From 543e4c4e7e946f96962e4cc387021ff86253fbab Mon Sep 17 00:00:00 2001 From: Lauren Hirata Singh Date: Wed, 2 Jul 2025 19:28:42 -0400 Subject: [PATCH] docs: Convert notebooks (#5322) * docs: Convert subgraphs notebook * graph api conversion * fix examples * add * fixes * fix links * fix links * be gone! * multi-agent conversion * fix link * fix links * fix link --- docs/_scripts/notebook_hooks.py | 38 +- docs/docs/concepts/agentic_concepts.md | 4 +- docs/docs/concepts/low_level.md | 20 +- docs/docs/concepts/mcp.md | 2 +- docs/docs/concepts/multi_agent.md | 6 +- docs/docs/concepts/server-mcp.md | 2 +- docs/docs/concepts/subgraphs.md | 4 +- .../docs/how-tos/assets/graph_api_image_1.png | Bin 0 -> 3954 bytes .../how-tos/assets/graph_api_image_10.png | Bin 0 -> 150474 bytes .../docs/how-tos/assets/graph_api_image_2.png | Bin 0 -> 7129 bytes .../docs/how-tos/assets/graph_api_image_3.png | Bin 0 -> 9696 bytes .../docs/how-tos/assets/graph_api_image_4.png | Bin 0 -> 10101 bytes .../docs/how-tos/assets/graph_api_image_5.png | Bin 0 -> 7389 bytes .../docs/how-tos/assets/graph_api_image_6.png | Bin 0 -> 11541 bytes .../docs/how-tos/assets/graph_api_image_7.png | Bin 0 -> 7675 bytes .../docs/how-tos/assets/graph_api_image_8.png | Bin 0 -> 15601 bytes .../docs/how-tos/assets/graph_api_image_9.png | Bin 0 -> 7923 bytes docs/docs/how-tos/graph-api.ipynb | 3438 ----------------- docs/docs/how-tos/graph-api.md | 1877 +++++++++ docs/docs/how-tos/multi_agent.md | 580 +++ docs/docs/how-tos/subgraph.ipynb | 559 --- docs/docs/how-tos/subgraph.md | 453 +++ docs/mkdocs.yml | 6 +- docs/uv.lock | 2 +- extract_images.py | 70 + 25 files changed, 3022 insertions(+), 4039 deletions(-) create mode 100644 docs/docs/how-tos/assets/graph_api_image_1.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_10.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_2.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_3.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_4.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_5.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_6.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_7.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_8.png create mode 100644 docs/docs/how-tos/assets/graph_api_image_9.png delete mode 100644 docs/docs/how-tos/graph-api.ipynb create mode 100644 docs/docs/how-tos/graph-api.md create mode 100644 docs/docs/how-tos/multi_agent.md delete mode 100644 docs/docs/how-tos/subgraph.ipynb create mode 100644 docs/docs/how-tos/subgraph.md create mode 100644 extract_images.py diff --git a/docs/_scripts/notebook_hooks.py b/docs/_scripts/notebook_hooks.py index 34dceb064..7b7f049eb 100644 --- a/docs/_scripts/notebook_hooks.py +++ b/docs/_scripts/notebook_hooks.py @@ -34,20 +34,20 @@ REDIRECT_MAP = { "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb", "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain", # graph-api - "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state", - "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps", - "how-tos/branching.ipynb": "how-tos/graph-api#create-branches", - "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops", - "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph", - "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas", - "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes", - "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state", - "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api", - "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command", - "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration", - "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies", - "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit", - "how-tos/async.ipynb": "how-tos/graph-api/#async", + "how-tos/state-reducers.ipynb": "how-tos/graph-api.md#define-and-update-state", + "how-tos/sequence.ipynb": "how-tos/graph-api.md#create-a-sequence-of-steps", + "how-tos/branching.ipynb": "how-tos/graph-api.md#create-branches", + "how-tos/recursion-limit.ipynb": "how-tos/graph-api.md#create-and-control-loops", + "how-tos/visualization.ipynb": "how-tos/graph-api.md#visualize-your-graph", + "how-tos/input_output_schema.ipynb": "how-tos/graph-api.md#define-input-and-output-schemas", + "how-tos/pass_private_state.ipynb": "how-tos/graph-api.md#pass-private-state-between-nodes", + "how-tos/state-model.ipynb": "how-tos/graph-api.md#use-pydantic-models-for-graph-state", + "how-tos/map-reduce.ipynb": "how-tos/graph-api.md#map-reduce-and-the-send-api", + "how-tos/command.ipynb": "how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command", + "how-tos/configuration.ipynb": "how-tos/graph-api.md#add-runtime-configuration", + "how-tos/node-retries.ipynb": "how-tos/graph-api.md#add-retry-policies", + "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api.md#impose-a-recursion-limit", + "how-tos/async.ipynb": "how-tos/graph-api.md#async", # memory how-tos "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory/add-memory.md", "how-tos/memory/delete-messages.ipynb": "how-tos/memory/add-memory.md#delete-messages", @@ -55,8 +55,8 @@ REDIRECT_MAP = { "how-tos/memory.ipynb": "how-tos/memory/add-memory.md", "agents/memory.ipynb": "how-tos/memory/add-memory.md", # subgraph how-tos - "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas", - "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence", + "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.md#different-state-schemas", + "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.md#add-persistence", # persistence how-tos "how-tos/persistence_postgres.ipynb": "how-tos/memory/add-memory.md#use-in-production", "how-tos/persistence_mongodb.ipynb": "how-tos/memory/add-memory.md#use-in-production", @@ -73,9 +73,9 @@ REDIRECT_MAP = { "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state", "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state", # multi-agent how-tos - "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs", - "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system", - "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation", + "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs", + "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system", + "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.md#multi-turn-conversation", # cloud redirects "cloud/index.md": "index.md", "cloud/how-tos/index.md": "concepts/langgraph_platform", diff --git a/docs/docs/concepts/agentic_concepts.md b/docs/docs/concepts/agentic_concepts.md index 5c18fce31..0dce56cd9 100644 --- a/docs/docs/concepts/agentic_concepts.md +++ b/docs/docs/concepts/agentic_concepts.md @@ -97,7 +97,7 @@ Parallel processing is vital for efficient multi-agent systems and complex tasks - Implementation of map-reduce-like operations - Efficient handling of independent subtasks -For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api.ipynb#map-reduce-and-the-send-api) +For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api.md#map-reduce-and-the-send-api) ### Subgraphs @@ -107,7 +107,7 @@ For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api - Hierarchical organization of agent teams - Controlled communication between agents and the main system -Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.ipynb). +Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.md). ### Reflection diff --git a/docs/docs/concepts/low_level.md b/docs/docs/concepts/low_level.md index f277b742e..13c0fa0f7 100644 --- a/docs/docs/concepts/low_level.md +++ b/docs/docs/concepts/low_level.md @@ -45,9 +45,9 @@ The first thing you do when you define a graph is define the `State` of the grap ### Schema -The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/graph-api.ipynb#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation. +The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation. -By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [guide here](../how-tos/graph-api.ipynb#define-input-and-output-schemas) for how to use. +By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [guide here](../how-tos/graph-api.md#define-input-and-output-schemas) for how to use. #### Multiple schemas @@ -56,9 +56,9 @@ Typically, all graph nodes communicate with a single schema. This means that the - Internal nodes can pass information that is not required in the graph's input / output. - We may also want to use different input / output schemas for the graph. The output might, for example, only contain a single relevant output key. -It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`. See [this guide](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) for more detail. +It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`. See [this guide](../how-tos/graph-api.md#pass-private-state-between-nodes) for more detail. -It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this guide](../how-tos/graph-api.ipynb#define-input-and-output-schemas) for more detail. +It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this guide](../how-tos/graph-api.md#define-input-and-output-schemas) for more detail. Let's look at an example: @@ -406,7 +406,7 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]: When returning `Command` in your node functions, you must add return type annotations with the list of node names the node is routing to, e.g. `Command[Literal["my_other_node"]]`. This is necessary for the graph rendering and tells LangGraph that `my_node` can navigate to `my_other_node`. -Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`. +Check out this [how-to guide](../how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`. ### When should I use Command instead of conditional edges? @@ -433,17 +433,17 @@ def my_node(state: State) -> Command[Literal["other_subgraph"]]: !!! important "State updates with `Command.PARENT`" - When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](#schema), you **must** define a [reducer](#reducers) for the key you're updating in the parent graph state. See this [example](../how-tos/graph-api.ipynb#navigate-to-a-node-in-a-parent-graph). + When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](#schema), you **must** define a [reducer](#reducers) for the key you're updating in the parent graph state. See this [example](../how-tos/graph-api.md#navigate-to-a-node-in-a-parent-graph). This is particularly useful when implementing [multi-agent handoffs](./multi_agent.md#handoffs). -Check out [this guide](../how-tos/graph-api.ipynb#navigate-to-a-node-in-a-parent-graph) for detail. +Check out [this guide](../how-tos/graph-api.md#navigate-to-a-node-in-a-parent-graph) for detail. ### Using inside tools A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. -Refer to [this guide](../how-tos/graph-api.ipynb#use-inside-tools) for detail. +Refer to [this guide](../how-tos/graph-api.md#use-inside-tools) for detail. ### Human-in-the-loop @@ -489,7 +489,7 @@ def node_a(state, config): ... ``` -See [this guide](../how-tos/graph-api.ipynb#add-runtime-configuration) for a full breakdown on configuration. +See [this guide](../how-tos/graph-api.md#add-runtime-configuration) for a full breakdown on configuration. ### Recursion Limit @@ -503,4 +503,4 @@ Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-li ## Visualization -It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/graph-api.ipynb#visualize-your-graph) for more info. +It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/graph-api.md#visualize-your-graph) for more info. diff --git a/docs/docs/concepts/mcp.md b/docs/docs/concepts/mcp.md index a82356462..4b05d008e 100644 --- a/docs/docs/concepts/mcp.md +++ b/docs/docs/concepts/mcp.md @@ -53,5 +53,5 @@ sequenceDiagram LangGraph -->> ClientApp: 12. Return resources / tool output ``` -For more information, see [MCP endpoint in LangGraph Server](../concepts/server-mcp.md#use-mcp-tools-in-your-deployment). +For more information, see [MCP endpoint in LangGraph Server](../concepts/server-mcp.md#use-user-scoped-mcp-tools-in-your-deployment). diff --git a/docs/docs/concepts/multi_agent.md b/docs/docs/concepts/multi_agent.md index 0cd4e7f61..81967f610 100644 --- a/docs/docs/concepts/multi_agent.md +++ b/docs/docs/concepts/multi_agent.md @@ -166,7 +166,7 @@ network = builder.compile() ### Supervisor -In this architecture, we define agents as nodes and add a supervisor node (LLM) that decides which agent nodes should be called next. We use [`Command`](./low_level.md#command) to route execution to the appropriate agent node based on supervisor's decision. This architecture also lends itself well to running multiple agents in parallel or using [map-reduce](../how-tos/graph-api.ipynb#map-reduce-and-the-send-api) pattern. +In this architecture, we define agents as nodes and add a supervisor node (LLM) that decides which agent nodes should be called next. We use [`Command`](./low_level.md#command) to route execution to the appropriate agent node based on supervisor's decision. This architecture also lends itself well to running multiple agents in parallel or using [map-reduce](../how-tos/graph-api.md#map-reduce-and-the-send-api) pattern. ```python from typing import Literal @@ -414,5 +414,5 @@ There are two high-level approaches to achieve that: An agent might need to have a different state schema from the rest of the agents. For example, a search agent might only need to keep track of queries and retrieved documents. There are two ways to achieve this in LangGraph: -- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it’s important to [add input / output transformations](../how-tos/subgraph.ipynb#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs. -- Define agent node functions with a [private input state schema](../how-tos/graph-api.ipynb/#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent. +- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it’s important to [add input / output transformations](../how-tos/subgraph.md#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs. +- Define agent node functions with a [private input state schema](../how-tos/graph-api.md/#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent. diff --git a/docs/docs/concepts/server-mcp.md b/docs/docs/concepts/server-mcp.md index ba1ebf49d..0d40bfb2e 100644 --- a/docs/docs/concepts/server-mcp.md +++ b/docs/docs/concepts/server-mcp.md @@ -198,7 +198,7 @@ print(graph.invoke({"question": "hi"})) For more details, see the [low-level concepts guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#state). -## Use User-Scoped MCP tools in your deployment +## Use user-scoped MCP tools in your deployment !!! tip "Prerequisites" diff --git a/docs/docs/concepts/subgraphs.md b/docs/docs/concepts/subgraphs.md index 6a4aefb23..218bf8cac 100644 --- a/docs/docs/concepts/subgraphs.md +++ b/docs/docs/concepts/subgraphs.md @@ -12,7 +12,7 @@ Some reasons for using subgraphs are: The main question when adding subgraphs is how the parent graph and subgraph communicate, i.e. how they pass the [state](./low_level.md#state) between each other during the graph execution. There are two scenarios: -* parent and subgraph have **shared state keys** in their state [schemas](./low_level.md#state). In this case, you can [include the subgraph as a node in the parent graph](../how-tos/subgraph.ipynb#shared-state-schemas) +* parent and subgraph have **shared state keys** in their state [schemas](./low_level.md#state). In this case, you can [include the subgraph as a node in the parent graph](../how-tos/subgraph.md#shared-state-schemas) ```python from langgraph.graph import StateGraph, MessagesState, START @@ -40,7 +40,7 @@ The main question when adding subgraphs is how the parent graph and subgraph com graph.invoke({"messages": [{"role": "user", "content": "hi!"}]}) ``` -* parent graph and subgraph have **different schemas** (no shared state keys in their state [schemas](./low_level.md#state)). In this case, you have to [call the subgraph from inside a node in the parent graph](../how-tos/subgraph.ipynb#different-state-schemas): this is useful when the parent graph and the subgraph have different state schemas and you need to transform state before or after calling the subgraph +* parent graph and subgraph have **different schemas** (no shared state keys in their state [schemas](./low_level.md#state)). In this case, you have to [call the subgraph from inside a node in the parent graph](../how-tos/subgraph.md#different-state-schemas): this is useful when the parent graph and the subgraph have different state schemas and you need to transform state before or after calling the subgraph ```python from typing_extensions import TypedDict, Annotated diff --git a/docs/docs/how-tos/assets/graph_api_image_1.png b/docs/docs/how-tos/assets/graph_api_image_1.png new file mode 100644 index 0000000000000000000000000000000000000000..c1ac60da95c60406ff97447b87acbd758e64929f GIT binary patch literal 3954 zcmY*ccTm%9v;BdT2;rqlmktI3QYAEL0@4KOy(3aXuc1gsK$Idy2%-T55$V0YNReJb z4PA=Tg#glX^Ih-EH}{Wyc4p`7o_S{Xk8@r=)p<-o#!LnP0ELFSiatKO;Uf-2h<}Yp z*GTb+z*qmVGVpbTbqfF}1vONZ41@CakP(KoCheV`V(B*NHatW$>o7`cIu#4Q(g`vs z=DsTA@uj;^*mhZd^Rs#1=^G4tfQRmA^F#N*o%*nfvwHVYmlL_V%L~n#X5&jii^z|* z%a{TVg(wK8dU&b^U%$3f+BNrmf8GvtK1VO?`REW*)<$6=rwQ8@xL?_7Ev!yOyct`u0SaRkYkiiIZ4d|kBV5(lk%v+_w#Q(;3Np0+{@-KZj$_& 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\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM aps built with LangGraph — read more about how to get started in the docs. \n", - "

\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "b462f26d-8795-4dc2-9420-722d3e21656c", - "metadata": {}, - "source": [ - "## Define and update state\n", - "\n", - "Here we show how to define and update [state](../../concepts/low_level/#state) in LangGraph. We will demonstrate:\n", - "\n", - "1. How to use state to define a graph's [schema](../../concepts/low_level/#schema)\n", - "2. How to use [reducers](../../concepts/low_level/#reducers) to control how state updates are processed." - ] - }, - { - "cell_type": "markdown", - "id": "ca7ac66a-a3ae-43c6-bb9d-7ee2bd0030f0", - "metadata": {}, - "source": [ - "### Define state\n", - "\n", - "[State](../../concepts/low_level/#state) in LangGraph can be a `TypedDict`, `Pydantic` model, or dataclass. Below we will use `TypedDict`. See [this section](#use-pydantic-models-for-graph-state) for detail on using Pydantic.\n", - "\n", - "By default, graphs will have the same input and output schema, and the state determines that schema. See [this section](#define-input-and-output-schemas) for how to define distinct input and output schemas.\n", - "\n", - "Let's consider a simple example using [messages](../../concepts/low_level/#messagesstate). This represents a versatile formulation of state for many LLM applications. See our [concepts page](../../concepts/low_level/#working-with-messages-in-graph-state) for more detail." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e7c3b392-50fb-4af3-bf2d-7b769f47efc6", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.messages import AnyMessage\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "class State(TypedDict):\n", - " messages: list[AnyMessage]\n", - " extra_field: int" - ] - }, - { - "cell_type": "markdown", - "id": "c3555791-9dc9-4593-923e-9aa599d4c547", - "metadata": {}, - "source": [ - "This state tracks a list of [message](https://python.langchain.com/docs/concepts/messages/) objects, as well as an extra integer field.\n", - "\n", - "### Update state\n", - "\n", - "Let's build an example graph with a single node. Our [node](../../concepts/low_level/#nodes) is just a Python function that reads our graph's state and makes updates to it. The first argument to this function will always be the state:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f5db493b-c977-4f15-a06f-27b460cd7e4c", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.messages import AIMessage\n", - "\n", - "\n", - "def node(state: State):\n", - " messages = state[\"messages\"]\n", - " new_message = AIMessage(\"Hello!\")\n", - "\n", - " return {\"messages\": messages + [new_message], \"extra_field\": 10}" - ] - }, - { - "cell_type": "markdown", - "id": "9f6cef5d-2635-45bc-a415-b422bb571685", - "metadata": {}, - "source": [ - "This node simply appends a message to our message list, and populates an extra field.\n", - "\n", - "!!! important\n", - "\n", - " Nodes should return updates to the state directly, instead of mutating the state.\n", - "\n", - "Let's next define a simple graph containing this node. We use [StateGraph](../../concepts/low_level/#stategraph) to define a graph that operates on this state. We then use [add_node](../../concepts/low_level/#nodes) populate our graph." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "92402ca2-9e46-4ad9-8378-83f98f597c00", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(node)\n", - "builder.set_entry_point(\"node\")\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "f765d37d-3793-4ac9-90e5-fe28c6142202", - "metadata": {}, - "source": [ - "LangGraph provides built-in utilities for visualizing your graph. Let's inspect our graph. See [this section](#visualize-your-graph) for detail on visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "263470d0-a9fa-48bc-86b1-c5a28b242aec", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "ca3cd1a1-38b1-4cbb-9301-5aec62659e70", - "metadata": {}, - "source": [ - "In this case, our graph just executes a single node. Let's proceed with a simple invocation:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7d932703-932f-48cb-842c-c372f82510f9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'messages': [HumanMessage(content='Hi', additional_kwargs={}, response_metadata={}),\n", - " AIMessage(content='Hello!', additional_kwargs={}, response_metadata={})],\n", - " 'extra_field': 10}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n", - "\n", - "result = graph.invoke({\"messages\": [HumanMessage(\"Hi\")]})\n", - "result" - ] - }, - { - "cell_type": "markdown", - "id": "e5918e84-49ac-4ab1-80bb-863a2e5461e2", - "metadata": {}, - "source": [ - "Note that:\n", - "\n", - "- We kicked off invocation by updating a single key of the state.\n", - "- We receive the entire state in the invocation result.\n", - "\n", - "For convenience, we frequently inspect the content of [message objects](https://python.langchain.com/docs/concepts/messages/) via pretty-print:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5009dbb2-77c3-47de-9d9d-3fc6e5b649b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Hi\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Hello!\n" - ] - } - ], - "source": [ - "for message in result[\"messages\"]:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "4c4da6ff-a677-41bf-b51e-f2826fc51926", - "metadata": {}, - "source": [ - "### Process state updates with reducers\n", - "\n", - "Each key in the state can have its own independent [reducer](../../concepts/low_level/#reducers) function, which controls how updates from nodes are applied. If no reducer function is explicitly specified then it is assumed that all updates to the key should override it.\n", - "\n", - "For `TypedDict` state schemas, we can define reducers by annotating the corresponding field of the state with a reducer function.\n", - "\n", - "In the earlier example, our node updated the `\"messages\"` key in the state by appending a message to it. Below, we add a reducer to this key, such that updates are automatically appended:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "35db8bfc-8747-423f-858a-b4f069d6199f", - "metadata": {}, - "outputs": [], - "source": [ - "from typing_extensions import Annotated\n", - "\n", - "\n", - "def add(left, right):\n", - " \"\"\"Can also import `add` from the `operator` built-in.\"\"\"\n", - " return left + right\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # highlight-next-line\n", - " messages: Annotated[list[AnyMessage], add]\n", - " extra_field: int" - ] - }, - { - "cell_type": "markdown", - "id": "4979a906-fff3-48de-9215-8a12b9bc4acf", - "metadata": {}, - "source": [ - "Now our node can be simplified:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f24f8d3f-973f-468e-8895-8c4cf01951dd", - "metadata": {}, - "outputs": [], - "source": [ - "def node(state: State):\n", - " new_message = AIMessage(\"Hello!\")\n", - " # highlight-next-line\n", - " return {\"messages\": [new_message], \"extra_field\": 10}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6ef429ed-a0fa-4c00-a88c-bb61df59f578", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Hi\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Hello!\n" - ] - } - ], - "source": [ - "from langgraph.graph import START\n", - "\n", - "\n", - "graph = StateGraph(State).add_node(node).add_edge(START, \"node\").compile()\n", - "\n", - "result = graph.invoke({\"messages\": [HumanMessage(\"Hi\")]})\n", - "\n", - "for message in result[\"messages\"]:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "7827723b-64aa-4d8b-b17a-5a3bcd8206f7", - "metadata": {}, - "source": [ - "#### MessagesState\n", - "\n", - "In practice, there are additional considerations for updating lists of messages:\n", - "\n", - "- We may wish to update an existing message in the state.\n", - "- We may want to accept short-hands for [message formats](../../concepts/low_level/#using-messages-in-your-graph), such as [OpenAI format](https://python.langchain.com/docs/concepts/messages/#openai-format).\n", - "\n", - "LangGraph includes a built-in reducer `add_messages` that handles these considerations:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "89880b92-5d00-421f-83c3-46382c1d1c17", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph.message import add_messages\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # highlight-next-line\n", - " messages: Annotated[list[AnyMessage], add_messages]\n", - " extra_field: int\n", - "\n", - "\n", - "def node(state: State):\n", - " new_message = AIMessage(\"Hello!\")\n", - " return {\"messages\": [new_message], \"extra_field\": 10}\n", - "\n", - "\n", - "graph = StateGraph(State).add_node(node).set_entry_point(\"node\").compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "51aeb731-e7f0-4d28-a096-436ef7fe004a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Hi\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Hello!\n" - ] - } - ], - "source": [ - "# highlight-next-line\n", - "input_message = {\"role\": \"user\", \"content\": \"Hi\"}\n", - "\n", - "result = graph.invoke({\"messages\": [input_message]})\n", - "\n", - "for message in result[\"messages\"]:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "17583dde-2520-464c-a962-eae511d0928e", - "metadata": {}, - "source": [ - "This is a versatile representation of state for applications involving [chat models](https://python.langchain.com/docs/concepts/chat_models/). LangGraph includes a pre-built `MessagesState` for convenience, so that we can have:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "05956122-5ba0-4b4c-8b66-6362982227f2", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import MessagesState\n", - "\n", - "\n", - "class State(MessagesState):\n", - " extra_field: int" - ] - }, - { - "cell_type": "markdown", - "id": "f262985e-e973-4a27-9c9e-dbb3a06a35b7", - "metadata": {}, - "source": [ - "### Define input and output schemas\n", - "\n", - "By default, `StateGraph` operates with a single schema, and all nodes are expected to communicate using that schema. However, it's also possible to define distinct input and output schemas for a graph.\n", - "\n", - "When distinct schemas are specified, an internal schema will still be used for communication between nodes. The input schema ensures that the provided input matches the expected structure, while the output schema filters the internal data to return only the relevant information according to the defined output schema.\n", - "\n", - "Below, we'll see how to define distinct input and output schema." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ec0eb77-874e-443e-8c73-93125b515106", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'answer': 'bye'}\n" - ] - } - ], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "# Define the schema for the input\n", - "class InputState(TypedDict):\n", - " question: str\n", - "\n", - "\n", - "# Define the schema for the output\n", - "class OutputState(TypedDict):\n", - " answer: str\n", - "\n", - "\n", - "# Define the overall schema, combining both input and output\n", - "class OverallState(InputState, OutputState):\n", - " pass\n", - "\n", - "\n", - "# Define the node that processes the input and generates an answer\n", - "def answer_node(state: InputState):\n", - " # Example answer and an extra key\n", - " return {\"answer\": \"bye\", \"question\": state[\"question\"]}\n", - "\n", - "\n", - "# Build the graph with input and output schemas specified\n", - "builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)\n", - "builder.add_node(answer_node) # Add the answer node\n", - "builder.add_edge(START, \"answer_node\") # Define the starting edge\n", - "builder.add_edge(\"answer_node\", END) # Define the ending edge\n", - "graph = builder.compile() # Compile the graph\n", - "\n", - "# Invoke the graph with an input and print the result\n", - "print(graph.invoke({\"question\": \"hi\"}))" - ] - }, - { - "cell_type": "markdown", - "id": "6a68836f-98e1-4684-a8a6-c1473c73460c", - "metadata": {}, - "source": [ - "Notice that the output of invoke only includes the output schema." - ] - }, - { - "cell_type": "markdown", - "id": "47ed5db3-bda5-49e1-bf75-23e08c9a3af0", - "metadata": {}, - "source": [ - "### Pass private state between nodes\n", - "\n", - "In some cases, you may want nodes to exchange information that is crucial for intermediate logic but doesn’t need to be part of the main schema of the graph. This private data is not relevant to the overall input/output of the graph and should only be shared between certain nodes.\n", - "\n", - "Below, we'll create an example sequential graph consisting of three nodes (node_1, node_2 and node_3), where private data is passed between the first two steps (node_1 and node_2), while the third step (node_3) only has access to the public overall state." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ce5b944d-4597-4af9-a7b5-15a00325f3e0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Entered node `node_1`:\n", - "\tInput: {'a': 'set at start'}.\n", - "\tReturned: {'private_data': 'set by node_1'}\n", - "Entered node `node_2`:\n", - "\tInput: {'private_data': 'set by node_1'}.\n", - "\tReturned: {'a': 'set by node_2'}\n", - "Entered node `node_3`:\n", - "\tInput: {'a': 'set by node_2'}.\n", - "\tReturned: {'a': 'set by node_3'}\n", - "\n", - "Output of graph invocation: {'a': 'set by node_3'}\n" - ] - } - ], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "# The overall state of the graph (this is the public state shared across nodes)\n", - "class OverallState(TypedDict):\n", - " a: str\n", - "\n", - "\n", - "# Output from node_1 contains private data that is not part of the overall state\n", - "class Node1Output(TypedDict):\n", - " private_data: str\n", - "\n", - "\n", - "# The private data is only shared between node_1 and node_2\n", - "def node_1(state: OverallState) -> Node1Output:\n", - " output = {\"private_data\": \"set by node_1\"}\n", - " print(f\"Entered node `node_1`:\\n\\tInput: {state}.\\n\\tReturned: {output}\")\n", - " return output\n", - "\n", - "\n", - "# Node 2 input only requests the private data available after node_1\n", - "class Node2Input(TypedDict):\n", - " private_data: str\n", - "\n", - "\n", - "def node_2(state: Node2Input) -> OverallState:\n", - " output = {\"a\": \"set by node_2\"}\n", - " print(f\"Entered node `node_2`:\\n\\tInput: {state}.\\n\\tReturned: {output}\")\n", - " return output\n", - "\n", - "\n", - "# Node 3 only has access to the overall state (no access to private data from node_1)\n", - "def node_3(state: OverallState) -> OverallState:\n", - " output = {\"a\": \"set by node_3\"}\n", - " print(f\"Entered node `node_3`:\\n\\tInput: {state}.\\n\\tReturned: {output}\")\n", - " return output\n", - "\n", - "\n", - "# Connect nodes in a sequence\n", - "# node_2 accepts private data from node_1, whereas\n", - "# node_3 does not see the private data.\n", - "builder = StateGraph(OverallState).add_sequence([node_1, node_2, node_3])\n", - "builder.add_edge(START, \"node_1\")\n", - "graph = builder.compile()\n", - "\n", - "# Invoke the graph with the initial state\n", - "response = graph.invoke(\n", - " {\n", - " \"a\": \"set at start\",\n", - " }\n", - ")\n", - "\n", - "print()\n", - "print(f\"Output of graph invocation: {response}\")" - ] - }, - { - "cell_type": "markdown", - "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", - "metadata": {}, - "source": [ - "### Use Pydantic models for graph state\n", - "\n", - "A [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph) accepts a `state_schema` argument on initialization that specifies the \"shape\" of the state that the nodes in the graph can access and update.\n", - "\n", - "In our examples, we typically use a python-native `TypedDict` for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).\n", - "\n", - "Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/). can be used for `state_schema` to add run time validation on **inputs**.\n", - "\n", - "\n", - "
\n", - "

Known Limitations

\n", - "

\n", - "

    \n", - "
  • \n", - " Currently, the output of the graph will NOT be an instance of a pydantic model.\n", - "
    • \n", - "
  • \n", - " Run-time validation only occurs on inputs into nodes, not on the outputs.\n", - "
    • \n", - "
  • \n", - " The validation error trace from pydantic does not show which node the error arises in.\n", - "
    • \n", - "
\n", - "

\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "efc46b36-425c-49c3-9f9e-d9785c70b034", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'a': 'goodbye'}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from typing_extensions import TypedDict\n", - "\n", - "from pydantic import BaseModel\n", - "\n", - "\n", - "# The overall state of the graph (this is the public state shared across nodes)\n", - "class OverallState(BaseModel):\n", - " a: str\n", - "\n", - "\n", - "def node(state: OverallState):\n", - " return {\"a\": \"goodbye\"}\n", - "\n", - "\n", - "# Build the state graph\n", - "builder = StateGraph(OverallState)\n", - "builder.add_node(node) # node_1 is the first node\n", - "builder.add_edge(START, \"node\") # Start the graph with node_1\n", - "builder.add_edge(\"node\", END) # End the graph after node_1\n", - "graph = builder.compile()\n", - "\n", - "# Test the graph with a valid input\n", - "graph.invoke({\"a\": \"hello\"})" - ] - }, - { - "cell_type": "markdown", - "id": "25b594c2-8198-4f76-9606-ea47151ff9d1", - "metadata": {}, - "source": [ - "Invoke the graph with an **invalid** input" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "05d7d43b-0b71-4e25-af6f-61d1560a46cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "An exception was raised because `a` is an integer rather than a string.\n", - "1 validation error for OverallState\n", - "a\n", - " Input should be a valid string [type=string_type, input_value=123, input_type=int]\n", - " For further information visit https://errors.pydantic.dev/2.9/v/string_type\n" - ] - } - ], - "source": [ - "try:\n", - " graph.invoke({\"a\": 123}) # Should be a string\n", - "except Exception as e:\n", - " print(\"An exception was raised because `a` is an integer rather than a string.\")\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "572ee9f1-45d2-428e-9b70-e7befaf2da80", - "metadata": {}, - "source": [ - "See below for additional features of Pydantic model state:\n", - "\n", - "
\n", - "Serialization Behavior\n", - "\n", - "When using Pydantic models as state schemas, it's important to understand how serialization works, especially when:\n", - "
    \n", - "
  • Passing Pydantic objects as inputs
    • \n", - "
  • Receiving outputs from the graph
    • \n", - "
  • Working with nested Pydantic models
    • \n", - "
\n", - "Let's see these behaviors in action.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e919cdc", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from pydantic import BaseModel\n", - "\n", - "\n", - "class NestedModel(BaseModel):\n", - " value: str\n", - "\n", - "\n", - "class ComplexState(BaseModel):\n", - " text: str\n", - " count: int\n", - " nested: NestedModel\n", - "\n", - "\n", - "def process_node(state: ComplexState):\n", - " # Node receives a validated Pydantic object\n", - " print(f\"Input state type: {type(state)}\")\n", - " print(f\"Nested type: {type(state.nested)}\")\n", - "\n", - " # Return a dictionary update\n", - " return {\"text\": state.text + \" processed\", \"count\": state.count + 1}\n", - "\n", - "\n", - "# Build the graph\n", - "builder = StateGraph(ComplexState)\n", - "builder.add_node(\"process\", process_node)\n", - "builder.add_edge(START, \"process\")\n", - "builder.add_edge(\"process\", END)\n", - "graph = builder.compile()\n", - "\n", - "# Create a Pydantic instance for input\n", - "input_state = ComplexState(text=\"hello\", count=0, nested=NestedModel(value=\"test\"))\n", - "print(f\"Input object type: {type(input_state)}\")\n", - "\n", - "# Invoke graph with a Pydantic instance\n", - "result = graph.invoke(input_state)\n", - "print(f\"Output type: {type(result)}\")\n", - "print(f\"Output content: {result}\")\n", - "\n", - "# Convert back to Pydantic model if needed\n", - "output_model = ComplexState(**result)\n", - "print(f\"Converted back to Pydantic: {type(output_model)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f13f28ce", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
Runtime Type Coercion\n", - "\n", - "Pydantic performs runtime type coercion for certain data types. This can be helpful but also lead to unexpected behavior if you're not aware of it.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "faf59316", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from pydantic import BaseModel\n", - "\n", - "\n", - "class CoercionExample(BaseModel):\n", - " # Pydantic will coerce string numbers to integers\n", - " number: int\n", - " # Pydantic will parse string booleans to bool\n", - " flag: bool\n", - "\n", - "\n", - "def inspect_node(state: CoercionExample):\n", - " print(f\"number: {state.number} (type: {type(state.number)})\")\n", - " print(f\"flag: {state.flag} (type: {type(state.flag)})\")\n", - " return {}\n", - "\n", - "\n", - "builder = StateGraph(CoercionExample)\n", - "builder.add_node(\"inspect\", inspect_node)\n", - "builder.add_edge(START, \"inspect\")\n", - "builder.add_edge(\"inspect\", END)\n", - "graph = builder.compile()\n", - "\n", - "# Demonstrate coercion with string inputs that will be converted\n", - "result = graph.invoke({\"number\": \"42\", \"flag\": \"true\"})\n", - "\n", - "# This would fail with a validation error\n", - "try:\n", - " graph.invoke({\"number\": \"not-a-number\", \"flag\": \"true\"})\n", - "except Exception as e:\n", - " print(f\"\\nExpected validation error: {e}\")" - ] - }, - { - "cell_type": "markdown", - "id": "2844475b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
Working with Message Models\n", - "\n", - "When working with LangChain message types in your state schema, there are important considerations for serialization. You should use AnyMessage (rather than BaseMessage) for proper serialization/deserialization when using message objects over the wire.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd0734b0", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from pydantic import BaseModel\n", - "from langchain_core.messages import HumanMessage, AIMessage, AnyMessage\n", - "from typing import List\n", - "\n", - "\n", - "class ChatState(BaseModel):\n", - " messages: List[AnyMessage]\n", - " context: str\n", - "\n", - "\n", - "def add_message(state: ChatState):\n", - " return {\"messages\": state.messages + [AIMessage(content=\"Hello there!\")]}\n", - "\n", - "\n", - "builder = StateGraph(ChatState)\n", - "builder.add_node(\"add_message\", add_message)\n", - "builder.add_edge(START, \"add_message\")\n", - "builder.add_edge(\"add_message\", END)\n", - "graph = builder.compile()\n", - "\n", - "# Create input with a message\n", - "initial_state = ChatState(\n", - " messages=[HumanMessage(content=\"Hi\")], context=\"Customer support chat\"\n", - ")\n", - "\n", - "result = graph.invoke(initial_state)\n", - "print(f\"Output: {result}\")\n", - "\n", - "# Convert back to Pydantic model to see message types\n", - "output_model = ChatState(**result)\n", - "for i, msg in enumerate(output_model.messages):\n", - " print(f\"Message {i}: {type(msg).__name__} - {msg.content}\")" - ] - }, - { - "cell_type": "markdown", - "id": "c2e52e1f-c07a-4ebf-b28e-c370c8f50550", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6e6a0a39-9a4c-47ae-a238-1a3a847eea5b", - "metadata": {}, - "source": [ - "## Add runtime configuration\n", - "\n", - "Sometimes you want to be able to configure your graph when calling it. For example, you might want to be able to specify what LLM or system prompt to use at runtime, *without polluting the graph state with these parameters*.\n", - "\n", - "To add runtime configuration:\n", - "\n", - "1. Specify a schema for your configuration\n", - "2. Add the configuration to the function signature for nodes or conditional edges\n", - "3. Pass the configuration into the graph.\n", - "\n", - "See below for a simple example:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "97fc508b-1011-402e-8769-573ac3acb53a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'my_state_value': 1}\n", - "{'my_state_value': 2}\n" - ] - } - ], - "source": [ - "from langchain_core.runnables import RunnableConfig\n", - "from langgraph.graph import END, StateGraph, START\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "# 1. Specify config schema\n", - "class ConfigSchema(TypedDict):\n", - " my_runtime_value: str\n", - "\n", - "\n", - "# 2. Define a graph that accesses the config in a node\n", - "class State(TypedDict):\n", - " my_state_value: str\n", - "\n", - "\n", - "# highlight-next-line\n", - "def node(state: State, config: RunnableConfig):\n", - " # highlight-next-line\n", - " if config[\"configurable\"][\"my_runtime_value\"] == \"a\":\n", - " return {\"my_state_value\": 1}\n", - " # highlight-next-line\n", - " elif config[\"configurable\"][\"my_runtime_value\"] == \"b\":\n", - " return {\"my_state_value\": 2}\n", - " else:\n", - " raise ValueError(\"Unknown values.\")\n", - "\n", - "\n", - "# highlight-next-line\n", - "builder = StateGraph(State, config_schema=ConfigSchema)\n", - "builder.add_node(node)\n", - "builder.add_edge(START, \"node\")\n", - "builder.add_edge(\"node\", END)\n", - "\n", - "graph = builder.compile()\n", - "\n", - "# 3. Pass in configuration at runtime:\n", - "# highlight-next-line\n", - "print(graph.invoke({}, {\"configurable\": {\"my_runtime_value\": \"a\"}}))\n", - "# highlight-next-line\n", - "print(graph.invoke({}, {\"configurable\": {\"my_runtime_value\": \"b\"}}))" - ] - }, - { - "cell_type": "markdown", - "id": "f080f50b-cec1-4dba-81e0-63cf40c990ee", - "metadata": {}, - "source": [ - "
Extended example: specifying LLM at runtime\n", - "\n", - "Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff4c1453-8cff-4679-9574-8602c530144e", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph \"langchain[anthropic,openai]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c031a059-7279-4e75-9164-58325d76242f", - "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\")\n", - "_set_env(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "75ebea6f-e75f-42d4-9d32-68b37150bdde", - "metadata": {}, - "source": [ - "Build the graph:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a6033c2a-3b56-46f5-9c3f-79310e31e545", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "claude-3-5-haiku-20241022\n", - "gpt-4.1-mini-2025-04-14\n" - ] - } - ], - "source": [ - "from langchain.chat_models import init_chat_model\n", - "from langchain_core.runnables import RunnableConfig\n", - "from langgraph.graph import MessagesState\n", - "from langgraph.graph import END, StateGraph, START\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "class ConfigSchema(TypedDict):\n", - " model: str\n", - "\n", - "\n", - "MODELS = {\n", - " \"anthropic\": init_chat_model(\"anthropic:claude-3-5-haiku-latest\"),\n", - " \"openai\": init_chat_model(\"openai:gpt-4.1-mini\"),\n", - "}\n", - "\n", - "\n", - "def call_model(state: MessagesState, config: RunnableConfig):\n", - " model = config[\"configurable\"].get(\"model\", \"anthropic\")\n", - " model = MODELS[model]\n", - " response = model.invoke(state[\"messages\"])\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "builder = StateGraph(MessagesState, config_schema=ConfigSchema)\n", - "builder.add_node(\"model\", call_model)\n", - "builder.add_edge(START, \"model\")\n", - "builder.add_edge(\"model\", END)\n", - "\n", - "graph = builder.compile()\n", - "\n", - "# Usage\n", - "input_message = {\"role\": \"user\", \"content\": \"hi\"}\n", - "# With no configuration, uses default (Anthropic)\n", - "response_1 = graph.invoke({\"messages\": [input_message]})[\"messages\"][-1]\n", - "# Or, can set OpenAI\n", - "config = {\"configurable\": {\"model\": \"openai\"}}\n", - "response_2 = graph.invoke({\"messages\": [input_message]}, config=config)[\"messages\"][-1]\n", - "\n", - "print(response_1.response_metadata[\"model_name\"])\n", - "print(response_2.response_metadata[\"model_name\"])" - ] - }, - { - "cell_type": "markdown", - "id": "bd1ac2bc-9e3e-42c4-a68f-238c841e58d1", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
Extended example: specifying model and system message at runtime\n", - "\n", - "Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "367bcedd-047c-4150-a059-0a7630ab2663", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph \"langchain[anthropic,openai]\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5d0dd7d-9564-4a5f-aa2e-2f7be20e1647", - "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\")\n", - "_set_env(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "04924627-9326-438b-aa52-91cea76f4477", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "hi\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Ciao! Come posso aiutarti oggi?\n" - ] - } - ], - "source": [ - "from typing import Optional\n", - "\n", - "from langchain.chat_models import init_chat_model\n", - "from langchain_core.messages import SystemMessage\n", - "from langchain_core.runnables import RunnableConfig\n", - "from langgraph.graph import END, MessagesState, StateGraph, START\n", - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "class ConfigSchema(TypedDict):\n", - " model: Optional[str]\n", - " system_message: Optional[str]\n", - "\n", - "\n", - "MODELS = {\n", - " \"anthropic\": init_chat_model(\"anthropic:claude-3-5-haiku-latest\"),\n", - " \"openai\": init_chat_model(\"openai:gpt-4.1-mini\"),\n", - "}\n", - "\n", - "\n", - "def call_model(state: MessagesState, config: RunnableConfig):\n", - " model = config[\"configurable\"].get(\"model\", \"anthropic\")\n", - " model = MODELS[model]\n", - " messages = state[\"messages\"]\n", - " if system_message := config[\"configurable\"].get(\"system_message\"):\n", - " messages = [SystemMessage(system_message)] + messages\n", - " response = model.invoke(messages)\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "builder = StateGraph(MessagesState, config_schema=ConfigSchema)\n", - "builder.add_node(\"model\", call_model)\n", - "builder.add_edge(START, \"model\")\n", - "builder.add_edge(\"model\", END)\n", - "\n", - "graph = builder.compile()\n", - "\n", - "# Usage\n", - "input_message = {\"role\": \"user\", \"content\": \"hi\"}\n", - "config = {\"configurable\": {\"model\": \"openai\", \"system_message\": \"Respond in Italian.\"}}\n", - "response = graph.invoke({\"messages\": [input_message]}, config)\n", - "for message in response[\"messages\"]:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "03eac38a-fca6-4baf-9ccf-94bf16321235", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "94715e3d-e98b-4be0-ae5f-1169cb795ce5", - "metadata": {}, - "source": [ - "## Add retry policies\n", - "\n", - "There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. LangGraph lets you add retry policies to nodes.\n", - "\n", - "To configure a retry policy, pass the `retry_policy` parameter to the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node:\n", - "\n", - "```python\n", - "from langgraph.pregel import RetryPolicy\n", - "\n", - "builder.add_node(\n", - " \"node_name\",\n", - " node_function,\n", - " retry_policy=RetryPolicy(),\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "af144b8f-761a-4456-a356-ce0812e92577", - "metadata": {}, - "source": [ - "By default, the `retry_on` parameter uses the `default_retry_on` function, which retries on any exception except for the following:\n", - "\n", - "* `ValueError`\n", - "* `TypeError`\n", - "* `ArithmeticError`\n", - "* `ImportError`\n", - "* `LookupError`\n", - "* `NameError`\n", - "* `SyntaxError`\n", - "* `RuntimeError`\n", - "* `ReferenceError`\n", - "* `StopIteration`\n", - "* `StopAsyncIteration`\n", - "* `OSError`\n", - "\n", - "In addition, for exceptions from popular http request libraries such as `requests` and `httpx` it only retries on 5xx status codes.\n", - "\n", - "
Extended example: customizing retry policies\n", - "\n", - "Consider an example in which we are reading from a SQL database. Below we pass two different retry policies to nodes:\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad92598c-b688-42fa-aae0-9de36273d584", - "metadata": {}, - "outputs": [], - "source": [ - "import sqlite3\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langchain.chat_models import init_chat_model\n", - "\n", - "from langgraph.graph import END, MessagesState, StateGraph, START\n", - "from langgraph.pregel import RetryPolicy\n", - "from langchain_community.utilities import SQLDatabase\n", - "from langchain_core.messages import AIMessage\n", - "\n", - "db = SQLDatabase.from_uri(\"sqlite:///:memory:\")\n", - "\n", - "model = init_chat_model(\"anthropic:claude-3-5-haiku-latest\")\n", - "\n", - "\n", - "def query_database(state: MessagesState):\n", - " query_result = db.run(\"SELECT * FROM Artist LIMIT 10;\")\n", - " return {\"messages\": [AIMessage(content=query_result)]}\n", - "\n", - "\n", - "def call_model(state: MessagesState):\n", - " response = model.invoke(state[\"messages\"])\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "# Define a new graph\n", - "builder = StateGraph(MessagesState)\n", - "builder.add_node(\n", - " \"query_database\",\n", - " query_database,\n", - " retry_policy=RetryPolicy(retry_on=sqlite3.OperationalError),\n", - ")\n", - "builder.add_node(\"model\", call_model, retry_policy=RetryPolicy(max_attempts=5))\n", - "builder.add_edge(START, \"model\")\n", - "builder.add_edge(\"model\", \"query_database\")\n", - "builder.add_edge(\"query_database\", END)\n", - "\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "068f806a", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6d99d63c", - "metadata": {}, - "source": [ - "## Add node caching\n", - "\n", - "Node caching is useful in cases where you want to avoid repeating operations, like when doing something expensive (either in terms of time or cost). LangGraph lets you add individualized caching policies to nodes in a graph.\n", - "\n", - "To configure a cache policy, pass the `cache_policy` parameter to the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.StateGraph.add_node) function. In the following example, a [`CachePolicy`](https://langchain-ai.github.io/langgraph/reference/types/?h=cachepolicy#langgraph.types.CachePolicy) object is instantiated with a time to live of 120 seconds and the default `key_func` generator. Then it is associated with a node:\n", - "\n", - "```python\n", - "from langgraph.types import CachePolicy\n", - "\n", - "builder.add_node(\n", - " \"node_name\",\n", - " node_function,\n", - " cache_policy=CachePolicy(ttl=120),\n", - ")\n", - "```\n", - "\n", - "Then, to enable node-level caching for a graph, set the `cache` argument when compiling the graph. The example below uses `InMemoryCache` to set up a graph with in-memory cache, but `SqliteCache` is also available.\n", - "\n", - "```python\n", - "from langgraph.cache.memory import InMemoryCache\n", - "\n", - "\n", - "graph = builder.compile(cache=InMemoryCache())\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "e1a0213e-282f-4fad-b048-5f7465edfccb", - "metadata": {}, - "source": [ - "## Create a sequence of steps\n", - "\n", - "!!! info \"Prerequisites\"\n", - " This guide assumes familiarity with the above section on [state](#define-and-update-state).\n", - "\n", - "Here we demonstrate how to construct a simple sequence of steps. We will show:\n", - "\n", - "1. How to build a sequential graph\n", - "2. Built-in short-hand for constructing similar graphs.\n", - "\n", - "\n", - "To add a sequence of nodes, we use the `.add_node` and `.add_edge` methods of our [graph](../../concepts/low_level/#stategraph):\n", - "```python\n", - "from langgraph.graph import START, StateGraph\n", - "\n", - "builder = StateGraph(State)\n", - "\n", - "# Add nodes\n", - "builder.add_node(step_1)\n", - "builder.add_node(step_2)\n", - "builder.add_node(step_3)\n", - "\n", - "# Add edges\n", - "builder.add_edge(START, \"step_1\")\n", - "builder.add_edge(\"step_1\", \"step_2\")\n", - "builder.add_edge(\"step_2\", \"step_3\")\n", - "```\n", - "\n", - "We can also use the built-in shorthand `.add_sequence`:\n", - "```python\n", - "builder = StateGraph(State).add_sequence([step_1, step_2, step_3])\n", - "builder.add_edge(START, \"step_1\")\n", - "```\n", - "\n", - "\n", - "
\n", - "Why split application steps into a sequence with LangGraph?\n", - "\n", - "LangGraph makes it easy to add an underlying persistence layer to your application.\n", - "This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern:\n", - "\n", - "
    \n", - "
  • How state updates are [checkpointed](../../concepts/persistence/)
    • \n", - "
  • How interruptions are resumed in [human-in-the-loop](../../concepts/human_in_the_loop/) workflows
    • \n", - "
  • How we can \"rewind\" and branch-off executions using LangGraph's [time travel](../../concepts/time-travel/) features
    • \n", - "
\n", - "\n", - "They also determine how execution steps are [streamed](../../concepts/streaming/), and how your application is visualized\n", - "and debugged using [LangGraph Studio](../../concepts/langgraph_studio/).\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "518cb5d1-c60f-44d7-b348-e03b6e487098", - "metadata": {}, - "source": [ - "Let's demonstrate an end-to-end example. We will create a sequence of three steps:\n", - "\n", - "1. Populate a value in a key of the state\n", - "2. Update the same value\n", - "3. Populate a different value\n", - "\n", - "Let's first define our [state](../../concepts/low_level/#state). This governs the [schema of the graph](../../concepts/low_level/#schema), and can also specify how to apply updates. See [this section](#process-state-updates-with-reducers) for more detail.\n", - "\n", - "In our case, we will just keep track of two values:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "aa1b04c6-2653-4ad4-a720-facc1f2906a7", - "metadata": {}, - "outputs": [], - "source": [ - "from typing_extensions import TypedDict\n", - "\n", - "\n", - "class State(TypedDict):\n", - " value_1: str\n", - " value_2: int" - ] - }, - { - "cell_type": "markdown", - "id": "6d4a8554-bc19-4bbe-a8c2-20adcfca8273", - "metadata": {}, - "source": [ - "Our [nodes](../../concepts/low_level/#nodes) are just Python functions that read our graph's state and make updates to it. The first argument to this function will always be the state:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c7db921a-dbfb-4039-a93b-8b2264143a3f", - "metadata": {}, - "outputs": [], - "source": [ - "def step_1(state: State):\n", - " return {\"value_1\": \"a\"}\n", - "\n", - "\n", - "def step_2(state: State):\n", - " current_value_1 = state[\"value_1\"]\n", - " return {\"value_1\": f\"{current_value_1} b\"}\n", - "\n", - "\n", - "def step_3(state: State):\n", - " return {\"value_2\": 10}" - ] - }, - { - "cell_type": "markdown", - "id": "2b454eb0-19c1-418e-a912-dc44c0c045e2", - "metadata": {}, - "source": [ - "!!! note\n", - "\n", - " Note that when issuing updates to the state, each node can just specify the value of the key it wishes to update.\n", - "\n", - "By default, this will **overwrite** the value of the corresponding key. You can also use [reducers](../../concepts/low_level/#reducers) to control how updates are processed— for example, you can append successive updates to a key instead. See [this section](#process-state-updates-with-reducers) for more detail.\n", - "\n", - "Finally, we define the graph. We use [StateGraph](../../concepts/low_level/#stategraph) to define a graph that operates on this state.\n", - "\n", - "We will then use [add_node](../../concepts/low_level/#messagesstate) and [add_edge](../../concepts/low_level/#edges) to populate our graph and define its control flow." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b02bdbcf-2bbb-4f08-b177-1a45b2a8bc6d", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import START, StateGraph\n", - "\n", - "builder = StateGraph(State)\n", - "\n", - "# Add nodes\n", - "builder.add_node(step_1)\n", - "builder.add_node(step_2)\n", - "builder.add_node(step_3)\n", - "\n", - "# Add edges\n", - "builder.add_edge(START, \"step_1\")\n", - "builder.add_edge(\"step_1\", \"step_2\")\n", - "builder.add_edge(\"step_2\", \"step_3\")" - ] - }, - { - "cell_type": "markdown", - "id": "79a696b2-ef9b-4f8d-ae83-ea674a16bab0", - "metadata": {}, - "source": [ - "!!! tip \"Specifying custom names\"\n", - "\n", - " You can specify custom names for nodes using `.add_node`:\n", - "\n", - " ```python\n", - " builder.add_node(\"my_node\", step_1)\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "id": "7452c5ea-cf1b-47a5-8da0-32479c142dce", - "metadata": {}, - "source": [ - "Note that:\n", - "\n", - "- `.add_edge` takes the names of nodes, which for functions defaults to `node.__name__`.\n", - "- We must specify the entry point of the graph. For this we add an edge with the [START node](../../concepts/low_level/#start-node).\n", - "- The graph halts when there are no more nodes to execute.\n", - "\n", - "We next [compile](../../concepts/low_level/#compiling-your-graph) our graph. This provides a few basic checks on the structure of the graph (e.g., identifying orphaned nodes). If we were adding persistence to our application via a [checkpointer](../../concepts/persistence/), it would also be passed in here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7aa2828c-0903-4775-b4ea-d62647f3bf0a", - "metadata": {}, - "outputs": [], - "source": [ - "graph = builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "4c85772e-788c-49df-9327-11cb92f02c6a", - "metadata": {}, - "source": [ - "LangGraph provides built-in utilities for visualizing your graph. Let's inspect our sequence. See [this guide](../../how-tos/visualization) for detail on visualization." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4162bc81-cbfd-49e3-b79f-8a97b9f417c2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "9406b427-c28c-4cd0-9f01-e5bc1f089f4c", - "metadata": {}, - "source": [ - "Let's proceed with a simple invocation:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3f7012ae-4f8f-4dd3-9f99-ebd179ff5fe9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'value_1': 'a b', 'value_2': 10}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"value_1\": \"c\"})" - ] - }, - { - "cell_type": "markdown", - "id": "648421de-e43a-4242-8e62-afd84bc91b7e", - "metadata": {}, - "source": [ - "Note that:\n", - "\n", - "- We kicked off invocation by providing a value for a single state key. We must always provide a value for at least one key.\n", - "- The value we passed in was overwritten by the first node.\n", - "- The second node updated the value.\n", - "- The third node populated a different value." - ] - }, - { - "cell_type": "markdown", - "id": "1acdf7f1-d2f8-4863-a7f3-d5de4cc0ef28", - "metadata": {}, - "source": [ - "!!! tip \"Built-in shorthand\"\n", - "\n", - " `langgraph>=0.2.46` includes a built-in short-hand `add_sequence` for adding node sequences. You can compile the same graph as follows:\n", - "\n", - " ```python\n", - " # highlight-next-line\n", - " builder = StateGraph(State).add_sequence([step_1, step_2, step_3])\n", - " builder.add_edge(START, \"step_1\")\n", - " \n", - " graph = builder.compile()\n", - " \n", - " graph.invoke({\"value_1\": \"c\"}) \n", - " ```" - ] - }, - { - "attachments": { - "51f122de-b2ce-4c21-a5a7-c3be70c28a91.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "710dc4f0-1c88-4386-9e9d-fec3de6bb774", - "metadata": {}, - "source": [ - "## Create branches\n", - "\n", - "Parallel execution of nodes is essential to speed up overall graph operation. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out and fan-in mechanisms, utilizing both standard edges and [conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph.add_conditional_edges). Below are some examples showing how to add create branching dataflows that work for you. \n", - "\n", - "![Screenshot 2024-07-09 at 2.55.56 PM.png](attachment:51f122de-b2ce-4c21-a5a7-c3be70c28a91.png)" - ] - }, - { - "cell_type": "markdown", - "id": "d6c05fc4-ecd8-483f-a9fd-b1a055f922d9", - "metadata": {}, - "source": [ - "### Run graph nodes in parallel\n", - "\n", - "In this example, we fan out from `Node A` to `B and C` and then fan in to `D`. With our state, [we specify the reducer add operation](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers). This will combine or accumulate values for the specific key in the State, rather than simply overwriting the existing value. For lists, this means concatenating the new list with the existing list. See the above section on [state reducers](#process-state-updates-with-reducers) for more detail on updating state with reducers." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "09372b8b-edea-4b9d-9ec3-3d93ce1ba819", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Any\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # The operator.add reducer fn makes this append-only\n", - " aggregate: Annotated[list, operator.add]\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Adding \"A\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Adding \"B\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "def c(state: State):\n", - " print(f'Adding \"C\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"C\"]}\n", - "\n", - "\n", - "def d(state: State):\n", - " print(f'Adding \"D\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"D\"]}\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(c)\n", - "builder.add_node(d)\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_edge(\"a\", \"b\")\n", - "builder.add_edge(\"a\", \"c\")\n", - "builder.add_edge(\"b\", \"d\")\n", - "builder.add_edge(\"c\", \"d\")\n", - "builder.add_edge(\"d\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "66f52a20", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "74dd577b-0474-44c4-b4bc-9113090e3121", - "metadata": {}, - "source": [ - "With the reducer, you can see that the values added in each node are accumulated." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "81646784-5e7d-4096-980d-9fdfafd6e7a3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"B\" to ['A']\n", - "Adding \"C\" to ['A']\n", - "Adding \"D\" to ['A', 'B', 'C']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'B', 'C', 'D']}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": []}, {\"configurable\": {\"thread_id\": \"foo\"}})" - ] - }, - { - "cell_type": "markdown", - "id": "ea5495cf-9564-40c6-bc2d-0b2a8f72a5df", - "metadata": {}, - "source": [ - "!!! note\n", - "\n", - " In the above example, nodes `\"b\"` and `\"c\"` are executed concurrently in the same [superstep](../../concepts/low_level/#graphs). Because they are in the same step, node `\"d\"` executes after both `\"b\"` and `\"c\"` are finished.\n", - "\n", - " Importantly, updates from a parallel superstep may not be ordered consistently. If you need a consistent, predetermined ordering of updates from a parallel superstep, you should write the outputs to a separate field in the state together with a value with which to order them." - ] - }, - { - "cell_type": "markdown", - "id": "c392b3d2", - "metadata": {}, - "source": [ - "
Exception handling?\n", - "

LangGraph executes nodes within \"supersteps\", meaning that while parallel branches are executed in parallel, the entire superstep is transactional. If any of these branches raises an exception, none of the updates are applied to the state (the entire superstep errors).

