From b4d815e32a032ecc8093e4def15486bf1a88eb3e Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Fri, 21 Jun 2024 18:15:56 -0700 Subject: [PATCH] Add example for context key (#762) --- examples/state-context-key.ipynb | 497 +++++++++++++++++++++++++++++++ 1 file changed, 497 insertions(+) create mode 100644 examples/state-context-key.ipynb diff --git a/examples/state-context-key.ipynb b/examples/state-context-key.ipynb new file mode 100644 index 000000000..02ed87ab4 --- /dev/null +++ b/examples/state-context-key.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53", + "metadata": {}, + "source": [ + "# How to use a context object in state\n", + "\n", + "Sometimes you need some resources (like a database connection, a requests session, etc) to live for the duration of your graph execution, without being ever saved by the checkpointer.\n", + "\n", + "LangGraph supports decorating a state key with the Context channel, which will take care to \n", + "\n", + "- initialize the value before the graph starts executing, with access to the config passed in to invoke/stream\n", + "- run any cleanup code you need at the end of the graph execution, whether the graph succeeded or errored\n", + "\n", + "The argument to context channel should be either a ContextManager class or function." + ] + }, + { + "cell_type": "markdown", + "id": "7cbd446a-808f-4394-be92-d45ab818953c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "First we need to install the packages required" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain_openai" + ] + }, + { + "cell_type": "markdown", + "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d", + "metadata": {}, + "source": [ + "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "\n", + "def _set_env(var: str):\n", + " if not os.environ.get(var):\n", + " os.environ[var] = getpass.getpass(f\"{var}: \")\n", + "\n", + "\n", + "_set_env(\"OPENAI_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c", + "metadata": {}, + "source": [ + "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "_set_env(\"LANGCHAIN_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "21ac643b-cb06-4724-a80c-2862ba4773f1", + "metadata": {}, + "source": [ + "## Set up the tools\n", + "\n", + "We will first define the tools we want to use.\n", + "For this simple example, we will use create a placeholder search engine.\n", + "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/v0.2/docs/how_to/custom_tools) on how to do that.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def search(query: str):\n", + " \"\"\"Call to surf the web.\"\"\"\n", + " # This is a placeholder for the actual implementation\n", + " # Don't let the LLM know this though 😊\n", + " return [\"The answer to your question lies within.\"]\n", + "\n", + "\n", + "tools = [search]" + ] + }, + { + "cell_type": "markdown", + "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58", + "metadata": {}, + "source": [ + "We can now wrap these tools in a simple [ToolExecutor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolexecutor).\n", + "This is a real simple class that takes in a [ToolInvocation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolinvocation) and calls that tool, returning the output.\n", + "\n", + "A ToolInvocation is any dict-like class with `tool` and `tool_input` attributes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.prebuilt import ToolExecutor\n", + "\n", + "tool_executor = ToolExecutor(tools)" + ] + }, + { + "cell_type": "markdown", + "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5", + "metadata": {}, + "source": [ + "## Set up the model\n", + "\n", + "Now we need to load the chat model we want to use.\n", + "Importantly, this should satisfy two criteria:\n", + "\n", + "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n", + "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n", + "\n", + "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "892b54b9-75f0-4804-9ed0-88b5e5532989", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI\n", + "\n", + "model = ChatOpenAI(temperature=0)" + ] + }, + { + "cell_type": "markdown", + "id": "a77995c0-bae2-4cee-a036-8688a90f05b9", + "metadata": {}, + "source": [ + "\n", + "After we've done this, we should make sure the model knows that it has these tools available to call.\n", + "We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107", + "metadata": {}, + "outputs": [], + "source": [ + "model = model.bind_tools(tools)" + ] + }, + { + "cell_type": "markdown", + "id": "5e8a84b6-78ad-4f89-b64b-fb8c9e215a87", + "metadata": {}, + "source": [ + "## Define the context object\n", + "\n", + "Here we're defining the context object as a pydantic model, which is created by the factory function decorated with @contextmanager. @contextmanager ensures any cleanup code you need can be run at the end of the execution" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dec610e7-728f-4a71-93e3-1c7d1a8d05bb", + "metadata": {}, + "outputs": [], + "source": [ + "import httpx\n", + "from contextlib import contextmanager\n", + "from langchain_core.pydantic_v1 import BaseModel\n", + "from langchain_core.runnables import RunnableConfig\n", + "\n", + "class AgentContext(BaseModel):\n", + " class Config:\n", + " arbitrary_types_allowed = True\n", + "\n", + " httpx_session: httpx.Client\n", + "\n", + "@contextmanager\n", + "def make_agent_context(config: RunnableConfig):\n", + " # here you could read the config values passed invoke/stream to customize the context object\n", + "\n", + " # as an example, we create an httpx session, which could then be used in your graph's nodes\n", + " session = httpx.Client()\n", + " try:\n", + " yield AgentContext(httpx_session=session)\n", + " finally:\n", + " session.close()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8e8b9211-93d0-4ad5-aa7a-9c09099c53ff", + "metadata": {}, + "source": [ + "## Define the agent state\n", + "\n", + "The main type of graph in `langgraph` is the [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph).\n", + "This graph is parameterized by a state object that it passes around to each node.\n", + "Each node then returns operations to update that state.\n", + "These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n", + "Whether to set or add is denoted by annotating the state object you construct the graph with.\n", + "\n", + "For this example, the state we will track will just be a list of messages.\n", + "We want each node to just add messages to that list.\n", + "Therefore, we will use a `pydantic.BaseModel` with one key (`messages`) and annotate it so that the `messages` attribute is treated as \"append-only\".