From ae74825ea721b7f6b99fdeb7cbb30bb20561d301 Mon Sep 17 00:00:00 2001 From: Vadym Barda Date: Wed, 31 Jul 2024 16:45:52 -0400 Subject: [PATCH] langgraph checkpoint: new library for checkpoint interfaces (#1163) --------- Co-authored-by: Nuno Campos --- README.md | 2 +- docs/docs/reference/checkpoints.md | 6 +- .../human-in-the-loop.ipynb | 692 +++--- .../langgraph_to_langgraph_cloud.ipynb | 2108 ++++++++--------- examples/create-react-agent-hitl.ipynb | 478 ++-- examples/create-react-agent-memory.ipynb | 496 ++-- examples/human_in_the_loop/breakpoints.ipynb | 930 ++++---- .../human_in_the_loop/edit-graph-state.ipynb | 1128 ++++----- examples/human_in_the_loop/time-travel.ipynb | 1118 ++++----- .../human_in_the_loop/wait-user-input.ipynb | 1304 +++++----- examples/persistence.ipynb | 1158 ++++----- examples/persistence_mongodb.ipynb | 1978 ++++++++-------- examples/persistence_postgres.ipynb | 2058 ++++++++-------- examples/persistence_redis.ipynb | 1660 ++++++------- examples/storm/storm.ipynb | 1882 ++++++++------- examples/subgraph.ipynb | 1348 +++++------ libs/checkpoint/LICENSE | 21 + libs/checkpoint/Makefile | 31 + libs/checkpoint/README.md | 85 + .../langgraph/checkpoint/base/__init__.py} | 74 +- .../langgraph/checkpoint/base}/id.py | 0 .../langgraph/checkpoint/memory.py | 0 .../langgraph/checkpoint/py.typed} | 0 .../langgraph/checkpoint/serde/__init__.py | 0 .../langgraph/checkpoint}/serde/base.py | 0 .../langgraph/checkpoint}/serde/jsonplus.py | 6 +- .../langgraph/checkpoint/serde/types.py | 66 + .../langgraph/checkpoint/sqlite/__init__.py} | 120 +- .../langgraph/checkpoint/sqlite/aio.py} | 9 +- .../langgraph/checkpoint/sqlite/utils.py | 114 + libs/checkpoint/poetry.lock | 879 +++++++ libs/checkpoint/pyproject.toml | 49 + libs/checkpoint/tests/__init__.py | 0 .../tests}/test_aiosqlite.py | 10 +- .../tests/test_jsonplus.py | 2 +- .../tests}/test_memory.py | 8 +- .../tests}/test_sqlite.py | 20 +- libs/langgraph/README.md | 2 +- libs/langgraph/langgraph/__init__.py | 3 - libs/langgraph/langgraph/channels/manager.py | 36 +- .../langgraph/checkpoint/__init__.py | 13 - libs/langgraph/langgraph/errors.py | 19 +- libs/langgraph/langgraph/graph/graph.py | 2 +- libs/langgraph/langgraph/graph/state.py | 4 +- .../langgraph/prebuilt/chat_agent_executor.py | 4 +- libs/langgraph/langgraph/pregel/__init__.py | 2 +- libs/langgraph/langgraph/pregel/algo.py | 9 +- libs/langgraph/langgraph/pregel/loop.py | 2 +- libs/langgraph/poetry.lock | 82 +- libs/langgraph/pyproject.toml | 4 +- libs/langgraph/tests/test_pregel.py | 4 +- libs/langgraph/tests/test_pregel_async.py | 10 +- poetry.lock | 38 +- pyproject.toml | 1 + 54 files changed, 10660 insertions(+), 9415 deletions(-) create mode 100644 libs/checkpoint/LICENSE create mode 100644 libs/checkpoint/Makefile create mode 100644 libs/checkpoint/README.md rename libs/{langgraph/langgraph/checkpoint/base.py => checkpoint/langgraph/checkpoint/base/__init__.py} (88%) rename libs/{langgraph/langgraph/checkpoint => checkpoint/langgraph/checkpoint/base}/id.py (100%) rename libs/{langgraph => checkpoint}/langgraph/checkpoint/memory.py (100%) rename libs/{langgraph/langgraph/serde/__init__.py => checkpoint/langgraph/checkpoint/py.typed} (100%) create mode 100644 libs/checkpoint/langgraph/checkpoint/serde/__init__.py rename libs/{langgraph/langgraph => checkpoint/langgraph/checkpoint}/serde/base.py (100%) rename libs/{langgraph/langgraph => checkpoint/langgraph/checkpoint}/serde/jsonplus.py (95%) create mode 100644 libs/checkpoint/langgraph/checkpoint/serde/types.py rename libs/{langgraph/langgraph/checkpoint/sqlite.py => checkpoint/langgraph/checkpoint/sqlite/__init__.py} (80%) rename libs/{langgraph/langgraph/checkpoint/aiosqlite.py => checkpoint/langgraph/checkpoint/sqlite/aio.py} (98%) create mode 100644 libs/checkpoint/langgraph/checkpoint/sqlite/utils.py create mode 100644 libs/checkpoint/poetry.lock create mode 100644 libs/checkpoint/pyproject.toml create mode 100644 libs/checkpoint/tests/__init__.py rename libs/{langgraph/tests/checkpoint => checkpoint/tests}/test_aiosqlite.py (92%) rename libs/{langgraph => checkpoint}/tests/test_jsonplus.py (98%) rename libs/{langgraph/tests/checkpoint => checkpoint/tests}/test_memory.py (96%) rename libs/{langgraph/tests/checkpoint => checkpoint/tests}/test_sqlite.py (90%) delete mode 100644 libs/langgraph/langgraph/__init__.py delete mode 100644 libs/langgraph/langgraph/checkpoint/__init__.py diff --git a/README.md b/README.md index cf8814eb6..79170f2f4 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ 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/docs/reference/checkpoints.md b/docs/docs/reference/checkpoints.md index eb8735dd6..3108091f4 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 @@ -33,7 +35,7 @@ LangGraph also natively provides the following checkpoint implementations. ### AsyncSqliteSaver -::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver +::: langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver ### SqliteSaver 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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q2u4286/bbtDkNomNFznDgKi2UFKg4oEFHdqrA8oT/i5evon66/Uy7m6QlrGby4o+gsob/vWsD++nV6u75E3OjtaMmKwYsVlkuuPltAQXXSCtehraiNdT3mtpw8gquN4n30g+LIb8Rhq3sODYU64PwFQQn/2j6CKjWUh7F8SueTZeh6245bme3lQbY2uXMdRsAglv3qevnBO+mzzpANWdid6tWR4vabpYnEO2SZFbfhLbaLSFMKSCgpQQCkcutAgdKkkqSavdvw/X6yOfisTGcejgbalKVScoUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlYkq6xIUqNFeksomSgvxeMp1KXH+UcyghJI5tDqdd3poDLqrLnmFy4n3PiBgViZyDDZ1sjNsNZe5BT4v4w4nmIY5z5/KkoOxroo6KSEqOEzjFy8IXDLe5nljvWACHefHWbPCvPK7KZaO2fGVM60CrS+RKtgoSQqrgoDS4jjfuVxu02py4TL0/b4qIpudyWHJUgJABU4sAbUdAk+k1uqUoDxddQy2txxaW20AqUtR0Egd5Jr4weEb4TV14g+Es5nlhmKYj2CU2zYVpPRLLCyUrI6b7RXMsg+hfKegr7E5jjEbNsRvmOzHpEaJd4L9veeiLCXm0OtqQpSCQQFAKJBII3roa+e3ED/Z/wDD3FePXCnCYl5yZy1ZYi7KnPPyo5fbMWOl1vsiGAkbUo83MlWx3a76A7x4RcS7bxh4bWDMLV5sS6xg6WubZZcBKXGifSULCkk/gqYVXXAvgXYPB8xKbjmNS7nJtci4O3BKLm+l5TClpQkttlKU6QOQEA7OyokndWLQCoXd+HD1w4i49lUTJrxambVGciO2KK6nyfMbUDrnbKTpSVcpCh10kDpU0pQED4Z8SLjmNouEjJMUn4HOiXNdtES7OtlMg7HZrZcSdOJUFJAI6c3MAVa3U8qK8SuF+M8XsXcx/K7Yi6WxTqX0oK1IW06nfK4haSFJUNnqD3EjuJFap685bi+cXuTeG7HH4Ww7T40zPQ64mbFcaSC6HUEFKkFPMQU9wR6SdUBP6VosJziw8R8Zg5DjVzYu9mmp52ZUc7SrR0QQeqVAggpIBBBBANb2gFKUoBSlKAUpSgFKUoBWP5Qi/fLPzgrIrntjjhbrnlMiz2fH8hvseJO8my7zboSVwY8gEJWhS1LCjyE6UUJUE9dnoaAvzyhF++WfnBTyhF++WfnBXOs/wjsbt91mNKt16dscG4C1zMmaiJNtjyecIKFuc/PoLUEFYQUAnRVXrvfhIWGxSshD1jyF63Y9O8Qu11YhoVFhq0g86lFwKUjTiSeRKlJHVSQCCQOjvKEX75Z+cFPKEX75Z+cFc4M8X7yvj9c8IGMz5Vmj26JIRPjIZ0hTq3Ap5xSngex0gJHKgq5kr2NcpOsx3j7Ft2N3a83prIJfaZY5YY9tctjCZUR0pTyRwhl1QcSDsBe+YlfUaG6AvzPslvEPFL4cOjQbplMeMHIUS4uqYjOuKJABd1o6AUeUEfyQSkKCq0lj4d2a53/Gc5y+HaJXEe22pMBy4RHVKYZWQS6WUqPTalLAURzcqiN6NV0rwg7HFsd/n3C0Xq1y7FMhQp9plsNCU2qU422wscrhbUhRcB2FnolXTY1W/wAh4sWDFMmnWa6uPQjCsi7/ACJriR4u3GQ52ahsHmK99dBPUenfSgLpbmR3VhKH21qPclKwSa91Udw4402/I84tdkl2DIMZm3Fp563eXISWUzUoRzL7MpWrSgk83IvlVrZ10NXjQClKUArnbjN/HC8HT5LI/wBCRXRNc7cZv44Xg6fJZH+hIoDomlKUApSlAK/O+v2lAV/l/Da6zJWJLw7JV4RDs1wMmXbIMJpUW4sLVt1paNDlUdrIUO5SySCdEZeJcUI+UZVlVhesl4scmwSENKkXWL2UeahfNyOx3NkLSeRXwEa6gVNaiHFXLbbg2HSL1dnVtQoziAQ22XHHFqPIhtCB1UtSlJSEjvJFASfyhF++WfnBTyhF++WfnBXOq/CIssCBfnrxYchx6baLU7el225xG0PyYjfRa2eVxSFaJSCkrBBUNgbrZY3xstGQZCm0SLZd8feegLukN+8x0MNTIqCkLcQQslPLzoJS4EKAUCU0BfKZ0ZaglMhpSidABY2a99cl/wDaIfynOuGLGNW2+26xXrIRHVdp9vbREucUMPK00pRKwCpKFAlKCoAkbG660oBSlKAUpSgFcp8N7bnfCd6XibWHJv8AY3LzIlxsgZujLKURpEhTqu2bX9sLiO0UNJSQrQ6jvrqytf5Bg/cPz1e2gONrtwtzz7HuRcJomPMu2S7XWQ61lZntBpiG/KMhfOyT2peSFKQAE8pPKeYCt5fOFuTS+GfHe0s2ztJ+TXOXItLRkNfsltcOO2hXMVaRtbax55B6b7iK6t8gwfuH56vbTyDB+4fnq9tAc4rx/KsW41x8ig48q+We62OFaJbrMxlpdvcZfcUpxSXFDnRyvE+Zs7QRrqDUYVwoyopcHkvqeKKcjH7Ia/c8FP27334D5nvv/LXWvkGD9w/PV7ahHEjK7NwufsUq42+6z4l7ukazIEJsONQ3HCrTzmiFhJ7ifOHROgNkkChuJHCLKMou3Fp+3wmiLs1j79qU9IQlMp2E8p5xs6JKN6SnagB5w9AOtVn3C7NON2R5Q7Px04hAuOHqtERyZOYkLEoS0PpDqWlK0k8veObzQd9Ty12L5Bg/cPz1e2nkGD9w/PV7aA5z4AYExb83t82bwVseCT4cZz/viG9EcUp8p5FJYDQKwhSVOecspOtDR2ddOVhsWmJGdS601yrT3HmJ/wD2sygFKVr7/f7di1knXi7zGrfbILKpEmU+rlQ02kbKifxUBpeJ/Eyw8IcIueVZJLES2QUcx11ceWfettp/lLUegH9ugCaqDgLw6yLOcxXxp4lRlQ8imMqZx3HlklNhgLHcQf59xJ8862ASOmylOg4cWG5eFjn8HijlcN2Fw3srxXh2Oyk6M1wHXlGQn0719rSe7vHQEudUUApSlAKUpQClKUAqoPCo4bzOKPCZ6025ER+4MT4twjxZ/wC1pSmXA52LvQ+asAp/KN9Kt+vTJiNTGwh5HOkHetkdfyUBxvK4UOXrhrn8W08GLXgV9nWJ+BCVHkwlPy3HEKCm+ZrzUo2G9FShv0gaqT5/wqvGZZJhzaWSxbmsYvFnnzA4jcZySzHbb83m5ldUL6p2By9SNiul/IMH7h+er21DOH2L5RGumXKy52FKgu3ZxdiTEJCmoPKORDmgPP3zd+/x0Bz7i2N8RrnP4NWW74Qi2sYddo5m3Vi6R3WH2mojrCXGmwoL5VFSTogKGwNEbI7FrBbssNpxK0s6Ukgg8yuh/trOoBSlKAUpSgFKUoBSlKAVj3BqQ/AktxXxFlLbUlp9SOcNrIPKop9Ojo69NZFQPjhh+V5zw2u1owrK38OyJ5siPcGUIIVtJSptaihSmwoKOnGuVxCglQPQpUBA7l4RVs8Hzhhavs1ZRaFZ4iMVSLfYz20iYr7YW1IZCUlPOG+XnUENBexzAEVddkvMPIrNAu1ue8Yt8+O3KjvcpTztrSFIVpQBGwQdEA18IeMXDvNOGmd3K3Z5DmMZA84qS9KmOF0zCtRJeDuz2vMdkq2eu99Qa+2/BQa4N4GP/QIH6O3QE0pSlAeLjiWkKWtQQhIJUpR0APhNcnTXZPhu5+uBGcdZ4FY1MAlvtkp9081s77NJHfGQdEke+7x1KSjN4uZbdvCY4gTODWDT3YOLW8gZtk0Q+8Rv9z2FdxcXohfwaIOwlaT0fiWJ2jBcat2P2GC1bbRb2UsRorI81CR/eSTsknqSSTsmgNlGjMwozUeO0hhhpAbbaaSEpQkDQSAOgAHTVe2lKAUpSgFKUoBSlKAUpSgI9l3EXFOH6YqsoyezY2mWVCObvcGooe5dc3J2ihza5k713bHw1TnC3irwUxC9Z1Kt/F2xzHb1fHLhKRc7ww0hl1SUgoYKikLa6DSklQ7+tbPwyOBo48cD7vaorPaX+3DylaiBtSn20nbQ+USVI13bKSe6vmX4FvAhXHLjjbIE6L2uO2g+UbsFp8xTaCOVk+g86+VJHfy85HdQH2lpSlAKUpQClKUApSlAKUrDu90j2O0zbjLUURYjK33VAbISlJUdfkFZScnZAwMlyuLjTbSVtuy5r+/F4ccbcc1rZ2dBKRsbUogdQO8gGGv5Plk9RWJFutLZ0Qy0wqSsfDtxSkg/kQPy1g21MmQXblcAPKk7TkjRJDfTzWk7/koB0O7Z5lEbUd5tWSqKm+bBJ9+vhst4/I7tHBwjG882QLi1wqa43455EzGXGukVCudlzxBCHo6/6TbiSFJPdvR0e4gipVZWcgx2zQLVb763HgQY7cWOz4ilXI2hIShO1KJOgANkk1s6w5d5gQLhAgyZjDE2epaYsdxwBx8oSVr5E950kEnXdWOsT3L4Y+RsdXo9k93lPLPjG39Xt+2sO8HKrzaZsBWVuRUymVsl+JDQ282FAgqQsHaVDfQjuPWvFOTW1eTuY8JBN4bhpnqj9mvowpZQFc2uX3ySNb307tVtKdYnuXwx8jHV6PZIfwpwiTwQxNjHsTet6ba0tTy25kNRckOq98446lwEqOu8g6AAA0ABa2N5w3d5Yt8+Kq13MglDalhbUgDqS0sa3odSlQSrvOtDdRmsefBbuEfsnOZJCgttxB0ttYO0rSfQoHqDRVlPKol70rW4WTKqmEpyXoqzLWpUfwa/u5DjzT0ooNwYWqLL7MaSXkHSlAegK6KA+BQqQVGUXCTi9hwWnF2YpSlRMClKUBCOIN8u9vu9jg2uaiCJaZC3XFMB0nkCNAA93vjWl8fyv4yN/V7ftrYcRP33Yv8AIzP8Gq9Fa2LxNShzI07Zrcnte9Hl+UsZXoV+ZTlZW7jG8fyv4yN/V7ftp4/lfxkb+r2/bWTStHSGI3r4Y+RytJYvt+C8jG8fyv4yN/V7ftqE8O+FCeFd0ye441cGoEvI5xuFwc8RQrncOzpOz5qAVLISOgK1aqf0ppDEb18MfIaSxfb8F5GN4/lfxkb+r2/bTx/K/jI39Xt+2sK95Va8cmWeLcZXi793l+IwkdmtXavci3OXaQQnzW1natDp37IrbU6/iN6+GPkZ0ji1nz/BeRjeP5X8ZG/q9v21hXq/Zba7NPmoyFpao0dx4JNvb0SlJOu/8FbatRl/7071/uT/APpqq6hjq8qsYtqza/LHf7idPlHFOcU57dy8i14DypEGM6vXO42lR18JANK8LT+5UL5FH+UUrpy1s9yZdKUqIFRLitz+4G6cm/5rn1/Q7VHP+bupbWJd7XHvlqm26Wkriy2VsOpB0ShSSk/3GraUlCpGT2NEouzTK8qluNbszJsztOJ2FzIV31FvduLjdqvxtEZpgrDaXXnUoWpaucEJQEke+KhrVW5bVSYxdttwI8qQeVuRoEBwa811O/5KwNjv0eZO9pOtBmPCrFs+nxJt8tnjUuK2plt5uQ6wotKIKm1ltSedBIG0K2n8FUTi4ScWeml/kh6O0onCc0yPi1B4SY9dsin2hm7WOZcrjOtb/i0m4vR3ENJaS6nRR0UXFcmidegVueI3DKGnilwdtD1+ySQ2V3VnxtV7kIk6EdTg+2oUlXN15eb3xSkAk1ZsvgVgs3GYGPuWBCbVb5DkmE0zIeaXEcWoqWWXErC2wSo+ahQHo1qvObwRwu4Yxb8ffsxNst8hUuIES30PMvKKipaXkrDnMorVs83XZ3uoFXRStZ56vCxUXGDMb7w0zniVNst0uDhawqNcmIsuU4/GiyFSnGC820olKeVCEqIA0Skk72a881TdeEV8gWy15ffr4xfsbvDsrypcFyVtPR4yXG5bKidtEqUU6QQnzk6AI3V4fY7x03F6c5bEPyX7UiyOl9xbqXISSpQaUlRKVDa17JGzvqTWlx7gTg2LIuKbdY+zM+Gq3PLelvvLEZQ0WW1OLUW0dfeoKR0HwCsmXTk3rKzwh2747kXBaarJb3dVZfbnhdmbnOU+y4sQRIStDZ81ohSSPMA2D12etdE1HmuH9gZcxhaIHKrGWy1aT2zn7GSWexI995/2s8vn7+Hv61uZ85u3sdo5zKKlBDbaBtbiydJQkelRPQCiTk7LWWwjzE7/AFkbvhfz+NZV39l5SRy7/peKsb1+Du/Lup3WgwewO49jzTMkIE99apUvsztPbLO1JB9IT0SD8CRW/raqtOeWyy4Kx5urJTm5IUpSqSoUpSgK+4ifvuxf5GZ/g1Xor38RP33Yv8jM/wAGqjeVRslksMDG7jarc8FHtlXWA7LSpOugSEPNcp36STXN5Q+1T/6/+pHjOV1fEpX2L9ze1V/hIZld8I4XyJlkdTFnyp0S3iYtwNpioefQ2pwrKVBGgogKKVcpIOjrR2QtnFHkIOS4jz7Gj7npWtdd9PHvxV7o2KZLkEebbM4mY1kGPzI6mXoESzPMKWSRrmU5JdBGt9OXe9EEarmxtFpt3OVBRhJSk00tmfkUrkVh4m8PsE4g3KRcpFvsreMTFtpcyh+6y2pqRtt5p1xhpbQ5ecEBRG+UgDVba6Xm88Jcqs0uJe7xkKLtid1ucqFdpipCHJUVth1C20no1zdopJS2Ep0R5o1Vm2ngRg9ksd7tEWzueIXmL4lOQ/PkvLdY0oBsLW4VJSAtWgkjWzqpG9hVlkXmz3VyEFz7RGeiQnS4vTTToQHE8u9K2G0dVAka6a2d2dJE2HiIPJq6z2a8str2nOEbGZQd4EZZPyy9ZHcr5eGZcoS5pXDC3YEhzbLPvWgnqkcuuhO9nu6rqtLb4O2B47cI1ystiRAuUF9cu3lUqSuPFfUlSeZDPahCU+edoSAD+MAjL8l8U/jLiB/+Oyv+uqM2p6mQrTjWas9W9W291ywK1GX/AL071/uT/wDpqqLeS+Kfxmw//wCuyv8ArqlOW79yV633+Ivd3yaqnQVq0M9q+ZVCKVSNnfNFqWn9yoXyKP8AKKUtP7lQvkUf5RSvQy+0z6QZdKUqIFKUoDSZLikTJW2lOLdizWObxeZHOnGt62OuwpJ0NpUCDoHWwCIa/jGWQFFKY9uu7YIAeafVGWR6dtqSoD8iz+SrNpVqqZc2STXf/WZfTr1KWUWVX5Myv4uN/WDfsp5Myv4uN/WDfsq1KVLnw9Wv9vMv67VKr8mZX8XG/rBv2U8mZX8XG/rBv2ValKc+Hq1/t5jrtUq9uw5dKVyptUCED/OypxVy/wDChB3+LY/HUoxvB27PKE+dKVdLmAQh1SAhpgHoQ0gb1sdCpRUrqRvR1UopWHUytGKXu/u7KqmIqVFaTyFKUqk1hSlKAUpSgIRxBsd2uF3sc61wkThETIQ62p8NEc4Rognv96a0viGV/FxH1g37KtGlJxp1EukgnbLbvvsa3mjWwVDES59SN372Vd4hlfxcR9YN+yniGV/FxH1g37KtGlQ6HD+qXGX8ijReE7Hi/Mq7xDK/i4j6wb9lPEMr+LiPrBv2VaNKdDh/VLjL+Q0XhOx4vzKu8Qyv4uI+sG/ZTxDK/i4j6wb9lWjSnQ4f1S4y/kNF4TseL8yrvEMr+LiPrBv2Vh3qw5ZdLNPhIx5tCpMdxkKM9vQKkkb7vw1btKlGnQhJSVNXXfL+RlcmYWLTUfF+ZjwGVR4MZpeudttKTr4QAKVkUqTd3c6h/9k=", + "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/cloud_examples/langgraph_to_langgraph_cloud.ipynb b/examples/cloud_examples/langgraph_to_langgraph_cloud.ipynb index 41a174f33..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": "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': []}" + "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 Cloud" - ] - }, - { - "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", - "{'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" - ] + "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/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/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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" - } - }, - "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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dJy0ndbDgVJupJyfaKUpVZWKUpQClKUApSlAc78gT6slh8+uXtr1dEVzvyBPqyWHz65e2vV0RQClKUApSlAKUpQClKUApSlAKUpQHO/IE+rJYfPrl7a9XRFc78gT6slh8+uXtr1dEUApSlAKUpQClKUApSlAKUrGmXOHb8fKpbEbPEc84EfrNZSbyQMmub+VlyublyXbhYd7QfSWz3Zpe5cE3b5Lzb6D4TSkcwv8AmqQoHeGcqGPBJq/OlVl74geso99VLypdn2ntvexe+aZFzthuqUfLLU6uS2ObltglHHPAKBU2T2Bw1PVz4WZszkvkP8sifB6HbH7doBV1el3J7nLqi7bnMsuvreddLXMnIbQpRxvje3OzNfSKuAPwZ+x6Doy2XvaFqVxi33mapVst0aa4ltxphKhzzm6o5BWtISMgEBtXYqu6+lVl74geso99NXPhYszaUrAjX62THA3HuMR9ZOAlt9KifuBrPqDTjtRgUpSsAUpSgFKVE9p1wch6UcjsOFp+4PNQUrBIKUuLAcII4ghvfII7cdXXU4R05KO8lGLk1FdppL5qmXqd11i2SnIFnQotqlsHdelKBwebV/Mb4EBY8JXWkpSApeka0rZ2VKWLbGcdUSpTrrYccUT1kqVkk/aa2LLLcdlDTSEttNpCUoSMBIHAAVErptc0nZ9YtaWlXUi+uKZQYzUZ50NqdOGkuOIQUNlXYFqGaSrS2Qdl97d56OnSp0YpEh6P2vu2H6BPup0ftfdsP0CfdUD2dbcbZr/VuqbCiJMhybPcHIba3IUkNvNtttqU4pxTSUIVvLUAgqyQkKGQQa2OntuGh9VX5mzWu/NyZz5WmNlh1tqUUAlQZdUgNvYAJ8BSuAJ7Kr1k+JlqlB9pK+j9r7th+gT7qdH7X3bD9An3VCtNcoLQOr5tsi2y+qcXdFFEFx+DIjsyVjOUIdcbShS+B8AHe4dVWJTWT4mZTjLNGte0xZ5LZQ7aYLqDkFK4yCP1VkWtU7SbiXLQ+67DT++Wp90raWnt5oqOWleIA7njTx3hU+utoW0DZ/eYlyuEDTr2lJd7j2lmFHW+q4rbecDaHgs4QVZO8WwjqBG921cVTVaosm7rc9hW4wqpxkiwLLeI1/tjM6KVFp0firG6tCgcKSodigQQR4xWdVdaDlm36xuNtBAYnxhPQgdjqFJbdP3hTP3pJ7asWpVIqLutjz+/dsPPVaeqm4ilKVWUioXtWjk6diTMEpg3CPIXgZwgr5tR+wBwk+QGppXomw2LjDfiSWkvxn21NOtLGUrQoYIPkIJqynLQmpMnCWjJS3FcVQO0IXWxbXkXDRFo1MzqOdKgM3MiAXLJdIo3QtbjpyG1tNlQCgUqykDCgavSZGe0fIRb7k4TFJ3IdxX+I8nOEtuKPBLoGAQfx/xk/wA5KMqqpwdN57Ox7z0ica0U4soGFabwzd9sOilWq6xJerJMqXa723EWqAEu29ttJU+kbqFJW2RunBzjGc1qNj2kLTNc0da73pXaDDv1iS26v52nTXLTDksNlIW2pbxZWk8QgNg8FYwBmulaVWNUr3OPNm064bQ9iWyzRlp01e0yYtxhT5F6kQlNQYzDEkuqcQ+fBWohO6lKcnwjnFdh1qdK6UtWitOwrFZovyO1Qm+aYj84pzcTknG8slR4k9ZNRf6ANmn5A6b/AO1s/wC2hiEJQW9lXax14/ctuyHr5o/WUrS+khm1pt+nZUhqbcFApXJKkowUtJJQjryVKUDjFdHg5AP668GGG4zLbLKEtNNpCEIQMBIAwAB4qx5FwCZbUCMj5Xc3xlmG2fCI6t5X9VA7VHgPKSAZRi5u0USS0LykzP0fHVL2hrkJB3IFrW0s44ZfdQQM+PEY/wCI8dWVWj0jpsabtq0urS9PlOc/LeQDurcIAwnPEJSAEgeIeMmt5WxUabUVsSt587nnq9TWVHJClKVUUClKUB6ZcRifGcjyWW5Ed1JStp1IUhY7QQeBFRR7ZTYyomKu4W1JJPNxJriWxnxIJKR9gAFTGlWRqThlFkoylH8rsQj6KIHe969d+FPoogd73r134VN6VZr6m/wLNdU4mc18lOHM2u7FLVqbUN7ujl0kSpjTio8jm0brclxtGEgf1Uird+iiB3vevXfhVXcgT6slh8+uXtr1dEU19Tf4DXVOJkKTsotZ4PXC8vo7Um4uIz96Ck/pqRWLTdr01HWzbILMNCzvOFtPhOHxrUeKj5SSa2dKhKrOSs3kQlOUvzO4pSlVEBSlKAUpSgFKUoBSlKA535An1ZLD59cvbXq6IrnfkCfVksPn1y9teroigFKUoBSlKAUpSgFKUoBSlKAUpXEX4TvYWrV2gbftFtkcLuenR8mn7g8JyEtfgnxnm3FZx4nVk9VAWryBPqyWHz65e2vV0RXyE/B4bEXNqW3OJf5bS/mPSSm7m64OAVKCsxm8+PfSV/Y0R219e6AUpSgFKUoBSlKAUpWt1Fe2dN2OZcn0lxEdsqDaThTiupKBntUohI8pqUU5NRW1mUr5I9Go9VwNMNN/KVLelPZ5iHHTvvPYxndT4hkZUcJGRkjIqJva91JJUTGs9vht5OBLlqccx2EhCMD7Ao/bWshsvrcdmz1h+5ysKfdzkJ6yG0eJtGSEj7ScqUonKqbqQg7RV/a/l9eWw7VLBQSvUzZ7Omerv7NZP8z1YN7vOodSWafablbrDMt05hcaTHcLxS62tJSpJ8hBIrJS4lalJSoKUg4UAeIOM8fuIr9rGvfCu4v6JR3Fa8nzZnP5OehnNN2BFsmJfluTJE2WXOdeWrAGd0AAJQlKQBw4E9ZNWd0z1d/ZrJ/mer10pr3wruHRKO49yNbarb4qt9mkf3BIdaz/ANW4r9Vbyw7Q41yltQbjEes1wdOGm3yFtPHxNup4E/3VbqjgkJwM1BF60083qFNhVfbYm+qG8LYZjYkkYznms73Vx6q2kqK1NjrZeRvtr6xnHlBBHEEHiCOIIprYyynFfDb5Fc8HSkvw5MtOlRLQF+fnNTLVOcL063FGHlnKn2Fg824ry5StJ8ZQT21LaxOOg7HEnFwk4vsFKUqBAVCdrC1fM1pa/wCU7dYwcz1YSStP/uhH34qbVodb2J3UWmpUWMQJqSiRGJOBzrawtAJ7ASkJPkJ66uotKomyym1GabIfVI6q1lqexbbWod61BI0to992G1aFt2tp+HcXFfvzD8ggqZdUrwUcUjGCN48Kua3zkXGIh9AUjOUrbXwU2tJKVIUOxSVApI7CDUH1ZsZt+tdRIuN0v1+dt4kR5S7CmYkW9xxkpU2oo3N4eElKiErAJGSDWq04tp7T0s05JaJWWzZ25aGnbcdVydQT7nAs14nSXbSpiOlEhTcGO6FlaWgsKCQEABQThIJBOSfLZvrPa1ebrpS6yoF4n2i7qbcuTMyDbY8GLHdb3g5GcakKfO6SjAcCipJOd04FWixsgtcXWl31Axcbm0xeMquVkDyFW+Yss8yVrbKCreKAnOFAEpBINY2hdi0LZ9OiKt2pdSv2qElTcOyTLgHIUZBBASlO4FqSkHCQtSt3hjqoVKEk1u95Umktoev7dsu0BtCumrVX1m7T4kO42Z63R2my2/I5kLaU2hKw4klKuJKTg8BXT1UBsU5Oblm0boper7he5E+yYlN6dlT23LfFlJUoocSltPhFOcjK1AE8BVg/P+0v8itN/wDlD3/w1gzT0ox/FfxIFym7LFgR7BeHLHCi2Vq+Qp161JGbR8vh7jzaW1ITgFQUd1Cl7xKU9SVdl91WeoNhlv1pdlTb9fNQyre++1Mf02q5b9tLqN0pG7uBZQFJSd3eCSRndqzKFkU1Jt9p+6VWpvaVHQj8V60SS7jxoeY3M+kc/TVm1A9nNvVNuFxv6weYdQmHCOchbaSVLcHkUo48obSR11PK3KuWjHtS+vK9vgcDEyUqraFKUqg1RSlKAiOptEOTZblyszzcO4uAc8y8D8nlEDAK8cUrwAN8Z4YCgrdTuxV4XyCoolaauBUCfDiKbfbPlBCgr/FINWxSrtNNWnG/j9+826eJqUlZbCovnCf+Tl69U+NPnCf+Tl69U+NW7SmlS4OZd06puRRGkNoUPX1iZvWnrddLta3lrbblR4uUKUhRQsDJ7FJI+6t184T/AMnL16p8ainIE+rJYfPrl7a9XRFNKlwcx06puRUiZV0d4M6YvLiz1JLTbf6VrSP01trXoe63taV30NW+3darcw5zrr48TrmAEp8aE5z/AFsZBsWlNZGOcI2e/b9ORXPF1Zq2w8GmkMNIaaQlttCQlKEDASB1ADsFedKVSaQpSlAKUpQClKUApSlAc78gT6slh8+uXtr1dEVzvyBPqyWHz65e2vV0RQClKUApSlAKUpQClKUApSlAKUrm/lZcrm5cl24WHe0H0ls92aXuXBN2+S82+g+E0pHML/mqQoHeGcqGPBJoDI5An1ZLD59cvbXq6Ir5u8h/lkT4PQ7Y/btAKur0u5Pc5dUXbc5ll19bzrpa5k5DaFKON8b252Zr6RUApSlAKUpQClKUArDm3m321xLcudGirUN4JeeSgkePBNZlVZrGBFn7THxJjMyAm0R93nWwrH/7P9WaleMYynLYlfml8zXxFZYelKq1e3mT/pVZe+IHrKPfTpVZe+IHrKPfVd9HrX3bD9Aj3U6PWvu2H6BHurS6bQ4XyOH13D1b7/oWJ0qsvfED1lHvqpeVLs+09t72L3zTIudsN1Sj5ZanVyWxzctsEo454BQKmyewOGtt0etfdsP0CPdTo9a+7YfoEe6nTaHC+Q67h6t9/wBDlj8Gfsfg6Mtl72halcYt95mqVbLdGmuJbcaYSoc85uqOQVrSEjIBAbV2KruvpVZe+IHrKPfVd9HrX3bD9Aj3U6PWvu2H6BHup02hwvkOu4erff8AQsTpVZe+IHrKPfTpVZe+IHrKPfVd9HrX3bD9Aj3U6PWvu2H6BHup02hwvkOu4erff9Cxm9S2h5xDbd1hLcWQlKUyEEknqAGa2VUpqGzW+LFhuswYzTqbjBwtDKUkf8U11ECrrrbhKFWmqsL7Ws/ZbzOvhMUsXTdRK2dvDzFKUoboqtNTfymSfzRG/wBaRVl1Wmpv5TJP5ojf60io1P0Kvu+aOb6R/aVPh4o86UpXmTwJp9V6vs+h7M5db5Pbt8FCko5xYKipajhKEJSCpaiepKQSewVGWdu+hHdOTL6dQNsW2FJZiS1yWHWXIzrqkpbDra0BbYUVDwlJAxk5wCai/KS0ncr5H0bd4cK7XWBYrx8ruECxSXGJq2VMuNFxlTakrK0FYO6kgkFQqEX/AERb7toW5XXTWmtZpucy+2VqQrUhmPy5LEeY05vpQ+tbiW0BbmSQnGFHq41fGEWk2b9KjSlGLk3dv2ZZ+WZd+m9rGlNVx7u9AuoQm0IDk9M5h2GuM2UlQcWl5KFBBSlRC8bpAODwqG2HlC2nXG1bTunNLymrjapttmzJT7sOQy4C2pkNFouBIU2rfc8IBQO6MEYOYdtz2eai1lq7aKxZrbIeE/RtvaZWUFDMt5qc+6uOHCN3fU34OM8A4M4Brc2XUMrX23DQ12i6R1JYrbbrJcmJC7vanIrbLi1Rt1rJGM+ArBHA48EnBxJQja/3sJKlTUXJZ5Pt2ZX+OezZsL4pSlaxzjUao/g+L+cYPtbVW/VQao/g+L+cYPtbVW/XocJ+1X+peET2fob9s/8AT8EKUpV53RVaam/lMk/miN/rSKsuozqHQEDUV2FydlTokoMJjlUN/mwpCVKUARg9q1f41nRU4Tpt2urc0/kauKovEUZUk7N+ZXGqdmmktcSmZOodNWq9yGUc227cIbbykJzndBUDgZOa0v7n/ZngDoDpzA44+bGcfs1aP0VQe+L3678KfRVB74vfrvwrQWBtsq8mefXonEJWVRcyHaU0BpnQolDTlgttiErdL4t8VDPO7ud3e3QM43lYz4zW/rZfRVB74vfrvwp9FUHvi9+u/CsPAJ7anJkH6GrSd3NczW1gXyxW7U1qkWy7QY9zt0gAPRZbQcbcAII3kngeIB+6pD9FUHvi9+u/Cn0VQe+L3678Kx1evWLuYXoWss1Ncyr0bAdmjZynQOnEnBGRbGRwIwR+L4q91v2G7O7TPjToWh9PxJkZ1LzD7NuaSttaSClSSE5BBAII8VWV9FUHvi9+u/Cn0VQe+L3678Kl0H+3kyzqrE+t8SLao/g+L+cYPtbVW/UJGye2F1hbtxu0hLLzb4bel5QVIWFpyMcRlIqbVu06ao0VSTvm33peR2cDhpYSk6cnfO/JeQpSlZOgKUpQClKUApSlAKUpQClKUApSlAKUpQH/2Q==", 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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/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/persistence.ipynb b/examples/persistence.ipynb index b99767a2c..03c8911f0 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.sqlite.aio 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", + "