\n", - "

Importantly, when using a checkpointer, results from successful nodes within a superstep are saved, and don't repeat when resumed.

\n", - " If you have error-prone (perhaps want to handle flakey API calls), LangGraph provides two ways to address this:
\n", - "
    \n", - "
  1. You can write regular python code within your node to catch and handle exceptions.
    2. \n", - "
  2. You can set a retry_policy to direct the graph to retry nodes that raise certain types of exceptions. Only failing branches are retried, so you needn't worry about performing redundant work.
    3. \n", - "

\n", - "Together, these let you perform parallel execution and fully control exception handling.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "48731230", - "metadata": {}, - "source": [ - "### Defer node execution\n", - "\n", - "Deferring node execution is useful when you want to delay the execution of a node until all other pending tasks are completed. This is particularly relevant when branches have different lengths, which is common in workflows like map-reduce flows.\n", - "\n", - "The above example showed how to fan-out and fan-in when each path was only one step. But what if one branch had more than one step? Let's add a node `\"b_2\"` in the `\"b\"` branch:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "3890af2f-fb14-4569-b48d-a91db2d3f026", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Any\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # The operator.add reducer fn makes this append-only\n", - " aggregate: Annotated[list, operator.add]\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Adding \"A\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Adding \"B\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "def b_2(state: State):\n", - " print(f'Adding \"B_2\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B_2\"]}\n", - "\n", - "\n", - "def c(state: State):\n", - " print(f'Adding \"C\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"C\"]}\n", - "\n", - "\n", - "def d(state: State):\n", - " print(f'Adding \"D\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"D\"]}\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(b_2)\n", - "builder.add_node(c)\n", - "# highlight-next-line\n", - "builder.add_node(d, defer=True)\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_edge(\"a\", \"b\")\n", - "builder.add_edge(\"a\", \"c\")\n", - "builder.add_edge(\"b\", \"b_2\")\n", - "builder.add_edge(\"b_2\", \"d\")\n", - "builder.add_edge(\"c\", \"d\")\n", - "builder.add_edge(\"d\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1a3508e6-bcaf-448e-bdc8-bf5701589d42", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b510379a-b82a-4658-973e-df56caf5cd01", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"B\" to ['A']\n", - "Adding \"C\" to ['A']\n", - "Adding \"B_2\" to ['A', 'B', 'C']\n", - "Adding \"D\" to ['A', 'B', 'C', 'B_2']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'B', 'C', 'B_2', 'D']}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": []})" - ] - }, - { - "cell_type": "markdown", - "id": "70e67ced", - "metadata": {}, - "source": [ - "In the above example, nodes `\"b\"` and `\"c\"` are executed concurrently in the same superstep. We set `defer=True` on node `d` so it will not execute until all pending tasks are finished. In this case, this means that `\"d\"` waits to execute until the entire `\"b\"` branch is finished." - ] - }, - { - "cell_type": "markdown", - "id": "1a940eec-f36f-4236-9cc8-8d5dfd5cc860", - "metadata": {}, - "source": [ - "### Conditional branching\n", - "\n", - "If your fan-out should vary at runtime based on the state, you can use [add_conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph.add_conditional_edges) to select one or more paths using the graph state. See example below, where node `a` generates a state update that determines the following node." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8b270199-f07d-4831-9674-f18715fa26de", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Literal, Sequence\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " aggregate: Annotated[list, operator.add]\n", - " # Add a key to the state. We will set this key to determine\n", - " # how we branch.\n", - " which: str\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Adding \"A\" to {state[\"aggregate\"]}')\n", - " # highlight-next-line\n", - " return {\"aggregate\": [\"A\"], \"which\": \"c\"}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Adding \"B\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "def c(state: State):\n", - " print(f'Adding \"C\" to {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"C\"]}\n", - "\n", - "\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(c)\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_edge(\"b\", END)\n", - "builder.add_edge(\"c\", END)\n", - "\n", - "\n", - "def conditional_edge(state: State) -> Literal[\"b\", \"c\"]:\n", - " # Fill in arbitrary logic here that uses the state\n", - " # to determine the next node\n", - " return state[\"which\"]\n", - "\n", - "\n", - "# highlight-next-line\n", - "builder.add_conditional_edges(\"a\", conditional_edge)\n", - "\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "43999312-0198-49e4-86a9-4f71e343ed64", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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420cIn+TBAh6PpOXiVovTYnxwUowbNTn3mzyowNJSejwwKTxPhwe0t66e1l481qSusUrlzG84IJLwGmutsR2kD2UFJ2XIELe5UNB48WiTE1KL6YaJ5+nQVt46naeur7INfjRaEYpxh1ltg/X0PpXJ4OicGYS4ypHtKofDNWJ2HL6k8CQd7pu3Tu1Taxvs/R6ORKxw+OvqDl1lXfpzUdihr+uEUv5DWWEIP22nQ+tRRmOdtb7SwpoqYFhOzLVzOqgVEMeoummy2xA7qtCD0qF1W6DK5WK78rdZXfDvIo6hum3hCVhNijbSoXVb+iZHRALb+2XFJAVq6jl3RLtR5wiPZTUp2kiH1m3ZLE6bme1CyWxwOG2c6wiGeN1uZTUp2kgHeqITSVBbJEFtkQS1RRLUFklQWyRBbZEEtUUS1BZJUFskQW2RBGPzMiZmD9/wJUvby/otNG+RBLVFEkzacjodH61990D+XqvV0rtXvxXPvKRUBiP/w2az/Wv9P/IO7NHrdSkp5Xs4pgAADIZJREFUqU88tjQjg5kjZpmcT/j9vu+cLuf/vvO3Z1e+cv7Cjx/89R3kl/z9k/f37N25aOHTH7z/aVxcwrOrnqytZeYQKCbzVmhI2NInV8KDtNTON24Ub9220Ww2E3dm03+IwWAAVU88vmzY0JHw9JnlL5qMxurqyqgoBs5NZtJW1649mh936dzNbrdXVd1OTk5B/kRZWYnVak1P+3lDYaFQ+Nqrf0YMwaQtmeyXFdESqRTdOW3ahPwMnU4LP8ViLCUKk7ZauoHsj+6cfCRFfoYyOATdWX9tQBhgMsq4dPlC8+Pia1egEIiNjUd+RkJ8O6iqCy+ecz91Op3Llj92pICZ5eJM5q2amirozhg+fAxUV9/t+mbIkOH+FmIAcrl87JhHNm3+PCI8sl375F27vrl27WpqamfEBIzZcjjss2b+CYQtXDTHZrNm9h24bOlzyC+BgDCAx/vkn381mYxJSSlvv/XXmOhYxASM2dr9XYH7wdIlzyL/RiwWL1m8Av5DTEN7nkiC2iIJaoskqC2SoLZIgtoiCWqLJKgtkqC2SILaIglqiySoLZKgtkiidVsiSYATsb1fhlTOF4g4t0OXRM5j+VO1kQ6tjx0rQoSqW2xPqai8YQiOECGOIVMIVLfNiEXaSIfWbUUmiANY/5bDFyoygXO2IhPFTjuru3i0kQ73zVtxKZKj3zAzZ9ET8r6s7DZIyePjOon+dxOVKJEp+ae/VyFWaDsd2tqfsOiHpusX9N2zwkKiRHwBlnS0WZwaleXHffWZ40Lbp3N3i8LT+9X11Za0viFhMWIc2196mA4P2E2ytMhwoUBTU2rme7YzlevubHg+z6OtJyWBPJPBkZAa2GNYcEx7rs9lKz6jLTzapNfY7Z5tb+R0OSF5eR7UKJ6ng6dnLVhMHu11ZDAYcnJydu/e7cmL4Z+WBDK/pSheXMji2Q5Ya9asSU9PHz9+/IN/pcfp4Gl7Syz1qCS0OQJsDqOHLyaSAE+TwhVg5QkczCYFbR2TBLVFEtQWSVBbJEFtkQS1RRLUFklQWyRBbZEEtUUS1BZJUFskQW2RBLVFEtQWSVBbJEFtkQS1RRLUFklQWyRBbZEEtUUSDNsSCoUpKSmIgpBWq+3QoQNiFE9nf3oOfMpJkyYdOnQI+TGvv/569+7dJ06ciBiF+WmaQUFBO3bs6Nevn83GueNw2WHVqlU4VCEctgClUnns2LHBgwc3NTUhP2Pp0qXDhw/HoQphsoXuVmAnT57Mzs6urq5GfsOCBQtycnJGjhyJ8IB3wjrUXo899lhJSQnyA2bNmrV48eKBAwcibGBfXrB79+7nn3/+0qVLyKeBUmT16tU9evRAOGE+JmyVuXPnLly4MDMzE/kio0eP/vTTTxMTExFmWLIFLFq0aOrUqcOGDUO+BQRTEAOHh4cj/LBnC1ixYgXY8mQBGhEYjcYhQ4YcPXo0MDAQsQKry+LWrFlz6tSpbdu2IfKpr6+HAvDMmTOsqUIs20J3G/kQIq5fvx6RTEVFBUSA0KZE7OKFJafQ1IdW88cff4zI5KefflqyZMn+/fsR63hngTA0+MVi8bvvvotI4/z582+88cbOnTuRN/Dacu758+fHxMRAwYjI4fjx42vXrt20aRPyEqzGhPeSm5sLHVRvv/024jwHDhzYtWvXhx9+iLyHl7dKgN5PiOmhYETcBr5VBw8e9K4q5HVbwKhRo6AnFLoTW16cMmUK8h4zZ85s+XTLli2FhYXvvOP9Q0s5sQ0J9IRCvxTExO6n0OVRVlbmrRgEmlDQlmpuwq9btw7i9ZdffhlxAK7My+jZsyf0ikLfqEAgKC0thSvQjnY4HHw+21sLQf2kVqvhwbhx48aOHQsfYOXKlYgbcGiLn7S0NKfT6VYFqFQq6NRB7GI2my9c+Pk8xbq6um+//Ra6NxFn4NaGTJWVlc2PtVptXl4eYheI0UFS81OdTvfwww8jzsAhW7169Wr5NCAgAHoNoApBLAI9FGCo5RUY+4b+QMQNOGSre/fuUVFRUE9Aeei+UltbW1BQgNgCctX169ebn/J4PPg8qampY8aMQdyAQ7M/P//8c/giQ0gGhiDV9Hq9RqPJz8+fPHkyYgWoJquqqqBLLCIiIjo6un///pDdMzIyEGfwWl+Gy+kqLTKUXzPXV1pMegd8EoO2xYw2F9x3QSaDjycUChFb2G22AN4dAlrs2RkSJTHpbFI5XxkujGknTu4mU4Sw95Fa4gVbtbfM5440lVzQKaMCFZEyvpAnEPGFEkEAj3Obwf+MEyza7RaH3eYwqM36BqNUxu8+WNltsBKxC6u2GussR7arNfW28KQQRTh7g3iMY9JaNFU6Y6Np0KTw1J5yxBbs2Tp9QHv9vF4RKQ+K4u7e1L8Jq9FWe0Mtk/MeeSJawEoAwJKtQ1tVNRWO2M4RyOfQVOubKjVzXkoMwH/gARu2TuxtvF1ij0wJRT6KWW+tL2mYsSIW0675zWBvbx3bWV9505dVoTsn04giOoZveLMcYQavreKzuvLrtogOvqzKjThQGJYUmvsJ3kn/GG2ZDI4f9jbGZUQi/yAoUmZ18C+fwLisBqOt4981BEUrkD8RmhD879wGhA1ctrRqW1mRMTQ+CPkTfCE/NF5xer8a4QGXrfOHNaGJbDf1Pafw8sEVqzMNBg1imtB2yiundAgPuGyVXDTIw7l+7A8OBEI+CuDV3MJyHB4WWw1VFhieEkm90/XpdWRhgSWFeoQBLB0mVaVmJc7upfMX8wqOb65VlYrFgT26jho7YqFIJIHrG7a8AP0JqR37Hz66oUmnigxvl/3winYJXeGWw2HP3fv+uYv7oGO/c+qglOTeCBvyMKm6FktkiCVvaettLmxHul6+UrBp2+pOKX2fWbwxJ3v1xaJD27/7efIony8ovVVYXlH01KINrz63LzBQ+fW3b7pvHTq6/tSZnY+MfWr5og1J7R/KL/gcYQNijboKC8IAFls6jUMgwtXNeejYhuT2PceNXBQelpDeacD4UYvPFe7TNNW671qtJlAiFkkht/XsNqauvsxqvVOFnC38PqNzVt+eE+BdA/pO7tQB4ypNoZhvNtgRBrDYgp5HoRTLzDIYn7xddRUyVvMVMAc/q2tuuJ+CDHepCARK77QfjCat3W6rb6hIiOvc/K7E+C4IGzBQFxojNeqYF4YlBzhsTofTo+P6fis2m9npdOQd+vTA4XUtr2t1P0+2EQjE97zJBRkO/idscQsqPISThkoTjFgipsFiSx4saKjHUhQIhRKonAb1y8ns9cj/+xdlbXVFCu/mNpPllzjNZMLVJAJglFkk5eMYCsdiSxHCr612IAzweLy4mLRGTXVkRHv3FSjloNIKDGyr00QoEIUEx1TX/DKf6VrJaYQNu9URqMBSxWD5pZEJEqseS1AEDB00+9KVwxDj1aluVVYVb97+ytrPHjebDW2/CwJ9CCZPntkJNVzB8U1V1dcQNoxNlogECcIAlryV0ClQ11ANdReOs8G7dRk2Y/Jrh49t2H/wnxKJvH1it4XzPpZIHtC8G/mHBQajZve+D50uZ3qngeNHPbnh6+fvHknMPEa1scd4LL1uuMaOd6+rcfCkymj2Zphwh8t5pYvf64Bj4B9XP2HXAQqD2oj8D02NPrWPEtMcDVxt2HbpslP7Gg1qkyy09b5d6AXflvs/rd6SSZUGU+s9N/16TXp4zBLEEKW3Lqzb+Eyrt6CdwAvgodYSfUj/6aP+8Nj9fmfddfWsVQkIDxhn0UA/9L4Nqva9Y1u9a7GaDIbGVm9B70NzC/dXiMUyWSBjVYLNZtHpG+53i88XQgh67y2JWH6/ELShvCk01DFsKq6pXXjnPB3ZrtI0CYNj/WIE2WFzVF6qmfMixr258M6iGTolwqLRGxqxDPZwjZunKic/GYtwgn2GWs4z8U23G01aXM0vjnC7sHrCY9EyJd4pu2ys35qxMq62WKWtNSBfxGF33jhRMWpWeHR77GPl7M2D/+6f1TaXKDReyd21JL+dxipdTXHDrOcSg8LYGChndY1J4VHNv3PrI5ODI5JDEOFoVQZVSWNsknjs3GjEFl5Yv3V8V8PNS0bE48vCAoMipfjGLRkH+tIgYtKpjPp6Y2SieEh2eGiUCLGId9ZGOp2uW1eM187pm9QOVblRFMiXKcVQASBOIlWImupMVrNdqhAoggWdesqTM2S4A4pW8fKuXOjOF9Zl0NqNWofd5uVPcj+gopXKebIggUji5UX13rdF8Rx6ohNJUFskQW2RBLVFEtQWSVBbJPF/AAAA//+JyG5+AAAABklEQVQDAK0cf6EVKQblAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c6e92bf6-5ee8-4a5a-8693-a0e028b73e3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding \"A\" to []\n", - "Adding \"C\" to ['A']\n", - "{'aggregate': ['A', 'C'], 'which': 'c'}\n" - ] - } - ], - "source": [ - "result = graph.invoke({\"aggregate\": []})\n", - "print(result)" - ] - }, - { - "cell_type": "markdown", - "id": "b1c54e61-393d-4359-88e2-6117a6022ce3", - "metadata": {}, - "source": [ - "!!! tip\n", - "\n", - " Your conditional edges can route to multiple destination nodes. For example:\n", - "\n", - " ```python\n", - " def route_bc_or_cd(state: State) -> Sequence[str]:\n", - " if state[\"which\"] == \"cd\":\n", - " return [\"c\", \"d\"]\n", - " return [\"b\", \"c\"]\n", - " ```" - ] - }, - { - "attachments": { - "f0038a5c-08d9-4eff-a1cb-d1ee4dde4fe5.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "931a0f15-b8d2-4ff6-8772-fd99f708a099", - "metadata": {}, - "source": [ - "### Map-reduce and the `Send` API\n", - "\n", - "By default, `Nodes` and `Edges` are defined ahead of time and operate on the same shared state. However, there can be cases where the exact edges are not known ahead of time and/or you may want different versions of state to exist at the same time. A common example of this is with map-reduce design patterns. In this design pattern, a first node may generate a list of objects, and you may want to apply some other node to all those objects. The number of objects may be unknown ahead of time (meaning the number of edges may not be known) and the input state to the downstream `Node` should be different (one for each generated object).\n", - "\n", - "To support this design pattern, LangGraph supports returning [Send](/langgraph/reference/types/#langgraph.types.Send) objects from conditional edges. `Send` takes two arguments: first is the name of the node, and second is the state to pass to that node.\n", - "\n", - "```python\n", - "def continue_to_jokes(state: OverallState):\n", - " return [Send(\"generate_joke\", {\"subject\": s}) for s in state['subjects']]\n", - "\n", - "graph.add_conditional_edges(\"node_a\", continue_to_jokes)\n", - "```\n", - "\n", - "Below we implement a simple example, where we simulate using LLMs to (1) generate a list of subjects (the length of which is unknown ahead of time), (2) generate jokes in parallel, and (3) select a \"best\" joke. Importantly, the input state to the fan-out nodes is different than the graph's overall state.\n", - "\n", - "![Screenshot 2025-05-05 at 4.21.35 PM.png](attachment:f0038a5c-08d9-4eff-a1cb-d1ee4dde4fe5.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8b971a9c-337a-4899-bcf7-080193832935", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.types import Send\n", - "from langgraph.graph import END, StateGraph, START\n", - "\n", - "\n", - "# This will be the overall state of the main graph.\n", - "# It will contain a topic (which we expect the user to provide)\n", - "# and then will generate a list of subjects, and then a joke for\n", - "# each subject\n", - "class OverallState(TypedDict):\n", - " topic: str\n", - " subjects: list\n", - " # Notice here we use the operator.add\n", - " # This is because we want combine all the jokes we generate\n", - " # from individual nodes back into one list - this is essentially\n", - " # the \"reduce\" part\n", - " jokes: Annotated[list, operator.add]\n", - " best_selected_joke: str\n", - "\n", - "\n", - "# This will be the state of the node that we will \"map\" all\n", - "# subjects to in order to generate a joke\n", - "class JokeState(TypedDict):\n", - " subject: str\n", - "\n", - "\n", - "# This is the function we will use to generate the subjects of the jokes.\n", - "# In general the length of the list generated by this node could vary each run.\n", - "def generate_topics(state: OverallState):\n", - " # Simulate a LLM.\n", - " return {\"subjects\": [\"lions\", \"elephants\", \"penguins\"]}\n", - "\n", - "\n", - "# Here we generate a joke, given a subject\n", - "def generate_joke(state: JokeState):\n", - " # Simulate a LLM.\n", - " joke_map = {\n", - " \"lions\": \"Why don't lions like fast food? Because they can't catch it!\",\n", - " \"elephants\": \"Why don't elephants use computers? They're afraid of the mouse!\",\n", - " \"penguins\": (\n", - " \"Why don’t penguins like talking to strangers at parties? \"\n", - " \"Because they find it hard to break the ice.\"\n", - " ),\n", - " }\n", - " return {\"jokes\": [joke_map[state[\"subject\"]]]}\n", - "\n", - "\n", - "# Here we define the logic to map out over the generated subjects\n", - "# We will use this as an edge in the graph\n", - "def continue_to_jokes(state: OverallState):\n", - " # We will return a list of `Send` objects\n", - " # Each `Send` object consists of the name of a node in the graph\n", - " # as well as the state to send to that node\n", - " return [Send(\"generate_joke\", {\"subject\": s}) for s in state[\"subjects\"]]\n", - "\n", - "\n", - "# Here we will judge the best joke\n", - "def best_joke(state: OverallState):\n", - " return {\"best_selected_joke\": \"penguins\"}\n", - "\n", - "\n", - "# Construct the graph: here we put everything together to construct our graph\n", - "builder = StateGraph(OverallState)\n", - "builder.add_node(\"generate_topics\", generate_topics)\n", - "builder.add_node(\"generate_joke\", generate_joke)\n", - "builder.add_node(\"best_joke\", best_joke)\n", - "builder.add_edge(START, \"generate_topics\")\n", - "builder.add_conditional_edges(\"generate_topics\", continue_to_jokes, [\"generate_joke\"])\n", - "builder.add_edge(\"generate_joke\", \"best_joke\")\n", - "builder.add_edge(\"best_joke\", END)\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2760846f-a297-455c-a07c-155743f5e55f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ixqnXBto6+OYvtx0Ao0ellJWWQCN4qRhcemh5QaMmvDP03drp5haNCCThqMDR1k6B1dqSV1RU8PkvJATh7e0c/t9vmVtAItRgTV3XUOPXwqAKQhB38NCez5cthACiSb4+0DQ+mDKJ8Qlx1KpEIrl79y9qGQJgsFcQ6EI9pf6gcYNLq//ovb3byeVyOGHNVlwuz9ra9tWSlfJKVMtgQFyWk5tdu2tALUPlAPsJ8a1mhw4OTuBfTU1M0b+h2ZtX67YJiU/BtFCr0EAhjPKuiaIpIOanFsABQZaLS4va+/Hw8AJ78+TJI2o1Le3Zh7OnpKamQB2Fw0bVkRqrQ4fOwdPmgEWEsAY1BU0ksKMzOKdjx76CA4UT27DpC4v/mVOIU6CnD7YR4ursnCwINCAQ/dehAwi4wEWt37DiwYO7INilmF9mfTgJgrVXS0JIBXUF4i/ogfx959aOneEQ6GU+T4cqxasBQvqk5AS4shPGvwchOsR3EAxDCuz84wXToS7WfyQmxibgb/b12gAAEABJREFU+6E8XHQI2isrZRC0wx7AbEBMDm0aYkaqJMgDoQA0QcjdvmMjpAwYMOSlkwIHvDliLRwnFIMYHmonePG/bt9YtmLR1T9isrKfww+dPh0NTd/Ozh41BU020AGdHDh08LXWVjaTJweD4YqPf9Gg587+BC7T/gM7QAPwo/49e08Pnlf/3uBibdq4E3q3K1eHQqfI3t5x6tQZ0MV6tSTYPejzREXtuvjbz61bt4G+U0Fh/tqwpYsWzz508NuJEz6IPvn1zZt/Hj3yY++3+n++dO2J6MOHDu8DYXx8/LZFRGpeyPI6Ro2asGHjF1AVoG/TrWvPzZt274/aOWPWRDhCX58OsAc4AE3hWTPm79y1+VlqMnSl1q7eAvW+9q7AMq8P275z9+ZVq0NZTBYEmxCHg+OYMjlYqVTs27e9sKiAOrCNG3a8asbfjCYb6IAoRqFUgJDU6qKQ2WA2qSiRDpz+4SR06GN+u430QT0DHU3WgiEugMA45JNlFhaWN2/9CaZ4w7rtiKBvmtJEwyDcipWLwUs5OjovCV31HweBdcbwkX1fl7UkdHVAQB/UnGkyE918gSDudVkW5pb1D1YbCLow0c2XVzvNOEEExhwiMOYQgTGHCIw5RGDMIQJjDhEYc4jAmEMExpy6BTYSsNQqNSI0E3jGLA6v7tuLdd/wN7Nm56RVIEIzITNebOXAqzOrboGdPQXyimb//mCaUF6isLDlmllz6sytW2AWm9F9iOXFb7IQweC5Ep391rvWr8ut73XCWSkVv36T26GPpbkdT2BCwjEDgsFAomKFqEh+81zBe8vdTK04ry1Z/wvBxaXKe5dLctNk0nIcLLZSoWAwmdSzT80avjGbw2U4tjLqHmhZ/+wtWnz5TEN4eLibm9v48eMRbaCX4R06dKixsTGiE/RqwTSEXq9wuHDhwu3b+pnZqi/oJfCjR49SU1MRnSA+GHOID8Yc4oMxh/hgzCE+GHOID8Yc4oMxh/hgzCE+GHOID8Yc4oMxh/hgzCE+GHOID8Yc4oMxh/hgzCE+GHOID8Yc4oMxh14mOjMzU/MyYJpAL4H9/f2JDyZgBfHBmEP6wZhD+sGYQ3ww5hAfjDnEB2MO8cGYQ3ww5hAfjDnEB2MO8cGYQ3ww5hAfjDnEB2MOLUz0hAkTmEwmnCnc7WfVUFVDdHQ0wh1aBFmgZWJi4kspHTp0QDSAFiZ67NixPN7/e52yQCCYNm0aogG0EHjMmDEuLi61U7y9vXv1ah4fR/2P0CXIAo25XC61bGJiEhwcjOgBjQSmGjF4X2i+PXv2RPSARt0k8MTQiE1NTadMmYJow5tH0aIiBYPJQM2Hgf1GfH/yvK2tbfu23ctLlKj5AD1ZU8s3VKrR/eCc1Ip7l0tT4ySO7nzQGBG0j6UDLytJ6uEn7D7UytSS06htGydw+lPpzZ+LAt61M7Pm1P8meULTolSoS/Pll0/mBM1zsrDlNnzDRggM6v71S1FgsAsi6I9TW1PHLHBueDtuRJB170rJgMk4f+6+WdBvvMOt88UNL99QgctLFKX5Ci6v2X+vpLljYcdLflDe8PINFbi0QOHsKUAEfcNiM1y9hKUF8gaWb2jwXaWu/oYSIhgAxXnyhke45HtmmEMExhwiMOYQgTGHCIw5RGDMIQJjDhEYc4jAmEMExhwiMObQ69GVJmHlqtCQxXPqL/PsWXK/AV0ePXqA9A3tWvAPP36bkPhkSegq9KYMGxakbD4vvKSdwImJT9F/o2uXHqj5oEWBlUrlnr1bL8X8olIpe781IMC/z4qVi09/d9HCwhKyjh47ePnKxby8HBsbu7FjJo8cMYbaatTogVMnT8/Lz7185deKCqmvb8fFi5ZbWVlTO6xzq9TUlOAZ49et3bo/aiffiL93zzcqleqbIwdiYn4pKMw3NTWDn/5w1gI+n79w0azY2Huwya+//rQ/8pinh1diUnxU1K6ExKdKpaJTx27z5obY2zvUf15gosXi8ogte2E5Pz9v775td+/+VSGrcHFxmzj+/YEDh766ydFjXx0/cWjb1v1erduUlpbs2bctNvZuWVmpu7vnzBkfdezQBWkNLfrg774/fu6n07Nmzt+7+xtra5t9+7+s/j1m9S/ui/zy5LdHJk+cdjDqJOi0a/eWn8//SG3FZrNPnPy6RQv3E8fOfRX1bVJS/JGjUVTW67bicKonKH39zf7x46Z+uvgL6qePnzgcHDz34IHo0E9XXr9xNeqr3ZAetmZra0/v/v0G/Xj6kntLj7y83EUhHzKYzG0RkRFb9onKy0I+nSOXN/ReukKh+PSzeZnP09euiTh08Nveb/Vfv/GL69evvlTs96uX4Ni+WLER1FWr1Z8tmR8X9/Cz0FWRe496e7VdsvRjcNhIa2ixBf968adeAX2HvTMKlqcHz33y5FFWViYsi8XiM2dPTZ40bfDgYbDq7OQCKoIe7wx9l9rQzbVl4JARsGBra9etq39CwpN/2arm7neHDl2orYC3BwR27dLT3d2juqSza7++g/66fR2WjY2NWWw2h8s1MzOH1bPnvoM758uXrTMxNoHVz5esnTh5+NU/Yga+HdiQE/zrr+sZGWmUJYDVD97/8O692z/8eDIgoI+mzNOnjzduWvnJwqU9ugfA6p27f4HN2Bqxj2q1H81bDCmnf4heHLIcaQdtteCqqqrnzzN82vlpUnr16kctpKQkgrHt0vkfT+bn1zk7+7lUKqVWwXBpskxMTEXlooZs1batryYL9ANF5370wbgJQ4PGDDr30/flNTt5Cbj63l7tKHUBOzt7Bwen5OQE1DCSkuN5PJ5Hq9aalNat2ySn/POcam5ezrIVi8aNnTI0cKTmF8HedPDrTK2CPWvv27Hhv/gGaKsFV1RUgB58wT/TuMAXUgtSqQT+/QRs4//mnVBTd4tLigQ15V961JPxb1tRq0LhP29X2blr82+Xzn+yYGk7Hz8el3ci+mvw6K8epEQiTkpOGDTkn+eUwOoWFReihiGWiI2M+LVnzwgFQuo4Kb7csRHqX1HRPzuEXPiJwYH+mhQIFywtrZDW0JbAlF+UyWSaFE0bopRY9nkYeMHam9ja2NWzw3q2yi/Iq50Cl+z8hTNTp8zQxDsg5Ov26evbIeSTZbUT+fyGzi00FhpDGAj1TKOxRCqpXc/AU3Tq1A2Csp493wJvRf0il8s9EHm89n6ouERLaFFg8KDxCXGalGvXrlALYIEht6Sk2LVPCyoFAku4RprHO+uk4VtBIAMaawyGRCK5cfOP2hdRM9e/TRsfCBQcHZ0hsqNSMjPTqYi9IXi1bgsRGfhUiJ6olCdxD72922kKDOg/BCrQkMHDt0SEtfH2gT1DLmwCh9eyZSuqTG5ujrm5BdIaWqw7fXq/ffXqJejVZGU/P/x1JPRYqHSIdGCsAFIgKzsn6/6DO4tD524MX1X/3hq+FdQDiHpAOfjdlJSkz5cv7N49AOwHBETgNcDjgs8Dywy9lOHDRkMT3BS+ClYhYvjmSNS06ePi4+NQw+jWzd/NrWVERNjT+Dj4rQNRu+ITnkB4/1IxiKQEfEH45tVQsTp36gbHtn7DigcP7ubkZkMfctaHkyB4RFpDi1H0tA9ml5QUbd6yhsczGjBgyJRJwdCLgBgWsubO/gQu9P4DO8A/gQfy79l7evC8f91hw7eCzhL8bvD0cfb2jsHT5kDriXscO2fee1EHokeNmrBh4xcfL5i+etXmbl17bo2I3L9/B6yyWKwWLVqFrd1aO1irH2j34Rt3QV8/9LN54IzAd6xdvaVTx64vFRMKhUuXrFnwyczTP5wcHTRh08adeyO3r1wdKpNVwOFNnTrj1TrRhDT02aSMeOndmNK3pzTi0RVoLjAgoLE/0D6gPwAdUNTMWfHFYtBmc/hupCd+2Jk+crajmXWDHk/Sook+dvzQpCkjoJsP5uva9d9B3cGDhqHmDATAYMzBwltZ26BmghZNNAxKyOWV+yK3FxcXQawLIxLvTZ2JmgNLly18/LiOG0EQHEH3z9HBKWjUBNRM0KKJbr6Aj5cr6h6wFAiEZv+Lz/VFo0w0ueFfBw3vKRk+RGDMIQJjDhEYc4jAmEMExhwiMOYQgTGHCIw5RGDMaajADCYysSS1wSCwtOch1ND3Ezb0bpKlHTfjqQQR9I1Crn6eKDGzbujrKhsqsNCMbe3MqxCTV2XpmeLcSs+OJg0v34j7wV0HWlw6mo0IeuXy8eyAEY2Yhdm41wnnZ8h++SY3YKSdqTXXSEDeW6k7JCJlWUHllejcqctchWaNeGV0o18IXpIvv/NbcdoTqakVp6ygmb0QXF2lro4Xm9ubrm2ceaX58pa+woDh1hxe4ybhvPmXz2QSNaO5PV385Zdfurq6jho1CjUrqtRVRsI3tJdv3vMxEjbDh8eZCgZLyePT6LF30rXFHCIw5hCBMYcIjDlEYMwhAmMOERhziMCYQwTGHCIw5hCBMYcIjDlEYMwhAmMOERhziMCYQwTGHCIw5hCBMYcIjDlEYMwhAmMOvQQ2Nzfn8/mITtDrw1ilpaUVFRWIThATjTlEYMwhAmMOERhziMCYQwTGHCIw5hCBMYcIjDlEYMwhAmMOERhziMCYQwTGHCIw5hCBMefN33TXjBg1alRGRgaDwVCr1UwmkzplLy+v48ePI9yhxYyOwYMHUx8Kp74DDkoLhcKpU6ciGkALgSdMmODi4lI7pWXLloGBgYgG0EJgc3PzIUOGsFgv3ucpEAjGjx+P6AFdJt0FBQW5urpSy9B8hw4diugBXQSmGjGbzaZV80W0mjY7evRoZ2dnNzc3+jRfpJtu0uPrZSkPJWp1VcHzSqRXlCoVhNAspp6rtYUdly9ktelh0srXGGkZrQv829E8njHb1sXIysGIyWpm79LXEgq5uihblhYndnQ36tjXHGkT7Qr888EcC3sj314WiFAXN8/lG5uz/Ic14isqjUWLxirhjsjYgkPUrYeew23LihQ5qVp8mkaLAqfHVzT8A120hW/MyUpungKrlFXgdxGhXiA6kYrVSGto8W5SSa6cBjcy/itqVZWkVIufnyK3CzGHCIw5RGDMIQJjDhEYc4jAmEMExhwiMOYQgTGHCIw5RGDMIQJjjgHNyXr2LLnfgC6PHj1AOqSBP7pyVWjI4jmoGYLzpLt3g97Oyc2uv4y1je3CBUscHZ0RpmBrovPycsvKSv+1mKmJ6cgRYxC+GJzAxSVFS5ctfPDgDpfLCxwyYtbM+dQDRYlJ8VFRuxISnyqVik4du82bG2Jv7wDpSqXyQNSu36/+VlJSbG5u0af327DJ47jYRSGzIXfS5BEBAX3C1kS87ufARE+fOWHH9ihf3w6wCrb6wMFdiYlPGQxGG2+fmTPnt/Fu99ImRUWF8+Z/4OvT4fOla6FYzOVfT506mp6RyucL+vcbPGP6PCMjA5rmYHAmOurg7q5den65PWrsmMknvz1y9tz3qKY5Lgr5kCNHCqUAAAyySURBVMFkbouIjNiyT1ReFvLpHLlcDlnHTxy++NvPi0NWHPrq1KKFn1/5/eLhryPh6n+xYgPkRu47uvSzNQ386czM9MWhc22sbXfvPLxrxyG+QLD40zn5+Xm1y8hksuVfhDg6OId+uhLUvXbt97B1yzp37n5g/wlI+ePPmIht65AhYXACB/j3CRo1vrWn95TJwW3b+l6KuQCJZ899B1dz+bJ17u4e3l5tP1+yNicn6+ofMZCVmprs3tKja5ceTo7OPXr02rpl35DBw2ueYBBCromJqVAobOBPnzn7HbTCpUvWtGrlCX/LloaBefj14k+aAlVVVRs2flFZKVuzeguHw4GU49GH/fw6zZzxkbOTS4/uATNnzL906cJLdUK/GJzA7X07apbbtW2fkZEGC0+fPvb2amdibEKl29nZOzg4JScnwLJ/z9737v+9Zu3S369eEpWLXF1buLi4oTciMekpVCyoHNSqQCCAXaWkJGoK7D+wE4z/xvU7jI2rJ6yr1Wow5l0699AU6ODXGVWb/SRkMBicDxYK/5nsz+fzZbLqGYcSiTgpOWHQkJ6aLIVCUVRcCAsDBw6Fxnrm7CloWyqVCgwARMUWFpao8UilEitL69opsGdIpJbjE+IexN7lcrnQgqkUMNfwi+ARvjlyoPZW1IEZCAYncIXsnzmkUqkUbCaqUR2CoJBPltUuSWUBEEbBX0VFxa2/ru3eE7E5Yu36sG2o8cCvQE2qnQKrGsk5HO7WiMht29avW798185D0NAhmIJ/g0ZNeGfou7W3Mn+j6qUlDM5EP378z5hDQuITN7eWsNCmjU9WVib0VsECU3/gkq2sqi89hDlUZxeae7++A+Fapz5L1uyhUc9teLVuC1E62AZqtVxcDg7C+39RdCt3T6/WbSByTkt/Bq0W1bwvwNPTOy8vR3NU4DhYbDZ0vZDBYHAC/3ntyuUrF3NzcyDkgU7L4EHDIHH4sNEVFdJN4avAUD9/nvHNkahp08fFx8dB1venT4ADjo29l52Tdf/BHfDEfh2qHSF1lW/dupaW9qyBPz1y5Fgwv+Fb1kA4Dd0nCI+hTVMHoAFUnDXz4xPRX1ODXxPGv/fHn5chkodN4NjWb1jx8YLpEokEGQwGZKKVKiX8Cx1c0Cx882ojI/7kSdOGBo6EROjygnncv38HXD4Wi9WiRauwtVshxoYs6A7t2bt15erQanNqZd2je68Z0z+C9Nat23Tr5r933zboMm2N2NeQA4A4fPOm3fujds6YNRF+BTaEXhn0rV8qNurdcbdu/QlaQteo91v9oU2fiD586PA+qA0+Pn6wScPjdh2gxYfPjm/M6BVkb2Fn0E+vJCcnzvxw0s4vD4I2SB+kPxFnxpcHTnNA2oHWd5NgTOrGzT9gwcraBmEK/gKDs/x8+cI6s6Cfw2SyRgwf7WDviDAFf4EhAj9+7Nzrco2FxhCQI3zBX2DoqmqGwGgImdGBOURgzCECYw4RGHOIwJhDBMYcIjDmEIExR4sCm1hwmEzy7sJ/gcVi8AQspDW0eD+YyUJlRXp++6jhU1IgNxJoUwWkNRzceZIyJSLUi7xCZePCQ1pDiwJ36m/5+HqJREQ0fi2Z8eKSvErPDlocKtfu22blMvXxTen+I+0cWgoQoRZw2VNiy1Mflr87z1GrkYrW3xetUlVdjs5PvFfu7mssEamQXqlSV78VkqHvF4KzOYznSVIff9O+Y2yRltHRh7FA5sLnlUqFnt9dGR0dbWdn169fP6RXuHymjZMW/W5tdNQPhs6AnZv+H8lScQs4pgInDz6iDWSgA3OIwJhDBMYcIjDmEIExhwiMOURgzCECYw4RGHOIwJhDBMYcIjDmEIExhwiMOURgzCECYw4RGHOIwJhDBMYcIjDmEIExhwiMOfQS2MTEhMs16FcrNjk4f1bnVcrLy6kvPdAHYqIxhwiMOURgzCECYw4RGHOIwJhDBMYcIjDmEIExhwiMOURgzCECYw4RGHOIwJhDBMYcIjDm6OhNd/olMDAwPz8f1bwhUvOdM0dHx3PnziHcocWMjr59+8K/IC2TyWTUwGKxxo0bh2gALQSeNGmSs7Nz7RQXFxciMD6AnAEBARpnBM135MiRPJ6OXgeqX+gy6W7ChAmaRuzk5DR27FhED+gisKura48ePVBN8w0KCuLz6fLCWRpNm504caJzDWPGjEG0wRC7SfJKddoTSVG2XFymkoiUanWVqonmMufm5nC4XCtLK9QU8E1YalWV0JRlbM62deG1bCdEhodhCRx3syzuVnlRdqWlszF0Zdg8FpsL/7GQQX5+CQ5KIVcp4a8S/hQlWRInT4GPv4lWP7LRWAxF4Kd/l18/U2juZGJkamRs2VwdpChfKhNVVJbLegdZu3kbxHdI9C+wWo3OROZKxVW2HpYcIxyGTitElQUpJdaO7MD37Rj6tj16Frg4t/JEeKZ7dye+KW7PhIkKJEWpJe8tc2Wx9SmyPgWWipXHNz5v1dOZgeknDisliqxHuVOXuXC4Wvw6Yf3oTeDyEkV0xHPPAFeENWqVOuFqxpzNrZCe0Fs/+PjGTPduTgh3mCymWyf7E5szkZ7QTwv+7XievEogtKTL9+7KckR2DuoegU3T/24UemjBWSkVuekK+qgLmDmYPvyjTFquh+906kHgP38otGphgWiGjYflnz8WIZ2ja4EzEyRVLLbAXP+fuasTiaR08YrusY9jUFNj4WhSlKuE0BLpFl0LnPxQyuXT4kbsqzA57LQ4CdItuhY49bHExIam3xIWWgmSYqVIt+h0aLA4T25qzeMKOEg7PM+OP//bHvhXpVR4tuo6IvATSwsHSL9x+/tfY/YHT4k4c35rfkGaQGA2oM+07p1HUFvdvH065o/DYkmJs4P3kIGzkdYwsRbk5pdBzxj6TkhX6LQFi0uVlVI10g4lpbn7vprLZDDnBO+ZHbxbKhVFHv5Ioay+0chismUy8aWrX703YcPaZTGdOww9fW5TaVn1PMtnafe/P7epfbsBi+YeHdB32rkLO5A2EZcodPyVbJ0KLBUpWVobtLv592nEYEweu9bBzsPFqe3EMauKS7IexV2mclVqZb+33jM3g9F/RrdOw1UqZXZuEqTffXDBxNjqnUEf2dq4tWnt36fXJKRN4G6KFGOBZRI1i6ctp5CR+djVqS2f/+JerIW5vaWFU1ZOoqaAo50ntSDgm1YfjKwc/s0rSHN28maxXlQ7V+d2SJtwhWwd94Z16oPh3plaoS0TXSGTZOcmfLaqlyZFpVKIygs1qxzO/4veqSG8ykqJqck/A0xcjnZvRSsr1Sy2ThuVTgUWmLJUCm0ZKCMjYUvXDmNGLqmdyOX+S8TO5fLBPWtWK2qatfZQVqrgIiAdotPaJDRjK+XaMlBuLj6FxZlWls62Ni2oPzAZpibW9W9lY+WanZesVr+wK0kpt5E2kVcohaY6bVQ6FdjClovU2jLRPbqMqqyURp9ek5WdUFCY8duVg1t2TczMiqt/q45+g8Xi4rMXtufkJT+Mu3Ln/nmkNcB6Cc04fGN8WzCcG5fPlJbKkBaALu/s4D3l4qLdUbO+3PdBQtKtaZO3uLn41r+Vl0f3EYELH8bFbN/7wdXrx8aOXIr+556bHFG+1NZF1xNXdH278M6l4uQnKntPS0Q/nj/K8w80dfc1RjpE10OVnh1MqhS6HnA3BKqfXEVqHauLdP8AuJk1x9KWVfxcZOlsWmeBMlHB5p0T6swy4hnLKsV1ZtnZtJw/Kwo1HcvXDXhdllqlZLLquG4uTm0+/GDX67bKSyr27qKHmfF6mNEhk6oOr0737utWZy6MMZWJ8uvMUigqX+rLamCxOGamNqjpKC7Jfl2WXFHJresw2Gzu64J26B09u501a31LpHP0M2XnbkxJRqrawskc0YPCZ0Ude/Fb+enhiQf9TLrrPMCCXSUX5en65qheKEorsXdh6UVdpMdZlcNnOpTnlZUX6vr+qI4pTCs14il7jdDDdDsKPT/Z8PXadHNnczN7XceWugHUNRaqhrxni/SH/p9N+ikqV67iWLpi5Y9VSnVRerG9E6v3KGukVwzi6cL7V0pv/FRo39rSytUMNX/yk4uLMkUDxtu27qz/50gN5fFRlbLq6unCvExFFWKZ2ApMrJvZvK0qdZWoQFpeIFUrFK07CnsEGspQnWE9AC4WKVMeSBLvi6Xl0B+uYnPZLC50cVmG+bY2Npslr5C/eABcobJz43t1ErbuaMziGNCLMQz0TXcKubqsUCEVqSRlSoW8Sq02SIE5DDaXAbf/4M/CjsPQ+7PAdUGLVxnSGfIyUswhAmMOERhziMCYQwTGHCIw5vwfAAAA//+6DtzUAAAABklEQVQDAFiqIpwyPw3aAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cd91131a-b640-4604-9b48-b6cb5207be31", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'generate_topics': {'subjects': ['lions', 'elephants', 'penguins']}}\n", - "{'generate_joke': {'jokes': [\"Why don't lions like fast food? Because they can't catch it!\"]}}\n", - "{'generate_joke': {'jokes': [\"Why don't elephants use computers? They're afraid of the mouse!\"]}}\n", - "{'generate_joke': {'jokes': ['Why don’t penguins like talking to strangers at parties? Because they find it hard to break the ice.']}}\n", - "{'best_joke': {'best_selected_joke': 'penguins'}}\n" - ] - } - ], - "source": [ - "# Call the graph: here we call it to generate a list of jokes\n", - "for step in graph.stream({\"topic\": \"animals\"}):\n", - " print(step)" - ] - }, - { - "cell_type": "markdown", - "id": "4c505843-5449-4e9b-8ad4-27b88a987cc4", - "metadata": {}, - "source": [ - "## Create and control loops\n", - "\n", - "When creating a graph with a loop, we require a mechanism for terminating execution. This is most commonly done by adding a [conditional edge](../../concepts/low_level/#conditional-edges) that routes to the [END](../../concepts/low_level/#end-node) node once we reach some termination condition.\n", - "\n", - "You can also set the graph recursion limit when invoking or streaming the graph. The recursion limit sets the number of [supersteps](../../concepts/low_level/#graphs) that the graph is allowed to execute before it raises an error. Read more about the concept of recursion limits [here](../../concepts/low_level/#recursion-limit). \n", - "\n", - "Let's consider a simple graph with a loop to better understand how these mechanisms work.\n", - "\n", - "!!! tip\n", - "\n", - " To return the last value of your state instead of receiving a recursion limit error, see the [next section](#impose-a-recursion-limit).\n", - "\n", - "When creating a loop, you can include a conditional edge that specifies a termination condition:\n", - "```python\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "\n", - "def route(state: State) -> Literal[\"b\", END]:\n", - " if termination_condition(state):\n", - " return END\n", - " else:\n", - " return \"b\"\n", - "\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_conditional_edges(\"a\", route)\n", - "builder.add_edge(\"b\", \"a\")\n", - "graph = builder.compile()\n", - "```\n", - "\n", - "To control the recursion limit, specify `\"recursion_limit\"` in the config. This will raise a `GraphRecursionError`, which you can catch and handle:\n", - "```python\n", - "from langgraph.errors import GraphRecursionError\n", - "\n", - "try:\n", - " graph.invoke(inputs, {\"recursion_limit\": 3})\n", - "except GraphRecursionError:\n", - " print(\"Recursion Error\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "2de7cdff-3811-4d19-b93b-7b8dfbbdb4f1", - "metadata": {}, - "source": [ - "Let's define a graph with a simple loop. Note that we use a conditional edge to implement a termination condition." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f087c028-b115-42a0-a85d-f53d96223720", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Literal\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # The operator.add reducer fn makes this append-only\n", - " aggregate: Annotated[list, operator.add]\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Node A sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Node B sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "# Define nodes\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "\n", - "\n", - "# Define edges\n", - "def route(state: State) -> Literal[\"b\", END]:\n", - " if len(state[\"aggregate\"]) < 7:\n", - " return \"b\"\n", - " else:\n", - " return END\n", - "\n", - "\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_conditional_edges(\"a\", route)\n", - "builder.add_edge(\"b\", \"a\")\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c468e5b2-a1dd-4fac-84a9-9eb212a09752", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "255d288a-6e17-4f34-babe-0c6cf1e09a6f", - "metadata": {}, - "source": [ - "This architecture is similar to a [ReAct agent](../../agents/overview) in which node `\"a\"` is a tool-calling model, and node `\"b\"` represents the tools.\n", - "\n", - "In our `route` conditional edge, we specify that we should end after the `\"aggregate\"` list in the state passes a threshold length.\n", - "\n", - "Invoking the graph, we see that we alternate between nodes `\"a\"` and `\"b\"` before terminating once we reach the termination condition." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "83c759be-9bb7-4f96-8b79-028fe1ebb45f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node A sees []\n", - "Node B sees ['A']\n", - "Node A sees ['A', 'B']\n", - "Node B sees ['A', 'B', 'A']\n", - "Node A sees ['A', 'B', 'A', 'B']\n", - "Node B sees ['A', 'B', 'A', 'B', 'A']\n", - "Node A sees ['A', 'B', 'A', 'B', 'A', 'B']\n" - ] - }, - { - "data": { - "text/plain": [ - "{'aggregate': ['A', 'B', 'A', 'B', 'A', 'B', 'A']}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"aggregate\": []})" - ] - }, - { - "cell_type": "markdown", - "id": "8c596264-9a36-4c52-ba7d-9fa5dcb3467d", - "metadata": {}, - "source": [ - "### Impose a recursion limit\n", - "\n", - "In some applications, we may not have a guarantee that we will reach a given termination condition. In these cases, we can set the graph's [recursion limit](../../concepts/low_level/#recursion-limit). This will raise a `GraphRecursionError` after a given number of [supersteps](../../concepts/low_level/#graphs). We can then catch and handle this exception:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f7526e3c-357c-4eba-b101-751418523672", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node A sees []\n", - "Node B sees ['A']\n", - "Node A sees ['A', 'B']\n", - "Node B sees ['A', 'B', 'A']\n", - "Recursion Error\n" - ] - } - ], - "source": [ - "from langgraph.errors import GraphRecursionError\n", - "\n", - "try:\n", - " graph.invoke({\"aggregate\": []}, {\"recursion_limit\": 4})\n", - "except GraphRecursionError:\n", - " print(\"Recursion Error\")" - ] - }, - { - "cell_type": "markdown", - "id": "0793b71a-fd92-4284-8d58-cc10f49872f6", - "metadata": {}, - "source": [ - "Note that this time we terminate after the fourth step. The default recursion limit is 25.\n", - "\n", - "
Extended example: return state on hitting recursion limit\n", - "\n", - "Instead of raising GraphRecursionError, we can introduce a new key to the state that keeps track of the number of steps remaining until reaching the recursion limit. We can then use this key to determine if we should end the run.\n", - "\n", - "LangGraph implements a special RemainingSteps annotation. Under the hood, it creates a ManagedValue channel -- a state channel that will exist for the duration of our graph run and no longer.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7fe57f1a-ab55-45ed-b229-8f29fc3da05b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node A sees []\n", - "Node B sees ['A']\n", - "Node A sees ['A', 'B']\n", - "{'aggregate': ['A', 'B', 'A']}\n" - ] - } - ], - "source": [ - "import operator\n", - "from typing import Annotated, Literal\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "# highlight-next-line\n", - "from langgraph.managed.is_last_step import RemainingSteps\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # The operator.add reducer fn makes this append-only\n", - " aggregate: Annotated[list, operator.add]\n", - " # highlight-next-line\n", - " remaining_steps: RemainingSteps\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Node A sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Node B sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "# Define nodes\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "\n", - "\n", - "# Define edges\n", - "def route(state: State) -> Literal[\"b\", END]:\n", - " # highlight-next-line\n", - " if state[\"remaining_steps\"] <= 2:\n", - " return END\n", - " else:\n", - " return \"b\"\n", - "\n", - "\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_conditional_edges(\"a\", route)\n", - "builder.add_edge(\"b\", \"a\")\n", - "graph = builder.compile()\n", - "\n", - "# Test it out\n", - "result = graph.invoke({\"aggregate\": []}, {\"recursion_limit\": 4})\n", - "print(result)" - ] - }, - { - "cell_type": "markdown", - "id": "6c9e1818-079a-41bb-aed0-2bf993ca943f", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "308d93f4-6e78-411d-82de-78eac230e44d", - "metadata": {}, - "source": [ - "
Extended example: loops with branches\n", - "\n", - "To better understand how the recursion limit works, let's consider a more complex example. Below we implement a loop, but one step fans out into two nodes:\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "258d8613-8572-407a-941a-2ac50b8f1c3e", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing import Annotated, Literal\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "\n", - "\n", - "class State(TypedDict):\n", - " aggregate: Annotated[list, operator.add]\n", - "\n", - "\n", - "def a(state: State):\n", - " print(f'Node A sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"A\"]}\n", - "\n", - "\n", - "def b(state: State):\n", - " print(f'Node B sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"B\"]}\n", - "\n", - "\n", - "def c(state: State):\n", - " print(f'Node C sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"C\"]}\n", - "\n", - "\n", - "def d(state: State):\n", - " print(f'Node D sees {state[\"aggregate\"]}')\n", - " return {\"aggregate\": [\"D\"]}\n", - "\n", - "\n", - "# Define nodes\n", - "builder = StateGraph(State)\n", - "builder.add_node(a)\n", - "builder.add_node(b)\n", - "builder.add_node(c)\n", - "builder.add_node(d)\n", - "\n", - "\n", - "# Define edges\n", - "def route(state: State) -> Literal[\"b\", END]:\n", - " if len(state[\"aggregate\"]) < 7:\n", - " return \"b\"\n", - " else:\n", - " return END\n", - "\n", - "\n", - "builder.add_edge(START, \"a\")\n", - "builder.add_conditional_edges(\"a\", route)\n", - "builder.add_edge(\"b\", \"c\")\n", - "builder.add_edge(\"b\", \"d\")\n", - "builder.add_edge([\"c\", \"d\"], \"a\")\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bdb2a545-3f1f-408b-8aa3-3702d37eedf8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "a1d4cc42-590c-4de9-9e51-0bb8a79db86f", - "metadata": {}, - "source": [ - "This graph looks complex, but can be conceptualized as loop of [supersteps](../../concepts/low_level/#graphs):\n", - "\n", - "1. Node A\n", - "2. Node B\n", - "3. Nodes C and D\n", - "4. Node A\n", - "5. ...\n", - "\n", - "We have a loop of four supersteps, where nodes C and D are executed concurrently.\n", - "\n", - "Invoking the graph as before, we see that we complete two full \"laps\" before hitting the termination condition:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d5c692d0-9a69-4743-bd47-e462adab8700", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node A sees []\n", - "Node B sees ['A']\n", - "Node D sees ['A', 'B']\n", - "Node C sees ['A', 'B']\n", - "Node A sees ['A', 'B', 'C', 'D']\n", - "Node B sees ['A', 'B', 'C', 'D', 'A']\n", - "Node D sees ['A', 'B', 'C', 'D', 'A', 'B']\n", - "Node C sees ['A', 'B', 'C', 'D', 'A', 'B']\n", - "Node A sees ['A', 'B', 'C', 'D', 'A', 'B', 'C', 'D']\n" - ] - } - ], - "source": [ - "result = graph.invoke({\"aggregate\": []})" - ] - }, - { - "cell_type": "markdown", - "id": "42cb9253-93a9-4a33-b4fb-a235aa41d655", - "metadata": {}, - "source": [ - "However, if we set the recursion limit to four, we only complete one lap because each lap is four supersteps:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d0ff64b3-eb78-48a4-aab5-bb78499f92df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Node A sees []\n", - "Node B sees ['A']\n", - "Node C sees ['A', 'B']\n", - "Node D sees ['A', 'B']\n", - "Node A sees ['A', 'B', 'C', 'D']\n", - "Recursion Error\n" - ] - } - ], - "source": [ - "from langgraph.errors import GraphRecursionError\n", - "\n", - "try:\n", - " result = graph.invoke({\"aggregate\": []}, {\"recursion_limit\": 4})\n", - "except GraphRecursionError:\n", - " print(\"Recursion Error\")" - ] - }, - { - "cell_type": "markdown", - "id": "13579366-69fb-4aa4-9d95-a9865c1d5799", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "5a2d23ae-ea3f-478b-8db6-791cd29cfb6c", - "metadata": {}, - "source": [ - "## Async\n", - "\n", - "Using the [async](https://docs.python.org/3/library/asyncio.html) programming paradigm can produce significant performance improvements when running [IO-bound](https://en.wikipedia.org/wiki/I/O_bound) code concurrently (e.g., making concurrent API requests to a chat model provider).\n", - "\n", - "To convert a `sync` implementation of the graph to an `async` implementation, you will need to:\n", - "\n", - "1. Update `nodes` use `async def` instead of `def`.\n", - "2. Update the code inside to use `await` appropriately.\n", - "3. Invoke the graph with `.ainvoke` or `.astream` as desired.\n", - "\n", - "Because many LangChain objects implement the [Runnable Protocol](https://python.langchain.com/docs/expression_language/interface/) which has `async` variants of all the `sync` methods it's typically fairly quick to upgrade a `sync` graph to an `async` graph.\n", - "\n", - "See example below. To demonstrate async invocations of underlying LLMs, we will include a chat model:\n", - "\n", - "{!snippets/chat_model_tabs.md!}\n", - "\n", - "```python\n", - "from langchain.chat_models import init_chat_model\n", - "from langgraph.graph import MessagesState, StateGraph\n", - "\n", - "\n", - "# highlight-next-line\n", - "async def node(state: MessagesState): # (1)!\n", - " # highlight-next-line\n", - " new_message = await llm.ainvoke(state[\"messages\"]) # (2)!\n", - " return {\"messages\": [new_message]}\n", - "\n", - "\n", - "builder = StateGraph(MessagesState).add_node(node).set_entry_point(\"node\")\n", - "graph = builder.compile()\n", - "\n", - "input_message = {\"role\": \"user\", \"content\": \"Hello\"}\n", - "# highlight-next-line\n", - "result = await graph.ainvoke({\"messages\": [input_message]}) # (3)!\n", - "```\n", - "\n", - "1. Declare nodes to be async functions.\n", - "2. Use async invocations when available within the node.\n", - "3. Use async invocations on the graph object itself.\n", - "\n", - "!!! tip \"Async streaming\"\n", - " See the [streaming guide](../../how-tos/streaming) for examples of streaming with async." - ] - }, - { - "cell_type": "markdown", - "id": "d33ecddc-6818-41a3-9d0d-b1b1cbcd286d", - "metadata": {}, - "source": [ - "## Combine control flow and state updates with `Command`" - ] - }, - { - "cell_type": "markdown", - "id": "7c0a8d03-80b4-47fd-9b17-e26aa9b081f3", - "metadata": {}, - "source": [ - "It can be useful to combine control flow (edges) and state updates (nodes). For example, you might want to BOTH perform state updates AND decide which node to go to next in the SAME node. LangGraph provides a way to do so by returning a [Command](/langgraph/reference/types/#langgraph.types.Command) object from node functions:\n", - "\n", - "```python\n", - "def my_node(state: State) -> Command[Literal[\"my_other_node\"]]:\n", - " return Command(\n", - " # state update\n", - " update={\"foo\": \"bar\"},\n", - " # control flow\n", - " goto=\"my_other_node\"\n", - " )\n", - "```\n", - "\n", - "We show an end-to-end example below. Let's create a simple graph with 3 nodes: A, B and C. We will first execute node A, and then decide whether to go to Node B or Node C next based on the output of node A." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4539b81b-09e9-4660-ac55-1b1775e13892", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "from typing_extensions import TypedDict, Literal\n", - "\n", - "from langgraph.graph import StateGraph, START\n", - "from langgraph.types import Command\n", - "\n", - "\n", - "# Define graph state\n", - "class State(TypedDict):\n", - " foo: str\n", - "\n", - "\n", - "# Define the nodes\n", - "\n", - "\n", - "def node_a(state: State) -> Command[Literal[\"node_b\", \"node_c\"]]:\n", - " print(\"Called A\")\n", - " value = random.choice([\"a\", \"b\"])\n", - " # this is a replacement for a conditional edge function\n", - " if value == \"a\":\n", - " goto = \"node_b\"\n", - " else:\n", - " goto = \"node_c\"\n", - "\n", - " # note how Command allows you to BOTH update the graph state AND route to the next node\n", - " return Command(\n", - " # this is the state update\n", - " update={\"foo\": value},\n", - " # this is a replacement for an edge\n", - " goto=goto,\n", - " )\n", - "\n", - "\n", - "def node_b(state: State):\n", - " print(\"Called B\")\n", - " return {\"foo\": state[\"foo\"] + \"b\"}\n", - "\n", - "\n", - "def node_c(state: State):\n", - " print(\"Called C\")\n", - " return {\"foo\": state[\"foo\"] + \"c\"}" - ] - }, - { - "cell_type": "markdown", - "id": "badc25eb-4876-482e-bb10-d763023cdaad", - "metadata": {}, - "source": [ - "We can now create the `StateGraph` with the above nodes. Notice that the graph doesn't have [conditional edges](../../concepts/low_level#conditional-edges) for routing! This is because control flow is defined with `Command` inside `node_a`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d6711650-4380-4551-a007-2805f49ab2d8", - "metadata": {}, - "outputs": [], - "source": [ - "builder = StateGraph(State)\n", - "builder.add_edge(START, \"node_a\")\n", - "builder.add_node(node_a)\n", - "builder.add_node(node_b)\n", - "builder.add_node(node_c)\n", - "# NOTE: there are no edges between nodes A, B and C!\n", - "\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "0ab344c5-d634-4d7d-b3b4-edf4fa875311", - "metadata": {}, - "source": [ - "!!! important\n", - "\n", - " You might have noticed that we used `Command` as a return type annotation, e.g. `Command[Literal[\"node_b\", \"node_c\"]]`. This is necessary for the graph rendering and tells LangGraph that `node_a` can navigate to `node_b` and `node_c`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "eeb810e5-8822-4c09-8d53-c55cd0f5d42e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Image\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "58fb6c32-e6fb-4c94-8182-e351ed52a45d", - "metadata": {}, - "source": [ - "If we run the graph multiple times, we'd see it take different paths (A -> B or A -> C) based on the random choice in node A." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d88a5d9b-ee08-4ed4-9c65-6e868210bfac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Called A\n", - "Called C\n" - ] - }, - { - "data": { - "text/plain": [ - "{'foo': 'bc'}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"foo\": \"\"})" - ] - }, - { - "cell_type": "markdown", - "id": "68986cc4-97ec-43a1-b95d-5273d7ffc25a", - "metadata": {}, - "source": [ - "### Navigate to a node in a parent graph\n", - "\n", - "If you are using [subgraphs](../../concepts/subgraphs), you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`:\n", - "\n", - "```python\n", - "def my_node(state: State) -> Command[Literal[\"my_other_node\"]]:\n", - " return Command(\n", - " update={\"foo\": \"bar\"},\n", - " goto=\"other_subgraph\", # where `other_subgraph` is a node in the parent graph\n", - " graph=Command.PARENT\n", - " )\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "02ccddf2-978c-41bf-b2eb-2d0c4b3f5d81", - "metadata": {}, - "source": [ - "Let's demonstrate this using the above example. We'll do so by changing `node_a` in the above example into a single-node graph that we'll add as a subgraph to our parent graph.\n", - "\n", - "!!! important \"State updates with `Command.PARENT`\"\n", - "\n", - " When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](../../concepts/low_level#schema), you **must** define a [reducer](../../concepts/low_level#reducers) for the key you're updating in the parent graph state. See the example below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "91351541-67af-4c73-9437-426599dcf81e", - "metadata": {}, - "outputs": [], - "source": [ - "import operator\n", - "from typing_extensions import Annotated\n", - "\n", - "\n", - "class State(TypedDict):\n", - " # NOTE: we define a reducer here\n", - " # highlight-next-line\n", - " foo: Annotated[str, operator.add]\n", - "\n", - "\n", - "def node_a(state: State):\n", - " print(\"Called A\")\n", - " value = random.choice([\"a\", \"b\"])\n", - " # this is a replacement for a conditional edge function\n", - " if value == \"a\":\n", - " goto = \"node_b\"\n", - " else:\n", - " goto = \"node_c\"\n", - "\n", - " # note how Command allows you to BOTH update the graph state AND route to the next node\n", - " return Command(\n", - " update={\"foo\": value},\n", - " goto=goto,\n", - " # this tells LangGraph to navigate to node_b or node_c in the parent graph\n", - " # NOTE: this will navigate to the closest parent graph relative to the subgraph\n", - " # highlight-next-line\n", - " graph=Command.PARENT,\n", - " )\n", - "\n", - "\n", - "subgraph = StateGraph(State).add_node(node_a).add_edge(START, \"node_a\").compile()\n", - "\n", - "\n", - "def node_b(state: State):\n", - " print(\"Called B\")\n", - " # NOTE: since we've defined a reducer, we don't need to manually append\n", - " # new characters to existing 'foo' value. instead, reducer will append these\n", - " # automatically (via operator.add)\n", - " # highlight-next-line\n", - " return {\"foo\": \"b\"}\n", - "\n", - "\n", - "def node_c(state: State):\n", - " print(\"Called C\")\n", - " # highlight-next-line\n", - " return {\"foo\": \"c\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "beb61d02-c868-4c2b-b83f-1dfd280f1c8e", - "metadata": {}, - "outputs": [], - "source": [ - "builder = StateGraph(State)\n", - "builder.add_edge(START, \"subgraph\")\n", - "builder.add_node(\"subgraph\", subgraph)\n", - "builder.add_node(node_b)\n", - "builder.add_node(node_c)\n", - "\n", - "graph = builder.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3f07b704-1fe2-48a3-ad40-c9bc7698cb1c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Called A\n", - "Called C\n" - ] - }, - { - "data": { - "text/plain": [ - "{'foo': 'bc'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "graph.invoke({\"foo\": \"\"})" - ] - }, - { - "cell_type": "markdown", - "id": "bcd31ceb-f96f-4325-878d-ae1dea8cde8a", - "metadata": {}, - "source": [ - "### Use inside tools\n", - "\n", - "A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. To update the graph state from the tool, you can return `Command(update={\"my_custom_key\": \"foo\", \"messages\": [...]})` from the tool:\n", - "\n", - "```python\n", - "@tool\n", - "def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: RunnableConfig):\n", - " \"\"\"Use this to look up user information to better assist them with their questions.\"\"\"\n", - " user_info = get_user_info(config.get(\"configurable\", {}).get(\"user_id\"))\n", - " return Command(\n", - " update={\n", - " # update the state keys\n", - " \"user_info\": user_info,\n", - " # update the message history\n", - " \"messages\": [ToolMessage(\"Successfully looked up user information\", tool_call_id=tool_call_id)]\n", - " }\n", - " )\n", - "```\n", - "\n", - "!!! important\n", - " You MUST include `messages` (or any state key used for the message history) in `Command.update` when returning `Command` from a tool and the list of messages in `messages` MUST contain a `ToolMessage`. This is necessary for the resulting message history to be valid (LLM providers require AI messages with tool calls to be followed by the tool result messages).\n", - "\n", - "If you are using tools that update state via `Command`, we recommend using prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] which automatically handles tools returning `Command` objects and propagates them to the graph state. If you're writing a custom node that calls tools, you would need to manually propagate `Command` objects returned by the tools as the update from the node." - ] - }, - { - "cell_type": "markdown", - "id": "8bcd1a3d-7c50-4f58-be4e-1ed654aa33be", - "metadata": {}, - "source": [ - "## Visualize your graph\n", - "\n", - "Here we demonstrate how to visualize the graphs you create." - ] - }, - { - "cell_type": "markdown", - "id": "e130cf70-a30e-47d7-8fd5-464f1a92e374", - "metadata": {}, - "source": [ - "You can visualize any arbitrary [Graph](https://langchain-ai.github.io/langgraph/reference/graphs/), including [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.StateGraph). Let's have some fun by drawing fractals :)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6d604311", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "from typing import Annotated, Literal\n", - "\n", - "from typing_extensions import TypedDict\n", - "\n", - "from langgraph.graph import StateGraph, START, END\n", - "from langgraph.graph.message import add_messages\n", - "\n", - "\n", - "class State(TypedDict):\n", - " messages: Annotated[list, add_messages]\n", - "\n", - "\n", - "class MyNode:\n", - " def __init__(self, name: str):\n", - " self.name = name\n", - "\n", - " def __call__(self, state: State):\n", - " return {\"messages\": [(\"assistant\", f\"Called node {self.name}\")]}\n", - "\n", - "\n", - "def route(state) -> Literal[\"entry_node\", \"__end__\"]:\n", - " if len(state[\"messages\"]) > 10:\n", - " return \"__end__\"\n", - " return \"entry_node\"\n", - "\n", - "\n", - "def add_fractal_nodes(builder, current_node, level, max_level):\n", - " if level > max_level:\n", - " return\n", - "\n", - " # Number of nodes to create at this level\n", - " num_nodes = random.randint(1, 3) # Adjust randomness as needed\n", - " for i in range(num_nodes):\n", - " nm = [\"A\", \"B\", \"C\"][i]\n", - " node_name = f\"node_{current_node}_{nm}\"\n", - " builder.add_node(node_name, MyNode(node_name))\n", - " builder.add_edge(current_node, node_name)\n", - "\n", - " # Recursively add more nodes\n", - " r = random.random()\n", - " if r > 0.2 and level + 1 < max_level:\n", - " add_fractal_nodes(builder, node_name, level + 1, max_level)\n", - " elif r > 0.05:\n", - " builder.add_conditional_edges(node_name, route, node_name)\n", - " else:\n", - " # End\n", - " builder.add_edge(node_name, \"__end__\")\n", - "\n", - "\n", - "def build_fractal_graph(max_level: int):\n", - " builder = StateGraph(State)\n", - " entry_point = \"entry_node\"\n", - " builder.add_node(entry_point, MyNode(entry_point))\n", - " builder.add_edge(START, entry_point)\n", - "\n", - " add_fractal_nodes(builder, entry_point, 1, max_level)\n", - "\n", - " # Optional: set a finish point if required\n", - " builder.add_edge(entry_point, END) # or any specific node\n", - "\n", - " return builder.compile()\n", - "\n", - "\n", - "app = build_fractal_graph(3)" - ] - }, - { - "cell_type": "markdown", - "id": "edcd9ad2", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-18T12:18:30.629307Z", - "start_time": "2024-04-18T12:18:30.609323Z" - } - }, - "source": [ - "### Mermaid\n", - "\n", - "We can also convert a graph class into Mermaid syntax." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "66007b2d", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T11:25:38.733126Z", - "start_time": "2024-04-19T11:25:38.726838Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "%%{init: {'flowchart': {'curve': 'linear'}}}%%\n", - "graph TD;\n", - "\t__start__([

