迁移出 ConversationBufferMemory 或 ConversationStringBufferMemory

ConversationBufferMemory 和 ConversationStringBufferMemory 用于跟踪人类和 AI 助手之间的对话，而无需任何额外的处理。

注意

ConversationStringBufferMemory 与 ConversationBufferMemory 等效，但目标是那些非聊天模型的 LLM。

使用现有现代原语处理对话历史记录的方法有：

1. 使用 LangGraph 持久化 以及对消息历史记录的适当处理
2. 使用 LCEL 和 RunnableWithMessageHistory 以及对消息历史记录的适当处理。

大多数用户会发现 LangGraph 持久化 比等效的 LCEL 更易于使用和配置，尤其是在更复杂的使用场景中。

设置

%%capture --no-stderr
%pip install --upgrade --quiet langchain-openai langchain

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

与 LLMChain / ConversationChain 的用法

本节展示如何从与 LLMChain 或 ConversationChain 一起使用的 ConversationBufferMemory 或 ConversationStringBufferMemory 迁移。

旧版

以下是 ConversationBufferMemory 与 LLMChain 或等效的 ConversationChain 一起使用的示例。

详情

from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_core.messages import SystemMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
)
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate(
    [
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{text}"),
    ]
)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

legacy_chain = LLMChain(
    llm=ChatOpenAI(),
    prompt=prompt,
    memory=memory,
)

legacy_result = legacy_chain.invoke({"text": "my name is bob"})
print(legacy_result)

legacy_result = legacy_chain.invoke({"text": "what was my name"})

API 参考：LLMChain | ConversationBufferMemory | SystemMessage | ChatPromptTemplate | ChatPromptTemplate | HumanMessagePromptTemplate | MessagesPlaceholder | ChatOpenAI

{'text': 'Hello Bob! How can I assist you today?', 'chat_history': [HumanMessage(content='my name is bob', additional_kwargs={}, response_metadata={}), AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, response_metadata={})]}

legacy_result["text"]

'Your name is Bob. How can I assist you today, Bob?'

注意

请注意，单个内存对象不支持分离对话线程

LangGraph

下面的示例演示如何使用 LangGraph 实现带有 ConversationBufferMemory 的 ConversationChain 或 LLMChain。

本示例假设你已经对 LangGraph 有所了解。 如果没有，请参阅 LangGraph 快速入门指南 以了解更多详细信息。

LangGraph 提供了许多附加功能（例如，时间旅行和中断），并且可以很好地适用于其他更复杂（和现实）的架构。

详情

import uuid

from IPython.display import Image, display
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, MessagesState, StateGraph

# Define a new graph
workflow = StateGraph(state_schema=MessagesState)

# Define a chat model
model = ChatOpenAI()


# Define the function that calls the model
def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    # We return a list, because this will get added to the existing list
    return {"messages": response}


# Define the two nodes we will cycle between
workflow.add_edge(START, "model")
workflow.add_node("model", call_model)


# Adding memory is straight forward in langgraph!
memory = MemorySaver()

app = workflow.compile(
    checkpointer=memory
)


# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
# This enables a single application to manage conversations among multiple users.
thread_id = uuid.uuid4()
config = {"configurable": {"thread_id": thread_id}}


input_message = HumanMessage(content="hi! I'm bob")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

# Here, let's confirm that the AI remembers our name!
input_message = HumanMessage(content="what was my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

API 参考：HumanMessage | MemorySaver | StateGraph

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

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

Hello Bob! How can I assist you today?
================================ Human Message =================================

what was my name?
================================== Ai Message ==================================

Your name is Bob. How can I help you today, Bob?

LCEL RunnableWithMessageHistory

或者，如果你有一个简单的链，你可以将链的聊天模型包装在 RunnableWithMessageHistory 中。

有关更多信息，请参阅以下 迁移指南。

与预构建代理的用法

此示例演示如何使用使用 create_tool_calling_agent 函数构建的预构建代理的 Agent Executor 的用法。

如果你使用的是 旧的 LangChain 预构建代理，你应该能够用新的 langgraph 预构建代理 替换该代码，该代理利用聊天模型的原生工具调用功能，并且很可能开箱即用。

旧版用法

详情

from langchain import hub
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain.memory import ConversationBufferMemory
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

model = ChatOpenAI(temperature=0)


