX2MFgK3fkYdH4gDaTIINhXDIGy0VAQvkt995IzwQBcyOw8VCpcsrx97LO39BQivB1oEjfw2nlqG1ohrODaZ5DUvKqsWpLroLfg+RKzzsllH6bAzE8qC25/ljNIbzy9VHkl9bh3uXde/Jf8vQWFJTW4unrx2FugnHe9x+szUQdOfe8eUuiub8OUr8gIAgIAl0iMKhJ+osvvrjLwVvqSY2k1yYFuJ9MDhvuG/adj3dH4huW7+02k2ZlZWWwt7fXE0pMIHF/DdfaNhNI2jntGK810whvXrcnwPmY5hnb/nxH12nXG17T2XVclpf2ZQ3La2W0vvZ0vXPnTvBiaGFhYWBpIlMS9Vy/eNK36tH7ePTNc9Xwfpliu6yiVl9NLMnUTJ8QgfAW6Rv9iR5uMLHPkj5M9H/zywF8/jMsjqjv67wR/x9ZunTpoCfp+e8MG//tMZVxnTU1NaaqrsN6dibrkjT7WClJz4MK8HXDQYpsqalvptDa1t+lDgfczcH0whpc9dy2Nl5gfMm7a9Pxl4t6P4nW1NiOpG/SfU9cyMNsIKyx+Tj8vSzrb2xvcfjHZ4dRQLlBrp4f2dsq5DpBoEcIONrZK61dSw7ld7JzgruzFwI9g3s0NiksCAgCXSOw/UgJQihS1lptGHkfBBBRn0X5v/YQUT/dRFKBcSFuCA90RWZ+Nb59ZFankYJTRnjhx805+GJdJk6bEIDx0V1HTs8c44sVv2fh/rf24bdn5sPeiJdlduDYm2zaSAFrvd/Sb0FAEBhYBAbmTa8HY9YI6B5cYvVFmdhmooVJbs26IuEZo/z8fOXhz57d5rQNGzaYs/pBXTffV1MT9AxY67dkUMPX6eBYj76B9NpjI307LTMQJ55+awOaiHz19nJBaVkNqusa+kzQG45jTIwfhiwaja9/tkCivq8sPQ2UE6ZyLhJtwkyLbOG14TafNzym7WtrY8pq1/OaCXNtX6uj/Vqr03Cij8vwvuFkH293tGhluR7tGq0cH+MoMMN6DNvh49XV1UqOjctppk2A8vVsHI1gjr83HEK8L1kXGeLt2Xsvca3fA7X28tD1vYgSs0Y49t6zjT3Krn1xhyLol50Shj+RVuuafQV46J39WEkvlH9cFgc3A2/4noy3ie6ls8G1TJKzGXrX96S+vpTl+87m6z44SHo7ehnfQwnwMDB5l/tyK+RaK0WA4ngllN9K7510WxDoCwK55P1dSAlQx1mpRKA29kCSCmSSfl96hclIeq7bw9VeNWFn13kc7umJQVhIHvZJGeUYHtx9/qX7z43H7acPx+HsSj1Bz88x65IKsfloCXYcLkUpyRLefUE8zp+hk250dhqqnuVYgsfOBO8xGm6yFgQEAUGgpwhYNEmvEfQa6dDTwVlreY2k5/FrVldXp4gjbb/9mrXyWV7FnMaaxwNtGqnFpJW2MGmlkVqdrbmsOY2jCzinAa9Z8mjdunX4+OOPVZMsR3HZZZfh6quvNksXDOZyzFK/pVeamqPz6rUUPXrG663/7VIEfeLYMCyZEYOv1x5G0pF8/LYzE3MTw00GaQJJ5hROilLSNz9vccKiadEmq7svFbX+5epLLcD06dP7VsEgunrPnj34/vvv1cK5VNjGjh2L0047DYsWLUJ/5MHgNndSsmo2Zyf7DnMnqJNW9MGapRF9kHB5/7dMVFU3YOGkIDxAL4VsHKr9vNcxlJDkTXJuZbceX53B1UCe9IbSNjX1OpLe1aFnj24HMiuwnvRcNx8qxhF6ufbzccIn90zr1GOto/4UUw4CNh8P3ct0R2VMcYwTx/2ytxA5xbUI8XXGGYRrQB+0/XnOyuBRSt9FByLpswpbI530Jww2+EV9K+kIbzhQohLZZZOn39zEQDx5eYJBKdkUBAQBQUAQEAQ6RyCzSPdbE+TXPbnceS0DfyaYPOnZ9mXonkFN3aMqkg30IsK+rLoRe9PLsD+jCnlltYjyd8W1CyIV2T6hxYO+nBLxfr8jFxfMDEMRRcXd8cZulFc14u0/TEK4n84Jw93ZDpNHeKtuPrviKD7/LaNNl53oWYodLTRzstdFVZbTM53mkGCKaEutflkLAoKAIGAsAj170zO2VhOWMySqTVitRVfFJD2TyoZjZwJY85Rs33n2uGeimq8b7MaYMBHOiyUZJwxeuXKlWtasWQMfHx9ceOGFegLNnH3lEERbNk2PPtRCwkg///kg8osqsWRuPBLjA9WtWTRjOHIKKrFheypGkQd8gAk1KbXksaxRP5JI+/AA634JsOXvcvuxs878Tz/9pBb+u8IWEhKiJvyYnJ81a1b7S8y+vz9DR9J7uFunHn17gIoqdMni2x83Zp8J4PdWpaqifz5PR9Br1y2bHkzn0vQeYtpxXu9IKcXqPYWoIxI+wNMBF88OVy+mhmV4u5404F0cW3/rqiliiM3VwLteHejkg4mBG/61A6UtBDsXs6cfjEZq13BydzOF4f+8uwB3nDH8pH7k00RDoJcTcsgTkM2dJmf6akx+1zUcPynCgLVpr3x2K7SIAW7n398l445z43DJ7DDVbDZ5I36xKRsllY3UVzsi8YPB4fLtjZrAf35Jw1srk0kSbgjmEbl+11kj9Ml8HSj0vZKIAEMz1NldtTsff/vgQJu+MHb1hJ2YICAICAKCgCBgLALNLRGOTibScTe2XVOXc2n5/W9uSWJv6vrP/Mt64jqG0vNBW8e66BB3XH1qJD7+PRMjgl0xLdYHn6zPwNs/pCrt+sc/PqT057k/T3xxGK/dPBGNlNPnBcoTdA1d5+Jkpyfo+Xng8WvG4JTR/noPe20cDtQ2W30TRbTS9X96bz827ivE6BgvvH37pA4n/LVrZS0ICAKCgCkRsHhWl0lZW/OkZzKeSXdj5W64HHuQWxpxbcovqqXWtW3bNkXM//DDD8jNzcWYMWPw0EMPYfny5fDz8+uXbts4R48MCr10d3MyKfHd2xv3zW9HcTS1EFNJe14j6Lku1pCeP204/rdqH75bewjXnjOxt010eB0T9bsP5GDL3iyEL+y9BnaHlcvBfkdAI+Z5XV5eDo6U4r8pp556qpr4c3IaHAR5vwPbQYN9Iek5WSwTyrPGBZCOc9vHqZsXx4CX9vb8t0fx6a8ZbQ6v3l2IN25LhI9bWwKc63ZzbtXLrybSns3VSE3698lrTCPoZ1Mf7142AqE+J0v7vPJ9Mo6QZ9yEGE+cNblVi3pnahluIZL/1TsScTSnSrX90858JBOZzp5m3m4O6poFY/2V11kWeb+/tzaDnl9OKLK/kiYV3Ejvf2lisHqxrib9/398dRi/bM0DkxZnUoj5wxeOVPXyx1NfHlZ4xoZ7YNn0EFQSvp+vz8I/6bgDhcH7Unv3vbVXX55ftr/ekIMXbxp/UrTCQx8mYTUlh2Xjtnh7AyUK/uDeKcrLzoE85tjrji2PJiJu+NdO9ZJ/1WlRuJVC5F+lyQFtsuCmM0fg/Jmh8Gh3j9XF8iEICAKCgCAgCHSBQFmtbnKXPbet2TTy3Jki0cxh/JvLS0SQG2Yl+OLUcf4YHeahl55556c0hFDk47Q/+BDBrns2+uMbe9Q1cREeSM2uwoFUnZd/eW0jviT9eg8XO/Usds2SaPz3x1T1PPDkp4dRf+5xsISOoTk76sZVRI4Nd765Bxl5uueeAxRB+smGLL2zgOE1si0ICAKCgDkQsPhfC1sj6Pkms0c8k/SGnvS8bbhv+GXQPOmFpDdExXzbrPXM2vw//vgjNm3apBpauHAhHnzwQZx11lnma7iTmofZsG5eAYVBsh79iAifTtDpv8PHsig083AufLxdMXvCyZI2IyO9ibwPx9bdmVi3KxNzJp5cpi+9TUwIxXry1N912AcTWzz4+1JfX641ldxNX/pgTdcyEb9+/Xq18N+W9PR0uLi44Mwzz8TcuXPV4u5uWRESDY2tIcLWhHX7vvbFM7qMiGq2YCOTqWoEfbC/Cx67fDTGRXjiD2/vxSby1HqSvL+euXqMvnstjndtCHkOu2YzNtEth4evI499JurX7y0AS7zcbeBNrjWWma9LNDwhqm0itq+35KoieeRF30BeZWz80soLe5WzMfn96jfJWPHwTCLZj2DbwSJ13PCDnNpx3vRQXPXCdiUNxOQ6e8ZxIj32jH/rl3Tl7bbnWKmq97VbJuonPa6aH4FtdDyXiHQm6JnkuO/CeCyhl+vPN2bhecLtHkoM9+Njs6H9FrKXvkbQcz9epgmQ3w8U4dM1Gbj/vSR8cPcUcIK4JhoTTzhcZeC9/+5PaVg8MQj3UmTEn6k9Jgz+Q5MYDZS099oFUTRZYB5ywhAv2RYEBAFBQBAYPAiUkBwLm2OLnIq1jqyhxcPdXHlx7qVJ+4XkUMCSNx0Zt5vVIh3EEjds/BsdRNJ4/75tEp7/7ii+/j0LJTQB79YShZicq3u+YaeJM8kJ4flvjmEDPQ89+n4STcan4LazYrB4QpDykre30xH///fqbqVNP56kci6YHapyDK3YmC0kfUc3RY4JAoKAWRCweJLeLKO28Eo1Tfr2nvSddZs979mT3hbkbjrDwNzHd+7cid9//12RaFu3blXNjRgxAtdcc40i0iZPnmzuLnRaP/EdNmFHM0ux4qck2JMXKYdcOtEDmI+3TncwOMBzwDHYTF7sbDNJc76zkNaFU6ORlVeJ9dtI9ibaF/6UVNZUNmNcGHYmZeNwWvGAk/SmGpOt1HPttddi+/bt6m84E/M84cfkvCV7zDdZOUlf36jzSo/0P9mz3Njvnb+Ho5JS+Z7I7MvnRSDYu/MIB9ZYZQ96b0q8+uEfpyqtefbE37pfR2qv28Me6tEqdJvb5xdPNnv71j/wdS1EuUZGqwJdfASRTM3Kv87GV1uy1cvor0So87JkWghuOz1Gr/XOZDVbuIE2P0vl/LglRx2fGe+HLzdnq203Vwe8dutEJTHDsjU3vLIT7GXG8jB/uWikSsjmRAR4VIArWHKmuLIOfP2NVI61+8fHeeOVGybAvoXsvp281ZjYDyTZH7boUDc9Qc/7PNbpcT44468beBdvkFf/yDDdhNVXG3X9q6B6PyUvt0tP0U18ZhbrXsq5POcKmEIv2rxspURxR0mfP40mJeypj4yxRtBffGqE6u9nROR/vS2HJjNiservc/DmTyn4fE2m8sD7+NdMXLEoEpfNCYezlcsWMDZigoAgIAgIAuZHoIq8utk6ezcwfw9M00J9i367E0XImcNmjvTtlKDn9nwoP01ecRk9V9SDk/GyOdM74dt3TlI5dqbHeiuSfhdJCi4gsp8dAva15FHismFE5j9/zVjw882rP6ao56G/0MT9e6sz8J87JqG6Tnefasn5axrJ4fzrhnF8GZ52PYK03Cp6ZjhxkkSOKiAfgoAgIAiYGAGLdwli73Fb86Znsp0J+uLiYv3t7s6TnjXsxZNeD5dJNn777Tc8/vjjWLJkiZKaeP7558EJfG+//XZ8+umnWL16NR599FEMJEHPAyVp3cFnLWMqLKtBUkoR1u7IICmXbEWqjI2jRIK+bmgmBmgvSbywFZZWI7+06ySA5gRp56E8ZGSVICLMB+OGB3TZ1EJKJEtxMSR7c7TLcj09aU9yEONHBSMlvQhlVToPk57WIeUHBoE77rgDr732Go4cOYIXX3wRixcvtmiCnlFiz2Jrttz8StV9LcFYb8bCHu3XLo1RHlfn/W0jfthJMi7MTHdgKQXV6ujiKYF6gv6213ep0Gt+kWR79GPWQNdd39Sy1iRZ+Hwz6aSyVdUZjz0HWrEX+yryNH/g0tFqkoDJ97MeXa+Ida7PtyUS4KsWUp614a/+53Y+payKZHZCWiYg7KnC4UGu6rgdbWvSO5mUhNWfXqBZLocT58YGuyGetOKZoGcrbNH+jwqka2m8LDHz+qoUved9AkUVsHWmdVtE5dn7ngl6nhx45qsjSM2pVC/hfN0b5BFXWK77u5dR2ErSp9JYtFsS5KubRCkjwqS2BUMm6u8kzfu7iJS/Ym4EV4XtROazcdJePs5k/VXkhddMZf9NXvWnP7IejJGYICAICAKCgCDQHQJ+LQnXswp08indlbfU83UtJL2LmSapC1p+wzsbf1DLc0h5dRNKWp4pOLJOS/I6tuU5YuOhElWFq4s9qmp0xDvn1eHJ/u+25yKUyHpOAL/ikVmYFO+D5OxKPPv1UeSX6aIj2RnhqStbE8TPT9S91+0yIPw766McFwQEAUHAFAiIJ70pUDRxHUzS19fXIyCglezjiYrO5G7Yk15IetPeBCbL/va3v4GlbRYsWIDLL78cU6dORVxcnGkbMkFtTMJYuzEflVtUhczccuQXV6GwpBpFtHQ0QbdldwY8PZwwItIfNbX1aKCHRibreZkyPkJ5aGrJVPsLl3TSxWcbb4TMTJi/G+ZNj8GazclYsz0d8ydHmqybEcFe2IR07D1aYHI5HZN1Uio6CYF58+addMxSD4yJ0EmiNLW8rFlqP7vrV3GJ7mU5xKdz7/fu6uDz1y+MogmLE3iXCGcOn/77RwdJT9UVhWX1qKaXwwDyTl/xwAxE++uI7U/Im34tSdAUltQpgp69tZ6kl8HLntuqdOEvp/ULN4wHe8GzHcjQTSbwtqZPn5RZrhKn8bHu7JFPDtDzwQncTklhl00JxtlEon9DL6n/+PggHnl3v6rnljNiwN5kT31yEG98n6LXsY8iop29xz4jWZlbiKT+B0nNsHTOuU9swvBQd/JKr1Q67jzJcD7py3dlN5DW+18/SFJebhyObmiclM2rRY8/n3DryAK8nRUpv+ih31VCN/Z08yOM3r17Ml6iPvPEwwVPbsbT143TJ7nlevjl+7ynNisPuq0tUjyh9KJf3EIGTBnlp/fAD6BJBpYi4mt4suSSZ7Zg4cQAXDkvEreSnu3VJL3zn19S8SHJ87Be7bePzOyoq3JMEBAEBAFBQBDQIxAVoIuazS6sQFiAm/64tW3kFuj03iPpd9KUFkYRjUlEgHPema6MI+I4GjCYntvuPS8O/9uU00ZXnh0Fxg33JrkbHdk+gcpntDhIsAMAT/b/7cMDePaLI4iLdIcj8ScpOboJ932p5Zg5Wied+sx1Y9vICt60KArfrc/GUXoemkre+qa04+RkyTaUuJ8Tx5tpOYGhrLff4rzRvq2a5GPI/+Jz1Ken4gRxRfYhoRjxtyfaFzNq/3hdLY498pByDI157O8Y6tCKf/qLz6OxqFW+0HX0GARfcqlR9UqhgUegp9+rge+x9KA9AkLSt0fEAvaZpD9+/HgbUr4rT3pN7kY86U1385iMZ8/W2NhY01Vqppp0vpVmqtzM1aZkl2HL/hxk55UpbXmtOUd7O4SHeiMqxAuhAR7aYbWuqK5DMWnRl5TXoLJCF+6oFdi2J0NtHkouxMKZwzEirK3GslbO1OvC4mq4ODtg7HB/o6qeMS4UWfkV2LQzXenphweYRm88MtiT5CGG4sCxgSPp+dlSbPAikEiEKhsn4ywlTVBvknyxNssgL3qOBAiiJKpuTn1/DGIC94IZIfj3L2nYebQUaZRklfFhndQLTwlT8HgTCf0nkoN57rPDFK5dqzzALyeC//alw9W72Lt/mIIrn9+qSPFHPzqAN0lHnT3H2VucPefdSIt11kg/MMn/064Co0n6WtJnX7crX2m0M7HuS/erighu7h/bgaxyLCENdtaY/xdptXLfOAHbNQsjMTfBH1e/uB15JfXqhfW/d03GAzQRwX3icmxc5wM0rqjArl/al5LszDAK+1qxOQd5pfUqCWs+TVQw6X8eYefvrns5ZKmcjuzJa8bg9ld2gaVt2GZSstrHLhmtpHEevWgUaggjlgz6w2u7cCPp7rM9QWHtH/6WqV7+c+hFnft61zmxyuPfl17mGxqP44krWr3l+Jo/nD1Cad9nkWROKenacqK591alYWS0p4oaKKCXfLYCitxinX7RqFdwyIcgIAgIAoJAJwhEtxDziuROCOmklOUfzqVJBrZJRISb0v50TryKZDOU3Ouo/nMpoTzL2LDc3BhKMM9Le3ud5Pg0ScCnrxpDz086EpzrfpekBp8nj3nOf7PniC5ijq/nZPV/Oj9O5Qm6gqQL/UiW0NDYU/+l2yfqnScMz/Vlu+rwIRy99TrY+wdhzCdf4sAVl6IhLwsj/vUG3BPGnFR1Ocntptx/l/74MGcXHCe5495aZVISqndsVpdXHzkM9zFj9VXV7N2DurRj+n3ZsAwE0p5/Fk2lJYh54GEMde5YrrOn3yvLGJn0oj0CfX87bV+j7PcZAY2k74kmPXvS83VipkPAGgh6Hi3N51ilfbPuCPaTTIwz6cu7kL48J4ANIm35mRPCMJxkY1i+pWNrfSj7eYszeVM6I9jPjYijMhxLL0FmTilKyqrx2cq9GBMfjFMmRcDbre0DV8f19u4oq36w1/+MxJ55xC+YFkMSOaX49tdDuPXiKb1rvN1VLPMcEuSpMKglT+eB0E2uZ01Hup9igxMBJotHR3vhQGoZeSCVYpJHkNUNtKDFiz4soOMH3N4MiD24HqBko5qx7I32oqgdY9kZ9mQvJi+vQE+nNo5Sni52WPHgTOwjL/lRobq/cS9cPw4HSEOdZVfY2IPrH3RseKDxnnhPkMTN+0ROv/dzmvIQZy9xNibll80KxYw4nRwNv/jy0t7ev6v1b1MMRQh8cu9UpQdbUN5AGvYuavKg/TWd7S+eEEgJ2gL1p+8gb/StB+uRSC/8geQVzwleNa16faGWDX4Z//XJuRSOXgc/mmiwN9B5Y4czTrqbQpr+3uSJx5rybJwj4G3Sma0k7X8er5ND6wTA+5Q8lhPxeji3fW6aN4bIf3qxD6bflU/vm4pXV7KXfq4i+lu6Ah/q680kcyQEvYaIrAUBQUAQEAQ6Q8CHJun5XSfXiuVuONq5nt7TAsi5IcIgf01nY+7JcX6uZIk8Y4yflboyw+cufjZwN/iNZ7k8doBgybxcchIgnyZ4k7QNSxdq1p6g145PijHtxATXO2Sorl092dri5TTUseMIz6yX/6m647VoKYIvvxJOYZSHpw8kgPu48fA+61zlSe82erQ2VLUe9da7al2xZzeS776tzTnZGTgEKjb+jubSIpqc+XOnJH1Pv1cDNxppuSsEuv5L19WV/XiuM5mXfuxCvzalkfSG4y6ikKPm5o51aLm8yN306y2yqMas0ZP+x00piqBfMCsWm3dlkGZgA2ZNjsIpEyPQE/memZQs1bXlASzI1xUzxuokFzLpQfj37WnYfzhXLafOHIHpY8zjvZLXQvi5kid9T8yHiKZ5pE+/iiYrvlt/DGfOHtGTyzstG07e9DxRUVZRC2eavOhvK6CoAn/P/olg6O+xSXs6BKbGeyuSPp0mxiaNtD6SPo0ieNgWGhDGupGZ7tPwRdGwViahNSkbw+O8zS+U41o0VXmfSeb2yWjnkXd7T4zbu3ZBpFo4eW0peaIzMc0Tlz35W2vYJnuVaRqwhsd7up1ZRAlciTzX9O45lL0r4/62x8OwPE8isA1tCRPX5q8NX9K18kzyd/ayr00kODk44hHy0ueFJwfYc97DxaHT67S6ZS0ICAKCgCAgCBgiwE4BRzMqcCi9FCMju/6tM7zOUrazC3UT/ONboiktpV+96Qfn0+nOa7839fb0miEt8jJD7HXvj0OddOT8EPuTHZ0ayyniPDtdNRF+6x2w82hxWOOZhhZrrq5GM0ngDHN0xDBX3fOQds5wrWR1KMcOW8Rt/6fWGrGrdnryQZMEx4mfUhI9Bn0xlFvh6gzbHELPfao9iuisLyiAY2Cr84Zh0xwl0JCfB3tvny7HY3hN+22Tt0t95rFpYzheW4vG8nLdGFqePdv34TjdkwYapwPJWA+le2NoxvavfcQE7+uPUbssl6RZT75X2jWytjwEWu+o5fVN9agjTWpju7p27Vo89thjqKmpga+vL2lX68Kk+XrWfHemMBEmwvlceno63N3bSk4w+d1I/wnaS82U039GLqt5unOZ9lIzhYWFSlOe6/j666+N7bIqV1FRgXpKUGpI0ldVVYGPd2SiSd8RKrZzzNpI+o17s7BzXxaWzo3Hmi0p8PJ0wZJZw5U3fE/vmkbQt78unEJLL106Bt/8dlSR9L9uPGY2kl5r28X55Icq7VxnayY4UzJLlZ5+fJQfYk0gz9PQqJvMK61sIEw7a1mOCwK9R0AjkvMKWvXSe19b/15ZRv8vUtJK4EsE+JKJHb8Y9G+P+q81L1d78GIpVlBUizHDTT+hx16LbMUkx2QqY09/MUFAEBAEBAFBoDcIzBjpo0j6XQdzrJOkJ4lOtgkxuiTvvcFArmmLgEastifnh3ZA0jfk5auLnYbHtxL0batD5huvofT7r9RRlsJxShiPkGuvh1v8yDYlj971f6jev7vNsXHf/NQrIrx8+3YlweO18HRE3/+Qvs7955yO5toajF/1myKQM156ESXffKnOu06ajsALL0bms0+hsTAPjhHRCLzmBvjOmavO82RDxr9eQNkvP+jrcx0zAZH3PwzHoJ45Bpm63erkZBy56SpwNMMQO3uU/qDj+Ow8vBDx4KPwnNwafdpUWYmM559B+brV+nF4zlmAiLvvhV0L52hM/1iGKOmCs/R18EbShWe32R/71Ur6Xuj+b/bke9WmEtmxKAQsnqRvT5D3BL28vDzMnz8f7IXuQTOOGunNxD8T7XyMjb3Q3dzc4O+v81DTyvFxJuKZBNeOcXmul8s60AwoH68jQt3FxUVt8z4vnHh01KhReiKfrzPW/Pz81IykNgnA1w0fPhwHDx7ssAqeIBC5mw6hsY2DVsTS7yWt+LWbUzB/xnD8vP4ooiN9ceacOJIhaA01NOVNO3tuLCVorEI+eYCs3JCMpTQZYGpzadG0dnXqmSe91o/TyYOeZW++/ikJ91w7Szvc63Uj6++QlVfV9roOuVAQ6AqBWaN8MYFkUnYfKUI+aXMHUlJPa7FDaUUU6txMXvShAyIHZS04maOfz31zFOXkzc+e6eXk0c+6+IkjTE/Sc1I5tiTyWmRdfTFBQBAQBAQBQWAgETh1bADe+ykNqRnFVudNz/lb0ikC0Yui6OaNEe8fU32PtEStQ1q8qzWyfphBAlflkU7PSk1EeLPZ+/i2elDTvqEHu1NEJDxmz0dzVSWqd29H9fZNOEpL/NsfwSUyUl3PHx6z51LC2TC1X/bTd/rjfdk4QZxZh0Z9Z3MbPxEn6P20dOUKNGZnIf+j9+E0PBYOkdGqn9lPPw7vmbMUoZ/61N9RufE3dZ371FmoPUTa+TSpkPLIAxj1+n9o0K3RA6pQFx/mardqy0Y0VZSB+8f9qdz8O1Lu+wMSPlkBhxY+Me2Jv6Fy6wbVOxeaMKlJ2qMI+zTiDUc8+bQ6bkz/Rn/yFbzPWE7hCJQLjPBj40mRIRTtqdmQYa2UrjHfK+06WVsuAq131HL72OueXXzxxb2+diAvZHKeJxIMJwa0Yx31iycRONEsr8VsDwFr4eirSctww/Z0zJwUhTWbkjFpbBgWk9yLue265RPx2mfbsTspmxLRepOeto9Jm3R00P0ZzS2qpESPLeGHPWjBjTzwz12cgI+/24MvVh/C+Qvaejz0oCpVtLHFk76pyVq+GT0doZS3BASumB+qSPrDqUVE0odbQpeM6sORtGJVbpEZpW6M6oiNFeIkrJ+tyVCjziIPek0uZ3iLRI0p4eBIj2HkLPHlhhzcskSXmNeU9UtdgoAgIAgIAoJATxAYRXrokaS7np5bBWvzpt95OA9V1fW48cyYk5Kq9gQDKdsWAfamDrzmJjiG6KRafZeeBbeJkzCMHEfZWM5kz5J5alv7qNy2sc2xqMf+Ae9Zs9XpoPMvAHghYxmV9H++oDzri1Z+i4hbblfH+SPoggv12/s2r1dks/6AmTZ8580HL0wyc3Jct6nTEHnn3aq1fcvPUH1oLCkm7/s6RdBzJMDoj75UUQOMw+Gbr0dd8mFUHkhqk+C2u+6aq10m6A0T/Cb/5SFUrF+Dwu+/RejV16ImLU1P0Me/9SFcoqJQm5qCQ9dfoY7XkIIHT5wY07/mmmpE3X2PGmrFpvVKkz6cZIr0kkftQOjue9WuuOxaKAJWQdIbktUWiqNJu8XjZZLe0JNeO9ZRQ1zO1jDqCAdbPUb5b6zCNu3JhJ096TH7uWNUbEC/EPQaMDMnROD7tYew4uf98L9wCvwpKaCpzEkj6Vv0GntTb3SIJ1g3/wgRnn21RtJNZnOmRExigoC5EJg90g+jYnyx60AOZlBuiM4TPZurBz2vl3NVZOVSsttRfhgb0fMJtZ63KFdoCHiTBM0fL4jHc58fbpOEdfII006acnuswz+NvP027itEHU1aDkQCbW3cshYEBAFBQBAQBBiBWQkkr0skvfKmJ4eBkVG+VgFM0pE8eFIem0tmR1hFf62lk+w5H0IJYDXzXbBQ21Rr1j33XnqO2mZt9uodmzHM2w8eM3SkPJ9wCDSQfyGHzYp9+1CXlYHjNbV0Tifp2ECEsKWZ3xmtci2Rf30SzdVVsHN1Q1WLaoTLxCloKC1VC/fdKW4k6tKOoT47u0ckfftxm6pdlrdxH9WabNedJh2YpK9PT1NN1rasnaJGKIKeDzpHx4D3eRy1aaltohvURfTRWf+088asu/teGVOHlBl4BCyepO/Kg3zg4TNPDxTh3gNP+vZe9+bpldRqqQhYA0efkV+JrbszFYQ/rjuMK86e0K9wjo8LQBJJ7aRlFuPb347g0tPHmExih/IPwsfbVUnq9GVQnNjWFMltNU96B3uL//PeF7jkWgtA4I/LRuD6F7ZguyLqdZ5AFtCtTruwcXeGSlR685KoTsvICfMhcOHMMEyK8caf392PjLwqXLYwEt5m0sh/7JLROHhKpRD05rudUrMgIAgIAoJADxCYN9oPH/2iI0zXbE1FWIAH3FwsJ09MR0PJpNxD+UVVuOmsWLg4ivNPRxiZ6xgnV436472q+oq9e5FMJL1LbLz+mGG7x0lC5fBtNykC2PA4bx9v7kSKpn3Bftx3Cg7Wt+Yxbpx+m73p2Vju5nCL5I3+JG00EZnfFzNVu44RUW1kd5yjolW3Got1znba2mlEbJvuOg3XkfSNpSVtjms7nfVPOy9r20HA4lkc1lq3NRkXnphoptlQQ096w+32X09bw6f9+G19/wQsn6bfsEsnc8D3akZiBHw9+z8J37RxoYqkz8svx6/b0kyqTx8e7Ik9RFQeSi8d8IRQZZU6LXpnJ8t+8Lf1/7eDYfxjI9wwbWwQ1lIC6JBAD0QGulvssLYezEUy6dGfMy8aWuJbi+3sIO4Yy9t89qdpyCquRbif6SKa2kPm7myHqbHe7Q/LviAgCAgCgoAgMCAIjI/2wuKpwVi1NRelZTXqXYRzZ1mybSepUHc3B1x7qnjRW/J9ynnnbUXQu8+cC//l58GRPOyrjx5Gxt8e7mO3yRONjSRnjDWW2uGksV3ZMFfXDk87Bui8/x2CwhB07Q0nlXEd2TdJWHO1W5+Vpfo6zFOXZ8neV5e7ofbIoTZjqKV7wsa5BTqyzvqnleWksJx5rqmKIg9acmtq52Q9uBAwPvPCAI67K4J6ALtltqY1aRtDCRveZt35jozxYW96MdtEwBpufQ4R42xR4b6YlhAyIDdqeKgXxsTrZu4PHstHowk128OCddIZrDM5kMYRC2XlOpI+KkiX5X0g+yNtD34Erjo1XP3+/G/VflRQMlBLtOLyOmykfBihQR646wzz58GwRAwsqU/0OGNWgt6Sxip9EQQEAUFAEBAENARuWhwNf2/dBPX+w7nYeShPO2Vx67U7MnDwaAGWzxyY9zaLA8SCO1R9YL/qXeDFl8IzcRKcQkPRWKTzSu9Lt+28dKRzXVoKVBLbdpU5Regmb6p3blM6+Hy6ZN26dqWM33WN1U1asW79UBcXsASQ4eIUGmZ8ZT0o2dN265KPkI5+ha4FlhnaulltO0VGqbXmWV+fkYrqY8fUseqjR8H7bM4t5dRODz40eaOyDet7cJXpi/J3oeinVeAIj/bWUFiI/K+/Aq/bW+X+feo6zjMg1jUCFu9Jb4tSLhpJbzg5wdvJycm47LLL0NzcTBOajeAog/LycpSXlaGMFjHbRMDSSfpsCpOsp6SxbAkj/Af0JkWHeYEfirk/e4monzTSQMuvDz2LDNI9xCidyXbe9KVV9fAmLcf+sN0tD/tjaVzWoBHeH5hIG+ZFYFKUB6aQxvu2g0X44Ns9uPXiKeZtsBe1r9uZhpraBtyxfCScLP6ppxcDlEsEAUFAEBAEBAFBwOIRCPVxxnWLo/DUJwdVX9dRZG84OdWYMleWKUDYn1KEjTvSsGBKCG47fbgpqpQ6zIiAY1QMapL2IPPZp5RmvdKwJ+KcNezrDu7HsQf/jLBbbyeivRn5H3+g7wknQGVLf/4ZDLG3hwOR4KFXXKU/z/IrnMS1sTAPh264Gs4j4lBHeush198Mz6lT4RgUBKfh8Sqp674Lz4UDecI3EBHN17A3PbcbceddKPjqSzST97dmqU/9XW2G3nAzHHxbvcr5ev9LrkLhx+8i7aF7kUn9d588lTLhkm5AQwNi/vKYVoVR64yXXzRLuzy2QzdeA9fxE1FDxDNPKrD5n7VMrTkpLEc1sGzPkZuu0mPEJ/k4n2cztn+qMH24TZ2B6v27kfv6iyhZ+Q2c40ehmTjA4Guuh2t8vFbM7OuSNb8i8x+6ezH2qx/aePVnPPc0OMFx+cYNiPvHs/q+8KTGsTtv1u/7nbZYvy0bJyNgFa+rhmT1yUMYfEd4vI6Ojm0855m4t6MQl+joaHXOiZKNcJmioiLw9ogRIwYfEDIioxCw9MSxeUTSszlQgtWxIwKMGpO5CoWRJIdmSeQdYiqS3svVAZPHhWP73kxspqWqpg6ZeeWUpLIClVV18PFyxXmnjTb7Q3jSYZ1HzpQEy9cH1+6DrK0fgUcujMe1/6pGYWkt3vpqF65bPtFiBrVuV6byBFs4IxLnThnYSUKLAUU6IggIAoKAICAICAIDgsDyaSHYlVKmZG/YgWD1llRcvLg1CeWAdMqg0XKKivzmlwOICnHHE5eOMjgjmwOFACeRVUYcUUcWet31OFFfh8oNv6Hw0/cVOR965x9R8NEHRKAXoXLz72i85DLydj+Bsp9XnlRF+dqf1THXMRMAA5J+qIMDQv/0ELKfflx5gWue4I0Vugh5vogJ+/THHkRzaREa6moQetd91O77aKYEqZzstpESwJb9+F0bCRytD8EGbWmdCrv2ejgQ+Z/3zluqTq2sOn/80TYL1b7wAABAAElEQVRa8No1na3N1S7jZB8UjLJfflBNcyLZsD8/TDJDOrkePhj95weR8aIzylb/qCYx+JjXgiU0aXE3byrrSf/4gqALL8LxhnqUfvO/NvfD69SF/UrSO7bkFLD3D1IRD7rR6D6dKEEuk/TOxFka2jA3V3B5nvBxDJHoHENsOtoeQp7qFq2TkpCQgP/+97+YSrN1tmK7d+9WHvP79++HJnnz/vvvKxx+/fVXW4FBxtkNAqv3FuCB/+7Dz0+dijUplpcURuv+t+uOYB95eI8bHYIzZw/8ZNKrn25TkjBhwV648qzWZDVaf3uyZhmN7KJK5BZUIaegAvkk63O8pQIXZwcEUVIoXy9njI8LRIC3S0+q7nHZ/SmF9FB9EFMnRGDh1KgeX2+qC554cx0SYrzw9h2TTFWl1GMFCOxMKcUtL+1UPfX3c8MN5yYOeK+3JuXilw1HMW18GP5+aRzcHVq0NQe8Z9IBQUAQEAQEAUFAELBVBLJLanHnm+T5nF+tIBgdG4hz5vefJ2xnuDNB/8qHOumOn/8+Fx4uVuHP2dlwbO84Sa80ECmueac3V1eDCf4hRLZzItpeG9VbX1Cg6rIj3XUm79sYny8qhIOfn2rHVO0er61FQ0mJas/e16dvY2jT4a53OmuXpWvYM55J+rgXXwEn7G2qrISDf+dOQCwN00QJce1bsOm6ZePOsu5/fV6+ijCwc3cjT/b+l7hlz3iWJGKd/PbG98zBx6f9YSWJ1FxTCzs3t5POyYG2CJyMatvzFrFna570DDrrz2sEPe8fOHAAxcV91xbjusQGBwLuzvZqIBY9y0Y9LCUim83HQ6fBqHYG8COEiHOdbrvxhF1lTSMKSmuQX1xFSyUKi2tQVNIatjeMIl0iInwRR5ECh44VwIvGehV5E7s69t+f2E27MxEV5jOgBP0A3lZpeoARSIzxxgOXjMITHx9EIUXPvPbZdtxy4eQB69UeipRhgn7i6GAh6AfsLkjDgoAgIAgIAoKAINAeAZa9eeH68Xjg/f04klGBA0fzYWc/bECdmfYmF+C71bpEl2/eOVkI+vY3zRr2ydNeI+i5u90lIjV6SKzyQN7tnRqfb0n6ymVM1e5QZ2elr99pu2Y6YWy7Q0nNwoGWroxJbJbxMaXxhIvTAHujd5W4tiOCnsfP/RaC3rhvQv8xSMb1p8NStkbSG5LzGiATJ07Epk2btF1ZCwJ6BE5ortv6I5a54dSPhHVXCDBJzw/DnVlhWQ0KSmpIuoMWIuULiIwvr9BNNGjXMAkfH+MPrmt4hA8CyFtes8+bT+BoaiE27ckkwrxtqJdWxtTrt1fsQW1tI86eP9LUVUt9goDRCCybGgL2Dnt3VRpK6f8RE/UXLRlLE3SORtdhioKb9mVjzaZkJMQH4JmrRokHvSlAlToEAUFAEBAEBAFBwGQIhPs545/XT8D97+3DnmOl2Hsgh/JJDcPi6f3z7qANpJrydK0hbXxu39vTEW/93yTwJIKYICAICAKCwMAgYPEkPSdJHTasD+E5A4Nrn1ttPzHhQGFFnChWTBBoj4Cle9Jr/XVy0Hn+a/sDtfbx1D14urm27c/nPx9ETn4Fqmvq23RNI+SDA9wR7OeOEH93ONp3rAnIF16waBQ+J2m/reTZHuDrinHDzafDX1pRj/e/3Y2q6npcsHQsAs0sqdMGGNkRBDpA4NYlwzEu0gt/fHO3Iuo//XEfzjl1JP3fMX9oY3VtE1heKyW9CEtIg/7hC0bAzviAmQ5GI4cEAUFAEBAEBAFBQBAwDwK+7vYtHvX7sDmpGDsor5UjedTPSYzA0H54fjmUVoy1RNCXlFZjyig/vHzjePMMVGoVBAYBAsMoH6RjRDTsQ8IGwWhkCJaMgMWT9AyerZH0HaUJsKeM2yyBIyYItEfAWr4VzhbiSV9BiVzZwoJb9ds4ua27myMac5oxIsoPYUEeRhHy7e+Fts9E/WufVauw0bq6ZkxNCNZOmWy9L7kQ364+qOo7deYIxIZ5m6xuqUgQ6AsCs0f54tU7EnEP6a2yR/17lEx24tgwTB8bCg9KsmwO27w/B79uPKYmxh66YizOSjTf5Jg5+i91CgKCgCAgCAgCgoDtIeDqOAzPXzseL353DJ+uycDGHWnIyCnBzIlRGBHmZRZAMvIrsS0pG4dJotPNxQGXL4zCHWcMN0tbUqkgMFgQcAoPx+j/fjBYhiPjsGAEhKS34Jtj2DX2pOeoAjFBoD0C1uJJn5lXjugQj/bd7/f9CkqKxBZKHvGaBZGX7xJeZsRoh/q8Zj1uTqLKuti1FEo6NzG8z3VyBayNvzUpR4Wl8n5EqA+mj5Es6YyFmOUgMIk06l+9fRL+/tkhHM2swHbyDjucko/EBCLrx4VhmAk8xPglc8/hXHrJLERDUzPmJwbjT8tj4ePWNkrGclCRnggCgoAgIAgIAoKAINAWgWHkNn/32bGYM8YP//05DdsPleCz3L2YPC4ccyZFwqmLCN62NXW+l19cjZScMhxNL0FWTilcnB1w5ilRuOnUUJLt7FpXu/Na5YwgIAgIAoKAqRGweJKevcdtzZO+o5vMnvRC0neEjBw7ThrolmxhQZ7Iyi1DFknJWIKVV9bC1cURof0gv/HAjXPwX9KL37A9FTxJMWtCRK8nKkqr6rGVvIX3kudLY7MufiKRvJNNObFgqvsTF9Y6AWKqOqUe60NgFH0PPrh7ClbvK8CPOwqwbk8+ftuSgmPpBQgP8lY5HSKCvODiZJykXUPTCeQUVqrlCIVo5+SVwc3NAXMnBuK0CX6YM9rf+kCSHgsCgoAgIAgIAoKAIEAITCYHh8k3eePj9Zn4YHWGcnDIJEI9OtwXsZG+CA/omXQgOzMcSi1COtVRSFHDbEzOL54RhavnhSEmoH9zBqkOyIcgIAgIAoJAlwhYPEnPvW+vz97liAbpSfGkH6Q31gTD2pFSBthbLikaQbIym3eB9N7LUVnTCHeXgfVyrahqQPzw/iPzrjlnPH7ekoptlEg2I6tEecVMHBkIfy+Xbu8+a2xnFpQjr6gau4icr61rVNe4uzlhzpQojI+1TEkPD2fjSNduAZACgwKBBWMDwMvhnCis3JGLjQdKsJlyNmgWHOiJqFAvOJAOq6PDMJU4jZOn8X51fQNy86uQT+R8XkEFmk+cAHucTUnwxzWnjsb8sf5wd7KKRxltuLIWBAQBQUAQEAQEAUGgUwQumR2OpRQd+PuBQqwjrfrfdqXTu1Q6vOndYWRMAJxIIsfRwQ7O9MzkQDm/Ghoa1TtWVU0DeKmubVDPTTW01iws2Atjor1wyZxQjAwUz3kNF1kLAoKAIGBpCFjFm62tedKzJv2QIUNw3333IT8/HzU1NcjJyaEf4NYfWkv7Ikl/Bg6B5uOW7UkfQZ709sOG0ve3CTsO5mLepIgBAys5uwzskZIQ238kPQ920bRoRNDD8aGUAuw+kKM8Y/zJkz820k8liGJi0oEetp1oqaLEtRm5FcgrrEBRSXUbrDgCYPyoYEymxW2AJzvadEx2BAEjEIgPcUN8SCzuOgsoqqzHQZLBOZxTjVU78rFpZ3qnNfhRGHZ0sBtOSQhHbIg7xkd6ItxPlwC604vkhCAgCAgCgoAgIAgIAlaKgKeLHc6cHKyW8gtGYtXuAnyzJQcHjuWhvEKXX6u7oY2M8UNivB+mxXojMcIF9LohJggIAoKAIGDhCFg8Sc8SL7ZG0jNBz8YSNyEhIfDw8MD8+fMxffp0C/86SfcGAoFGUj4ZOhANG9mmg90QhIV6IzWjWBHUk4hgHihv+t2H8lSv/b17Fi5q5FC7LBYf4Q1eSidHIYmSvh4luQ4m7A29XDqqgL2GfbxdEUsJbYWc7wghOWaNCPi5O+IUkqfh5XpKWNZEEk41DbRQ/oYaSrZc00gLreNC3eFFL6pigoAgIAgIAoKAICAI2CICTNhfODNELTz+OpL/S86vQ2pBNdILa1FZ1wRfysfj7+FIkboOCPV2QpSvSNnY4ndFxiwICALWj4DFv/myV7mtkfQ8ZrbHH3/c+r9hMgKzI8CS9JZM0jMAU8eGKpKeCentREzPnxxpdlzaN5BZUIXDyQUIC/HusaZj+7r6su/t5ojZ48PUwvWUUyLbQkoGW0eRBhxtUN/QjHoiKAN8XeHn6QxfCm01RZLNvvRZrhUEzI2AHUXbeDjzYvGPJeaGQuoXBAQBQUAQEAQEAUGgUwScyAEqIdRZLZ0WkhOCgCAgCAgCVomAxb8NM2FtZ2fx3TT5zde86U1esVQ46BDgHKIDq/LePaTDSW965IgAHDpWoGQtWEdx5rjQ7i80YYkdSVmqtpHRfiaste9Vebo6gBcxQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAdtEwNIdcNVdsUWS3ja/jjLq3iDQZOGa9NqYJie0kvJrNycjObtcO2X29ZYk0nA8WqB030dG+Zq9PWlAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAwFgGLJ+mPHz9uc3I3xt48KScIMALNxy3+v7G6URGB7pg2oVXm5tPv9/TLDcwimZvft6SqtiaMphwP4rXeL7hLI4KAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICALGIWDx7J4tyt1omvTG3UIpZesINLXkMLAGHBZMjURCfLC+q0+8uQ4VpMluTluzNRUNTZSAMsYfC6dGmbMpqVsQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEOgxAkLS9xgyuUAQsCwEKMeoVdlZc2IRTslbNXv5w83gpK7msBVrDiMzp1RVvezUUeZoQuoUBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAT6hIDFZ2QtLy/H4sWL1SCbm5vBXua88DYbrxsaGkhr2gEsjcPGSVe5jGHyVcPtYcOG6cs1NjbC0dERQ4cOVQuX4/O85mOsh89rbZ/P8T6bdryqqgqenp76NjvyhOe+aX03XPPx9guPWRuLakg+BIEuEGhqPtHFWcs7NXQIsGx+PD75YR+KSqpVB99fsRPLF43GKBMmdf1hQzLp0Oer+q9cngh7i5+StLx7JT0SBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQMD8CFk/Sr127FseOHVPEORPmTJJrRLm2nZGRgaioqDakPEOnkeXa2pAo14jxgoIC+Pj4KLKfCX8+zmtempqa1KLtG57Xrud1bm4uAgIC9HeL29MmBXitbWukPq8NF208huMbM2aMvj7ZEAS6QqCxmVhvKzPWhb/y7AlYsz0Nu/Znq95/9fMBHBsZhFmkW+/j4djrETU0HceG3VnYlZQNP183XLp0HNycLf5PXa/Ha4kXxoe4W2K3pE+CgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCFgkAhbPXCUmJoIXMUFAEBhcCDg5DMPpM4cjOsQL64isZ6/6fYfykJxRgukTIjCGNOTdXOyNHnRZVT32Hi3ATiLn6+ubkDg2DEtmxBh9vRQ0HQLuzsbfN9O1KjUJAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAIGCdCFg8SW+dsEqvBQFBwFgERkb5IoqI+u0HcnAwuRCFxVX4deMxrNmUjNAgT0SFeiMy1AtM6js52MHRntfDkFdcjdyiKhSV1aK4rAbpGcVopiiWGYlRGB8X2CdvfGP7LuXaIpCaU9H2gOwJAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAIdIuAkPTdQiQFBAFBwNwIMOk+e0K4Wg6lFWP/sQIcSSlEVm6ZWtZv77oHzk72iB8RgPHxweSZ79F1YTkrCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAhYEAJC0lvQzZCuCAKCAMCe9bzU1MeiuLwOJeQlX8gLyeE0NuqSQ2s4+Xk7Iy7KDzHkac8JacUEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFrQ0BIemu7Y9JfQcBGEHBxtINLgBvCaRETBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQGKwJDB+vAZFyCgCAgCAgCA4OAh7PM/w4M8oOz1eS86sE5MBmVICAICAKCgCAgCAgCgoAgIAgIAoKAINCCgJD08lUQBAQBQUAQMAkC9Q2Nqp6RYe4mqU8qEQRe/TEZ972zT4AQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAY1AkLSD+rbK4MTBAQBQaD/EMgtquq/xqQlm0AgJbcGOQU1NjFWGaQgIAgIAoKAICAICAKCgCAgCAgCgoDtIiAkve3eexm5ICAICAImRaC5uQlRwZJDwKSg2nhlZdUNGCpZoW38WyDDFwQEAUFAEBAEBAFBQBAQBAQBQWDwI2DzJH1TUxM+//xzZGRkDP67LSMUBAQBQcCMCOQXVcNV9OjNiLBtVj3U5p9UbPO+y6gFAUFAEBAEBAFBQBAQBAQBQUAQsCUEbP7V9+uvv8aFF16IdevW2dJ9l7EOAgSmxnqrUZRV1g6C0cgQBgMCBSR3E+LjPBiGImOwIASGiSe9Bd0N6YogIAgIAoKAICAICAKCgCAgCAgCgoA5ELB5kr6oqEjhOnXqVHPgK3UKAmZFIDzQFfnF1WZtQyoXBIxBII++h7X1TZg/1s+Y4lJGEDAagSHDbP5RxWispKAgIAgIAoKAICAICAKCgCAgCAgCgoB1ImDzb75fffUVAgICEBsba513UHpt0wjEh3sgT5J12vR3wFIGfyyjZcIz1sdSuiT9GCQIDBsyZJCMRIYhCAgCgoAgIAgIAoKAICAICAKCgCAgCHSMgE2T9CUlJVi1ahUuv/xyDOknEiApKQmvvvoqLrroIgQGBmLOnDmoq6vr+O7IUUGgGwRGhrkhJ6+sm1JyWhAwPwJZueUqaay7aNKbH2wba2GYnZD0NnbLZbiCgCAgCAgCgoAgIAgIAoKAICAI2BwCNk3Sf/nll+qGL1u2rMsbzyT6s88+i9mzZysyn8n1e+65B0eOHOnwutraWtTX17c5x4lpx48fjzFjxuC2227DZ599ps5zuf6aIGjTIdkZFAjEh7ircew6nD8oxiODsE4ESqvqkZJVhokteRKscxTSa0tFQH4jLfXOSL8EAUFAEBAEBAFBQBAQBAQBQUAQEARMhYBNk/Svv/66krqZNWtWp3geOHAACQkJuPfee7FhwwZMnz4dYWFheO655xAfH48VK1bor01NTcVll10GFxcX+Pv7tzn39ttvY+/evarsrbfeiqysLOTn52PLli1wdHTU1yEbgkBPEODksT6eTti2L6snl0lZQcCkCGzYmabqu2JuuEnrlcoEgebjJ2DXT5FugrYgIAgIAoKAICAICAKCgCAgCAgCgoAgMFAI2CxJz4T5zp07cd1112HYsGGd4r9p0yakpKSo82+88QZ4f8eOHYpwHzduHJYvX459+/bh119/VZ7yH330EWJiYjBlyhRVduPGjXj88cdVO1yejeVumOQvKChQ+/IhCPQFgbOmB6GopBqpORV9qUauFQR6hUBtQzMOpxYjIcYLoT7OvapDLhIEOkOgofE4horcTWfwyHFBQBAQBAQBQUAQEAQEAUFAEBAEBIFBgoDNkvScMJaNSfauzNvbW52+/fbbceONN+qLjh07Fk888YTaX7t2LS655BJUVlbi5ZdfRnJyMlavXq3Os/b8ww8/jMbGRuzatUvJ3MTFxeGFF15QmvT333+/8qjXVywbgkAPEVg2NURd8fuOtB5eKcUFgb4jsIWiOOrrm3Dzkpi+VyY1CALtEKhrOC6e9O0wkV1BQBAQBAQBQUAQEAQEAUFAEBAEBIHBh4DNkvTbtm1Td1Mj4Tu7tXZ2duqUm5tbmyJMyL/yyivqGJPumlc8S+AcP35c6dXfcccdStaGz7N3/dChQ3HBBReAk8fyJAF71j/11FMICgrCDz/80KZ+2REEjEWAvZenjPJFVm4Z1mxPN/YyKScI9BmBvOJqbNyRrrzoWXpJTBAwNQLKk36oJI41Na5SnyAgCAgCgoAgIAgIAoKAICAICAKCgGUhoGOgLatP/dIbJtLZEhMTlTQNE+U1NTXIy8tThHtVVRXuvvtu3Hzzzaock+l8LDIyUnnEf/vtt3rP+cWLFyvN+meeeQaL/p+98wCPourC8Jfeew/pJKH3KiAWEERUQAXFhiIiNlT4UWwoKoqCooiCXUQUFQsoShEpIr1DgAQI6aT3Xv977u5sNmWTTUjb5Fyf3Zm5c8u57yxx98yZ79xwg2yv/fbUU0/Jw/nz58uI+rlz52LixImghLUbNmzA/fffj5tuugkZGRlwdHTU7sr7TEAvAs/f0QVT3zmIfUej4eVqi64BLnr140ZMoLEESOZm7cYTsLIwxev3dG/sMNyPCdRJoKi0HDZWHfarSp1s+CQTYAJMgAkwASbABJgAE2ACTIAJtB8CRhWitJ/l6L8S0pWfMmWKRm9eV8+YmBgZFf/ss89KDXul3S233ILZs2dj9OjRsoqc/itXrsSuXbuQkpICb29vKI781NRUuLi4gJz1y5cvl+3Hjx8vI+izs7Px008/ybo9e/agriS2yty8ZQK1Edh+MhkvfHUKFmamuOfWPvB0samtGdcxgSYh8On6IzIXwpsP9sKo3u5NMiYPwgSqE7hu/k54u1lj7dzB1U/xMRNgAkyACTABJsAEmAATYAJMgAkwgXZDoMM66ZUrSJHzmZmZKCsrg7W1NUjWhvbJsZ6Xl4chQ4YoTWUkfWFhIZydnaV0jeaEjh1fX1+Ym5tLjXpqUlxcjNWrV0uJGyUZLdW7u7tLBz7p0xsZ8WP9xIRL4wi8+UsENvwbKx31t43tiUBv+8YNxL2YgA4CFEG/ZuNx6aCfNNIX8yeF6mjJ1UzgygkMm/sPOvvYYc0zg658MB6BCTABJsAEmAATYAJMgAkwASbABJhAGyXQ4Z30zXVdyJlvZWWF6dOn44svvqgxDd0YSE9Ph4ODA0gXn/TquTCBpiAwb00Ydh9NlEONu6YL+nXxaIpheQwmANKgJ4mbopJSsIOePxDNTaC0vALDhZO+W6ADvp49sLmn4/GZABNgAkyACTABJsAEmAATYAJMgAm0GgEWem0m9FFRUXLkoUOH1joDac+z/nytaLjyCgksua8Hfg11xuJ1Z/DXrnCcikjC1QMCOKr+Crl25O4ZuUXYfTgKYeKzRGX+1G6YNNi7IyPhtbcAAUoaS8WUE8e2AG2eggkwASbABJgAE2ACTIAJMAEmwARakwA76ZuIflZWFkiuZvDgwXjggQekjj0NPXAgR/81EWIepgEEJg3xQrdOtli0/jwiojPw/R/H4ePlyM76BjDkppCR8wdOxWmc80E+jnjlzlB0FfIjXJhAcxMoLlOlzOEnzZqbNI/PBJgAE2ACTIAJMAEmwASYABNgAq1NgJ30TXQF9u7dKxPHUvLYy5cvY9OmTXLk0FDWa24ixDxMAwmQI3XN0/2xPyIDKzZdxPmYTOmst7O1hJ+3A/y9HUVyWVtOMNtAru21OUnZkN58YmoOohOyEJ+QKWVtaL3+nRzx0NhAjO3l3F6Xz+tqgwSKS8qkVSasBtcGrw6bxASYABNgAkyACTABJsAEmAATYAJNSYA16ZuIZkVFBebMmYP3339fMyJJ3ezbt09zzDtMoDUJnIvLwfbT6TgamYnYxBxk5RTVMMe/k5O6ThXBquQxpoTG4o+F6pxxBWR6YyPhOatQyVEoPjQjUVehrqvsq9WulfIiSyuF+dI+7VWrl1QB1TrolLJM7WaVZ7VrRVs6LK9cVIWyaFFddZyKasdVx2mNo8KiEiSl5umcOiTAFV39HXBtTyeMCHbQ2Y5PMIHmIhCbmo87Fu3DoG6uWDGzT3NNw+MyASbABJgAE2ACTIAJMAEmwASYABNodQIcSd9El4CcmMuWLcPNN9+MmTNnIjIyEkuWLGmi0XkYJnDlBCiyXiVT4i8Hi08vQHxaodwPT8hBTkGJXpMcPp+pbkcu6koHtaqy9rq8gjJEXc7Va3xupB8BKwtTBAlJI32Kt7MVvF0samnqJuvKyo0R6GkDGtPC1Agjuig3a2rpwlVMoIUIFCma9CbV/860kAE8DRNgAkyACTABJsAEmAATYAJMgAkwgRYiwE76JgY9atQonDp1Crm5uXB3d2/i0Xk4JtB0BDoJxy29qAwOaYBT9sams6GxI+UUlOKseDLAUIu9lSnruhvqxWO7W4xAcan6SR1OHNtizHkiJsAEmAATYAJMgAkwASbABJgAE2gdAgbtpD969CjWrVuHYcOGYeLEia1DsJZZra2tQa/mLtu3b8e3336LxYsXw8PDo7mn4/GZQJshYCec3A26sdBmLGdDmAAT0JeA4qQ34Uh6fZFxOybABJgAE2ACTIAJMAEmwASYABMwUAKKlLTBmb9161YMGDAAq1ev7rAR6xEREfj666/lTYqcHMONKja4Dx8bzASYABNgAs1OoLhUZn0A++ibHTVPwASYABNgAkyACTABJsAEmAATYAKtTMAgnfR5eXm47777pHP+2LFj0kndyhxbZXrSvp81a5bUv3/llVdaxQaelAkwASbABJhAcxDQRNJrJWRujnl4TCbABJgAE2ACTIAJMAEmwASYABNgAq1NwCDlbt59910kJyfjt99+g7e3d50MS0pKcOTIEakRTzrxKSkpyMrKQnFxMcrKytCzZ09MmjSpzjHa6kkTExO8/fbbWLt2rUxa+8ILL8DV1bWtmst2MQEmwASYABPQm0CJ+H80FVNTg4wn0Hud3JAJMAEmwASYABNgAkyACTABJsAEmIDBOenJsf7RRx+hd+/euPXWW2tcwdOnT2Pv3r2gCHvSrD948GCNNtUrzp49i65du1avNohje3t7LFy4EHPmzMGnn34KctRzYQJMgAkwASZg6AQUuRvOG2voV5LtZwJMgAkwASbABJgAE2ACTIAJMIH6CBick/6///6TUfTPPPMMjLQegSd9doqIP3PmTJU1BwUFSTkYqly+fDnc3Nzg7OwMikKn/uXl5fD396/Sx9AOHn74YZDcDSWQnTdvHszMzAxtCWwvE2ACTIAJMIEqBEpKy+Wxidb/66s04AMmwASYABNgAkyACTABJsAEmAATYALthIDBOekjIyMl+k6dOlW5BOSkJwe9nZ0dFixYgFGjRqFXr17iMXlTdO7cGba2tnjyySer9Kl+cPjwYezZswfZ2dno1q0bbr75ZlhZWVVvpvfx5cuXERsbix49esDGxqbWfiS7c/78eSlT4+HhUaVNaWkp9u/fj927d2PHjh04cOAAZsyYgffee69KO1rbY489JqVvdu3ahdGjR1c5zwdMgAkwASbABAyNQEmZKnGssamRoZnO9jIBJsAEmAATYAJMoFEELibmobNn7b6DRg3InZgAE2ACTMBgCBick56c8FSOHz8uk8cqpMmhTg72/v3713CskyOcoufrKr///nsN+ZzQ0FB89913GDBggOwaHx+PdevWITU1VY43YcIEUJvqJT8/X94Q+PLLLzWnfvnllxra96SpTzcO4uLiZLvrr78eP/30kxx78+bNmDZtmnxqQBnE3d0dhYWFymGV7fTp06WT/ueff2YnfRUyfKAQoCSMMSkF8HezgpmBaTzPXxOG5MzKz37fIAfMHh+sLE2z1bedpkMDdvKLyhCfXgA3ews42vDTKg1Ax02ZQKMIKIljzTiSvlH8uBMTYAJMgAkwASbQtgkkZRXh+KVMnLyUhRPidT42Wxo8oIszPp7Vr20bz9YxASbABJhAkxMwOCf94MGDJYTPPvsML730EpycnDRQhg8frtnX3qFEsRRRr6tQxDpFqFOZO3cuhg0bBorMX7ZsGQYOHIjo6GhcuHBBRucrY9DNglWrVmH9+vXo16/yf6BFRUW47bbbsGXLFhnVTw7+nTt3Soc73UhQpGjIgf/QQw/J4R544AH5FMA///yDDz74QGrMv/HGGxoHPWnN33nnnSD9eV2FbhaQTadOndLVhOs7KIHUnCLM/uQELsbnaAh07mSHDx/pAxc7C01dW945EZmJdC0nvSq+tqbF+rar2VN3TaKYd95XpxARo/rSTC2tLEzx9G0hmDi4auLqeV+fRprgvfzhvrC1NNE9qJ5nmno8PaflZkygTRAoVSLpOZC+TVwPNoIJtCcC+eK7vaV4Kte4jt8H7Wm9+q4lT/zesQmuGQShb39uxwSYQN0EwsTviaPCKX8iMgth0VlIF056pZipnxy0tTFnB70ChbdMgAkwgQ5GwNjQ1uvr64tbbrkFOTk50kmvr/2kPa+rUHLZ5ORkOe7SpUulk33+/PnSMb99+3aEh4dLB72Pjw8o4p6S137yySdS6/7ee++Vx8rYH3/8sXTQ080EcvSTTA3p55O9FNFPheoVBz2NRzccHnzwQXkuIyNDbt966y1Q5DwVunHwzTffgG4A1FVIoqekpKSuJnyugxEgTec73z4oHfT0hW9QN1fQlhz2U0S9IifR1rH89cpwHFg2Cu/O7Funqfq2q3OQaiff/iVCOujNTIwxWPAL9LZDQVEp3vr+LPZHpFdpffBsGsLEDQVFS7vKyUYcNPV4jTCBuzCBViNQrHbSmxjYkz+tBownZgJMQG8CFx6fgcvffat3+47QkBz0EY9MQ/aJEx1hubxGJtAiBA6eT8cnW6Pw6KpjGPnsDkxfdggrfjuPf08mSwf9QPHb4tr+KsnbktIKUB6e9c8PbRHbeBImwASYABNoewR0h5e3PVs1FlEEO0Wnk0O8e/fuePzxxzXndO3k5ubqOgXSjqdyww03VGlDkekkQTNkyBBZv3XrVqlVTwdr1qyRdaSD//3334Oc9VQ2btwotz/88AM8PT3l/jvvvINnn31Wo0u/cOFCWU9vdMNBuygR/VdffbW8CUBrfP3116Uszttvvy1vTNx///01JH1ojKioKAQEBGgPx/sdnMDGw5eRm1cMdycr/PDcEFhbmIBkWyYv3o9UESH++6HLuG2ot8pZX1EBU+GI1laWEFUoLRM3uESlmUnVcFZyREcL+RwPRwvYWVX9U1JaXoEK8aJiZGwEU/GiseLSCuDrWjXPQ05BKQqLy2BhbgL7auO09uWjKPq9p1KkGb++PAxuDqonD3aGpSA2tQBDQ1UyWtWd8sWlZcJRr46kr8ZOn/U2ZDyFUV3XQ2nDWyZgSARKSkqluZw41pCuGtvKBAyDQFlBPsoyVYExhmFxC1gpgpColDCXFoDNU7RHAikiKn6fCOA5F5eDhPRCnBLyNfQ7jAJ9/L1scXUfDwR6WCPA3QqdPWxhZ22KV78/h51Hk+RTuhQE9L8pXeHEsprt8ePBa2ICTIAJ6EWgqmdNry6t38jb2xskDTNo0CA88cQTSEtLwwsvvKBT0oaSvyYkJOg0PDMzU57TFYVOkfbjxo2TDnqSxiFn+V9//SXlZShC/vnnn8fYsWPh5uammYMSvgaoHeYktaM47CkKn6LnyQn/4Ycf4v3338fJkyfRtWtX6YCnaHilULLZefPmYdasWfj6669BEji0T05/umFACXGVQglj6WmAKVOmKFW8ZQL4/aDqBtSDYwKkg56QkKN++tgAvPPDOY2TfuIbe6XTfuH9PXBjP9XNJWq75XgiXvkmDJ4uVtjw0jCqQrZwqr8gNOIPnU2Vx/Tm62GDZTN6Cwe8tax7ae0Z7DiaKPdJWmeGmO+1tWdlBLqTkNh5/NbOuGWglzz/mrBj94kkuU/OuCAfO8yZFIL+gY6yrjXf1PcZZFSLo625xpRre1T+W6cv5De/ukdzjnZuXfhfleO/Xh8JZ1uVjn19623oePpcjyrG8AETMBACityNKSeONZArxmYygeYjUCy+q5ckJaE0Pw8WHp6wFL8FrrSUF6mecL3ScVqsv4h2SPtnO6w6B8O6GYJyKipUTx1XqJ/8bbF18URMwMAJrNwSiYMRGTgjnqal0tXfAX07O+CGvm4IFU/ghggHffXy3b+x+EA8rWtpbopugQ44Kxz6w3u7y+Cp6m35mAkwASbABDoOAYOTu1EuDWnFk2OayiuvvCKj4OuSg6HkrAUFBUr3KlvFOX/p0qUq9crB0KFDpVO+R48e6NKli3Smk5xNZGQkSBaHxqY2Bw4cwMyZM2W3qVOngrTmf/31V6lpX0FhxKKQI50c+yYmJujTpw+++uorHDlyBGvXrtVE6cuG4m3UqFFYsWKFbEsJZsm+d999VyOzQ+3oKYA333wT1157LR3isccek1t+YwJEIClDJZE0VCQf0i5KBHiyiPKgMmFYJ7n945DKWS4PxNufh5Pl7rhBlY77pz8/oXHQ05dQig6JTcrDrI+OQXFqD+3ihBHiiyaVpMwirPzzEgK8bRDia48Modn+1ndnNVI7nb2s0buzEwLEF9gy8e+EEiY9uvwIIi7rfvpFDtwCb95OlvIGBdk15Z0DOBmTVWNWG0tTjBSRMcp6qQHJCtGx8tJ+CqG+9TZ0PH2uRw2juYIJGAABRe6m6jM8BmA4m8gEmMAVEyhKTETqtq2Iem8pTt4xAWG3j0fEY9MR+b8ncfa+ycg6dvSK5zC2qLz5fsWDtcAA6Xv+RcybryBh1UfNMluFCESiYmShemqwWSbhQZlAOyOQLIJ1CsQTwYHu1nhtWk9sfHUEVj89EM/cEoLxA7xqOOhjUvPx8Ioj0kF/nZC5uXuUr3TQ02+klyd3bWd0eDlMgAkwASbQUAIGGUmvLHLkyJHYt28fxowZI+VvfvzxR9x3333Kac3W0dFROsfJiU9R9dVLJ5E4igrJ29RWKHHrxIkTZXJXOq9Es1N7cpCTlA4500kuh6LyabzJkydj9erV8kV9qC050t977z1QktedO3dKLfqHH36YTmsK6dbHx8fDz89POv/JOU9PCVAkP41BNwSoUKQ+radv375ybVRH2vbakfhUx6VjE8jJUUWJOQsdeu3irI4Kz1KfnzTYC1/8eRFHz6WhWMjYmAsNaNKrP6yOlp+gTpAakZArNdcp4n3Ta1fDSUSHk8zKrYv2yUj8QxfSMSTEWSZUpaSqQ57ZLh/zHCRuEiy+r4c0YcT/doixy5GYUSilb2aNDQLGqqyjOSlKnyLrv90Vg9fu6q5tdqvsL7ynO54WiXcTkvPw8LLD8kbDW/f31Mj20JMJSx7oKW275rldQrqnFK/f3V2yqc3g+tbbkPH0vR612VFb3cyVRxERXZlguHqbbgH2WDmrMlF29fN8zASakkBJiSqqU/y54cIEmEAHIFBRXobYT1Yie5f4npCiehqPlm3m5gmbEdfB3MsLJnYOyD1xDKgj11R1VMUpKSCHvKm9gzxF81AxtraRW0N5y/h7qzTVWDxp2xylXO2kN7Ws+VupsfOViwCp4vR0maS3sWNwPybQlgm4CynMOcIhr0/5/O8ofLbpogwAekP8drC1MsPcVcdBTxm/MLmLzt8O+ozNbZgAE2ACTKB9EDBoJz1dAopgp8SuL7/8Mjw8VElXql8aSrpKhZz1tRXShaeI92HDhtV2Gr169cL58+eRKKJ6SNLGzEwlW0GNjYT3gGRrnn76aTg7O8PY2Bh084Ci3vfs2QOSyqEXRdnTixLYfvnllxgxYoSMuqd56SZDXl6evOGwadMmaQMlnP3vv/+wbNkyOT7dgFBK//795XotRKSLosc/bdo0+Pv7K014ywQkgXJ1aLtJNT150oinopwnrXWK4KAo9h2nUzC2rwf2hafKyHaKcPcSEeVUzsRly20X8VhmcnaRfFFFF1877BP67ZFJ+dJJLxtpvd19jY/maOnMPsgU+oyOar1FMnF/RCouJuYjp6AMXi6qCK5Ll/M0fVpzp6+Q3fldJK596+dwbD+SKBnd+eY+qRlJev4NLU253sZeD102m6k/F7rOm4i/b1yYQEsRKFE/gWZsxJ+7lmLO8zCB1iRQEBuHtPXfSxPMO/nD5dZJcBTf8y19fKuadY8qD1TVytqPSrOzEHbXROGgd0TXz1bDzNUVFcUlsrGxpeq7Te0921Ytyf1k79khjTJ1UN1saHIL1VyMmpBLxDNPouD8WQQtWQ6H/gOa3GQekAkYAoFjlzKx+KdwRImnhO8Y6Yt5k0KlfOjDHx6Rv7WemxKK7uJ3GBcmwASYABNgAgbvpKdL6CUiaz7//HOdV1NJ/KqzgThBkfJ1FXK+kxa+rqKtD09trK2tpfOdHPDVC0XSnzhxAkuWLMGGDRuklI7Shm46PPLII9KJT1r2ixYtki+6QUCR83SjwUHry/mCBQuUrrxlAjUIOAjne7pwnqcJiRlPx8ofo6nqCHo6r5RJw7yFTn02NgnJG3LS/3VUJXVzq5YjOjW7WDYnzcX7lxxQumq2uUKvvrYS6FYZ9aVI7VA7SmJ7u0hiSzZWLyWlKomo6vWtcUyJcd+8twfSJgRj2caL2CYS8i798ZzQ1feEmXjqQN/S1Ott7PXQZe9Hj3CUvC42XN/yBErVfwM4kr7l2fOMTKA1CFiLYBObnn2Rd/o4un3+NYzNqz4F2BibjEVUuN2gYcg5tBfR776D4LfegRIxbmxl3ZghW6VP4ndrNPMa29b+5K+mQSN3ystU3+FMxG+Ypiq2Q66STvroBfPRa+NmGBmbNNXQPA4TaPMEKNbgrV/CsWFPnNSdXzV7APqpc2598PsF6bR/+vYuuK6nSiK0zS+IDWQCTIAJMIFmJ9AunPTNTqkZJujduzfWrFkD0sM/fvy4THpLjn57+9rvoiuJZ5vBFB6yHRNwtTeXDvAD5zMwYZAqUSst98D5dLlqekRTKeNEwlhKJksSNyR58+9xlZN+/IBKPXofF5Wj31bI5zw6PkjpqtnqSvZKTu7aypLfIqR9PYIcMe16P/i5WeO4SJy0eN3Z2pqLJ1VUjvtSYV9dRd92dY1R2zkX8TjqG0L+JlJEwlyMz8HOsBTcIPTolaI4E7MLSmp9ZLWh661vvMZeD8Ve3jKBtkygVMhiUTFW/iG0ZWPZNibABJqEgLFaksbItGmcueToD168BCR5U5qfL22sKFQFBhhbVn4H0td40srPPLgfJSLHlJm7O5yGXw1zF5ca3YvT0lCamQGrwMArdkzniSeG035ep5nDuJk040mahoqJwkU8/Vsocl9ZqmVBNQaQ55FeIoCJ+mQdPYKck8eRJ/IElCTEotMzz8Fl1GjZ3OfBh+A15S4UxERpONTXRzMP7zABAyaw+Vgi3vkxQspgPnJzMKaP8tesZsvxJPyxLx6Tr/HD1BE+mnreYQJMgAkwASZQu+eMubQYAZLOGTRoUIvNxxN1LAI3C63592Ky8flfl3CDSGRKeucUzf3F5igJYvzgSgc8nRvS3Q0HzqRg6cbzUje+u3CeK7I01KGnn+oR61whV0OOd4q4v5JyPFKViHXWjUEYHOIkh9pyrGryWu3xXexUNwlihawO6ddrJ2RtTDvtPrXtJwjd/ALBq7Nn5ZMANG98iuqHbPU+ro4WIoluKTYdScJjNwZWP42Grre+8Zr6etQwmCuYQCsSoH9rVNhH34oXgadmAq1EoCwvH6YiF1NpTg7yLpxHwYULKE5NhoV3J3hMmKSySjiR0//bg/zwc9JhbNnJB643iCdYa5FmMxdylUpcflmh2hmtjqQnjfqEb1bDOiRUON1HVFlxmZCjNDIxAUnjFMbH4fwTj6A0O1PTJunTj+D95Fy4jr1R1pVkZSJu5UfI3PanPDYRc3g9MQduN47T9GnITrm4oRC1aKHs4jv/FcQuXqhxdpeKnFiZYv0uIidWSVo6ot54FSUZGQh+5z1YeFZ+vysX66UkvMUi55WRyM1l27MXHAYMrGFGWYHqJoaJWpM+6t0lyNi8Ed2++VHjqCd7Tt97J1CYD+ebJyHlp7VVxjFxcoXCVzlhIjT0bbv1kIfx33yN5NWfKafktrY+VRrwARMwIAIpIpHsovXh2CfkQ4eL315P3xIMP9fKPA8nolTBSF39HTD75s4GtDI2lQkwASbABFqCADvpW4Iyz8EEWonApCHeWPn7RSRnFOCW1/eii48twuNyZTJXKwtTTFInhFXMu32Yl3TSb/g3TlbddlVViScfFyvcOqwTNu6Nx4LVp/Hm9+fQu7MjyJVWXFKGTx/vL/u9+G0YsvMrpW+e/PSErH9pShd4aMnuBAu9e0rIukAkix3W0xWX0wtw6kImLM1NESOi1R9YfhgLpnRDkNpJ7u9qDUpaW1BUilsW/ocgbxtEC4f9nEkhGCW+CCtF33ZKe13bbSeS8fGG8zKhU6CYi5T8wy5ly6gYMxNjDAx2rtJ1hFjD90l5WL0lUjIK8bGT+vtzJ4aAtO0but76xtP3elQxkg+YgIEQUBLHGteTK8FAlsNmMgEm0AACZ++/CxUmpijLSK3Sy6b/YHjcMkFK1kS+8hJyDv4nz5MznJzM+RHn4PfEU1Xu7hWnJCPlz03odN806cCnGwBUjNWyLvnnLyB5zRcgHfzqTvqLr70i24a+vRQJn38qHfQ2A6+C8+gxKMvPQ/rG3xD7zuswEhKVDiLo5vzTT6Ao5hIs/AJh3b2XdHLHLXkDzlePBDmrG1qi3xNPAcRHw/upebAOUj3BSHNRSd2yGZc/XgYLEekev+IDKStD9fGfrULQy6/SLoqSknBhzmwUJ6q+15E2f/I3n4Mc/vKGhmyleisvUD9hIBz5VPLPnJJa/hZelQ7/LBEtT9fEVCTwVRz0xN5/wSLYiZxZxmrbVCOK3EfiieHLa9fAbeJtMBUR+oqDvq4+Sl/eMgFDI/D1jhisFIFOzuK3zgt3d6/yFDOtJUnIe74hnlouKi7H4+KJZPMGSGYaGgu2lwkwASbABBpHQH8x5caNz72YABNoRQL05W/9i0MR6mcvHfNHwtPllo5/FvXV9dRHdHMV0emqPwvkDB+t5fhWljFfaCc+NiFEOtILi0txUMjjHBKvExcyoM5Tix1Hk2S90ofa0CuvsEypktvnbg/BIDFndm4xNonHPs8IB/jcyV1gay1+mItHqc8K6ZsUoaevFEtzYzx/dzfpqM8Q9bSeVPGFNyNPlQSuoe2U9rq2vkLep5tIkkvR9EfFXDQfrZmS6X48uz+c1Mlvlf6PiycCJl7tIxmSfbTmCPEkQ3hCrmzS0PXWNx4Nqs/1UOzjLRMwJAKlJKcgCsvdGNJVY1uZQNMQoGh1cgbb9BkAj+mPIvST1ei7bTdClyyTE1x48TnpoHe68Vb0+HEjev+xDRSRnfbbT0jbtaOKETlnz0rHdN7Fi7K+TOR4okKOYioFsTFya92jl9wqbzKK//A+lCYngqLts3Zvl07rzgsWSgc3RfR3++wrdF62Ena9eiF2xXLpoCdd/dAPV6GTkHohxz+VMrXUjjK2PtukDb8ic/tmOFwzGh63TkRFqeo7FN0cILkZRRIo6uX50kFP2vvkhM89cUwOT8lmI2ZNlw56r5lPos+f/yB01ZfyHEXkFyVXfXKRIu6pmKid9KUpSbAICtZE7tO5jL+30gZu04SMzaNPy326ORK34n1kHdgvj7XfyFa6AZK+/W/xNIKVXn20+/M+EzAEAidjsnD30oPSQT9ByNf89NyQGg56WscbP4YjJjEXM8Z3Fk8QVw30MYR1so1MgAkwASbQ/AQ4kr75GfMMTKBVCbgKHfU1zwyS8jAUweHpZAlTHZGpJqJ+z9Lr6rSX2ky71k++coXTPVU81mkunOekb68Mu/e96+scQzlJtq2Y2Qdlwrufml2kibKnmwMUPUs3GapL2twy0As39fdEgoi6pzZu9ha1RqLo206xpbbt9b3cQS8qdCOgoLgMbnbmNW5uKH3ppsfzt3XBvImhiE8rEDctKuBgbQ5nWzPZpKHrrW88GlSf66HYx1smYEgElMSxwhtlSGazrUyACTQBAd/nX4XjkKFS8qb6cBmHDiLv6EE4jrkZAfOek6dzTp/SRN1fXvURnEaM1ER1m6oj2AvjY2ETEoKKkmLZR3FylxepIsitOgdXmSrhq8/lse2gIUJGRiVxY9Wle9WIeCGtYy/yTBVcipQOdepAiW9PTRirGctu2DUguZ2GlJyw00hYvlR2MRb5qi4tXoS840flMUXCF0VHiWh91Q0AuqFh1aUHgl5bhIRvv0HK2q9ExH8Wkn/4Xkb++z77skaOJ03tZKeBEr78HIHzX5Rj0lt5sYqL1JoXEfDkfCfHulJIGz9r5zaYe/rAXTzNQFHzziNHSqmgjL82IGrBc0gK6QaPadPhNPQq+TSDciOkMCZaDuN5x+R6+yjz8ZYJtHUC9Ptl6YYI/LI7DiG+9lg2qw+GdXGt1exPtkbK4B2SwNHWp6+1MVcyASbABJhAhyXATvoOe+l54R2NADm7SR6lKYutpQlsLVWRaFcyLjmatWVwdCWaVeag9r5C+qa+om+7+sah8xQ1Xz1yXlc/ugniL5Lg6ioNXW994ynzNNX1UMbjLRNoTQLFZaqoUY6kb82rwHMzgdYhYN+nb60OerKmMCpKGuU6/ma5LYyLRfQbr8p9cgqXpCQi8Yd18L7nXlln5ugot3mnTsLlWhFEoE5KTZruVCw8veQ2468/4H6rcD6LfFHxwtmdtmG9rC/LyYXStlzo4NdWso4ckdUBbyxBkdB+z9q9Q0S+l8Jx1Bi4T5hYWxeddRThHvWS6uYDNcr4/RfZ1sxNJTtDTxcEPPs8Yj78QFNPyXEpSa5d7z7CSS9uFJwLR36k6skB5+tUgRMZ+/Yi6ctVsg+9kW5+zs23wk5o1Mui/ptLiV2NRTQ9ReXn7P8XJHFDYys2eTw4Q3MDxNzdAwH/exZe909D0rrvJLOol+YhZcBQBIubBqTlT9ck/6Qqup/m0aePyiB+ZwJtl8DWE0lY8lMEskWerunjAvHImCCdxsaLwKI1W6Ph426D/02sejNQZyc+wQSYABNgAh2SADvpO+Rl50UzASbABJgAE2jbBJRIek4c27avE1vHBJqDQImQatEVfW4dqHKGXXhqFqy69kTBudPShE5z5sO+b3+R3HWmcEavRGlGGnwengVTF1Vka5lIQkvF2FoVsFAgEtI6iWh9h379YSUiwAvOn0XYnZNgZGEpHf0kVUNyLZlb/4Cb2tFeIpLH1laUSHFyblO0OL0aU/Kjo3Fh7myUieh4t8n3wFKs1bprN1j7+opI9yKcGD8alLyVnN+l6Wlyik5PPiMc6g5y3yo4RG5zhOSNtdjPE3I9tCZTNw8UXgyXDvOgdz9ESWqqjHwnhj7/exFu427SaPTnXbwgHfcud9wlnfqR/3uyylKcR1wtj4tTUnBRyA553PcAnEXCXb/Zz8Bz6r2I/3QlMv/ZIiLsv4bPzFkwcXZDWWZGg/pUmZAPmEAbIpAkniB++5cI/HcyGQO7OuPJ8cHoKnJQ1VWW/HZePNFcLnJoBcPbqWkDpuqal88xASbABJiA4RFgJ73hXTO2mAkwASbABJhAuydQUqqKWOVI+nZ/qXmBTEBDwNxblbDe1MFeU1d9x2HwYLjcMRVp67+XDnqSX/EWDmJyuFPpvPQDXPzfU0j79UdY+PpJPXdqY+EfIM/bCFkbiu7O/neXiLa/TyaTDX3/Q8R+shJZ2/6STnq3qdPgNfUe5IaFIeathZpEtMY6kr/aigj2jE2/Ik4kerVZsVLjNJcTireSrEySkYe5Oqpfqa+yFQ2kg15o8WtL1ChtitPS5W6xSExLxXvGTKQLJ752slsa337EdSgVNzl8Zj0mE+mSNBBJ4lAEvt8z82ApHP7oIh4oEDI4lPQ2buki2ISGwq5vPySJcbNExD1F13vffS+MjIyR8suPcj7KEeAwcpS8QUAVFRXl0vEf/erzSBBR/tbde8JIRNwXXIiQ7fPPnZFbG3HjpFhIDVHRt49szG9MoI0RWLMrBiuEw93SGucJ/gAAQABJREFU3BRPCXnLu0UeqvrKwfMZ2HcqBVNH+WN419qlcOobg88zASbABJhAxyFgVCFKx1kur5QJMAEmwASYABMwBAJ3LTmISwk5Mpn0lGH1/xA2hDWxjUyACdRNgKRWikWUt3Qk190UZXl5qCgrreEQp26kr14oEsJaq3XmFb11km2hki+ixSkRq00X4a3Ws5BOvLG5hdS1r9GFHOzPP4ucQ3vlDQDH8ROErIsnCoXkDGnJFyfGyaj/rh99UqOrUkE2Rr7+KrzunabTrugP3kNJYiKC33pH6VZzS5I8QitfKXSDwETYTVH+1Qtp15fm5sFSfXMk68hhWPr4wsLDo0rTtO1/I+bNVxCwcLHQ+1dF0lOD/KgoJK7+UibV1e5gM/AqdBJPMdgEC2kPwYYS55qob3Do1Ud7MN5nAq1M4FRsNt5ZH46ImGyM7OOO2TcHC9nNmv+edJn5w39xuHM4f4/RxYfrmQATYAJMoJIAO+krWfAeE2ACTIAJMAEm0EYI3P7WfsQl52HelK6446pObcQqNoMJMAEmUDuBivIyJK5bJxLIbhW6+Rc0jShq327EtfC6bxosOxmmo45uQOSfOoaev/4pNfs1i1Pv0NqLU1JhJHLymNrZa6Ltq7fTPm5MH+3+vM8EmptAaVkFPth0AT/uiIGTnQVm3dwZEwerclg099w8PhNgAkyACXRMAix30zGvO6+aCTABJsAEmECbJlCslrthTfo2fZnYOCbABNQEjIxN4HX3PfJFeu2FCQkwd3WFpZdw6mlFthsasOK0NOQc/A8uE+6o1UFP66G1V4++r2+djelT35h8ngk0FYF/TiXjHZEYNiOnCOOGemO20J53tjVrquF5HCbABJgAE2ACtRJgJ32tWLiSCbQvAiUiEoSKmYkRSssrpC6qqYh2qs/59eYv4bgQn4uFU7s36LHO5qLX2HU0lz08LhNgAs1HoJQ16ZsPLo/MBJhAsxKgpLe6Et8268TNMHjatq1yVOcxNzbD6DwkE2hbBC5nFuK93y5g94kkeLvbYO7tobhBSNxwYQJMgAkwASbQEgTYSd8SlHkOJtCKBI5eysSjy4/AysIUOxdfg7EL9iA3rxgrHu+PQcFOdVp2NCIDsUl5yCoogS/0116sc9BGnrySdTRySu7GBJhAKxJQIulb0QSemgkwASbQ4Qmk//4bTJxcYdu1W4dnwQDaN4Hv/o3FB7+oEh/fMdJXas9bmFXmd2jfq+fVMQEmwASYQFsgwE76tnAV2AYm0IwEjNXh8pbmJnIW5aumhbmy14yTN+HQ7WUdTYiEh2IC7ZpAaanqCSADVolo19eHF8cEmED7J5AXHi6T3jqO4ij69n+1O+4KT0RlYdnG8zh7KQshvvZ4XGjPXxXq3HGB8MqZABNgAkyg1Qiwk77V0PPETKBlCFipnfMmQuqGirk6IsTSVOW017aiQvjE4tLy4WRrAVvLmue12xYUl4m2BfBxsYIyh/Z5ZZ/GTBSPjpYJmR1qq6tQ1GxaTjGy8kpgI+a2tzaHg3XlnyhlDn3Wocyh2Kcc85YJMAHDIVBSWiaNVW7QGY7lbCkTYAJMoH0QMLKwkAsx9+bk3e3jivIqtAkUl5Tj4y2R+H57tKyeNiYAj43rrN2E95kAE2ACTIAJtCiBSg9Yi07LkzEBJtBSBCxMVBHz5mpnvZnaSV/98U1KkLRgdRhKysqlaSN6166/mCmc6M9+fQonLmRoltBHyOa880AvONpUJlQqEU735X9exM87Y1FGnnpRzIQtowZ64tU7u2n08C9czsOCtWG4GJ+jGU/Z2bP0eqmjT8f6rkPp++K3Yfj7SCJuGOiFN+7prlTzlgkwAQMhQDf2qIj0GVyYABNgAkygFQhY+/sjYOFi2PXu0wqz85RMoPkI/H0yCe8L7fmUjELQ75jHxndG3wCH5puQR2YCTIAJMAEmoAcBdtLrAYmbMAFDJqDI2liYqpz1mq2WxmKC+IL6/Jen5DK7BTqgTCSa3XMyGSa1ZJZ96rMTOBedJdtSQqWE5DzpsKf61U8P1KB64bsz2H0sSR7b25jDzdFCOuI3H0iAjYUJnp0UKqPrZ3xwGAVFpbA0N0XPIHvYiQh6cvCXC8c+JbpVij7rUNrSNi61QB7GpuRrV/M+E2ACBkaAI+kN7IKxuUyACbQfAuJ7oNOIq9vPenglHZ4A/T74aHMk/hGBPPTbY9YtwXjwev8Oz4UBMAEmwASYQNsgwE76tnEd2Aom0GwEHKzNcOd1fggQDnUqU0b6IDo5H/ZWlVHvq3eoHvOkSJJPRUJZKgfOp2P2x8fkvvJ2Li5H46BfPXcwuvrY4ayoe+Ddg7I+PCEXXbxtEZWUr3HQL7y/B27s5ymHOCSi7z/+KxIPqL8MXxY3B8hBT2Xd/CHwcrKU+7W96bMO7X5LH+yFrSeSMaZP7U8EaLflfSbABNouAaNabha2XWvZMibABJgAE2ACTKAtEvh+TyyW/xwBemb4qp5ueOKmzgj2Uv0+aov2sk1MgAkwASbQ8Qiwk77jXXNecQcjYC2i1ufcGqJZ9e1Da+qKRibkyfNX93TVtBsU7CzlaRT5GzpxLkElSePsaCkd9FTXTTjq6Thd6M6fE5I15KQPi1NF2ltZmGoc9NR2kLgJ8NWTA2hXlk7OVrAVUfa5ecW4/91DuK6fO4Z1ccbQUBcR3VI1sa0+61DGpa2bgwXuGemrXcX7TIAJGCAB9tEb4EVjk5kAE2ACTIAJtBECxy9lYvkfFxEWmQkHkXdrxrgATBnm00asYzOYABNgAkyACVQSYCd9JQveYwIdlkBKdpFcexdvOw0D0oH2dLVCbJLKgU8nUrKK5fnOXraadrQT5GktnfQpWapxLmeotiHCgV9XIefbkod64c0fzsl5NuyJA71Iu/6p20IxeVjNGwp1jcfnmAATaH8E2Enf/q4pr4gJMAEmwASYQHMTKCwuw2fbovDt31FyqusHeGK20J6v68nd5raJx2cCTIAJMAEmUBcBdtLXRYfPMYEOQsDJzhyXhXZ7VEoeBoc46Vy1u9CVp3JBSNxol4vxKke+h/p8J2eVbA1FrJQIfXttbXntfrTfP9AR6+cPRbJw8P93Lg3bT6Tg0NlULFsfjvHiyzRF0HNhAkyg4xLgxLEd99rzypkAE2ACTIAJNIbA1hNJ+GjjRSSmF8DTzRozxwaI3xVejRmK+zABJsAEmAATaDECVfUkWmxanogJMIG2RCDIQ6XHuONkikzmSrZl5JaIpLBVk66StI08l1OEkzEqSZvjUVnIEMdUunRSne/p5yCPy0Ty1y/+voRikQi2vuIu5GkmDfHGO9N6ykh66ns8KqO+bjrPk979l9ujQVt9C91Q+OG/OOyPSK/Rpa7xqD31o4S3XJgAE2haApw4tml58mhMgAkwASbABNorgdjUfLy09gxe/vq0dNDfKmRtvn1mMDvo2+sF53UxASbABNoZAY6kb2cXlJfDBBpD4IHr/fDHvngcDU/HmAV70M3PHqcuZoIc5dolVMjcdA9yxBkRIf/wssNwFVr0qUKLnkoPUU/nqfgKmZyxg72w5eBlfLX5Er7ZEoU+oU5yvGQhhbPqsX7wFH1jUwsw86Oj8BSR97ZCvz5TaNPHJOaL6PtymAiNC235HTlwA97mfXUK52OzseNkMtY8M0ivnr8eiMd7IoKfyubXR8LJtjK57rNfn0JETDb+Eclov51TOR7dzHhqZWWC3TuHs8alXrC5ERPQkwA76fUExc2YQDsgkBcRgdK83IavpFzcJC8tQ0VFOcqzs5F/8bzYr4BQ1atzq2mjnlH7743yDagwOgrl+fmgYxpPNahqS1+TSJJL2WqflGOLk8qWusqiS8Ortnp965Sxm3lr06dfo2awCgmFqV3dEojVB7bw8ISlt3f1aj5mAjoJfPdvLD7ecEH+jugsAodm3hiAa3u662zPJ5gAE2ACTIAJtDUC7KRva1eE7WECrUDA19UabzzQEwvXnJFJXEluhpzuFmbG0nGvbdLyGX0w/5tTOHwuXeOgH9jVGYvv76XdDAvu7AZv4Xz/dlu0/LJMNwCUEk+PngonPW0p4Sy9tIunixXmT+kKFzuVvI72OX33QzrZSid9iEhkq2/xF4/DUqGEt7ZWVf880jjkpO9cbTxqR+0Likqh9Nd3Pm7HBJhA7QTyi8o0J2rzUWlO8g4TYAIGSyDr2FHkHD2C7P/+RVH0RYNdR0cyPD/sRKss18K/M2z69IVtvwFw6Ne/wQ7/VjGaJ20xAkdFYthVmyNxIkL1BO5d1/sL7flgiBRXXJgAE2ACTIAJGBQBIxHdoQSKGJThbCwTYALNQ4BkXRyszaQWPDnKKKrM0rzmt1yShkkSznUP4WyvS3OerMzIK0F6TrEcx93eAmamleMViKROqdnFUhLHSujPO9uY1zpfY1abKmR4XBvo6CdbbS1Na10TJcZ1E7I81QuxIFYO1lUd+9Xb8TETYAL6EaB/u+PFUz1Ulj7cF1d3d9GvI7diAkygTRMozclB4o/rkP7bepTlV0bMmzq5wDqkq8r5KqLiLb07wcRadeNc14LyLpzXdUqv+uKEeJQVFOjVtikamVhZwVysq7WLTXBIa5ugc/4y8cRCobgu9HhCaVYmiuLjUZQYV6O946gb4XzjTXDoP6DGOa7oOATou/dnQlbzu7+j5aIpwOjRcUEYFKw7v1bHocMrZQJMgAkwAUMkwE56Q7xqbDMTYAJMgAkwgXZMIEZIYU1etFeu8L1H+mB4V9d2vFpeGhNo/wSqO+dNrGzgMPI6WPkFwKaTD0ysVAnn2z8JXmFDCdCNlIL4OORFXkRe2OkqTnvrHn3g88TTsAkNbeiw3N7ACWw5noRVf10S+bPyZC6r+8f4Y+aYIANfFZvPBJgAE2ACHZ0Ah3129E8Ar58JMAEmwASYQBsjkFdYqrGI5W40KHiHCRgkAXLQRzz1uJS0MXVxh/ukyXASkiXGZZWyVga5MDa6RQjQEwi2IvqfXhhzI3LFExQZu3ciNzwMJL9zce6T8Jz1BNzH39Ii9vAkrUsgJiUfn/8dJfNekSWDu7vhUaE9393XvnUN49mZABNgAkyACTQBAXbSNwFEHoIJMAEmwASYABNoOgL5xVpOelWqxqYbnEdiAkygxQhQIlhyopK0jcf9M+Ay9CpUCKc92EHfYtegvU2kOOyL0tMR/9VnKEq+jPj3FiPvxHEEvvBye1sur0eLwNrdsfjkjwsoKimHvZDHnCai5+8d6afVgneZABNgAkyACRg2AXbSG/b1Y+uZABNgAkyACbQ7AmUiz4NSjDiUXkHBWyZgUAQUBz1E+quA5xbAytlZ5aA3qFWwsW2VgIX4PAXNfQ6Xf12PzP17kLl9M6JtbOD/1Jy2ajLb1UgChy9k4EsRPX8kPF2OcF1/Tzx2YyD83OrOW9HI6bgbE2ACTIAJMIFWI8BO+lZDzxMzASbABJgAE2ACtRMw0lRT8mouTIAJGBYBkriJXvwGTF3dEPD4U6hMF29Y62Br2z4Br0l3wELkNUj6WSQk3vgzbPv2h8s117Z9w9nCegnkFpYJaZtL+H67KjGsu7MVpo8NwKTB3vX25QZMgAkwASbABAyRADvpDfGqsc1MgAkwASbABDoAgdmTQjE4xKkDrJSXyATaF4HYD99HaXIi/GbPZQd9+7q0bXI1zoOHokgkl6WI+vgli2Dp5c3JZNvkldLfqM3HEvH5lijEJuXJTuOv6oTHxgXC1c5C/0G4JRNgAkyACTABAyPATnoDu2BsLhNgAkyACTCB9k6AHPNX93bHPSN92/tSeX1MoN0RSNu1U0qPdJrxOCyFJAkXJtASBCiiviDyokqj/rOVCF2yrCWm5TmamEBUkiox7LbDl+XIgd52mC6058f08WjimXg4JsAEmAATYAJtj4BRhShtzyy2iAkwgaYiUFquSsBoamyK8vIylKMCJsYmIhUjS0g0FeOOOA5/rjriVec1MwEmwATqJ3D6zttgISKZfe+dVn9jbsEEmpAAJZONfPs1OWK3NT/B0ptlUZoQb7MPtWZXDL7YHIWCwhI515Rr/PDYTUGwMjdp9rl5AibABJgAE2ACbYEAS0S2havANjCBZiJwOjEMd351B6atvV/OMO27afL41OXTzTQjD2voBN76523M3/QC8kvydS6FP1c60fAJJsAEmECHJkBR9CWpSXAaOrxDc+DFtw4BSiZr5RckJ0/8dnXrGMGzNpjAwfMZeHTVMaz47bx00HcPcsSyWX0wd2IIO+gbTJM7MAEmwASYgCETYLkbQ756bDsTqIeAiYiep2JmYi63xkaq+3Lm6mNZyW9MQIvAybhjKC4pREmZiGIy0zqhtcufKy0YvMsEmAATYAIaAqk//whTR2fYBgdr6niHCbQkAdex4xD72UfI3rUdpY8+AVM7u5acnudqAIGcglJ89ncUfvhHlRjWxNgI9wlpm0fHdm7AKNyUCTABJsAEmED7IcCR9O3nWvJKmEANAhZqZzzJ21AxNVF5Xc3NVE776h2Ky4qRnJeCi2mRSMi6jOyiXE2TcqGMRY7b8opyTR3tkOyJIn1CxySpIx28Yv9ydhJoTCopeakoLC2S+/Smbztqm1uch7T8dOSIra6ijCdtFDZQqRD/JeSoNC2VYzqvba9sqG5L58rU8kBKfX1bfeatPgbNE50ZLddV/RzZrL0Gimina0H1ugpxjUqPrsJXaauvfTQnvZRSrD6muuq8Gvq5ojHps8CFCTABJsAE2i+BvIgI5IedgMuYce13kbyyNk/ANjgEpvaOKCssQNa+vW3e3o5q4J9HE/HgB4c1DvpB3Vyx8on+7KDvqB8IXjcTYAJMgAlIAhxJzx8EJtCOCZiZqpzyilPVzFjtpK8WSU8O42U730ds2qUaNH54cD1Iz35/zH68+/fb6OXbH6+OWaBpN3X1ndLhrrR7ZetCnIk/CT/XzohJvShvDFwbOhp/n/0LxuJmwQtjX0E/797Qtx1N9OGeFTh8aZ+ck8bwcQ7AQ0Omo6dnD40dS3ctw4HIPfLY1yUQd/a7Eyt2L0dhcT7srBxx3+BpGOY/FPd+M1W2WXXX53CzcdX03x99AEv/XgxPB198dMeHmvr6dvSZd1TwdXIYusnw7s53cSr2qGZYmu/FG16At4OXrAtPjsCLvz+H3oKzqbhOR6P2y3q6wfL0dXNxlViDUnKKsrF4+zs4pyVf1NWrJ+aPehZ2FvaymT729fXug5nfT1eGldtZ62ZUOf7intVwtHSQdfp+rpQB3hXXZu+FXRgWfC3mXvO0Us1bJsAEmAATaEcE8s6Hy9U4dO/ZjlbFSzFEArbiM5i5fw8KIs4BY8Ya4hLarc2RiXn4fHs0tqsTw9pYm+HBMQG4T+jPc2ECTIAJMAEm0NEJcCR9R/8E8PrbNQELUwu5PnMzS/VWFUFvbqKqp0qKjH/h9+elg57a9fTphyFBIzAgYCj6+g+WDnrZWf2mK9K8eg7qUhFBb2tpj1IRiX1CSKgEe3STzvzdF3drDyfO19/Oz8kPoV490MnZT45Bzv9XNr2IS+lRmrH6CbvJZippuSn47shaeDv6ypsFOQWZWPXvCiH7Y4aBgVfJNn+e3Sy3ytveqH1y96rOqvNKfX1bfeZVItEXbVukcdAHuofKGxiJWbF4+a+XxHWoGil/JuGUdNBTuyD3LpIj3USgJx2UsnDrGxoHvYe9yslPDnuqV4o+9pEcEnFR+FFfuhlDx8qLbtQoRZ/PldKWtpezL8vDy9kJ2tW8zwSYABNgAu2IQMGF81LqxsTKqh2tipdiiASsO4dIswsiLxqi+e3W5tU7Y/Dwh0c0DvqRfTzwiYieZwd9u73kvDAmwASYABNoIIFKr0sDO3JzJsAE2j4BO3NbjOs9ET4OnaSxN3UfjzjhMLUzt9EYn5STIqPNqeKD2z+Eu42b5tyV7NzW5w4pk7Jq94e4pfcEEdlthw+SziJNyN5oF33a3dNvKtBP1YtuErwjotEpsn7D6Q14euRT8sQNIaNAr9u/mIh8IdPTq1NfPHvd/+S5O7+eLJ3c5OCe0GOC7Pv3uc2YNvBejSlHYw7J/ZFBIzV1+uzoO29haSHOJ56RTxN8PvVLOIiodJKSmfXjI8jMS8PJy6fQVzxhoBS6ubHgptfRx6uXrHr+zxcRcTkMv576DY8MfVhKEl0SUfdU3pn4Hjq7BOFCaiSe2zAHVB8pnooIEk8U6GNfbnEOnr/+OTnW1G/ukpr0zwiuZGNtRZ/PlXa/50fNx79R/+HqgOHa1bzPBJgAE2AC7YhAwfkImAmZES5MoLUJ2IaEShMKY6Ja2xSeXxA4EJGOL7ZF4cSFDMnDzclKRM/74/ahqt8nDIkJMAEmwASYABNQEWAnPX8SmEA7JmBlZoUZgx7QrPDGLmM1+8qOp707rC1spWN73m9zMSRwGPr59BeSNH1haaqKvFfaNmRrIfqWFpfKLhS5ryStrT6GPu0oyvxY/DHEZMUhrzgX7rYecpjYzNjqw2mOb+1xi2b/uTEvIacwG/ZCAsbbzguONi7SMX484aR0jMdkxmhkcfwcfDT9GrOja94w4aCnEuQWKm5UpMsXHfu7BiMz5iBiRUS9tpOe5G16eVZKBgzyGySd9DFCe55KZLpKmsjR2lk66Kku2DUIdJwp9PsvivPkpK9edNlXvV1dx/p8rrT7uwibJnavvB7a53ifCTABJsAE2geBgrOn4HzdmPaxGF6FQROgpzks3L1QlHwZpTk5nDy2la5mVr5IDLs1Ej/tqvy+Pm6oNx4ZEwgvJ9VTvq1kGk/LBJgAE2ACTKBNEmAnfZu8LGwUE2g5AkYwwnOjn8fKPatA0ivbhQwMvchJPG3oQ7ip640tZ0wtMxWUFOCJ9Y9Lx3P102XqBLHV6+nYx7HS2d5faK5rl/E9b8HaA1/j9zN/SMf4f0KPnsrwoKu1mzVqX9e8lPiWygXxNMG8356pMXau0M7XLq62nuLGhpGmKtils9zPEFH3VFLV4/m6BMhj5c3H2V+ySlefV+qVrS77lPO8ZQJMgAkwASbQWAKW3hwZq7CjAIOihHgUJl5GvnjKoDQrC0bCeUwOZBNzC5g5OcOmS1dYeqgCD5R+vG0aAsSXnPR5gr1D/wFNMyiPojeBP48k4ksRPR+blCf7+Hna4iERPX9jP0+9x+CGTIAJMAEmwAQ6GgF20ne0K87rZQK1EKAErJQsNS0/FYfjjmNf1F6pnf7Vvs9wXedrQJHTtRWSnimvw1FeW5+G1n26/wvpdA7x7I5JvSbBSyRYPZt0Dp8Kjfm6iq2WpE/1dmNCb5BO+uPRB5Ffko8Dl/bKJtd0bpjUTfVx6VjXvJ52qh8l9NTC3YPuq9G1h3v3GnXaFdEi2p8KJcGl4mrtIrdRQuJGu0Srk/+6iqcFaiu67FPaKk885IgnFnTJ3ShtecsEmAATYAJMQJuAibW19mGH3c84dABpO7ajJC1ZMjC1sRV6/S4oTUtBWX4eKkqKVWw2AWbObrAN7QKrgCA49OvfYZk19cLN6YZReFhTD8vj1UMgOiVfRM9HYZs6MSw1nyySws4cGwh7K3Y91IOPTzMBJsAEmEAHJ8D/p+zgHwBePhPQJuBi7YqxoaMxMnA4Hlh7v9RxPyMivwcI+ZsAxwDZNCLxrHTMGxub4J+Lu7S7N8t+eKLqB9bU/lM1+uz/Rv57RXORo7q/SIp6NGo/NpzZJJPmWppbI9RNlWjsigbX0bmLa6g8Q3r5NmL+kYEjdLRUVSfnJCC7MAv2QheekvseVEf7+4okulSChQY9FUqKey45HF1FctmzYkvHVIKca0rdyBP1vDlauSBRRPXvFNf23n5319Nav9OUC2CnSBh8rbgJ0lQ5D5SZM8R6t1/4B0P8BsO3mlQR8QhPjcDYkNFVbjQRz63nt8NB5Em4yl+VbFgZj7dMgAkwASbQcAJZR480vFM77JEXFYXc8LPIPXlc46B3ERJAxlbW0jlfFB+H8pISVJSWojAuShIoSU9Bxn567UH67p1wHD4CTgMHt0M6vKT2TmDt7lis/jsaWTlFcqld/R0wQ0TPX93drb0vndfHBJgAE2ACTKBJCLCTvkkw8iBMwHAJJORcxst/vAhXOw9Ym1sJx3A2EjLjpIOeHPFBamewt4hgV7Tc7/n2HhEN44D03FSZCJWi6ef9/izmXDOnyUGQnEuSSHa7bMe76C8csSm5iaAbBeZmlojPiBHzPocnRzyBn06uR25Rjmb+hVtfk/uPDX8MbjaumnplZ4LQrCcn/frDa2XVIOG0b0x5d9cyveb1svfAdV3HYse5Lfjgn6VYabYCXcXTARVi0pLSYiy66Y0q0xPTR396FCEeXXFRJIIl5z6V20QiYCqBzgEI9ugm5XNeFAyUa0Pn6KkDOk9FX/tkY/E20H8Q/jgZi1+P/oi/z25FoNC5p8/E9CEPoYeYrzHlrb8XIyb1Ig5E7cO7ty5tzBA6+6z472PQExF/hW3CF3d9oWlHSXkXbHpB3lCifAT39b9Hc25v9H589u9H8vjDO1aCPttcmAATYAJMgAk0hoB0zJ8NQ35EOAoTVE+9KeOYWFgi+8QxmNrawdjSUjjrrUREvZPctwoIRHlRoXDe56OiUGwLCmT/xJ++Q/bhQ3AcNgIOvavK9Snj8rZ+Aiy9VD+jpmpx/FImPt8WjUNnU+WQJKV5v3DOzxwbBFPjSunGppqPx2ECTIAJMAEm0F4JsJO+vV5ZXhcT0JNAUk6KlJOhZKPaxVU4lR8Z9iic1PIqdO6hq2ZgmXAwF5cUIl04QacPm4n1R3+Q/WOFzEp6QYb2EJp9E6GtbmJkrDnWtVNbu0eumomi0iKExZ/ArvCt0jk/fdgj+PHo9zL5a6SIlqZ590fuqSK9czL2qJymoLgAsKk5I0n8KElW6ezYWpLq1uxVs6Yh8z4m7PZ28MRPghkxVGykUUm7VluD3tPBF272blJ2iM5TjoAnr31GJr6lYyovi4S4S3Ysxem4Y5IF1fX06Yd51/2PdmVpiH3U4V7xxEJRaSF2hG+TUfmKjZfSoxrtpA8QUf3kpPdvZHS/aiW1vwcIDX5y0vuKrXYxE7ycRMLatNwU+GrlJ6A2lDyYCjG1t7KX+/zGBJgAE2ACTKAhBHLCwpCxb4/QPD8LEbEAK58AuIweB5vgUJja28NMvIzNzBoyJNL370Xm3v+Qf+m8fBXEXA/Pm29t0BjcWEWApZea/5NQVl6BT7ZE4lvhoC8T32Op9A91wsM3BqF/oEqesfmt4BmYABNgAkyACbQfAkYVorSf5fBKmAATaAyBQuEETy9Il9HzFqaWQovcEZam5rUORVIhiblJ8LRxl1H0ucV50rlsYWIOE+Pmu+9H81LyVSUqvinmPRBzCO9sWwQPey98PHllrettrkrSwqf1mAtubkJfnp5aoELSNRQZT056yhNAiXNJH74umZhSkRsgJTcN7rYuTXYNKJI/QTy1QDcP7IUsjKOQ3bmSQjdSnK2crmQInX2Jo4twyFcv9JnJElH02jealDbE39jIVOfnXGnHWybABJgAE6ifAMndRM6bDd+HH4dtcPNJx9VvSfO3yIuMFHrzfyMv4gxMrW3hfO1o2PfrJ5zyV/b/SW3LE35ah6zD+2WVw6Cr4H3HndqneV8PArkXziP2s48QtGQ5J47Vg1dDm+w8nYIvRGLYiJhs2dXK0gzTbvDHg9dXDZpo6LjcngkwASbABJhARybQfB61jkyV184EDIyApamFJrq4PtMpsagSiUxt60tEWt94+p6neRUH/ZXOSw7aHUJz/Zv9X8rp7xzQNNrr+q6F2lmbWcPaof4Ee5S0V1fiXmU+U3FzhOR0mrLQTQMfe5F0rYlKcznoybzaHPRUT5+Z2hz0dI74c2ECTIAJMAEm0BAC6Xv+RfJfG6SmvF3v/nAdPRaWHk37/1+yx3vyXUIKpwg5p44h69A+lAs5HJ97pzXEVG7LBJqFQKrQm/9k8yVs3BuvGX94b3fMvCEAXX3sNHW8wwSYABNgAkyACTScADvpG86MezABJmCgBAqF9vsjP8xAroiuVsqEfpNxTdDVyiFvmQATYAJMgAkwASZQg0DWyRNI+v1nWe84eDi8bp9co01TVnjcOhFmrm5I37FVOuuT/+kE9+tHN+UUPBYTaBCBXw8k4OutUUhML5D9nBwsMH1MIKYMa7qgjgYZxI2ZABNgAkyACbQzAuykb2cXlJfDBJiAbgIWpmbSQW9tYQs/l0CM734ThvlfpbtDK5yxFHJDdiIPgAcnM20F+jwlE2ACTIAJMIHaCWTs2S1P2IR0a3YHPU1E8jkeN96Eopho5F0MR9buHbDv2RuW7u61G8i1TKCZCIQn5OLzrZew+0SyZoaxg70w44ZA+Llaaep4hwkwASbABJgAE7gyAuykvzJ+3JsJMAEDImAEI/z80G9t2mJKhPr13V+3aRvZOCbABJgAE2ACHYlA+sH9KIi+CHM3T3g2cwR9da5+Mx/FpeXLUBgfjbR/tqHTXfdUb8LHTKDZCHz5TzTWCAd9flGZnMPb3QYPCe35mwd6NducPDATYAJMgAkwgY5KgJ30HfXK87qZABNgAkyACTABJsAEmAATqJdA1r69so3jkGEwd6qZqLzeAa6wgdeddyN25XJkHzsE+z79YNet+xWO2BG6G3WERTbbGvdHpOPL7VE4EZGhmePWEZ3w6NjOcLY109TxDhNgAkyACTABJtB0BNhJ33QseSQmwASYABNgAkyACTABJsAE2hGBgrhYFCbEwMzZFU5DhrbKyig5rXXX7tJJnxcRzk56fa6CKf/M1QdT9TYlZRX48M8L+OGfGM2pEF97PDTGH9f1ZKklDRTeYQJMgAkwASbQDAT420szQOUhmQATYAJMgAkwASbABJhARyGQfexou11qzpkwuTb7AYNhbG7eauu07d5T5aS/ENFqNhjSxEamJoZkbpuwdfOxRHy1LRpRl3M19kwd5Y9ZIjmspTnz1EDhHSbABJgAE2ACzUSAnfTNBJaHZQJMgAkwASbABJgAE2ACTMCwCeSdPQMTc8tWi6JX6Nn37IVkR2cUJ19GcUZ6q8juKLYYwtbYrPVuqBgCH20bkzIL8dHmSGw5cFlT3buzIx4eG4TBIU6aOt5hAkyACTABJsAEmpcAO+mbly+PzgSYABNgAkyACTABJsAEmICBEiCpG6uAYJjZ2bfqCoyMjWEdEITs4+koy8sDWkEbv1UBNHDyCpa70YvY93ti8Y2Ink/PLpLtzc1NMU0khp0xOkCv/tyICTABJsAEmAATaDoC7KRvOpY8EhNokwTKS0ulXcbix0pFeZl4VcDYRDyyalR7Qq38ixeQtP4nFEVfQkVREcy8OyH49TcbtbbywgJcWPASjMUPy6DXFlV5TDz6g/dQkpqqGddGPMbtNfVuzTHvtG0CDf1cte3VsHVMgAkwASbABHQTsPT1032yBc9YevsIJ/1hmNq37g2DFlxy46cS3z256CZwNi4HK/+6hANnUjSNhnR3E9HzAejlx58vDRTeYQJMgAkwASbQggTYSd+CsHkqJtDSBHLDz+H8Yw/BzM0TPdf9jDP33Y3ixDgEL/8Edj161jAn6+BBRD7/jKbexMoa5SUlmuOG7uSEhSHvyH7ZTSY6E49qKyX/5AkURl1QDnnbRghEvbcUpeIx+qAXXoaxlVWtVjX0c1XrIFzJBJgAE2ACTKCNEygrKJAWtnYUvYLJolMnlT32DkoVb3UQMDY303GGq1dticS3Inq+pKxcwrC3MceDYwNx99U+DIcJMAEmwASYABNoRQLspG9F+Dw1E2huAkbGqiRPGmcrRdCLYmxhWevUcSvel/WON9wEr3vvh6WPL1Cu+gJfa4d6Ku1694HTLbfJSHrb7t2rtO72xWp5nH3iOC7OebzKOT5oPQLZe/9FWUaquDkzX6eTvqGfq9ZbTcebuaysDMXFxbDScYPlSomcPXsWH3/8MZYsWQJLy9r/jlzpHK3V//fffwet79lnn20tE3heJmDwBIrT08QaQgx+HcoCCuLj5K6xrY1S1Sa2BbFCgqeNRPe3CSC1GFFhwj9zq2P572waPt1yCeeiszSnrh/giZljAhDo3rY+4xoDeYcJMAEmwASYQAciwM8BdqCLzUvteASMzFVJs4zUybOM1U41I7Oa0UUlWZkojo+WkHwfe1LloKcjrceFSQO1OF2thVoHTpLVUSLw/R6fDZ9Hn4Di2K2jW+2nxE0COVa1mwUkt6JIrlBHZU5qS/uyVFSgKClJtV/LO7UtjItVabvWcl6fqiafV9isvYZyEcVXlJgoFlih05xyIUtUGBsL2lYv+tpHc9JLKcqx3Kolk5RzDflcKX14e2UESsU1KNG6PrWNViE+I6NHj0bXrl1x/PhxnDp1qrZmV1RHY27cuBGHDx++onHaYud//vkHH330EQrUkbPaNhaJf1t0ngsTYAJ1EyjJyKi7gYGeNbVuGw7M8kLV/+d1fyMwUMDNYLaROjClGYY2uCELS8rx1i8RmPPpcY2D3tPFCvPv6oa37u3BDnqDu6JsMBNgAkyACbRXAhxi0F6vLK+LCQgCpENPpbpz3rgWJ31xosqZbdm5i06t09hPViJj069yTJLCsezRB97TZ8C2S1dZp7ydf2Y28k4fVw7ltvfGrTCxafiP3CzhDCQJHsfR4xD4/EuaMU9PHIeygnz02bJLrjPmww+QvvFned5mwFB4TLkLsUsXoyQlERZ+gfB48GG4jLxGnqebDTHLlyHz778049n07Av/51+Ghaenpk6fnaaeN+/iRUQ8Mg30NIORqRky/togzTC1d4Tfi6/CYeAgjVmlOTmIeW8JsnZv19Q5jBwFvznzYGpnJ+v0sc9OyBCFTb5FMwbthE25tcpxr1//FJ8L1eP1DflcVRmEDxpFoLCwEOPGjZPO4507d+qMYD9//jwiIyPlHORMX7ZsGY4ePdqoOXV1KlffLIuKikLnzp2RnJwMU/F3JiAgADaN+Peta57WqKenEKgQQ1pLhnA20tMCISEhSBX5Mx588EHs2LEDQUFBrWEez8kEDIOAjnw3hmG8bivLxd/htlAKYqKkGSY6nohsCza2GRu0gkzajE2tYMjvhy/ji61RuJySr5l9/FBvzLoxCO4OFpo63mECTIAJMAEmwARanwA76Vv/GrAFTKDZCBgrkfQWqi/hirPeRF1PE8todBGBWyoc3lTMnF2qRFTLJLPqHzqWfv6wH3EdynJzkCcSl+Ud3ofz4tXly+9g7e8v+9Ob/YhrRMJZla5l5tY/NPVXslNRLZpbM5Y6wty2Tz9UlJYh48/fUCIeT0/6bg0sO4fA3D9Q2hn/zhtwGjZcOvQvLV6EnL275BB2g4ej4JzQzhc3FSIXvIBuqz6v8vSAZh4dO801b+6BvSjNzgTZR08z5Oz/F5HPPY0e636DuZubtCbqzdeRc/A/uW8tbpjkh52QDvso4UwIfusdWa+Pfd3X/Qqn8ZNktD7xo0I3RYzMK3+8GWk9Nq7P50oOwm9NQiAhIUHjfCcpG10yM7/+qrqBNmPGDDg7OyMtLQ3U19vb+4rsIGf1oUOHEBcXh23btsmxXnzxRdBLKbNnz8bcuXOVQ4PZ0s0GuqFBa6OnD6jcdNNNVexfu3atdNRT5enTp9lJX4UOHzCBqgSM2qmTvvByAhz69a+62FY4KhIyN1RMrGvPGdMKJrXZKY2MOvYD4/HpBfhwUyR2HBVPY6pLoLcdpo/xx5g+HkoVb5kAE2ACTIAJMIE2RICd9G3oYrApTKCpCVA0tceDj8DCu5Mc2uWmW2DbbwBMbG3lMUmZnLjx2irT5hzaW6Uu4LW34TR8hGzjecdkgF6ikIxK9PvLZGR96p+/w09I2ijFc/IUZRen9u+RzmZNRTPtuFx7HehFTmZKjms7eAj8n5ojZzs1aby0oURo5ZYVFEoHPT0J0P27n+VTA8QhfNYMFF4MR86ZMFBkub6lueYlB712gt+Lr7yE7D07kLLpd3R6YDryhXNRcdB3+WItrEUkc8GlSJybcZ+sz4+OljdO9LGvLD8PAXP+J5ecvW+P1KT3FTJFpvb2tWKo73NVayeubDQBYz2iAfPE0yHffPONnGP69OmgqHoqTeGknzVrFvbv3y/HU95sxd+Qq6++Gr1790Z3kW9i4MCByqkm38bHx+Pff//VrIUkfVxdXWvMk5KSIqPfKdKdovvrK8TsmmtUT9dot/Xy8sKIESPk2rp06YIBAwZoJHBihayUvoVuAFChpwy4MIGOQqCinTnpTdQygcWJl1v1EmYdO4qEdd/A2NxS2sGR9HpcDlNVHiY9Wra7Jqt3RGP139HIyy/RrG3KdX6YeUMg7Kzq//+jphPvMAEmwASYABNgAi1KgP8v3aK4eTIm0LIEKHLeWySAVYrLqNHKrtwamRjD6aaJcr84KRF5R/bDxMkV9lepnPJ0wtxDS/5FSF1ki6jTwrgYlOcXiHOqSJxi4RBua8V1fKVci//Ct4TufC5MbWyRKxJDUrHuNwjFIkKYXlQsQ7uiMOoCioRDsCFOetlZ662p5iV5G7tulcl27cRNB3LSF0VHydkK1FvLgGDpoKdKq8Ag0DGtoyDqUpWnG2Qn8abLPuW8Ptv6Plf6jMFt9CfgL55ScXFxkZHxSq99+/bhhRdewIoVK9CjRw+pl56bmytlcTp16oRo9b9JioJXCkXhk/O6utM/UeQ8oKSp6SLfhKOjI2644YYq0eLBwcHS6U+OayobNmzAyy+/jLvuuksZusFbks3Zvn27jGI3EbrBfn5+mDBhQg3baB2TJk2qsna6QbBo0SJMnKj620V2L168GD/88IO0g86//vrruO222+q0izh069YNpDc/bNgwnDt3TmrtU+Q8SfloF3P100faPEkeh9ZhVk0+jJLPvvrqq5obG3T9Vq5cKa+T9pi8zwTaIwEjPW4qGtK6rXx8QTf1CxMTWtVsctBTKS8uhKmDE2rLLdSqBra1yUmPvo5cPm3N3Kay51RMNj7+8yKOhqdrhuwW6CASwwZhWFdnTR3vMAEmwASYABNgAm2TADvp2+Z1YauYQIsQoGSuAXPnybmyT57EReGktw7poqnTNoL0WMMff0Q6gLXrab+8rLR6VasfW4poWKXYi2hfpVA0PRWSuwlXS94o52hbKpz5V1Kaal4Lv4AqsjtWAYHSrJK01Cpby+CQKuZadlY56UsyKn+gaTfQZZ92G95vWwRIPoIiuvfu3asxbNWqVVICZ/fu3dL5qzio7733XtlGcSqTY57KunXr8Nxzz0nn+/r166XTn+ppzKlTp9KuLOTgJie1tlOZHOL0onLs2DHppM/Pr9S2lSca8EZO8SeffBJbtmyRvWhOusEQFhYmJXS05TLeffdd6aCn6Hly4lM7su+pp56SzvHhw4fj7rvvBjnGyeFOUe/ffvstnnnmGYwZMwY0tq5iZWWFzZs3a05/8skn0klfW+JYJTKfbKdCHB566P/s3Qd81OX9B/BP9l32JAkJAcLeG0QRxYngqAP3FuvetbV/rbXWtlatilZrtYgTJ4IoiHuhshEZMsJKSMjee/6f73P5HZdBuJDL5cbn6etyv/uNZ7x/kcL399z3uU4H+F9++WVMmTJF75cxXHjhhbqfJ510EqSNZcuW4cEHH8SiRYv0OfxBAQq4l4CpTz9U7NyGOpXqLyA0rEc6H33iqSj85nPdtn9Iz/ShRwZ+lI3qRWMPlybxKOt09cue/jgNb315aNKMn/q7w5Wn91MB+v7w9bBvuLj6vWD/KEABClCAAkcrwCD90crxOgp4mUDWKy/rAH3YsScg7tzzEaRm2Ffs2oH0v/6pixI+lutVyhl7i6TakUVjOyqHW6Q2qJdl9n9gQjISrr2+TRUhQ1sugtvmhCPs6K52a1TObCl+EZH6PSDGku6jaud2/dn4UaXuiRRZW6C9crj+GefKorCyfGa9CoYeLt2NcS7fnSeQ0Lygsczk3rt3L7755hvdeHBwMH5RD9gkHYykaZk6dareH9S8DoUE0x9//HE9414OyKKo8llmnq9Zs0YH6OU6CcLPmDFDz6iX/PISBJcgusxyty1GPvyjDdLLzPPf/va3uv/yQOHOO+9EnFpj4ZhjjsFLL72kg+yySK6UehVgkVn78i2CZ555xrow7UUXXaTzx0uu/UceeUQH6GUm/IsvvqjT0qxevVrP/Jd0Nh0F6W3HJdvG2NoL0hsOcky+ASAphYxy33336Vz9MrabbrpJB+hlXLfeeiu2bdumg/Qy25+FAt4g0OSBgwxOHaCD9KVq3YqYacf3yAgjp0xF0Xdf6VSD1VnpqFJr75iTknukL27RqPq7jPo/Ebfoalc7+d22ArygZs/vziyzVjVxWCyuP7Uvxva3/J3ReoAbFKAABShAAQq4tIB3r6jj0reGnaOAawlUbNuiOxR/8aWIGD8BJpVSoy7fMiu9Kz31V+k1pFTv22NZxLZVZSaVBkNKxYa1+h+nsl2oZg8fbQkZZJl5LnnrfVWAU1IA2b5M3fSP3s62W717p8qjX2oZpgr+la6x5AQ39e2n9xkz62vS96IiLU3vq1B5yOWzFHPzefpDJ34Y6Y2Kf1jZiascf6osaJz/2aeQb3i0LrUq93jOh4sh761L2ZbN+jpZZ8CTiiwEK2X37t061YwxtjC17sSzzz6rP0rw2wgmGzO/5ZikxJFZ5kuWLNEB77feegt1ykcC3FJk5vnJJ5+sU8188MEHep/ktF+6dKnetv0hgXMpkurFKJKSZufOncbHDt9XrlypA/Qy+10eDEiAXmalHzxoyff8j3/8QwfnpRIjtYzMjg8JCbHWK2OUfTKr/r333tP75RsBI0eOxKRJk3SA/swzz0R8czou64VH2DDGZrzL6evWrbP2QwL+0n8J0Mv2q6++CpktLw8+ZPFZmSlvpBl6+umnIWmCzj77bN2qzLpnoYA3CHjijN2QQYNVeplAlG3a2GO3sGTdGsvfgZpnRGcvfr/H+uIWDbd6wOwWfe5kJ8urG/CXd37FvS/9bA3Qh5gCcPPZA/Hcb8cwQN9JT55OAQpQgAIUcAUBNc2AhQIUoMCRBYL6paJy6yZkPPGozlmvc9irwLnksK/+dQvS7r8PyTffqgLtDch56w1rhbIAqpT9Tz6uc6gGqiB40hVXWY9L+hXJ91qXl43t118N88DBqN6/D73n3oiIyZMRpGYQmwYM0Yu6br7wPASqmfC1KhAt18hsemk35Y67kLt4ERpU0M4oex+1pOdIuv5GBKqZuEaR6+MuuQp5b72KfQ/ciwzV/7CJk3Xu0iaVGiT1zw8bp9r1nv7ved3Sroxt+2+vQciYcahUgWd5qCAl7qxz9HuwynMt32qQtD07b7jKaiQHZb8cl2Jv//TJ6kfo5Kmo2PIzDr4wD4XLl8I8ZBgaiouReM1chKiUK84qhV9/hYx/Wu7FqMWftJjVn/6vxyALHJf8+AMG//MJa5fkoUbaHTdaP8eedrp12903JFe8FMlDLwFtmf0u7zLT/Bs1q15mm9umralW6amkSNB4kHowtXjxYp16RfK4z58/X8/w3rRpE0488UQdTJbAtKS4kbokAC0B8H/+85+YPn26NTWO1Gf0o6SkRD7qIgHoPn36YMGCBcauw74bC9pKShgpsriqzNqXIu1KfyV9jDxwKCuzzAq0DZrrE5t/GOl/JFgu1y1fvlwH+CUtjjwE6GwxHoSUNj8cy8/Px/nnn48HHngAc+fO1SbiIkUeYEjeekl/89VXX+lg/tq1a/Wxr7/+Gu+++65OJRQVFYVrrrlGO+uD/EEBDxdo8sDxSV766ONnoOCrT1GtHg6b1MNFZ5ZG9XeTEvX3LSmhQ0eqtXXU2kAZ+5D72Qr0Om2mM7viNm1JOkdPLotXZ+F/K/aqh+vN30ZVg50+Jh7Xn94PgxMPn+bNk004NgpQgAIUoIAnCDBI7wl3kWOggAMEZBFZXQ6z6FvSdXPRVFONsh++Rd47r+vgfNId9yB34RsqgJ6PslXfo+6Sy9RMryYUf768TY9KmnOphowcC9gE6X0DA5H0+weQ+dgjeha4MRO8rvRQEFAC9vsfvh8NRfmora5E0l1/UO2+jga1QKosdlunUoAUr/i4RQocow+JNm0ZnUq+di4CVfA/+5X5uk7jXH288aEWueCNaw733l3tilNAQiKKv/hENy0LySbf9yeVZsiSrkd29r/vfqTPM6P4yxX6IYbsizx5pnpocbds6tKZ/skFCRdepBamq0HR0g9a3I/Ik05xapA+SAWhpQTEJehvPOgPzT9MaoFcCdKb+1vy9BvH/EJD9PnywCdIpULxpCJpbaRIYF4C65JiZebMmTqoLvsl57nkPzdKbm6usQnJX28ckzztEqSXlDDjx4/X10u+d8ldL4HuMWPG4JVXXtGpZ55//nm9aOu8efMwbtw4XZ/MfJciM8wlYC3Bagm8n3XWWXr/kX5Ibn0p8rBgsnoIJyl3pEgKHknVIwF2mWEv/ZdtKRLIb6/ItwqkyNiuuuoq/WrvPHv39erVS58qfZL0O4899pj+LDP0bdPVPPXUU9aFZSUnvhSZYZ+enq63ZQa/3B8WCnQkkFNSg/iIoI5OcctjtmtKuOUADtPp2Bkno3yr+qbWimVIvuLqw5zVPbuL1q5BfVEBQgYNQ5+rr0P6awtQoSZNFKj/7w9RExtCUlO7p2F3rtW/+e+07jyGdvqekV+FJ5fuwo+b8/DQFSPw0OtbERtpwjWn9cMFU5PauYK7KEABClCAAhRwJwGfJlXcqcPsKwUo0MMCKvVKrQqKG7PTG1TeZwnw+6hge5dmLql6a1RgTuryV3nXJXjfosjx/DwExsbqdhzVbqPKMV2r8kVLewEx0V0bQ4sOd/zhcO1K6hqZGS9B+sHznoMs2FuvZhQHdjBzT1LD1KsFcQOabTpu2b6jkve/JjtHf8PAPyxUzWSPsO9CB54lM+MlJZHkyW9d5J4FNqeAsT2m1yuorIK/mpXtSUWC4bLYaqoKxkhKGpmhLTPfJbe8BLMlkG4bHJMguswCf/jhh9sEr+fMmYN+/frphU9lxroE56VIjniZqS+pZeSvBn/+8591SheZ4S7pXHybH+DJwwFZqNUoMqv/888/h6TesadIn6XvUmQ8f/nLX/SMffm8fft2PQu+oKBAz2CXlDzy4KC91DsfffSRzvsu3xSQtDdiYlsksC7jkG8Z2FNk5ryk0bEtp59+us51L7Pr5eHIxIkT9WfbcyS1zWuvvYZZs2bh9ddfxwUXXKC/hWCkHJJzJV99Tk4OwsPDW6Tusa2H294jMPff67F5dzH6qRmvEwdFYXT/CJw0qhcC/A7NinU3jYz5LyF/4SuImXk2es04yd26b1d/SzauR9bbryNy8nFIPH+OXdd09aSybVuR+cbLaFIpxlJuuF0H5KvVn1UZLz6H+pIiBCX2Qeqd93S1GY+73k99a7JS/Z1xz723I/XxZ3SaRncf5IKv9mPBij0Y2Cccc1VQ/q4XfsbpkxNxw2mpSIoxufvw2H8KUIACFKAABZQAg/T8NaAABSjgQgKtg/Qu1DV2pYcFZMZ6X5XGKNDmAZaknZHguBFAt+1isUpTFBER0SJ4L8czMzMhx0aMGKGDx3kqfYMEsm2DykY9EsCXFDdSj1F+Vosn3n+/+maLChqdd955kNQ1Rhoc45wjvcuirjJ7v3VgXa6T/ZLnfahaxFnakkVwJad+6yIBeHnI8Nlnn+lUOVdccQVkMdkdO3boVDNSh8zWN/LWt76+vc/yAERm0Es+eXnIId8QMFykz2IfEBDQ4lJZRHfDhg26j+eee65+6CEPDuTbAjLLX9IKyUx7efBw9913W9P7tKiEH7xKoAUjynIAAEAASURBVKSyHt9vy8MP2wux+tcCVFTWoU98CGZNSsTZExMQ64Yz7I0gfewZZyPuRM8M0ssv6cEP3kfx6pVIvup6hA0fYffv7d5nn0aUWnQ2clzLB4EdVVChvi2U+br6xp9Kf9f74isRMW689fSSTT8jSz0UkRI1/WQkzLbv20z6Ai/44a/+v+DgJ8v1gyN3D9Jv2luMpz9Kw7a9Jfjv7RNwwzPr9Z8X16lA/RnjE7zgbnKIFKAABShAAe8RYJDee+41R0oBCriBAIP0bnCT2EWXEZB89ZILXxbF3bLFsri1dE5m/8+ePRu33HKLfrDhrA7Lww9ZrHfZsmXWxXClbfnGgCwiK7nt7f3GgbP6zHZ6VkAC9t9szcPXv+Thpy15CAkOwJRhMThuaDSOHx6HiOC23yTq2R6337oRpPfkmfTGyDPffhOlG9ci/jdzED3VkvLKONbee4Nau2Lng39QKeNGIOXa69s7pc2+qswDOPDqfD1bvnWA3jg5e9lHKPruS/0x+ZobEDa07cNM41xve/eUIP2/luzCu9+mY8b4eEwZEo1H3/oV507vo2bP90dUSMsHxt52jzleClCAAhSggCcKuMff/D1RnmOiAAUo0I6An5o1HJTSHwG9k9s5yl0UoICtgMxyv/766/VL0slIXvgEtd5EUlJSu98usL22O7blGwV/+tOf9Eu++SB5+2VR3ViVioqFAu0JSBD+HDWDXl5pByvwxS85OmD/1frsFgH7E0f2QqjJ9RfDtE271d54PWFf0sWXoVEF3nOWvIeqAwf0LHa/5nVD2htfVUa63h08YGB7h9vsK/hhJQq/+QJN6iHk4QL0cpHMnq/NykRF2nbkfrREpcIZ0DZVYJvavWSHmy8c+/WWXPz74z3IyqnAkgePw28e/gGZKh/9Y3NH44QRzl242Et+YzhMClCAAhSggEsIMEjvEreBnaAABShgETCpgN7wBW+QgwIU6KSALNgqL1cpkvKGhQKdERiYGIKBiam48fRUPbt+6ZpsSLBeXv8O243jRsZixqg4TFMz7V2tNJaX6S55y0JXfa66FtkfL0XR91+hOn0fIiZOQZRas8IvpO16KOU7tmubkIGH/zOhQa0/U/LLzyj56UdUZ6Ujcso0RJ8wA0FHWFOjlwrUZ8zPRG1+DnKXf4yE35znar8aPdMfH/dc36GkshaPL07D5+sO4uKT+iJ4fC8doL/8lH7qzwU1gcNDF8TtmV8StkoBClCAAhRwPQEG6V3vnrBHFKAABShAAQpQwGkCElhtbGyCvKtU/5aiNozPjbKtPhif9RGbz3JcDh463oimRks1+trmei3Hm+vS51su0vvV+VKv0Y7tdVJTk7V/lnOken2+vk5/aN6jxiL9bd4v9bljCQ70x8XTkjFOLSr746/52LCzCB//lKlfCdFmjEyNxFh1bM6xSS4xvOq0Xbof3jCT3gBPOPNslZd+pAqOL0Xe8iU69UzYmAkqYD8J5t6H7kv1vr3wCQiEOantN+TKlVvJ+rWo3L4N9ZXlCB0+Giln/UYvEGu009G7SbUTe8ZZyH5vIYp++g5mNZs+YvSYji7xjmNuGKR/X/33/cLyPfBXff/4oWk486GVGDM4Cv++ZTwmDWy5MLp33ESOkgIUoAAFKOB9AgzSe98954gpQAEKUIACFKAAdmdX4A+vbEaGSqnA4j4C2YVVkNcXarbtiyqod9kpKbj6xL4uMQA3fSZy1HYhar2J/rfeicKffkD5tq0o+uEb/QqIjkNQXC/4R0WjOnM/ghKSIQH52oJ8Nes9H/WFBWrG/AHUFebDPywc5kFDEDZqDCJGje50X6ImTkZ1RgaKV32vHxgEqz4FhIZ1uh6PusDXfWbS78upVLPnd2DdjkLccd5gpGWV44K/r8Jvz0zFdSf396jbwsFQgAIUoAAFKNCxAIP0HfvwKAUoQAEKUMBrBHJzcyF53qOjo71mzN480AEJIThpjOQ3jsOrn+3zZgq3HXtpRS0WfZ/pMkF69wmNOvaWywKy8qqvKEfFzp0o3bQRNbk5qNi9A00NDTqFTcZLz8FH5UoPjEuAr1p/JnzMeJj79YekwfFVf+52pSSccy5qDmahav9u5H2yDL3nXNyV6nitkwT+98U+/G/ZHgxIDsPLd03CtU+txbGjeuE/t47D8D7hTuoFm6EABShAAQpQwFUEuvY3QlcZBftBAQpQgAIUoECXBR566CGUlpbijTfe6HJdrMA9BG4+YwB+t2AzfNT/fH0Bfz+15eOrgok+8FOf/dROmZTqo/ZLGgY5x1cdUB/VMTlH9qmXWtNUzpX9vvJuvVZd56+OKw5dh1wj19rUIfsDpG71rutTx/2b69V16m11jTrHT53jq96ln2p3i3qkH9Kuv/RZ2lfjsJyv2tZ9l7GpbelL80t26G1Vn7FPvzfvlz5JkaOW/c3Xqvrls7RpOSpnqS310XhJPy3XWN71cTlHXesrP+Rc+Z9+t1wn50idlusOHd+dXY4vf8nDajXbdldGKaLCgnD2sUmYkBqBKYNd56Gat82kl/tlW/xVTvqIceP1S/Ybeevjz78YwSn9YOqmdTN81C98nEq/kzn/BZSsWwVT/1REqxn23lvkvyDXLRv2FuNfH+xC2oFS/P2aUViy6iDu+d8vuPP8IbhEpblioQAFKEABClDAOwUYpPfO+85RU4ACFKCAmwpkZWXhvffeQ0REBC677DIEBAQ4bCR1dXXIzs52WH3uWNHevXvxxRdf4IorroDJZHLHIXS6z0+oIBGL6wlsVIG8n7YXYM2uIvy6twQmlad+0tBonDk5ESerBWTjIoJcrtM1Gftdrk892aGqfXvUwyk/RE+a0u3dCEnpi9jTZyPnw/dRsGIZQvoPOOLCs93eqR5qwEee1rloeXzxTrz/XQYmD4/DY3NH4/cqOH/yhAS8ePt4pMQGu2iv2S0KUIACFKAABZwhwCC9M5TZBgUoQAEKUMABAg0qbYIE5vfs2aNr++CDD/Dss8+ib1/H5KNubGxEbW2tA3rqvlXIA5DnnnsOw4YNw7Rp09x3IOy52wnU1jfixx0FWKUWiZX81LJWgATmp42Ow+UnpmDqkBiEBKmvLLhwaayqcuHeObdrtUWFKlf8PgT3H+S0hqOPnaZS62ShZO2PKu3Nx0i+/Cqnte1SDck3VVysfL4pB898uBv5xVU6tc3D7/yKJxfvwv2XDsfZkxJdrLfsDgUoQAEKUIACPSHAIH1PqLNNClCAAhSgwFEIbN++XQfoBw0ahDPOOAPPPPMMZs2ahXfffRcjRow4ihpbXlJfX4/AwMCWO73s065du/SIKyoct5iqfEPhhRdewP79+5GXl4eioiLExcVhzJgxuPbaaxEaGuplyhyurcBXm3NVKpt8rNtZiOLSGn1o4pBozDk+GTPHJSAi2H3+ut7U5O0Jbw7d2YrdafqDecDAQzudsBU/+2xUp+9H2eaNKPhhAGKO88KHjS6U7aaovA6PLdmJr9ZnY/bUJIxMCde55yVd1W9P6++S34hxwq8pm6AABShAAQpQoB0B9/lbfzud5y4KUIACFKCANwlIqhspDz74IKZPn45TTz0VV199NS688MJOB+pl1vxOtcDh0KFDrYTV1dU6jY51hxdu7NixQ4+6pKTEYaOXup544ok29UlanVdeeUXfu4EDnRvIa9MZ7nC6wNdb8vDWtxnYlFak2w4NDsA5Kh/1rPHxGNs/0un9YYOOFajcs1tXGJLq3P+2/cwmxM2chQOvvoTCL1YgODUV5sTejh2ci9cma1C4Qnn3x0w892Eags1+eP33U3DLcxuxPb0Uf7tmJE4ZHe8KXWQfKEABClCAAhRwIQEG6V3oZrArFKAABShAgY4EZBa2lLCwMP0+evRovP/++zjrrLN0DvUff/zR7jzqTz75pE6V87vf/Q633Xabrk9S3YSHh+ttb/whM95ltrsUR36jIDY2Vue5l3RFffr0QUhICAoKCnDRRRdBZu6vWbMGDNJ7z2+c5Jh//pPdOs+8jHqwmll7ytheKjif4Pazal0lOOoKv01Ve1VaMhUsDhkwwOndCRs+AlHTT0bRd18i/5Nl6HPt9U7vQ8822LNB+rSD5fjXh7uwQaWtuur0VLWgdROueGw1Lj6pL25Qs+eDXTxtVc/eO7ZOAQpQgAIU8F4B10vY5733giOnAAUoQAEKdCgg6Whal1Q1S/LPf/6zDvoaM+1bn9Pe5/Hjx+s0KzLDe+vWrfoUCdKbzeb2TveKfcZDEBlsdHS0Q8csKYrkWwsSoJeyb98+HaCXbUl9w+I9AktWZ6GpEbjhzIF47d4peP2uSbhqRl+3DtCbBjov77o7/KZUqP++6wrzYE5J7bHu9jpjtm6/fMdW5H/9ZY/1o0ca7sGZ9C98ugeXqYB8aUUdnr5pHF5Vn1fvKNLbd501kAH6HvmFYKMUoAAFKEAB9xDgTHr3uE/sJQUoQAEKeLlAWVkZJEVNe0Vm0ksu6P79+7d3uN19J510EjZt2gRJ79KvXz99TmVl5VHlR5e2v/76ax3s9/f3x6hRo3DcccepSaQtZzOWl5cjIyNDL3QbHBzcbr96cqek+zGKYWJ8lveO+p+TkwP5JoPMiv/hhx/0ArwLFy6EPESxLfKg5fnnn8e//vUvvVsesEjaIhbvEfj75V1fP8LVtHxDLd/uYUZ6y52p2mtJdRPs5Hz0tr8Xvr6+iDvjTGS89Bzyv/gE5r79EdLqzyPb89vb3vfcM6ivKEdTfR2C4uIRGK/WSBg/AebkPu2d7jr71NidXeQbMv9Suef3ZZXj3guHYqe8v7gJ157RX82eb/n/A87uG9ujAAUoQAEKUMA9BBikd4/7xF5SgAIUoICXC5xwwgl6trwwfPLJJ6iqqsIAlUahV69eeva7pE4xSnZ2Nj766CMUFhYiMjJSB4FbB4vlXAmo2y44K7nTbYPnixcv1oucyuKmtkUCzfLQICoqSu/+5z//if/85z+2p+CUU07Bo48+qmeJy/kvvvgi5DyjXHPNNfjTn/4EPz8/Y5fd7/JQwHgAINvbtm1rMY7c3Fz897//xVdffWVdaFfy9stLPIxy8OBBfPfdd/oBhyzEW1NjWbRTZr2npKQYp6Gj/kv7t956q74nxgWyEGxMTIy+ztgn7/KQ5aabbsJnn32md0uwfvbs2bancJsCbi3Q8rGcWw+lS52v3G1ZNLYng/QyAAnKx5wyE/mfLUPeimUIudmS2qy9wTWpP6cr9u3Vh0o2rENl2k7UlxQhaup0RB47DQ3qz/yD77+Foh++QfiEKYg56VSYVCovVyw+TgzS1zc26YVhP/z+AKaOjMOlJ/TB39/6FZOGxeK5W8djTL8IVyRinyhAAQpQgAIUcEEBBuld8KawSxSgAAUoQIHWAhL0fuedd/RuCUDLyygnnngibrjhBhx77LF6Nvcll1xiHNIz4998800dRLcNyMsJH3zwgZ59P27cOH2+5EmXALMUyZ9+55136u3f/OY3LdK/vPrqq5Cc9jITX3K4GwF6WdA2KSlJzyafP3++Dop/+eWXuO+++/Dee+/pwPWsWbN0fvYFCxboQP60adN0Gx39kFzxzz77LK677jps3LgRt9xyC6RPf/vb3/DII4/gf//7n973+9//HitWrMA999yjZ71LnWIj3xaQc19++WWIhTzc+Oabb3DVVVdZm5UFXKUu4xrjgATWO+q/PCyQhyZSJk6cqOsYNmyYcXmLd5lpbwToX3rpJZx22mktjvMDBSjg/gIN6htJlft2w0c9gAx1gTRAcSefimoVfC/fuQ05Kj99vEqDY5SSTT+j6kCGPl6VrnLot1OKfvoO8rItpetVOhf1knQ+wUOGImzYcJiTkm1P6dltJz0tWrExG/OWpKGkvBZPXD8ai1cdxDNLd+PW3wzCFSccetDbsxhsnQIUoAAFKEABdxFgkN5d7hT7SQEKUIACXi3w2GOPQWbT33zzzTrwfMwxx+DAgQPYvn071q1bp4PqMjNeAvSJiYk6KD1jxgw9o/7222/HHXfcgU8//bTFzHWZyS0z7GWWu8wIl2LkTDfys0tdrfOzf/jhhzoIXlpaag06S4BeguhSZs6cqR8a7N27F4sWLbIG6GWRW6lP0spI0F5m49tT5BsB8+bNQ3Jysg60S9qZn3/+GW+88YYO0Esd8uDg3nvv1UF6OS4PG+S4PICQscnDAjGYM2cOli9froP6ct0f//hHnSs+PT1dO8g+mYlvlCP1XwLt5557LuRbB3If5D794Q9/0HUadRjvxgMQ+Sz3hoUCnibAdDcqLdaunWiqq0XIoPYf1vXEPY+dOUsF49NR+M3nCFYPDury81D804+oycm0dsc/KgZhQ4cjdORo+JlM8A1SL1MQ/NS7LIBbq66pyc9XufYLUF9UiLriYlSphxES3C/4fDkip0xD4nkXWOvz5I28kho8tngXvtuUgzOnJiEpxozfvfQLpo+Lx4tq9vyABMvaI55swLFRgAIUoAAFKOB4AQbpHW/KGilAAQpQgALdImCkYJF88razwI3Gzj77bL0pwemBAwfqbZktL2XXrl1YunSpDijrHepHRESEdfFSma0uJTAwUL8b+dlHjx6tPxs/JNAvM+hltrgE7yW1jhR5aGBb4uPj9cx5eaggRWbp2wamJR3MySefbHvJEbclCG+ULVu24P7779dtDBkyRH+DQILrRjobCZYb3xCQ2e7yTYTLL78cL7zwgn5AIIF8mSF/44036ioll7yk35EiAX1JcSPlH//4h34/XP8lXc/TTz8NSd8j3y6QFDvyknshY7edVT927FjIAw6xDggI0PXyBwU8ScBJE5hdmqxyjyUffcigIS7TT5nlHjtzNnI+eAdZr85HY50ltZd0MHz8ZISPHqtnw3fUYVNib8jLtlRnH0T+l5+j7JcNKF69Eo0qd33v8+bARz0w7tHi49ttzS/8PgP/XrILUeFB+Pct4/Hvj3dj9fYC/OHiYThvSkufbusEK6YABShAAQpQwCMFuu9vMB7JxUFRgAIUoAAFek7AyAEvi6+2VyR4LuldJEAvQWZJESNpXYwZ3JITXoLNRpFA+p49e3TeeUlvI6VYzY6UEhcXp99lEVRJaSNF8rfffffdelsWmZUZ6pLHXooR1NYfmn9ImhlpT66RlDQy63zMmDF6lr0Eso0HArbXHGlbZv5LgF+KjEseQpxzzjn6s8yGl28TSDG+EaA/qB8yBpm9L9cYueeNhwSrVq3SQXY5V/LWSwBfFsLtTP9lXDKbXx6EyDjlXb5RIN8AsC3Sv4ceegjr16+33c1tClDAQwSqM9L1SEIGD3apEdWpbyRJMQL05r4DkHzV9Ui66NIjBugPNxBTQiKSL7sSiRddDv+IKJ0CJ2PB/yApf3q0qAez3VXmfbATV57WDyeOjsOtz21AamIIXrptAgP03QXOeilAAQpQgAJeJNDD0xy8SJpDpQAFKEABCnRRQFLFSJFZ8e2V8ePH66C8zBqvra3VgWkJHr+i8q1LDnRJbyOpWSRwLLPMjfokbY3MqpciM9SlSJBbZoLLNZJHvk+fPvj11191kFtyr0tqlw0bNuhZ4XJ+ZmamDsDLtlGM4L6vr69evPbUU081Dh31u8xWl7Q1EvyXhxD9+vXT6XOkwp07d+p2ZKySt37u3Lk6aC/9lIcCUpYsWQLjIYfMxJdvCsgDBCmSW/+4447T6XAWLlyoU+PI/iP1X75dIGlxpF/iLe1v3bpVp9KR/or19OnTpSq8++672lhm60+YMEHv4w8KeIqAkTbLU8bT2XHUFRehOnM//MMi2sw672xdjjxfctFLqhspkpJIQtgxKld9mMon74gSOX6iyojji4Kvv0RF2nak/++/6H35VQhS37bqkdKNQfrVT52Mq59Zh4qqBvzlyhGYOS6hR4bIRilAAQpQgAIU8DwBzqT3vHvKEVGAAhSggIcKSGoVyaleVFTU7gglNUvfvn11EF8C5JLe5a233tJpaWRRVUmRY+yXBVGNILHMqJdA9IlqFr7MvK+oqND1yzUPP/wwevXqpQPbl156KT7++GMduJcZ7ZLqxgj0BwcHt+mTkSrniSeewObNm9scl3aysrLa7G+9Izw8XD8ckNQ0EvD+3e9+p/sgaX+kDB06FLIt3wKYMmUK/vKXvyAsLEynoZG2JZXNlVdeqdPYyPWSmkdm48t+CdDLtsyCl4cR8rBC8uvLMXv7L4FJCdTL9eeff77un9QrawZIsR27zLKX9hzxwEJXzh8UcCEBSS3lzaVSrcMhJdiFUt3YBuilb9HHqz831Z/3OUsWyUeHlYhx45F6970IGzlWP6jIfPm/qFW563uk+HXfP3H35Vbg5jMGYP7tExig75Gby0YpQAEKUIACnivgo/5hyTWePPf+cmQUoAAFKOBhApJWpqqqSgeh2xuaBN9l0VcJBBupX2zPkyC95G03Zs7L4q0S0JaSk5OjA/wym9zeYJv05bPPPsPs2bPbbW/+/Pk60C/1X3TRRRg5cqQOzK9du1bPxpf98s2AI6W+kXG3Nx65XoqMW/ps228jFY8xVsuZlp9S38aNG3WdkjfepBZKtC0yU18M7e2/pMyRbyjINwxsizwokYcd7fXB9jxuU8CdBTLmv4T8ha/APGAI+v32JnceSpf6fnDxIhSv+h4Jcy5F1MTJXarLEReX7tiOzJdfsFaVPPdmhA0ajHw14z1vxUcIn3gMkuZcbD3uqI2Di99XDiu7rf4j9TNIfavswIKX9e9k6uPPIGI8v7V0JDMepwAFKEABClCg5wWY7qbn7wF7QAEKUIACFLBbQALVRlC9vYtkRrzMjD9ckZn2tsW2Lrmuo2ttrzO2zWazNSe8sc/2/brrrtOpct5880288847+mUcl9nvMjv+SAF6Ob+jAL0cl3G3Lh0FxqW+SZMmtb7E+lkC9FLs7b+ks5GXLLgrC9jKNwvkYciR+m1tkBsUoIDbC1Sn79djCO4/wCXGUvyNJc2XdCb5yut1gF62Y2ecjOoDGShdtwrhw0chbMQI2e2wknjuBerJaROK1/yA8BGq/uGOrf9IHVWPa490Co9TgAIUoAAFKEABlxNgkN7lbgk7RAEKUIACFPAsAUnxIi9JbyN57SV4nZKSYldw3hUkOtN/mZEvY2OhgDcKeHNotFp9E6k6Kx1BCckIan7I15O/AwXff4eKPTt1F8JGj28TiO915tmoyjyA7A/eVsf+6vCuJp4/BzXZWchd/hFMyckICLese+LwhtqrsJ2Htu2dxn0UoAAFKEABClDAlQQYpHelu8G+UIACFKAABTxYQBajlUVn3bW4e//d1Z39dh8BT86hWa3W4KjYuQM1uTmoVwvE1ql86w1lJTq/e0BUrF6QVe5UYELPLyTaUFGOwu8ss+jNfQcg+bIr2/wSBUZFo9ess5D15gJkvrMQSRdd2uacru6Im30O0v/zNLLfewd9rvttV6uz/3qV/oyFAhSgAAUoQAEKuJsAg/TudsfYXwpQgAIUoAAFKEABCriigIcsdVWycQOqsw/ClJCI8p3bUbknTQXmWy6CGhQXj5ChI+BrMqOhqhJVe3bpO1L28zqkZaQjdOhwhKj872HDhjv9TpX8sgn1pcXwDwlFwgUXHbb9iNFjUJV+Eoq+/0rNph+NcLVmiCNLSL9+8I+IUobbULxxPSLHOSc3fJP1kYkjR8O6KEABClCAAhSgQPcKMEjfvb6snQIUoAAFKEABClCAAl4hYLtws7sOuPCHlchZ+n6L7kug2ZTUF/Vq5nx9eamsVI2avBz9khOD4nujoaYGPr5+8AkIQF1BLop+kNc3iJp6POJOnwU/tX6Hs0r5ls26qbBxk2Dq1avDZhNU2psalZ8+5/23YO77BwSEhXd4fmcPSn7+UvXgolQ9+HBWkB4NnEnf2fvE8ylAAQpQgAIU6HkBBul7/h6wBxSgAAUoQAEKUIACFHB7gSY3n0mf/fFSPatcboQ5JRWxp5+BIBXkbp1Pva6sFPUlJagtLNRpb6rVzPmanCz4BpraLFla9NP3aib+bsSccjpk5np3l/ryMlSkbYePWhw7cvIxdjUn+ekzXnoeuUuXIKmd1Dh2VXKYk8wDBuogfcWOrWiorISfWlS72wvT3XQ7MRugAAUoQAEKUMDxAgzSO96UNVKAAhSgAAUoQAEKUIACbiRw8IP3Ubx6JcLHT0b08SfA3DvpsL2X2ebyMif3sZ6TsyJOb8fPnIUaFbyvSNuJqv370FBQgIq9u3Tu99rcWYg75TTrNd2xUbJxo642XGbRx8fb1YSMI/b02cj58H0EyfYJM+y6zp6TAqNjrKeV705DxKjR1s/dttHEmfTdZsuKKUABClCAAhToNgEG6buNlhVTgAIUoAAFKEABClDAewTcNd3NwcWWAH3MjNPQSwXZj6bETDse/qFh+tKg6GgEySz25pnsRevWqMVTFyL/8+Xwj4xE1MTJR9OEXdfUq0VjpZj6pNh1vnFS9LHTVH76/aqPn6i0N/0h+eQdXeqLihxdZbv1NTHdTbsu3EkBClCAAhSggGsL+Lp299g7ClCAAhSgAAUoQAEKUMAtBNww3U3uiuUoXrUSXQnQy70xAvTt3ScJyseff7E+lLPobZTt2N7eaQ7dJwvbdrb0mnUm/MMikL9iWWcvtev8uqKWi+/addHRnOSGv4dHM0xeQwEKUIACFKCAZwkwSO9Z95OjoQAFKEABClCAAhSgAAXsEChRi5kWfP1ZlwP0djSFaDWrPmzUODSpfOnZi95B9cEsey476nNM8R0vGNtexZJ7Xxa5rVTpeXI//aS9U7q0r67YOTPpZWFfFgpQgAIUoAAFKOBuAgzSu9sdY38pQAEKUIACFKAABShAgS4JSIA+6+3X9AKxR5viprMdSL78Kt1efUkRCr75qrOXO+X8iLHjEHX8SSj46lOUbdva5TabamoP1VFff2i7G7eamJO+G3VZNQUoQAEKUIAC3SXAIH13ybJeClCAAhSgAAUoQAEKeJOAm6QZqTqQoQP0cmtiTz/DqXcoYc7FKqVMOEp/XoeyXTsd3rYpPkHXWZ2Te9R1J5x5NsJHj1ffMvjyqOswLqw6mGlswtS3n3W7Wzc4k75beVk5BShAAQpQgALdI8Agffe4slYKUIACFKAABShAAQp4l4CPj1uMt/DHlbqf0Wqh2NCBg5zaZ1OvXgifqBaVVaX4+28d3nZw//66zpq8nC7VnXTZlYg748wu1SEX12YftNZh7mfpm3VHN21w4dhugmW1FKAABShAAQp0qwCD9N3Ky8opQAEKUIACFKAABSjgJQJuMJNeZtGXrl+NoPjeiJ85q0duTPSxx+mFZst3bEVV5gGH9iEgMgqyaGzNga7XG5Ka2uW+1TQH6f0CTQh21kx6prvp8n1jBRSgAAUoQAEKOF+AQXrnm7NFClCAAhSgAAUoQAEKUKAHBCr37tWthk+Y1AOtW5qUBVrDJ03VHyrSdjm8H0F9+qLslw2oKy52eN2dqbA6KxO1edn6ElO/VPgGBHTm8qM/t7Hp6K/llRSgAAUoQAEKUKCHBBik7yF4NksBClCAAhSgAAUoQAFPEnCH0Gh1c470qMmWlDM95R8xboJuump3msO7ED5yDBqqq1Dy8waH192ZCos3rLeeHnXc8dbtbt9obOj2JtgABShAAQpQgAIUcLQAg/SOFmV9FKAABShAAQpQgAIU8EIBd8hIX6PS3YQMGgY/s7lH75ApPh4B0bGo2rfb4f0IGzECoYOHo2TtatQWF1nrl1Q/zioNVdUo22R5SBA2cizChg5zVtMAF451njVbogAFKEABClDAYQL+DquJFVGAAhSgAAUoQAEKUIACFHBhgZqcLIQOH+kSPTT37Y/SjWu7pS8Rk6Yg880FSH/h3wiMjEblvjQ0qTUDIqdMQ+J5F3RLm7aVFq9fi/pSS7qdqGOdOIteOuEGayPYWnGbAhSgAAUoQAEKiACD9Pw9oAAFKEABClCAAhSgAAW6LOBrMnW5ju6soCY/X1ffWFXZnc3YXXdgr3i7z+3oxLrSElRnHFCL0GaoBWMzULV3Nxpqq/UldUUFqCspgimxDwLU7P3g/l1fDLajvsgxmbFf8NkyfVr4xGMQMmDAkS5x6PEmlZM+fNx45C98xaH1sjIKUIACFKAABSjQnQIM0nenLuumAAUoQAEKUIACFKCAlwgEJfdx6ZFWZx7Q/WuotgSwe7qzfiEhnepCQ00NanKyIeOozT6Imuxs1OZmo76y3FqPf1g4TCn9ENg7CUEJCSj/ZRPKt29B7MxZCBsy1Hped25kvf0mGmqqETJwKBLP7f5Z+23GwnQ3bUi4gwIUoAAFKEAB1xdgkN717xF7SAEKUIACFKAABShAAZcXkHQqrlxqVGBbSmNVlUt00y/48EH6qoNZKhCvAvIqKF+rXhKcryvIbdHvgJheMKcOhEk9HAlSQXlzUhL8Q8NanBM1YRIyXn4JB15+AXGzzkHsCTNaHHf0h8z33kZtXrbqUz/0vuwK+Po7/5+bTU0Njh4W66MABShAAQpQgALdLuD8vzV1+5DYAAUoQAEKUIACFKAABSjgdAEXD9IHREVpkgYXCdL7+vq1e4tyVixH4deftTgWFJ+E8AlTVNoaNUO+OSDvZ2d6oT7XXq8D9XnLP1RpcdIRd8aZCIqJaVG/Iz4cePM1lP2yAYFxCUi6/Er4d/AQwhHtHbaOBtd+WHTYfvMABShAAQpQgAJeLcAgvVfffg6eAhSgAAUoQAEKUIACXROw5v929SB9tCUwXZW+B421tfANDOzawLt4teSQb10q9u1DXX6eDsib+6TAlJQMkwrKd3VGugTqsz9cjKIfv1U569MQMWkqoo49DgHhEa270OnP5Wm7kPvhB6jJPYjwsRMRe/osBEZFd7oeh13g4r+HDhsnK6IABShAAQpQwKMEGKT3qNvJwVCAAhSgAAUoQAEKUKCHBFw8OBrQHKQXnYpduxA2YkQPQVmarcnK1LPObTsR0q8f5NUdJeGcc2FOSUHuJx+jQM3UL1n7U5eC9WVbt6Jk4zqUbd4I/4goxJ9/MaInH9MdXe9UnU1NjZ06nydTgAIUoAAFKEABVxBgkN4V7gL7QAEKUIACFKAABShAAXcXcPEgfVD0odndFbt29HiQvvpgJszJKU696xHjJiB0yDCUblYLym7dooP1xT99r3LbD0LI0GGIGDkKfiGh7fapprDQsmhtXi7Kt21RqXP2wTcgCOETpyL25FNh69tuBc7aWVPrrJbYDgUoQAEKUIACFHCYAIP0DqNkRRSgAAUoQAEKUIACFPBiAR8flx986PDRKsD8Cyr3pPVoX8t27UR9cSECx010ej/8goMRNUWlu1Gvmvx8lG7aiOI1P2mXnA/egV+gCT6BAeoVpIPwvkFBajHYHDRUVVj7GhAZjZgZpyFi4mQExcZa97vCRlN9nSt0g32gAAUoQAEKUIACnRJgkL5TXDyZAhSgAAUoQAEKUIACFHBXgfDRY3UwuiYnC2W/bkPYsOE9MpTSnzfAz2RGRA+nh5EAe5yaBS8vyS1ftmUzqtP3o6G6Ck211agtKwVU+hj/iGg9699f5ZoPSkhUwflJ8FPBe5csDQ0u2S12igIUoAAFKEABCnQkwCB9Rzo8RgEKUIACFKAABShAAQp4jEDEuPHIXbYE9Sr4XLxmVY8E6WsKClC6bhUijznedVLEqDscOnCQfnnEzWag3iNuIwdBAQpQgAIU8CYBX28aLMdKAQpQgAIUoAAFKEABCni3QMjQkRpA0t5U7NnjdIzC777WbUZOnuL0tr2lwcY6przxlnvNcVKAAhSgAAU8RYBBek+5kxwHBShAAQpQgAIUoAAFKHBEgV5nzEJgbLw+r3jd6iOe78gTSjZuQPGqlYg742yYk5IdWTXrshVoqLf9xG0KUIACFKAABSjg8gIM0rv8LWIHKUABClCAAhSgAAUoQAFHCfiHhCLhvAt1daXrV6Ng5feOqrrDeiRAn/X2azD1TkHsiSd1eC4Pdk2gqY5B+q4J8moKUIACFKAABZwtwCC9s8XZHgUoQAEKUIACFKAABSjQowIhAwYg5pQzdB9yP1qEutKSbu2PEaCXRpKvndutbbFyJcCc9Pw1oAAFKEABClDAzQQYpHezG8buUoACFKAABShAAQpQgAJdF+h16ukITh2sK9r37FNdr7CdGuqKi5G9ZJGeQW9O6Y/Uex9AQFh4O2dylyMFfOo5k96RnqyLAhSgAAUoQIHuF/Dv/ibYAgUoQAEKUIACFKAABShAAdcT6HvDzch8ZyFKN6zBrkcfQb+bb0NAeESXO1qdnY3iNatQtmEt6qsqEHnM8Ug89/wu18sK7BNorOfCsfZJ8SwKUIACFKAABVxFgEF6V7kT7AcFKEABClCAAhSgAAXcWCBk4CC37H3SRZfClJCI3OUfIu1vf0bs6Wcieupx8DObOzWe6rw8VKXvQ9XePTro36RSroSNmYDkS6/oVD08uesCzEnfdUPWQAEKUIACFKCAcwUYpHeuN1ujAAUoQAEKUIACFKAABVxMIOaEGQgeMBCFK79D/qcfo/jH72HuPwCRU6bCJzAQstisf3AIfAL8ISls6oqLml/FqMlIR3XWAdSXFltHFRDTC7GnnIbI8ROt+7jhRAHmpHciNpuiAAUoQAEKUMARAgzSO0KRdVCAAhSgAAUoQAEKUIACbi1gTu6DpIsvU8H105H/5WeoVsH3jJees3tMAdFxMKcOgDmlLyJGjYFfcLDd1/JEBws0NTq4QlZHAQpQgAIUoAAFuleAQfru9WXtFKAABShAAQpQgAIUoIAbCQTFxkJS4EipKSzUwfqqvbtRnZnZZhQBMTEwqzQ/IWrWfZDaZqEABShAAQpQgAIUoMDRCDBIfzRqvIYCFKAABShAAQpQgAIU8HiBoOhoyCtizFiPHysHSAEKUIACFKAABSjQcwK+Pdc0W6YABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKeLcAg/Teff85egpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFOhBAQbpexCfTVOAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoIB3CzBI7933n6OnAAUoQAEKUIACFKCAQwRqCwscUg8roUBXBRoqK3UVIYMGd7UqXk8BClCAAhSgAAWcIsAgvVOY2QgFKEABClCAAhSgAAU8W6CusNCzB8jRuY1AdVam7qt/WJjb9JkdpQAFKEABClDAuwUYpPfu+8/RU4ACFKAABShAAQpQwCEC9UUM0jsEkpVQgAIUoAAFKEABCnidAIP0XnfLOWAKUIACFKAABShAAQo4XoAz6R1vyhqPTqC2eSb90V3NqyhAAQpQgAIUoIDzBRikd745W6QABShAAQpQgAIUoIDHCESMn6DHUpt70GPGxIG4t0B1dhaCh49270Gw9xSgAAUoQAEKeJUAg/Redbs5WApQgAIUoAAFKEABCjheICglFQ3VVajKPOD4ylkjBTohUKPWRqgvKULw2PGduIqnUoACFKAABShAgZ4VYJC+Z/3ZOgUoQAEKUIACFKAABdxewDxwkB5DRdoutx8LB+DeAmWbN+kBRB9/gnsPhL2nAAUoQAEKUMCrBBik96rbzcFSgAIUoAAFKEABClDA8QKmgYN1peVbNju+ctZIgU4IVO1Og585BCGDLb+TnbiUp1KAAhSgAAUoQIEeE2CQvsfo2TAFKEABClCAAhSgAAU8QyDq+Ol6IFXpe9BQVeUZg+Io3E5AUt2U79iKsKnT3K7v7DAFKEABClCAAt4twCC9d99/jp4CFKAABShAAQpQgAJdFjD17m1dqDP/26+7XB8roMDRCBSvWaUvS7xm7tFczmsoQAEKUIACFKBAjwkwSN9j9GyYAhSgAAUoQAEKUIACniOQ0BwYLfnpe86m95zb6jYjkW9wyO9exAmnQB4asVCAAhSgAAUoQAF3EmCQ3p3uFvtKAQpQgAIUoAAFKEABFxWIGD8B/jG90FBdBc6md9Gb5MHdyl2xTP/u9Z57gwePkkOjAAUoQAEKUMBTBRik99Q7y3FRgAIUoAAFKEABClDAyQIp9z2gW5QZzVWZB5zcOpvzVoFCleameNVK9L7pTs6i99ZfAo6bAhSgAAUo4OYCDNK7+Q1k9ylAAQpQgAIUoAAFKOAqAjKbPvbSq/WM5oPvLGTaG1e5MR7cD3kYlLPobURMmYb4C+Z48Eg5NApQgAIUoAAFPFmAQXpPvrscGwUoQAEKUIACFKAABZws0Oe66xE6cSpqcrKQ9dYbTm6dzXmTQHnaLmS8+ByCEpKR+rdHvWnoHCsFKEABClCAAh4m4NOkioeNicOhAAUoQAEKUIACFKAABXpQoL6sDDtvvRE1B/bBnJKKPtdeDz+zuQd7xKY9TaDg22+Qu3wJgnolYuCTzyAwkYvFeto95ngoQAEKUIAC3iTAIL033W2OlQIUoAAFKEABClCAAk4U2P3nB1C68mv4mcyIP/9iRIwe48TW2ZQnCtQUFiJ3ySKU79iKqKnT0efeP8AvItITh8oxUYACFKAABSjgRQIM0nvRzeZQKUABClCAAhSgAAUo4GyBnA/eR878F3SeeplVHzX9RESMGu3sbrA9NxeQ4Hz+p8tR+vM6+AWZkHDJlYj9zXnwDQtz85Gx+xSgAAUoQAEKUABgkJ6/BRSgAAUoQAEKUIACFKBAtwpUZ2Uh+/VXUPrdVzpY7x8RhejjTkDwwIEwJyV3a9us3H0FZFHYyrQ0lG35BVXpe3RwPkL93sSfPwdB/frBR31Dg4UCFKAABShAAQp4ggCD9J5wFzkGClCAAhSgAAUoQAEKuIGA5KrPfm0BCpcv1cF66bKkwjH3TUVQ7yQVtB+kRyGBe2flsJfFR1m6JuCI+yUB+YaqKhWU34W6okJU7t2N+pIi3TH/8EiETzoG8RdciKCUvio4b+pah3k1BShAAQpQgAIUcDEBBuld7IawOxSgAAUoQAEKUIACFPAGgYqdO1G2fh1KN6xF9bbN1qC97dglgN9QXWW7i9teIhA6ciyCR4xC9AknwiSB+aAgwNfXS0bPYVKAAhSgAAUo4G0CDNJ72x3neClAAQpQgAIUoAAFKOCiAiUb1lt7Vrpxg3Xb2GgsL0N1F2e+16gZ2g1VFUaVLv3uH9MLgfEJLtVHk/q2g2+oY/PA+6v6ggdZvkURMX6CS42XnaEABShAAQpQgALOEGCQ3hnKbIMCFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUo0I4Avy/YDgp3UYACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAWcIcAgvTOU2QYFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoB0BBunbQeEuClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoIAzBBikd4Yy26AABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKtCPAIH07KNxFAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFHCGAIP0zlBmGxSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBdgQYpG8HhbsoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAs4Q8HdGI2yDAhSgAAUoQAEKUIACFKCAVaCpCY0NDdaPrTd8fH3g4+vXerddnxurq5D24APw9fVF6sN/g29goPW6/fOeRF1+vvVzyPCRSLzkUutnd9xoamxAU2OT8jp6M3vH7Qp+Hd1fe8dxNOc1VlejNi8P/pGR8A8LO5oqeA0FKEABClCAAhQ4rACD9Iel4QEKUIACFKAABShAAQpQoDsESn/ZhN1333LYqkNGjsXgec8d9nhHB8q2bkXF+lX6lIqdOxA2cpT19ErVbvW+NOtnd9+QAP3ms2eioaoSfuZgjFq64qgfbthj4Qp+Hd1fe8bQ2XPkoc7+J/6JsrU/Wi8NiEtA4k23IeaEE637ZGPfk0+gvqgQqf/3J/iazS2OdfZDXUmxavcxBCYmIuXm2zp7Oc+nAAUoQAEKUMDNBBikd7Mbxu5SgAIUoAAFKEABClDAUwT8wyMR1H9Am+EEDxnWZp+9O8JGj0HUWefpmfShw4e3uGzY/Ff159JNP3f4kKDFRS78oWzLVh2gly5KoL5827YWDyUc3XVX8Ovo/jp6vFJf5oL5OkAvv6uhU45FXUE+KjasQfrD9yNw3gstvEt//B4NRflorLuvy0H6xuoalP34LYJS+qteMEjfHfeWdVKAAhSgAAVcSYBBele6G+wLBShAAQpQgAIUoAAFvEgg/MST0feOuzsccUNFBRpqauAXFAS/kJDDnqvTvjQ06uMpt9yu3482ZQ4aG3U6Hl8/lXJHpc0xSmN9vd709bf8M8q2TR8/X8ssdpXKpyY3F0Hx8cZlLd4b6+pQm5ONgKjoDsfT4qLDfChZu1ofiZ1zKfLfW4iSNatbBI0haYVUn42+iWVdcTFMana27bikEnudD9OVQ7ub24SPDwwn68FW/bHuVxuNtbWoLylBveqjn8kEv9BQ+KuXUWyt7bm/R6rPqLejd5lFX7RiqT5lyII3EahS3UgpWb8ONdkHrdZyT22LfLbua+VwROfm372mulprlda61B7jXloPNm/oe6tm8QfGJ8A3IKD1YX6mAAUoQAEKUMDFBRikd/EbxO5RgAIUoAAFKEABClDAmwUy/vsfFC1brAkkpYtpxBj0vnYuQocMbcGy667bUbHl5xb7Ri/97KgC4SXr1mHPH+9C5ClnoP8fH7DWueU3Z+gZ62M+/VYHoNOfnYfCpYv08ZAJxyD+wouR8cSjqMvL1jOg46+5HjHTT9DHJYia/sxTKP7iE2t9ktan7x//hKCEBOu+zmyU/7hSn5546RU6SF/2w/eAsjFKxe7d2HnDVYg8dZbeVfz5cv0us8L7/+MJhA499I0Fe52Nug/33qgeqGyafbI+POLdpQiMibGeWrJ2rXa1TWdUfSBDuTxtTVFkPVltGM6yz977a299tu0cbrtJPVSQIr93AeFh1tMiJky0btcWFmLrnLOsn2Vj64Vnt/g8avFy+IdH6H1Hci74/js9S9+ooCZ9LzbNPNH4qH+vhi94w/pZxrvv739F1Y6t1n1Rs89VKXJuha962MFCAQpQgAIUoIB7CDBI7x73ib2kAAUoQAEKUIACFKCAVwqYUvoifNoMNJSXoeLndahY9xN2qdeQlxciuG9fq0n4tBMQ0DtZfy7+7GPr/q5sNDXPnG9TR3PwNnTMODTVN6Bo+RLUZR5AzsLXYRowCIF9++t+Zj72CKKOPU4H9Pc++jedvkTqCpt8HKq2q9z56qHCngf/D8Ne+F+bme1t2my1QxYxlfz6IeMnqwBwuH6XNCyyPzAursXZ5at/RH1pMULGTkT1/n0qb3o+st98HQP/+nfrefY6Wy84zIYEhiVILA9WCj7/DIkXX2I9s2TNT3pb7pUuatZ42u/u1A81/KJiETZ+InzVtyXEVL7NYDsT367724n6rJ3qYEMczUNG6AD4zjtvQ/LtdyNk4MAWV/gHB+vxyrcW5PdAijzc8QkMsp7n43fon91HcjYnJyNq1m9QX1ykf1/kAUH4jNOsdflHR1u35YHIrttu1PdWHryYh45A2ZoftL2v2YSUm261nssNClCAAhSgAAVcW+DQ3xZcu5/sHQUoQAEKUIACFKAABSjgYQI1e3Yjd9lHLUYVecyxLWZfJ1wwB5CXKpLyZP/TT+kgZP7yj1oEIRPmXGitZ/OqlTpwad3RTRsxJ86AvCQ4W5t9AKGTp1jT92w+d7buQ11hgZp9X20NuA5fuEgH1SWFyY4b56J69w6UbdtqTZ1ib1dL1q3Vp4ZPnKLfw9S7BOklFUvczDNaVCMB+oHN+dMbq6qw6cxTdH8aq6uts63tdW5R8WE+xM0+S9+jwo+WtAjSl678Tl8RqR5cSKnNz9MBetke+p//tXm4IPuNYs/97Ux9Rr1Heu9zxz3Y++f/Q+XWTfpbCSETp6Lv3fda0xnJQ4l+d/9OV1P600qdk76PSrckD07aK0dyDh4wEP3uuRc1OTnYpnLS+8fF68/t1ZX/2QrLwxf1oGbQP5/Q6ZYkRc+Wi85Bwftvoc8NN9m/kLB6wLHlUst/Z+21Jfti51yChPMvONxh7qcABShAAQpQoAsCDNJ3AY+XUoACFKAABShAAQpQgAJHLyAzyVunqDE9+XyLIL3MqC7dvBnVB9LRWFmlcm5bcr3X7t9/9A1305Wxsw+lOen7l3+oPO/l8A8JRfmvv+oWg8dNQm1RkX7JDtPgoXo2fE1mZueD9M2pbsKaU6+Eq/dsVWeJSnnTOkgvs6zDRozUffA1m9WM65FqJv8WVKvZ/xIU1sWBziFDhqhvFAyxPIDYslmPTdKyGGmATElJusnAuF4ITEjWDzh23XMHIk6YgbCx4xA2ajR8AwMt/erET0fXJ03LWIbNfw2ZL7+EgiXv6W9I7LjuciTdfR9iTrKk9elEF/U3BBz1+1ylHnJJCR07AVUZB6zdCEzqi9rM/ajNzetUKiWZ8d/UYFl3wVqZzYaPv1qjgYUCFKAABShAgW4RYJC+W1hZKQUoQAEKUIACFKAABShwJAFJ1RIzc3aL00w2KWxkpveOW27QgewWJ6kPjR0EE1uf66zPekHW5sbCR4+2Niuz6aWUqZnRO9SrdalXwfzOFJmFL3VJyXnrDTVb2lcFV1WKGFUqN67Vi8XapooJSumnVhz10cflh+0x+dwdzjHnnIvMJx9FwSfLdZC+WC1qKyVCLRZsLapPfR/8C7Kef1Y/rMlb+ArkJQ8VEm++A7GnHkrzYr2mow1H19fclixYnHLbneh91TXIeuM1FCx6W48t6vjpnVqk1dHOMmteSs7L/9Gv5u5a3+Qhkd1F/Q6NePMdu0/niRSgAAUoQAEKOFaAQXrHerI2ClCAAhSgAAUoQAEKUMBOgaDkPog5+ZTDnp31yss6QB927AmIO/d8lWIkARW7diD9r3867DX2HWgOWKtgt71FUu00VFV2eLoEc9srQb0ss/9l1njCtde3OSVkaMtFcNuc0GpH2dYt1j0l33xu3ZYN6WO5Oh4+ZmyL/R196Lzzkf1iTjxJB7KLVixFyq23o3SVJR991LTjW3RFFgAePO851BYUoGzzJpT+9KNeXDdr3uOIliB4Jxc/dXR9tp2VxV9Tbr5N/U7u0wvdlmxYj6gpx1hPkYcf8qikvlx9g6KddDedcTaeqdSXlVnrb70R2Lu33iVrHESd0vaBRlBCYutL+JkCFKAABShAARcVYJDeRW8Mu0UBClCAAhSgAAUoQAFvF6jYZglGx198qTVdS7EK4na1+EdG6iqq9+1pM+tcDphSUvTxig1rdR58H18/FH5nyaeuD3TyR8igQfoKyVvvKwuNTj22kzW0PL2sOeCdePNdiJ7evAirOqXwu29x8PmndEC8M0H6zjofyU96Kw8sos44B0WffIjcT5bpoHZAXMKh9Doth6RTHElgP0qtSVCuFpiVPPrlO3aohw1jWp1p38fAmBi1XkDX6pNFeBtrqmFSD5OMIg9rqvekGR9bvAeqh0iS0qf4h5WwzaFvnNQZZ//oGH1Zg1rkt0I5SNqd1iVkyDDIdzQqN29En7vugfEwqPV5/EwBClCAAhSggOsLMEjv+veIPaQABShAAQpQgAIUoIBXCgT1S9ULdmY88SjCp05DbU62Whx1LfyiYlH96xak3X8fkm++VQXaG3TaFwNJArxS9j/5OHwCAhCYlIykK64yDkPS0viZg3VAdfv1V8M8cDCq9+9D77k3ImLyZJ3H28ipvvnC8xCoZsLXpu/V18hMdWk35Y67kLt4ERrUrGlm7uuCAAAvEUlEQVSj7H30b3oz6fobW+TVl+vjLrkKeW+9in0P3IsM1f+wiZPVSrhNaKqtReqfHzaqsOu9pDkfffTxx7dYbDVKfZYgveSlT1aLhtpb7HU2KUcpR/Iz2o0962wdpD/43FN6V4QKmtsWWRx1z//9HgG9k3RQv6G4GJVpO9Gg7p/cn+D+/fTplWr9AUnrY5TD3V976zPqOdJ78eqfkPnUPxGU0h+m1IE6rVDZxvV6cVid53/Y8BZVhE6eqtP2HHxhHgqXL4VZBdFlTInXzNVB9s44y6x8SQcliwHvvPlahIwciwA1M15SJw38x+M6ZZEsWpy3yLK+wLZLzoOp30CYh49EY3kZTAMHofdlV7ToHz9QgAIUoAAFKOC6AgzSu+69Yc8oQAEKUIACFKAABSjgmQI+vnaNK+m6uWhSM5nLfvgWee+8roPzSXfcg9yFb6hFSfNRtup71F1ymZrt3oTiz5e3qdNIBSMBTtgE6WVR0qTfP4DMxx5BjQq+y0tKXWmJtQ4J2O9/+H4dkK2trkTSXX9Q7b6Ohn1pelZ4nVoAtnjFxy1S4Bh9SLRpy6gw+dq5apHUBGS/Ml/XaZyrjzc+pBLF22dSm5erFwWV1DkS/LctMpNaL8Qqi4aqWeBHKpLLXoq9zkaQ3h4/qVdSz0jguFqZSYk+uWVKFhmLHDOO65PUD/OQEeh9460qZUyE3lVfUmLX/bW3PqOdI71LupiwY45H5Y5fYfwuyTUhYyci6abb2qS0SbjwIjTW1qBo6Qctfq8iTzpFB+k769zv3vuQ9eorkJRBeoFltdCylDqVGihIFlBW92/QY08i87UFKF72YQvL+kqVmolBeu3FHxSgAAUoQAF3EPBpUsUdOso+UoACFKAABShAAQpQgAJeKtDYiFoVFJcUJlIaKirg4+cLHxVsl1Q0R11UvTW5ubou/4hISPC5RZHj+XkIjI3V7Tiq3caqKtQWFur2AmKiuzaGFh3u4ofOOh/JT3VH8rbvufd2BI8YgyHPPN+mg401NagrKdbfKPANMsE/IqLtfWhz1eF3OLo+o6X60lKV+qYGkurHV307o6MiKXFqsnP0NyX8w0KtDxus13TSWRacrS3IV7+n/ggQH7PZWpXthjg2lFfo44FRUS0WC7Y9j9sUoAAFKEABCrieAIP0rndP2CMKUIACFKAABShAAQpQgAJuLdBYXYXC779X6Xfm6fzy/R55vMu5+N0ahJ2nAAUoQAEKUIACHQgw3U0HODxEAQpQgAIUoAAFKEABClCAAvYLNKoc+7/OvVqn5DGuSvjtbQzQGxh8pwAFKEABClCAAu0IMEjfDgp3UYACFKAABShAAQpQgAIUoEDnBSQVTK3KiS+58c0qJ33MzNl6Md7O18QrKEABClCAAhSggPcIMN2N99xrjpQCFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAVcTMDXxfrD7lCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFvEaAQXqvudUcKAUoQAEKUIACFKAABVxToK6hCfKSUt9o2W6yfOyww3//YAeufXY9MvKrOjzPWQePdhzO6h/boQAFKEABClCAAhRwTQEG6V3zvrBXFKAABShAAQpQgAIU8AqBDXuLMe13X+HU+7/T4z39wZX687rdRUcc/4adRdi6pxglVXVHPLe7T+jKOLq7b6yfAhSgAAUoQAEKUMC1BRikd+37w95RgAIUoAAFKEABClDAowV8fXz0+EyBfvrd+AdKUKCx5R7D95RxuIc2e0kBClCAAhSgAAU8S8C9/ubrWfYcDQUoQAEKUIACFKAABbxewNwcnPfzswTrAwMs/0Qx+VuC9rZAkgInI78S5dUNtrvb3a6qbcCug+WQ946K1HmwqBoHCjpOmVNb36jP236gTPehpLK+RbWdGYdx4ZHaNM7jOwUoQAEKUIACFKCAZwv4e/bwODoKUIACFKAABShAAQpQwJUFgvwsQfnA5mB9QHOQPqj53ej7V5tz8eCrW1Xu+ka9a9roXsahFu/FFXX4/SubsSntULqcMQOj8NjVoxAZEmA9t04F3Z9ZvhuLvslAQ3MC/ADVl5MnJuChi4aheYI/0g5W4ME3t2J3Zpn1WmNj5RMnIaD54YK94zCuvf+NrfhifTZOnZiIRy4bbuzmOwUoQAEKUIACFKCAFwowSO+FN51DpgAFKEABClCAAhSggKsIGGltgvwtwXrru02QPkvNdP/jy5t1l4f1j0CDWmR25S+58DMi6TaDueOlTdi+v0Tv6d0rBFm5FTpgL/tfvXOi9cz/W7gN323M0Z/DQwIRFxmkA/ErVmchJMgPvz93MBrUIrZz561DVU09TIH+GJkajrDgQEiAv1EF9o0AvVRizzisjauNA82L3WbkVdru5jYFKEABClCAAhSggBcKMEjvhTedQ6YABShAAQpQgAIUoICrCEQEB+CiGSnopwLqUi6cnoz9uZUINx+a9f7q1/v1MZkR/+It4/X26l2FuP35jXrb+CGpaIwA/av3TMbQ5DD8qvZd/a81ev+OrHIM6R2KfTmV1gD9X64cgZnjEnQVa9Xs++c/2YOrT+qrP0saHAnQS3n7vilIjDLp7fZ+2DMO2+ueuGYUPtuUi9PGtP+NANtzuU0BClCAAhSgAAUo4NkCDNJ79v3l6ChAAQpQgAIUoAAFKODSAsFq1vrdZw+y9vH8Y5Ks28bGnqwKvXn8yFhjFyYNjFYz2X2t6W/kwPYsS0qa6EiTDtDLvmEqUC+fC4ursV2lrJEg/dYDlpn25iB/a4Bezp2kHgIsuG2CbOqSFG1GqJplX15Riyv/tRYzxvXCsUOicczgGDWzvuXyXvaMw6hX3uMignDZ9D62u7hNAQpQgAIUoAAFKOClAi3/ZumlCBw2BShAAQpQgAIUoAAFKOC6AnmlNbpzQ3qHWTvpq9aZTYg1Wz/LRl5Jrf48IDG0xf7UhGD9Oa/EUs/BIsv7IBXA76hINp3HrxuFPvEhKFWB+g9XHsAf5v+CU/7vW7z3Y2ZHl/IYBShAAQpQgAIUoAAF7BZgkN5uKp5IAQpQgAIUoAAFKEABCvSEQFRYoG52X55lRv3h+tBL5ZWXkqZS3NiW3ZmW6+KbjydFW9LWbN1TrGbiN9me2mZ7fP9IvH/fMfjooWm47+JhmDQsVs/ef+r9HaisaWhzPndQgAIUoAAFKEABClCgswIM0ndWjOdTgAIUoAAFKEABClCAAk4VSFUz2aV8/UueXsxVtovK69SisC0XXZXUNvpYWQ1+SbektPl5XwmK1GcpQ5Isx0emROjPDWrx1/lf7EWtWgj2SKWXSk9z7pTeeOyqkTrNjlz7876iI1122OOS7/7lL/dD3u0t8kDhnR8OYNXOwjaXdFSfnC/XyYK3LBSgAAUoQAEKUIACrifAnPSud0/YIwpQgAIUoAAFKEABClDARuDqk1Lw8U+Z2LCjEKc9uBLDUsKxeXcxJFBuWwarNDfDUyOxTc2Qv/6pdYhVuejzVS56KSPUfjkupY9Kk3P65ER8uuYgFqzYi9c+3Ycxg6N0fbkqFc4LN49Dgro2I78Kv31uAxLUzPtQlb++WKW8Sc+u1DPp/VQuHNv0O7riTvy4d8Fm7MooVQ8ecvH6XZPsunLx6kw8qWbwS1nx1+mICj20uO7vX9mMneml+EotRvvG3Yfqk4cZd/zn0AK7Fx2XbFdbPIkCFKAABShAAQpQwHkCnEnvPGu2RAEKUIACFKAABShAAQochUCf2GA8crVlBrss4rr213wMSA7FeLWIa+vyzNwxmDjUst8I0MvneWq/bXnwomG4ZmZ/66x4eQCwaWcRDuZVIrOwSp8q77LgrAT916g2JQheXVuPhBgz/nXjWMSEWdLr2NZr7/agJMsDg0FqIVt7S984S259WfA21NxyvpVRz4BW9cl5cr4U43p72+N5FKAABShAAQpQgALOEfBpUsU5TbEVClCAAhSgAAUoQAEKUIACXROQtC4RwQEIDvLTOeF91Yx2U2DbuUeSGiZHBdjj1Yz4AD+1AmwHpaiiDoVltbqeXuFBCPA/VF9VbQPyS2t1ShyzajM6JLDd9jqo/rCH8lUanthOBvqlr6Em/3bHJAvjxqm0PK2LWEj+/IjgloH91ufxMwUoQAEKUIACFKBAzwgwSN8z7myVAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKIBDU0SIQQEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSggFMFGKR3KjcbowAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQocEmCQ/pAFtyhAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACThVgkN6p3GyMAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKHBIgEH6QxbcogAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQo4VYBBeqdyszEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgwCEBBukPWXCLAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKOBUAX+ntsbGKEABClCAAhSgAAUoQAEKNAs01tfrLV9/fzQ1NqhXE3z9/AAfn3aNKnenIef991Czfy+aamoQ0DsJA//693bPPdLOxuoqpD34AHx9fZH68N/gGxhovWT/vCdRl59v/RwyfCQSL7nU+tndNjrr7G7jY38pQAEKUIACFKCAuwswSO/ud5D9pwAFKEABClCAAhSggBsKlO/Yjl03X4eAuASMfHsRtl1xKWqzD2DgM/9F2IiRbUZUsmYN9vzxLut+P3MwGuvqrJ87u1G2dSsq1q/Sl1Xs3IGwkaOsVVT+sgnV+9Ksn915o7PO7jxW9p0CFKAABShAAQq4qwCD9O5659hvClCAAhSgAAUoQAEKuLGAj6+aMa+Kr9lsGYXMoJfPQSbL51Y/D/z7ab0n8tRZSLz8SpiS+wCNja3Osv9j2OgxiDrrPD2TPnT48BYXDpv/qv5cuuln7L77lhbH3O1DZ53dbXzsLwUoQAEKUIACFPAEAQbpPeEucgwUoAAFKEABClCAAhRwMwGf5vQyPgGWNDO+Jktw3icgoM1I6kqKUZu5X+/vc/Nt8A8Pt5yjUtUYpaGiAg0qBY5fUBD8QkKM3W3edVqdBktwP+WW2/VxI5Dd5uQj7VAPCRobGiwpemz6YpteRqqwbdPHzxe6vaYm1OTmIig+vt1W5FsCtTnZCIiK7nA8jbW1qC8pQb0av58y9AsNhb96GaUzzsY1tbk5COzVfr+Mc/hOAQpQgAIUoAAFKOA4AQbpHWfJmihAAQpQgAIUoAAFKEABOwUkD72U1sF533aC9LXZOfpc04AhhwL0es+hHxn//Q+Kli3WOyQVjmnEGPS+di5Chwz9//buPMqvqk4Q+DeV1JJUKntIZTELWxDDNmKUpUWUtgWGbtGGEXscBMNAqyMNNgoCLijdbI1G7enuc1oPHmycnhmGHrTBdm0QAo3YB0wQAgGyECYb2SqV1JKqzLuv8vulqrJDQvJePvec3++9d99y7/3c+qPO993fvdsuyvZeuOoz0TrvqT55x9//k10Gwvtc3Otg3ZNP5lPwjDjr7Jh23Q3VM/M+eHZ0bdoYJ/zLQ5Haufhbs2P1/ffm5xvf/q4Yd+FHYskdt0TnymVRP3lajLvkshj97jPy8+llw+Jvfj3W/uzB6vMaZ5wYU667Meqbm6t5ba8sya77RnXKnuqJbKdSbsrbG+d0/eJvz47X7vufMfrDH4nJ2QsRiQABAgQIECBAYP8LCNLvf2MlECBAgAABAgQIECDQT6CyUOuAbOR7SpVg/cBeC7jmI9KzEeebs4B3SrWjRveZhz5fZHbrCPaGyVNi2OlnRteGlmh96sloffKxeCH7TP/uPTFkypT8/vQ17PQzsgVnJ+XHa3/yo2r+G9nZsnUB3O2ekdU9paEnnBRbNnfFmgf+KTqXvhLL77k7Go44KuqmTMvrufS2r8XIU0/LA+ov33JztMx5KL+vaeZpsem5bO787KXCS1/8Qrz1b/8+g8p+PZCN4F/w53+WB/kHjhwTTf/h5KjJfj2QykjnKoH59JA9cc4L2/rVsXRpvtexZEnvbPsECBAgQIAAAQL7UUCQfj/iejQBAgQIECBAgAABAjsWGNTUlI0gvzzqJ0zMLxh9znkx9KS359O1pIw03cvTH3hPfq7y1fLrOX3ypt50a4w87fT8dPMfXxCRPllK08ss+sbX85H1qx74YUz+00/n+emr+YILq/tzH38kNq9fWz3eXzuj33NmpE8K0qfFcYfOfGdMufLqvLi555+b16Fz9WvZ6Pu2PECffglw7D335r8aSA7zr5gVbS/Oj5bfPZMvcNuxamUeoE8POOZv/j7qxo7dadV359z/xsnXXBvr/u3xGP7Od/U/5ZgAAQIECBAgQGA/CQjS7ydYjyVAgAABAgQIECBAYOcCaeT8hGwB2Eoa/b6zKrv5Ns3dPvKcD+b7aW721t88HmnU+LBTeoLy6UTduG3Tv6QR5Ovnzo22VxZH98ZN2bmeOdU7FvXMZZ8/6CD5GnPuH1ZrMuUrfxldrRtiUOPQ2PDss3n+kJPeER1r1uSflNFw9DHRtnBBtGej3JtmHJcF5Q+LuuZJecD/hc9eGcPPODOaTjwpmo47vjpyvlLA7pwr11W2daNGxdizz6kc2hIgQIAAAQIECLwJAoL0bwKyIggQIECAAAECBAgQ2DuBtLjq1M9ek9+0/re/jRezIP2Qo6ZX83o/rbutLeZ/6vI8kN07P+13d23un3XAjxvGj6/WYdjxx1f302j6lNJ0N/O3TnlTPZntbM6C+XkaMCCmfPEr8ep//1Y+Fc7Ke+6K9Bk0bESM/+SVMeb339/7NvsECBAgQIAAAQIHuYAg/UHeQapHgAABAgQIECBAgMCuBV6967t5gL7p1DNi7PkfjvpshH3rC/Nj8Vdv3PWNuz07oOeKbMqZPU1pqp20aOyu0sBs/vgdpfrDekb/p1HyzZdett0ljcdsWwQ3LYh79Oy/jo7XXouWuU/H+sfm5IvNvjr79hj1e++uzvG/3UNkECBAgAABAgQIHHQCgvQHXZeoEAECBAgQIECAAAECeyPQ+rt5+eXjPvLRaHrbjHx/bRa0fqNp0IgR+SPaFr4UaRHb3guyphMNkyfn51v//df5PPhp9P/qhx/O817PV+NRR+W3pXnra4YMiZGnnLrbx9SNHp3Nd//eGPmuU2PDE4/l89tvmD8/hp1wwm7v3dEFHStXxpo5j2QL2Z6+y7nud3Tv7vKS4epf/Dybqmd89P4FQbpvV+W2zJsb7a++GqPOfG/U1NburhjnCRAgQIAAAQKFExCkL1yXqTABAgQIECBAgAABAr0F6qceHhufeTqW3HFLPmd9Pod9FjhPc9i3PTsvFlx/bUz65KezQHtXLP/B96u3VhaNXXTn7TEgC/7WTZwUEz92cfV8mpYmLeLauXJZPHfZx2PwkUdH26KFMWHWFTF85syob26OhiOm54u6zr3wQ1GXjYTvWPxyfk8aTZ/KnXzlVbHivnuja8PWqWqyp798y815GRMvuyJSkL2S0v1jL7o4Vv7ge7HwhmtiSVb/ppNnZivhboktHR1x+Jduyi9tX748XvrC56I2W3Q3jcrvWrs2Ni54PrqyRXBTfYdMm1p55F5vF95xa7Q++Vi0ZC85jrzl9r2+f1c3rP7lL2LJrT1tOO6+B/OFcSvXL/6r2yItDLxuzqNx9K13VLKzlw7rY8GVV1SPx7z/D6r7dggQIECAAAECZREQpC9LT2oHAQIECBAgQIAAgZIKpEVk81SzdduvnRM/MSu2tLdFy6MPxcp/vDsPzk+88rOx4p7vZwH0VdHy+K+i86I/yUa7b4m1P32g390R6/71p3le44wTI3oF6Wvq6mLi526Ipbd9Ldqz4Hv6pNS5fl2+TV8pYL/opuuja82q6GjbGBOv+nxW7t3RlS30mha77cwWgF374x/1mQKnUofxvcqqPHDSpbOykebNseyu7+TPrFybn+/+ckRm0LFyRT69T1pMtncaPP1tMeGKT2fB7+G9s/dqf8i0w/MgfUO23depfutc/LVjm/NfCvR+fiovBekHT5vWOzsGDm2MdH16UVI/YUKfcw4IECBAgAABAmURGLAlS2VpjHYQIECAAAECBAgQIHAIC3R3R0cWFK+MTu9qbY0U4B+QBdvTVDSvO2XPbV+xIn/WoOEjIgXv+6R0ftXKqBszJi9nX5XbvWlTdKxenZdXO3pUnzZ0t7dH57q1+Qj7mvqGGDR8+Pb16lPJPT9Iz63N2rk/UhoZn6by6T91UCortbVu1Kjtis3n+d+4KQYNHbrdORkECBAgQIAAgTIICNKXoRe1gQABAgQIECBAgAABAgQIECBAgAABAgQKKbDj34sWsikqTYAAAQIECBAgQIAAAQIECBAgQIAAAQIEiiUgSF+s/lJbAgQIECBAgAABAgQIECBAgAABAgQIECiRgCB9iTpTUwgQIECAAAECBAgQIECAAAECBAgQIECgWAKC9MXqL7UlQIAAAQIECBAgQIAAAQIECBAgQIAAgRIJCNKXqDM1hQABAgQIECBAgAABAgQIECBAgAABAgSKJSBIX6z+UlsCBAgQIECAAAECBAgQIECAAAECBAgQKJHAoBK1RVMIECBAgAABAgQIECiQQPfmzXltawYNii3dXdlnS9QMHBgxYMAOW7HxxQWx/H//r2hf9HJsaW+P2gkT48iv/sUOr91dZnfbpljwxRuipqYmDr/p5qipq6vesmj2ndG5alX1uPHYGTH+oo9Wj+0c3AJ7+3d1cLdG7QgQIECAAIFDQUCQ/lDoZW0kQIAAAQIECBAgcJAJbJj/XLzwyU9E7djmmPE/7o3ffeyj0bHslTjym38XTW+bsV1t1z3xRLx03VXV/IGDh0R3Z2f1eG93Wp55Jlp/83h+W+vz86NpxnHVR2z87dPRtnBB9djOwSGw8M47YvOa1XH4F26MmsGDd1ipvf272uFDZBIgQIAAAQIE3mQBQfo3GVxxBAgQIECAAAECBAhkg+VrshHzWaoGW9MI+nRc35Bv+3+98u1v5Fkjfv+cGP+f/0s0THpLRHd3/8v2+Ljp+BNi5HkfykfSDz322D73vfU738uP1z/9VLx49af6nHNw4ATWz/lVdK1Zlb2cuXbb302/6uzt31W/2x0SIECAAAECBA6IgCD9AWFXKAECBAgQIECAAIFDW2DA1ullBtT2TDNT09ATnB9QW7sdTOe6tdGxdFGe/5ZP/rcYNGxYzzXZVDWV1NXaGl3ZFDgD6+tjYGNjJXu7bT6tTldPcH/ypz6Tn68Edre7eHcZ2UuC7q6unil6etWl93Qr6RG9yxwwsKbnBcWWLdG+YkXUjxu3w1LSrwQ6li+L2pGjdtmeHd68NXOfl5vVObWt0obuTZuic926njbsZIqi7qxPOrJ21h12WPYCpr5Pdfe0fv1/MZGOq3lZuWm6pEram7+ryj22BAgQIECAAIEDLbDtv5kDXRPlEyBAgAABAgQIECBwyAhUAqv9g/M1OwjSdyxbnrs0HDF9W4C+n9SSv/ubWPPP9+W5aSqchredEBMunRVDpx/T58oXrvpMtM57qk/e8ff/5HUFwtc9+WQ+Bc+Is86OadfdUH3mvA+eHV2bNsYJ//JQHkBe/K3Zsfr+e/PzjW9/V4y78COx5I5bonPlsqifPC3GXXJZjH73Gfn59LJh8Te/Hmt/9mD1eY0zTowp190Y9c3N1bw92dnX5ba++GI8f/nFkX7NMGBQbax58P/m1Rg0bERMvv7LMfzkd1SrtbmlJRbfeXuse/jn1bzh735fTL76mhjU1JTn7Un90jREz1xwXvUZaeeZC/+wz/Fx9z2Q/V0Mz/P25u+qz0McECBAgAABAgQOoMC2oScHsBKKJkCAAAECBAgQIEDg0BKoLNQ6YOvo6kqwfmCvBVzTqO00YnpzFvBOqXbU6Py4OpK613Q3DZOnxLDTz4zGE0/OA+StTz6Wz3m/cVHPCPyK7rDTz4gR7/+P+aeS90a3W7YugLvdc7KR5ykNPeGkGHnOB/P9zqWvxPJ77o6GI46KxpNPifbFL8fS276Wj1BPF7x8y83VAH3TzNOy4POI/KXCS1/8wl5P77O/yt3wb3PyAH2qX9O7fi82r18bL33+z6Jj5cq8jelr4V98tRqgH5K9MEkpBexTfiXtSf3S38nIc8+v+qV700uR5Fn5DBi4bezZnvxdVcq3JUCAAAECBAgcLALb/ps5WGqkHgQIECBAgAABAgQIlF4gjaYed8nlUT9hYt7W0eecF0NPensMHDo0P06B+Kc/8J4+Di2/ntMnb+pNt8bI007Pr2n+4wsi0idLaRqVRd/4ej6yftUDP4zJf/rpPD99NV9wYXV/7uOP5AHmasZ+2hn9njMjfdY88E/54rhDZ74zplx5dV7a3PPPzevQufq17OVCW7TMeSjSLwGOvefe/FcDyWH+FbOi7cX50fK7Z/oscLu76u6vclNQvvcCvy9+6YZY/8gvY+U//zAmfvzS2LhwYbQ88Whevenf+YcYMnVqbHr5pXhu1sfy/PTiZMiUKbnJ7ly6NrbG1Kv/PH/W+sceyeekf0s2TVF1yqN+CLv7u+p3uUMCBAgQIECAwEEhIEh/UHSDShAgQIAAAQIECBA4tATSyPkJ2QKwlTT6fWdVdvNtmve8Mvo8zc3e+pvHY+DIMTHslJ6gfLqoblyv6V+yUfXr586NtlcWR/fGTdm5nrneO/qNpO9TyAE6GHPutulapnzlL6OrdUMMahwaG559Nq/RkJPeER1r1uSflNFw9DHRtnBBtC9duldB+v7N21flptH9TW/dtthuU/bSIQXp2xctzIvctHXbMPXIPECfMgdPOzzScWrHpoUv50H6/OJeXzurX69Ldru7u7+r3T7ABQQIECBAgACBAyAgSH8A0BVJgAABAgQIECBAgMCuBdJirlM/e01+0frf/jZezIL0Q46aXs3rfXd3W1vM/9TleQC4d37a7+7a3D/rgB83jB9frcOw44+v7qfR9Cml0fTzs0//tDkL5r+RtK/KrZ88NaLXQrmDp07Lq9X52qo+24Yjj+pT3YYjeoL0nWtW98mvHOysfpXztgQIECBAgACBsgoI0pe1Z7WLAAECBAgQIECAwCEi8Opd380D9E2nnhFjz/9w1Gcj7FtfmB+Lv3rjGxQY0HN/NuXMnqY01U5aNHZXaWBj4w5P1x/WM/q/rnlSNF962XbXNB7TdxHc7S7YTcb+Krf9lVfykgcOH5Fva0ePybebnn+uT402ZX2SUlpbYEdpZ/WrXJsWhe3KDjZvyH55MGxYJduWAAECBAgQIFB4AUH6wnehBhAgQIAAAQIECBA4tAVafzcvBxj3kY9G09tm5PtrH5vzhlEGjegJOrctfClf2DUFiXunhsmT88PWf/91Pg9+Gv2/+uGHe1+yV/uNR/WMPO9Y9krUDBkSI085da/uf70X7225bS8+n82jv74nUJ6mGXri8bzohilT821lZH1aFLd1wYJoPPLI7KXJC/kiuemCwVuvyy/ei680vVHnymWx9tFH+qwtsBeP2CeXpgWNV//i51HXPD56/xIiPTwtnrtmziMx8tTTo27s2D7ltcybG+2vvhqjznxv1NTW9jnngAABAgQIEDi0Bfr+l3loW2g9AQIECBAgQIAAAQIFFKifenhsfObpWHLHLfmc9fkc9lngPM1h3/bsvFhw/bUx6ZOfzgLtXbH8B9+vtjAtgJrSojtvjwFZ0LRu4qSY+LGLq+fT9CtpEdcUGH7uso/H4COPjrZsvvUJs66I4TNnRn1zczQcMT1f1HXuhR+KumwkfEcWmE73pNH0qdzJV14VK+67N7qy0d+V9PItN+e7Ey+7IupGbxtVnu4fe9HFsfIH34uFN1wTS7L6N508M1sJd0ts6eiIw790U+URe7Rd/O3Z+6Xc1Lbn/usl0XjCSbExCzynlwopjT3vj/JtWhQ2/aohTdvz/OUXV43SyZSfzqe0p/XLL86+hs48JVrnPRX/729nx+oH7o/B098aXWvXxvhLZkXj9OmVy/b7dvUvfxFLbu3pi+Pue7DPqP7Ff3VbpAWO1815NI6+9Y5qXdJLjQVXXlE9HvP+P6ju2yFAgAABAgQICNL7GyBAgAABAgQIECBA4KAWSIvI5qnXPOi9KzzxE7NiS3tbtDz6UKz8x7vz4PzEKz8bK+75fhZAXxUtj/8qOi/6k2y0+5ZY+9MHet+a76/715/m28YZJ0b0CtLX1NXFxM/dEEtv+1o+CjyNDE+pc/26fJu+UsB+0U3XR9eaVdHRtjEmXvX5rNy7oytbIDUtdtuZLQC79sc/6jMFTqUO43uVVXngpEtnZSO0m2PZXd/Jn1m5Nj/f/eU+c8FX7tnZdn+Vm5xqs1Hka3/2YF50Wkh20rU3ZtMM9UzXkzKnXXt9LJ49ONb+/Mf5S4yUN+J9H8heWlyddvO0N/VLNzRf+J+iu6M91tz/f/r0x4j3nvWmBunrt64pUDu2Of/FQ09rer4bsgVyU5B+8LSeefor5wYObYx0fXrhUz9hQiXblgABAgQIECCQCwzYkiUWBAgQIECAAAECBAgQKLxANvVKRxYUr4xO72ptjRTgH5AF29NUNK87Zc9tX7Eif9agbN71FLzvk9L5VSujbsyYvJx9VW73pk3RsXp1Xl7t6FFvrA19Krzrg52Vm6auSSPjU5D+6Nl/HWnB3s0tLdtN69L76WlqmM3Zgri1W216n3u9+2ne//Zly/NfGAxqGpqNZB/+eh/1uu9LI+PTlET9p0BKD0x9Vjdq1HbPztcr2LgpBg0dut05GQQIECBAgMChLWAk/aHd/1pPgAABAgQIECBAoDwC2Uj7SoA+NWp3C5HuccOz56apbXaa0vmti76ma/ZVuTWDB0fDxIk7LXZ/ndjTcmsaGqIu++wqpSB2msZnX6b0wqXhAI9G39XCtTsK0Kf2p3oL0O/LvwTPIkCAAAEC5RHY+rvR8jRISwgQIECAAAECBAgQIECAAAECBAgQIECAQFEEBOmL0lPqSYAAAQIECBAgQIAAgQMoMLC+PuonT4vaCZMOYC0UTYAAAQIECBAon4A56cvXp1pEgAABAgQIECBAgAABAgQIECBAgAABAgURMJK+IB2lmgQIECBAgAABAgQIECBAgAABAgQIECBQPgFB+vL1qRYRIECAAAECBAgQIECAAAECBAgQIECAQEEEBOkL0lGqSYAAAQIECBAgQIAAAQIECBAgQIAAAQLlExCkL1+fahEBAgQIECBAgAABAgQIECBAgAABAgQIFERAkL4gHaWaBAgQIECAAAECBAgQIECAAAECBAgQIFA+AUH68vWpFhEgQIAAAQIECBAgQIAAAQIECBAgQIBAQQQE6QvSUapJgAABAgQIECBAgAABAgQIECBAgAABAuUTEKQvX59qEQECBAgQIECAAAECBAgQIECAAAECBAgURECQviAdpZoECBAgQIAAAQIECBAgQIAAAQIECBAgUD4BQfry9akWESBAgAABAgQIECBAgAABAgQIECBAgEBBBATpC9JRqkmAAAECBAgQIECAAAECBAgQIECAAAEC5RMQpC9fn2oRAQIECBAgQIAAAQIECBAgQIAAAQIECBREQJC+IB2lmgQIECBAgAABAgQIECBAgAABAgQIECBQPgFB+vL1qRYRIECAAAECBAgQIECAAAECBAgQIECAQEEEBOkL0lGqSYAAAQIECBAgQIAAAQIECBAgQIAAAQLlExCkL1+fahEBAgQIECBAgAABAgQIECBAgAABAgQIFERAkL4gHaWaBAgQIECAAAECBAgQIECAAAECBAgQIFA+AUH68vWpFhEgQIAAAQIECBAgQIAAAQIECBAgQIBAQQQE6QvSUapJgAABAgQIECBAgAABAgQIECBAgAABAuUTEKQvX59qEQECBAgQIECAAAECBAgQIECAAAECBAgURECQviAdpZoECBAgQIAAAQIECBAgQIAAAQIECBAgUD4BQfry9akWESBAgAABAgQIECBAgAABAgQIECBAgEBBBATpC9JRqkmAAAECBAgQIECAAAECBAgQIECAAAEC5RMQpC9fn2oRAQIECBAgQIAAAQIECBAgQIAAAQIECBREQJC+IB2lmgQIECBAgAABAgQIECBAgAABAgQIECBQPgFB+vL1qRYRIECAAAECBAgQIECAAAECBAgQIECAQEEEBOkL0lGqSYAAAQIECBAgQIAAAQIECBAgQIAAAQLlExCkL1+fahEBAgQIECBAgAABAgQIECBAgAABAgQIFERAkL4gHaWaBAgQIECAAAECBAgQIECAAAECBAgQIFA+AUH68vWpFhEgQIAAAQIECBAgQIAAAQIECBAgQIBAQQQE6QvSUapJgAABAgQIECBAgAABAgQIECBAgAABAuUTEKQvX59qEQECBAgQIECAAAECBAgQIECAAAECBAgURECQviAdpZoECBAgQIAAAQIECBAgQIAAAQIECBAgUD4BQfry9akWESBAgAABAgQIECBAgAABAgQIECBAgEBBBATpC9JRqkmAAAECBAgQIECAAAECBAgQIECAAAEC5RMQpC9fn2oRAQIECBAgQIAAAQIECBAgQIAAAQIECBREQJC+IB2lmgQIECBAgAABAgQIECBAgAABAgQIECBQPgFB+vL1qRYRIECAAAECBAgQIECAAAECBAgQIECAQEEEBOkL0lGqSYAAAQIECBAgQIAAAQIECBAgQIAAAQLlExCkL1+fahEBAgQIECBAgAABAgQIECBAgAABAgQIFERAkL4gHaWaBAgQIECAAAECBAgQIECAAAECBAgQIFA+AUH68vWpFhEgQIAAAQIECBAgQIAAAQIECBAgQIBAQQQE6QvSUapJgAABAgQIECBAgAABAgQIECBAgAABAuUTEKQvX59qEQECBAgQIECAAAECBAgQIECAAAECBAgURECQviAdpZoECBAgQIAAAQIECBAgQIAAAQIECBAgUD4BQfry9akWESBAgAABAgQIECBAgAABAgQIECBAgEBBBATpC9JRqkmAAAECBAgQIECAAAECBAgQIECAAAEC5RMQpC9fn2oRAQIECBAgQIAAAQIECBAgQIAAAQIECBREQJC+IB2lmgQIECBAgAABAgQIECBAgAABAgQIECBQPgFB+vL1qRYRIECAAAECBAgQIECAAAECBAgQIECAQEEEBOkL0lGqSYAAAQIECBAgQIAAAQIECBAgQIAAAQLlExCkL1+fahEBAgQIECBAgAABAgQIECBAgAABAgQIFERAkL4gHaWaBAgQIECAAAECBAgQIECAAAECBAgQIFA+AUH68vWpFhEgQIAAAQIECBAgQIAAAQIECBAgQIBAQQQE6QvSUapJgAABAgQIECBAgAABAgQIECBAgAABAuUTEKQvX59qEQECBAgQIECAAAECBAgQIECAAAECBAgURECQviAdpZoECBAgQIAAAQIECBAgQIAAAQIECBAgUD4BQfry9akWESBAgAABAgQIECBAgAABAgQIECBAgEBBBATpC9JRqkmAAAECBAgQIECAAAECBAgQIECAAAEC5RMQpC9fn2oRAQIECBAgQIAAAQIECBAgQIAAAQIECBREQJC+IB2lmgQIECBAgAABAgQIECBAgAABAgQIECBQPgFB+vL1qRYRIECAAAECBAgQIECAAAECBAgQIECAQEEEBOkL0lGqSYAAAQIECBAgQIAAAQIECBAgQIAAAQLlExCkL1+fahEBAgQIECBAgAABAgQIECBAgAABAgQIFERAkL4gHaWaBAgQIECAAAECBAgQIECAAAECBAgQIFA+AUH68vWpFhEgQIAAAQIECBAgQIAAAQIECBAgQIBAQQQE6QvSUapJgAABAgQIECBAgAABAgQIECBAgAABAuUTEKQvX59qEQECBAgQIECAAAECBAgQIECAAAECBAgURECQviAdpZoECBAgQIAAAQIECBAgQIAAAQIECBAgUD6B/w+jDwOxnklQVQAAAABJRU5ErkJggg==" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple example\n", + "\n", + "Let's consider a toy example: I have a system that accepts logs and perform two separate sub-tasks. First, it will summarize them. Second, it will summarize any failure modes captured in the logs. I want to perform these two operations in two different sub-graphs.\n", + "\n", + "The most important thing to recognize is the information transfer between the graphs. `Entry Graph` is the parent, and each of the two sub-graphs are defined as nodes in `Entry Graph`. Both subgraphs inherit state from the parent `Entry Graph`; I can access `docs` in each of the sub-graphs simply by specifying it in the sub-graph state (see diagram). Each subgraph can have its own private state. And any values that I want propagated back to the parent `Entry Graph` (for final reporting) simply need to be defined in my `Entry Graph` state (e.g., `summary report` and `failure report`).\n", + "\n", + "![Screenshot 2024-07-12 at 10.35.41 AM.png](attachment:9145adc1-ce9d-4a22-8183-e13796d4a388.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from operator import add\n", + "from typing import List, TypedDict, Optional, Annotated, Dict\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import StateGraph, START, END\n", + "\n", + "\n", + "# The structure of the logs\n", + "class Logs(TypedDict):\n", + " id: str\n", + " question: str\n", + " docs: Optional[List]\n", + " answer: str\n", + " grade: Optional[int]\n", + " grader: Optional[str]\n", + " feedback: Optional[str]\n", + "\n", + "\n", + "# Failure Analysis Sub-graph\n", + "class FailureAnalysisState(TypedDict):\n", + " docs: List[Logs]\n", + " failures: List[Logs]\n", + " fa_summary: str\n", + "\n", + "\n", + "def get_failures(state):\n", + " docs = state[\"docs\"]\n", + " failures = [doc for doc in docs if \"grade\" in doc]\n", + " return {\"failures\": failures}\n", + "\n", + "\n", + "def generate_summary(state):\n", + " failures = state[\"failures\"]\n", + " # Add fxn: fa_summary = summarize(failures)\n", + " fa_summary = \"Poor quality retrieval of Chroma documentation.\"\n", + " return {\"fa_summary\": fa_summary}\n", + "\n", + "\n", + "fa_builder = StateGraph(FailureAnalysisState)\n", + "fa_builder.add_node(\"get_failures\", get_failures)\n", + "fa_builder.add_node(\"generate_summary\", generate_summary)\n", + "fa_builder.add_edge(START, \"get_failures\")\n", + "fa_builder.add_edge(\"get_failures\", \"generate_summary\")\n", + "fa_builder.add_edge(\"generate_summary\", END)\n", + "\n", + "\n", + "# Summarization subgraph\n", + "class QuestionSummarizationState(TypedDict):\n", + " docs: List[Logs]\n", + " qs_summary: str\n", + " report: str\n", + "\n", + "\n", + "def generate_summary(state):\n", + " docs = state[\"docs\"]\n", + " # Add fxn: summary = summarize(docs)\n", + " summary = \"Questions focused on usage of ChatOllama and Chroma vector store.\"\n", + " return {\"qs_summary\": summary}\n", + "\n", + "\n", + "def send_to_slack(state):\n", + " qs_summary = state[\"qs_summary\"]\n", + " # Add fxn: report = report_generation(qs_summary)\n", + " report = \"foo bar baz\"\n", + " return {\"report\": report}\n", + "\n", + "\n", + "def format_report_for_slack(state):\n", + " report = state[\"report\"]\n", + " # Add fxn: formatted_report = report_format(report)\n", + " formatted_report = \"foo bar\"\n", + " return {\"report\": formatted_report}\n", + "\n", + "\n", + "qs_builder = StateGraph(QuestionSummarizationState)\n", + "qs_builder.add_node(\"generate_summary\", generate_summary)\n", + "qs_builder.add_node(\"send_to_slack\", send_to_slack)\n", + "qs_builder.add_node(\"format_report_for_slack\", format_report_for_slack)\n", + "qs_builder.add_edge(START, \"generate_summary\")\n", + "qs_builder.add_edge(\"generate_summary\", \"send_to_slack\")\n", + "qs_builder.add_edge(\"send_to_slack\", \"format_report_for_slack\")\n", + "qs_builder.add_edge(\"format_report_for_slack\", END)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that each sub-graph has its own state, `QuestionSummarizationState` and `FailureAnalysisState`.\n", + " \n", + "After defining each sub-graph, we put everything together." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Dummy logs\n", + "question_answer = Logs(\n", + " id=\"1\",\n", + " question=\"How can I import ChatOllama?\",\n", + " answer=\"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\",\n", + ")\n", + "\n", + "question_answer_feedback = Logs(\n", + " id=\"2\",\n", + " question=\"How can I use Chroma vector store?\",\n", + " answer=\"To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).\",\n", + " grade=0,\n", + " grader=\"Document Relevance Recall\",\n", + " feedback=\"The retrieved documents discuss vector stores in general, but not Chroma specifically\",\n", + ")\n", + "\n", + "\n", + "# Entry Graph\n", + "class EntryGraphState(TypedDict):\n", + " raw_logs: Annotated[List[Dict], add]\n", + " docs: Annotated[List[Logs], add] # This will be used in sub-graphs\n", + " fa_summary: str # This will be generated in the FA sub-graph\n", + " report: str # This will be generated in the QS sub-graph\n", + "\n", + "\n", + "def convert_logs_to_docs(state):\n", + " # Get logs\n", + " raw_logs = state[\"raw_logs\"]\n", + " docs = [question_answer, question_answer_feedback]\n", + " return {\"docs\": docs}\n", + "\n", + "\n", + "entry_builder = StateGraph(EntryGraphState)\n", + "entry_builder.add_node(\"convert_logs_to_docs\", convert_logs_to_docs)\n", + "entry_builder.add_node(\"question_summarization\", qs_builder.compile())\n", + "entry_builder.add_node(\"failure_analysis\", fa_builder.compile())\n", + "\n", + "entry_builder.add_edge(START, \"convert_logs_to_docs\")\n", + "entry_builder.add_edge(\"convert_logs_to_docs\", \"failure_analysis\")\n", + "entry_builder.add_edge(\"convert_logs_to_docs\", \"question_summarization\")\n", + "entry_builder.add_edge(\"failure_analysis\", END)\n", + "entry_builder.add_edge(\"question_summarization\", END)\n", + "\n", + "graph = entry_builder.compile()\n", + "\n", + "from IPython.display import Image, display\n", + "\n", + "# Setting xray to 1 will show the internal structure of the nested graph\n", + "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'raw_logs': [{'foo': 'bar'}, {'foo': 'baz'}],\n", + " 'docs': [{'id': '1',\n", + " 'question': 'How can I import ChatOllama?',\n", + " 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n", + " {'id': '2',\n", + " 'question': 'How can I use Chroma vector store?',\n", + " 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n", + " 'grade': 0,\n", + " 'grader': 'Document Relevance Recall',\n", + " 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'},\n", + " {'id': '1',\n", + " 'question': 'How can I import ChatOllama?',\n", + " 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n", + " {'id': '2',\n", + " 'question': 'How can I use Chroma vector store?',\n", + " 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n", + " 'grade': 0,\n", + " 'grader': 'Document Relevance Recall',\n", + " 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'},\n", + " {'id': '1',\n", + " 'question': 'How can I import ChatOllama?',\n", + " 'answer': \"To import ChatOllama, use: 'from langchain_community.chat_models import ChatOllama.'\"},\n", + " {'id': '2',\n", + " 'question': 'How can I use Chroma vector store?',\n", + " 'answer': 'To use Chroma, define: rag_chain = create_retrieval_chain(retriever, question_answer_chain).',\n", + " 'grade': 0,\n", + " 'grader': 'Document Relevance Recall',\n", + " 'feedback': 'The retrieved documents discuss vector stores in general, but not Chroma specifically'}],\n", + " 'fa_summary': 'Poor quality retrieval of Chroma documentation.',\n", + " 'report': 'foo bar'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_logs = [{\"foo\": \"bar\"}, {\"foo\": \"baz\"}]\n", + "graph.invoke({\"raw_logs\": raw_logs}, debug=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom reducer functions to manage state\n", + "\n", + "Now, let's highlight a possible stumbling block when we use the same `State` across multiple sub-graphs.\n", + " \n", + "We will create two graphs: a parent graph with a few nodes and a child graph that is added as a node in the parent.\n", + "\n", + "We define a custom [reducer](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) function for our state." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Annotated\n", + "\n", + "from typing_extensions import TypedDict\n", + "\n", + "\n", + "def reduce_list(left: list | None, right: list | None) -> list:\n", + " if not left:\n", + " left = []\n", + " if not right:\n", + " right = []\n", + " return left + right\n", + "\n", + "\n", + "class ChildState(TypedDict):\n", + " name: str\n", + " path: Annotated[list[str], reduce_list]\n", + "\n", + "\n", + "class ParentState(TypedDict):\n", + " name: str\n", + " path: Annotated[list[str], reduce_list]\n", + "\n", + "\n", + "child_builder = StateGraph(ChildState)\n", + "\n", + "child_builder.add_node(\"child_start\", lambda state: {\"path\": [\"child_start\"]})\n", + "child_builder.add_edge(START, \"child_start\")\n", + "child_builder.add_node(\"child_middle\", lambda state: {\"path\": [\"child_middle\"]})\n", + "child_builder.add_node(\"child_end\", lambda state: {\"path\": [\"child_end\"]})\n", + "child_builder.add_edge(\"child_start\", \"child_middle\")\n", + "child_builder.add_edge(\"child_middle\", \"child_end\")\n", + "child_builder.add_edge(\"child_end\", END)\n", + "\n", + "builder = StateGraph(ParentState)\n", + "\n", + "builder.add_node(\"grandparent\", lambda state: {\"path\": [\"grandparent\"]})\n", + "builder.add_edge(START, \"grandparent\")\n", + "builder.add_node(\"parent\", lambda state: {\"path\": [\"parent\"]})\n", + "builder.add_node(\"child\", child_builder.compile())\n", + "builder.add_node(\"sibling\", lambda state: {\"path\": [\"sibling\"]})\n", + "builder.add_node(\"fin\", lambda state: {\"path\": [\"fin\"]})\n", + "\n", + "# Add connections\n", + "builder.add_edge(\"grandparent\", \"parent\")\n", + "builder.add_edge(\"parent\", \"child\")\n", + "builder.add_edge(\"parent\", \"sibling\")\n", + "builder.add_edge(\"child\", \"fin\")\n", + "builder.add_edge(\"sibling\", \"fin\")\n", + "builder.add_edge(\"fin\", END)\n", + "graph = builder.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "# Setting xray to 1 will show the internal structure of the nested graph\n", + "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36;1m\u001b[1;3m[0:tasks]\u001b[0m \u001b[1mStarting step 0 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3m__start__\u001b[0m -> {'name': 'test'}\n", + "\u001b[36;1m\u001b[1;3m[0:writes]\u001b[0m \u001b[1mFinished step 0 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", + "\u001b[36;1m\u001b[1;3m[0:checkpoint]\u001b[0m \u001b[1mState at the end of step 0:\n", + "\u001b[0m{'name': 'test', 'path': []}\n", + "\u001b[36;1m\u001b[1;3m[1:tasks]\u001b[0m \u001b[1mStarting step 1 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mgrandparent\u001b[0m -> {'name': 'test', 'path': []}\n", + "\u001b[36;1m\u001b[1;3m[1:writes]\u001b[0m \u001b[1mFinished step 1 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['grandparent']\n", + "\u001b[36;1m\u001b[1;3m[1:checkpoint]\u001b[0m \u001b[1mState at the end of step 1:\n", + "\u001b[0m{'name': 'test', 'path': ['grandparent']}\n", + "\u001b[36;1m\u001b[1;3m[2:tasks]\u001b[0m \u001b[1mStarting step 2 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mparent\u001b[0m -> {'name': 'test', 'path': ['grandparent']}\n", + "\u001b[36;1m\u001b[1;3m[2:writes]\u001b[0m \u001b[1mFinished step 2 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['parent']\n", + "\u001b[36;1m\u001b[1;3m[2:checkpoint]\u001b[0m \u001b[1mState at the end of step 2:\n", + "\u001b[0m{'name': 'test', 'path': ['grandparent', 'parent']}\n", + "\u001b[36;1m\u001b[1;3m[3:tasks]\u001b[0m \u001b[1mStarting step 3 with 2 tasks:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mchild\u001b[0m -> {'name': 'test', 'path': ['grandparent', 'parent']}\n", + "- \u001b[32;1m\u001b[1;3msibling\u001b[0m -> {'name': 'test', 'path': ['grandparent', 'parent']}\n", + "\u001b[36;1m\u001b[1;3m[3:writes]\u001b[0m \u001b[1mFinished step 3 with writes to 2 channels:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", + "- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['grandparent', 'parent', 'child_start', 'child_middle', 'child_end'], ['sibling']\n", + "\u001b[36;1m\u001b[1;3m[3:checkpoint]\u001b[0m \u001b[1mState at the end of step 3:\n", + "\u001b[0m{'name': 'test',\n", + " 'path': ['grandparent',\n", + " 'parent',\n", + " 'grandparent',\n", + " 'parent',\n", + " 'child_start',\n", + " 'child_middle',\n", + " 'child_end',\n", + " 'sibling']}\n", + "\u001b[36;1m\u001b[1;3m[4:tasks]\u001b[0m \u001b[1mStarting step 4 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mfin\u001b[0m -> {'name': 'test',\n", + " 'path': ['grandparent',\n", + " 'parent',\n", + " 'grandparent',\n", + " 'parent',\n", + " 'child_start',\n", + " 'child_middle',\n", + " 'child_end',\n", + " 'sibling']}\n", + "\u001b[36;1m\u001b[1;3m[4:writes]\u001b[0m \u001b[1mFinished step 4 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['fin']\n", + "\u001b[36;1m\u001b[1;3m[4:checkpoint]\u001b[0m \u001b[1mState at the end of step 4:\n", + "\u001b[0m{'name': 'test',\n", + " 'path': ['grandparent',\n", + " 'parent',\n", + " 'grandparent',\n", + " 'parent',\n", + " 'child_start',\n", + " 'child_middle',\n", + " 'child_end',\n", + " 'sibling',\n", + " 'fin']}\n" + ] + }, + { + "data": { + "text/plain": [ + "{'name': 'test',\n", + " 'path': ['grandparent',\n", + " 'parent',\n", + " 'grandparent',\n", + " 'parent',\n", + " 'child_start',\n", + " 'child_middle',\n", + " 'child_end',\n", + " 'sibling',\n", + " 'fin']}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph.invoke({\"name\": \"test\"}, debug=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice here that the `[\"grandparent\", \"parent\"]` sequence is duplicated! \n", + "\n", + "This is because our child state has received the full parent state and returns the full parent state once it terminates. \n", + "\n", + "To avoid duplication or conflicts in state, you typically would do one or more of the following:\n", + "\n", + "1. Handle duplicates in your `reducer` function.\n", + "2. Call the child graph from within a python function. In that function, handle the state as needed. \n", + "3. Update the child graph keys to avoid conflicts. You would still need to ensure the output can be interpreted by the parent, however.\n", + "\n", + "Let's re-implement the graph using technique (1) and add unique IDs for every value in the list. This is what is done in [`MessageGraph`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "\n", + "\n", + "def reduce_list(left: list | None, right: list | None) -> list:\n", + " \"\"\"Append the right-hand list, replacing any elements with the same id in the left-hand list.\"\"\"\n", + " if not left:\n", + " left = []\n", + " if not right:\n", + " right = []\n", + " left_, right_ = [], []\n", + " for orig, new in [(left, left_), (right, right_)]:\n", + " for val in orig:\n", + " if not isinstance(val, dict):\n", + " val = {\"val\": val}\n", + " if \"id\" not in val:\n", + " val[\"id\"] = str(uuid.uuid4())\n", + " new.append(val)\n", + " # Merge the two lists\n", + " left_idx_by_id = {val[\"id\"]: i for i, val in enumerate(left_)}\n", + " merged = left_.copy()\n", + " for val in right_:\n", + " if (existing_idx := left_idx_by_id.get(val[\"id\"])) is not None:\n", + " merged[existing_idx] = val\n", + " else:\n", + " merged.append(val)\n", + " return merged\n", + "\n", + "\n", + "class ChildState(TypedDict):\n", + " name: str\n", + " path: Annotated[list[str], reduce_list]\n", + "\n", + "\n", + "class ParentState(TypedDict):\n", + " name: str\n", + " path: Annotated[list[str], reduce_list]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "child_builder = StateGraph(ChildState)\n", + "\n", + "child_builder.add_node(\"child_start\", lambda state: {\"path\": [\"child_start\"]})\n", + "child_builder.add_edge(START, \"child_start\")\n", + "child_builder.add_node(\"child_middle\", lambda state: {\"path\": [\"child_middle\"]})\n", + "child_builder.add_node(\"child_end\", lambda state: {\"path\": [\"child_end\"]})\n", + "child_builder.add_edge(\"child_start\", \"child_middle\")\n", + "child_builder.add_edge(\"child_middle\", \"child_end\")\n", + "child_builder.add_edge(\"child_end\", END)\n", + "\n", + "builder = StateGraph(ParentState)\n", + "\n", + "builder.add_node(\"grandparent\", lambda state: {\"path\": [\"grandparent\"]})\n", + "builder.add_edge(START, \"grandparent\")\n", + "builder.add_node(\"parent\", lambda state: {\"path\": [\"parent\"]})\n", + "builder.add_node(\"child\", child_builder.compile())\n", + "builder.add_node(\"sibling\", lambda state: {\"path\": [\"sibling\"]})\n", + "builder.add_node(\"fin\", lambda state: {\"path\": [\"fin\"]})\n", + "\n", + "# Add connections\n", + "builder.add_edge(\"grandparent\", \"parent\")\n", + "builder.add_edge(\"parent\", \"child\")\n", + "builder.add_edge(\"parent\", \"sibling\")\n", + "builder.add_edge(\"child\", \"fin\")\n", + "builder.add_edge(\"sibling\", \"fin\")\n", + "builder.add_edge(\"fin\", END)\n", + "graph = builder.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "# Setting xray to 1 will show the internal structure of the nested graph\n", + "display(Image(graph.get_graph(xray=1).draw_mermaid_png()))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36;1m\u001b[1;3m[0:tasks]\u001b[0m \u001b[1mStarting step 0 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3m__start__\u001b[0m -> {'name': 'test'}\n", + "\u001b[36;1m\u001b[1;3m[0:writes]\u001b[0m \u001b[1mFinished step 0 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", + "\u001b[36;1m\u001b[1;3m[0:checkpoint]\u001b[0m \u001b[1mState at the end of step 0:\n", + "\u001b[0m{'name': 'test', 'path': []}\n", + "\u001b[36;1m\u001b[1;3m[1:tasks]\u001b[0m \u001b[1mStarting step 1 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mgrandparent\u001b[0m -> {'name': 'test', 'path': []}\n", + "\u001b[36;1m\u001b[1;3m[1:writes]\u001b[0m \u001b[1mFinished step 1 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['grandparent']\n", + "\u001b[36;1m\u001b[1;3m[1:checkpoint]\u001b[0m \u001b[1mState at the end of step 1:\n", + "\u001b[0m{'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'}]}\n", + "\u001b[36;1m\u001b[1;3m[2:tasks]\u001b[0m \u001b[1mStarting step 2 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mparent\u001b[0m -> {'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'}]}\n", + "\u001b[36;1m\u001b[1;3m[2:writes]\u001b[0m \u001b[1mFinished step 2 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['parent']\n", + "\u001b[36;1m\u001b[1;3m[2:checkpoint]\u001b[0m \u001b[1mState at the end of step 2:\n", + "\u001b[0m{'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'}]}\n", + "\u001b[36;1m\u001b[1;3m[3:tasks]\u001b[0m \u001b[1mStarting step 3 with 2 tasks:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mchild\u001b[0m -> {'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'}]}\n", + "- \u001b[32;1m\u001b[1;3msibling\u001b[0m -> {'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'}]}\n", + "\u001b[36;1m\u001b[1;3m[3:writes]\u001b[0m \u001b[1mFinished step 3 with writes to 2 channels:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mname\u001b[0m -> 'test'\n", + "- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", + " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", + " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", + " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'}], ['sibling']\n", + "\u001b[36;1m\u001b[1;3m[3:checkpoint]\u001b[0m \u001b[1mState at the end of step 3:\n", + "\u001b[0m{'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", + " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", + " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", + " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'},\n", + " {'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718', 'val': 'sibling'}]}\n", + "\u001b[36;1m\u001b[1;3m[4:tasks]\u001b[0m \u001b[1mStarting step 4 with 1 task:\n", + "\u001b[0m- \u001b[32;1m\u001b[1;3mfin\u001b[0m -> {'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", + " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", + " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", + " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'},\n", + " {'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718', 'val': 'sibling'}]}\n", + "\u001b[36;1m\u001b[1;3m[4:writes]\u001b[0m \u001b[1mFinished step 4 with writes to 1 channel:\n", + "\u001b[0m- \u001b[33;1m\u001b[1;3mpath\u001b[0m -> ['fin']\n", + "\u001b[36;1m\u001b[1;3m[4:checkpoint]\u001b[0m \u001b[1mState at the end of step 4:\n", + "\u001b[0m{'name': 'test',\n", + " 'path': [{'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49', 'val': 'grandparent'},\n", + " {'id': '2a6f0263-3949-4e47-a210-57f817e6097d', 'val': 'parent'},\n", + " {'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088', 'val': 'child_start'},\n", + " {'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783', 'val': 'child_middle'},\n", + " {'id': '669dd810-360f-4694-a9f3-49597f23376a', 'val': 'child_end'},\n", + " {'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718', 'val': 'sibling'},\n", + " {'id': 'a4328c5f-845a-43de-b3d7-53a39208e316', 'val': 'fin'}]}\n" + ] + }, + { + "data": { + "text/plain": [ + "{'name': 'test',\n", + " 'path': [{'val': 'grandparent', 'id': '79a81f03-d16d-4d12-94a6-4ba29fc9ce49'},\n", + " {'val': 'parent', 'id': '2a6f0263-3949-4e47-a210-57f817e6097d'},\n", + " {'val': 'child_start', 'id': 'd1c1bab0-6e19-4846-a470-e9cc2eb85088'},\n", + " {'val': 'child_middle', 'id': 'e0fcb647-1e9e-4ae4-b560-0046515d5783'},\n", + " {'val': 'child_end', 'id': '669dd810-360f-4694-a9f3-49597f23376a'},\n", + " {'val': 'sibling', 'id': '137dbc2f-b33c-4ea4-8b04-a62215ba9718'},\n", + " {'val': 'fin', 'id': 'a4328c5f-845a-43de-b3d7-53a39208e316'}]}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph.invoke({\"name\": \"test\"}, debug=True)" ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "graph.invoke({\"name\": \"test\"}, debug=True)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/examples/time-travel.ipynb b/examples/time-travel.ipynb index 4eccaf4e5..dcdd035c0 100644 --- a/examples/time-travel.ipynb +++ b/examples/time-travel.ipynb @@ -246,7 +246,7 @@ "id": "6845ed6a-d155-4105-9160-28849877248b", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\n\nmemory = SqliteSaver.from_conn_string(\":memory:\")"] + "source": ["from langgraph.checkpoint.memory import MemorySaver\n\nmemory = MemorySaver()"] }, { "cell_type": "code", diff --git a/examples/tutorials/rag-agent-testing.ipynb b/examples/tutorials/rag-agent-testing.ipynb index bfbd5fd5a..5d83ae068 100644 --- a/examples/tutorials/rag-agent-testing.ipynb +++ b/examples/tutorials/rag-agent-testing.ipynb @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "8d0866a9-70d4-4c43-b538-80ddb50e0e32", "metadata": {}, "outputs": [], @@ -145,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "id": "b7dc9931-f037-40b2-a9e9-da22aee2fe62", "metadata": {}, "outputs": [], @@ -153,10 +153,32 @@ "from langchain_fireworks import ChatFireworks\n", "\n", "model_tested = \"firefunction-v2\"\n", - "metadata = \"CRAG, firefunction-v\"\n", + "metadata = \"CRAG, firefunction-v2\"\n", "llm = ChatFireworks(model=\"accounts/fireworks/models/firefunction-v2\", temperature=0)" ] }, + { + "cell_type": "markdown", + "id": "f3e646bb-0f9b-40dc-b382-8e4338df2cfe", + "metadata": {}, + "source": [ + "We can test [Mistal-Lage-v2](https://mistral.ai/news/mistral-large-2407/)." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "9a454a8f-80a7-417a-819d-37a3e52e0412", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_mistralai.chat_models import ChatMistralAI\n", + "\n", + "model_tested = \"mistral-large-2407\"\n", + "metadata = \"CRAG, mistral-large-2407\"\n", + "llm = ChatMistralAI(model=model_tested, temperature=0)" + ] + }, { "attachments": { "599c43c4-9da4-4875-b644-f3ed2b34a179.png": { @@ -196,10 +218,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d3560759-9e2d-4362-a06f-3a257d0a3088", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "USER_AGENT environment variable not set, consider setting it to identify your requests.\n" + ] + } + ], "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", @@ -254,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "a8cd1080-f8da-41b8-8e52-9a75d9ce1258", "metadata": {}, "outputs": [], @@ -276,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 43, "id": "3a4f8620-d072-49d8-bbeb-76fbf9ee4097", "metadata": {}, "outputs": [], @@ -308,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 45, "id": "f3411e9a-53ef-45d3-9c95-0f64b7218c25", "metadata": {}, "outputs": [], @@ -339,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 46, "id": "e9b474e9-cebd-4452-a694-4e4cc622fd52", "metadata": {}, "outputs": [], @@ -363,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 47, "id": "c3856ef6-e5db-4fa6-97e2-c9495f159d77", "metadata": {}, "outputs": [], @@ -383,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 48, "id": "13f4be82-ef3b-4d3a-901d-82eb28abb594", "metadata": {}, "outputs": [], @@ -472,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 49, "id": "a0c66d91-8804-4fd7-a29c-2852faa31d19", "metadata": {}, "outputs": [], @@ -528,13 +558,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 50, "id": "3e13495c-dfe2-4eea-8786-7d3b8f8138e9", "metadata": {}, "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ "" ] @@ -544,7 +574,7 @@ } ], "source": [ - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import START, END, StateGraph\n", "from langgraph.prebuilt import tools_condition\n", "from IPython.display import Image, display\n", @@ -567,7 +597,7 @@ "builder.add_edge(\"tools\", \"assistant\")\n", "\n", "# The checkpointer lets the graph persist its state\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "react_graph = builder.compile(checkpointer=memory)\n", "\n", "# Show\n", @@ -584,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 51, "id": "71301681-ddc5-46a9-8092-96693dcdef1b", "metadata": {}, "outputs": [], @@ -606,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 52, "id": "87c2aea6-49d9-4d32-9da6-7851fda021ea", "metadata": {}, "outputs": [ @@ -616,7 +646,7 @@ "['retrieve_documents', 'grade_document_retrieval', 'generate_answer']" ] }, - "execution_count": 12, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -676,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 53, "id": "9520cb91-5ba2-4915-8aa3-ce0e86d4381b", "metadata": {}, "outputs": [], @@ -712,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 54, "id": "c949a92e-9f76-41a0-8008-728b9e51dc1f", "metadata": {}, "outputs": [], @@ -770,13 +800,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 58, "id": "769ade7f-e19f-467c-b359-ca7e5ded45c2", "metadata": {}, "outputs": [ { "data": { - "image/jpeg": 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", 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", "text/plain": [ "" ] @@ -955,25 +985,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "3dbbfde9-37cc-427f-abee-64cc3366c3b7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'response': 'The types of agent memory are short-term memory and long-term memory. Short-term memory involves in-context learning and temporary information processing. Long-term memory allows the agent to retain and recall information over extended periods, often using an external vector store for fast retrieval.',\n", - " 'steps': ['retrieve_documents',\n", - " 'grade_document_retrieval',\n", - " 'web_search',\n", - " 'generate_answer']}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "def predict_custom_agent_answer(example: dict):\n", " config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n", @@ -1018,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "2ef03dad-d161-4002-abb4-857dc034d2fb", "metadata": {}, "outputs": [], @@ -1072,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "88a78e4e-c1d4-456a-9f4c-a207e3255086", "metadata": {}, "outputs": [], @@ -1124,7 +1141,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "a6d07b7c-a6ab-4b71-9e31-fdb61348b7cb", "metadata": {}, "outputs": [], @@ -1199,7 +1216,7 @@ " data=dataset_name,\n", " evaluators=[answer_evaluator, check_trajectory_react],\n", " experiment_prefix=experiment_prefix + \"-answer-and-tool-use\",\n", - " num_repetitions=3,\n", + " num_repetitions=5,\n", " metadata={\"version\": metadata},\n", ")\n", "\n", @@ -1209,7 +1226,7 @@ " data=dataset_name,\n", " evaluators=[answer_evaluator, check_trajectory_custom],\n", " experiment_prefix=experiment_prefix + \"-answer-and-tool-use\",\n", - " num_repetitions=3,\n", + " num_repetitions=5,\n", " metadata={\"version\": metadata},\n", ")" ] diff --git a/examples/tutorials/sql-agent-diagram.png b/examples/tutorials/sql-agent-diagram.png index 9c8d8a27c..698ef98e6 100644 Binary files a/examples/tutorials/sql-agent-diagram.png and b/examples/tutorials/sql-agent-diagram.png differ diff --git a/examples/tutorials/tnt-llm/img/tnt_llm.png b/examples/tutorials/tnt-llm/img/tnt_llm.png index dbe96813e..400d083ed 100644 Binary files a/examples/tutorials/tnt-llm/img/tnt_llm.png and b/examples/tutorials/tnt-llm/img/tnt_llm.png differ diff --git a/examples/tutorials/tool-calling-agent-local.ipynb b/examples/tutorials/tool-calling-agent-local.ipynb index 4ccde6444..5c60336e8 100644 --- a/examples/tutorials/tool-calling-agent-local.ipynb +++ b/examples/tutorials/tool-calling-agent-local.ipynb @@ -15,6 +15,7 @@ "\n", "```\n", "ollama pull llama3-groq-tool-use\n", + "ollama pull llama3.1\n", "```\n", "\n", "And also, we'll use the Ollama partner package.\n", @@ -39,35 +40,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "120c1da8-e45e-4ffa-9ac1-a536026c7e1c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install -qU langchain-ollama" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "32c0504b-007a-4af6-9976-c7294ed26b73", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "USER_AGENT environment variable not set, consider setting it to identify your requests.\n" - ] - } - ], + "outputs": [], "source": [ "# /// LLM ///\n", "\n", "from langchain_ollama import ChatOllama\n", "\n", "llm = ChatOllama(\n", - " model=\"llama3-groq-tool-use\",\n", + " # model=\"llama3-groq-tool-use\",\n", + " model=\"llama3.1\",\n", " temperature=0,\n", ")\n", "\n", @@ -129,14 +134,13 @@ " for d in web_results\n", " ]\n", "\n", - "\n", "# Tool list\n", "tools = [retrieve_documents, web_search]" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "30052f47-2b5d-46f5-9873-eb716145cda1", "metadata": {}, "outputs": [], @@ -148,11 +152,9 @@ "from langgraph.graph.message import AnyMessage, add_messages\n", "from typing_extensions import TypedDict\n", "\n", - "\n", "class State(TypedDict):\n", " messages: Annotated[list[AnyMessage], add_messages]\n", "\n", - "\n", "class Assistant:\n", " def __init__(self, runnable: Runnable):\n", " \"\"\"\n", @@ -209,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "40504a0b-8a99-4420-a6bf-561c62e893d1", "metadata": {}, "outputs": [ @@ -251,7 +253,7 @@ "\n", "\n", "from IPython.display import Image, display\n", - "from langgraph.checkpoint.sqlite import SqliteSaver\n", + "from langgraph.checkpoint.memory import MemorySaver\n", "from langgraph.graph import END, START, StateGraph\n", "from langgraph.prebuilt import tools_condition\n", "\n", @@ -273,7 +275,7 @@ "builder.add_edge(\"tools\", \"assistant\")\n", "\n", "# The checkpointer lets the graph persist its state\n", - "memory = SqliteSaver.from_conn_string(\":memory:\")\n", + "memory = MemorySaver()\n", "react_graph = builder.compile(checkpointer=memory)\n", "\n", "# Show\n", @@ -282,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "43c633d5-e7a7-4b7c-8dc7-760a3b032e95", "metadata": {}, "outputs": [], @@ -301,9 +303,19 @@ "response = predict_react_agent_answer(example)" ] }, + { + "cell_type": "markdown", + "id": "bf82fa52-9e6c-4f37-94ae-91450dac602e", + "metadata": {}, + "source": [ + "See trace with llama3.1 here:\n", + "\n", + "https://smith.langchain.com/public/44d0c7dd-a756-47ad-8025-ee7ae6469ecb/r" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "cd74a0b3-be40-46cd-97bf-ef9676878289", "metadata": {}, "outputs": [], @@ -311,6 +323,24 @@ "example = {\"input\": \"Get me information about the current weather in SF.\"}\n", "response = predict_react_agent_answer(example)" ] + }, + { + "cell_type": "markdown", + "id": "8cac91bf-c975-44a2-a9fd-99706fee5735", + "metadata": {}, + "source": [ + "See trace with llama3.1 here:\n", + "\n", + "https://smith.langchain.com/public/7a4938e3-f94f-4e04-a162-bf592fba4643/r" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74b813cb-18ed-42d8-b313-6ee56ded4bcc", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/usaco/img/benchmark.png b/examples/usaco/img/benchmark.png index a6199631a..6b96e6568 100644 Binary files a/examples/usaco/img/benchmark.png and b/examples/usaco/img/benchmark.png differ diff --git a/examples/usaco/img/diagram-part-1.png b/examples/usaco/img/diagram-part-1.png index f002232c6..b9b49a80a 100644 Binary files a/examples/usaco/img/diagram-part-1.png and b/examples/usaco/img/diagram-part-1.png differ diff --git a/examples/usaco/img/diagram-part-2.png b/examples/usaco/img/diagram-part-2.png index 0bb6ca168..502b97391 100644 Binary files a/examples/usaco/img/diagram-part-2.png and b/examples/usaco/img/diagram-part-2.png differ diff --git a/examples/usaco/img/diagram.png b/examples/usaco/img/diagram.png index 125f8cdbe..57c04d1eb 100644 Binary files a/examples/usaco/img/diagram.png and b/examples/usaco/img/diagram.png differ diff --git a/examples/usaco/img/usaco.png b/examples/usaco/img/usaco.png index b15be575e..fdba6c2b2 100644 Binary files a/examples/usaco/img/usaco.png and b/examples/usaco/img/usaco.png differ diff --git a/examples/usaco/usaco.ipynb b/examples/usaco/usaco.ipynb index c83c84df6..0a4d4e445 100644 --- a/examples/usaco/usaco.ipynb +++ b/examples/usaco/usaco.ipynb @@ -598,7 +598,7 @@ "id": "e6e73e85-1232-4848-beba-3139ac7d0a64", "metadata": {}, "outputs": [], - "source": ["from langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\n# Add connectivity\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\n\n\ncheckpointer = SqliteSaver.from_conn_string(\":memory:\")\ngraph = builder.compile(checkpointer=checkpointer)"] + "source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\n# Add connectivity\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\n\n\ncheckpointer = MemorySaver()\ngraph = builder.compile(checkpointer=checkpointer)"] }, { "cell_type": "code", @@ -809,7 +809,7 @@ "id": "3c6456ba-363c-4133-8631-6dabb042b6ce", "metadata": {}, "outputs": [], - "source": ["# This is all the same as before\nfrom langgraph.checkpoint.sqlite import SqliteSaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nprompt = hub.pull(\"wfh/usaco-draft-solver\")\nllm = ChatAnthropic(model=\"claude-3-opus-20240229\", max_tokens_to_sample=4000)\n\ndraft_solver = Solver(llm, prompt.partial(examples=\"\"))\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nsolver = Solver(llm, prompt)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\ncheckpointer = SqliteSaver.from_conn_string(\":memory:\")"] + "source": ["# This is all the same as before\nfrom langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(State)\nprompt = hub.pull(\"wfh/usaco-draft-solver\")\nllm = ChatAnthropic(model=\"claude-3-opus-20240229\", max_tokens_to_sample=4000)\n\ndraft_solver = Solver(llm, prompt.partial(examples=\"\"))\nbuilder.add_node(\"draft\", draft_solver)\nbuilder.add_edge(START, \"draft\")\nbuilder.add_node(\"retrieve\", retrieve_examples)\nsolver = Solver(llm, prompt)\nbuilder.add_node(\"solve\", solver)\nbuilder.add_node(\"evaluate\", evaluate)\nbuilder.add_edge(\"draft\", \"retrieve\")\nbuilder.add_edge(\"retrieve\", \"solve\")\nbuilder.add_edge(\"solve\", \"evaluate\")\n\n\ndef control_edge(state: State):\n if state.get(\"status\") == \"success\":\n return END\n return \"solve\"\n\n\nbuilder.add_conditional_edges(\"evaluate\", control_edge, {END: END, \"solve\": \"solve\"})\ncheckpointer = MemorySaver()"] }, { "cell_type": "markdown", diff --git a/examples/visualization.ipynb b/examples/visualization.ipynb index 43f06aefb..7e7c83ab6 100644 --- a/examples/visualization.ipynb +++ b/examples/visualization.ipynb @@ -268,7 +268,7 @@ ], "source": [ "from IPython.display import Image, display\n", - "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeColors\n", + "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles\n", "\n", "display(\n", " Image(\n", @@ -340,7 +340,7 @@ " Image(\n", " app.get_graph().draw_mermaid_png(\n", " curve_style=CurveStyle.LINEAR,\n", - " node_colors=NodeColors(start=\"#ffdfba\", end=\"#baffc9\", other=\"#fad7de\"),\n", + " node_colors=NodeStyles(first=\"#ffdfba\", last=\"#baffc9\", default=\"#fad7de\"),\n", " wrap_label_n_words=9,\n", " output_file_path=None,\n", " draw_method=MermaidDrawMethod.PYPPETEER,\n", diff --git a/examples/web-navigation/img/web-voyager.excalidraw.png b/examples/web-navigation/img/web-voyager.excalidraw.png index 54563b056..a2c1cd935 100644 Binary files a/examples/web-navigation/img/web-voyager.excalidraw.png and b/examples/web-navigation/img/web-voyager.excalidraw.png differ diff --git a/libs/checkpoint-postgres/Makefile b/libs/checkpoint-postgres/Makefile new file mode 100644 index 000000000..043fef898 --- /dev/null +++ b/libs/checkpoint-postgres/Makefile @@ -0,0 +1,48 @@ +.PHONY: test test_watch lint format + +###################### +# TESTING AND COVERAGE +###################### + +start-postgres: + docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait + +stop-postgres: + docker compose -f tests/compose-postgres.yml down + +test: + make start-postgres; \ + poetry run pytest; \ + EXIT_CODE=$$?; \ + make stop-postgres; \ + exit $$EXIT_CODE + +test_watch: + make start-postgres; \ + poetry run ptw .; \ + EXIT_CODE=$$?; \ + make stop-postgres; \ + exit $$EXIT_CODE + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langgraph +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) diff --git a/libs/checkpoint-postgres/README.md b/libs/checkpoint-postgres/README.md new file mode 100644 index 000000000..24a77673a --- /dev/null +++ b/libs/checkpoint-postgres/README.md @@ -0,0 +1,101 @@ +# LangGraph Checkpoint Postgres + +Implementation of LangGraph CheckpointSaver that uses Postgres. + +## Usage + +> [!IMPORTANT] +> When using Postgres checkpointers for the first time, make sure to call `.setup()` method on them to create required tables. See example below. + +> [!IMPORTANT] +> When manually creating Postgres connections and passing them to `PostgresSaver` or `AsyncPostgresSaver`, make sure to include `autocommit=True` and `row_factory=dict_row` (`from psycopg.rows import dict_row`). See a full example in this [how-to guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/). + +```python +from langgraph.checkpoint.postgres import PostgresSaver + +write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}} +read_config = {"configurable": {"thread_id": "1"}} + +DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable" +with PostgresSaver.from_conn_string(DB_URI) as checkpointer: + # call .setup() the first time you're using the checkpointer + checkpointer.setup() + checkpoint = { + "v": 1, + "ts": "2024-07-31T20:14:19.804150+00:00", + "id": "1ef4f797-8335-6428-8001-8a1503f9b875", + "channel_values": { + "my_key": "meow", + "node": "node" + }, + "channel_versions": { + "__start__": 2, + "my_key": 3, + "start:node": 3, + "node": 3 + }, + "versions_seen": { + "__input__": {}, + "__start__": { + "__start__": 1 + }, + "node": { + "start:node": 2 + } + }, + "pending_sends": [], + "current_tasks": {} + } + + # store checkpoint + checkpointer.put(write_config, checkpoint, {}, {}) + + # load checkpoint + checkpointer.get(read_config) + + # list checkpoints + list(checkpointer.list(read_config)) +``` + +### Async + +```python +from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver + +async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer: + checkpoint = { + "v": 1, + "ts": "2024-07-31T20:14:19.804150+00:00", + "id": "1ef4f797-8335-6428-8001-8a1503f9b875", + "channel_values": { + "my_key": "meow", + "node": "node" + }, + "channel_versions": { + "__start__": 2, + "my_key": 3, + "start:node": 3, + "node": 3 + }, + "versions_seen": { + "__input__": {}, + "__start__": { + "__start__": 1 + }, + "node": { + "start:node": 2 + } + }, + "pending_sends": [], + "current_tasks": {} + } + + # store checkpoint + await checkpointer.aput(write_config, checkpoint, {}, {}) + + # load checkpoint + await checkpointer.aget(read_config) + + # list checkpoints + [c async for c in checkpointer.alist(read_config)] +``` diff --git a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py new file mode 100644 index 000000000..e07f0aa5f --- /dev/null +++ b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py @@ -0,0 +1,350 @@ +import threading +from contextlib import contextmanager +from typing import Any, Iterator, List, Optional + +from langchain_core.runnables import RunnableConfig +from psycopg import Connection, Cursor, Pipeline +from psycopg.errors import UndefinedTable +from psycopg.rows import dict_row +from psycopg.types.json import Jsonb + +from langgraph.checkpoint.base import ( + ChannelVersions, + Checkpoint, + CheckpointMetadata, + CheckpointTuple, + get_checkpoint_id, +) +from langgraph.checkpoint.postgres.base import ( + BasePostgresSaver, +) +from langgraph.checkpoint.serde.base import SerializerProtocol + + +class PostgresSaver(BasePostgresSaver): + lock: threading.Lock + + def __init__( + self, + conn: Connection, + pipe: Optional[Pipeline] = None, + serde: Optional[SerializerProtocol] = None, + ) -> None: + super().__init__(serde=serde) + self.conn = conn + self.pipe = pipe + self.lock = threading.Lock() + + @classmethod + @contextmanager + def from_conn_string( + cls, conn_string: str, *, pipeline: bool = False + ) -> Iterator["PostgresSaver"]: + """Create a new PostgresSaver instance from a connection string. + + Args: + conn_string (str): The Postgres connection info string. + pipeline (bool): whether to use Pipeline + + Returns: + PostgresSaver: A new PostgresSaver instance. + """ + with Connection.connect( + conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row + ) as conn: + if pipeline: + with conn.pipeline() as pipe: + yield PostgresSaver(conn, pipe) + else: + yield PostgresSaver(conn) + + def setup(self) -> None: + """Set up the checkpoint database asynchronously. + + This method creates the necessary tables in the Postgres database if they don't + already exist and runs database migrations. It MUST be called directly by the user + the first time checkpointer is used. + """ + with self.lock: + with self.conn.cursor(binary=True) as cur: + try: + version = cur.execute( + "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1" + ).fetchone()["v"] + except UndefinedTable: + version = -1 + for v, migration in zip( + range(version + 1, len(self.MIGRATIONS)), + self.MIGRATIONS[version + 1 :], + ): + cur.execute(migration) + cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})") + if self.pipe: + self.pipe.sync() + + def list( + self, + config: Optional[RunnableConfig], + *, + filter: Optional[dict[str, Any]] = None, + before: Optional[RunnableConfig] = None, + limit: Optional[int] = None, + ) -> Iterator[CheckpointTuple]: + """List checkpoints from the database. + + This method retrieves a list of checkpoint tuples from the Postgres database based + on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first). + + Args: + config (RunnableConfig): The config to use for listing the checkpoints. + filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None. + before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None. + limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None. + + Yields: + Iterator[CheckpointTuple]: An iterator of checkpoint tuples. + + Examples: + >>> from langgraph.checkpoint.postgres import PostgresSaver + >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable" + >>> with PostgresSaver.from_conn_string(DB_URI) as memory: + ... # Run a graph, then list the checkpoints + >>> config = {"configurable": {"thread_id": "1"}} + >>> checkpoints = list(memory.list(config, limit=2)) + >>> print(checkpoints) + [CheckpointTuple(...), CheckpointTuple(...)] + + >>> config = {"configurable": {"thread_id": "1"}} + >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}} + >>> with PostgresSaver.from_conn_string(DB_URI) as memory: + ... # Run a graph, then list the checkpoints + >>> checkpoints = list(memory.list(config, before=before)) + >>> print(checkpoints) + [CheckpointTuple(...), ...] + """ + where, args = self._search_where(config, filter, before) + query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC" + if limit: + query += f" LIMIT {limit}" + # if we change this to use .stream() we need to make sure to close the cursor + for value in self.conn.execute(query, args, binary=True): + yield CheckpointTuple( + { + "configurable": { + "thread_id": value["thread_id"], + "checkpoint_ns": value["checkpoint_ns"], + "checkpoint_id": value["checkpoint_id"], + } + }, + { + **self._load_checkpoint(value["checkpoint"]), + "channel_values": self._load_blobs(value["channel_values"]), + }, + self._load_metadata(value["metadata"]), + { + "configurable": { + "thread_id": value["thread_id"], + "checkpoint_ns": value["checkpoint_ns"], + "checkpoint_id": value["parent_checkpoint_id"], + } + } + if value["parent_checkpoint_id"] + else None, + ) + + def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + """Get a checkpoint tuple from the database. + + This method retrieves a checkpoint tuple from the Postgres database based on the + provided config. If the config contains a "checkpoint_id" key, the checkpoint with + the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint + for the given thread ID is retrieved. + + Args: + config (RunnableConfig): The config to use for retrieving the checkpoint. + + Returns: + Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found. + + Examples: + + Basic: + >>> config = {"configurable": {"thread_id": "1"}} + >>> checkpoint_tuple = memory.get_tuple(config) + >>> print(checkpoint_tuple) + CheckpointTuple(...) + + With timestamp: + + >>> config = { + ... "configurable": { + ... "thread_id": "1", + ... "checkpoint_ns": "", + ... "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875", + ... } + ... } + >>> checkpoint_tuple = memory.get_tuple(config) + >>> print(checkpoint_tuple) + CheckpointTuple(...) + """ # noqa + thread_id = config["configurable"]["thread_id"] + checkpoint_id = get_checkpoint_id(config) + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") + if checkpoint_id: + args = (thread_id, checkpoint_ns, checkpoint_id) + where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s" + else: + args = (thread_id, checkpoint_ns) + where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1" + + with self._cursor() as cur: + cur = self.conn.execute( + self.SELECT_SQL + where, + args, + binary=True, + ) + + for value in cur: + return CheckpointTuple( + { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": value["checkpoint_id"], + } + }, + { + **self._load_checkpoint(value["checkpoint"]), + "channel_values": self._load_blobs(value["channel_values"]), + }, + self._load_metadata(value["metadata"]), + { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": value["parent_checkpoint_id"], + } + } + if value["parent_checkpoint_id"] + else None, + self._load_writes(value["pending_writes"]), + ) + + def put( + self, + config: RunnableConfig, + checkpoint: Checkpoint, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, + ) -> RunnableConfig: + """Save a checkpoint to the database. + + This method saves a checkpoint to the Postgres database. The checkpoint is associated + with the provided config and its parent config (if any). + + Args: + config (RunnableConfig): The config to associate with the checkpoint. + checkpoint (Checkpoint): The checkpoint to save. + metadata (CheckpointMetadata): Additional metadata to save with the checkpoint. + new_versions (ChannelVersions): New channel versions as of this write. + + Returns: + RunnableConfig: Updated configuration after storing the checkpoint. + + Examples: + + >>> from langgraph.checkpoint.postgres import PostgresSaver + >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable" + >>> with PostgresSaver.from_conn_string(DB_URI) as memory: + >>> config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}} + >>> checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "data": {"key": "value"}} + >>> saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {}) + >>> print(saved_config) + {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}} + """ + configurable = config["configurable"].copy() + thread_id = configurable.pop("thread_id") + checkpoint_ns = configurable.pop("checkpoint_ns") + checkpoint_id = configurable.pop( + "checkpoint_id", configurable.pop("thread_ts", None) + ) + + copy = checkpoint.copy() + next_config = { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint["id"], + } + } + + with self._cursor(pipeline=True) as cur: + cur.executemany( + self.UPSERT_CHECKPOINT_BLOBS_SQL, + self._dump_blobs( + thread_id, + checkpoint_ns, + copy.pop("channel_values"), + new_versions, + ), + ) + cur.execute( + self.UPSERT_CHECKPOINTS_SQL, + ( + thread_id, + checkpoint_ns, + checkpoint["id"], + checkpoint_id, + Jsonb(self._dump_checkpoint(copy)), + self._dump_metadata(metadata), + ), + ) + return next_config + + def put_writes( + self, + config: RunnableConfig, + writes: List[tuple[str, Any]], + task_id: str, + ) -> None: + """Store intermediate writes linked to a checkpoint. + + This method saves intermediate writes associated with a checkpoint to the Postgres database. + + Args: + config (RunnableConfig): Configuration of the related checkpoint. + writes (List[Tuple[str, Any]]): List of writes to store. + task_id (str): Identifier for the task creating the writes. + """ + with self._cursor() as cur: + cur.executemany( + self.UPSERT_CHECKPOINT_WRITES_SQL, + self._dump_writes( + config["configurable"]["thread_id"], + config["configurable"]["checkpoint_ns"], + config["configurable"]["checkpoint_id"], + task_id, + writes, + ), + ) + + @contextmanager + def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor]: + if self.pipe: + # a connection in pipeline mode can be used concurrently + # in multiple threads/coroutines, but only one cursor can be + # used at a time + try: + with self.conn.cursor(binary=True) as cur: + yield cur + finally: + if pipeline: + self.pipe.sync() + elif pipeline: + # a connection not in pipeline mode can only be used by one + # thread/coroutine at a time, so we acquire a lock + with self.lock, self.conn.pipeline(), self.conn.cursor(binary=True) as cur: + yield cur + else: + with self.lock, self.conn.cursor(binary=True) as cur: + yield cur diff --git a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py new file mode 100644 index 000000000..c325399e2 --- /dev/null +++ b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py @@ -0,0 +1,309 @@ +import asyncio +from contextlib import asynccontextmanager +from typing import Any, AsyncIterator, Optional + +from langchain_core.runnables import RunnableConfig +from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline +from psycopg.errors import UndefinedTable +from psycopg.rows import dict_row +from psycopg.types.json import Jsonb + +from langgraph.checkpoint.base import ( + ChannelVersions, + Checkpoint, + CheckpointMetadata, + CheckpointTuple, + get_checkpoint_id, +) +from langgraph.checkpoint.postgres.base import BasePostgresSaver +from langgraph.checkpoint.serde.base import SerializerProtocol + + +class AsyncPostgresSaver(BasePostgresSaver): + lock: asyncio.Lock + + def __init__( + self, + conn: AsyncConnection, + pipe: Optional[AsyncPipeline] = None, + serde: Optional[SerializerProtocol] = None, + ) -> None: + super().