" ] - }, - "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.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": [ + { + "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..0eec3667c 100644 --- a/examples/persistence_mongodb.ipynb +++ b/examples/persistence_mongodb.ipynb @@ -1,1013 +1,1013 @@ { - "cells": [ - { - "cell_type": "markdown", - "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", - "\n", - "This example shows how to use `MongoDB` 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", - "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": [ + "cells": [ { - "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'}})" + "cell_type": "markdown", + "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", + "\n", + "This example shows how to use `MongoDB` 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." ] - }, - "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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checkpointer implementation" ] - }, - "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': []}" + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U langgraph pymongo" ] - }, - "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'}})" + "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", + " }" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get_tuple(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup environment" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "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(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup model and tools for the graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install langchain_openai" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Literal\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", - "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')]}" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MongoDB connection" ] - }, - "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'}})" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "MONGO_URI = \"mongodb://localhost:27017/\"" ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get_tuple(config)" - ] - }, - { - "cell_type": "markdown", - "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", - "{'_id': ObjectId('668d39a9b975d3e766de42d5'), 'thread_id': '1', 'thread_ts': '1ef3df65-6b84-63fd-8003-888bcef289e3', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:49.794047+00:00\", \"id\": \"1ef3df65-6b84-63fd-8003-888bcef289e3\", \"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\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"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\": null, \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0\", \"tool_calls\": [], \"invalid_tool_calls\": []}}], \"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': b'{\"source\": \"loop\", \"step\": 3, \"writes\": {\"agent\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"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\": null, \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0\", \"tool_calls\": [], \"invalid_tool_calls\": []}}]}}}', 'parent_ts': '1ef3df65-6252-6b30-8002-2c9e1e68364b'}\n" - ] - } - ], - "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)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic example using graph" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "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': []}" + "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)" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "await checkpointer.aget(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "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'}})" + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "await checkpointer.aget_tuple(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "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" - ] - } - ], - "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" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + "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)" + ] + }, { - "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" - ] + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup environment" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup model and tools for the graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install langchain_openai" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\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", + "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)" + ] + }, + { + "cell_type": "markdown", + "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", + "{'_id': ObjectId('668d39a9b975d3e766de42d5'), 'thread_id': '1', 'thread_ts': '1ef3df65-6b84-63fd-8003-888bcef289e3', 'checkpoint': b'{\"v\": 1, \"ts\": \"2024-07-09T13:22:49.794047+00:00\", \"id\": \"1ef3df65-6b84-63fd-8003-888bcef289e3\", \"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\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"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\": null, \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0\", \"tool_calls\": [], \"invalid_tool_calls\": []}}], \"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': b'{\"source\": \"loop\", \"step\": 3, \"writes\": {\"agent\": {\"messages\": [{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"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\": null, \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"run-e9984eca-f132-46d0-94ba-41d8ba5b7046-0\", \"tool_calls\": [], \"invalid_tool_calls\": []}}]}}}', 'parent_ts': '1ef3df65-6252-6b30-8002-2c9e1e68364b'}\n" + ] + } + ], + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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': []}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "await checkpointer.aget(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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'}})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "await checkpointer.aget_tuple(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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" + ] + } + ], + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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" + ] + } + ], + "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)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myenv", + "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": [ - "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)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "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": 2 + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/persistence_postgres.ipynb b/examples/persistence_postgres.ipynb index 5874aa819..d67e674b6 100644 --- a/examples/persistence_postgres.ipynb +++ b/examples/persistence_postgres.ipynb @@ -1,1043 +1,1043 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "# How to create a custom checkpointer using Postgres\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" - ] - }, - { - "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": [ + "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\")" - ] - }, - { - "cell_type": "markdown", - "id": "e26b3204-cca2-414c-800e-7e09032445ae", - "metadata": {}, - "source": [ - "## Setup model and tools for the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Literal\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", - "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)" - ] - }, - { - "cell_type": "markdown", - "id": "e9342c62-dbb4-40f6-9271-7393f1ca48c4", - "metadata": {}, - "source": [ - "## Use sync connection" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2b9d13b1-9d72-48a0-b63a-adc062c06c29", - "metadata": {}, - "outputs": [], - "source": [ - "DB_URI = \"postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable\"" - ] - }, - { - "cell_type": "markdown", - "id": "e39fc712-9e1c-4831-9077-dd07b0c13594", - "metadata": {}, - "source": [ - "### With a connection pool" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2657c1c4-d8a5-4fe3-8f77-95415a98ed6c", - "metadata": {}, - "outputs": [], - "source": [ - "from psycopg_pool import ConnectionPool\n", - "\n", - "pool = ConnectionPool(\n", - " # Example configuration\n", - " conninfo=DB_URI,\n", - " max_size=20,\n", - ")\n", - "\n", - "checkpointer = PostgresSaver(sync_connection=pool)\n", - "checkpointer.create_tables(pool)" - ] - }, - { - "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})]}" + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to create a custom checkpointer using Postgres\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." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "96efd8b2-97c9-4207-83b2-00131723a75a", - "metadata": {}, - "outputs": [ + }, { - "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': []}" + "cell_type": "markdown", + "id": "ddc5cc5f-89fa-4d7a-9f2b-5b099feb6ecc", + "metadata": {}, + "source": [ + "## Checkpointer implementation" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get(config)" - ] - }, - { - "cell_type": "markdown", - "id": "967c95c7-e392-4819-bd71-f29e91c68df3", - "metadata": {}, - "source": [ - "### With a connection" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "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", - " 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)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "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=[])" + "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" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpoint_tuple" - ] - }, - { - "cell_type": "markdown", - "id": "c0a47d3e-e588-48fc-a5d4-2145dff17e77", - "metadata": {}, - "source": [ - "## Use async connection" - ] - }, - { - "cell_type": "markdown", - "id": "ee6b6cf7-d8f7-4777-a48d-93b5855fe681", - "metadata": {}, - "source": [ - "### With a connection pool" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "20cea8b7-8f13-4dc7-a3c9-825040eb4c57", - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/vadymbarda/.virtualenvs/langgraph-postgres/lib/python3.11/site-packages/psycopg_pool/pool_async.py:138: RuntimeWarning: opening the async pool AsyncConnectionPool in the constructor is deprecated and will not be supported anymore in a future release. Please use `await pool.open()`, or use the pool as context manager using: `async with AsyncConnectionPool(...) as pool: `...