{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to pass config to tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Prerequisites

\n", "

\n", " This guide assumes familiarity with the following:\n", "

\n", "

\n", "
\n", "\n", "\n", "\n", "At runtime, you may need to pass values to a tool, like a user ID, which should be set by the application logic, not controlled by the LLM, for security reasons. The LLM should only manage its intended parameters.\n", "\n", "LangChain tools use the `Runnable` interface, where methods like `invoke` accept runtime information through the `RunnableConfig` argument.\n", "\n", "In the following example, we’ll set up an agent with tools to manage a user's favorite pets—adding, reading, and deleting entries—while fixing the user ID through application logic and letting the chat model control other parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "First, let's install the required packages and set our API keys" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%%capture --no-stderr\n", "%pip install --quiet -U langgraph langchain_anthropic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import getpass\n", "import os\n", "\n", "\n", "def _set_env(var: str):\n", " if not os.environ.get(var):\n", " os.environ[var] = getpass.getpass(f\"{var}: \")\n", "\n", "\n", "_set_env(\"ANTHROPIC_API_KEY\")" ] }, { "cell_type": "markdown", "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", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define tools and model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from typing import List\n", "\n", "from langchain_core.tools import tool\n", "from langchain_core.runnables.config import RunnableConfig\n", "\n", "from langgraph.prebuilt import ToolNode\n", "\n", "user_to_pets = {}\n", "\n", "\n", "@tool(parse_docstring=True)\n", "def update_favorite_pets(\n", " # NOTE: config arg does not need to be added to docstring, as we don't want it to be included in the function signature attached to the LLM\n", " pets: List[str],\n", " config: RunnableConfig,\n", ") -> None:\n", " \"\"\"Add the list of favorite pets.\n", "\n", " Args:\n", " pets: List of favorite pets to set.\n", " \"\"\"\n", " user_id = config.get(\"configurable\", {}).get(\"user_id\")\n", " user_to_pets[user_id] = pets\n", "\n", "\n", "@tool\n", "def delete_favorite_pets(config: RunnableConfig) -> None:\n", " \"\"\"Delete the list of favorite pets.\"\"\"\n", " user_id = config.get(\"configurable\", {}).get(\"user_id\")\n", " if user_id in user_to_pets:\n", " del user_to_pets[user_id]\n", "\n", "\n", "@tool\n", "def list_favorite_pets(config: RunnableConfig) -> None:\n", " \"\"\"List favorite pets if any.\"\"\"\n", " user_id = config.get(\"configurable\", {}).get(\"user_id\")\n", " return \", \".join(user_to_pets.get(user_id, []))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "tools = [update_favorite_pets, delete_favorite_pets, list_favorite_pets]\n", "tool_node = ToolNode(tools)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll be using a small chat model from Anthropic in our example. To use chat models with tool calling, we need to first ensure that the model is aware of the available tools. We do this by calling `.bind_tools` method on `ChatAnthropic` moodel" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from langchain_anthropic import ChatAnthropic\n", "from langgraph.graph import StateGraph, MessagesState\n", "from langgraph.prebuilt import ToolNode\n", "\n", "\n", "model_with_tools = ChatAnthropic(\n", " model=\"claude-3-haiku-20240307\", temperature=0\n", ").bind_tools(tools)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## ReAct Agent\n", "\n", "Let's set up a graph implementation of the [ReAct agent](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-agent). This agent takes some query as input, then repeatedly call tools until it has enough information to resolve the query. We'll be using prebuilt `ToolNode` and the Anthropic model with tools we just defined." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from typing import Literal\n", "\n", "from langgraph.graph import StateGraph, MessagesState, START, END\n", "\n", "\n", "def should_continue(state: MessagesState):\n", " messages = state[\"messages\"]\n", " last_message = messages[-1]\n", " if last_message.tool_calls:\n", " return \"tools\"\n", " return END\n", "\n", "\n", "def call_model(state: MessagesState):\n", " messages = state[\"messages\"]\n", " response = model_with_tools.invoke(messages)\n", " return {\"messages\": [response]}\n", "\n", "\n", "builder = StateGraph(MessagesState)\n", "\n", "# Define the two nodes we will cycle between\n", "builder.add_node(\"agent\", call_model)\n", "builder.add_node(\"tools\", tool_node)\n", "\n", "builder.add_edge(START, \"agent\")\n", "builder.add_conditional_edges(\"agent\", should_continue, [\"tools\", END])\n", "builder.add_edge(\"tools\", \"agent\")\n", "\n", "graph = builder.compile()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Image, display\n", "\n", "try:\n", " display(Image(graph.get_graph().draw_mermaid_png()))\n", "except Exception:\n", " # This requires some extra dependencies and is optional\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use it!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User information prior to run: {}\n", "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "my favorite pets are cats and dogs\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "[{'text': \"Okay, let's update your favorite pets:\", 'type': 'text'}, {'id': 'toolu_01SU6vhbKDjSsPj2z86QA3wy', 'input': {'pets': ['cats', 'dogs']}, 'name': 'update_favorite_pets', 'type': 'tool_use'}]\n", "Tool Calls:\n", " update_favorite_pets (toolu_01SU6vhbKDjSsPj2z86QA3wy)\n", " Call ID: toolu_01SU6vhbKDjSsPj2z86QA3wy\n", " Args:\n", " pets: ['cats', 'dogs']\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: update_favorite_pets\n", "\n", "null\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "Your favorite pets have been updated to cats and dogs.\n", "User information after the run: {'123': ['cats', 'dogs']}\n" ] } ], "source": [ "from langchain_core.messages import HumanMessage\n", "\n", "user_to_pets.clear() # Clear the state\n", "\n", "print(f\"User information prior to run: {user_to_pets}\")\n", "\n", "inputs = {\"messages\": [HumanMessage(content=\"my favorite pets are cats and dogs\")]}\n", "for chunk in graph.stream(\n", " inputs, {\"configurable\": {\"user_id\": \"123\"}}, stream_mode=\"values\"\n", "):\n", " chunk[\"messages\"][-1].pretty_print()\n", "\n", "print(f\"User information after the run: {user_to_pets}\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User information prior to run: {'123': ['cats', 'dogs']}\n", "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "what are my favorite pets\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "[{'id': 'toolu_01DdpiqiCxzbR4RjQdEoR6mJ', 'input': {}, 'name': 'list_favorite_pets', 'type': 'tool_use'}]\n", "Tool Calls:\n", " list_favorite_pets (toolu_01DdpiqiCxzbR4RjQdEoR6mJ)\n", " Call ID: toolu_01DdpiqiCxzbR4RjQdEoR6mJ\n", " Args:\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: list_favorite_pets\n", "\n", "cats, dogs\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "Based on the list_favorite_pets tool, your favorite pets are cats and dogs.\n", "User information prior to run: {'123': ['cats', 'dogs']}\n" ] } ], "source": [ "from langchain_core.messages import HumanMessage\n", "\n", "print(f\"User information prior to run: {user_to_pets}\")\n", "\n", "inputs = {\"messages\": [HumanMessage(content=\"what are my favorite pets\")]}\n", "for chunk in graph.stream(\n", " inputs, {\"configurable\": {\"user_id\": \"123\"}}, stream_mode=\"values\"\n", "):\n", " chunk[\"messages\"][-1].pretty_print()\n", "\n", "print(f\"User information prior to run: {user_to_pets}\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User information prior to run: {'123': ['cats', 'dogs']}\n", "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "please forget what i told you about my favorite animals\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "[{'id': 'toolu_013TXG6yTxvuWiugbdKGTKSF', 'input': {}, 'name': 'delete_favorite_pets', 'type': 'tool_use'}]\n", "Tool Calls:\n", " delete_favorite_pets (toolu_013TXG6yTxvuWiugbdKGTKSF)\n", " Call ID: toolu_013TXG6yTxvuWiugbdKGTKSF\n", " Args:\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: delete_favorite_pets\n", "\n", "null\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "I have deleted the information about your favorite pets. The list of favorite pets has been cleared.\n", "User information prior to run: {}\n" ] } ], "source": [ "print(f\"User information prior to run: {user_to_pets}\")\n", "\n", "inputs = {\n", " \"messages\": [\n", " HumanMessage(content=\"please forget what i told you about my favorite animals\")\n", " ]\n", "}\n", "for chunk in graph.stream(\n", " inputs, {\"configurable\": {\"user_id\": \"123\"}}, stream_mode=\"values\"\n", "):\n", " chunk[\"messages\"][-1].pretty_print()\n", "\n", "print(f\"User information prior to run: {user_to_pets}\")" ] } ], "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 4 }