__init__(serde=serde) + self.conn = conn + self.pipe = pipe + self.lock = asyncio.Lock() + + @classmethod + @asynccontextmanager + async def from_conn_string( + cls, conn_string: str, *, pipeline: bool = False + ) -> AsyncIterator["AsyncPostgresSaver"]: + """Create a new PostgresSaver instance from a connection string. + + Args: + conn_string (str): The Postgres connection info string. + pipeline (bool): whether to use AsyncPipeline + + Returns: + PostgresSaver: A new PostgresSaver instance. + """ + async with await AsyncConnection.connect( + conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row + ) as conn: + if pipeline: + async with conn.pipeline() as pipe: + yield AsyncPostgresSaver(conn, pipe) + else: + yield AsyncPostgresSaver(conn) + + async def setup(self) -> None: + """Set up the checkpoint database asynchronously. + + This method creates the necessary tables in the Postgres database if they don't + already exist and runs database migrations. It MUST be called directly by the user + the first time checkpointer is used. + """ + async with self.lock: + async with self.conn.cursor(binary=True) as cur: + try: + results = await cur.execute( + "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1" + ) + version = (await results.fetchone())["v"] + except UndefinedTable: + version = -1 + for v, migration in zip( + range(version + 1, len(self.MIGRATIONS)), + self.MIGRATIONS[version + 1 :], + ): + await cur.execute(migration) + await cur.execute( + f"INSERT INTO checkpoint_migrations (v) VALUES ({v})" + ) + if self.pipe: + await self.pipe.sync() + + async def alist( + self, + config: Optional[RunnableConfig], + *, + filter: Optional[dict[str, Any]] = None, + before: Optional[RunnableConfig] = None, + limit: Optional[int] = None, + ) -> AsyncIterator[CheckpointTuple]: + """List checkpoints from the database asynchronously. + + This method retrieves a list of checkpoint tuples from the Postgres database based + on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first). + + Args: + config (Optional[RunnableConfig]): Base configuration for filtering checkpoints. + filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. + before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None. + limit (Optional[int]): Maximum number of checkpoints to return. + + Yields: + AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples. + """ + where, args = self._search_where(config, filter, before) + query = self.SELECT_SQL + where + " ORDER BY checkpoint_id DESC" + if limit: + query += f" LIMIT {limit}" + # if we change this to use .stream() we need to make sure to close the cursor + async for value in await self.conn.execute(query, args, binary=True): + yield CheckpointTuple( + { + "configurable": { + "thread_id": value["thread_id"], + "checkpoint_ns": value["checkpoint_ns"], + "checkpoint_id": value["checkpoint_id"], + } + }, + { + **self._load_checkpoint(value["checkpoint"]), + "channel_values": await asyncio.to_thread( + self._load_blobs, value["channel_values"] + ), + }, + self._load_metadata(value["metadata"]), + { + "configurable": { + "thread_id": value["thread_id"], + "checkpoint_ns": value["checkpoint_ns"], + "checkpoint_id": value["parent_checkpoint_id"], + } + } + if value["parent_checkpoint_id"] + else None, + ) + + async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + """Get a checkpoint tuple from the database asynchronously. + + This method retrieves a checkpoint tuple from the Postgres database based on the + provided config. If the config contains a "checkpoint_id" key, the checkpoint with + the matching thread ID and "checkpoint_id" is retrieved. Otherwise, the latest checkpoint + for the given thread ID is retrieved. + + Args: + config (RunnableConfig): The config to use for retrieving the checkpoint. + + Returns: + Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found. + """ + thread_id = config["configurable"]["thread_id"] + checkpoint_id = get_checkpoint_id(config) + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") + if checkpoint_id: + args = (thread_id, checkpoint_ns, checkpoint_id) + where = "WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id = %s" + else: + args = (thread_id, checkpoint_ns) + where = "WHERE thread_id = %s AND checkpoint_ns = %s ORDER BY checkpoint_id DESC LIMIT 1" + + async with self._cursor() as cur: + cur = await self.conn.execute( + self.SELECT_SQL + where, + args, + binary=True, + ) + + async for value in cur: + return CheckpointTuple( + { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": value["checkpoint_id"], + } + }, + { + **self._load_checkpoint(value["checkpoint"]), + "channel_values": await asyncio.to_thread( + self._load_blobs, value["channel_values"] + ), + }, + self._load_metadata(value["metadata"]), + { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": value["parent_checkpoint_id"], + } + } + if value["parent_checkpoint_id"] + else None, + await asyncio.to_thread(self._load_writes, value["pending_writes"]), + ) + + async def aput( + self, + config: RunnableConfig, + checkpoint: Checkpoint, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, + ) -> RunnableConfig: + """Save a checkpoint to the database asynchronously. + + This method saves a checkpoint to the Postgres database. The checkpoint is associated + with the provided config and its parent config (if any). + + Args: + config (RunnableConfig): The config to associate with the checkpoint. + checkpoint (Checkpoint): The checkpoint to save. + metadata (CheckpointMetadata): Additional metadata to save with the checkpoint. + new_versions (ChannelVersions): New channel versions as of this write. + + Returns: + RunnableConfig: Updated configuration after storing the checkpoint. + """ + configurable = config["configurable"].copy() + thread_id = configurable.pop("thread_id") + checkpoint_ns = configurable.pop("checkpoint_ns") + checkpoint_id = configurable.pop( + "checkpoint_id", configurable.pop("thread_ts", None) + ) + + copy = checkpoint.copy() + next_config = { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint["id"], + } + } + + async with self._cursor(pipeline=True) as cur: + await cur.executemany( + self.UPSERT_CHECKPOINT_BLOBS_SQL, + await asyncio.to_thread( + self._dump_blobs, + thread_id, + checkpoint_ns, + copy.pop("channel_values"), + new_versions, + ), + ) + await cur.execute( + self.UPSERT_CHECKPOINTS_SQL, + ( + thread_id, + checkpoint_ns, + checkpoint["id"], + checkpoint_id, + Jsonb(self._dump_checkpoint(copy)), + self._dump_metadata(metadata), + ), + ) + return next_config + + async def aput_writes( + self, + config: RunnableConfig, + writes: list[tuple[str, Any]], + task_id: str, + ) -> None: + """Store intermediate writes linked to a checkpoint asynchronously. + + This method saves intermediate writes associated with a checkpoint to the database. + + Args: + config (RunnableConfig): Configuration of the related checkpoint. + writes (Sequence[Tuple[str, Any]]): List of writes to store, each as (channel, value) pair. + task_id (str): Identifier for the task creating the writes. + """ + async with self._cursor() as cur: + await cur.executemany( + self.UPSERT_CHECKPOINT_WRITES_SQL, + await asyncio.to_thread( + self._dump_writes, + config["configurable"]["thread_id"], + config["configurable"]["checkpoint_ns"], + config["configurable"]["checkpoint_id"], + task_id, + writes, + ), + ) + + @asynccontextmanager + async def _cursor(self, *, pipeline: bool = False) -> AsyncIterator[AsyncCursor]: + if self.pipe: + # a connection in pipeline mode can be used concurrently + # in multiple threads/coroutines, but only one cursor can be + # used at a time + try: + async with self.conn.cursor(binary=True) as cur: + yield cur + finally: + if pipeline: + await self.pipe.sync() + elif pipeline: + # a connection not in pipeline mode can only be used by one + # thread/coroutine at a time, so we acquire a lock + async with self.lock, self.conn.pipeline(), self.conn.cursor( + binary=True + ) as cur: + yield cur + else: + async with self.lock, self.conn.cursor(binary=True) as cur: + yield cur diff --git a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py new file mode 100644 index 000000000..e9ca12305 --- /dev/null +++ b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py @@ -0,0 +1,273 @@ +from base64 import b64decode, b64encode +from hashlib import md5 +from typing import Any, List, Optional, Tuple + +from langchain_core.runnables import RunnableConfig +from psycopg.types.json import Jsonb + +from langgraph.checkpoint.base import ( + BaseCheckpointSaver, + Checkpoint, + EmptyChannelError, + get_checkpoint_id, +) +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer +from langgraph.checkpoint.serde.types import ChannelProtocol + +MetadataInput = Optional[dict[str, Any]] + +""" +To add a new migration, add a new string to the MIGRATIONS list. +The position of the migration in the list is the version number. +""" +MIGRATIONS = [ + """CREATE TABLE IF NOT EXISTS checkpoint_migrations ( + v INTEGER PRIMARY KEY +);""", + """CREATE TABLE IF NOT EXISTS checkpoints ( + thread_id TEXT NOT NULL, + checkpoint_ns TEXT NOT NULL DEFAULT '', + checkpoint_id TEXT NOT NULL, + parent_checkpoint_id TEXT, + type TEXT, + checkpoint JSONB NOT NULL, + metadata JSONB NOT NULL DEFAULT '{}', + PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id) +);""", + """CREATE TABLE IF NOT EXISTS checkpoint_blobs ( + thread_id TEXT NOT NULL, + checkpoint_ns TEXT NOT NULL DEFAULT '', + channel TEXT NOT NULL, + version TEXT NOT NULL, + type TEXT NOT NULL, + blob BYTEA, + PRIMARY KEY (thread_id, checkpoint_ns, channel, version) +);""", + """CREATE TABLE IF NOT EXISTS checkpoint_writes ( + thread_id TEXT NOT NULL, + checkpoint_ns TEXT NOT NULL DEFAULT '', + checkpoint_id TEXT NOT NULL, + task_id TEXT NOT NULL, + idx INTEGER NOT NULL, + channel TEXT NOT NULL, + type TEXT, + blob BYTEA NOT NULL, + PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) +);""", + "ALTER TABLE checkpoint_blobs ALTER COLUMN blob DROP not null;", +] + +SELECT_SQL = """ +select + thread_id, + checkpoint, + checkpoint_ns, + checkpoint_id, + parent_checkpoint_id, + metadata, + ( + select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob]) + from jsonb_each_text(checkpoint -> 'channel_versions') + inner join checkpoint_blobs bl + on bl.thread_id = checkpoints.thread_id + and bl.checkpoint_ns = checkpoints.checkpoint_ns + and bl.channel = jsonb_each_text.key + and bl.version = jsonb_each_text.value + ) as channel_values, + ( + select + array_agg(array[cw.task_id::text::bytea, cw.channel::bytea, cw.type::bytea, cw.blob]) + from checkpoint_writes cw + where cw.thread_id = checkpoints.thread_id + and cw.checkpoint_ns = checkpoints.checkpoint_ns + and cw.checkpoint_id = checkpoints.checkpoint_id + ) as pending_writes +from checkpoints """ + +UPSERT_CHECKPOINT_BLOBS_SQL = """ + INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, version, type, blob) + VALUES (%s, %s, %s, %s, %s, %s) + ON CONFLICT (thread_id, checkpoint_ns, channel, version) DO NOTHING +""" + +UPSERT_CHECKPOINTS_SQL = """ + INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, checkpoint, metadata) + VALUES (%s, %s, %s, %s, %s, %s) + ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id) + DO UPDATE SET + checkpoint = EXCLUDED.checkpoint, + metadata = EXCLUDED.metadata; +""" + +UPSERT_CHECKPOINT_WRITES_SQL = """ + INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, blob) + VALUES (%s, %s, %s, %s, %s, %s, %s, %s) + ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING +""" + + +class BasePostgresSaver(BaseCheckpointSaver): + SELECT_SQL = SELECT_SQL + MIGRATIONS = MIGRATIONS + UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL + UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL + UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL + jsonplus_serde = JsonPlusSerializer() + + def _load_checkpoint(self, checkpoint: dict[str, Any]) -> Checkpoint: + if len(checkpoint["pending_sends"]) == 2 and all( + isinstance(a, str) for a in checkpoint["pending_sends"] + ): + type, bs = checkpoint["pending_sends"] + return { + **checkpoint, + "pending_sends": self.serde.loads_typed((type, b64decode(bs))), + } + + return checkpoint + + def _dump_checkpoint(self, checkpoint: Checkpoint) -> dict[str, Any]: + type, bs = self.serde.dumps_typed(checkpoint["pending_sends"]) + return { + **checkpoint, + "pending_sends": (type, b64encode(bs).decode()), + } + + def _load_blobs( + self, blob_values: list[tuple[bytes, bytes, bytes]] + ) -> dict[str, Any]: + if not blob_values: + return {} + return { + k.decode(): self.serde.loads_typed((t.decode(), v)) + for k, t, v in blob_values + if t.decode() != "empty" + } + + def _dump_blobs( + self, + thread_id: str, + checkpoint_ns: str, + values: dict[str, Any], + versions: dict[str, str], + ) -> list[tuple[str, str, str, str, str, bytes]]: + if not versions: + return [] + + return [ + ( + thread_id, + checkpoint_ns, + k, + ver, + *( + self.serde.dumps_typed(values[k]) + if k in values + else ("empty", None) + ), + ) + for k, ver in versions.items() + ] + + def _load_writes( + self, writes: list[tuple[bytes, bytes, bytes, bytes]] + ) -> list[tuple[str, str, Any]]: + return ( + [ + ( + tid.decode(), + channel.decode(), + self.serde.loads_typed((t.decode(), v)), + ) + for tid, channel, t, v in writes + ] + if writes + else [] + ) + + def _dump_writes( + self, + thread_id: str, + checkpoint_ns: str, + checkpoint_id: str, + task_id: str, + writes: list[tuple[str, Any]], + ) -> list[tuple[str, str, str, int, str, str, bytes]]: + return [ + ( + thread_id, + checkpoint_ns, + checkpoint_id, + task_id, + idx, + channel, + *self.serde.dumps_typed(value), + ) + for idx, (channel, value) in enumerate(writes) + ] + + def _load_metadata(self, metadata: dict[str, Any]) -> dict[str, Any]: + return self.jsonplus_serde.loads(self.jsonplus_serde.dumps(metadata)) + + def _dump_metadata(self, metadata) -> str: + serialized_metadata_type, serialized_metadata = self.jsonplus_serde.dumps_typed( + metadata + ) + if serialized_metadata_type != "json": + raise TypeError( + f"Failed to properly serialize metadata -- expected 'json', got '{serialized_metadata_type}'" + ) + return serialized_metadata.decode() + + def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str: + if current is None: + current_v = 0 + elif isinstance(current, int): + current_v = current + else: + current_v = int(current.split(".")[0]) + next_v = current_v + 1 + try: + next_h = md5(self.serde.dumps_typed(channel.checkpoint())[1]).hexdigest() + except EmptyChannelError: + next_h = "" + return f"{next_v:032}.{next_h}" + + def _search_where( + self, + config: Optional[RunnableConfig], + filter: MetadataInput, + before: Optional[RunnableConfig] = None, + ) -> Tuple[str, List[Any]]: + """Return WHERE clause predicates for alist() given config, filter, cursor. + + This method returns a tuple of a string and a tuple of values. The string + is the parametered WHERE clause predicate (including the WHERE keyword): + "WHERE column1 = $1 AND column2 IS $2". The list of values contains the + values for each of the corresponding parameters. + """ + wheres = [] + param_values = [] + + # construct predicate for config filter + if config: + wheres.append("thread_id = %s ") + param_values.append(config["configurable"]["thread_id"]) + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") + wheres.append("checkpoint_ns = %s") + param_values.append(checkpoint_ns) + + # construct predicate for metadata filter + if filter: + wheres.append("metadata @> %s ") + param_values.append(Jsonb(filter)) + + # construct predicate for `before` + if before is not None: + wheres.append("checkpoint_id < %s ") + param_values.append(get_checkpoint_id(before)) + + return ( + "WHERE " + " AND ".join(wheres) if wheres else "", + param_values, + ) diff --git a/libs/langgraph/langgraph/serde/__init__.py b/libs/checkpoint-postgres/langgraph/checkpoint/py.typed similarity index 100% rename from libs/langgraph/langgraph/serde/__init__.py rename to libs/checkpoint-postgres/langgraph/checkpoint/py.typed diff --git a/libs/checkpoint-postgres/poetry.lock b/libs/checkpoint-postgres/poetry.lock new file mode 100644 index 000000000..813bdf9b0 --- /dev/null +++ b/libs/checkpoint-postgres/poetry.lock @@ -0,0 +1,972 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] + +[[package]] +name = "certifi" +version = "2024.7.4" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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+readme = "README.md" +repository = "https://www.github.com/langchain-ai/langgraph" +packages = [{ include = "langgraph" }] + +[tool.poetry.dependencies] +python = "^3.9.0,<4.0" +langgraph-checkpoint = "^1.0.1" +orjson = ">=3.10.1" +psycopg = {extras = ["binary"], version = ">=3.1.19"} + +[tool.poetry.group.dev.dependencies] +ruff = "^0.1.4" +codespell = "^2.2.0" +pytest = "^7.2.1" +anyio = "^4.4.0" +pytest-asyncio = "^0.21.1" +pytest-mock = "^3.11.1" +pytest-watch = "^4.2.0" +mypy = "^1.10.0" +psycopg-pool = "^3.2.2" +langgraph-checkpoint = {path = "../checkpoint", develop = true} + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +addopts = "--strict-markers --strict-config --durations=5 -vv" +asyncio_mode = "auto" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +lint.select = [ + "E", # pycodestyle + "F", # Pyflakes + "UP", # pyupgrade + "B", # flake8-bugbear + "I", # isort +] +lint.ignore = ["E501", "B008", "UP007", "UP006"] diff --git a/libs/checkpoint-postgres/tests/compose-postgres.yml b/libs/checkpoint-postgres/tests/compose-postgres.yml new file mode 100644 index 000000000..42d8c3781 --- /dev/null +++ b/libs/checkpoint-postgres/tests/compose-postgres.yml @@ -0,0 +1,16 @@ +services: + postgres-test: + image: postgres:16 + ports: + - "5441:5432" + environment: + POSTGRES_DB: postgres + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + healthcheck: + test: pg_isready -U postgres + start_period: 10s + timeout: 1s + retries: 5 + interval: 60s + start_interval: 1s diff --git a/libs/checkpoint-postgres/tests/conftest.py b/libs/checkpoint-postgres/tests/conftest.py new file mode 100644 index 000000000..6100be1d4 --- /dev/null +++ b/libs/checkpoint-postgres/tests/conftest.py @@ -0,0 +1,25 @@ +import pytest +from psycopg import AsyncConnection +from psycopg.errors import UndefinedTable +from psycopg.rows import dict_row + +DEFAULT_URI = "postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable" + + +@pytest.fixture(scope="function") +async def conn(): + async with await AsyncConnection.connect( + DEFAULT_URI, autocommit=True, prepare_threshold=0, row_factory=dict_row + ) as conn: + yield conn + + +@pytest.fixture(scope="function", autouse=True) +async def clear_test_db(conn): + """Delete all tables before each test.""" + try: + await conn.execute("DELETE FROM checkpoints") + await conn.execute("DELETE FROM checkpoint_blobs") + await conn.execute("DELETE FROM checkpoint_writes") + except UndefinedTable: + pass diff --git a/libs/checkpoint-postgres/tests/test_async.py b/libs/checkpoint-postgres/tests/test_async.py new file mode 100644 index 000000000..e94cf32ae --- /dev/null +++ b/libs/checkpoint-postgres/tests/test_async.py @@ -0,0 +1,115 @@ +import pytest +from conftest import DEFAULT_URI +from langchain_core.runnables import RunnableConfig + +from langgraph.checkpoint.base import ( + Checkpoint, + CheckpointMetadata, + create_checkpoint, + empty_checkpoint, +) +from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver + + +class TestAsyncPostgresSaver: + @pytest.fixture(autouse=True) + async def setup(self): + # objects for test setup + self.config_1: RunnableConfig = { + "configurable": { + "thread_id": "thread-1", + # for backwards compatibility testing + "thread_ts": "1", + "checkpoint_ns": "", + } + } + self.config_2: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2", + "checkpoint_ns": "", + } + } + self.config_3: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2-inner", + "checkpoint_ns": "inner", + } + } + + self.chkpnt_1: Checkpoint = empty_checkpoint() + self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) + self.chkpnt_3: Checkpoint = empty_checkpoint() + + self.metadata_1: CheckpointMetadata = { + "source": "input", + "step": 2, + "writes": {}, + "score": 1, + } + self.metadata_2: CheckpointMetadata = { + "source": "loop", + "step": 1, + "writes": {"foo": "bar"}, + "score": None, + } + self.metadata_3: CheckpointMetadata = {} + async with AsyncPostgresSaver.from_conn_string(DEFAULT_URI) as saver: + await saver.setup() + + async def test_asearch(self): + async with AsyncPostgresSaver.from_conn_string(DEFAULT_URI) as saver: + await saver.aput(self.config_1, self.chkpnt_1, self.metadata_1, {}) + await saver.aput(self.config_2, self.chkpnt_2, self.metadata_2, {}) + await saver.aput(self.config_3, self.chkpnt_3, self.metadata_3, {}) + + # call method / assertions + query_1: CheckpointMetadata = {"source": "input"} # search by 1 key + query_2: CheckpointMetadata = { + "step": 1, + "writes": {"foo": "bar"}, + } # search by multiple keys + query_3: CheckpointMetadata = {} # search by no keys, return all checkpoints + query_4: CheckpointMetadata = {"source": "update", "step": 1} # no match + + search_results_1 = [c async for c in saver.alist(None, filter=query_1)] + assert len(search_results_1) == 1 + assert search_results_1[0].metadata == self.metadata_1 + + search_results_2 = [c async for c in saver.alist(None, filter=query_2)] + assert len(search_results_2) == 1 + assert search_results_2[0].metadata == self.metadata_2 + + search_results_3 = [c async for c in saver.alist(None, filter=query_3)] + assert len(search_results_3) == 3 + + search_results_4 = [c async for c in saver.alist(None, filter=query_4)] + assert len(search_results_4) == 0 + + # search by config (defaults to root graph checkpoints) + search_results_5 = [ + c + async for c in saver.alist({"configurable": {"thread_id": "thread-2"}}) + ] + assert len(search_results_5) == 1 + assert search_results_5[0].config["configurable"]["checkpoint_ns"] == "" + + # search by config and checkpoint_ns + search_results_6 = [ + c + async for c in saver.alist( + { + "configurable": { + "thread_id": "thread-2", + "checkpoint_ns": "inner", + } + } + ) + ] + assert len(search_results_6) == 1 + assert ( + search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner" + ) + + # TODO: test before and limit params diff --git a/libs/checkpoint-postgres/tests/test_sync.py b/libs/checkpoint-postgres/tests/test_sync.py new file mode 100644 index 000000000..dfae82907 --- /dev/null +++ b/libs/checkpoint-postgres/tests/test_sync.py @@ -0,0 +1,114 @@ +import pytest +from conftest import DEFAULT_URI +from langchain_core.runnables import RunnableConfig + +from langgraph.checkpoint.base import ( + Checkpoint, + CheckpointMetadata, + create_checkpoint, + empty_checkpoint, +) +from langgraph.checkpoint.postgres import PostgresSaver + + +class TestPostgresSaver: + @pytest.fixture(autouse=True) + def setup(self): + # objects for test setup + self.config_1: RunnableConfig = { + "configurable": { + "thread_id": "thread-1", + # for backwards compatibility testing + "thread_ts": "1", + "checkpoint_ns": "", + } + } + self.config_2: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2", + "checkpoint_ns": "", + } + } + self.config_3: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2-inner", + "checkpoint_ns": "inner", + } + } + + self.chkpnt_1: Checkpoint = empty_checkpoint() + self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) + self.chkpnt_3: Checkpoint = empty_checkpoint() + + self.metadata_1: CheckpointMetadata = { + "source": "input", + "step": 2, + "writes": {}, + "score": 1, + } + self.metadata_2: CheckpointMetadata = { + "source": "loop", + "step": 1, + "writes": {"foo": "bar"}, + "score": None, + } + self.metadata_3: CheckpointMetadata = {} + with PostgresSaver.from_conn_string(DEFAULT_URI) as saver: + saver.setup() + + def test_search(self): + with PostgresSaver.from_conn_string(DEFAULT_URI) as saver: + # save checkpoints + saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {}) + saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {}) + saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {}) + + # call method / assertions + query_1: CheckpointMetadata = {"source": "input"} # search by 1 key + query_2: CheckpointMetadata = { + "step": 1, + "writes": {"foo": "bar"}, + } # search by multiple keys + query_3: CheckpointMetadata = {} # search by no keys, return all checkpoints + query_4: CheckpointMetadata = {"source": "update", "step": 1} # no match + + search_results_1 = list(saver.list(None, filter=query_1)) + assert len(search_results_1) == 1 + assert search_results_1[0].metadata == self.metadata_1 + + search_results_2 = list(saver.list(None, filter=query_2)) + assert len(search_results_2) == 1 + assert search_results_2[0].metadata == self.metadata_2 + + search_results_3 = list(saver.list(None, filter=query_3)) + assert len(search_results_3) == 3 + + search_results_4 = list(saver.list(None, filter=query_4)) + assert len(search_results_4) == 0 + + # search by config (defaults to root graph checkpoints) + search_results_5 = list( + saver.list({"configurable": {"thread_id": "thread-2"}}) + ) + assert len(search_results_5) == 1 + assert search_results_5[0].config["configurable"]["checkpoint_ns"] == "" + + # search by config and checkpoint_ns + search_results_6 = list( + saver.list( + { + "configurable": { + "thread_id": "thread-2", + "checkpoint_ns": "inner", + } + } + ) + ) + assert len(search_results_6) == 1 + assert ( + search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner" + ) + + # TODO: test before and limit params diff --git a/libs/checkpoint-sqlite/Makefile b/libs/checkpoint-sqlite/Makefile new file mode 100644 index 000000000..734e46308 --- /dev/null +++ b/libs/checkpoint-sqlite/Makefile @@ -0,0 +1,34 @@ +.PHONY: test test_watch lint format + +###################### +# TESTING AND COVERAGE +###################### + +test: + poetry run pytest tests + +test_watch: + poetry run ptw . + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langgraph +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) diff --git a/libs/checkpoint-sqlite/README.md b/libs/checkpoint-sqlite/README.md new file mode 100644 index 000000000..86ca9cafc --- /dev/null +++ b/libs/checkpoint-sqlite/README.md @@ -0,0 +1,92 @@ +# LangGraph SQLite Checkpoint + +Implementation of LangGraph CheckpointSaver that uses SQLite DB (both sync and async, via `aiosqlite`) + +## Usage + +```python +from langgraph.checkpoint.sqlite import SqliteSaver + +write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}} +read_config = {"configurable": {"thread_id": "1"}} + +with SqliteSaver.from_conn_string(":memory:") as checkpointer: + checkpoint = { + "v": 1, + "ts": "2024-07-31T20:14:19.804150+00:00", + "id": "1ef4f797-8335-6428-8001-8a1503f9b875", + "channel_values": { + "my_key": "meow", + "node": "node" + }, + "channel_versions": { + "__start__": 2, + "my_key": 3, + "start:node": 3, + "node": 3 + }, + "versions_seen": { + "__input__": {}, + "__start__": { + "__start__": 1 + }, + "node": { + "start:node": 2 + } + }, + "pending_sends": [], + "current_tasks": {} + } + + # store checkpoint + checkpointer.put(write_config, checkpoint, {}, {}) + + # load checkpoint + checkpointer.get(read_config) + + # list checkpoints + list(checkpointer.list(read_config)) +``` + +### Async + +```python +from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver + +async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer: + checkpoint = { + "v": 1, + "ts": "2024-07-31T20:14:19.804150+00:00", + "id": "1ef4f797-8335-6428-8001-8a1503f9b875", + "channel_values": { + "my_key": "meow", + "node": "node" + }, + "channel_versions": { + "__start__": 2, + "my_key": 3, + "start:node": 3, + "node": 3 + }, + "versions_seen": { + "__input__": {}, + "__start__": { + "__start__": 1 + }, + "node": { + "start:node": 2 + } + }, + "pending_sends": [], + "current_tasks": {} + } + + # store checkpoint + await checkpointer.aput(write_config, checkpoint, {}, {}) + + # load checkpoint + await checkpointer.aget(read_config) + + # list checkpoints + [c async for c in checkpointer.alist(read_config)] +``` \ No newline at end of file diff --git a/libs/checkpoint-sqlite/langgraph/checkpoint/py.typed b/libs/checkpoint-sqlite/langgraph/checkpoint/py.typed new file mode 100644 index 000000000..e69de29bb diff --git a/libs/langgraph/langgraph/checkpoint/sqlite.py b/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py similarity index 56% rename from libs/langgraph/langgraph/checkpoint/sqlite.py rename to libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py index c1c0b2217..ac2f64f32 100644 --- a/libs/langgraph/langgraph/checkpoint/sqlite.py +++ b/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py @@ -1,59 +1,29 @@ -import json -import pickle import sqlite3 import threading -from contextlib import AbstractContextManager, contextmanager +from contextlib import contextmanager from hashlib import md5 -from types import TracebackType from typing import Any, AsyncIterator, Dict, Iterator, Optional, Sequence, Tuple from langchain_core.runnables import RunnableConfig -from typing_extensions import Self -from langgraph.channels.base import BaseChannel from langgraph.checkpoint.base import ( BaseCheckpointSaver, + ChannelVersions, Checkpoint, CheckpointMetadata, CheckpointTuple, + EmptyChannelError, SerializerProtocol, + get_checkpoint_id, ) -from langgraph.errors import EmptyChannelError -from langgraph.serde.jsonplus import JsonPlusSerializer - - -class JsonPlusSerializerCompat(JsonPlusSerializer): - """A serializer that supports loading pickled checkpoints for backwards compatibility. - - This serializer extends the JsonPlusSerializer and adds support for loading pickled - checkpoints. If the input data starts with b"\x80" and ends with b".", it is treated - as a pickled checkpoint and loaded using pickle.loads(). Otherwise, the default - JsonPlusSerializer behavior is used. - - Examples: - >>> import pickle - >>> from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat - >>> - >>> serializer = JsonPlusSerializerCompat() - >>> pickled_data = pickle.dumps({"key": "value"}) - >>> loaded_data = serializer.loads(pickled_data) - >>> print(loaded_data) # Output: {"key": "value"} - >>> - >>> json_data = '{"key": "value"}'.encode("utf-8") - >>> loaded_data = serializer.loads(json_data) - >>> print(loaded_data) # Output: {"key": "value"} - """ - - def loads(self, data: bytes) -> Any: - if data.startswith(b"\x80") and data.endswith(b"."): - return pickle.loads(data) - return super().loads(data) - +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer +from langgraph.checkpoint.serde.types import ChannelProtocol +from langgraph.checkpoint.sqlite.utils import search_where _AIO_ERROR_MSG = ( "The SqliteSaver does not support async methods. " "Consider using AsyncSqliteSaver instead.\n" - "from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver\n" + "from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver\n" "Note: AsyncSqliteSaver requires the aiosqlite package to use.\n" "Install with:\n`pip install aiosqlite`\n" "See https://langchain-ai.github.io/langgraph/reference/checkpoints/asyncsqlitesaver" @@ -61,7 +31,7 @@ _AIO_ERROR_MSG = ( ) -class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): +class SqliteSaver(BaseCheckpointSaver): """A checkpoint saver that stores checkpoints in a SQLite database. Note: @@ -92,11 +62,9 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): >>> graph.get_state(config) >>> result = graph.invoke(3, config) >>> graph.get_state(config) - StateSnapshot(values=4, next=(), config={'configurable': {'thread_id': '1', 'thread_ts': '2024-05-04T06:32:42.235444+00:00'}}, parent_config=None) + StateSnapshot(values=4, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '0c62ca34-ac19-445d-bbb0-5b4984975b2a'}}, parent_config=None) """ # noqa - serde = JsonPlusSerializerCompat() - conn: sqlite3.Connection is_setup: bool @@ -107,48 +75,40 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): serde: Optional[SerializerProtocol] = None, ) -> None: super().__init__(serde=serde) + self.jsonplus_serde = JsonPlusSerializer() self.conn = conn self.is_setup = False self.lock = threading.Lock() @classmethod - def from_conn_string(cls, conn_string: str) -> "SqliteSaver": + @contextmanager + def from_conn_string(cls, conn_string: str) -> Iterator["SqliteSaver"]: """Create a new SqliteSaver instance from a connection string. Args: conn_string (str): The SQLite connection string. - Returns: + Yields: SqliteSaver: A new SqliteSaver instance. Examples: In memory: - memory = SqliteSaver.from_conn_string(":memory:") + with SqliteSaver.from_conn_string(":memory:") as memory: + ... To disk: - memory = SqliteSaver.from_conn_string("checkpoints.sqlite") + with SqliteSaver.from_conn_string("checkpoints.sqlite") as memory: + ... """ - return SqliteSaver( - conn=sqlite3.connect( - conn_string, - # https://ricardoanderegg.com/posts/python-sqlite-thread-safety/ - check_same_thread=False, - ) - ) - - def __enter__(self) -> Self: - return self - - def __exit__( - self, - __exc_type: Optional[type[BaseException]], - __exc_value: Optional[BaseException], - __traceback: Optional[TracebackType], - ) -> Optional[bool]: - return self.conn.close() + with sqlite3.connect( + conn_string, + # https://ricardoanderegg.com/posts/python-sqlite-thread-safety/ + check_same_thread=False, + ) as conn: + yield SqliteSaver(conn) def setup(self) -> None: """Set up the checkpoint database. @@ -165,20 +125,24 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): PRAGMA journal_mode=WAL; CREATE TABLE IF NOT EXISTS checkpoints ( thread_id TEXT NOT NULL, - thread_ts TEXT NOT NULL, - parent_ts TEXT, + checkpoint_ns TEXT NOT NULL DEFAULT '', + checkpoint_id TEXT NOT NULL, + parent_checkpoint_id TEXT, + type TEXT, checkpoint BLOB, metadata BLOB, - PRIMARY KEY (thread_id, thread_ts) + PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id) ); CREATE TABLE IF NOT EXISTS writes ( thread_id TEXT NOT NULL, - thread_ts TEXT NOT NULL, + checkpoint_ns TEXT NOT NULL DEFAULT '', + checkpoint_id TEXT NOT NULL, task_id TEXT NOT NULL, idx INTEGER NOT NULL, channel TEXT NOT NULL, + type TEXT, value BLOB, - PRIMARY KEY (thread_id, thread_ts, task_id, idx) + PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) ); """ ) @@ -211,8 +175,8 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): """Get a checkpoint tuple from the database. This method retrieves a checkpoint tuple from the SQLite database based on the - provided config. If the config contains a "thread_ts" key, the checkpoint with - the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint + provided config. If the config contains a "checkpoint_id" key, the checkpoint with + the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint for the given thread ID is retrieved. Args: @@ -229,68 +193,82 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): >>> print(checkpoint_tuple) CheckpointTuple(...) - With timestamp: + With checkpoint ID: >>> config = { ... "configurable": { ... "thread_id": "1", - ... "thread_ts": "2024-05-04T06:32:42.235444+00:00", + ... "checkpoint_ns": "", + ... "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875", ... } ... } >>> checkpoint_tuple = memory.get_tuple(config) >>> print(checkpoint_tuple) CheckpointTuple(...) """ # noqa + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") with self.cursor(transaction=False) as cur: # find the latest checkpoint for the thread_id - if config["configurable"].get("thread_ts"): + if checkpoint_id := get_checkpoint_id(config): cur.execute( - "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND thread_ts = ?", + "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?", ( str(config["configurable"]["thread_id"]), - str(config["configurable"]["thread_ts"]), + checkpoint_ns, + checkpoint_id, ), ) else: cur.execute( - "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? ORDER BY thread_ts DESC LIMIT 1", - (str(config["configurable"]["thread_id"]),), + "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1", + (str(config["configurable"]["thread_id"]), checkpoint_ns), ) # if a checkpoint is found, return it if value := cur.fetchone(): - if not config["configurable"].get("thread_ts"): + ( + thread_id, + checkpoint_id, + parent_checkpoint_id, + type, + checkpoint, + metadata, + ) = value + if not get_checkpoint_id(config): config = { "configurable": { - "thread_id": value[0], - "thread_ts": value[1], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint_id, } } # find any pending writes cur.execute( - "SELECT task_id, channel, value FROM writes WHERE thread_id = ? AND thread_ts = ?", + "SELECT task_id, channel, type, value FROM writes WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?", ( str(config["configurable"]["thread_id"]), - str(config["configurable"]["thread_ts"]), + checkpoint_ns, + str(config["configurable"]["checkpoint_id"]), ), ) # deserialize the checkpoint and metadata return CheckpointTuple( config, - self.serde.loads(value[3]), - self.serde.loads(value[4]) if value[4] is not None else {}, + self.serde.loads_typed((type, checkpoint)), + self.jsonplus_serde.loads(metadata) if metadata is not None else {}, ( { "configurable": { - "thread_id": value[0], - "thread_ts": value[2], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if value[2] + if parent_checkpoint_id else None ), [ - (task_id, channel, self.serde.loads(value)) - for task_id, channel, value in cur + (task_id, channel, self.serde.loads_typed((type, value))) + for task_id, channel, type, value in cur ], ) @@ -301,17 +279,16 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): filter: Optional[Dict[str, Any]] = None, before: Optional[RunnableConfig] = None, limit: Optional[int] = None, - as_prefix: bool = False, ) -> Iterator[CheckpointTuple]: """List checkpoints from the database. This method retrieves a list of checkpoint tuples from the SQLite database based - on the provided config. The checkpoints are ordered by timestamp in descending order. + on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first). Args: config (RunnableConfig): The config to use for listing the checkpoints. filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. Defaults to None. - before (Optional[RunnableConfig]): If provided, only checkpoints before the specified timestamp are returned. Defaults to None. + before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None. limit (Optional[int]): The maximum number of checkpoints to return. Defaults to None. Yields: @@ -319,41 +296,58 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): Examples: >>> from langgraph.checkpoint.sqlite import SqliteSaver - >>> memory = SqliteSaver.from_conn_string(":memory:") + >>> with SqliteSaver.from_conn_string(":memory:") as memory: ... # Run a graph, then list the checkpoints - >>> config = {"configurable": {"thread_id": "1"}} - >>> checkpoints = list(memory.list(config, limit=2)) + >>> config = {"configurable": {"thread_id": "1"}} + >>> checkpoints = list(memory.list(config, limit=2)) >>> print(checkpoints) [CheckpointTuple(...), CheckpointTuple(...)] >>> config = {"configurable": {"thread_id": "1"}} - >>> before = {"configurable": {"thread_ts": "2024-05-04T06:32:42.235444+00:00"}} - >>> checkpoints = list(memory.list(config, before=before)) + >>> before = {"configurable": {"checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875"}} + >>> with SqliteSaver.from_conn_string(":memory:") as memory: + ... # Run a graph, then list the checkpoints + >>> checkpoints = list(memory.list(config, before=before)) >>> print(checkpoints) [CheckpointTuple(...), ...] """ - where, param_values = search_where(config, filter, before, as_prefix=as_prefix) - query = f"""SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata + where, param_values = search_where(config, filter, before) + query = f"""SELECT thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints {where} - ORDER BY thread_ts DESC""" + ORDER BY checkpoint_id DESC""" if limit: query += f" LIMIT {limit}" with self.cursor(transaction=False) as cur: cur.execute(query, param_values) - for thread_id, thread_ts, parent_ts, value, metadata in cur: + for ( + thread_id, + checkpoint_ns, + checkpoint_id, + parent_checkpoint_id, + type, + checkpoint, + metadata, + ) in cur: yield CheckpointTuple( - {"configurable": {"thread_id": thread_id, "thread_ts": thread_ts}}, - self.serde.loads(value), - self.serde.loads(metadata) if metadata is not None else {}, + { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint_id, + } + }, + self.serde.loads_typed((type, checkpoint)), + self.jsonplus_serde.loads(metadata) if metadata is not None else {}, ( { "configurable": { "thread_id": thread_id, - "thread_ts": parent_ts, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if parent_ts + if parent_checkpoint_id else None ), ) @@ -363,6 +357,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: """Save a checkpoint to the database. @@ -372,37 +367,44 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): Args: config (RunnableConfig): The config to associate with the checkpoint. checkpoint (Checkpoint): The checkpoint to save. - metadata (Optional[dict[str, Any]]): Additional metadata to save with the checkpoint. Defaults to None. + metadata (CheckpointMetadata): Additional metadata to save with the checkpoint. + new_versions (ChannelVersions): New channel versions as of this write. Returns: - RunnableConfig: The updated config containing the saved checkpoint's timestamp. + RunnableConfig: Updated configuration after storing the checkpoint. Examples: >>> from langgraph.checkpoint.sqlite import SqliteSaver - >>> memory = SqliteSaver.from_conn_string(":memory:") - ... # Run a graph, then list the checkpoints - >>> config = {"configurable": {"thread_id": "1"}} - >>> checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "data": {"key": "value"}} - >>> saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}) + >>> with SqliteSaver.from_conn_string(":memory:") as memory: + >>> config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}} + >>> checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "data": {"key": "value"}} + >>> saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {}) >>> print(saved_config) - {"configurable": {"thread_id": "1", "thread_ts": 2024-05-04T06:32:42.235444+00:00"}} + {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}} """ + thread_id = config["configurable"]["thread_id"] + checkpoint_ns = config["configurable"]["checkpoint_ns"] + type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint) + serialized_metadata = self.jsonplus_serde.dumps(metadata) with self.lock, self.cursor() as cur: cur.execute( - "INSERT OR REPLACE INTO checkpoints (thread_id, thread_ts, parent_ts, checkpoint, metadata) VALUES (?, ?, ?, ?, ?)", + "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata) VALUES (?, ?, ?, ?, ?, ?, ?)", ( str(config["configurable"]["thread_id"]), + checkpoint_ns, checkpoint["id"], - config["configurable"].get("thread_ts"), - self.serde.dumps(checkpoint), - self.serde.dumps(metadata), + config["configurable"].get("checkpoint_id"), + type_, + serialized_checkpoint, + serialized_metadata, ), ) return { "configurable": { - "thread_id": config["configurable"]["thread_id"], - "thread_ts": checkpoint["id"], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint["id"], } } @@ -423,15 +425,16 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): """ with self.lock, self.cursor() as cur: cur.executemany( - "INSERT OR REPLACE INTO writes (thread_id, thread_ts, task_id, idx, channel, value) VALUES (?, ?, ?, ?, ?, ?)", + "INSERT OR REPLACE INTO writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, value) VALUES (?, ?, ?, ?, ?, ?, ?, ?)", [ ( str(config["configurable"]["thread_id"]), - str(config["configurable"]["thread_ts"]), + str(config["configurable"]["checkpoint_ns"]), + str(config["configurable"]["checkpoint_id"]), task_id, idx, channel, - self.serde.dumps(value), + *self.serde.dumps_typed(value), ) for idx, (channel, value) in enumerate(writes) ], @@ -477,7 +480,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): """ raise NotImplementedError(_AIO_ERROR_MSG) - def get_next_version(self, current: Optional[str], channel: BaseChannel) -> str: + def get_next_version(self, current: Optional[str], channel: ChannelProtocol) -> str: """Generate the next version ID for a channel. This method creates a new version identifier for a channel based on its current version. @@ -495,89 +498,7 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): current_v = int(current.split(".")[0]) next_v = current_v + 1 try: - next_h = md5(self.serde.dumps(channel.checkpoint())).hexdigest() + next_h = md5(self.serde.dumps_typed(channel.checkpoint())[1]).hexdigest() except EmptyChannelError: next_h = "" return f"{next_v:032}.{next_h}" - - -def _metadata_predicate( - metadata_filter: Dict[str, Any], -) -> Tuple[Sequence[str], Sequence[Any]]: - """Return WHERE clause predicates for (a)search() given metadata filter. - - This method returns a tuple of a string and a tuple of values. The string - is the parametered WHERE clause predicate (excluding the WHERE keyword): - "column1 = ? AND column2 IS ?". The tuple of values contains the values - for each of the corresponding parameters. - """ - - def _where_value(query_value: Any) -> Tuple[str, Any]: - """Return tuple of operator and value for WHERE clause predicate.""" - if query_value is None: - return ("IS ?", None) - elif ( - isinstance(query_value, str) - or isinstance(query_value, int) - or isinstance(query_value, float) - ): - return ("= ?", query_value) - elif isinstance(query_value, bool): - return ("= ?", 1 if query_value else 0) - elif isinstance(query_value, dict) or isinstance(query_value, list): - # query value for JSON object cannot have trailing space after separators (, :) - # SQLite json_extract() returns JSON string without whitespace - return ("= ?", json.dumps(query_value, separators=(",", ":"))) - else: - return ("= ?", str(query_value)) - - predicates = [] - param_values = [] - - # process metadata query - for query_key, query_value in metadata_filter.items(): - operator, param_value = _where_value(query_value) - predicates.append( - f"json_extract(CAST(metadata AS TEXT), '$.{query_key}') {operator}" - ) - param_values.append(param_value) - - return (predicates, param_values) - - -def search_where( - config: Optional[RunnableConfig], - filter: Optional[Dict[str, Any]], - before: Optional[RunnableConfig] = None, - as_prefix: bool = False, -) -> Tuple[str, Sequence[Any]]: - """Return WHERE clause predicates for (a)search() given metadata filter - and `before` config. - - This method returns a tuple of a string and a tuple of values. The string - is the parametered WHERE clause predicate (including the WHERE keyword): - "WHERE column1 = ? AND column2 IS ?". The tuple of values contains the - values for each of the corresponding parameters. - """ - wheres = [] - param_values = [] - - # construct predicate for config filter - if config is not None: - thread_filter = "thread_id LIKE ? || '%'" if as_prefix else "thread_id = ?" - wheres.append(thread_filter) - - param_values.append(config["configurable"]["thread_id"]) - - # construct predicate for metadata filter - if filter: - metadata_predicates, metadata_values = _metadata_predicate(filter) - wheres.extend(metadata_predicates) - param_values.extend(metadata_values) - - # construct predicate for `before` - if before is not None: - wheres.append("thread_ts < ?") - param_values.append(before["configurable"]["thread_ts"]) - - return ("WHERE " + " AND ".join(wheres) if wheres else "", param_values) diff --git a/libs/langgraph/langgraph/checkpoint/aiosqlite.py b/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py similarity index 64% rename from libs/langgraph/langgraph/checkpoint/aiosqlite.py rename to libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py index 5ac9a9197..95b77209a 100644 --- a/libs/langgraph/langgraph/checkpoint/aiosqlite.py +++ b/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py @@ -1,7 +1,6 @@ import asyncio import functools -from contextlib import AbstractAsyncContextManager -from types import TracebackType +from contextlib import asynccontextmanager from typing import ( Any, AsyncIterator, @@ -15,16 +14,18 @@ from typing import ( import aiosqlite from langchain_core.runnables import RunnableConfig -from typing_extensions import Self from langgraph.checkpoint.base import ( BaseCheckpointSaver, + ChannelVersions, Checkpoint, CheckpointMetadata, CheckpointTuple, SerializerProtocol, + get_checkpoint_id, ) -from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat, search_where +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer +from langgraph.checkpoint.sqlite.utils import search_where T = TypeVar("T", bound=callable) @@ -43,7 +44,7 @@ def not_implemented_sync_method(func: T) -> T: return wrapper -class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): +class AsyncSqliteSaver(BaseCheckpointSaver): """An asynchronous checkpoint saver that stores checkpoints in a SQLite database. This class provides an asynchronous interface for saving and retrieving checkpoints @@ -84,19 +85,18 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): ```pycon >>> import asyncio - >>> import aiosqlite >>> - >>> from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver + >>> from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver >>> from langgraph.graph import StateGraph >>> >>> builder = StateGraph(int) >>> builder.add_node("add_one", lambda x: x + 1) >>> builder.set_entry_point("add_one") >>> builder.set_finish_point("add_one") - >>> memory = AsyncSqliteSaver.from_conn_string("checkpoints.sqlite") - >>> graph = builder.compile(checkpointer=memory) - >>> coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}}) - >>> asyncio.run(coro) + >>> async with AsyncSqliteSaver.from_conn_string("checkpoints.db") as memory: + >>> graph = builder.compile(checkpointer=memory) + >>> coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}}) + >>> print(asyncio.run(coro)) Output: 2 ``` Raw usage: @@ -104,23 +104,20 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): ```pycon >>> import asyncio >>> import aiosqlite - >>> from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver + >>> from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver >>> >>> async def main(): >>> async with aiosqlite.connect("checkpoints.db") as conn: ... saver = AsyncSqliteSaver(conn) ... config = {"configurable": {"thread_id": "1"}} ... checkpoint = {"ts": "2023-05-03T10:00:00Z", "data": {"key": "value"}} - ... saved_config = await saver.aput(config, checkpoint) + ... saved_config = await saver.aput(config, checkpoint, {}, {}) ... print(saved_config) >>> asyncio.run(main()) - {"configurable": {"thread_id": "1", "thread_ts": "2023-05-03T10:00:00Z"}} + {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}} ``` """ - serde = JsonPlusSerializerCompat() - - conn: aiosqlite.Connection lock: asyncio.Lock is_setup: bool @@ -131,33 +128,26 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): serde: Optional[SerializerProtocol] = None, ): super().__init__(serde=serde) + self.jsonplus_serde = JsonPlusSerializer() self.conn = conn self.lock = asyncio.Lock() self.is_setup = False @classmethod - def from_conn_string(cls, conn_string: str) -> "AsyncSqliteSaver": + @asynccontextmanager + async def from_conn_string( + cls, conn_string: str + ) -> AsyncIterator["AsyncSqliteSaver"]: """Create a new AsyncSqliteSaver instance from a connection string. Args: conn_string (str): The SQLite connection string. - Returns: + Yields: AsyncSqliteSaver: A new AsyncSqliteSaver instance. """ - return AsyncSqliteSaver(conn=aiosqlite.connect(conn_string)) - - async def __aenter__(self) -> Self: - return self - - async def __aexit__( - self, - __exc_type: Optional[type[BaseException]], - __exc_value: Optional[BaseException], - __traceback: Optional[TracebackType], - ) -> Optional[bool]: - if self.is_setup: - return await self.conn.close() + async with aiosqlite.connect(conn_string) as conn: + yield AsyncSqliteSaver(conn) @not_implemented_sync_method def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: @@ -210,20 +200,24 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): PRAGMA journal_mode=WAL; CREATE TABLE IF NOT EXISTS checkpoints ( thread_id TEXT NOT NULL, - thread_ts TEXT NOT NULL, - parent_ts TEXT, + checkpoint_ns TEXT NOT NULL DEFAULT '', + checkpoint_id TEXT NOT NULL, + parent_checkpoint_id TEXT, + type TEXT, checkpoint BLOB, metadata BLOB, - PRIMARY KEY (thread_id, thread_ts) + PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id) ); CREATE TABLE IF NOT EXISTS writes ( thread_id TEXT NOT NULL, - thread_ts TEXT NOT NULL, + checkpoint_ns TEXT NOT NULL DEFAULT '', + checkpoint_id TEXT NOT NULL, task_id TEXT NOT NULL, idx INTEGER NOT NULL, channel TEXT NOT NULL, + type TEXT, value BLOB, - PRIMARY KEY (thread_id, thread_ts, task_id, idx) + PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) ); """ ): @@ -235,8 +229,8 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): """Get a checkpoint tuple from the database asynchronously. This method retrieves a checkpoint tuple from the SQLite database based on the - provided config. If the config contains a "thread_ts" key, the checkpoint with - the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint + provided config. If the config contains a "checkpoint_id" key, the checkpoint with + the matching thread ID and checkpoint ID is retrieved. Otherwise, the latest checkpoint for the given thread ID is retrieved. Args: @@ -246,56 +240,69 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found. """ await self.setup() + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") async with self.conn.cursor() as cur: # find the latest checkpoint for the thread_id - if config["configurable"].get("thread_ts"): + if checkpoint_id := get_checkpoint_id(config): await cur.execute( - "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND thread_ts = ?", + "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?", ( str(config["configurable"]["thread_id"]), - str(config["configurable"]["thread_ts"]), + checkpoint_ns, + checkpoint_id, ), ) else: await cur.execute( - "SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata FROM checkpoints WHERE thread_id = ? ORDER BY thread_ts DESC LIMIT 1", - (str(config["configurable"]["thread_id"]),), + "SELECT thread_id, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints WHERE thread_id = ? AND checkpoint_ns = ? ORDER BY checkpoint_id DESC LIMIT 1", + (str(config["configurable"]["thread_id"]), checkpoint_ns), ) # if a checkpoint is found, return it if value := await cur.fetchone(): - if not config["configurable"].get("thread_ts"): + ( + thread_id, + checkpoint_id, + parent_checkpoint_id, + type, + checkpoint, + metadata, + ) = value + if not get_checkpoint_id(config): config = { "configurable": { - "thread_id": value[0], - "thread_ts": value[1], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint_id, } } # find any pending writes await cur.execute( - "SELECT task_id, channel, value FROM writes WHERE thread_id = ? AND thread_ts = ?", + "SELECT task_id, channel, type, value FROM writes WHERE thread_id = ? AND checkpoint_ns = ? AND checkpoint_id = ?", ( str(config["configurable"]["thread_id"]), - str(config["configurable"]["thread_ts"]), + checkpoint_ns, + str(config["configurable"]["checkpoint_id"]), ), ) # deserialize the checkpoint and metadata return CheckpointTuple( config, - self.serde.loads(value[3]), - self.serde.loads(value[4]) if value[4] is not None else {}, + self.serde.loads_typed((type, checkpoint)), + self.jsonplus_serde.loads(metadata) if metadata is not None else {}, ( { "configurable": { - "thread_id": value[0], - "thread_ts": value[2], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if value[2] + if parent_checkpoint_id else None ), [ - (task_id, channel, self.serde.loads(value)) - async for task_id, channel, value in cur + (task_id, channel, self.serde.loads_typed((type, value))) + async for task_id, channel, type, value in cur ], ) @@ -306,44 +313,58 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): filter: Optional[Dict[str, Any]] = None, before: Optional[RunnableConfig] = None, limit: Optional[int] = None, - as_prefix: bool = False, ) -> AsyncIterator[CheckpointTuple]: """List checkpoints from the database asynchronously. This method retrieves a list of checkpoint tuples from the SQLite database based - on the provided config. The checkpoints are ordered by timestamp in descending order. + on the provided config. The checkpoints are ordered by checkpoint ID in descending order (newest first). Args: config (Optional[RunnableConfig]): Base configuration for filtering checkpoints. filter (Optional[Dict[str, Any]]): Additional filtering criteria for metadata. - before (Optional[RunnableConfig]): List checkpoints created before this configuration. + before (Optional[RunnableConfig]): If provided, only checkpoints before the specified checkpoint ID are returned. Defaults to None. limit (Optional[int]): Maximum number of checkpoints to return. Yields: AsyncIterator[CheckpointTuple]: An asynchronous iterator of matching checkpoint tuples. """ await self.setup() - where, param_values = search_where(config, filter, before, as_prefix=as_prefix) - query = f"""SELECT thread_id, thread_ts, parent_ts, checkpoint, metadata + where, param_values = search_where(config, filter, before) + query = f"""SELECT thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata FROM checkpoints {where} - ORDER BY thread_ts DESC""" + ORDER BY checkpoint_id DESC""" if limit: query += f" LIMIT {limit}" async with self.conn.execute(query, param_values) as cursor: - async for thread_id, thread_ts, parent_ts, value, metadata in cursor: + async for ( + thread_id, + checkpoint_ns, + checkpoint_id, + parent_checkpoint_id, + type, + checkpoint, + metadata, + ) in cursor: yield CheckpointTuple( - {"configurable": {"thread_id": thread_id, "thread_ts": thread_ts}}, - self.serde.loads(value), - self.serde.loads(metadata) if metadata is not None else {}, + { + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint_id, + } + }, + self.serde.loads_typed((type, checkpoint)), + self.jsonplus_serde.loads(metadata) if metadata is not None else {}, ( { "configurable": { "thread_id": thread_id, - "thread_ts": parent_ts, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if parent_ts + if parent_checkpoint_id else None ), ) @@ -353,6 +374,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: """Save a checkpoint to the database asynchronously. @@ -363,26 +385,34 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): config (RunnableConfig): The config to associate with the checkpoint. checkpoint (Checkpoint): The checkpoint to save. metadata (CheckpointMetadata): Additional metadata to save with the checkpoint. + new_versions (ChannelVersions): New channel versions as of this write. Returns: - RunnableConfig: The updated config containing the saved checkpoint's timestamp. + RunnableConfig: Updated configuration after storing the checkpoint. """ await self.setup() + thread_id = config["configurable"]["thread_id"] + checkpoint_ns = config["configurable"]["checkpoint_ns"] + type_, serialized_checkpoint = self.serde.dumps_typed(checkpoint) + serialized_metadata = self.jsonplus_serde.dumps(metadata) async with self.conn.execute( - "INSERT OR REPLACE INTO checkpoints (thread_id, thread_ts, parent_ts, checkpoint, metadata) VALUES (?, ?, ?, ?, ?)", + "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint_ns, checkpoint_id, parent_checkpoint_id, type, checkpoint, metadata) VALUES (?, ?, ?, ?, ?, ?, ?)", ( str(config["configurable"]["thread_id"]), + checkpoint_ns, checkpoint["id"], - config["configurable"].get("thread_ts"), - self.serde.dumps(checkpoint), - self.serde.dumps(metadata), + config["configurable"].get("checkpoint_id"), + type_, + serialized_checkpoint, + serialized_metadata, ), ): await self.conn.commit() return { "configurable": { - "thread_id": config["configurable"]["thread_id"], - "thread_ts": checkpoint["id"], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint["id"], } } @@ -403,15 +433,16 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): """ await self.setup() async with self.conn.executemany( - "INSERT OR REPLACE INTO writes (thread_id, thread_ts, task_id, idx, channel, value) VALUES (?, ?, ?, ?, ?, ?)", + "INSERT OR REPLACE INTO writes (thread_id, checkpoint_ns, checkpoint_id, task_id, idx, channel, type, value) VALUES (?, ?, ?, ?, ?, ?, ?, ?)", [ ( str(config["configurable"]["thread_id"]), - str(config["configurable"]["thread_ts"]), + str(config["configurable"]["checkpoint_ns"]), + str(config["configurable"]["checkpoint_id"]), task_id, idx, channel, - self.serde.dumps(value), + *self.serde.dumps_typed(value), ) for idx, (channel, value) in enumerate(writes) ], diff --git a/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py b/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py new file mode 100644 index 000000000..6e1baf5ae --- /dev/null +++ b/libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/utils.py @@ -0,0 +1,88 @@ +import json +from typing import Any, Dict, Optional, Sequence, Tuple + +from langchain_core.runnables import RunnableConfig + +from langgraph.checkpoint.base import get_checkpoint_id + + +def _metadata_predicate( + metadata_filter: Dict[str, Any], +) -> Tuple[Sequence[str], Sequence[Any]]: + """Return WHERE clause predicates for (a)search() given metadata filter. + + This method returns a tuple of a string and a tuple of values. The string + is the parametered WHERE clause predicate (excluding the WHERE keyword): + "column1 = ? AND column2 IS ?". The tuple of values contains the values + for each of the corresponding parameters. + """ + + def _where_value(query_value: Any) -> Tuple[str, Any]: + """Return tuple of operator and value for WHERE clause predicate.""" + if query_value is None: + return ("IS ?", None) + elif ( + isinstance(query_value, str) + or isinstance(query_value, int) + or isinstance(query_value, float) + ): + return ("= ?", query_value) + elif isinstance(query_value, bool): + return ("= ?", 1 if query_value else 0) + elif isinstance(query_value, dict) or isinstance(query_value, list): + # query value for JSON object cannot have trailing space after separators (, :) + # SQLite json_extract() returns JSON string without whitespace + return ("= ?", json.dumps(query_value, separators=(",", ":"))) + else: + return ("= ?", str(query_value)) + + predicates = [] + param_values = [] + + # process metadata query + for query_key, query_value in metadata_filter.items(): + operator, param_value = _where_value(query_value) + predicates.append( + f"json_extract(CAST(metadata AS TEXT), '$.{query_key}') {operator}" + ) + param_values.append(param_value) + + return (predicates, param_values) + + +def search_where( + config: Optional[RunnableConfig], + filter: Optional[Dict[str, Any]], + before: Optional[RunnableConfig] = None, +) -> Tuple[str, Sequence[Any]]: + """Return WHERE clause predicates for (a)search() given metadata filter + and `before` config. + + This method returns a tuple of a string and a tuple of values. The string + is the parametered WHERE clause predicate (including the WHERE keyword): + "WHERE column1 = ? AND column2 IS ?". The tuple of values contains the + values for each of the corresponding parameters. + """ + wheres = [] + param_values = [] + + # construct predicate for config filter + if config is not None: + wheres.append("thread_id = ?") + param_values.append(config["configurable"]["thread_id"]) + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") + wheres.append("checkpoint_ns = ?") + param_values.append(checkpoint_ns) + + # construct predicate for metadata filter + if filter: + metadata_predicates, metadata_values = _metadata_predicate(filter) + wheres.extend(metadata_predicates) + param_values.extend(metadata_values) + + # construct predicate for `before` + if before is not None: + wheres.append("checkpoint_id < ?") + param_values.append(get_checkpoint_id(before)) + + return ("WHERE " + " AND ".join(wheres) if wheres else "", param_values) diff --git a/libs/checkpoint-sqlite/poetry.lock b/libs/checkpoint-sqlite/poetry.lock new file mode 100644 index 000000000..cd7aa8376 --- /dev/null +++ b/libs/checkpoint-sqlite/poetry.lock @@ -0,0 +1,835 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "aiosqlite" +version = "0.20.0" +description = "asyncio bridge to the standard sqlite3 module" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"}, + {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"}, +] + +[package.dependencies] +typing_extensions = ">=4.0" + +[package.extras] +dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"] +docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"] + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "certifi" +version = "2024.7.4" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +addopts = "--strict-markers --strict-config --durations=5 -vv" +asyncio_mode = "auto" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +lint.select = [ + "E", # pycodestyle + "F", # Pyflakes + "UP", # pyupgrade + "B", # flake8-bugbear + "I", # isort +] +lint.ignore = ["E501", "B008", "UP007", "UP006"] + +[tool.pytest-watcher] +now = true +delay = 0.1 +runner_args = ["--ff", "-v", "--tb", "short"] +patterns = ["*.py"] diff --git a/libs/checkpoint-sqlite/tests/__init__.py b/libs/checkpoint-sqlite/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/checkpoint-sqlite/tests/test_aiosqlite.py b/libs/checkpoint-sqlite/tests/test_aiosqlite.py new file mode 100644 index 000000000..59f830dae --- /dev/null +++ b/libs/checkpoint-sqlite/tests/test_aiosqlite.py @@ -0,0 +1,112 @@ +import pytest +from langchain_core.runnables import RunnableConfig + +from langgraph.checkpoint.base import ( + Checkpoint, + CheckpointMetadata, + create_checkpoint, + empty_checkpoint, +) +from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver + + +class TestAsyncSqliteSaver: + @pytest.fixture(autouse=True) + def setup(self): + # objects for test setup + self.config_1: RunnableConfig = { + "configurable": { + "thread_id": "thread-1", + # for backwards compatibility testing + "thread_ts": "1", + "checkpoint_ns": "", + } + } + self.config_2: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2", + "checkpoint_ns": "", + } + } + self.config_3: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2-inner", + "checkpoint_ns": "inner", + } + } + + self.chkpnt_1: Checkpoint = empty_checkpoint() + self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) + self.chkpnt_3: Checkpoint = empty_checkpoint() + + self.metadata_1: CheckpointMetadata = { + "source": "input", + "step": 2, + "writes": {}, + "score": 1, + } + self.metadata_2: CheckpointMetadata = { + "source": "loop", + "step": 1, + "writes": {"foo": "bar"}, + "score": None, + } + self.metadata_3: CheckpointMetadata = {} + + async def test_asearch(self): + async with AsyncSqliteSaver.from_conn_string(":memory:") as saver: + await saver.aput(self.config_1, self.chkpnt_1, self.metadata_1, {}) + await saver.aput(self.config_2, self.chkpnt_2, self.metadata_2, {}) + await saver.aput(self.config_3, self.chkpnt_3, self.metadata_3, {}) + + # call method / assertions + query_1: CheckpointMetadata = {"source": "input"} # search by 1 key + query_2: CheckpointMetadata = { + "step": 1, + "writes": {"foo": "bar"}, + } # search by multiple keys + query_3: CheckpointMetadata = {} # search by no keys, return all checkpoints + query_4: CheckpointMetadata = {"source": "update", "step": 1} # no match + + search_results_1 = [c async for c in saver.alist(None, filter=query_1)] + assert len(search_results_1) == 1 + assert search_results_1[0].metadata == self.metadata_1 + + search_results_2 = [c async for c in saver.alist(None, filter=query_2)] + assert len(search_results_2) == 1 + assert search_results_2[0].metadata == self.metadata_2 + + search_results_3 = [c async for c in saver.alist(None, filter=query_3)] + assert len(search_results_3) == 3 + + search_results_4 = [c async for c in saver.alist(None, filter=query_4)] + assert len(search_results_4) == 0 + + # search by config (defaults to root graph checkpoints) + search_results_5 = [ + c + async for c in saver.alist({"configurable": {"thread_id": "thread-2"}}) + ] + assert len(search_results_5) == 1 + assert search_results_5[0].config["configurable"]["checkpoint_ns"] == "" + + # search by config and checkpoint_ns + search_results_6 = [ + c + async for c in saver.alist( + { + "configurable": { + "thread_id": "thread-2", + "checkpoint_ns": "inner", + } + } + ) + ] + assert len(search_results_6) == 1 + assert ( + search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner" + ) + + # TODO: test before and limit params diff --git a/libs/checkpoint-sqlite/tests/test_sqlite.py b/libs/checkpoint-sqlite/tests/test_sqlite.py new file mode 100644 index 000000000..2147cca87 --- /dev/null +++ b/libs/checkpoint-sqlite/tests/test_sqlite.py @@ -0,0 +1,166 @@ +import pytest +from langchain_core.runnables import RunnableConfig + +from langgraph.checkpoint.base import ( + Checkpoint, + CheckpointMetadata, + create_checkpoint, + empty_checkpoint, +) +from langgraph.checkpoint.sqlite import SqliteSaver +from langgraph.checkpoint.sqlite.utils import _metadata_predicate, search_where + + +class TestSqliteSaver: + @pytest.fixture(autouse=True) + def setup(self): + # objects for test setup + self.config_1: RunnableConfig = { + "configurable": { + "thread_id": "thread-1", + # for backwards compatibility testing + "thread_ts": "1", + "checkpoint_ns": "", + } + } + self.config_2: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2", + "checkpoint_ns": "", + } + } + self.config_3: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2-inner", + "checkpoint_ns": "inner", + } + } + + self.chkpnt_1: Checkpoint = empty_checkpoint() + self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) + self.chkpnt_3: Checkpoint = empty_checkpoint() + + self.metadata_1: CheckpointMetadata = { + "source": "input", + "step": 2, + "writes": {}, + "score": 1, + } + self.metadata_2: CheckpointMetadata = { + "source": "loop", + "step": 1, + "writes": {"foo": "bar"}, + "score": None, + } + self.metadata_3: CheckpointMetadata = {} + + def test_search(self): + with SqliteSaver.from_conn_string(":memory:") as saver: + # set up test + # save checkpoints + saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {}) + saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {}) + saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {}) + + # call method / assertions + query_1: CheckpointMetadata = {"source": "input"} # search by 1 key + query_2: CheckpointMetadata = { + "step": 1, + "writes": {"foo": "bar"}, + } # search by multiple keys + query_3: CheckpointMetadata = {} # search by no keys, return all checkpoints + query_4: CheckpointMetadata = {"source": "update", "step": 1} # no match + + search_results_1 = list(saver.list(None, filter=query_1)) + assert len(search_results_1) == 1 + assert search_results_1[0].metadata == self.metadata_1 + + search_results_2 = list(saver.list(None, filter=query_2)) + assert len(search_results_2) == 1 + assert search_results_2[0].metadata == self.metadata_2 + + search_results_3 = list(saver.list(None, filter=query_3)) + assert len(search_results_3) == 3 + + search_results_4 = list(saver.list(None, filter=query_4)) + assert len(search_results_4) == 0 + + # search by config (defaults to root graph checkpoints) + search_results_5 = list( + saver.list({"configurable": {"thread_id": "thread-2"}}) + ) + assert len(search_results_5) == 1 + assert search_results_5[0].config["configurable"]["checkpoint_ns"] == "" + + # search by config and checkpoint_ns + search_results_6 = list( + saver.list( + { + "configurable": { + "thread_id": "thread-2", + "checkpoint_ns": "inner", + } + } + ) + ) + assert len(search_results_6) == 1 + assert ( + search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner" + ) + + # TODO: test before and limit params + + def test_search_where(self): + # call method / assertions + expected_predicate_1 = "WHERE json_extract(CAST(metadata AS TEXT), '$.source') = ? AND json_extract(CAST(metadata AS TEXT), '$.step') = ? AND json_extract(CAST(metadata AS TEXT), '$.writes') = ? AND json_extract(CAST(metadata AS TEXT), '$.score') = ? AND checkpoint_id < ?" + expected_param_values_1 = ["input", 2, "{}", 1, "1"] + assert search_where(None, self.metadata_1, self.config_1) == ( + expected_predicate_1, + expected_param_values_1, + ) + + def test_metadata_predicate(self): + # call method / assertions + expected_predicate_1 = [ + "json_extract(CAST(metadata AS TEXT), '$.source') = ?", + "json_extract(CAST(metadata AS TEXT), '$.step') = ?", + "json_extract(CAST(metadata AS TEXT), '$.writes') = ?", + "json_extract(CAST(metadata AS TEXT), '$.score') = ?", + ] + expected_predicate_2 = [ + "json_extract(CAST(metadata AS TEXT), '$.source') = ?", + "json_extract(CAST(metadata AS TEXT), '$.step') = ?", + "json_extract(CAST(metadata AS TEXT), '$.writes') = ?", + "json_extract(CAST(metadata AS TEXT), '$.score') IS ?", + ] + expected_predicate_3 = [] + + expected_param_values_1 = ["input", 2, "{}", 1] + expected_param_values_2 = ["loop", 1, '{"foo":"bar"}', None] + expected_param_values_3 = [] + + assert _metadata_predicate(self.metadata_1) == ( + expected_predicate_1, + expected_param_values_1, + ) + assert _metadata_predicate(self.metadata_2) == ( + expected_predicate_2, + expected_param_values_2, + ) + assert _metadata_predicate(self.metadata_3) == ( + expected_predicate_3, + expected_param_values_3, + ) + + async def test_informative_async_errors(self): + with SqliteSaver.from_conn_string(":memory:") as saver: + # call method / assertions + with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"): + await saver.aget(self.config_1) + with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"): + await saver.aget_tuple(self.config_1) + with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"): + async for _ in saver.alist(self.config_1): + pass diff --git a/libs/checkpoint/LICENSE b/libs/checkpoint/LICENSE new file mode 100644 index 000000000..fc0602fee --- /dev/null +++ b/libs/checkpoint/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/checkpoint/Makefile b/libs/checkpoint/Makefile new file mode 100644 index 000000000..734e46308 --- /dev/null +++ b/libs/checkpoint/Makefile @@ -0,0 +1,34 @@ +.PHONY: test test_watch lint format + +###################### +# TESTING AND COVERAGE +###################### + +test: + poetry run pytest tests + +test_watch: + poetry run ptw . + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langgraph +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) diff --git a/libs/checkpoint/README.md b/libs/checkpoint/README.md new file mode 100644 index 000000000..7f6b26d6e --- /dev/null +++ b/libs/checkpoint/README.md @@ -0,0 +1,88 @@ +# LangGraph Checkpoint + +This library defines the base interface for LangGraph checkpointers. Checkpointers provide persistence layer for LangGraph. They allow you to interact with and manage the graph's state. When you use a graph with a checkpointer, the checkpointer saves a _checkpoint_ of the graph state at every superstep, enabling several powerful capabilities like human-in-the-loop, "memory" between interactions and more. + +## Key concepts + +### Checkpoint + +Checkpoint is a snapshot of the graph state at a given point in time. Checkpoint tuple refers to an object containing checkpoint and the associated config, metadata and pending writes. + +### Thread + +Threads enable the checkpointing of multiple different runs, making them essential for multi-tenant chat applications and other scenarios where maintaining separate states is necessary. A thread is a unique ID assigned to a series of checkpoints saved by a checkpointer. When using a checkpointer, you must specify a `thread_id` and optionally `checkpoint_id` when running the graph. + +- `thread_id` is simply the ID of a thread. This is always required +- `checkpoint_id` can optionally be passed. This identifier refers to a specific checkpoint within a thread. This can be used to kick of a run of a graph from some point halfway through a thread. + +You must pass these when invoking the graph as part of the configurable part of the config, e.g. + +```python +{"configurable": {"thread_id": "1"}} # valid config +{"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}} # also valid config +``` + +### Serde + +`langgraph_checkpoint` also defines protocol for serialization/deserialization (serde) and provides an default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more. + +### Pending writes + +When a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes. + +## Interface + +Each checkpointer should conform to `langgraph.checkpoint.base.BaseCheckpointSaver` interface and must implement the following methods: + +- `.put` - Store a checkpoint with its configuration and metadata. +- `.put_writes` - Store intermediate writes linked to a checkpoint (i.e. pending writes). +- `.get_tuple` - Fetch a checkpoint tuple using for a given configuration (`thread_id` and `thread_ts`). +- `.list` - List checkpoints that match a given configuration and filter criteria. + +If the checkpointer will be used with asynchronous graph execution (i.e. executing the graph via `.ainvoke`, `.astream`, `.abatch`), checkpointer must implement asynchronous versions of the above methods (`.aput`, `.aput_writes`, `.aget_tuple`, `.alist`). + +## Usage + +```python +from langgraph.checkpoint.memory import MemorySaver + +write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}} +read_config = {"configurable": {"thread_id": "1"}} + +checkpointer = MemorySaver() +checkpoint = { + "v": 1, + "ts": "2024-07-31T20:14:19.804150+00:00", + "id": "1ef4f797-8335-6428-8001-8a1503f9b875", + "channel_values": { + "my_key": "meow", + "node": "node" + }, + "channel_versions": { + "__start__": 2, + "my_key": 3, + "start:node": 3, + "node": 3 + }, + "versions_seen": { + "__input__": {}, + "__start__": { + "__start__": 1 + }, + "node": { + "start:node": 2 + } + }, + "pending_sends": [], + "current_tasks": {} +} + +# store checkpoint +checkpointer.put(write_config, checkpoint, {}, {}) + +# load checkpoint +checkpointer.get(read_config) + +# list checkpoints +list(checkpointer.list(read_config)) +``` diff --git a/libs/langgraph/langgraph/checkpoint/base.py b/libs/checkpoint/langgraph/checkpoint/base/__init__.py similarity index 81% rename from libs/langgraph/langgraph/checkpoint/base.py rename to libs/checkpoint/langgraph/checkpoint/base/__init__.py index cca902050..86c8b0eec 100644 --- a/libs/langgraph/langgraph/checkpoint/base.py +++ b/libs/checkpoint/langgraph/checkpoint/base/__init__.py @@ -7,6 +7,7 @@ from typing import ( Iterator, List, Literal, + Mapping, NamedTuple, Optional, Tuple, @@ -17,11 +18,13 @@ from typing import ( from langchain_core.runnables import ConfigurableFieldSpec, RunnableConfig -from langgraph.channels.base import BaseChannel -from langgraph.checkpoint.id import uuid6 -from langgraph.constants import Send -from langgraph.serde.base import SerializerProtocol -from langgraph.serde.jsonplus import JsonPlusSerializer +from langgraph.checkpoint.base.id import uuid6 +from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer +from langgraph.checkpoint.serde.types import ( + ChannelProtocol, + SendProtocol, +) V = TypeVar("V", int, float, str) PendingWrite = Tuple[str, str, Any] @@ -50,7 +53,7 @@ class CheckpointMetadata(TypedDict, total=False): """ score: Optional[int] """The score of the checkpoint. - + The score can be used to mark a checkpoint as "good". """ @@ -59,35 +62,38 @@ class TaskInfo(TypedDict): status: Literal["scheduled", "success", "error"] +ChannelVersions = dict[str, Union[str, int, float]] + + class Checkpoint(TypedDict): """State snapshot at a given point in time.""" v: int """The version of the checkpoint format. Currently 1.""" id: str - """The ID of the checkpoint. This is both unique and monotonically + """The ID of the checkpoint. This is both unique and monotonically increasing, so can be used for sorting checkpoints from first to last.""" ts: str """The timestamp of the checkpoint in ISO 8601 format.""" channel_values: dict[str, Any] """The values of the channels at the time of the checkpoint. - + Mapping from channel name to channel snapshot value. """ - channel_versions: dict[str, Union[str, int, float]] + channel_versions: ChannelVersions """The versions of the channels at the time of the checkpoint. - + The keys are channel names and the values are the logical time step at which the channel was last updated. """ - versions_seen: dict[str, dict[str, Union[str, int, float]]] + versions_seen: dict[str, ChannelVersions] """Map from node ID to map from channel name to version seen. - + This keeps track of the versions of the channels that each node has seen. - + Used to determine which nodes to execute next. """ - pending_sends: List[Send] + pending_sends: List[SendProtocol] """List of packets sent to nodes but not yet processed. Cleared by the next checkpoint.""" current_tasks: Dict[str, TaskInfo] @@ -120,6 +126,36 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint: ) +def create_checkpoint( + checkpoint: Checkpoint, + channels: Optional[Mapping[str, ChannelProtocol]], + step: int, + *, + id: Optional[str] = None, +) -> Checkpoint: + """Create a checkpoint for the given channels.""" + ts = datetime.now(timezone.utc).isoformat() + if channels is None: + values = checkpoint["channel_values"] + else: + values: dict[str, Any] = {} + for k, v in channels.items(): + try: + values[k] = v.checkpoint() + except EmptyChannelError: + pass + return Checkpoint( + v=1, + ts=ts, + id=id or str(uuid6(clock_seq=step)), + channel_values=values, + channel_versions=checkpoint["channel_versions"], + versions_seen=checkpoint["versions_seen"], + pending_sends=checkpoint.get("pending_sends", []), + current_tasks={}, + ) + + class CheckpointTuple(NamedTuple): """A tuple containing a checkpoint and its associated data.""" @@ -139,10 +175,19 @@ CheckpointThreadId = ConfigurableFieldSpec( is_shared=True, ) -CheckpointThreadTs = ConfigurableFieldSpec( - id="thread_ts", +CheckpointNS = ConfigurableFieldSpec( + id="checkpoint_ns", + annotation=str, + name="Checkpoint NS", + description='Checkpoint namespace. Denotes the path to the subgraph node the checkpoint originates from, separated by `|` character, e.g. `"child|grandchild"`. Defaults to "" (root graph).', + default=None, + is_shared=True, +) + +CheckpointId = ConfigurableFieldSpec( + id="checkpoint_id", annotation=Optional[str], - name="Thread Timestamp", + name="Checkpoint ID", description="Pass to fetch a past checkpoint. If None, fetches the latest checkpoint.", default=None, is_shared=True, @@ -170,7 +215,7 @@ class BaseCheckpointSaver(ABC): *, serde: Optional[SerializerProtocol] = None, ) -> None: - self.serde = serde or self.serde + self.serde = maybe_add_typed_methods(serde or self.serde) @property def config_specs(self) -> list[ConfigurableFieldSpec]: @@ -179,7 +224,7 @@ class BaseCheckpointSaver(ABC): Returns: list[ConfigurableFieldSpec]: List of configuration field specs. """ - return [CheckpointThreadId, CheckpointThreadTs] + return [CheckpointThreadId, CheckpointNS, CheckpointId] def get(self, config: RunnableConfig) -> Optional[Checkpoint]: """Fetch a checkpoint using the given configuration. @@ -214,7 +259,6 @@ class BaseCheckpointSaver(ABC): filter: Optional[Dict[str, Any]] = None, before: Optional[RunnableConfig] = None, limit: Optional[int] = None, - as_prefix: bool = False, ) -> Iterator[CheckpointTuple]: """List checkpoints that match the given criteria. @@ -237,6 +281,7 @@ class BaseCheckpointSaver(ABC): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: """Store a checkpoint with its configuration and metadata. @@ -244,6 +289,7 @@ class BaseCheckpointSaver(ABC): config (RunnableConfig): Configuration for the checkpoint. checkpoint (Checkpoint): The checkpoint to store. metadata (CheckpointMetadata): Additional metadata for the checkpoint. + new_versions (ChannelVersions): New channel versions as of this write. Returns: RunnableConfig: Updated configuration after storing the checkpoint. @@ -269,9 +315,7 @@ class BaseCheckpointSaver(ABC): Raises: NotImplementedError: Implement this method in your custom checkpoint saver. """ - raise NotImplementedError( - "This method was added in langgraph 0.1.7. Please update your checkpoint saver to implement it." - ) + raise NotImplementedError async def aget(self, config: RunnableConfig) -> Optional[Checkpoint]: """Asynchronously fetch a checkpoint using the given configuration. @@ -329,6 +373,7 @@ class BaseCheckpointSaver(ABC): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: """Asynchronously store a checkpoint with its configuration and metadata. @@ -336,6 +381,7 @@ class BaseCheckpointSaver(ABC): config (RunnableConfig): Configuration for the checkpoint. checkpoint (Checkpoint): The checkpoint to store. metadata (CheckpointMetadata): Additional metadata for the checkpoint. + new_versions (ChannelVersions): New channel versions as of this write. Returns: RunnableConfig: Updated configuration after storing the checkpoint. @@ -361,11 +407,9 @@ class BaseCheckpointSaver(ABC): Raises: NotImplementedError: Implement this method in your custom checkpoint saver. """ - raise NotImplementedError( - "This method was added in langgraph 0.1.7. Please update your checkpoint saver to implement it." - ) + raise NotImplementedError - def get_next_version(self, current: Optional[V], channel: BaseChannel) -> V: + def get_next_version(self, current: Optional[V], channel: ChannelProtocol) -> V: """Generate the next version ID for a channel. Default is to use integer versions, incrementing by 1. If you override, you can use str/int/float versions, @@ -379,3 +423,17 @@ class BaseCheckpointSaver(ABC): V: The next version identifier, which must be increasing. """ return current + 1 if current is not None else 1 + + +class EmptyChannelError(Exception): + """Raised when attempting to get the value of a channel that hasn't been updated + for the first time yet.""" + + pass + + +def get_checkpoint_id(config: RunnableConfig) -> Optional[str]: + """Get checkpoint ID in a backwards-compatible manner (fallback on thread_ts).""" + return config["configurable"].get( + "checkpoint_id", config["configurable"].get("thread_ts") + ) diff --git a/libs/langgraph/langgraph/checkpoint/id.py b/libs/checkpoint/langgraph/checkpoint/base/id.py similarity index 100% rename from libs/langgraph/langgraph/checkpoint/id.py rename to libs/checkpoint/langgraph/checkpoint/base/id.py diff --git a/libs/langgraph/langgraph/checkpoint/memory.py b/libs/checkpoint/langgraph/checkpoint/memory/__init__.py similarity index 65% rename from libs/langgraph/langgraph/checkpoint/memory.py rename to libs/checkpoint/langgraph/checkpoint/memory/__init__.py index 1aa89f881..d989b1fbc 100644 --- a/libs/langgraph/langgraph/checkpoint/memory.py +++ b/libs/checkpoint/langgraph/checkpoint/memory/__init__.py @@ -1,20 +1,26 @@ import asyncio from collections import defaultdict +from contextlib import AbstractAsyncContextManager, AbstractContextManager from functools import partial +from types import TracebackType from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Tuple from langchain_core.runnables import RunnableConfig from langgraph.checkpoint.base import ( BaseCheckpointSaver, + ChannelVersions, Checkpoint, CheckpointMetadata, CheckpointTuple, SerializerProtocol, + get_checkpoint_id, ) -class MemorySaver(BaseCheckpointSaver): +class MemorySaver( + BaseCheckpointSaver, AbstractContextManager, AbstractAsyncContextManager +): """An in-memory checkpoint saver. This checkpoint saver stores checkpoints in memory using a defaultdict. @@ -44,7 +50,8 @@ class MemorySaver(BaseCheckpointSaver): asyncio.run(coro) # Output: 2 """ - storage: defaultdict[str, dict[str, tuple[bytes, bytes, Optional[str]]]] + # thread ID -> checkpoint NS -> checkpoint ID -> checkpoint mapping + storage: defaultdict[str, dict[str, dict[str, tuple[bytes, bytes, Optional[str]]]]] def __init__( self, @@ -52,14 +59,36 @@ class MemorySaver(BaseCheckpointSaver): serde: Optional[SerializerProtocol] = None, ) -> None: super().