\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from psycopg_pool import AsyncConnectionPool\n", - "\n", - "pool = AsyncConnectionPool(\n", - " # Example configuration\n", - " conninfo=DB_URI,\n", - " max_size=20,\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", - ")" - ] - }, - { - "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=[])" + "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.base 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" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpoint_tuple" - ] - }, - { - "cell_type": "markdown", - "id": "56552584-9eb8-40df-a6a0-44151018b509", - "metadata": {}, - "source": [ - "### Use connection" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "386b78bc-2f73-49ba-a2a4-47bce6fc49b7", - "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", - " 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)]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d1ed1344-c923-4a46-b04e-cc3646737d48", - "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)]" + "cell_type": "markdown", + "id": "456fa19c-93a5-4750-a410-f2d810b964ad", + "metadata": {}, + "source": [ + "## Setup environment" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "eca9aafb-a155-407a-8036-682a2f1297d7", + "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": "markdown", + "id": "e26b3204-cca2-414c-800e-7e09032445ae", + "metadata": {}, + "source": [ + "## Setup model and tools for the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\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", + "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)" + ] + }, + { + "cell_type": "markdown", + "id": "e9342c62-dbb4-40f6-9271-7393f1ca48c4", + "metadata": {}, + "source": [ + "## Use sync connection" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2b9d13b1-9d72-48a0-b63a-adc062c06c29", + "metadata": {}, + "outputs": [], + "source": [ + "DB_URI = \"postgresql://postgres:postgres@localhost:5432/postgres?sslmode=disable\"" + ] + }, + { + "cell_type": "markdown", + "id": "e39fc712-9e1c-4831-9077-dd07b0c13594", + "metadata": {}, + "source": [ + "### With a connection pool" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2657c1c4-d8a5-4fe3-8f77-95415a98ed6c", + "metadata": {}, + "outputs": [], + "source": [ + "from psycopg_pool import ConnectionPool\n", + "\n", + "pool = ConnectionPool(\n", + " # Example configuration\n", + " conninfo=DB_URI,\n", + " max_size=20,\n", + ")\n", + "\n", + "checkpointer = PostgresSaver(sync_connection=pool)\n", + "checkpointer.create_tables(pool)" + ] + }, + { + "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})]}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "96efd8b2-97c9-4207-83b2-00131723a75a", + "metadata": {}, + "outputs": [ + { + "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': []}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpointer.get(config)" + ] + }, + { + "cell_type": "markdown", + "id": "967c95c7-e392-4819-bd71-f29e91c68df3", + "metadata": {}, + "source": [ + "### With a connection" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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", + " 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)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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=[])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_tuple" + ] + }, + { + "cell_type": "markdown", + "id": "c0a47d3e-e588-48fc-a5d4-2145dff17e77", + "metadata": {}, + "source": [ + "## Use async connection" + ] + }, + { + "cell_type": "markdown", + "id": "ee6b6cf7-d8f7-4777-a48d-93b5855fe681", + "metadata": {}, + "source": [ + "### With a connection pool" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "20cea8b7-8f13-4dc7-a3c9-825040eb4c57", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vadymbarda/.virtualenvs/langgraph-postgres/lib/python3.11/site-packages/psycopg_pool/pool_async.py:138: RuntimeWarning: opening the async pool AsyncConnectionPool in the constructor is deprecated and will not be supported anymore in a future release. Please use `await pool.open()`, or use the pool as context manager using: `async with AsyncConnectionPool(...) as pool: `...\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from psycopg_pool import AsyncConnectionPool\n", + "\n", + "pool = AsyncConnectionPool(\n", + " # Example configuration\n", + " conninfo=DB_URI,\n", + " max_size=20,\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", + ")" + ] + }, + { + "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=[])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_tuple" + ] + }, + { + "cell_type": "markdown", + "id": "56552584-9eb8-40df-a6a0-44151018b509", + "metadata": {}, + "source": [ + "### Use connection" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "386b78bc-2f73-49ba-a2a4-47bce6fc49b7", + "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", + " 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)]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d1ed1344-c923-4a46-b04e-cc3646737d48", + "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)]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_tuples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a39d1ff-ca37-4457-8b52-07d33b59c36e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langgraph-postgres", + "language": "python", + "name": "langgraph-postgres" + }, + "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": [ - "checkpoint_tuples" - ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a39d1ff-ca37-4457-8b52-07d33b59c36e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "langgraph-postgres", - "language": "python", - "name": "langgraph-postgres" - }, - "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_redis.ipynb b/examples/persistence_redis.ipynb index e42963f21..65d5d8980 100644 --- a/examples/persistence_redis.ipynb +++ b/examples/persistence_redis.ipynb @@ -1,842 +1,842 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "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", - "\n", - "This example shows how to use `Redis` 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": "0aac2830", - "metadata": {}, - "source": [ - "## Install the necessary libraries for Redis on Python" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "faadfb1b-cebe-4dcf-82fd-34044c380bc4", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%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": [], - "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", - "id": "e26b3204-cca2-414c-800e-7e09032445ae", - "metadata": {}, - "source": [ - "## Setup model and tools for the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Literal\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", - "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)" - ] - }, - { - "cell_type": "markdown", - "id": "e9342c62-dbb4-40f6-9271-7393f1ca48c4", - "metadata": {}, - "source": [ - "## 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": [ + "cells": [ { - "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", - "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)" - ] - }, - { - "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='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})]}" + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "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", + "\n", + "This example shows how to use `Redis` 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." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "96efd8b2-97c9-4207-83b2-00131723a75a", - "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': []}" + "cell_type": "markdown", + "id": "0aac2830", + "metadata": {}, + "source": [ + "## Install the necessary libraries for Redis on Python" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpointer.get(config)" - ] - }, - { - "cell_type": "markdown", - "id": "967c95c7-e392-4819-bd71-f29e91c68df3", - "metadata": {}, - "source": [ - "### With a connection" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b7d3687b", - "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" - ] - } - ], - "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)" - ] - }, - { - "cell_type": "markdown", - "id": "c0a47d3e-e588-48fc-a5d4-2145dff17e77", - "metadata": {}, - "source": [ - "## 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", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:__main__:Asynchronous Redis pool initialized with url=redis://172.25.0.4:6379/0\n" - ] - } - ], - "source": [ - "# Initialize a synchronous Redis connection pool\n", - "async_pool = initialize_async_pool(url=\"redis://172.25.0.4:6379/0\")\n", - "\n", - "checkpointer = RedisSaver(async_connection=async_pool)" - ] - }, - { - "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", - "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)" + "cell_type": "code", + "execution_count": 1, + "id": "faadfb1b-cebe-4dcf-82fd-34044c380bc4", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install -U redis langgraph" ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "checkpoint_tuple" - ] - }, - { - "cell_type": "markdown", - "id": "56552584-9eb8-40df-a6a0-44151018b509", - "metadata": {}, - "source": [ - "### Use connection" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a7bf32bd", - "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" - ] + "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.base 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": [], + "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", + "id": "e26b3204-cca2-414c-800e-7e09032445ae", + "metadata": {}, + "source": [ + "## Setup model and tools for the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e5213193-5a7d-43e7-aeba-fe732bb1cd7a", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\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", + "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)" + ] + }, + { + "cell_type": "markdown", + "id": "e9342c62-dbb4-40f6-9271-7393f1ca48c4", + "metadata": {}, + "source": [ + "## 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", + "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)" + ] + }, + { + "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='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})]}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "96efd8b2-97c9-4207-83b2-00131723a75a", + "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': []}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpointer.get(config)" + ] + }, + { + "cell_type": "markdown", + "id": "967c95c7-e392-4819-bd71-f29e91c68df3", + "metadata": {}, + "source": [ + "### With a connection" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b7d3687b", + "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" + ] + } + ], + "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)" + ] + }, + { + "cell_type": "markdown", + "id": "c0a47d3e-e588-48fc-a5d4-2145dff17e77", + "metadata": {}, + "source": [ + "## 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", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:__main__:Asynchronous Redis pool initialized with url=redis://172.25.0.4:6379/0\n" + ] + } + ], + "source": [ + "# Initialize a synchronous Redis connection pool\n", + "async_pool = initialize_async_pool(url=\"redis://172.25.0.4:6379/0\")\n", + "\n", + "checkpointer = RedisSaver(async_connection=async_pool)" + ] + }, + { + "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", + "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)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_tuple" + ] + }, + { + "cell_type": "markdown", + "id": "56552584-9eb8-40df-a6a0-44151018b509", + "metadata": {}, + "source": [ + "### Use connection" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a7bf32bd", + "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" + ] + } + ], + "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)]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": [ - "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)]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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/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": [ - { - 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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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" + } + }, + "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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", + "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 a2b4bec3a..