__start__

]):::first\n", - "\tentry_node(entry_node)\n", - "\tnode_entry_node_A(node_entry_node_A)\n", - "\tnode_entry_node_B(node_entry_node_B)\n", - "\tnode_node_entry_node_B_A(node_node_entry_node_B_A)\n", - "\tnode_node_entry_node_B_B(node_node_entry_node_B_B)\n", - "\tnode_node_entry_node_B_C(node_node_entry_node_B_C)\n", - "\t__end__([

__end__

]):::last\n", - "\t__start__ --> entry_node;\n", - "\tentry_node --> __end__;\n", - "\tentry_node --> node_entry_node_A;\n", - "\tentry_node --> node_entry_node_B;\n", - "\tnode_entry_node_B --> node_node_entry_node_B_A;\n", - "\tnode_entry_node_B --> node_node_entry_node_B_B;\n", - "\tnode_entry_node_B --> node_node_entry_node_B_C;\n", - "\tnode_entry_node_A -.-> entry_node;\n", - "\tnode_entry_node_A -.-> __end__;\n", - "\tnode_node_entry_node_B_A -.-> entry_node;\n", - "\tnode_node_entry_node_B_A -.-> __end__;\n", - "\tnode_node_entry_node_B_B -.-> entry_node;\n", - "\tnode_node_entry_node_B_B -.-> __end__;\n", - "\tnode_node_entry_node_B_C -.-> entry_node;\n", - "\tnode_node_entry_node_B_C -.-> __end__;\n", - "\tclassDef default fill:#f2f0ff,line-height:1.2\n", - "\tclassDef first fill-opacity:0\n", - "\tclassDef last fill:#bfb6fc\n", - "\n" - ] - } - ], - "source": [ - "print(app.get_graph().draw_mermaid())" - ] - }, - { - "cell_type": "markdown", - "id": "8f77ad75", - "metadata": {}, - "source": [ - "### PNG\n", - "\n", - "If preferred, we could render the Graph into a `.png`. Here we could use three options:\n", - "\n", - "- Using Mermaid.ink API (does not require additional packages)\n", - "- Using Mermaid + Pyppeteer (requires `pip install pyppeteer`)\n", - "- Using graphviz (which requires `pip install graphviz`)\n", - "\n", - "\n", - "**Using Mermaid.Ink**\n", - "\n", - "By default, `draw_mermaid_png()` uses Mermaid.Ink's API to generate the diagram." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "967f116d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles\n", - "\n", - "display(Image(app.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "b9e767fc", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-18T12:18:30.873950Z", - "start_time": "2024-04-18T12:18:30.871750Z" - } - }, - "source": [ - "**Using Mermaid + Pyppeteer**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d403e1e7", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T11:25:44.798703Z", - "start_time": "2024-04-19T11:25:44.793438Z" - } - }, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install --quiet pyppeteer\n", - "%pip install --quiet nest_asyncio" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "058546ee", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T11:25:47.412695Z", - "start_time": "2024-04-19T11:25:45.405158Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import nest_asyncio\n", - "\n", - "nest_asyncio.apply() # Required for Jupyter Notebook to run async functions\n", - "\n", - "display(\n", - " Image(\n", - " app.get_graph().draw_mermaid_png(\n", - " curve_style=CurveStyle.LINEAR,\n", - " node_colors=NodeStyles(first=\"#ffdfba\", last=\"#baffc9\", default=\"#fad7de\"),\n", - " wrap_label_n_words=9,\n", - " output_file_path=None,\n", - " draw_method=MermaidDrawMethod.PYPPETEER,\n", - " background_color=\"white\",\n", - " padding=10,\n", - " )\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d821b2f6", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-18T12:18:30.629629Z", - "start_time": "2024-04-18T12:18:30.620092Z" - } - }, - "source": [ - "**Using Graphviz**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4234400-75cd-4b13-aeff-828f7fb68ab1", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T11:25:42.057704Z", - "start_time": "2024-04-19T11:25:42.019017Z" - } - }, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install pygraphviz" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ee026342-f560-4ce0-ab43-1718bd19a366", - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T11:25:42.631675Z", - "start_time": "2024-04-19T11:25:42.452377Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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Px9y5c9GxY0fRaX+qsLAQUVFRiIiIQHh4OMLCwpCeng4VFRVoamqi5D975TVq1AhWVlYIDQ2FpqZmldx70qRJKCsrw759+6rk84mSlZUFf39/bNy4EZqampg3bx7mzJlTJ1a81Rfbt2/HggULUFRUhBs3bsDKykp00hvZtm0bAgMDkZSUhGfPnkFLS0t0EhEREREREVFDxkEUEdUe2dnZvxk2RUVF4ebNm8jPzwcANG3aFKamprCxsakYOPXo0aNOrfqgvyaXy3Hy5Els2bIFISEh6NGjB6ZPn45PPvmkVq5sOHXqFI4fP47Lly/j9u3bkMvl0NDQgFwuh1wu/8P1qqqqaNasGWJiYtC2bdsqaUhPT0e7du1gYGCA9PT0KvmcomVmZmL9+vXw9/eHrq4u5s6dCzc3Nw4VaohcLkdSUhKMjY0rXRG1ZcsWvP3227CxscFbb71Vg4X/XGFhIYyNjWFubo5PPvkEn332megkIiIiIiIiovqIgygiqnm/HzglJibi5s2bFQ/QXw6c/neVk6WlJVq2bCm4nKpTdnY2AgICsHXrVqSlpeGDDz7A7Nmz0a9fP9FplQoODoazs/NrX6+uro4rV67Azs6uyhqWLVuGlStXQqlU4tatW+jatWuVfW7RMjIysGHDBmzZsgUtWrTAvHnzMG3atCpbSUZ/n0wmg6mpKVJSUuDi4oKgoCDRSW+koKAAO3fuRFxcHOzt7TF58uRXXltUVARtbW2oqKjUYCERERERERFRvcBBFBFVn7KyMsTHxyM2NhaxsbG4efMm4uLikJmZCQBo3rw5zM3NYWpqCgsLC5iamsLc3BzNmzcXXE41KSUlBZs2bcKePXugpqaGqVOnYubMmejQoYPotNc2fPhwnD59GjKZ7C+v3bVrV6UPvN9UcXExDA0NkZubCzU1NWzevBkzZsyoss9fWzx+/Bjr1q3Djh070Lp1ayxatAiTJk2Cmpqa6LQGLzc3F/n5+ZWe2xQWFobY2FhYWVnBysqqzg0S586di127dsHc3BxHjx7lGyOIiIiIiIiIXh8HUURUNTIzMxETE1MxdIqJicHt27chk8mgra0Nc3Nz9OjRA2ZmZjAzM4O5uTnatGkjOpsEioqKwubNm3HgwAG0b98erq6ucHV1hYGBgei0N/bo0SO88847KC4ufuU1qqqqmDNnDr766qsqvXdAQABmzJgBhUIBVVVVjBgxAj/++GOV3qM2efToEdavX4+AgAAYGhrC09OTA6k6YMuWLfDy8kJeXh7u3LkDExMT0UlvJCEhAaGhoYiPj8fGjRuhqqr6ymsTExPRpUuXSrc0JCIiIiIiImpAOIgiojf39OlTREVFVXxLTEzEvXv3oFQqf3OO08uznCwsLKChoSE6m2oBhUKBEydOwNfXF2FhYbCxscGcOXMwduzYOj9IWLduHTw8PKBQKP7wmrq6Ouzt7XHu3Lkq/fdUKpUwMTFBSkoKXv51rqenh+zsbDRq1KjK7lMbPXjwAD4+Pvj222/RoUMHuLu7Y/LkyZUOCEgspVKJ1NRUGBkZVfrr08fHB126dIGtrS3efvvtmgusApmZmWjZsiU0NDQwf/58rF69WnQSERERERERkWgcRBHRq5WVlSE5Ofk3Q6eYmBgUFhZCVVUVHTt2/M3QqWfPnjA0NBSdTbVQQUEB9u/fjw0bNuDu3bt4//334ebmBolEIjqtypSXl8PS0hJJSUkoLy+v+Hk1NTW0adMG0dHRaNGiRZXe81XnU0VHR6NHjx5Veq/a6v79+1izZg2+/fZbmJiYwN3dHZ988km9H8TVV4WFhbC1tUVSUhI++eQT7NmzR3TSG5HL5YiPj0d8fDzat29f6Rl3KSkpkMvlMDY25q9XIiIiIiIiqs84iCKiXz19+hSJiYlISEioGDrdvn0bCoUCTZo0gYmJyW+GTtbW1tDRFt6oXQAAIABJREFU0RGdTbXcs2fPEBAQgC1btkAmk2Hs2LGYO3cu3nnnHdFp1SI0NBSOjo4Vq5NUVFSgpaWFyMhImJubV/n9+vXrh/Dw8N8MvtTV1bFmzRrMmzevyu9Xm92+fRs+Pj7Yv38/unXrhqVLl2LkyJFQUVERnUZ/Q1FREQoKCtCqVatXXvPLL78gISEB1tbWsLW1rXPnTrm6umLHjh1o0qQJ7t69W+m/KxEREREREVEdxkEUUUNTXl6OW7duITo6uuJMp5iYGLx48QIA8Pbbb8PS0hKWlpbo0aMHLC0tYWRkxIe59Eaio6OxceNG/PDDD2jevDlcXV0xZ84cNGvWTHRatZs0aRL27dsHmUwGFRUV/PzzzxgxYkSV3+fmzZvo0aMHfv/XeKNGjTB48GCcOnWqyu9ZFyQmJsLX1xfff/89zMzM4OXlxYFUPfXVV1/Bx8cHWVlZePz4Md566y3RSW+kuLgYcXFxuHnzJqZMmVLptdevX4e5uTm0tLRqqI6IiIiIiIioynAQRVTfvTzPKTQ0FFKptGJrPXV1dXTp0qXiHCdTU1P07t0bLVu2FJ1MdZRCocD58+exefNmBAcHo0ePHpg+fTrGjx9fbx6eFhUVYc+ePZg2bdort9LKysqCsbExcnJy4O3tDS8vr2ppGTduHIKCgiCTyf7wmra2NnJzc6Gurl4t964L4uPj4e3tjcOHD6N79+5YvHgxRo0aJTqLqsHDhw/RoUOHV76uVCqxYcMGWFpawtraGs2bN6/Bun/u/v37MDIygpqaGry8vLB06VLRSURERERERERvgoMoovpCqVQiJSUF169fr/h248YN5OfnQ0NDA927d0fPnj0rvpmZmUFNTU10NtUDpaWlOHjwIHx9fXH79m0MGjQIc+bMwbBhw+rVKpSLFy9i/PjxyM3NRVhYGMzMzF557e7duxEcHIzDhw9Xy3+Dp0+fomPHjr/Zku/3wsLC0KdPnyq/d10TFxeHlStX4vDhw+jduzc8PT3/9Fwtqr/S0tJga2uLR48eYebMmdi6davopDeiUCiQnJyM6OhoGBkZwc7O7pXXPnz4EI0bN65zwzYiIiIiIiKq1ziIIqqrXq50evktIiICmZmZUFNTg4mJScVZTjY2NujZs2e9WZFCtUd6ejq2b9+Obdu2IT8/H6NHj4aHhwdMTU1Fp1WLpKQkrF+/HitXrkTr1q0rvVapVKKkpATa2trV0uLp6YkNGzb86WooANDQ0ICXlxeWLFlSLfevi65evYpVq1YhODgY9vb2WLFiBQYNGiQ6i2pQRkYGZDIZ2rZt+8prQkNDkZmZCWtra7Rv374G66rGuHHjsH//fnTs2BGJiYk8y5GIiIiIiIhqAw6iiOqC3w+drl27hrS0NABAp06dYG9vXzF0sra25oMnqlZJSUnYtm0bdu7ciSZNmmDixIlwc3ODoaGh6LQGoaioCIaGhsjLy3vlNSoqKnB0dMSlS5dqsKxuCA8Ph4+PT8VAauXKlRgwYIDoLKol3NzcsHXrViiVSrx48QIGBgaik95IWloabty4geTkZMyZM+eV1ykUCpSVlfFNKkRERERERFQTOIgiqm1ycnIQHx9fMXSSSqW4d+8eAMDQ0PA3K50cHBzQtGlTwcXUUEilUvj5+eHEiRMwNjbGzJkzMXXq1Gpb9UN/btu2bZg1axbU1NSgqqoKpVIJmUyG3/91rq6ujtzcXP7v8wqhoaFYunQpzp8/D4lEgtWrV6NXr16is6gWyMvLQ2JiInr37v3Ka8rLy3Hs2DHY2NigY8eONVhXNRISEmBlZQVLS0t4eHjAxcVFdBIRERERERHVXxxEEYmUm5uLyMjI35zr9PDhQwCAkZERbG1t0bNnz4rBk76+vuBiamgUCgWOHTsGPz8/REREoH///nB3d8d7771Xr85/eunp06eVbttVG8TFxeHu3bt4/vw5MjMzkZGRgX379kFXVxdaWlrIyspCbm4u5HI5QkJCuP3cX5BKpfDy8sLFixchkUjg4+MDW1tb0VlUy926dQsWFhaQy+VYsmQJVq5cKTrpjeTk5ODkyZOIjIzEsGHDIJFIRCcRERERERFR/cVBFFFNSk1NhVQqRVRUFEJDQxEdHQ2FQvGHlU52dnZo1aqV6FxqwGQyGQ4cOAA/Pz/cunULQ4cOhaenJ/r27Ss6rVrcunULX3zxBeLj45GamgpNTU3RSW9k48aNsLS0xMCBAyt+LjMzExoaGtDT0xNYVneEhIRg8eLFiIyMhEQigZ+fH6ytrUVnUS1WUFCA6OhotG7dGiYmJq+8LikpCbq6urV+yP0qrq6uyMzMRN++fTFv3jzROURERERERFT3cBBFVF0yMzMRERGBq1evIjw8HJGRkcjPz0fjxo3Rs2dP9OnTB71794adnR3atGkjOpcIAFBYWIhdu3bhq6++wrNnzzBmzBi4u7vDzMxMdFq1mj9/Ps6fP4/NmzfD0dFRdA4JFBISAk9PT0RFRWHo0KHw9vaGlZWV6Cyqw4YNG4YTJ06gQ4cOSElJgZqamuikN/L111/jzJkzKCoqwtmzZ0XnEBERERERUd3DQRRRVSgvL0dsbCwiIiIqhk/JyclQUVGBiYlJxcCpT58+MDc3r3MPoaj+y8zMxNatW7F161aUlJRg8uTJmDdvHjp06CA6rUYUFxdDU1MTjRo1Ep1CtURISAjc3d0RExMDFxcXeHt7o2vXrqKzqA7Kzc3FjRs3cP/+fUycOPGV18lkMmRnZ9fZFdGJiYn45ptv0KdPHwwcOBDNmjUTnURERERERES1AwdRRH9HQUEBYmJiEBoaCqlUiitXriA3NxdNmjRB9+7d4eDgAHt7e/Tp0wctWrQQnUv0Sg8fPsSGDRvwzTffQE1NDdOmTcOCBQvQvHlz0WlEwimVSgQHB2Pp0qW4efMmXFxcsGrVqkq3YSP6u8LDw9G3b1906NABa9euxUcffSQ66Y1cvHgRX375JeLi4vDTTz9h+PDhopOIiIiIiIioduAgiuh1PHjwAFKpFGFhYZBKpYiPj4dCoYCJiQn69u0LBwcH9OnTB127duWKCqoT7t69C39/fwQEBKBp06ZwdXXFl19+CX19fdFp1aa8vJyrEelvUSgU+PHHH7F06VIkJSXBxcUFPj4+MDY2Fp1G9UhBQQHCwsIQFRWFwYMHw8bG5pXXKhSKWvv1RkFBAdTV1Ss9ay8qKgpdunThGXZEREREREQNAwdRRL8nl8tx+/btitVOUqkU9+7dg5qaGiwtLWFvbw8bGxsMGDAA7du3F51L9EZu3LiBTZs2Yf/+/TAyMsKsWbMwbdq0Sh8Y1nVRUVFYsGAB7O3tsXLlStE5VIe9HEgtWbIE9+7dw5gxY7Bs2TJ07txZdBo1MIMHD0ZaWhr69++PLVu2iM55IwqFAs2aNUNBQQHGjBmDffv2iU4iIiIiIiKi6sVBFFFJSQkiIyNx6dIlXLlyBREREcjPz4eBgQHs7e0rVjzZ2tpCW1tbdC7R3yKVSuHn54fg4GBYW1vDzc0N48aNg6qqqui0alVSUoKOHTuiU6dO2LBhA/r27Ss6ieqBlwOpRYsW4cGDB5g4cSK8vLzQrl070WnUQBw/fhxXrlxBcXEx/P39Ree8sefPnyM8PByqqqrcwo+IiIiIiKj+4yCKGp7CwkKEhYXhypUruHTpEiIjI1FSUoL27dujf//+Fec7mZqa1tptb4heh0KhwIkTJ+Dt7Y3r16/D3t4e7u7ucHZ2Fp1Wo1JTU2FkZAQVFRXRKVTPyGQyHDhwAN7e3nj06BEmTJiAZcuWoW3btqLTiAAAly9fxvLly2Fra4vJkyfXufPNAgICcPr0afTr1w+TJk2q19vHEhERERER1WPZfMpO9V5BQQFCQkKwfPlyODk5oXnz5hg8eDB27dqF1q1bY/PmzYiPj8fDhw+xd+9euLq6wtzcnEMoqrNKS0sRGBiIbt264YMPPkCbNm0QGRkJqVTa4IZQANCpU6d6PYQ6evQot7YSRF1dHePHj8etW7fg7++PEydOoFOnTnB1dcWzZ89E5xFBV1cXb7/9Nk6ePIm0tDTROW+sTZs2UCqV8PPzq9d/jhMREREREdV3XBFF9c6LFy9w+fJlXLp0CZcvX0ZsbCwUCgW6deuG/v37o1+/fujXrx/fsU71Tk5ODrZt24YtW7YgPz8fEyZMwPz589GpUyfRaVSNxo0bh8LCQhw5ckR0SoNXVlaGPXv2YMWKFcjNzcXkyZOxaNEitG7dWnQa0V/y8PBA8+bNK7YlrkvKy8sRERGBXr16QUNDQ3QOERERERER/Ra35qO6r6ioCGFhYQgJCYFUKsXVq1ehUCjQtWtXODg4QCKR4N1330XLli1FpxJVi8zMTGzduhWbN2+GXC7HxIkT4e7u3iCGrREREVi0aBF27tyJzp07i84RYvDgwejYsSN27twpOoX+o7S0FN999x2WL1+OvLw8zJo1CwsXLkSzZs1EpxG90ujRoyGVSmFhYYEzZ86Iznkj169fh62tLXR0dLBx40ZMnTpVdBIRERERERH9FwdRVPeUlpYiIiIC58+fx/nz53H16lXIZDKYmZlh4MCBGDhwIN59910YGBiITiWqVs+fP8f69esREBAALS0tfPnll5g1axb09PREp9WIU6dO4f3334dEIsH27dvRpUsX0UlCPH36FAqFAu3atROdQr9TWFiIXbt2Yc2aNSgsLMTMmTPh4eHBv5+oVissLETjxo1f+XpCQgLu3LmDXr161ao/d1JTU3Hx4kXY2NjA0tJSdA4RERERERH9FwdRVPvJ5XLExMRAKpUiNDQUZ86cQV5eHgwNDStWPP3rX/9C+/btRacS1Yi0tDRs3LgR/v7+0NXVxfTp0zF37twGM4B6qby8HJcvX8bAgQNFpxBVqqCgANu2bcPatWshk8kwY8YMeHp6Ql9fX3Qa0RvbtGkT5s+fD7lcjqioKFhbW4tOeiPLli2Dvr4+Bg8eDHNzc9E5REREREREDQEHUVQ7JScn4+zZszh37hwuXryI3NxctGnTpmLF08CBA2FkZCQ6k6hGPXjwAF999RV27NgBfX19fPnll5gzZw60tbVFpxHRa3g5kPL19YWqqipmzZrVIIfIVPfl5+cjKioK9vb2UFdXf+V1t2/fhrGxMdTU1GqwrnKTJk3CsWPHYGxsjIiICNE5REREREREDQEHUVQ75Obm4vz58zh79izOnDmDe/fuQU9PDwMGDMCgQYMwcOBAmJmZic4kEuLevXvw9fXF7t270bZtW3z55ZdwdXWFlpaW6DQi+hvy8/Oxfft2rFmzBurq6pg/fz5mz54NHR0d0WlEVSY/Px9NmzaFlpYW5syZAx8fH9FJFRQKBdLT09GmTRvRKURERERERA0BB1EkhkKhQHR0NEJCQhASEoLLly+jvLwcVlZWkEgkkEgk6NevHzQ0NESnEgmTkJAAPz8/HDhwAB06dIC7uzsmTZpUq95ZXl3KysoQEBCAxMREfP3116JziKpFVlYW/P39sXHjRmhqamLevHlc5Uj1hlwuR0JCAiIjI9G2bVu8//77opPeyPr163HkyBFIJBK4ubmhadOmopOIiIiIiIjqKg6iqOY8f/4cV65cwfHjx3HixAm8ePECrVu3Rr9+/SCRSODs7AxDQ0PRmUTCxcXFYd26ddi/fz+6du2KhQsXYuzYsQ1iAPXSBx98gDNnzmDGjBlYt24dGjVqJDqJqNpkZmZi/fr1Fee+zZ07F25ublz1SA3G/Pnzcfr0afTp0wcbNmyoFdtVXrhwAd9//z0uX76M6OhoNG7cWHQSERERERFRXcVBFFWf8vJySKVSnDx5EidOnEBiYiJ0dHTQr18/DB48GEOGDIGpqanoTKJaIyYmBj4+Pjh8+DAsLCwwb948jBs3DqqqqqLTalxsbCyaNWuG9u3bi04hqjEZGRnYsGEDtmzZghYtWmDevHmYNm0aNDU1RacRVavLly8jODgYN2/exMmTJ+vUmw8UCgXkcnmlZ2URERERERE1cBxEUdXKyMjAqVOncOLECZw9exY5OTl45513MGzYMLz33ntwcHDgO7yJfic0NBS+vr4IDg5Gjx49sGjRIowcORIqKiqi04hIgPT0dHz11VfYvHkzWrVqhcWLFzeYbTmJKpOZmYkFCxagd+/eGDx4MIyMjEQnQSqVYujQoRg0aBDc3d1hZ2cnOomIiIiIiKi2ya47bzekWuvlOTZOTk5o27YtJk+ejCdPnsDDwwOJiYm4ffs21q9fD4lEwiEU0f+QSqVwdnaGg4MDsrOzcezYMURHR2PUqFEcQtFr2bRpE4YMGSI6g6pYq1at4Ovri6SkJHzwwQeYM2cOunTpgh07dqC8vFx0HpEwWVlZuH//PubPn49jx46JzgEAdOnSBT4+PpDJZCgrKxOdQ0REREREVCtxRRS9saKiIoSFheH48eP4+eef8ejRI7Rq1QpDhgyBs7MzhgwZUiv29ieqrUJCQrB06VKEh4fD3t4e7u7ucHZ2Fp1VYxITE+Hl5YVZs2ZhwIABonPqtC+++ALXrl1DaGio6BSqRg8ePICPjw++/fZbtG/fHh4eHpg8eXKD3LaTCADkcjnKysqgra39ymsuXboEPT09WFhY1JrVhCUlJXxTFhERERERNURcEUWvJysrC4GBgRg9enTF0Ck0NBRjx47FlStX8Pz5cwQGBmLUqFEcQhH9CaVSiePHj6NXr15wcnJC48aNER4eXrEqqqFITk5G9+7dkZyczIfoVaBVq1awtbUVnUHVrGPHjggICEBycjKcnJwwc+ZMWFhYIDAwEHK5XHQeUY1TVVWtdAgFAJ6enrC2toaJiUkNVVWuuLgYbdq0gUQiwaFDh0TnEBERERER1SiuiKJXSklJwZEjR3DkyBGEhYVBS0sLgwcPxogRIzB06FC0bNlSdCJRradUKvHjjz/C29sb8fHx+OCDD7BkyRJYW1uLThPml19+wYABA+rUYfREtcm9e/fg6+uLb775Bl27dsWyZct4rhzR7ygUCiQkJODp06eVbmFaVlYGFRUVqKurV2tPSUkJfv75Zxw/fhx2dnZwc3Or1vsRERERERHVItkcRNFvJCQkICgoCMHBwYiKikLTpk0hkUgwbNgwfPjhh2jSpInoRKI6QalU4siRI1i+fDni4+MxcuRIeHl5wdzcXHQaEdUTiYmJ8PX1xf79+2FqagovLy8OpIjeUFBQECZMmABbW1usW7eOq0yJiIiIiIiqHrfma+jkcjmkUik8PDxgYmICc3Nz7N69GzY2Njh27BjS0tJw6NAhjB8/nkMootcUEhKCXr16wcXFBR06dMD169dx8OBBDqGIqEqZmpoiMDAQsbGx6Nq1Kz766CNYWVkhKChIdBpRnWFvbw9/f3907twZ+vr6onMAAMOGDcPHH3+MI0eOiE4hIiIiIiKqElwR1QApFAqEhYUhKCgIBw8eRFpaGkxNTeHs7Ixhw4bB3t6e76Ym+htCQkKwaNEiXLt2DRKJBH5+fg1qC76ysjLs2bMHQUFBOHPmDLfeI6phcXFxWLlyJQ4fPgw7OzssWrSoQZ1BR1Tdpk2bBiMjIwwYMAC9evWqtvt8/fXXOHz4MLS0tHDixIlquw8REREREVEN4dZ8DYVcLsfly5dx6NAh/PTTT0hPT4e1tTU++ugjuLi4oHPnzqITieqskJAQLF68GJGRkZBIJFizZg169uwpOqvGTZ8+HYGBgZgxYwa8vb3/8iB5IqoesbGxWL16NYKCgtC3b194e3tj0KBBorOI6rTy8nKMHTsWoaGhcHR0xA8//FDt95TL5VBVVa32+xAREREREVUzDqLqs/9d+RQUFIRnz57B1NQUo0aNwscff4x33nlHdCJRnSaVSrFkyRJcunQJEokEPj4+DfpsicePH0NdXR2tW7cWnUJEACIiIrB69WoEBwfD3t4eK1euxIABA0RnEdV5RUVF0NHReeXrd+7cQWlpKczMzKp1kLRnzx4UFxdj1KhRaNGiRbXdh4iIiIiI6B/iGVH1UUJCAjw8PNChQwc4OjoiJCQEU6dOxa1bt5CQkIDly5dzCEX0D0ilUgwYMACOjo5QV1fH1atXce7cuQY9hAKAdu3acQhFVIv07t0bx48fh1QqhZaWFgYOHAgHBwdcunRJdBpRnVbZEAoAtm7dCktLSzRr1gzPnj2rto5bt25hwYIF6NatG+RyebXdh4iIiIiI6J/iiqh6IiEhAfv27cP333+PR48eoVu3bhg9ejRGjx4NU1NT0XlE9YJUKsWyZctw/vx52NvbY/Xq1ejfv7/orBojl8uhVCqhpqYmOoUAPH/+HKmpqejbt6/oFKojpFIpli5digsXLnAVJ1E1ksvlSEhIQGRkJCZPnlytZ68WFhYiPj4ednZ21XYPIiIiIiKif4grouqytLQ0bNq0CTY2NjA3N8f+/fvx6aef4ubNm0hMTMTy5cs5hCKqAqGhoZBIJHB0dERpaSkuXLgAqVTaYIZQCoUCQUFBMDMzw65du0Tn0H8cO3YMQ4cOFZ1BdYiDgwPOnz+PK1euoKysDL169YKTkxOioqJEpxHVK6qqqujevTumTJlS6RDq8ePHeOuttzBmzBiEhIT8rXs1btz4L4dQWVlZKCsr+1ufn4iIiIiIqCpwEFXHlJSU4Pjx4xg9ejTat2+PZcuWwczMDMeOHUNqaipWr14NCwsL0ZlE9UJYWBicnZ3h4OCAkpIS/PLLL5BKpXj33XdFp9Wo58+fY+LEibCzs4OTk5PoHPqPjIwMtGzZUnQG1UEvt+c7d+4ccnNzYWtrC2dnZ0RHR4tOI2pQNDU1MWvWLBQUFCAtLa3a7uPh4QFDQ0O4urqioKCg2u5DRERERET0Ktyarw5QKBQICwvD3r178cMPP6CwsBADBgzAp59+ChcXFzRu3Fh0IlG9Eh4eDh8fHwQHB8Pe3h4rVqzAoEGDRGcJ9eLFCzRr1kx0Bv2PyMhI3L17F2PHjhWdQnVcSEgIPDw8EB0djffffx+rVq2CpaWl6Cwi+h8//vgjtLS0YG9vDwMDgzf62IcPH+LgwYO4cuUKjh49Wq1bBRIREREREf2JbA6iarGUlBR888032LdvHx49egQbGxt8+umn+Pjjj9GqVSvReUT1TkREBFavXo3g4GD07dsXHh4ecHZ2Fp1FRFTtlEolgoODsWzZMsTGxsLFxQWrVq2CiYmJ6DQiAjBs2DCcPHkSHTt2xL1790TnEBERERERvQmeEVXblJaW4uDBg5BIJOjSpQsCAwPxySefICEhAdevX4ebmxuHUERVLDIyEu+//z769OmD7OxsnDt3DqGhoQ1qCPXgwQPRCUQkkIqKCpydnXH9+nUcOXIESUlJ6NatG0aPHo3k5GTReUQNXnBwMNLT03H48OFKrysvL8fffZ9hREQELl269Lc/noiIiIiI6FU4iKolkpKS4OHhgfbt22Ps2LFQUVHBwYMHcf/+ffj4+MDU1FR0IlG9k5iYiNGjR6N3797Izs7GsWPHIJVKIZFIRKfVmIsXL8Le3h42NjYoLi4WnUNEgjVq1AjOzs64ceMGfvjhB8TGxsLMzAzjx49HSkqK6DyiBq1FixawsbGp9Jr9+/ejZcuWGDFiBBISEt7o8+/btw/vvvsujI2NeZYUERERERFVKQ6iBCotLUVQUBCcnJzQtWtX7Nu3D5MmTUJqairOnTuHUaNGQU1NTXQmUb2TkpKCTz/9FBYWFkhOTsbx48cRHh7eoFZAvXTp0iXo6Ojg5MmT0NbWFp1DRLVEo0aNMGrUKNy6dQvff/89IiIi0K1bN4wfP57bghHVYg4ODliyZAnU1NSgo6PzRh+7detW3Lx5E19++SV0dXWrqZCIiIiIiBoinhElQFxcHHbu3Im9e/eisLAQw4cPx+effw4nJyc0asTZIFF1ycjIwIYNG7Bp0yYYGhrC09MTkydPhqqqqug0YRQKBf/cIaK/JJPJcODAAXh7e+PRo0eYMGECli1bhrZt24pOI6K/aenSpbCysoKjoyNatGghOoeIiIiIiOqvbA6iaohCocCJEyewZcsWhISEwNjYGFOmTMGECRPQunVr0XlE9dqLFy+wdu1abNmyBc2bN4eXlxcmTZrEFYdERG9IJpNh9+7dWLlyJdLT0zFhwgQsX74choaGotOI6A3k5eWhf//+uHnzJsaPH4/du3e/9seePHkS6urqkEgkUFFRqcZKIiIiIiKqJziIqm4vXrzAzp07sX37djx+/BjDhg3D7NmzMWjQIP4fN6JqVlhYiK1bt8LX1xeqqqpYsGAB5syZ02C2oFMqlQgODoatrS3atGkjOoeI6pGysjLs2bMHK1asQE5ODqZMmQJPT0/+WUNUx+Tk5KCgoADt2rV75TUymQzq6uoVP540aRJ2794NCwsLREdHN+iV5URERERE9FqyuR9TNblz5w7c3NzQoUMH+Pj44L333kNCQgKOHj3Kdw8SVbOysjLs2LEDxsbGWLVqFVxdXZGSkgJ3d/cGM4RKSUmBjY0NRowYgRMnTojOoSqWmJiIKVOm8DB5EkZDQwNTp07FvXv3sHHjRgQFBcHY2Bhubm5IT08XnUdEr8nAwKDSIRQAfP755+jSpQsmT56MsrIyfPvtt4iPj8fChQs5hCIiIiIiotfCFVFVSKFQ4Pz589i8eTNOnDiBzp07Y8qUKXB1dYWBgYHoPKJ6T6FQ4Mcff4S7uzuePHmCCRMmYOXKlWjVqpXotBpXUlKCWbNmYfbs2bC0tBSdQ1Xsp59+wsiRI1FaWvqbd6kTiVJYWIhdu3bB19cXBQUFmDlzJtzd3dG0aVPRaUT0D4WGhuL06dO4c+cODh06JDqHiIiIiIjqHm7NVxUKCgqwc+dO+Pv748GDBxgyZAjc3NwwePBgrnwiqgFKpRKHDx/GkiVLcO/ePUxH7iEyAAAgAElEQVScOBHLli1D27ZtRacRVYuAgAB4enrixYsXolOIfuPllqhr166FTCbDjBkz4OHhwTfkEDUAT58+xbFjx9C/f39069YNV69ehbm5ORo3biw6jYiIiIiIxOIg6p948eIF/P394e/vj5KSEkyaNAmzZs2CiYmJ6DSiBiMkJATu7u6IiYmBi4sLfHx8YGxsLDqLqNrl5eVBT09PdAbRnyooKMC2bdvg5+cHFRUVzJ49G3PnzuWvWaJ67PTp0xg1ahQKCgqwd+9euLu7o7i4GO7u7nB3dxedR0RERERE4nAQ9Xekp6dj+/bt2LRpE5RKJSZMmMADuolqWFhYGBYvXoyLFy9CIpFg3bp16NGjh+isGlFWVobAwECkpqbCx8dHdA4R0Svl5+dj+/btWLNmDdTV1TF//nzMnj0bOjo6otOIqBqUl5cjKioKXbp0gUKhwI4dO6Crq4s5c+aITiMiIiIiInE4iHoT9+/fx8aNG7Fz5040adIE06dPxxdffMHtZohqUHx8PLy9vREUFAR7e3usWbMGjo6OorNq1HvvvYcLFy5gypQp2LZtm+gcIqK/lJWVBX9/f2zcuBGampqYN28e5syZA21tbdFpRCRA9+7d0alTJ4wcORKffPKJ6BwiIiIiIqpeHES9jvj4eKxduxYHDhxAu3bt8MUXX2Dq1Kl8eEJUg27fvg0fHx98//336NmzJ1avXg2JRCI6S4jIyEgYGhqiffv2olOIiN5IZmYm1q9fD39/f+jq6mLu3Llwc3ODlpaW6DQiqiFyuRzbt2/HhQsX0LlzZ6xbt050EhERERERVS8OoioTHx8PLy8vHD16FGZmZvDw8MBHH30ENTU10WlEDcbjx4+xdOlSBAYGolu3bli1ahVGjBghOouIiP6BjIwMbNiwAVu2bEGLFi0wb948TJs2DZqamqLTiKgGPX36FBMmTMCCBQvg5OT0h9eTkpKgqqqKzp07C6gjIiIiIqIqkt1IdEFtdO/ePYwfPx6Wlpa4f/8+jhw5gps3b2LcuHEcQhHVkNzcXHh6esLExAQXL17Enj17EBsb2yCGUMXFxaITiIiqVcuWLeHr64v79+9j7Nix8PDwgImJCXbs2IHy8nLReURUQ/Lz8wEAgwcPxqFDh/7w+urVq2FsbIy3334beXl5NZ1HRERERERVhCui/sfL7WI2bdoEQ0NDeHp6YvLkyVBVVRWdRtRgyGQy7N69G15eXigvL8fChQsbzLZNmZmZ2Lp1K/z9/XHmzBn07NlTdBIRUY149OgR1q9fj4CAgIqvwSZNmsQ3ABE1ENeuXUP37t3/sCqyrKwMkZGRiIqKgpubm6A6IiIiIiL6h7g1H/DrO/G2b98OHx8fNGnSBAsWLOD2MEQ1TKlU4vDhw1i0aBEePnyIadOmYcWKFTAwMBCdVmPOnj2LcePGYc6cOZg1axaaNm0qOomIqEY9fPgQq1evxrfffov27dvDw8ODbwoior+UmpqKr776CgMGDMDAgQP5NRQRERERUe3SsLfmKyoqgp+fHzp27Ii1a9di0aJFSE5OhpubG4dQRDUoPDwcjo6OGDNmDKysrHD79m1s3ry5QQ2hgF+3pXnw4AG8vLz4AIVeKSkpCRoaGoiPjxedQlTlOnTogICAACQnJ8PJyQkzZ86EhYUFAgMDIZfLRecRUS2VkZGByMhIfPTRRzh9+rToHCIiIiIi+p0GOYhSKpXYt28funTpglWrVmHWrFm4d+8e3N3doa2tLTqPqMG4ffs2Ro8ejb59+0JbWxvXr1/HoUOHYGRkJDpNGB0dHdEJVMulpaVBJpOhefPmolOIqs3bb7+NgIAAJCUlwdHREZMmTYKlpSWCgoLAxfxEDc+ECRPg6uqKZ8+e/enrdnZ2iIyMRFZW1l+eJ8pz6IiIiIiIal6DG0TFxMSgX79++Oyzz+Ds7IzU1FR4e3tDT09PdBpRg/H06VO4urrCwsICiYmJOH78OM6dOwcrKyvRadXq4sWLCA8PF51BdZyuri5GjBjBQRQ1CEZGRggICEBcXBysra3x8ccfcyBF1AA5ODjgxIkT+PDDDyu9Tl9fv9I39SiVSnTu3BmOjo7497//XdWZRERERET0Cg1mEJWTkwM3Nzf07NkTJSUlCAsLw7///W+0bNlSdBpRg1FYWAg/Pz907doVJ0+exLZt2xAbG4thw4aJTqtWxcXF6Nu3LwYMGIBDhw6JzqE6zsrKCkeOHIGGhoboFKIa061bNwQGBiI2NhZdu3bFRx99hB49eiAoKEh0GhHVgClTpiA5ORmBgYH/6PMoFAr4+vrCxMQEz58/r6I6IiIiIiL6KyrKev52UqVSib1792LBggWQy+Xw8vLC7Nmz0ahRg5nBEQknk8mwe/duLFu2DKWlpXB3d4ebmxu0tLREp9WYVatWQSKRoHfv3qJTiIjqvLi4OKxcuRKHDx+GnZ0dFi1aBGdnZ9FZRFSPJCcnQ1dXF4aGhqJTiIiIiIjquux6PY2JiopC3759MXnyZIwZMwYpKSlwc3PjEIqoBh0/fhxmZmaYPXs2hg8fjjt37sDd3b1BDaEAYMmSJRxCERFVEQsLCxw6dAgxMTFo3749hg8fDnt7e/zyyy+i04ionvDy8kLbtm1hYWGB4uJi0TlERERERHVavZzI5OfnY/r06bC1tYWOjg5iYmKwefNm6Ovri04jajCuXr2Kfv36YcSIEejRowdu3bqFgIAAbodJRERVpnv37jh06BDCw8PRrFkzSCQSODg44MKFC6LTiKiGXbt2rUq3QP7mm29w6tQpfPLJJ9DW1q6yz0tERERE1BDVu0HUhQsXYGlpicOHD2P//v345ZdfYGZmJjqLqMG4c+cORo8ejT59+kBDQwPXr1/HoUOH0KlTJ9Fp1SIzMxPLly9Hr169IJfLRecQETVIvXv3xvHjxxEaGgotLS0MHDgQDg4OuHTpkug0IqohISEhGDNmDIYMGYLCwsJ//PkaN26M9957D+7u7pVed//+fWzduhW3b9/+x/ckIiIiIqqv6s0gqri4GB4eHpBIJOjWrRtiY2MxZswY0VlEDUZGRgamT58Oc3Nz3L59GydOnEBISAisra1Fp1WrRYsWYfv27RgxYgRkMpnoHCKiBq1v374ICQnBlStXoKGhgXfffRdOTk64du2a6DQiqmaenp4IDQ2FhYUFGjduXGP3jY+Px5IlS9CtWzf89NNPNXZfIiIiIqK6REWpVCpFR/xT4eHhmDBhAtLT0+Hn54epU6eKTiJqMGQyGfz9/bFy5Uo0btwYK1euxGeffdZgzmLLzMyEjo4OdHR0RKcQEdHvSKVSLFmyBJcuXYJEIoGvry9sbGxEZxFRPVNeXo5r167B1NSU28ETEREREf1Rdp1+UlxSUgIPDw84OjqiU6dOiIuL4xCKqAaFhITAysoKnp6eGD9+PG7duoWJEyc2mCEUALRo0YJDKKoxSqUSe/fuxZMnT0SnENUJDg4OuHjxIs6dO4fc3FzY2trC2dkZ0dHRotOIqB5RU1NDnz59Kh1CKZVKjBgxAqtXr0ZcXFwN1hERERERiVdnnxZHR0fDysoK//73v7Fr1y6cOnUK7dq1E51F1CAkJSXB2dkZTk5OMDIywu3bt7F582Y0adJEdFqVkslkOHDgAIqKikSnEAEAsrOzK4a+RPT6JBIJIiMjcfbsWTx79gw2NjZwdnZGbGys6DQiaiDy8/PRrFkzbN++HTt27BCdQ0RERERUo+rkIOrrr79G37590bZtW8TFxWHChAmik4gahJycHHh4eMDCwgKPHz/GxYsXcfz4cRgZGYlOq3JhYWEwNjbG+PHjceXKFdE5RACA9PR0AECrVq0ElxDVTRKJBNeuXcPRo0fx5MkTWFtbY/To0bhz547oNCKqAT/99BOCg4OF3FtPTw+7d+/GkydP4OfnJ6SBiIiIiEiUOjWIys/Px9ixYzFz5ky4ubnh7NmzaN++vegsonpPoVAgMDAQ77zzDnbt2oW1a9fi+vXr6N+/v+i0amNsbIyRI0fi7t27GDJkiOgcIgBA27ZtcfToUXTu3Fl0ClGdpaKiAmdnZ0RFReHIkSNISkqCqakpRo8ejeTkZNF5RFSNLly4gOHDh8PT01Nox19t6zxhwgR8+OGHCAgIqKEiIiIiIqLqpaJUKpWiI17HnTt3MGLECOTk5OD777/HoEGDRCcR1WlyuRyqqqp/ed2FCxfwxRdf4NatW5g+fTq8vb15CDMREdUbCoUCP/74I7y8vJCamooxY8Zg2bJlHPgS1VN79uxBRkYGFixYIDrllb777jv8/PPPyMrK4sp8IiIiIqoPsuvEIOrUqVMYO3Ysunbtip9++gmGhoaik4jqtPDwcPj5+eHIkSOvvObRo0dYvHgx9u7dC4lEgk2bNsHMzKwGK6tfaWkpNDU1RWcQEVEt8HIgtXjxYty/fx9jxozB8uXL0alTJ9FpRER/qqysDBoaGqIziIiIiIj+Snat35pv3bp1cHZ2xogRI3Dx4kUOoYj+ocePH2P48OE4evQofv755z+8XlhYiOXLl8PExARXr15FcHAwzp07V2+GUDKZDPv27YOVlRW8vb1F5xARUS3RqFEjjBo1CgkJCdi1axfCwsLQrVs3uLq64smTJ6/9eUpLS6uxkojov7Zt24a33noLkydPxuPHj0XnEBERERG9Uq0dRMnlckyfPh2enp5Yt24d9uzZw5ULRP9QcXExhg8fjtzcXKioqGDmzJkoLCwEACiVSgQGBsLY2BhbtmzB8uXLERcXh6FDhwqurlrl5eVYuHAhTE1NMWrUKNE5RERUy6irq2P8+PG4desWdu7ciZCQEHTq1Amurq549uxZpR977do1ODk5VfzdSkRUnf71r39hxowZuHv3LnR1dUXnEBERERG9Uq3cmq+oqAhjxozBuXPnEBgYyIfFRFXk448/xuHDh1FeXg4AUFNTw9y5czFy5Ei4ubnh6tWrGDduHNavX49WrVoJrq0+xcXF0NbWFp1BRER1QFlZGfbs2QNvb29kZ2djypQp8PT0RJs2bf5w7eDBg3Hu3Dm8++67OHXqFLS0tAQUE9E/oVAo0KhRrX2/5t+iUCjw7NkzvPXWW6JTiIiIiKhhqn1b87148QIDBw5EeHg4Lly4wCEUURXx8/PDwYMHK4ZQwK+rg9atWwc7Ozvo6OggNjYWgYGB9XoIBYBDKCIiem0aGhqYOnUqUlNTsXHjRgQFBcHY2Bhubm5IS0uruC48PBznzp0DAEilUnz44YeQyWSisonobzh58iTs7e1/83u7Prhx4wbatWsHKysrHD9+XHQOERERETVAtWoQlZGRgUGDBuH58+cIDQ1F7969RScR1QtnzpzBokWL8GcLIFVUVNClSxecO3cO5ubmAuqqjkKhwNGjR7F+/XrRKUREVM+8HEjdvXsXq1evxqFDh2BsbAwPDw9kZ2dj0aJFUFNTA/DrGz3OnTuHUaNG/eYNIERUu5mYmCArKwuOjo4oKCgQnVNlzM3NcerUKTg4OMDAwEB0DhERERE1QLVma7709HRIJBLk5+fjl19+QadOnUQnEdULd+7cQc+ePVFUVASFQvGn16ioqGD37t347LPPariuar3//vs4ffo0hg8fjp9//hkqKiqik4iq1P/93/9h6tSpeO+990SnEDV4BQUF2LJlCzZs2ICysrI/fWitqqqKjz76CHv37q13W30R1VdpaWk4duwYPv/8c9EpQjx+/Bjt2rUTnUFERERE9Ut2rRhEZWRkoH///pDL5fjll1/4hS9RFcnLy0PPnj1x//79SrcHUlFRgZ6eHu7evYsWLVrUYGHVunLlClq3bg0TExPRKURVTqlUQkNDA3v37sWYMWNE5xDRf+Tl5cHCwgJPnjyBXC7/w+uqqqr49NNP8e233/INEkRUq5WVlaFly5Zo2rQpZsyYgYULF4pOIiIiIqL6QfwZUXl5efjXv/6FsrIyXLhwgUMooioil8vh4uLyl0Mo4NcH3Lm5uVi8eHEN1VUPR0dHDqGo3srPz4e+vn6dHhYT1UfXrl3Dw4cP/3QIBfz693FgYCC++OKLGi4jInozampqOHv2LD799FNu4UdEREREVUpN5M2Li4sxfPhwPHv2DFKpFG3bthWZQ1SvuLu748KFC698MKaiogJVVVWUl5dDVVUVXbt2haamJkpLS6GpqVnDtX8tPz8fZ8+ehYuLi+gUIiH09PSQmZkpOoOIfsfLywtqamqVngWlUCiwdetWGBgYYMWKFTVYR0T0+ho1agQ7OzvY2dn95bXPnz9Hy5YtoaqqWgNlRERERFTXCduaT6lUYvTo0Th//jwuXboEc3NzERlE9VJgYOAfzntSV1evWBnVpk0b9OvXD71794adnR2sra2hpaUlIvW17NixAwsXLoRMJkNKSgratGkjOomIiAhnz57FkCFD3uhjfH194e7uXk1FREQ1w8nJCTdu3MDQoUPx3XffcetRIiIiIqpMtrAVUStWrMDRo0dx5swZDqGIqtDVq1d/c7iyjo4ObG1t4eDggF69esHOzg6tW7cWWPjmjI2N4enpic8//xzNmjUTnUNERAQA+O67736zGkpNTQ2qqqqQyWRQKBR/+jEeHh7Q0dHB7NmzazKViKpAbGwszMzMoKYmdGORWmHbtm0IDg7GkydPOIQiIiIior8kZEXUTz/9hJEjR2L79u2YNm1aTd+e6I2Vl5cjPz8fAFBUVITS0lIAvx7oW1hYWOnH5uTk4E1+m+np6VW6xYWmpiZ0dHQA/HoAup6eHgBAS0sLeXl5mDhxIjp06AA7Ozv06tUL3bp1Q6NGwo+DIyIiqpfKy8vx6NEjpKamIjU1FSkpKUhNTUVycjJSUlIqvn5QUVGBhoYGysrKAAA7d+7E5MmTq72tLnz9oq2t/dr3IRIlPz8fRkZGcHFxQUBAgOicOiM3NxcaGhr8fU5ERETUsGXX+CDqzp076NmzJz777DNs3bq1Jm9N9VBeXh5yc3ORm5uLoqIi5OXlVTxoyc7ORmlpacXPl5SUoKCgAAUFBSgtLUVubiaKi4tQUlL8uwc1xSgt/fUhUW5uIRQKIbtX/iN6ejpQVW30nwc9ugD++6BHVVUNenr60NFpAi0tHRgYGFS8pq+vD01NTejq6kJXVxeamprQ19eHjo5OxetNmzaFvr5+lQ63Hj16hHbt2vHdlEREVO+8ePHiD0OqxMREPH78GHPmzIGjoyO/fvmPuvb1CzU8R44cgYuLC06fPg0nJyfROXXCihUrsHbtWgwZMgRbt27ludBEREREDVPNDqJKS0vRp08fqKqqIjQ0FBoaGjV1a6ql5HI5MjMzkZWVVfHPrKysiuFSTk7Of76fjdzcF8jJefGfn89DTk5hpe/UbdpUHZqaKtDRUYGeHqCpqUSTJko0biyHhoYCTZsCmpqAjg6gogIYGPz6cS9/DgCaNAHU1IBGjQB9/T++/ut9Kv931NH59WNe778HkJdX+TX5+cDL89BlMqCg4NfvFxYC/3mTNXJyAKXyz19/+XMFBUBpKZCbq47iYhWUlKggNxcoLVWioECJggI5ZLI/31YIAJo00YaBgR709fWgr28Aff1m0NdvBgMDA+jr68PAwKDi+y1btkSLFi3QokULNG/evOI8qoSEBCxbtgxHjhzByZMnMXjw4Nf7D0VERCQQv375/X+PhvX1CzVskZGR6NWrl+iMOuP58+c4duwYTp06hQMHDvD3EREREVHDVLODqFmzZuG7775DVFQUTExMauq2VIMUCgXS0tLw/PlzPH36FBkZGcjMzERGRgYyMjKQlZWBzMznyMrKRGbmC2Rl/fGphb6+GvT1G0FfXwUGBkro68v/8+3Xhy0v//ny+y9/rK0N6Om92YMTqpxCAeTmAsXFQFHRr9/Pzv71ny+/5eT8749VkJOjhtzcRsjJAXJyFMjNLYdc/ts/ZnR1tdGihQEaN26CJ0/SYGHRHVZWVmjVqhVatmyJVq1aoXXr1njrrbf+n707D4uq7N8AfrODiKAgCu4YoICYoqKCZpktpuaSmruWS+7WT7PcXn3DiqzcUnPfkjSzXlPrNU1fDRRQRJFdRXFh30GWgZnz++OEOIoKCDzMzP25rnPNcObMmfvMiDzzfM95HjRt2pRFayIiqlFsv2iXmm6/2Nrawtq6CWxsmsDa2ho2NjZsvxA9J5VKBT09PY6QQERERKSdaq8QdfToUQwaNAh79+7FmDFjauMlqZolJiYiMTERCQkJD26TkpJw794dJCXdxb1795CSkomSEuWD59SrZwAbGwM0bqyHxo2VsLYugY0NYGMDWFsDtrZQ+9nGRj6Dl7RLejqQllZ2W7qkppbe10d6uiHS0vSQmqpEVlaJ2vMbN7ZCkyaN0bx5azRpYo/mzZurdfQ0a9YM9vb2MDIyEnSERDUvOTkZDRo04BwLRJXE9gtVFdsvRLXn1KlTGDduHAYNGoS5c+eiXbt2oiMRERERUfWpnULUnTt30KlTJwwdOhRbtmyp6ZejKsrMzHwwh0HpkpBwG4mJtxETE4e8vMIH25qa6sPe3hB2dhLs7YthZwfY20PttlmzsuFiiCpDoZA7eBITgYSER28NkJhohIQEFVJSitXOVm7Y0AIODm3g4OAMBwcHODg4wM7ODvb29nB2dkb9+vUFHhXR8+nQoQOGDBmCf//736KjENUpbL9QXcH2C1HV3b59G7t27cLhw4exYcMGdO/eXXQkIiIiIqo+NV+IKikpQZ8+fZCVlYXg4GDUe3hweqpVCoUCN27cQExMDGJjY3Ht2jVcvx6N+PibuHs3GcXF8lmcxsb6aNHCCK1aSWjVSoFWrYA2bYBWrco6aMzNBR8MEeQOn+Rk4O5d4PZtID6+9FYfN28aIj5eiby8sjPc7ewaoXXrVmjd2hlOTs5wdnaGo6MjnJyc0KBBA4FHQvRsTZo0wZIlSzB79mzRUYhqFdsvpG3YfiEiIiIiIh1T84WoRYsWYfXq1QgKCoK7u3tNvhT9486dO4iNjX2wxMREITY2Crdu3YVSqYKeHtCihSkcHVV44QW5o6Z0ad1a7qzR1xd9FETVIz1d7uApXW7dAm7e1EdMjAHi4kpQXCz/F9i0aSM4OzvCyckdTk5OcHJygrOzM9q2bQtDjrdEdcDChQsxZMgQniFMWovtF6IybL8QlS83Nxevvvoq+vfvj5EjR3IIPyIiIiLNULOFqPPnz8Pb2xvff/89pkyZUlMvo7MUCgWuXbuGkJAQhISEIDLyCq5cuYzU1GwAgJWVIdq21YeDgwIODoCLC+DqCjg7AxzhgwgoKZHPQI6LK1siIowQGamPW7cUUKkkGBkZwNGxDVxdO8HFxQUeHh7o0qUL7OzsRMcnItJIbL8QPR+2X+oePz8/SJLEuZBrQXJyMlasWIEjR45gzpw5WLBggehIRERERPRsNVeIKioqgoeHB5o2bYoTJ05AT0+vJl5GZyQnJyMoKAihoaG4ejUMV65cRFzcHahUEiwsDOHmZgh390J07Ai4uQHt28sTZxNR1dy/D8TGAuHhwNWrwJUrhggL00NSUjEA+QzkDh3c8eKLXeHu7o5u3brByclJcGoiorqF7Rei2sX2ixjz58/H9u3bERMTA1tbW9FxdIIkSVAoFDAxMREdhYiIiIiereYKUUuXLsXq1asRFhYGBweHmngJraVQKBAaGoqgoCAEBp5HYODfuHnzHvT19eDgYISOHUvg7q5Chw5Ax47y/Aes8xHVjtRU4MoVICxM7uAJCzNGREQJiopUsLZuAE/PHvD07Inu3bvD09MTlpaWoiMTEdUKtl+I6i62X2rW/fv34e7uDl9fX7zzzjui49A/li1bho4dO+LNN9/kXNVEREREYtVMIerq1avw8PDAt99+i1mzZlX37rVOTk4OTp8+jTNnziAoyB+XLl1BYaECjRoZoXt3wNOz+J9bgN8JieoehQIIDQWCgoDAQD0EBhri5s1i6OvroV07B3Tv3hve3r3w6quvokWLFqLjEhFVC7ZfiDQb2y/Vq6ioiFfn1CEKhQJvvPEGzp49i0GDBuGXX34RHYmIiIhIl1V/IUqpVKJHjx4wMDBAQEAA9Dlr9GNKSkoQHByMEydO4MSJ3xEUFAKVSoUOHYzQs6fiQaeNkxPPFCbSVMnJQGCgvJw/b4jgYAkFBUq0a+eAfv3eQr9+/dCnTx9YWFiIjkpEVCFsvxBpP7ZfSNukpKQgKyuLQ1ASERERiVX9hahVq1Zh6dKluHTpElxcXKpz1xotJSUFv/zyC44f/x2nT59CdvZ9tGxpjH79itGvn4S+fTknApE2KygA/P2BkyeBEyeMcOVKCQwM9NG9exe89toADB06lP9nElGdw/YLkW5j+4V0QWBgINq0aYMmTZqIjkJERESkraq3EHX9+nV07NgRixYtwuLFi6trtxorIyMDv/zyCw4c2IfTp8/CzEwPffsC/fop0a+ffMYwEemm1FTgr7+AEyf0cPy4Ee7dU8DNzQkjR47DyJEj4ejoKDoiEekotl+I6EnYfiFt5O7ujsjISLz99ts4dOiQ6DhERERE2qh6C1EDBw5EfHw8QkJCYGRkVF271Sj5+fn4+eefceCAH06cOAlDQ6B/f2DkSCUGDADMzEQnJKK6RqUCzp0DDhwAfv7ZEElJJejc2RUjR47HmDFj0KxZM9ERSbCDBw+ifv36ePPNN0VHIS3F9gsRVRbbL6QtCgoK8McffyAjIwOTJ08WHYeIiIhIG1VfIerUqVPo27cv/vzzT/Tr1686dqlRYmNjsXHjRuzevR0FBfl47TU9jBypxKBBAIdQJ6KKUiqBM2eAAwf0cOiQIbKzVRg0aABmzJiNvn37io5HgrzyyitwdnbGpk2bREchLcP2S9X8739AZiZgZAQMGCA6DVWX2vxcIyKA2NjH1xsYACYmQLqTXfsAACAASURBVNOmgLu75sy3xvYL6YKioiKYmJiIjkFERESkiaqnEKVSqdC1a1c0a9YMv/32W3UE0xhXr17FqlVfws9vP5o21cfkySWYORNo3Fh0Ms3QtStw8aLc2ZWTIzrN88nOBlasAF55hZ1SIj/XV14BTp+W7zdqBNy7B5ia1m6G6qJQAIcPA1u2GOHkyWJ07OiKjz76GGPGjIGBgYHoeFSL3NzcMGzYMKxYsUJ0FNISbL88H7ZftFNtfq6ffAL4+j59m7ZtgXXr5KsTNQnbL7Lc3FxERESge/fuoqNQNZk0aRIuXLiAoUOHYsmSJTA2NhYdiYiIiEhTZOpXx1527NiBsLAwfPHFF9WxO41w9+5djB07Gh07dkRY2EH8+KMKt2+XYPlyduLoorNnAUdHYPVqoLhYdBrddeOGfDZzqYwM4OefhcV5bsbGwPDhwIkTxbhwAWjTJhoTJ05Ajx5dcP78edHxqBYdOnQIU6dOFR2DtADbL/Qwtl/qths3gGHDgEuXRCepHLZfZF9++SWGDRsGhUIhOgpVk6lTp6Jv374ICAhgEYqIiIiokp67EJWXl4dly5Zh+vTpcHV1rY5MdZokSdi0aRPatXPE+fM/4+BBCaGhxRg+HNCvlrIeaaLQUHnyZkBzhlDRRjt2AI9e47lli5gs1a1LF+DXX5UIDQUsLMLh5eWFqVMn4/79+6KjUS1wdnbmXBv0XNh+ofKw/VI3bNkiDwmYkSF/HuHhwNCh8mOFhYCfn9h8z0OX2y8zZsxAWloa/DT5AyQ1PXr0wNq1a/HXX3+JjkJERESkcQyfdwe+vr4oKCjAsmXLqiNPnXb//n2MHTsKR44cxaJFEhYvlsdw13RBQUB0NGBoCIwZA9y6Bfz5JxATA7i6yl+Erawef55SKXdgnD0LJCcDbm7ysC5P6isNCpKvVsnKAnr0AAYOfHqusDB5+1u3gHbtgN695dvnVZH9VuY9OXUKCA4ue+7p0/IwNwMHykPD/e9/QHy8PMzLwIHAzp3AnTvAa6/Jw76kpcnPe+cdwNy8bD8FBcBPP8n37e2Byk69pmufq1IJ7Nol32/eHGjTBvj7b3mJigLat3/+16gLOnYE/vqrBAcPAtOn78bZs6dx+PDvcHZ2Fh2NiOootl/UaerfObZftPNzBeT37+FjsrEBVq4EfvlF/jk+vnpeRyRdbL80a9YMP/30E3r16iU6CtWyo0eP4urVqxgxYgTatm0rOg4RERFR3SA9hzt37kj16tWTVq1a9Ty70QjZ2dlSjx5dJRsbI+nMGUiSpD3LjBmQAEhmZpB++QVSvXryz6VLq1aQrl9Xf861a5CaN1ffDoBUvz6kjRvVt1WpIH300ePbvvUWJGdn+b6FRdn2SiWkxYsh6eurb29oCOmLL+T9VeU4K7PfyrwnAwc+fmwApNBQ+fEhQ+Sf27SB9N57ZY+7ukJauLDsZz8/9bw//1z22MqV/FyftRw5UrbPhQsh7dhR9vO8eeJ/z2piuXsXUvfuhlLjxlZSaGioRET0KLZfNP/vHNsv2vm5Pvwe7tv3+ONbtpQ9vmWL+N+36lzYfiFt991330k2NjaSvr6+lJycLDoOERERUV2Qged59syZM6VWrVpJhYWF1RWoTlKpVNKwYYMlOzsjKTpa/Je36l5Kv/Dr6clfsrt1gzRnDqTWrcu+AE+ZUrZ9XBykFi3KHuvRA1L//uodBTt3lm2/f3/Zej09ueOjZ0/1L/MPf+HfurVsvbW1/NoPdy4cOFC146zMfivznsyZA8nevmx969aQOnbEg38rpR05enryrbm53Hnx+efyNqXPGzxYPe/IkWXPi4/n5/qs5e23y/YVHg4pJ6cse6NGkAoKxP+u1cSSlwepb18DqWVLOyktLU0iIirF9ot2/J1j+0U7P9eHC1G9e0OaOBHShAmQxo6F5O1d9thrr0HKzRX/+1bdC9svpO2Ki4ulwMBA0TGIiIiI6oqqF6KSk5MlMzMzaf369dUZqE46fPiwpK+vJ509K/5LW00spV/4H+1MuHatbL2nZ9n60aPL1q9ZU7Y+KgqSsbG8vmFDSBkZ8noXl7Lt//ij/C/2pV/4i4og2drK66ys5C+pkgSpuBhSy5by+vbtK3/2aWX3W9n3ZM2asvW//qr+2qUdOQCkPn3kgkhqatn706OH/JipKaTsbHldQYF8Fm/pc/i5Pn1JTJQ7xwBInTqVrR87tizL3r3if9dqaklPh9SihZE0Y8Z0iYioFNsvmv93ju0X7fxcJUm9EPWkpX59SBER4n/Xamph+4V0XVpampSQkCA6BhEREVFtyKjy9NSrV6+GhYUF3n///aruQmNs2rQeAwfqQxeG954xo+z+Cy/IY9QDQHp62frTp+VbExPg4Y+/XTvg5Zfl+5mZwIULQHGxPKY/AFhby/MKlHrvPcDSUv31b9wAUlLk+337yhM0p6fLcxb07y+vj4oCkpIqd1zPs9+KvCcV9fHHgKmpvI+GDeV1kybJt4WFwG+/yff/+18gL0++P3585V/nUdr6uZbavRsoKZHvP/x+TZhQdn/z5qrtWxM0agR88kkxdu7cjoKCAtFxiKiOYPtF8//Osf1Sdl+bPtdHOTsDvXrJi6en/LOBgfxeengA69Y93/7rKrZfSNdt27YNzZs3R58+fXDjxg3RcYiIiIhqVJUKUTk5Ofj+++8xb948mJmZVXemOics7DJeflkpOkataNxY/ed69eRb5T+Hf/MmkJgo3+/TB6hfX337AQPK7kdEyJNalz73pZcA/Yf+xenrA82bqz//2rWy+4cOyR0Opcv335c9du9epQ7rufb7rPekMhwdH183cmTZPksn9/7557LXeuedyr/Oo7T1cy21fXvZ/WPHgBEj5OXhffv7A5GRVdu/JujbFygoUODaw282Eek0tl80/+8c2y/qP2vL5/qoZcuAs2flJTAQiI4GQkMBW1u5+LVwYVmBT9uw/UK6bM6cOTh48CCaNWuGJk2aiI5DREREVKMMq/Kk7777DiqVCtOnT6/uPHWSiYkxCgtFp6gdJibqP+s/Uqps2hQwMpLPKC3vS/fdu2X3GzYEGjQo+7m4WH3bkhLg9m31dUZGZfdffBHo0qX8nKam5a9/kufZ77Pek8p4tIMEkN+jYcOAvXuB48flM2+PHJEfGzwYsLCo+uuV0tbPFQDOnFHvKDp58snbbtkCrFlT+dfQBKX/R5lW5U2kOm358uW4d+8etm7dKjoKaRi2X8po6t85tl/Uf9aWz7UiOnQA3noL2LlT/hv/++/ySTbahu0X0mVmZmYYOnQohg4dKjoKERERUY2rdCGqsLAQ69evx8yZM2FlZVUTmeqc7t174ddff8HChcXP3ljD6ek9/XEzM/mL+IULQHg4EBcHODiUPV46NAsgf4G2sZGHOcnOBkJCAJWqrBPh/HkgN1d9/w/vy8ICeLjfNSJC7ghp2fLZOR/1PPutyGs9vI1K9eTtjI3LXz9pktyRo1AAM2cCOTny+uoY1ubRfOXR1M8VUL8aysOj/I6vM2cASQL27AG+/LJmOoxE++UXwNa2IRwefrNJK0RFRUFZlUsYSOex/VJGU//Osf3y9Mc19XOtqKtXy+5r6yAUutR+uXHjBoKDgzFq1CjRUUjDBAcH4/PPP8fo0aMxcOBAnRiVhoiIiLRPpc+N3L59O7KzszF37tyayFMnffTRfFy4UKLW4a3LXnml7P7MmfLVKGlpgI+PPE4+IA930rmzfH/IEPk2IQGYNUvupCjd/lHOzmXP8/cHDhyQh1CJjwe8vIDWreUOB4Wicplrar+lHu6gCQ8Hrl8v64x5mIFB+c/v00fOAJQNa2NnB7z6atXyVIUmfq5ZWWXvl6WlvO/Tpx9fHp4j4uDByr2GJoiKAr791hDz5i2AoWGVLnSlOsza2hrt2rUTHYM0ENsv6jTx7xzbL8+miZ/ro06eBDZtkpcNG4Cvv5bni7p4UX7c2Bjw9n6+16iLdK398r///Q/vvfcesrOzRUchDSNJEgoLCzF69GgEBQWJjkNERERUNVIlubm5SZMnT67s0zTekiVLJFNTA+m33yBJknYtM2ZAAuQlKkr9sdat5fVt2pStUyggvfNO2XMeXaytIV27Vrb93buQGjQoe9zAAJKeHiRDQ0ht28rrLCzKtj91ClK9emXb29hA0teX7xsaQgoMrNpxVma/lX1PTp16/H3480/5sSFDytZlZT053/Ll6s+fP5+f67OWDRvK9vf++0/e7ocfyrbz9hb/O1edy40bkBwcjCQvL09JoVBIREQPY/ulbJ0m/p2r7H7ZftGcz3XhwidnfnTZtUv871t1L7rYfsnIyJCMjY2lPXv2iI5CGio5OVlSKpWiYxARERFVRUalrogKDAxEeHg4pkyZUpmnaYUVK1ZgwoT3MXSoPlavlof50lVGRsD+/fLEyQ+fpG9iIs8VEBEBvPBC2fpmzYCgIKBTJ/lnpVI+W/a33+QJih/18svAuXPyMGuGhvJZqsbGQL9+wL59gKdn1XLX1H4B+Uzbh4f2NjZ+fHiXZ5kwQX1ol3Hjqp6nKjTxc922rez+096vYcOA0pFE/f2ByMjKv1ZddPo04O1thEaNXPDrr0dg9PBkFkREYPvlYZr4d64m9wuw/QKI+1yfxNBQHoavcWNg+HDg6FH5PdYmutp+adiwIf73v/9hhDZO9kW1wtbWFvpPmfCvsLAQYWFhtZiIiIiIqOL0JKniXRJTpkxBUFCQTjdufH19sXTpYvTpo4cNG0rg6Cg6kXhpafIE1U5O8pfnp0lOljs4Hu4QeJrCQnmIlRdeqN6x8Wtqv0lJ8vvh7Kw+wXVFREcDrq7yfAUdOwKXL1dfrqrQxM9VV+TkAP/6F7BunR6GDh2MHTt2w6I6ZoUnIq3F9svjNPHvHNsvz6aJn6uuYPuFqGYdOXIEgwYNgqurKzZu3IjevXuLjkRERERUKrPChai8vDzY29vDx8cHc+bMqelgdVpwcDDef388YmOvYc4cFT7+WD5rkaiySkrkJSoK+L//k88QBYC1awEd/zWjcigUwK5dwLJlRigpqYevv16DiRMnio5FRBqC7ReqLmy/UGWw/UJUOyRJwrlz5/Djjz9izpw5cHJyEh2JiIiIqFTFC1Hbtm3DrFmzcPfuXdjY2NR0sDqvpKQEW7ZswfLlS5Cfn4OpU5X48EOgRQvRyXRDTIw8XElF/fAD4O5ec3mq6uZNwMFBfZ2dHXDjhvqZttpyvM+iK8dZWffvAzt3AqtWGSEpScK0aR9g+fIVaNSokehoRKRh2H4RS1v+zrH9ok5XjrOy2H4hIiIiIqJ/ZD5jwIoy27dvx7Bhw1iE+oehoSFmzJiBiRMnYvPmzfj66y+wbl0aBg3Sx/TpSvTtCzxl+GZ6TgoFEBdX8e2Limouy/No1kyeV6G0HOzuDuze/fhwL9pyvM+iK8dZUZGRwObNwO7dhlAo9DF58lQsWPAxWrDHmIiqiO