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如何为你的图添加线程级持久化

先决条件

本指南假定您熟悉以下内容

许多 AI 应用程序需要内存来跨多个交互共享上下文。在 LangGraph 中，这种内存可以使用线程级持久化添加到任何 StateGraph 中。

在创建任何 LangGraph 图时，您可以通过在编译图时添加 检查点器 来设置持久化其状态

from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
graph.compile(checkpointer=checkpointer)

API 参考：MemorySaver

本指南展示了如何为你的图添加线程级持久化。

注意

如果您需要跨多个对话或用户共享的内存（跨线程持久化），请查看此操作指南。

设置

首先，我们需要安装所需的软件包

%%capture --no-stderr
%pip install --quiet -U langgraph langchain_anthropic

接下来，我们需要为 Anthropic（我们将使用的 LLM）设置 API 密钥。

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")

ANTHROPIC_API_KEY:  ········

为 LangGraph 开发设置 LangSmith

注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用程序 — 阅读此处了解更多关于如何开始的信息。

定义图

我们将使用一个调用 聊天模型 的单节点图。

让我们首先定义我们将要使用的模型

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-3-5-sonnet-20240620")

API 参考：ChatAnthropic

现在我们可以定义我们的 StateGraph 并添加我们的模型调用节点

from typing import Annotated
from typing_extensions import TypedDict

from langgraph.graph import StateGraph, MessagesState, START


def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": response}


builder = StateGraph(MessagesState)
builder.add_node("call_model", call_model)
builder.add_edge(START, "call_model")
graph = builder.compile()

API 参考：StateGraph | START

如果我们尝试使用此图，对话的上下文将不会在交互之间持久化

input_message = {"role": "user", "content": "hi! I'm bob"}
for chunk in graph.stream({"messages": [input_message]}, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

input_message = {"role": "user", "content": "what's my name?"}
for chunk in graph.stream({"messages": [input_message]}, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

================================ Human Message =================================

hi! I'm bob
================================== Ai Message ==================================

Hello Bob! It's nice to meet you. How are you doing today? Is there anything I can help you with or would you like to chat about something in particular?
================================ Human Message =================================

what's my name?
================================== Ai Message ==================================

I apologize, but I don't have access to your personal information, including your name. I'm an AI language model designed to provide general information and answer questions to the best of my ability based on my training data. I don't have any information about individual users or their personal details. If you'd like to share your name, you're welcome to do so, but I won't be able to recall it in future conversations.

添加持久化

为了添加持久化，我们需要在编译图时传入一个 Checkpointer。

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
graph = builder.compile(checkpointer=memory)
# If you're using LangGraph Cloud or LangGraph Studio, you don't need to pass the checkpointer when compiling the graph, since it's done automatically.

API 参考：MemorySaver

注意

如果您正在使用 LangGraph Cloud 或 LangGraph Studio，则不需要在编译图时传递检查点器，因为它会自动完成。

我们现在可以与代理交互，并看到它记住了之前的消息！

config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

================================ Human Message =================================

hi! I'm bob
================================== Ai Message ==================================

Hello Bob! It's nice to meet you. How are you doing today? Is there anything in particular you'd like to chat about or any questions you have that I can help you with?
您可以随时恢复之前的线程

input_message = {"role": "user", "content": "what's my name?"}
for chunk in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

================================ Human Message =================================

what's my name?
================================== Ai Message ==================================

Your name is Bob, as you introduced yourself at the beginning of our conversation.
如果我们想开始新的对话，我们可以传入不同的 thread_id。嘭！所有的记忆都消失了！

input_message = {"role": "user", "content": "what's my name?"}
for chunk in graph.stream(
    {"messages": [input_message]},
    {"configurable": {"thread_id": "2"}},
    stream_mode="values",
):
    chunk["messages"][-1].pretty_print()

================================ Human Message =================================

what's is my name?
================================== Ai Message ==================================

I apologize, but I don't have access to your personal information, including your name. As an AI language model, I don't have any information about individual users unless it's provided within the conversation. If you'd like to share your name, you're welcome to do so, but otherwise, I won't be able to know or guess it.

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