在 LangGraph 中集成 Mem0 记忆系统教程

米落枫 · 发表于 9 小时前

作者：CSDN博客
简介

LangGraph 是一个强大的对话流程编排框架，而 Mem0 则是一个高效的记忆系统。本教程将介绍如何将两者结合，创建一个具有记忆能力的客服助手系统。
环境准备

首先安装必要的依赖：

pip install langgraph mem0 langchain openai

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基础配置

1. 导入必要的模块

from openai import OpenAI
from mem0 import Memory
from mem0.configs.base import MemoryConfig
from mem0.embeddings.configs import EmbedderConfig
from mem0.llms.configs import LlmConfig
from typing import Annotated, TypedDict, List
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage

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2. 配置基础参数

# 集中管理配置
API_KEY ="your-api-key"
BASE_URL ="your-base-url"# 配置 LLM
llm = ChatOpenAI(
temperature=0,
openai_api_key=API_KEY,
openai_api_base=BASE_URL,
model="qwen-turbo")# 配置 Mem0
config = MemoryConfig(
llm = LlmConfig(
provider="openai",
config={"model":"qwen-turbo","api_key": API_KEY,"openai_base_url": BASE_URL
}),
embedder = EmbedderConfig(
provider="openai",
config={"embedding_dims":1536,"model":"text-embedding-v2","api_key": API_KEY,"openai_base_url": BASE_URL
}))

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核心概念解析

1. 状态定义

在 LangGraph 中，我们需要定义对话状态：

class State(TypedDict):
messages: Annotated[List[HumanMessage | AIMessage], add_messages]
mem0_user_id: str

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这个状态包含：

2. 对话节点实现

defchatbot(state: State):
messages = state["messages"]
user_id = state["mem0_user_id"]# 检索相关记忆
memories = mem0.search(messages[-1].content, user_id=user_id)
context ="\n".join([f"- {memory['memory']}"for memory in memories["results"]])# 构建系统提示
system_prompt =f"""You are a helpful customer support assistant. Use the provided context to personalize your responses and remember user preferences and past interactions.
Relevant information from previous conversations:
{context}"""# 生成回复并存储记忆
response = llm.invoke([SystemMessage(content=system_prompt)]+ messages)
mem0.add(f"User: {messages[-1].content}\nAssistant: {response.content}", user_id=user_id)return{"messages":[response]}

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3. 图结构构建

graph = StateGraph(State)
graph.add_node("chatbot", chatbot)
graph.add_edge(START,"chatbot")
graph.add_edge("chatbot","chatbot")
compiled_graph = graph.compile()

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工作流程解析

状态初始化

记忆检索

上下文整合

响应生成

运行对话

defrun_conversation(user_input:str, mem0_user_id:str):
config ={"configurable":{"thread_id": mem0_user_id}}
state ={"messages":[HumanMessage(content=user_input)],"mem0_user_id": mem0_user_id}for event in compiled_graph.stream(state, config):for value in event.values():if value.get("messages"):print("Customer Support:", value["messages"][-1].content)return

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主程序示例

if __name__ =="__main__":
mem0_user_id ="customer_123"print("Welcome to Customer Support! How can I assist you today?")while(user_input :=input("You: ").lower())notin['quit','exit','bye']:
run_conversation(user_input, mem0_user_id)

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关键特性

状态管理

记忆检索

流程编排

完整代码与示例

from openai import OpenAI
from mem0 import Memory
from mem0.configs.base import MemoryConfig
from mem0.embeddings.configs import EmbedderConfig
from mem0.llms.configs import LlmConfig
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from typing import List, Dict
API_KEY ="your api key"
BASE_URL ="https://dashscope.aliyuncs.com/compatible-mode/v1"
openai_client = OpenAI(
api_key=API_KEY,
base_url=BASE_URL,)
llm = ChatOpenAI(
temperature=0,
openai_api_key=API_KEY,
openai_api_base=BASE_URL,
model="qwen-turbo")
config = MemoryConfig(
llm = LlmConfig(
provider="openai",
config={"model":"qwen-turbo","api_key": API_KEY,"openai_base_url": BASE_URL
}),
embedder = EmbedderConfig(
provider="openai",
config={"embedding_dims":1536,"model":"text-embedding-v2","api_key": API_KEY,"openai_base_url": BASE_URL
}))
mem0 = Memory(config=config)
prompt = ChatPromptTemplate.from_messages([
SystemMessage(content="""You are a helpful travel agent AI. Use the provided context to personalize your responses and remember user preferences and past interactions.
Provide travel recommendations, itinerary suggestions, and answer questions about destinations.
If you don't have specific information, you can make general suggestions based on common travel knowledge."""),
MessagesPlaceholder(variable_name="context"),
HumanMessage(content="{input}")])defretrieve_context(query:str, user_id:str)-> List[Dict]:"""Retrieve relevant context from Mem0"""
memories = mem0.search(query, user_id=user_id)
seralized_memories =' '.join([mem["memory"]for mem in memories["results"]])
context =[{"role":"system","content":f"Relevant information: {seralized_memories}"},{"role":"user","content": query
}]return context
defgenerate_response(input:str, context: List[Dict])->str:"""Generate a response using the language model"""
chain = prompt | llm
response = chain.invoke({"context": context,"input":input})return response.content
defsave_interaction(user_id:str, user_input:str, assistant_response:str):"""Save the interaction to Mem0"""
interaction =[{"role":"user","content": user_input
},{"role":"assistant","content": assistant_response
}]
mem0.add(interaction, user_id=user_id)defchat_turn(user_input:str, user_id:str)->str:# Retrieve context
context = retrieve_context(user_input, user_id)# Generate response
response = generate_response(user_input, context)# Save interaction
save_interaction(user_id, user_input, response)return response
if __name__ =="__main__":print("Welcome to your personal Travel Agent Planner! How can I assist you with your travel plans today?")
user_id ="john"whileTrue:
user_input =input("You: ")if user_input.lower()in['quit','exit','bye']:print("Travel Agent: Thank you for using our travel planning service. Have a great trip!")break
response = chat_turn(user_input, user_id)print(f"Travel Agent: {response}")

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原文地址：https://blog.csdn.net/qq_41472205/article/details/148260565

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