0XsbTl7xzbL+p05Tgriu0XorotJSUFM2fOxKhRo9C/f3+YmpqKjkREREQ6oEJXREVHR6N9+/Y4efIk+pY3iy9BoVDg8OHD2LJlA/766ywaNTJA//4lGD9envj44UmciR6WkiLPzWBjA9jbi05DosXHA//5D3DwoAkCAorQrFkTTJ78AWbNmsUTAYio2rH9QlXF9gs9jO0XIs0RGRmJ2bNn48yZM9iwYQOmTZsmOhIRERFpv4oNzffFF19gzZo1SExMhD5Pk32mmJgYHDhwAAcO/IDIyGto0cIEI0YUYeRIoEsXduoQkbr4eODgQeDAAUNcvFgCW9uGGDbsXYwYMQK9e/fm/7s67O7du8jJyYGLi4voKKQD2H4hospg+4VIsyUkJMDCwgIWFhaioxAREZH2q1ghqmfPnmjfvj22b99eG6G0SkREBA4ePIj9+/cgJuYmGjc2Qp8+JXj1VQlvvsk5GYh0UX4+cO4ccPIkcPKkCS5dKoKVlQUGDHgbw4ePwBtvvAEjIyPRMakOWL58OX766SdERkaKjkI6hu0XInoU2y/VR6FQIDw8HJ07dxYdheip7t27h2bNmomOQURERJrv2YWolJQU2Nvb4+eff8bgwYNrK5hWunz5Mv773//i5Mnj8PcPQFFRMdzczNCvXwH69QN69wbMzUWnJKLqplQCFy/KHTcnThjh/HkliosldOzYHv36vYV+/fqhT58+7Lyhx8ycORPh4eE4c+aM6Cikw9h+IdJNbL/UnP3792PcuHFIT09HgwYNRMchKld4eDjc3d3Rq1cvLFu2jNM0EBER0fN4diFqx44dmDVrFlJTU2HOXoZqU1BQgICAAJw8eRInTx7DpUsR0NcHnJ0N4eFRDG9vwMsLcHHhUDhEmiY7G7hwAfD3B0JCjBAQAGRmFqNJk0bo3fsVvPpqP7z11ls8u5Ce6cSJE8jKysLw4cNFRyECwPYLkTZj+6X2pKSkoGnTpjh27BjefPNN0XGIylVSUoLjx4/jhx9+wIQJE/DGG2+IjkRERESa69mFqCFDhqCkpARHjhyprVA6KSkpCWfOnEFgYCCCgvxx6dIVFBUVw9raGN27S/D0LEaPHoCHB9Cwoei0RFSqsBAICwOCgoDAQD0EBhohLk4BPT09tGvXBt2790b37j3g7e3NeX6ISOuw/UKkmdh+EW/NmjV466234OjoKDoKEREREVFNe3ohqqioCDY2Nvjmm28wderU2gym80pKShATE4OAgAD4+/+NkJBziIq6CUmSYGdnBFdXFVxclPDwAFxdATc3wMREdGoi7ZaQAERGAhERQEiIHiIjTRAerkBRkQoNGtRDhw7u8PZ+CV5eXujZsyesra1FRyYiqlVsvxDVPWy/EFFNUiqV+Oqrr/DOO++wsEpERERP8vRC1F9//YVXX30Vd+7cQfPmzWszGJUjKSkJV65cwZUrVxAWFoawsBBER19DcbESJiYGcHU1grt7EdzcJLRvDzg6Am3aAIaGopMTaZaEBCAmBrh2Dbh6Fbh61RBhYUBmZgkAoE0bO7i7e6BDhxfh7u6OF198ES+88AL0OA4VEdFj2H4hqh1svxCRCNeuXUOfPn2QkJCAb775Bh999JHoSERERFT3PL0Q5ePjg23btuHWrVu1mIkqQ6FQIDIyElevXkVYWBiuXAlBeHgYEhPTAQBGRvpo08YIzs7FcHJSwclJ7uBxcgI4vDvpsqwsIDZW7qyJiQFiY/Vw7ZoxYmNLkJenBAA0aFAPLi7t0LFjV7i7u8Pd3R0dOnSApaWl4PRERJqN7ReiqmH7hYjqIqVSib/++gtt2rThVVFERERUnqcXogYOHAhzc3Ps37+/NkNRNcjJycG1a9cQGxuL2NhYxMRE49q1CMTG3kBOTj4AoH59A7Rta4hWrYrRurUKrVpBbWncWPBBED2HvDwgPh64dUu+lRc9xMcb4cYNCampxQAAY2NDtG3bEs7ObnB0dIaTkxOcnJzg7OyMJk2aiD0IIiIdw/YL6Tq2X4iIiIiISAs9uRAlSRKaNGmCTz/9FB9++GFtB6MalJSUhJiYGFy7dg1xcXGIj49HfPwN3LwZh8TENJT+k6hXzwCtWxuhdesStGxZglat5LOQ7ewAe3v5lhOPkwgFBcC9e0BSUtmt3GGjj/h4A8THS0hPL3mwfaNGFmjVqjlatXoBrVo5oG3btnB0dISTkxNatWoFAwMDcQdDREQVwvYLabrKtl/MzU1haGgIO7tmcHJyhqenJzp37sz2CxFppJycHHh6emLIkCGYOHEinJycREciIiKi2vPkQtT169fh6OiIc+fOoUePHrUdjARRKBS4ffv2P5078nLr1i3cunUNt2/fQmJiGoqKih9sb2qqD3t7I9jZSbC3L/7nVu7ksbMDmjYFrK0BGxvA2FjggVGdJ0lAWlrZ8mhHTUKCERIT9ZGQoER2dlknjb6+Hpo0aYQWLVr8U2hqjVatWqF169Zo3Vq+b2FhIfDIiIioprH9QqLUZPvl9OnT8PPzQ0hICK5fvw4AaN68Obp06fJg8fDwgI2NjajDJyKqsPT0dHzzzTfYt28fZs2ahQULFoiORERERLXnyYWovXv3YsqUKcjKyoKpqWltB6M6LC0tDUlJSbh3757abULCPSQm3kZCwj0kJaWjsFCh9rwGDQxha2sAGxsJNjZK2NgoYW0N2NrKw+jY2MidPtbWgKWlvJiZCTpIei4lJUB2trykp5d1zpTeT00FUlP1kJZmhLQ0PaSlqZCeXgKVquy/IwMDfTRp0gh2dk1hZ9cS9vbNYWdnBzs7O9jb2z+4tbW1hSFntCctdOHCBezfvx/ffPON6ChEWoHtF3qWutx+ycnJQVhYGEJCQh4sUVFRkCQJdnZ28PDweLD06NGDxSkiqrNUKhWKi4thYmIiOgoRERHVnicXombPno3g4GAEBQXVdijSEunp6UhJSUF6ejrS0tKQlpaGlJSUB/fT01OQlpb0z7os5OUVPLYPY2N9WFoawNJSH5aWQMOGKlhalsDSUnrQ2VO61K8PWFgAJiZAgwaAubl838oKMDVlp9CzlJQAubny3ARFRXInTEEBUFgoT4xdWFjWOZOdLa/LygKysw2Rna2P7Gw9ZGdLyM5W4v595WP7NzExgo2NFaytG6Fx46awtbWHtbU1bGxsHiy2trZq9znkDOmyDRs2YMWKFUhJSREdhUinsP2iWXS5/ZKdnY2rV6+qFaciIyMB4LHiVM+ePWFtbV0ruaji7ty5g/Hjx2Pbtm1o27at6DhEdUZMTAycnZ1FxyAiIqLqk/nE0/CioqLg7u5em2FIy1hbW1fqC29hYSHS0tKQkZGB7OzscpfMzExkZ2cjISEdUVGZyM7OQnZ2DrKz88rtCHqUpaUhTEz0UL++/j+dPhIaNJBrsebmShgbqwCUzR1hZCR3EMmPlw3P8+jcEhYWwNNOarW0BPT1n/0eZGcDKtWTH8/KkoeAKZWXBxQXy+uysuR1RUVAvjyfO3Jz5Q4a+XEjAEBBgR4KC/WQmQkUFUnIz5eQk1MCpbLcmvQDJiZGsLSsD0tLC1haWsLKqhGsrBqjeXNLuLpawtKy/KX03wGHxyOqnNTUVDRu3Fh0DCKdU1Ptlxs3buDs2SuwtW0ESVKy/VLF9ktGBqBQsP0CAJaWlvD29oa3t/eDdVlZWQgPD0dAQAD8/f2xZcsWJCYmAlAvTnl7e6NHjx4wNzcXFZ8g/39z9uxZhIWFsRBF9I/4+Hi0b98erq6uWLFiBYYOHSo6EhEREVWDJ14R1aZNG0ybNg2ffPJJbWciqrK8vDwUFhYiJycH9+/fR1FREbKyslBQUIDCwkJkZWWhsLAQ+fn5yMnJQWFhIfLy8gDIQ54olUqoVCpkZ6cBAAoLC1BQIPeK5ObmoqSk5J/Hcx+8ptxJklcrx2dubgpj47Ieo3r1zGBiIvcuWVlZQU9PD0ZGxqhf3+Kfxy1gYmKm9ripqSnMzMxgaWkJU1NTmJubw8LCAqamprCwsIC5uTlMTU1haWkJMzMzmJqaoiFndSeqdYWFhcjNzWUxikiDqVQq/Oc//4Gvry+Cg4PRp08ffPXVV+jatavadmy/VLz9EhAQgOzsbEyaNAlNmzZl+6UCEhIS1K6aunDhApKTk2FgYABnZ2e1K6c6d+6MevXqiY6sUzp37ozp06djypQpoqMQ1Rnnzp3Dnj178Prrr2PIkCGi4xAREdHzK39ovuLiYpiZmcHPzw8jRowQEYxI4x06dAgjR45EampqhZ9T2nFCREREmkuhUGD//v348ssvER0djbfeeguLFi1Cjx49REerMKVSiZycnAptW5vtl/Pnz2PMmDHIz8/Hrl278MYbb9TK62qbR4tTQUFBSE1NhaGhIZycnNSKUx4eHjDjGJFERERERFR15Reirl+/DkdHR1y8eBEeHh4ighFpvP/7v//DqVOnEBoaKjoKERER1YK8vDxs374d33zzDZKSkvDuu+/ik08+gYuLi+hoWiU7OxvTp0/H/v37MXv2bKxatQrGpeMPUpU9Wpw6f/480tPTyy1Ode3aFSYmJqIjE5EOkyQJ69evx5AhQ9CiRQvRcYiIiOjpyi9EHT9+HG+88QYyMjI4pAVRFcXFxSEpKQk9e/YUHYWIiIhqUFpaGr777jusX78eRUVFeP/99zF//nx2jNWwPXv2YObMmXB2doafnx+cnJxER9I6jxanAgICkJmZCSMjIzg6OqoVp7p168aCIBHVmlu3bqFLly7IzMyEr68v5s+fLzoSERERPVn5hahNmzZh0aJFyMzMFBGKiIiIiKjOi4+Px7fffott27ahXr16mDlzJmbPng1ra2vR0XTGzZs3MWbMGFy+fBlffPEF5s6dKzqSVlMqlYiOjlYrToWGhiI/P/9Bccrb2xteXl7w8PBA+/btoa+vLzo2EWkphUKBY8eOoW3btnB3dxcdh4iIiJ6s/ELUp59+iuPHj+PSpUsiQhERERER1Vnh4eH46quvsH//ftjZ2eGjjz7C5MmTYW5uLjqaTiopKYGPjw8+++wzDB48GFu3bkWjRo1Ex9IZ5RWnLl26hIKCAtSvXx8dO3ZUu3KKxSkiIiIiIp1TfiFq+vTpiImJwalTp0SEIiIiIiKqc/z9/eHr6/vg7OtZs2bhgw8+4Fw5dcTp06cxbtw46OvrY+/evXjppZdER9JZJSUliImJUStOhYSEoLCwEBYWFnB3d1crTrm4uEBPT090bCLSQoWFhRg+fDiGDx+Od955B/Xq1RMdiYiISBdlGpa3Njc3FxYWFrUdhoiIiIioziktQB09ehSdO3fGrl27MGbMGBgYGIiORg95+eWXER4ejg8++AAvv/wyZs+ejVWrVnHeIgEMDQ3h6uoKV1dXjB8/HgBQXFyM2NhYtcLU999/D4VCAUtLS7i5ubE4RUTVLjMzEyYmJpgyZQritOpSGQAAIABJREFU4uKwfPly0ZGIiIh0UrlXRL399tto0KAB9u7dKyITERGRTjtz5gwGDRqEmzdvcngpIkFUKhWOHTuGFStWICQkBF5eXli4cCEGDhwoOhpVwJ49ezBjxgy4uLhg3759cHR0FB2JyvFwcSogIAD+/v6IiYmBUqmElZUVXF1dHxSmvL294eDgIDpyjbhz5w4kSULLli1FRyHSWmlpadDT0+M8jkRERGKUPzTfyy+/jPbt22Pjxo0iQhEREem0n376CaNHj0ZRURGvuCCqZUVFRThw4AB8fHxw48YN9O/fH//617/QpUsX0dGokqKjozF69Ghcv34dX3/9NaZOnSo6ElVAXl4eLl++rHblVHR0NFQqFezs7NSumurWrRuaNGkiOvJz69+/P2xtbbFr1y7RUYh0mkql4hx2RERENYND8xFVNzZeieh5NWrUCG+//TaLUES1KCcnBzt37oSvry8yMjIwYsQIHD16FE5OTqKjURW1a9cOQUFBWLlyJaZPn44TJ05gy5YtaNiwoeho9BT169eHt7c3vL29H6zLzc3FlStXHhSmDh48iH//+9+QJOmx4pSnpydsbW0FHkHl2dvb4/bt26JjEOm05ORkdO7cGSNHjsS0adPg7OwsOhIREZFWKfeKKGdnZ4wbNw5LliwRkYlIoy1ZsgQXLlzA8ePHRUchIiKiZ0hOTsamTZuwdu1aKJVKTJo0CQsXLoS9vb3oaFSN/vrrL4wfPx6GhobYu3cvevfuLToSPaecnByEhYWpXTkVFRVVbnGqR48esLGxER35iY4fP47k5OQH82kRUe3LyMjAhg0bsHv3bixYsADTpk0THYmIiEiblD80n5OTEyZMmIDFixeLCEWk0V5//XU0adIEe/bsER2FiIiInuDGjRtYt24dtmzZAktLS3zwwQeYN28erKysREejGpKamorJkyfj2LFjmD9/Pj777DMYGRmJjkXVKCsrC+Hh4WrFqcjISAB4rDjl5eXFeRiJ6DGSJEGpVMLQsNwBhIiIiKhqyh+az9TUFEVFRbUdhkgrtG3bFl5eXqJjEBERUTkuX76Mb7/9Fn5+fmjVqhW+/PJLTJ06FWZmZqKjUQ1r3LgxDh8+jD179mD69Ok4ffo09u3bhxdeeEF0NKomVlZWjw3rl5iYiIsXLz4oTG3evBlJSUkAyopT3t7e8PLyQqdOnWBubi4qPhHVAXp6es8sQt25cwctWrSopURERETaodwrojw9PfHSSy/hq6++EpGJiIiIiKha+fv7w9fXF0ePHsWLL76IDz/8EGPGjOFcbDoqKioKo0ePRlxcHL777juMGzdOdCSqRQkJCWpXTQUHByMlJQUGBgZwdnZWu3LKw8ODhWoieuDKlSvo1KkT+vTpg3/961946aWXREciIiLSBE++IqqwsLC2wxARERERVRuVSoVjx45h5cqVCAoKgpeXF3777TcMGDAAenp6ouORQO3bt8f58+exfPlyTJw4EUeOHMGWLVs4NKOOsLe3h729PQYOHPhg3aPFKR8fH6SlpcHQ0BBOTk5qhakuXbrA1NRU4BEQkSiurq747bffsHPnTvabERERVUK5V0S9/vrraNWqFbZs2SIiExERERFRlSkUCuzfvx9ffPEFYmNj0b9/fyxZsgSenp6io1EddOLECUyYMAHGxsb44Ycf1IZ1I932aHHq3LlzyMjIKLc41bVrV5iYmIiOTERERERUF2WWW4h6++23YWlpiT179ogIRURERERUabm5udixYwdWrVqF1NRUjBw5EosWLUK7du1ER6M6LjU1Fe+99x7++9//YvHixVi6dCmHbaRylRanAgIC4O/vj8uXL+P+/fswMjKCo6OjWnGqW7duMDY2Fh2ZiARQKpU4deoU+vbtC319fdFxiIiIRCu/EDV27Fjk5ubi8OHDIkIRERHprPz8fBw/fhyvvPIKLC0tRcch0gipqanYsGED1q1bh+LiYrz33ntYsGABmjdvLjoaaRBJkrB161Z8+OGHcHd3x759++Dg4CA6FtVxSqUS0dHRaldOhYaGIj8/H+bm5njxxRfVilPt27dnpzSRDvD390evXr3QsmVLbN68GW+88YboSERERCKVX4j65JNP8Oeff+LSpUsiQhEREemsiIgIuLm5ISIiAi4uLqLjENVpt27dwurVq7Ft2zaYm5tjxowZmDNnDho1aiQ6GmmwiIgIjB49Grdu3cLGjRsxZswY0ZFIw5SUlCAmJkatOBUSEoLCwkJYWFjA3d1drTjVqlUr+Pj4YPLkyXjhhRdExyeiahITE4Pt27djwoQJcHV1FR2HiIhIpPILURs2bMDy5cuRmpoqIhQREZHOOn36NF555RWkpKSgcePGouMQ1UlhYWH4+uuv8eOPP6J58+aYN28epkyZgnr16omORlqisLAQCxcuxPr16zF27Fhs3LgR9evXFx2LNFh5xamLFy+iqKgIFhYWyM3NxaBBgzBs2DB4eHjAxcUFenp6omMTEREREVWH8gtRv/32G95++23cv3+fX+iJKujvv/9GfHw8xo4dKzoKEWmwpKQk/PXXX3j33Xc5PwnRI/z9/eHr64tjx47Bzc0N8+fPx+jRo2FoaCg6Gmmp48ePY+LEiTA1NcW+ffvQs2dP0ZFIixQXFyM2NhYXLlzApEmT0K5dO8TFxUGhUMDS0hJubm5qV07xigoi7ZOdnY1Dhw5hxIgRPOGBiIi0WfmFqNDQUHTu3BkxMTFwcnISEYxI43zwwQeIiorCmTNnREchIiLSGpIk4ejRo/jyyy9x7tw5eHl5YeHChRgwYACvFqBakZKSgkmTJuHPP//E4sWLsXTpUp4oQNUuLi4Otra2MDY2xtWrV+Hv7//gyqno6GioVCpYWVnB1dUV3t7e8PLyQpcuXWBnZyc6OhE9hxMnTmDgwIEwNjbGwYMH8frrr4uOREREVBPKL0SlpaWhcePGOHnyJPr27SsiGJHG6d27N9zc3LBx40bRUYiIiDRecXExfvzxR/j6+iIqKgpvvfUWPv30U16RQkJIkoR169Zh4cKF6NmzJ/bs2YPmzZuLjkU6Ii8vD5cvX1Yb1q+0OGVnZ6d21VS3bt3QpEkT0ZGJqBLS09Pxww8/YNSoUbC1tRUdh4iIqCaUX4iSJAn169fHunXr8P7774sIRqRxbt68CT09PbRu3Vp0FCIiIo11//59bNu2Dd9++y0SExPx7rvvYuHChRySiuqE8PBwjB49Grdv38amTZswatQo0ZFIR+Xk5CAsLEytOBUVFQVJkh4rTnXv3p3zThIRERGRSOUXogDA09MTnp6eWLduXW2HIiIiIiIdk5aWhu+++w7fffcd8vLyMGHCBCxevBgtW7YUHY1ITUFBAT755BOsW7cO48aNw8aNGzmvB9UJ2dnZuHr1qlpxKjIyEgAeK0717NkT1tbWghMTUUXdunULP/zwAyZNmoRmzZqJjkNERFRZTy5EzZgxA2FhYfD396/tUERERESkI5KSkvD9999j9erVAICJEyfi008/RdOmTQUnI3q6//znP5g8eTJsbGzg5+eHzp07i45E9JisrCyEh4cjJCQEAQEB8Pf3R2JiIoDHi1Pe3t5o2LCh4MREVJ4//vgD48ePR2ZmJk6ePIk+ffqIjkRERFQZTy5Ebdu2DXPnzkVOTg4n4yUiIiKianX9+nWsX78emzdvRsOGDTFt2jR8+OGHsLS0FB2NqMKSk5MxceJEnDx5EosXL8ayZcugr68vOhbRUyUkJKhdNXXhwgUkJycDABwcHODl5fWgONW5c2fUq1dPcGIiAgCFQoHDhw9j0KBBMDExER2HiIioMp5ciLp06RI8PDwQEREBFxeX2g5GRERERFro0qVLWLNmDfz8/NC6dWvMnj0b06ZNg6mpqehoRFUiSRLWrVuHjz/+GN7e3tizZw+HTSKN82hxKigoCKmpqTAwMICzs7PalVMeHh4wMzMTHZmIiIiINMeTC1EKhQINGjTAtm3bMHbs2NoORkREpHOUSiWGDBmCpUuXomvXrqLjEFUrf39/+Pr64ujRo+jUqRPmzZuHMWPG8Mp70hoXL17EmDFjkJGRgW3btuHtt98WHYnouTxanDp//jzS09NhaGgIJycntcJUly5deEIBUR0QHBz8YOhYBwcH0XGIiIhKZT5x3AhjY2O4ubkhJCSkNgMRERHprLS0NBw5cgT5+fmioxBVC5VKhSNHjqBbt27o1asXMjMz8dtvv+HSpUsYP348i1CkVbp06YLLly9j9OjRGDx4MMaPH4/79++LjkUaws3NDT///LPoGGrs7e0xcOBALF++HEeOHEFaWhru3buHX375BcOHD0dmZiZWrFiBXr16oUGDBnB1dcX48eOxdu1a+Pv7Q6FQiD4EIp1z9+5d7N69G46OjggLCxMdh4iI6AHDpz3o7e2N06dP11YWIiIinZaZmQkTExPY2NiIjkL0XIqKinDgwAGsXLkS169fR//+/REcHMwr/UjrmZmZYe3atXjppZcwZcoUdOnSBX5+fujUqZPoaFTHxcbGoqioSHSMZ7K3t39QoCoVFxcHf3//B1dOHTp0CPn5+TAyMoKjoyM8PDzg7e0NLy8vtGvXjichENWgoUOHYtCgQTh58iTc3d1FxyEiInrgiUPzAcDvv/+OAQMG4N69e7Czs6vNXEQa4/bt2+jatStOnjyJDh06iI5DREQkTG5uLnbs2IGvvvoKaWlpGDlyJBYvXgxnZ2fR0Yhq3Z07dzBu3DgEBgZixYoVWLBgAfT1nzggBem4rl27YuXKlXjttddER3luSqUS0dHRasP6Xbp0CQUFBahfvz46duyoNqxf+/bt+btBREREpN2ePEcUAOTn56NRo0bYvHkzJkyYUJvBiDTGmTNn0KdPHyQmJqJp06ai4xAREdW6lJQUbNy4EWvXroVSqcSkSZPw8ccfo1mzZqKjEQklSRLWrVuHjz/+GL1798bu3bthb28vOhZRrSspKUFMTIxacSokJASFhYWwsLCAu7u7WnHKxcUFenp6omMTaa3Dhw/j7NmzmDJlCtq1ayc6DhERab+nF6IA4NVXX4WtrS38/PxqKxSRRvn777+xdOlSnD59ml+WiIhIp8TFxWHt2rXYunUrLCwsMH36dMydOxcNGzYUHY2oTrlw4QJGjx6N7OxsbN++XW1YMyJdVVxcjNjYWLXC1MWLF1FUVIQGDRqgQ4cOLE4R1RA/Pz8sWrQId+7cwZ07d3iSBBER1bRnF6JWrVqFr776CsnJybxcnoiIiIhw5coVfPPNN/jxxx/RokULzJ07F1OnToWZmZnoaER1Vm5uLubPn4+tW7diypQpWL16NerVqyc6FlGd8mhxKiAgAJcvX4ZSqYSVlRVcXV3VilOurq6iIxNpLJVKhYsXL6Jbt26ioxARkfZ7diHq6tWrcHd35wTTRERERDrO398fvr6+OHbsGNzd3fHRRx9h9OjRMDQ0FB2NSGP8/PPPmDp1Kuzs7ODn54eOHTuKjkRUp+Xl5eHy5ctqV05FR0dDpVLBzs5OrTDVtWtXDpdOREREVPc8uxAlSRIcHBwwYsQI+Pr61lYwIiIiIqoDVCoVjh07hs8//xyBgYHw8vLCwoULMWDAAA6RRFRFt2/fxtixY3HhwgV8+eWXmDNnDn+fiCohNzcXV65cUStORUVFQZKkx4pTnp6esLW1FR2ZSCN9//33uHbtGqZOnQpnZ2fRcYiISHM9uxAFAIsXL8bu3bsRHx8PAwOD2ghGRERERAIpFArs378fX375JWJiYtC/f38sXrwY3bt3Fx2NSCsolUp8/fXXWLp0KV5++WXs2rULdnZ2omMRaaycnByEhYVVqDjVo0cP2NjYiI5MVOdt27YNK1euRFpaGpKTkzmkLBERVVXFClFRUVFwcXHBqVOn8PLLL9dGMCIiIp2TmZkJKysrnhVPQuXl5WH79u34+uuvkZKSgpEjR+LTTz9F+/btRUcj0krBwcEYPXo0cnNzsWPHDrz11luiIxFpjezsbFy9elWtOBUZGQkAjxWnvLy80KhRI8GJieoelUqFyMhIuLm5iY5CRESaq2KFKADw8PBAp06dsG3btpoORUREpJNsbGzw2WefYfr06aKjkA5KTU3Fhg0bsH79eigUCrz33nuYP38+WrRoIToakdbLycnBzJkzsW/fPkyZMgVr1qyBmZmZ6FhUSxISEpCcnIxOnTqJjqITMjMzERISAn9/f4SEhODixYtISkoCoF6c8vb2Ro8ePWBubi44MREREZHGq3gh6ttvv8WKFSuQlJTEL0VERETVTKlUwtjYGAcOHMA777wjOg7pkFu3bmH16tXYtm0b6tWrh5kzZ2LOnDk8K5xIgIMHD2Lq1Klo1qwZ/Pz84O7uLjoS1YINGzbg3//+N5KTk0VH0VkJCQlqV00FBwcjJSUFBgYGcHZ2VrtyqnPnzhyejOgRPj4+UCgUmDJlCk9iIiKi8mTqV3TLUaNG4f79+zh69GhNBiLSKPHx8fydIKJqUVRUhKlTp6Jdu3aio5COuHr1KsaPHw9HR0ccPnwYn3/+OeLj47F8+XIWoYgEGT58OC5fvoyGDRvC09MTa9euRQXPGyQNlpWVBSsrK9ExdJq9vT0GDhyI5cuX48iRI0hOTsa9e/fw66+/Yvjw4cjMzISPjw969eoFS0tLuLq6Yvz48Vi7di38/f1RWFgo+hCIhDI0NMTWrVvRq1cv/t0iIqJyVfiKKADo378/FAoFTp48WZOZiDTGpk2bsGTJEqSnp4uOQkREVCH+/v7w9fXFsWPH4OrqigULFmDUqFEwMjISHY2I/lFSUgIfHx/4+Pjg1Vdfxa5du9C0aVPRsaiGBAcHIzIyEhMnThQdhZ7h0Sunzp8/j/T0dBgaGsLJyUntyqmuXbvCxMREdGSiWlNcXIzr169zXlEiIipPxYfmA4D//ve/ePPNN3H58mV07NixJoMRaYTly5fj4MGDiIiIEB2FiIjoiSRJwtGjR+Hr64uAgAB4eXlh4cKFGDBgAPT09ETHI6InCAwMxJgxY3D//n3s3LkTb775puhIRPSIh4tTAQEBOH/+PO7fvw8jIyM4OjqqFae6desGY2Nj0ZGJiIiIalvlClGSJMHNzQ3du3fH9u3bazIYkUYIDw/HnTt32ClARER1kkqlwqFDh7BixQpEREQ8KEANHDhQdDQiqqDs7GzMmDEDP/74I2bPno1Vq1axI5uoDlMqlYiOjla7cio0NBT5+fkwNjZGhw4d4OXl9aA41b59e+jrV3jWBCKNNm/ePNjb22PixImwtbUVHYeIiGpP5QpRALB582bMnTsX8fHxaNKkSU0FIyIiIqIqKioqwoEDB/DZZ58hLi4O/fv3x/Lly+Hh4SE6GhFV0Z49ezBz5ky0bt0aP/74I9zc3ERHIqIKKikpQUxMjFpx6tKlSygoKED9+vXRsWNHtSunWJwibfV///d/2LFjB1xcXBAQECA6DhER1Z7KF6IKCgrQsmVLzJ49G8uWLaupYERERERUSTk5Odi5cyd8fX2RkZGBESNGYOnSpXB0dBQdjYiqwa1btzBmzBiEhobiiy++wNy5c0VHIqIqKq84dfHiRRQVFaFBgwbo0KGDWnHKxcWFw+mSVsjPz8e9e/fYPiUi0i2VL0QBwOLFi7Fjxw7cunWLk28SERERCZaUlITvv/8ea9asgSRJmDhxIj755BPY2dmJjkZE1aykpAQ+Pj7w8fHBoEGDsHXrVlhbW4uORUTVoLi4GLGxsWrFqQsXLkChUMDS0hJubm5qxSlXV1fRkYmIiIgqomqFqHv37qFt27b49ttvMWPGjJoIRkRERETPcOPGDaxbtw5btmyBpaUlPvjgA8ybNw9WVlaioxFRDTt9+jTGjx+PkpIS7Nq1C6+//rroSERUA0qLUwEBAfD390dISAiio6OhUqlgZWUFV1dXeHh4wNvbG97e3jwJhbTCrFmz8NJLL2Hw4MEwMjISHYeIiJ5f1QpRADB37lwcPHgQ169fR7169ao7GBERkU7x8/NDmzZt0KNHD9FRSAOEhoZi9erV8PPzQ+vWrTF79mxMmzYNpqamoqMRUS3Kzs7GBx98gAMHDmD27NlYtWoVjI2NRcciohqWl5eHy5cvq105VVqcsrOzU7tqqlu3bpzfmzRKfn4+3n33Xfz+++8YP348duzYIToSERE9v6oXolJTU9G2bVssXboUCxYsqO5gREREOqV9+/YYNWoU51+kp/L394evry+OHj2KF198ER9++CHGjBkDAwMD0dGISKA9e/Zg5syZaNeuHfz8/DjvhgYJCQnBrl27sHr1ahgaGoqOQxosNzcXV65cUStORUVFQZKkx4pT3bt3R+PGjUVHJnqq+Ph4FBYWwtnZWXQUIiJ6flUvRAHAokWLsGXLFsTFxaFBgwbVGYyIiEinWFtbw8fHB9OnTxcdheoYlUqFY8eOwcfHB8HBwfDy8sLChQsxcOBA0dGIqA6JiYnB6NGjERUVhS+++AJz584VHYkqYOvWrZg/fz6ys7NFRyEtlJ2djatXr1aoONWzZ0/ON0dEREQ1JVP/eZ798ccfQ5IkrF69uroCEWmMqKgoDBs2DOnp6aKjEJEW+OOPPzB48GDRMagOUSgU2LNnD1xcXDB48GDY2toiMDAQ/v7+LEIR0WOcnZ0RGBiIjz/+GB999BHeeecdZGRkiI5Fz3Dv3j3Y29uLjkFaytLSEt7e3pg7dy727NmDiIgIZGRk4O+//8bChQvRsGFD7N27F4MGDYKNjQ3s7e0xcOBALF++HEeOHEFmZqboQyB6IqVSifnz5yM0NFR0FCIiqoDnuiIKAFauXImvvvoKcXFxPHuGdMqJEyfw2muvISsrC5aWlqLjEBGRlsjNzcWOHTuwatUqpKamYuTIkVi0aBHatWsnOhr9P3v3HR9FnT5w/LMtvRICpFASeqihiJgQQFROBA77iQ3xlDsLcFhiO/Wnp4KHCiKo2AvKWU/BgnAqEBQkoYckCIkYSAjpfZPs7vz+mGw2SwJSkkyy+7xfr++L3dnZmWd2wjzfmWeKEB3E//73P26++WYMBgPvvfceCQkJWockTsJqtVJaWkqnTp20DkW4sZycHKerprZv305eXh4AYWFhxMfHExcXx8iRIxkxYoQ8J1y0C7/99htTpkwhLS2NxYsXc88992gdkhBCiJM7t1vzgfqQzL59+3L55ZezYsWKlgpMiHZvzZo1zJgxg9raWnk2hxBCiHN2/PhxVqxYwYsvvkhdXR2zZ8/m/vvvJyIiQuvQhBAdUEFBAbfeeitr167lrrvuYvHixZhMJq3DEkJ0ECcWp3755ReOHz+OwWCgf//+Trf1GzlyJN7e3lqHLNyQoij8+OOP9OrVi6ioKK3DEUIIcXLnXogCeO+995g1axY///wz5513XksEJoQQQgjhFrKysliyZAmvvfYafn5+3HHHHcybN4/g4GCtQxNCuIB3332XO+64g0GDBrFq1Sr69OmjdUhCiA7qxOLU1q1bKSgowGg00q9fP6fC1KhRo/Dy8tI6ZCGEEEK0Dy1TiFIUhQsvvJCKigq2bduGXn9Oj54SQgghhHB5e/bsYfHixXz44YdERkYyf/58brvtNrndjRCixaWlpXH99ddz8OBBFi9ezO233651SEIIF3Ficeqnn36iqKgIk8lE3759nYpTo0ePxtPTU+uQhZspLS1l5cqV3HLLLXTu3FnrcIQQwl21TCEKIDU1ldjYWF5++WVuvfXWlpikEEIIIYTLSUpKYtGiRXz11VcMGTKEe+65h5kzZ2I0GrUOTQjhwmpqanjsscf497//zZVXXsmrr74qV14KIVpFTk4OW7ZsISkpiZSUFHbt2kVlZWWT4lR8fDzDhw+XW92LVrVp0yamT59OTU0Nn3zyCZdddpnWIQkhhDtquUIUwLx581i1ahXp6elyloEQQgghRD2bzcZXX33FM888w88//0xcXByJiYlMnToVnU6ndXhCCDeyYcMGbr75ZkwmE++99x7jxo3TOiQhhIuzWq2kp6c7XTm1Y8cOqqur8fPzY9iwYU5XTg0cOFDutCNaVGVlJR988AGXX365HK8UQghttGwhqrS0lAEDBjBjxgxefvnllpqsEEIIIUSHVFdXx4cffsiiRYtIS0vjsssu48EHH+SCCy7QOjQhhBvLz89n9uzZfPvtt9xzzz08+eSTmEwmrcMSQrgRi8VCRkaGU3EqJSUFs9mMv78/Q4cOdSpOxcTEyMk7QgghRMfVsoUogFWrVnHTTTexYcMGJk6c2JKTFkIIIVzSP/7xDwICAvi///s/rUMRLaSiooI33niD5557jmPHjvGXv/yFBx54gJiYGK1DE0IIQH3O72uvvcY//vEPhgwZwqpVq+jdu7fWYbkVs9mMl5eX1mEI0W40V5xKTk6mpqaGgIAAhgwZIsUp0Wp+//13UlJSmDZtmtwyWwghWl7LF6IArrrqKlJSUti9ezcBAQEtPXkhhBDCpSQkJDBs2DCWLVumdSjiHBUUFPDSSy/x0ksvYTabufXWW7nnnnvo0aOH1qEJIUSz9u/fz8yZM8nKymL58uXccMMNWofkNmbMmEFgYCDvvPOO1qEI0W7V1dVx4MABp+LU9u3bqa2tJTAwkMGDBzsVpwYNGqR1yKKDeuONN7j99tsJDw9n3bp1cgKZEEK0rNYpROXn5zNkyBCmT5/OypUrW3ryQrQLtbW1eHh4aB2GEMIFTJs2jYkTJ7JgwQKtQxFn6fDhwzz//PO88cYbeHt7c+edd3L33XcTEhKidWhCCPGHzGYziYmJLFu2jKuuuoqVK1cSFBSkdVgub/DgwVx++eU8+eSTWociRIdSWVnJzp07nYpT6enp2Gw2goODGTlyJHFxcYwcOZLRo0fTrVs3rUMWHURmZiZvv/02jzzyiBzvEUKzHvJBAAAgAElEQVSIltU6hSiAL774gssvv5y1a9cyZcqU1piFEJqaOXMmZrOZzz77TOtQhBBCaGTfvn08++yzrF69mpCQEObMmcOCBQvkinAhRIf03XffMWvWLDw9PXn//feJi4vTOiSXNm/ePC699FL+9Kc/aR2KEB1eeXk5u3fvbrY4FRYW5nTV1JgxY+jSpYvWIQshhBDupPUKUaAeqN+4cSN79+6lU6dOrTUbITRx9dVXo9Pp+Oijj7QORQghRBtLSUlh6dKlrFq1iujoaO666y7+9re/4enpqXVoQghxTo4fP87s2bNZt24dDz/8MP/85z8xGAxahyWEEGesrKyMPXv2OBWn0tLSUBSlSXHq/PPPJzQ0VOuQRQdw8OBBbDYb/fr10zoUIYToSFq3EFVYWMiQIUOYOHEiq1ataq3ZCKGJJUuWoNPpmDdvntahCCGEaCNJSUksWrSItWvXMmLECObNm8f1118vB2mFEC5FURRefPFFEhMTGTFiBKtWrSIqKkrrsIQQ4pyVlpayd+9ep+LU/v37AZoUpy644AK5zbJo4u6772bFihVccsklrF69msDAQK1DEkKIjqB1C1EA33zzDVOnTuXVV1/lr3/9a2vOSgghhBCixdlsNr766iueeOIJkpOTiYuLIzExkWnTpmkdmhBCtKrU1FSuu+46fv/9d15++WWuu+46rUMSQogWV1JSwr59+9iyZQtJSUmkpKSQm5sLNC1OxcfHExwcrHHEQks2m401a9bwzTff8Morr2gdjhBCdBStX4gCeOihh3jhhRf46aefiI2Nbe3ZCSGEEEKcs5qaGv7zn//wr3/9i0OHDjFlyhQeffRRRo8erXVoQgjRZsxmM4mJiSxbtowbbriBFStW4Ofnp3VYQgjRqnJycpyumtq+fTt5eXkYDAb69+/vVJwaMWIEPj4+WocshBBCtGdtU4iyWq1MnjyZw4cPk5ycLJetCiGEEKLdKisr46233uLZZ5+lsLCQa665hkceeUTuAy+EcGv//e9/ue222/D392fVqlWMHTtW65CEEKJNnVic2rZtG/n5+RiNRvr16+dUnBo5ciTe3t5ahyw0lJubS5cuXeQW3kIIoWqbQhRAXl4esbGxxMfH89FHH7XFLIUQQgghTlteXh4vv/wyS5cuxWq1csstt5CYmEh4eLjWoQkhRLuQl5fHLbfcwvr163n44Yd59NFH0ev1WoclhBCaObE49fPPP1NYWNhscWr06NF4enpqHbJoIwkJCWRnZ3PnnXdy7733ah2OEEJore0KUQA//PADF198MUuXLuXOO+9sq9kKIYQQ7daRI0dQFIXu3btrHYrbyszMZOnSpaxcuZKAgAD+/ve/M3/+fIKCgrQOTQgh2h1FUXjxxRe5//77iYuL47333iMiIkLrsIQQot04sTi1ZcsWiouLMZlM9O3b16k4dd555+Hh4aF1yKIV/Prrr6xYsYK8vDw++OADrcMRQgittW0hCuCJJ57g6aef5ocffpDbOQghhHB7c+bM4eDBg/zvf//TOhS3s2vXLp5//nk++OADevbsydy5c7n99tvlNipCCHEaUlJSmDlzJsePH+eVV17h2muv1TqkDmPr1q1s3bqV+fPnax2KEKINWK1W0tPTnYpTO3fupKqqqqE4FR8fT1xcHCNHjmTgwIFytakQQghX0/aFKJvNxowZM/jll1/Ytm0bPXv2bMvZCyGEEO3KFVdcgYeHB6tXr9Y6FLeRlJTEokWLWLt2LcOGDWPBggXMnDkTo9GodWhCCNGhVFdX88ADD/Diiy9y44038vLLL+Pr66t1WO3e/fffz3fffceuXbu0DkUIoZHmilM7duyguroaPz8/hg0b5nTllBSnXJfFYpH9ECGEO2j7QhRARUUFcXFxWCwWfvrpJwIDA9s6BCHOWUpKCgUFBUyePFnrUIQQHdjq1avx9PTk8ssv1zoUl2az2fjqq6946qmn2LZtG3FxcSQmJjJ16lR0Op3W4QkhRIf2+eefc9tttxEaGsoHH3xAbGys1iG1axdeeCHR0dG8/vrrWocihGhHLBYLGRkZTsWplJQUzGYz/v7+DB061Kk4FRMTI/3YDs5mszFkyBBGjRrF3LlzGTlypNYhCSFEa9GmEAVw+PBhxowZw4gRI1izZg0Gg0GLMIQ4a3feeSepqan8+OOPWocihBDiJGpra1m9ejXPPPMMBw4cYMqUKTz88MOcf/75WocmhBAuJTs7m5tuuoktW7bw0EMP8eijj8rZ+ydRVFREVVUVkZGRWocihGjn6urqOHDggFNhavv27dTW1hIYGMjgwYOlONWBWSwW3n77bV566SXGjBnDq6++qnVIQgjRWrQrRAH89NNPXHjhhcybN49FixZpFYYQZ2XBggX8/PPP/Pzzz1qHIoQQ4gQVFRW88cYbLF68mOPHj3Pttdfy4IMPMnDgQK1DE0IIl6UoCi+++CL3338/48aN49133yU8PFzrsIQQwqU0Lk5t2bKFpKQkMjIysFqtBAUFMWjQoIbCVHx8PNHR0VqHLE5DdXW1PKtWCOHKtC1EAbz33nvcfPPNvPnmm8yaNUvLUIQ4I3v27KGsrIz4+HitQxFCCFEvPz+f5cuXs2zZMmpra5k9ezb33XefnHUuhBBtKDk5mZkzZ1JSUsLrr7/O9OnTtQ5JCCFcWkVFBbt27XK6cio9PR2bzUZYWJjTVVOjR4+mW7duWocshBDCvWhfiAJ46KGHeO6551izZg2XXHKJ1uEIIYQQooP57bffeOGFF3j99dfx9fXljjvuYO7cuXTq1Enr0IQQwi2Vl5dz7733snLlSm688UZeeeUVfHx8tA5LCCHcRnl5Obt373YqTqWlpaEoSpPi1JgxY+jSpYvWIYuTqKysZNq0adx4441cd911eHl5aR2SEEKcqfZRiFIUhVtvvZX//Oc/rF+/ngsuuEDrkIQQQgjRAezdu5d///vffPjhh0RGRjJ//nxuu+02OdgphBDtxKeffsrtt99O165d+eCDDxg+fLjWIQkhhNsqKytjz549p1WcGjt2LJ07d9Y6ZAHk5uZy77338sknn/D444/z4IMPah2SEEKcqfZRiAKwWq1cffXVbNq0ic2bN8szHIQQQghxUklJSSxatIivvvqKwYMHc++99zJz5kyMRqPWoQkhhDjB77//zo033sgvv/zC448/zn333Yder9c6LCGEEEBJSQn79u1zKk7t378foElxKi4uTu44oKFjx47h6elJcHCw1qEIIcSZaj+FKFAfzHfJJZeQlZXFli1b6Nmzp9YhCSGEEKKdUBSFtWvXsnDhQn766Sfi4uJITExk6tSp6HQ6rcMTQghxClarlcWLF/PPf/6TCRMm8M477xAWFqZ1WEIIIZqRm5tLcnJyQ2EqOTmZY8eOAY7iVHx8PHFxccTGxuLr66txxEIIIdq59lWIAigtLWX8+PFUVVWRlJQk96gVQgjhsr7//ntSUlK47777tA6lXaurq+PDDz/k2WefZf/+/Vx22WU88MADxMXFaR2aEEKIM/TLL79w/fXXU1payptvvsnUqVObHc9qtfLxxx/zl7/8pY0jbD0lJSX4+/tjMBi0DkUIIc5YTk6O01VTv/zyC8ePH8dgMNC/f3+nK6dGjhyJt7e31iG7naNHj7J8+XLuvPNOIiIitA5HCCEaa3+FKFA3nHFxcYSFhfHdd9/h7++vdUhCCCFEi3vkkUdYs2YNu3fv1jqUdqmyspLXX3+d559/niNHjnDllVfy2GOPMWjQIK1DE0IIcQ7Kysq46667eP/997ntttt44YUXmjzb78knn+Txxx/nxx9/ZNy4cRpF2rJuu+02fv/9d9atW6d1KEII0SJOLE5t3bqVgoICjEYj/fr1cypMjRo1Ci8vL61Ddmk//PAD119/PQUFBXz77bdceOGFWockhBB27bMQBZCRkcH48ePp27cv33zzDX5+flqHJIQQQrSoOXPmcOjQITZs2KB1KK2uoqLitHN5WVkZb731FgsXLqS4uJhrrrmGRx99lD59+rRylEIIIdrSxx9/zJw5cwgPD+eDDz5g6NChAPz888/Ex8ejKArh4eGkpqYSGBiocbTnLiYmhhkzZvD0009rHYoQQrSaE4tTP/30E0VFRc0Wp0aPHo2np6fWIbuU2tpaPvnkE6644gop/Akh2pP2W4gCtRg1ceJEoqOj+fbbb6UYJdoVm83Gn//8Z+6//36XOUtTCNG2SktLqaqqcvlnZCxdupTvv/+eL7744pTjHTt2jFdeeYUlS5agKAqzZs3igQcecPnfRwgh3Nnhw4e54YYbSE5OZuHChcyePZuhQ4dy5MgRLBYLJpOJa665hvfff1/rUM+JoigkJiYyffp04uPjtQ5HCCHalL04tWXLFpKSkti1axeVlZWYTCb69u3rVJw677zz8PDw0DpkIYQQLat9F6IA0tPTmThxIn369JEro0S7ExgYyHPPPcdf//pXrUMRQoh2afHixdx3333odDr27dtHTExMk3EOHjzIsmXLWLlyJUFBQcyZM4d//OMfLnH2uxBCiD9msVh44oknePrpp+nWrRvHjx+nrq7OaZwPP/zQpZ4XJYQQ7sxqtZKenu505dTOnTupqqrC19eX4cOHOxWnBg4ciF6v1zpsl3Ho0CGqq6sZPHiw1qEIIdxH+y9EgaMY1bdvX77++mspRol2IyYmhr///e/cfffdWocihBDtzrPPPktiYiIAJpOJv/zlL7z77rsNn+/YsYMlS5bwwQcf0KtXL+6++27mzJkjt5AQQgg39cQTT/DYY481Ga7T6fD19SU1NZUePXpoEJkQQojWZrFYyMjIcCpOpaSkYDab8ff3Z+jQoVKcaiH3338/ixcvZtKkSaxevZqQkBCtQxJCuL6OUYgC2LNnD5MmTSImJoavv/4aX19frUMSQgghxEksWrSIBx54wGmYwWDg0KFDZGdns2jRItauXUtsbCzz58/n+uuvx2AwaBStEEIIrWVlZTFkyBCqqqpobhfVZDJx3nnnsWnTJjnwKIQQbqK54lRycjI1NTUEBAQwZMgQp+JUTEwMOp1O67DbPUVRWLduHZ999hmvvvqq/GZCiLbQcQpRALt27eKiiy5i6NChfPHFF/j7+2sdkhBCCCFO8Nhjj/HEE080GW40GgkKCqKgoIDJkyeTmJjIxIkTNYhQCCFEe2KxWIiLi2Pnzp1NbsnXmF6v59///jcLFixow+iEEEK0J3V1dRw4cMCpOLV9+3Zqa2sJDAxk8ODBTsWpQYMGaR2yEEKIjlaIAti/fz+XXHIJ3bp149tvv6Vz585ahySEEEKIeo888ghPP/10s2ezg3pV1Pr166UAJYQQosGjjz7Kk08+eVrjmkwmUlJSGDJkSCtHJYQQoqOora1l7969JCUlNRSn0tPTsdlsBAUFMWjQIOLj44mLi2PUqFGEhYW1eAwffvghEyZMaJVpa6WkpISgoCCtwxBCuIaOV4gC9bYNF198MR4eHnz33XdERkZqHZIQQgjh1hRFYcGCBSxduvSkRShQr4p6+OGHefzxx9suOCGEEO1WXV0d8+fP57PPPuPYsWN4eHhgsViw2WzNjm80GunTpw+7du3C09OzjaMVQgjRUVRUVLBr1y6nK6fsxamwsDCnq6bOO+88unbtek7zGzBgANnZ2Tz55JPcfffdmEymFloS7Vx00UUUFhYyb948Zs2apXU4QoiOrWMWogByc3OZPHky5eXlrF+/nj59+mgdkhBCCOGWFEVh7ty5LF++/JRFKLuAgACOHj2Kn59fG0QnhBCio8jMzGTNmjV8/vnnJCUlYbPZMBgMWCwWp/GMRiNz587lueee0yhSIYQQHVFZWRl79uxxKk6lpaWhKEqT4tT5559PaGjoaU23srKSgIAAbDYber2e3r1788orr3DhhRe28hK1rp07d/LKK6+Qm5vLl19+qXU4QoiOreMWogCKioqYMmUKhw8fZt26dQwdOlTrkIQQQojT8umnn3LPPfeQlZXVoR8Oa7PZmDNnDm+++eZJz14/kU6n4/nnn2f+/PmtHJ0QQoiOqqioiHXr1rFmzRq+/vprSktL8fT0pKamBlBzyfr165k0aZLGkZ5aTU0N11xzDU8++aTsrwohRDtUWlrK3r17nYpT+/fvB2hSnLrgggsICQlpMo3NmzeTkJDQ8N5gMGC1WpkyZQorVqygZ8+ebbY8rcFeYBNCiHPQsQtRoJ7NMG3aNFJTU1m7di3nn3++1iEJIYQQf2jp0qUsWrSInJwcrUM5a1arldmzZ/Puu+82+7lOp8NkMqHT6airq3MqVPXr14/9+/djMBjaKlwhhBAdlNVqZevWrXz11Vf897//JS0tDYBu3bqRmppKp06dNI7w5DZt2sT48eM5ePAgvXv31jocIYQQp6GkpIR9+/aRkpLCli1bSEpKIjc3F2hanIqPj+ftt98mMTGRuro6p+mYTCb0ej0PPPAADz74oNxSVgjhzjp+IQqgurqaa6+9lg0bNrBq1Souv/xyrUMSbqSoqAhFUZo9K0YI0XGUlpY2FEqsVitlZWWnNe4fOdm0NmzYQHJyMg888ADe3t54eXmddqwBAQEnLeCcOK1TjXsurFYrN998M6tWrXIabjKZ6Ny5MxEREfTo0YPIyEjCwsIIDw93avLQWyGEEGcrOzubr7/+mq+//pqQkBDefPPNJuNYLBbKy8sb3ldXV2M2m5ud3onj/hGz2Ux1dfUpxwkMDESv1/P555/z1ltvnfKWRgaDgYCAgD+c1umMK4QQonUcPXqU5ORkUlJSSE5OJjk5mfz8fAwGA35+flRUVGC1Wpv9rtFoJCIighUrVjBlypQ2jrx1KYrCpZdeyqRJk7jttttadT+vqqqq4cpoOPV++anyfnPKyspOuv5AXYf+/v6nPb1T7eOfmMu9vLzw9vY+7WkL0UG5RiEK1ANi8+bNY8WKFTzzzDMkJiZqHZJwEzExMVx11VU88cQTWociRIdQXl6O2WymvLyciooK6urqGg7o1NXVUVFRgaIolJSUAI4OYWVlJbW1tdTU1FBVVeV00Ki4OB9QKC8vw2JRz0JrPA2A2to6KiurGt5XVpqprXV+5oSr0+t1BAb6Nrz39PTAx8fR4fX19cXDw6PhfVBQCDod+PkFYTJ5NHSmTSYThYWF5OTk0LVrV/z9/YmIiCAoKIiuXbvi4eHR0Jm2d9h1Oh1BQUENw4OCgjr0LQmFEEKobDYbpaWlDQeHiouLAUf+tg+35+/GJ2iUlJSgKArl5eVYLJaGg0a1tWYqK8uw2RRKS4sa5mWfhl1lZRU1NXXY00lJSeVpPavQlXh6mvDxcZxh7+fni8lkbHgfHBzc8Npk8sDPzx+dTk9QUGfAccKKvQ/g6emJj4+P0wE3+zT8/f0xGo34+Pjg6emJh4cHvr6+BAQE4OXlJc9+FEK4ncOHD5OSksLs2bMpLS095bh6vR6bzcaf/vQnVqxYQVRUVBtFeebs++clJSXU1NRQWVnZbP6uqKigsrKSTz/9lK1btzJo0CAuuuiihr4BOIpFlZVl1NaaqakxU1VV2TAv+zEBu+LiMkDN5XV1VioqTn3ihyvy9/fGaFRPJlX3ox1FK3vutfP19a/P3974+Pg7Fbns+9z2/G3fn7dPQ6/XExgY6DSun58fnp6eBAYGNuR7IVqI6xSi7JYuXcqCBQu46667eOGFF+QepqLVzZgxAy8vL1avXq11KEK0KHtHs7i4mLKysoZWWVnZcMDJbDZTUlLScOCouLgYs7mK6uoKSkqKMJvNVFVVUVZWgdlce0adyKAgY31HSIfJpMPbG7y8wGRS8PNT0OsVAgPVQlJgIOj14OMDjftJ9uEABgM0PoHYywsan3Tk7w/G+uM2Oh2c6kQuT091XqfLzw9MppN/Xn/M7rTU1UFFxck/r6hQx2lu2id+t7oaGp8kVlYG9pPAbDaw70vZh1dWGqit1VNTA1VVeiwWsJ9AXlxsBXSUlVmwWk+va2E/eBYY6I+Xl1f9wawgvLx88PMLxN9fHe7v74+fnx9eXl4EBATg4+PTcOArKCiIwMBAAgICGg6ECSGEaF51dbVTTrfn+MrKyiYHnMrKyjCbzVRUVFBeXozZXE15eSkVFRWYzWbKyirO6KQOk0mPn5+hPseqyTkgQM3Pvr42PDzA09OGj48Vo1HNy6DmY3uhqfFwOLNc7uEBjY7bNNH4u3/kj/oJtbVQWXnyz09UWal+52ROlcurqqDRyeGUlqo5HNTc3fiibLNZzf2Nh5eUGFEUHeXlOiwWXUPfoLZWobJSqS8Gnv6JO/7+3nh5eeLv79sodwfh4+OPl5cPQUFBDQfCgoODnU5Qsed2e7PneNmnF0K0Z+Xl5QQGBp72iRBGoxG9Xs/DDz/M/ffff877L43zur2VlpY2OQFUzd1qzjebqyktLaSqSn2t7tObMZtrKC4+xc7mCXx8DHh66vH01OHlpUOvVwgOVpN2UJC1vghiwWhUGvbnT8zl9uF29r4BqPvy9XUSoOl++Kn2s/8oV5/oj/oJJ+5n/5FT7eOf2E84sR9QUgL2P6fG+9xw8n14+/C6Oh0VFcb6/Xk1f9r7BpWVNmprwWy2UV198qu/GlMLYb54e3vh5eVZn7u98fb2ISgoFC8vL3x8fAgMDGy0T+84QSUoKMgptwcEBJzRlWXCpbheIQrUB8DfeOON/OlPf+L999/H50yOFgpxhj7++GMqKyuZNWuW1qEI4aSsrIzCwkIKCwspKiqitLSUkpKSZjuqJSX5lJbaC07llJVVUlnZ/GXsBoOOgAAjPj5qZzMoCLy9Fby8FIKD6xo6h4GBaofS11ftTHp6qh1OPz91eECAo3Bk7/Sd2NEUHZf9wJhatHIc9LIPLy52HBArKVFfV1Wp45jNake/vNxITY2OsjI9lZXq8NJSG1VVVmpqmr8Fg4eHkYAAXwIC/AgODiYgIIiAAPu/asc3MDDQqUPcqVMnQkJC6NSpE506dZIDXkKIdslqtVJUVERRUVFDbm98wMk5x5dQVlZMWVkJJSUllJaqub2urvmDDl5eery9DQQF6fHysudxG56eNvz8rPj7q7nb31/N115ear728VFfBwU5ikL2k0DsB4dOLBaJjst+YMx+MM5+0Ku0VP23stI5j1dUqK/LytTPamqgpMREdbUOs1lX3xdQqK5Wi102W/OHJvz8vOtzu3997g4iOLhLkwNbjXN8SEhIQ26XA15CiNa0ceNGJkyYcNrjG41GLBa1wB8VFcWjjz7KyJEjG3J80331EkpLSykrK6rP76X1n1WcsmgUEGDEy0uPn5+u0T64DV9fK15eNgIDHUWg4GBHvg4KUvfRT9yPB3U8ddqOYpHouOxFLkVRczyoObumRh1eUaG+Li1V99XNZnU8e/5X9+P1VFUZKC1VT1hV9+NtmM0K5eXNn8hiL24FBtrzemD9PnswAQEB9fvxzvndvq9ub3ICaofkmoUogM2bNzNjxgwGDBjAF198QefOnbUOSQghzkpFRQUFBQUNB57sB5+cWwGFhXkUFRVSVFRCUVEZFkvTg01BQUYCAvQEBOgICFAICLAREGAhKEg9cBQQ0LQFBzu/l4NJoj2wWNROckmJ2jEuK2vaiosbv9dTVmagrExPaSmUlNgoK7NgsTTtBgUH+xESElzfyQ2lU6cuTYpVjZv9YJcQQpwORVFOms+dhx2nqCi/ftwSSkubXmJjMukJCDAQGKgnKIj6vG6t/1fN26eT4+VgkmgPqqudc/of53hjfX7XUVamUFZma/bqLZPJQKdOAXTqFEynTiGEhHSlU6fOzeZy+/vOnTs73fpICCFO5rnnniMxMdHp+UL2Z/Q2Hubp6YmnpwdGox6r1Up1dfNXFfv6GggIMNTvs6snhgQF1TWbx0+V44VoD05vX93edJSWGikp0de/Vygrs1JV1fTYlo+PJyEhQfW5vTOdOnUhJKRzk332E3O76VS3qhGtzXULUQDp6ekNDwH84osvGDJkiMYRCSGEqrq6mtzcXHJyciguLj7h9RFycn6nuLiQnJzjlJQ0PfDk5aUnONhAcDAEB1sJD7cRFkb9++Zb165yoEmIE1VXq53gUzc9xcUmcnN15OTYKCqyNHtFVnCwP2FhXQkO7kx4eHfCwsIIDg4mPDzc6XVkZKTTs7iEEB1fTU0NhYWFFBcXN5PXc+vz+nFyc4+RnZ3X7JVJwcFGwsIMBAfbCA62EBysnDKvBwdDeLgGCytEO3eq3J6bCzk5UFxsoLjYSHGxjuJiG/n5dU1OTvHy8iA4OKA+f3cnLCyi2bweFhZG165dGw48CyFcQ3V19Ulyeg7FxUXk5v5OTs5RsrKyqa5uen9Vk0mHv7+B4GAdXboohIRYms3jjffjO3dW7xYihHBmz+2OPN5cM9XndzW35+XVNrna2svLg/DwLoSFhRMc3MUppzeX40WLcu1CFEBhYSHXXHMNv/zyC++88w5XXHGF1iEJIVyU1WolLy+PI0eOkJOTQ3Z2NkePHuXo0aMcO5ZDXl4O+fn55OcXY7U6DmIbjTpCQz0IDdURFqbQpUsNoaHQrRt06QKhoWqHtFMnCAlxXA4vhNBORQUUFamtsBDy8uD4ccjPVzvH+fl6jh83cOwYHD9uxWx2Llx16uRPly4hdOnSja5dIwkPj6B79+4NhaqIiAgiIiLk4bBCaMxsNjfk9d9//53c3FyOHDnC0aNHycs7Sn7+MY4dy29ytZK3t4EuXYyEhekJDa0jNNRCWJia07t0UXN8p06OJhdeCKE9e04vKoKCAjWnq3lcbXl5evLy9OTn68jPd34mptFoIDQ0kNDQzoSFhdOlS3ciIyMJDw+nR48ehIeHExERQbdu3dDZH3omhNBEcXExR48eJTs7m5ycHI4cOcKRI0c4diyH/PxccnOPcamsjU0AACAASURBVPx4IWazc3EpONhE164GQkNtdO1aR7duCqGh6rN9evaE/v3VPG/P7XLhhRDaqq117LMXFTXeV3e8Pn7cSH6+nmPHrJSWOp8o5uPjSWhoMGFhXQkNDSc8XM3njffbIyMjCZTnS5wu1y9EAVgsFu655x6WLVvG/fffz9NPPy3PfxBCnJGamhqnAlPjDmtOzu/8/vth8vIKnW6H17WrB2FheiIjLXTt6jgAFRqK08Go0FANF0wI0SbKy5vr9Dpe5+Yayc7Wc+yY8xnZXboEER7ejcjIXkRE9HQ6oBUZGUn37t3l2RdCnKXS0lKOHDnSkNft/x49mk12dhY5ObkUFJQ2jG8y6QkLM9G9u46wsJqGA1Bdu6qFpcZFJj8/DRdMCNHqFMWRx48fVwtW9tdqjteTnW0gJ0ehoMBx6y2TyUC3biF07x5JREQ0ERFqLg8LC6N79+4NJ6LIldNCnDlFUTh27JjTCSRqXj9KdvZhcnOPkJ19lKqqmobv+Poa6dHDSHi4Qnh40xNCu3VT83xoqFypJISrq6lxnISSl6e+zstzvD56VE9OjoHsbJvT7QJ9fT3p0SOcsLBwIiN7N5yIYs/r9qushJsUouxWrlzJXXfdxYwZM3jrrbfkns9CCCfFxcVkZmae0NLJzDzE4cO5TlcxBQcbiY7WERZmITxcITpaLS7ZL63v2VMOQgkhzk5xsXq7gdxcyMx0vM7J8SA318DBg3VOz8AIDvYnOroXYWE9CA+PIDo6uqENGDBA+jvCbdXW1nLkyJET8vpBMjMzyMnJITe3qGFcLy894eFGwsJshIdbGnJ64/zes6fc4lYIceZqatQrrZrmdR25uR5kZurIzq6lrq7xvoZ/fS7v55TXo6Oj6dmzp9wGULit4uLi+hye2yi3HyAzM4OMjEwqKswN46q3vNXX53Vbk7xu/1cIIc5UdbXjNoHO+d1AZqaJnBwbeXl1DbcG9PAwEhnZjejovvXNObcHu8etj9yrEAWQlJTElVdeSbdu3fjiiy/o1auX1iEJIdqI2WwmIyODjIwMMjMz+e2338jKOkRW1kEOHz7S8KBQLy8DUVEmoqIs9Q2ioqBHD4iIUM+KkjtqCCG0VFICR4/C779DVpa96cjKMpKVZaO4WD1DS6fTER4eQlRUL6KiBhAd3ZuoqCj69evHgAED3KXDK1xYQUEB6enp/Prrr2RlZdW3X8nKyiQnJ79hvJAQE1FReqKiaomKUoiKgl69HLld7qghhNCSzaaegX30KBw+3Di368nKMvDbb9aG51N6epro1SucqKi+9S2KqKgo+vfvT79+/eS2vqJDs9lsHD58mPT0dA4ePFif1w/x228Hyco63HArXINBR0SEZ/2+urlhnz0qSs3r4eEg/xWEEFoym9W8fvRo47xO/T67npwcR6EqONiPqKge9OrVl6ioPkRFRdGnTx8GDBhAjx49XOW2vu5XiALIysriz3/+M3l5eXz44YdceOGFWockhGhB9iubUlNT2b9/P5mZv5KauouMjN8armoKDjYQHa0jOtpCdDROrVcvkLt3CiE6MrNZPSMrM7NxM5CZ6UFGRh0VFWrh3X7GdUzMUAYNGlT/OoaBAwfKbYxFu5KTk1Of0+35fReZmb+SmZkLgKennogIA9HRVqKjbU55vXdvCArSeAGEEOIcFRefmNchM1O9ourw4dqGZ1aFhXVm0KChxMQMbsjtgwcPplu3bhovgRAO9quW7fvsqan72L9/F+npB6msVK9qUu9Coic6uo7oaMXpauWBA8HHR+OFEEKIc1BbC0eOOOd19WppLzIzISurGkVRr6bq06cXgwYNJzq6NzExMQwaNIiBAwfi07E2hO5ZiAKoqKjg1ltv5dNPP+WJJ57gwQcfdJXqotCAoijcfffdXHPNNSQkJGgdjtsoLi5m586d7Nq1i7S0NNLT95GWlkZhofo8B39/I/37GxgwoJaBAxX691c7rH36yP2dhRDuS1HUs60zMiAtDdLTISPDyP79cPy4WqDy8fGkf/9o+vcfSkzMIIYMGcKIESPo0aOHxtELV5eVlcWOHTvYu3cvaWlpZGTsIyPjYMMDw8PCPOtzei0DB8KAAerDweVPUwjhzmpq1LyekaHm9bQ0yMgwkZFho7JSvUo6NDSQmJgB9O8/jJiYGIYPH87w4cPlIeuiVdXW1rJv3z527tzJ/v372b9/HxkZqRw+nIPNpmA06oiO9mTgwDoGDLDSvz/ExKi5XU4iEUK4s6Kixjkd0tL0pKcbyMqyYLUqGAx6evUKZ8CAQQwcOIRBgwYRGxtLTEwMJpNJ6/Cb476FKLuVK1dy9913c/HFF/Pee+/JLWrEWRs4cCBXXHEFTz31lNahuKTc3Fx27tzJjh072LkzhZ07t5OVdRSAbt1MDBqk0L+/xemgVPfuGgcthBAdTHFx085uWpqJzMxabDaFkJAAYmNHMGLEecTGxjJixAj69OkjV0+JM2a1Wjlw4EB9Xt/Jzp3b2bFjByUlFRgMOnr39iAmpo4BA2xOB6XkeKkQQpw+RVFv49v45JP0dBOpqZCfX4dOp6N37+7Exp7HiBEjiY2NJTY2li5dumgduuiAKisr2b17d31e38mOHVtJTc2gttaCr6+BmBj1JNEBA9R99oED1auW5SRRIYQ4fTU18Ouv9pwOaWk6MjI82L/fQnW1FU9PE0OGDGTEiPMb8vrQoUPx9vbWOnQpRAFs376dq6++GkVR+PjjjznvvPO0Dkl0QLNmzaK0tJTPP/9c61A6vJKSEpKSkti6dSs7d6awY0cyx44VABAV5UVsbB2xsVZGjIDYWPUho0IIIVpPeTns2gU7d8KOHbBzp4n9+y1YLAr+/t4MGzaEESPOZ9SoUYwfP16unBJNZGZmsmnTJpKTk9mx4xf27NlLZaUZk0nP4MEexMaaG/L6sGHg66t1xEII4dqOHGmc1/Xs2GEgO7sOgMjIUGJjRxMbO4qxY8cSFxeHv7+/xhGL9sRisZCSkkJSUlL9SSXbOXDgEFarjaAgE7GxMGJEHbGxam7v3x8MBq2jFkII12WxqIUpNa/Djh1Gdu1SKCuzYjQaGDAgmtjYMYwcOYr4+HiGDx+OoW03zFKIsisoKGDmzJls3ryZhQsXMm/ePK1DEh1MeXm5dM7PUkFBAZs3b2bjxo1s2rSB3bv3oygK/ft71HdelfqOLMhFi0II0T6YzbB3r+Mg1o4dHuzZY6GmxkavXuEkJExi/PgJJCQk0KdPH63DFW0sPT2dTZs2sWnTRjZu3MCRI8fx9jYwfLiB2Njahrw+eLCcCS2EEO1FQUHjA1g6du704NdfazAaDcTGDiYh4SLGjx9PfHy83E3GzdTU1LB9+/b6ffbv+emnn6moqKZLFxMjR9qcThSNjtY6WiGEEKBeGX3wYOMTT4ykpOgoLKwjIMCH+PhxJCRMJCEhgVGjRrX2Lf2kENWY1Wrln//8JwsXLmTWrFksW7YMXzkdU4gWV1RUxIYNG9i0aRMbN/6P1NQM9HoYOtSDhIQaxo+HceOgc2etIxVCCHEmqqth2zbYuBE2bjSwdatCdbWN8PDOjB8/iYSECVx00UVSmHJBGRkZ9bldPUB17Fghfn5GLrhAx7hxdUyYAKNHg6en1pEKIYQ4E8eOwaZNatu40UhqqgWdTseQIf0YP34yCQkJTJo0iSB5oI9LsVgsbNmyhR9++IGNG79n27ZfqK6uISLCgwkTLCQk2Bg3Tr29nhBCiI5DUSA1Vd1n37RJx6ZNRo4dq8PX15OxY88nIWESEydOZOzYsS19xZQUopqzZs0abrnlFkJDQ1m9ejXDhg3TOiQhOry0tDTWrFnDV1/9ly1btqHTKYwcaSIhoZaEBIiPl4eRCiGEq6mthe3b7Z1cA1u2QEWFlQEDopk27UqmTp1KXFxcW98SQLQAi8XCxo0bWbt2LWvXfsbBg78TGGgkPl5h/Hgr48bBqFFgNGodqWv48Uf1GW4mE0ydqnU0rcddltPdtOV6TU2FAweaDjcY1EJ4t24wdCjodO17Hu1ZYSFs3mzP7R7s3l2HXq9n3Lg4pk6dwfTp0+ndu7fWYYqzUFRUxNdff83atV+ybt03lJRUEBXlQUJCHRMmKIwbpz7TSbQMd8l57rKc7sbVcntjNpv6XMXdu9V/u3VTi+5xca57Ul1Ghv2kEx0bN5rIzq6lU6cALr10KlOnTuPSSy8l8Nwf1luMIpqVnZ2tJCQkKJ6ensqSJUsUm82mdUhCdDiHDx9WlixZosTFjVIAJSTEqFx9tU555x2U4mIURZHWGm3UKBRA8ffXPhZZztNrJSUo//gHypo12seidWvL9ZqYqM7rVK13b5Svvmq5eU6c6Jh2p04o1dXa/+Zt2SwWlORklMceQxk50qs+NwQqt99+u7J582bpb7VzVqtV2bx5szJ37lyla9dOCqBER5uUuXNR1q9Hqa3V/m/MVZsr5Tx3WU7J7dqs17bI7Vr0H9pzKypC+egjlNtv1ytdu3oogBIT00957LHHlEOHDimifauqqlK+/PJL5eqrL1c8PIyK0ahT4uIMysKFKGlp2v99uXJzpZznLsspuV2b9dqWeTcrC2XcuObn0a8fyoYN2v/2bdEOHUJ59VWUqVNNioeHXvH0NClTp05R3nnnHaW8vFw5S0X6cy1luarIyEh++OEHFi1axH333ceMGTMoLCzUOiwh2j2LxcKnn37KRRdNoFevXvzrX/cxdGgymzbB8eMWPvpI4aab5OonIew2bYK+feGFF6CuTutoxIkOHYIrr1Tvp9wS0/rxR8f7oiL45JNzn25HYjDAyJHw+OOQnGwmIwPmzi1l8+a3GTduHAMH9mHJkiWUlJRoHapopKCggGeffZa+fXsxbtw4fvjhZebNKyIzEw4dqmPpUrjoIvWMSCGE5Pb2riVzu5bzaC+Cg+Hqq+HVV20cPVrLhg0wduyvvPjiU/Tt24cpUyazdu1abDab1qGKRnbv3s2cObfTpUsIV145g6qqL3nzTQtFRQpJSVYSE2HAAK2jFKL9kNzevrVE3k1KUq+s2rzZMczLy/H6wAG4+GL48suzn0dHER0Nt98Oa9bUkZdn45VX6qit/Y7Zs2cRGdmNu+++m7S0tDOerhSiTkGv1zNv3jw2b97M3r17GT58OBs3btQ6LCHapdraWpYvX05UVCTXXHM1Xl5JfPmlQm5uHStWqM980ssWR4gmdu6E/Hz1tavewqUjWLlSvbVAUZG6PvbtgyuuUD8zm+GDD859Hm++qd6P+cT5urN+/eDRR2H//lp27ICJE7N49NH7iIjoxoIFCzh+/LjWIbq1o0ePcscdf6d793CeeeZhpk7NZu9e2LOnjgcfhKgorSMUon2S3N4+tEVub4t5dCQGA0yaBK+/rnDsmIWPP1awWL5n+vRp9O3bizfffBOLxaJ1mG5t48aNTJgQz/Dhw9m8+W2eeqqa3Fwba9dauf568PfXOkIh2ifJ7e1Da+VdqxXuuAPKy9X3s2fDkSNQWakWIe23H1QUmDPHvYqRQUEwaxasW2chJ0fhkUcq+eabVxk0aBB/+tNFbNu27bSnJXdtPw1jxowhOTmZW2+9lUmTJvHwww/zz3/+E6Pc9F4IAD7++GPuu28+eXl5zJljY/58hV69rFqHpbl33lGfjXH99fDbb/Ddd+p9VwcNUhNlc1eFWa1qB2fTJsjLg8GD4cILISKi+Xls26ZeYVFSAmPHwrRpp45pzx51/N9+U89wS0g4tzPdtm2D9HTXX84zme6Z/Cbffw+//OL47g8/QGmpunydOqnzOnxY3SGcNg3eeguys+GSS6CsDAoK1O9ddRX4+jqmU10NH32kvg4PV8/aORPutl5B/f0aL1PnzvDUU/DZZ+r7w4fPbfpWK7z9tvo6MlI9gL95s9rS0uRBzwCxsfDyywqLFll46y0LixYtY+XKFdx7byIPPfQwHh4eWofoNqqqqvi///s/XnxxCV26wJIlddxwg/N2xh25y7bRXZbzTKYrub3jrVdo/dzeVvPoqDw81L+rK66wcOAAPP/8Ef72t9tYuPAJlixZwZQpU7QO0a0cOHCAu+76O+vXf8+kSUb+9z+YOLHO7Q+ou8u20V2W80ymK7m9461XaL28u3Il7N2rvp42Dd54w/HZuHHqlVL9+sHx4+qVwOnpMGTI2c2rI+vSBe69FxYsqOO77+Bf/9rI+eefz/TpU3jxxRX07Nnz1BM425v6uavly5cr3t7eyujRo5X09HStwxHtUHFxsfL5559rHUabKC4uVq644s+KTodyyy16JTtb+/uYtqcGKN7eKJ99huLj43xv2Z49UQ4edB7/119RIiOb3ofWzw9lxQrncW02lAULmo572WUo/fs3vVev1Yry8MMoer3z+EYjyjPPqNM7m2W84w73WM4zme6Z/CbTpjV/7+GdO9XPL79cfR8VhTJ7tuPzQYOc75P8wQfO8X7yieOzp56S9Xqy1vg3XLWq6ecrVzo+X7ny3LYHa9Y4ppWYiPLmm4738+e37bapo7SqKpRnn0Xx8zMogwf3V3bv3q2I1rd161alT5+eSmCgUVm6FMVs1v5vob00d9k2ustySm53zfXaFrm9LfsPrtYyM1H+8he9Aig33XSDUlFRoYjW98ILLyje3h7K8OEm5fvvtf87aE/NXbaN7rKckttdc722Rd6dPt0xjc2bmx8nPR0lP7/1t0sdrX3zDUpMjFHx9/dWXnvtNeUUijjVp6J5hw4dUuLi4hRvb29l4cKFitVq1Tok0Y6sX79eAZSsrCytQ2lVx44dU4YNi1EiI03K+vXab/jaYwMUnU5NwuedhzJ3LkqvXo7kdtttjnEzM1G6d3d8NnYsypQpzh2Jt95yjL96tWO4Tqd2jC64wDnZN+4QvPaaY3hIiDrvxp2P//zn7JbR3vFx9eU8k+meyW8ydy5KeLhjeK9eKMOGqR0cRXF0aHU69V9fX7UT9/TT6jj2782Y4Rzvtdc6vnf4sKzXk7XGHdqEBJRZs1BuvhnlhhtQ4uMdn11yCUp5+bltD/78Z8f09u1DKStz/D6dOqFUV2u/zWqvLSsLZfx4oxIY6Kts2rRJEa3n22+/VXx8PJXJkw3KkSPar/v21txl2+guyym53TXXa1vk9rbsP7hq+/JLlC5dTMrYsaOVoqIiRbQOm82mzJs3VzEYdMqTT6LU1mq/7ttbc5dto7ssp+R211yvbZF3Bw50TKeoSPttU0drZjPKQw+pfyePPPKIchJSiDpbdXV1ysKFCxWTyaRcdNFFSnZ2ttYhiXaipqZG8ff3V5YtW6Z1KK3GZrMpEybEK/36mc4qWbpLa66z8euvjuFjxjiGz5zpGL5kiWN4WhqKh4c6PDjYkRBjYhzjf/ONY/zGid/eIaipQenSRR0WFIRSUaEOr6tD6dFDHT5w4NmdnWLv+Ljycp7pdM/0N1myxDH888+d523v0ALKhAlqsSI/3/H7jB2rfublhVJaqg6rrlbPZrJ/52z+dt1hvSqKc4f2ZM3PDyU19dy2Bbm56o4IoMTGOobfcINjPu+917LbH1drNTUoV11lUIKD/ZWcnBxFtLzMzEzF19dLmTVLr1gs2q/z9tjcZdvoDsspud0116uitE1ub6v+g6u3jAyUHj1MyvTplymidbz00kuKyaRXPvlE+/XdXpu7bBvdYTklt7vmelWU1s+7Vqtj+X18Wneb4+rt7bdR9Hqd8u677yrNkELUufrll1+U/v37K4GBgSf7kYUbWrt2rXLkyBGtw2g1n332mWIy6ZVdu7TfyLXnZk+I333nPLxzZ3V4nz6OYWFh6jBPz6ZncEye7JjWunXqmWwGg/o+JERNmvZxrVaUwEDnDsH+/Y7vX3klSkGBo/3tb47PcnLOfBkbd3xcdTnPdLpn+pucbof266+bxtb4EnR7IePzzx3D3nzz7P523WG9Kopzh7Z/f5Rx49Q2Zoz63h6nlxfK0qVnvy1YuNAxnxdecAxfv94xPD6+ZbY7rtyqqlD69jUpc+bcroiWd9111yjDhpnkbOlTNHfZNrrDckpud831qihtk9vbqv/gDm3zZvWA1YYNGxTRsqqqqhR/fx/lsce0X8/tubnLttEdllNyu2uuV0Vp/bxrs6m3NwT1yjI5Ke/c2vz5OiU0NFixWCzKCYr0iHMyevRoduzYwQ033MDNN9/MzJkzKSws1DosobHLLruMiJM95c8FbNq0idGjjQwbpnUkHUNoqPN7Hx/1X6tV/TcrC3Jz1dcTJoCfn/P4U6c6Xqemqg+9tH93/HjQN9qS6/UQGen8/V9/dbz+9FP1YY729sorjs+OHj2jxWrCVZfzXKb7R7/Jmejbt+mwa691TNP+kNNPPnHM66qrznw+J3LV9XqiRx9VH9a6aRNs3ao+fHTnTvVhnGYzJCZCRcXZTbvxg06/+gquuUZtjeNPSoL9+89tGVydtzfceGMdP/74ndahuKRNm37g5pvrMJm0jqRjcJdto6sup+R25/eusl5P1Jq5vS3n4cri4yEmxoONGzdqHYrL2blzJ+XlVfz1r1pH0nG4y7bRVZdTcrvze1dZrydqjbyr0znWm80Gx441P15enjoPcWq3366Qn1/M/mYOcBg1iMfl+Pj48NJLLzF9+nRuvfVWBg8ezPLly7niiiu0Dk2IVuHl5UV1tU7rMDoMT0/n940TOEC3bmAyQV1d80n5yBHH6+BgCAhwvK+rcx7XYoHff3ce1vig4vDhMGpU83F6eTU//HS56nKey3T/6Dc5Eyd2FEH9ja68Et57D9atg+PHYc0a9bMZM8Df/+znZ+eq6/V0DBkCl10Gb72ldji//lotIJ2JjRudO+UbNpx83JUrYcmSs4vVXVRXg7e3t9ZhuCQvL0/ZsToD7rJtdNXllNzu/N5V1uvpaInc3h7m4Uqqq9X9S9Gy7L+p5PbT5y7bRlddTsntzu9dZb2ejpbIu/36wZ496usNG+Dmm5uOM38+rF0Ll14KTz4J/fufe+yuyJ53msvtUohqQZdccgmpqancd999XHXVVVx22WW8+uqrhIeHax2aEC1q8uTJLFq0kO++g0su0Tqa9k/3BzU7b281UW/fDvv2QWYmREc7Pv/yS8frIUPUM0oCA6G0FFJS1DM27J2Mn3+G8nLn6Teelr8/vPaa431qqtpR6tHjj+P8I666nOcy3dOZV+NxbLaTj+fh0fzwW25RO7S1tXDnnVBWpg6/6aY/nvfpcNX1err27nW8Ppv6R+OroUaObH4nY+NGUBR4911YuLB1OueuoKAA3njDxC23TNc6FJc0efJUXn75De68s85px1M0z122ja66nJLbT/15R12vp+tcc3t7mYcr+OQTyMysZfLkyVqH4nKGDBlCeHgoixYV8tprp9gQiQbusm101eWU3H7qzzvqej1d55p3b7nFcZXaM8+oBcLAQMfnO3bA559DTY36W61YcW7xurKFCw306RNJnz59mnwmt+ZrYQEBAbz66qv8+OOPpKenM3jwYFauXKl1WEK0qAkTJnDddX9h5kwj27ZpHY1ruPBCx+s771SvoCgogH/9C9LS1OHjx8OIEerryy9X/83JgbvuUjsx9vFP1L+/43tJSfCf/6iXWB8+DHFx0KuX2iGprW21xWvQEZeztX+/xh3Vffvg4EFHp7Qxg6H570+YoMYAjo5TWBhcdNHZxXM2OuJ6PdGGDfDyy2pbvhwWL4Zx4yA5Wf3cw0O9fcyZKClxrJPAQDX+H35o2iZOVMcpLoaPPz635XBVhYVw2WUmfHy68PDDD2sdjkt64ol/UVcXwPTpxma3QeLMucK28XR0xOWU3P7HOuJ6PVFr5HYt5uGqfvgBbr3VyN/+NoeRI0dqHY7LMZlMLF++kjfeUHj6aa2jcR2usG08HR1xOSW3/7GOuF5P1Fp5d8oUmF5/vmNGhnpF1/Ll6pVrs2er66+mRv38ppvUQp1wpijw4IPwySc2XnnlDXTNVR1PfGqUaDmVlZVKYmKiotfrlSlTpijZ2dlahyREi6mqqlKmTr1U8fU1KMuWOT+4UJraqH+YYlqa8/BevdThUVGOYbW1KFdd5fjOiS0kBOXXXx3jHzmCEhDg+NxgQNHpUIxGlN69nR8aqSgo33+P4uPjGL9zZ/UhjKB+Z+vWs1vGxg/HdOXlPJPpnulv8v33TX8H+4NGGz/0tKTk5PE9/rjz9++999z+dt1lvTZ+6OkftbffPvPpL1/u+P6tt558vPffd4wXH39u684V248/ovTubVKioiKVgwcPKqL17Nu3TwkPD1UGDjSd9f8rV27usm10l+WU3O6a67W1c3tbzcOVm8WCsmgRiqenXrnuumuV2tpaRbSel19+WdHrdcp11+mVggLt1397a+6ybXSX5ZTc7prrta3y7pEjKGPGnHr6Y8eiVFW17HbIFVpuLsqMGQbFZDIo77//vnISRXJFVCvy8fFh4cKF/O9//yMjI4OhQ4fy1ltvoSiK1qEJcc68vb357LMvWLDgIRYsMDBunJFNm7SOquMymWD1avXBigMGOIZ7eqr3Ek5NhcZXtUZEwLZtEBurvrda1bNpvvwSJk1qOv2JE+Gnn9RbgxmN6lksHh5w8cWwahWMGdO6y2fXUZezNX+/8eOh8SMFPTyaXub+R26+2fkS9xtvPPt4zkZHXa8nYzSql/OHhsLVV6v3gW7uHtF/5PXXHa9PtU6uvBKCgtTXSUnQzDM93dJvv8Hs2XomToTBgyfz88/J9O7dW+uwXNqgQYPYujWFyMh44uJ03HGH7pwfKOzOXG3beDIddTklt59aR12vJ9NSuV3reXR0330HY8aYeOyx/2fvzuOiKvc/gH9mmGHfkX3JAQwFl4TciczdktTcStO6muVt0X73Wl29Lbbc0qu3rpaaWtfSMpcsEy1KxVAsV1wSFRhBZAfZhhkYWo2YtQAAIABJREFUmO33xzAsJqglc4bh8369zotxeGb4jBbfc873Oc+RYsmSf+GLL7ZA2vzGInTHzZs3D3v3fo/Dh70RFSXFmjXmuaLGWlnb78bWdNTPydreto7679qaO113AwOBI0eAN99sunrNxMPD+PyPP3LJ3eZqa4EPPgCioiQ4dy4A+/YdwIwZM1p/QWstKrqzVCqV4cUXXzTY2NgYhg4dakhPTxc6EplJdna24fLly0LHaFenT582DBt2vwGAYfhwieH773mF1J/dSkthSEuDQaO5+diiopazVm621dbCcO6cZczi6Iifs73et7AQht9+M85Sut3XXrzYNNOoTx/+u3Lr2Nu5czDMnSsy2NqKDWFhIYatW7cayLz0er1h48aNhpAQf4O9vY3h+edFhkuXhP9vo6NvneV3Y0f8nKzt1vnvys0yNq0Whm+/hWHIEIkBgOHBB0cZLly4YCDzqqysNCxYsMBgb29rCAmxNaxa1faVG9xubessvxs74udkbbfOf1dzbhUVMJw4AUN+Pgx6vfB5LGkrK4Nh+XIY/P0lBicnO8Mrr7xiUCqVhpsoFxkMBl6eY0anT5/GM888gzNnzuBvf/sb3nzzTdjZ2Qkdi9rRvffei549e+Kzzz4TOkq7+/nnn/HOO0uQlHQIMpkETz+twYwZQFCQ0MmIrI9Wa9wuXgT+/nfjOvsAsHIlMH++sNmIbpdSCezaBaxfL8XhwxpERITipZcWY9asWZwpLaC6ujp88skneP/9ZcjOzsWwYRLMnavFww9zJiBRe2BtJ2uSnQ188QWwYYMU+flajB07Cq++ugQDBw4UOlqnlpeXh6VL38Nnn/0PIpEG06fr8eSTBgwc2PJKDSK6M1jbyVro9cYVXDZuFGPbNhFsbe0xZ84zePnll+Hr63srb1HBRpQAtFotVq9ejVdffRV+fn74+OOPMfxG1zSSVVi3bh1efPFF5ObmoksnuZvdpUuXsHbtWmzevBFVVUrExtrgsce0GD/eeBkvWab0dOPlzLfqiy+A3r3bL097sZbPmZ0NhIa2fM7fH7h8ueUJYmv5vDdjjs/ZWf4uzUWlMi5tsG2bGAkJImi1wLhxD+HZZ1/A8OHDb3xzUxKEXq9HYmIi1qz5EImJP8HRUYzx43WYNs2A4cPZlLJkneX3lrV8Ttb2lljbO57cXODbb4GvvpLi2DENunRxx5NPzsUzzzzD5XUtTFVVFT7//HOsW/cRLlzIhExmi8ceq8fkycA997ApZck6y+8ta/mcrO0tsbZ3LHo9cPIksH07sH27FLm5GtxzTxTmzXsBM2bMgLOz8+28XYWkvYJS6yQSCRYsWIDJkydj/vz5GDlyJB5//HG8//77naZR0Zk8/vjjcHFxgaurq9BRzKZ79+5YuXIlli9fjsTERGzdugULF36HZ59V4957pRg3ToOHHjLu4NrYCJ2WTOrrgaysWx9fV9d+WdqTtXzOwEDjAaJpOknv3sDnn//+hLC1fN6bMcfn7Cx/l+3pyhUgMRHYvdsGBw8C9fV6xMUNxn//OxOTJk2Cl5eX0BHpBsRiMR588EE8+OCDKCkpwfbt27F162Y8/PAJODiIMWIEMG6cDmPH8ipoS9NZfm9Zy+dkbW+Jtd3yabXAqVPG+3Ls2WOLM2fq4erqiAkTJuH11x/DyJEjIZHwtJMlcnNzw/z58zF//nycOXMGX331Fb78cjPefbcQQUG2eOihesTHG++Lc3vnGam9dZbfW9byOVnbW2Jtt3wKBZCUBOzZI8LevRIUFWkQFhaCJ56YhUcffRRRUVF/+L15RZQF2LZtG1588UXo9Xr85z//wYwZMzgLmKyOWq1GSkoKEhIS8O2325CbWwxnZxsMHAgMGaJDbCwQF2e8kSER3ZqSEqCoCOjSBQgIEDoN0e9lZRkv3z9yRIT9+22RlVUHR0c7DBs2HPHx4zFu3DgE8D/eDqu0tBQ//PAD9uz5DomJP6C6uhb+/lLExmoxYoQBQ4YAf+I4hahTYm0nS6bVAmfPmmq7BPv3i1BRocFdd/lj9Oh4jBs3DqNGjeLtBzqwtLQ07NmzBwkJO/HrrychEgH33CPBkCEaxMYCw4cDnp5CpyTqWFjbyZJVVwPHjgH79wMpKXY4flwDnc6Avn17Ydy4iYiPj0d0dPSd6FVwaT5LUVlZiUWLFmH9+vWIjY3FRx99hF69egkdi6hdGAwGnD9/HsnJyTh0KBmHDiWhuLgczs4SDB4swn33aXD//UD//gCPYYiIOga9HrhwAUhOBg4fFuHQIQkKCzVwcrLDoEEDERc3HPfffz8GDBjAE1RWqLa2Fr/++isOHTqE5OQkHDt2HLW1dQgKssX992tx3316xMUBPXoInZSIiG5VbS1w9Chw6BBw6JANjh4Famp0CAjogvvvH464uKGIi4tDZGSk0FGpHRQVFTUcsx9CcvIBXLiQAbEY6N3bDnFxasTFAffdB3h7C52UiIhuVVGRsa4fPgwkJ0uQlqYDAPTqdTfuv3804uLiEBcXB+87/8udjShLk5qaiueffx4nTpzAs88+i7feegtubm5CxyJqd1lZWUhJScGRIyn46ac9uHKlEBKJCHffLUVMTD1iYoCYGONyflwagIhIWFqtce3tU6eMzae0NFv8+qsBZWUaODs7YODAgRgyJA6xsbG477772HjqhLRaLc6ePYv9+/cjJeVnpKQcRmWlCq6uEvTqBcTEaBETY7xiqlcvXhFNRCS06mrj1U5Ntd0BJ0/Woa5OD3//LoiNHYoRI0ZiyJAhiIyM5CounZBCocDx48cbavsBHD9+GhqNDv7+UsTE6BATo2+s7dffE4eIiMyvoKB5XbfBqVO2uHixFmKxGBER4Q21fQSGDRtmjmXy2YiyRAaDAZs3b8bChQthY2ODZcuWYebMmdzRo04lKysLR48exenTp5GaehynT59GRUU1bGxEuPtuW/TtW4/oaAP69gWiowF3d6ETExFZp7o64LffgNRU43b6tBTnzumgVuthZydB797d0bfvYERHR6Nfv37o06cPbHgDQLqOVqvFqVOncOrUKaSmpuL06eM4f/4i6uu1cHS0QZ8+UkRHq9G3L9C3L9CzJ5tTRETtpayseV0XITVVCrm8HgYD0KWLK/r2jUZ09AD07dsXgwYNQkhIiNCRyQJVVlbi119/bajrqUhNPYbs7HwAgJ+fLfr21SE6Wtd4zC6TCRyYiMhKGQzA5cvA6dOm+m6D06fFKC3VQCQSISwsENHRA9G3bwxiYmIwcOBAuLi4mDsmG1GWrKKiAkuWLMHq1asxZMgQLtdHnV5BQUHjSaxTp47j1KnjKCwsAwB4eEgQGQlERWkRGoqGx0DXroBYLGxuIqKOoLLSuPOalQWkpQEXLtggLU2K9PQ66HQGODs7oE+fXoiKugeRkZGIiYlBv379eLUT/WFarRbp6enNavuvOHPmN6hUakgkIoSESBEaqkVkpL5xdnWvXoCvr9DJiYg6hoIC4yzoptouRVaWDbKz1TAYAH//LoiK6o3IyJ6IiTGenOLVTvRnKBQKnDt3rqGun8SpU78iPT0bOp0ednZihIXZICpK0+KYPTIScHAQOjkRkeXTaIDcXFNNB7KyREhLs8O5c1pUV2thYyNGRERowzF7FGJiYjBo0CB06dJF6OgAG1Edw8mTJ/Hcc88hNTUVzz33HN544w14eHgIHYvIIhQWFuLs2bNIS0tDeno6Ll06j4sXL+LatUoAgLOzBBEREnTvXocePQzo3h2IiADCwrizS0Sdj1YLXL0KZGQAFy8Cly4B6ekSXLgAlJZqAQCOjnbo3j0MERG90aNHJLp3747evXujW7duELOzT+1Mp9MhPT0dv/32Gy5evIhLly4hPf08Ll3KhFpdD8A4y7pHDyAioh49eqCxtgcFAbwYj4g6G5XKOJHk0iXjdvEikJ4uRXq6HjU1xvs++Pi4IzIyAhER96B79+6IjIzEPffcAx8fH4HTU2egUqlaHLNfuHAe6elpuHIlH3q9ARKJCDKZLXr00KJ7dx26dzfeUzI8HLCMc6dEROZVUgJkZppqOnDxohiXLtngyhUtdDoDbGzEkMkC0b17T/To0RMRERHo2bMnevfuDQfLPdnJRlRHodfr8b///Q///Oc/odfr8dZbb+Hpp5/m0jcdUFpaGnbs2IElS5YIHcWqVVRUICsrC2lpabhw4QKysjKRlnYG6elXoNPpARivogoNFSE01Dgjq/kWEgJIJAJ/CCKiP6CiwjjzueUmRVaWGFev1kOrNe76eXi4IDQ0FJGRvREVFYXIyEhERUWha9eubDiRRSooKGio6ab6fgZZWXJkZRUAAKRSMYKDJfD31yEgQPe72i6TAZzkT0QdjUYDlJYChYXN67oIWVm2yMoSNV7dBLS8wikqKgqhoaHo2bMn/Pz8hP0QRDdQX1+PvLy8xmP2tLTzuHDhDNLT5VAq1QAAe3sxAgJsEBqqQ2iovkVd79YNcHUV+EMQEf0BdXVAfv71x+w2yMqSIjNTC4XCOEnU1laC8HAZoqL6IDQ0rPGYvUePHnB0dBT4U9w2NqI6GqVSiRUrVmDp0qWQyWR4//33MXbsWKFj0W1ITk7G0KFD8eOPP2LUqFFCx+l01Go1MjMzkZWVhezs7IbtMrKzM5GdfRUqlXGH13gySwqZDJDJ6iCTGZtTwcGAv7/xq+VOMiAia6XRAMXFxquaCguNl+VnZwNZWWJkZ0uQna1rnP0sldogJMQfMlk4ZLJukMlkjdvdd9/Nq6vJapSVlSEjI6NZXc9uqOuXkZtbBK3W+P+Es7MEMpkUMpkGMpkWMpmxngcEGL/6+nISChGZX01NU13PywNycoy13VjXxcjL0zROInFxcYBMFgSZLKKhvhvrelhYGMLDw2HLm+uRFdDr9cjJycHly5dvUNuzUVJS3jjW19cWMpkIMlk9ZDIDunY11vWQEONXLy/hPgcRdV6lpcblcfPyjNuVK81re9NqJADg5+cJmewuyGTdIZOFtqjtISEh1rRcLhtRHVVmZib++c9/YseOHRg3bhz++9//IiwsTOhYdIvi4+Ph5+eHDRs2CB2FrlNSUnLdzq5ph1eOvLxi1NVpGsd6ekoRECBGcLAW/v46BAcDgYFNJ7T8/QFvbwE/DBF1KEqlsbFUUGCcHZWXZzwpdfWqGAUFUuTnG1BcrIFeb9x1E4tF8PPzathRjWjRaJLJZAgKCuKV09TpabVa5Obm3qC2p+PKlSsoKiqH6XDIxkYEX18pgoKAgAAtgoP1CAgw1nbjc8b63vEmHxKRUIqLmxpMTXUdKCyUIC/PBvn5OlRWNp2Msre3RXCw3w0nkchkMku5xwORoFQq1Q3q+mVkZ2fgypVcKBQ1jWPt7cUICpLC31+PkBBNi7ru729sWPn5cSIKEd0ajaZpQmjz4/aCAiA31xYFBSLk52tQV6dvfI27uzO6dg2GTHY3ZLKw39V2C15K705jI6qjO3DgAP7v//4Ply5dwl//+le89dZbcHNzEzoW3YRCoYCLi4s1dbU7jeLiYhQUFCA/Px95eXkoKCjA1atXUViYi7y8HOTlFf5uxzcwUAo/PwO8vbXw89PDx8fYoPLzM86+9vY2fuXFCUTWp6bGeBKqqMg4K6qkpOXjwkIpSkvFyMvTNV5+DxhPRAUG+iAgIBDBwWEICAhAUFAQAgMDERAQgJCQEPj6+kIqlQr46Yg6vvr6ehQVFSE3Nxf5+fkoKChAbm4uCgoKkJeXhfz8PBQUlLaYiOLuLkFgoA18fPTw99fA29tYy00TUEyPfXx49TSRNSorM9bw0lJjTS8uNj4uLgaKi8UoKZGgsBAoLNT+7kRUYKAfgoNl8PcPQnBwcGNdDw4ORkBAALw5i43oT1MqlY21PD8/H7m5uSgsLERubg7y86+goKCwxUQUsdg4ESUgQARfXy28vHTw9zcer3t7G+u56bG3N5tWRNbGtASuaRnc5sftxnpvg8JCCYqKDCgurm9cDtc0MdRYy0MQEtIV/v7+CAoKQlBQEPz9/RESEtIRl9BrL2xEWQOtVos1a9bgzTffhFQqxZtvvok5c+ZAwupIJAiVSoWrV6+2aFgVFxejtLQUhYX5KC0tRGnpNZSWVqL5r2BbWzG8vSXw9RU3NK7qGnd6vbwAT8+Wm5cXd4KJzK28vOVWVmb82vJklATFxWIUF+ugUulavN7FxQH+/l3g7e0Db+9A+PsHwsfHBwEBAQgMDERwcDD8/f15IorIwhQXF6OwsBB5eXnIy8tDYWEhiouLUVRUhNLSQpSUFKGoqBRKZW2L1zk7S+DnJ2mYhFIPPz994yQUUy1vXtfd3QX6gESdlEbTVMuvr++mk1GlpWIUFto0nIzSQqNp2n8Xi0Xw9naDt7cXfHz84OcXAm9vH/j5+SEwMBBBQUGNjSaeiCKyHBqNprGu5+fnIz8/HwkJCThx4gSUSiV69uyO8vIylJaWo75e2+K1Pj528PYWw9tb1zAhxdA4ubR5TTc95v/6ROalUv3+eL35JBJjk0mM0lIJSkoMuHZN0+L1dnZSeHu7w8/PB76+gfD2DoCvr29jk8lU1/38/Hju/fawEWVNysrK8NZbb+Hjjz9GWFgYli1bhvj4eKFjEVErtFotSktLUVpaiqKiIpSUlDQ+NjauilBcnI/i4hKUl1ehpqbud+/h6iqBp6dNw86uDl5e2t81rEybh4fxZq6mjaizqq0FFArjVlFx48aScbNp2EQoL9ejrEyD6/eapFIbeHq6wtu7C3x9A+DnFwRvb2/4+BhPQpkem5pL9vb2wnxoIjKL2tpalJSUoLCwsLHGt3x8FSUlRSgtLUN5uaLx/lUmYrEIXl4SeHqK4elpgKenrmG78aSU5rWdv16oM6uqar22t6zzUpSViRv+rEN1tfZ37+XkZA9PTzf4+fnBx8cf3t5+8PPzg6+vL7y9veHr69v42MfHB2KxWIBPTES3oqamBvv27YNcLkevXr3avE93YmIiUlNTER4ejrFjx8LFxQUAUFFR0TixtGkSSilKSkpQVFTQMCGlGCUlZaiqUv3ufe3txfD0lMDTUwQvLz08PbUNNb6ptpu+ursba7qHB+DmBvDXC3VWOp2xtldWNn01Ha+3PGYXo7xcgrIyEcrLDSgvb3k1somHhwt8fLwaJoQGwN/feBWyt7c3/P394ePjA29vb/j5+XGlsfbDRpQ1ysnJwWuvvYYvvvgCAwcOxIoVKzB48GChYxHRn1RbW4vy8vIbbmVlZSgrK0N5eRnKy0tQXn4N5eUVrTawAMDDQwo3N3HDCSwDXF11DVvTjm/zxpVp8/AwLjVkb2/cUeYKk2Qu1dVAXZ3xJJNS2XTCSaFouZPa9LwYCoUNqqrEDd/TQ6HQQaP5/Y6pqaHk6ekBT08veHr6wMvLG56eni02Ly8veHl5Nf7ZdIBKRPRHKBSK6+r4jWp8aUNtL0N5eeUNG1iA8cpqV1cbuLqKG+q4vqGu639Xx6+v8U5Oxufs7ABnZwH+IqhT0uuNdbu21rj9vo7fqMbbNGyihu+1vMdSc6aGkrG2d2mo7V1a1PEbbZw0QtRx1NXVwc7OrtXvFxcXN16h+OKLL2LhwoXtmken093wWP339b0E5eWlDfW/8oYNLABwdrZpqO2ihpqth4eHps1jdVdXY023t2+q8ba27fqxiRrV1RmXp6+qAtTqto7Vm39PiqoqccNzBigUv19ZxMTDwxleXh4NNdsbnp4+Nzxmv/45ThqxCGxEWbPjx4/jpZdewuHDhzF58mQsXboUoaGhQsciIjMzNbAqKyuhUCgat4qKCigUClRVVTV7vgoKRTkqKsoa/lyNqioV1Or6Vt/fzk4MR0fjiS97e+MJLGdnPeztDXB11cLRsalpZW9vbGK5uxtPdjk5Gb86OhqXGXRxMTa2TEsTmWaBOTsDvBWO5aqoMH5VKIwzl1QqoL7euONZW2tc9kapND6nUhnH1dUZG0tKpfFxVRVQU2ODujoxKirEja+tqtKjrk4PpfLGO6IAIJHYwNXVEe7urnBzc4OrqztcXT3g5uYBV1dXuLq6wt3dveF7ro2bm5sb3N3d2VAiog7H1MCqrKy8ro63rPHGOl8OhaKiocYrUFFRBYWiBjrd75vyJs7ONrC3F8PVVdysjuthb6+Dg4O+RR13dTU+dnFpqtcuLsa67uho/F5btZ4sj6le63TGmg20Xutb1nHj44oKNKvjEqjVYqhUIlRXA2q1AdXVBqhUOtTXt/7foIODLdzcnOHq6tJQxz3h5uYFV1e3xhreWo031XY2lIis18svv4wtW7agvLwcSqWyzZPMNTU1Fr80pqmBZTpmv/GxevMaXw6FohJVVZVQKKqhUChb3Kv6esa6K4WDgwj29qKGY3MDHB2Nx+z29oaG43hjzXZza6rhHh7GY3LTRSKmyaimmm+apGpra9wvIMukVBqPy2trjTX6RrW+stI4SaSysqmOV1Yaa7tKhYY6LkJ1tQQqlQhqtahhQokBarUBFRWaNjO4uTnB1dVY293c3BuO2z1bPVZvXuNNtZ0NpQ6NjShrZzAY8M033+Af//gH8vLyMH/+fCxatAjuPPIjottQX1/fYsdXrVajtrYWlZWVUKvVqKmpgUKhgFqthlKpRHV1Nerq6qBQKKBSKaBW16CqqgK1tbVQq9WoqFBAra5DbW3rDa4bcXAwnhgz7uSKYWPTtMygh4fxZIZYbICbW9PMWNPJL5PrZ4Q1v6rLdIKs6efd2lJHt7PTbWtr/Dk1rR8ntGCaUXQzzXcggaYdR5PqakDb8NdiMBh3KE1MO6EmKpUN6uvFDa8TQasVNZ5cqqszoKbGAK3WcMPlbNoildrA2dkBzs5OsLe3g6urK5ycnGFn5wB39y5wcHCAvb09PDw8YGdnB0dHR7i5ucHe3h5OTk5wcXGBvb09XFxc4Ozs3LhzaukHlkRElkilUjWe4Dp69CieffZZ1NbWYu7cuRg0aBDUanVDHVehrq4OlZWVzep4KerqalFTY3wPtboOKlXNTRtcN+LmJoFYLIKTkwi2tqLGSStSqQHOzsZDVTs7HRwdm973+gkqHh5Nj6XSlld1mU6kNf28W1vq6HZmkDs5GWuppu3zL41MJ4NuxnSyyMTUBAKariYyuX5/wdQoMqmslMBgEMFgMKCy0gaA8fV6PaBU6htOTumgVt/ev5+jox3s7W3h7u7aWMfd3b1gb+8AR0cXuLm5wc7ODs7Ozi3quJOTE+zt7eHm5gZHR8eG1zWdiJJyBhJRp1NWVoYDBw5ALpdjxIgR6N+/f6tjt23bhry8PISFhWHcuHG8T0uD5hNRamtroVAoUFNTA7Va3aKONz+Or6qqQl1dLZTKKlRXK6BW16K6uhoqVQ3U6vpWr9ZqjY2NCK6uEgAGeHgY642rK2BjAzg56Rtrq4uLFhKJsc5fP0Hl+uPr2zmGb0vz/YWbcXdveczcluuPr9tyO7X8+v0F06QQANBoRFAqJc1eJ4ZWazzuN+Yx1vOqKi30+ts79e/u7gx7e1s4OjrA1dUV9vYOcHZ2gYuLB+zs7BuO45vqePPjeHt7ezg4ODQ+33ySCHV6bER1FhqNBmvXrsXbb78NnU6HV155BS+88AJP3lmQXbt2ITY2Fl26dBE6CpFZ1dTUoK6uDvX19VCpVNDpdFA0dFQqGva0FAoFdDodVCoV6uvrGxthGo0GSqUSer0eVQ17cKbnTGprVVCrm3aeFYoq6HTGBkrz1wFAXV09amqaujdKZS00mtavxLE0Hh5NZ96kUimcnZt+xzs6OrZYtsLNzaNxNpGNjQSurp6N3zPtPAKAk5MTbG1tG58zvq8zRCJR486km5sbxGIxnJ2dIZVKG3dEbW1t4eTkBBsbG7jyxmRERBZLoVDgyy+/xJgxYyCTyf70+1VVVUGv10OpVEKj0TSe+Gqr1ldXV0Or1TbuF9TV1aGm4WyM6bmm9y+DXm+sz8b3ajr7Y9pHMKmuVkHbMBPDeGKmaR/B0tnaSuDk1DQjxsnJEba2TQ0ad3d3iBrOxEkkUri4NNVae3snODg0ncVzcXFpPFFrep3pOdM+gmkSiEQigYuLyy3VeiKiW1VbW9t4jHEjx48fx+DBgxESEoK3334bM2bMMGM6aotWq0V1dXXDZAZjzTXV+ls5Rq+srITBYGis9c2fM76/BtXVTV2W2toaqNVNtdx0PgBAQ4amGZj19VqoVM1mbVg4Z2cHSKU2DX8SwcOjqXab6q+Jg4NTi1rr6uoFGxvja8ViceO9jEzH6M2fu1mtb36M7tHQoXN1dW18f6J2wEZUZ6NUKrF69Wq8++67sLOzw9///nf83//9H2y5YKygamtr0b17d/Tv3x87duwQOg4R3abmO9Q389prr+HgwYM4fPhw48mjm2l+oomIiOh2yOVy7Nu3D08//TRPLtyi6yeq3MpY00mgW3E7Y4mIOrrp06dj37598PT0RHp6eqvj9Ho9tFotz09Ru2g+weVWx5omZd6K6yd+EtHvsBHVWV27dg0rVqzAypUr4efnh0WLFuGpp57iWpsC2r9/P8aMGYOTJ0/innvuEToOEbWTxYsXIzExEampqUJHISIiK/f222/j9ddfh6urK06ePIlu3boJHYmIiKxAXl4efv75Z2RmZmLGjBm4++67Wx27bt06aDQaREREYOTIkWZMSUREFoSNqM7u6tWr+Ne//oVPP/0UPXr0wOuvv44pU6YIHavTysrKQmhoqNAxiKgdJScnIzs7G08++aTQUYiIyMplZGSgsLAQQ4YM4f0ziIjoltXU1LR5K4evv/4aM2bMQGhoKNasWYMHHnjAjOmIiKgDYiOKjH777Tf885//REJCAoYOHYq33noL9913n9CxiIiIiIjoOkqlEgcOHEBBQQH++te/Ch2HiIisxH333Ydz585hwIAB+OlVtk5vAAAgAElEQVSnn1odp9VqIRKJuOQrERHdqgquw0YAgF69emH37t1ISUkBAMTFxWHEiBGNfyYiIiIiIsswatQoPPLII9i+fbvQUYiIyMKlp6fj008/xaJFi1BWVtbm2AkTJmD58uV488032xwnkUjYhCIiotvCK6LohlJSUrBkyRIcOHAAQ4YMwdtvv81LrYmIiIiILMBvv/0GPz8/eHt7Cx2FiIgEZDAYUFNTAycnp1bHrFixAkuWLEF4eDi++uor9OjRw4wJiYiIAHBpPrqZlJQUvPHGG0hKSsKQIUPwzjvvYOjQoULHIiIiIiKyKgaDAadPn0ZiYiJCQ0Px6KOPCh2JiIgslFKpxKBBgyCXy/H4449jw4YNrY6tr6+Hra2tGdMRERH9Dpfmo7bFxsbiwIEDOHz4MOzs7PDAAw8gNjYWP//8s9DRiIiIiIishumk4ocffoi8vDyh4xARkQBOnTqFNWvWYOHChdDr9a2Oc3Z2xuTJk7F27VosWLCgzfdkE4qIiCwBr4ii25KSkoLXX38dBw8exIgRI/DOO+9gwIABQseyakuWLEG3bt0wY8YMoaMQERERUTuSy+UICwuDSCQSOgoREd1hWq0W9fX1cHR0bHXM/Pnz8eWXXyIsLAw//PADvLy8zJiQiIio3fCKKLo9sbGxSEpKwuHDh6HVajFw4ECMHDkSx44dEzqa1aqqqsJf//pXZGRkCB2FiIiIiG5TeXk5vvjiC8yYMQMnT55sc2x4eDibUEREViYjIwPdunWDo6Mj3n///TbH/uc//0FZWRmOHz/OJhQREVkVNqLoD4mNjcXBgwd/15A6fvy40NGszrJly9C/f39cuXJF6ChEdAckJiZi1apVQscgIiIzOX36NObMmYOioiKo1Wqh4xAR0R2SnJyMDz74AG+88Uab4/z9/fHEE0/g888/x2OPPdbmWKlUeicjEhERWQwuzUd3xP79+/Hqq6/i2LFjGDFiBN59913069dP6FhERBZn8eLFSExMRGpqqtBRiIjIDLRaLdRqNZydnYWOQkREt0itVsNgMMDBwaHVMQ8//DBOnDiByMhIHDhwwIzpiIiIOhwuzUd3xogRI3D06FHs3bsXCoUCAwYMQHx8PH755RehoxERERER3XGpqalYtGgRBg0a1OaVThKJhE0oIqIOIjk5GXfddRccHR2xY8eONsd+8803KCwsZBOKiIjoFrARRXfUgw8+iGPHjiEhIQFlZWUYMmQI4uLi8P3334MX3xERAa+99hp+/vlnoWMQEdGftHv3buzcuRP9+vWDSqUSOg4REbUhISEBy5Ytu+k9mmQyGZ577jl8/fXXGDlyZJtjJRLJnYxIRERk1bg0H7WrlJQULFu2DHv37kXPnj2xcOFCTJ8+nTtsRERERNShabVa7tMSEVmA6upq2NnZwdbWttUx0dHRKC8vx3333YfNmzebMR0RERGBS/NRe4uNjUVCQgLOnDmDe+65B3PmzEG3bt2wcuVK1NTUCB2PiIiIiKiFTz75BA8//DAeeuihNsexCUVEJKwtW7bAz88Prq6uOHr0aJtjT5w4gStXrrAJRUREJBBeEUVmdeXKFXzwwQfYsGEDnJ2d8eyzz2LBggXw8PAQOlqH9Pnnn2Pq1Klt3kCViIiIiG7dxIkTAQDx8fGYPXu2wGmIiDoXvV6Pbdu2QS6Xw9/fH0899VSrY8+dO4ekpCR069YNgwcP5nkFIiIiy1XBRhQJorS0FKtXr8aqVaug0Wgwe/ZsvPzyywgMDBQ6WodRWFiInj174oEHHsD27dshFvMCRyIiIqKb0Wg0kEqlQscgIuqUrl27Bi8vL4hEolbH+Pj4wN3dHY888giWLl1qxnRERETUTrg0HwnD29sbS5YsQU5ODt555x3s3LkToaGhmDVrFtLT04WO1yH4+/vj22+/xf79+3H27Fmh4xARERFZrPLycjz//PMICwvDm2++KXQcIqJOZ/ny5fDw8IC3tzdycnLaHFtUVISMjAw2oYiIiKwIr4gii1BfX4+tW7fi3XffRWZmJh588EG89tpr6N+/v9DRLF5lZSXc3d2FjkFERERkserr6zF8+HDExcVh8uTJ6Nu3r9CRiIg6PJVKhe3bt0MulyMmJgaPPPJIq2NTUlJw/vx5hIWFYfDgwXBycjJjUiIiIhIYl+Yjy6LT6fD1119j6dKlOHPmDEaPHo1//OMfGDp0qNDRiIiIiMhCVVdXw97enkvuERHdQUVFRfDz82v1+1VVVfD390d4eDieeuopzJ8/34zpiIiIqAPh0nxkWWxsbDBt2jScPn0ahw8fhlQqxQMPPIDo6GisX78earVa6IhERH/KW2+9xeY6EdEdcuTIETzwwAPw8vJCUlKS0HGIiKzCggUL4OTkhODgYGg0mlbHubm5oaamBufOnWMTioiIiNrERhRZrNjYWCQkJODYsWOIiIhoXNf/3XffxbVr14SOR0T0h6jVaigUCqFjEBFZBUdHRwQHB+Ozzz7jks5ERG0oLi7GunXr8NJLL+HgwYNtjh0zZgw+/PBDHDhwAGIxTxsRERHRn8el+ajDKCoqwscff4yPPvoISqUSU6dOxcsvv4yePXsKHY2I6JZ9+umnOHnyJNauXSt0FCIii2YwGJCRkYGIiAihoxARWTSdTodr167B19e31THnzp1DXFwcwsPD8dJLL2HatGlmTEhERESdHO8RRR2PWq3G9u3bsWzZMly4cAFDhgzBK6+8gnHjxkEkEgkdz2KsXbsWQ4cORY8ePYSOQkRERHRbVqxYgRUrVqC8vBylpaVwc3MTOhIRkUWaOHEivv/+e/j5+SEnJ0foOEREREQ3wkYUdVwGgwEHDhzAypUrsXfvXoSHh+O5557D3Llz4ejoKHQ8QWk0GgwbNgxyuRwHDx5E9+7dhY5EREREdMu+/vpryOVyjBkzBn369OFkIyLqVLKysrBnzx5kZmZi7ty56N27d6tjt23bBq1Wi7vvvhv9+vUzY0oiIiKiW8ZGFFmHjIwMrF69Ghs2bICtrS2eeOIJLFy4EMHBwUJHE0x1dTXi4+PxwgsvYNKkSULHISIiIgJgvE9JQUEB+vbtK3QUIiKzq6+vR0VFRZvL6O3evRtPPfUUwsPD8d577+H+++83Y0IiIiKiO46NKLIuJSUl2LhxIz788EOUlpZi/Pjx+Nvf/oaBAwcKHU0QBoOBM4iJiIjIYgwbNgw///wz+vXrh2PHjgkdh4jIrKKjo3Hu3DkMHjwYhw4dEjoOERERkbmwEUXWqb6+Ht999x1WrFiB48ePIyYmBvPnz8f06dMhkUiEjkdERETUKa1fvx7+/v4YNmwYnJychI5DRPSnnT17Fj/99BPkcjneeOMNBAQEtDp248aNcHZ2RmRkJKKiosyYkoiIiEhQbESR9UtKSsJ///tf7N27F127dsW8efMwe/ZseHl5CR2NiIiIyCpoNBqkpKTA39+f96YkIquhUqmgUqng4+PT6pjVq1dj2bJlCA8Px0cffYTIyEgzJiQiIiLqENiIos4jMzMTH374ITZt2oS6ujpMmzYNzz77LPr37y90NCIiIqIO6/Lly+jbty+qq6uxZMkSvPHGG0JHIiL6U2praxEWFobCwkLMnDkTmzZtEjoSERERUUfGRhR1Pmq1Gtu3b8cHH3yAM2fOIDo6Gs888wxmzJjRqZaIyc/PR2BgoNAxiDoduVyOsrIyDBgwQOgoRER3hF6vxyeffIIRI0YgNDRU6DhERK1KSUlBcnIy5HI51q1bB1tb21bHrl69GsHBwejVqxdkMpkZUxIRERFZHTaiqHM7deoU1q9fj82bN8PW1hbTpk3DggULrH45hV9++QXDhw/H559/jqlTpwodh6hTWbx4MRITE5Gamip0FCKim7p8+TISExMxY8YMuLu7Cx2HiKhV5eXlMBgMbS7BvmjRIuzYsQPh4eH44osv0KVLFzMmJCIiIuq0KsRCJyASUkxMDNatW4crV65g0aJF2LdvH3r27ImRI0dix44d0Gq1QkdsF4MGDWq8CiwzM1PoOERERGSBNmzYgPDwcCxevBhpaWlCxyEiuiG5XI4uXbrAy8sLH3zwQZtj33vvPcjlciQmJrIJRURERGRGvCKKqBm9Xo+kpCSsX78e33zzDXx9fTFz5kw8//zzCAoKEjreHXf06FEMHDhQ6BhEnUpycjKys7Px5JNPCh2FiKhNhYWFyMjIwODBgyGVSoWOQ0SdzPfff49ff/0V5eXlWL16davjamtr8b///Q/h4eHo3bs3/P39zZiSiIiIiG4Bl+Yjas3ly5exYcMGfPrpp1AoFBg/fjyefvppDB8+HCKRSOh4RERERH+IUqlEUlISzp49i9dee03oOETUCRUVFcHe3r7NJT9nzpyJU6dOISIiAt988w2PwYiIiIg6LjaiiG6mrq4Ou3fvxsqVK3HkyBF0794d8+bNw5w5c+Ds7Cx0PCIiIqLbMnXqVOzcuRMxMTFITk6Gg4OD0JGIqJNITk5GfHw8qqursX79esydO1foSERERETU/tiIIrodJ06cwJo1a7B161bY2dlh+vTpeOqppxAdHS10NCIiIqJbIpfL4erqCh8fH6GjEJGV2LJlC86ePQupVIp33nmn1XHFxcXYvXs3wsPD0adPH3h6epoxJREREREJhI0ooj+ivLwcn332GT755BNcvHgR0dHReOqppzB9+nS4ubkJHe+OKC0thbe3t9AxiIiI6BYYDAacOXMGiYmJcHFxwfPPPy90JCKyAnq9Hrm5ufD29oajo2Or44YPH47y8nIMGDAAH3/8sRkTEhEREVEHUCEWOgFRR+Tp6Ym//e1vuHDhAk6ePIl7770XL730Evz8/DB16lTs378fHbnHe+LECchkMmzatEnoKERERHSLxo4di1WrVqGoqEjoKERkBb744gs4Ojqia9euSElJaXPsgQMHcPr0aTahiIiIiOiGeEUU0R2iUCiwdetWrF+/vvGmun/5y1/wl7/8pcMtfaPX67Fo0SIsX74cJ06cQExMjNCRiIiI6CZyc3MRFBQEkUgkdBQislAGgwFr1qxBZmYmgoKCsHDhwlbHZmRk4OjRo+jWrRt69erF++MSERER0R/FpfmI2kNaWho2b96MDRs2QKlUYvz48Zg5cyYefPBB2NjYCB3vlqWmpvL+V0RERAJSKBRISEjADz/8gEcffRTjxo0TOhIRWSiNRoPs7GyEhoZCIpG0Oq5nz55wdnbGgw8+iNdff92MCYmIiIiok+LSfETtISoqCkuXLkV+fj6++OILVFRUYPz48ejatSv+8Y9/ICcnR+iIt4RNKKI7LzExEatWrRI6BhF1EEVFRZg9ezYKCws71GQWIjKv9957D46OjoiIiMDly5fbHHv+/HkcPXqUTSgiIiIiMhteEUVkJunp6di4cSM2btyIa9euYdiwYXj66acxYcIESKVSoeMRkZksXrwYiYmJSE1NFToKEXUQSqWSS2IRdUJKpRLr1q2DXC7HgAED8OSTT7Y69vTp08jIyEB4eDh69uwJOzs78wUlIiIiImobr4giMpeIiAgsXboUV69exZYtWwAAjz76KLp27YpXX331pjMXiYiIyHqcP38eixYtQnR0NPLz89scyyYUkfWpra3FxYsX2xxjY2ODDz74ABcuXEBdXV2bY/v27Ytp06YhJiaGTSgiIiIisji8IopIQHl5efjyyy+xdu1a5OTkICYmBjNnzsTMmTPh6ekpdLw2ffvtt7j33nsRHBwsdBSiDqW2thYajQaurq5CRyEiAf3vf//De++9hzFjxuDll19mPSXqRObNm4f169cDAFQqFRwcHARORERERETUrirYiCKyAHq9HklJSdi0aRN27twJvV6P+Ph4zJw5E2PHjm3zZsNC0Ol06NevH/Ly8rB582aMHj1a6EhEREQdil6vh1jMxQmIrEVhYSE+++wzyOVyTJgwAfHx8a2OPXz4MK5du4Zu3bohMjKSvwuIiIiIyNpxaT4iSyAWizFixAhs2rQJBQUFWLduHSoqKjB+/Hh07doVCxYswLlz54SO2cjGxgaHDh3C6NGjIZfLhY5DRERkMb788kuMHz8effr0aXMcTzwTdRxVVVVIT09vc4xSqcT69etx9epVaDSaNsfed999mDhxInr27MnfBURERETUKfCKKCILlp6ejq+++gqbNm1CdnZ249J9M2bMQJcuXYSOR0RERNeZP38+cnNzMWbMGDz11FOwsbEROhIR/QljxozBjz/+CF9fXxQVFQkdh4iIiIioI+LSfEQdgV6vxy+//ILNmzdjy5Yt0Gg0GDlyJGbNmoWJEyda3NJ9RERE1qq2tpb3cyHq4DIyMrBlyxbI5XK88MILGDBgQKtjf/zxR+j1eoSHh6Nbt25mTElEREREZDW4NB9RRyAWixEbG4t169YhPz8f69evh1qtxrRp0xASEoIFCxbgzJkzQsckIiKySvX19Xj++ecRHh6OefPmCR2HiNpQUlKCzMzMNsfk5eVh69atqKysvOkyeqNHj8bYsWPZhCIiIiIi+hN4RRRRB5abm4stW7Zgw4YNuHz5MiIjIzFr1izMnj0b3t7eQsfDb7/9hl69egkdg4iI6E+bOHEiIiMjMX78ePTv31/oOER0AxEREcjIyEC/fv1w/PhxoeMQEREREZERl+YjsgbNl+776quvUFdXh1GjRmHKlCmYNGkSnJyczJ7p2LFjGDRoEObNm4fly5cLkoGIiOhWVFdXQywWs1YRWaATJ07gm2++gVwux7///W/IZLJWx3733XdwdnZGREQEgoKCzJiSiIiIiIjawKX5iKxB86X7CgoKsGHDBmg0GsyePRsBAQH4y1/+gv3790On05kt04ABA/Dll19ix44dOH36tNl+LpGle+uttzB06FChYxARjFfuPvDAA/Dy8sKWLVuEjkPUqRgMBuTm5iI7O7vNcRcvXsT3338PvV4PtVrd5tjx48dj+PDhbEIREREREVkYXhFFZMXKy8vx9ddfY9OmTfjll1/g6emJSZMmYebMmRgyZAhEIlG7Z1AqlXB2dm73n0PUUSxevBiJiYlITU0VOgpRp1dYWIhXXnkFY8aMwejRo+Hl5SV0JKJOob6+Hh4eHqipqcGUKVOwfft2oSMREREREVH74dJ8RJ1FTk4Otm7dio0bNyI9PR1du3bFtGnTMGfOHN58mciMPv30U5w8eRJr164VOgqRVdPr9Th37hzuueceoaMQdRr79+/HDz/8ALlcji1btrS53OW2bdsQGBiI7t27o0uXLmZMSUREREREZsZGFFFnlJaWhs2bN+Pzzz9HUVERIiMjMWvWLDzxxBPw8/MTOh4REdGf8umnn2Lx4sUoKSnBxYsX0b17d6EjEXVoWq0WOTk5sLOza3PZu2XLluHrr79GeHg4Vq5cCR8fHzOmJCIiIiIiC8VGFFFnptfr8csvv2Dz5s3YunUrVCoVBg4ciFmzZuGxxx6Di4tLu/58rVaL5ORkDB8+vF1/DhERdS7Jyck4evQoxowZg969e5tlKVoia5WdnY2IiAhoNBosXLgQy5cvFzoSERERERF1LGxEEZFRbW0tvvvuO2zZsgWJiYmwtbXF+PHjMX36dIwaNQpSqfSO/8xdu3Zh4sSJmDx5MlatWgV/f/87/jOIiMi6FBcXIzMzE7GxsUJHIerQtm/fjkOHDqG0tBTbtm1rdZxGo8GOHTsQHh6OiIgIuLm5mTElERERERFZgQqx0AmIyDI4ODjg0Ucfxe7du1FcXIw1a9agpKQE8fHx8PX1xaxZs5CQkACNRnPHfuaECRPw448/IisrC+yJExHRzUyZMgUBAQGYPn260FGILFZdXR0uXryI4uLiNscdOXIEZ8+ehaOjI3Q6XavjpFIppk+fjv79+7MJRUREREREfwiviCKiNuXk5GD79u3Ytm0bTp06BR8fH0yaNAlTp05FXFwcxGL2s4mIyDx27NgBe3t7DBs2DE5OTkLHIbI4hw4dwgMPPAC9Xo8PP/wQzz//vNCRiIiIiIiIuDQfEd26nJwc7Nq1Czt27MCRI0fQpUsXjB07FrNmzcKwYcPYlCIioj9Eo9HgyJEjcHR0RP/+/YWOQ2RxPv74Y5w6dQo2Njb4+OOPWx1XUVGBpKQkhIeHo1u3bnB0dDRjSiIiIiIiohtiI4qI/pjs7Gzs3r0bmzZtQmpqKoKCgvDII49gypQpGDJkCG8MT0REt0ShUCAkJARVVVWYN28e1q5dK3QkIrOprq6GXC5HWFgYXF1dWx336KOPorS0FNHR0Vi+fLkZExIREREREf1pbEQR0Z+XlpaGHTt2YNu2bbh06RKCg4MxceLEO9KUMt2nasmSJRg7duwdTE0kDLlcjrKyMgwYMEDoKEQW46uvvkL//v0RFhYmdBQis9m8eTNmzZoFANi9ezfi4+MFTkRERERERNQu2IgiojvL1JTasmULMjMzcdddd2H8+PGYMmUKYmNjb/v98vPz8dxzz+G7777Dtm3bMHXq1HZITWQ+ixcvRmJiIlJTU4WOQtTusrKykJiYiLFjx0Imkwkdh8gs/vWvfyEjIwPBwcF45513Wh139epVnDt3Dt26dYNMJoOtra0ZUxIREREREZlNhUToBERkXaKiohAVFYUlS5bg+PHj2L59O3bs2IFVq1ahe/fumDp1KiZNmoTevXvf0vsFBgZi165dSEpKwuDBg9s5PRER3Sl79+7FuHHj4OLigsDAQDaiqMMrKyuDXC5H375922wa7d+/Hw4ODujVq1eb7xcSEoKQkJA7HZOIiIiIiMji8IooImp3BoMBv/76K7Zt24ZvvvkGeXl56NatGx555BFMnjwZMTExvKcUdRrJycnIzs7Gk08+KXQUonZVXV2NU6dOYciQIZBKpULHIfpT3n77bbz++usAgDNnzqBPnz4CJyIiIiIiIuowuDQfEZmfafm+r776qnHpmokTJyI+Ph5Dhw6FRMKLNYmILJVKpUJSUhIOHjyIFStWQCwWCx2J6A9RKpVYunQp5HI5Bg0ahAULFrQ6Nj09HTk5OejWrRtCQkJgY2NjxqREREREREQdWgXPHBCR2ZmW7ktPT8f58+cxe/Zs7Nu3DyNHjoS/vz9mzZqFhIQEaDSaW37P9957D3v27GnH1EREBACvvvoqJkyYgEOHDqG4uFjoOEQ3VFBQgCNHjrQ5xs7ODgkJCairq4Obm1ubYyMiIjBq1CjIZDI2oYiIiIiIiG4Tr4giIouRlpaGPXv2ICEhAUeOHIGnpyceeughTJkyBaNHj271fgwGgwFTp07Fzp078eijj2LLli1mTk5E1HkUFBRAIpHAx8dH6ChENzR79mxs3LgRAFBZWXnTJhMRERERERG1Ky7NR0SW6cqVK/juu++wY8cO/PLLL3Bzc8PIkSMxbtw4TJo0CU5OTr97zcmTJ6FQKDBs2DABEhMRdWxnz57FDz/8AJVKhbffflvoOEQt5OfnY+XKlZDL5ZgyZQoee+yxVseeOXMGVVVVCA8PR2BgoBlTEhERERER0Q2wEUVEli8nJwc7d+7Ezp07cfToUTg6OuKhhx7CI488grFjx8LFxUXoiEREHV6/fv2Qm5uLSZMmYfXq1ULHoU5Cp9Ph6tWrKC4uxsCBA1sdl5ubiwkTJiA8PByPP/444uPjzZiSiIiIiIiI/gQ2ooioYykoKMC3336LnTt34tChQ5BIJBg2bBgmTJiAhx9+GH5+fkJHJCLqkIqLi+Hj4wORSCR0FOpEhg8fjqSkJLi5uaGyslLoOERERERERHTnsRFFRB1XeXk5Dhw4gISEBOzatQvV1dWIjIzElClTMHXqVERGRrYYf/r0abzzzjt488030bNnT4FSExGZT01NDb799lskJiYiLi4Oc+fOFToSdQIXLlzAxx9/DLlcjoULF7a5ZO7Ro0chEokQHh4OLy8vM6YkIiIiIiIiM6kQC52AiOiP8vT0xJQpU7Bp0yaUlJRg3759GDFiBNatW4eoqCiEhYVhwYIFSElJgV6vh1qtxuXLl9GnTx+kpqYKHZ86qcTERKxatUroGNRJaLVaPPPMM8jPz4e7u7vQcaiDq6+vR3p6Ok6ePNnmOIVCgZMnT6JLly5wcHBoc+zAgQMxYMAANqGIiIiIiIisGK+IIiKro9fr8csvv2DPnj3YtWsX0tPT4e3tjTFjxmDy5MkQi8UYN26c0DGpk1q8eDESExPZDCWzUavVsLe3FzoGWYGwsDBkZWWhd+/eOHv2rNBxiIiIiIiIqGPgFVFEZH3EYjFiY2OxdOlSXLp0CefPn8ff//53ZGVlYcKECZg2bRri4+OxadMmVFVVCR2XiOi2ZWZmYvHixYiOjsbx48fbHMsmFLXlyJEjmDdvHkaMGIGLFy+2OXbjxo1ITU1FSkqKmdIRERERERGRNWAjioisXlRUFF555RWkpKTgypUrWLp0KWprazFnzhz4+Phg9OjRWLNmDa5evQoAyM7OhkqlEjg1WavXXnsNP//8s9AxqIO7cuUKtm/fjsGDB8PNzU3oOGSBampqcO7cOfz2229tjispKUF6ejpkMhnE4rYPDeLi4tC3b1+4uLjcyahERERERERk5bg0HxF1WhUVFdi7dy++++47/Pjjj6iurkafPn2gUChQUVGBF198Ea+//jpEIpHQUYmok9Hr9TdtChC1RqfTwcHBARqNBvHx8di9e7fQkYiIiIiIiKjzqmAjiogIgFarxdGjR7Fnzx7s3LkTcrkcdnZ2ePjhhzFu3DhMmDABrq6uQsekDuann37C1KlTodPp2hwnFovxzDPP4N///reZkpGl2rVrFz777DMcPXoUOTk5sLOzEzoSWRBTjbp8+TJ2794Nd3f3Vsfu27cPAQEBCAsL4/KMREREREREJCTeI4qICAAkEknjfaUyMzNx+fJlLFu2DBUVFZgzZ9C8pkQAACAASURBVA68vLwQGxuLZcuWISMjQ+i41EEMHToUer0eSqWyzU2hUCA+Pl7ouGQB0tPTAQBLliy5aQOTrEdFRQVOnjx50/qSl5eHgoIC9OrVC/X19W2OHTlyJKKiotiEIiIiIiIiIsHxiigiopsoKytDUlISEhISsHv3blRVVSE0NBQPPfQQHn74YQwdOhQSiUTomGSh5s6di88//xwajabVMX5+fsjPz+dSbJ2AUqmEs7Oz0DHIgly9ehV33XUXAOCFF17AqlWrBE5EREREREREdEdxaT4iottRV1eH5ORkJCQkYP369aivr4enpyfGjx+PcePGYdSoUTzJTC0cPHgQw4YNa/X7tv/f3r2HRVnn/x9/zjAzHATPIHhAJDQRPEVWaHkgPGe2bmWrlZWuv2q72i7Nas1yc6vd0nY72zfL3M2OtulGmqJ5tlITxQA1OagoCYhyGBEYZub3x2yoq5SHgRvx9biuueae+/7c97w+6HXR1dv352Oz8cgjj/DCCy/UYyqpb1OmTOGLL74gPDyc1atXGx1H6sGCBQtITk4mLy+PtWvX1jrO5XKRnJxMVFQUHTt2xGq11l9IERERERERkbp3TP+EX0TkPPj6+jJkyBAGDhxI586defnll2nTpg25ubmMHTsWgGuvvZZRo0aRmJhIXFycwYnFaAMGDCAkJISCgoKzXq+qquJ3v/tdPaeS+lZRUcHtt9/OyJEjjY4iFyk/P5+9e/cSHh5OeHh4reNycnKw2+3ExcVRVVWFzWY76ziz2cywYcPqKq6IiIiIiIiI4dQRJSJyEdxuN3a7naCgIAoLC1m+fDnLli0jOTmZo0ePEhkZyfDhwxkxYgSDBg3C39/f6MhigClTpvD666+fdXm+yMhIsrKyDEgl3lJWVkZVVRWtWrUyOorUsfXr1zNgwAAA5syZw9SpUw1OJCIiIiIiItLgaWk+EZG64HQ62bFjB6tWrSIpKYlvvvkGX19frr/+ehITE7n55puJjo42OqbUk61bt3LNNdeccd5qtfL0008zY8YMA1LJxcrNzWXChAls3LiRJ554glmzZhkdSS7QnDlz+O677zCZTCxatKjWcXa7na1btxIVFUX79u0xmUz1mFJERERERETkkqRClIhIXSovLycuLo4hQ4YQFRXF1q1bSUpKori4mMjISBITE2v2lvL19TU6rtShyMhIcnJyzjj/448/0rlzZwMSycWqrKzk/vvvZ/DgwQwePJjg4GCjI8kp3G43ubm5ZGZm0qNHD1q3bl3r2D/84Q8UFBQQGxvLzJkz6zGliIiIiIiISKOnQpSISF0qKSnhpZdeYsGCBUyaNImnn34ah8PBxo0b+eqrr1i2bBnp6ekEBgZy4403MmLECIYPH06HDh2Mji5eNnPmTP7617/WLM9nMpno2bMn27dvNziZnI3b7Wb79u3ExMSoSHyJWrhwIXfddRcAixYt4tZbbzU4kYiIiIiIiMhlSYUoEZH64HK5qKioICAg4Ixr+fn5rFixgi+//JIVK1ZQWlpa0y2VmJjI8OHDCQwMNCC1eNPu3btPW47RYrEwe/ZsHnnkEQNTydksXryYBx54gPz8fFauXEliYqLRkeQUjz/+OBkZGYSHh/PGG2/UOi4/P5/MzEw6d+5MSEhIPSYUERERERERkVOoECUi0lAUFhYSFBTEhg0bWL58OStWrCA9PR1/f38GDBjA0KFDGTZsGF27djU6qlyg7t27k56ejtvtxmQykZubS7t27YyOJf8jPT2dpKQkhg0bRs+ePbUPUD1wOBzs27ePrKws+vfvf9ai/c9uu+02bDYb/fr148EHH6zHlCIiIiIiIiJyAVSIEhFpCH766SfCw8OJj4/n8ccfZ+TIkYDnX/SvX7+epKQkli5dytGjRwkNDWXw4MGMGjWKxMREWrRoYXB6OVezZ89m+vTpuFwubrjhBtauXWt0pMtOQUEBKSkpDBs2zOgocornnnuOGTNmALBlyxb69OljcCIRERERERER8RIVokREGoKqqipWrFjBhx9+yMiRI7nzzjvPGON0OtmxYwerVq1i1apVrFu3DpfLRa9evWqW8Rs4cCAWi8WAGci5yM3NpWPHjrjdbubNm8ekSZOMjnRZeeCBB3j77bcJCAigoKAAf39/oyM1asePH2fatGlkZWXRr18/nn766VrHHjhwgIKCAqKiomjevHk9phQRERERERGROqZClIjIpaqoqIhVq1axYsUKVqxYQV5eHq1bt2bw4MEMHTqUxMRELfvWAPXr14+tW7eSn5+vbrZ69vXXX1NeXk5CQgJNmjQxOs4lq6KigszMTLKyshg9enSt41wuF4MHD6Zjx44MHTqUsWPH1mNKEREREREREWkgVIgSEbnUzJw5k4EDB9K/f398fHxqzu/cubOmKLVx40YqKyuJjo4+rVuqadOmBiYXgLfeeovly5ezZMkSo6M0CAUFBYSEhFzUMxwOB5s2baKqqoohQ4Z4KZnUZvLkycybNw/w7G3XunVrgxOJiIiIiIiISAOmQpSIyKWkqKiIhIQEdu7cyf3338/cuXPPOu7EiRNs2rSpZhm/7du3YzKZTlvG74YbbsDX17eeZ9D4lZeXY7fbsdvtNefsdjsOhwOA0tJSUlJSGDhwYM31UzujfH19CQoKavRFw8rKSmbMmMFXX31FWlraBT/H5XLRoUMH8vLyGDNmDP/+97+9mPLycejQIWbOnElmZibjxo1j8uTJtY7du3cvFRUVREVFaXlDEREREREREfk1KkSJiFyK9u7di9vtpkuXLuc0/siRI6xZs6amMJWdnU1AQAB9+/atKUz17t0bs9nslXwul8trzzJSUVEReXl5HD58mPz8fAoLC8nPz/ccFxzm2NEjlJWVUlZWRklJGaX2cpxOl9e+v1nTJgQFNiEoMJDAoEBatGxNcEgYwcHBtGnThtDQ0Jrjtm3bEhYWhslk8tr315X09HTGjh1LRkYGbrebAwcO0KFDhwt+3tKlS4mOjiYyMtKLKRuH0tJSMjMzyc/PZ/jw4bWOKygoYPz48URFRTFmzBgGDx5cjylFREREREREpBFTIUpEpLHasmULXbt2PWtnTXZ2Nhs3bmTTpk18+eWX5OXlERwczMCBA0lMTGTIkCFERERc0PdmZWVx1113MX/+fLp27XqRs6hbTqeT7Oxsdu3aRU5OjueVncW+nL3s259LaVl5zVirxUxIcwshTU2ENq0mJMhJiyYQ5A+BftA8AIL8PMdB/p7jn/nbwM969gzVTiirOPn5RBXYKzznisuh7ITns70SjtqhsMxMod3C4RIT+ceqKa901tzra7PSMbwtEZ2uIKJTFBEREXTq1IkuXboQHR1tePeK2+1m3rx5PPzwwzidTqqrq7FYLMydO5dJkyadMT4rK4vly5dz3XXXERcXZ0DiS9/QoUNJTk4mKCiIkpKSS6JQKSIiIiIiIiKNigpRIiKNkdvtpn379hQVFXHffffx5ptv/uL47Ozsmm6p5cuXU1ZWRmRkZE231I033kjLli3P6bvfffddJk2ahM1m49lnn2XKlCmn7WVllP3797Nt2zZ27dpFWtoP7M7Yya7dmVRWeZbMC21pI6I1RLSqolMwRARDp2Bo3xJCmkGrQIMnUIvjlXC4GH4qhpxCyCmAfUdgX5GVnEITB484qHa6MZtNdOrYjpjYXnSLiaVbt2707NmTmJiYevnzyc/PZ8KECaxcuRKX62TXmI+PD6NGjWLx4sWnjd++fTtXXXUVTZs25e9//zsTJ06s84yXirS0NF544QUyMzOZPn06o0aNqnVseno6VquViIgIbDZbPaYUEREREREREQFUiBIRabyOHTtGUlISTqeTe++995zvq66uJjU1taYwtW7dOpxOJ717964pTF1//fX4+flRWVmJ1Wo9bRm+8ePH8+mnn1JdXY3ZbKZXr168//77dOvWrS6meValpaXs3LmTbdu2sWnjetavW0t+4VEAwlpaiWnnpFtbFzHtoVs76NXR08nUGFU74UARpB+EjEOQfshExk82dh2sprzCSZMAP3r17EFcH0/XUVxcHN26dfNq58znn3/OfffdR3l5ec1eWacKCAiguLgYq/Vk25jL5WLDhg307dv3tPON2ZEjR8jMzKS8vJyEhIRax6WmpjJ9+nSioqK46667uPrqq+sxpYiIiIiIiIjIeVEhSkTkcpednU3z5s1r7Xiy2+189913NYWplJQU/Pz86NevH6GhoezYsYOPP/6YmJgYAEJCQigsLKy532Kx4Ha7efTRR5k1a1addGWUlJSwfv16vv76a9Z8vYIf0vfgdrvpFGrjmk4O+kS6ueYKuCoCmvh6/esvSdVOSD8EW7NgcxZs3Wcj/YCne6p92xASEoeSkHAjCQkJF7x/U1lZGVOmTOGdd97BbDaf1gn1v9atW0f//v0vdDqNQteuXdmzZw+xsbH88MMPRscREREREREREfEGFaJERC53Y8aMISkpiX79+pGcnPyrhaLc3NyaotSSJUsoL/fsoxQbG8uYMWOYNWvWWe/z8fGhS5cuLFy4kKuuuuqic6ekpLB48WKSly9l2/ZU3G43PTpaGdS1ioQYuPYKCD5zeyz5BeVVkJIDa3fB6gwfvt3rpqLKRVSnDiQOHcno0aNJSEg4p2Li5s2bGTt2LHl5eWftgjqVzWZj6tSpPP/8896aSoOxdu1a3nzzTbKyspg3b94v/t1PTU2lWbNmdOjQoUEsZykiIiIiIiIi4gUqRImIXO6Ki4tJTk4mLS2t1iJSbWJiYsjIyACoWcrNZDLV2vlyMd1RTqeTDRs2sGTJEpZ8voj9uXmEh/gyskclCTEwMBpaB51XfPkVFQ745kdYnQHLd1pIyakmKDCAESNu4jdjfsvw4cMJCjr9h15dXc1LL73Ek08+CXj+3M5FTEwMaWlpvzru0KFDtGvX7vwn42UHDx4kKysLq9VK3759ax23cuVK5s6dS1RUFL///e/p3LlzPaYUERERERERETGcClEiInJuDh48SFpaGv379ycgIICSkhJatmx5WtHJZDJxLr9WfHx86Ny5MwsXLiQuLu4Xx+7Zs4ePPvqI9979Pw4cPExkqJWbejq47Vro1wW8uJWR/IrcIvgqFZJ2WFmR6sRs9uHmm2/mrrsnMGLECLKyshg3bhzbtm0772ebTCYOHTpEWFjYWa/v3r2bp556is2bN3PgwIGLncpFcbvdBAYGUl5ezk033URSUpKheUREREREREREGjAVokRE5NzMnz+fiRMn4uvrS3JyMqWlpYwaNeqCn2exWHC5XEybNo1nnnkGX9+TmzcdP36cDz74gPfmz+O7zd/TMcTGPddXMb4fdA71xmzkYhXZ4fOt8N56C9/+WE14+1C6dutBREQELpeL4uJiCgsLOXr0KCUlJZSWlmK326murq71me+99x733HPPaeeys7OZOXMmH374YU233fHjx/H39/f6nD777DMWLlzI3r17Wb16NW3atKl17Pfff0+7du1qLZyJiIiIiIiIiAigQpSIiJyPvLw8Vq5cyS233MJzzz3HK6+8QlVV1QU96+el/NxuN7GxsSxYsIDw8HBef/113njtFcrL7fy2j5t7+rsY1A3M6nxqsHYdggXr4f1vrBwpdXHHHXcw7bHH6d69+xljy8vLKSkpoaSkhOLi4tPeO3XqxODBgwFPB96LL77IW2+9BXDaPlNpaWnExMScUzan08n+/fvJzMwkODiY3r171zp24cKFLF26lKioKB5++GGCg4PP58cgIiIiIiIiIiJnUiFKREQuTFxcHCkpKed1T5MmTQgJCSEkJIT27dsTFhZGcHAwNpuNjRs3sPrrVTTxdfNQooOHhkCrwDoKL3XC4YSPvoHZyyyk51YzbOhgZjz151/cQ+l/HTlyhDlz5vCPf/wDt9t9WgHqZ0uWLGH06NHn9LyCgoKazqaHHnqI11577ZyziIiIiIiIiIjIRVMhSkREzp/dbqd58+Y4nc5zGv/z3lHjxo3j1VdfpVWrVoBnCb7Zs2cz+8W/ERLkYupwB/cNhABb3WWXuud2w7Id8MJSCxt3O7n9tlt54cXZdOzYsdZ7jh49yquvvsrs2bOpqqqqdQk/m83G888/T5MmTfjPf/5Dbm4uO3fuxGw21/rsLVu2EBUVRcuWLS96biIiIiIiIiIicl5UiBIRkfO3cuVKhgwZck5jbTYbJpOJyspKAEJCQnjttddwOBw8/thUyoqLeHJ0NX8cCr7WukwtRvgiBR79yMLBo2amTJ3GjBkz8PPzq7lut9t54403ePbZZ6msrDxrB9SprFYrEydOpEuXLqSmphIVFcW0adNO22NMREREREREREQaDBWiRETk/D311FM899xz2Gw2HA4HLper5prFYqFVq1a0b9+etm3b0qFDB9q0aUPbtm0JDQ0lKCiIl+bMZumyZUwaCH+5zU1IU+PmInWvqhreWAnPLLbQoeMVfPTJZ0RGRvLGG2/w/PPPU1paetrfoV8zYMAA1q5dW3eBRURERERERETEW1SIEhGR8/fiiy+Sl5dHWFgYYWFhhIaG1hSaWrduXet9Gzdu5M5xY3GUF/LPyQ4SY+sxtBhuXyHcOdeHbfvMXH/DAPLy8ti9ezculwur1Yrb7a51Sb5ThYWFkZeXVw+JRURERERERETkIqkQJSIi9eOzzz5j/LjfMaynm3cnOWkdZHQiMYLTBc8tgb8sMXP/Aw8wZ85LZGZmsm3bNrZt28a3337Ljh07cDgcWCwWTCbTGcv1mUwmysvLT1viT0REREREREREGiQVokREpO4tXLiQe++ZwOQEN69NcGM2GZ3o0tPnKfg+G4L8oPRdo9NcvKQUuP01H8b89jb++a/3sVgsNdfKy8tJTU0lJSWlpji1d+9enE4nZrMZl8tFRkYG0dHRBs5ARERERERERETOwTHLr48RERG5cIsWLeLuu+/mqd+4eea3RqeRhmLUVfCfKU7GvLyIBx9swttvv1NzLSAggPj4eOLj42vOnThxgp07d7Jt2zZSUlIoLS01IraIiIiIiIiIiJwndUSJiEidycrKIu6qntx1XTmvTdCvm4vR2DqifpaUAqP/DgsXfsC4ceOMjiMiIiIiIiIiIt6ljigREakbTqeTO26/lStaVzFnXOMrQu08AGt3wb5C6NoW+nf1vJ9qcybs/gksZhjfzzM2+QfY8xPEtIcxfaB5wJnP3pzpeXZxOcR3hlG962dORhh1FTwy3MT9/28S1113HZGRkUZHEhERERERERERL1IhSkRE6sT777/PjtRUfvibG1+r0Wm8x+WGpz+Dv/7Hc/wziw/85VZ4fBSY/rsH1r82wpsrwd8GAb5w5xtQXnXynlmfw9fT4Yo2ns9uNzz6Ifx92enfObI3lJ2o23kZ6W9j3SzfWc2MJ6fz4UcfGx1HRERERERERES8yGx0ABERaZxeffklxvc7s0voUjd/LTy3xFOEahUIvx8E7VtCtRP+9Aks2nzmPRUOuPVliO0ADw+FiGDP+f1H4IWkk+M+3XyyCGUyebqF+naBpds9XVSNlc0CM0Y7WLToM376qRFPVERERERERETkMqRClIiIeF12djbbU9OYcEPjWpKvqhqe/NRz3DwA9r8Kb0+CnJchvJXn/J8/93Q2ncrthpvjYPMseOVuWPmnk9d25p48nvX5yeNl0+CLqbBpJsybVDfzaUjG9IEAXxNLliwxOoqIiIiIiIiIiHiRClEiIuJ1mzdvxmY1E9/Z6CTelZUPBaWe4xtjPZ1ORXYoOQEjennO7zoEh0vOvPfBxJPHUW2gdZDnuKjM8+5wnux6ahUIQ3qcHH/fQGh2lr2kGhM/K1wbBVu2bDE6ioiIiIiIiIiIeJH2iBIREa87fPgwwc0s+Fmrfn3wJWRv/snjf2/xvM7m0FEIa376ueCmp38OsHnenS7Pe27RyeMB0WA2nRxrNnmW/yspv/Dsl4LwltXkHjpgdAwREREREREREfEiFaJERMTrzGYzLpfRKbzP6nPyuFdHuDry7OP8bGee8/2f37jm/+lJbup/8tjhPP1atRMOHDn3nJcqpwt8fPSfJiIiIiIiIiIijYn+b4+IiHhdu3btKCxxcLwSmvgancZ7IkNOHgf5nb53U/pBCPTz7BVlMp1579nOnap1kGf5vZJy2JYDLvfJrqhvM6Gs4uLzN3Q5R6x06dfR6BgiIiIiIiIiIuJF2iNKRES8Lj4+HqcL1u0yOol3XRkGV0V4jjf+CJ985+ni2X8E+j0DEX+EXtOhqvrCnv+bqz3vecfgoQVQegKOlMGzi72RvmErq4AtWS7i4+ONjiIiIiIiIiIiIl6kQpSIiHhdu3bt6Bd/De+u8/n1wZeYOeM9+zu53XDHaxD6IEQ+4ulksvjA2xPBdoH9xs/efnKJvrmroOVkCHkAVmfAFW28N4eG6MNN4HKbueWWW4yOIiIiIiIiIiIiXqRClIiI1Imp055g8VYnW7KMTuJdg7rBN89AXCdP4elImafwNLg7fPAgXBt14c9u1wI2z4LeEZ7PTheENYcvpsKNMV6J3yDZK+D5JCv3TZxIixYtjI4jIiIiIiIiIiJeZHK73W6jQ4iISOPjdrsZNnQwWWnrSfmLo6bTpzGpcMDewxDVBvxt3n12folnubqoRt4JBXDnXDMrdzcj9YcMQkNDjY4jIiIiIiIiIiLec0yFKBERqTMFBQX06hHD9Z2O8clDTkwmoxNJQzNvDdw/38RXXy1nyJAhRscRERERERERERHvUiFKRETq1po1axg2dAjj+7qYN8mFTz0sCtvjiXMbt+cnqKqG7h3ObfzCB6FH+IXn8pY9P8Ftr5z72IY6x39tgPveNvHkjKd45pln6udLRURERERERESkPh27wO3URUREzs2gQYP4avkKRt88kqPHq/jkIRe+1rr9zuyCcxtXVX1+4yurLyyPt1VVX/pzfOtr+MMCE9OmPaYilIiIiIiIiIhII6aOKBERqRcbNmzgppHD6BJSxYcPVtNZWwFdlk5UwROfmHl1uYs///nPzJw50+hIIiIiIiIiIiJSd7Q0n4iI1J89e/Yw7o7b2ftjBnN+V83kBKMTSX3algPj51rJt/vy1v+9w9ixY42OJCIiIiIiIiIidetYPezUISIi4nHllVfyzXdbmPzAH7l/vokRs33YdcjoVFLXjh2HKQsh/s9m2l/Zl7T03SpCiYiIiIiIiIhcJlSIEhGReuXr68ucOXNYs2YNP7m60nO6mYf/ZaLIbnQy8bZqJ7y5Ero8amHhlua89vqbJK9cTbt27YyOJiIiIiIiIiIi9URL84mIiGFcLhfz58/nqSef4MTxEh64sZo/DoXQ5kYnk4tR4YB/roc5X1k5cMTNw398hBkzZtCsWTOjo4mIiIiIiIiISP3SHlEiImK8srIyXn/9dV59+SWOHSvm7hvcTB3h4sowo5PJ+Th2HOaugleTLRSXw90TJvD443/iiiuuMDqaiIiIiIiIiIgYQ4UoERFpOKqqqvj444/52/Oz2LUni7hIHyYPcjKuLwT6GZ1Ozsblhm9+hPc3mflgkwkfqy/33DuJxx57TEvwiYiIiIiIiIiIClEiItLwuFwuli9fzvx33yEpKQlfK9x+jYs7r3dxw5Xgox0ODZdxCD79DhZstLK/wEH8tVdz78TJ3HHHHQQFBRkdT0REREREREREGgYVokREpGE7cuQIH3zwAQvmz2PHznRaNbUyqlc1t1ztZkh38LcZnfDy4HLD5kxY8j0s2W7jx0NVhLVpzZ1338u9995LdHS00RFFRERERERERKThUSFKREQuHZmZmSxevJglny/iuy3f42czk9ANEro5SYiBHh3AZDI6ZeORWwSrM2B1uonkNAuHjzmIigznljG3c8sttxAfH4/ZrPY0ERERERERERGplQpRIiJyaTp8+DBffPEFyckrWLvma4qOltC6mZVB0U4GdnUR3xliO4DVx+ikl47MfNiSBet2wZrdVvbmOfDztRJ/3bUkDhnOzTffTGxsrNExRURERERERETk0qFClIiIXPpcLhepqamsWbOG1V+vZP36dZTZT+Dv60OvCDPXdHLQJxKujoSoNtpjCuDQMUjJga3ZsDXHhy1ZJo6WVWO1+HB1XG8SEoeSkJBAfHw8/v7+RscVEREREREREZFLkwpRIiLS+LhcLnbv3s2WLVvYunUrW77bSOrOdBzVTnytZqLbW4kOqyK2vZtu7SCmPXRsDTaL0cm9y+32FJx250H6Qcg4BGmHrGQcclNsr8ZkMtH5inD6XNuPa665lj59+tC7d2/8/PyMji4iIiIiIiIiIo2DClEiInJ5qKys5IcffiA9PZ2MjAzS03aSkf4D+w7k4Xa7MZtNtG1pJSLYTadWDiKCISIY2reE0GYQ0hSCmzasbqqjdsgvgcIyOHgU9hX+91VkYd8RHw4UOqh0uABo3bIZsbGxRMf0oHv37kRHR9OrVy+aN29u8CxERERERERERKQRUyFKREQub3a7nT179pCTk8O+ffs8r5wscrL3sm//QcpPVNaMNZtNBDezEtzURGgzFy38HTQLgEBfCPSDIH9oHgBBfmDx8exPFXhKc5G/DfysnmOnC0pPnLxW6YDyKs/xseNgr/C8yio840rKwV7lw+ESC4dL3BSWVFP13yITgNXiQ/t2oUREdCIisjMRERF06tSJiIgIunbtSnBwcF3+GEVERERERERERM5GhSgREZFfcuzYMQ4fPkxhYSGHDx8mPz+/5ri4uJiS4iLsZaWUlZVSVlZGSUkZpfZynE7Xrz+8Fs2aNiEosAmBTZoQ1DSIZs1a0KxFKwIDgwgJCSEsLIzg4GDatGlDaGgowcHBhISEYDY3oHYtERERERERERERFaJERETqTkVFBSdOnGx7stvtOByOms8tWrSoObZYL2SkjgAAAF9JREFULAQFBdVrPhERERERERERkTqmQpSIiIiIiIiIiIiIiIjUiWNaw0dERERERERERERERETqhApRIiIiIiIiIiIiIiIiUicswDajQ4iIiIiIiIiIiIiIiEijU/r/ARN5/xGPrsbJAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "try:\n", - " display(Image(app.get_graph().draw_png()))\n", - "except