__init__(serde=serde) - self.storage = defaultdict(dict) + self.storage = defaultdict(lambda: defaultdict(dict)) self.writes = defaultdict(list) + def __enter__(self) -> "MemorySaver": + return self + + def __exit__( + self, + exc_type: Optional[type[BaseException]], + exc_value: Optional[BaseException], + traceback: Optional[TracebackType], + ) -> Optional[bool]: + return + + async def __aenter__(self) -> "MemorySaver": + return self + + async def __aexit__( + self, + __exc_type: Optional[type[BaseException]], + __exc_value: Optional[BaseException], + __traceback: Optional[TracebackType], + ) -> Optional[bool]: + return + def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: """Get a checkpoint tuple from the in-memory storage. This method retrieves a checkpoint tuple from the in-memory storage based on the - provided config. If the config contains a "thread_ts" key, the checkpoint with + provided config. If the config contains a "checkpoint_id" key, the checkpoint with the matching thread ID and timestamp is retrieved. Otherwise, the latest checkpoint for the given thread ID is retrieved. @@ -70,45 +99,54 @@ class MemorySaver(BaseCheckpointSaver): Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found. """ thread_id = config["configurable"]["thread_id"] - if ts := config["configurable"].get("thread_ts"): - if saved := self.storage[thread_id].get(ts): - checkpoint, metadata, parent_ts = saved - writes = self.writes[(thread_id, ts)] + checkpoint_ns = config["configurable"].get("checkpoint_ns", "") + if checkpoint_id := get_checkpoint_id(config): + if saved := self.storage[thread_id][checkpoint_ns].get(checkpoint_id): + checkpoint, metadata, parent_checkpoint_id = saved + writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)] return CheckpointTuple( config=config, - checkpoint=self.serde.loads(checkpoint), - metadata=self.serde.loads(metadata), + checkpoint=self.serde.loads_typed(checkpoint), + metadata=self.serde.loads_typed(metadata), pending_writes=[ - (id, c, self.serde.loads(v)) for id, c, v in writes + (id, c, self.serde.loads_typed(v)) for id, c, v in writes ], parent_config={ "configurable": { "thread_id": thread_id, - "thread_ts": parent_ts, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if parent_ts + if parent_checkpoint_id else None, ) else: - if checkpoints := self.storage[thread_id]: - ts = max(checkpoints.keys()) - checkpoint, metadata, parent_ts = checkpoints[ts] - writes = self.writes[(thread_id, ts)] + if checkpoints := self.storage[thread_id][checkpoint_ns]: + checkpoint_id = max(checkpoints.keys()) + checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id] + writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)] return CheckpointTuple( - config={"configurable": {"thread_id": thread_id, "thread_ts": ts}}, - checkpoint=self.serde.loads(checkpoint), - metadata=self.serde.loads(metadata), + config={ + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint_id, + } + }, + checkpoint=self.serde.loads_typed(checkpoint), + metadata=self.serde.loads_typed(metadata), pending_writes=[ - (id, c, self.serde.loads(v)) for id, c, v in writes + (id, c, self.serde.loads_typed(v)) for id, c, v in writes ], parent_config={ "configurable": { "thread_id": thread_id, - "thread_ts": parent_ts, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if parent_ts + if parent_checkpoint_id else None, ) @@ -119,7 +157,6 @@ class MemorySaver(BaseCheckpointSaver): filter: Optional[Dict[str, Any]] = None, before: Optional[RunnableConfig] = None, limit: Optional[int] = None, - as_prefix: bool = False, ) -> Iterator[CheckpointTuple]: """List checkpoints from the in-memory storage. @@ -135,29 +172,26 @@ class MemorySaver(BaseCheckpointSaver): Yields: Iterator[CheckpointTuple]: An iterator of matching checkpoint tuples. """ - if config: - config_thread_id = config["configurable"]["thread_id"] - if as_prefix: - thread_ids = ( - thread_id - for thread_id in self.storage - if thread_id.startswith(config_thread_id) - ) - else: - thread_ids = (config_thread_id,) - else: - thread_ids = self.storage.keys() - + thread_ids = (config["configurable"]["thread_id"],) if config else self.storage + checkpoint_ns = ( + config["configurable"].get("checkpoint_ns", "") if config else "" + ) for thread_id in thread_ids: - for ts, (checkpoint, metadata_b, parent_ts) in sorted( - self.storage[thread_id].items(), key=lambda x: x[0], reverse=True + for checkpoint_id, (checkpoint, metadata_b, parent_checkpoint_id) in sorted( + self.storage[thread_id][checkpoint_ns].items(), + key=lambda x: x[0], + reverse=True, ): - # filter by thread_ts - if before and ts >= before["configurable"]["thread_ts"]: + # filter by checkpoint ID + if ( + before + and (before_checkpoint_id := get_checkpoint_id(before)) + and checkpoint_id >= before_checkpoint_id + ): continue # filter by metadata - metadata = self.serde.loads(metadata_b) + metadata = self.serde.loads_typed(metadata_b) if filter and not all( query_value == metadata[query_key] for query_key, query_value in filter.items() @@ -171,16 +205,23 @@ class MemorySaver(BaseCheckpointSaver): limit -= 1 yield CheckpointTuple( - config={"configurable": {"thread_id": thread_id, "thread_ts": ts}}, - checkpoint=self.serde.loads(checkpoint), + config={ + "configurable": { + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint_id, + } + }, + checkpoint=self.serde.loads_typed(checkpoint), metadata=metadata, parent_config={ "configurable": { "thread_id": thread_id, - "thread_ts": parent_ts, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": parent_checkpoint_id, } } - if parent_ts + if parent_checkpoint_id else None, ) @@ -189,6 +230,7 @@ class MemorySaver(BaseCheckpointSaver): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: """Save a checkpoint to the in-memory storage. @@ -199,23 +241,27 @@ class MemorySaver(BaseCheckpointSaver): config (RunnableConfig): The config to associate with the checkpoint. checkpoint (Checkpoint): The checkpoint to save. metadata (CheckpointMetadata): Additional metadata to save with the checkpoint. + new_versions (dict): New versions as of this write Returns: RunnableConfig: The updated config containing the saved checkpoint's timestamp. """ - self.storage[config["configurable"]["thread_id"]].update( + thread_id = config["configurable"]["thread_id"] + checkpoint_ns = config["configurable"]["checkpoint_ns"] + self.storage[thread_id][checkpoint_ns].update( { checkpoint["id"]: ( - self.serde.dumps(checkpoint), - self.serde.dumps(metadata), - config["configurable"].get("thread_ts"), # parent + self.serde.dumps_typed(checkpoint), + self.serde.dumps_typed(metadata), + config["configurable"].get("checkpoint_id"), # parent ) } ) return { "configurable": { - "thread_id": config["configurable"]["thread_id"], - "thread_ts": checkpoint["id"], + "thread_id": thread_id, + "checkpoint_ns": checkpoint_ns, + "checkpoint_id": checkpoint["id"], } } @@ -239,9 +285,11 @@ class MemorySaver(BaseCheckpointSaver): RunnableConfig: The updated config containing the saved writes' timestamp. """ thread_id = config["configurable"]["thread_id"] - ts = config["configurable"]["thread_ts"] - self.writes[(thread_id, ts)].extend( - [(task_id, c, self.serde.dumps(v)) for c, v in writes] + checkpoint_ns = config["configurable"]["checkpoint_ns"] + checkpoint_id = config["configurable"]["checkpoint_id"] + key = (thread_id, checkpoint_ns, checkpoint_id) + self.writes[key].extend( + [(task_id, c, self.serde.dumps_typed(v)) for c, v in writes] ) async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: @@ -267,7 +315,6 @@ class MemorySaver(BaseCheckpointSaver): filter: Optional[Dict[str, Any]] = None, before: Optional[RunnableConfig] = None, limit: Optional[int] = None, - as_prefix: bool = False, ) -> AsyncIterator[CheckpointTuple]: """Asynchronous version of list. @@ -288,7 +335,6 @@ class MemorySaver(BaseCheckpointSaver): before=before, limit=limit, filter=filter, - as_prefix=as_prefix, ), config, ) @@ -305,6 +351,7 @@ class MemorySaver(BaseCheckpointSaver): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: """Asynchronous version of put. @@ -312,12 +359,13 @@ class MemorySaver(BaseCheckpointSaver): config (RunnableConfig): The config to associate with the checkpoint. checkpoint (Checkpoint): The checkpoint to save. metadata (CheckpointMetadata): Additional metadata to save with the checkpoint. + new_versions (dict): New versions as of this write Returns: RunnableConfig: The updated config containing the saved checkpoint's timestamp. """ return await asyncio.get_running_loop().run_in_executor( - None, self.put, config, checkpoint, metadata + None, self.put, config, checkpoint, metadata, new_versions ) async def aput_writes( diff --git a/libs/checkpoint/langgraph/checkpoint/py.typed b/libs/checkpoint/langgraph/checkpoint/py.typed new file mode 100644 index 000000000..e69de29bb diff --git a/libs/checkpoint/langgraph/checkpoint/serde/__init__.py b/libs/checkpoint/langgraph/checkpoint/serde/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/checkpoint/langgraph/checkpoint/serde/base.py b/libs/checkpoint/langgraph/checkpoint/serde/base.py new file mode 100644 index 000000000..5e4fba5be --- /dev/null +++ b/libs/checkpoint/langgraph/checkpoint/serde/base.py @@ -0,0 +1,45 @@ +from typing import Any, Protocol + + +class SerializerProtocol(Protocol): + """Protocol for serialization and deserialization of objects. + + - `dumps`: Serialize an object to bytes. + - `dumps_typed`: Serialize an object to a tuple (type, bytes). + - `loads`: Deserialize an object from bytes. + - `loads_typed`: Deserialize an object from a tuple (type, bytes). + + Valid implementations include the `pickle`, `json` and `orjson` modules. + """ + + def dumps(self, obj: Any) -> bytes: + ... + + def dumps_typed(self, obj: Any) -> tuple[str, bytes]: + ... + + def loads(self, data: bytes) -> Any: + ... + + def loads_typed(self, data: tuple[str, bytes]) -> Any: + ... + + +class SerializerCompat(SerializerProtocol): + def __init__(self, serde: SerializerProtocol) -> None: + self.serde = serde + + def dumps_typed(self, obj: Any) -> tuple[str, bytes]: + return type(obj).__name__, self.serde.dumps(obj) + + def loads_typed(self, data: tuple[str, bytes]) -> Any: + return self.serde.loads(data[1]) + + +def maybe_add_typed_methods(serde: SerializerProtocol) -> SerializerProtocol: + """Wrap serde old serde implementations in a class with loads_typed and dumps_typed for backwards compatibility.""" + + if not hasattr(serde, "loads_typed") or not hasattr(serde, "dumps_typed"): + return SerializerCompat(serde) + + return serde diff --git a/libs/langgraph/langgraph/serde/jsonplus.py b/libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py similarity index 79% rename from libs/langgraph/langgraph/serde/jsonplus.py rename to libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py index d28e6b640..2b7bce5d4 100644 --- a/libs/langgraph/langgraph/serde/jsonplus.py +++ b/libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py @@ -9,8 +9,8 @@ from uuid import UUID from langchain_core.load.load import Reviver from langchain_core.load.serializable import Serializable -from langgraph.constants import Send -from langgraph.serde.base import SerializerProtocol +from langgraph.checkpoint.serde.base import SerializerProtocol +from langgraph.checkpoint.serde.types import SendProtocol LC_REVIVER = Reviver() @@ -64,10 +64,14 @@ class JsonPlusSerializer(SerializerProtocol): ) elif isinstance(obj, Enum): return self._encode_constructor_args(obj.__class__, args=[obj.value]) - elif isinstance(obj, Send): + elif isinstance(obj, SendProtocol): return self._encode_constructor_args( obj.__class__, kwargs={"node": obj.node, "arg": obj.arg} ) + elif isinstance(obj, (bytes, bytearray)): + return self._encode_constructor_args( + obj.__class__, method="fromhex", args=[obj.hex()] + ) else: raise TypeError( f"Object of type {obj.__class__.__name__} is not JSON serializable" @@ -99,5 +103,24 @@ class JsonPlusSerializer(SerializerProtocol): "utf-8", "ignore" ) + def dumps_typed(self, obj: Any) -> tuple[str, bytes]: + if isinstance(obj, bytes): + return "bytes", obj + elif isinstance(obj, bytearray): + return "bytearray", obj + else: + return "json", self.dumps(obj) + def loads(self, data: bytes) -> Any: return json.loads(data, object_hook=self._reviver) + + def loads_typed(self, data: tuple[str, bytes]) -> Any: + type_, data_ = data + if type_ == "bytes": + return data_ + elif type_ == "bytearray": + return bytearray(data_) + elif type_ == "json": + return self.loads(data_) + else: + raise NotImplementedError(f"Unknown serialization type: {type_}") diff --git a/libs/checkpoint/langgraph/checkpoint/serde/types.py b/libs/checkpoint/langgraph/checkpoint/serde/types.py new file mode 100644 index 000000000..de61ef78b --- /dev/null +++ b/libs/checkpoint/langgraph/checkpoint/serde/types.py @@ -0,0 +1,66 @@ +from typing import ( + Any, + AsyncGenerator, + Generator, + Optional, + Protocol, + Sequence, + TypeVar, + runtime_checkable, +) + +from langchain_core.runnables import RunnableConfig +from typing_extensions import Self + +Value = TypeVar("Value") +Update = TypeVar("Update") +C = TypeVar("C") + + +class ChannelProtocol(Protocol[Value, Update, C]): + # Mirrors langgraph.channels.base.BaseChannel + @property + def ValueType(self) -> Any: + ... + + @property + def UpdateType(self) -> Any: + ... + + def checkpoint(self) -> Optional[C]: + ... + + def from_checkpoint( + self, checkpoint: Optional[C], config: RunnableConfig + ) -> Generator[Self, None, None]: + ... + + async def afrom_checkpoint( + self, checkpoint: Optional[C], config: RunnableConfig + ) -> AsyncGenerator[Self, None]: + ... + + def update(self, values: Sequence[Update]) -> bool: + ... + + def get(self) -> Value: + ... + + def consume(self) -> bool: + ... + + +@runtime_checkable +class SendProtocol(Protocol): + # Mirrors langgraph.constants.Send + node: str + arg: Any + + def __hash__(self) -> int: + ... + + def __repr__(self) -> str: + ... + + def __eq__(self, value: object) -> bool: + ... diff --git a/libs/checkpoint/poetry.lock b/libs/checkpoint/poetry.lock new file mode 100644 index 000000000..7bed6ad81 --- /dev/null +++ b/libs/checkpoint/poetry.lock @@ -0,0 +1,850 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "certifi" +version = "2024.7.4" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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"https://www.github.com/langchain-ai/langgraph" +packages = [{ include = "langgraph" }] + +[tool.poetry.dependencies] +python = "^3.9.0,<4.0" +langchain-core = ">=0.2.22,<0.3" + +[tool.poetry.group.dev.dependencies] +ruff = "^0.1.4" +codespell = "^2.2.0" +pytest = "^7.2.1" +pytest-asyncio = "^0.21.1" +pytest-mock = "^3.11.1" +pytest-watcher = "^0.4.1" +mypy = "^1.10.0" +dataclasses-json = "^0.6.7" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +addopts = "--strict-markers --strict-config --durations=5 -vv" +asyncio_mode = "auto" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +lint.select = [ + "E", # pycodestyle + "F", # Pyflakes + "UP", # pyupgrade + "B", # flake8-bugbear + "I", # isort +] +lint.ignore = ["E501", "B008", "UP007", "UP006"] + +[tool.pytest-watcher] +now = true +delay = 0.1 +runner_args = ["--ff", "-v", "--tb", "short"] +patterns = ["*.py"] diff --git a/libs/checkpoint/tests/__init__.py b/libs/checkpoint/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/checkpoint/tests/test_jsonplus.py b/libs/checkpoint/tests/test_jsonplus.py new file mode 100644 index 000000000..a42278b65 --- /dev/null +++ b/libs/checkpoint/tests/test_jsonplus.py @@ -0,0 +1,132 @@ +import dataclasses +import sys +import uuid +from datetime import datetime, timezone +from enum import Enum + +import dataclasses_json +from langchain_core.pydantic_v1 import BaseModel as LcBaseModel +from langchain_core.runnables import RunnableMap +from pydantic import BaseModel + +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer + + +class MyPydantic(BaseModel): + foo: str + bar: int + + +class MyFunnyPydantic(LcBaseModel): + foo: str + bar: int + + +@dataclasses.dataclass +class MyDataclass: + foo: str + bar: int + + def something(self) -> None: + pass + + +if sys.version_info < (3, 10): + + class MyDataclassWSlots(MyDataclass): + pass + +else: + + @dataclasses.dataclass(slots=True) + class MyDataclassWSlots: + foo: str + bar: int + + def something(self) -> None: + pass + + +class MyEnum(Enum): + FOO = "foo" + BAR = "bar" + + +@dataclasses_json.dataclass_json +@dataclasses.dataclass +class Person: + name: str + + +def test_serde_jsonplus() -> None: + uid = uuid.UUID(int=1) + current_time = datetime(2024, 4, 19, 23, 4, 57, 51022, timezone.max) + + to_serialize = { + "uid": uid, + "time": current_time, + "my_slotted_class": MyDataclassWSlots("bar", 2), + "my_dataclass": MyDataclass("foo", 1), + "my_enum": MyEnum.FOO, + "my_pydantic": MyPydantic(foo="foo", bar=1), + "my_funny_pydantic": MyFunnyPydantic(foo="foo", bar=1), + "person": Person(name="foo"), + "a_bool": True, + "a_none": None, + "a_str": "foo", + "a_str_nuc": "foo\u0000", + "a_str_uc": "foo ⛰️", + "a_str_ucuc": "foo \u26f0\ufe0f\u0000", + "a_str_ucucuc": "foo \\u26f0\\ufe0f", + "text": [ + "Hello\ud83d\ude00", + "Python\ud83d\udc0d", + "Surrogate\ud834\udd1e", + "Example\ud83c\udf89", + "String\ud83c\udfa7", + "With\ud83c\udf08", + "Surrogates\ud83d\ude0e", + "Embedded\ud83d\udcbb", + "In\ud83c\udf0e", + "The\ud83d\udcd6", + "Text\ud83d\udcac", + "收花🙄·到", + ], + "an_int": 1, + "a_float": 1.1, + "runnable_map": RunnableMap({}), + } + + serde = JsonPlusSerializer() + + dumped = serde.dumps_typed(to_serialize) + + assert dumped == ( + "json", + b"""{"uid": {"lc": 2, "type": "constructor", "id": ["uuid", "UUID"], "method": null, "args": ["00000000000000000000000000000001"], "kwargs": {}}, "time": {"lc": 2, "type": "constructor", "id": ["datetime", "datetime"], "method": "fromisoformat", "args": ["2024-04-19T23:04:57.051022+23:59"], "kwargs": {}}, "my_slotted_class": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyDataclassWSlots"], "method": null, "args": [], "kwargs": {"foo": "bar", "bar": 2}}, "my_dataclass": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyDataclass"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "my_enum": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyEnum"], "method": null, "args": ["foo"], "kwargs": {}}, "my_pydantic": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "my_funny_pydantic": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyFunnyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "person": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "Person"], "method": null, "args": [], "kwargs": {"name": "foo"}}, "a_bool": true, "a_none": null, "a_str": "foo", "a_str_nuc": "foo\\u0000", "a_str_uc": "foo \xe2\x9b\xb0\xef\xb8\x8f", "a_str_ucuc": "foo \xe2\x9b\xb0\xef\xb8\x8f\\u0000", "a_str_ucucuc": "foo \\\\u26f0\\\\ufe0f", "text": ["Hello", "Python", "Surrogate", "Example", "String", "With", "Surrogates", "Embedded", "In", "The", "Text", "\xe6\x94\xb6\xe8\x8a\xb1\xf0\x9f\x99\x84\xc2\xb7\xe5\x88\xb0"], "an_int": 1, "a_float": 1.1, "runnable_map": {"lc": 1, "type": "constructor", "id": ["langchain", "schema", "runnable", "RunnableParallel"], "kwargs": {"steps__": {}}, "name": "RunnableParallel<>", "graph": {"nodes": [{"id": 0, "type": "schema", "data": "Parallel<>Input"}, {"id": 1, "type": "schema", "data": "Parallel<>Output"}], "edges": []}}}""", + ) + + assert serde.loads_typed(dumped) == { + **to_serialize, + "text": [v.encode("utf-8", "ignore").decode() for v in to_serialize["text"]], + } + + +def test_serde_jsonplus_bytes() -> None: + serde = JsonPlusSerializer() + + some_bytes = b"my bytes" + dumped = serde.dumps_typed(some_bytes) + + assert dumped == ("bytes", some_bytes) + assert serde.loads_typed(dumped) == some_bytes + + +def test_serde_jsonplus_bytearray() -> None: + serde = JsonPlusSerializer() + + some_bytearray = bytearray([42]) + dumped = serde.dumps_typed(some_bytearray) + + assert dumped == ("bytearray", some_bytearray) + assert serde.loads_typed(dumped) == some_bytearray diff --git a/libs/langgraph/tests/checkpoint/test_memory.py b/libs/checkpoint/tests/test_memory.py similarity index 68% rename from libs/langgraph/tests/checkpoint/test_memory.py rename to libs/checkpoint/tests/test_memory.py index bac957180..a0bc8d738 100644 --- a/libs/langgraph/tests/checkpoint/test_memory.py +++ b/libs/checkpoint/tests/test_memory.py @@ -1,8 +1,12 @@ import pytest from langchain_core.runnables import RunnableConfig -from langgraph.channels.manager import create_checkpoint -from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, empty_checkpoint +from langgraph.checkpoint.base import ( + Checkpoint, + CheckpointMetadata, + create_checkpoint, + empty_checkpoint, +) from langgraph.checkpoint.memory import MemorySaver @@ -13,14 +17,31 @@ class TestMemorySaver: # objects for test setup self.config_1: RunnableConfig = { - "configurable": {"thread_id": "thread-1", "thread_ts": "1"} + "configurable": { + "thread_id": "thread-1", + "checkpoint_ns": "", + # for backwards compatibility testing + "thread_ts": "1", + } } self.config_2: RunnableConfig = { - "configurable": {"thread_id": "thread-2", "thread_ts": "2"} + "configurable": { + "thread_id": "thread-2", + "checkpoint_ns": "", + "checkpoint_id": "2", + } + } + self.config_3: RunnableConfig = { + "configurable": { + "thread_id": "thread-2", + "checkpoint_id": "2-inner", + "checkpoint_ns": "inner", + } } self.chkpnt_1: Checkpoint = empty_checkpoint() self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) + self.chkpnt_3: Checkpoint = empty_checkpoint() self.metadata_1: CheckpointMetadata = { "source": "input", @@ -34,12 +55,14 @@ class TestMemorySaver: "writes": {"foo": "bar"}, "score": None, } + self.metadata_3: CheckpointMetadata = {} async def test_search(self): # set up test # save checkpoints - self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1) - self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2) + self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {}) + self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {}) + self.memory_saver.put(self.config_3, self.chkpnt_3, self.metadata_3, {}) # call method / assertions query_1: CheckpointMetadata = {"source": "input"} # search by 1 key @@ -64,13 +87,29 @@ class TestMemorySaver: search_results_4 = list(self.memory_saver.list(None, filter=query_4)) assert len(search_results_4) == 0 + # search by config (defaults to root graph checkpoints) + search_results_5 = list( + self.memory_saver.list({"configurable": {"thread_id": "thread-2"}}) + ) + assert len(search_results_5) == 1 + assert search_results_5[0].config["configurable"]["checkpoint_ns"] == "" + + # search by config and checkpoint_ns + search_results_6 = list( + self.memory_saver.list( + {"configurable": {"thread_id": "thread-2", "checkpoint_ns": "inner"}} + ) + ) + assert len(search_results_6) == 1 + assert search_results_6[0].config["configurable"]["checkpoint_ns"] == "inner" + # TODO: test before and limit params async def test_asearch(self): # set up test # save checkpoints - self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1) - self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2) + self.memory_saver.put(self.config_1, self.chkpnt_1, self.metadata_1, {}) + self.memory_saver.put(self.config_2, self.chkpnt_2, self.metadata_2, {}) # call method / assertions query_1: CheckpointMetadata = {"source": "input"} # search by 1 key diff --git a/libs/langgraph/Makefile b/libs/langgraph/Makefile index 8403fbf33..fd513f65f 100644 --- a/libs/langgraph/Makefile +++ b/libs/langgraph/Makefile @@ -14,11 +14,28 @@ coverage: --cov-report xml \ --cov-report term-missing:skip-covered +start-postgres: + docker compose -f tests/compose-postgres.yml up -V --force-recreate --wait + +stop-postgres: + docker compose -f tests/compose-postgres.yml down + test: - poetry run pytest + make start-postgres; \ + poetry run pytest; \ + EXIT_CODE=$$?; \ + make stop-postgres; \ + exit $$EXIT_CODE test_watch: - poetry run ptw . + make start-postgres; \ + poetry run ptw .; \ + EXIT_CODE=$$?; \ + make stop-postgres; \ + exit $$EXIT_CODE + +test_watch_all: + npx concurrently -n langgraph,checkpoint,checkpoint-sqlite,postgres "make test_watch" "make -C ../checkpoint test_watch" "make -C ../checkpoint-sqlite test_watch" "make -C ../checkpoint-postgres test_watch" ###################### # LINTING AND FORMATTING diff --git a/libs/langgraph/README.md b/libs/langgraph/README.md index 10cd6932b..87d19961f 100644 --- a/libs/langgraph/README.md +++ b/libs/langgraph/README.md @@ -3,7 +3,6 @@ ![Version](https://img.shields.io/pypi/v/langgraph) [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph) [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues) -[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.com/channels/1038097195422978059/1170024642245832774) [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/) ⚡ Building language agents as graphs ⚡ @@ -11,9 +10,6 @@ > [!NOTE] > Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)). -> [!TIP] -> Looking to deploy your LangGraph application? [Join the waitlist](https://www.langchain.com/langgraph-cloud-beta) for [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/), our managed service for deploying and hosting LangGraph applications. - ## Overview [LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. @@ -62,7 +58,7 @@ from typing import Annotated, Literal, TypedDict from langchain_core.messages import HumanMessage from langchain_anthropic import ChatAnthropic from langchain_core.tools import tool -from langgraph.checkpoint import MemorySaver +from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import END, StateGraph, MessagesState from langgraph.prebuilt import ToolNode diff --git a/libs/langgraph/langgraph/__init__.py b/libs/langgraph/langgraph/__init__.py deleted file mode 100644 index 959f4ab2e..000000000 --- a/libs/langgraph/langgraph/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from langgraph.version import __version__ - -__all__ = ["__version__"] diff --git a/libs/langgraph/langgraph/_api/deprecation.py b/libs/langgraph/langgraph/_api/deprecation.py index 6ce811f90..6fa419e83 100644 --- a/libs/langgraph/langgraph/_api/deprecation.py +++ b/libs/langgraph/langgraph/_api/deprecation.py @@ -1,6 +1,6 @@ import functools import warnings -from typing import Any, Callable, TypeVar, cast +from typing import Any, Callable, Type, TypeVar, Union, cast class LangGraphDeprecationWarning(DeprecationWarning): @@ -8,31 +8,57 @@ class LangGraphDeprecationWarning(DeprecationWarning): F = TypeVar("F", bound=Callable[..., Any]) +C = TypeVar("C", bound=Type[Any]) def deprecated( since: str, alternative: str, *, removal: str = "", example: str = "" ) -> Callable[[F], F]: - def decorator(func: F) -> F: - @functools.wraps(func) - def wrapper(*args: Any, **kwargs: Any) -> Any: - removal_str = removal if removal else "a future version" - message = ( - f"{func.__name__} is deprecated as of version {since} and will be" - f" removed in {removal_str}. Use {alternative} instead.{example}" - ) - warnings.warn(message, LangGraphDeprecationWarning, stacklevel=2) - return func(*args, **kwargs) - - docstring = ( - f"**Deprecated**: This function is deprecated as of version {since}. " - f"Use `{alternative}` instead." + def decorator(obj: Union[F, C]) -> Union[F, C]: + removal_str = removal if removal else "a future version" + message = ( + f"{obj.__name__} is deprecated as of version {since} and will be" + f" removed in {removal_str}. Use {alternative} instead.{example}" ) - if func.__doc__: - docstring = docstring + f"\n\n{func.__doc__}" - wrapper.__doc__ = docstring + if isinstance(obj, type): + original_init = obj.__init__ - return cast(F, wrapper) + @functools.wraps(original_init) + def new_init(self, *args: Any, **kwargs: Any) -> None: + warnings.warn(message, LangGraphDeprecationWarning, stacklevel=2) + original_init(self, *args, **kwargs) + + obj.__init__ = new_init + + docstring = ( + f"**Deprecated**: This class is deprecated as of version {since}. " + f"Use `{alternative}` instead." + ) + if obj.__doc__: + docstring = docstring + f"\n\n{obj.__doc__}" + obj.__doc__ = docstring + + return cast(C, obj) + elif callable(obj): + + @functools.wraps(obj) + def wrapper(*args: Any, **kwargs: Any) -> Any: + warnings.warn(message, LangGraphDeprecationWarning, stacklevel=2) + return obj(*args, **kwargs) + + docstring = ( + f"**Deprecated**: This function is deprecated as of version {since}. " + f"Use `{alternative}` instead." + ) + if obj.__doc__: + docstring = docstring + f"\n\n{obj.__doc__}" + wrapper.__doc__ = docstring + + return cast(F, wrapper) + else: + raise TypeError( + f"Can only add deprecation decorator to classes or callables, got '{type(obj)}' instead." + ) return decorator diff --git a/libs/langgraph/langgraph/channels/__init__.py b/libs/langgraph/langgraph/channels/__init__.py index 975fb93e0..6f9ba2119 100644 --- a/libs/langgraph/langgraph/channels/__init__.py +++ b/libs/langgraph/langgraph/channels/__init__.py @@ -1,11 +1,17 @@ +from langgraph.channels.any_value import AnyValue from langgraph.channels.binop import BinaryOperatorAggregate from langgraph.channels.context import Context +from langgraph.channels.ephemeral_value import EphemeralValue from langgraph.channels.last_value import LastValue from langgraph.channels.topic import Topic +from langgraph.channels.untracked_value import UntrackedValue __all__ = [ "LastValue", "Topic", "Context", "BinaryOperatorAggregate", + "UntrackedValue", + "EphemeralValue", + "AnyValue", ] diff --git a/libs/langgraph/langgraph/channels/manager.py b/libs/langgraph/langgraph/channels/manager.py index c5fa373f3..9a7c8d87e 100644 --- a/libs/langgraph/langgraph/channels/manager.py +++ b/libs/langgraph/langgraph/channels/manager.py @@ -1,13 +1,10 @@ from contextlib import AsyncExitStack, ExitStack, asynccontextmanager, contextmanager -from datetime import datetime, timezone -from typing import Any, AsyncGenerator, Generator, Mapping, Optional +from typing import AsyncGenerator, Generator, Mapping from langchain_core.runnables import RunnableConfig from langgraph.channels.base import BaseChannel from langgraph.checkpoint.base import Checkpoint -from langgraph.checkpoint.id import uuid6 -from langgraph.errors import EmptyChannelError @contextmanager @@ -40,31 +37,3 @@ async def AsyncChannelsManager( ) for k, v in channels.items() } - - -def create_checkpoint( - checkpoint: Checkpoint, - channels: Mapping[str, BaseChannel], - step: int, - *, - id: Optional[str] = None, -) -> Checkpoint: - """Create a checkpoint for the given channels.""" - ts = datetime.now(timezone.utc).isoformat() - values: dict[str, Any] = {} - for k, v in channels.items(): - try: - values[k] = v.checkpoint() - except EmptyChannelError: - pass - return Checkpoint( - v=1, - ts=ts, - id=id or str(uuid6(clock_seq=step)), - channel_values=values, - channel_versions=checkpoint["channel_versions"], - versions_seen=checkpoint["versions_seen"], - pending_sends=checkpoint.get("pending_sends", []), - # checkpoints are saved only at the end of a step, ie. when current tasks should be cleared - current_tasks={}, - ) diff --git a/libs/langgraph/langgraph/channels/untracked_value.py b/libs/langgraph/langgraph/channels/untracked_value.py new file mode 100644 index 000000000..989bba35e --- /dev/null +++ b/libs/langgraph/langgraph/channels/untracked_value.py @@ -0,0 +1,62 @@ +from contextlib import contextmanager +from typing import Generator, Generic, Optional, Sequence, Type + +from langchain_core.runnables import RunnableConfig +from typing_extensions import Self + +from langgraph.channels.base import BaseChannel, Value +from langgraph.errors import EmptyChannelError, InvalidUpdateError + + +class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]): + """Stores the last value received, never checkpointed.""" + + def __init__(self, typ: Type[Value], guard: bool = True) -> None: + self.typ = typ + self.guard = guard + + def __eq__(self, value: object) -> bool: + return isinstance(value, UntrackedValue) and value.guard == self.guard + + @property + def ValueType(self) -> Type[Value]: + """The type of the value stored in the channel.""" + return self.typ + + @property + def UpdateType(self) -> Type[Value]: + """The type of the update received by the channel.""" + return self.typ + + def checkpoint(self) -> Value: + raise EmptyChannelError() + + @contextmanager + def from_checkpoint( + self, checkpoint: Optional[Value], config: RunnableConfig + ) -> Generator[Self, None, None]: + empty = self.__class__(self.typ, self.guard) + try: + yield empty + finally: + try: + del empty.value + except AttributeError: + pass + + def update(self, values: Sequence[Value]) -> bool: + if len(values) == 0: + return False + if len(values) != 1 and self.guard: + raise InvalidUpdateError( + "UntrackedValue can only receive one value per step." + ) + + self.value = values[-1] + return True + + def get(self) -> Value: + try: + return self.value + except AttributeError: + raise EmptyChannelError() diff --git a/libs/langgraph/langgraph/checkpoint/__init__.py b/libs/langgraph/langgraph/checkpoint/__init__.py deleted file mode 100644 index 50f9db11b..000000000 --- a/libs/langgraph/langgraph/checkpoint/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from langgraph.checkpoint.base import ( - BaseCheckpointSaver, - Checkpoint, - SerializerProtocol, -) -from langgraph.checkpoint.memory import MemorySaver - -__all__ = [ - "BaseCheckpointSaver", - "Checkpoint", - "MemorySaver", - "SerializerProtocol", -] diff --git a/libs/langgraph/langgraph/constants.py b/libs/langgraph/langgraph/constants.py index edab6d6b7..ae447d62e 100644 --- a/libs/langgraph/langgraph/constants.py +++ b/libs/langgraph/langgraph/constants.py @@ -18,20 +18,24 @@ RESERVED = { } TAG_HIDDEN = "langsmith:hidden" -THREAD_ID_SEPARATOR = "__" - START = "__start__" END = "__end__" +CHECKPOINT_NAMESPACE_SEPARATOR = "|" + class Send: """A message or packet to send to a specific node in the graph. - The `Send` class is used within a `StateGraph`'s conditional edges to dynamically - route states to different nodes based on certain conditions. This enables - creating "map-reduce" like workflows, where a node can be invoked multiple times - in parallel on different states, and the results can be aggregated back into the - main graph's state. + The `Send` class is used within a `StateGraph`'s conditional edges to + dynamically invoke a node with a custom state at the next step. + + Importantly, the sent state can differ from the core graph's state, + allowing for flexible and dynamic workflow management. + + One such example is a "map-reduce" workflow where your graph invokes + the same node multiple times in parallel with different states, + before aggregating the results back into the main graph's state. Attributes: node (str): The name of the target node to send the message to. @@ -55,6 +59,8 @@ class Send: >>> builder.add_conditional_edges(START, continue_to_jokes) >>> builder.add_edge("generate_joke", END) >>> graph = builder.compile() + >>> + >>> # Invoking with two subjects results in a generated joke for each >>> graph.invoke({"subjects": ["cats", "dogs"]}) {'subjects': ['cats', 'dogs'], 'jokes': ['Joke about cats', 'Joke about dogs']} """ diff --git a/libs/langgraph/langgraph/errors.py b/libs/langgraph/langgraph/errors.py index eed9cb7af..ea54cc55f 100644 --- a/libs/langgraph/langgraph/errors.py +++ b/libs/langgraph/langgraph/errors.py @@ -1,3 +1,6 @@ +from langgraph.checkpoint.base import EmptyChannelError + + class GraphRecursionError(RecursionError): """Raised when the graph has exhausted the maximum number of steps. @@ -17,13 +20,6 @@ class GraphRecursionError(RecursionError): pass -class EmptyChannelError(Exception): - """Raised when attempting to get the value of a channel that hasn't been updated - for the first time yet.""" - - pass - - class InvalidUpdateError(Exception): """Raised when attempting to update a channel with an invalid sequence of updates.""" @@ -40,3 +36,12 @@ class EmptyInputError(Exception): """Raised when graph receives an empty input.""" pass + + +__all__ = [ + "GraphRecursionError", + "InvalidUpdateError", + "GraphInterrupt", + "EmptyInputError", + "EmptyChannelError", +] diff --git a/libs/langgraph/langgraph/graph/graph.py b/libs/langgraph/langgraph/graph/graph.py index 4841642f5..dc927446d 100644 --- a/libs/langgraph/langgraph/graph/graph.py +++ b/libs/langgraph/langgraph/graph/graph.py @@ -24,8 +24,14 @@ from langchain_core.runnables.graph import Graph as DrawableGraph from langchain_core.runnables.graph import Node as DrawableNode from langgraph.channels.ephemeral_value import EphemeralValue -from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.constants import END, START, TAG_HIDDEN, Send +from langgraph.checkpoint.base import BaseCheckpointSaver +from langgraph.constants import ( + CHECKPOINT_NAMESPACE_SEPARATOR, + END, + START, + TAG_HIDDEN, + Send, +) from langgraph.errors import InvalidUpdateError from langgraph.pregel import Channel, Pregel from langgraph.pregel.read import PregelNode @@ -154,6 +160,11 @@ class Graph: *, metadata: Optional[dict[str, Any]] = None, ) -> None: + if isinstance(node, str) and CHECKPOINT_NAMESPACE_SEPARATOR in node: + raise ValueError( + f"'{CHECKPOINT_NAMESPACE_SEPARATOR}' is a reserved character and is not allowed in the node names." + ) + if self.compiled: logger.warning( "Adding a node to a graph that has already been compiled. This will " @@ -482,6 +493,11 @@ class CompiledGraph(Pregel): for key, n in self.builder.nodes.items(): node = n.runnable + metadata = n.metadata or {} + if key in self.interrupt_before_nodes: + metadata["__interrupt"] = "before" + elif key in self.interrupt_after_nodes: + metadata["__interrupt"] = "after" if xray: subgraph = ( node.get_graph( @@ -498,11 +514,11 @@ class CompiledGraph(Pregel): subgraph, prefix=key ) else: - n = graph.add_node(node, key) + n = graph.add_node(node, key, metadata=metadata or None) start_nodes[key] = n end_nodes[key] = n else: - n = graph.add_node(node, key, metadata=n.metadata) + n = graph.add_node(node, key, metadata=metadata or None) start_nodes[key] = n end_nodes[key] = n for start, end in sorted(self.builder._all_edges): diff --git a/libs/langgraph/langgraph/graph/state.py b/libs/langgraph/langgraph/graph/state.py index 5411ae02b..5810aaf29 100644 --- a/libs/langgraph/langgraph/graph/state.py +++ b/libs/langgraph/langgraph/graph/state.py @@ -29,8 +29,8 @@ from langgraph.channels.dynamic_barrier_value import DynamicBarrierValue, WaitFo from langgraph.channels.ephemeral_value import EphemeralValue from langgraph.channels.last_value import LastValue from langgraph.channels.named_barrier_value import NamedBarrierValue -from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.constants import TAG_HIDDEN +from langgraph.checkpoint.base import BaseCheckpointSaver +from langgraph.constants import CHECKPOINT_NAMESPACE_SEPARATOR, TAG_HIDDEN from langgraph.errors import InvalidUpdateError from langgraph.graph.graph import ( END, @@ -85,7 +85,7 @@ class StateGraph(Graph): Examples: >>> from langchain_core.runnables import RunnableConfig >>> from typing_extensions import Annotated, TypedDict - >>> from langgraph.checkpoint import MemorySaver + >>> from langgraph.checkpoint.memory import MemorySaver >>> from langgraph.graph import StateGraph >>> >>> def reducer(a: list, b: int | None) -> int: @@ -311,6 +311,12 @@ class StateGraph(Graph): raise ValueError(f"Node `{node}` already present.") if node == END or node == START: raise ValueError(f"Node `{node}` is reserved.") + + if CHECKPOINT_NAMESPACE_SEPARATOR in node: + raise ValueError( + f"'{CHECKPOINT_NAMESPACE_SEPARATOR}' is a reserved character and is not allowed in the node names." + ) + try: if isfunction(action) and ( hints := get_type_hints(action.__call__) or get_type_hints(action) @@ -412,6 +418,15 @@ class StateGraph(Graph): if not isinstance(val, Context) and not is_managed_value(val) ] ) + stream_channels = ( + "__root__" + if len(self.channels) == 1 and "__root__" in self.channels + else [ + key + for key, val in self.channels.items() + if not isinstance(val, Context) and not is_managed_value(val) + ] + ) compiled = CompiledStateGraph( builder=self, @@ -421,7 +436,7 @@ class StateGraph(Graph): input_channels=START, stream_mode="updates", output_channels=output_channels, - stream_channels=output_channels, + stream_channels=stream_channels, checkpointer=checkpointer, interrupt_before_nodes=interrupt_before, interrupt_after_nodes=interrupt_after, @@ -452,7 +467,11 @@ class CompiledStateGraph(CompiledGraph): def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - if isclass(self.builder.input) and issubclass(self.builder.input, BaseModel): + from pydantic import BaseModel as BaseModelP + + if isclass(self.builder.input) and issubclass( + self.builder.input, (BaseModel, BaseModelP) + ): return self.builder.input else: keys = list(self.builder.schemas[self.builder.input].keys()) @@ -475,7 +494,11 @@ class CompiledStateGraph(CompiledGraph): def get_output_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - if isclass(self.builder.input) and issubclass(self.builder.output, BaseModel): + from pydantic import BaseModel as BaseModelP + + if isclass(self.builder.input) and issubclass( + self.builder.output, (BaseModel, BaseModelP) + ): return self.builder.output return super().get_output_schema(config) @@ -497,7 +520,7 @@ class CompiledStateGraph(CompiledGraph): return SKIP_WRITE elif isinstance(input, dict): return input.get(key, SKIP_WRITE) - elif get_type_hints(type(input)).get(key): + elif get_type_hints(type(input)): value = getattr(input, key, SKIP_WRITE) return value if value is not None else SKIP_WRITE else: @@ -602,7 +625,7 @@ class CompiledStateGraph(CompiledGraph): if branch.then and branch.then != END: writes.append( ChannelWriteEntry( - f"branch:{start}:{name}:then", + f"branch:{start}:{name}::then", WaitForNames( {p.node if isinstance(p, Send) else p for p in filtered} ), @@ -622,12 +645,12 @@ class CompiledStateGraph(CompiledGraph): for end in ends: if end != END: channel_name = f"branch:{start}:{name}:{end}" - self.channels[channel_name] = EphemeralValue(Any) + self.channels[channel_name] = EphemeralValue(Any, guard=False) self.nodes[end].triggers.append(channel_name) # attach then subscriber if branch.then and branch.then != END: - channel_name = f"branch:{start}:{name}:then" + channel_name = f"branch:{start}:{name}::then" self.channels[channel_name] = DynamicBarrierValue(str) self.nodes[branch.then].triggers.append(channel_name) for end in ends: @@ -691,6 +714,8 @@ def _is_field_channel(typ: Type[Any]) -> Optional[BaseChannel]: meta = typ.__metadata__ if len(meta) >= 1 and isinstance(meta[-1], BaseChannel): return meta[-1] + elif len(meta) >= 1 and isclass(meta[-1]) and issubclass(meta[-1], BaseChannel): + return meta[-1](typ.__origin__ if hasattr(typ, "__origin__") else typ) return None @@ -700,14 +725,14 @@ def _is_field_binop(typ: Type[Any]) -> Optional[BinaryOperatorAggregate]: if len(meta) >= 1 and callable(meta[-1]): sig = signature(meta[0]) params = list(sig.parameters.values()) - if len(params) == 2 and len( - [ - p - for p in params - if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) - ] + if len(params) == 2 and all( + p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) for p in params ): return BinaryOperatorAggregate(typ, meta[0]) + else: + raise ValueError( + f"Invalid reducer signature. Expected (a, b) -> c. Got {sig}" + ) return None diff --git a/libs/langgraph/langgraph/managed/base.py b/libs/langgraph/langgraph/managed/base.py index b06f6a7c7..0e1476895 100644 --- a/libs/langgraph/langgraph/managed/base.py +++ b/libs/langgraph/langgraph/managed/base.py @@ -17,26 +17,23 @@ from typing import ( from langchain_core.runnables import RunnableConfig from typing_extensions import Self, TypeGuard -from langgraph.pregel.types import PregelTaskDescription - if TYPE_CHECKING: - from langgraph.pregel import Pregel + from langgraph.pregel.types import PregelTaskDescription V = TypeVar("V") class ManagedValue(ABC, Generic[V]): - def __init__(self, config: RunnableConfig, graph: "Pregel") -> None: + def __init__(self, config: RunnableConfig) -> None: self.config = config - self.graph = graph @classmethod @contextmanager def enter( - cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any + cls, config: RunnableConfig, **kwargs: Any ) -> Generator[Self, None, None]: try: - value = cls(config, graph, **kwargs) + value = cls(config, **kwargs) yield value finally: # because managed value and Pregel have reference to each other @@ -49,10 +46,10 @@ class ManagedValue(ABC, Generic[V]): @classmethod @asynccontextmanager async def aenter( - cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any + cls, config: RunnableConfig, **kwargs: Any ) -> AsyncGenerator[Self, None]: try: - value = cls(config, graph, **kwargs) + value = cls(config, **kwargs) yield value finally: # because managed value and Pregel have reference to each other @@ -63,7 +60,7 @@ class ManagedValue(ABC, Generic[V]): pass @abstractmethod - def __call__(self, step: int, task: PregelTaskDescription) -> V: + def __call__(self, step: int, task: "PregelTaskDescription") -> V: ... @@ -87,15 +84,14 @@ def is_managed_value(value: Any) -> TypeGuard[ManagedValueSpec]: def ManagedValuesManager( values: dict[str, ManagedValueSpec], config: RunnableConfig, - graph: "Pregel", ) -> Generator[ManagedValueMapping, None, None]: if values: with ExitStack() as stack: yield { key: stack.enter_context( - value.cls.enter(config, graph, **value.kwargs) + value.cls.enter(config, **value.kwargs) if isinstance(value, ConfiguredManagedValue) - else value.enter(config, graph) + else value.enter(config) ) for key, value in values.items() } @@ -107,7 +103,6 @@ def ManagedValuesManager( async def AsyncManagedValuesManager( values: dict[str, ManagedValueSpec], config: RunnableConfig, - graph: "Pregel", ) -> AsyncGenerator[ManagedValueMapping, None]: if values: async with AsyncExitStack() as stack: @@ -115,9 +110,9 @@ async def AsyncManagedValuesManager( tasks = { asyncio.create_task( stack.enter_async_context( - value.cls.aenter(config, graph, **value.kwargs) + value.cls.aenter(config, **value.kwargs) if isinstance(value, ConfiguredManagedValue) - else value.aenter(config, graph) + else value.aenter(config) ) ): key for key, value in values.items() diff --git a/libs/langgraph/langgraph/managed/few_shot.py b/libs/langgraph/langgraph/managed/few_shot.py deleted file mode 100644 index 6dcab2aa8..000000000 --- a/libs/langgraph/langgraph/managed/few_shot.py +++ /dev/null @@ -1,105 +0,0 @@ -from contextlib import asynccontextmanager, contextmanager -from typing import ( - TYPE_CHECKING, - Any, - AsyncGenerator, - AsyncIterator, - Callable, - Dict, - Generator, - Generic, - Iterator, - Optional, - Sequence, - Union, -) - -from langchain_core.runnables import RunnableConfig -from typing_extensions import Self - -from langgraph.channels.manager import AsyncChannelsManager, ChannelsManager -from langgraph.managed.base import ConfiguredManagedValue, ManagedValue, V -from langgraph.pregel import Pregel -from langgraph.pregel.io import read_channels -from langgraph.pregel.types import PregelTaskDescription - -if TYPE_CHECKING: - from langgraph.pregel import Pregel - -# Metadata filter can be a dict (static) or a function (dynamic) that takes a -# RunnableConfig and returns a dict. Functions are used for filtering on -# metadata values that are only available at runtime. -MetadataFilter = Union[Dict[str, Any], Callable[[RunnableConfig], Dict[str, Any]]] - - -class FewShotExamples(ManagedValue[Sequence[V]], Generic[V]): - examples: list[V] - - def __init__( - self, - config: RunnableConfig, - graph: Pregel, - k: int = 5, - metadata_filter: Optional[MetadataFilter] = None, - ) -> None: - super().__init__(config, graph) - self.k = k - self.metadata_filter = metadata_filter or {} - - @classmethod - def configure( - cls, k: int = 5, metadata_filter: Optional[MetadataFilter] = None - ) -> ConfiguredManagedValue: - return ConfiguredManagedValue( - cls, - { - "k": k, - "metadata_filter": metadata_filter, - }, - ) - - @property - def metadata_filter_dict(self) -> Dict[str, Any]: - if isinstance(self.metadata_filter, Callable): - return self.metadata_filter(self.config) - else: - return self.metadata_filter - - def iter(self, score: int = 1) -> Iterator[V]: - for example in self.graph.checkpointer.list( - None, filter={"score": score, **self.metadata_filter_dict}, limit=self.k - ): - with ChannelsManager( - self.graph.channels, example.checkpoint, self.config - ) as channels: - yield read_channels(channels, self.graph.output_channels) - - async def aiter(self, score: int = 1) -> AsyncIterator[V]: - async for example in self.graph.checkpointer.alist( - None, filter={"score": score, **self.metadata_filter_dict}, limit=self.k - ): - async with AsyncChannelsManager( - self.graph.channels, example.checkpoint, self.config - ) as channels: - yield read_channels(channels, self.graph.output_channels) - - @classmethod - @contextmanager - def enter( - cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any - ) -> Generator[Self, None, None]: - with super().enter(config, graph, **kwargs) as value: - value.examples = list(value.iter()) - yield value - - @classmethod - @asynccontextmanager - async def aenter( - cls, config: RunnableConfig, graph: "Pregel", **kwargs: Any - ) -> AsyncGenerator[Self, None]: - async with super().aenter(config, graph, **kwargs) as value: - value.examples = [e async for e in value.aiter()] - yield value - - def __call__(self, step: int, task: PregelTaskDescription) -> Sequence[V]: - return self.examples diff --git a/libs/langgraph/langgraph/prebuilt/__init__.py b/libs/langgraph/langgraph/prebuilt/__init__.py index 615de4c7b..4ad055730 100644 --- a/libs/langgraph/langgraph/prebuilt/__init__.py +++ b/libs/langgraph/langgraph/prebuilt/__init__.py @@ -1,14 +1,10 @@ """langgraph.prebuilt exposes a higher-level API for creating and executing agents and tools.""" -from langgraph.prebuilt import chat_agent_executor -from langgraph.prebuilt.agent_executor import create_agent_executor from langgraph.prebuilt.chat_agent_executor import create_react_agent from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation from langgraph.prebuilt.tool_node import InjectedState, ToolNode, tools_condition from langgraph.prebuilt.tool_validator import ValidationNode __all__ = [ - "create_agent_executor", - "chat_agent_executor", "create_react_agent", "ToolExecutor", "ToolInvocation", diff --git a/libs/langgraph/langgraph/prebuilt/agent_executor.py b/libs/langgraph/langgraph/prebuilt/agent_executor.py deleted file mode 100644 index c8b541a1b..000000000 --- a/libs/langgraph/langgraph/prebuilt/agent_executor.py +++ /dev/null @@ -1,181 +0,0 @@ -import operator -from typing import Annotated, Sequence, TypedDict, Union - -from langchain_core.agents import AgentAction, AgentFinish -from langchain_core.messages import BaseMessage - -from langgraph._api.deprecation import deprecated -from langgraph.graph import END, StateGraph -from langgraph.graph.state import CompiledStateGraph -from langgraph.prebuilt.tool_executor import ToolExecutor -from langgraph.utils import RunnableCallable - - -def _get_agent_state(input_schema=None): - if input_schema is None: - - class AgentState(TypedDict): - # The input string - input: str - # The list of previous messages in the conversation - chat_history: Sequence[BaseMessage] - # The outcome of a given call to the agent - # Needs `None` as a valid type, since this is what this will start as - agent_outcome: Union[AgentAction, AgentFinish, None] - # List of actions and corresponding observations - # Here we annotate this with `operator.add` to indicate that operations to - # this state should be ADDED to the existing values (not overwrite it) - intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add] - - else: - - class AgentState(input_schema): - # The outcome of a given call to the agent - # Needs `None` as a valid type, since this is what this will start as - agent_outcome: Union[AgentAction, AgentFinish, None] - # List of actions and corresponding observations - # Here we annotate this with `operator.add` to indicate that operations to - # this state should be ADDED to the existing values (not overwrite it) - intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add] - - return AgentState - - -@deprecated( - "0.0.44", - alternative="create_react_agent", - removal="0.2.0", - example=""" -from langgraph.prebuilt import create_react_agent - -create_react_agent(...) -""", -) -def create_agent_executor( - agent_runnable, tools, input_schema=None -) -> CompiledStateGraph: - """This is a helper function for creating a graph that works with LangChain Agents. - - Args: - agent_runnable (RunnableLike): The agent runnable. - tools (list): A list of tools to be used by the agent. - input_schema (dict, optional): The input schema for the agent. Defaults to None. - - Returns: - The `CompiledStateGraph` object. - - - Examples: - - # Since this is deprecated, you should use `create_react_agent` instead. - # Example usage: - from langgraph.prebuilt import create_react_agent - from langchain_openai import ChatOpenAI - from langchain_community.tools.tavily_search import TavilySearchResults - - tools = [TavilySearchResults(max_results=1)] - model = ChatOpenAI() - - app = create_react_agent(model, tools) - - inputs = {"messages": [("user", "what is the weather in sf")]} - for s in app.stream(inputs): - print(list(s.values())[0]) - print("----") - """ - - if isinstance(tools, ToolExecutor): - tool_executor = tools - else: - tool_executor = ToolExecutor(tools) - - state = _get_agent_state(input_schema) - - # Define logic that will be used to determine which conditional edge to go down - - def should_continue(data): - # If the agent outcome is an AgentFinish, then we return `exit` string - # This will be used when setting up the graph to define the flow - if isinstance(data["agent_outcome"], AgentFinish): - return "end" - # Otherwise, an AgentAction is returned - # Here we return `continue` string - # This will be used when setting up the graph to define the flow - else: - return "continue" - - def run_agent(data, config): - agent_outcome = agent_runnable.invoke(data, config) - return {"agent_outcome": agent_outcome} - - async def arun_agent(data, config): - agent_outcome = await agent_runnable.ainvoke(data, config) - return {"agent_outcome": agent_outcome} - - # Define the function to execute tools - def execute_tools(data, config): - # Get the most recent agent_outcome - this is the key added in the `agent` above - agent_action = data["agent_outcome"] - if not isinstance(agent_action, list): - agent_action = [agent_action] - output = tool_executor.batch(agent_action, config, return_exceptions=True) - return { - "intermediate_steps": [ - (action, str(out)) for action, out in zip(agent_action, output) - ] - } - - async def aexecute_tools(data, config): - # Get the most recent agent_outcome - this is the key added in the `agent` above - agent_action = data["agent_outcome"] - if not isinstance(agent_action, list): - agent_action = [agent_action] - output = await tool_executor.abatch( - agent_action, config, return_exceptions=True - ) - return { - "intermediate_steps": [ - (action, str(out)) for action, out in zip(agent_action, output) - ] - } - - # Define a new graph - workflow = StateGraph(state) - - # Define the two nodes we will cycle between - workflow.add_node("agent", RunnableCallable(run_agent, arun_agent)) - workflow.add_node("tools", RunnableCallable(execute_tools, aexecute_tools)) - - # Set the entrypoint as `agent` - # This means that this node is the first one called - workflow.set_entry_point("agent") - - # We now add a conditional edge - workflow.add_conditional_edges( - # First, we define the start node. We use `agent`. - # This means these are the edges taken after the `agent` node is called. - "agent", - # Next, we pass in the function that will determine which node is called next. - should_continue, - # Finally we pass in a mapping. - # The keys are strings, and the values are other nodes. - # END is a special node marking that the graph should finish. - # What will happen is we will call `should_continue`, and then the output of that - # will be matched against the keys in this mapping. - # Based on which one it matches, that node will then be called. - { - # If `tools`, then we call the tool node. - "continue": "tools", - # Otherwise we finish. - "end": END, - }, - ) - - # We now add a normal edge from `tools` to `agent`. - # This means that after `tools` is called, `agent` node is called next. - workflow.add_edge("tools", "agent") - - # Finally, we compile it! - # This compiles it into a LangChain Runnable, - # meaning you can use it as you would any other runnable - return workflow.compile() diff --git a/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py b/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py index f5759478b..546ad53bc 100644 --- a/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py +++ b/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py @@ -1,5 +1,3 @@ -import json -import types from typing import ( Annotated, Callable, @@ -15,20 +13,18 @@ from langchain_core.language_models import LanguageModelLike from langchain_core.messages import ( AIMessage, BaseMessage, - FunctionMessage, SystemMessage, ) from langchain_core.runnables import Runnable, RunnableConfig, RunnableLambda from langchain_core.tools import BaseTool -from langchain_core.utils.function_calling import convert_to_openai_function -from langgraph._api.deprecation import deprecated, deprecated_parameter -from langgraph.checkpoint import BaseCheckpointSaver +from langgraph._api.deprecation import deprecated_parameter +from langgraph.checkpoint.base import BaseCheckpointSaver from langgraph.graph import END, StateGraph from langgraph.graph.graph import CompiledGraph from langgraph.graph.message import add_messages from langgraph.managed import IsLastStep -from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation +from langgraph.prebuilt.tool_executor import ToolExecutor from langgraph.prebuilt.tool_node import ToolNode @@ -64,136 +60,6 @@ StateModifier = Union[ ] -@deprecated("0.0.44", "create_react_agent", removal="0.2.0") -def create_function_calling_executor( - model: LanguageModelLike, tools: Union[ToolExecutor, Sequence[BaseTool]] -) -> CompiledGraph: - """Creates a graph that works with a chat model that utilizes function calling. - - Examples: - ```pycon - >>> # Since this is deprecated, you should use `create_react_agent` instead. - >>> # Example usage: - >>> from langgraph.prebuilt import create_react_agent - >>> from langchain_openai import ChatOpenAI - >>> from langchain_community.tools.tavily_search import TavilySearchResults - >>> - >>> tools = [TavilySearchResults(max_results=1)] - >>> model = ChatOpenAI() - >>> - >>> app = create_react_agent(model, tools) - >>> inputs = {"messages": [("user", "what is the weather in sf")]} - >>> for s in app.stream(inputs): - ... print(list(s.values())[0]) - ... print("----") - ``` - """ - if isinstance(tools, ToolExecutor): - tool_executor = tools - tool_classes = tools.tools - else: - tool_executor = ToolExecutor(tools) - tool_classes = tools - model = model.bind(functions=[convert_to_openai_function(t) for t in tool_classes]) - - # Define the function that determines whether to continue or not - def should_continue(state: AgentState): - messages = state["messages"] - last_message = messages[-1] - # If there is no function call, then we finish - if "function_call" not in last_message.additional_kwargs: - return "end" - # Otherwise if there is, we continue - else: - return "continue" - - # Define the function that calls the model - def call_model(state: AgentState, config: RunnableConfig): - messages = state["messages"] - response = model.invoke(messages, config) - # We return a list, because this will get added to the existing list - return {"messages": [response]} - - async def acall_model(state: AgentState, config: RunnableConfig): - messages = state["messages"] - response = await model.ainvoke(messages, config) - # We return a list, because this will get added to the existing list - return {"messages": [response]} - - # Define the function to execute tools - def _get_action(state: AgentState): - messages = state["messages"] - # Based on the continue condition - # we know the last message involves a function call - last_message = messages[-1] - # We construct an AgentAction from the function_call - return ToolInvocation( - tool=last_message.additional_kwargs["function_call"]["name"], - tool_input=json.loads( - last_message.additional_kwargs["function_call"]["arguments"] - ), - ) - - def call_tool(state: AgentState, config: RunnableConfig): - action = _get_action(state) - # We call the tool_executor and get back a response - response = tool_executor.invoke(action, config) - # We use the response to create a FunctionMessage - function_message = FunctionMessage(content=str(response), name=action.tool) - # We return a list, because this will get added to the existing list - return {"messages": [function_message]} - - async def acall_tool(state: AgentState, config: RunnableConfig): - action = _get_action(state) - # We call the tool_executor and get back a response - response = await tool_executor.ainvoke(action, config) - # We use the response to create a FunctionMessage - function_message = FunctionMessage(content=str(response), name=action.tool) - # We return a list, because this will get added to the existing list - return {"messages": [function_message]} - - # Define a new graph - workflow = StateGraph(AgentState) - - # Define the two nodes we will cycle between - workflow.add_node("agent", RunnableLambda(call_model, acall_model)) - workflow.add_node("tools", RunnableLambda(call_tool, acall_tool)) - - # Set the entrypoint as `agent` - # This means that this node is the first one called - workflow.set_entry_point("agent") - - # We now add a conditional edge - workflow.add_conditional_edges( - # First, we define the start node. We use `agent`. - # This means these are the edges taken after the `agent` node is called. - "agent", - # Next, we pass in the function that will determine which node is called next. - should_continue, - # Finally we pass in a mapping. - # The keys are strings, and the values are other nodes. - # END is a special node marking that the graph should finish. - # What will happen is we will call `should_continue`, and then the output of that - # will be matched against the keys in this mapping. - # Based on which one it matches, that node will then be called. - { - # If `tools`, then we call the tool node. - "continue": "tools", - # Otherwise we finish. - "end": END, - }, - ) - - # We now add a normal edge from `tools` to `agent`. - # This means that after `tools` is called, `agent` node is called next. - workflow.add_edge("tools", "agent") - - # Finally, we compile it! - # This compiles it into a LangChain Runnable, - # meaning you can use it as you would any other runnable - return workflow.compile() - - def _get_state_modifier_runnable(state_modifier: Optional[StateModifier]) -> Runnable: state_modifier_runnable: Runnable if state_modifier is None: @@ -231,7 +97,7 @@ def _convert_messages_modifier_to_state_modifier( state_modifier: StateModifier if isinstance(messages_modifier, (str, SystemMessage)): return messages_modifier - elif isinstance(messages_modifier, types.FunctionType): + elif callable(messages_modifier): def state_modifier(state: AgentState) -> Sequence[BaseMessage]: return messages_modifier(state["messages"]) @@ -261,7 +127,7 @@ def _get_model_preprocessing_runnable( return _get_state_modifier_runnable(state_modifier) -@deprecated_parameter("messages_modifier", "0.1.9", "state_modifier", removal="0.2.0") +@deprecated_parameter("messages_modifier", "0.1.9", "state_modifier", removal="0.3.0") def create_react_agent( model: LanguageModelLike, tools: Union[ToolExecutor, Sequence[BaseTool]], @@ -472,7 +338,7 @@ def create_react_agent( Add "chat memory" to the graph: ```pycon - >>> from langgraph.checkpoint import MemorySaver + >>> from langgraph.checkpoint.memory import MemorySaver >>> graph = create_react_agent(model, tools, checkpointer=MemorySaver()) >>> config = {"configurable": {"thread_id": "thread-1"}} >>> def print_stream(graph, inputs, config): @@ -608,7 +474,7 @@ def create_react_agent( # Define the two nodes we will cycle between workflow.add_node("agent", RunnableLambda(call_model, acall_model)) - workflow.add_node("tools", ToolNode(tools)) + workflow.add_node("tools", ToolNode(tool_classes)) # Set the entrypoint as `agent` # This means that this node is the first one called @@ -656,6 +522,5 @@ create_tool_calling_executor = create_react_agent __all__ = [ "create_react_agent", "create_tool_calling_executor", - "create_function_calling_executor", "AgentState", ] diff --git a/libs/langgraph/langgraph/prebuilt/tool_executor.py b/libs/langgraph/langgraph/prebuilt/tool_executor.py index cfd217044..7590b3836 100644 --- a/libs/langgraph/langgraph/prebuilt/tool_executor.py +++ b/libs/langgraph/langgraph/prebuilt/tool_executor.py @@ -5,6 +5,7 @@ from langchain_core.runnables import RunnableConfig from langchain_core.tools import BaseTool from langchain_core.tools import tool as create_tool +from langgraph._api.deprecation import deprecated from langgraph.utils import RunnableCallable INVALID_TOOL_MSG_TEMPLATE = ( @@ -13,6 +14,7 @@ INVALID_TOOL_MSG_TEMPLATE = ( ) +@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0") class ToolInvocationInterface: """Interface for invoking a tool. @@ -26,6 +28,7 @@ class ToolInvocationInterface: tool_input: Union[str, dict] +@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0") class ToolInvocation(Serializable): """Information about how to invoke a tool. @@ -47,6 +50,7 @@ class ToolInvocation(Serializable): tool_input: Union[str, dict] +@deprecated("0.2.0", "langgraph.prebuilt.ToolNode", removal="0.3.0") class ToolExecutor(RunnableCallable): """Executes a tool invocation. diff --git a/libs/langgraph/langgraph/pregel/__init__.py b/libs/langgraph/langgraph/pregel/__init__.py index c0b3a511d..2a31ec281 100644 --- a/libs/langgraph/langgraph/pregel/__init__.py +++ b/libs/langgraph/langgraph/pregel/__init__.py @@ -52,15 +52,16 @@ from langgraph.channels.base import ( BaseChannel, ) from langgraph.channels.context import Context +from langgraph.channels.last_value import LastValue from langgraph.channels.manager import ( AsyncChannelsManager, ChannelsManager, - create_checkpoint, ) from langgraph.checkpoint.base import ( BaseCheckpointSaver, CheckpointTuple, copy_checkpoint, + create_checkpoint, empty_checkpoint, ) from langgraph.constants import ( @@ -102,6 +103,7 @@ from langgraph.pregel.types import ( StateSnapshot, StreamMode, ) +from langgraph.pregel.utils import get_new_channel_versions from langgraph.pregel.validate import validate_graph, validate_keys from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry @@ -358,9 +360,14 @@ class Pregel( checkpoint = saved.checkpoint if saved else empty_checkpoint() config = saved.config if saved else config with ChannelsManager( - self.channels, checkpoint, config + { + k: LastValue(None) if isinstance(c, Context) else c + for k, c in self.channels.items() + }, + checkpoint, + config, ) as channels, ManagedValuesManager( - self.managed_values_dict, ensure_config(config), self + self.managed_values_dict, ensure_config(config) ) as managed: next_tasks = prepare_next_tasks( checkpoint, @@ -373,7 +380,7 @@ class Pregel( ) return StateSnapshot( values=read_channels(channels, self.stream_channels_asis), - next=tuple(name for name, _ in next_tasks), + next=tuple(t.name for t in next_tasks), config=saved.config if saved else config, metadata=saved.metadata if saved else None, created_at=saved.checkpoint["ts"] if saved else None, @@ -386,9 +393,14 @@ class Pregel( checkpoint = saved.checkpoint if saved else empty_checkpoint() config = saved.config if saved else config async with AsyncChannelsManager( - self.channels, checkpoint, config + { + k: LastValue(None) if isinstance(c, Context) else c + for k, c in self.channels.items() + }, + checkpoint, + config, ) as channels, AsyncManagedValuesManager( - self.managed_values_dict, ensure_config(config), self + self.managed_values_dict, ensure_config(config) ) as managed: next_tasks = prepare_next_tasks( checkpoint, @@ -401,7 +413,7 @@ class Pregel( ) return StateSnapshot( values=read_channels(channels, self.stream_channels_asis), - next=tuple(name for name, _ in next_tasks), + next=tuple(t.name for t in next_tasks), config=saved.config if saved else config, metadata=saved.metadata if saved else None, created_at=saved.checkpoint["ts"] if saved else None, @@ -554,9 +566,14 @@ class Pregel( yield state_snapshot else: with ChannelsManager( - self.channels, checkpoint, config + { + k: LastValue(None) if isinstance(c, Context) else c + for k, c in self.channels.items() + }, + checkpoint, + config, ) as channels, ManagedValuesManager( - self.managed_values_dict, ensure_config(config), self + self.managed_values_dict, ensure_config(config) ) as managed: next_tasks = prepare_next_tasks( checkpoint, @@ -567,9 +584,10 @@ class Pregel( -1, for_execution=False, ) + yield StateSnapshot( read_channels(channels, self.stream_channels_asis), - tuple(name for name, _ in next_tasks), + tuple(t.name for t in next_tasks), config, metadata, checkpoint["ts"], @@ -602,14 +620,18 @@ class Pregel( ) in self.checkpointer.alist(config, before=before, limit=limit, filter=filter): if include_subgraph_state: state_snapshot = await self.aget_state( - config, include_subgraph_state=True - ) + config, include_subgraph_state=True) yield state_snapshot else: - async with AsyncChannelsManager( - self.channels, checkpoint, config + async with AsyncChannelsManager( + { + k: LastValue(None) if isinstance(c, Context) else c + for k, c in self.channels.items() + }, + checkpoint, + config, ) as channels, AsyncManagedValuesManager( - self.managed_values_dict, ensure_config(config), self + self.managed_values_dict, ensure_config(config) ) as managed: next_tasks = prepare_next_tasks( checkpoint, @@ -622,7 +644,7 @@ class Pregel( ) yield StateSnapshot( read_channels(channels, self.stream_channels_asis), - tuple(name for name, _ in next_tasks), + tuple(t.name for t in next_tasks), config, metadata, checkpoint["ts"], @@ -632,7 +654,7 @@ class Pregel( def update_state( self, config: RunnableConfig, - values: dict[str, Any] | Any, + values: Optional[Union[dict[str, Any], Any]], as_node: Optional[str] = None, ) -> RunnableConfig: """Update the state of the graph with the given values, as if they came from @@ -645,8 +667,39 @@ class Pregel( # get last checkpoint saved = self.checkpointer.get_tuple(config) checkpoint = copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint() + checkpoint_previous_versions = ( + saved.checkpoint["channel_versions"] if saved else {} + ) + step = saved.metadata.get("step", -1) if saved else -1 + # merge configurable fields with previous checkpoint config + checkpoint_config = { + **config, + "configurable": { + **config["configurable"], + # TODO: add proper support for updating nested subgraph state + "checkpoint_ns": "", + }, + } + if saved: + checkpoint_config = { + "configurable": { + **config.get("configurable", {}), + **saved.config["configurable"], + } + } # find last node that updated the state, if not provided - if as_node is None and not any( + if values is None and as_node is None: + return self.checkpointer.put( + checkpoint_config, + create_checkpoint(checkpoint, None, step), + { + "source": "update", + "step": step, + "writes": {}, + }, + {}, + ) + elif as_node is None and not any( v for vv in checkpoint["versions_seen"].values() for v in vv.values() ): if ( @@ -705,26 +758,19 @@ class Pregel( apply_writes( checkpoint, channels, [task], self.checkpointer.get_next_version ) - step = saved.metadata.get("step", -2) + 1 if saved else -1 - - # merge configurable fields with previous checkpoint config - checkpoint_config = config - if saved: - checkpoint_config = { - "configurable": { - **config.get("configurable", {}), - **saved.config["configurable"], - } - } + new_versions = get_new_channel_versions( + checkpoint_previous_versions, checkpoint["channel_versions"] + ) return self.checkpointer.put( checkpoint_config, - create_checkpoint(checkpoint, channels, step), + create_checkpoint(checkpoint, channels, step + 1), { "source": "update", - "step": step, + "step": step + 1, "writes": {as_node: values}, }, + new_versions, ) async def aupdate_state( @@ -739,8 +785,39 @@ class Pregel( # get last checkpoint saved = await self.checkpointer.aget_tuple(config) checkpoint = copy_checkpoint(saved.checkpoint) if saved else empty_checkpoint() + checkpoint_previous_versions = ( + saved.checkpoint["channel_versions"] if saved else {} + ) + step = saved.metadata.get("step", -1) if saved else -1 + # merge configurable fields with previous checkpoint config + checkpoint_config = { + **config, + "configurable": { + **config["configurable"], + # TODO: add proper support for updating nested subgraph state + "checkpoint_ns": "", + }, + } + if saved: + checkpoint_config = { + "configurable": { + **config.get("configurable", {}), + **saved.config["configurable"], + } + } # find last node that updated the state, if not provided - if as_node is None and not saved: + if values is None and as_node is None: + return await self.checkpointer.aput( + checkpoint_config, + create_checkpoint(checkpoint, None, step), + { + "source": "update", + "step": step, + "writes": {}, + }, + {}, + ) + elif as_node is None and not saved: if ( isinstance(self.input_channels, str) and self.input_channels in self.nodes @@ -797,26 +874,19 @@ class Pregel( apply_writes( checkpoint, channels, [task], self.checkpointer.get_next_version ) - step = saved.metadata.get("step", -2) + 1 if saved else -1 - - # merge configurable fields with previous checkpoint config - checkpoint_config = config - if saved: - checkpoint_config = { - "configurable": { - **config.get("configurable", {}), - **saved.config["configurable"], - } - } + new_versions = get_new_channel_versions( + checkpoint_previous_versions, checkpoint["channel_versions"] + ) return await self.checkpointer.aput( checkpoint_config, - create_checkpoint(checkpoint, channels, step), + create_checkpoint(checkpoint, channels, step + 1), { "source": "update", - "step": step, + "step": step + 1, "writes": {as_node: values}, }, + new_versions, ) def _defaults( @@ -850,8 +920,10 @@ class Pregel( if config and config.get("configurable", {}).get(CONFIG_KEY_READ) is not None: # if being called as a node in another graph, always use values mode stream_mode = ["values"] - if config is not None and config.get("configurable", {}).get( - CONFIG_KEY_CHECKPOINTER + if ( + config is not None + and config.get("configurable", {}).get(CONFIG_KEY_CHECKPOINTER) + and (interrupt_after or interrupt_before) ): checkpointer: Optional[BaseCheckpointSaver] = config["configurable"][ CONFIG_KEY_CHECKPOINTER diff --git a/libs/langgraph/langgraph/pregel/algo.py b/libs/langgraph/langgraph/pregel/algo.py index 00d9e7809..2ddbcd873 100644 --- a/libs/langgraph/langgraph/pregel/algo.py +++ b/libs/langgraph/langgraph/pregel/algo.py @@ -25,9 +25,15 @@ from langchain_core.runnables.config import ( from langgraph.channels.base import BaseChannel from langgraph.channels.context import Context -from langgraph.channels.manager import ChannelsManager, create_checkpoint -from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint, copy_checkpoint +from langgraph.channels.manager import ChannelsManager +from langgraph.checkpoint.base import ( + BaseCheckpointSaver, + Checkpoint, + copy_checkpoint, + create_checkpoint, +) from langgraph.constants import ( + CHECKPOINT_NAMESPACE_SEPARATOR, CONFIG_KEY_CHECKPOINTER, CONFIG_KEY_READ, CONFIG_KEY_RESUMING, @@ -36,7 +42,6 @@ from langgraph.constants import ( RESERVED, TAG_HIDDEN, TASKS, - THREAD_ID_SEPARATOR, Send, ) from langgraph.errors import EmptyChannelError, InvalidUpdateError @@ -215,6 +220,7 @@ def prepare_next_tasks( managed: ManagedValueMapping, config: RunnableConfig, step: int, + *, for_execution: Literal[False], is_resuming: bool = False, checkpointer: Literal[None] = None, @@ -231,6 +237,7 @@ def prepare_next_tasks( managed: ManagedValueMapping, config: RunnableConfig, step: int, + *, for_execution: Literal[True], is_resuming: bool, checkpointer: Optional[BaseCheckpointSaver], @@ -252,12 +259,16 @@ def prepare_next_tasks( checkpointer: Optional[BaseCheckpointSaver] = None, manager: Union[None, ParentRunManager, AsyncParentRunManager] = None, ) -> Union[list[PregelTaskDescription], list[PregelExecutableTask]]: + parent_ns = config.get("configurable", {}).get("checkpoint_ns", "") tasks: Union[list[PregelTaskDescription], list[PregelExecutableTask]] = [] # Consume pending packets for packet in checkpoint["pending_sends"]: if not isinstance(packet, Send): logger.warn(f"Ignoring invalid packet type {type(packet)} in pending sends") continue + if packet.node not in processes: + logger.warn(f"Ignoring unknown node name {packet.node} in pending sends") + continue if for_execution: proc = processes[packet.node] if node := proc.get_node(): @@ -268,7 +279,14 @@ def prepare_next_tasks( "langgraph_triggers": triggers, "langgraph_task_idx": len(tasks), } - task_id = str(uuid5(UUID(checkpoint["id"]), json.dumps(metadata))) + checkpoint_ns = ( + f"{parent_ns}{CHECKPOINT_NAMESPACE_SEPARATOR}{packet.node}" + if parent_ns + else packet.node + ) + task_id = str( + uuid5(UUID(checkpoint["id"]), json.dumps((checkpoint_ns, metadata))) + ) writes = deque() tasks.append( PregelExecutableTask( @@ -310,7 +328,7 @@ def prepare_next_tasks( ) ) else: - tasks.append(PregelTaskDescription(packet.node, packet.arg)) + tasks.append(PregelTaskDescription(packet.node)) # Check if any processes should be run in next step # If so, prepare the values to be passed to them version_type = type(next(iter(checkpoint["channel_versions"].values()), None)) @@ -330,7 +348,11 @@ def prepare_next_tasks( > seen.get(chan, null_version) ): try: - val = next(_proc_input(step, name, proc, managed, channels)) + val = next( + _proc_input( + step, name, proc, managed, channels, for_execution=for_execution + ) + ) except StopIteration: continue @@ -342,15 +364,18 @@ def prepare_next_tasks( "langgraph_triggers": triggers, "langgraph_task_idx": len(tasks), } - task_id = str(uuid5(UUID(checkpoint["id"]), json.dumps(metadata))) - if parent_thread_id := config.get("configurable", {}).get( - "thread_id" - ): - thread_id: Optional[ - str - ] = f"{parent_thread_id}{THREAD_ID_SEPARATOR}{name}" - else: - thread_id = None + checkpoint_ns = ( + f"{parent_ns}{CHECKPOINT_NAMESPACE_SEPARATOR}{name}" + if parent_ns + else name + ) + task_id = str( + uuid5( + UUID(checkpoint["id"]), + json.dumps((checkpoint_ns, metadata)), + ) + ) + writes = deque() tasks.append( PregelExecutableTask( @@ -384,8 +409,8 @@ def prepare_next_tasks( ), CONFIG_KEY_CHECKPOINTER: checkpointer, CONFIG_KEY_RESUMING: is_resuming, - "thread_id": thread_id, - "thread_ts": checkpoint["id"], + "checkpoint_id": checkpoint["id"], + "checkpoint_ns": checkpoint_ns, }, ), triggers, @@ -394,7 +419,7 @@ def prepare_next_tasks( ) ) else: - tasks.append(PregelTaskDescription(name, val)) + tasks.append(PregelTaskDescription(name)) return tasks @@ -404,6 +429,8 @@ def _proc_input( proc: PregelNode, managed: ManagedValueMapping, channels: Mapping[str, BaseChannel], + *, + for_execution: bool, ) -> Iterator[Any]: # If all trigger channels subscribed by this process are not empty # then invoke the process with the values of all non-empty channels @@ -423,7 +450,7 @@ def _proc_input( for key, chan in proc.channels.items(): if is_managed_value(chan): managed_values[key] = managed[key]( - step, PregelTaskDescription(name, val) + step, PregelTaskDescription(name) ) val.update(managed_values) @@ -444,7 +471,7 @@ def _proc_input( ) # If the process has a mapper, apply it to the value - if proc.mapper is not None: + if for_execution and proc.mapper is not None: val = proc.mapper(val) yield val diff --git a/libs/langgraph/langgraph/pregel/debug.py b/libs/langgraph/langgraph/pregel/debug.py index 70c1e69b7..9e152cf87 100644 --- a/libs/langgraph/langgraph/pregel/debug.py +++ b/libs/langgraph/langgraph/pregel/debug.py @@ -71,7 +71,7 @@ def map_debug_tasks( continue metadata = config["metadata"].copy() - metadata.pop("thread_ts", None) + metadata.pop("checkpoint_id", None) yield { "type": "task", @@ -97,7 +97,8 @@ def map_debug_task_results( continue metadata = config["metadata"].copy() - metadata.pop("thread_ts", None) + metadata.pop("checkpoint_id", None) + # TODO: make task IDs deterministic in tests and reuse task IDs for payload ID yield { "type": "task_result", diff --git a/libs/langgraph/langgraph/pregel/io.py b/libs/langgraph/langgraph/pregel/io.py index 71ad967e5..53e77557e 100644 --- a/libs/langgraph/langgraph/pregel/io.py +++ b/libs/langgraph/langgraph/pregel/io.py @@ -1,4 +1,3 @@ -from collections import defaultdict from typing import Any, Iterator, Mapping, Optional, Sequence, TypeVar, Union from langchain_core.runnables.utils import AddableDict @@ -102,36 +101,33 @@ def map_output_updates( output_tasks = [ t for t in tasks if not t.config or TAG_HIDDEN not in t.config.get("tags") ] + if not output_tasks: + return if isinstance(output_channels, str): - if updated := [ + updated = [ (task.name, value) for task in output_tasks for chan, value in task.writes if chan == output_channels - ]: - grouped = defaultdict(list) - for node, value in updated: - grouped[node].append(value) - for node, value in grouped.items(): - if len(value) == 1: - grouped[node] = value[0] - yield AddableUpdatesDict(grouped) + ] else: - if updated := [ + updated = [ ( task.name, {chan: value for chan, value in task.writes if chan in output_channels}, ) for task in output_tasks if any(chan in output_channels for chan, _ in task.writes) - ]: - grouped = defaultdict(list) - for node, value in updated: - grouped[node].append(value) - for node, value in grouped.items(): - if len(value) == 1: - grouped[node] = value[0] - yield AddableUpdatesDict(grouped) + ] + grouped = {t.name: [] for t in output_tasks} + for node, value in updated: + grouped[node].append(value) + for node, value in grouped.items(): + if len(value) == 0: + grouped[node] = None + if len(value) == 1: + grouped[node] = value[0] + yield AddableUpdatesDict(grouped) T = TypeVar("T") diff --git a/libs/langgraph/langgraph/pregel/loop.py b/libs/langgraph/langgraph/pregel/loop.py index ae2ba2edf..0508cd942 100644 --- a/libs/langgraph/langgraph/pregel/loop.py +++ b/libs/langgraph/langgraph/pregel/loop.py @@ -28,7 +28,6 @@ from langgraph.channels.base import BaseChannel from langgraph.channels.manager import ( AsyncChannelsManager, ChannelsManager, - create_checkpoint, ) from langgraph.checkpoint.base import ( BaseCheckpointSaver, @@ -37,6 +36,7 @@ from langgraph.checkpoint.base import ( CheckpointTuple, PendingWrite, copy_checkpoint, + create_checkpoint, empty_checkpoint, ) from langgraph.constants import CONFIG_KEY_READ, CONFIG_KEY_RESUMING, INPUT, INTERRUPT @@ -61,6 +61,7 @@ from langgraph.pregel.executor import ( ) from langgraph.pregel.io import map_input, map_output_updates, map_output_values, single from langgraph.pregel.types import PregelExecutableTask +from langgraph.pregel.utils import get_new_channel_versions if TYPE_CHECKING: from langgraph.pregel import Pregel @@ -100,8 +101,11 @@ class PregelLoop: checkpoint_config: RunnableConfig checkpoint_metadata: CheckpointMetadata checkpoint_pending_writes: List[PendingWrite] + # (thread_id, checkpoint_ns -> channel_versions) + checkpoint_previous_versions: dict[str, Union[str, float, int]] step: int + stop: int status: Literal[ "pending", "done", "interrupt_before", "interrupt_after", "out_of_steps" ] @@ -142,7 +146,10 @@ class PregelLoop: **self.checkpoint_config, "configurable": { **self.checkpoint_config["configurable"], - "thread_ts": self.checkpoint["id"], + "checkpoint_ns": self.config["configurable"].get( + "checkpoint_ns", "" + ), + "checkpoint_id": self.checkpoint["id"], }, }, writes, @@ -203,7 +210,7 @@ class PregelLoop: return False # check if iteration limit is reached - if self.step > self.config["recursion_limit"]: + if self.step > self.stop: self.status = "out_of_steps" return False @@ -314,8 +321,28 @@ class PregelLoop: # this is achieved by writing child checkpoints as progress is made # (so that error recovery / resuming from interrupt don't lose work) # but doing so always with an id equal to that of the parent checkpoint - id=self.config["configurable"]["thread_ts"] if self.is_nested else None, + id=self.config["configurable"]["checkpoint_id"] + if self.is_nested + else None, ) + + self.checkpoint_config = { + **self.checkpoint_config, + "configurable": { + **self.checkpoint_config["configurable"], + "checkpoint_ns": self.config["configurable"].get( + "checkpoint_ns", "" + ), + }, + } + + channel_versions = self.checkpoint["channel_versions"].copy() + new_versions = get_new_channel_versions( + self.checkpoint_previous_versions, channel_versions + ) + + self.checkpoint_previous_versions = channel_versions + # save it, without blocking # if there's a previous checkpoint save in progress, wait for it # ensuring checkpointers receive checkpoints in order @@ -325,12 +352,13 @@ class PregelLoop: self.checkpoint_config, copy_checkpoint(self.checkpoint), self.checkpoint_metadata, + new_versions, ) self.checkpoint_config = { **self.checkpoint_config, "configurable": { **self.checkpoint_config["configurable"], - "thread_ts": self.checkpoint["id"], + "checkpoint_id": self.checkpoint["id"], }, } # produce debug output @@ -383,12 +411,13 @@ class SyncPregelLoop(PregelLoop, ContextManager): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: Optional[dict[str, Union[str, float, int]]], ) -> RunnableConfig: try: if prev is not None: prev.result() finally: - self.checkpointer.put(config, checkpoint, metadata) + self.checkpointer.put(config, checkpoint, metadata, new_versions) # context manager @@ -413,12 +442,12 @@ class SyncPregelLoop(PregelLoop, ContextManager): ChannelsManager(self.graph.channels, self.checkpoint, self.config) ) self.managed = self.stack.enter_context( - ManagedValuesManager( - self.graph.managed_values_dict, self.config, self.graph - ) + ManagedValuesManager(self.graph.managed_values_dict, self.config) ) self.status = "pending" self.step = self.checkpoint_metadata["step"] + 1 + self.stop = self.step + self.config["recursion_limit"] + 1 + self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy() return self @@ -459,12 +488,13 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: Optional[dict[str, Union[str, float, int]]], ) -> RunnableConfig: try: if prev is not None: await prev finally: - await self.checkpointer.aput(config, checkpoint, metadata) + await self.checkpointer.aput(config, checkpoint, metadata, new_versions) # context manager @@ -491,12 +521,13 @@ class AsyncPregelLoop(PregelLoop, AsyncContextManager): AsyncChannelsManager(self.graph.channels, self.checkpoint, self.config) ) self.managed = await self.stack.enter_async_context( - AsyncManagedValuesManager( - self.graph.managed_values_dict, self.config, self.graph - ) + AsyncManagedValuesManager(self.graph.managed_values_dict, self.config) ) self.status = "pending" self.step = self.checkpoint_metadata["step"] + 1 + self.stop = self.step + self.config["recursion_limit"] + 1 + + self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy() return self diff --git a/libs/langgraph/langgraph/pregel/types.py b/libs/langgraph/langgraph/pregel/types.py index d0648cf3b..f3d25f0b1 100644 --- a/libs/langgraph/langgraph/pregel/types.py +++ b/libs/langgraph/langgraph/pregel/types.py @@ -58,7 +58,6 @@ class RetryPolicy(NamedTuple): class PregelTaskDescription(NamedTuple): name: str - input: Any class PregelExecutableTask(NamedTuple): diff --git a/libs/langgraph/langgraph/pregel/utils.py b/libs/langgraph/langgraph/pregel/utils.py new file mode 100644 index 000000000..d3d0d989f --- /dev/null +++ b/libs/langgraph/langgraph/pregel/utils.py @@ -0,0 +1,19 @@ +from langgraph.checkpoint.base import ChannelVersions + + +def get_new_channel_versions( + previous_versions: ChannelVersions, current_versions: ChannelVersions +) -> ChannelVersions: + """Get new channel versions.""" + if previous_versions: + version_type = type(next(iter(current_versions.values()), None)) + null_version = version_type() + new_versions = { + k: v + for k, v in current_versions.items() + if v > previous_versions.get(k, null_version) + } + else: + new_versions = current_versions + + return new_versions diff --git a/libs/langgraph/langgraph/serde/base.py b/libs/langgraph/langgraph/serde/base.py deleted file mode 100644 index 2fbb0ab71..000000000 --- a/libs/langgraph/langgraph/serde/base.py +++ /dev/null @@ -1,17 +0,0 @@ -from typing import Any, Protocol - - -class SerializerProtocol(Protocol): - """Protocol for serialization and deserialization of objects. - - - `dumps`: Serialize an object to bytes. - - `loads`: Deserialize an object from bytes. - - Valid implementations include the `pickle`, `json` and `orjson` modules. - """ - - def dumps(self, obj: Any) -> bytes: - ... - - def loads(self, data: bytes) -> Any: - ... diff --git a/libs/langgraph/poetry.lock b/libs/langgraph/poetry.lock index 0b7a1755f..aedca6476 100644 --- a/libs/langgraph/poetry.lock +++ b/libs/langgraph/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "aiohttp" @@ -112,18 +112,21 @@ frozenlist = ">=1.1.0" [[package]] name = "aiosqlite" -version = "0.19.0" +version = "0.20.0" description = "asyncio bridge to the standard sqlite3 module" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "aiosqlite-0.19.0-py3-none-any.whl", hash = "sha256:edba222e03453e094a3ce605db1b970c4b3376264e56f32e2a4959f948d66a96"}, - {file = "aiosqlite-0.19.0.tar.gz", hash = "sha256:95ee77b91c8d2808bd08a59fbebf66270e9090c3d92ffbf260dc0db0b979577d"}, + {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"}, + {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"}, ] +[package.dependencies] +typing_extensions = ">=4.0" + [package.extras] -dev = ["aiounittest (==1.4.1)", "attribution (==1.6.2)", "black (==23.3.0)", "coverage[toml] (==7.2.3)", "flake8 (==5.0.4)", 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version = "1.3.0" @@ -4179,4 +4282,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = ">=3.9.0,<4.0" -content-hash = "18b26895b05f2f7cdcd08d59ba164ac788e0e8005e3ec5d7089469b4f3a96aea" +content-hash = "4ef9e25016072ce08554c8ff8d091104fb59da7cdab9a128312bd787e6f35146" diff --git a/libs/langgraph/pyproject.toml b/libs/langgraph/pyproject.toml index a856e7f69..9701f5933 100644 --- a/libs/langgraph/pyproject.toml +++ b/libs/langgraph/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langgraph" -version = "0.1.12" +version = "0.2.3" description = "Building stateful, multi-actor applications with LLMs" authors = [] license = "MIT" @@ -9,7 +9,8 @@ repository = "https://www.github.com/langchain-ai/langgraph" [tool.poetry.dependencies] python = ">=3.9.0,<4.0" -langchain-core = ">=0.2.22,<0.3" +langchain-core = ">=0.2.27,<0.3" +langgraph-checkpoint = "^1.0.2" [tool.poetry.group.dev.dependencies] @@ -22,7 +23,6 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"metadata": { + "version": 2, + "variant": "b" } }, { @@ -508,6 +512,95 @@ ''' # --- +# name: test_conditional_graph.5 + dict({ + 'edges': list([ + dict({ + 'source': '__start__', + 'target': 'agent', + }), + dict({ + 'source': 'tools', + 'target': 'agent', + }), + dict({ + 'conditional': True, + 'data': 'continue', + 'source': 'agent', + 'target': 'tools', + }), + dict({ + 'conditional': True, + 'data': 'exit', + 'source': 'agent', + 'target': '__end__', + }), + ]), + 'nodes': list([ + dict({ + 'data': '__start__', + 'id': '__start__', + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain', + 'schema', + 'runnable', + 'RunnableAssign', + ]), + 'name': 'agent', + }), + 'id': 'agent', + 'metadata': dict({ + '__interrupt': 'after', + }), + 'type': 'runnable', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'tools', + }), + 'id': 'tools', + 'metadata': dict({ + 'variant': 'b', + 'version': 2, + }), + 'type': 'runnable', + }), + dict({ + 'data': '__end__', + 'id': '__end__', + 'type': 'schema', + }), + ]), + }) +# --- +# name: test_conditional_graph.6 + ''' + %%{init: {'flowchart': {'curve': 'linear'}}}%% + graph TD; + __start__([__start__]):::first + agent(agent
__interrupt = after) + tools(tools
version = 2 + variant = b) + __end__([__end__]):::last + __start__ --> agent; + tools --> agent; + agent -.  continue  .-> tools; + agent -.  exit  .-> __end__; + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc + + ''' +# --- # name: test_conditional_state_graph '{"title": "LangGraphInput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' # --- @@ -615,6 +708,146 @@ ''' # --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1 + ''' + graph TD; + __start__ --> rewrite_query; + analyzer_one --> retriever_one; + qa --> __end__; + retriever_one --> qa; + retriever_two --> qa; + rewrite_query --> analyzer_one; + rewrite_query -.-> retriever_two; + + ''' +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1.1 + dict({ + 'definitions': dict({ + 'InnerObject': dict({ + 'properties': dict({ + 'yo': dict({ + 'title': 'Yo', + 'type': 'integer', + }), + }), + 'required': list([ + 'yo', + ]), + 'title': 'InnerObject', + 'type': 'object', + }), + }), + 'properties': dict({ + 'inner': dict({ + '$ref': '#/definitions/InnerObject', + }), + 'query': dict({ + 'title': 'Query', + 'type': 'string', + }), + }), + 'required': list([ + 'query', + 'inner', + ]), + 'title': 'Input', + 'type': 'object', + }) +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1.2 + dict({ + 'properties': dict({ + 'answer': dict({ + 'title': 'Answer', + 'type': 'string', + }), + 'docs': dict({ + 'items': dict({ + 'type': 'string', + }), + 'title': 'Docs', + 'type': 'array', + }), + }), + 'required': list([ + 'answer', + 'docs', + ]), + 'title': 'Output', + 'type': 'object', + }) +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2 + ''' + graph TD; + __start__ --> rewrite_query; + analyzer_one --> retriever_one; + qa --> __end__; + retriever_one --> qa; + retriever_two --> qa; + rewrite_query --> analyzer_one; + rewrite_query -.-> retriever_two; + + ''' +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.1 + dict({ + '$defs': dict({ + 'InnerObject': dict({ + 'properties': dict({ + 'yo': dict({ + 'title': 'Yo', + 'type': 'integer', + }), + }), + 'required': list([ + 'yo', + ]), + 'title': 'InnerObject', + 'type': 'object', + }), + }), + 'properties': dict({ + 'inner': dict({ + '$ref': '#/$defs/InnerObject', + }), + 'query': dict({ + 'title': 'Query', + 'type': 'string', + }), + }), + 'required': list([ + 'query', + 'inner', + ]), + 'title': 'Input', + 'type': 'object', + }) +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.2 + dict({ + 'properties': dict({ + 'answer': dict({ + 'title': 'Answer', + 'type': 'string', + }), + 'docs': dict({ + 'items': dict({ + 'type': 'string', + }), + 'title': 'Docs', + 'type': 'array', + }), + }), + 'required': list([ + 'answer', + 'docs', + ]), + 'title': 'Output', + 'type': 'object', + }) +# --- # name: test_in_one_fan_out_state_graph_waiting_edge_via_branch ''' graph TD; @@ -629,10 +862,10 @@ ''' # --- # name: test_message_graph - '{"title": "LangGraphInput", "type": "array", "items": {"anyOf": [{"$ref": "#/definitions/AIMessage"}, {"$ref": "#/definitions/HumanMessage"}, {"$ref": "#/definitions/ChatMessage"}, {"$ref": "#/definitions/SystemMessage"}, {"$ref": "#/definitions/FunctionMessage"}, {"$ref": "#/definitions/ToolMessage"}]}, "definitions": {"ToolCall": {"title": "ToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "object"}, "id": {"title": "Id", "type": "string"}, "type": {"title": "Type", "enum": ["tool_call"], "type": "string"}}, "required": ["name", "args", "id"]}, "InvalidToolCall": {"title": "InvalidToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "string"}, "id": {"title": "Id", "type": "string"}, "error": {"title": "Error", "type": "string"}, "type": {"title": "Type", "enum": ["invalid_tool_call"], "type": "string"}}, "required": ["name", "args", "id", "error"]}, "UsageMetadata": {"title": "UsageMetadata", "type": "object", "properties": {"input_tokens": {"title": "Input Tokens", "type": "integer"}, "output_tokens": {"title": "Output Tokens", "type": "integer"}, "total_tokens": {"title": "Total Tokens", "type": "integer"}}, "required": ["input_tokens", "output_tokens", "total_tokens"]}, "AIMessage": {"title": "AIMessage", "description": "Message from an AI.\\n\\nAIMessage is returned from a chat model as a response to a prompt.\\n\\nThis message represents the output of the model and consists of both\\nthe raw output as returned by the model together standardized fields\\n(e.g., tool calls, usage metadata) added by the LangChain framework.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "ai", "enum": ["ai"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}, "tool_calls": {"title": "Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/ToolCall"}}, "invalid_tool_calls": {"title": "Invalid Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/InvalidToolCall"}}, "usage_metadata": {"$ref": "#/definitions/UsageMetadata"}}, "required": ["content"]}, "HumanMessage": {"title": "HumanMessage", "description": "Message from a human.\\n\\nHumanMessages are messages that are passed in from a human to the model.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Instantiate a chat model and invoke it with the messages\\n model = ...\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "human", "enum": ["human"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}}, "required": ["content"]}, "ChatMessage": {"title": "ChatMessage", "description": "Message that can be assigned an arbitrary speaker (i.e. role).", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "chat", "enum": ["chat"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "role": {"title": "Role", "type": "string"}}, "required": ["content", "role"]}, "SystemMessage": {"title": "SystemMessage", "description": "Message for priming AI behavior.\\n\\nThe system message is usually passed in as the first of a sequence\\nof input messages.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Define a chat model and invoke it with the messages\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "system", "enum": ["system"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content"]}, "FunctionMessage": {"title": "FunctionMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nFunctionMessage are an older version of the ToolMessage schema, and\\ndo not contain the tool_call_id field.\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "function", "enum": ["function"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content", "name"]}, "ToolMessage": {"title": "ToolMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nToolMessages contain the result of a tool invocation. Typically, the result\\nis encoded inside the `content` field.\\n\\nExample: A ToolMessage representing a result of 42 from a tool call with id\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n ToolMessage(content=\'42\', tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\')\\n\\n\\nExample: A ToolMessage where only part of the tool output is sent to the model\\n and the full output is passed in to artifact.\\n\\n .. versionadded:: 0.2.17\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n tool_output = {\\n \\"stdout\\": \\"From the graph we can see that the correlation between x and y is ...\\",\\n \\"stderr\\": None,\\n \\"artifacts\\": {\\"type\\": \\"image\\", \\"base64_data\\": \\"/9j/4gIcSU...\\"},\\n }\\n\\n ToolMessage(\\n content=tool_output[\\"stdout\\"],\\n artifact=tool_output,\\n tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\',\\n )\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "tool", "enum": ["tool"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "tool_call_id": {"title": "Tool Call Id", "type": "string"}, "artifact": {"title": "Artifact"}}, "required": ["content", "tool_call_id"]}}}' + '{"title": "LangGraphInput", "type": "array", "items": {"anyOf": [{"$ref": "#/definitions/AIMessage"}, {"$ref": "#/definitions/HumanMessage"}, {"$ref": "#/definitions/ChatMessage"}, {"$ref": "#/definitions/SystemMessage"}, {"$ref": "#/definitions/FunctionMessage"}, {"$ref": "#/definitions/ToolMessage"}]}, "definitions": {"ToolCall": {"title": "ToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "object"}, "id": {"title": "Id", "type": "string"}, "type": {"title": "Type", "enum": ["tool_call"], "type": "string"}}, "required": ["name", "args", "id"]}, "InvalidToolCall": {"title": "InvalidToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "string"}, "id": {"title": "Id", "type": "string"}, "error": {"title": "Error", "type": "string"}, "type": {"title": "Type", "enum": ["invalid_tool_call"], "type": "string"}}, "required": ["name", "args", "id", "error"]}, "UsageMetadata": {"title": "UsageMetadata", "type": "object", "properties": {"input_tokens": {"title": "Input Tokens", "type": "integer"}, "output_tokens": {"title": "Output Tokens", "type": "integer"}, "total_tokens": {"title": "Total Tokens", "type": "integer"}}, "required": ["input_tokens", "output_tokens", "total_tokens"]}, "AIMessage": {"title": "AIMessage", "description": "Message from an AI.\\n\\nAIMessage is returned from a chat model as a response to a prompt.\\n\\nThis message represents the output of the model and consists of both\\nthe raw output as returned by the model together standardized fields\\n(e.g., tool calls, usage metadata) added by the LangChain framework.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "ai", "enum": ["ai"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}, "tool_calls": {"title": "Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/ToolCall"}}, "invalid_tool_calls": {"title": "Invalid Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/InvalidToolCall"}}, "usage_metadata": {"$ref": "#/definitions/UsageMetadata"}}, "required": ["content"]}, "HumanMessage": {"title": "HumanMessage", "description": "Message from a human.\\n\\nHumanMessages are messages that are passed in from a human to the model.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Instantiate a chat model and invoke it with the messages\\n model = ...\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "human", "enum": ["human"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}}, "required": ["content"]}, "ChatMessage": {"title": "ChatMessage", "description": "Message that can be assigned an arbitrary speaker (i.e. role).", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "chat", "enum": ["chat"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "role": {"title": "Role", "type": "string"}}, "required": ["content", "role"]}, "SystemMessage": {"title": "SystemMessage", "description": "Message for priming AI behavior.\\n\\nThe system message is usually passed in as the first of a sequence\\nof input messages.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Define a chat model and invoke it with the messages\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "system", "enum": ["system"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content"]}, "FunctionMessage": {"title": "FunctionMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nFunctionMessage are an older version of the ToolMessage schema, and\\ndo not contain the tool_call_id field.\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "function", "enum": ["function"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content", "name"]}, "ToolMessage": {"title": "ToolMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nToolMessages contain the result of a tool invocation. Typically, the result\\nis encoded inside the `content` field.\\n\\nExample: A ToolMessage representing a result of 42 from a tool call with id\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n ToolMessage(content=\'42\', tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\')\\n\\n\\nExample: A ToolMessage where only part of the tool output is sent to the model\\n and the full output is passed in to artifact.\\n\\n .. versionadded:: 0.2.17\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n tool_output = {\\n \\"stdout\\": \\"From the graph we can see that the correlation between x and y is ...\\",\\n \\"stderr\\": None,\\n \\"artifacts\\": {\\"type\\": \\"image\\", \\"base64_data\\": \\"/9j/4gIcSU...\\"},\\n }\\n\\n ToolMessage(\\n content=tool_output[\\"stdout\\"],\\n artifact=tool_output,\\n tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\',\\n )\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "tool", "enum": ["tool"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "tool_call_id": {"title": "Tool Call Id", "type": "string"}, "artifact": {"title": "Artifact"}, "status": {"title": "Status", "default": "success", "enum": ["success", "error"], "type": "string"}}, "required": ["content", "tool_call_id"]}}}' # --- # name: test_message_graph.1 - '{"title": "LangGraphOutput", "type": "array", "items": {"anyOf": [{"$ref": "#/definitions/AIMessage"}, {"$ref": "#/definitions/HumanMessage"}, {"$ref": "#/definitions/ChatMessage"}, {"$ref": "#/definitions/SystemMessage"}, {"$ref": "#/definitions/FunctionMessage"}, {"$ref": "#/definitions/ToolMessage"}]}, "definitions": {"ToolCall": {"title": "ToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "object"}, "id": {"title": "Id", "type": "string"}, "type": {"title": "Type", "enum": ["tool_call"], "type": "string"}}, "required": ["name", "args", "id"]}, "InvalidToolCall": {"title": "InvalidToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "string"}, "id": {"title": "Id", "type": "string"}, "error": {"title": "Error", "type": "string"}, "type": {"title": "Type", "enum": ["invalid_tool_call"], "type": "string"}}, "required": ["name", "args", "id", "error"]}, "UsageMetadata": {"title": "UsageMetadata", "type": "object", "properties": {"input_tokens": {"title": "Input Tokens", "type": "integer"}, "output_tokens": {"title": "Output Tokens", "type": "integer"}, "total_tokens": {"title": "Total Tokens", "type": "integer"}}, "required": ["input_tokens", "output_tokens", "total_tokens"]}, "AIMessage": {"title": "AIMessage", "description": "Message from an AI.\\n\\nAIMessage is returned from a chat model as a response to a prompt.\\n\\nThis message represents the output of the model and consists of both\\nthe raw output as returned by the model together standardized fields\\n(e.g., tool calls, usage metadata) added by the LangChain framework.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "ai", "enum": ["ai"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}, "tool_calls": {"title": "Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/ToolCall"}}, "invalid_tool_calls": {"title": "Invalid Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/InvalidToolCall"}}, "usage_metadata": {"$ref": "#/definitions/UsageMetadata"}}, "required": ["content"]}, "HumanMessage": {"title": "HumanMessage", "description": "Message from a human.\\n\\nHumanMessages are messages that are passed in from a human to the model.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Instantiate a chat model and invoke it with the messages\\n model = ...\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "human", "enum": ["human"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}}, "required": ["content"]}, "ChatMessage": {"title": "ChatMessage", "description": "Message that can be assigned an arbitrary speaker (i.e. role).", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "chat", "enum": ["chat"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "role": {"title": "Role", "type": "string"}}, "required": ["content", "role"]}, "SystemMessage": {"title": "SystemMessage", "description": "Message for priming AI behavior.\\n\\nThe system message is usually passed in as the first of a sequence\\nof input messages.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Define a chat model and invoke it with the messages\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "system", "enum": ["system"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content"]}, "FunctionMessage": {"title": "FunctionMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nFunctionMessage are an older version of the ToolMessage schema, and\\ndo not contain the tool_call_id field.\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "function", "enum": ["function"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content", "name"]}, "ToolMessage": {"title": "ToolMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nToolMessages contain the result of a tool invocation. Typically, the result\\nis encoded inside the `content` field.\\n\\nExample: A ToolMessage representing a result of 42 from a tool call with id\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n ToolMessage(content=\'42\', tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\')\\n\\n\\nExample: A ToolMessage where only part of the tool output is sent to the model\\n and the full output is passed in to artifact.\\n\\n .. versionadded:: 0.2.17\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n tool_output = {\\n \\"stdout\\": \\"From the graph we can see that the correlation between x and y is ...\\",\\n \\"stderr\\": None,\\n \\"artifacts\\": {\\"type\\": \\"image\\", \\"base64_data\\": \\"/9j/4gIcSU...\\"},\\n }\\n\\n ToolMessage(\\n content=tool_output[\\"stdout\\"],\\n artifact=tool_output,\\n tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\',\\n )\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "tool", "enum": ["tool"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "tool_call_id": {"title": "Tool Call Id", "type": "string"}, "artifact": {"title": "Artifact"}}, "required": ["content", "tool_call_id"]}}}' + '{"title": "LangGraphOutput", "type": "array", "items": {"anyOf": [{"$ref": "#/definitions/AIMessage"}, {"$ref": "#/definitions/HumanMessage"}, {"$ref": "#/definitions/ChatMessage"}, {"$ref": "#/definitions/SystemMessage"}, {"$ref": "#/definitions/FunctionMessage"}, {"$ref": "#/definitions/ToolMessage"}]}, "definitions": {"ToolCall": {"title": "ToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "object"}, "id": {"title": "Id", "type": "string"}, "type": {"title": "Type", "enum": ["tool_call"], "type": "string"}}, "required": ["name", "args", "id"]}, "InvalidToolCall": {"title": "InvalidToolCall", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}, "args": {"title": "Args", "type": "string"}, "id": {"title": "Id", "type": "string"}, "error": {"title": "Error", "type": "string"}, "type": {"title": "Type", "enum": ["invalid_tool_call"], "type": "string"}}, "required": ["name", "args", "id", "error"]}, "UsageMetadata": {"title": "UsageMetadata", "type": "object", "properties": {"input_tokens": {"title": "Input Tokens", "type": "integer"}, "output_tokens": {"title": "Output Tokens", "type": "integer"}, "total_tokens": {"title": "Total Tokens", "type": "integer"}}, "required": ["input_tokens", "output_tokens", "total_tokens"]}, "AIMessage": {"title": "AIMessage", "description": "Message from an AI.\\n\\nAIMessage is returned from a chat model as a response to a prompt.\\n\\nThis message represents the output of the model and consists of both\\nthe raw output as returned by the model together standardized fields\\n(e.g., tool calls, usage metadata) added by the LangChain framework.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "ai", "enum": ["ai"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}, "tool_calls": {"title": "Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/ToolCall"}}, "invalid_tool_calls": {"title": "Invalid Tool Calls", "default": [], "type": "array", "items": {"$ref": "#/definitions/InvalidToolCall"}}, "usage_metadata": {"$ref": "#/definitions/UsageMetadata"}}, "required": ["content"]}, "HumanMessage": {"title": "HumanMessage", "description": "Message from a human.\\n\\nHumanMessages are messages that are passed in from a human to the model.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Instantiate a chat model and invoke it with the messages\\n model = ...\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "human", "enum": ["human"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "example": {"title": "Example", "default": false, "type": "boolean"}}, "required": ["content"]}, "ChatMessage": {"title": "ChatMessage", "description": "Message that can be assigned an arbitrary speaker (i.e. role).", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "chat", "enum": ["chat"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "role": {"title": "Role", "type": "string"}}, "required": ["content", "role"]}, "SystemMessage": {"title": "SystemMessage", "description": "Message for priming AI behavior.\\n\\nThe system message is usually passed in as the first of a sequence\\nof input messages.\\n\\nExample:\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import HumanMessage, SystemMessage\\n\\n messages = [\\n SystemMessage(\\n content=\\"You are a helpful assistant! Your name is Bob.\\"\\n ),\\n HumanMessage(\\n content=\\"What is your name?\\"\\n )\\n ]\\n\\n # Define a chat model and invoke it with the messages\\n print(model.invoke(messages))", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "system", "enum": ["system"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content"]}, "FunctionMessage": {"title": "FunctionMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nFunctionMessage are an older version of the ToolMessage schema, and\\ndo not contain the tool_call_id field.\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "function", "enum": ["function"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}}, "required": ["content", "name"]}, "ToolMessage": {"title": "ToolMessage", "description": "Message for passing the result of executing a tool back to a model.\\n\\nToolMessages contain the result of a tool invocation. Typically, the result\\nis encoded inside the `content` field.\\n\\nExample: A ToolMessage representing a result of 42 from a tool call with id\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n ToolMessage(content=\'42\', tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\')\\n\\n\\nExample: A ToolMessage where only part of the tool output is sent to the model\\n and the full output is passed in to artifact.\\n\\n .. versionadded:: 0.2.17\\n\\n .. code-block:: python\\n\\n from langchain_core.messages import ToolMessage\\n\\n tool_output = {\\n \\"stdout\\": \\"From the graph we can see that the correlation between x and y is ...\\",\\n \\"stderr\\": None,\\n \\"artifacts\\": {\\"type\\": \\"image\\", \\"base64_data\\": \\"/9j/4gIcSU...\\"},\\n }\\n\\n ToolMessage(\\n content=tool_output[\\"stdout\\"],\\n artifact=tool_output,\\n tool_call_id=\'call_Jja7J89XsjrOLA5r!MEOW!SL\',\\n )\\n\\nThe tool_call_id field is used to associate the tool call request with the\\ntool call response. This is useful in situations where a chat model is able\\nto request multiple tool calls in parallel.", "type": "object", "properties": {"content": {"title": "Content", "anyOf": [{"type": "string"}, {"type": "array", "items": {"anyOf": [{"type": "string"}, {"type": "object"}]}}]}, "additional_kwargs": {"title": "Additional Kwargs", "type": "object"}, "response_metadata": {"title": "Response Metadata", "type": "object"}, "type": {"title": "Type", "default": "tool", "enum": ["tool"], "type": "string"}, "name": {"title": "Name", "type": "string"}, "id": {"title": "Id", "type": "string"}, "tool_call_id": {"title": "Tool Call Id", "type": "string"}, "artifact": {"title": "Artifact"}, "status": {"title": "Status", "default": "success", "enum": ["success", "error"], "type": "string"}}, "required": ["content", "tool_call_id"]}}}' # --- # name: test_message_graph.2 ''' @@ -1073,6 +1306,7 @@ down --> __end__; side --> down; up --> down; + up --> other; up --> side; ''' @@ -1098,3 +1332,270 @@ # name: test_state_graph_w_config '{"title": "LangGraphConfig", "type": "object", "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, "definitions": {"Configurable": {"title": "Configurable", "type": "object", "properties": {"tools": {"title": "Tools", "type": "array", "items": {"type": "string"}}}}}}' # --- +# name: test_state_graph_w_config.1 + '{"title": "LangGraphInput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' +# --- +# name: test_state_graph_w_config.2 + '{"title": "LangGraphOutput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' +# --- +# name: test_state_graph_w_config_inherited_state + '{"title": "LangGraphConfig", "type": "object", "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, "definitions": {"Configurable": {"title": "Configurable", "type": "object", "properties": {"tools": {"title": "Tools", "type": "array", "items": {"type": "string"}}}}}}' +# --- +# name: test_state_graph_w_config_inherited_state.1 + '{"title": "LangGraphInput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' +# --- +# name: test_state_graph_w_config_inherited_state.2 + '{"title": "LangGraphOutput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' +# --- +# name: test_state_graph_w_config_inherited_state_keys + '{"title": "LangGraphConfig", "type": "object", "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, "definitions": {"Configurable": {"title": "Configurable", "type": "object", "properties": {"tools": {"title": "Tools", "type": "array", "items": {"type": "string"}}}}}}' +# --- +# name: test_state_graph_w_config_inherited_state_keys.1 + '{"title": "LangGraphInput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' +# --- +# name: test_state_graph_w_config_inherited_state_keys.2 + '{"title": "LangGraphOutput", "type": "object", "properties": {"input": {"title": "Input", "type": "string"}, "agent_outcome": {"title": "Agent Outcome", "anyOf": [{"$ref": "#/definitions/AgentAction"}, {"$ref": "#/definitions/AgentFinish"}]}, "intermediate_steps": {"title": "Intermediate Steps", "type": "array", "items": {"type": "array", "minItems": 2, "maxItems": 2, "items": [{"$ref": "#/definitions/AgentAction"}, {"type": "string"}]}}}, "definitions": {"AgentAction": {"title": "AgentAction", "description": "Represents a request to execute an action by an agent.\\n\\nThe action consists of the name of the tool to execute and the input to pass\\nto the tool. The log is used to pass along extra information about the action.", "type": "object", "properties": {"tool": {"title": "Tool", "type": "string"}, "tool_input": {"title": "Tool Input", "anyOf": [{"type": "string"}, {"type": "object"}]}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentAction", "enum": ["AgentAction"], "type": "string"}}, "required": ["tool", "tool_input", "log"]}, "AgentFinish": {"title": "AgentFinish", "description": "Final return value of an ActionAgent.\\n\\nAgents return an AgentFinish when they have reached a stopping condition.", "type": "object", "properties": {"return_values": {"title": "Return Values", "type": "object"}, "log": {"title": "Log", "type": "string"}, "type": {"title": "Type", "default": "AgentFinish", "enum": ["AgentFinish"], "type": "string"}}, "required": ["return_values", "log"]}}}' +# --- +# name: test_xray_lance + dict({ + 'edges': list([ + dict({ + 'source': '__start__', + 'target': 'ask_question', + }), + dict({ + 'source': 'ask_question', + 'target': 'answer_question', + }), + dict({ + 'conditional': True, + 'source': 'answer_question', + 'target': 'ask_question', + }), + dict({ + 'conditional': True, + 'source': 'answer_question', + 'target': '__end__', + }), + ]), + 'nodes': list([ + dict({ + 'data': '__start__', + 'id': '__start__', + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'ask_question', + }), + 'id': 'ask_question', + 'type': 'runnable', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'answer_question', + }), + 'id': 'answer_question', + 'type': 'runnable', + }), + dict({ + 'data': '__end__', + 'id': '__end__', + 'type': 'schema', + }), + ]), + }) +# --- +# name: test_xray_lance.1 + dict({ + 'edges': list([ + dict({ + 'source': '__start__', + 'target': 'generate_analysts', + }), + dict({ + 'source': 'conduct_interview', + 'target': 'generate_sections', + }), + dict({ + 'source': 'generate_sections', + 'target': '__end__', + }), + dict({ + 'conditional': True, + 'source': 'generate_analysts', + 'target': 'conduct_interview', + }), + ]), + 'nodes': list([ + dict({ + 'data': '__start__', + 'id': '__start__', + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'generate_analysts', + }), + 'id': 'generate_analysts', + 'type': 'runnable', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'graph', + 'state', + 'CompiledStateGraph', + ]), + 'name': 'conduct_interview', + }), + 'id': 'conduct_interview', + 'type': 'runnable', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'generate_sections', + }), + 'id': 'generate_sections', + 'type': 'runnable', + }), + dict({ + 'data': '__end__', + 'id': '__end__', + 'type': 'schema', + }), + ]), + }) +# --- +# name: test_xray_lance.2 + dict({ + 'edges': list([ + dict({ + 'source': 'conduct_interview:__start__', + 'target': 'conduct_interview:ask_question', + }), + dict({ + 'source': 'conduct_interview:ask_question', + 'target': 'conduct_interview:answer_question', + }), + dict({ + 'conditional': True, + 'source': 'conduct_interview:answer_question', + 'target': 'conduct_interview:ask_question', + }), + dict({ + 'conditional': True, + 'source': 'conduct_interview:answer_question', + 'target': 'conduct_interview:__end__', + }), + dict({ + 'source': '__start__', + 'target': 'generate_analysts', + }), + dict({ + 'source': 'conduct_interview:__end__', + 'target': 'generate_sections', + }), + dict({ + 'source': 'generate_sections', + 'target': '__end__', + }), + dict({ + 'conditional': True, + 'source': 'generate_analysts', + 'target': 'conduct_interview:__start__', + }), + ]), + 'nodes': list([ + dict({ + 'data': '__start__', + 'id': '__start__', + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'generate_analysts', + }), + 'id': 'generate_analysts', + 'type': 'runnable', + }), + dict({ + 'data': 'conduct_interview:__start__', + 'id': 'conduct_interview:__start__', + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'conduct_interview:ask_question', + }), + 'id': 'conduct_interview:ask_question', + 'type': 'runnable', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'conduct_interview:answer_question', + }), + 'id': 'conduct_interview:answer_question', + 'type': 'runnable', + }), + dict({ + 'data': 'conduct_interview:__end__', + 'id': 'conduct_interview:__end__', + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langgraph', + 'utils', + 'RunnableCallable', + ]), + 'name': 'generate_sections', + }), + 'id': 'generate_sections', + 'type': 'runnable', + }), + dict({ + 'data': '__end__', + 'id': '__end__', + 'type': 'schema', + }), + ]), + }) +# --- diff --git a/libs/langgraph/tests/__snapshots__/test_pregel_async.ambr b/libs/langgraph/tests/__snapshots__/test_pregel_async.ambr index 809ff8731..c0d2e782c 100644 --- a/libs/langgraph/tests/__snapshots__/test_pregel_async.ambr +++ b/libs/langgraph/tests/__snapshots__/test_pregel_async.ambr @@ -36,6 +36,127 @@ +---------+ ''' # --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2 + ''' + graph TD; + __start__ --> rewrite_query; + analyzer_one --> retriever_one; + qa --> __end__; + retriever_one --> qa; + retriever_two --> qa; + rewrite_query --> analyzer_one; + rewrite_query -.-> retriever_two; + + ''' +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.1 + dict({ + '$defs': dict({ + 'InnerObject': dict({ + 'properties': dict({ + 'yo': dict({ + 'title': 'Yo', + 'type': 'integer', + }), + }), + 'required': list([ + 'yo', + ]), + 'title': 'InnerObject', + 'type': 'object', + }), + }), + 'properties': dict({ + 'answer': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Answer', + }), + 'docs': dict({ + 'items': dict({ + 'type': 'string', + }), + 'title': 'Docs', + 'type': 'array', + }), + 'inner': dict({ + '$ref': '#/$defs/InnerObject', + }), + 'query': dict({ + 'title': 'Query', + 'type': 'string', + }), + }), + 'required': list([ + 'query', + 'inner', + 'docs', + ]), + 'title': 'State', + 'type': 'object', + }) +# --- +# name: test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2.2 + dict({ + '$defs': dict({ + 'InnerObject': dict({ + 'properties': dict({ + 'yo': dict({ + 'title': 'Yo', + 'type': 'integer', + }), + }), + 'required': list([ + 'yo', + ]), + 'title': 'InnerObject', + 'type': 'object', + }), + }), + 'properties': dict({ + 'answer': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Answer', + }), + 'docs': dict({ + 'items': dict({ + 'type': 'string', + }), + 'title': 'Docs', + 'type': 'array', + }), + 'inner': dict({ + '$ref': '#/$defs/InnerObject', + }), + 'query': dict({ + 'title': 'Query', + 'type': 'string', + }), + }), + 'required': list([ + 'query', + 'inner', + 'docs', + ]), + 'title': 'State', + 'type': 'object', + }) +# --- # name: test_in_one_fan_out_state_graph_waiting_edge_via_branch ''' +-----------+ diff --git a/libs/langgraph/tests/checkpoint/test_aiosqlite.py b/libs/langgraph/tests/checkpoint/test_aiosqlite.py deleted file mode 100644 index abcf6cb71..000000000 --- a/libs/langgraph/tests/checkpoint/test_aiosqlite.py +++ /dev/null @@ -1,76 +0,0 @@ -import pytest -from langchain_core.runnables import RunnableConfig - -from langgraph.channels.manager import create_checkpoint -from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver -from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, empty_checkpoint - - -class TestAsyncSqliteSaver: - @pytest.fixture(autouse=True) - def setup(self): - self.sqlite_saver = AsyncSqliteSaver.from_conn_string(":memory:") - - # objects for test setup - self.config_1: RunnableConfig = { - "configurable": {"thread_id": "thread-1", "thread_ts": "1"} - } - self.config_2: RunnableConfig = { - "configurable": {"thread_id": "thread-2", "thread_ts": "2"} - } - - self.chkpnt_1: Checkpoint = empty_checkpoint() - self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) - - self.metadata_1: CheckpointMetadata = { - "source": "input", - "step": 2, - "writes": {}, - "score": 1, - } - self.metadata_2: CheckpointMetadata = { - "source": "loop", - "step": 1, - "writes": {"foo": "bar"}, - "score": None, - } - - async def test_asearch(self): - # set up test - # save checkpoints - await self.sqlite_saver.aput(self.config_1, self.chkpnt_1, self.metadata_1) - await self.sqlite_saver.aput(self.config_2, self.chkpnt_2, self.metadata_2) - - # call method / assertions - query_1: CheckpointMetadata = {"source": "input"} # search by 1 key - query_2: CheckpointMetadata = { - "step": 1, - "writes": {"foo": "bar"}, - } # search by multiple keys - query_3: CheckpointMetadata = {} # search by no keys, return all checkpoints - query_4: CheckpointMetadata = {"source": "update", "step": 1} # no match - - async with self.sqlite_saver as sqlite_saver: - search_results_1 = [ - c async for c in sqlite_saver.alist(None, filter=query_1) - ] - assert len(search_results_1) == 1 - assert search_results_1[0].metadata == self.metadata_1 - - search_results_2 = [ - c async for c in sqlite_saver.alist(None, filter=query_2) - ] - assert len(search_results_2) == 1 - assert search_results_2[0].metadata == self.metadata_2 - - search_results_3 = [ - c async for c in sqlite_saver.alist(None, filter=query_3) - ] - assert len(search_results_3) == 2 - - search_results_4 = [ - c async for c in sqlite_saver.alist(None, filter=query_4) - ] - assert len(search_results_4) == 0 - - # TODO: test before and limit params diff --git a/libs/langgraph/tests/checkpoint/test_sqlite.py b/libs/langgraph/tests/checkpoint/test_sqlite.py deleted file mode 100644 index cd2aa8e1e..000000000 --- a/libs/langgraph/tests/checkpoint/test_sqlite.py +++ /dev/null @@ -1,125 +0,0 @@ -import pytest -from langchain_core.runnables import RunnableConfig - -from langgraph.channels.manager import create_checkpoint -from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, empty_checkpoint -from langgraph.checkpoint.sqlite import ( - _AIO_ERROR_MSG, - SqliteSaver, - _metadata_predicate, - search_where, -) - - -class TestSqliteSaver: - @pytest.fixture(autouse=True) - def setup(self): - self.sqlite_saver = SqliteSaver.from_conn_string(":memory:") - - # objects for test setup - self.config_1: RunnableConfig = { - "configurable": {"thread_id": "thread-1", "thread_ts": "1"} - } - self.config_2: RunnableConfig = { - "configurable": {"thread_id": "thread-2", "thread_ts": "2"} - } - - self.chkpnt_1: Checkpoint = empty_checkpoint() - self.chkpnt_2: Checkpoint = create_checkpoint(self.chkpnt_1, {}, 1) - - self.metadata_1: CheckpointMetadata = { - "source": "input", - "step": 2, - "writes": {}, - "score": 1, - } - self.metadata_2: CheckpointMetadata = { - "source": "loop", - "step": 1, - "writes": {"foo": "bar"}, - "score": None, - } - self.metadata_3: CheckpointMetadata = {} - - def test_search(self): - # set up test - # save checkpoints - self.sqlite_saver.put(self.config_1, self.chkpnt_1, self.metadata_1) - self.sqlite_saver.put(self.config_2, self.chkpnt_2, self.metadata_2) - - # call method / assertions - query_1: CheckpointMetadata = {"source": "input"} # search by 1 key - query_2: CheckpointMetadata = { - "step": 1, - "writes": {"foo": "bar"}, - } # search by multiple keys - query_3: CheckpointMetadata = {} # search by no keys, return all checkpoints - query_4: CheckpointMetadata = {"source": "update", "step": 1} # no match - - search_results_1 = list(self.sqlite_saver.list(None, filter=query_1)) - assert len(search_results_1) == 1 - assert search_results_1[0].metadata == self.metadata_1 - - search_results_2 = list(self.sqlite_saver.list(None, filter=query_2)) - assert len(search_results_2) == 1 - assert search_results_2[0].metadata == self.metadata_2 - - search_results_3 = list(self.sqlite_saver.list(None, filter=query_3)) - assert len(search_results_3) == 2 - - search_results_4 = list(self.sqlite_saver.list(None, filter=query_4)) - assert len(search_results_4) == 0 - - # TODO: test before and limit params - - def test_search_where(self): - # call method / assertions - expected_predicate_1 = "WHERE json_extract(CAST(metadata AS TEXT), '$.source') = ? AND json_extract(CAST(metadata AS TEXT), '$.step') = ? AND json_extract(CAST(metadata AS TEXT), '$.writes') = ? AND json_extract(CAST(metadata AS TEXT), '$.score') = ? AND thread_ts < ?" - expected_param_values_1 = ["input", 2, "{}", 1, "1"] - assert search_where(None, self.metadata_1, self.config_1) == ( - expected_predicate_1, - expected_param_values_1, - ) - - def test_metadata_predicate(self): - # call method / assertions - expected_predicate_1 = [ - "json_extract(CAST(metadata AS TEXT), '$.source') = ?", - "json_extract(CAST(metadata AS TEXT), '$.step') = ?", - "json_extract(CAST(metadata AS TEXT), '$.writes') = ?", - "json_extract(CAST(metadata AS TEXT), '$.score') = ?", - ] - expected_predicate_2 = [ - "json_extract(CAST(metadata AS TEXT), '$.source') = ?", - "json_extract(CAST(metadata AS TEXT), '$.step') = ?", - "json_extract(CAST(metadata AS TEXT), '$.writes') = ?", - "json_extract(CAST(metadata AS TEXT), '$.score') IS ?", - ] - expected_predicate_3 = [] - - expected_param_values_1 = ["input", 2, "{}", 1] - expected_param_values_2 = ["loop", 1, '{"foo":"bar"}', None] - expected_param_values_3 = [] - - assert _metadata_predicate(self.metadata_1) == ( - expected_predicate_1, - expected_param_values_1, - ) - assert _metadata_predicate(self.metadata_2) == ( - expected_predicate_2, - expected_param_values_2, - ) - assert _metadata_predicate(self.metadata_3) == ( - expected_predicate_3, - expected_param_values_3, - ) - - async def test_informative_async_errors(self): - # call method / assertions - with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG): - await self.sqlite_saver.aget(self.config_1) - with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG): - await self.sqlite_saver.aget_tuple(self.config_1) - with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG): - async for _ in self.sqlite_saver.alist(self.config_1): - pass diff --git a/libs/langgraph/tests/compose-postgres.yml b/libs/langgraph/tests/compose-postgres.yml new file mode 100644 index 000000000..80904ce90 --- /dev/null +++ b/libs/langgraph/tests/compose-postgres.yml @@ -0,0 +1,16 @@ +services: + postgres-test: + image: postgres:16 + ports: + - "5442:5432" + environment: + POSTGRES_DB: postgres + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + healthcheck: + test: pg_isready -U postgres + start_period: 10s + timeout: 1s + retries: 5 + interval: 60s + start_interval: 1s diff --git a/libs/langgraph/tests/conftest.py b/libs/langgraph/tests/conftest.py index 42278f79a..1219ff0f1 100644 --- a/libs/langgraph/tests/conftest.py +++ b/libs/langgraph/tests/conftest.py @@ -1,8 +1,22 @@ -from uuid import UUID +import asyncio +import sys +from concurrent.futures import ThreadPoolExecutor +from contextlib import asynccontextmanager, contextmanager +from typing import AsyncIterator, Iterator, TypeVar +from uuid import UUID, uuid4 import pytest +from psycopg import AsyncConnection, Connection from pytest_mock import MockerFixture +from langgraph.checkpoint.postgres import PostgresSaver +from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver +from langgraph.checkpoint.sqlite import SqliteSaver +from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver +from tests.memory_assert import MemorySaverAssertImmutable + +DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5442/" + @pytest.fixture() def deterministic_uuids(mocker: MockerFixture) -> MockerFixture: @@ -12,4 +26,162 @@ def deterministic_uuids(mocker: MockerFixture) -> MockerFixture: return mocker.patch("uuid.uuid4", side_effect=side_effect) -pytest.register_assert_rewrite("tests.memory_assert") +""" +pytest-asyncio doesn't support calling async fixtures with getfixturevalue +so we need to use ThreadPoolExecutor to run the async fixture in a thread +https://github.com/pytest-dev/pytest-asyncio/issues/112#issuecomment-462062890 +""" +T = TypeVar("T") + + +def close_loop(loop: asyncio.AbstractEventLoop) -> None: + loop.run_until_complete(loop.shutdown_asyncgens()) + loop.run_until_complete(loop.shutdown_default_executor()) + asyncio.set_event_loop(None) + loop.close() + + +@contextmanager +def agen_to_gen(agen: AsyncIterator[T]) -> Iterator[T]: + with ThreadPoolExecutor(1) as bg: + loop = asyncio.new_event_loop() + bg.submit(asyncio.set_event_loop, loop).result() + try: + yield bg.submit(loop.run_until_complete, agen.__aenter__()).result() + finally: + bg.submit( + loop.run_until_complete, agen.__aexit__(None, None, None) + ).result() + bg.submit(close_loop, loop).result() + + +# checkpointer fixtures + + +@pytest.fixture(scope="function") +def checkpointer_memory(): + yield MemorySaverAssertImmutable() + + +@pytest.fixture(scope="function") +def checkpointer_sqlite(): + with SqliteSaver.from_conn_string(":memory:") as checkpointer: + yield checkpointer + + +@pytest.fixture(scope="function") +def checkpointer_sqlite_aio(): + with agen_to_gen(_checkpointer_sqlite_aio()) as checkpointer: + yield checkpointer + + +@asynccontextmanager +async def _checkpointer_sqlite_aio(): + async with AsyncSqliteSaver.from_conn_string(":memory:") as checkpointer: + yield checkpointer + + +@pytest.fixture(scope="function") +def checkpointer_postgres(): + database = f"test_{uuid4().hex[:16]}" + # create unique db + with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn: + conn.execute(f"CREATE DATABASE {database}") + try: + # yield checkpointer + with PostgresSaver.from_conn_string( + DEFAULT_POSTGRES_URI + database + ) as checkpointer: + checkpointer.setup() + yield checkpointer + finally: + # drop unique db + with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn: + conn.execute(f"DROP DATABASE {database}") + + +@pytest.fixture(scope="function") +def checkpointer_postgres_pipe(): + database = f"test_{uuid4().hex[:16]}" + # create unique db + with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn: + conn.execute(f"CREATE DATABASE {database}") + try: + # yield checkpointer + with PostgresSaver.from_conn_string( + DEFAULT_POSTGRES_URI + database + ) as checkpointer: + checkpointer.setup() + # setup can't run inside pipeline because of implicit transaction + with checkpointer.conn.pipeline() as pipe: + checkpointer.pipe = pipe + yield checkpointer + finally: + # drop unique db + with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn: + conn.execute(f"DROP DATABASE {database}") + + +@pytest.fixture(scope="function") +def checkpointer_postgres_aio(): + if sys.version_info < (3, 10): + pytest.skip("Async Postgres tests require Python 3.10+") + with agen_to_gen(_checkpointer_postgres_aio()) as checkpointer: + yield checkpointer + + +@asynccontextmanager +async def _checkpointer_postgres_aio(): + database = f"test_{uuid4().hex[:16]}" + # create unique db + async with await AsyncConnection.connect( + DEFAULT_POSTGRES_URI, autocommit=True + ) as conn: + await conn.execute(f"CREATE DATABASE {database}") + try: + # yield checkpointer + async with AsyncPostgresSaver.from_conn_string( + DEFAULT_POSTGRES_URI + database + ) as checkpointer: + await checkpointer.setup() + yield checkpointer + finally: + # drop unique db + async with await AsyncConnection.connect( + DEFAULT_POSTGRES_URI, autocommit=True + ) as conn: + await conn.execute(f"DROP DATABASE {database}") + + +@pytest.fixture(scope="function") +def checkpointer_postgres_aio_pipe(): + if sys.version_info < (3, 10): + pytest.skip("Async Postgres tests require Python 3.10+") + with agen_to_gen(_checkpointer_postgres_aio_pipe()) as checkpointer: + yield checkpointer + + +@asynccontextmanager +async def _checkpointer_postgres_aio_pipe(): + database = f"test_{uuid4().hex[:16]}" + # create unique db + async with await AsyncConnection.connect( + DEFAULT_POSTGRES_URI, autocommit=True + ) as conn: + await conn.execute(f"CREATE DATABASE {database}") + try: + # yield checkpointer + async with AsyncPostgresSaver.from_conn_string( + DEFAULT_POSTGRES_URI + database + ) as checkpointer: + await checkpointer.setup() + # setup can't run inside pipeline because of implicit transaction + async with checkpointer.conn.pipeline() as pipe: + checkpointer.pipe = pipe + yield checkpointer + finally: + # drop unique db + async with await AsyncConnection.connect( + DEFAULT_POSTGRES_URI, autocommit=True + ) as conn: + await conn.execute(f"DROP DATABASE {database}") diff --git a/libs/langgraph/tests/memory_assert.py b/libs/langgraph/tests/memory_assert.py index c0ede2f20..b02cbb65b 100644 --- a/libs/langgraph/tests/memory_assert.py +++ b/libs/langgraph/tests/memory_assert.py @@ -5,8 +5,10 @@ from typing import Any, Optional from langchain_core.runnables import RunnableConfig from langgraph.checkpoint.base import ( + ChannelVersions, Checkpoint, CheckpointMetadata, + CheckpointTuple, SerializerProtocol, copy_checkpoint, ) @@ -14,17 +16,17 @@ from langgraph.checkpoint.memory import MemorySaver class NoopSerializer(SerializerProtocol): - def loads(self, data: bytes) -> Any: - return data + def loads_typed(self, data: tuple[str, bytes]) -> Any: + return data[1] - def dumps(self, obj: Any) -> bytes: - return obj + def dumps_typed(self, obj: Any) -> tuple[str, bytes]: + return "type", obj class MemorySaverAssertImmutable(MemorySaver): serde = NoopSerializer() - storage_for_copies: defaultdict[str, dict[str, Checkpoint]] + storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]] def __init__( self, @@ -33,14 +35,15 @@ class MemorySaverAssertImmutable(MemorySaver): put_sleep: Optional[float] = None, ) -> None: super().__init__(serde=serde) - self.storage_for_copies = defaultdict(dict) + self.storage_for_copies = defaultdict(lambda: defaultdict(dict)) self.put_sleep = put_sleep def put( self, config: dict, checkpoint: Checkpoint, - metadata: Optional[CheckpointMetadata] = None, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> None: if self.put_sleep: import time @@ -48,16 +51,19 @@ class MemorySaverAssertImmutable(MemorySaver): time.sleep(self.put_sleep) # assert checkpoint hasn't been modified since last written thread_id = config["configurable"]["thread_id"] + checkpoint_ns = config["configurable"]["checkpoint_ns"] if saved := super().get(config): assert ( - self.serde.loads(self.storage_for_copies[thread_id][saved["id"]]) + self.serde.loads_typed( + self.storage_for_copies[thread_id][checkpoint_ns][saved["id"]] + ) == saved ) - self.storage_for_copies[thread_id][checkpoint["id"]] = self.serde.dumps( - copy_checkpoint(checkpoint) - ) + self.storage_for_copies[thread_id][checkpoint_ns][ + checkpoint["id"] + ] = self.serde.dumps_typed(copy_checkpoint(checkpoint)) # call super to write checkpoint - return super().put(config, checkpoint, metadata) + return super().put(config, checkpoint, metadata, new_versions) class MemorySaverAssertCheckpointMetadata(MemorySaver): @@ -81,7 +87,8 @@ class MemorySaverAssertCheckpointMetadata(MemorySaver): self, config: RunnableConfig, checkpoint: Checkpoint, - metadata: Optional[CheckpointMetadata] = None, + metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> None: """The implementation of put() merges config["configurable"] (a run's configurable fields) with the metadata field. The state of the @@ -90,23 +97,24 @@ class MemorySaverAssertCheckpointMetadata(MemorySaver): """ configurable = config["configurable"].copy() - # remove thread_ts to make testing simpler - thread_ts = configurable.pop("thread_ts", None) - - self.storage[config["configurable"]["thread_id"]].update( + # remove checkpoint_id to make testing simpler + checkpoint_id = configurable.pop("checkpoint_id", None) + thread_id = config["configurable"]["thread_id"] + checkpoint_ns = config["configurable"]["checkpoint_ns"] + self.storage[thread_id][checkpoint_ns].update( { checkpoint["id"]: ( - self.serde.dumps(checkpoint), + self.serde.dumps_typed(checkpoint), # merge configurable fields and metadata - self.serde.dumps({**configurable, **metadata}), - thread_ts, + self.serde.dumps_typed({**configurable, **metadata}), + checkpoint_id, ) } ) return { "configurable": { "thread_id": config["configurable"]["thread_id"], - "thread_ts": checkpoint["id"], + "checkpoint_id": checkpoint["id"], } } @@ -115,7 +123,16 @@ class MemorySaverAssertCheckpointMetadata(MemorySaver): config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: return await asyncio.get_running_loop().run_in_executor( - None, self.put, config, checkpoint, metadata + None, self.put, config, checkpoint, metadata, new_versions ) + + +class MemorySaverNoPending(MemorySaver): + def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]: + result = super().get_tuple(config) + if result: + return CheckpointTuple(result.config, result.checkpoint, result.metadata) + return result diff --git a/libs/langgraph/tests/messages.py b/libs/langgraph/tests/messages.py new file mode 100644 index 000000000..be67db1f1 --- /dev/null +++ b/libs/langgraph/tests/messages.py @@ -0,0 +1,42 @@ +"""Redefined messages as a work-around for pydantic issue with AnyStr. + +The code below creates version of pydantic models +that will work in unit tests with AnyStr as id field +Please note that the `id` field is assigned AFTER the model is created +to workaround an issue with pydantic ignoring the __eq__ method on +subclassed strings. +""" +from typing import Any + +from langchain_core.documents import Document +from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage + +from tests.any_str import AnyStr + + +def _AnyIdDocument(**kwargs: Any) -> Document: + """Create a document with an id field.""" + message = Document(**kwargs) + message.id = AnyStr() + return message + + +def _AnyIdAIMessage(**kwargs: Any) -> AIMessage: + """Create ai message with an any id field.""" + message = AIMessage(**kwargs) + message.id = AnyStr() + return message + + +def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk: + """Create ai message with an any id field.""" + message = AIMessageChunk(**kwargs) + message.id = AnyStr() + return message + + +def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage: + """Create a human with an any id field.""" + message = HumanMessage(**kwargs) + message.id = AnyStr() + return message diff --git a/libs/langgraph/tests/test_algo.py b/libs/langgraph/tests/test_algo.py new file mode 100644 index 000000000..cdf179c24 --- /dev/null +++ b/libs/langgraph/tests/test_algo.py @@ -0,0 +1,28 @@ +from langgraph.channels.manager import ChannelsManager +from langgraph.checkpoint.base import empty_checkpoint +from langgraph.managed.base import ManagedValuesManager +from langgraph.pregel.algo import prepare_next_tasks + + +def test_prepare_next_tasks() -> None: + config = {} + processes = {} + checkpoint = empty_checkpoint() + + with ManagedValuesManager({}, config) as managed, ChannelsManager( + {}, checkpoint, config + ) as channels: + assert ( + prepare_next_tasks( + checkpoint, processes, channels, managed, config, 0, for_execution=False + ) + == [] + ) + assert ( + prepare_next_tasks( + checkpoint, processes, channels, managed, config, 0, for_execution=True + ) + == [] + ) + + # TODO: add more tests diff --git a/libs/langgraph/tests/test_interruption.py b/libs/langgraph/tests/test_interruption.py index 0c9da279a..e6682e964 100644 --- a/libs/langgraph/tests/test_interruption.py +++ b/libs/langgraph/tests/test_interruption.py @@ -1,10 +1,18 @@ from typing import TypedDict -from langgraph.checkpoint.memory import MemorySaver +import pytest +from pytest_mock import MockerFixture + from langgraph.graph import END, START, StateGraph -def test_interruption_without_state_updates(): +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_interruption_without_state_updates( + request: pytest.FixtureRequest, checkpointer_name: str, mocker: MockerFixture +) -> None: """Test interruption without state updates. This test confirms that interrupting doesn't require a state key having been updated in the prev step""" @@ -23,9 +31,8 @@ def test_interruption_without_state_updates(): builder.add_edge("step_2", "step_3") builder.add_edge("step_3", END) - memory = MemorySaver() - - graph = builder.compile(checkpointer=memory, interrupt_after="*") + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + graph = builder.compile(checkpointer=checkpointer, interrupt_after="*") initial_input = {"input": "hello world"} thread = {"configurable": {"thread_id": "1"}} @@ -40,7 +47,13 @@ def test_interruption_without_state_updates(): assert graph.get_state(thread).next == () -async def test_interruption_without_state_updates_async(): +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], +) +async def test_interruption_without_state_updates_async( + request: pytest.FixtureRequest, checkpointer_name: str, mocker: MockerFixture +): """Test interruption without state updates. This test confirms that interrupting doesn't require a state key having been updated in the prev step""" @@ -59,9 +72,8 @@ async def test_interruption_without_state_updates_async(): builder.add_edge("step_2", "step_3") builder.add_edge("step_3", END) - memory = MemorySaver() - - graph = builder.compile(checkpointer=memory, interrupt_after="*") + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + graph = builder.compile(checkpointer=checkpointer, interrupt_after="*") initial_input = {"input": "hello world"} thread = {"configurable": {"thread_id": "1"}} diff --git a/libs/langgraph/tests/test_jsonplus.py b/libs/langgraph/tests/test_jsonplus.py deleted file mode 100644 index d2df4c747..000000000 --- a/libs/langgraph/tests/test_jsonplus.py +++ /dev/null @@ -1,112 +0,0 @@ -import dataclasses -import sys -import uuid -from datetime import datetime, timezone -from enum import Enum - -import dataclasses_json -from langchain_core.pydantic_v1 import BaseModel as LcBaseModel -from langchain_core.runnables import RunnableMap -from pydantic import BaseModel - -from langgraph.serde.jsonplus import JsonPlusSerializer - - -class MyPydantic(BaseModel): - foo: str - bar: int - - -class MyFunnyPydantic(LcBaseModel): - foo: str - bar: int - - -@dataclasses.dataclass -class MyDataclass: - foo: str - bar: int - - def something(self) -> None: - pass - - -if sys.version_info < (3, 10): - - class MyDataclassWSlots(MyDataclass): - pass - -else: - - @dataclasses.dataclass(slots=True) - class MyDataclassWSlots: - foo: str - bar: int - - def something(self) -> None: - pass - - -class MyEnum(Enum): - FOO = "foo" - BAR = "bar" - - -@dataclasses_json.dataclass_json -@dataclasses.dataclass -class Person: - name: str - - -def test_serde_jsonplus() -> None: - uid = uuid.UUID(int=1) - current_time = datetime(2024, 4, 19, 23, 4, 57, 51022, timezone.max) - - to_serialize = { - "uid": uid, - "time": current_time, - "my_slotted_class": MyDataclassWSlots("bar", 2), - "my_dataclass": MyDataclass("foo", 1), - "my_enum": MyEnum.FOO, - "my_pydantic": MyPydantic(foo="foo", bar=1), - "my_funny_pydantic": MyFunnyPydantic(foo="foo", bar=1), - "person": Person(name="foo"), - "a_bool": True, - "a_none": None, - "a_str": "foo", - "a_str_nuc": "foo\u0000", - "a_str_uc": "foo ⛰️", - "a_str_ucuc": "foo \u26f0\ufe0f\u0000", - "a_str_ucucuc": "foo \\u26f0\\ufe0f", - "text": [ - "Hello\ud83d\ude00", - "Python\ud83d\udc0d", - "Surrogate\ud834\udd1e", - "Example\ud83c\udf89", - "String\ud83c\udfa7", - "With\ud83c\udf08", - "Surrogates\ud83d\ude0e", - "Embedded\ud83d\udcbb", - "In\ud83c\udf0e", - "The\ud83d\udcd6", - "Text\ud83d\udcac", - "收花🙄·到", - ], - "an_int": 1, - "a_float": 1.1, - "runnable_map": RunnableMap({}), - } - - serde = JsonPlusSerializer() - - dumped = serde.dumps(to_serialize) - - assert ( - dumped - == b"""{"uid": {"lc": 2, "type": "constructor", "id": ["uuid", "UUID"], "method": null, "args": ["00000000000000000000000000000001"], "kwargs": {}}, "time": {"lc": 2, "type": "constructor", "id": ["datetime", "datetime"], "method": "fromisoformat", "args": ["2024-04-19T23:04:57.051022+23:59"], "kwargs": {}}, "my_slotted_class": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyDataclassWSlots"], "method": null, "args": [], "kwargs": {"foo": "bar", "bar": 2}}, "my_dataclass": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyDataclass"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "my_enum": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyEnum"], "method": null, "args": ["foo"], "kwargs": {}}, "my_pydantic": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "my_funny_pydantic": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyFunnyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}, "person": {"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "Person"], "method": null, "args": [], "kwargs": {"name": "foo"}}, "a_bool": true, "a_none": null, "a_str": "foo", "a_str_nuc": "foo\\u0000", "a_str_uc": "foo \xe2\x9b\xb0\xef\xb8\x8f", "a_str_ucuc": "foo \xe2\x9b\xb0\xef\xb8\x8f\\u0000", "a_str_ucucuc": "foo \\\\u26f0\\\\ufe0f", "text": ["Hello", "Python", "Surrogate", "Example", "String", "With", "Surrogates", "Embedded", "In", "The", "Text", "\xe6\x94\xb6\xe8\x8a\xb1\xf0\x9f\x99\x84\xc2\xb7\xe5\x88\xb0"], "an_int": 1, "a_float": 1.1, "runnable_map": {"lc": 1, "type": "constructor", "id": ["langchain", "schema", "runnable", "RunnableParallel"], "kwargs": {"steps__": {}}, "name": "RunnableParallel<>", "graph": {"nodes": [{"id": 0, "type": "schema", "data": "Parallel<>Input"}, {"id": 1, "type": "schema", "data": "Parallel<>Output"}], "edges": []}}}""" - ) - - assert serde.loads(dumped) == { - **to_serialize, - "text": [v.encode("utf-8", "ignore").decode() for v in to_serialize["text"]], - } diff --git a/libs/langgraph/tests/test_prebuilt.py b/libs/langgraph/tests/test_prebuilt.py index f08e9e7cc..ba9c88afc 100644 --- a/libs/langgraph/tests/test_prebuilt.py +++ b/libs/langgraph/tests/test_prebuilt.py @@ -18,11 +18,9 @@ from langchain_core.tools import BaseTool from langchain_core.tools import tool as dec_tool from pydantic import BaseModel