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](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)" - ] - }, - { - "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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" + } + }, + "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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SgVVtpJd7fwTZepC5GwslfWsxO7SC1FsJIJNiA9p/KQQehBIIIJB7vRuonalwjLEzGQ3Ynur2oozu1krTsdvmI2cN+uzhv1XGrP4Zvc3O6qib/BdtXlO32wxBp/LysZ/Ytqn7VOUXw2+qX39DUy2CcM/ijv0RFUcQIiIAiIgCIiAIiIAiIgCIiALieJeOfHHj87E1zhjnPZZa0/8Al5OXnd/0FrHn+a1y7ZFOEsyVycJuElJcCMGuD2hzSHNI3BHcVXmPgDqyHXzMnj7GK03RbmTknXcVmMmZpYTMZXwmm95rgyAlrj73znENHcrK5TQV3DvdJp0QSUSS7yVO4sEfzQv6hrfiYRsN9mloAatJJYy1d3LPpjLsf6ezjjlH52PKzoJP/G7r16do7qrUayTbsayjoHT+OweWw0ONjOLys1mxdqzOdKyd9hznTE8xPRxc7oOg36ALn8LwE0Lp+5Fbo4V8dyOvLUbZkvWJZTBI3ldEXvkJLNu5hJDT1aAeq7DyhkPvbzfqo+knlDIfe3m/VR9JNXq4FufRfFGqr8ONO1amlqsWO5YNL8oxDO3kPi3LCYR15t3/AFtxb5/N379/Vc6/wduHr84csNPCO54+zKARXLDIW2mPD2ythbIIw7mAJIb16g7gldv5QyH3t5v1UfSTyhkPvbzfqo+kmr1cA50XvaIo0d4NWn6uYzeY1Pjq+WyVrUdzM1eS3O6BrJJjJCZISWxukaD3lrtvQSur1NwK0LrDL38nmMBHds5BgjttdYmbDY2bytdJE14Y57RsA8t5m7DYjYLZ1+IdS3qy3piHG5STP1KzLk9AVfrkcLjs1569xPRbnyhkPvbzfqo+kmr1cDClQStdGFhND4XTuWnydCo6K/PTr0JZ3zySOfDBzdk087j1HO7zu879SVoYuBuh6+rHakhwbYMq60LznRWZmQusd/bGAP7Iv3683Lvv13XV+UMh97eb9VH0k8oZD72836qPpJq9XAlpKOKNJb4WaXvaf1DhJ8Xz4vUFqS5koPGJR28z+XndzB3M3fkb0aQOncvbNcMdNag1bj9T3cc52dotYyG5DZlhcWtfzta8MeBI0O6hrw4dT06rc+UMh97eb9VH0l9IpMzb82tpjKOf6DOIoW/lLnj+wFY1erxXqjDqUcUZFmzFSrS2J5GwwRNL3yPOzWtA3JJ+JdPw5w0+Ow1i5cifBdyc5tyQye+ibytZGw/EQxjSR6HF34Th4HQtma1Fez74ZDC8SQY6uS6Fjgd2vkcQDI5pG46BrT12JDXDt1LZTi4p3b3/AI7+Ry8qyhVfYhuCIiqOcEREAREQBERAEREAREQBERAEREAREQBERAEREBXfSfw69d/0OoftyrEKu+k/h167/odQ/blWIQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREBXfSfw69d/0OoftyrEKu+k/h167/odQ/blWIQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBEXzmnirRmSaRkUY73PcAB+UoD6KNvCO19qPhdwT1TqzSmOq5TOYmCOzHWuxvfCYhKztnODHsds2IyP6OHvfT3LtTqrCg7HL0Af+ZZ7V8Lme07kac9S1ksbYrTxuilhksRua9jhs5pBPUEEjZWaOfKzNmfkvhv8oFr7F8YMjxDdg9OTZPJY+DF2arYLDYewjfzgs+vEtedyOYlw/mr9T+DOc1TqfhbpvMa0pU8dqa/VFq3ToRPjih5yXRs5Xuc4ODCwOBcfO5u7uX5zcO/A2ioeGHYxOT5H8N8PZOWhyUzx4tcgBD4awkJ5Xv5nNa9u++zHn4l+nHuqwnyxQ9aZ7U0c+VizNqi1bdU4ZzgG5egSe4Cyz2rZRyMmYHxua9h6hzTuCouMo70YPZERRAREQBERAEREAREQBERAEREAREQBERAEREARF6ve2NjnuOzWjck+gIDldXatmo2RisUGvyT2h8s7xzR1GHuJH2Tz15W/MSemwdxMumqV2YT5Nrszb9NjI7TO/ICOVn4GAD5k03O7JY3ytKB4xlXePyHrv54HI3r/JjDGfgYFp+IvEelw2p4ie5j8jlJMrkY8XVrYyJkkrpnse9u4c5o22jI336bj0bkXVJypSdODtbf5/rveehoUYUYXe833kDGfJ1T9A32J5AxnydU/QN9i4rF8aqGVxuoHx6f1BHmsG+FlzTzqbXZAdqfrTmNa8se13U8wfsA1xJGxWE7whcDW0dqrP38Vm8XJpjkOTxF2sxl2IPAMbg3tCxwcDuCH7dCqNJPFmxnwJC8gYz5OqfoG+xPIGM+Tqn6BvsXE2uNdKliq9ufTeoY7N6z4ti8YakZuZLaPtDJFGJPNYG7kmUx8ux3AOywp/CI09UxBt2cXnK16PKw4afDSUh49BZlYXxB0YcQ5rxtyuYXA8w+fZpJ8zGfAkI6fxbgQcbTIPoMDPYvStgYcVN2+HkfhLO4PPR2Yx23ofHtyOH4Rv8AEQeqifXnhCyYvhjrvL4fA5GnqbTMbBPistBGH1zI3mjmeGSlroiNzux5Pmkbb9FKelM7Y1HhIL1rD38FM/cGnkhEJm7ek9lI9ux7x534dlJVqkd0mYvCfs2uSDpDVbs4JaV6JlbL1gDIyPfs5mHulj368p7i09Wnodxyud0iiO/aOHyOJy7CGvq244pD186GV7Y5G/g85rvwsHxKXFZNJxU1x+q7RwsppKlOy3MIiKo1AiIgCIiAIiIAiIgCIiAIiIAiIgCIiAL5WYG2q0sLujZGFh2+IjZfVE3AhvSQfHprHQSgtnrQirM1w2Ikj+tvH/c0qMfCUuXKDOGlihQOUuRaxquiptlbEZj4ta80Od5oP4dh84U36swE2ByVnL04XTY227tLsUQJfBJsG9q1o72kAcwHUEc3XdxGklp4rUkNG1JBTykUErbdSZzGTNjkAIbLG7rs4BzgHDrsT8asrK8nUW5927+J6OnNV6ex7SvepOHPEHWkmrdVzYZ2Ft5STF0xpqDKRts2sbWlc+xG6yx3Ix8vaOA2dsGt2Luq0tvgpqaTTHF2liNBV9OVdT47HjF4uveru5ZIXvEjJdnBrXuDg/cFzdj74norXIta5N0Yve++2Q5x/wCF9vWuX0bqCrp2hrKPATWW2dO5B0bG3IZ2NaXMdJ5gkY5jHDm2B69QtNLwtsWcBpeTCcOaGiJ4NXUMjdx9KatzeKwl280jo9mlw5j5jS47d2+6nxEuSdKLbeJBWuuEmoNW3eNkMEEdeHU+Fx9TF2JZW8k00Uc4e0gEuaA57BuQPfdN9ipV0Ll8zmcBFPntPS6ZyDT2bqUtqKwSA0eeHxOLdiSQBvv06gbroF8Lt+tjoDNanjrxbhvNI7YEnuA+Mn0D0rKTk7IyoKLuYefrm/BToMBMly7XhaAN+natc8/kY1x/IphXDaL03Ys32Z7JV3VnMY5lCpKNpI2u25pZB9i9wGwb3taTvsXOa3uVtT9mKp4Xb+L/AOkcPK6qqVPZ3IIiKk0giIgCIiAIiIAiIgCIiAIiIAiIgCIiALDzGYoafxlnJZS7Xx2PrMMk9q1K2OKJo73Oc4gAfOVF/FfwkMFw7y8WmMRSta24gWh/mulsLs+fu6Pnf72CPqCXP7gdwCN1yGH8HnUfF3KVtRcc8nDlY4XiejoTFuc3EUj3tM3XezIPSXeaPOA5mnYAfG3xr1vx/szYrgvTGI00HmK1xFzVY9gNjs4Ua7tjO4dfPcAwEEHvBUsY3hFiq+Ho17tq7eysFdkVjMMndWsXHgAGWURFrXOcRuSQV2lWrDRrRVq0MdevCwRxwxNDWMaBsGgDoAB6AvqpxnKDvF2JKTi7pnEHhPjyf9LZofN46fYn1J8f8r5v10+xduis09TEs01TmZxH1J8f8r5v10+xPqT4/wCV8366fYu3RNPUxGmqczOIHCfHb9ctmnD4vHiP1BYeqOD8F/TN+rpzL29M6ila3xXUP8dsVntcCCBMXDlO3K5o5dw4jcb7qQ0WHWqNWuRdSclZsrrhvCH1DwmytXTfHPGQ4btniCjrnGNLsPePoE3prSH0h3m9HHzWgb2FrWYbtaKxXlZPBK0PjlicHNe0jcEEdCCPSsfM4XH6jxVrGZWjXyWOtMMc9S3E2SKVp7w5rgQR+FV5s8FtceD5YlynBm35b0tzGWzw6zdk9kATu40LDtzC7v8AMdu0kknfoFSVlk0UZcIfCD0vxgdZx9R1nCaqodMjpjMx+L5Cm4d+8Z983qPPbuOo32J2UmoAiIgCIiAIiIAiIgCIiAIiIAiIgNJrLW2B4eaetZ3UmWq4XE1hvJatyBjR8TR6XOPoaNyT0AKgM624meE2ex0LHa4Z8OJej9W5CHlyuSj/APooD/BMI7pX9diC3YghajgfoKhx74hcQdb8QprGq7emNZZHA4PF33A4/HwQOZyPZXADTIQ/YvduTytPeN1a0DYIDheE/BPSXBfES0tM43sp7J7S7k7Lu2u3pO8vmmPnPJJJ26AbnYBd2iIAiIgCIiAIiIAiIgCIiAjXi94P+leMbatvIxWMTqSh52O1JiJfF8hScO4slHUjqfNduOp6A9VGsHGLXng7Tx47jDVOpNIcwjr8Q8LVO0Q32Av1m7mI/wDzGbtO4HU7kWUXzngitQSQzRsmhkaWPjkaHNc0jYgg94I9CAxMFnsbqfEVcrh79bKY20wSQW6krZYpW/G1zSQVnqqmb0RU8HrwkuGcGgbVrAae1xdvQ5nTcTw7HOdFX7RssUTgexeSRvyEDZoAAG4Nq0AREQBERAEREAREQBERAFzuruI+k+H/AIp7qNUYXTfjfP4v5XyENXtuTl5+TtHDm5eZu+3dzD4wuiVWP8ojwQPFbghJnKEJlzmkjJkYQ3vfWIHjLB/0sa/4/rWw70Bq/BJ40cPtP1eLTcprvTWNdd4hZm9Vbcy9eIz139kWTM5njmjcAdnjcHY7Hordr8UPA34GHjxxuxOKtwOk0/jv/Ecs4jzTAwjaI/8AEcWs279nOI7l+16AIiIAiIgCIiAIiIAiIgCIiAIiICu3hCfCM8HT8a5T9zCsSq7eEJ8IzwdPxrlP3MKxKAIiIAiIgCIiAw8tmKWCovuX7DK1dhAL3+kk7BoA6ucT0AG5JOwC4qzxFy9xxOLwccMH2M2UsGN7vnETGuIHp85wPduB6NS/LO1dkXZd7i6k1zmY6Lm3YIxu3ttv5Ug3IPoYWjoS7fJV0nGi81q7434d94nYoZHFxzqh9DrLVu52r4Xb8Mye7LV33PhfzzLTZLVWLxGdw+Gt2uyyWXMwpQdm93amJnPJ5wBDdm9fOI39G686j1NjdJY5t/K2TVqOnirCQRvf9cke2Ng2aCernNG/cN9zsFHTvlXQ2dWoYG492WrvufC/nmXrJq7Vc0b45KuDfG8Frmu7Ugg94IWoxOqsXnMvmcZStdvew80de9F2b29i98bZWjcgB27HtO7SR127+iTaqxcGqa2nH2uXM2akl6Kt2b/OgY9jHv5tuUbOkYNid+vQdCmnfKug1ahgcLwL4Px+DyzUI0nSx4Obti1Yfcle90bRvyQsLWt2jZzP2B3d5x3cem0p+7LV33PhfzzL5rGyeTqYXHWb9+zDSo1Y3TT2Z3hkcTGjdznOPQAAb7lNO+VdDOq0cDN92WrvufC/nmT3Zau+58L+eZfCCeOzBHNE8SRSND2Pb3OBG4IXumnfKug1WjgfdmttVRHd+Pw9gD7BtiWIn5ty136lv9P6+q5e3HRuV5cRkn/wcFggsmO25EUg6PIAJ5ejtgTy7DdcbJmaEWYhxT7kDcnNC+zHULx2romua1zw3v5QXtG/xuC+1upFegdDM3mYSD0Ja5pB3DmkdWuBAII2IIBBBCaWMtk4/Nd273lc8jpyXs7GSoi5bQOoJ8rSs0L0na5LHPbFLKdt52EbxykDYAuG4OwA5mu2AGy6lYnFwdmcOUXBuLCIigRCIiAIiIAiIgK7eEJ8IzwdPxrlP3MKxKrt4QnwjPB0/GuU/cwrEoAiIgCIiALTazsTU9H52euSJ4qE74yO/mEbiP7VuV6TQssQvikaHxvaWuae4g9CFODUZJvgZRFOHijgxFGKLYRMgY1mw2GwaNlGHGTJZLIa64e6LrZi5gMbqCa7Jeu46XsbL214WvZBHJ3sLy4kluztoyAR1UlYerLhmyYS04m1jdoQ553dLD1EUv8A1NHX+cHjrylYGtdA4DiJimY7UONZkascrZ4t3ujkhkb3PjkYQ5jhuerSD1KhVTjUaffmen/yQvEhniDw6ZW4i8JNPM1JqIwyWcs835Mk595rfFQTG2cgvA6bb782xOzh3rnMnls5U0BloDqbOS2NL8Q62IqXTkJGzz1Xz1d47DmkdsOWw9vn77gBTxguEWk9N2MRPj8W6KfFTWLFSWS3NK9kkzAyVznPeS8uaAPP3226bL62OFel7VPJVZcXzQZHKszdpnjEo7S4wxlsu/NuNjDH5o2b5vd1O9dyt0nta72EH6r1hkdCu8IDK4mwynkRmcRWjuSNDm1RNVpwmYg9DyCQu69N29em632mtDN0N4S+Brtz2czvbaSvOdLm77rbw4Wqu5aXe9Dt+rR5o26AKVL/AAt0rlMzmcrbwsFi7mqQx+Rc8uLLcA22bIzflcQAAHEcwA2B2WgxHAzT2hZn5TRdGHFajZUdRrXsnPbvxxwuexzozG6cEt8wbAOHKe7oSCGjle/e+53uXhtWMTdio2G1Lz4Htgne3mbHIWkNcR6QDsdvmVUMrDbqcFeKGktWX9Ux62qaYOQuRZDMPtVrTWCT/Oar2npFI9uz4jygABpbtvvYGtg9fW5mwZrOaXu4mXeO3XrYSzDJJERs5rXm44NJB7y0/gXvpfgrovR1fJwYzBsbHkq/idvxueW0ZYNiOx3le4iPZzvMGzevcsEpxcyN9U0IsFonQukcPc1XkcxnHmxUiq6jmrySNZAHS9rbeXvjhaHNIazrvygDbdcZU1Jq/LcOdM4e5qPJ47J1+I7tOz36t4yWXVWmYGN83K3tSBs3mcwblrXFu6m2PwftCRYKrh2YedtKpYNqsRk7fbV5CwRns5u17RjSwBvK1wbsO5Z2L4K6LwtatWoYNlStWykeZhghnlaxlxjORsobz7b8veO5x6kE9Vkg6cmyL73DLH1vCU0pS8sajdHFpm7YZJJnrbpXuZdruDXPMnM5h5zuwkghrdweUbWGXLa04Y6a4hT46fO451qzjy81bENmWvLFzgB4D4ntdyu2G7SdjsNwup7lguhHNbPpot7o+IlpjPey4ppl2+Nkx5N/++T+341JS4bhpj3WX5DUDwRFfEcNPc7h1ePmLZB8z3PeR8bQw/g7lblbY1Hikl38Nx5/KZKVWTQREVBrBERAEREAREQFdvCE+EZ4On41yn7mFYlV28IT4Rng6fjXKfuYViUAREQBERAEREBo9T6Ug1HHHI2V1LIwb9hciaC5oPexwPvmHpu35gQQ4Bw4ezj9SYlxZawb8g0d1nFysc134WPc1zT6dhzAfGfTKiK1TVrSV0bNLKKlLZF7CIjfyAJHuczR2+KqPpLx5QyH3t5v1UfSUvIs51Lk9TY16pgiIfKGQ+9vN+qj6SeUMh97eb9VH0lLyJnUuT1GvVMEQRpjiHU1m3JuwmNymSbjL0uMuGGrv2FmLbtInbn3zeYb/hW58oZD72836qPpLlfAz/ifGb+szOfrhViEzqXJ6jXqmCIh8oZD72836qPpJ5QyH3t5v1UfSUvImdS5PUa9UwREkdnKznlh0xmHvO+wfHHEPzveAt1itCX8y9smoBDXodD5Lrv7QzfNM/YAt+NjRsdti5zSWmQUTSRjthGzx4lc8rqzVtx4ADQAAAB0AC8oipNIIiIAiIgCIiAIiICu3hCfCM8HT8a5T9zCsSq7eEJ8IzwdPxrlP3MKxKAIiIAiIgCIiAIiIAiIgCIiArv4Gf8AE+M39Zmc/XCrEKu/gZ/xPjN/WZnP1wqxCAIiIAiIgCIiAIiIAiIgCIiAIiICu3hCfCM8HT8a5T9zCsSq7eEJ8IzwdPxrlP3MKxKAIiIAiIgCIiAIiIAiIgCIq0+G74QuvvBy03prPaQw2HyeMt2ZqmSmy0E0vYScrXQBojlZsHATbk7+9b3b9QM7wM/4nxm/rMzn64VYhfkhwC8NfingdS5HTek9PabyeR1nqWfKmK3WsO5bltzAWtLZxyxAtB67kDfd3pH63oAiIgCIiAIiIAiIgCIiAIiIAiIgK7eEJ8IzwdPxrlP3MKxKrt4QnwjPB0/GuU/cwrEoAiIgCIiAIi5HV2rp6lvyTieQ5AtD57Mg5o6jD3dPspHfYt7gAXO+xa+cYuTJwhKpLNjvOqnsxVYzJNKyGMd75HBo/OVrjqzBg/6Zx/rTPaoyfpqjam8YyEZy9sjY2chtM89d+gI2aPmaAPmX28g4wf8Aw6p+gb7FK9FcW/Q6SyB22yJG91mD+Wcf60z2p7rMH8s4/wBaZ7VHPkHGfJ1T9A32J5BxnydU/QN9iZ1Hz9CWoe8SN7rMH8s4/wBaZ7VxfGfTmk+MvDDUOj8lmca2LJ1nRxTOssPYTDzopR1+xeGu29O23pWt8g4z5OqfoG+xPIOM+Tqn6BvsTOo+foNQ94p7/k8/B7foniLqHWOuY4cRcwjn43Fw3XtZ2szt2y2Iy7bmYGea17d2u7R2x81foR7rMH8s4/1pntUc+QcZ8nVP0DfYnkHGfJ1T9A32JnUfP0Goe8SN7rMH8s4/1pntT3WYP5Zx/rTPao58g4z5OqfoG+xPIOM+Tqn6BvsTOo+foNQ94kb3WYP5Zx/rTPasipncbfkDK2RqWXn7GKdrj+YFRj5BxnydU/QN9i+U+mMPZbyy4qlIO7zq7D/6JnUfP0Goe8TCiivGZDIaQcJKUljIY1u3aYyWTnc1vpML3dQ7+Y48p22HLvupLx2Qr5ajBcqSiatM0PY8bjcH5j1B+Y9QsSiks6Luu95z6tGVF2kZKIirKAiIgCIiAIiICu3hCfCM8HT8a5T9zCsSq7eEJ8IzwdPxrlP3MKxKAIiIAiIgPnYnZVryzSHaONpe4/MBuVEOmnyW8VHkZ9jbyR8dncNzu54BA6+hreVo+ZoUs5GoL+PtVSdhPE6Pf4twR/6qJtKyOfpzGh7XMljgbDKxw2LXsHK8H8DmkK3/AMLtivudXIEs6T4ms19xExfDvH0578du5avWG1KOOx0PbWbcxBPJGzcdwBJJIAA6kKOde8asti26Ev1cHqDDxXtQOx17D2sfG+5ajFWZ7WMaHPGxeGee14HmndwAcuh4v6Uz9zPaL1bpujFmclpm5PI7Eyztg8ahngdDJySO81sjdw4c2wPUEhazOY7V2vsvw9ytzSj8D5I1G+1ZrS5CCd8dXxOZglcWO5dzJIG8jC47bH49tU6MnJtpGLrTj1Xm4W57MY2DUGAyGNvx4y8w46vLcxUrixwfJDJKI3MLXMALXO37QEb7Hbd4PiJPT19xXjz2TZDp3TTaM0JkY1rasTqnazEuA5nbnc9SfiHxLi9f8K9UZvG8a4qWL7Z+or2MmxY8Yib4wyKGq2Q9XDl2Mbx522+3TfcL7674Qaj1XmuL2KirRRYnWWLqvpZY2G8sFqCJsYglj99s4gO5gCNgQeuwOdhC87374/ozMZxwyGr+LOgsZj8TncBgspUyNiUZmhHC2+1kcToXxndzmgbuOx5Ds5u46hTeoGN3V2U4iaG1XrDSsGisNpylkWZLIWsxVfAx80cTWluz9wwlnQnr16gemSqHGLQOVvV6VLXGnLlyxI2KGvXy1d8kr3HZrWtD9ySSAAO9YLIS35z+3BHEY/wpMFkqOAvQ6W1WaGfd2OMs+IR8lqxyk9g0CXcO813nOAYeUkP5RuvGsOO9exw6vZXGw57A3Keagw1/fH15rOMmMsX8LE+UMcx4exvMxz/4YEb7HbSaT4WaoxnDrghi7OL7O9pzMNtZSLxiI+LxCC0zm3Dtn+dKwbNJPX5js1fwr1RlNPcV61XF9rPnNT4zI49vjEQ7eCJ1EyP3Ltm7djL0dsTy9Adxvkqzqmb3h+Tf8X+PMWl8XrPGafx2aymbw2LmmsZHGUmTVsXMYXPiMznuAJHmvLWtfs3q4bLPj431sRj9M46XGZnVOpbuHgydutg6bJXwROYN5pN3Ma0OdzANHU7HZq4zU+jdd6crcWtP4TSbdS4vWRt3aeSiyMFd1aaeo2F8UrJCCQCwFpbuCDseX0Z2ntK624X6vbmMbpYamqZrA4yjdhjyENebH2qkb2dS88r43CTqWEkFp2B9IznTzv0YvCPirqjVM/B4ZLJmw3UGFytzIg14mGeWGSERO81o5eUPd0bsDv1BXanwgcF4+0jE51+AddGPGpm0gcaZjJ2QHPzc/L2nmdpycm/2W3VcHw54Yax0dV4L258IyWfBU7+MzFQXIg+q2zJG5szTuWyBvZ9Q077OGwJ6LW8MeBR0NNj9O5fhFgtQeJXncms3S1QZK/al7JXscDN2zWkDl2IJb75CMZVEku9y/ZZxbDhtaNTLZzDggQMMeQhYN/N7YvEg/SRuf+GQrXrM4fV3WNXZ66A7soa1amCR0Mm8kjh+Rr4vzrYo/wBZry+6MZYlonckJERVnnwiIgCIiAIiICu3hCfCM8HT8a5T9zCsSq7eEJ8IzwdPxrlP3MKxKAIiIAiIgCjrVeBl05kbOVqQOmxVt/a3I4hu+tLsAZQ30xu287bq13nbEOcWyKinGWbdPamW0qkqUs6JFta1Ddrxz15o54JBzMkicHNcPjBHQr6Lp8pw4wOUsyWfFpaNmQ7vmoWJK5ed9yXBhAcd/SQStf8AUnx3ytmh/wDfH2KWjpPdK3y/Z1ll0LbUzUItv9SfH/K2b9ePsT6k+P8AlbN+vH2JoqfP6Gdep4M072NkaWvaHNPeCNwV8m0q7HBza8TXA7ghg3C3v1J8f8rZv14+xPqT4/5Wzfrx9iaKnz+g16ngzUIo48HKjb4l1+I783mspI7B61yeDp9jZ5OWrAY+zDth1d5x3PpUvfUnx/ytm/Xj7E0VPn9Br1PBmoRbf6k+P+Vs368fYn1J8f8AK2b9ePsTRU+f0GvU8GahFt/qT4/5Wzfrx9i94+E+I3+v3MvaZ3FkmRlaD+HkLd00VPn9Br1PBnMyXJbV3ybi2Mu5ZwB7Dm2bC090kpHvGd/XvO2zQT0UiaY09DpnER0o3mZ5e6Wadw2MsryXPcfi6noPQAAOgCycRhaGBpirjqkNOuDvyQsDQT8Z+M/OeqzUcoqOZDd9TnV8odZ4IIiKo1AiIgCIiAIiICu3hCfCM8HT8a5T9zCsSq7eEJ8IzwdPxrlP3MKxKAIiIAiIgCIiAIiIAiIgCIiArv4Gf8T4zf1mZz9cKsQq7+Bn/E+M39Zmc/XCrEIAiIgCIiAIiIAiIgCIiAIiIAiIgK7eEJ8IzwdPxrlP3MKxKrt4QnwjPB0/GuU/cwrEoAiIgCIiAIiIAiIgCIiAIi53V3EfSfD/AMU91GqMLpvxvn8X8r5CGr23Jy8/J2jhzcvM3fbu5h8YQEM+Bn/E+M39Zmc/XCrEKovglcaOH2n6vFpuU13prGuu8Qszeqi5mK8Rnrv7IsmZzPHNG4A7PG4Ox2Kt0gCIiAIiIAiIgCIiAIiIAiIgCIsbJZKphsdav37UNGhVifPYtWZBHFDG0FznvcSA1oAJJPQAICv/AIQnwjPB0/GuU/cwrEqofHTjVw8y/HvgLkKOvNM3aGNyWSkvWq+YryRVWuqhrXSvDyGAnoC4jc9FaXTOsMDrWg+7p7N47PUo5OyfZxluOzG1/K1/KXMJAPK9jtvic09xCA26IiAIiIAiIgCIuSzvEKvjrctHG05szfiPLI2IhkMJ+J8rum/xtbzOHpA3U4wlPcSjGU3aKudaijZ2tdWSbltHDV/5hmll2/6uVu/5l6+7LV/2jCf33tVmiXMups6pWwJLVWP8ojwQdxX4ISZyhCZc5pIyZGEN731iB4ywf9LGv+P61sO9S57stX/aMJ/fe1esmrdWTRvjkrYN8bwWua4SkEHvBCaJcy6mdUrYH5O+BvwMPHjjdicVbgdJp/Hf+I5Vxb5pgYRtEf8AiOLWbd+znEdy/a9Vi4G8H4/B6GohpOljmnOW/Gp3W5JHmNo37OCMta3aNnM/lDt3ecd3HopS92Wr/tGE/vvamiXMuo1StgSWijT3Zav+0YT++9q8jWWrtxvBhdvTt23tTRLmXUapWwJKRR7W4i5mk4HJ4KKxB9lLirPO9vz9lI1u49PRxPxAnbftMPmqWfosuULDbFdxLeYAtLXDva5p2LXD0tIBHpCjKnKKvvXltKJ0p0/7IzkRFUVBERAEREARFxWW4lRtnkr4PHvzUsbi19gyiCoxwOxb2pBLjv8AyGuA2IJBGynGEp7u/mThCU3aKudqsPMYinn8TdxeRrst4+7A+tYryDdssb2lr2n5iCR+VcE/WmrHHdtTDRDf3pfM/wDt2H6l6+7LV/2jCf33tVmiXMups6pWwPxv8IHhBd4IcXtQaPsB8kVWxzUpnd89Z/nQv322JLSAdu5wcPQv1s8Dfgo/gTwIw2FuNezNX3HK5Njv/d2ZWsBj29HIxkbD8ZYT6Vx3E7g1Dxa4iaQ1pnqGNdmNNSB9cQPe2KyGvD2MnBaS9rH7uABb752+4OylX3Zav+0YT++9qaJcy6jVK2BJaKNPdlq/7RhP75ZdXiNk6bgcvhGugA3dPi5jM5vX0xOa1xHp80uPzJom/wCsk/mReS1kr5pICLFxuTq5ijFcpTss1pQS2Rh6HY7EfMQQQQeoIIPULKVLTTszVCIiwDkeIGenpx08TRldDcyHOXzMds+GBoHO9v8AO3cxo+Iv39C5qrVipV2QQMEcTBs1o/8A7qfnX21O8ycSrrX90WJqmMbd3PNZ5j+Xkb/2hYuUtvoYy5ajiM8kML5GxDveQ0kN/LtsrK3spU1gn83t+jsd/JIKNJSxMlarSmqsXrfT1HOYW147irrO0r2OzdHzt3I35XgOHUHvAUWcBNPW9VaV0vxBymsc9lMvla3j1iq3IObjgZGn6y2sPMa2Pfbp527OpPUKK+ENLI6I4Z8FdR0dSZqV2Xy0GKt4yxbLqLq83bDlbBsGtc0taQ8edvvuTutYv0r2O2x/r8lk7HFDTNWaWKXJFj4svFgXg15el2UNMcXveu4e3zvejfqRsV0GUyVbDY23kLknY1KkL55pOUu5GNaXOOwBJ2APQdVVq1Lbjr26NnJ5DJwY7jBjq1Z+SuSWXxRctVwjDpCSGhz3EDuG5W91lWyHFDI8Y3XtS5nE1NK13UaGIxVw1mEGkJnTztH8Lzl5ADt2hrSNt+qEdK8O9pYXC5ipqHD0Mrj5vGKF6COzXl5S3nje0Oa7ZwBG4IOxAKzFx3Bj/Y9oX8Q0P3dii/itk9W6t41s0XhXTMx9LBMypr1tQS4aSxI+d8Zf2sUMj3tYGNHIOUbv3O/QAWuebFPEsCirezEa3dqnhdpPVupchWktw5zxx2Fyj2vtQRmu6u2WZrIy6RrSAZGtY4+dsRzO31eP1XqLJRYXQD9TZOpVta1yuClz/jH+f+J1Y3zRwiY9RI/zWdp77Zp9JSxDS+Xez8lj8ZqrF5jOZnD07XbZHDviZeh7N7exdJGJGDmIAdu0g+aTt6diss5V2kb3lqMltUEDIxc2zHQ9AZSP5UY87f0tBHXptD3AzAN0vxU4u41l+/kmQ28Zy2MnZdYnINJp2dI7q7bfYE7nYDqVMl+GOxRsRTbdk+NzX7jccpGxVtOWZNN7uPwMtKrBqSxJVRaDQFma5oPTc9gkzy42s+Qnv5jE0n+1b9TnHMk44HmXsCIigYCIiA4bX2Zls34dPVpHxNfD4zelifyvbEXFrIwR1HOWv3I+xY4d5BGoiiZBGyONjY42ANaxo2DQO4AL42XmXXOq3P8AfR2YIWb/AGsVonD8nM9/9q1+saeVyOkc3UwdxmOzU9KaOjckG7YJywiN56HoHEHuPd3FWV/ZagtySfVXPQ5NBQpJrez7al1HjtIYG9mctYNXG0ojNYmEbpORg7zytBcfyAr5YvVuJzWcyuHp2u2yOLbA+5D2b29kJml0XUgA7tBPQnb07Kr+ocrexXAjiNjZsxrTCa707Sr3bcd3OyzPjkIcGTV7DHbuhkIeS3cAFuxa3ZdXY0MdYcZuLszdUZvTE1OpinxWcVedXYx/i0hEkrR0kDeX3rtxsT067rXsWaVtqy72/gnd2qsWzVcemza2zUlJ2RbV7N/Wu2RsZfzbcvvnNG2+/Xu2XnTupsbqunPaxdk2YILM1ORxjezllieWSN2cATs5pG/cfQSFAHB7VGQ4h8RtD5rM7w5LKcOJZLD4vrZc43IAZG7e95vfDbu5hstJpS7m8tBw7wEmqtQR1rerdQY6zZbk5XWZ60AtGON0riXHYRNAO/MO9pB2IWCq32272fktciqtmtVZ7TuntQ4SbUWbGnMPrqDGX8y2d81+ripa8cvL23WTpLIxnadXhru/0qSvB1iv+L6ysSX8zlcDNmT5FvZyeaSeaq2GJu7e168geHhrtgXbbknoVglGrnSzbEsUcmdI5lmQjPJj7krIshFvs3c+ayfb+U08rXH0s79+Rm0qqHdVsZJpfLtk25DTm3JG+3mFSth5pLGIoyzbiZ8DHP3/AJRaCVtv2qak962fjv4HKy6CjJSXEzERFSc04LiLQdRyWPzzQfF2MdTuHfYMY4h0ch+ZrgW/MJSe4LXKS5oY7EL4pWNlie0tex43a4HoQR6Qo+ymiMrhHudgxFkcf9jj55Ozlh+aOQ7hzfia7bb+URsBc0qqSvZr1/f29epkuUxgsyZHeF4HaI07qZuexmDFLIsmfYYIrUwgjleCHvZBz9k1xDnAlrR3lZ9ThZpejp/T2DgxfJi8BajuY2DxiU9hMzm5HcxdzO253dHEjr1C3zrWVi6TaXzMbx3tEcUn9rJHD+1enlDIfe1m/VR9JR1erh6o6KnR4NGgyXCPSWYxGdxlzDssUs3e8p343zSbyWeVjRK13NvG4CJm3IW7cvTvO+DqjgTobWWTORy2D8YuvrCnLNFbnhM8IGwZL2b2iUAdBz7ldb5QyH3tZv1UfSTyhkPvazfqo+kmr1cA50XvaOUfpjWWEZBjdLZfTmM09SgirUad7EWbU0UTI2tDXSi2zm7uh5Qdtt9z1PpluEuP1/Rx8mvK9HMZqi5/YZDENsY10bHd7WubO6QAjvHPsfiW00xxDqa0blHYTGZTJNxl+XGXDDW/gLUW3aRO3Pvm8w3/AArdeUMh97Wb9VH0k1erh9Bn0eMl1NVj+G+nMVY09PUxjYJNPwTVsYWSybV45Q0SDbm2dzcjertz06d5WBleDWjc3hMjiL2EZYoX8k/MTsdPKHeOOO5mY8O5o3f7hbtudttyuk8oZD72s36qPpLyL+QJA9zeaG/x1h9JNXq4DSUcUabRPDLTfDp+Sfp/HupSZF8cluSSzNO6ZzG8rXOMj3Hfb0+nvO5W4zME+UiZh6bi27kt4GuYRzRRnpJL/wBDST+HlH2QWTWo6kyrmsqYF9Bp77OVmYxjfwMY573H5iGg/GPR2+l9Jw6dZJNJMb2SnAE1x7A0kehjB9iwehu5+MkkkmUYaJqc7XXDf18u/M162Uwpxzae83NavHTrxQQsEcMTAxjB3NaBsB+ZfVEVO84QREQBERAR1rXHuxGqo8pttRyUTK0r99mxzsJ7Mn/fa7l3+ONg73LV5jE1M/ibuMyEIs0bkL69iFxIEkb2lrmkjr1BIUo36FbKU5qluBlmtM3lkikbu1w+cKPcjpHPYFx8nsGfoD3kb5WxW4x/J3dsyT8JLD3b8x3KucdMlZ+0vlf7HWyXKYqOjmR7R4EaGx2m85gocITjs4xsWREtyeWayxo2a10zpDJs0E7AOAG5223TVPAfQ2tMzbyuYwhtXrgY2zI25YiFhrGhrWSNZIGvYAB5rgR3nbcldg+3k4zs/TOZa7fbYQMd/a15H9q9fKGQ+9rN+qj6Sjq9XD6G/n0LWuvQ1tzh5p29mMDlZMVCzIYJjo8dNAXRGuwtDSwBhALNgPNILeg6LGx/CvS+Ks4uxVxfZTYy/aydR3jEp7OxZDxO/q7rzdq/odwOboBsNvOZ4h1NPZvCYjJYzKVMnmpJIsdWkredZexvM8N6+hvVbryhkPvazfqo+kmr1cDOko4oj/iXwdr57T+UiwGLxsmTyWUiy1rynduQMlmZGI+dssDw+J/I1oBZ06Hdp3K9+CHDrUHD6pmhm8kyWO9OySriq9+1egoNazZwZNZJkdznziDsBt0Heu98fyP3tZr1YfSWZVxepMu4Mr4c4phHWzlJGbN6+iONxc7p6CW/hTV6i37Pmit1KMXn5yMTJUn5+evgYdy++eWctOxirD+FefyeYP5z2qXAA0AAbAdwC02mdLVtNQzFj3Wrtgh1m5L7+UjflH81jdzytHQbk9S5xO6WZNKKhHcvqcfKK2mndbkERFUaoREQBERAEREBXbwMv4nxm/rMzf64VYlV28DL+J8Zv6zM3+uFWJQBERAEREAREQBERAEREAREQBERAV28IT4Rng6fjXKfuYViVXbwhPhGeDp+Ncp+5hWJQBERAEREAREQBERAEREAREQFavAyzNDyhxrwvjtfyvFxEzNt9DtR27YXuja2Qs335CWuAdtsSCrKqJ+L3g46c4qXoM9BPa0nrikP8x1VhXdjciIGwbJtsJWegsd6NwCNyuEx/H7V/Ay/XwXHLHMOKkeIafEPDQk4+wT0aLcQG9eQ/Htykk7AAFyAsmixsbk6eZoV72PtQXqVhglhs1pBJHKw9Q5rgSCD8YWSgCIiAIiIAiIgCIiAIiIAi1mpdT4nR2EtZjO5KricXVbzzXLkojjjHzk/H3Aek9Aq8y8WuIXhJyvocJK0mj9EOcWT8QczWPa2W77HxCs7Yu/4j9h3+9cBuB9OPmax9zwpfB+xMF2vPlKt/JWLFKOUOmhjdU2a97Ad2tPK7Ynv2PxKyqjfhBwC0nwYr2ZcRXmv568efI6hyknjGQvPPUuklPXYnryjZvp236qSEAREQBERAEREAREQBERAEREAWNksbUzNCxRv1Yb1KwwxTVrMYkjlYRsWuadwQfiKyUQFacn4P+seBmQsZ7gXkWHFSPM13h5mZicfOT1cakhO9eQ/FvykkbkABq7ng/4S2muKuRn0/ZgtaR11T6XdK5tvY24yBuTHvsJWekOb6NiQNwpdUc8YeAWj+N2Pgj1BRfFlKh5qGbx7+wv0Xg7h0Uw6jY9eU7t367bgICRkX5ReGNxv4kaKq5bgLm9XUNYY+s+tNbzXiL4shLFsyaGvO53mFw+tSF8e5d5oL/ftVwfAJ8IyTjlwoGNzd02dX6d5at18ry6S1CR9ZsEnq4kAtcdyS5hJ25ggLOIixrmRqY9odatQ1mnuM0gYD+dZSb2IGSi1Puswfyzj/Wme1Z9W7XvR89axFYZ/KieHD84UnCUdrRmx90RUr/ykvhHe4HQzOHOEtcme1FCTffG7zq9Aktc0/EZSC3/dD9+8FQMF1FCXFTwocTpHUJ0bo7F2OIfEWTdrMBiHAtqnu5rU3vYWj079R03AB3VN/A84j8RPCG03S4Mw64bpDC4Ci+exkKTJpMzdo9qGiGOZxcyJsfaMjBBaWsMYa1wa5foBwq4N6R4LaeGH0lh4cbA7Z09g+fYtP/lyyHznu6nvOw32AA6ICKNNeDRmeI2bq6s46ZeHVmThd21HSdMFuExh/wCGf4d47i5+47x5wAKsTFEyCJkUTGxxsaGtYwbBoHcAPQF7ogCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgKw8Sf8nbwn4l6py2obRzuJyWVtSXLTsbfbyvmkdzPcBKyTbdxcdh0HMdgBsB68DfAPwPg/wDE6pq7TesM5KyOGavYx9xsTm2ontIDHua1vRruzeOnfGFaBc5xDy02G0helqydjbmMdSCQb7skmkbE1w2+Iv3/ACKcIuclFcTKWc7I5/UOqreesz0sTZkoY6Fxjlvw7dpO4dHNiJB5WjqC/bcnfl225joItK4iOV0xx0E1hx3dPYZ2srj873buP5Ss+nUhx9SCrXjEVeFjY4429zWgbAfmUNcUOIephxh0poTBw5jFVbsMty3lqFSpOXsa+Jg5e2eQ2NhkJkdyF3vQwHqRmVaX9absu9/fwPRQpwoR3EueQcZ8nVP0DfYvgdL4tsomr1GUbLd+WzS+sSt3+JzNiotpcd6uFk1hezTM7LXp6irYSHGOxsPbVpJY4gxsfZSOMzHl4eCfP8/bl6LuND8S6et8lmcX5MyeDzGJMRtY7LRMZK1koJjkBje9rmu5XdQ7oWkEBVqrUjtUn1Lc6EthImltW26t6HE5mU2RN5tTJFoaZHfa5QAAHnva4AB3UbNIHPBXEz/J3aP4ucSs7rDUertTTWMrP2xgryV2di0NDWxtcYneY0ABvTcNABJO7jKuWoeU8dNXDuze4B0cg745AeZjx87XAEfOFIeksydRaXxOTcA19urHM9o7g4tBcPyHdXO04aRb9z+336HGyuiqUlKO5kFcKfAL4WcINU0NR4hmctZqg/tK1u1lZGOjd6ekPZhwI3aWuBa5rnBwIKsWiKk0AiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgC5HipXdLouzO0OPiU9e67lG55IpmPk6f7jXLrl6yRsmjfHIxr43gtc1w3BB7wQrKcsyalgSi81p4EZ96j7M6VylvjrpnUMVXmw9PB36c9ntGDklklruY3l35juI39QNht1I3C7fIY86EkZTuPDMOCI6V6R3mhvQNilce546AEnzht9luFlKucHTflweJ6aMo1oqSK+Z/hXqi7m9SWIcXzw3NfYbNQO8YiHPTgbWE0vV3Tl7N/mnzjt0B3CkLA6VylLjhq7UE1XkxF/EY6rXsdow9pLE+wZG8oPMNhIzqQAd+m+xUgrw97Y2Oe9waxo3LnHYAfGqt5lU0nfvj+T527UdGpNZlO0ULHSPI9AA3K7Lh9jpcTofB1bDSywypGZWnva8jdw/ISVx+Cw51zZifyB2nYntlfYPvbrmkOayP+VHuAXO7ne9G+7uWUVttaOnmPe3d+Vt31focjLaqnJQjwCIipOaEREAREQBERAEREAREQBERAEREAREQBERAEREAREQHpNDHYifFKxskT2lr2PG7XA9CCPSFyU/CrAOkL6rbmL3O/Z0LksUQ/BGHcg/I0LsEVkak4f1diUZSj/V2K1YGlbyHhR6p0NNmsocBj9O1clBGLO0gmklLXEu23I2Hcpop8LtP15WSWILGTew7t8o2pLDAfj5HEs/sUSaT+HXrv8AodQ/blWIU9PV5ibq1HscmeAAAABsAvKIqCoIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiICu+k/h167/AKHUP25ViFXfSfw69d/0OoftyrEIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgC12odR4nSWHsZbOZSlhcVX5e2vZCwyCCLmcGt5nvIaN3OaBuepIHpWxXA8eeF0PGjg/qnRksnYuylTlglJIaydjmyQudt9iJGMJHpAIQEDaR4xaCk8NbV+UZrfTjsZd0vRp1boy1cwzzic7xMfz7Of1Hmgk9e5Wm09qPE6tw9fLYPKUs1irHN2N7H2GTwS8ri13K9hLTs5rgdj0II9C/B7SfC7Pat4o0dA16j4dQWcj5NfBI3rBIHlshft3Bmzi74g0r9z+HWhMZwx0Lg9K4dhZjsTVZViJA5n7Dznu2+yc7dx+dxQHRoiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgC5zXmormmsNDYoRQTWZrUVZosE8g53bbnbr0XRriuK3+g8b+Nav7RW07Z6uQqNxhKS4Jmr912r/tOE/vvanuu1f9pwn997V5RcXxCrguh4rxbKsV0R4912r/ALThP772p7rtX/acJ/fe1eUTxCrgug8WyrFdERTheC8WB46ZTixUo41upshXMD4nPf4rG4hrXzMZy8wkc1uxPMQeZ3TdxKlX3Xav+04T++9q8rU6q1Vi9E4OfMZq14ljoXxskm7N8nK6SRsbBytBPVz2ju9PXosr+QrPYkuhlfyuVt2T9EbX3Xav+04T++9qe67V/wBpwn997V5RY8Qq4LoY8WyrFdEePddq/wC04T++9q63ROen1NpejkrMUcM8wdzsiJLQWvc3pv19C5NbrhP/AKgYv8M37Z66GT15ZRTm5pbGtytvv+Dt/wAZldXKnNVXut9zrkRFad0IiIAiIgCIiAIiIAiIgCIiAIiIAiIgC4rit/oPG/jWr+0XariuK3+g8b+Nav7RW0/7FVX/ABy+D+hrEWt1BDl58VKzB2qVLJEt7ObIVn2IQNxzbsZJGT03284bH4+5ckMXxS676l0h83/s7a//AHl5RK/E+bximtrt1Oh1/nrOldCajzVOAWreNxtm5DAQSJHxxOe1vT4y0BQNwkwXFDKS6L1W3KGxRyAit5We3qmW7Bdryx8zhHTNVkcDgS1zRG8cvKWnm3JUx4jG8QW5GA5fO6Yt43f6/BUwdiGV7du5r3W3gH8LT+BfDS3A7RGic63MYTBihcjMhiay1M6GAv35+yhc8xx77nfkaO9WRkopo2YThThKO9vu3D7kF6OyGeocPuGWtn6sz97K5LVMOKtwXMg+WrLVluS1zGYT5u4aGkPI59x77botTrOtkOInAzP8QctqXMm9JnmQMwcN0soVIYsqyBkD4B5rnbNDy93ncxB3277M1+FWlquncPgosXy4rEXmZKlX8YlPZWGSmZr+bm5nbPcTs4kddttui0eZ8HXh5n8vfyV3TwdZv2G27IhuWIYpZ2uDhKYmSBnPu0Eu5dz13J3KsVWN7+fpgbEcqpqeda22+5br7iSEXC28bxMdbmdV1FpSKsXuMTJsBZe9rN/NDnC6ATttuQBv8QXydjOKJPm6l0gBsO/T1o9fT/55a9liaGYuZev4O/W64T/6gYv8M37Z65nEx34sbXZk569nIBgE8tSF0MT3ektY57y0fMXH8K6bhP8A6gYv8M37Z67OQf4qnxj/APR6L+F/tU+X3OuREW6epCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAuP4oU7dvAVDTqTXpIL9ed0NdvM8sa/ckBdginCWbJMjJKScXxIl8pX/vbzfqo+knlK/8Ae3m/VR9JS0i1tWybkfU4/hGS+fX9ES+Ur/3t5v1UfSTylf8Avbzfqo+kpaRNWybkfUeEZL59f0RL5Sv/AHt5v1UfSTylf+9vN+qj6SlpE1bJuR9R4Rkvn1/REvlK/wDe3m/VR9JPKV/72836qPpKWkTVsm5H1HhGS+fX9ES+Ur/3t5v1UfSXZ8NKNnG6Ixle5Xkq2GiQuhlGzm7yOIB/IQunRXQjTpxcacbXtxvuv+TdybI6WS30d9oREQ3QiIgCIiAIiID/2Q==", - "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/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..93d1b4ca0 --- /dev/null +++ b/libs/checkpoint/Makefile @@ -0,0 +1,31 @@ +.PHONY: test lint format + +###################### +# TESTING AND COVERAGE +###################### + +test: + poetry run pytest tests + +###################### +# 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_checkpoint +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..d15480132 --- /dev/null +++ b/libs/checkpoint/README.md @@ -0,0 +1,85 @@ +# 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` or `thread_ts` when running the graph. + +- `thread_id` is simply the ID of a thread. This is always required +- `thread_ts` 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", "thread_ts": "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.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 + +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(thread_config, checkpoint, {}) + +# load checkpoint +checkpointer.get(thread_config) + +# list checkpoints +list(checkpointer.list(thread_config)) +``` diff --git a/libs/langgraph/langgraph/checkpoint/base.py b/libs/checkpoint/langgraph/checkpoint/base/__init__.py similarity index 88% rename from libs/langgraph/langgraph/checkpoint/base.py rename to libs/checkpoint/langgraph/checkpoint/base/__init__.py index b6694db89..5ea3099d4 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 +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". """ @@ -65,29 +68,29 @@ class Checkpoint(TypedDict): 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]] """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]]] """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 +123,36 @@ 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.""" @@ -268,9 +301,7 @@ 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. @@ -360,11 +391,9 @@ 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, @@ -378,3 +407,10 @@ 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 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.py similarity index 100% rename from libs/langgraph/langgraph/checkpoint/memory.py rename to libs/checkpoint/langgraph/checkpoint/memory.py diff --git a/libs/langgraph/langgraph/serde/__init__.py b/libs/checkpoint/langgraph/checkpoint/py.typed similarity index 100% rename from libs/langgraph/langgraph/serde/__init__.py rename to libs/checkpoint/langgraph/checkpoint/py.typed 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/langgraph/langgraph/serde/base.py b/libs/checkpoint/langgraph/checkpoint/serde/base.py similarity index 100% rename from libs/langgraph/langgraph/serde/base.py rename to libs/checkpoint/langgraph/checkpoint/serde/base.py diff --git a/libs/langgraph/langgraph/serde/jsonplus.py b/libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py similarity index 95% rename from libs/langgraph/langgraph/serde/jsonplus.py rename to libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py index d28e6b640..c76d90fe4 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() @@ -64,7 +64,7 @@ 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} ) 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/langgraph/langgraph/checkpoint/sqlite.py b/libs/checkpoint/langgraph/checkpoint/sqlite/__init__.py similarity index 80% rename from libs/langgraph/langgraph/checkpoint/sqlite.py rename to libs/checkpoint/langgraph/checkpoint/sqlite/__init__.py index eee6e05c7..48a34106b 100644 --- a/libs/langgraph/langgraph/checkpoint/sqlite.py +++ b/libs/checkpoint/langgraph/checkpoint/sqlite/__init__.py @@ -1,5 +1,3 @@ -import json -import pickle import sqlite3 import threading from contextlib import AbstractContextManager, contextmanager @@ -10,50 +8,21 @@ 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, Checkpoint, CheckpointMetadata, CheckpointTuple, + EmptyChannelError, SerializerProtocol, ) -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.types import ChannelProtocol +from langgraph.checkpoint.sqlite.utils import JsonPlusSerializerCompat, 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" @@ -476,7 +445,7 @@ 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. @@ -498,82 +467,3 @@ class SqliteSaver(BaseCheckpointSaver, AbstractContextManager): 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, -) -> 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"]) - - # 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/langgraph/checkpoint/sqlite/aio.py similarity index 98% rename from libs/langgraph/langgraph/checkpoint/aiosqlite.py rename to libs/checkpoint/langgraph/checkpoint/sqlite/aio.py index 76ab9bde3..77b4486ee 100644 --- a/libs/langgraph/langgraph/checkpoint/aiosqlite.py +++ b/libs/checkpoint/langgraph/checkpoint/sqlite/aio.py @@ -24,7 +24,7 @@ from langgraph.checkpoint.base import ( CheckpointTuple, SerializerProtocol, ) -from langgraph.checkpoint.sqlite import JsonPlusSerializerCompat, search_where +from langgraph.checkpoint.sqlite.utils import JsonPlusSerializerCompat, search_where T = TypeVar("T", bound=callable) @@ -84,9 +84,8 @@ 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) @@ -104,7 +103,7 @@ 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: @@ -120,7 +119,6 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): serde = JsonPlusSerializerCompat() - conn: aiosqlite.Connection lock: asyncio.Lock is_setup: bool @@ -145,6 +143,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver, AbstractAsyncContextManager): Returns: AsyncSqliteSaver: A new AsyncSqliteSaver instance. """ + return AsyncSqliteSaver(conn=aiosqlite.connect(conn_string)) async def __aenter__(self) -> Self: diff --git a/libs/checkpoint/langgraph/checkpoint/sqlite/utils.py b/libs/checkpoint/langgraph/checkpoint/sqlite/utils.py new file mode 100644 index 000000000..f9334f42e --- /dev/null +++ b/libs/checkpoint/langgraph/checkpoint/sqlite/utils.py @@ -0,0 +1,114 @@ +import json +import pickle +from typing import Any, Dict, Optional, Sequence, Tuple + +from langchain_core.runnables import RunnableConfig + +from langgraph.checkpoint.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) + + +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"]) + + # 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/checkpoint/poetry.lock b/libs/checkpoint/poetry.lock new file mode 100644 index 000000000..d8cccfcc1 --- /dev/null +++ b/libs/checkpoint/poetry.lock @@ -0,0 +1,879 @@ +# This file is automatically @generated by Poetry 1.8.2 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://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-watch = "^4.2.0" +mypy = "^1.10.0" +dataclasses-json = "^0.6.7" +aiosqlite = "^0.20.0" + +[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/tests/__init__.py b/libs/checkpoint/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/langgraph/tests/checkpoint/test_aiosqlite.py b/libs/checkpoint/tests/test_aiosqlite.py similarity index 92% rename from libs/langgraph/tests/checkpoint/test_aiosqlite.py rename to libs/checkpoint/tests/test_aiosqlite.py index abcf6cb71..35f038d73 100644 --- a/libs/langgraph/tests/checkpoint/test_aiosqlite.py +++ b/libs/checkpoint/tests/test_aiosqlite.py @@ -1,9 +1,13 @@ 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 +from langgraph.checkpoint.base import ( + Checkpoint, + CheckpointMetadata, + create_checkpoint, + empty_checkpoint, +) +from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver class TestAsyncSqliteSaver: diff --git a/libs/langgraph/tests/test_jsonplus.py b/libs/checkpoint/tests/test_jsonplus.py similarity index 98% rename from libs/langgraph/tests/test_jsonplus.py rename to libs/checkpoint/tests/test_jsonplus.py index d2df4c747..254238c1f 100644 --- a/libs/langgraph/tests/test_jsonplus.py +++ b/libs/checkpoint/tests/test_jsonplus.py @@ -9,7 +9,7 @@ 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 +from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer class MyPydantic(BaseModel): diff --git a/libs/langgraph/tests/checkpoint/test_memory.py b/libs/checkpoint/tests/test_memory.py similarity index 96% rename from libs/langgraph/tests/checkpoint/test_memory.py rename to libs/checkpoint/tests/test_memory.py index bac957180..1622444c3 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 diff --git a/libs/langgraph/tests/checkpoint/test_sqlite.py b/libs/checkpoint/tests/test_sqlite.py similarity index 90% rename from libs/langgraph/tests/checkpoint/test_sqlite.py rename to libs/checkpoint/tests/test_sqlite.py index cd2aa8e1e..d920aeb5e 100644 --- a/libs/langgraph/tests/checkpoint/test_sqlite.py +++ b/libs/checkpoint/tests/test_sqlite.py @@ -1,14 +1,14 @@ 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, +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: @@ -116,10 +116,10 @@ class TestSqliteSaver: async def test_informative_async_errors(self): # call method / assertions - with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG): + with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"): await self.sqlite_saver.aget(self.config_1) - with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG): + with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"): await self.sqlite_saver.aget_tuple(self.config_1) - with pytest.raises(NotImplementedError, match=_AIO_ERROR_MSG): + with pytest.raises(NotImplementedError, match="AsyncSqliteSaver"): async for _ in self.sqlite_saver.alist(self.config_1): pass diff --git a/libs/langgraph/README.md b/libs/langgraph/README.md index cf8814eb6..79170f2f4 100644 --- a/libs/langgraph/README.md +++ b/libs/langgraph/README.md @@ -61,7 +61,7 @@ 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/channels/manager.py b/libs/langgraph/langgraph/channels/manager.py index 8bee9e545..