迁移出 ConversationBufferWindowMemory 或 ConversationTokenBufferMemory

如果您尝试迁移出下面列出的旧内存类，请遵循本指南

内存类型	描述
ConversationBufferWindowMemory	保留对话的最后 n 条消息。当消息超过 n 条时，删除最旧的消息。
ConversationTokenBufferMemory	仅保留对话中最新的消息，约束条件是对话中的 token 总数不超过某个限制。

ConversationBufferWindowMemory 和 ConversationTokenBufferMemory 在原始对话历史记录之上应用额外的处理，以将对话历史记录修剪到适合聊天模型上下文窗口的大小。

此处理功能可以使用 LangChain 的内置 trim_messages 函数完成。

重要提示

我们将首先探索一种直接的方法，该方法涉及将处理逻辑应用于整个对话历史记录。

虽然这种方法易于实现，但它有一个缺点：随着对话的增长，延迟也会增加，因为逻辑会在每次轮次中重新应用于对话中的所有先前交流。

更高级的策略侧重于增量更新对话历史记录，以避免冗余处理。

例如，langgraph 关于摘要的指南演示了如何在保持对话的运行摘要的同时丢弃较旧的消息，确保它们不会在以后的轮次中重新处理。

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()

使用 LLMChain / Conversation Chain 的旧版用法

详情

from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
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(
    [
        SystemMessage(content="You are a helpful assistant."),
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{text}"),
    ]
)

memory = ConversationBufferWindowMemory(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"})
print(legacy_result)

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

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

重新实现 ConversationBufferWindowMemory 逻辑

让我们首先创建适当的逻辑来处理对话历史记录，然后我们将了解如何将其集成到应用程序中。您稍后可以将此基本设置替换为更高级的逻辑，以满足您的特定需求。

我们将使用 trim_messages 来实现保留对话最后 n 条消息的逻辑。当消息数量超过 n 时，它将删除最旧的消息。

此外，如果存在系统消息，我们也会保留它 -- 当存在时，它是对话中的第一条消息，其中包含聊天模型的指令。

from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    trim_messages,
)
from langchain_openai import ChatOpenAI

messages = [
    SystemMessage("you're a good assistant, you always respond with a joke."),
    HumanMessage("i wonder why it's called langchain"),
    AIMessage(
        'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!'
    ),
    HumanMessage("and who is harrison chasing anyways"),
    AIMessage(
        "Hmmm let me think.\n\nWhy, he's probably chasing after the last cup of coffee in the office!"
    ),
    HumanMessage("why is 42 always the answer?"),
    AIMessage(
        "Because it’s the only number that’s constantly right, even when it doesn’t add up!"
    ),
    HumanMessage("What did the cow say?"),
]

API 参考：AIMessage | BaseMessage | HumanMessage | SystemMessage | trim_messages | ChatOpenAI

from langchain_core.messages import trim_messages

selected_messages = trim_messages(
    messages,
    token_counter=len,  # <-- len will simply count the number of messages rather than tokens
    max_tokens=5,  # <-- allow up to 5 messages.
    strategy="last",
    # Most chat models expect that chat history starts with either:
    # (1) a HumanMessage or
    # (2) a SystemMessage followed by a HumanMessage
    # start_on="human" makes sure we produce a valid chat history
    start_on="human",
    # Usually, we want to keep the SystemMessage
    # if it's present in the original history.
    # The SystemMessage has special instructions for the model.
    include_system=True,
    allow_partial=False,
)

for msg in selected_messages:
    msg.pretty_print()

API 参考：trim_messages

================================ System Message ================================

you're a good assistant, you always respond with a joke.
================================== Ai Message ==================================

Hmmm let me think.

Why, he's probably chasing after the last cup of coffee in the office!
================================ Human Message =================================

why is 42 always the answer?
================================== Ai Message ==================================

Because it’s the only number that’s constantly right, even when it doesn’t add up!
================================ Human Message =================================

What did the cow say?

重新实现 ConversationTokenBufferMemory 逻辑

在这里，我们将使用 trim_messages 来保留系统消息和对话中最新的消息，约束条件是对话中的 token 总数不超过某个限制。

from langchain_core.messages import trim_messages

selected_messages = trim_messages(
    messages,
    # Please see API reference for trim_messages for other ways to specify a token counter.
    token_counter=ChatOpenAI(model="gpt-4o"),
    max_tokens=80,  # <-- token limit
    # The start_on is specified
    # Most chat models expect that chat history starts with either:
    # (1) a HumanMessage or
    # (2) a SystemMessage followed by a HumanMessage
    # start_on="human" makes sure we produce a valid chat history
    start_on="human",
    # Usually, we want to keep the SystemMessage
    # if it's present in the original history.
    # The SystemMessage has special instructions for the model.
    include_system=True,
    strategy="last",
)

for msg in selected_messages:
    msg.pretty_print()

API 参考：trim_messages

================================ System Message ================================

you're a good assistant, you always respond with a joke.
================================ Human Message =================================

why is 42 always the answer?
================================== Ai Message ==================================

Because it’s the only number that’s constantly right, even when it doesn’t add up!
================================ Human Message =================================

What did the cow say?

使用 LangGraph 的现代用法

下面的示例展示了如何使用 LangGraph 添加简单的对话预处理逻辑。

注意

如果您想避免每次都在整个对话历史记录上运行计算，您可以遵循 关于摘要的指南，该指南演示了如何丢弃较旧的消息，确保它们不会在以后的轮次中重新处理。

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):
    selected_messages = trim_messages(
        state["messages"],
        token_counter=len,  # <-- len will simply count the number of messages rather than tokens
        max_tokens=5,  # <-- allow up to 5 messages.
        strategy="last",
        # Most chat models expect that chat history starts with either:
        # (1) a HumanMessage or
        # (2) a SystemMessage followed by a HumanMessage
        # start_on="human" makes sure we produce a valid chat history
        start_on="human",
        # Usually, we want to keep the SystemMessage
        # if it's present in the original history.
        # The SystemMessage has special instructions for the model.
        include_system=True,
        allow_partial=False,
    )

    response = model.invoke(selected_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.
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!
config = {"configurable": {"thread_id": thread_id}}
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, Bob?