as BaseModelV2 -from langgraph.checkpoint.base import BaseCheckpointSaver from langgraph.prebuilt import ToolNode, ValidationNode, create_react_agent from langgraph.prebuilt.tool_node import InjectedState -from tests.any_str import AnyStr -from tests.memory_assert import MemorySaverAssertImmutable +from tests.messages import _AnyIdHumanMessage class FakeToolCallingModel(BaseChatModel): @@ -53,18 +51,13 @@ class FakeToolCallingModel(BaseChatModel): @pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - None, - ], - ids=[ - "memory", - "none", - ], + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], ) -def test_no_modifier(checkpointer: Optional[BaseCheckpointSaver]): +def test_no_modifier(request: pytest.FixtureRequest, checkpointer_name: str) -> None: + checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name) model = FakeToolCallingModel() + agent = create_react_agent(model, [], checkpointer=checkpointer) inputs = [HumanMessage("hi?")] thread = {"configurable": {"thread_id": "123"}} @@ -75,30 +68,12 @@ def test_no_modifier(checkpointer: Optional[BaseCheckpointSaver]): if checkpointer: saved = checkpointer.get_tuple(thread) assert saved is not None - assert saved.checkpoint == { - "v": 1, - "ts": AnyStr(), - "id": AnyStr(), - "channel_values": { - "messages": [ - HumanMessage(content="hi?", id=AnyStr()), - AIMessage(content="hi?", id="0"), - ], - "agent": "agent", - }, - "channel_versions": { - "__start__": 2, - "messages": 3, - "start:agent": 3, - "agent": 3, - }, - "versions_seen": { - "__input__": {}, - "__start__": {"__start__": 1}, - "agent": {"start:agent": 2}, - }, - "pending_sends": [], - "current_tasks": {}, + assert saved.checkpoint["channel_values"] == { + "messages": [ + _AnyIdHumanMessage(content="hi?"), + AIMessage(content="hi?", id="0"), + ], + "agent": "agent", } assert saved.metadata == { "source": "loop", @@ -109,18 +84,16 @@ def test_no_modifier(checkpointer: Optional[BaseCheckpointSaver]): @pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - None, - ], - ids=[ - "memory", - "none", - ], + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], ) -async def test_no_modifier_async(checkpointer: Optional[BaseCheckpointSaver]): +async def test_no_modifier_async( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + model = FakeToolCallingModel() + agent = create_react_agent(model, [], checkpointer=checkpointer) inputs = [HumanMessage("hi?")] thread = {"configurable": {"thread_id": "123"}} @@ -131,30 +104,12 @@ async def test_no_modifier_async(checkpointer: Optional[BaseCheckpointSaver]): if checkpointer: saved = await checkpointer.aget_tuple(thread) assert saved is not None - assert saved.checkpoint == { - "v": 1, - "ts": AnyStr(), - "id": AnyStr(), - "channel_values": { - "messages": [ - HumanMessage(content="hi?", id=AnyStr()), - AIMessage(content="hi?", id="0"), - ], - "agent": "agent", - }, - "channel_versions": { - "__start__": 2, - "messages": 3, - "start:agent": 3, - "agent": 3, - }, - "versions_seen": { - "__input__": {}, - "__start__": {"__start__": 1}, - "agent": {"start:agent": 2}, - }, - "pending_sends": [], - "current_tasks": {}, + assert saved.checkpoint["channel_values"] == { + "messages": [ + _AnyIdHumanMessage(content="hi?"), + AIMessage(content="hi?", id="0"), + ], + "agent": "agent", } assert saved.metadata == { "source": "loop", diff --git a/libs/langgraph/tests/test_pregel.py b/libs/langgraph/tests/test_pregel.py index 34e4028ff..af7f0920a 100644 --- a/libs/langgraph/tests/test_pregel.py +++ b/libs/langgraph/tests/test_pregel.py @@ -8,9 +8,9 @@ from contextlib import contextmanager from typing import ( Annotated, Any, - Callable, Dict, Generator, + Iterator, List, Literal, Optional, @@ -18,6 +18,7 @@ from typing import ( Tuple, TypedDict, Union, + get_type_hints, ) import httpx @@ -36,15 +37,18 @@ from syrupy import SnapshotAssertion from langgraph.channels.base import BaseChannel from langgraph.channels.binop import BinaryOperatorAggregate from langgraph.channels.context import Context +from langgraph.channels.ephemeral_value import EphemeralValue from langgraph.channels.last_value import LastValue from langgraph.channels.topic import Topic +from langgraph.channels.untracked_value import UntrackedValue from langgraph.checkpoint.base import ( - BaseCheckpointSaver, Checkpoint, CheckpointMetadata, CheckpointTuple, ) from langgraph.checkpoint.memory import MemorySaver +from langgraph.checkpoint.serde.base import SerializerProtocol +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer from langgraph.checkpoint.sqlite import SqliteSaver from langgraph.constants import Send from langgraph.errors import InvalidUpdateError @@ -52,22 +56,20 @@ from langgraph.graph import END, Graph from langgraph.graph.graph import START from langgraph.graph.message import MessageGraph, add_messages from langgraph.graph.state import StateGraph -from langgraph.managed.few_shot import FewShotExamples from langgraph.prebuilt.chat_agent_executor import ( - create_function_calling_executor, create_tool_calling_executor, ) from langgraph.prebuilt.tool_node import ToolNode from langgraph.pregel import Channel, GraphRecursionError, Pregel, StateSnapshot from langgraph.pregel.retry import RetryPolicy -from langgraph.serde.base import SerializerProtocol -from langgraph.serde.jsonplus import JsonPlusSerializer from tests.any_str import AnyStr from tests.memory_assert import ( MemorySaverAssertCheckpointMetadata, MemorySaverAssertImmutable, + MemorySaverNoPending, NoopSerializer, ) +from tests.messages import _AnyIdAIMessage, _AnyIdHumanMessage def test_graph_validation() -> None: @@ -181,6 +183,15 @@ def test_graph_validation() -> None: with pytest.raises(ValueError, match="Found edge starting at unknown node "): graph.compile() + def bad_reducer(a): + ... + + class BadReducerState(TypedDict): + hello: Annotated[str, bad_reducer] + + with pytest.raises(ValueError, match="Invalid reducer"): + StateGraph(BadReducerState) + def test_checkpoint_errors() -> None: class FaultyGetCheckpointer(MemorySaver): @@ -193,6 +204,7 @@ def test_checkpoint_errors() -> None: config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: Optional[dict[str, Union[str, int, float]]] = None, ) -> RunnableConfig: raise ValueError("Faulty put") @@ -234,8 +246,6 @@ def test_checkpoint_errors() -> None: def test_node_schemas_custom_output() -> None: - from langchain_core.messages import HumanMessage - class State(TypedDict): hello: str bye: str @@ -251,7 +261,7 @@ def test_node_schemas_custom_output() -> None: def node_a(state: StateForA) -> State: assert state == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } class StateForB(TypedDict): @@ -288,7 +298,7 @@ def test_node_schemas_custom_output() -> None: graph = builder.compile() assert graph.invoke({"hello": "there", "bye": "world", "messages": "hello"}) == { - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } builder = StateGraph(input=State, output=Output) @@ -308,13 +318,27 @@ def test_node_schemas_custom_output() -> None: "now": 345, # ignored because not in input schema } ) == { - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } + assert [ + c + for c in graph.stream( + { + "hello": "there", + "bye": "world", + "messages": "hello", + "now": 345, # ignored because not in input schema + } + ) + ] == [ + {"a": None}, + {"b": {"hello": "again", "now": 123}}, + {"c": None}, + ] + def test_reducer_before_first_node() -> None: - from langchain_core.messages import HumanMessage - class State(TypedDict): hello: str messages: Annotated[list[str], add_messages] @@ -322,7 +346,7 @@ def test_reducer_before_first_node() -> None: def node_a(state: State) -> State: assert state == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } builder = StateGraph(State) @@ -332,7 +356,7 @@ def test_reducer_before_first_node() -> None: graph = builder.compile() assert graph.invoke({"hello": "there", "messages": "hello"}) == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } class State(TypedDict): @@ -342,7 +366,7 @@ def test_reducer_before_first_node() -> None: def node_a(state: State) -> State: assert state == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } builder = StateGraph(State) @@ -352,7 +376,7 @@ def test_reducer_before_first_node() -> None: graph = builder.compile() assert graph.invoke({"hello": "there", "messages": "hello"}) == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } class State(TypedDict): @@ -362,7 +386,7 @@ def test_reducer_before_first_node() -> None: def node_a(state: State) -> State: assert state == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } builder = StateGraph(State) @@ -372,7 +396,7 @@ def test_reducer_before_first_node() -> None: graph = builder.compile() assert graph.invoke({"hello": "there", "messages": "hello"}) == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } @@ -548,12 +572,18 @@ def test_invoke_two_processes_in_out(mocker: MockerFixture) -> None: assert step == 2 -def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_invoke_two_processes_in_out_interrupt( + request: pytest.FixtureRequest, checkpointer_name: str, mocker: MockerFixture +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") add_one = mocker.Mock(side_effect=lambda x: x + 1) one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox") two = Channel.subscribe_to("inbox") | add_one | Channel.write_to("output") - memory = MemorySaverAssertImmutable() app = Pregel( nodes={"one": one, "two": two}, channels={ @@ -563,64 +593,67 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: }, input_channels="input", output_channels="output", - checkpointer=memory, + checkpointer=checkpointer, interrupt_after_nodes=["one"], ) + thread1 = {"configurable": {"thread_id": "1"}} + thread2 = {"configurable": {"thread_id": "2"}} # start execution, stop at inbox - assert app.invoke(2, {"configurable": {"thread_id": "1"}}) is None + assert app.invoke(2, thread1) is None # inbox == 3 - checkpoint = memory.get({"configurable": {"thread_id": "1"}}) + checkpoint = checkpointer.get(thread1) assert checkpoint is not None assert checkpoint["channel_values"]["inbox"] == 3 # resume execution, finish - assert app.invoke(None, {"configurable": {"thread_id": "1"}}) == 4 + assert app.invoke(None, thread1) == 4 # start execution again, stop at inbox - assert app.invoke(20, {"configurable": {"thread_id": "1"}}) is None + assert app.invoke(20, thread1) is None # inbox == 21 - checkpoint = memory.get({"configurable": {"thread_id": "1"}}) + checkpoint = checkpointer.get(thread1) assert checkpoint is not None assert checkpoint["channel_values"]["inbox"] == 21 # send a new value in, interrupting the previous execution - assert app.invoke(3, {"configurable": {"thread_id": "1"}}) is None - assert app.invoke(None, {"configurable": {"thread_id": "1"}}) == 5 + assert app.invoke(3, thread1) is None + assert app.invoke(None, thread1) == 5 # start execution again, stopping at inbox - assert app.invoke(20, {"configurable": {"thread_id": "2"}}) is None + assert app.invoke(20, thread2) is None # inbox == 21 - snapshot = app.get_state({"configurable": {"thread_id": "2"}}) + snapshot = app.get_state(thread2) assert snapshot.values["inbox"] == 21 assert snapshot.next == ("two",) # update the state, resume - app.update_state({"configurable": {"thread_id": "2"}}, 25, as_node="one") - assert app.invoke(None, {"configurable": {"thread_id": "2"}}) == 26 + app.update_state(thread2, 25, as_node="one") + assert app.invoke(None, thread2) == 26 # no pending tasks - snapshot = app.get_state({"configurable": {"thread_id": "2"}}) + snapshot = app.get_state(thread2) assert snapshot.next == () # list history - thread1 = {"configurable": {"thread_id": "1"}} - assert [c for c in app.get_state_history(thread1)] == [ + history = [c for c in app.get_state_history(thread1)] + assert history == [ StateSnapshot( values={"inbox": 4, "output": 5, "input": 3}, next=(), config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 6, "writes": 5}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][1].config, + parent_config=history[1].config, ), StateSnapshot( values={"inbox": 4, "output": 4, "input": 3}, @@ -628,12 +661,13 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 5, "writes": None}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][2].config, + parent_config=history[2].config, ), StateSnapshot( values={"inbox": 21, "output": 4, "input": 3}, @@ -641,12 +675,13 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "input", "step": 4, "writes": 3}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][3].config, + parent_config=history[3].config, ), StateSnapshot( values={"inbox": 21, "output": 4, "input": 20}, @@ -654,12 +689,13 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 3, "writes": None}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][4].config, + parent_config=history[4].config, ), StateSnapshot( values={"inbox": 3, "output": 4, "input": 20}, @@ -667,12 +703,13 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "input", "step": 2, "writes": 20}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][5].config, + parent_config=history[5].config, ), StateSnapshot( values={"inbox": 3, "output": 4, "input": 2}, @@ -680,12 +717,13 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 1, "writes": 4}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][6].config, + parent_config=history[6].config, ), StateSnapshot( values={"inbox": 3, "input": 2}, @@ -693,12 +731,13 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 0, "writes": None}, created_at=AnyStr(), - parent_config=[*app.checkpointer.list(thread1)][7].config, + parent_config=history[7].config, ), StateSnapshot( values={"input": 2}, @@ -706,7 +745,8 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "input", "step": -1, "writes": 2}, @@ -715,6 +755,185 @@ def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: ), ] + # forking from any previous checkpoint w/out forking should do nothing + assert [c for c in app.stream(None, history[0].config, stream_mode="updates")] == [] + assert [c for c in app.stream(None, history[1].config, stream_mode="updates")] == [] + assert [c for c in app.stream(None, history[2].config, stream_mode="updates")] == [] + + # forking and re-running from any prev checkpoint should re-run nodes + fork_config = app.update_state(history[0].config, None) + assert [c for c in app.stream(None, fork_config, stream_mode="updates")] == [] + + fork_config = app.update_state(history[1].config, None) + assert [c for c in app.stream(None, fork_config, stream_mode="updates")] == [ + {"two": {"output": 5}} + ] + + fork_config = app.update_state(history[2].config, None) + assert [c for c in app.stream(None, fork_config, stream_mode="updates")] == [ + {"one": {"inbox": 4}} + ] + + +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_fork_always_re_runs_nodes( + request: pytest.FixtureRequest, checkpointer_name: str, mocker: MockerFixture +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + add_one = mocker.Mock(side_effect=lambda _: 1) + + builder = StateGraph(Annotated[int, operator.add]) + builder.add_node("add_one", add_one) + builder.add_edge(START, "add_one") + builder.add_conditional_edges("add_one", lambda cnt: "add_one" if cnt < 6 else END) + graph = builder.compile(checkpointer=checkpointer) + + thread1 = {"configurable": {"thread_id": "1"}} + + # start execution, stop at inbox + assert [*graph.stream(1, thread1, stream_mode=["values", "updates"])] == [ + ("values", 1), + ("updates", {"add_one": 1}), + ("values", 2), + ("updates", {"add_one": 1}), + ("values", 3), + ("updates", {"add_one": 1}), + ("values", 4), + ("updates", {"add_one": 1}), + ("values", 5), + ("updates", {"add_one": 1}), + ("values", 6), + ] + + # list history + history = [c for c in graph.get_state_history(thread1)] + assert history == [ + StateSnapshot( + values=6, + next=(), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 5, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[1].config, + ), + StateSnapshot( + values=5, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 4, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[2].config, + ), + StateSnapshot( + values=4, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 3, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[3].config, + ), + StateSnapshot( + values=3, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 2, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[4].config, + ), + StateSnapshot( + values=2, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 1, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[5].config, + ), + StateSnapshot( + values=1, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 0, "writes": None}, + created_at=AnyStr(), + parent_config=history[6].config, + ), + StateSnapshot( + values=0, + next=("__start__",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "input", "step": -1, "writes": 1}, + created_at=AnyStr(), + parent_config=None, + ), + ] + + # forking from any previous checkpoint w/out forking should do nothing + assert [ + c for c in graph.stream(None, history[0].config, stream_mode="updates") + ] == [] + assert [ + c for c in graph.stream(None, history[1].config, stream_mode="updates") + ] == [] + + # forking and re-running from any prev checkpoint should re-run nodes + fork_config = graph.update_state(history[0].config, None) + assert [c for c in graph.stream(None, fork_config, stream_mode="updates")] == [] + + fork_config = graph.update_state(history[1].config, None) + assert [c for c in graph.stream(None, fork_config, stream_mode="updates")] == [ + {"add_one": 1} + ] + + fork_config = graph.update_state(history[2].config, None) + assert [c for c in graph.stream(None, fork_config, stream_mode="updates")] == [ + {"add_one": 1}, + {"add_one": 1}, + ] + def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None: add_one = mocker.Mock(side_effect=lambda x: x + 1) @@ -744,6 +963,7 @@ def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None: {"input": 2, "inbox": 12}, output_keys="output", stream_mode="updates" ) ] == [ + {"one": None}, {"two": 13}, {"two": 4}, ] @@ -1038,92 +1258,84 @@ def test_invoke_checkpoint(mocker: MockerFixture) -> None: @pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - SqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], ) -def test_pending_writes_resume(checkpointer: BaseCheckpointSaver) -> None: - try: +def test_pending_writes_resume( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") - class State(TypedDict): - value: Annotated[int, operator.add] + class State(TypedDict): + value: Annotated[int, operator.add] - class AwhileMaker: - def __init__(self, sleep: float, rtn: Union[Dict, Exception]) -> None: - self.sleep = sleep - self.rtn = rtn - self.reset() + class AwhileMaker: + def __init__(self, sleep: float, rtn: Union[Dict, Exception]) -> None: + self.sleep = sleep + self.rtn = rtn + self.reset() - def __call__(self, input: State) -> Any: - self.calls += 1 - time.sleep(self.sleep) - if isinstance(self.rtn, Exception): - raise self.rtn - else: - return self.rtn + def __call__(self, input: State) -> Any: + self.calls += 1 + time.sleep(self.sleep) + if isinstance(self.rtn, Exception): + raise self.rtn + else: + return self.rtn - def reset(self): - self.calls = 0 + def reset(self): + self.calls = 0 - one = AwhileMaker(0.2, {"value": 2}) - two = AwhileMaker(0.6, ConnectionError("I'm not good")) - builder = StateGraph(State) - builder.add_node("one", one) - builder.add_node("two", two, retry=RetryPolicy(max_attempts=2)) - builder.add_edge(START, "one") - builder.add_edge(START, "two") - graph = builder.compile(checkpointer=checkpointer) + one = AwhileMaker(0.2, {"value": 2}) + two = AwhileMaker(0.6, ConnectionError("I'm not good")) + builder = StateGraph(State) + builder.add_node("one", one) + builder.add_node("two", two, retry=RetryPolicy(max_attempts=2)) + builder.add_edge(START, "one") + builder.add_edge(START, "two") + graph = builder.compile(checkpointer=checkpointer) - thread1: RunnableConfig = {"configurable": {"thread_id": "1"}} - with pytest.raises(ConnectionError, match="I'm not good"): - graph.invoke({"value": 1}, thread1) + thread1: RunnableConfig = {"configurable": {"thread_id": "1"}} + with pytest.raises(ConnectionError, match="I'm not good"): + graph.invoke({"value": 1}, thread1) - # both nodes should have been called once - assert one.calls == 1 - assert two.calls == 2 # two attempts + # both nodes should have been called once + assert one.calls == 1 + assert two.calls == 2 # two attempts - # latest checkpoint should be before nodes "one", "two" - state = graph.get_state(thread1) - assert state is not None - assert state.values == {"value": 1} - assert state.next == ("one", "two") - assert state.metadata == {"source": "loop", "step": 0, "writes": None} - # should contain pending write of "one" - checkpoint = checkpointer.get_tuple(thread1) - assert checkpoint is not None - assert checkpoint.pending_writes == [ - (AnyStr(), "one", "one"), - (AnyStr(), "value", 2), - ] - # both pending writes come from same task - assert checkpoint.pending_writes[0][0] == checkpoint.pending_writes[1][0] + # latest checkpoint should be before nodes "one", "two" + state = graph.get_state(thread1) + assert state is not None + assert state.values == {"value": 1} + assert state.next == ("one", "two") + assert state.metadata == {"source": "loop", "step": 0, "writes": None} + # should contain pending write of "one" + checkpoint = checkpointer.get_tuple(thread1) + assert checkpoint is not None + assert checkpoint.pending_writes == [ + (AnyStr(), "one", "one"), + (AnyStr(), "value", 2), + ] + # both pending writes come from same task + assert checkpoint.pending_writes[0][0] == checkpoint.pending_writes[1][0] - # resume execution - with pytest.raises(ConnectionError, match="I'm not good"): - graph.invoke(None, thread1) + # resume execution + with pytest.raises(ConnectionError, match="I'm not good"): + graph.invoke(None, thread1) - # node "one" succeeded previously, so shouldn't be called again - assert one.calls == 1 - # node "two" should have been called once again - assert two.calls == 4 # two attempts before + two attempts now + # node "one" succeeded previously, so shouldn't be called again + assert one.calls == 1 + # node "two" should have been called once again + assert two.calls == 4 # two attempts before + two attempts now - # confirm no new checkpoints saved - state_two = graph.get_state(thread1) - assert state_two == state + # confirm no new checkpoints saved + state_two = graph.get_state(thread1) + assert state_two == state - # resume execution, without exception - two.rtn = {"value": 3} - # both the pending write and the new write were applied, 1 + 2 + 3 = 6 - assert graph.invoke(None, thread1) == {"value": 6} - finally: - if getattr(checkpointer, "__exit__", None): - checkpointer.__exit__(None, None, None) + # resume execution, without exception + two.rtn = {"value": 3} + # both the pending write and the new write were applied, 1 + 2 + 3 = 6 + assert graph.invoke(None, thread1) == {"value": 6} def test_cond_edge_after_send() -> None: @@ -1133,15 +1345,15 @@ def test_cond_edge_after_send() -> None: setattr(self, "__name__", name) def __call__(self, state): - return state + [self.name] + return [self.name] def send_for_fun(state): - return [Send("2", state)] + return [Send("2", state), Send("2", state)] def route_to_three(state) -> Literal["3"]: return "3" - builder = StateGraph(list) + builder = StateGraph(Annotated[list, operator.add]) builder.add_node(Node("1")) builder.add_node(Node("2")) builder.add_node(Node("3")) @@ -1149,8 +1361,30 @@ def test_cond_edge_after_send() -> None: builder.add_conditional_edges("1", send_for_fun) builder.add_conditional_edges("2", route_to_three) graph = builder.compile() + assert graph.invoke(["0"]) == ["0", "1", "2", "2", "3"] - assert graph.invoke(["0"]) == ["0", "1", "2", "3"] + +async def test_checkpointer_null_pending_writes() -> None: + class Node: + def __init__(self, name: str): + self.name = name + setattr(self, "__name__", name) + + def __call__(self, state): + return [self.name] + + builder = StateGraph(Annotated[list, operator.add]) + builder.add_node(Node("1")) + builder.add_edge(START, "1") + graph = builder.compile(checkpointer=MemorySaverNoPending()) + assert graph.invoke([], {"configurable": {"thread_id": "foo"}}) == ["1"] + assert graph.invoke([], {"configurable": {"thread_id": "foo"}}) == ["1"] * 2 + assert (await graph.ainvoke([], {"configurable": {"thread_id": "foo"}})) == [ + "1" + ] * 3 + assert (await graph.ainvoke([], {"configurable": {"thread_id": "foo"}})) == [ + "1" + ] * 4 def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None: @@ -1188,13 +1422,17 @@ def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None: assert state is not None assert state.values.get("total") == 2 assert state.next == () - assert state.config["configurable"]["thread_ts"] == memory.get(thread_1)["id"] + assert ( + state.config["configurable"]["checkpoint_id"] == memory.get(thread_1)["id"] + ) # total is now 2, so output is 2+3=5 assert app.invoke(3, thread_1) == 5 state = app.get_state(thread_1) assert state is not None assert state.values.get("total") == 7 - assert state.config["configurable"]["thread_ts"] == memory.get(thread_1)["id"] + assert ( + state.config["configurable"]["checkpoint_id"] == memory.get(thread_1)["id"] + ) # total is now 2+5=7, so output would be 7+4=11, but raises ValueError with pytest.raises(ValueError): app.invoke(4, thread_1) @@ -1234,8 +1472,8 @@ def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None: } # sorted descending assert ( - thread_1_history[0].config["configurable"]["thread_ts"] - > thread_1_history[1].config["configurable"]["thread_ts"] + thread_1_history[0].config["configurable"]["checkpoint_id"] + > thread_1_history[1].config["configurable"]["checkpoint_id"] ) # cursor pagination cursored = list( @@ -1250,18 +1488,18 @@ def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None: # can get each checkpoint using aget with config assert ( memory.get(thread_1_history[0].config)["id"] - == thread_1_history[0].config["configurable"]["thread_ts"] + == thread_1_history[0].config["configurable"]["checkpoint_id"] ) assert ( memory.get(thread_1_history[1].config)["id"] - == thread_1_history[1].config["configurable"]["thread_ts"] + == thread_1_history[1].config["configurable"]["checkpoint_id"] ) thread_1_next_config = app.update_state(thread_1_history[1].config, 10) # update creates a new checkpoint assert ( - thread_1_next_config["configurable"]["thread_ts"] - > thread_1_history[0].config["configurable"]["thread_ts"] + thread_1_next_config["configurable"]["checkpoint_id"] + > thread_1_history[0].config["configurable"]["checkpoint_id"] ) # update makes new checkpoint child of the previous one assert ( @@ -1686,6 +1924,9 @@ def test_conditional_graph(snapshot: SnapshotAssertion) -> None: ) config = {"configurable": {"thread_id": "1"}} + assert app_w_interrupt.get_graph().to_json() == snapshot + assert app_w_interrupt.get_graph().draw_mermaid() == snapshot + assert [ c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config) ] == [ @@ -1729,7 +1970,7 @@ def test_conditional_graph(snapshot: SnapshotAssertion) -> None: ) assert ( app_w_interrupt.checkpointer.get_tuple(config).config["configurable"][ - "thread_ts" + "checkpoint_id" ] is not None ) @@ -2315,21 +2556,47 @@ def test_conditional_entrypoint_to_multiple_state_graph( } -def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: +def test_conditional_state_graph( + snapshot: SnapshotAssertion, mocker: MockerFixture +) -> None: from langchain_core.agents import AgentAction, AgentFinish from langchain_core.language_models.fake import FakeStreamingListLLM from langchain_core.prompts import PromptTemplate from langchain_core.tools import tool + setup = mocker.Mock() + teardown = mocker.Mock() + + @contextmanager + def assert_ctx_once() -> Iterator[None]: + assert setup.call_count == 0 + assert teardown.call_count == 0 + try: + yield + finally: + assert setup.call_count == 1 + assert teardown.call_count == 1 + setup.reset_mock() + teardown.reset_mock() + + @contextmanager + def make_httpx_client() -> Iterator[httpx.Client]: + setup() + with httpx.Client() as client: + try: + yield client + finally: + teardown() + class AgentState(TypedDict, total=False): - input: str + input: Annotated[str, UntrackedValue] agent_outcome: Optional[Union[AgentAction, AgentFinish]] intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add] - session: Annotated[httpx.Client, Context(httpx.Client)] + session: Annotated[httpx.Client, Context(make_httpx_client)] class ToolState(TypedDict, total=False): agent_outcome: Union[AgentAction, AgentFinish] - session: Annotated[httpx.Client, Context(httpx.Client)] + session: Annotated[httpx.Client, Context(make_httpx_client)] # Assemble the tools @tool() @@ -2412,84 +2679,88 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot assert app.get_graph().draw_mermaid(with_styles=False) == snapshot - assert app.invoke({"input": "what is weather in sf"}) == { - "input": "what is weather in sf", - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:query", - ), - "result for query", - ), - ( - AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - "result for another", - ), - ], - "agent_outcome": AgentFinish( - return_values={"answer": "answer"}, log="finish:answer" - ), - } - - assert [*app.stream({"input": "what is weather in sf"})] == [ - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", tool_input="query", log="tool:search_api:query" - ), - } - }, - { - "tools": { - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:query", - ), - "result for query", - ) - ], - } - }, - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - } - }, - { - "tools": { - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - "result for another", + with assert_ctx_once(): + assert app.invoke({"input": "what is weather in sf"}) == { + "input": "what is weather in sf", + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", ), - ], - } - }, - { - "agent": { - "agent_outcome": AgentFinish( - return_values={"answer": "answer"}, log="finish:answer" + "result for query", ), - } - }, - ] + ( + AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + "result for another", + ), + ], + "agent_outcome": AgentFinish( + return_values={"answer": "answer"}, log="finish:answer" + ), + } + + with assert_ctx_once(): + assert [*app.stream({"input": "what is weather in sf"})] == [ + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", + ), + } + }, + { + "tools": { + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", + ), + "result for query", + ) + ], + } + }, + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + } + }, + { + "tools": { + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + "result for another", + ), + ], + } + }, + { + "agent": { + "agent_outcome": AgentFinish( + return_values={"answer": "answer"}, log="finish:answer" + ), + } + }, + ] # test state get/update methods with interrupt_after @@ -2499,21 +2770,24 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: ) config = {"configurable": {"thread_id": "1"}} - assert [ - c for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config) - ] == [ - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", tool_input="query", log="tool:search_api:query" - ), - } - }, - ] + with assert_ctx_once(): + assert [ + c + for c in app_w_interrupt.stream({"input": "what is weather in sf"}, config) + ] == [ + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", + ), + } + }, + ] assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -2538,20 +2812,20 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: parent_config=[*app_w_interrupt.checkpointer.list(config, limit=2)][-1].config, ) - app_w_interrupt.update_state( - config, - { - "agent_outcome": AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:a different query", - ) - }, - ) + with assert_ctx_once(): + app_w_interrupt.update_state( + config, + { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:a different query", + ) + }, + ) assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", @@ -2578,45 +2852,46 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: parent_config=[*app_w_interrupt.checkpointer.list(config, limit=2)][-1].config, ) - assert [c for c in app_w_interrupt.stream(None, config)] == [ - { - "tools": { - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:a different query", - ), - "result for query", - ) - ], - } - }, - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - } - }, - ] + with assert_ctx_once(): + assert [c for c in app_w_interrupt.stream(None, config)] == [ + { + "tools": { + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:a different query", + ), + "result for query", + ) + ], + } + }, + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + } + }, + ] - app_w_interrupt.update_state( - config, - { - "agent_outcome": AgentFinish( - return_values={"answer": "a really nice answer"}, - log="finish:a really nice answer", - ) - }, - ) + with assert_ctx_once(): + app_w_interrupt.update_state( + config, + { + "agent_outcome": AgentFinish( + return_values={"answer": "a really nice answer"}, + log="finish:a really nice answer", + ) + }, + ) assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentFinish( return_values={"answer": "a really nice answer"}, log="finish:a really nice answer", @@ -2674,7 +2949,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -2712,7 +2986,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", @@ -2777,7 +3050,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentFinish( return_values={"answer": "a really nice answer"}, log="finish:a really nice answer", @@ -2826,7 +3098,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "intermediate_steps": [], }, next=("agent",), @@ -2848,7 +3119,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -2892,7 +3162,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -2965,7 +3234,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -3009,7 +3277,6 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -3061,17 +3328,25 @@ def test_conditional_state_graph(snapshot: SnapshotAssertion) -> None: ] -def test_state_graph_w_config(snapshot: SnapshotAssertion) -> None: +def test_state_graph_w_config_inherited_state_keys(snapshot: SnapshotAssertion) -> None: from langchain_core.agents import AgentAction, AgentFinish from langchain_core.language_models.fake import FakeStreamingListLLM from langchain_core.prompts import PromptTemplate from langchain_core.tools import tool - class AgentState(TypedDict, total=False): + class BaseState(TypedDict): input: str agent_outcome: Optional[Union[AgentAction, AgentFinish]] + + class AgentState(BaseState, total=False): intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add] + assert get_type_hints(AgentState).keys() == { + "input", + "agent_outcome", + "intermediate_steps", + } + class Config(TypedDict, total=False): tools: list[str] @@ -3129,249 +3404,49 @@ def test_state_graph_w_config(snapshot: SnapshotAssertion) -> None: return "continue" # Define a new graph - workflow = StateGraph(AgentState, Config) + builder = StateGraph(AgentState, Config) - workflow.add_node("agent", agent) - workflow.add_node("tools", execute_tools) + builder.add_node("agent", agent) + builder.add_node("tools", execute_tools) - workflow.set_entry_point("agent") + builder.set_entry_point("agent") - workflow.add_conditional_edges( + builder.add_conditional_edges( "agent", should_continue, {"continue": "tools", "exit": END} ) - workflow.add_edge("tools", "agent") + builder.add_edge("tools", "agent") - app = workflow.compile() + app = builder.compile() assert app.config_schema().schema_json() == snapshot + assert app.get_input_schema().schema_json() == snapshot + assert app.get_output_schema().schema_json() == snapshot + assert builder.channels.keys() == {"input", "agent_outcome", "intermediate_steps"} -def test_state_graph_few_shot() -> None: - from langchain_core.language_models.fake_chat_models import ( - FakeMessagesListChatModel, - ) - from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage - from langchain_core.prompts import ChatPromptTemplate - from langchain_core.tools import tool - - def filter_by_source(config: RunnableConfig) -> Dict[str, Any]: - """This function is a trivial example that demonstrates that passing - a Callable to metadata_filter works as expected. - """ - return {"source": "loop"} - - class BaseState(TypedDict): - messages: Annotated[list[AnyMessage], add_messages] - - class AgentState(BaseState): - examples: Annotated[ - Sequence[BaseState], - FewShotExamples[BaseState].configure(k=1, metadata_filter=filter_by_source), - ] - - # Assemble the tools - @tool() - def search_api(query: str) -> str: - """Searches the API for the query.""" - return f"result for {query}" - - tools = [search_api] - - prompt = ChatPromptTemplate.from_messages( - [ + assert app.invoke({"input": "what is weather in sf"}) == { + "agent_outcome": AgentFinish( + return_values={"answer": "answer"}, log="finish:answer" + ), + "input": "what is weather in sf", + "intermediate_steps": [ ( - "system", - """You are a nice assistant. -Some examples of past conversations: -{examples}""", - ), - ("placeholder", "{messages}"), - ] - ) - - model = FakeMessagesListChatModel( - responses=[ - AIMessage( - content="", - tool_calls=[ - { - "id": "tool_call123", - "name": "search_api", - "args": {"query": "query"}, - }, - ], - ), - AIMessage(content="answer"), - ] - ) - - def agent(state: AgentState, config: RunnableConfig) -> AgentState: - # begin: testing code - assert state["examples"] == config["configurable"]["expected_examples"] - # end: testing code - formatted = prompt.invoke(state) - response = model.invoke(formatted) - return {"messages": response} - - # Define decision-making logic - def should_continue(data: AgentState) -> str: - # Logic to decide whether to continue in the loop or exit - if not data["messages"][-1].tool_calls: - return "exit" - else: - return "continue" - - # Define a new graph - workflow = StateGraph(AgentState) - - workflow.add_node("agent", agent) - workflow.add_node("tools", ToolNode(tools)) - workflow.set_entry_point("agent") - workflow.add_conditional_edges( - "agent", should_continue, {"continue": "tools", "exit": END} - ) - workflow.add_edge("tools", "agent") - - with SqliteSaver.from_conn_string(":memory:") as saver: - app = workflow.compile(checkpointer=saver) - - first_messages = [ - HumanMessage(content="what is weather in sf", id=AnyStr()), - AIMessage( - content="", - id=AnyStr(), - tool_calls=[ - { - "name": "search_api", - "args": {"query": "query"}, - "id": "tool_call123", - "type": "tool_call", - } - ], - ), - ToolMessage( - content="result for query", - name="search_api", - id=AnyStr(), - tool_call_id="tool_call123", - ), - AIMessage(content="answer", id=AnyStr()), - ] - actual = app.invoke( - {"messages": "what is weather in sf"}, - { - "configurable": { - "thread_id": "1", - "expected_examples": [], - }, - }, - ) - expected = {"messages": first_messages} - assert actual == expected - - # get first checkpoint - chkpnt_tuple_1 = saver.get_tuple({"configurable": {"thread_id": "1"}}) - config = chkpnt_tuple_1.config - checkpoint = chkpnt_tuple_1.checkpoint - metadata = chkpnt_tuple_1.metadata - - # not needed in application code, only for testing - hiscored = list(saver.list(None, filter={"score": 1})) - assert hiscored == [] - - # mark as "good" - metadata["score"] = 1 - saver.put(config, checkpoint, metadata) - - # not needed in application code, only for testing - hiscored = list(saver.list(None, filter={"score": 1})) - assert len(hiscored) == 1 - assert hiscored[0].checkpoint["channel_values"]["messages"] == first_messages - - second_messages = [ - HumanMessage(content="what is weather in la", id=AnyStr()), - AIMessage( - content="", - id=AnyStr(), - tool_calls=[ - { - "name": "search_api", - "args": {"query": "query"}, - "id": "tool_call123", - "type": "tool_call", - } - ], - ), - ToolMessage( - content="result for query", - name="search_api", - id=AnyStr(), - tool_call_id="tool_call123", - ), - AIMessage(content="answer", id=AnyStr()), - ] - assert app.invoke( - {"messages": "what is weather in la"}, - { - "configurable": { - "thread_id": "2", - # below is only for testing purposes, not part of few shot api - "expected_examples": [{"messages": first_messages}], - } - }, - ) == {"messages": second_messages} - - # get first checkpoint - chkpnt_tuple_2 = saver.get_tuple({"configurable": {"thread_id": "2"}}) - config = chkpnt_tuple_2.config - checkpoint = chkpnt_tuple_2.checkpoint - metadata = chkpnt_tuple_2.metadata - - # not needed in application code, only for testing - hiscored = list(saver.list(None, filter={"score": 1})) - assert len(hiscored) == 1 - - # mark as "good" - metadata["score"] = 1 - saver.put(config, checkpoint, metadata) - - hiscored = list(saver.list(None, filter={"score": 1})) - assert len(hiscored) == 2 - - assert app.invoke( - {"messages": "what is weather in ny"}, - { - "configurable": { - "thread_id": "3", - # below is only for testing purposes, not part of few shot api - "expected_examples": [{"messages": second_messages}], - } - }, - ) == { - "messages": [ - HumanMessage(content="what is weather in ny", id=AnyStr()), - AIMessage( - content="", - id=AnyStr(), - tool_calls=[ - { - "name": "search_api", - "args": {"query": "query"}, - "id": "tool_call123", - "type": "tool_call", - } - ], + AgentAction( + tool="search_api", tool_input="query", log="tool:search_api:query" ), - ToolMessage( - content="result for query", - name="search_api", - id=AnyStr(), - tool_call_id="tool_call123", + "result for query", + ), + ( + AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", ), - AIMessage(content="answer", id=AnyStr()), - ] - } + "result for another", + ), + ], + } def test_conditional_entrypoint_graph_state(snapshot: SnapshotAssertion) -> None: @@ -3485,7 +3560,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None: {"messages": [HumanMessage(content="what is weather in sf")]} ) == { "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id=AnyStr(), content="", @@ -3531,7 +3606,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None: tool_call_id="tool_call567", id=AnyStr(), ), - AIMessage(content="answer", id=AnyStr()), + _AnyIdAIMessage(content="answer"), ] } @@ -3541,10 +3616,8 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None: debug=True, ) == { "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), - AIMessage( - content="Sorry, need more steps to process this request.", id=AnyStr() - ), + _AnyIdHumanMessage(content="what is weather in sf"), + _AnyIdAIMessage(content="Sorry, need more steps to process this request."), ] } @@ -3623,16 +3696,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None: ] } }, - { - "agent": { - "messages": [ - AIMessage( - content="answer", - id=AnyStr(), - ) - ] - } - }, + {"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}, ] assert [ @@ -3707,141 +3771,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None: ] } }, - {"agent": {"messages": [AIMessage(content="answer", id=AnyStr())]}}, - ] - - -def test_prebuilt_chat(snapshot: SnapshotAssertion) -> None: - from langchain_core.language_models.fake_chat_models import ( - FakeMessagesListChatModel, - ) - from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage - from langchain_core.tools import tool - - class FakeFuntionChatModel(FakeMessagesListChatModel): - def bind_functions(self, functions: list): - return self - - @tool() - def search_api(query: str) -> str: - """Searches the API for the query.""" - return f"result for {query}" - - tools = [search_api] - - app = create_function_calling_executor( - FakeFuntionChatModel( - responses=[ - AIMessage( - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": json.dumps("query"), - } - }, - ), - AIMessage( - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": json.dumps("another"), - } - }, - ), - AIMessage(content="answer"), - ] - ), - tools, - ) - - assert app.get_input_schema().schema_json() == snapshot - assert app.get_output_schema().schema_json() == snapshot - assert json.dumps(app.get_graph().to_json(), indent=2) == snapshot - assert app.get_graph().draw_mermaid(with_styles=False) == snapshot - - assert app.invoke( - {"messages": [HumanMessage(content="what is weather in sf")]} - ) == { - "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": {"name": "search_api", "arguments": '"query"'} - }, - ), - FunctionMessage(content="result for query", name="search_api", id=AnyStr()), - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": {"name": "search_api", "arguments": '"another"'} - }, - ), - FunctionMessage( - content="result for another", name="search_api", id=AnyStr() - ), - AIMessage(content="answer", id=AnyStr()), - ] - } - - assert [ - *app.stream({"messages": [HumanMessage(content="what is weather in sf")]}) - ] == [ - { - "agent": { - "messages": [ - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": '"query"', - } - }, - ) - ] - } - }, - { - "tools": { - "messages": [ - FunctionMessage( - content="result for query", name="search_api", id=AnyStr() - ) - ] - } - }, - { - "agent": { - "messages": [ - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": '"another"', - } - }, - ) - ] - } - }, - { - "tools": { - "messages": [ - FunctionMessage( - content="result for another", name="search_api", id=AnyStr() - ) - ] - } - }, - {"agent": {"messages": [AIMessage(content="answer", id=AnyStr())]}}, + {"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}, ] @@ -3954,7 +3884,7 @@ def test_state_graph_packets(serde: SerializerProtocol) -> None: assert app.invoke({"messages": HumanMessage(content="what is weather in sf")}) == { "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4110,10 +4040,7 @@ def test_state_graph_packets(serde: SerializerProtocol) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4163,10 +4090,7 @@ def test_state_graph_packets(serde: SerializerProtocol) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4242,10 +4166,7 @@ def test_state_graph_packets(serde: SerializerProtocol) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4323,10 +4244,7 @@ def test_state_graph_packets(serde: SerializerProtocol) -> None: assert app_w_interrupt.get_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4611,7 +4529,7 @@ def test_message_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", tool_calls=[ @@ -4655,7 +4573,7 @@ def test_message_graph( # message was replaced instead of appended assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -4719,10 +4637,7 @@ def test_message_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -4783,10 +4698,7 @@ def test_message_graph( # replaces message even if object identity is different, as long as id is the same assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -4842,10 +4754,7 @@ def test_message_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", tool_calls=[ @@ -4889,10 +4798,7 @@ def test_message_graph( # message was replaced instead of appended assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -4956,10 +4862,7 @@ def test_message_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5020,10 +4923,7 @@ def test_message_graph( # replaces message even if object identity is different, as long as id is the same assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5061,10 +4961,7 @@ def test_message_graph( # now the next node is "agent" per the graph edges assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5083,7 +4980,7 @@ def test_message_graph( id=AnyStr(), ), AIMessage(content="answer", id="ai2"), - AIMessage(content="an extra message", id=AnyStr()), + _AnyIdAIMessage(content="an extra message"), ], next=("agent",), config=app_w_interrupt.checkpointer.get_tuple(config).config, @@ -5342,7 +5239,7 @@ def test_root_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", tool_calls=[ @@ -5386,7 +5283,7 @@ def test_root_graph( # message was replaced instead of appended assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5450,10 +5347,7 @@ def test_root_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5514,10 +5408,7 @@ def test_root_graph( # replaces message even if object identity is different, as long as id is the same assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5573,10 +5464,7 @@ def test_root_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", tool_calls=[ @@ -5620,10 +5508,7 @@ def test_root_graph( # message was replaced instead of appended assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5687,10 +5572,7 @@ def test_root_graph( assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5751,10 +5633,7 @@ def test_root_graph( # replaces message even if object identity is different, as long as id is the same assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5792,10 +5671,7 @@ def test_root_graph( # now the next node is "agent" per the graph edges assert app_w_interrupt.get_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5814,7 +5690,7 @@ def test_root_graph( id=AnyStr(), ), AIMessage(content="answer", id="ai2"), - AIMessage(content="an extra message", id=AnyStr()), + _AnyIdAIMessage(content="an extra message"), ], next=("agent",), config=app_w_interrupt.checkpointer.get_tuple(config).config, @@ -5865,10 +5741,7 @@ def test_root_graph( assert new_app.get_state(config) == StateSnapshot( values={ "__root__": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id="ai1", @@ -5887,7 +5760,7 @@ def test_root_graph( id=AnyStr(), ), AIMessage(content="answer", id="ai2"), - AIMessage(content="an extra message", id=AnyStr()), + _AnyIdAIMessage(content="an extra message"), ] }, next=("agent",), @@ -6377,7 +6250,8 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": {"my_key": ""}, @@ -6400,7 +6274,8 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { @@ -6447,7 +6322,8 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { @@ -6494,7 +6370,8 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { @@ -6513,10 +6390,10 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "timestamp": AnyStr(), "step": 3, "payload": { - "id": "ceada3c5-5f25-59e4-9ea5-544599ce1d2f", + "id": "9b590c54-15ef-54b1-83a7-140d27b0bc52", "name": "finish", "input": {"my_key": "value prepared slow", "market": "DE"}, - "triggers": ["branch:prepare:condition:then"], + "triggers": ["branch:prepare:condition::then"], }, }, { @@ -6524,7 +6401,7 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "timestamp": AnyStr(), "step": 3, "payload": { - "id": "ceada3c5-5f25-59e4-9ea5-544599ce1d2f", + "id": "9b590c54-15ef-54b1-83a7-140d27b0bc52", "name": "finish", "result": [("my_key", " finished")], }, @@ -6541,7 +6418,8 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { @@ -6775,7 +6653,7 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: created_at=AnyStr(), metadata={ "source": "update", - "step": -1, + "step": 0, "writes": {START: {"my_key": "key", "market": "DE"}}, }, ) @@ -6792,7 +6670,7 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"], metadata={ "source": "loop", - "step": 0, + "step": 1, "writes": {"prepare": {"my_key": " prepared"}}, }, parent_config=uconfig, @@ -6809,7 +6687,7 @@ def test_branch_then(snapshot: SnapshotAssertion) -> None: created_at=tool_two.checkpointer.get_tuple(thread3).checkpoint["ts"], metadata={ "source": "loop", - "step": 2, + "step": 3, "writes": {"finish": {"my_key": " finished"}}, }, parent_config=[*tool_two.checkpointer.list(thread3, limit=2)][-1].config, @@ -7025,11 +6903,35 @@ def test_in_one_fan_out_state_graph_waiting_edge_via_branch( ] -def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( - snapshot: SnapshotAssertion, +def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1( + snapshot: SnapshotAssertion, mocker: MockerFixture ) -> None: from langchain_core.pydantic_v1 import BaseModel, ValidationError + setup = mocker.Mock() + teardown = mocker.Mock() + + @contextmanager + def assert_ctx_once() -> Iterator[None]: + assert setup.call_count == 0 + assert teardown.call_count == 0 + try: + yield + finally: + assert setup.call_count == 1 + assert teardown.call_count == 1 + setup.reset_mock() + teardown.reset_mock() + + @contextmanager + def make_httpx_client() -> Iterator[httpx.Client]: + setup() + with httpx.Client() as client: + try: + yield client + finally: + teardown() + def sorted_add( x: list[str], y: Union[list[str], list[tuple[str, str]]] ) -> list[str]: @@ -7039,10 +6941,26 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( y = [t[1] for t in y] return sorted(operator.add(x, y)) + class InnerObject(BaseModel): + yo: int + class State(BaseModel): + class Config: + arbitrary_types_allowed = True + query: str + inner: InnerObject answer: Optional[str] = None docs: Annotated[list[str], sorted_add] + client: Annotated[httpx.Client, Context(make_httpx_client)] + + class Input(BaseModel): + query: str + inner: InnerObject + + class Output(BaseModel): + answer: str + docs: list[str] class StateUpdate(BaseModel): query: Optional[str] = None @@ -7066,10 +6984,11 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( return {"answer": ",".join(data.docs)} def decider(data: State) -> str: + print("decider", data) assert isinstance(data, State) return "retriever_two" - workflow = StateGraph(State) + workflow = StateGraph(State, input=Input, output=Output) workflow.add_node("rewrite_query", rewrite_query) workflow.add_node("analyzer_one", analyzer_one) @@ -7089,23 +7008,28 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( app = workflow.compile() assert app.get_graph().draw_mermaid(with_styles=False) == snapshot + assert app.get_input_schema().schema() == snapshot + assert app.get_output_schema().schema() == snapshot - with pytest.raises(ValidationError): + with pytest.raises(ValidationError), assert_ctx_once(): app.invoke({"query": {}}) - assert app.invoke({"query": "what is weather in sf"}) == { - "query": "analyzed: query: what is weather in sf", - "docs": ["doc1", "doc2", "doc3", "doc4"], - "answer": "doc1,doc2,doc3,doc4", - } + with assert_ctx_once(): + assert app.invoke({"query": "what is weather in sf", "inner": {"yo": 1}}) == { + "docs": ["doc1", "doc2", "doc3", "doc4"], + "answer": "doc1,doc2,doc3,doc4", + } - assert [*app.stream({"query": "what is weather in sf"})] == [ - {"rewrite_query": {"query": "query: what is weather in sf"}}, - {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, - {"retriever_two": {"docs": ["doc3", "doc4"]}}, - {"retriever_one": {"docs": ["doc1", "doc2"]}}, - {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, - ] + with assert_ctx_once(): + assert [ + *app.stream({"query": "what is weather in sf", "inner": {"yo": 1}}) + ] == [ + {"rewrite_query": {"query": "query: what is weather in sf"}}, + {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, + {"retriever_two": {"docs": ["doc3", "doc4"]}}, + {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + ] app_w_interrupt = workflow.compile( checkpointer=MemorySaverAssertImmutable(), @@ -7113,18 +7037,196 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( ) config = {"configurable": {"thread_id": "1"}} - assert [ - c for c in app_w_interrupt.stream({"query": "what is weather in sf"}, config) - ] == [ - {"rewrite_query": {"query": "query: what is weather in sf"}}, - {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, - {"retriever_two": {"docs": ["doc3", "doc4"]}}, - {"retriever_one": {"docs": ["doc1", "doc2"]}}, - ] + with assert_ctx_once(): + assert [ + c + for c in app_w_interrupt.stream( + {"query": "what is weather in sf", "inner": {"yo": 1}}, config + ) + ] == [ + {"rewrite_query": {"query": "query: what is weather in sf"}}, + {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, + {"retriever_two": {"docs": ["doc3", "doc4"]}}, + {"retriever_one": {"docs": ["doc1", "doc2"]}}, + ] - assert [c for c in app_w_interrupt.stream(None, config)] == [ - {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, - ] + with assert_ctx_once(): + assert [c for c in app_w_interrupt.stream(None, config)] == [ + {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + ] + + with assert_ctx_once(): + assert app_w_interrupt.update_state( + config, {"docs": ["doc5"]}, as_node="rewrite_query" + ) == { + "configurable": { + "thread_id": "1", + "checkpoint_id": AnyStr(), + "checkpoint_ns": "", + } + } + + +def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2( + snapshot: SnapshotAssertion, mocker: MockerFixture +) -> None: + from pydantic import BaseModel, ConfigDict, ValidationError + + setup = mocker.Mock() + teardown = mocker.Mock() + + @contextmanager + def assert_ctx_once() -> Iterator[None]: + assert setup.call_count == 0 + assert teardown.call_count == 0 + try: + yield + finally: + assert setup.call_count == 1 + assert teardown.call_count == 1 + setup.reset_mock() + teardown.reset_mock() + + @contextmanager + def make_httpx_client() -> Iterator[httpx.Client]: + setup() + with httpx.Client() as client: + try: + yield client + finally: + teardown() + + def sorted_add( + x: list[str], y: Union[list[str], list[tuple[str, str]]] + ) -> list[str]: + if isinstance(y[0], tuple): + for rem, _ in y: + x.remove(rem) + y = [t[1] for t in y] + return sorted(operator.add(x, y)) + + class InnerObject(BaseModel): + yo: int + + class State(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + query: str + inner: InnerObject + answer: Optional[str] = None + docs: Annotated[list[str], sorted_add] + client: Annotated[httpx.Client, Context(make_httpx_client)] + + class StateUpdate(BaseModel): + query: Optional[str] = None + answer: Optional[str] = None + docs: Optional[list[str]] = None + + class Input(BaseModel): + query: str + inner: InnerObject + + class Output(BaseModel): + answer: str + docs: list[str] + + def rewrite_query(data: State) -> State: + return {"query": f"query: {data.query}"} + + def analyzer_one(data: State) -> State: + return StateUpdate(query=f"analyzed: {data.query}") + + def retriever_one(data: State) -> State: + return {"docs": ["doc1", "doc2"]} + + def retriever_two(data: State) -> State: + time.sleep(0.1) + return {"docs": ["doc3", "doc4"]} + + def qa(data: State) -> State: + return {"answer": ",".join(data.docs)} + + def decider(data: State) -> str: + assert isinstance(data, State) + return "retriever_two" + + workflow = StateGraph(State, input=Input, output=Output) + + workflow.add_node("rewrite_query", rewrite_query) + workflow.add_node("analyzer_one", analyzer_one) + workflow.add_node("retriever_one", retriever_one) + workflow.add_node("retriever_two", retriever_two) + workflow.add_node("qa", qa) + + workflow.set_entry_point("rewrite_query") + workflow.add_edge("rewrite_query", "analyzer_one") + workflow.add_edge("analyzer_one", "retriever_one") + workflow.add_conditional_edges( + "rewrite_query", decider, {"retriever_two": "retriever_two"} + ) + workflow.add_edge(["retriever_one", "retriever_two"], "qa") + workflow.set_finish_point("qa") + + app = workflow.compile() + + assert app.get_graph().draw_mermaid(with_styles=False) == snapshot + assert app.get_input_schema().schema() == snapshot + assert app.get_output_schema().schema() == snapshot + + with pytest.raises(ValidationError), assert_ctx_once(): + app.invoke({"query": {}}) + + with assert_ctx_once(): + assert app.invoke({"query": "what is weather in sf", "inner": {"yo": 1}}) == { + "docs": ["doc1", "doc2", "doc3", "doc4"], + "answer": "doc1,doc2,doc3,doc4", + } + + with assert_ctx_once(): + assert [ + *app.stream({"query": "what is weather in sf", "inner": {"yo": 1}}) + ] == [ + {"rewrite_query": {"query": "query: what is weather in sf"}}, + {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, + {"retriever_two": {"docs": ["doc3", "doc4"]}}, + {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + ] + + app_w_interrupt = workflow.compile( + checkpointer=MemorySaverAssertImmutable(), + interrupt_after=["retriever_one"], + ) + config = {"configurable": {"thread_id": "1"}} + + with assert_ctx_once(): + assert [ + c + for c in app_w_interrupt.stream( + {"query": "what is weather in sf", "inner": {"yo": 1}}, config + ) + ] == [ + {"rewrite_query": {"query": "query: what is weather in sf"}}, + {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, + {"retriever_two": {"docs": ["doc3", "doc4"]}}, + {"retriever_one": {"docs": ["doc1", "doc2"]}}, + ] + + with assert_ctx_once(): + assert [c for c in app_w_interrupt.stream(None, config)] == [ + {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + ] + + with assert_ctx_once(): + assert app_w_interrupt.update_state( + config, {"docs": ["doc5"]}, as_node="rewrite_query" + ) == { + "configurable": { + "thread_id": "1", + "checkpoint_id": AnyStr(), + "checkpoint_ns": "", + } + } def test_in_one_fan_out_state_graph_waiting_edge_plus_regular() -> None: @@ -7286,6 +7388,7 @@ def test_in_one_fan_out_state_graph_waiting_edge_multiple() -> None: {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"rewrite_query": {"query": "query: analyzed: query: what is weather in sf"}}, { "analyzer_one": { @@ -7294,6 +7397,7 @@ def test_in_one_fan_out_state_graph_waiting_edge_multiple() -> None: }, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"qa": {"answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4"}}, ] @@ -7421,6 +7525,7 @@ def test_in_one_fan_out_state_graph_waiting_edge_multiple_cond_edge() -> None: {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"rewrite_query": {"query": "query: analyzed: query: what is weather in sf"}}, { "analyzer_one": { @@ -7429,6 +7534,7 @@ def test_in_one_fan_out_state_graph_waiting_edge_multiple_cond_edge() -> None: }, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"qa": {"answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4"}}, ] @@ -7443,6 +7549,9 @@ def test_simple_multi_edge(snapshot: SnapshotAssertion) -> None: def side(state: State): pass + def other(state: State): + return {"my_key": "_more"} + def down(state: State): pass @@ -7450,17 +7559,33 @@ def test_simple_multi_edge(snapshot: SnapshotAssertion) -> None: graph.add_node("up", up) graph.add_node("side", side) + graph.add_node("other", other) graph.add_node("down", down) graph.set_entry_point("up") graph.add_edge("up", "side") + graph.add_edge("up", "other") graph.add_edge(["up", "side"], "down") graph.set_finish_point("down") app = graph.compile() assert app.get_graph().draw_mermaid(with_styles=False) == snapshot - assert app.invoke({"my_key": "my_value"}) == {"my_key": "my_value"} + assert app.invoke({"my_key": "my_value"}) == {"my_key": "my_value_more"} + assert [*app.stream({"my_key": "my_value"})] in ( + [ + {"up": None}, + {"side": None}, + {"other": {"my_key": "_more"}}, + {"down": None}, + ], + [ + {"up": None}, + {"other": {"my_key": "_more"}}, + {"side": None}, + {"down": None}, + ], + ) def test_nested_graph_xray(snapshot: SnapshotAssertion) -> None: @@ -7570,1298 +7695,140 @@ def test_nested_graph(snapshot: SnapshotAssertion) -> None: @pytest.mark.repeat(10) @pytest.mark.parametrize( - "checkpointer_fct", - [ - lambda: MemorySaverAssertImmutable(put_sleep=0.2), - lambda: SqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], ) def test_nested_graph_interrupts( - checkpointer_fct: Callable[[], BaseCheckpointSaver], + request: pytest.FixtureRequest, checkpointer_name: str ) -> None: - try: - checkpointer = checkpointer_fct() + checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name) - class InnerState(TypedDict): - my_key: str - my_other_key: str + class InnerState(TypedDict): + my_key: str + my_other_key: str - def inner_1(state: InnerState): - return { - "my_key": state["my_key"] + " here", - "my_other_key": state["my_key"], - } - - def inner_2(state: InnerState): - return { - "my_key": state["my_key"] + " and there", - "my_other_key": state["my_key"], - } - - inner = StateGraph(InnerState) - inner.add_node("inner_1", inner_1) - inner.add_node("inner_2", inner_2) - inner.add_edge("inner_1", "inner_2") - inner.set_entry_point("inner_1") - inner.set_finish_point("inner_2") - - class State(TypedDict): - my_key: str - - def outer_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - def outer_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("outer_1", outer_1) - graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) - graph.add_node("outer_2", outer_2) - graph.set_entry_point("outer_1") - graph.add_edge("outer_1", "inner") - graph.add_edge("inner", "outer_2") - graph.set_finish_point("outer_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - assert app.invoke({"my_key": "my value"}, config, debug=True) == { - "my_key": "hi my value", - } - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert app.invoke(None, config, debug=True) == { - "my_key": "hi my value here and there and back again", - } - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - - # test stream updates w/ nested interrupt - config = {"configurable": {"thread_id": "2"}} - assert [*app.stream({"my_key": "my value"}, config)] == [ - {"outer_1": {"my_key": "hi my value"}}, - ] - assert [*app.stream(None, config)] == [ - {"inner": {"my_key": "hi my value here and there"}}, - {"outer_2": {"my_key": "hi my value here and there and back again"}}, - ] - - # test stream values w/ nested interrupt - config = {"configurable": {"thread_id": "3"}} - assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - { - "my_key": "hi my value here and there and back again", - }, - ] - - # test interrupts BEFORE the node w/ interrupts - app = graph.compile(checkpointer=checkpointer, interrupt_before=["inner"]) - config = {"configurable": {"thread_id": "4"}} - assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - # while we're waiting for the node w/ interrupt inside to finish - assert [*app.stream(None, config, stream_mode="values")] == [] - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - { - "my_key": "hi my value here and there and back again", - }, - ] - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - - # test interrupts AFTER the node w/ interrupts - app = graph.compile(checkpointer=checkpointer, interrupt_after=["inner"]) - config = {"configurable": {"thread_id": "5"}} - assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - # interrupted after "inner" - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - ] - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there and back again", - }, - ] - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - - # test restarting from thread_ts - config = {"configurable": {"thread_id": "6"}} - app = graph.compile(checkpointer=checkpointer) - assert app.invoke({"my_key": "my value"}, config, debug=True) == { - "my_key": "hi my value" - } - state_history = [c for c in app.get_state_history(config)] - assert state_history == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - child_state_history = [ - c - for c in app.get_state_history({"configurable": {"thread_id": "6__inner"}}) - ] - assert child_state_history == [ - StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": { - "thread_id": "6__inner", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "inner_1": { - "my_key": "hi my value here", - "my_other_key": "hi my value", - } - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "6__inner", - "thread_ts": AnyStr(), - } - }, - ), - # there should be a single child checkpoint because we only keep - # one child checkpoint per parent checkpoint (in which child ran) - ] - - # check that child snapshot matches id of parent - child_snapshot = child_state_history[0] - assert ( - child_snapshot.config["configurable"]["thread_ts"] - == state_history[0].config["configurable"]["thread_ts"] - ) - # check resuming from interrupt w/ thread_ts - interrupt_state_snapshot, before_interrupt_state_snapshot = state_history[:2] - before_interrupt_config = before_interrupt_state_snapshot.config - # going to get to interrupt again here, so the output is None - assert app.invoke(None, before_interrupt_config, debug=True) == { - "my_key": "hi my value" - } - # one more "identical" snapshot than before, at top of list - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - # going to restart from interrupt - interrupt_config = interrupt_state_snapshot.config - assert app.invoke(None, interrupt_config, debug=True) == { - "my_key": "hi my value here and there and back again", - } - assert list(app.get_state_history(config)) == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - finally: - if hasattr(checkpointer, "__exit__"): - checkpointer.__exit__(None, None, None) - - -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - SqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -def test_nested_graph_interrupts_parallel(checkpointer: BaseCheckpointSaver) -> None: - try: - - class InnerState(TypedDict): - my_key: Annotated[str, operator.add] - my_other_key: str - - def inner_1(state: InnerState): - time.sleep(0.1) - return {"my_key": "got here", "my_other_key": state["my_key"]} - - def inner_2(state: InnerState): - return { - "my_key": " and there", - "my_other_key": state["my_key"], - } - - inner = StateGraph(InnerState) - inner.add_node("inner_1", inner_1) - inner.add_node("inner_2", inner_2) - inner.add_edge("inner_1", "inner_2") - inner.set_entry_point("inner_1") - inner.set_finish_point("inner_2") - - class State(TypedDict): - my_key: Annotated[str, operator.add] - - def outer_1(state: State): - return {"my_key": " and parallel"} - - def outer_2(state: State): - return {"my_key": " and back again"} - - graph = StateGraph(State) - graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) - graph.add_node("outer_1", outer_1) - graph.add_node("outer_2", outer_2) - - graph.add_edge(START, "inner") - graph.add_edge(START, "outer_1") - graph.add_edge(["inner", "outer_1"], "outer_2") - graph.set_finish_point("outer_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - assert app.invoke({"my_key": ""}, config, debug=True) == { - "my_key": "", + def inner_1(state: InnerState): + return { + "my_key": state["my_key"] + " here", + "my_other_key": state["my_key"], } - assert app.invoke(None, config, debug=True) == { - "my_key": "got here and there and parallel and back again", + def inner_2(state: InnerState): + return { + "my_key": state["my_key"] + " and there", + "my_other_key": state["my_key"], } - # below combo of assertions is asserting two things - # - outer_1 finishes before inner interrupts (because we see its output in stream, which only happens after node finishes) - # - the writes of outer are persisted in 1st call and used in 2nd call, ie outer isn't called again (because we dont see outer_1 output again in 2nd stream) - # test stream updates w/ nested interrupt - config = {"configurable": {"thread_id": "2"}} - assert [*app.stream({"my_key": ""}, config)] == [ - # we got to parallel node first - {"outer_1": {"my_key": " and parallel"}}, - ] - assert [*app.stream(None, config)] == [ - {"inner": {"my_key": "got here and there"}}, - {"outer_2": {"my_key": " and back again"}}, - ] + inner = StateGraph(InnerState) + inner.add_node("inner_1", inner_1) + inner.add_node("inner_2", inner_2) + inner.add_edge("inner_1", "inner_2") + inner.set_entry_point("inner_1") + inner.set_finish_point("inner_2") - # test stream values w/ nested interrupt - config = {"configurable": {"thread_id": "3"}} - assert [*app.stream({"my_key": ""}, config, stream_mode="values")] == [ - { - "my_key": "", - }, - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "got here and there and parallel", - }, - { - "my_key": "got here and there and parallel and back again", - }, - ] + class State(TypedDict): + my_key: str - # test interrupts BEFORE the parallel node - app = graph.compile(checkpointer=checkpointer, interrupt_before=["outer_1"]) - config = {"configurable": {"thread_id": "4"}} - assert [*app.stream({"my_key": ""}, config, stream_mode="values")] == [ - {"my_key": ""} - ] - # while we're waiting for the node w/ interrupt inside to finish - assert [*app.stream(None, config, stream_mode="values")] == [] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "got here and there and parallel", - }, - { - "my_key": "got here and there and parallel and back again", - }, - ] + def outer_1(state: State): + return {"my_key": "hi " + state["my_key"]} - # test interrupts AFTER the parallel node - app = graph.compile(checkpointer=checkpointer, interrupt_after=["outer_1"]) - config = {"configurable": {"thread_id": "5"}} - assert [*app.stream({"my_key": ""}, config, stream_mode="values")] == [ - {"my_key": ""} - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - {"my_key": "got here and there and parallel"}, - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "got here and there and parallel and back again", - }, - ] - finally: - if hasattr(checkpointer, "__exit__"): - checkpointer.__exit__(None, None, None) + def outer_2(state: State): + return {"my_key": state["my_key"] + " and back again"} + graph = StateGraph(State) + graph.add_node("outer_1", outer_1) + graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) + graph.add_node("outer_2", outer_2) + graph.set_entry_point("outer_1") + graph.add_edge("outer_1", "inner") + graph.add_edge("inner", "outer_2") + graph.set_finish_point("outer_2") -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - SqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -def test_doubly_nested_graph_interrupts(checkpointer: BaseCheckpointSaver) -> None: - try: + app = graph.compile(checkpointer=checkpointer) - class State(TypedDict): - my_key: str - - class ChildState(TypedDict): - my_key: str - - class GrandChildState(TypedDict): - my_key: str - - def grandchild_1(state: ChildState): - return {"my_key": state["my_key"] + " here"} - - def grandchild_2(state: ChildState): - return { - "my_key": state["my_key"] + " and there", - } - - grandchild = StateGraph(GrandChildState) - grandchild.add_node("grandchild_1", grandchild_1) - grandchild.add_node("grandchild_2", grandchild_2) - grandchild.add_edge("grandchild_1", "grandchild_2") - grandchild.set_entry_point("grandchild_1") - grandchild.set_finish_point("grandchild_2") - - child = StateGraph(ChildState) - child.add_node("child_1", grandchild.compile(interrupt_before=["grandchild_2"])) - child.set_entry_point("child_1") - child.set_finish_point("child_1") - - def parent_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - def parent_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("parent_1", parent_1) - graph.add_node("child", child.compile()) - graph.add_node("parent_2", parent_2) - graph.set_entry_point("parent_1") - graph.add_edge("parent_1", "child") - graph.add_edge("child", "parent_2") - graph.set_finish_point("parent_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - assert app.invoke({"my_key": "my value"}, config, debug=True) == { - "my_key": "hi my value", - } - - assert app.invoke(None, config, debug=True) == { - "my_key": "hi my value here and there and back again", - } - - # test stream updates w/ nested interrupt - config = {"configurable": {"thread_id": "2"}} - assert [*app.stream({"my_key": "my value"}, config)] == [ - {"parent_1": {"my_key": "hi my value"}}, - ] - assert [*app.stream(None, config)] == [ - {"child": {"my_key": "hi my value here and there"}}, - {"parent_2": {"my_key": "hi my value here and there and back again"}}, - ] - - # test stream values w/ nested interrupt - config = {"configurable": {"thread_id": "3"}} - assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert [*app.stream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - { - "my_key": "hi my value here and there and back again", - }, - ] - finally: - if hasattr(checkpointer, "__exit__"): - checkpointer.__exit__(None, None, None) - - -@pytest.mark.parametrize( - "checkpointer_fct", - [ - lambda: MemorySaverAssertImmutable(put_sleep=0.2), - lambda: SqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -def test_nested_graph_state( - checkpointer_fct: Callable[[], BaseCheckpointSaver], -) -> None: - try: - checkpointer = checkpointer_fct() - - class InnerState(TypedDict): - my_key: str - my_other_key: str - - def inner_1(state: InnerState): - return { - "my_key": state["my_key"] + " here", - "my_other_key": state["my_key"], - } - - def inner_2(state: InnerState): - return { - "my_key": state["my_key"] + " and there", - "my_other_key": state["my_key"], - } - - inner = StateGraph(InnerState) - inner.add_node("inner_1", inner_1) - inner.add_node("inner_2", inner_2) - inner.add_edge("inner_1", "inner_2") - inner.set_entry_point("inner_1") - inner.set_finish_point("inner_2") - - class State(TypedDict): - my_key: str - - def outer_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - def outer_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("outer_1", outer_1) - graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) - graph.add_node("outer_2", outer_2) - graph.set_entry_point("outer_1") - graph.add_edge("outer_1", "inner") - graph.add_edge("inner", "outer_2") - graph.set_finish_point("outer_2") - - app = graph.compile(checkpointer=checkpointer) - - config = {"configurable": {"thread_id": "1"}} - app.invoke({"my_key": "my value"}, config, debug=True) - # test state w/ nested subgraph state (right after interrupt) - assert app.get_state(config, include_subgraph_state=False) == StateSnapshot( + # test invoke w/ nested interrupt + config = {"configurable": {"thread_id": "1"}} + assert app.invoke({"my_key": "my value"}, config, debug=True) == { + "my_key": "hi my value", + } + assert list(app.get_state_history(config)) == [ + StateSnapshot( values={"my_key": "hi my value"}, next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", "writes": {"outer_1": {"my_key": "hi my value"}}, "step": 1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots=None, - ) - assert app.get_state(config, include_subgraph_state=True) == StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - metadata={ - "source": "loop", - "writes": { - "inner_1": { - "my_key": "hi my value here", - "my_other_key": "hi my value", - } - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots=None, - ) - }, - ) - assert list(app.get_state_history(config, include_subgraph_state=True)) == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "inner_1": { - "my_key": "hi my value here", - "my_other_key": "hi my value", - } - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - subgraph_state_snapshots=None, - ), - ] - app.invoke(None, config, debug=True) - # test state w/ nested subgraph state (after resuming from interrupt) - assert app.get_state(config, include_subgraph_state=True) == StateSnapshot( + parent_config=None, + ), + ] + assert app.invoke(None, config, debug=True) == { + "my_key": "hi my value here and there and back again", + } + assert list(app.get_state_history(config)) == [ + StateSnapshot( values={"my_key": "hi my value here and there and back again"}, next=(), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", "writes": { @@ -8870,377 +7837,1260 @@ def test_nested_graph_state( "step": 3, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - metadata={ - "source": "loop", - "writes": { - "inner_2": { - "my_key": "hi my value here and there", - "my_other_key": "hi my value here", - } - }, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots=None, - ) + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } }, - ) - assert list(app.get_state_history(config, include_subgraph_state=True)) == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - # TODO: this is likely very confusing for an end user, and we'll probably need to update this. - # right now this is happening due to us overwriting the - # subgraph snapshot after we finish the graph with while the thread_ts - # is the same as when we interrupted - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "inner_2": { - "my_key": "hi my value here and there", - "my_other_key": "hi my value here", - } - }, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - subgraph_state_snapshots=None, - ), - ] - finally: - if hasattr(checkpointer, "__exit__"): - checkpointer.