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如何为你的图添加线程级持久化功能

前提条件

本指南假设你熟悉以下内容：

LangGraph API 用户无需操作

如果你使用的是 LangGraph API，则无需手动实现检查点保存器。API 会自动为你处理检查点保存。本指南适用于在你自己的自定义服务器中实现 LangGraph 的情况。

许多 AI 应用程序需要内存来在多次交互中共享上下文。在 LangGraph 中，可以使用线程级持久化功能，将这种内存添加到任何 StateGraph 中。

在创建任何 LangGraph 图时，你可以在编译图时添加一个检查点保存器，从而设置该图以持久保存其状态：

from langgraph.checkpoint.memory import MemorySaver

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

本指南将展示如何为你的图添加线程级持久化功能。

注意

如果你需要在多个对话或用户之间**共享**的内存（跨线程持久化），请查看此操作指南。

安装设置

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（我们将使用的大语言模型）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，以便快速发现问题并提升你的 LangGraph 项目的性能。LangSmith 允许你使用跟踪数据来调试、测试和监控使用 LangGraph 构建的大语言模型应用程序 — 点击 此处 了解更多关于如何开始使用的信息。

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使用的模型：

from langchain_anthropic import ChatAnthropic

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

现在我们可以定义我们的 StateGraph 并添加调用模型的节点：

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()

如果我们尝试使用此图，对话上下文将不会在各交互之间持久保存：

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.

添加持久化功能

要添加持久化功能，我们需要在编译图时传入一个 检查点保存器。

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.

注意

如果您使用的是 LangGraph Cloud 或 LangGraph Studio，在编译图时无需传递检查点器，因为这会自动完成。

现在我们可以与智能体进行交互，并发现它能记住之前的消息！

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?
你始终可以恢复之前的对话线程：

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.
如果我们想开启一个新的对话，我们可以传入一个不同的 thread_id。噗！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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