如何删除消息¶

图的常见状态之一是消息列表。通常，你只会向该状态添加消息。然而，有时你可能想要删除消息（可以通过直接修改状态，也可以作为图的一部分来进行）。要实现这一点，你可以使用 RemoveMessage 修饰符。在本指南中，我们将介绍如何操作。

关键思路是每个状态键都有一个 reducer 键。这个键指定了如何合并对状态的更新。默认的 MessagesState 有一个消息键，该键的归约器（reducer）接受这些 RemoveMessage 修饰符。然后，该归约器使用这些 RemoveMessage 从键中删除消息。

因此请注意，仅仅因为你的图状态有一个是消息列表的键，并不意味着 RemoveMessage 修饰符就能起作用。你还必须定义一个知道如何处理此操作的 reducer。

注意：许多模型对消息列表有特定的规则要求。例如，有些模型要求消息列表以 user 消息开头，有些则要求所有带有工具调用的消息后面都跟着一条工具消息。删除消息时，你需要确保不违反这些规则。

配置¶

首先，让我们构建一个使用消息的简单图。请注意，它使用了具有所需 reducer 的 MessagesState。

%%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。

构建代理¶

现在，让我们来构建一个简单的 ReAct 风格的代理。

from typing import Literal

from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import MessagesState, StateGraph, START, END
from langgraph.prebuilt import ToolNode

memory = MemorySaver()


@tool
def search(query: str):
    """Call to surf the web."""
    # This is a placeholder for the actual implementation
    # Don't let the LLM know this though 😊
    return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."


tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model_name="claude-3-haiku-20240307")
bound_model = model.bind_tools(tools)


def should_continue(state: MessagesState):
    """Return the next node to execute."""
    last_message = state["messages"][-1]
    # If there is no function call, then we finish
    if not last_message.tool_calls:
        return END
    # Otherwise if there is, we continue
    return "action"


# 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 a new graph
workflow = StateGraph(MessagesState)

# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)

# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START, "agent")

# We now add a conditional edge
workflow.add_conditional_edges(
    # First, we define the start node. We use `agent`.
    # This means these are the edges taken after the `agent` node is called.
    "agent",
    # Next, we pass in the function that will determine which node is called next.
    should_continue,
    # Next, we pass in the path map - all the possible nodes this edge could go to
    ["action", END],
)

# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("action", "agent")

# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
app = workflow.compile(checkpointer=memory)

API Reference: tool

from langchain_core.messages import HumanMessage

config = {"configurable": {"thread_id": "2"}}
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()


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

API Reference: HumanMessage

================================[1m Human Message [0m=================================

hi! I'm bob
==================================[1m Ai Message [0m==================================

It's nice to meet you, Bob! I'm an AI assistant created by Anthropic. I'm here to help out with any questions or tasks you might have. Please let me know if there's anything I can assist you with.
================================[1m Human Message [0m=================================

what's my name?
==================================[1m Ai Message [0m==================================

You said your name is Bob.

手动删除消息¶

首先，我们将介绍如何手动删除消息。让我们看看当前线程的状态：

messages = app.get_state(config).values["messages"]
messages

[HumanMessage(content="hi! I'm bob", additional_kwargs={}, response_metadata={}, id='db576005-3a60-4b3b-8925-dc602ac1c571'),
 AIMessage(content="It's nice to meet you, Bob! I'm an AI assistant created by Anthropic. I'm here to help out with any questions or tasks you might have. Please let me know if there's anything I can assist you with.", additional_kwargs={}, response_metadata={'id': 'msg_01BKAnYxmoC6bQ9PpCuHk8ZT', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 52}}, id='run-3a60c536-b207-4c56-98f3-03f94d49a9e4-0', usage_metadata={'input_tokens': 12, 'output_tokens': 52, 'total_tokens': 64}),
 HumanMessage(content="what's my name?", additional_kwargs={}, response_metadata={}, id='2088c465-400b-430b-ad80-fad47dc1f2d6'),
 AIMessage(content='You said your name is Bob.', additional_kwargs={}, response_metadata={'id': 'msg_013UWTLTzwZi81vke8mMQ2KP', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 72, 'output_tokens': 10}}, id='run-3a6883be-0c52-4938-af98-e9e7476659eb-0', usage_metadata={'input_tokens': 72, 'output_tokens': 10, 'total_tokens': 82})]