__exit__(None, None, None) - - -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - SqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -def test_doubly_nested_graph_state(checkpointer: BaseCheckpointSaver) -> None: - try: - - class State(TypedDict): - my_key: str - - class ChildState(TypedDict): - my_key: str - - class GrandChildState(TypedDict): - my_key: str - - def grandchild_1(state: ChildState): - return {"my_key": state["my_key"] + " here"} - - def grandchild_2(state: ChildState): - return { - "my_key": state["my_key"] + " and there", - } - - grandchild = StateGraph(GrandChildState) - grandchild.add_node("grandchild_1", grandchild_1) - grandchild.add_node("grandchild_2", grandchild_2) - grandchild.add_edge("grandchild_1", "grandchild_2") - grandchild.set_entry_point("grandchild_1") - grandchild.set_finish_point("grandchild_2") - - child = StateGraph(ChildState) - child.add_node("child_1", grandchild.compile(interrupt_before=["grandchild_2"])) - child.set_entry_point("child_1") - child.set_finish_point("child_1") - - def parent_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - def parent_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("parent_1", parent_1) - graph.add_node("child", child.compile()) - graph.add_node("parent_2", parent_2) - graph.set_entry_point("parent_1") - graph.add_edge("parent_1", "child") - graph.add_edge("child", "parent_2") - graph.set_finish_point("parent_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - app.invoke({"my_key": "my value"}, config, debug=True) - assert app.get_state(config) == StateSnapshot( - values={"my_key": "hi my value"}, - next=("child",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, metadata={ "source": "loop", - "writes": {"parent_1": {"my_key": "hi my value"}}, + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, "step": 1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots=None, - ) - assert app.get_state(config, include_subgraph_state=True) == StateSnapshot( + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + + # test stream updates w/ nested interrupt + config = {"configurable": {"thread_id": "2"}} + assert [*app.stream({"my_key": "my value"}, config)] == [ + {"outer_1": {"my_key": "hi my value"}}, + ] + assert [*app.stream(None, config)] == [ + {"inner": {"my_key": "hi my value here and there"}}, + {"outer_2": {"my_key": "hi my value here and there and back again"}}, + ] + + # test stream values w/ nested interrupt + config = {"configurable": {"thread_id": "3"}} + assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + { + "my_key": "hi my value here and there and back again", + }, + ] + + # test interrupts BEFORE the node w/ interrupts + app = graph.compile(checkpointer=checkpointer, interrupt_before=["inner"]) + config = {"configurable": {"thread_id": "4"}} + assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert list(app.get_state_history(config)) == [ + StateSnapshot( values={"my_key": "hi my value"}, - next=("child",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + next=("inner",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", - "writes": {"parent_1": {"my_key": "hi my value"}}, + "writes": {"outer_1": {"my_key": "hi my value"}}, "step": 1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "child": StateSnapshot( - values={"my_key": "hi my value"}, - next=(), - config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots={ - "child_1": StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "grandchild_1": {"my_key": "hi my value here"} - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ) + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } }, - ) - app.invoke(None, config, debug=True) - assert app.get_state(config, include_subgraph_state=True) == StateSnapshot( + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + # while we're waiting for the node w/ interrupt inside to finish + assert [*app.stream(None, config, stream_mode="values")] == [] + assert list(app.get_state_history(config)) == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + { + "my_key": "hi my value here and there and back again", + }, + ] + assert list(app.get_state_history(config)) == [ + StateSnapshot( values={"my_key": "hi my value here and there and back again"}, next=(), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", "writes": { - "parent_2": {"my_key": "hi my value here and there and back again"} + "outer_2": {"my_key": "hi my value here and there and back again"} }, "step": 3, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "child": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - metadata={ - "source": "loop", - "writes": {"child_1": {"my_key": "hi my value here and there"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots={ - "child_1": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "grandchild_2": { - "my_key": "hi my value here and there" - } - }, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ) + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } }, - ) + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] - finally: - if hasattr(checkpointer, "__exit__"): - checkpointer.__exit__(None, None, None) + # test interrupts AFTER the node w/ interrupts + app = graph.compile(checkpointer=checkpointer, interrupt_after=["inner"]) + config = {"configurable": {"thread_id": "5"}} + assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + # interrupted after "inner" + assert list(app.get_state_history(config)) == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + ] + assert list(app.get_state_history(config)) == [ + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there and back again", + }, + ] + assert list(app.get_state_history(config)) == [ + StateSnapshot( + values={"my_key": "hi my value here and there and back again"}, + next=(), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": { + "outer_2": {"my_key": "hi my value here and there and back again"} + }, + "step": 3, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + + # test restarting from checkpoint_id + config = {"configurable": {"thread_id": "6"}} + app = graph.compile(checkpointer=checkpointer) + assert app.invoke({"my_key": "my value"}, config, debug=True) == { + "my_key": "hi my value" + } + state_history = [c for c in app.get_state_history(config)] + assert state_history == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + child_state_history = [ + c + for c in app.get_state_history( + {"configurable": {"thread_id": "6", "checkpoint_ns": "inner"}} + ) + ] + assert child_state_history == [ + StateSnapshot( + values={"my_key": "hi my value here"}, + next=(), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "inner", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": { + "inner_1": { + "my_key": "hi my value here", + "my_other_key": "hi my value", + } + }, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "inner", + "checkpoint_id": AnyStr(), + } + }, + ), + # there should be a single child checkpoint because we only keep + # one child checkpoint per parent checkpoint (in which child ran) + ] + + # check that child snapshot matches id of parent + child_snapshot = child_state_history[0] + assert ( + child_snapshot.config["configurable"]["checkpoint_id"] + == state_history[0].config["configurable"]["checkpoint_id"] + ) + # check resuming from interrupt w/ checkpoint_id + interrupt_state_snapshot, before_interrupt_state_snapshot = state_history[:2] + before_interrupt_config = before_interrupt_state_snapshot.config + # going to get to interrupt again here, so the output is None + assert app.invoke(None, before_interrupt_config, debug=True) == { + "my_key": "hi my value" + } + # one more "identical" snapshot than before, at top of list + assert list(app.get_state_history(config)) == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + # going to restart from interrupt + interrupt_config = interrupt_state_snapshot.config + assert app.invoke(None, interrupt_config, debug=True) == { + "my_key": "hi my value here and there and back again", + } + assert list(app.get_state_history(config)) == [ + StateSnapshot( + values={"my_key": "hi my value here and there and back again"}, + next=(), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": { + "outer_2": {"my_key": "hi my value here and there and back again"} + }, + "step": 3, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + + +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_nested_graph_interrupts_parallel( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name) + + class InnerState(TypedDict): + my_key: Annotated[str, operator.add] + my_other_key: str + + def inner_1(state: InnerState): + time.sleep(0.1) + return {"my_key": "got here", "my_other_key": state["my_key"]} + + def inner_2(state: InnerState): + return { + "my_key": " and there", + "my_other_key": state["my_key"], + } + + inner = StateGraph(InnerState) + inner.add_node("inner_1", inner_1) + inner.add_node("inner_2", inner_2) + inner.add_edge("inner_1", "inner_2") + inner.set_entry_point("inner_1") + inner.set_finish_point("inner_2") + + class State(TypedDict): + my_key: Annotated[str, operator.add] + + def outer_1(state: State): + return {"my_key": " and parallel"} + + def outer_2(state: State): + return {"my_key": " and back again"} + + graph = StateGraph(State) + graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) + graph.add_node("outer_1", outer_1) + graph.add_node("outer_2", outer_2) + + graph.add_edge(START, "inner") + graph.add_edge(START, "outer_1") + graph.add_edge(["inner", "outer_1"], "outer_2") + graph.set_finish_point("outer_2") + + app = graph.compile(checkpointer=checkpointer) + + # test invoke w/ nested interrupt + config = {"configurable": {"thread_id": "1"}} + assert app.invoke({"my_key": ""}, config, debug=True) == { + "my_key": "", + } + + assert app.invoke(None, config, debug=True) == { + "my_key": "got here and there and parallel and back again", + } + + # below combo of assertions is asserting two things + # - outer_1 finishes before inner interrupts (because we see its output in stream, which only happens after node finishes) + # - the writes of outer are persisted in 1st call and used in 2nd call, ie outer isn't called again (because we dont see outer_1 output again in 2nd stream) + # test stream updates w/ nested interrupt + config = {"configurable": {"thread_id": "2"}} + assert [*app.stream({"my_key": ""}, config)] == [ + # we got to parallel node first + {"outer_1": {"my_key": " and parallel"}}, + ] + assert [*app.stream(None, config)] == [ + {"inner": {"my_key": "got here and there"}}, + {"outer_2": {"my_key": " and back again"}}, + ] + + # test stream values w/ nested interrupt + config = {"configurable": {"thread_id": "3"}} + assert [*app.stream({"my_key": ""}, config, stream_mode="values")] == [ + { + "my_key": "", + }, + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "got here and there and parallel", + }, + { + "my_key": "got here and there and parallel and back again", + }, + ] + + # test interrupts BEFORE the parallel node + app = graph.compile(checkpointer=checkpointer, interrupt_before=["outer_1"]) + config = {"configurable": {"thread_id": "4"}} + assert [*app.stream({"my_key": ""}, config, stream_mode="values")] == [ + {"my_key": ""} + ] + # while we're waiting for the node w/ interrupt inside to finish + assert [*app.stream(None, config, stream_mode="values")] == [] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "got here and there and parallel", + }, + { + "my_key": "got here and there and parallel and back again", + }, + ] + + # test interrupts AFTER the parallel node + app = graph.compile(checkpointer=checkpointer, interrupt_after=["outer_1"]) + config = {"configurable": {"thread_id": "5"}} + assert [*app.stream({"my_key": ""}, config, stream_mode="values")] == [ + {"my_key": ""} + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + {"my_key": "got here and there and parallel"}, + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "got here and there and parallel and back again", + }, + ] + + +@pytest.mark.skip +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_doubly_nested_graph_interrupts( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(checkpointer_name) + + class State(TypedDict): + my_key: str + + class ChildState(TypedDict): + my_key: str + + class GrandChildState(TypedDict): + my_key: str + + def grandchild_1(state: ChildState): + return {"my_key": state["my_key"] + " here"} + + def grandchild_2(state: ChildState): + return { + "my_key": state["my_key"] + " and there", + } + + grandchild = StateGraph(GrandChildState) + grandchild.add_node("grandchild_1", grandchild_1) + grandchild.add_node("grandchild_2", grandchild_2) + grandchild.add_edge("grandchild_1", "grandchild_2") + grandchild.set_entry_point("grandchild_1") + grandchild.set_finish_point("grandchild_2") + + child = StateGraph(ChildState) + child.add_node("child_1", grandchild.compile(interrupt_before=["grandchild_2"])) + child.set_entry_point("child_1") + child.set_finish_point("child_1") + + def parent_1(state: State): + return {"my_key": "hi " + state["my_key"]} + + def parent_2(state: State): + return {"my_key": state["my_key"] + " and back again"} + + graph = StateGraph(State) + graph.add_node("parent_1", parent_1) + graph.add_node("child", child.compile()) + graph.add_node("parent_2", parent_2) + graph.set_entry_point("parent_1") + graph.add_edge("parent_1", "child") + graph.add_edge("child", "parent_2") + graph.set_finish_point("parent_2") + + app = graph.compile(checkpointer=checkpointer) + + # test invoke w/ nested interrupt + config = {"configurable": {"thread_id": "1"}} + assert app.invoke({"my_key": "my value"}, config, debug=True) == { + "my_key": "hi my value", + } + + assert app.invoke(None, config, debug=True) == { + "my_key": "hi my value here and there and back again", + } + + # test stream updates w/ nested interrupt + config = {"configurable": {"thread_id": "2"}} + assert [*app.stream({"my_key": "my value"}, config)] == [ + {"parent_1": {"my_key": "hi my value"}}, + ] + assert [*app.stream(None, config)] == [ + {"child": {"my_key": "hi my value here and there"}}, + {"parent_2": {"my_key": "hi my value here and there and back again"}}, + ] + + # test stream values w/ nested interrupt + config = {"configurable": {"thread_id": "3"}} + assert [*app.stream({"my_key": "my value"}, config, stream_mode="values")] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert [*app.stream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + { + "my_key": "hi my value here and there and back again", + }, + ] def test_repeat_condition(snapshot: SnapshotAssertion) -> None: @@ -9295,7 +9145,7 @@ def test_checkpoint_metadata() -> None: from langchain_core.language_models.fake_chat_models import ( FakeMessagesListChatModel, ) - from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage + from langchain_core.messages import AIMessage, AnyMessage, ToolMessage from langchain_core.prompts import ChatPromptTemplate from langchain_core.tools import tool @@ -9381,7 +9231,7 @@ def test_checkpoint_metadata() -> None: }, ) == { "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", id=AnyStr(), @@ -9400,7 +9250,7 @@ def test_checkpoint_metadata() -> None: id=AnyStr(), tool_call_id="tool_call123", ), - AIMessage(content="answer", id=AnyStr()), + _AnyIdAIMessage(content="answer"), ] } @@ -9469,7 +9319,13 @@ def test_checkpoint_metadata() -> None: assert chkpnt_tuple.metadata["test_config_4"] == "bar" -def test_remove_message_via_state_update(): +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_remove_message_via_state_update( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage workflow = MessageGraph() @@ -9485,7 +9341,7 @@ def test_remove_message_via_state_update(): workflow.set_entry_point("chatbot") workflow.add_edge("chatbot", END) - checkpointer = MemorySaver() + checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name) app = workflow.compile(checkpointer=checkpointer) config = {"configurable": {"thread_id": "1"}} output = app.invoke([HumanMessage(content="Hi")], config=config) @@ -9521,3 +9377,156 @@ def test_remove_message_from_node(): output = app.invoke([HumanMessage(content="Hi")]) assert len(output) == 2 assert output[-1].content == "How can I help you?" + + +def test_xray_lance(snapshot: SnapshotAssertion): + from langchain_core.messages import AnyMessage, HumanMessage + from langchain_core.pydantic_v1 import BaseModel, Field + + class Analyst(BaseModel): + affiliation: str = Field( + description="Primary affiliation of the investment analyst.", + ) + name: str = Field( + description="Name of the investment analyst.", + pattern=r"^[a-zA-Z0-9_-]{1,64}$", + ) + role: str = Field( + description="Role of the investment analyst in the context of the topic.", + ) + description: str = Field( + description="Description of the investment analyst focus, concerns, and motives.", + ) + + @property + def persona(self) -> str: + return f"Name: {self.name}\nRole: {self.role}\nAffiliation: {self.affiliation}\nDescription: {self.description}\n" + + class Perspectives(BaseModel): + analysts: List[Analyst] = Field( + description="Comprehensive list of investment analysts with their roles and affiliations.", + ) + + class Section(BaseModel): + section_title: str = Field(..., title="Title of the section") + context: str = Field( + ..., title="Provide a clear summary of the focus area that you researched." + ) + findings: str = Field( + ..., + title="Give a clear and detailed overview of your findings based upon the expert interview.", + ) + thesis: str = Field( + ..., + title="Give a clear and specific investment thesis based upon these findings.", + ) + + class InterviewState(TypedDict): + messages: Annotated[List[AnyMessage], add_messages] + analyst: Analyst + section: Section + + class ResearchGraphState(TypedDict): + analysts: List[Analyst] + topic: str + max_analysts: int + sections: List[Section] + interviews: Annotated[list, operator.add] + + # Conditional edge + def route_messages(state): + return "ask_question" + + def generate_question(state): + return ... + + def generate_answer(state): + return ... + + # Add nodes and edges + interview_builder = StateGraph(InterviewState) + interview_builder.add_node("ask_question", generate_question) + interview_builder.add_node("answer_question", generate_answer) + + # Flow + interview_builder.add_edge(START, "ask_question") + interview_builder.add_edge("ask_question", "answer_question") + interview_builder.add_conditional_edges("answer_question", route_messages) + + # Set up memory + memory = MemorySaver() + + # Interview + interview_graph = interview_builder.compile(checkpointer=memory).with_config( + run_name="Conduct Interviews" + ) + + # View + assert interview_graph.get_graph().to_json() == snapshot + + def run_all_interviews(state: ResearchGraphState): + """Edge to run the interview sub-graph using Send""" + return [ + Send( + "conduct_interview", + { + "analyst": Analyst(), + "messages": [ + HumanMessage( + content="So you said you were writing an article on ...?" + ) + ], + }, + ) + for s in state["analysts"] + ] + + def generate_sections(state: ResearchGraphState): + return ... + + def generate_analysts(state: ResearchGraphState): + return ... + + builder = StateGraph(ResearchGraphState) + builder.add_node("generate_analysts", generate_analysts) + builder.add_node("conduct_interview", interview_builder.compile()) + builder.add_node("generate_sections", generate_sections) + + builder.add_edge(START, "generate_analysts") + builder.add_conditional_edges( + "generate_analysts", run_all_interviews, ["conduct_interview"] + ) + builder.add_edge("conduct_interview", "generate_sections") + builder.add_edge("generate_sections", END) + + graph = builder.compile() + + # View + assert graph.get_graph().to_json() == snapshot + assert graph.get_graph(xray=1).to_json() == snapshot + + +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite", "postgres", "postgres_pipe"], +) +def test_channel_values(request: pytest.FixtureRequest, checkpointer_name: str) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + + config = {"configurable": {"thread_id": "1"}} + chain = Channel.subscribe_to("input") | Channel.write_to("output") + app = Pregel( + nodes={ + "one": chain, + }, + channels={ + "ephemeral": EphemeralValue(Any), + "input": LastValue(int), + "output": LastValue(int), + }, + input_channels=["input", "ephemeral"], + output_channels="output", + checkpointer=checkpointer, + ) + app.invoke({"input": 1, "ephemeral": "meow"}, config) + assert checkpointer.get(config)["channel_values"] == {"input": 1, "output": 1} diff --git a/libs/langgraph/tests/test_pregel_async.py b/libs/langgraph/tests/test_pregel_async.py index a9653b546..67b62941b 100644 --- a/libs/langgraph/tests/test_pregel_async.py +++ b/libs/langgraph/tests/test_pregel_async.py @@ -8,13 +8,11 @@ from typing import ( Any, AsyncGenerator, AsyncIterator, - Callable, Dict, Generator, List, Literal, Optional, - Sequence, Tuple, TypedDict, Union, @@ -39,18 +37,21 @@ from langgraph.channels.binop import BinaryOperatorAggregate from langgraph.channels.context import Context from langgraph.channels.last_value import LastValue from langgraph.channels.topic import Topic -from langgraph.checkpoint import BaseCheckpointSaver -from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver -from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, CheckpointTuple +from langgraph.channels.untracked_value import UntrackedValue +from langgraph.checkpoint.base import ( + ChannelVersions, + Checkpoint, + CheckpointMetadata, + CheckpointTuple, +) from langgraph.checkpoint.memory import MemorySaver +from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver from langgraph.constants import Send from langgraph.errors import InvalidUpdateError from langgraph.graph import END, Graph, StateGraph from langgraph.graph.graph import START from langgraph.graph.message import MessageGraph, add_messages -from langgraph.managed.few_shot import FewShotExamples from langgraph.prebuilt.chat_agent_executor import ( - create_function_calling_executor, create_tool_calling_executor, ) from langgraph.prebuilt.tool_executor import ToolExecutor @@ -62,6 +63,7 @@ from tests.memory_assert import ( MemorySaverAssertCheckpointMetadata, MemorySaverAssertImmutable, ) +from tests.messages import _AnyIdAIMessage, _AnyIdHumanMessage async def test_checkpoint_errors() -> None: @@ -75,6 +77,7 @@ async def test_checkpoint_errors() -> None: config: RunnableConfig, checkpoint: Checkpoint, metadata: CheckpointMetadata, + new_versions: ChannelVersions, ) -> RunnableConfig: raise ValueError("Faulty put") @@ -230,184 +233,162 @@ async def test_step_timeout_on_stream_hang() -> None: @pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - None, - ], - ids=[ - "memory", - "aiosqlite", - "none", - ], + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], ) async def test_cancel_graph_astream( - checkpointer: Optional[BaseCheckpointSaver], + request: pytest.FixtureRequest, checkpointer_name: str ) -> None: - try: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") - class State(TypedDict): - value: Annotated[int, operator.add] + class State(TypedDict): + value: Annotated[int, operator.add] - class AwhileMaker: - def __init__(self) -> None: - self.reset() + class AwhileMaker: + def __init__(self) -> None: + self.reset() - async def __call__(self, input: State) -> Any: - self.started = True - try: - await asyncio.sleep(1.5) - except asyncio.CancelledError: - self.cancelled = True - raise + async def __call__(self, input: State) -> Any: + self.started = True + try: + await asyncio.sleep(1.5) + except asyncio.CancelledError: + self.cancelled = True + raise - def reset(self): - self.started = False - self.cancelled = False + def reset(self): + self.started = False + self.cancelled = False - async def alittlewhile(input: State) -> None: - await asyncio.sleep(0.6) - return {"value": 2} + async def alittlewhile(input: State) -> None: + await asyncio.sleep(0.6) + return {"value": 2} - awhile = AwhileMaker() - aparallelwhile = AwhileMaker() - builder = StateGraph(State) - builder.add_node("awhile", awhile) - builder.add_node("aparallelwhile", aparallelwhile) - builder.add_node(alittlewhile) - builder.add_edge(START, "alittlewhile") - builder.add_edge(START, "aparallelwhile") - builder.add_edge("alittlewhile", "awhile") - graph = builder.compile(checkpointer=checkpointer) + awhile = AwhileMaker() + aparallelwhile = AwhileMaker() + builder = StateGraph(State) + builder.add_node("awhile", awhile) + builder.add_node("aparallelwhile", aparallelwhile) + builder.add_node(alittlewhile) + builder.add_edge(START, "alittlewhile") + builder.add_edge(START, "aparallelwhile") + builder.add_edge("alittlewhile", "awhile") + graph = builder.compile(checkpointer=checkpointer) - # test interrupting astream - got_event = False - thread1: RunnableConfig = {"configurable": {"thread_id": "1"}} - async with aclosing(graph.astream({"value": 1}, thread1)) as stream: - async for chunk in stream: - assert chunk == {"alittlewhile": {"value": 2}} - got_event = True - break + # test interrupting astream + got_event = False + thread1: RunnableConfig = {"configurable": {"thread_id": "1"}} + async with aclosing(graph.astream({"value": 1}, thread1)) as stream: + async for chunk in stream: + assert chunk == {"alittlewhile": {"value": 2}} + got_event = True + break - assert got_event + assert got_event - # node aparallelwhile should start, but be cancelled - assert aparallelwhile.started is True - assert aparallelwhile.cancelled is True + # node aparallelwhile should start, but be cancelled + assert aparallelwhile.started is True + assert aparallelwhile.cancelled is True - # node "awhile" should never start - assert awhile.started is False + # node "awhile" should never start + assert awhile.started is False - # checkpoint with output of "alittlewhile" should not be saved - if checkpointer is not None: - state = await graph.aget_state(thread1) - assert state is not None - assert state.values == {"value": 1} - assert state.next == ( - "aparallelwhile", - "alittlewhile", - ) - assert state.metadata == {"source": "loop", "step": 0, "writes": None} - finally: - if getattr(checkpointer, "__aexit__", None): - await checkpointer.__aexit__(None, None, None) + # checkpoint with output of "alittlewhile" should not be saved + if checkpointer is not None: + state = await graph.aget_state(thread1) + assert state is not None + assert state.values == {"value": 1} + assert state.next == ( + "aparallelwhile", + "alittlewhile", + ) + assert state.metadata == {"source": "loop", "step": 0, "writes": None} @pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - None, - ], - ids=[ - "memory", - "aiosqlite", - "none", - ], + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe", None], ) async def test_cancel_graph_astream_events_v2( - checkpointer: Optional[BaseCheckpointSaver], + request: pytest.FixtureRequest, checkpointer_name: Optional[str] ) -> None: - try: + checkpointer = ( + request.getfixturevalue(f"checkpointer_{checkpointer_name}") + if checkpointer_name + else None + ) - class State(TypedDict): - value: int + class State(TypedDict): + value: int - class AwhileMaker: - def __init__(self) -> None: - self.reset() + class AwhileMaker: + def __init__(self) -> None: + self.reset() - async def __call__(self, input: State) -> Any: - self.started = True - try: - await asyncio.sleep(1.5) - except asyncio.CancelledError: - self.cancelled = True - raise + async def __call__(self, input: State) -> Any: + self.started = True + try: + await asyncio.sleep(1.5) + except asyncio.CancelledError: + self.cancelled = True + raise - def reset(self): - self.started = False - self.cancelled = False + def reset(self): + self.started = False + self.cancelled = False - async def alittlewhile(input: State) -> None: - await asyncio.sleep(0.6) - return {"value": 2} + async def alittlewhile(input: State) -> None: + await asyncio.sleep(0.6) + return {"value": 2} - awhile = AwhileMaker() - anotherwhile = AwhileMaker() - builder = StateGraph(State) - builder.add_node(alittlewhile) - builder.add_node("awhile", awhile) - builder.add_node("anotherwhile", anotherwhile) - builder.add_edge(START, "alittlewhile") - builder.add_edge("alittlewhile", "awhile") - builder.add_edge("awhile", "anotherwhile") - graph = builder.compile(checkpointer=checkpointer) + awhile = AwhileMaker() + anotherwhile = AwhileMaker() + builder = StateGraph(State) + builder.add_node(alittlewhile) + builder.add_node("awhile", awhile) + builder.add_node("anotherwhile", anotherwhile) + builder.add_edge(START, "alittlewhile") + builder.add_edge("alittlewhile", "awhile") + builder.add_edge("awhile", "anotherwhile") + graph = builder.compile(checkpointer=checkpointer) - # test interrupting astream_events v2 - got_event = False - thread2: RunnableConfig = {"configurable": {"thread_id": "2"}} - async with aclosing( - graph.astream_events({"value": 1}, thread2, version="v2") - ) as stream: - async for chunk in stream: - if chunk["event"] == "on_chain_stream" and not chunk["parent_ids"]: - got_event = True - assert chunk["data"]["chunk"] == {"alittlewhile": {"value": 2}} - break + # test interrupting astream_events v2 + got_event = False + thread2: RunnableConfig = {"configurable": {"thread_id": "2"}} + async with aclosing( + graph.astream_events({"value": 1}, thread2, version="v2") + ) as stream: + async for chunk in stream: + if chunk["event"] == "on_chain_stream" and not chunk["parent_ids"]: + got_event = True + assert chunk["data"]["chunk"] == {"alittlewhile": {"value": 2}} + break - # did break - assert got_event + # did break + assert got_event - # node "awhile" maybe starts (impl detail of astream_events) - # if it does start, it must be cancelled - if awhile.started: - assert awhile.cancelled is True + # node "awhile" maybe starts (impl detail of astream_events) + # if it does start, it must be cancelled + if awhile.started: + assert awhile.cancelled is True - # node "anotherwhile" should never start - assert anotherwhile.started is False + # node "anotherwhile" should never start + assert anotherwhile.started is False - # checkpoint with output of "alittlewhile" should not be saved - if checkpointer is not None: - state = await graph.aget_state(thread2) - assert state is not None - assert state.values == {"value": 2} - assert state.next == ("awhile",) - assert state.metadata == { - "source": "loop", - "step": 1, - "writes": {"alittlewhile": {"value": 2}}, - } - finally: - if getattr(checkpointer, "__aexit__", None): - await checkpointer.__aexit__(None, None, None) + # checkpoint with output of "alittlewhile" should not be saved + if checkpointer is not None: + state = await graph.aget_state(thread2) + assert state is not None + assert state.values == {"value": 2} + assert state.next == ("awhile",) + assert state.metadata == { + "source": "loop", + "step": 1, + "writes": {"alittlewhile": {"value": 2}}, + } async def test_node_schemas_custom_output() -> None: - from langchain_core.messages import HumanMessage - class State(TypedDict): hello: str bye: str @@ -423,7 +404,7 @@ async def test_node_schemas_custom_output() -> None: async def node_a(state: StateForA): assert state == { "hello": "there", - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } class StateForB(TypedDict): @@ -462,7 +443,7 @@ async def test_node_schemas_custom_output() -> None: assert await graph.ainvoke( {"hello": "there", "bye": "world", "messages": "hello"} ) == { - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } builder = StateGraph(input=State, output=Output) @@ -482,9 +463,25 @@ async def test_node_schemas_custom_output() -> None: "now": 345, # ignored because not in input schema } ) == { - "messages": [HumanMessage(content="hello", id=AnyStr())], + "messages": [_AnyIdHumanMessage(content="hello")], } + assert [ + c + async for c in graph.astream( + { + "hello": "there", + "bye": "world", + "messages": "hello", + "now": 345, # ignored because not in input schema + } + ) + ] == [ + {"a": None}, + {"b": {"hello": "again", "now": 123}}, + {"c": None}, + ] + async def test_invoke_single_process_in_out(mocker: MockerFixture) -> None: add_one = mocker.Mock(side_effect=lambda x: x + 1) @@ -665,12 +662,17 @@ async def test_invoke_two_processes_in_out(mocker: MockerFixture) -> None: assert step == 2 -async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None: +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], +) +async def test_invoke_two_processes_in_out_interrupt( + request: pytest.FixtureRequest, checkpointer_name: str, mocker: MockerFixture +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") add_one = mocker.Mock(side_effect=lambda x: x + 1) one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox") two = Channel.subscribe_to("inbox") | add_one | Channel.write_to("output") - - memory = MemorySaverAssertImmutable() app = Pregel( nodes={"one": one, "two": two}, channels={ @@ -680,64 +682,67 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N }, input_channels="input", output_channels="output", - checkpointer=memory, + checkpointer=checkpointer, interrupt_after_nodes=["one"], ) + thread1 = {"configurable": {"thread_id": "1"}} + thread2 = {"configurable": {"thread_id": "2"}} # start execution, stop at inbox - assert await app.ainvoke(2, {"configurable": {"thread_id": "1"}}) is None + assert await app.ainvoke(2, thread1) is None # inbox == 3 - checkpoint = await memory.aget({"configurable": {"thread_id": "1"}}) + checkpoint = await checkpointer.aget(thread1) assert checkpoint is not None assert checkpoint["channel_values"]["inbox"] == 3 # resume execution, finish - assert await app.ainvoke(None, {"configurable": {"thread_id": "1"}}) == 4 + assert await app.ainvoke(None, thread1) == 4 # start execution again, stop at inbox - assert await app.ainvoke(20, {"configurable": {"thread_id": "1"}}) is None + assert await app.ainvoke(20, thread1) is None # inbox == 21 - checkpoint = await memory.aget({"configurable": {"thread_id": "1"}}) + checkpoint = await checkpointer.aget(thread1) assert checkpoint is not None assert checkpoint["channel_values"]["inbox"] == 21 # send a new value in, interrupting the previous execution - assert await app.ainvoke(3, {"configurable": {"thread_id": "1"}}) is None - assert await app.ainvoke(None, {"configurable": {"thread_id": "1"}}) == 5 + assert await app.ainvoke(3, thread1) is None + assert await app.ainvoke(None, thread1) == 5 # start execution again, stopping at inbox - assert await app.ainvoke(20, {"configurable": {"thread_id": "2"}}) is None + assert await app.ainvoke(20, thread2) is None # inbox == 21 - snapshot = await app.aget_state({"configurable": {"thread_id": "2"}}) + snapshot = await app.aget_state(thread2) assert snapshot.values["inbox"] == 21 assert snapshot.next == ("two",) # update the state, resume - await app.aupdate_state({"configurable": {"thread_id": "2"}}, 25, as_node="one") - assert await app.ainvoke(None, {"configurable": {"thread_id": "2"}}) == 26 + await app.aupdate_state(thread2, 25, as_node="one") + assert await app.ainvoke(None, thread2) == 26 # no pending tasks - snapshot = await app.aget_state({"configurable": {"thread_id": "2"}}) + snapshot = await app.aget_state(thread2) assert snapshot.next == () # list history - thread1 = {"configurable": {"thread_id": "1"}} - assert [c async for c in app.aget_state_history(thread1)] == [ + history = [c async for c in app.aget_state_history(thread1)] + assert history == [ StateSnapshot( values={"inbox": 4, "output": 5, "input": 3}, next=(), config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 6, "writes": 5}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][1].config, + parent_config=history[1].config, ), StateSnapshot( values={"inbox": 4, "output": 4, "input": 3}, @@ -745,12 +750,13 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 5, "writes": None}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][2].config, + parent_config=history[2].config, ), StateSnapshot( values={"inbox": 21, "output": 4, "input": 3}, @@ -758,12 +764,13 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "input", "step": 4, "writes": 3}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][3].config, + parent_config=history[3].config, ), StateSnapshot( values={"inbox": 21, "output": 4, "input": 20}, @@ -771,12 +778,13 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 3, "writes": None}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][4].config, + parent_config=history[4].config, ), StateSnapshot( values={"inbox": 3, "output": 4, "input": 20}, @@ -784,12 +792,13 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "input", "step": 2, "writes": 20}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][5].config, + parent_config=history[5].config, ), StateSnapshot( values={"inbox": 3, "output": 4, "input": 2}, @@ -797,12 +806,13 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 1, "writes": 4}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][6].config, + parent_config=history[6].config, ), StateSnapshot( values={"inbox": 3, "input": 2}, @@ -810,12 +820,13 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "loop", "step": 0, "writes": None}, created_at=AnyStr(), - parent_config=[c async for c in app.checkpointer.alist(thread1)][7].config, + parent_config=history[7].config, ), StateSnapshot( values={"input": 2}, @@ -823,7 +834,8 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N config={ "configurable": { "thread_id": "1", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), } }, metadata={"source": "input", "step": -1, "writes": 2}, @@ -832,6 +844,199 @@ async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> N ), ] + # forking from any previous checkpoint w/out forking should do nothing + assert [ + c async for c in app.astream(None, history[0].config, stream_mode="updates") + ] == [] + assert [ + c async for c in app.astream(None, history[1].config, stream_mode="updates") + ] == [] + assert [ + c async for c in app.astream(None, history[2].config, stream_mode="updates") + ] == [] + + # forking and re-running from any prev checkpoint should re-run nodes + fork_config = await app.aupdate_state(history[0].config, None) + assert [ + c async for c in app.astream(None, fork_config, stream_mode="updates") + ] == [] + + fork_config = await app.aupdate_state(history[1].config, None) + assert [c async for c in app.astream(None, fork_config, stream_mode="updates")] == [ + {"two": {"output": 5}} + ] + + fork_config = await app.aupdate_state(history[2].config, None) + assert [c async for c in app.astream(None, fork_config, stream_mode="updates")] == [ + {"one": {"inbox": 4}} + ] + + +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], +) +async def test_fork_always_re_runs_nodes( + request: pytest.FixtureRequest, checkpointer_name: str, mocker: MockerFixture +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + add_one = mocker.Mock(side_effect=lambda _: 1) + + builder = StateGraph(Annotated[int, operator.add]) + builder.add_node("add_one", add_one) + builder.add_edge(START, "add_one") + builder.add_conditional_edges("add_one", lambda cnt: "add_one" if cnt < 6 else END) + graph = builder.compile(checkpointer=checkpointer) + + thread1 = {"configurable": {"thread_id": "1"}} + + # start execution, stop at inbox + assert [ + c async for c in graph.astream(1, thread1, stream_mode=["values", "updates"]) + ] == [ + ("values", 1), + ("updates", {"add_one": 1}), + ("values", 2), + ("updates", {"add_one": 1}), + ("values", 3), + ("updates", {"add_one": 1}), + ("values", 4), + ("updates", {"add_one": 1}), + ("values", 5), + ("updates", {"add_one": 1}), + ("values", 6), + ] + + # list history + history = [c async for c in graph.aget_state_history(thread1)] + assert history == [ + StateSnapshot( + values=6, + next=(), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 5, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[1].config, + ), + StateSnapshot( + values=5, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 4, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[2].config, + ), + StateSnapshot( + values=4, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 3, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[3].config, + ), + StateSnapshot( + values=3, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 2, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[4].config, + ), + StateSnapshot( + values=2, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 1, "writes": {"add_one": 1}}, + created_at=AnyStr(), + parent_config=history[5].config, + ), + StateSnapshot( + values=1, + next=("add_one",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "step": 0, "writes": None}, + created_at=AnyStr(), + parent_config=history[6].config, + ), + StateSnapshot( + values=0, + next=("__start__",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "input", "step": -1, "writes": 1}, + created_at=AnyStr(), + parent_config=None, + ), + ] + + # forking from any previous checkpoint w/out forking should do nothing + assert [ + c async for c in graph.astream(None, history[0].config, stream_mode="updates") + ] == [] + assert [ + c async for c in graph.astream(None, history[1].config, stream_mode="updates") + ] == [] + + # forking and re-running from any prev checkpoint should re-run nodes + fork_config = await graph.aupdate_state(history[0].config, None) + assert [ + c async for c in graph.astream(None, fork_config, stream_mode="updates") + ] == [] + + fork_config = await graph.aupdate_state(history[1].config, None) + assert [ + c async for c in graph.astream(None, fork_config, stream_mode="updates") + ] == [{"add_one": 1}] + + fork_config = await graph.aupdate_state(history[2].config, None) + assert [ + c async for c in graph.astream(None, fork_config, stream_mode="updates") + ] == [ + {"add_one": 1}, + {"add_one": 1}, + ] + async def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None: add_one = mocker.Mock(side_effect=lambda x: x + 1) @@ -861,6 +1066,7 @@ async def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None: {"input": 2, "inbox": 12}, output_keys="output", stream_mode="updates" ) ] == [ + {"one": None}, {"two": 13}, {"two": 4}, ] @@ -1162,92 +1368,84 @@ async def test_invoke_checkpoint(mocker: MockerFixture) -> None: @pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], ) -async def test_pending_writes_resume(checkpointer: BaseCheckpointSaver) -> None: - try: +async def test_pending_writes_resume( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") - class State(TypedDict): - value: Annotated[int, operator.add] + class State(TypedDict): + value: Annotated[int, operator.add] - class AwhileMaker: - def __init__(self, sleep: float, rtn: Union[Dict, Exception]) -> None: - self.sleep = sleep - self.rtn = rtn - self.reset() + class AwhileMaker: + def __init__(self, sleep: float, rtn: Union[Dict, Exception]) -> None: + self.sleep = sleep + self.rtn = rtn + self.reset() - async def __call__(self, input: State) -> Any: - self.calls += 1 - await asyncio.sleep(self.sleep) - if isinstance(self.rtn, Exception): - raise self.rtn - else: - return self.rtn + async def __call__(self, input: State) -> Any: + self.calls += 1 + await asyncio.sleep(self.sleep) + if isinstance(self.rtn, Exception): + raise self.rtn + else: + return self.rtn - def reset(self): - self.calls = 0 + def reset(self): + self.calls = 0 - one = AwhileMaker(0.2, {"value": 2}) - two = AwhileMaker(0.6, ValueError("I'm not good")) - builder = StateGraph(State) - builder.add_node("one", one) - builder.add_node("two", two) - builder.add_edge(START, "one") - builder.add_edge(START, "two") - graph = builder.compile(checkpointer=checkpointer) + one = AwhileMaker(0.2, {"value": 2}) + two = AwhileMaker(0.6, ValueError("I'm not good")) + builder = StateGraph(State) + builder.add_node("one", one) + builder.add_node("two", two) + builder.add_edge(START, "one") + builder.add_edge(START, "two") + graph = builder.compile(checkpointer=checkpointer) - thread1: RunnableConfig = {"configurable": {"thread_id": "1"}} - with pytest.raises(ValueError, match="I'm not good"): - await graph.ainvoke({"value": 1}, thread1) + thread1: RunnableConfig = {"configurable": {"thread_id": "1"}} + with pytest.raises(ValueError, match="I'm not good"): + await graph.ainvoke({"value": 1}, thread1) - # both nodes should have been called once - assert one.calls == 1 - assert two.calls == 1 + # both nodes should have been called once + assert one.calls == 1 + assert two.calls == 1 - # latest checkpoint should be before nodes "one", "two" - state = await graph.aget_state(thread1) - assert state is not None - assert state.values == {"value": 1} - assert state.next == ("one", "two") - assert state.metadata == {"source": "loop", "step": 0, "writes": None} - # should contain pending write of "one" - checkpoint = await checkpointer.aget_tuple(thread1) - assert checkpoint is not None - assert checkpoint.pending_writes == [ - (AnyStr(), "one", "one"), - (AnyStr(), "value", 2), - ] - # both pending writes come from same task - assert checkpoint.pending_writes[0][0] == checkpoint.pending_writes[1][0] + # latest checkpoint should be before nodes "one", "two" + state = await graph.aget_state(thread1) + assert state is not None + assert state.values == {"value": 1} + assert state.next == ("one", "two") + assert state.metadata == {"source": "loop", "step": 0, "writes": None} + # should contain pending write of "one" + checkpoint = await checkpointer.aget_tuple(thread1) + assert checkpoint is not None + assert checkpoint.pending_writes == [ + (AnyStr(), "one", "one"), + (AnyStr(), "value", 2), + ] + # both pending writes come from same task + assert checkpoint.pending_writes[0][0] == checkpoint.pending_writes[1][0] - # resume execution - with pytest.raises(ValueError, match="I'm not good"): - await graph.ainvoke(None, thread1) + # resume execution + with pytest.raises(ValueError, match="I'm not good"): + await graph.ainvoke(None, thread1) - # node "one" succeeded previously, so shouldn't be called again - assert one.calls == 1 - # node "two" should have been called once again - assert two.calls == 2 + # node "one" succeeded previously, so shouldn't be called again + assert one.calls == 1 + # node "two" should have been called once again + assert two.calls == 2 - # confirm no new checkpoints saved - state_two = await graph.aget_state(thread1) - assert state_two == state + # confirm no new checkpoints saved + state_two = await graph.aget_state(thread1) + assert state_two == state - # resume execution, without exception - two.rtn = {"value": 3} - # both the pending write and the new write were applied, 1 + 2 + 3 = 6 - assert await graph.ainvoke(None, thread1) == {"value": 6} - finally: - if getattr(checkpointer, "__aexit__", None): - await checkpointer.__aexit__(None, None, None) + # resume execution, without exception + two.rtn = {"value": 3} + # both the pending write and the new write were applied, 1 + 2 + 3 = 6 + assert await graph.ainvoke(None, thread1) == {"value": 6} async def test_cond_edge_after_send() -> None: @@ -1257,15 +1455,15 @@ async def test_cond_edge_after_send() -> None: setattr(self, "__name__", name) async def __call__(self, state): - return state + [self.name] + return [self.name] async def send_for_fun(state): - return [Send("2", state)] + return [Send("2", state), Send("2", state)] async def route_to_three(state) -> Literal["3"]: return "3" - builder = StateGraph(list) + builder = StateGraph(Annotated[list, operator.add]) builder.add_node(Node("1")) builder.add_node(Node("2")) builder.add_node(Node("3")) @@ -1274,7 +1472,7 @@ async def test_cond_edge_after_send() -> None: builder.add_conditional_edges("2", route_to_three) graph = builder.compile() - assert await graph.ainvoke(["0"]) == ["0", "1", "2", "3"] + assert await graph.ainvoke(["0"]) == ["0", "1", "2", "2", "3"] async def test_invoke_checkpoint_aiosqlite(mocker: MockerFixture) -> None: @@ -1313,7 +1511,7 @@ async def test_invoke_checkpoint_aiosqlite(mocker: MockerFixture) -> None: assert state is not None assert state.values.get("total") == 2 assert ( - state.config["configurable"]["thread_ts"] + state.config["configurable"]["checkpoint_id"] == (await memory.aget(thread_1))["id"] ) # total is now 2, so output is 2+3=5 @@ -1322,7 +1520,7 @@ async def test_invoke_checkpoint_aiosqlite(mocker: MockerFixture) -> None: assert state is not None assert state.values.get("total") == 7 assert ( - state.config["configurable"]["thread_ts"] + state.config["configurable"]["checkpoint_id"] == (await memory.aget(thread_1))["id"] ) # total is now 2+5=7, so output would be 7+4=11, but raises ValueError @@ -1364,8 +1562,8 @@ async def test_invoke_checkpoint_aiosqlite(mocker: MockerFixture) -> None: } # sorted descending assert ( - thread_1_history[0].config["configurable"]["thread_ts"] - > thread_1_history[1].config["configurable"]["thread_ts"] + thread_1_history[0].config["configurable"]["checkpoint_id"] + > thread_1_history[1].config["configurable"]["checkpoint_id"] ) # cursor pagination cursored = [ @@ -1383,16 +1581,16 @@ async def test_invoke_checkpoint_aiosqlite(mocker: MockerFixture) -> None: # can get each checkpoint using aget with config assert (await memory.aget(thread_1_history[0].config))[ "id" - ] == thread_1_history[0].config["configurable"]["thread_ts"] + ] == thread_1_history[0].config["configurable"]["checkpoint_id"] assert (await memory.aget(thread_1_history[1].config))[ "id" - ] == thread_1_history[1].config["configurable"]["thread_ts"] + ] == thread_1_history[1].config["configurable"]["checkpoint_id"] thread_1_next_config = await app.aupdate_state(thread_1_history[1].config, 10) # update creates a new checkpoint assert ( - thread_1_next_config["configurable"]["thread_ts"] - > thread_1_history[0].config["configurable"]["thread_ts"] + thread_1_next_config["configurable"]["checkpoint_id"] + > thread_1_history[0].config["configurable"]["checkpoint_id"] ) # 1 more checkpoint in history assert len([c async for c in app.aget_state_history(thread_1)]) == 8 @@ -2466,12 +2664,27 @@ async def test_conditional_graph() -> None: ] -async def test_conditional_graph_state() -> None: +async def test_conditional_graph_state(mocker: MockerFixture) -> None: from langchain_core.agents import AgentAction, AgentFinish from langchain_core.language_models.fake import FakeStreamingListLLM from langchain_core.prompts import PromptTemplate from langchain_core.tools import tool + setup = mocker.Mock() + teardown = mocker.Mock() + + @asynccontextmanager + async def assert_ctx_once() -> AsyncIterator[None]: + assert setup.call_count == 0 + assert teardown.call_count == 0 + try: + yield + finally: + assert setup.call_count == 1 + assert teardown.call_count == 1 + setup.reset_mock() + teardown.reset_mock() + class MyPydanticContextModel(BaseModel): class Config: arbitrary_types_allowed = True @@ -2484,14 +2697,16 @@ async def test_conditional_graph_state() -> None: config: RunnableConfig, ) -> AsyncIterator[MyPydanticContextModel]: assert isinstance(config, dict) + setup() session = httpx.AsyncClient() try: yield MyPydanticContextModel(session=session, something_else="hello") finally: await session.aclose() + teardown() class AgentState(TypedDict): - input: str + input: Annotated[str, UntrackedValue] agent_outcome: Optional[Union[AgentAction, AgentFinish]] intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add] context: Annotated[MyPydanticContextModel, Context(make_context)] @@ -2570,86 +2785,91 @@ async def test_conditional_graph_state() -> None: app = workflow.compile() - assert await app.ainvoke({"input": "what is weather in sf"}) == { - "input": "what is weather in sf", - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:query", - ), - "result for query", - ), - ( - AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - "result for another", - ), - ], - "agent_outcome": AgentFinish( - return_values={"answer": "answer"}, log="finish:answer" - ), - } - - assert [c async for c in app.astream({"input": "what is weather in sf"})] == [ - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", tool_input="query", log="tool:search_api:query" - ), - } - }, - { - "tools": { - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:query", - ), - "result for query", - ) - ], - } - }, - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - } - }, - { - "tools": { - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - "result for another", + async with assert_ctx_once(): + assert await app.ainvoke({"input": "what is weather in sf"}) == { + "input": "what is weather in sf", + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", ), - ], - } - }, - { - "agent": { - "agent_outcome": AgentFinish( - return_values={"answer": "answer"}, log="finish:answer" + "result for query", ), - } - }, - ] + ( + AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + "result for another", + ), + ], + "agent_outcome": AgentFinish( + return_values={"answer": "answer"}, log="finish:answer" + ), + } - patches = [c async for c in app.astream_log({"input": "what is weather in sf"})] + async with assert_ctx_once(): + assert [c async for c in app.astream({"input": "what is weather in sf"})] == [ + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", + ), + } + }, + { + "tools": { + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", + ), + "result for query", + ) + ], + } + }, + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + } + }, + { + "tools": { + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + "result for another", + ), + ], + } + }, + { + "agent": { + "agent_outcome": AgentFinish( + return_values={"answer": "answer"}, log="finish:answer" + ), + } + }, + ] + + async with assert_ctx_once(): + patches = [c async for c in app.astream_log({"input": "what is weather in sf"})] patch_paths = {op["path"] for log in patches for op in log.ops} # Check that agent (one of the nodes) has its output streamed to the logs @@ -2689,24 +2909,26 @@ async def test_conditional_graph_state() -> None: ) config = {"configurable": {"thread_id": "1"}} - assert [ - c - async for c in app_w_interrupt.astream( - {"input": "what is weather in sf"}, config - ) - ] == [ - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", tool_input="query", log="tool:search_api:query" - ), - } - }, - ] + async with assert_ctx_once(): + assert [ + c + async for c in app_w_interrupt.astream( + {"input": "what is weather in sf"}, config + ) + ] == [ + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:query", + ), + } + }, + ] assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", @@ -2737,20 +2959,20 @@ async def test_conditional_graph_state() -> None: ][-1].config, ) - await app_w_interrupt.aupdate_state( - config, - { - "agent_outcome": AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:a different query", - ) - }, - ) + async with assert_ctx_once(): + await app_w_interrupt.aupdate_state( + config, + { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:a different query", + ) + }, + ) assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", @@ -2781,45 +3003,46 @@ async def test_conditional_graph_state() -> None: ][-1].config, ) - assert [c async for c in app_w_interrupt.astream(None, config)] == [ - { - "tools": { - "intermediate_steps": [ - ( - AgentAction( - tool="search_api", - tool_input="query", - log="tool:search_api:a different query", - ), - "result for query", - ) - ], - } - }, - { - "agent": { - "agent_outcome": AgentAction( - tool="search_api", - tool_input="another", - log="tool:search_api:another", - ), - } - }, - ] + async with assert_ctx_once(): + assert [c async for c in app_w_interrupt.astream(None, config)] == [ + { + "tools": { + "intermediate_steps": [ + ( + AgentAction( + tool="search_api", + tool_input="query", + log="tool:search_api:a different query", + ), + "result for query", + ) + ], + } + }, + { + "agent": { + "agent_outcome": AgentAction( + tool="search_api", + tool_input="another", + log="tool:search_api:another", + ), + } + }, + ] - await app_w_interrupt.aupdate_state( - config, - { - "agent_outcome": AgentFinish( - return_values={"answer": "a really nice answer"}, - log="finish:a really nice answer", - ) - }, - ) + async with assert_ctx_once(): + await app_w_interrupt.aupdate_state( + config, + { + "agent_outcome": AgentFinish( + return_values={"answer": "a really nice answer"}, + log="finish:a really nice answer", + ) + }, + ) assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentFinish( return_values={"answer": "a really nice answer"}, log="finish:a really nice answer", @@ -2883,7 +3106,6 @@ async def test_conditional_graph_state() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", log="tool:search_api:query" ), @@ -2925,7 +3147,6 @@ async def test_conditional_graph_state() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentAction( tool="search_api", tool_input="query", @@ -2994,7 +3215,6 @@ async def test_conditional_graph_state() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ - "input": "what is weather in sf", "agent_outcome": AgentFinish( return_values={"answer": "a really nice answer"}, log="finish:a really nice answer", @@ -3033,188 +3253,6 @@ async def test_conditional_graph_state() -> None: ) -async def test_state_graph_few_shot() -> None: - from langchain_core.language_models.fake_chat_models import ( - FakeMessagesListChatModel, - ) - from langchain_core.messages import ( - AIMessage, - AnyMessage, - HumanMessage, - ToolCall, - ToolMessage, - ) - from langchain_core.prompts import ChatPromptTemplate - from langchain_core.tools import tool - - def filter_by_source(config: RunnableConfig) -> Dict[str, Any]: - """This function is a trivial example that demonstrates that passing - a Callable to metadata_filter works as expected. - """ - return {"source": "loop"} - - class BaseState(TypedDict): - messages: Annotated[list[AnyMessage], add_messages] - - class AgentState(BaseState): - examples: Annotated[ - Sequence[BaseState], - FewShotExamples[BaseState].configure(k=1, metadata_filter=filter_by_source), - ] - - # Assemble the tools - @tool() - def search_api(query: str) -> str: - """Searches the API for the query.""" - return f"result for {query}" - - tools = [search_api] - tools_by_name = {t.name: t for t in tools} - - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - """You are a nice assistant. -Some examples of past conversations: -{examples}""", - ), - ("placeholder", "{messages}"), - ] - ) - - model = FakeMessagesListChatModel( - responses=[ - AIMessage( - content="", - tool_calls=[ - { - "id": "tool_call123", - "name": "search_api", - "args": {"query": "query"}, - }, - ], - ), - AIMessage(content="answer"), - ] - ) - - async def agent(state: AgentState, config: RunnableConfig) -> AgentState: - # begin: testing code - assert state["examples"] == config["configurable"]["expected_examples"] - # end: testing code - formatted = await prompt.ainvoke(state) - response = await model.ainvoke(formatted) - return {"messages": response} - - # Define decision-making logic - def should_continue(data: AgentState) -> str: - # Logic to decide whether to continue in the loop or exit - if tool_calls := data["messages"][-1].tool_calls: - return [Send("tools", tool_call) for tool_call in tool_calls] - else: - return "exit" - - def tools_node(tool_call: ToolCall, config: RunnableConfig) -> AgentState: - output = tools_by_name[tool_call["name"]].invoke(tool_call["args"], config) - return { - "messages": ToolMessage( - content=output, name=tool_call["name"], tool_call_id=tool_call["id"] - ) - } - - # Define a new graph - workflow = StateGraph(AgentState) - - workflow.add_node("agent", agent) - workflow.add_node("tools", tools_node) - workflow.set_entry_point("agent") - workflow.add_conditional_edges( - "agent", should_continue, {"continue": "tools", "exit": END} - ) - workflow.add_edge("tools", "agent") - - async with AsyncSqliteSaver.from_conn_string(":memory:") as saver: - app = workflow.compile(checkpointer=saver) - - first_messages = [ - HumanMessage(content="what is weather in sf", id=AnyStr()), - AIMessage( - content="", - id=AnyStr(), - tool_calls=[ - { - "name": "search_api", - "args": {"query": "query"}, - "id": "tool_call123", - } - ], - ), - ToolMessage( - content="result for query", - name="search_api", - id=AnyStr(), - tool_call_id="tool_call123", - ), - AIMessage(content="answer", id=AnyStr()), - ] - assert await app.ainvoke( - {"messages": "what is weather in sf"}, - {"configurable": {"thread_id": "1", "expected_examples": []}}, - ) == {"messages": first_messages} - - # get first checkpoint - chkpnt_tuple_1 = await saver.aget_tuple({"configurable": {"thread_id": "1"}}) - config = chkpnt_tuple_1.config - checkpoint = chkpnt_tuple_1.checkpoint - metadata = chkpnt_tuple_1.metadata - - # not needed in application code, only for testing - assert [c async for c in saver.alist(None, filter={"score": 1})] == [] - - # mark as "good" - metadata["score"] = 1 - await saver.aput(config, checkpoint, metadata) - - # not needed in application code, only for testing - hiscored = [c async for c in saver.alist(None, filter={"score": 1})] - assert len(hiscored) == 1 - assert hiscored[0].checkpoint["channel_values"]["messages"] == first_messages - - assert await app.ainvoke( - {"messages": "what is weather in la"}, - { - "configurable": { - "thread_id": "2", - # below is only for testing purposes, not part of few shot api - "expected_examples": [{"messages": first_messages}], - } - }, - ) == { - "messages": [ - HumanMessage(content="what is weather in la", id=AnyStr()), - AIMessage( - content="", - id=AnyStr(), - tool_calls=[ - { - "name": "search_api", - "args": {"query": "query"}, - "id": "tool_call123", - } - ], - ), - ToolMessage( - content="result for query", - name="search_api", - id=AnyStr(), - tool_call_id="tool_call123", - ), - AIMessage(content="answer", id=AnyStr()), - ] - } - - async def test_conditional_entrypoint_graph() -> None: async def left(data: str) -> str: return data + "->left" @@ -3353,7 +3391,7 @@ async def test_prebuilt_tool_chat() -> None: {"messages": [HumanMessage(content="what is weather in sf")]} ) == { "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id=AnyStr(), content="", @@ -3399,7 +3437,7 @@ async def test_prebuilt_tool_chat() -> None: tool_call_id="tool_call567", id=AnyStr(), ), - AIMessage(content="answer", id=AnyStr()), + _AnyIdAIMessage(content="answer"), ] } @@ -3478,139 +3516,7 @@ async def test_prebuilt_tool_chat() -> None: ] } }, - {"agent": {"messages": [AIMessage(content="answer", id=AnyStr())]}}, - ] - - -async def test_prebuilt_chat() -> None: - from langchain_core.language_models.fake_chat_models import ( - FakeMessagesListChatModel, - ) - from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage - from langchain_core.tools import tool - - class FakeFuntionChatModel(FakeMessagesListChatModel): - def bind_functions(self, functions: list): - return self - - @tool() - def search_api(query: str) -> str: - """Searches the API for the query.""" - return f"result for {query}" - - tools = [search_api] - - app = create_function_calling_executor( - FakeFuntionChatModel( - responses=[ - AIMessage( - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": json.dumps("query"), - } - }, - ), - AIMessage( - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": json.dumps("another"), - } - }, - ), - AIMessage(content="answer"), - ] - ), - tools, - ) - - assert await app.ainvoke( - {"messages": [HumanMessage(content="what is weather in sf")]} - ) == { - "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": {"name": "search_api", "arguments": '"query"'} - }, - ), - FunctionMessage(content="result for query", name="search_api", id=AnyStr()), - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": {"name": "search_api", "arguments": '"another"'} - }, - ), - FunctionMessage( - content="result for another", name="search_api", id=AnyStr() - ), - AIMessage(content="answer", id=AnyStr()), - ] - } - - assert [ - c - async for c in app.astream( - {"messages": [HumanMessage(content="what is weather in sf")]} - ) - ] == [ - { - "agent": { - "messages": [ - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": '"query"', - } - }, - ) - ] - } - }, - { - "tools": { - "messages": [ - FunctionMessage( - content="result for query", name="search_api", id=AnyStr() - ) - ] - } - }, - { - "agent": { - "messages": [ - AIMessage( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": { - "name": "search_api", - "arguments": '"another"', - } - }, - ) - ] - } - }, - { - "tools": { - "messages": [ - FunctionMessage( - content="result for another", name="search_api", id=AnyStr() - ) - ] - } - }, - {"agent": {"messages": [AIMessage(content="answer", id=AnyStr())]}}, + {"agent": {"messages": [_AnyIdAIMessage(content="answer")]}}, ] @@ -3717,7 +3623,7 @@ async def test_state_graph_packets() -> None: {"messages": HumanMessage(content="what is weather in sf")} ) == { "messages": [ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -3873,10 +3779,7 @@ async def test_state_graph_packets() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -3928,10 +3831,7 @@ async def test_state_graph_packets() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4010,10 +3910,7 @@ async def test_state_graph_packets() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4092,10 +3989,7 @@ async def test_state_graph_packets() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values={ "messages": [ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( id="ai1", content="", @@ -4248,7 +4142,7 @@ async def test_message_graph() -> None: app = workflow.compile() assert await app.ainvoke(HumanMessage(content="what is weather in sf")) == [ - HumanMessage(content="what is weather in sf", id=AnyStr()), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", additional_kwargs={ @@ -4327,10 +4221,7 @@ async def test_message_graph() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", additional_kwargs={ @@ -4370,10 +4261,7 @@ async def test_message_graph() -> None: # message was replaced instead of appended assert await app_w_interrupt.aget_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", additional_kwargs={ @@ -4432,10 +4320,7 @@ async def test_message_graph() -> None: assert await app_w_interrupt.aget_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", additional_kwargs={ @@ -4493,10 +4378,7 @@ async def test_message_graph() -> None: # replaces message even if object identity is different, as long as id is the same assert await app_w_interrupt.aget_state(config) == StateSnapshot( values=[ - HumanMessage( - content="what is weather in sf", - id=AnyStr(), - ), + _AnyIdHumanMessage(content="what is weather in sf"), AIMessage( content="", additional_kwargs={ @@ -4991,7 +4873,11 @@ async def test_branch_then() -> None: "metadata": {"thread_id": "10"}, "callbacks": None, "recursion_limit": 25, - "configurable": {"thread_id": "10", "thread_ts": AnyStr()}, + "configurable": { + "thread_id": "10", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + }, }, "values": {"my_key": ""}, "metadata": { @@ -5013,14 +4899,19 @@ async def test_branch_then() -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { "my_key": "value", "market": "DE", }, - "metadata": {"source": "loop", "step": 0, "writes": None}, + "metadata": { + "source": "loop", + "step": 0, + "writes": None, + }, }, }, { @@ -5056,10 +4947,14 @@ async def test_branch_then() -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, - "values": {"my_key": "value prepared", "market": "DE"}, + "values": { + "my_key": "value prepared", + "market": "DE", + }, "metadata": { "source": "loop", "step": 1, @@ -5100,7 +4995,8 @@ async def test_branch_then() -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { @@ -5119,10 +5015,10 @@ async def test_branch_then() -> None: "timestamp": AnyStr(), "step": 3, "payload": { - "id": "ceada3c5-5f25-59e4-9ea5-544599ce1d2f", + "id": "9b590c54-15ef-54b1-83a7-140d27b0bc52", "name": "finish", "input": {"my_key": "value prepared slow", "market": "DE"}, - "triggers": ["branch:prepare:condition:then"], + "triggers": ["branch:prepare:condition::then"], }, }, { @@ -5130,7 +5026,7 @@ async def test_branch_then() -> None: "timestamp": AnyStr(), "step": 3, "payload": { - "id": "ceada3c5-5f25-59e4-9ea5-544599ce1d2f", + "id": "9b590c54-15ef-54b1-83a7-140d27b0bc52", "name": "finish", "result": [("my_key", " finished")], }, @@ -5147,7 +5043,8 @@ async def test_branch_then() -> None: "recursion_limit": 25, "configurable": { "thread_id": "10", - "thread_ts": AnyStr(), + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), }, }, "values": { @@ -5369,7 +5266,7 @@ async def test_branch_then() -> None: created_at=AnyStr(), metadata={ "source": "update", - "step": -1, + "step": 0, "writes": {START: {"my_key": "key", "market": "DE"}}, }, ) @@ -5388,7 +5285,7 @@ async def test_branch_then() -> None: ], metadata={ "source": "loop", - "step": 0, + "step": 1, "writes": {"prepare": {"my_key": " prepared"}}, }, parent_config=uconfig, @@ -5407,7 +5304,7 @@ async def test_branch_then() -> None: ], metadata={ "source": "loop", - "step": 2, + "step": 3, "writes": {"finish": {"my_key": " finished"}}, }, parent_config=[ @@ -5593,10 +5490,34 @@ async def test_in_one_fan_out_state_graph_waiting_edge_via_branch( async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( - snapshot: SnapshotAssertion, + snapshot: SnapshotAssertion, mocker: MockerFixture ) -> None: from langchain_core.pydantic_v1 import BaseModel, ValidationError + setup = mocker.Mock() + teardown = mocker.Mock() + + @asynccontextmanager + async def assert_ctx_once() -> AsyncIterator[None]: + assert setup.call_count == 0 + assert teardown.call_count == 0 + try: + yield + finally: + assert setup.call_count == 1 + assert teardown.call_count == 1 + setup.reset_mock() + teardown.reset_mock() + + @asynccontextmanager + async def make_httpx_client() -> AsyncIterator[httpx.AsyncClient]: + setup() + async with httpx.AsyncClient() as client: + try: + yield client + finally: + teardown() + def sorted_add( x: list[str], y: Union[list[str], list[tuple[str, str]]] ) -> list[str]: @@ -5607,9 +5528,173 @@ async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( return sorted(operator.add(x, y)) class State(BaseModel): + class Config: + arbitrary_types_allowed = True + query: str answer: Optional[str] = None docs: Annotated[list[str], sorted_add] + client: Annotated[httpx.AsyncClient, Context(make_httpx_client)] + + class Input(BaseModel): + query: str + + class Output(BaseModel): + answer: str + docs: list[str] + + class StateUpdate(BaseModel): + query: Optional[str] = None + answer: Optional[str] = None + docs: Optional[list[str]] = None + + async def rewrite_query(data: State) -> State: + return {"query": f"query: {data.query}"} + + async def analyzer_one(data: State) -> State: + return StateUpdate(query=f"analyzed: {data.query}") + + async def retriever_one(data: State) -> State: + return {"docs": ["doc1", "doc2"]} + + async def retriever_two(data: State) -> State: + await asyncio.sleep(0.1) + return {"docs": ["doc3", "doc4"]} + + async def qa(data: State) -> State: + return {"answer": ",".join(data.docs)} + + async def decider(data: State) -> str: + assert isinstance(data, State) + return "retriever_two" + + workflow = StateGraph(State, input=Input, output=Output) + + workflow.add_node("rewrite_query", rewrite_query) + workflow.add_node("analyzer_one", analyzer_one) + workflow.add_node("retriever_one", retriever_one) + workflow.add_node("retriever_two", retriever_two) + workflow.add_node("qa", qa) + + workflow.set_entry_point("rewrite_query") + workflow.add_edge("rewrite_query", "analyzer_one") + workflow.add_edge("analyzer_one", "retriever_one") + workflow.add_conditional_edges( + "rewrite_query", decider, {"retriever_two": "retriever_two"} + ) + workflow.add_edge(["retriever_one", "retriever_two"], "qa") + workflow.set_finish_point("qa") + + app = workflow.compile() + + assert app.get_graph().draw_ascii() == snapshot + + async with assert_ctx_once(): + with pytest.raises(ValidationError): + await app.ainvoke({"query": {}}) + + async with assert_ctx_once(): + assert await app.ainvoke({"query": "what is weather in sf"}) == { + "docs": ["doc1", "doc2", "doc3", "doc4"], + "answer": "doc1,doc2,doc3,doc4", + } + + async with assert_ctx_once(): + assert [c async for c in app.astream({"query": "what is weather in sf"})] == [ + {"rewrite_query": {"query": "query: what is weather in sf"}}, + {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, + {"retriever_two": {"docs": ["doc3", "doc4"]}}, + {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + ] + + app_w_interrupt = workflow.compile( + checkpointer=MemorySaverAssertImmutable(), + interrupt_after=["retriever_one"], + ) + config = {"configurable": {"thread_id": "1"}} + + async with assert_ctx_once(): + assert [ + c + async for c in app_w_interrupt.astream( + {"query": "what is weather in sf"}, config + ) + ] == [ + {"rewrite_query": {"query": "query: what is weather in sf"}}, + {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, + {"retriever_two": {"docs": ["doc3", "doc4"]}}, + {"retriever_one": {"docs": ["doc1", "doc2"]}}, + ] + + async with assert_ctx_once(): + assert [c async for c in app_w_interrupt.astream(None, config)] == [ + {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + ] + + assert await app_w_interrupt.aget_state(config) == StateSnapshot( + values={ + "query": "analyzed: query: what is weather in sf", + "answer": "doc1,doc2,doc3,doc4", + "docs": ["doc1", "doc2", "doc3", "doc4"], + }, + next=(), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, + "step": 4, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ) + + async with assert_ctx_once(): + assert await app_w_interrupt.aupdate_state( + config, {"docs": ["doc5"]}, as_node="rewrite_query" + ) == { + "configurable": { + "thread_id": "1", + "checkpoint_id": AnyStr(), + "checkpoint_ns": "", + } + } + + +async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2( + snapshot: SnapshotAssertion, +) -> None: + from pydantic import BaseModel, ValidationError + + def sorted_add( + x: list[str], y: Union[list[str], list[tuple[str, str]]] + ) -> list[str]: + if isinstance(y[0], tuple): + for rem, _ in y: + x.remove(rem) + y = [t[1] for t in y] + return sorted(operator.add(x, y)) + + class InnerObject(BaseModel): + yo: int + + class State(BaseModel): + query: str + inner: InnerObject + answer: Optional[str] = None + docs: Annotated[list[str], sorted_add] class StateUpdate(BaseModel): query: Optional[str] = None @@ -5655,18 +5740,28 @@ async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( app = workflow.compile() - assert app.get_graph().draw_ascii() == snapshot + assert app.get_graph().draw_mermaid(with_styles=False) == snapshot + assert app.get_input_schema().schema() == snapshot + assert app.get_output_schema().schema() == snapshot with pytest.raises(ValidationError): await app.ainvoke({"query": {}}) - assert await app.ainvoke({"query": "what is weather in sf"}) == { + assert await app.ainvoke( + {"query": "what is weather in sf", "inner": {"yo": 1}} + ) == { "query": "analyzed: query: what is weather in sf", "docs": ["doc1", "doc2", "doc3", "doc4"], "answer": "doc1,doc2,doc3,doc4", + "inner": {"yo": 1}, } - assert [c async for c in app.astream({"query": "what is weather in sf"})] == [ + assert [ + c + async for c in app.astream( + {"query": "what is weather in sf", "inner": {"yo": 1}} + ) + ] == [ {"rewrite_query": {"query": "query: what is weather in sf"}}, {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, {"retriever_two": {"docs": ["doc3", "doc4"]}}, @@ -5683,7 +5778,7 @@ async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( assert [ c async for c in app_w_interrupt.astream( - {"query": "what is weather in sf"}, config + {"query": "what is weather in sf", "inner": {"yo": 1}}, config ) ] == [ {"rewrite_query": {"query": "query: what is weather in sf"}}, @@ -5696,6 +5791,16 @@ async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class( {"qa": {"answer": "doc1,doc2,doc3,doc4"}}, ] + assert await app_w_interrupt.aupdate_state( + config, {"docs": ["doc5"]}, as_node="rewrite_query" + ) == { + "configurable": { + "thread_id": "1", + "checkpoint_id": AnyStr(), + "checkpoint_ns": "", + } + } + async def test_in_one_fan_out_state_graph_waiting_edge_plus_regular() -> None: def sorted_add( @@ -5859,6 +5964,7 @@ async def test_in_one_fan_out_state_graph_waiting_edge_multiple() -> None: {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"rewrite_query": {"query": "query: analyzed: query: what is weather in sf"}}, { "analyzer_one": { @@ -5867,6 +5973,7 @@ async def test_in_one_fan_out_state_graph_waiting_edge_multiple() -> None: }, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"qa": {"answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4"}}, ] @@ -5943,6 +6050,7 @@ async def test_in_one_fan_out_state_graph_waiting_edge_multiple_cond_edge() -> N {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}}, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"rewrite_query": {"query": "query: analyzed: query: what is weather in sf"}}, { "analyzer_one": { @@ -5951,6 +6059,7 @@ async def test_in_one_fan_out_state_graph_waiting_edge_multiple_cond_edge() -> N }, {"retriever_two": {"docs": ["doc3", "doc4"]}}, {"retriever_one": {"docs": ["doc1", "doc2"]}}, + {"decider": None}, {"qa": {"answer": "doc1,doc1,doc2,doc2,doc3,doc3,doc4,doc4"}}, ] @@ -6081,1327 +6190,140 @@ async def test_nested_graph(snapshot: SnapshotAssertion) -> None: @pytest.mark.repeat(10) @pytest.mark.parametrize( - "checkpointer_fct", - [ - lambda: MemorySaverAssertImmutable(put_sleep=0.2), - lambda: AsyncSqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], ) async def test_nested_graph_interrupts( - checkpointer_fct: Callable[[], BaseCheckpointSaver], + request: pytest.FixtureRequest, checkpointer_name: str ) -> None: - try: - checkpointer = checkpointer_fct() + checkpointer = request.getfixturevalue("checkpointer_" + checkpointer_name) - class InnerState(TypedDict): - my_key: str - my_other_key: str + class InnerState(TypedDict): + my_key: str + my_other_key: str - async def inner_1(state: InnerState): - return { - "my_key": state["my_key"] + " here", - "my_other_key": state["my_key"], - } - - async def inner_2(state: InnerState): - return { - "my_key": state["my_key"] + " and there", - "my_other_key": state["my_key"], - } - - inner = StateGraph(InnerState) - inner.add_node("inner_1", inner_1) - inner.add_node("inner_2", inner_2) - inner.add_edge("inner_1", "inner_2") - inner.set_entry_point("inner_1") - inner.set_finish_point("inner_2") - - class State(TypedDict): - my_key: str - - async def outer_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - async def outer_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("outer_1", outer_1) - graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) - graph.add_node("outer_2", outer_2) - graph.set_entry_point("outer_1") - graph.add_edge("outer_1", "inner") - graph.add_edge("inner", "outer_2") - graph.set_finish_point("outer_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - assert await app.ainvoke({"my_key": "my value"}, config, debug=True) == { - "my_key": "hi my value", - } - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert await app.ainvoke(None, config, debug=True) == { - "my_key": "hi my value here and there and back again", - } - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - # test stream updates w/ nested interrupt - config = {"configurable": {"thread_id": "2"}} - assert [c async for c in app.astream({"my_key": "my value"}, config)] == [ - {"outer_1": {"my_key": "hi my value"}}, - ] - assert [c async for c in app.astream(None, config)] == [ - {"inner": {"my_key": "hi my value here and there"}}, - {"outer_2": {"my_key": "hi my value here and there and back again"}}, - ] - - # test stream values w/ nested interrupt - config = {"configurable": {"thread_id": "3"}} - assert [ - c - async for c in app.astream( - {"my_key": "my value"}, config, stream_mode="values" - ) - ] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - { - "my_key": "hi my value here and there and back again", - }, - ] - - # test interrupts BEFORE the node w/ interrupts - app = graph.compile(checkpointer=checkpointer, interrupt_before=["inner"]) - config = {"configurable": {"thread_id": "4"}} - assert [ - c - async for c in app.astream( - {"my_key": "my value"}, config, stream_mode="values" - ) - ] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - # while we're waiting for the node w/ interrupt inside to finish - assert [c async for c in app.astream(None, config, stream_mode="values")] == [] - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - { - "my_key": "hi my value here and there and back again", - }, - ] - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "4", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "4", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "4", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - - # test interrupts AFTER the node w/ interrupts - app = graph.compile(checkpointer=checkpointer, interrupt_after=["inner"]) - config = {"configurable": {"thread_id": "5"}} - assert [ - c - async for c in app.astream( - {"my_key": "my value"}, config, stream_mode="values" - ) - ] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - ] - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there and back again", - }, - ] - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "5", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "5", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "5", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - - # test restarting from thread_ts - config = {"configurable": {"thread_id": "6"}} - app = graph.compile(checkpointer=checkpointer) - await app.ainvoke({"my_key": "my value"}, config, debug=True) - - state_history = [c async for c in app.aget_state_history(config)] - assert state_history == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - - child_state_history = [ - c - async for c in app.aget_state_history( - {"configurable": {"thread_id": "6__inner"}} - ) - ] - assert child_state_history == [ - StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": { - "thread_id": "6__inner", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "inner_1": { - "my_key": "hi my value here", - "my_other_key": "hi my value", - } - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "6__inner", - "thread_ts": AnyStr(), - } - }, - ), - ] - - # check that child snapshot matches id of parent - child_snapshot = child_state_history[0] - assert ( - child_snapshot.config["configurable"]["thread_ts"] - == state_history[0].config["configurable"]["thread_ts"] - ) - # check resuming from interrupt w/ thread_ts - interrupt_state_snapshot, before_interrupt_state_snapshot = state_history[:2] - before_interrupt_config = before_interrupt_state_snapshot.config - # going to get to interrupt again here - assert await app.ainvoke(None, before_interrupt_config, debug=True) == { - "my_key": "hi my value" - } - # one more "identical" snapshot than before, at top of list - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - # going to resume from interrupt - interrupt_config = interrupt_state_snapshot.config - assert (await app.ainvoke(None, interrupt_config, debug=True)) == { - "my_key": "hi my value here and there and back again", - } - assert [s async for s in app.aget_state_history(config)] == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "6", - "thread_ts": AnyStr(), - } - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "6", "thread_ts": AnyStr()} - }, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "6", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - ), - ] - finally: - if hasattr(checkpointer, "__aexit__"): - await checkpointer.