ImportError:\n", - " print(\n", - " \"You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt\"\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/how-tos/graph-api.md b/docs/docs/how-tos/graph-api.md new file mode 100644 index 000000000..2e15cacb1 --- /dev/null +++ b/docs/docs/how-tos/graph-api.md @@ -0,0 +1,1877 @@ +# How to use the graph API + +This guide demonstrates the basics of LangGraph's Graph API. It walks through [state](#define-and-update-state), as well as composing common graph structures such as [sequences](#create-a-sequence-of-steps), [branches](#create-branches), and [loops](#create-and-control-loops). It also covers LangGraph's control features, including the [Send API](#map-reduce-and-the-send-api) for map-reduce workflows and the [Command API](#combine-control-flow-and-state-updates-with-command) for combining state updates with "hops" across nodes. + +## Setup + +Install `langgraph`: + +```bash +pip install -U langgraph +``` + +!!! tip "Set up LangSmith for better debugging" + Sign up for [LangSmith](https://smith.langchain.com) to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started in the [docs](https://docs.smith.langchain.com). + +## Define and update state + +Here we show how to define and update [state](../concepts/low_level.md#state) in LangGraph. We will demonstrate: + +1. How to use state to define a graph's [schema](../concepts/low_level.md#schema) +2. How to use [reducers](../concepts/low_level.md#reducers) to control how state updates are processed. + +### Define state + +[State](../concepts/low_level.md#state) in LangGraph can be a `TypedDict`, `Pydantic` model, or dataclass. Below we will use `TypedDict`. See [this section](#use-pydantic-models-for-graph-state) for detail on using Pydantic. + +By default, graphs will have the same input and output schema, and the state determines that schema. See [this section](#define-input-and-output-schemas) for how to define distinct input and output schemas. + +Let's consider a simple example using [messages](../concepts/low_level.md#messagesstate). This represents a versatile formulation of state for many LLM applications. See our [concepts page](../concepts/low_level.md#working-with-messages-in-graph-state) for more detail. + +```python +from langchain_core.messages import AnyMessage +from typing_extensions import TypedDict + +class State(TypedDict): + messages: list[AnyMessage] + extra_field: int +``` + +This state tracks a list of [message](https://python.langchain.com/docs/concepts/messages/) objects, as well as an extra integer field. + +### Update state + +Let's build an example graph with a single node. Our [node](../concepts/low_level.md#nodes) is just a Python function that reads our graph's state and makes updates to it. The first argument to this function will always be the state: + +```python +from langchain_core.messages import AIMessage + +def node(state: State): + messages = state["messages"] + new_message = AIMessage("Hello!") + return {"messages": messages + [new_message], "extra_field": 10} +``` + +This node simply appends a message to our message list, and populates an extra field. + +!!! important + Nodes should return updates to the state directly, instead of mutating the state. + +Let's next define a simple graph containing this node. We use [StateGraph](../concepts/low_level.md#stategraph) to define a graph that operates on this state. We then use [add_node](../concepts/low_level.md#nodes) populate our graph. + +```python +from langgraph.graph import StateGraph + +builder = StateGraph(State) +builder.add_node(node) +builder.set_entry_point("node") +graph = builder.compile() +``` + +LangGraph provides built-in utilities for visualizing your graph. Let's inspect our graph. See [this section](#visualize-your-graph) for detail on visualization. + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Simple graph with single node](assets/graph_api_image_1.png) + +In this case, our graph just executes a single node. Let's proceed with a simple invocation: + +```python +from langchain_core.messages import HumanMessage + +result = graph.invoke({"messages": [HumanMessage("Hi")]}) +result +``` +``` +{'messages': [HumanMessage(content='Hi'), AIMessage(content='Hello!')], 'extra_field': 10} +``` + +Note that: + +- We kicked off invocation by updating a single key of the state. +- We receive the entire state in the invocation result. + +For convenience, we frequently inspect the content of [message objects](https://python.langchain.com/docs/concepts/messages/) via pretty-print: + +```python +for message in result["messages"]: + message.pretty_print() +``` +``` +================================ Human Message ================================ + +Hi +================================== Ai Message ================================== + +Hello! +``` + +### Process state updates with reducers + +Each key in the state can have its own independent [reducer](../concepts/low_level.md#reducers) function, which controls how updates from nodes are applied. If no reducer function is explicitly specified then it is assumed that all updates to the key should override it. + +For `TypedDict` state schemas, we can define reducers by annotating the corresponding field of the state with a reducer function. + +In the earlier example, our node updated the `"messages"` key in the state by appending a message to it. Below, we add a reducer to this key, such that updates are automatically appended: + +```python +from typing_extensions import Annotated + +def add(left, right): + """Can also import `add` from the `operator` built-in.""" + return left + right + +class State(TypedDict): + # highlight-next-line + messages: Annotated[list[AnyMessage], add] + extra_field: int +``` + +Now our node can be simplified: + +```python +def node(state: State): + new_message = AIMessage("Hello!") + # highlight-next-line + return {"messages": [new_message], "extra_field": 10} +``` +```python +from langgraph.graph import START + +graph = StateGraph(State).add_node(node).add_edge(START, "node").compile() + +result = graph.invoke({"messages": [HumanMessage("Hi")]}) + +for message in result["messages"]: + message.pretty_print() +``` +``` +================================ Human Message ================================ + +Hi +================================== Ai Message ================================== + +Hello! +``` + +#### MessagesState + +In practice, there are additional considerations for updating lists of messages: + +- We may wish to update an existing message in the state. +- We may want to accept short-hands for [message formats](../concepts/low_level.md#using-messages-in-your-graph), such as [OpenAI format](https://python.langchain.com/docs/concepts/messages.md#openai-format). + +LangGraph includes a built-in reducer `add_messages` that handles these considerations: + +```python +from langgraph.graph.message import add_messages + +class State(TypedDict): + # highlight-next-line + messages: Annotated[list[AnyMessage], add_messages] + extra_field: int + +def node(state: State): + new_message = AIMessage("Hello!") + return {"messages": [new_message], "extra_field": 10} + +graph = StateGraph(State).add_node(node).set_entry_point("node").compile() +``` + +```python +# highlight-next-line +input_message = {"role": "user", "content": "Hi"} + +result = graph.invoke({"messages": [input_message]}) + +for message in result["messages"]: + message.pretty_print() +``` +``` +================================ Human Message ================================ + +Hi +================================== Ai Message ================================== + +Hello! +``` + +This is a versatile representation of state for applications involving [chat models](https://python.langchain.com/docs/concepts/chat_models/). LangGraph includes a pre-built `MessagesState` for convenience, so that we can have: + +```python +from langgraph.graph import MessagesState + +class State(MessagesState): + extra_field: int +``` + +### Define input and output schemas + +By default, `StateGraph` operates with a single schema, and all nodes are expected to communicate using that schema. However, it's also possible to define distinct input and output schemas for a graph. + +When distinct schemas are specified, an internal schema will still be used for communication between nodes. The input schema ensures that the provided input matches the expected structure, while the output schema filters the internal data to return only the relevant information according to the defined output schema. + +Below, we'll see how to define distinct input and output schema. + +```python +from langgraph.graph import StateGraph, START, END +from typing_extensions import TypedDict + +# Define the schema for the input +class InputState(TypedDict): + question: str + +# Define the schema for the output +class OutputState(TypedDict): + answer: str + +# Define the overall schema, combining both input and output +class OverallState(InputState, OutputState): + pass + +# Define the node that processes the input and generates an answer +def answer_node(state: InputState): + # Example answer and an extra key + return {"answer": "bye", "question": state["question"]} + +# Build the graph with input and output schemas specified +builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState) +builder.add_node(answer_node) # Add the answer node +builder.add_edge(START, "answer_node") # Define the starting edge +builder.add_edge("answer_node", END) # Define the ending edge +graph = builder.compile() # Compile the graph + +# Invoke the graph with an input and print the result +print(graph.invoke({"question": "hi"})) +``` +``` +{'answer': 'bye'} +``` + +Notice that the output of invoke only includes the output schema. + +### Pass private state between nodes + +In some cases, you may want nodes to exchange information that is crucial for intermediate logic but doesn't need to be part of the main schema of the graph. This private data is not relevant to the overall input/output of the graph and should only be shared between certain nodes. + +Below, we'll create an example sequential graph consisting of three nodes (node_1, node_2 and node_3), where private data is passed between the first two steps (node_1 and node_2), while the third step (node_3) only has access to the public overall state. + +```python +from langgraph.graph import StateGraph, START, END +from typing_extensions import TypedDict + +# The overall state of the graph (this is the public state shared across nodes) +class OverallState(TypedDict): + a: str + +# Output from node_1 contains private data that is not part of the overall state +class Node1Output(TypedDict): + private_data: str + +# The private data is only shared between node_1 and node_2 +def node_1(state: OverallState) -> Node1Output: + output = {"private_data": "set by node_1"} + print(f"Entered node `node_1`:\n\tInput: {state}.\n\tReturned: {output}") + return output + +# Node 2 input only requests the private data available after node_1 +class Node2Input(TypedDict): + private_data: str + +def node_2(state: Node2Input) -> OverallState: + output = {"a": "set by node_2"} + print(f"Entered node `node_2`:\n\tInput: {state}.\n\tReturned: {output}") + return output + +# Node 3 only has access to the overall state (no access to private data from node_1) +def node_3(state: OverallState) -> OverallState: + output = {"a": "set by node_3"} + print(f"Entered node `node_3`:\n\tInput: {state}.\n\tReturned: {output}") + return output + +# Connect nodes in a sequence +# node_2 accepts private data from node_1, whereas +# node_3 does not see the private data. +builder = StateGraph(OverallState).add_sequence([node_1, node_2, node_3]) +builder.add_edge(START, "node_1") +graph = builder.compile() + +# Invoke the graph with the initial state +response = graph.invoke( + { + "a": "set at start", + } +) + +print() +print(f"Output of graph invocation: {response}") +``` +``` +Entered node `node_1`: + Input: {'a': 'set at start'}. + Returned: {'private_data': 'set by node_1'} +Entered node `node_2`: + Input: {'private_data': 'set by node_1'}. + Returned: {'a': 'set by node_2'} +Entered node `node_3`: + Input: {'a': 'set by node_2'}. + Returned: {'a': 'set by node_3'} + +Output of graph invocation: {'a': 'set by node_3'} +``` + +### Use Pydantic models for graph state + +A [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.StateGraph) accepts a `state_schema` argument on initialization that specifies the "shape" of the state that the nodes in the graph can access and update. + +In our examples, we typically use a python-native `TypedDict` for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects). + +Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/). can be used for `state_schema` to add run time validation on **inputs**. + +!!! note "Known Limitations" + - Currently, the output of the graph will **NOT** be an instance of a pydantic model. + - Run-time validation only occurs on inputs into nodes, not on the outputs. + - The validation error trace from pydantic does not show which node the error arises in. + +```python +from langgraph.graph import StateGraph, START, END +from typing_extensions import TypedDict +from pydantic import BaseModel + +# The overall state of the graph (this is the public state shared across nodes) +class OverallState(BaseModel): + a: str + +def node(state: OverallState): + return {"a": "goodbye"} + +# Build the state graph +builder = StateGraph(OverallState) +builder.add_node(node) # node_1 is the first node +builder.add_edge(START, "node") # Start the graph with node_1 +builder.add_edge("node", END) # End the graph after node_1 +graph = builder.compile() + +# Test the graph with a valid input +graph.invoke({"a": "hello"}) +``` + +Invoke the graph with an **invalid** input + +```python +try: + graph.invoke({"a": 123}) # Should be a string +except Exception as e: + print("An exception was raised because `a` is an integer rather than a string.") + print(e) +``` +``` +An exception was raised because `a` is an integer rather than a string. +1 validation error for OverallState +a + Input should be a valid string [type=string_type, input_value=123, input_type=int] + For further information visit https://errors.pydantic.dev/2.9/v/string_type +``` + +See below for additional features of Pydantic model state: + +??? example "Serialization Behavior" + + When using Pydantic models as state schemas, it's important to understand how serialization works, especially when: + - Passing Pydantic objects as inputs + - Receiving outputs from the graph + - Working with nested Pydantic models + + Let's see these behaviors in action. + + ```python + from langgraph.graph import StateGraph, START, END + from pydantic import BaseModel + + class NestedModel(BaseModel): + value: str + + class ComplexState(BaseModel): + text: str + count: int + nested: NestedModel + + def process_node(state: ComplexState): + # Node receives a validated Pydantic object + print(f"Input state type: {type(state)}") + print(f"Nested type: {type(state.nested)}") + # Return a dictionary update + return {"text": state.text + " processed", "count": state.count + 1} + + # Build the graph + builder = StateGraph(ComplexState) + builder.add_node("process", process_node) + builder.add_edge(START, "process") + builder.add_edge("process", END) + graph = builder.compile() + + # Create a Pydantic instance for input + input_state = ComplexState(text="hello", count=0, nested=NestedModel(value="test")) + print(f"Input object type: {type(input_state)}") + + # Invoke graph with a Pydantic instance + result = graph.invoke(input_state) + print(f"Output type: {type(result)}") + print(f"Output content: {result}") + + # Convert back to Pydantic model if needed + output_model = ComplexState(**result) + print(f"Converted back to Pydantic: {type(output_model)}") + ``` + +??? example "Runtime Type Coercion" + + Pydantic performs runtime type coercion for certain data types. This can be helpful but also lead to unexpected behavior if you're not aware of it. + + ```python + from langgraph.graph import StateGraph, START, END + from pydantic import BaseModel + + class CoercionExample(BaseModel): + # Pydantic will coerce string numbers to integers + number: int + # Pydantic will parse string booleans to bool + flag: bool + + def inspect_node(state: CoercionExample): + print(f"number: {state.number} (type: {type(state.number)})") + print(f"flag: {state.flag} (type: {type(state.flag)})") + return {} + + builder = StateGraph(CoercionExample) + builder.add_node("inspect", inspect_node) + builder.add_edge(START, "inspect") + builder.add_edge("inspect", END) + graph = builder.compile() + + # Demonstrate coercion with string inputs that will be converted + result = graph.invoke({"number": "42", "flag": "true"}) + + # This would fail with a validation error + try: + graph.invoke({"number": "not-a-number", "flag": "true"}) + except Exception as e: + print(f"\nExpected validation error: {e}") + ``` + +??? example "Working with Message Models" + + When working with LangChain message types in your state schema, there are important considerations for serialization. You should use `AnyMessage` (rather than `BaseMessage`) for proper serialization/deserialization when using message objects over the wire. + + ```python + from langgraph.graph import StateGraph, START, END + from pydantic import BaseModel + from langchain_core.messages import HumanMessage, AIMessage, AnyMessage + from typing import List + + class ChatState(BaseModel): + messages: List[AnyMessage] + context: str + + def add_message(state: ChatState): + return {"messages": state.messages + [AIMessage(content="Hello there!")]} + + builder = StateGraph(ChatState) + builder.add_node("add_message", add_message) + builder.add_edge(START, "add_message") + builder.add_edge("add_message", END) + graph = builder.compile() + + # Create input with a message + initial_state = ChatState( + messages=[HumanMessage(content="Hi")], context="Customer support chat" + ) + + result = graph.invoke(initial_state) + print(f"Output: {result}") + + # Convert back to Pydantic model to see message types + output_model = ChatState(**result) + for i, msg in enumerate(output_model.messages): + print(f"Message {i}: {type(msg).__name__} - {msg.content}") + ``` + +## Add runtime configuration + +Sometimes you want to be able to configure your graph when calling it. For example, you might want to be able to specify what LLM or system prompt to use at runtime, *without polluting the graph state with these parameters*. + +To add runtime configuration: + +1. Specify a schema for your configuration +2. Add the configuration to the function signature for nodes or conditional edges +3. Pass the configuration into the graph. + +See below for a simple example: + +```python +from langchain_core.runnables import RunnableConfig +from langgraph.graph import END, StateGraph, START +from typing_extensions import TypedDict + +# 1. Specify config schema +class ConfigSchema(TypedDict): + my_runtime_value: str + +# 2. Define a graph that accesses the config in a node +class State(TypedDict): + my_state_value: str + +# highlight-next-line +def node(state: State, config: RunnableConfig): + # highlight-next-line + if config["configurable"]["my_runtime_value"] == "a": + return {"my_state_value": 1} + # highlight-next-line + elif config["configurable"]["my_runtime_value"] == "b": + return {"my_state_value": 2} + else: + raise ValueError("Unknown values.") + +# highlight-next-line +builder = StateGraph(State, config_schema=ConfigSchema) +builder.add_node(node) +builder.add_edge(START, "node") +builder.add_edge("node", END) + +graph = builder.compile() + +# 3. Pass in configuration at runtime: +# highlight-next-line +print(graph.invoke({}, {"configurable": {"my_runtime_value": "a"}})) +# highlight-next-line +print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}})) +``` +``` +{'my_state_value': 1} +{'my_state_value': 2} +``` + +??? example "Extended example: specifying LLM at runtime" + Below we demonstrate a practical example in which we configure what LLM to use at runtime. We will use both OpenAI and Anthropic models. + + ```python + from langchain.chat_models import init_chat_model + from langchain_core.runnables import RunnableConfig + from langgraph.graph import MessagesState + from langgraph.graph import END, StateGraph, START + from typing_extensions import TypedDict + + class ConfigSchema(TypedDict): + model: str + + MODELS = { + "anthropic": init_chat_model("anthropic:claude-3-5-haiku-latest"), + "openai": init_chat_model("openai:gpt-4.1-mini"), + } + + def call_model(state: MessagesState, config: RunnableConfig): + model = config["configurable"].get("model", "anthropic") + model = MODELS[model] + response = model.invoke(state["messages"]) + return {"messages": [response]} + + builder = StateGraph(MessagesState, config_schema=ConfigSchema) + builder.add_node("model", call_model) + builder.add_edge(START, "model") + builder.add_edge("model", END) + + graph = builder.compile() + + # Usage + input_message = {"role": "user", "content": "hi"} + # With no configuration, uses default (Anthropic) + response_1 = graph.invoke({"messages": [input_message]})["messages"][-1] + # Or, can set OpenAI + config = {"configurable": {"model": "openai"}} + response_2 = graph.invoke({"messages": [input_message]}, config=config)["messages"][-1] + + print(response_1.response_metadata["model_name"]) + print(response_2.response_metadata["model_name"]) + ``` + ``` + claude-3-5-haiku-20241022 + gpt-4.1-mini-2025-04-14 + ``` + +??? example "Extended example: specifying model and system message at runtime" + Below we demonstrate a practical example in which we configure two parameters: the LLM and system message to use at runtime. + + ```python + from typing import Optional + from langchain.chat_models import init_chat_model + from langchain_core.messages import SystemMessage + from langchain_core.runnables import RunnableConfig + from langgraph.graph import END, MessagesState, StateGraph, START + from typing_extensions import TypedDict + + class ConfigSchema(TypedDict): + model: Optional[str] + system_message: Optional[str] + + MODELS = { + "anthropic": init_chat_model("anthropic:claude-3-5-haiku-latest"), + "openai": init_chat_model("openai:gpt-4.1-mini"), + } + + def call_model(state: MessagesState, config: RunnableConfig): + model = config["configurable"].get("model", "anthropic") + model = MODELS[model] + messages = state["messages"] + if system_message := config["configurable"].get("system_message"): + messages = [SystemMessage(system_message)] + messages + response = model.invoke(messages) + return {"messages": [response]} + + builder = StateGraph(MessagesState, config_schema=ConfigSchema) + builder.add_node("model", call_model) + builder.add_edge(START, "model") + builder.add_edge("model", END) + + graph = builder.compile() + + # Usage + input_message = {"role": "user", "content": "hi"} + config = {"configurable": {"model": "openai", "system_message": "Respond in Italian."}} + response = graph.invoke({"messages": [input_message]}, config) + for message in response["messages"]: + message.pretty_print() + ``` + ``` + ================================ Human Message ================================ + + hi + ================================== Ai Message ================================== + + Ciao! Come posso aiutarti oggi? + ``` + +## Add retry policies + +There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. LangGraph lets you add retry policies to nodes. + +To configure a retry policy, pass the `retry_policy` parameter to the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node: + +```python +from langgraph.pregel import RetryPolicy + +builder.add_node( + "node_name", + node_function, + retry_policy=RetryPolicy(), +) +``` + +By default, the `retry_on` parameter uses the `default_retry_on` function, which retries on any exception except for the following: + +* `ValueError` +* `TypeError` +* `ArithmeticError` +* `ImportError` +* `LookupError` +* `NameError` +* `SyntaxError` +* `RuntimeError` +* `ReferenceError` +* `StopIteration` +* `StopAsyncIteration` +* `OSError` + +In addition, for exceptions from popular http request libraries such as `requests` and `httpx` it only retries on 5xx status codes. + +??? example "Extended example: customizing retry policies" + Consider an example in which we are reading from a SQL database. Below we pass two different retry policies to nodes: + + ```python + import sqlite3 + from typing_extensions import TypedDict + from langchain.chat_models import init_chat_model + from langgraph.graph import END, MessagesState, StateGraph, START + from langgraph.pregel import RetryPolicy + from langchain_community.utilities import SQLDatabase + from langchain_core.messages import AIMessage + + db = SQLDatabase.from_uri("sqlite:///:memory:") + model = init_chat_model("anthropic:claude-3-5-haiku-latest") + + def query_database(state: MessagesState): + query_result = db.run("SELECT * FROM Artist LIMIT 10;") + return {"messages": [AIMessage(content=query_result)]} + + def call_model(state: MessagesState): + response = model.invoke(state["messages"]) + return {"messages": [response]} + + # Define a new graph + builder = StateGraph(MessagesState) + builder.add_node( + "query_database", + query_database, + retry_policy=RetryPolicy(retry_on=sqlite3.OperationalError), + ) + builder.add_node("model", call_model, retry_policy=RetryPolicy(max_attempts=5)) + builder.add_edge(START, "model") + builder.add_edge("model", "query_database") + builder.add_edge("query_database", END) + graph = builder.compile() + ``` + +## Add node caching + +Node caching is useful in cases where you want to avoid repeating operations, like when doing something expensive (either in terms of time or cost). LangGraph lets you add individualized caching policies to nodes in a graph. + +To configure a cache policy, pass the `cache_policy` parameter to the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.state.StateGraph.add_node) function. In the following example, a [`CachePolicy`](https://langchain-ai.github.io/langgraph/reference/types/?h=cachepolicy#langgraph.types.CachePolicy) object is instantiated with a time to live of 120 seconds and the default `key_func` generator. Then it is associated with a node: + +```python +from langgraph.types import CachePolicy + +builder.add_node( + "node_name", + node_function, + cache_policy=CachePolicy(ttl=120), +) +``` + +Then, to enable node-level caching for a graph, set the `cache` argument when compiling the graph. The example below uses `InMemoryCache` to set up a graph with in-memory cache, but `SqliteCache` is also available. + +```python +from langgraph.cache.memory import InMemoryCache + +graph = builder.compile(cache=InMemoryCache()) +``` + +## Create a sequence of steps + +!!! info "Prerequisites" + This guide assumes familiarity with the above section on [state](#define-and-update-state). + +Here we demonstrate how to construct a simple sequence of steps. We will show: + +1. How to build a sequential graph +2. Built-in short-hand for constructing similar graphs. + +To add a sequence of nodes, we use the `.add_node` and `.add_edge` methods of our [graph](../concepts/low_level.md#stategraph): + +```python +from langgraph.graph import START, StateGraph + +builder = StateGraph(State) + +# Add nodes +builder.add_node(step_1) +builder.add_node(step_2) +builder.add_node(step_3) + +# Add edges +builder.add_edge(START, "step_1") +builder.add_edge("step_1", "step_2") +builder.add_edge("step_2", "step_3") +``` + +We can also use the built-in shorthand `.add_sequence`: + +```python +builder = StateGraph(State).add_sequence([step_1, step_2, step_3]) +builder.add_edge(START, "step_1") +``` + +??? info "Why split application steps into a sequence with LangGraph?" + LangGraph makes it easy to add an underlying persistence layer to your application. + This allows state to be checkpointed in between the execution of nodes, so your LangGraph nodes govern: + + - How state updates are [checkpointed](../concepts/persistence.md) + - How interruptions are resumed in [human-in-the-loop](../concepts/human_in_the_loop.md) workflows + - How we can "rewind" and branch-off executions using LangGraph's [time travel](../concepts/time-travel.md) features + + They also determine how execution steps are [streamed](../concepts/streaming.md), and how your application is visualized + and debugged using [LangGraph Studio](../concepts/langgraph_studio.md). + +Let's demonstrate an end-to-end example. We will create a sequence of three steps: + +1. Populate a value in a key of the state +2. Update the same value +3. Populate a different value + +Let's first define our [state](../concepts/low_level.md#state). This governs the [schema of the graph](../concepts/low_level.md#schema), and can also specify how to apply updates. See [this section](#process-state-updates-with-reducers) for more detail. + +In our case, we will just keep track of two values: + +```python +from typing_extensions import TypedDict + +class State(TypedDict): + value_1: str + value_2: int +``` + +Our [nodes](../concepts/low_level.md#nodes) are just Python functions that read our graph's state and make updates to it. The first argument to this function will always be the state: + +```python +def step_1(state: State): + return {"value_1": "a"} + +def step_2(state: State): + current_value_1 = state["value_1"] + return {"value_1": f"{current_value_1} b"} + +def step_3(state: State): + return {"value_2": 10} +``` + +!!! note + Note that when issuing updates to the state, each node can just specify the value of the key it wishes to update. + + By default, this will **overwrite** the value of the corresponding key. You can also use [reducers](../concepts/low_level.md#reducers) to control how updates are processed— for example, you can append successive updates to a key instead. See [this section](#process-state-updates-with-reducers) for more detail. + +Finally, we define the graph. We use [StateGraph](../concepts/low_level.md#stategraph) to define a graph that operates on this state. + +We will then use [add_node](../concepts/low_level.md#messagesstate) and [add_edge](../concepts/low_level.md#edges) to populate our graph and define its control flow. + +```python +from langgraph.graph import START, StateGraph + +builder = StateGraph(State) + +# Add nodes +builder.add_node(step_1) +builder.add_node(step_2) +builder.add_node(step_3) + +# Add edges +builder.add_edge(START, "step_1") +builder.add_edge("step_1", "step_2") +builder.add_edge("step_2", "step_3") +``` + +!!! tip "Specifying custom names" + You can specify custom names for nodes using `.add_node`: + + ```python + builder.add_node("my_node", step_1) + ``` + +Note that: + +- `.add_edge` takes the names of nodes, which for functions defaults to `node.