\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ea793afa-2eab-4901-910d-6eed90cd6564", + "metadata": {}, + "outputs": [], + "source": [ + "import operator\n", + "from typing import Annotated, Sequence\n", + "\n", + "from langchain_core.messages import BaseMessage\n", + "from langchain_core.pydantic_v1 import BaseModel\n", + "from langgraph.channels.context import Context\n", + "\n", + "\n", + "class AgentState(BaseModel):\n", + " messages: Annotated[Sequence[BaseMessage], operator.add]\n", + " context: Annotated[AgentContext, Context(make_agent_context)]" + ] + }, + { + "cell_type": "markdown", + "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4", + "metadata": {}, + "source": [ + "## Define the nodes\n", + "\n", + "We now need to define a few different nodes in our graph.\n", + "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel).\n", + "There are two main nodes we need for this:\n", + "\n", + "1. The agent: responsible for deciding what (if any) actions to take.\n", + "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n", + "\n", + "We will also need to define some edges.\n", + "Some of these edges may be conditional.\n", + "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n", + "The path that is taken is not known until that node is run (the LLM decides).\n", + "\n", + "1. Conditional Edge: after the agent is called, we should either:\n", + " a. If the agent said to take an action, then the function to invoke tools should be called\n", + " b. If the agent said that it was finished, then it should finish\n", + "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n", + "\n", + "Let's define the nodes, as well as a function to decide how what conditional edge to take.\n", + "\n", + "**MODIFICATION**\n", + "\n", + "We define each node to receive the AgentState base model as its first argument." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3b541bb9-900c-40d0-964d-7b5dfee30667", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import ToolMessage\n", + "\n", + "from langgraph.prebuilt import ToolInvocation\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "def should_continue(state):\n", + " messages = state.messages\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return \"end\"\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return \"continue\"\n", + "\n", + "\n", + "# Define the function that calls the model\n", + "def call_model(state):\n", + " # using context value\n", + " req = state.context.httpx_session.get('https://www.langchain.com/')\n", + " assert req.status_code == 200, req\n", + " \n", + " messages = state.messages\n", + " response = model.invoke(messages)\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [response]}\n", + "\n", + "\n", + "# Define the function to execute tools\n", + "def call_tool(state):\n", + " messages = state.messages\n", + " # Based on the continue condition\n", + " # we know the last message involves a function call\n", + " last_message = messages[-1]\n", + " # We construct an ToolInvocation from the function_call\n", + " tool_call = last_message.tool_calls[0]\n", + " action = ToolInvocation(\n", + " tool=tool_call[\"name\"],\n", + " tool_input=tool_call[\"args\"],\n", + " )\n", + " # We call the tool_executor and get back a response\n", + " response = tool_executor.invoke(action)\n", + " # We use the response to create a ToolMessage\n", + " tool_message = ToolMessage(\n", + " content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n", + " )\n", + " # We return a list, because this will get added to the existing list\n", + " return {\"messages\": [tool_message]}" + ] + }, + { + "cell_type": "markdown", + "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b", + "metadata": {}, + "source": [ + "## Define the graph\n", + "\n", + "We can now put it all together and define the graph!" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "813ae66c-3b58-4283-a02a-36da72a2ab90", + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.graph import END, StateGraph\n", + "\n", + "# Define a new graph\n", + "workflow = StateGraph(AgentState)\n", + "\n", + "# Define the two nodes we will cycle between\n", + "workflow.add_node(\"agent\", call_model)\n", + "workflow.add_node(\"action\", call_tool)\n", + "\n", + "# Set the entrypoint as `agent`\n", + "# This means that this node is the first one called\n", + "workflow.set_entry_point(\"agent\")\n", + "\n", + "# We now add a conditional edge\n", + "workflow.add_conditional_edges(\n", + " # First, we define the start node. We use `agent`.\n", + " # This means these are the edges taken after the `agent` node is called.\n", + " \"agent\",\n", + " # Next, we pass in the function that will determine which node is called next.\n", + " should_continue,\n", + " # Finally we pass in a mapping.\n", + " # The keys are strings, and the values are other nodes.\n", + " # END is a special node marking that the graph should finish.\n", + " # What will happen is we will call `should_continue`, and then the output of that\n", + " # will be matched against the keys in this mapping.\n", + " # Based on which one it matches, that node will then be called.\n", + " {\n", + " # If `tools`, then we call the tool node.\n", + " \"continue\": \"action\",\n", + " # Otherwise we finish.\n", + " \"end\": END,\n", + " },\n", + ")\n", + "\n", + "# We now add a normal edge from `tools` to `agent`.\n", + "# This means that after `tools` is called, `agent` node is called next.\n", + "workflow.add_edge(\"action\", \"agent\")\n", + "\n", + "# Finally, we compile it!\n", + "# This compiles it into a LangChain Runnable,\n", + "# meaning you can use it as you would any other runnable\n", + "app = workflow.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e09aaa63", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "display(Image(app.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "547c3931-3dae-4281-ad4e-4b51305594d4", + "metadata": {}, + "source": [ + "## Use it!\n", + "\n", + "We can now use it!\n", + "This now exposes the [same interface](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) as all other LangChain runnables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8edb04b9-40b6-46f1-a7a8-4b2d8aba7752", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n", + "for chunk in app.stream(inputs, stream_mode=\"values\"):\n", + " chunk[\"messages\"][-1].pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "296c7456-da05-4326-95dc-47d6b312da9d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}