__aexit__(None, None, None) - - -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -async def test_nested_graph_interrupts_parallel( - checkpointer: BaseCheckpointSaver, -) -> None: - try: - - class InnerState(TypedDict): - my_key: Annotated[str, operator.add] - my_other_key: str - - async def inner_1(state: InnerState): - await asyncio.sleep(0.1) - return {"my_key": "got here", "my_other_key": state["my_key"]} - - async def inner_2(state: InnerState): - return { - "my_key": " and there", - "my_other_key": state["my_key"], - } - - inner = StateGraph(InnerState) - inner.add_node("inner_1", inner_1) - inner.add_node("inner_2", inner_2) - inner.add_edge("inner_1", "inner_2") - inner.set_entry_point("inner_1") - inner.set_finish_point("inner_2") - - class State(TypedDict): - my_key: Annotated[str, operator.add] - - async def outer_1(state: State): - return {"my_key": " and parallel"} - - async def outer_2(state: State): - return {"my_key": " and back again"} - - graph = StateGraph(State) - graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) - graph.add_node("outer_1", outer_1) - graph.add_node("outer_2", outer_2) - - graph.add_edge(START, "inner") - graph.add_edge(START, "outer_1") - graph.add_edge(["inner", "outer_1"], "outer_2") - graph.set_finish_point("outer_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - assert await app.ainvoke({"my_key": ""}, config, debug=True) == { - "my_key": "", + async def inner_1(state: InnerState): + return { + "my_key": state["my_key"] + " here", + "my_other_key": state["my_key"], } - assert await app.ainvoke(None, config, debug=True) == { - "my_key": "got here and there and parallel and back again", + async def inner_2(state: InnerState): + return { + "my_key": state["my_key"] + " and there", + "my_other_key": state["my_key"], } - # below combo of assertions is asserting two things - # - outer_1 finishes before inner interrupts (because we see its output in stream, which only happens after node finishes) - # - the writes of outer are persisted in 1st call and used in 2nd call, ie outer isn't called again (because we dont see outer_1 output again in 2nd stream) - # test stream updates w/ nested interrupt - config = {"configurable": {"thread_id": "2"}} - assert [c async for c in app.astream({"my_key": ""}, config)] == [ - # we got to parallel node first - {"outer_1": {"my_key": " and parallel"}}, - ] - assert [c async for c in app.astream(None, config)] == [ - {"inner": {"my_key": "got here and there"}}, - {"outer_2": {"my_key": " and back again"}}, - ] + inner = StateGraph(InnerState) + inner.add_node("inner_1", inner_1) + inner.add_node("inner_2", inner_2) + inner.add_edge("inner_1", "inner_2") + inner.set_entry_point("inner_1") + inner.set_finish_point("inner_2") - # test stream values w/ nested interrupt - config = {"configurable": {"thread_id": "3"}} - assert [ - c async for c in app.astream({"my_key": ""}, config, stream_mode="values") - ] == [ - { - "my_key": "", - }, - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "got here and there and parallel", - }, - { - "my_key": "got here and there and parallel and back again", - }, - ] + class State(TypedDict): + my_key: str - # # test interrupts BEFORE the parallel node - app = graph.compile(checkpointer=checkpointer, interrupt_before=["outer_1"]) - config = {"configurable": {"thread_id": "4"}} - assert [ - c async for c in app.astream({"my_key": ""}, config, stream_mode="values") - ] == [{"my_key": ""}] - # while we're waiting for the node w/ interrupt inside to finish - assert [c async for c in app.astream(None, config, stream_mode="values")] == [] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "got here and there and parallel", - }, - { - "my_key": "got here and there and parallel and back again", - }, - ] + async def outer_1(state: State): + return {"my_key": "hi " + state["my_key"]} - # test interrupts AFTER the parallel node - app = graph.compile(checkpointer=checkpointer, interrupt_after=["outer_1"]) - config = {"configurable": {"thread_id": "5"}} - assert [ - c async for c in app.astream({"my_key": ""}, config, stream_mode="values") - ] == [{"my_key": ""}] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - {"my_key": "got here and there and parallel"}, - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "got here and there and parallel and back again", - }, - ] - finally: - if hasattr(checkpointer, "__aexit__"): - await checkpointer.__aexit__(None, None, None) + async def outer_2(state: State): + return {"my_key": state["my_key"] + " and back again"} + graph = StateGraph(State) + graph.add_node("outer_1", outer_1) + graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) + graph.add_node("outer_2", outer_2) + graph.set_entry_point("outer_1") + graph.add_edge("outer_1", "inner") + graph.add_edge("inner", "outer_2") + graph.set_finish_point("outer_2") -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -async def test_doubly_nested_graph_interrupts( - checkpointer: BaseCheckpointSaver, -) -> None: - try: + app = graph.compile(checkpointer=checkpointer) - class State(TypedDict): - my_key: str - - class ChildState(TypedDict): - my_key: str - - class GrandChildState(TypedDict): - my_key: str - - async def grandchild_1(state: ChildState): - return {"my_key": state["my_key"] + " here"} - - async def grandchild_2(state: ChildState): - return { - "my_key": state["my_key"] + " and there", - } - - grandchild = StateGraph(GrandChildState) - grandchild.add_node("grandchild_1", grandchild_1) - grandchild.add_node("grandchild_2", grandchild_2) - grandchild.add_edge("grandchild_1", "grandchild_2") - grandchild.set_entry_point("grandchild_1") - grandchild.set_finish_point("grandchild_2") - - child = StateGraph(ChildState) - child.add_node("child_1", grandchild.compile(interrupt_before=["grandchild_2"])) - child.set_entry_point("child_1") - child.set_finish_point("child_1") - - async def parent_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - async def parent_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("parent_1", parent_1) - graph.add_node("child", child.compile()) - graph.add_node("parent_2", parent_2) - graph.set_entry_point("parent_1") - graph.add_edge("parent_1", "child") - graph.add_edge("child", "parent_2") - graph.set_finish_point("parent_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - assert await app.ainvoke({"my_key": "my value"}, config, debug=True) == { - "my_key": "hi my value", - } - - assert await app.ainvoke(None, config, debug=True) == { - "my_key": "hi my value here and there and back again", - } - - # test stream updates w/ nested interrupt - config = {"configurable": {"thread_id": "2"}} - assert [c async for c in app.astream({"my_key": "my value"}, config)] == [ - {"parent_1": {"my_key": "hi my value"}}, - ] - assert [c async for c in app.astream(None, config)] == [ - {"child": {"my_key": "hi my value here and there"}}, - {"parent_2": {"my_key": "hi my value here and there and back again"}}, - ] - - # test stream values w/ nested interrupt - config = {"configurable": {"thread_id": "3"}} - assert [ - c - async for c in app.astream( - {"my_key": "my value"}, config, stream_mode="values" - ) - ] == [ - { - "my_key": "my value", - }, - { - "my_key": "hi my value", - }, - ] - assert [c async for c in app.astream(None, config, stream_mode="values")] == [ - { - "my_key": "hi my value here and there", - }, - { - "my_key": "hi my value here and there and back again", - }, - ] - finally: - if hasattr(checkpointer, "__aexit__"): - await checkpointer.__aexit__(None, None, None) - - -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -async def test_nested_graph_state(checkpointer: BaseCheckpointSaver) -> None: - try: - - class InnerState(TypedDict): - my_key: str - my_other_key: str - - async def inner_1(state: InnerState): - return { - "my_key": state["my_key"] + " here", - "my_other_key": state["my_key"], - } - - async def inner_2(state: InnerState): - return { - "my_key": state["my_key"] + " and there", - "my_other_key": state["my_key"], - } - - inner = StateGraph(InnerState) - inner.add_node("inner_1", inner_1) - inner.add_node("inner_2", inner_2) - inner.add_edge("inner_1", "inner_2") - inner.set_entry_point("inner_1") - inner.set_finish_point("inner_2") - - class State(TypedDict): - my_key: str - - async def outer_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - async def outer_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("outer_1", outer_1) - graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) - graph.add_node("outer_2", outer_2) - graph.set_entry_point("outer_1") - graph.add_edge("outer_1", "inner") - graph.add_edge("inner", "outer_2") - graph.set_finish_point("outer_2") - - app = graph.compile(checkpointer=checkpointer) - - config = {"configurable": {"thread_id": "1"}} - await app.ainvoke({"my_key": "my value"}, config, debug=True) - # test state w/ nested subgraph state (right after interrupt) - assert await app.aget_state( - config, include_subgraph_state=False - ) == StateSnapshot( + # test invoke w/ nested interrupt + config = {"configurable": {"thread_id": "1"}} + assert await app.ainvoke({"my_key": "my value"}, config, debug=True) == { + "my_key": "hi my value", + } + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( values={"my_key": "hi my value"}, next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", "writes": {"outer_1": {"my_key": "hi my value"}}, "step": 1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots=None, - ) - assert await app.aget_state( - config, include_subgraph_state=True - ) == StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - metadata={ - "source": "loop", - "writes": { - "inner_1": { - "my_key": "hi my value here", - "my_other_key": "hi my value", - } - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots=None, - ) - }, - ) - assert [ - c async for c in app.aget_state_history(config, include_subgraph_state=True) - ] == [ - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "inner_1": { - "my_key": "hi my value here", - "my_other_key": "hi my value", - } - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - subgraph_state_snapshots=None, - ), - ] - await app.ainvoke(None, config, debug=True) - # test state w/ nested subgraph state (after resuming from interrupt) - assert await app.aget_state( - config, include_subgraph_state=True - ) == StateSnapshot( + parent_config=None, + ), + ] + assert await app.ainvoke(None, config, debug=True) == { + "my_key": "hi my value here and there and back again", + } + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( values={"my_key": "hi my value here and there and back again"}, next=(), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", "writes": { @@ -7410,385 +6332,1272 @@ async def test_nested_graph_state(checkpointer: BaseCheckpointSaver) -> None: "step": 3, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - metadata={ - "source": "loop", - "writes": { - "inner_2": { - "my_key": "hi my value here and there", - "my_other_key": "hi my value here", - } - }, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__inner", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots=None, - ) + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } }, - ) - assert [ - c async for c in app.aget_state_history(config, include_subgraph_state=True) - ] == [ - StateSnapshot( - values={"my_key": "hi my value here and there and back again"}, - next=(), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "outer_2": { - "my_key": "hi my value here and there and back again" - } - }, - "step": 3, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=("outer_2",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"inner": {"my_key": "hi my value here and there"}}, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={"my_key": "hi my value"}, - next=("inner",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": {"outer_1": {"my_key": "hi my value"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - # TODO: this is likely very confusing for an end user, and we'll probably need to update this. - # right now this is happening due to us overwriting the - # subgraph snapshot after we finish the graph with while the thread_ts - # is the same as when we interrupted - subgraph_state_snapshots={ - "inner": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "inner_2": { - "my_key": "hi my value here and there", - "my_other_key": "hi my value here", - } - }, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__inner", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ), - StateSnapshot( - values={"my_key": "my value"}, - next=("outer_1",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ), - StateSnapshot( - values={}, - next=("__start__",), - config={ - "configurable": { - "thread_id": "1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "input", - "writes": {"my_key": "my value"}, - "step": -1, - }, - created_at=AnyStr(), - parent_config=None, - subgraph_state_snapshots=None, - ), - ] - finally: - if hasattr(checkpointer, "__aexit__"): - await checkpointer.__aexit__(None, None, None) - - -@pytest.mark.parametrize( - "checkpointer", - [ - MemorySaverAssertImmutable(), - AsyncSqliteSaver.from_conn_string(":memory:"), - ], - ids=[ - "memory", - "sqlite", - ], -) -async def test_doubly_nested_graph_state( - checkpointer: BaseCheckpointSaver, -) -> None: - try: - - class State(TypedDict): - my_key: str - - class ChildState(TypedDict): - my_key: str - - class GrandChildState(TypedDict): - my_key: str - - async def grandchild_1(state: ChildState): - return {"my_key": state["my_key"] + " here"} - - async def grandchild_2(state: ChildState): - return { - "my_key": state["my_key"] + " and there", - } - - grandchild = StateGraph(GrandChildState) - grandchild.add_node("grandchild_1", grandchild_1) - grandchild.add_node("grandchild_2", grandchild_2) - grandchild.add_edge("grandchild_1", "grandchild_2") - grandchild.set_entry_point("grandchild_1") - grandchild.set_finish_point("grandchild_2") - - child = StateGraph(ChildState) - child.add_node("child_1", grandchild.compile(interrupt_before=["grandchild_2"])) - child.set_entry_point("child_1") - child.set_finish_point("child_1") - - async def parent_1(state: State): - return {"my_key": "hi " + state["my_key"]} - - async def parent_2(state: State): - return {"my_key": state["my_key"] + " and back again"} - - graph = StateGraph(State) - graph.add_node("parent_1", parent_1) - graph.add_node("child", child.compile()) - graph.add_node("parent_2", parent_2) - graph.set_entry_point("parent_1") - graph.add_edge("parent_1", "child") - graph.add_edge("child", "parent_2") - graph.set_finish_point("parent_2") - - app = graph.compile(checkpointer=checkpointer) - - # test invoke w/ nested interrupt - config = {"configurable": {"thread_id": "1"}} - await app.ainvoke({"my_key": "my value"}, config, debug=True) - assert await app.aget_state(config) == StateSnapshot( - values={"my_key": "hi my value"}, - next=("child",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, metadata={ "source": "loop", - "writes": {"parent_1": {"my_key": "hi my value"}}, + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, "step": 1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots=None, - ) - assert await app.aget_state( - config, include_subgraph_state=True - ) == StateSnapshot( + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "1", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + # test stream updates w/ nested interrupt + config = {"configurable": {"thread_id": "2"}} + assert [c async for c in app.astream({"my_key": "my value"}, config)] == [ + {"outer_1": {"my_key": "hi my value"}}, + ] + assert [c async for c in app.astream(None, config)] == [ + {"inner": {"my_key": "hi my value here and there"}}, + {"outer_2": {"my_key": "hi my value here and there and back again"}}, + ] + + # test stream values w/ nested interrupt + config = {"configurable": {"thread_id": "3"}} + assert [ + c + async for c in app.astream({"my_key": "my value"}, config, stream_mode="values") + ] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + { + "my_key": "hi my value here and there and back again", + }, + ] + + # test interrupts BEFORE the node w/ interrupts + app = graph.compile(checkpointer=checkpointer, interrupt_before=["inner"]) + config = {"configurable": {"thread_id": "4"}} + assert [ + c + async for c in app.astream({"my_key": "my value"}, config, stream_mode="values") + ] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( values={"my_key": "hi my value"}, - next=("child",), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + next=("inner",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", - "writes": {"parent_1": {"my_key": "hi my value"}}, + "writes": {"outer_1": {"my_key": "hi my value"}}, "step": 1, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "child": StateSnapshot( - values={"my_key": "hi my value"}, - next=(), - config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - metadata={"source": "loop", "writes": None, "step": 0}, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots={ - "child_1": StateSnapshot( - values={"my_key": "hi my value here"}, - next=(), - config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "grandchild_1": {"my_key": "hi my value here"} - }, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ) + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } }, - ) - await app.ainvoke(None, config, debug=True) - assert await app.aget_state( - config, include_subgraph_state=True - ) == StateSnapshot( + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + # while we're waiting for the node w/ interrupt inside to finish + assert [c async for c in app.astream(None, config, stream_mode="values")] == [] + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + { + "my_key": "hi my value here and there and back again", + }, + ] + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( values={"my_key": "hi my value here and there and back again"}, next=(), - config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, metadata={ "source": "loop", "writes": { - "parent_2": {"my_key": "hi my value here and there and back again"} + "outer_2": {"my_key": "hi my value here and there and back again"} }, "step": 3, }, created_at=AnyStr(), - parent_config={"configurable": {"thread_id": "1", "thread_ts": AnyStr()}}, - subgraph_state_snapshots={ - "child": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - metadata={ - "source": "loop", - "writes": {"child_1": {"my_key": "hi my value here and there"}}, - "step": 1, - }, - created_at=AnyStr(), - parent_config={ - "configurable": {"thread_id": "1__child", "thread_ts": AnyStr()} - }, - subgraph_state_snapshots={ - "child_1": StateSnapshot( - values={"my_key": "hi my value here and there"}, - next=(), - config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - metadata={ - "source": "loop", - "writes": { - "grandchild_2": { - "my_key": "hi my value here and there" - } - }, - "step": 2, - }, - created_at=AnyStr(), - parent_config={ - "configurable": { - "thread_id": "1__child__child_1", - "thread_ts": AnyStr(), - } - }, - subgraph_state_snapshots=None, - ) - }, - ) + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } }, - ) + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "4", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] - finally: - if hasattr(checkpointer, "__aexit__"): - await checkpointer.__aexit__(None, None, None) + # test interrupts AFTER the node w/ interrupts + app = graph.compile(checkpointer=checkpointer, interrupt_after=["inner"]) + config = {"configurable": {"thread_id": "5"}} + assert [ + c + async for c in app.astream({"my_key": "my value"}, config, stream_mode="values") + ] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + ] + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there and back again", + }, + ] + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( + values={"my_key": "hi my value here and there and back again"}, + next=(), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": { + "outer_2": {"my_key": "hi my value here and there and back again"} + }, + "step": 3, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "5", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + + # test restarting from checkpoint_id + config = {"configurable": {"thread_id": "6"}} + app = graph.compile(checkpointer=checkpointer) + await app.ainvoke({"my_key": "my value"}, config, debug=True) + + state_history = [c async for c in app.aget_state_history(config)] + assert state_history == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + + child_state_history = [ + c + async for c in app.aget_state_history( + {"configurable": {"thread_id": "6", "checkpoint_ns": "inner"}} + ) + ] + assert child_state_history == [ + StateSnapshot( + values={"my_key": "hi my value here"}, + next=(), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "inner", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": { + "inner_1": { + "my_key": "hi my value here", + "my_other_key": "hi my value", + } + }, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "inner", + "checkpoint_id": AnyStr(), + } + }, + ), + # there should be a single child checkpoint because we only keep + # one child checkpoint per parent checkpoint (in which child ran) + ] + + # check that child snapshot matches id of parent + child_snapshot = child_state_history[0] + assert ( + child_snapshot.config["configurable"]["checkpoint_id"] + == state_history[0].config["configurable"]["checkpoint_id"] + ) + # check resuming from interrupt w/ checkpoint_id + interrupt_state_snapshot, before_interrupt_state_snapshot = state_history[:2] + before_interrupt_config = before_interrupt_state_snapshot.config + # going to get to interrupt again here + assert await app.ainvoke(None, before_interrupt_config, debug=True) == { + "my_key": "hi my value" + } + # one more "identical" snapshot than before, at top of list + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + # going to resume from interrupt + interrupt_config = interrupt_state_snapshot.config + assert (await app.ainvoke(None, interrupt_config, debug=True)) == { + "my_key": "hi my value here and there and back again", + } + assert [s async for s in app.aget_state_history(config)] == [ + StateSnapshot( + values={"my_key": "hi my value here and there and back again"}, + next=(), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": { + "outer_2": {"my_key": "hi my value here and there and back again"} + }, + "step": 3, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value here and there"}, + next=("outer_2",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"inner": {"my_key": "hi my value here and there"}}, + "step": 2, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "hi my value"}, + next=("inner",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "loop", + "writes": {"outer_1": {"my_key": "hi my value"}}, + "step": 1, + }, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={"my_key": "my value"}, + next=("outer_1",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={"source": "loop", "writes": None, "step": 0}, + created_at=AnyStr(), + parent_config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + ), + StateSnapshot( + values={}, + next=("__start__",), + config={ + "configurable": { + "thread_id": "6", + "checkpoint_ns": "", + "checkpoint_id": AnyStr(), + } + }, + metadata={ + "source": "input", + "writes": {"my_key": "my value"}, + "step": -1, + }, + created_at=AnyStr(), + parent_config=None, + ), + ] + + +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], +) +async def test_nested_graph_interrupts_parallel( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + + class InnerState(TypedDict): + my_key: Annotated[str, operator.add] + my_other_key: str + + async def inner_1(state: InnerState): + await asyncio.sleep(0.1) + return {"my_key": "got here", "my_other_key": state["my_key"]} + + async def inner_2(state: InnerState): + return { + "my_key": " and there", + "my_other_key": state["my_key"], + } + + inner = StateGraph(InnerState) + inner.add_node("inner_1", inner_1) + inner.add_node("inner_2", inner_2) + inner.add_edge("inner_1", "inner_2") + inner.set_entry_point("inner_1") + inner.set_finish_point("inner_2") + + class State(TypedDict): + my_key: Annotated[str, operator.add] + + async def outer_1(state: State): + return {"my_key": " and parallel"} + + async def outer_2(state: State): + return {"my_key": " and back again"} + + graph = StateGraph(State) + graph.add_node("inner", inner.compile(interrupt_before=["inner_2"])) + graph.add_node("outer_1", outer_1) + graph.add_node("outer_2", outer_2) + + graph.add_edge(START, "inner") + graph.add_edge(START, "outer_1") + graph.add_edge(["inner", "outer_1"], "outer_2") + graph.set_finish_point("outer_2") + + app = graph.compile(checkpointer=checkpointer) + + # test invoke w/ nested interrupt + config = {"configurable": {"thread_id": "1"}} + assert await app.ainvoke({"my_key": ""}, config, debug=True) == { + "my_key": "", + } + + assert await app.ainvoke(None, config, debug=True) == { + "my_key": "got here and there and parallel and back again", + } + + # below combo of assertions is asserting two things + # - outer_1 finishes before inner interrupts (because we see its output in stream, which only happens after node finishes) + # - the writes of outer are persisted in 1st call and used in 2nd call, ie outer isn't called again (because we dont see outer_1 output again in 2nd stream) + # test stream updates w/ nested interrupt + config = {"configurable": {"thread_id": "2"}} + assert [c async for c in app.astream({"my_key": ""}, config)] == [ + # we got to parallel node first + {"outer_1": {"my_key": " and parallel"}}, + ] + assert [c async for c in app.astream(None, config)] == [ + {"inner": {"my_key": "got here and there"}}, + {"outer_2": {"my_key": " and back again"}}, + ] + + # test stream values w/ nested interrupt + config = {"configurable": {"thread_id": "3"}} + assert [ + c async for c in app.astream({"my_key": ""}, config, stream_mode="values") + ] == [ + { + "my_key": "", + }, + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "got here and there and parallel", + }, + { + "my_key": "got here and there and parallel and back again", + }, + ] + + # # test interrupts BEFORE the parallel node + app = graph.compile(checkpointer=checkpointer, interrupt_before=["outer_1"]) + config = {"configurable": {"thread_id": "4"}} + assert [ + c async for c in app.astream({"my_key": ""}, config, stream_mode="values") + ] == [{"my_key": ""}] + # while we're waiting for the node w/ interrupt inside to finish + assert [c async for c in app.astream(None, config, stream_mode="values")] == [] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "got here and there and parallel", + }, + { + "my_key": "got here and there and parallel and back again", + }, + ] + + # test interrupts AFTER the parallel node + app = graph.compile(checkpointer=checkpointer, interrupt_after=["outer_1"]) + config = {"configurable": {"thread_id": "5"}} + assert [ + c async for c in app.astream({"my_key": ""}, config, stream_mode="values") + ] == [{"my_key": ""}] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + {"my_key": "got here and there and parallel"}, + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "got here and there and parallel and back again", + }, + ] + + +@pytest.mark.skip +@pytest.mark.parametrize( + "checkpointer_name", + ["memory", "sqlite_aio", "postgres_aio", "postgres_aio_pipe"], +) +async def test_doubly_nested_graph_interrupts( + request: pytest.FixtureRequest, checkpointer_name: str +) -> None: + checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}") + + class State(TypedDict): + my_key: str + + class ChildState(TypedDict): + my_key: str + + class GrandChildState(TypedDict): + my_key: str + + async def grandchild_1(state: ChildState): + return {"my_key": state["my_key"] + " here"} + + async def grandchild_2(state: ChildState): + return { + "my_key": state["my_key"] + " and there", + } + + grandchild = StateGraph(GrandChildState) + grandchild.add_node("grandchild_1", grandchild_1) + grandchild.add_node("grandchild_2", grandchild_2) + grandchild.add_edge("grandchild_1", "grandchild_2") + grandchild.set_entry_point("grandchild_1") + grandchild.set_finish_point("grandchild_2") + + child = StateGraph(ChildState) + child.add_node("child_1", grandchild.compile(interrupt_before=["grandchild_2"])) + child.set_entry_point("child_1") + child.set_finish_point("child_1") + + async def parent_1(state: State): + return {"my_key": "hi " + state["my_key"]} + + async def parent_2(state: State): + return {"my_key": state["my_key"] + " and back again"} + + graph = StateGraph(State) + graph.add_node("parent_1", parent_1) + graph.add_node("child", child.compile()) + graph.add_node("parent_2", parent_2) + graph.set_entry_point("parent_1") + graph.add_edge("parent_1", "child") + graph.add_edge("child", "parent_2") + graph.set_finish_point("parent_2") + + app = graph.compile(checkpointer=checkpointer) + + # test invoke w/ nested interrupt + config = {"configurable": {"thread_id": "1"}} + assert await app.ainvoke({"my_key": "my value"}, config, debug=True) == { + "my_key": "hi my value", + } + + assert await app.ainvoke(None, config, debug=True) == { + "my_key": "hi my value here and there and back again", + } + + # test stream updates w/ nested interrupt + config = {"configurable": {"thread_id": "2"}} + assert [c async for c in app.astream({"my_key": "my value"}, config)] == [ + {"parent_1": {"my_key": "hi my value"}}, + ] + assert [c async for c in app.astream(None, config)] == [ + {"child": {"my_key": "hi my value here and there"}}, + {"parent_2": {"my_key": "hi my value here and there and back again"}}, + ] + + # test stream values w/ nested interrupt + config = {"configurable": {"thread_id": "3"}} + assert [ + c + async for c in app.astream({"my_key": "my value"}, config, stream_mode="values") + ] == [ + { + "my_key": "my value", + }, + { + "my_key": "hi my value", + }, + ] + assert [c async for c in app.astream(None, config, stream_mode="values")] == [ + { + "my_key": "hi my value here and there", + }, + { + "my_key": "hi my value here and there and back again", + }, + ] async def test_checkpoint_metadata() -> None: diff --git a/libs/sdk-js/.gitignore b/libs/sdk-js/.gitignore index a43bf2e13..4b85d8c2a 100644 --- a/libs/sdk-js/.gitignore +++ b/libs/sdk-js/.gitignore @@ -1,4 +1,5 @@ /docs +## GENERATED create-entrypoints.js /client.cjs /client.js /client.d.ts @@ -7,3 +8,4 @@ /index.js /index.d.ts /index.d.cts +## END GENERATED create-entrypoints.js diff --git a/libs/sdk-js/package.json b/libs/sdk-js/package.json index f8c9c5a1a..300578131 100644 --- a/libs/sdk-js/package.json +++ b/libs/sdk-js/package.json @@ -1,6 +1,6 @@ { "name": "@langchain/langgraph-sdk", - "version": "0.0.2", + "version": "0.0.4", "description": "Client library for interacting with the LangGraph API", "type": "module", "packageManager": "yarn@1.22.19", diff --git a/libs/sdk-js/scripts/create-entrypoints.js b/libs/sdk-js/scripts/create-entrypoints.js index 69af43c6d..63ddd2ce9 100644 --- a/libs/sdk-js/scripts/create-entrypoints.js +++ b/libs/sdk-js/scripts/create-entrypoints.js @@ -97,11 +97,15 @@ const updateConfig = () => { const endMarker = "## END GENERATED create-entrypoints.js"; const startIdx = lines.findIndex((line) => line.includes(startMarker)); const endIdx = lines.findIndex((line) => line.includes(endMarker)); - const newLines = [ - ...lines.slice(0, startIdx + 1), - ...filenames.map((fname) => `/${fname}`), - ...lines.slice(endIdx), - ]; + const newLines = lines.slice(0, startIdx + 1); + if (startIdx === -1) { + newLines.push(startMarker); + } + newLines.push(...filenames.map((fname) => `/${fname}`)); + if (endIdx === -1) { + newLines.push(endMarker); + } + newLines.push(...lines.slice(endIdx)); fs.writeFileSync("./.gitignore", newLines.join("\n")); }; diff --git a/libs/sdk-js/src/client.mts b/libs/sdk-js/src/client.mts index 642832eed..bf98dccb4 100644 --- a/libs/sdk-js/src/client.mts +++ b/libs/sdk-js/src/client.mts @@ -337,6 +337,17 @@ export class ThreadsClient extends BaseClient { }); } + /** + * Copy an existing thread + * @param threadId ID of the thread to be copied + * @returns Newly copied thread + */ + async copy(threadId: string): Promise { + return this.fetch(`/threads/${threadId}/copy`, { + method: "POST", + }); + } + /** * Update a thread. * @@ -427,15 +438,18 @@ export class ThreadsClient extends BaseClient { async updateState( threadId: string, options: { values: ValuesType; checkpointId?: string; asNode?: string }, - ): Promise { - return this.fetch(`/threads/${threadId}/state`, { - method: "POST", - json: { - values: options.values, - checkpoint_id: options.checkpointId, - as_node: options?.asNode, + ): Promise> { + return this.fetch>( + `/threads/${threadId}/state`, + { + method: "POST", + json: { + values: options.values, + checkpoint_id: options.checkpointId, + as_node: options?.asNode, + }, }, - }); + ); } /** diff --git a/libs/sdk-js/src/schema.ts b/libs/sdk-js/src/schema.ts index d9f8458a2..897ed1ed8 100644 --- a/libs/sdk-js/src/schema.ts +++ b/libs/sdk-js/src/schema.ts @@ -27,7 +27,7 @@ export interface Config { /** * Timestamp of the state checkpoint */ - thread_ts?: string; + checkpoint_id?: string; [key: string]: unknown; }; } @@ -39,14 +39,28 @@ export interface GraphSchema { graph_id: string; /** - * The schema for the graph state + * The schema for the input state. + * Missing if unable to generate JSON schema from graph. */ - state_schema: JSONSchema7; + input_schema?: JSONSchema7; /** - * The schema for the graph config + * The schema for the output state. + * Missing if unable to generate JSON schema from graph. */ - config_schema: JSONSchema7; + output_schema?: JSONSchema7; + + /** + * The schema for the graph state. + * Missing if unable to generate JSON schema from graph. + */ + state_schema?: JSONSchema7; + + /** + * The schema for the graph config. + * Missing if unable to generate JSON schema from graph. + */ + config_schema?: JSONSchema7; } export type Metadata = Optional>; diff --git a/libs/sdk-py/langgraph_sdk/client.py b/libs/sdk-py/langgraph_sdk/client.py index b76798243..13d5560db 100644 --- a/libs/sdk-py/langgraph_sdk/client.py +++ b/libs/sdk-py/langgraph_sdk/client.py @@ -11,7 +11,6 @@ from typing import ( List, NamedTuple, Optional, - TypedDict, Union, overload, ) @@ -31,6 +30,7 @@ from langgraph_sdk.schema import ( MultitaskStrategy, OnConflictBehavior, Run, + RunCreate, StreamMode, Thread, ThreadState, @@ -40,35 +40,31 @@ from langgraph_sdk.schema import ( logger = logging.getLogger(__name__) -class RunCreate(TypedDict): - """Payload for creating a background run.""" - - thread_id: Optional[str] - assistant_id: str - input: Optional[dict] - metadata: Optional[dict] - config: Optional[Config] - checkpoint_id: Optional[str] - interrupt_before: Optional[list[str]] - interrupt_after: Optional[list[str]] - webhook: Optional[str] - multitask_strategy: Optional[MultitaskStrategy] - - def get_client( - *, url: str = "http://localhost:8123", api_key: Optional[str] = None + *, url: Optional[str] = None, api_key: Optional[str] = None ) -> LangGraphClient: """Get a LangGraphClient instance. Args: - url (str, optional): The URL of the LangGraph API. Defaults to "http://localhost:8123". - api_key (str, optional): The API key. If not provided, it will be read from the environment. + url: The URL of the LangGraph API. + api_key: The API key. If not provided, it will be read from the environment. Precedence: 1. explicit argument 2. LANGGRAPH_API_KEY 3. LANGSMITH_API_KEY 4. LANGCHAIN_API_KEY """ + transport: Optional[httpx.AsyncBaseTransport] = None + if url is None: + try: + from langgraph_api.server import app # type: ignore + + url = "http://api" + transport = httpx.ASGITransport(app, root_path="/noauth") + except Exception: + url = "http://localhost:8123" + if transport is None: + transport = httpx.AsyncHTTPTransport(retries=5) headers = { "User-Agent": f"langgraph-sdk-py/{langgraph_sdk.__version__}", } @@ -77,7 +73,7 @@ def get_client( headers["x-api-key"] = api_key client = httpx.AsyncClient( base_url=url, - transport=httpx.AsyncHTTPTransport(retries=5), + transport=transport, timeout=httpx.Timeout(connect=5, read=60, write=60, pool=5), headers=headers, ) @@ -239,15 +235,174 @@ class AssistantsClient: self.http = http async def get(self, assistant_id: str) -> Assistant: - """Get an assistant by ID.""" + """Get an assistant by ID. + + Args: + assistant_id: The ID of the assistant to get. + + Returns: + Assistant: Assistant Object. + + Example Usage: + + assistant = await client.assistants.get( + assistant_id="my_assistant_id" + ) + print(assistant) + + ---------------------------------------------------- + + { + 'assistant_id': 'my_assistant_id', + 'graph_id': 'agent', + 'created_at': '2024-06-25T17:10:33.109781+00:00', + 'updated_at': '2024-06-25T17:10:33.109781+00:00', + 'config': {}, + 'metadata': {'created_by': 'system'} + } + + """ # noqa: E501 return await self.http.get(f"/assistants/{assistant_id}") async def get_graph(self, assistant_id: str) -> dict[str, list[dict[str, Any]]]: - """Get the graph of an assistant by ID.""" + """Get the graph of an assistant by ID. + + Args: + assistant_id: The ID of the assistant to get the graph of. + + Returns: + Graph: The graph information for the assistant in JSON format. + + Example Usage: + + graph_info = await client.assistants.get_graph( + assistant_id="my_assistant_id" + ) + print(graph_info) + + -------------------------------------------------------------------------------------------------------------------------- + + { + 'nodes': + [ + {'id': '__start__', 'type': 'schema', 'data': '__start__'}, + {'id': '__end__', 'type': 'schema', 'data': '__end__'}, + {'id': 'agent','type': 'runnable','data': {'id': ['langgraph', 'utils', 'RunnableCallable'],'name': 'agent'}}, + ], + 'edges': + [ + {'source': '__start__', 'target': 'agent'}, + {'source': 'agent','target': '__end__'} + ] + } + + + """ # noqa: E501 return await self.http.get(f"/assistants/{assistant_id}/graph") async def get_schemas(self, assistant_id: str) -> GraphSchema: - """Get the schemas of an assistant by ID.""" + """Get the schemas of an assistant by ID. + + Args: + assistant_id: The ID of the assistant to get the schema of. + + Returns: + GraphSchema: The graph schema for the assistant. + + Example Usage: + + schema = await client.assistants.get_schemas( + assistant_id="my_assistant_id" + ) + print(schema) + + ---------------------------------------------------------------------------------------------------------------------------- + + { + 'graph_id': 'agent', + 'state_schema': + { + 'title': 'LangGraphInput', + '$ref': '#/definitions/AgentState', + 'definitions': + { + 'BaseMessage': + { + 'title': 'BaseMessage', + 'description': 'Base abstract Message class. Messages are the inputs and outputs of ChatModels.', + 'type': 'object', + 'properties': + { + 'content': + { + 'title': 'Content', + 'anyOf': [ + {'type': 'string'}, + {'type': 'array','items': {'anyOf': [{'type': 'string'}, {'type': 'object'}]}} + ] + }, + 'additional_kwargs': + { + 'title': 'Additional Kwargs', + 'type': 'object' + }, + 'response_metadata': + { + 'title': 'Response Metadata', + 'type': 'object' + }, + 'type': + { + 'title': 'Type', + 'type': 'string' + }, + 'name': + { + 'title': 'Name', + 'type': 'string' + }, + 'id': + { + 'title': 'Id', + 'type': 'string' + } + }, + 'required': ['content', 'type'] + }, + 'AgentState': + { + 'title': 'AgentState', + 'type': 'object', + 'properties': + { + 'messages': + { + 'title': 'Messages', + 'type': 'array', + 'items': {'$ref': '#/definitions/BaseMessage'} + } + }, + 'required': ['messages'] + } + } + }, + 'config_schema': + { + 'title': 'Configurable', + 'type': 'object', + 'properties': + { + 'model_name': + { + 'title': 'Model Name', + 'enum': ['anthropic', 'openai'], + 'type': 'string' + } + } + } + } + + """ # noqa: E501 return await self.http.get(f"/assistants/{assistant_id}/schemas") async def create( @@ -259,7 +414,31 @@ class AssistantsClient: assistant_id: Optional[str] = None, if_exists: Optional[OnConflictBehavior] = None, ) -> Assistant: - """Create a new assistant.""" + """Create a new assistant. + + Useful when graph is configurable and you want to create different assistants based on different configurations. + + Args: + graph_id: The ID of the graph the assistant should use. The graph ID is normally set in your langgraph.json configuration. + config: Configuration to use for the graph. + metadata: Metadata to add to assistant. + assistant_id: Assistant ID to use, will default to a random UUID if not provided. + if_exists: How to handle duplicate creation. Defaults to 'raise' under the hood. + Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing assistant). + + Returns: + Assistant: The created assistant. + + Example Usage: + + assistant = await client.assistants.create( + graph_id="agent", + config={"configurable": {"model_name": "openai"}}, + metadata={"number":1}, + assistant_id="my-assistant-id", + if_exists="do_nothing" + ) + """ # noqa: E501 payload: Dict[str, Any] = { "graph_id": graph_id, } @@ -281,7 +460,30 @@ class AssistantsClient: config: Optional[Config] = None, metadata: Metadata = None, ) -> Assistant: - """Update an assistant.""" + """Update an assistant. + + Use this to point to a different graph, update the configuration, or change the metadata of an assistant. + + Args: + assistant_id: Assistant to update. + graph_id: The ID of the graph the assistant should use. + The graph ID is normally set in your langgraph.json configuration. If None, assistant will keep pointing to same graph. + config: Configuration to use for the graph. + metadata: Metadata to add to assistant. + + Returns: + Assistant: The updated assistant. + + Example Usage: + + assistant = await client.assistants.update( + assistant_id='e280dad7-8618-443f-87f1-8e41841c180f', + graph_id="other-graph", + config={"configurable": {"model_name": "anthropic"}}, + metadata={"number":2} + ) + + """ # noqa: E501 payload: Dict[str, Any] = {} if graph_id: payload["graph_id"] = graph_id @@ -298,7 +500,21 @@ class AssistantsClient: self, assistant_id: str, ) -> None: - """Delete an assistant.""" + """Delete an assistant. + + Args: + assistant_id: The assistant ID to delete. + + Returns: + None + + Example Usage: + + await client.assistants.delete( + assistant_id="my_assistant_id" + ) + + """ # noqa: E501 await self.http.delete(f"/assistants/{assistant_id}") async def search( @@ -312,14 +528,23 @@ class AssistantsClient: """Search for assistants. Args: - metadata (dict, optional): Metadata to filter by. Defaults to None. - graph_id (str, optional): The ID of the graph to filter by. Defaults to None. + metadata: Metadata to filter by. Exact match filter for each KV pair. + graph_id: The ID of the graph to filter by. The graph ID is normally set in your langgraph.json configuration. - limit (int, optional): The maximum number of results to return. Defaults to 10. - offset (int, optional): The number of results to skip. Defaults to 0. + limit: The maximum number of results to return. + offset: The number of results to skip. Returns: list[Assistant]: A list of assistants. + + Example Usage: + + assistants = await client.assistants.search( + metadata = {"name":"my_name"}, + graph_id="my_graph_id", + limit=5, + offset=5 + ) """ payload: Dict[str, Any] = { "limit": limit, @@ -340,7 +565,32 @@ class ThreadsClient: self.http = http async def get(self, thread_id: str) -> Thread: - """Get a thread by ID.""" + """Get a thread by ID. + + Args: + thread_id: The ID of the thread to get. + + Returns: + Thread: Thread object. + + Example Usage: + + thread = await client.threads.get( + thread_id="my_thread_id" + ) + print(thread) + + ----------------------------------------------------- + + { + 'thread_id': 'my_thread_id', + 'created_at': '2024-07-18T18:35:15.540834+00:00', + 'updated_at': '2024-07-18T18:35:15.540834+00:00', + 'metadata': {'graph_id': 'agent'} + } + + """ # noqa: E501 + return await self.http.get(f"/threads/{thread_id}") async def create( @@ -350,7 +600,26 @@ class ThreadsClient: thread_id: Optional[str] = None, if_exists: Optional[OnConflictBehavior] = None, ) -> Thread: - """Create a new thread.""" + """Create a new thread. + + Args: + metadata: Metadata to add to thread. + thread_id: ID of thread. + If None, ID will be a randomly generated UUID. + if_exists: How to handle duplicate creation. Defaults to 'raise' under the hood. + Must be either 'raise' (raise error if duplicate), or 'do_nothing' (return existing thread). + + Returns: + Thread: The created thread. + + Example Usage: + + thread = await client.threads.create( + metadata={"number":1}, + thread_id="my-thread-id", + if_exists="raise" + ) + """ # noqa: E501 payload: Dict[str, Any] = {} if thread_id: payload["thread_id"] = thread_id @@ -361,13 +630,42 @@ class ThreadsClient: return await self.http.post("/threads", json=payload) async def update(self, thread_id: str, *, metadata: dict[str, Any]) -> Thread: - """Update a thread.""" + """Update a thread. + + Args: + thread_id: ID of thread to update. + metadata: Metadata to add/update to thread. + + Returns: + Thread: The created thread. + + Example Usage: + + thread = await client.threads.update( + thread_id="my-thread-id", + metadata={"number":1}, + ) + """ # noqa: E501 return await self.http.patch( f"/threads/{thread_id}", json={"metadata": metadata} ) async def delete(self, thread_id: str) -> None: - """Delete a thread.""" + """Delete a thread. + + Args: + thread_id: The ID of the thread to delete. + + Returns: + None + + Example Usage: + + await client.threads.delete( + thread_id="my_thread_id" + ) + + """ # noqa: E501 await self.http.delete(f"/threads/{thread_id}") async def search( @@ -378,7 +676,28 @@ class ThreadsClient: limit: int = 10, offset: int = 0, ) -> list[Thread]: - """Search for threads.""" + """Search for threads. + + Args: + metadata: Thread metadata to search for. + status: Status to search for. + Must be one of 'idle', 'busy', or 'interrupted'. + limit: Limit on number of threads to return. + offset: Offset in threads table to start search from. + + Returns: + list[Thread]: List of the threads matching the search parameters. + + Example Usage: + + threads = await client.threads.search( + metadata={"number":1}, + status="interrupted", + limit=15, + offset=5 + ) + + """ # noqa: E501 payload: Dict[str, Any] = { "limit": limit, "offset": offset, @@ -393,13 +712,124 @@ class ThreadsClient: ) async def copy(self, thread_id: str) -> None: - """Copy a thread.""" + """Copy a thread. + + Args: + thread_id: The ID of the thread to copy. + + Returns: + None + + Example Usage: + + await client.threads.copy( + thread_id="my_thread_id" + ) + + """ # noqa: E501 return await self.http.post(f"/threads/{thread_id}/copy", json=None) async def get_state( self, thread_id: str, checkpoint_id: Optional[str] = None ) -> ThreadState: - """Get the state of a thread.""" + """Get the state of a thread. + + Args: + thread_id: The ID of the thread to get the state of. + checkpoint_id: The ID of the checkpoint to get the state of. + + Returns: + ThreadState: the thread of the state. + + Example Usage: + + thread_state = await client.threads.get_state( + thread_id="my_thread_id", + checkpoint_id="my_checkpoint_id" + ) + print(thread_state) + + ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + { + 'values': { + 'messages': [ + { + 'content': 'how are you?', + 'additional_kwargs': {}, + 'response_metadata': {}, + 'type': 'human', + 'name': None, + 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', + 'example': False + }, + { + 'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.", + 'additional_kwargs': {}, + 'response_metadata': {}, + 'type': 'ai', + 'name': None, + 'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b', + 'example': False, + 'tool_calls': [], + 'invalid_tool_calls': [], + 'usage_metadata': None + } + ] + }, + 'next': [], + 'config': + { + 'configurable': + { + 'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2', + 'checkpoint_ns': '', + 'checkpoint_id': '1ef4a9b8-e6fb-67b1-8001-abd5184439d1' + } + }, + 'metadata': + { + 'step': 1, + 'run_id': '1ef4a9b8-d7da-679a-a45a-872054341df2', + 'source': 'loop', + 'writes': + { + 'agent': + { + 'messages': [ + { + 'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b', + 'name': None, + 'type': 'ai', + 'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.", + 'example': False, + 'tool_calls': [], + 'usage_metadata': None, + 'additional_kwargs': {}, + 'response_metadata': {}, + 'invalid_tool_calls': [] + } + ] + } + }, + 'user_id': None, + 'graph_id': 'agent', + 'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2', + 'created_by': 'system', + 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, + 'created_at': '2024-07-25T15:35:44.184703+00:00', + 'parent_config': + { + 'configurable': + { + 'thread_id': 'e2496803-ecd5-4e0c-a779-3226296181c2', + 'checkpoint_ns': '', + 'checkpoint_id': '1ef4a9b8-d80d-6fa7-8000-9300467fad0f' + } + } + } + + """ # noqa: E501 if checkpoint_id: return await self.http.get(f"/threads/{thread_id}/state/{checkpoint_id}") else: @@ -413,7 +843,28 @@ class ThreadsClient: as_node: Optional[str] = None, checkpoint_id: Optional[str] = None, ) -> None: - """Update the state of a thread.""" + """Update the state of a thread. + + Args: + thread_id: The ID of the thread to update. + values: The values to update to the state. + as_node: Update the state as if this node had just executed. + + checkpoint_id: The ID of the checkpoint to get the state of. + + Returns: + None + + Example Usage: + + await client.threads.get_state( + thread_id="my_thread_id", + values={"messages":[{"role": "user", "content": "hello!"}]}, + as_node="my_node", + checkpoint_id="my_checkpoint_id" + ) + + """ # noqa: E501 payload: Dict[str, Any] = { "values": values, } @@ -428,7 +879,23 @@ class ThreadsClient: thread_id: Union[str, Config], metadata: dict, ) -> None: - """Patch the state of a thread.""" + """Patch the state of a thread. + + Args: + thread_id: The ID of the thread to get the state of. + metadata: The metadata to assign to the state. + + Returns: + None + + Example Usage: + + await client.threads.patch_state( + thread_id="my_thread_id", + metadata={"name":"new_name"}, + ) + + """ # noqa: E501 if isinstance(thread_id, dict): thread_id_: str = thread_id["configurable"]["thread_id"] else: @@ -445,7 +912,27 @@ class ThreadsClient: before: Optional[str] = None, metadata: Optional[dict] = None, ) -> list[ThreadState]: - """Get the history of a thread.""" + """Get the state history of a thread. + + Args: + thread_id: The ID of the thread to get the state of. + limit: The maximum number of results to return. + before: Thread timestamp to get history before. + metadata: The metadata of the thread history to get. + + Returns: + list[ThreadState]: the state history of the thread. + + Example Usage: + + thread_state = await client.threads.get_history( + thread_id="my_thread_id", + limit=5, + before="my_timestamp", + metadata={"name":"my_name"} + ) + + """ # noqa: E501 payload: Dict[str, Any] = { "limit": limit, } @@ -510,7 +997,56 @@ class RunsClient: webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, ) -> AsyncIterator[StreamPart]: - """Create a run and stream the results.""" + """Create a run and stream the results. + + Args: + thread_id: the thread ID to assign to the thread. + If None will create a stateless run. + assistant_id: The assistant ID or graph name to stream from. + If using graph name, will default to first assistant created from that graph. + input: The input to the graph. + stream_mode: The stream mode(s) to use. + metadata: Metadata to assign to the run. + config: The configuration for the assistant. + checkpoint_id: The checkpoint to start streaming from. + interrupt_before: Nodes to interrupt immediately before they get executed. + + interrupt_after: Nodes to Nodes to interrupt immediately after they get executed. + + feedback_keys: Feedback keys to assign to run. + webhook: Webhook to call after LangGraph API call is done. + multitask_strategy: Multitask strategy to use. + Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'. + + Returns: + AsyncIterator[StreamPart]: Asynchronous iterator of stream results. + + Example Usage: + + async for chunk in client.runs.stream( + thread_id=None, + assistant_id="agent", + input={"messages": [{"role": "user", "content": "how are you?"}]}, + stream_mode=["values","debug"], + metadata={"name":"my_run"}, + config={"configurable": {"model_name": "anthropic"}}, + checkpoint_id="my_checkpoint", + interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"], + interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"], + feedback_keys=["my_feedback_key_1","my_feedback_key_2"], + webhook="https://my.fake.webhook.com", + multitask_strategy="interrupt" + ): + print(chunk) + + ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + StreamPart(event='metadata', data={'run_id': '1ef4a9b8-d7da-679a-a45a-872054341df2'}) + StreamPart(event='values', data={'messages': [{'content': 'how are you?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', 'example': False}]}) + StreamPart(event='values', data={'messages': [{'content': 'how are you?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'fe0a5778-cfe9-42ee-b807-0adaa1873c10', 'example': False}, {'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-159b782c-b679-4830-83c6-cef87798fe8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}) + StreamPart(event='end', data=None) + + """ # noqa: E501 payload = { "input": input, "config": config, @@ -579,7 +1115,93 @@ class RunsClient: webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, ) -> Run: - """Create a background run.""" + """Create a background run. + + Args: + thread_id: the thread ID to assign to the thread. + If None will create a stateless run. + assistant_id: The assistant ID or graph name to stream from. + If using graph name, will default to first assistant created from that graph. + input: The input to the graph. + metadata: Metadata to assign to the run. + config: The configuration for the assistant. + checkpoint_id: The checkpoint to start streaming from. + interrupt_before: Nodes to interrupt immediately before they get executed. + + interrupt_after: Nodes to Nodes to interrupt immediately after they get executed. + + webhook: Webhook to call after LangGraph API call is done. + multitask_strategy: Multitask strategy to use. + Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'. + + Returns: + Run: The created background run. + + Example Usage: + + background_run = await client.runs.create( + thread_id="my_thread_id", + assistant_id="my_assistant_id", + input={"messages": [{"role": "user", "content": "hello!"}]}, + metadata={"name":"my_run"}, + config={"configurable": {"model_name": "openai"}}, + checkpoint_id="my_checkpoint", + interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"], + interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"], + webhook="https://my.fake.webhook.com", + multitask_strategy="interrupt" + ) + print(background_run) + + -------------------------------------------------------------------------------- + + { + 'run_id': 'my_run_id', + 'thread_id': 'my_thread_id', + 'assistant_id': 'my_assistant_id', + 'created_at': '2024-07-25T15:35:42.598503+00:00', + 'updated_at': '2024-07-25T15:35:42.598503+00:00', + 'metadata': {}, + 'status': 'pending', + 'kwargs': + { + 'input': + { + 'messages': [ + { + 'role': 'user', + 'content': 'how are you?' + } + ] + }, + 'config': + { + 'metadata': + { + 'created_by': 'system' + }, + 'configurable': + { + 'run_id': 'my_run_id', + 'user_id': None, + 'graph_id': 'agent', + 'thread_id': 'my_thread_id', + 'checkpoint_id': None, + 'model_name': "openai", + 'assistant_id': 'my_assistant_id' + } + }, + 'webhook': "https://my.fake.webhook.com", + 'temporary': False, + 'stream_mode': ['values'], + 'feedback_keys': None, + 'interrupt_after': ["node_to_stop_after_1","node_to_stop_after_2"], + 'interrupt_before': ["node_to_stop_before_1","node_to_stop_before_2"] + }, + 'multitask_strategy': 'interrupt' + } + + """ # noqa: E501 payload = { "input": input, "config": config, @@ -650,7 +1272,73 @@ class RunsClient: webhook: Optional[str] = None, multitask_strategy: Optional[MultitaskStrategy] = None, ) -> Union[list[dict], dict[str, Any]]: - """Create a run, wait for and return the final state.""" + """Create a run, wait until it finishes and return the final state. + + Args: + thread_id: the thread ID to create the run on. + If None will create a stateless run. + assistant_id: The assistant ID or graph name to run. + If using graph name, will default to first assistant created from that graph. + input: The input to the graph. + metadata: Metadata to assign to the run. + config: The configuration for the assistant. + checkpoint_id: The checkpoint to start streaming from. + interrupt_before: Nodes to interrupt immediately before they get executed. + + interrupt_after: Nodes to Nodes to interrupt immediately after they get executed. + + webhook: Webhook to call after LangGraph API call is done. + multitask_strategy: Multitask strategy to use. + Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'. + + Returns: + Union[list[dict], dict[str, Any]]: The output of the run. + + Example Usage: + + final_state_of_run = await client.runs.wait( + thread_id=None, + assistant_id="agent", + input={"messages": [{"role": "user", "content": "how are you?"}]}, + metadata={"name":"my_run"}, + config={"configurable": {"model_name": "anthropic"}}, + checkpoint_id="my_checkpoint", + interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"], + interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"], + webhook="https://my.fake.webhook.com", + multitask_strategy="interrupt" + ) + print(final_state_of_run) + + ------------------------------------------------------------------------------------------------------------------------------------------- + + { + 'messages': [ + { + 'content': 'how are you?', + 'additional_kwargs': {}, + 'response_metadata': {}, + 'type': 'human', + 'name': None, + 'id': 'f51a862c-62fe-4866-863b-b0863e8ad78a', + 'example': False + }, + { + 'content': "I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, honest, and harmless.", + 'additional_kwargs': {}, + 'response_metadata': {}, + 'type': 'ai', + 'name': None, + 'id': 'run-bf1cd3c6-768f-4c16-b62d-ba6f17ad8b36', + 'example': False, + 'tool_calls': [], + 'invalid_tool_calls': [], + 'usage_metadata': None + } + ] + } + + """ # noqa: E501 payload = { "input": input, "config": config, @@ -672,28 +1360,113 @@ class RunsClient: async def list( self, thread_id: str, *, limit: int = 10, offset: int = 0 ) -> List[Run]: - """List runs.""" + """List runs. + + Args: + thread_id: The thread ID to list runs for. + limit: The maximum number of results to return. + offset: The number of results to skip. + + Returns: + List[Run]: The runs for the thread. + + Example Usage: + + await client.runs.delete( + thread_id="thread_id_to_delete", + limit=5, + offset=5, + ) + + """ # noqa: E501 return await self.http.get( f"/threads/{thread_id}/runs?limit={limit}&offset={offset}" ) async def get(self, thread_id: str, run_id: str) -> Run: - """Get a run.""" + """Get a run. + + Args: + thread_id: The thread ID to get. + run_id: The run ID to get. + + Returns: + Run: Run object. + + Example Usage: + + run = await client.runs.get( + thread_id="thread_id_to_delete", + run_id="run_id_to_delete", + ) + + """ # noqa: E501 + return await self.http.get(f"/threads/{thread_id}/runs/{run_id}") async def cancel(self, thread_id: str, run_id: str, *, wait: bool = False) -> None: - """Get a run.""" + """Get a run. + + Args: + thread_id: The thread ID to cancel. + run_id: The run ID to cancek. + wait: Whether to wait until run has completed. + + Returns: + None + + Example Usage: + + await client.runs.cancel( + thread_id="thread_id_to_cancel", + run_id="run_id_to_cancel", + wait=True + ) + + """ # noqa: E501 return await self.http.post( f"/threads/{thread_id}/runs/{run_id}/cancel?wait={1 if wait else 0}", json=None, ) async def join(self, thread_id: str, run_id: str) -> None: - """Block until a run is done.""" + """Block until a run is done. + + Args: + thread_id: The thread ID to join. + run_id: The run ID to join. + + Returns: + None + + Example Usage: + + await client.runs.join( + thread_id="thread_id_to_join", + run_id="run_id_to_join" + ) + + """ # noqa: E501 return await self.http.get(f"/threads/{thread_id}/runs/{run_id}/join") async def delete(self, thread_id: str, run_id: str) -> None: - """Delete a run.""" + """Delete a run. + + Args: + thread_id: The thread ID to delete. + run_id: The run ID to delete. + + Returns: + None + + Example Usage: + + await client.runs.delete( + thread_id="thread_id_to_delete", + run_id="run_id_to_delete" + ) + + """ # noqa: E501 await self.http.delete(f"/threads/{thread_id}/runs/{run_id}") @@ -715,7 +1488,43 @@ class CronClient: webhook: Optional[str] = None, multitask_strategy: Optional[str] = None, ) -> Run: - """Create a background run.""" + """Create a cron job for a thread. + + Args: + thread_id: the thread ID to run the cron job on. + assistant_id: The assistant ID or graph name to use for the cron job. + If using graph name, will default to first assistant created from that graph. + schedule: The cron schedule to execute this job on. + input: The input to the graph. + metadata: Metadata to assign to the cron job runs. + config: The configuration for the assistant. + interrupt_before: Nodes to interrupt immediately before they get executed. + + interrupt_after: Nodes to Nodes to interrupt immediately after they get executed. + + webhook: Webhook to call after LangGraph API call is done. + multitask_strategy: Multitask strategy to use. + Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'. + + Returns: + Run: The cron run. + + Example Usage: + + cron_run = await client.crons.create_for_thread( + thread_id="my-thread-id", + assistant_id="agent", + schedule="27 15 * * *", + input={"messages": [{"role": "user", "content": "hello!"}]}, + metadata={"name":"my_run"}, + config={"configurable": {"model_name": "openai"}}, + interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"], + interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"], + webhook="https://my.fake.webhook.com", + multitask_strategy="interrupt" + ) + + """ # noqa: E501 payload = { "schedule": schedule, "input": input, @@ -744,7 +1553,39 @@ class CronClient: webhook: Optional[str] = None, multitask_strategy: Optional[str] = None, ) -> Run: - """Create a background run.""" + """Create a cron run. + + Args: + assistant_id: The assistant ID or graph name to use for the cron job. + If using graph name, will default to first assistant created from that graph. + schedule: The cron schedule to execute this job on. + input: The input to the graph. + metadata: Metadata to assign to the cron job runs. + config: The configuration for the assistant. + interrupt_before: Nodes to interrupt immediately before they get executed. + interrupt_after: Nodes to Nodes to interrupt immediately after they get executed. + webhook: Webhook to call after LangGraph API call is done. + multitask_strategy: Multitask strategy to use. + Must be one of 'reject', 'interrupt', 'rollback', or 'enqueue'. + + Returns: + Run: The cron run. + + Example Usage: + + cron_run = await client.crons.create( + assistant_id="agent", + schedule="27 15 * * *", + input={"messages": [{"role": "user", "content": "hello!"}]}, + metadata={"name":"my_run"}, + config={"configurable": {"model_name": "openai"}}, + interrupt_before=["node_to_stop_before_1","node_to_stop_before_2"], + interrupt_after=["node_to_stop_after_1","node_to_stop_after_2"], + webhook="https://my.fake.webhook.com", + multitask_strategy="interrupt" + ) + + """ # noqa: E501 payload = { "schedule": schedule, "input": input, @@ -761,7 +1602,21 @@ class CronClient: return await self.http.post("/runs/crons", json=payload) async def delete(self, cron_id: str) -> None: - """Delete a cron.""" + """Delete a cron. + + Args: + cron_id: The cron ID to delete. + + Returns: + None + + Example Usage: + + await client.crons.delete( + cron_id="cron_to_delete" + ) + + """ # noqa: E501 await self.http.delete(f"/runs/crons/{cron_id}") async def search( @@ -772,7 +1627,50 @@ class CronClient: limit: int = 10, offset: int = 0, ) -> list[Cron]: - """Get a list of cron jobs.""" + """Get a list of cron jobs. + + Args: + assistant_id: The assistant ID or graph name to search for. + thread_id: the thread ID to search for. + limit: The maximum number of results to return. + offset: The number of results to skip. + + Returns: + list[Cron]: The list of cron jobs returned by the search, + + Example Usage: + + cron_jobs = await client.crons.search( + assistant_id="my_assistant_id", + thread_id="my_thread_id", + limit=5, + offset=5, + ) + print(cron_jobs) + + ---------------------------------------------------------- + + [ + { + 'cron_id': '1ef3cefa-4c09-6926-96d0-3dc97fd5e39b', + 'assistant_id': 'my_assistant_id', + 'thread_id': 'my_thread_id', + 'user_id': None, + 'payload': + { + 'input': {'start_time': ''}, + 'schedule': '4 * * * *', + 'assistant_id': 'my_assistant_id' + }, + 'schedule': '4 * * * *', + 'next_run_date': '2024-07-25T17:04:00+00:00', + 'end_time': None, + 'created_at': '2024-07-08T06:02:23.073257+00:00', + 'updated_at': '2024-07-08T06:02:23.073257+00:00' + } + ] + + """ # noqa: E501 payload = { "assistant_id": assistant_id, "thread_id": thread_id, diff --git a/libs/sdk-py/langgraph_sdk/schema.py b/libs/sdk-py/langgraph_sdk/schema.py index 3e9d6c064..a0fac51a0 100644 --- a/libs/sdk-py/langgraph_sdk/schema.py +++ b/libs/sdk-py/langgraph_sdk/schema.py @@ -42,10 +42,15 @@ class GraphSchema(TypedDict): graph_id: str """The ID of the graph.""" - state_schema: dict - """The schema for the graph state.""" - config_schema: dict - """The schema for the graph config.""" + input_schema: Optional[dict] + """The schema for the graph state. + Missing if unable to generate JSON schema from graph.""" + state_schema: Optional[dict] + """The schema for the graph state. + Missing if unable to generate JSON schema from graph.""" + config_schema: Optional[dict] + """The schema for the graph config. + Missing if unable to generate JSON schema from graph.""" class Assistant(TypedDict): @@ -128,3 +133,18 @@ class Cron(TypedDict): """The last time the cron was updated.""" payload: dict """The run payload to use for creating new run.""" + + +class RunCreate(TypedDict): + """Payload for creating a background run.""" + + thread_id: Optional[str] + assistant_id: str + input: Optional[dict] + metadata: Optional[dict] + config: Optional[Config] + checkpoint_id: Optional[str] + interrupt_before: Optional[list[str]] + interrupt_after: Optional[list[str]] + webhook: Optional[str] + multitask_strategy: Optional[MultitaskStrategy] diff --git a/libs/sdk-py/pyproject.toml b/libs/sdk-py/pyproject.toml index b892985e8..3c50594db 100644 --- a/libs/sdk-py/pyproject.toml +++ b/libs/sdk-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langgraph-sdk" -version = "0.1.26" +version = "0.1.27" description = "SDK for interacting with LangGraph API" authors = [] license = "MIT" diff --git a/poetry.lock b/poetry.lock index ea06529e8..7e00b52aa 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,23 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +[[package]] +name = "aiosqlite" +version = "0.20.0" +description = "asyncio bridge to the standard sqlite3 module" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"}, + {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"}, +] + +[package.dependencies] +typing_extensions = ">=4.0" + +[package.extras] +dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"] +docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"] + [[package]] name = "annotated-types" version = "0.7.0" @@ -927,26 +945,30 @@ test-ui = ["calysto-bash"] [[package]] name = "langchain-core" -version = "0.2.8" +version = "0.2.27" description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_core-0.2.8-py3-none-any.whl", hash = "sha256:172c81c858dc1f3123cc72b7e44e10f44c92f8a761cae18c364081f6c208e9f6"}, - {file = "langchain_core-0.2.8.tar.gz", hash = "sha256:2db866a4514672c4875b69d5590aa2ed50aa0d144874268bef68d74b5e7f33f9"}, + {file = "langchain_core-0.2.27-py3-none-any.whl", hash = "sha256:b12f58d4e3590e8e0b4b727acb0457b00e80862d1bf8b9d1ae36128adb08a7d0"}, + {file = "langchain_core-0.2.27.tar.gz", hash = "sha256:5d2e4b9bc84285bbfe19864363b1a21dd04c2eed12d4a6b39f239ee02237ce40"}, ] [package.dependencies] jsonpatch = ">=1.33,<2.0" langsmith = ">=0.1.75,<0.2.0" packaging = ">=23.2,<25" -pydantic = ">=1,<3" +pydantic = [ + {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""}, + {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""}, +] PyYAML = ">=5.3" -tenacity = ">=8.1.0,<9.0.0" +tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0" +typing-extensions = ">=4.7" [[package]] name = "langgraph" -version = "0.1.0" +version = "0.2.2" description = "Building stateful, multi-actor applications with LLMs" optional = false python-versions = ">=3.9.0,<4.0" @@ -954,15 +976,67 @@ files = [] develop = true [package.dependencies] -langchain-core = ">=0.2,<0.3" +langchain-core = ">=0.2.27,<0.3" +langgraph-checkpoint = "^1.0.2" [package.source] type = "directory" url = "libs/langgraph" +[[package]] +name = "langgraph-checkpoint" +version = "1.0.2" +description = "Library with base interfaces for LangGraph checkpoint savers." +optional = false +python-versions = "^3.9.0,<4.0" +files = [] +develop = true + +[package.dependencies] +langchain-core = ">=0.2.22,<0.3" + +[package.source] +type = "directory" +url = "libs/checkpoint" + +[[package]] +name = "langgraph-checkpoint-postgres" +version = "1.0.1" +description = "Library with a Postgres implementation of LangGraph checkpoint saver." +optional = false +python-versions = "^3.9.0,<4.0" +files = [] +develop = true + +[package.dependencies] +langgraph-checkpoint = "^1.0.1" +orjson = ">=3.10.1" +psycopg = {version = ">=3.1.19", extras = ["binary"]} + +[package.source] +type = "directory" +url = "libs/checkpoint-postgres" + +[[package]] +name = "langgraph-checkpoint-sqlite" +version = "1.0.0" +description = "Library with a SQLite implementation of LangGraph checkpoint saver." +optional = false +python-versions = "^3.9.0" +files = [] +develop = true + +[package.dependencies] +aiosqlite = "^0.20.0" +langgraph-checkpoint = "^1.0.1" + +[package.source] +type = "directory" +url = "libs/checkpoint-sqlite" + [[package]] name = "langgraph-sdk" 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