__name__`. +- We must specify the entry point of the graph. For this we add an edge with the [START node](../concepts/low_level.md#start-node). +- The graph halts when there are no more nodes to execute. + +We next [compile](../concepts/low_level.md#compiling-your-graph) our graph. This provides a few basic checks on the structure of the graph (e.g., identifying orphaned nodes). If we were adding persistence to our application via a [checkpointer](../concepts/persistence.md), it would also be passed in here. + +```python +graph = builder.compile() +``` + +LangGraph provides built-in utilities for visualizing your graph. Let's inspect our sequence. See [this guide](#visualize-your-graph) for detail on visualization. + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Sequence of steps graph](assets/graph_api_image_2.png) + +Let's proceed with a simple invocation: + +```python +graph.invoke({"value_1": "c"}) +``` +``` +{'value_1': 'a b', 'value_2': 10} +``` + +Note that: + +- We kicked off invocation by providing a value for a single state key. We must always provide a value for at least one key. +- The value we passed in was overwritten by the first node. +- The second node updated the value. +- The third node populated a different value. + +!!! tip "Built-in shorthand" + `langgraph>=0.2.46` includes a built-in short-hand `add_sequence` for adding node sequences. You can compile the same graph as follows: + + ```python + # highlight-next-line + builder = StateGraph(State).add_sequence([step_1, step_2, step_3]) + builder.add_edge(START, "step_1") + + graph = builder.compile() + + graph.invoke({"value_1": "c"}) + ``` + +## Create branches + +Parallel execution of nodes is essential to speed up overall graph operation. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out and fan-in mechanisms, utilizing both standard edges and [conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.MessageGraph.add_conditional_edges). Below are some examples showing how to add create branching dataflows that work for you. + +### Run graph nodes in parallel + +In this example, we fan out from `Node A` to `B and C` and then fan in to `D`. With our state, [we specify the reducer add operation](https://langchain-ai.github.io/langgraph/concepts/low_level.md#reducers). This will combine or accumulate values for the specific key in the State, rather than simply overwriting the existing value. For lists, this means concatenating the new list with the existing list. See the above section on [state reducers](#process-state-updates-with-reducers) for more detail on updating state with reducers. + +```python +import operator +from typing import Annotated, Any +from typing_extensions import TypedDict +from langgraph.graph import StateGraph, START, END + +class State(TypedDict): + # The operator.add reducer fn makes this append-only + aggregate: Annotated[list, operator.add] + +def a(state: State): + print(f'Adding "A" to {state["aggregate"]}') + return {"aggregate": ["A"]} + +def b(state: State): + print(f'Adding "B" to {state["aggregate"]}') + return {"aggregate": ["B"]} + +def c(state: State): + print(f'Adding "C" to {state["aggregate"]}') + return {"aggregate": ["C"]} + +def d(state: State): + print(f'Adding "D" to {state["aggregate"]}') + return {"aggregate": ["D"]} + +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) +builder.add_node(c) +builder.add_node(d) +builder.add_edge(START, "a") +builder.add_edge("a", "b") +builder.add_edge("a", "c") +builder.add_edge("b", "d") +builder.add_edge("c", "d") +builder.add_edge("d", END) +graph = builder.compile() +``` + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Parallel execution graph](assets/graph_api_image_3.png) + +With the reducer, you can see that the values added in each node are accumulated. + +```python +graph.invoke({"aggregate": []}, {"configurable": {"thread_id": "foo"}}) +``` +``` +Adding "A" to [] +Adding "B" to ['A'] +Adding "C" to ['A'] +Adding "D" to ['A', 'B', 'C'] +``` + +!!! note + In the above example, nodes `"b"` and `"c"` are executed concurrently in the same [superstep](../concepts/low_level.md#graphs). Because they are in the same step, node `"d"` executes after both `"b"` and `"c"` are finished. + + Importantly, updates from a parallel superstep may not be ordered consistently. If you need a consistent, predetermined ordering of updates from a parallel superstep, you should write the outputs to a separate field in the state together with a value with which to order them. + +??? note "Exception handling?" + LangGraph executes nodes within [supersteps](../concepts/low_level.md#graphs), meaning that while parallel branches are executed in parallel, the entire superstep is **transactional**. If any of these branches raises an exception, **none** of the updates are applied to the state (the entire superstep errors). + + Importantly, when using a [checkpointer](../concepts/persistence.md), results from successful nodes within a superstep are saved, and don't repeat when resumed. + + If you have error-prone (perhaps want to handle flakey API calls), LangGraph provides two ways to address this: + + 1. You can write regular python code within your node to catch and handle exceptions. + 2. You can set a **[retry_policy](../reference/types.md#langgraph.types.RetryPolicy)** to direct the graph to retry nodes that raise certain types of exceptions. Only failing branches are retried, so you needn't worry about performing redundant work. + + Together, these let you perform parallel execution and fully control exception handling. + +### Defer node execution + +Deferring node execution is useful when you want to delay the execution of a node until all other pending tasks are completed. This is particularly relevant when branches have different lengths, which is common in workflows like map-reduce flows. + +The above example showed how to fan-out and fan-in when each path was only one step. But what if one branch had more than one step? Let's add a node `"b_2"` in the `"b"` branch: + +```python +import operator +from typing import Annotated, Any +from typing_extensions import TypedDict +from langgraph.graph import StateGraph, START, END + +class State(TypedDict): + # The operator.add reducer fn makes this append-only + aggregate: Annotated[list, operator.add] + +def a(state: State): + print(f'Adding "A" to {state["aggregate"]}') + return {"aggregate": ["A"]} + +def b(state: State): + print(f'Adding "B" to {state["aggregate"]}') + return {"aggregate": ["B"]} + +def b_2(state: State): + print(f'Adding "B_2" to {state["aggregate"]}') + return {"aggregate": ["B_2"]} + +def c(state: State): + print(f'Adding "C" to {state["aggregate"]}') + return {"aggregate": ["C"]} + +def d(state: State): + print(f'Adding "D" to {state["aggregate"]}') + return {"aggregate": ["D"]} + +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) +builder.add_node(b_2) +builder.add_node(c) +# highlight-next-line +builder.add_node(d, defer=True) +builder.add_edge(START, "a") +builder.add_edge("a", "b") +builder.add_edge("a", "c") +builder.add_edge("b", "b_2") +builder.add_edge("b_2", "d") +builder.add_edge("c", "d") +builder.add_edge("d", END) +graph = builder.compile() +``` + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Deferred execution graph](assets/graph_api_image_4.png) + +```python +graph.invoke({"aggregate": []}) +``` +``` +Adding "A" to [] +Adding "B" to ['A'] +Adding "C" to ['A'] +Adding "B_2" to ['A', 'B', 'C'] +Adding "D" to ['A', 'B', 'C', 'B_2'] +``` + +In the above example, nodes `"b"` and `"c"` are executed concurrently in the same superstep. We set `defer=True` on node `d` so it will not execute until all pending tasks are finished. In this case, this means that `"d"` waits to execute until the entire `"b"` branch is finished. + +### Conditional branching + +If your fan-out should vary at runtime based on the state, you can use [add_conditional_edges](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.StateGraph.add_conditional_edges) to select one or more paths using the graph state. See example below, where node `a` generates a state update that determines the following node. + +```python +import operator +from typing import Annotated, Literal, Sequence +from typing_extensions import TypedDict +from langgraph.graph import StateGraph, START, END + +class State(TypedDict): + aggregate: Annotated[list, operator.add] + # Add a key to the state. We will set this key to determine + # how we branch. + which: str + +def a(state: State): + print(f'Adding "A" to {state["aggregate"]}') + # highlight-next-line + return {"aggregate": ["A"], "which": "c"} + +def b(state: State): + print(f'Adding "B" to {state["aggregate"]}') + return {"aggregate": ["B"]} + +def c(state: State): + print(f'Adding "C" to {state["aggregate"]}') + return {"aggregate": ["C"]} + +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) +builder.add_node(c) +builder.add_edge(START, "a") +builder.add_edge("b", END) +builder.add_edge("c", END) + +def conditional_edge(state: State) -> Literal["b", "c"]: + # Fill in arbitrary logic here that uses the state + # to determine the next node + return state["which"] + +# highlight-next-line +builder.add_conditional_edges("a", conditional_edge) + +graph = builder.compile() +``` + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Conditional branching graph](assets/graph_api_image_5.png) + +```python +result = graph.invoke({"aggregate": []}) +print(result) +``` +``` +Adding "A" to [] +Adding "C" to ['A'] +{'aggregate': ['A', 'C'], 'which': 'c'} +``` + +!!! tip + Your conditional edges can route to multiple destination nodes. For example: + + ```python + def route_bc_or_cd(state: State) -> Sequence[str]: + if state["which"] == "cd": + return ["c", "d"] + return ["b", "c"] + ``` + +## Map-Reduce and the Send API + +LangGraph supports map-reduce and other advanced branching patterns using the Send API. Here is an example of how to use it: + +```python +from langgraph.graph import StateGraph, START, END, Send +from typing_extensions import TypedDict + +class OverallState(TypedDict): + topic: str + subjects: list[str] + jokes: list[str] + best_selected_joke: str + +def generate_topics(state: OverallState): + return {"subjects": ["lions", "elephants", "penguins"]} + +def generate_joke(state: OverallState): + joke_map = { + "lions": "Why don't lions like fast food? Because they can't catch it!", + "elephants": "Why don't elephants use computers? They're afraid of the mouse!", + "penguins": "Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice." + } + return {"jokes": [joke_map[state["subject"]]]} + +def continue_to_jokes(state: OverallState): + return [Send("generate_joke", {"subject": s}) for s in state["subjects"]] + +def best_joke(state: OverallState): + return {"best_selected_joke": "penguins"} + +builder = StateGraph(OverallState) +builder.add_node("generate_topics", generate_topics) +builder.add_node("generate_joke", generate_joke) +builder.add_node("best_joke", best_joke) +builder.add_edge(START, "generate_topics") +builder.add_conditional_edges("generate_topics", continue_to_jokes, ["generate_joke"]) +builder.add_edge("generate_joke", "best_joke") +builder.add_edge("best_joke", END) +builder.add_edge("generate_topics", END) +graph = builder.compile() +``` + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Map-reduce graph with fanout](assets/graph_api_image_2.png) + +```python +# Call the graph: here we call it to generate a list of jokes +for step in graph.stream({"topic": "animals"}): + print(step) +``` +``` +{'generate_topics': {'subjects': ['lions', 'elephants', 'penguins']}} +{'generate_joke': {'jokes': ["Why don't lions like fast food? Because they can't catch it!"]}} +{'generate_joke': {'jokes': ["Why don't elephants use computers? They're afraid of the mouse!"]}} +{'generate_joke': {'jokes': ['Why don't penguins like talking to strangers at parties? Because they find it hard to break the ice.']}} +{'best_joke': {'best_selected_joke': 'penguins'}} +``` + +## Create and control loops + +When creating a graph with a loop, we require a mechanism for terminating execution. This is most commonly done by adding a [conditional edge](../concepts/low_level.md#conditional-edges) that routes to the [END](../concepts/low_level.md#end-node) node once we reach some termination condition. + +You can also set the graph recursion limit when invoking or streaming the graph. The recursion limit sets the number of [supersteps](../concepts/low_level.md#graphs) that the graph is allowed to execute before it raises an error. Read more about the concept of recursion limits [here](../concepts/low_level.md#recursion-limit). + +Let's consider a simple graph with a loop to better understand how these mechanisms work. + +!!! tip + To return the last value of your state instead of receiving a recursion limit error, see the [next section](#impose-a-recursion-limit). + +When creating a loop, you can include a conditional edge that specifies a termination condition: + +```python +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) + +def route(state: State) -> Literal["b", END]: + if termination_condition(state): + return END + else: + return "b" + +builder.add_edge(START, "a") +builder.add_conditional_edges("a", route) +builder.add_edge("b", "a") +graph = builder.compile() +``` + +To control the recursion limit, specify `"recursion_limit"` in the config. This will raise a `GraphRecursionError`, which you can catch and handle: + +```python +from langgraph.errors import GraphRecursionError + +try: + graph.invoke(inputs, {"recursion_limit": 3}) +except GraphRecursionError: + print("Recursion Error") +``` + +Let's define a graph with a simple loop. Note that we use a conditional edge to implement a termination condition. + +```python +import operator +from typing import Annotated, Literal +from typing_extensions import TypedDict +from langgraph.graph import StateGraph, START, END + +class State(TypedDict): + # The operator.add reducer fn makes this append-only + aggregate: Annotated[list, operator.add] + +def a(state: State): + print(f'Node A sees {state["aggregate"]}') + return {"aggregate": ["A"]} + +def b(state: State): + print(f'Node B sees {state["aggregate"]}') + return {"aggregate": ["B"]} + +# Define nodes +builder = StateGraph(State) +builder.add_node(a) +builder.add_node(b) + +# Define edges +def route(state: State) -> Literal["b", END]: + if len(state["aggregate"]) < 7: + return "b" + else: + return END + +builder.add_edge(START, "a") +builder.add_conditional_edges("a", route) +builder.add_edge("b", "a") +graph = builder.compile() +``` + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Simple loop graph](assets/graph_api_image_3.png) + +This architecture is similar to a [ReAct agent](../agents/overview.md) in which node `"a"` is a tool-calling model, and node `"b"` represents the tools. + +In our `route` conditional edge, we specify that we should end after the `"aggregate"` list in the state passes a threshold length. + +Invoking the graph, we see that we alternate between nodes `"a"` and `"b"` before terminating once we reach the termination condition. + +```python +graph.invoke({"aggregate": []}) +``` +``` +Node A sees [] +Node B sees ['A'] +Node A sees ['A', 'B'] +Node B sees ['A', 'B', 'A'] +Node A sees ['A', 'B', 'A', 'B'] +Node B sees ['A', 'B', 'A', 'B', 'A'] +Node A sees ['A', 'B', 'A', 'B', 'A', 'B'] +``` + +### Impose a recursion limit + +In some applications, we may not have a guarantee that we will reach a given termination condition. In these cases, we can set the graph's [recursion limit](../concepts/low_level.md#recursion-limit). This will raise a `GraphRecursionError` after a given number of [supersteps](../concepts/low_level.md#graphs). We can then catch and handle this exception: + +```python +from langgraph.errors import GraphRecursionError + +try: + graph.invoke({"aggregate": []}, {"recursion_limit": 4}) +except GraphRecursionError: + print("Recursion Error") +``` +``` +Node A sees [] +Node B sees ['A'] +Node C sees ['A', 'B'] +Node D sees ['A', 'B'] +Node A sees ['A', 'B', 'C', 'D'] +Recursion Error +``` + +??? example "Extended example: return state on hitting recursion limit" + + Instead of raising `GraphRecursionError`, we can introduce a new key to the state that keeps track of the number of steps remaining until reaching the recursion limit. We can then use this key to determine if we should end the run. + + LangGraph implements a special `RemainingSteps` annotation. Under the hood, it creates a `ManagedValue` channel -- a state channel that will exist for the duration of our graph run and no longer. + + ```python + import operator + from typing import Annotated, Literal + from typing_extensions import TypedDict + from langgraph.graph import StateGraph, START, END + from langgraph.managed.is_last_step import RemainingSteps + + class State(TypedDict): + aggregate: Annotated[list, operator.add] + remaining_steps: RemainingSteps + + def a(state: State): + print(f'Node A sees {state["aggregate"]}') + return {"aggregate": ["A"]} + + def b(state: State): + print(f'Node B sees {state["aggregate"]}') + return {"aggregate": ["B"]} + + # Define nodes + builder = StateGraph(State) + builder.add_node(a) + builder.add_node(b) + + # Define edges + def route(state: State) -> Literal["b", END]: + if state["remaining_steps"] <= 2: + return END + else: + return "b" + + builder.add_edge(START, "a") + builder.add_conditional_edges("a", route) + builder.add_edge("b", "a") + graph = builder.compile() + + # Test it out + result = graph.invoke({"aggregate": []}, {"recursion_limit": 4}) + print(result) + ``` + ``` + Node A sees [] + Node B sees ['A'] + Node A sees ['A', 'B'] + {'aggregate': ['A', 'B', 'A']} + ``` + +??? example "Extended example: loops with branches" + + To better understand how the recursion limit works, let's consider a more complex example. Below we implement a loop, but one step fans out into two nodes: + + ```python + import operator + from typing import Annotated, Literal + from typing_extensions import TypedDict + from langgraph.graph import StateGraph, START, END + + class State(TypedDict): + aggregate: Annotated[list, operator.add] + + def a(state: State): + print(f'Node A sees {state["aggregate"]}') + return {"aggregate": ["A"]} + + def b(state: State): + print(f'Node B sees {state["aggregate"]}') + return {"aggregate": ["B"]} + + def c(state: State): + print(f'Node C sees {state["aggregate"]}') + return {"aggregate": ["C"]} + + def d(state: State): + print(f'Node D sees {state["aggregate"]}') + return {"aggregate": ["D"]} + + # Define nodes + builder = StateGraph(State) + builder.add_node(a) + builder.add_node(b) + builder.add_node(c) + builder.add_node(d) + + # Define edges + def route(state: State) -> Literal["b", END]: + if len(state["aggregate"]) < 7: + return "b" + else: + return END + + builder.add_edge(START, "a") + builder.add_conditional_edges("a", route) + builder.add_edge("b", "c") + builder.add_edge("b", "d") + builder.add_edge(["c", "d"], "a") + graph = builder.compile() + ``` + + ```python + from IPython.display import Image, display + + display(Image(graph.get_graph().draw_mermaid_png())) + ``` + + ![Complex loop graph with branches](assets/graph_api_image_4.png) + + This graph looks complex, but can be conceptualized as loop of [supersteps](../concepts/low_level.md#graphs): + + 1. Node A + 2. Node B + 3. Nodes C and D + 4. Node A + 5. ... + + We have a loop of four supersteps, where nodes C and D are executed concurrently. + + Invoking the graph as before, we see that we complete two full "laps" before hitting the termination condition: + + ```python + result = graph.invoke({"aggregate": []}) + ``` + ``` + Node A sees [] + Node B sees ['A'] + Node D sees ['A', 'B'] + Node C sees ['A', 'B'] + Node A sees ['A', 'B', 'C', 'D'] + Node B sees ['A', 'B', 'C', 'D', 'A'] + Node D sees ['A', 'B', 'C', 'D', 'A', 'B'] + Node C sees ['A', 'B', 'C', 'D', 'A', 'B'] + Node A sees ['A', 'B', 'C', 'D', 'A', 'B', 'C', 'D'] + ``` + + However, if we set the recursion limit to four, we only complete one lap because each lap is four supersteps: + + ```python + from langgraph.errors import GraphRecursionError + + try: + result = graph.invoke({"aggregate": []}, {"recursion_limit": 4}) + except GraphRecursionError: + print("Recursion Error") + ``` + ``` + Node A sees [] + Node B sees ['A'] + Node C sees ['A', 'B'] + Node D sees ['A', 'B'] + Node A sees ['A', 'B', 'C', 'D'] + Recursion Error + ``` + +## Async + +Using the [async](https://docs.python.org/3/library/asyncio.html) programming paradigm can produce significant performance improvements when running [IO-bound](https://en.wikipedia.org/wiki/I/O_bound) code concurrently (e.g., making concurrent API requests to a chat model provider). + +To convert a `sync` implementation of the graph to an `async` implementation, you will need to: + +1. Update `nodes` use `async def` instead of `def`. +2. Update the code inside to use `await` appropriately. +3. Invoke the graph with `.ainvoke` or `.astream` as desired. + +Because many LangChain objects implement the [Runnable Protocol](https://python.langchain.com/docs/expression_language/interface/) which has `async` variants of all the `sync` methods it's typically fairly quick to upgrade a `sync` graph to an `async` graph. + +See example below. To demonstrate async invocations of underlying LLMs, we will include a chat model: + +{!snippets/chat_model_tabs.md!} + +```python +from langchain.chat_models import init_chat_model +from langgraph.graph import MessagesState, StateGraph + +# highlight-next-line +async def node(state: MessagesState): # (1)! + # highlight-next-line + new_message = await llm.ainvoke(state["messages"]) # (2)! + return {"messages": [new_message]} + +builder = StateGraph(MessagesState).add_node(node).set_entry_point("node") +graph = builder.compile() + +input_message = {"role": "user", "content": "Hello"} +# highlight-next-line +result = await graph.ainvoke({"messages": [input_message]}) # (3)! +``` + +1. Declare nodes to be async functions. +2. Use async invocations when available within the node. +3. Use async invocations on the graph object itself. + +!!! tip "Async streaming" + See the [streaming guide](./streaming.md) for examples of streaming with async. + +## Combine control flow and state updates with `Command` + +It can be useful to combine control flow (edges) and state updates (nodes). For example, you might want to BOTH perform state updates AND decide which node to go to next in the SAME node. LangGraph provides a way to do so by returning a [Command](../reference/types.md#langgraph.types.Command) object from node functions: + +```python +def my_node(state: State) -> Command[Literal["my_other_node"]]: + return Command( + # state update + update={"foo": "bar"}, + # control flow + goto="my_other_node" + ) +``` + +We show an end-to-end example below. Let's create a simple graph with 3 nodes: A, B and C. We will first execute node A, and then decide whether to go to Node B or Node C next based on the output of node A. + +```python +import random +from typing_extensions import TypedDict, Literal +from langgraph.graph import StateGraph, START +from langgraph.types import Command + +# Define graph state +class State(TypedDict): + foo: str + +# Define the nodes + +def node_a(state: State) -> Command[Literal["node_b", "node_c"]]: + print("Called A") + value = random.choice(["a", "b"]) + # this is a replacement for a conditional edge function + if value == "a": + goto = "node_b" + else: + goto = "node_c" + + # note how Command allows you to BOTH update the graph state AND route to the next node + return Command( + # this is the state update + update={"foo": value}, + # this is a replacement for an edge + goto=goto, + ) + +def node_b(state: State): + print("Called B") + return {"foo": state["foo"] + "b"} + +def node_c(state: State): + print("Called C") + return {"foo": state["foo"] + "c"} +``` + +We can now create the `StateGraph` with the above nodes. Notice that the graph doesn't have [conditional edges](../concepts/low_level.md#conditional-edges) for routing! This is because control flow is defined with `Command` inside `node_a`. + +```python +builder = StateGraph(State) +builder.add_edge(START, "node_a") +builder.add_node(node_a) +builder.add_node(node_b) +builder.add_node(node_c) +# NOTE: there are no edges between nodes A, B and C! + +graph = builder.compile() +``` + +!!! important + You might have noticed that we used `Command` as a return type annotation, e.g. `Command[Literal["node_b", "node_c"]]`. This is necessary for the graph rendering and tells LangGraph that `node_a` can navigate to `node_b` and `node_c`. + +```python +from IPython.display import display, Image + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Command-based graph navigation](assets/graph_api_image_6.png) + +If we run the graph multiple times, we'd see it take different paths (A -> B or A -> C) based on the random choice in node A. + +```python +graph.invoke({"foo": ""}) +``` +``` +Called A +Called C +``` + +### Navigate to a node in a parent graph + +If you are using [subgraphs](../concepts/subgraphs.md), you might want to navigate from a node within a subgraph to a different subgraph (i.e. a different node in the parent graph). To do so, you can specify `graph=Command.PARENT` in `Command`: + +```python +def my_node(state: State) -> Command[Literal["my_other_node"]]: + return Command( + update={"foo": "bar"}, + goto="other_subgraph", # where `other_subgraph` is a node in the parent graph + graph=Command.PARENT + ) +``` + +Let's demonstrate this using the above example. We'll do so by changing `node_a` in the above example into a single-node graph that we'll add as a subgraph to our parent graph. + +!!! important "State updates with `Command.PARENT`" + When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](../concepts/low_level.md#schema), you **must** define a [reducer](../concepts/low_level.md#reducers) for the key you're updating in the parent graph state. See the example below. + +```python +import operator +from typing_extensions import Annotated + +class State(TypedDict): + # NOTE: we define a reducer here + # highlight-next-line + foo: Annotated[str, operator.add] + +def node_a(state: State): + print("Called A") + value = random.choice(["a", "b"]) + # this is a replacement for a conditional edge function + if value == "a": + goto = "node_b" + else: + goto = "node_c" + + # note how Command allows you to BOTH update the graph state AND route to the next node + return Command( + update={"foo": value}, + goto=goto, + # this tells LangGraph to navigate to node_b or node_c in the parent graph + # NOTE: this will navigate to the closest parent graph relative to the subgraph + # highlight-next-line + graph=Command.PARENT, + ) + +subgraph = StateGraph(State).add_node(node_a).add_edge(START, "node_a").compile() + +def node_b(state: State): + print("Called B") + # NOTE: since we've defined a reducer, we don't need to manually append + # new characters to existing 'foo' value. instead, reducer will append these + # automatically (via operator.add) + # highlight-next-line + return {"foo": "b"} + +def node_c(state: State): + print("Called C") + # highlight-next-line + return {"foo": "c"} + +builder = StateGraph(State) +builder.add_edge(START, "subgraph") +builder.add_node("subgraph", subgraph) +builder.add_node(node_b) +builder.add_node(node_c) + +graph = builder.compile() +``` + +```python +graph.invoke({"foo": ""}) +``` +``` +Called A +Called C +``` + +### Use inside tools + +A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation. To update the graph state from the tool, you can return `Command(update={"my_custom_key": "foo", "messages": [...]})` from the tool: + +```python +@tool +def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: RunnableConfig): + """Use this to look up user information to better assist them with their questions.""" + user_info = get_user_info(config.get("configurable", {}).get("user_id")) + return Command( + update={ + # update the state keys + "user_info": user_info, + # update the message history + "messages": [ToolMessage("Successfully looked up user information", tool_call_id=tool_call_id)] + } + ) +``` + +!!! important + You MUST include `messages` (or any state key used for the message history) in `Command.update` when returning `Command` from a tool and the list of messages in `messages` MUST contain a `ToolMessage`. This is necessary for the resulting message history to be valid (LLM providers require AI messages with tool calls to be followed by the tool result messages). + +If you are using tools that update state via `Command`, we recommend using prebuilt [`ToolNode`](../reference/agents.md#langgraph.prebuilt.tool_node.ToolNode) which automatically handles tools returning `Command` objects and propagates them to the graph state. If you're writing a custom node that calls tools, you would need to manually propagate `Command` objects returned by the tools as the update from the node. + +## Visualize your graph + +Here we demonstrate how to visualize the graphs you create. + +You can visualize any arbitrary [Graph](https://langchain-ai.github.io/langgraph/reference/graphs/), including [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.state.StateGraph). Let's have some fun by drawing fractals :). + +```python +import random +from typing import Annotated, Literal +from typing_extensions import TypedDict +from langgraph.graph import StateGraph, START, END +from langgraph.graph.message import add_messages + +class State(TypedDict): + messages: Annotated[list, add_messages] + +class MyNode: + def __init__(self, name: str): + self.name = name + def __call__(self, state: State): + return {"messages": [("assistant", f"Called node {self.name}")]} + +def route(state) -> Literal["entry_node", "__end__"]: + if len(state["messages"]) > 10: + return "__end__" + return "entry_node" + +def add_fractal_nodes(builder, current_node, level, max_level): + if level > max_level: + return + # Number of nodes to create at this level + num_nodes = random.randint(1, 3) # Adjust randomness as needed + for i in range(num_nodes): + nm = ["A", "B", "C"][i] + node_name = f"node_{current_node}_{nm}" + builder.add_node(node_name, MyNode(node_name)) + builder.add_edge(current_node, node_name) + # Recursively add more nodes + r = random.random() + if r > 0.2 and level + 1 < max_level: + add_fractal_nodes(builder, node_name, level + 1, max_level) + elif r > 0.05: + builder.add_conditional_edges(node_name, route, node_name) + else: + # End + builder.add_edge(node_name, "__end__") + +def build_fractal_graph(max_level: int): + builder = StateGraph(State) + entry_point = "entry_node" + builder.add_node(entry_point, MyNode(entry_point)) + builder.add_edge(START, entry_point) + add_fractal_nodes(builder, entry_point, 1, max_level) + # Optional: set a finish point if required + builder.add_edge(entry_point, END) # or any specific node + return builder.compile() + +app = build_fractal_graph(3) +``` + +### Mermaid + +We can also convert a graph class into Mermaid syntax. + +```python +print(app.get_graph().draw_mermaid()) +``` +``` +%%{init: {'flowchart': {'curve': 'linear'}}}%% +graph TD; + __start__([