使用预构建的 langgraph agent

此示例展示了如何将 Agent Executor 与使用 create_tool_calling_agent 函数构建的预构建 agent 一起使用。

如果您正在使用 旧版 LangChain 预构建 agents 之一，您应该能够将该代码替换为新的 langgraph 预构建 agent，后者利用聊天模型的原生工具调用功能，并且可能开箱即用效果更好。

详情

import uuid

from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    trim_messages,
)
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()


def prompt(state) -> list[BaseMessage]:
    """Given the agent state, return a list of messages for the chat model."""
    # We're using the message processor defined above.
    return trim_messages(
        state["messages"],
        token_counter=len,  # <-- len will simply count the number of messages rather than tokens
        max_tokens=5,  # <-- allow up to 5 messages.
        strategy="last",
        # Most chat models expect that chat history starts with either:
        # (1) a HumanMessage or
        # (2) a SystemMessage followed by a HumanMessage
        # start_on="human" makes sure we produce a valid chat history
        start_on="human",
        # Usually, we want to keep the SystemMessage
        # if it's present in the original history.
        # The SystemMessage has special instructions for the model.
        include_system=True,
        allow_partial=False,
    )



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

# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
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：AIMessage | BaseMessage | HumanMessage | SystemMessage | trim_messages | tool | ChatOpenAI | MemorySaver | create_react_agent

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

hi! I'm bob. What is my age?
================================== Ai Message ==================================
Tool Calls:
  get_user_age (call_jsMvoIFv970DhqqLCJDzPKsp)
 Call ID: call_jsMvoIFv970DhqqLCJDzPKsp
  Args:
    name: bob
================================= Tool Message =================================
Name: get_user_age

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

Bob, you are 42 years old.
================================ Human Message =================================

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

Yes, your name is Bob.

LCEL：添加预处理步骤

添加复杂对话管理的最简单方法是在聊天模型前面引入一个预处理步骤，并将完整的对话历史记录传递给预处理步骤。

这种方法在概念上很简单，并且在许多情况下都适用；例如，如果使用 RunnableWithMessageHistory 而不是包装聊天模型，则使用预处理器包装聊天模型。

这种方法的明显缺点是，由于以下两个原因，延迟开始随着对话历史记录的增长而增加

1. 随着对话变得更长，可能需要从您用于存储对话历史记录的任何存储中获取更多数据（如果不是将其存储在内存中）。
2. 预处理逻辑最终将进行大量冗余计算，重复对话先前步骤的计算。

注意

如果您想使用聊天模型的工具调用功能，请记住在向模型添加历史记录预处理步骤之前将工具绑定到模型！

详情

from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    trim_messages,
)
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

model = ChatOpenAI()


@tool
def what_did_the_cow_say() -> str:
    """Check to see what the cow said."""
    return "foo"


message_processor = trim_messages(  # Returns a Runnable if no messages are provided
    token_counter=len,  # <-- len will simply count the number of messages rather than tokens
    max_tokens=5,  # <-- allow up to 5 messages.
    strategy="last",
    # The start_on is specified
    # to make sure we do not generate a sequence where
    # a ToolMessage that contains the result of a tool invocation
    # appears before the AIMessage that requested a tool invocation
    # as this will cause some chat models to raise an error.
    start_on=("human", "ai"),
    include_system=True,  # <-- Keep the system message
    allow_partial=False,
)

# Note that we bind tools to the model first!
model_with_tools = model.bind_tools([what_did_the_cow_say])

model_with_preprocessor = message_processor | model_with_tools

full_history = [
    SystemMessage("you're a good assistant, you always respond with a joke."),
    HumanMessage("i wonder why it's called langchain"),
    AIMessage(
        'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!'
    ),
    HumanMessage("and who is harrison chasing anyways"),
    AIMessage(
        "Hmmm let me think.\n\nWhy, he's probably chasing after the last cup of coffee in the office!"
    ),
    HumanMessage("why is 42 always the answer?"),
    AIMessage(
        "Because it’s the only number that’s constantly right, even when it doesn’t add up!"
    ),
    HumanMessage("What did the cow say?"),
]


# We pass it explicity to the model_with_preprocesor for illustrative purposes.
# If you're using `RunnableWithMessageHistory` the history will be automatically
# read from the source the you configure.
model_with_preprocessor.invoke(full_history).pretty_print()

API 参考：AIMessage | BaseMessage | HumanMessage | SystemMessage | trim_messages | tool | ChatOpenAI

================================== Ai Message ==================================
Tool Calls:
  what_did_the_cow_say (call_urHTB5CShhcKz37QiVzNBlIS)
 Call ID: call_urHTB5CShhcKz37QiVzNBlIS
  Args:

如果您需要实现更高效的逻辑，并且现在想使用 RunnableWithMessageHistory，实现此目的的方法是从 BaseChatMessageHistory 继承并为 add_messages 定义适当的逻辑（不只是简单地附加历史记录，而是重写它）。

除非您有充分的理由实施此解决方案，否则您应该改用 LangGraph。

下一步

探索 LangGraph 的持久性

• LangGraph 快速入门教程
• 如何向图中添加持久性（“内存”）
• 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 添加持久性（对于更复杂的使用案例，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