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 @@ -40,34 +37,3 @@ async def AsyncChannelsManager( ) for k, v in channels.items() } - - -def create_checkpoint( - checkpoint: Checkpoint, - channels: Optional[Mapping[str, BaseChannel]], - 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", []), - # 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/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/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..f55520223 100644 --- a/libs/langgraph/langgraph/graph/graph.py +++ b/libs/langgraph/langgraph/graph/graph.py @@ -24,7 +24,7 @@ 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.checkpoint.base import BaseCheckpointSaver from langgraph.constants import END, START, TAG_HIDDEN, Send from langgraph.errors import InvalidUpdateError from langgraph.pregel import Channel, Pregel diff --git a/libs/langgraph/langgraph/graph/state.py b/libs/langgraph/langgraph/graph/state.py index b67d4e9d1..702fbbfb9 100644 --- a/libs/langgraph/langgraph/graph/state.py +++ b/libs/langgraph/langgraph/graph/state.py @@ -29,7 +29,7 @@ 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.checkpoint.base import BaseCheckpointSaver from langgraph.constants import TAG_HIDDEN from langgraph.errors import InvalidUpdateError from langgraph.graph.graph import ( @@ -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: diff --git a/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py b/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py index f5759478b..221b5b725 100644 --- a/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py +++ b/libs/langgraph/langgraph/prebuilt/chat_agent_executor.py @@ -23,7 +23,7 @@ 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.checkpoint.base import BaseCheckpointSaver from langgraph.graph import END, StateGraph from langgraph.graph.graph import CompiledGraph from langgraph.graph.message import add_messages @@ -472,7 +472,7 @@ 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): diff --git a/libs/langgraph/langgraph/pregel/__init__.py b/libs/langgraph/langgraph/pregel/__init__.py index 52ed515ab..47c7df81b 100644 --- a/libs/langgraph/langgraph/pregel/__init__.py +++ b/libs/langgraph/langgraph/pregel/__init__.py @@ -55,11 +55,11 @@ from langgraph.channels.context import Context from langgraph.channels.manager import ( AsyncChannelsManager, ChannelsManager, - create_checkpoint, ) from langgraph.checkpoint.base import ( BaseCheckpointSaver, copy_checkpoint, + create_checkpoint, empty_checkpoint, ) from langgraph.constants import ( diff --git a/libs/langgraph/langgraph/pregel/algo.py b/libs/langgraph/langgraph/pregel/algo.py index d94d4e166..17fde77bd 100644 --- a/libs/langgraph/langgraph/pregel/algo.py +++ b/libs/langgraph/langgraph/pregel/algo.py @@ -25,8 +25,13 @@ 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 ( CONFIG_KEY_CHECKPOINTER, CONFIG_KEY_READ, diff --git a/libs/langgraph/langgraph/pregel/loop.py b/libs/langgraph/langgraph/pregel/loop.py index 8373a1fba..678ff1217 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 diff --git a/libs/langgraph/poetry.lock b/libs/langgraph/poetry.lock index f7173efbe..c851c9ee0 100644 --- a/libs/langgraph/poetry.lock +++ b/libs/langgraph/poetry.lock @@ -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)", "flake8-bugbear (==23.3.12)", "flit (==3.7.1)", "mypy (==1.2.0)", "ufmt (==2.1.0)", "usort (==1.0.6)"] -docs = ["sphinx (==6.1.3)", "sphinx-mdinclude (==0.5.3)"] +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" @@ -654,21 +657,6 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1 [package.extras] toml = ["tomli"] -[[package]] -name = "dataclasses-json" -version = "0.6.7" -description = "Easily serialize dataclasses to and from JSON." -optional = false -python-versions = "<4.0,>=3.7" -files = [ - {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"}, - {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"}, -] - -[package.dependencies] -marshmallow = ">=3.18.0,<4.0.0" -typing-inspect = ">=0.4.0,<1" - [[package]] name = "debugpy" version = "1.8.1" @@ -1829,6 +1817,22 @@ packaging = ">=23.2,<25" requests = ">=2,<3" types-requests = ">=2.31.0.2,<3.0.0.0" +[[package]] +name = "langgraph-checkpoint" +version = "0.1.0" +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 = "../checkpoint" + [[package]] name = "langsmith" version = "0.1.79" @@ -1914,25 +1918,6 @@ files = [ {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"}, ] -[[package]] -name = "marshmallow" -version = "3.21.3" -description = "A lightweight library for converting complex datatypes to and from native Python datatypes." -optional = false -python-versions = ">=3.8" -files = [ - {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"}, - {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"}, -] - -[package.dependencies] -packaging = ">=17.0" - -[package.extras] -dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"] -docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"] -tests = ["pytest", "pytz", "simplejson"] - [[package]] name = "matplotlib-inline" version = "0.1.7" @@ -3904,21 +3889,6 @@ files = [ {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"}, ] -[[package]] -name = "typing-inspect" -version = "0.9.0" -description = "Runtime inspection utilities for typing module." -optional = false -python-versions = "*" -files = [ - {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"}, - {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"}, -] - -[package.dependencies] -mypy-extensions = ">=0.3.0" -typing-extensions = ">=3.7.4" - [[package]] name = "uri-template" version = "1.3.0" @@ -4179,4 +4149,4 @@ 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 = "67b7cf32bfc11e115fa71c28af7880d63faff72a499d54fc2735ebff2bc13c90" diff --git a/libs/langgraph/pyproject.toml b/libs/langgraph/pyproject.toml index b72f9302d..f316932b1 100644 --- a/libs/langgraph/pyproject.toml +++ b/libs/langgraph/pyproject.toml @@ -22,7 +22,6 @@ syrupy = "^4.0.2" httpx = "^0.26.0" pytest-watcher = "^0.4.1" langchain = ">=0.1.0" -aiosqlite = "^0.19.0" grandalf = "^0.8" mypy = "^1.6.0" ruff = "^0.1.4" @@ -30,9 +29,10 @@ jupyter = "^1.0.0" langchainhub = "^0.1.14" langchain-openai = ">=0.1.2" langchain-anthropic = ">=0.1.8" -dataclasses-json = "^0.6.7" pytest-xdist = {extras = ["psutil"], version = "^3.6.1"} pytest-repeat = "^0.9.3" +langgraph-checkpoint = {path = "../checkpoint", develop = true} +aiosqlite = "^0.20.0" [tool.poetry.group.dev] optional = true diff --git a/libs/langgraph/tests/test_pregel.py b/libs/langgraph/tests/test_pregel.py index 25b356ab6..4427b4613 100644 --- a/libs/langgraph/tests/test_pregel.py +++ b/libs/langgraph/tests/test_pregel.py @@ -45,6 +45,8 @@ from langgraph.checkpoint.base import ( 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 @@ -60,8 +62,6 @@ from langgraph.prebuilt.chat_agent_executor import ( 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, diff --git a/libs/langgraph/tests/test_pregel_async.py b/libs/langgraph/tests/test_pregel_async.py index a36309365..83fe0951f 100644 --- a/libs/langgraph/tests/test_pregel_async.py +++ b/libs/langgraph/tests/test_pregel_async.py @@ -39,10 +39,14 @@ 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.checkpoint.base import ( + BaseCheckpointSaver, + 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 diff --git a/poetry.lock b/poetry.lock index ea06529e8..9de4c5ce9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -927,26 +927,30 @@ test-ui = ["calysto-bash"] [[package]] name = "langchain-core" -version = "0.2.8" +version = "0.2.26" 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-0.2.26-py3-none-any.whl", hash = "sha256:ab7bc58d8037349d06ad8c3eee2ea776e26af7f57cce330eecbed5b98e1c4d56"}, + {file = "langchain_core-0.2.26.tar.gz", hash = "sha256:20c7792eb6c256dc50892d41f07f7fd8e12e5868dbb059fa316a278977bdf4f6"}, ] [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 = "0.1.17" description = "Building stateful, multi-actor applications with LLMs" optional = false python-versions = ">=3.9.0,<4.0" @@ -954,15 +958,31 @@ files = [] develop = true [package.dependencies] -langchain-core = ">=0.2,<0.3" +langchain-core = ">=0.2.22,<0.3" [package.source] type = "directory" url = "libs/langgraph" +[[package]] +name = "langgraph-checkpoint" +version = "0.1.0" +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-sdk" -version = "0.1.23" +version = "0.1.26" description = "SDK for interacting with LangGraph API" optional = false python-versions = "^3.9.0,<4.0" @@ -2669,4 +2689,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "70e41571a1230938107dc420734b8dfdae4e71cdc3dca8281366c45441e8d73d" +content-hash = "350503bbf9ee5685fdbcd93b1fb4e9adc967e71205d2a26bf25c93ce108c2cf7" diff --git a/pyproject.toml b/pyproject.toml index 5636aa1ef..9d0482c8a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,6 +11,7 @@ python = "^3.10" [tool.poetry.group.docs.dependencies] langgraph = { path = "libs/langgraph/", develop = true } +langgraph-checkpoint = { path = "libs/checkpoint/", develop = true } langgraph-sdk = {path = "libs/sdk-py", develop = true} mkdocs = "^1.6.0" mkdocstrings = "^0.25.1"