__start__

]):::first + entry_node(entry_node) + node_entry_node_A(node_entry_node_A) + node_entry_node_B(node_entry_node_B) + node_node_entry_node_B_A(node_node_entry_node_B_A) + node_node_entry_node_B_B(node_node_entry_node_B_B) + node_node_entry_node_B_C(node_node_entry_node_B_C) + __end__([

__end__

]):::last + __start__ --> entry_node; + entry_node --> __end__; + entry_node --> node_entry_node_A; + entry_node --> node_entry_node_B; + node_entry_node_B --> node_node_entry_node_B_A; + node_entry_node_B --> node_node_entry_node_B_B; + node_entry_node_B --> node_node_entry_node_B_C; + node_entry_node_A -.-> entry_node; + node_entry_node_A -.-> __end__; + node_node_entry_node_B_A -.-> entry_node; + node_node_entry_node_B_A -.-> __end__; + node_node_entry_node_B_B -.-> entry_node; + node_node_entry_node_B_B -.-> __end__; + node_node_entry_node_B_C -.-> entry_node; + node_node_entry_node_B_C -.-> __end__; + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc +``` + +### PNG + +If preferred, we could render the Graph into a `.png`. Here we could use three options: + +- Using Mermaid.ink API (does not require additional packages) +- Using Mermaid + Pyppeteer (requires `pip install pyppeteer`) +- Using graphviz (which requires `pip install graphviz`) + +**Using Mermaid.Ink** + +By default, `draw_mermaid_png()` uses Mermaid.Ink's API to generate the diagram. + +```python +from IPython.display import Image, display +from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles + +display(Image(app.get_graph().draw_mermaid_png())) +``` + +![Fractal graph visualization](assets/graph_api_image_5.png) + +**Using Mermaid + Pyppeteer** + +```python +import nest_asyncio + +nest_asyncio.apply() # Required for Jupyter Notebook to run async functions + +display( + Image( + app.get_graph().draw_mermaid_png( + curve_style=CurveStyle.LINEAR, + node_colors=NodeStyles(first="#ffdfba", last="#baffc9", default="#fad7de"), + wrap_label_n_words=9, + output_file_path=None, + draw_method=MermaidDrawMethod.PYPPETEER, + background_color="white", + padding=10, + ) + ) +) +``` + +**Using Graphviz** + +```python +try: + display(Image(app.get_graph().draw_png())) +except ImportError: + print( + "You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt" + ) +``` \ No newline at end of file diff --git a/docs/docs/how-tos/multi_agent.md b/docs/docs/how-tos/multi_agent.md new file mode 100644 index 000000000..5aead8b95 --- /dev/null +++ b/docs/docs/how-tos/multi_agent.md @@ -0,0 +1,580 @@ +# Build multi-agent systems + +A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md). + +In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent. + +This guide covers the following: + +* implementing [handoffs](#handoffs) between agents +* using handoffs and the prebuilt [agent](../agents/agents.md) to [build a custom multi-agent system](#build-a-multi-agent-system) + +To get started with building multi-agent systems, check out LangGraph [prebuilt implementations](#prebuilt-implementations) of two of the most popular multi-agent architectures — [supervisor](../agents/multi-agent.md#supervisor) and [swarm](../agents/multi-agent.md#swarm). + +## Handoffs + +To set up communication between the agents in a multi-agent system you can use [**handoffs**](../concepts/multi_agent.md#handoffs) — a pattern where one agent *hands off* control to another. Handoffs allow you to specify: + +- **destination**: target agent to navigate to (e.g., name of the LangGraph node to go to) +- **payload**: information to pass to that agent (e.g., state update) + +### Create handoffs + +To implement handoffs, you can return `Command` objects from your agent nodes or tools: + +```python +from typing import Annotated +from langchain_core.tools import tool, InjectedToolCallId +from langgraph.prebuilt import create_react_agent, InjectedState +from langgraph.graph import StateGraph, START, MessagesState +from langgraph.types import Command + +def create_handoff_tool(*, agent_name: str, description: str | None = None): + name = f"transfer_to_{agent_name}" + description = description or f"Transfer to {agent_name}" + + @tool(name, description=description) + def handoff_tool( + # highlight-next-line + state: Annotated[MessagesState, InjectedState], # (1)! + # highlight-next-line + tool_call_id: Annotated[str, InjectedToolCallId], + ) -> Command: + tool_message = { + "role": "tool", + "content": f"Successfully transferred to {agent_name}", + "name": name, + "tool_call_id": tool_call_id, + } + return Command( # (2)! + # highlight-next-line + goto=agent_name, # (3)! + # highlight-next-line + update={"messages": state["messages"] + [tool_message]}, # (4)! + # highlight-next-line + graph=Command.PARENT, # (5)! + ) + return handoff_tool +``` + +1. Access the [state](../concepts/low_level.md#state) of the agent that is calling the handoff tool using the [InjectedState][langgraph.prebuilt.InjectedState] annotation. +2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs. +3. Name of the agent or node to hand off to. +4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state. +5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph. + +!!! tip + + If you want to use tools that return `Command`, you can either use prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] / [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] components, or implement your own tool-executing node that collects `Command` objects returned by the tools and returns a list of them, e.g.: + + ```python + def call_tools(state): + ... + commands = [tools_by_name[tool_call["name"]].invoke(tool_call) for tool_call in tool_calls] + return commands + ``` + +!!! Important + + This handoff implementation assumes that: + + - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs) + - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function: + + ```python + def call_hotel_assistant(state): + # return agent's final response, + # excluding inner monologue + response = hotel_assistant.invoke(state) + # highlight-next-line + return {"messages": response["messages"][-1]} + ``` + +### Control agent inputs + +You can use the [`Send()`][langgraph.types.Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent: + +```python + +from typing import Annotated +from langchain_core.tools import tool, InjectedToolCallId +from langgraph.prebuilt import InjectedState +from langgraph.graph import StateGraph, START, MessagesState +# highlight-next-line +from langgraph.types import Command, Send + +def create_task_description_handoff_tool( + *, agent_name: str, description: str | None = None +): + name = f"transfer_to_{agent_name}" + description = description or f"Ask {agent_name} for help." + + @tool(name, description=description) + def handoff_tool( + # this is populated by the calling agent + task_description: Annotated[ + str, + "Description of what the next agent should do, including all of the relevant context.", + ], + # these parameters are ignored by the LLM + state: Annotated[MessagesState, InjectedState], + ) -> Command: + task_description_message = {"role": "user", "content": task_description} + agent_input = {**state, "messages": [task_description_message]} + return Command( + # highlight-next-line + goto=[Send(agent_name, agent_input)], + graph=Command.PARENT, + ) + + return handoff_tool +``` + +See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.ipynb#4-create-delegation-tasks) example for a full example of using [`Send()`][langgraph.types.Send] in handoffs. + +## Build a multi-agent system + +You can use handoffs in any agents built with LangGraph. We recommend using the prebuilt [agent](../agents/overview.md) or [`ToolNode`](./tool-calling.md#toolnode), as they natively support handoffs tools returning `Command`. Below is an example of how you can implement a multi-agent system for booking travel using handoffs: + +```python +from langgraph.prebuilt import create_react_agent +from langgraph.graph import StateGraph, START, MessagesState + +def create_handoff_tool(*, agent_name: str, description: str | None = None): + # same implementation as above + ... + return Command(...) + +# Handoffs +transfer_to_hotel_assistant = create_handoff_tool(agent_name="hotel_assistant") +transfer_to_flight_assistant = create_handoff_tool(agent_name="flight_assistant") + +# Define agents +flight_assistant = create_react_agent( + model="anthropic:claude-3-5-sonnet-latest", + # highlight-next-line + tools=[..., transfer_to_hotel_assistant], + # highlight-next-line + name="flight_assistant" +) +hotel_assistant = create_react_agent( + model="anthropic:claude-3-5-sonnet-latest", + # highlight-next-line + tools=[..., transfer_to_flight_assistant], + # highlight-next-line + name="hotel_assistant" +) + +# Define multi-agent graph +multi_agent_graph = ( + StateGraph(MessagesState) + # highlight-next-line + .add_node(flight_assistant) + # highlight-next-line + .add_node(hotel_assistant) + .add_edge(START, "flight_assistant") + .compile() +) +``` + +??? example "Full example: Multi-agent system for booking travel" + + ```python + from typing import Annotated + from langchain_core.messages import convert_to_messages + from langchain_core.tools import tool, InjectedToolCallId + from langgraph.prebuilt import create_react_agent, InjectedState + from langgraph.graph import StateGraph, START, MessagesState + from langgraph.types import Command + + # We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely + + def pretty_print_message(message, indent=False): + pretty_message = message.pretty_repr(html=True) + if not indent: + print(pretty_message) + return + + indented = "\n".join("\t" + c for c in pretty_message.split("\n")) + print(indented) + + + def pretty_print_messages(update, last_message=False): + is_subgraph = False + if isinstance(update, tuple): + ns, update = update + # skip parent graph updates in the printouts + if len(ns) == 0: + return + + graph_id = ns[-1].split(":")[0] + print(f"Update from subgraph {graph_id}:") + print("\n") + is_subgraph = True + + for node_name, node_update in update.items(): + update_label = f"Update from node {node_name}:" + if is_subgraph: + update_label = "\t" + update_label + + print(update_label) + print("\n") + + messages = convert_to_messages(node_update["messages"]) + if last_message: + messages = messages[-1:] + + for m in messages: + pretty_print_message(m, indent=is_subgraph) + print("\n") + + + def create_handoff_tool(*, agent_name: str, description: str | None = None): + name = f"transfer_to_{agent_name}" + description = description or f"Transfer to {agent_name}" + + @tool(name, description=description) + def handoff_tool( + # highlight-next-line + state: Annotated[MessagesState, InjectedState], # (1)! + # highlight-next-line + tool_call_id: Annotated[str, InjectedToolCallId], + ) -> Command: + tool_message = { + "role": "tool", + "content": f"Successfully transferred to {agent_name}", + "name": name, + "tool_call_id": tool_call_id, + } + return Command( # (2)! + # highlight-next-line + goto=agent_name, # (3)! + # highlight-next-line + update={"messages": state["messages"] + [tool_message]}, # (4)! + # highlight-next-line + graph=Command.PARENT, # (5)! + ) + return handoff_tool + + # Handoffs + transfer_to_hotel_assistant = create_handoff_tool( + agent_name="hotel_assistant", + description="Transfer user to the hotel-booking assistant.", + ) + transfer_to_flight_assistant = create_handoff_tool( + agent_name="flight_assistant", + description="Transfer user to the flight-booking assistant.", + ) + + # Simple agent tools + def book_hotel(hotel_name: str): + """Book a hotel""" + return f"Successfully booked a stay at {hotel_name}." + + def book_flight(from_airport: str, to_airport: str): + """Book a flight""" + return f"Successfully booked a flight from {from_airport} to {to_airport}." + + # Define agents + flight_assistant = create_react_agent( + model="anthropic:claude-3-5-sonnet-latest", + # highlight-next-line + tools=[book_flight, transfer_to_hotel_assistant], + prompt="You are a flight booking assistant", + # highlight-next-line + name="flight_assistant" + ) + hotel_assistant = create_react_agent( + model="anthropic:claude-3-5-sonnet-latest", + # highlight-next-line + tools=[book_hotel, transfer_to_flight_assistant], + prompt="You are a hotel booking assistant", + # highlight-next-line + name="hotel_assistant" + ) + + # Define multi-agent graph + multi_agent_graph = ( + StateGraph(MessagesState) + .add_node(flight_assistant) + .add_node(hotel_assistant) + .add_edge(START, "flight_assistant") + .compile() + ) + + # Run the multi-agent graph + for chunk in multi_agent_graph.stream( + { + "messages": [ + { + "role": "user", + "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel" + } + ] + }, + # highlight-next-line + subgraphs=True + ): + pretty_print_messages(chunk) + ``` + + 1. Access agent's state + 2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs. + 3. Name of the agent or node to hand off to. + 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state. + 5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph. + +## Multi-turn conversation + +Users might want to engage in a *multi-turn conversation* with one or more agents. To build a system that can handle this, you can create a node that uses an [`interrupt`][langgraph.types.interrupt] to collect user input and routes back to the **active** agent. + +The agents can then be implemented as nodes in a graph that executes agent steps and determines the next action: + +1. **Wait for user input** to continue the conversation, or +2. **Route to another agent** (or back to itself, such as in a loop) via a [handoff](#handoffs) + +```python +def human(state) -> Command[Literal["agent", "another_agent"]]: + """A node for collecting user input.""" + user_input = interrupt(value="Ready for user input.") + + # Determine the active agent. + active_agent = ... + + ... + return Command( + update={ + "messages": [{ + "role": "human", + "content": user_input, + }] + }, + goto=active_agent + ) + +def agent(state) -> Command[Literal["agent", "another_agent", "human"]]: + # The condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc. + goto = get_next_agent(...) # 'agent' / 'another_agent' + if goto: + return Command(goto=goto, update={"my_state_key": "my_state_value"}) + else: + return Command(goto="human") # Go to human node +``` + +??? example "Full example: multi-agent system for travel recommendations" + + In this example, we will build a team of travel assistant agents that can communicate with each other via handoffs. + + We will create 2 agents: + + * travel_advisor: can help with travel destination recommendations. Can ask hotel_advisor for help. + * hotel_advisor: can help with hotel recommendations. Can ask travel_advisor for help. + + ```python + from langchain_anthropic import ChatAnthropic + from langgraph.graph import MessagesState, StateGraph, START + from langgraph.prebuilt import create_react_agent, InjectedState + from langgraph.types import Command, interrupt + from langgraph.checkpoint.memory import MemorySaver + + + model = ChatAnthropic(model="claude-3-5-sonnet-latest") + + class MultiAgentState(MessagesState): + last_active_agent: str + + + # Define travel advisor tools and ReAct agent + travel_advisor_tools = [ + get_travel_recommendations, + make_handoff_tool(agent_name="hotel_advisor"), + ] + travel_advisor = create_react_agent( + model, + travel_advisor_tools, + prompt=( + "You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). " + "If you need hotel recommendations, ask 'hotel_advisor' for help. " + "You MUST include human-readable response before transferring to another agent." + ), + ) + + + def call_travel_advisor( + state: MultiAgentState, + ) -> Command[Literal["hotel_advisor", "human"]]: + # You can also add additional logic like changing the input to the agent / output from the agent, etc. + # NOTE: we're invoking the ReAct agent with the full history of messages in the state + response = travel_advisor.invoke(state) + update = {**response, "last_active_agent": "travel_advisor"} + return Command(update=update, goto="human") + + + # Define hotel advisor tools and ReAct agent + hotel_advisor_tools = [ + get_hotel_recommendations, + make_handoff_tool(agent_name="travel_advisor"), + ] + hotel_advisor = create_react_agent( + model, + hotel_advisor_tools, + prompt=( + "You are a hotel expert that can provide hotel recommendations for a given destination. " + "If you need help picking travel destinations, ask 'travel_advisor' for help." + "You MUST include human-readable response before transferring to another agent." + ), + ) + + + def call_hotel_advisor( + state: MultiAgentState, + ) -> Command[Literal["travel_advisor", "human"]]: + response = hotel_advisor.invoke(state) + update = {**response, "last_active_agent": "hotel_advisor"} + return Command(update=update, goto="human") + + + def human_node( + state: MultiAgentState, config + ) -> Command[Literal["hotel_advisor", "travel_advisor", "human"]]: + """A node for collecting user input.""" + + user_input = interrupt(value="Ready for user input.") + active_agent = state["last_active_agent"] + + return Command( + update={ + "messages": [ + { + "role": "human", + "content": user_input, + } + ] + }, + goto=active_agent, + ) + + + builder = StateGraph(MultiAgentState) + builder.add_node("travel_advisor", call_travel_advisor) + builder.add_node("hotel_advisor", call_hotel_advisor) + + # This adds a node to collect human input, which will route + # back to the active agent. + builder.add_node("human", human_node) + + # We'll always start with a general travel advisor. + builder.add_edge(START, "travel_advisor") + + + checkpointer = MemorySaver() + graph = builder.compile(checkpointer=checkpointer) + ``` + + Let's test a multi turn conversation with this application. + + ```python + import uuid + + thread_config = {"configurable": {"thread_id": str(uuid.uuid4())}} + + inputs = [ + # 1st round of conversation, + { + "messages": [ + {"role": "user", "content": "i wanna go somewhere warm in the caribbean"} + ] + }, + # Since we're using `interrupt`, we'll need to resume using the Command primitive. + # 2nd round of conversation, + Command( + resume="could you recommend a nice hotel in one of the areas and tell me which area it is." + ), + # 3rd round of conversation, + Command( + resume="i like the first one. could you recommend something to do near the hotel?" + ), + ] + + for idx, user_input in enumerate(inputs): + print() + print(f"--- Conversation Turn {idx + 1} ---") + print() + print(f"User: {user_input}") + print() + for update in graph.stream( + user_input, + config=thread_config, + stream_mode="updates", + ): + for node_id, value in update.items(): + if isinstance(value, dict) and value.get("messages", []): + last_message = value["messages"][-1] + if isinstance(last_message, dict) or last_message.type != "ai": + continue + print(f"{node_id}: {last_message.content}") + ``` + + ``` + --- Conversation Turn 1 --- + + User: {'messages': [{'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean'}]} + + travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known as "One Happy Island" and offers: + - Year-round warm weather with consistent temperatures around 82°F (28°C) + - Beautiful white sand beaches like Eagle Beach and Palm Beach + - Clear turquoise waters perfect for swimming and snorkeling + - Minimal rainfall and location outside the hurricane belt + - A blend of Caribbean and Dutch culture + - Great dining options and nightlife + - Various water sports and activities + + Would you like me to get some specific hotel recommendations in Aruba for your stay? I can transfer you to our hotel advisor who can help with accommodations. + + --- Conversation Turn 2 --- + + User: Command(resume='could you recommend a nice hotel in one of the areas and tell me which area it is.') + + hotel_advisor: Based on the recommendations, I can suggest two excellent options: + + 1. The Ritz-Carlton, Aruba - Located in Palm Beach + - This luxury resort is situated in the vibrant Palm Beach area + - Known for its exceptional service and amenities + - Perfect if you want to be close to dining, shopping, and entertainment + - Features multiple restaurants, a casino, and a world-class spa + - Located on a pristine stretch of Palm Beach + + 2. Bucuti & Tara Beach Resort - Located in Eagle Beach + - An adults-only boutique resort on Eagle Beach + - Known for being more intimate and peaceful + - Award-winning for its sustainability practices + - Perfect for a romantic getaway or peaceful vacation + - Located on one of the most beautiful beaches in the Caribbean + + Would you like more specific information about either of these properties or their locations? + + --- Conversation Turn 3 --- + + User: Command(resume='i like the first one. could you recommend something to do near the hotel?') + + travel_advisor: Near the Ritz-Carlton in Palm Beach, here are some highly recommended activities: + + 1. Visit the Palm Beach Plaza Mall - Just a short walk from the hotel, featuring shopping, dining, and entertainment + 2. Try your luck at the Stellaris Casino - It's right in the Ritz-Carlton + 3. Take a sunset sailing cruise - Many depart from the nearby pier + 4. Visit the California Lighthouse - A scenic landmark just north of Palm Beach + 5. Enjoy water sports at Palm Beach: + - Jet skiing + - Parasailing + - Snorkeling + - Stand-up paddleboarding + + Would you like more specific information about any of these activities or would you like to know about other options in the area? + ``` + +## Prebuilt implementations + +LangGraph comes with prebuilt implementations of two of the most popular multi-agent architectures: + +- [supervisor](../agents/multi-agent.md#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. You can use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent systems. +- [swarm](../agents/multi-agent.md#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent systems. \ No newline at end of file diff --git a/docs/docs/how-tos/subgraph.ipynb b/docs/docs/how-tos/subgraph.ipynb deleted file mode 100644 index a16d99216..000000000 --- a/docs/docs/how-tos/subgraph.ipynb +++ /dev/null @@ -1,559 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use subgraphs\n", - "\n", - "This guide explains the mechanics of using [subgraphs](../../concepts/subgraphs). A common application of subgraphs is to build [multi-agent](../../concepts/multi_agent) systems.\n", - "\n", - "When adding subgraphs, you need to define how the parent graph and the subgraph communicate:\n", - "\n", - "* [Shared state schemas](#shared-state-schemas) — parent and subgraph have **shared state keys** in their state [schemas](../../concepts/low_level#state)\n", - "* [Different state schemas](#different-state-schemas) — **no shared state keys** in parent and subgraph [schemas](../../concepts/low_level#state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Set up LangSmith for LangGraph development

\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", - "

\n", - "
" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Shared state schemas\n", - "\n", - "A common case is for the parent graph and subgraph to communicate over a shared state key (channel) in the [schema](../../concepts/low_level#state). For example, in [multi-agent](../../concepts/multi_agent) systems, the agents often communicate over a shared [messages](https://langchain-ai.github.io/langgraph/concepts/low_level/#why-use-messages) key.\n", - "\n", - "If your subgraph shares state keys with the parent graph, you can follow these steps to add it to your graph:\n", - "\n", - "1. Define the subgraph workflow (`subgraph_builder` in the example below) and compile it\n", - "2. Pass compiled subgraph to the `.add_node` method when defining the parent graph workflow\n", - "\n", - "```python\n", - "from typing_extensions import TypedDict\n", - "from langgraph.graph.state import StateGraph, START\n", - "\n", - "class State(TypedDict):\n", - " foo: str\n", - "\n", - "# Subgraph\n", - "\n", - "def subgraph_node_1(state: State):\n", - " return {\"foo\": \"hi! \" + state[\"foo\"]}\n", - "\n", - "subgraph_builder = StateGraph(State)\n", - "subgraph_builder.add_node(subgraph_node_1)\n", - "subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - "# highlight-next-line\n", - "subgraph = subgraph_builder.compile()\n", - "\n", - "# Parent graph\n", - "\n", - "builder = StateGraph(State)\n", - "# highlight-next-line\n", - "builder.add_node(\"node_1\", subgraph)\n", - "builder.add_edge(START, \"node_1\")\n", - "graph = builder.compile()\n", - "```\n", - "\n", - "??? example \"Full example: shared state schemas\"\n", - "\n", - " ```python\n", - " from typing_extensions import TypedDict\n", - " from langgraph.graph.state import StateGraph, START\n", - "\n", - " # Define subgraph\n", - " class SubgraphState(TypedDict):\n", - " foo: str # (1)! \n", - " bar: str # (2)!\n", - " \n", - " def subgraph_node_1(state: SubgraphState):\n", - " return {\"bar\": \"bar\"}\n", - " \n", - " def subgraph_node_2(state: SubgraphState):\n", - " # note that this node is using a state key ('bar') that is only available in the subgraph\n", - " # and is sending update on the shared state key ('foo')\n", - " return {\"foo\": state[\"foo\"] + state[\"bar\"]}\n", - " \n", - " subgraph_builder = StateGraph(SubgraphState)\n", - " subgraph_builder.add_node(subgraph_node_1)\n", - " subgraph_builder.add_node(subgraph_node_2)\n", - " subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - " subgraph_builder.add_edge(\"subgraph_node_1\", \"subgraph_node_2\")\n", - " subgraph = subgraph_builder.compile()\n", - " \n", - " # Define parent graph\n", - " class ParentState(TypedDict):\n", - " foo: str\n", - " \n", - " def node_1(state: ParentState):\n", - " return {\"foo\": \"hi! \" + state[\"foo\"]}\n", - " \n", - " builder = StateGraph(ParentState)\n", - " builder.add_node(\"node_1\", node_1)\n", - " # highlight-next-line\n", - " builder.add_node(\"node_2\", subgraph)\n", - " builder.add_edge(START, \"node_1\")\n", - " builder.add_edge(\"node_1\", \"node_2\")\n", - " graph = builder.compile()\n", - " \n", - " for chunk in graph.stream({\"foo\": \"foo\"}):\n", - " print(chunk)\n", - " ```\n", - "\n", - " 1. This key is shared with the parent graph state\n", - " 2. This key is private to the `SubgraphState` and is not visible to the parent graph\n", - " \n", - " ```\n", - " {'node_1': {'foo': 'hi! foo'}}\n", - " {'node_2': {'foo': 'hi! foobar'}}\n", - " ```\n", - "\n", - " ```" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Different state schemas\n", - "\n", - "For more complex systems you might want to define subgraphs that have a **completely different schema** from the parent graph (no shared keys). For example, you might want to keep a private message history for each of the agents in a [multi-agent](../concepts/multi_agent.md) system.\n", - "\n", - "If that's the case for your application, you need to define a node **function that invokes the subgraph**. This function needs to transform the input (parent) state to the subgraph state before invoking the subgraph, and transform the results back to the parent state before returning the state update from the node.\n", - "\n", - "```python\n", - "from typing_extensions import TypedDict\n", - "from langgraph.graph.state import StateGraph, START\n", - "\n", - "class SubgraphState(TypedDict):\n", - " bar: str\n", - "\n", - "# Subgraph\n", - "\n", - "def subgraph_node_1(state: SubgraphState):\n", - " return {\"bar\": \"hi! \" + state[\"bar\"]}\n", - "\n", - "subgraph_builder = StateGraph(SubgraphState)\n", - "subgraph_builder.add_node(subgraph_node_1)\n", - "subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - "# highlight-next-line\n", - "subgraph = subgraph_builder.compile()\n", - "\n", - "# Parent graph\n", - "\n", - "class State(TypedDict):\n", - " foo: str\n", - "\n", - "def call_subgraph(state: State):\n", - " # highlight-next-line\n", - " subgraph_output = subgraph.invoke({\"bar\": state[\"foo\"]}) # (1)!\n", - " # highlight-next-line\n", - " return {\"foo\": subgraph_output[\"bar\"]} # (2)!\n", - "\n", - "builder = StateGraph(State)\n", - "# highlight-next-line\n", - "builder.add_node(\"node_1\", call_subgraph)\n", - "builder.add_edge(START, \"node_1\")\n", - "graph = builder.compile()\n", - "```\n", - "\n", - "1. Transform the state to the subgraph state\n", - "2. Transform response back to the parent state\n", - "\n", - "??? example \"Full example: different state schemas\"\n", - "\n", - " ```python\n", - " from typing_extensions import TypedDict\n", - " from langgraph.graph.state import StateGraph, START\n", - "\n", - " # Define subgraph\n", - " class SubgraphState(TypedDict):\n", - " # note that none of these keys are shared with the parent graph state\n", - " bar: str\n", - " baz: str\n", - " \n", - " def subgraph_node_1(state: SubgraphState):\n", - " return {\"baz\": \"baz\"}\n", - " \n", - " def subgraph_node_2(state: SubgraphState):\n", - " return {\"bar\": state[\"bar\"] + state[\"baz\"]}\n", - " \n", - " subgraph_builder = StateGraph(SubgraphState)\n", - " subgraph_builder.add_node(subgraph_node_1)\n", - " subgraph_builder.add_node(subgraph_node_2)\n", - " subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - " subgraph_builder.add_edge(\"subgraph_node_1\", \"subgraph_node_2\")\n", - " subgraph = subgraph_builder.compile()\n", - " \n", - " # Define parent graph\n", - " class ParentState(TypedDict):\n", - " foo: str\n", - " \n", - " def node_1(state: ParentState):\n", - " return {\"foo\": \"hi! \" + state[\"foo\"]}\n", - " \n", - " def node_2(state: ParentState):\n", - " # highlight-next-line\n", - " response = subgraph.invoke({\"bar\": state[\"foo\"]}) # (1)!\n", - " # highlight-next-line\n", - " return {\"foo\": response[\"bar\"]} # (2)!\n", - " \n", - " \n", - " builder = StateGraph(ParentState)\n", - " builder.add_node(\"node_1\", node_1)\n", - " # highlight-next-line\n", - " builder.add_node(\"node_2\", node_2)\n", - " builder.add_edge(START, \"node_1\")\n", - " builder.add_edge(\"node_1\", \"node_2\")\n", - " graph = builder.compile()\n", - " \n", - " for chunk in graph.stream({\"foo\": \"foo\"}, subgraphs=True):\n", - " print(chunk)\n", - " ```\n", - "\n", - " 1. Transform the state to the subgraph state\n", - " 2. Transform response back to the parent state\n", - "\n", - " ```\n", - " ((), {'node_1': {'foo': 'hi! foo'}})\n", - " (('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'subgraph_node_1': {'baz': 'baz'}})\n", - " (('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'subgraph_node_2': {'bar': 'hi! foobaz'}})\n", - " ((), {'node_2': {'foo': 'hi! foobaz'}})\n", - " ```\n", - "\n", - "??? example \"Full example: different state schemas (two levels of subgraphs)\"\n", - "\n", - " This is an example with two levels of subgraphs: parent -> child -> grandchild.\n", - "\n", - " ```python\n", - " # Grandchild graph\n", - " from typing_extensions import TypedDict\n", - " from langgraph.graph.state import StateGraph, START, END\n", - " \n", - " class GrandChildState(TypedDict):\n", - " my_grandchild_key: str\n", - " \n", - " def grandchild_1(state: GrandChildState) -> GrandChildState:\n", - " # NOTE: child or parent keys will not be accessible here\n", - " return {\"my_grandchild_key\": state[\"my_grandchild_key\"] + \", how are you\"}\n", - " \n", - " \n", - " grandchild = StateGraph(GrandChildState)\n", - " grandchild.add_node(\"grandchild_1\", grandchild_1)\n", - " \n", - " grandchild.add_edge(START, \"grandchild_1\")\n", - " grandchild.add_edge(\"grandchild_1\", END)\n", - " \n", - " grandchild_graph = grandchild.compile()\n", - " \n", - " # Child graph\n", - " class ChildState(TypedDict):\n", - " my_child_key: str\n", - " \n", - " def call_grandchild_graph(state: ChildState) -> ChildState:\n", - " # NOTE: parent or grandchild keys won't be accessible here\n", - " grandchild_graph_input = {\"my_grandchild_key\": state[\"my_child_key\"]} # (1)!\n", - " # highlight-next-line\n", - " grandchild_graph_output = grandchild_graph.invoke(grandchild_graph_input)\n", - " return {\"my_child_key\": grandchild_graph_output[\"my_grandchild_key\"] + \" today?\"} # (2)!\n", - " \n", - " child = StateGraph(ChildState)\n", - " # highlight-next-line\n", - " child.add_node(\"child_1\", call_grandchild_graph) # (3)!\n", - " child.add_edge(START, \"child_1\")\n", - " child.add_edge(\"child_1\", END)\n", - " child_graph = child.compile()\n", - " \n", - " # Parent graph\n", - " class ParentState(TypedDict):\n", - " my_key: str\n", - " \n", - " def parent_1(state: ParentState) -> ParentState:\n", - " # NOTE: child or grandchild keys won't be accessible here\n", - " return {\"my_key\": \"hi \" + state[\"my_key\"]}\n", - " \n", - " def parent_2(state: ParentState) -> ParentState:\n", - " return {\"my_key\": state[\"my_key\"] + \" bye!\"}\n", - " \n", - " def call_child_graph(state: ParentState) -> ParentState:\n", - " child_graph_input = {\"my_child_key\": state[\"my_key\"]} # (4)!\n", - " # highlight-next-line\n", - " child_graph_output = child_graph.invoke(child_graph_input)\n", - " return {\"my_key\": child_graph_output[\"my_child_key\"]} # (5)!\n", - " \n", - " parent = StateGraph(ParentState)\n", - " parent.add_node(\"parent_1\", parent_1)\n", - " # highlight-next-line\n", - " parent.add_node(\"child\", call_child_graph) # (6)!\n", - " parent.add_node(\"parent_2\", parent_2)\n", - " \n", - " parent.add_edge(START, \"parent_1\")\n", - " parent.add_edge(\"parent_1\", \"child\")\n", - " parent.add_edge(\"child\", \"parent_2\")\n", - " parent.add_edge(\"parent_2\", END)\n", - " \n", - " parent_graph = parent.compile()\n", - " \n", - " for chunk in parent_graph.stream({\"my_key\": \"Bob\"}, subgraphs=True):\n", - " print(chunk)\n", - " ```\n", - "\n", - " 1. We're transforming the state from the child state channels (`my_child_key`) to the child state channels (`my_grandchild_key`)\n", - " 2. We're transforming the state from the grandchild state channels (`my_grandchild_key`) back to the child state channels (`my_child_key`)\n", - " 3. We're passing a function here instead of just compiled graph (`grandchild_graph`)\n", - " 4. We're transforming the state from the parent state channels (`my_key`) to the child state channels (`my_child_key`)\n", - " 5. We're transforming the state from the child state channels (`my_child_key`) back to the parent state channels (`my_key`)\n", - " 6. We're passing a function here instead of just a compiled graph (`child_graph`)\n", - "\n", - " ```\n", - " ((), {'parent_1': {'my_key': 'hi Bob'}})\n", - " (('child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b', 'child_1:781bb3b1-3971-84ce-810b-acf819a03f9c'), {'grandchild_1': {'my_grandchild_key': 'hi Bob, how are you'}})\n", - " (('child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b',), {'child_1': {'my_child_key': 'hi Bob, how are you today?'}})\n", - " ((), {'child': {'my_key': 'hi Bob, how are you today?'}})\n", - " ((), {'parent_2': {'my_key': 'hi Bob, how are you today? bye!'}})\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add persistence \n", - "\n", - "You only need to **provide the checkpointer when compiling the parent graph**. LangGraph will automatically propagate the checkpointer to the child subgraphs.\n", - "\n", - "```python\n", - "from langgraph.graph import START, StateGraph\n", - "from langgraph.checkpoint.memory import InMemorySaver\n", - "from typing_extensions import TypedDict\n", - "\n", - "class State(TypedDict):\n", - " foo: str\n", - "\n", - "# Subgraph\n", - "\n", - "def subgraph_node_1(state: State):\n", - " return {\"foo\": state[\"foo\"] + \"bar\"}\n", - "\n", - "subgraph_builder = StateGraph(State)\n", - "subgraph_builder.add_node(subgraph_node_1)\n", - "subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - "# highlight-next-line\n", - "subgraph = subgraph_builder.compile()\n", - "\n", - "# Parent graph\n", - "\n", - "builder = StateGraph(State)\n", - "# highlight-next-line\n", - "builder.add_node(\"node_1\", subgraph)\n", - "builder.add_edge(START, \"node_1\")\n", - "\n", - "checkpointer = InMemorySaver()\n", - "# highlight-next-line\n", - "graph = builder.compile(checkpointer=checkpointer)\n", - "``` \n", - "\n", - "If you want the subgraph to **have its own memory**, you can compile it `with checkpointer=True`. This is useful in [multi-agent](../../concepts/multi_agent) systems, if you want agents to keep track of their internal message histories:\n", - "\n", - "```python\n", - "subgraph_builder = StateGraph(...)\n", - "# highlight-next-line\n", - "subgraph = subgraph_builder.compile(checkpointer=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View subgraph state\n", - "\n", - "When you enable [persistence](../persistence), you can [inspect the graph state](../persistence#manage-checkpoints) (checkpoint) via `graph.get_state(config)`. To view the subgraph state, you can use `graph.get_state(config, subgraphs=True)`.\n", - "\n", - "!!! important \"Available **only** when interrupted\"\n", - "\n", - " Subgraph state can only be viewed **when the subgraph is interrupted**. Once you resume the graph, you won't be able to access the subgraph state.\n", - "\n", - "??? example \"View interrupted subgraph state\"\n", - "\n", - " ```python\n", - " from langgraph.graph import START, StateGraph\n", - " from langgraph.checkpoint.memory import InMemorySaver\n", - " from langgraph.types import interrupt, Command\n", - " from typing_extensions import TypedDict\n", - " \n", - " class State(TypedDict):\n", - " foo: str\n", - " \n", - " # Subgraph\n", - " \n", - " def subgraph_node_1(state: State):\n", - " # highlight-next-line\n", - " value = interrupt(\"Provide value:\")\n", - " return {\"foo\": state[\"foo\"] + value}\n", - " \n", - " subgraph_builder = StateGraph(State)\n", - " subgraph_builder.add_node(subgraph_node_1)\n", - " subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - " \n", - " subgraph = subgraph_builder.compile()\n", - " \n", - " # Parent graph\n", - " \n", - " builder = StateGraph(State)\n", - " # highlight-next-line\n", - " builder.add_node(\"node_1\", subgraph)\n", - " builder.add_edge(START, \"node_1\")\n", - " \n", - " checkpointer = InMemorySaver()\n", - " # highlight-next-line\n", - " graph = builder.compile(checkpointer=checkpointer)\n", - " \n", - " config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - " \n", - " graph.invoke({\"foo\": \"\"}, config)\n", - " parent_state = graph.get_state(config)\n", - " # highlight-next-line\n", - " subgraph_state = graph.get_state(config, subgraphs=True).tasks[0].state # (1)!\n", - " \n", - " # resume the subgraph\n", - " graph.invoke(Command(resume=\"bar\"), config)\n", - " ```\n", - " \n", - " 1. This will be available only when the subgraph is interrupted. Once you resume the graph, you won't be able to access the subgraph state." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stream subgraph outputs\n", - "\n", - "To include outputs from [subgraphs](../concepts/low_level.md#subgraphs) in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.\n", - "\n", - "```python\n", - "for chunk in graph.stream(\n", - " {\"foo\": \"foo\"},\n", - " # highlight-next-line\n", - " subgraphs=True, # (1)!\n", - " stream_mode=\"updates\",\n", - "):\n", - " print(chunk)\n", - "```\n", - "\n", - "1. Set `subgraphs=True` to stream outputs from subgraphs.\n", - "\n", - "??? example \"Stream from subgraphs\"\n", - "\n", - " ```python\n", - " from typing_extensions import TypedDict\n", - " from langgraph.graph.state import StateGraph, START\n", - "\n", - " # Define subgraph\n", - " class SubgraphState(TypedDict):\n", - " foo: str\n", - " bar: str\n", - " \n", - " def subgraph_node_1(state: SubgraphState):\n", - " return {\"bar\": \"bar\"}\n", - " \n", - " def subgraph_node_2(state: SubgraphState):\n", - " # note that this node is using a state key ('bar') that is only available in the subgraph\n", - " # and is sending update on the shared state key ('foo')\n", - " return {\"foo\": state[\"foo\"] + state[\"bar\"]}\n", - " \n", - " subgraph_builder = StateGraph(SubgraphState)\n", - " subgraph_builder.add_node(subgraph_node_1)\n", - " subgraph_builder.add_node(subgraph_node_2)\n", - " subgraph_builder.add_edge(START, \"subgraph_node_1\")\n", - " subgraph_builder.add_edge(\"subgraph_node_1\", \"subgraph_node_2\")\n", - " subgraph = subgraph_builder.compile()\n", - " \n", - " # Define parent graph\n", - " class ParentState(TypedDict):\n", - " foo: str\n", - " \n", - " def node_1(state: ParentState):\n", - " return {\"foo\": \"hi! \" + state[\"foo\"]}\n", - " \n", - " builder = StateGraph(ParentState)\n", - " builder.add_node(\"node_1\", node_1)\n", - " # highlight-next-line\n", - " builder.add_node(\"node_2\", subgraph)\n", - " builder.add_edge(START, \"node_1\")\n", - " builder.add_edge(\"node_1\", \"node_2\")\n", - " graph = builder.compile()\n", - "\n", - " for chunk in graph.stream(\n", - " {\"foo\": \"foo\"},\n", - " stream_mode=\"updates\",\n", - " # highlight-next-line\n", - " subgraphs=True, # (1)!\n", - " ):\n", - " print(chunk)\n", - " ```\n", - " \n", - " 1. Set `subgraphs=True` to stream outputs from subgraphs.\n", - "\n", - " ```\n", - " ((), {'node_1': {'foo': 'hi! foo'}})\n", - " (('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',), {'subgraph_node_1': {'bar': 'bar'}})\n", - " (('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',), {'subgraph_node_2': {'foo': 'hi! foobar'}})\n", - " ((), {'node_2': {'foo': 'hi! foobar'}})\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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/docs/how-tos/subgraph.md b/docs/docs/how-tos/subgraph.md new file mode 100644 index 000000000..9ad820f73 --- /dev/null +++ b/docs/docs/how-tos/subgraph.md @@ -0,0 +1,453 @@ +# Use subgraphs + +This guide explains the mechanics of using [subgraphs](../concepts/subgraphs.md). A common application of subgraphs is to build [multi-agent](../concepts/multi_agent.md) systems. + +When adding subgraphs, you need to define how the parent graph and the subgraph communicate: + +* [Shared state schemas](#shared-state-schemas) — parent and subgraph have **shared state keys** in their state [schemas](../concepts/low_level.md#state) +* [Different state schemas](#different-state-schemas) — **no shared state keys** in parent and subgraph [schemas](../concepts/low_level.md#state) + +## Setup + +```bash +pip install -U langgraph +``` + +!!! tip "Set up LangSmith for LangGraph development" + Sign up for [LangSmith](https://smith.langchain.com) to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started [here](https://docs.smith.langchain.com). + +## Shared state schemas + +A common case is for the parent graph and subgraph to communicate over a shared state key (channel) in the [schema](../concepts/low_level.md#state). For example, in [multi-agent](../concepts/multi_agent.md) systems, the agents often communicate over a shared [messages](https://langchain-ai.github.io/langgraph/concepts/low_level.md#why-use-messages) key. + +If your subgraph shares state keys with the parent graph, you can follow these steps to add it to your graph: + +1. Define the subgraph workflow (`subgraph_builder` in the example below) and compile it +2. Pass compiled subgraph to the `.add_node` method when defining the parent graph workflow + +```python +from typing_extensions import TypedDict +from langgraph.graph.state import StateGraph, START + +class State(TypedDict): + foo: str + +# Subgraph + +def subgraph_node_1(state: State): + return {"foo": "hi! " + state["foo"]} + +subgraph_builder = StateGraph(State) +subgraph_builder.add_node(subgraph_node_1) +subgraph_builder.add_edge(START, "subgraph_node_1") +subgraph = subgraph_builder.compile() + +# Parent graph + +builder = StateGraph(State) +builder.add_node("node_1", subgraph) +builder.add_edge(START, "node_1") +graph = builder.compile() +``` + +??? example "Full example: shared state schemas" + + ```python + from typing_extensions import TypedDict + from langgraph.graph.state import StateGraph, START + + # Define subgraph + class SubgraphState(TypedDict): + foo: str # (1)! + bar: str # (2)! + + def subgraph_node_1(state: SubgraphState): + return {"bar": "bar"} + + def subgraph_node_2(state: SubgraphState): + # note that this node is using a state key ('bar') that is only available in the subgraph + # and is sending update on the shared state key ('foo') + return {"foo": state["foo"] + state["bar"]} + + subgraph_builder = StateGraph(SubgraphState) + subgraph_builder.add_node(subgraph_node_1) + subgraph_builder.add_node(subgraph_node_2) + subgraph_builder.add_edge(START, "subgraph_node_1") + subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") + subgraph = subgraph_builder.compile() + + # Define parent graph + class ParentState(TypedDict): + foo: str + + def node_1(state: ParentState): + return {"foo": "hi! " + state["foo"]} + + builder = StateGraph(ParentState) + builder.add_node("node_1", node_1) + builder.add_node("node_2", subgraph) + builder.add_edge(START, "node_1") + builder.add_edge("node_1", "node_2") + graph = builder.compile() + + for chunk in graph.stream({"foo": "foo"}): + print(chunk) + ``` + + 1. This key is shared with the parent graph state + 2. This key is private to the `SubgraphState` and is not visible to the parent graph + + ``` + {'node_1': {'foo': 'hi! foo'}} + {'node_2': {'foo': 'hi! foobar'}} + ``` + +## Different state schemas + +For more complex systems you might want to define subgraphs that have a **completely different schema** from the parent graph (no shared keys). For example, you might want to keep a private message history for each of the agents in a [multi-agent](../concepts/multi_agent.md) system. + +If that's the case for your application, you need to define a node **function that invokes the subgraph**. This function needs to transform the input (parent) state to the subgraph state before invoking the subgraph, and transform the results back to the parent state before returning the state update from the node. + +```python +from typing_extensions import TypedDict +from langgraph.graph.state import StateGraph, START + +class SubgraphState(TypedDict): + bar: str + +# Subgraph + +def subgraph_node_1(state: SubgraphState): + return {"bar": "hi! " + state["bar"]} + +subgraph_builder = StateGraph(SubgraphState) +subgraph_builder.add_node(subgraph_node_1) +subgraph_builder.add_edge(START, "subgraph_node_1") +subgraph = subgraph_builder.compile() + +# Parent graph + +class State(TypedDict): + foo: str + +def call_subgraph(state: State): + subgraph_output = subgraph.invoke({"bar": state["foo"]}) # (1)! + return {"foo": subgraph_output["bar"]} # (2)! + +builder = StateGraph(State) +builder.add_node("node_1", call_subgraph) +builder.add_edge(START, "node_1") +graph = builder.compile() +``` + +1. Transform the state to the subgraph state +2. Transform response back to the parent state + +??? example "Full example: different state schemas" + + ```python + from typing_extensions import TypedDict + from langgraph.graph.state import StateGraph, START + + # Define subgraph + class SubgraphState(TypedDict): + # note that none of these keys are shared with the parent graph state + bar: str + baz: str + + def subgraph_node_1(state: SubgraphState): + return {"baz": "baz"} + + def subgraph_node_2(state: SubgraphState): + return {"bar": state["bar"] + state["baz"]} + + subgraph_builder = StateGraph(SubgraphState) + subgraph_builder.add_node(subgraph_node_1) + subgraph_builder.add_node(subgraph_node_2) + subgraph_builder.add_edge(START, "subgraph_node_1") + subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") + subgraph = subgraph_builder.compile() + + # Define parent graph + class ParentState(TypedDict): + foo: str + + def node_1(state: ParentState): + return {"foo": "hi! " + state["foo"]} + + def node_2(state: ParentState): + response = subgraph.invoke({"bar": state["foo"]}) # (1)! + return {"foo": response["bar"]} # (2)! + + + builder = StateGraph(ParentState) + builder.add_node("node_1", node_1) + builder.add_node("node_2", node_2) + builder.add_edge(START, "node_1") + builder.add_edge("node_1", "node_2") + graph = builder.compile() + + for chunk in graph.stream({"foo": "foo"}, subgraphs=True): + print(chunk) + ``` + + 1. Transform the state to the subgraph state + 2. Transform response back to the parent state + + ``` + ((), {'node_1': {'foo': 'hi! foo'}}) + (('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'grandchild_1': {'my_grandchild_key': 'hi Bob, how are you'}}) + (('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'grandchild_2': {'bar': 'hi! foobaz'}}) + ((), {'node_2': {'foo': 'hi! foobaz'}}) + ``` + +??? example "Full example: different state schemas (two levels of subgraphs)" + + This is an example with two levels of subgraphs: parent -> child -> grandchild. + + ```python + # Grandchild graph + from typing_extensions import TypedDict + from langgraph.graph.state import StateGraph, START, END + + class GrandChildState(TypedDict): + my_grandchild_key: str + + def grandchild_1(state: GrandChildState) -> GrandChildState: + # NOTE: child or parent keys will not be accessible here + return {"my_grandchild_key": state["my_grandchild_key"] + ", how are you"} + + + grandchild = StateGraph(GrandChildState) + grandchild.add_node("grandchild_1", grandchild_1) + + grandchild.add_edge(START, "grandchild_1") + grandchild.add_edge("grandchild_1", END) + + grandchild_graph = grandchild.compile() + + # Child graph + class ChildState(TypedDict): + my_child_key: str + + def call_grandchild_graph(state: ChildState) -> ChildState: + # NOTE: parent or grandchild keys won't be accessible here + grandchild_graph_input = {"my_grandchild_key": state["my_child_key"]} # (1)! + grandchild_graph_output = grandchild_graph.invoke(grandchild_graph_input) + return {"my_child_key": grandchild_graph_output["my_grandchild_key"] + " today?"} # (2)! + + child = StateGraph(ChildState) + child.add_node("child_1", call_grandchild_graph) # (3)! + child.add_edge(START, "child_1") + child.add_edge("child_1", END) + child_graph = child.compile() + + # Parent graph + class ParentState(TypedDict): + my_key: str + + def parent_1(state: ParentState) -> ParentState: + # NOTE: child or grandchild keys won't be accessible here + return {"my_key": "hi " + state["my_key"]} + + def parent_2(state: ParentState) -> ParentState: + return {"my_key": state["my_key"] + " bye!"} + + def call_child_graph(state: ParentState) -> ParentState: + child_graph_input = {"my_child_key": state["my_key"]} # (4)! + child_graph_output = child_graph.invoke(child_graph_input) + return {"my_key": child_graph_output["my_child_key"]} # (5)! + + parent = StateGraph(ParentState) + parent.add_node("parent_1", parent_1) + parent.add_node("child", call_child_graph) # (6)! + parent.add_node("parent_2", parent_2) + + parent.add_edge(START, "parent_1") + parent.add_edge("parent_1", "child") + parent.add_edge("child", "parent_2") + parent.add_edge("parent_2", END) + + parent_graph = parent.compile() + + for chunk in parent_graph.stream({"my_key": "Bob"}, subgraphs=True): + print(chunk) + ``` + + 1. We're transforming the state from the child state channels (`my_child_key`) to the child state channels (`my_grandchild_key`) + 2. We're transforming the state from the grandchild state channels (`my_grandchild_key`) back to the child state channels (`my_child_key`) + 3. We're passing a function here instead of just compiled graph (`grandchild_graph`) + 4. We're transforming the state from the parent state channels (`my_key`) to the child state channels (`my_child_key`) + 5. We're transforming the state from the child state channels (`my_child_key`) back to the parent state channels (`my_key`) + 6. We're passing a function here instead of just a compiled graph (`child_graph`) + + ``` + ((), {'parent_1': {'my_key': 'hi Bob'}}) + (('child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b', 'child_1:781bb3b1-3971-84ce-810b-acf819a03f9c'), {'grandchild_1': {'my_grandchild_key': 'hi Bob, how are you'}}) + (('child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b',), {'child_1': {'my_child_key': 'hi Bob, how are you today?'}}) + ((), {'child': {'my_key': 'hi Bob, how are you today?'}}) + ((), {'parent_2': {'my_key': 'hi Bob, how are you today? bye!'}}) + ``` + +## Add persistence + +You only need to **provide the checkpointer when compiling the parent graph**. LangGraph will automatically propagate the checkpointer to the child subgraphs. + +```python +from langgraph.graph import START, StateGraph +from langgraph.checkpoint.memory import InMemorySaver +from typing_extensions import TypedDict + +class State(TypedDict): + foo: str + +# Subgraph + +def subgraph_node_1(state: State): + return {"foo": state["foo"] + "bar"} + +subgraph_builder = StateGraph(State) +subgraph_builder.add_node(subgraph_node_1) +subgraph_builder.add_edge(START, "subgraph_node_1") +subgraph = subgraph_builder.compile() + +# Parent graph + +builder = StateGraph(State) +builder.add_node("node_1", subgraph) +builder.add_edge(START, "node_1") + +checkpointer = InMemorySaver() +graph = builder.compile(checkpointer=checkpointer) +``` + +If you want the subgraph to **have its own memory**, you can compile it `with checkpointer=True`. This is useful in [multi-agent](../concepts/multi_agent.md) systems, if you want agents to keep track of their internal message histories: + +```python +subgraph_builder = StateGraph(...) +subgraph = subgraph_builder.compile(checkpointer=True) +``` + +## View subgraph state + +When you enable [persistence](../concepts/persistence.md), you can [inspect the graph state](../concepts/persistence.md#checkpoints) (checkpoint) via `graph.get_state(config)`. To view the subgraph state, you can use `graph.get_state(config, subgraphs=True)`. + +!!! important "Available **only** when interrupted" + + Subgraph state can only be viewed **when the subgraph is interrupted**. Once you resume the graph, you won't be able to access the subgraph state. + +??? example "View interrupted subgraph state" + + ```python + from langgraph.graph import START, StateGraph + from langgraph.checkpoint.memory import InMemorySaver + from langgraph.types import interrupt, Command + from typing_extensions import TypedDict + + class State(TypedDict): + foo: str + + # Subgraph + + def subgraph_node_1(state: State): + value = interrupt("Provide value:") + return {"foo": state["foo"] + value} + + subgraph_builder = StateGraph(State) + subgraph_builder.add_node(subgraph_node_1) + subgraph_builder.add_edge(START, "subgraph_node_1") + + subgraph = subgraph_builder.compile() + + # Parent graph + + builder = StateGraph(State) + builder.add_node("node_1", subgraph) + builder.add_edge(START, "node_1") + + checkpointer = InMemorySaver() + graph = builder.compile(checkpointer=checkpointer) + + config = {"configurable": {"thread_id": "1"}} + + graph.invoke({"foo": ""}, config) + parent_state = graph.get_state(config) + subgraph_state = graph.get_state(config, subgraphs=True).tasks[0].state # (1)! + + # resume the subgraph + graph.invoke(Command(resume="bar"), config) + ``` + + 1. This will be available only when the subgraph is interrupted. Once you resume the graph, you won't be able to access the subgraph state. + +## Stream subgraph outputs + +To include outputs from subgraphs in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs. + +```python +for chunk in graph.stream( + {"foo": "foo"}, + subgraphs=True, # (1)! + stream_mode="updates", +): + print(chunk) +``` + +1. Set `subgraphs=True` to stream outputs from subgraphs. + +??? example "Stream from subgraphs" + + ```python + from typing_extensions import TypedDict + from langgraph.graph.state import StateGraph, START + + # Define subgraph + class SubgraphState(TypedDict): + foo: str + bar: str + + def subgraph_node_1(state: SubgraphState): + return {"bar": "bar"} + + def subgraph_node_2(state: SubgraphState): + # note that this node is using a state key ('bar') that is only available in the subgraph + # and is sending update on the shared state key ('foo') + return {"foo": state["foo"] + state["bar"]} + + subgraph_builder = StateGraph(SubgraphState) + subgraph_builder.add_node(subgraph_node_1) + subgraph_builder.add_node(subgraph_node_2) + subgraph_builder.add_edge(START, "subgraph_node_1") + subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") + subgraph = subgraph_builder.compile() + + # Define parent graph + class ParentState(TypedDict): + foo: str + + def node_1(state: ParentState): + return {"foo": "hi! " + state["foo"]} + + builder = StateGraph(ParentState) + builder.add_node("node_1", node_1) + builder.add_node("node_2", subgraph) + builder.add_edge(START, "node_1") + builder.add_edge("node_1", "node_2") + graph = builder.compile() + + for chunk in graph.stream( + {"foo": "foo"}, + stream_mode="updates", + subgraphs=True, # (1)! + ): + print(chunk) + ``` + + 1. Set `subgraphs=True` to stream outputs from subgraphs. + + ``` + ((), {'node_1': {'foo': 'hi! foo'}}) + (('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',), {'subgraph_node_1': {'bar': 'bar'}}) + (('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',), {'subgraph_node_2': {'foo': 'hi! foobar'}}) + ((), {'node_2': {'foo': 'hi! foobar'}}) + \ No newline at end of file diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 9fc53ac0d..a59c835dd 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -112,7 +112,7 @@ nav: - LangGraph APIs: - Graph API: - Overview: concepts/low_level.md - - Use the Graph API: how-tos/graph-api.ipynb + - Use the Graph API: how-tos/graph-api.md - Functional API: - Overview: concepts/functional_api.md - Use the Functional API: how-tos/use-functional-api.md @@ -150,11 +150,11 @@ nav: - Use Server API: cloud/how-tos/human_in_the_loop_time_travel.md - Subgraphs: - Overview: concepts/subgraphs.md - - Use subgraphs: how-tos/subgraph.ipynb + - Use subgraphs: how-tos/subgraph.md - Multi-agent: - Overview: concepts/multi_agent.md - Prebuilt implementation: agents/multi-agent.md - - Custom implementation: how-tos/multi_agent.ipynb + - Custom implementation: how-tos/multi_agent.md - MCP: - Overview: concepts/mcp.md - Use MCP: agents/mcp.md diff --git a/docs/uv.lock b/docs/uv.lock index f838b6d3a..0f4527769 100644 --- a/docs/uv.lock +++ b/docs/uv.lock @@ -2590,7 +2590,7 @@ wheels = [ [[package]] name = "langgraph" -version = "0.5.0" +version = "0.5.1" source = { editable = "../libs/langgraph" } dependencies = [ { name = "langchain-core" }, diff --git a/extract_images.py b/extract_images.py new file mode 100644 index 000000000..781ba0a22 --- /dev/null +++ b/extract_images.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +""" +Script to extract images from the graph-api.ipynb notebook and save them to assets folder. +""" + +import json +import base64 +import os +from pathlib import Path + +def extract_images_from_notebook(notebook_path, assets_dir): + """Extract images from notebook and save them to assets directory.""" + + # Read the notebook + with open(notebook_path, 'r') as f: + notebook = json.load(f) + + # Create assets directory if it doesn't exist + os.makedirs(assets_dir, exist_ok=True) + + image_count = 0 + + # Process each cell + for cell_idx, cell in enumerate(notebook['cells']): + if cell['cell_type'] == 'code': + # Check if this cell contains draw_mermaid_png + source = ''.join(cell.get('source', [])) + if 'draw_mermaid_png' in source: + print(f"Found draw_mermaid_png in cell {cell_idx}") + + # Check for outputs with images + if 'outputs' in cell: + for output_idx, output in enumerate(cell['outputs']): + if output.get('output_type') == 'display_data': + data = output.get('data', {}) + + # Check for PNG data + if 'image/png' in data: + png_data = data['image/png'] + + # Decode base64 data + try: + image_bytes = base64.b64decode(png_data) + + # Generate filename + image_count += 1 + filename = f"graph_api_image_{image_count}.png" + filepath = os.path.join(assets_dir, filename) + + # Save the image + with open(filepath, 'wb') as img_file: + img_file.write(image_bytes) + + print(f"Saved image: {filepath}") + + except Exception as e: + print(f"Error decoding image {image_count}: {e}") + + print(f"Extracted {image_count} images to {assets_dir}") + return image_count + +if __name__ == "__main__": + notebook_path = "docs/docs/how-tos/graph-api.ipynb" + assets_dir = "docs/docs/how-tos/assets" + + if os.path.exists(notebook_path): + count = extract_images_from_notebook(notebook_path, assets_dir) + print(f"Successfully extracted {count} images") + else: + print(f"Notebook not found: {notebook_path}") \ No newline at end of file

Set up LangSmith for better debugging