mirror of
https://github.com/langchain-ai/langgraph.git
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302 lines
8.8 KiB
Plaintext
302 lines
8.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6e6a0a39-9a4c-47ae-a238-1a3a847eea5b",
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"metadata": {},
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"source": [
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"# Configuration\n",
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"\n",
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"Sometimes you want to be able to configure your agent when calling it. \n",
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"Examples of this include configuring which LLM to use.\n",
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"Below we walk through an example of doing so."
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]
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},
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{
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"cell_type": "markdown",
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"id": "df1ff9cf-f8d2-4109-adf9-2adec83f5a95",
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"metadata": {},
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"source": [
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"## Base\n",
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"\n",
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"First, let's create a very simple graph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "816523d0-0b59-47cf-9f4c-4838024efe22",
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"metadata": {},
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"outputs": [],
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"source": [
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"import operator\n",
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"from typing import Annotated, Sequence, TypedDict\n",
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"\n",
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"from langchain_anthropic import ChatAnthropic\n",
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"from langchain_core.messages import BaseMessage, HumanMessage\n",
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"\n",
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"from langgraph.graph import END, StateGraph\n",
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"\n",
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"model = ChatAnthropic(model_name=\"claude-2.1\")\n",
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"\n",
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"\n",
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"class AgentState(TypedDict):\n",
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" messages: Annotated[Sequence[BaseMessage], operator.add]\n",
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"\n",
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"\n",
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"def _call_model(state):\n",
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" response = model.invoke(state[\"messages\"])\n",
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"# Define a new graph\n",
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"workflow = StateGraph(AgentState)\n",
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"workflow.add_node(\"model\", _call_model)\n",
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"workflow.set_entry_point(\"model\")\n",
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"workflow.add_edge(\"model\", END)\n",
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"\n",
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"app = workflow.compile()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "070f11a6-2441-4db5-9df6-e318f110e281",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [HumanMessage(content='hi'),\n",
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" AIMessage(content='Hello!', response_metadata={'id': 'msg_01YZj7CVCUSc76faX4VM9i5d', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 10, 'output_tokens': 6}}, id='run-d343db34-598c-46a2-93d6-ffa886d9b264-0')]}"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"app.invoke({\"messages\": [HumanMessage(content=\"hi\")]})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "69a1dd47-c5b3-4e04-af56-45682f74d61f",
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"metadata": {},
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"source": [
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"## Configure the graph\n",
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"\n",
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"Great! Now let's suppose that we want to extend this example so the user is able to choose from multiple llms.\n",
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"We can easily do that by passing in a config.\n",
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"This config is meant to contain things are not part of the input (and therefore that we don't want to track as part of the state)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "c01f1e7c-8e8b-4e26-98f7-56ac225077b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"openai_model = ChatOpenAI()\n",
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"\n",
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"models = {\n",
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" \"anthropic\": model,\n",
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" \"openai\": openai_model,\n",
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"}\n",
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"\n",
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"\n",
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"def _call_model(state, config):\n",
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" m = models[config[\"configurable\"].get(\"model\", \"anthropic\")]\n",
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" response = m.invoke(state[\"messages\"])\n",
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"# Define a new graph\n",
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"workflow = StateGraph(AgentState)\n",
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"workflow.add_node(\"model\", _call_model)\n",
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"workflow.set_entry_point(\"model\")\n",
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"workflow.add_edge(\"model\", END)\n",
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"\n",
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"app = workflow.compile()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7741b75c-55ba-4c78-bbb1-5dc20a210f11",
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"metadata": {},
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"source": [
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"If we call it with no configuration, it will use the default as we defined it (Anthropic)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "ef50f048-fc43-40c0-b713-346408fcf052",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [HumanMessage(content='hi'),\n",
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" AIMessage(content='Hello!', response_metadata={'id': 'msg_01EedReFyXmonWXPKhYre7Jb', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 10, 'output_tokens': 6}}, id='run-1c6feaa0-bd6f-433a-8264-209d72c85db7-0')]}"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"app.invoke({\"messages\": [HumanMessage(content=\"hi\")]})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6896b32-9b25-4342-bfd0-29a3d329a06a",
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"metadata": {},
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"source": [
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"We can also call it with a config to get it to use a different model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "f2f7c74b-9fb0-41c6-9728-dcf9d8a3c397",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [HumanMessage(content='hi'),\n",
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" AIMessage(content='Hello! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 8, 'total_tokens': 17}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'stop', 'logprobs': None}, id='run-d41ffb62-e164-45a1-862c-d288c6ad100a-0')]}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"config = {\"configurable\": {\"model\": \"openai\"}}\n",
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"app.invoke({\"messages\": [HumanMessage(content=\"hi\")]}, config=config)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b4c7eaf1-4ee0-42b3-971d-273a108f205f",
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"metadata": {},
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"source": [
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"We can also adapt our graph to take in more configuration! Like a system message for example."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "f0393a43-9fbe-4056-972f-3e91ea329041",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.messages import SystemMessage\n",
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"\n",
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"\n",
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"def _call_model(state, config):\n",
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" m = models[config[\"configurable\"].get(\"model\", \"anthropic\")]\n",
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" messages = state[\"messages\"]\n",
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" if \"system_message\" in config[\"configurable\"]:\n",
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" messages = [\n",
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" SystemMessage(content=config[\"configurable\"][\"system_message\"])\n",
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" ] + messages\n",
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" response = m.invoke(messages)\n",
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"# Define a new graph\n",
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"workflow = StateGraph(AgentState)\n",
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"workflow.add_node(\"model\", _call_model)\n",
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"workflow.set_entry_point(\"model\")\n",
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"workflow.add_edge(\"model\", END)\n",
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"\n",
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"app = workflow.compile()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "718685f7-4cdd-4181-9fc8-e7762d584727",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [HumanMessage(content='hi'),\n",
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" AIMessage(content='Hello!', response_metadata={'id': 'msg_01Ts56eVLSrUbzVMbzLnXc3M', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 10, 'output_tokens': 6}}, id='run-f75a4389-b72e-4d47-8f3e-bedc6a060f66-0')]}"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"app.invoke({\"messages\": [HumanMessage(content=\"hi\")]})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "e043a719-f197-46ef-9d45-84740a39aeb0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [HumanMessage(content='hi'),\n",
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" AIMessage(content='Ciao!', response_metadata={'id': 'msg_01RzFCii8WhbbkFm16nUquxk', 'model': 'claude-2.1', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 14, 'output_tokens': 7}}, id='run-9492f0e4-f223-41c2-81a6-6f0cb6a14fe6-0')]}"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"config = {\"configurable\": {\"system_message\": \"respond in italian\"}}\n",
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"app.invoke({\"messages\": [HumanMessage(content=\"hi\")]}, config=config)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a5c5f7f4-4b0e-4cde-93a6-c1c6329b8591",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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