@tool
def get_user_age(name: str) -> str:
    """Use this tool to find the user's age."""
    # This is a placeholder for the actual implementation
    if "bob" in name.lower():
        return "42 years old"
    return "41 years old"


tools = [get_user_age]

prompt = ChatPromptTemplate.from_messages(
    [
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}"),
    ]
)

# Construct the Tools agent
agent = create_tool_calling_agent(model, tools, prompt)
# Instantiate memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# Create an agent
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,  # Pass the memory to the executor
)

# Verify that the agent can use tools
print(agent_executor.invoke({"input": "hi! my name is bob what is my age?"}))
print()
# Verify that the agent has access to conversation history.
# The agent should be able to answer that the user's name is bob.
print(agent_executor.invoke({"input": "do you remember my name?"}))

API 参考：hub | AgentExecutor | create_tool_calling_agent | ConversationBufferMemory | tool | ChatOpenAI

{'input': 'hi! my name is bob what is my age?', 'chat_history': [HumanMessage(content='hi! my name is bob what is my age?', additional_kwargs={}, response_metadata={}), AIMessage(content='Bob, you are 42 years old.', additional_kwargs={}, response_metadata={})], 'output': 'Bob, you are 42 years old.'}

{'input': 'do you remember my name?', 'chat_history': [HumanMessage(content='hi! my name is bob what is my age?', additional_kwargs={}, response_metadata={}), AIMessage(content='Bob, you are 42 years old.', additional_kwargs={}, response_metadata={}), HumanMessage(content='do you remember my name?', additional_kwargs={}, response_metadata={}), AIMessage(content='Yes, your name is Bob.', additional_kwargs={}, response_metadata={})], 'output': 'Yes, your name is Bob.'}

LangGraph

你可以按照标准的 LangChain 教程进行 构建代理，其中深入解释了其工作原理。

此处显式显示此示例是为了方便用户比较旧版实现与相应的 langgraph 实现。

此示例演示如何向 langgraph 中的 预构建的 react 代理 添加内存。

有关更多详细信息，请参阅 langgraph 中 如何将内存添加到预构建的 ReAct 代理 指南。

详情

import uuid

from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent


@tool
def get_user_age(name: str) -> str:
    """Use this tool to find the user's age."""
    # This is a placeholder for the actual implementation
    if "bob" in name.lower():
        return "42 years old"
    return "41 years old"


memory = MemorySaver()
model = ChatOpenAI()
app = create_react_agent(
    model,
    tools=[get_user_age],
    checkpointer=memory,
)

# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
# This enables a single application to manage conversations among multiple users.
thread_id = uuid.uuid4()
config = {"configurable": {"thread_id": thread_id}}

# Tell the AI that our name is Bob, and ask it to use a tool to confirm
# that it's capable of working like an agent.
input_message = HumanMessage(content="hi! I'm bob. What is my age?")

for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

# Confirm that the chat bot has access to previous conversation
# and can respond to the user saying that the user's name is Bob.
input_message = HumanMessage(content="do you remember my name?")

for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

API 参考：HumanMessage | tool | ChatOpenAI | MemorySaver | create_react_agent

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

hi! I'm bob. What is my age?
================================== Ai Message ==================================
Tool Calls:
  get_user_age (call_oEDwEbIDNdokwqhAV6Azn47c)
 Call ID: call_oEDwEbIDNdokwqhAV6Azn47c
  Args:
    name: bob
================================= Tool Message =================================
Name: get_user_age

42 years old
================================== Ai Message ==================================

Bob, you are 42 years old! If you need any more assistance or information, feel free to ask.
================================ Human Message =================================

do you remember my name?
================================== Ai Message ==================================

Yes, your name is Bob. If you have any other questions or need assistance, feel free to ask!

如果我们使用不同的线程 ID，它将开始新的对话，并且机器人将不知道我们的名字！

config = {"configurable": {"thread_id": "123456789"}}

input_message = HumanMessage(content="hi! do you remember my name?")

for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

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

hi! do you remember my name?
================================== Ai Message ==================================

Hello! Yes, I remember your name. It's great to see you again! How can I assist you today?

后续步骤

探索 LangGraph 的持久化

• LangGraph 快速入门教程
• 如何将持久化（“内存”）添加到你的图中
• 如何管理对话历史记录
• 如何添加对话历史记录的摘要

使用简单的 LCEL 添加持久化（对于更复杂的使用场景，请优先选择 langgraph）

• 如何添加消息历史记录

使用消息历史记录

• 如何修剪消息
• 如何过滤消息
• 如何合并消息运行