我们可以调用 update_state 并传入第一条消息的 ID。这将删除该消息。

from langchain_core.messages import RemoveMessage

app.update_state(config, {"messages": RemoveMessage(id=messages[0].id)})

API Reference: RemoveMessage

{'configurable': {'thread_id': '2',
  'checkpoint_ns': '',
  'checkpoint_id': '1ef75157-f251-6a2a-8005-82a86a6593a0'}}

如果我们现在查看消息，就可以验证第一条消息已被删除。

messages = app.get_state(config).values["messages"]
messages

[AIMessage(content="It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I assist you today?", response_metadata={'id': 'msg_01XPSAenmSqK8rX2WgPZHfz7', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-1c69af09-adb1-412d-9010-2456e5a555fb-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44}),
 HumanMessage(content="what's my name?", id='f3c71afe-8ce2-4ed0-991e-65021f03b0a5'),
 AIMessage(content='Your name is Bob, as you introduced yourself at the beginning of our conversation.', response_metadata={'id': 'msg_01BPZdwsjuMAbC1YAkqawXaF', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 19}}, id='run-b2eb9137-2f4e-446f-95f5-3d5f621a2cf8-0', usage_metadata={'input_tokens': 52, 'output_tokens': 19, 'total_tokens': 71})]

以编程方式删除消息¶

我们还可以在图内部以编程方式删除消息。在这里，我们将修改图，以便在图运行结束时删除任何旧消息（即 3 条消息之前的消息）。

from langchain_core.messages import RemoveMessage
from langgraph.graph import END


def delete_messages(state):
    messages = state["messages"]
    if len(messages) > 3:
        return {"messages": [RemoveMessage(id=m.id) for m in messages[:-3]]}


# We need to modify the logic to call delete_messages rather than end right away
def should_continue(state: MessagesState) -> Literal["action", "delete_messages"]:
    """Return the next node to execute."""
    last_message = state["messages"][-1]
    # If there is no function call, then we call our delete_messages function
    if not last_message.tool_calls:
        return "delete_messages"
    # Otherwise if there is, we continue
    return "action"


# Define a new graph
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)

# This is our new node we're defining
workflow.add_node(delete_messages)


workflow.add_edge(START, "agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
)
workflow.add_edge("action", "agent")

# This is the new edge we're adding: after we delete messages, we finish
workflow.add_edge("delete_messages", END)
app = workflow.compile(checkpointer=memory)

API Reference: RemoveMessage

现在我们可以来试一试。我们可以调用该图两次，然后检查状态。

from langchain_core.messages import HumanMessage

config = {"configurable": {"thread_id": "3"}}
input_message = HumanMessage(content="hi! I'm bob")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    print([(message.type, message.content) for message in event["messages"]])


input_message = HumanMessage(content="what's my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
    print([(message.type, message.content) for message in event["messages"]])

API Reference: HumanMessage

[('human', "hi! I'm bob")]
[('human', "hi! I'm bob"), ('ai', "Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.")]
[('human', "hi! I'm bob"), ('ai', "Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you."), ('human', "what's my name?")]
[('human', "hi! I'm bob"), ('ai', "Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you."), ('human', "what's my name?"), ('ai', 'You said your name is Bob, so that is the name I have for you.')]
[('ai', "Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you."), ('human', "what's my name?"), ('ai', 'You said your name is Bob, so that is the name I have for you.')]

如果我们现在检查状态，应该会看到它只有三条消息。这是因为我们刚刚删除了较早的消息 —— 否则应该有四条！

messages = app.get_state(config).values["messages"]
messages

[AIMessage(content="Hello Bob! It's nice to meet you. I'm an AI assistant created by Anthropic. I'm here to help with any questions or tasks you might have. Please let me know how I can assist you.", response_metadata={'id': 'msg_01XPEgPPbcnz5BbGWUDWTmzG', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 48}}, id='run-eded3820-b6a9-4d66-9210-03ca41787ce6-0', usage_metadata={'input_tokens': 12, 'output_tokens': 48, 'total_tokens': 60}),
 HumanMessage(content="what's my name?", id='a0ea2097-3280-402b-92e1-67177b807ae8'),
 AIMessage(content='You said your name is Bob, so that is the name I have for you.', response_metadata={'id': 'msg_01JGT62pxhrhN4SykZ57CSjW', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 68, 'output_tokens': 20}}, id='run-ace3519c-81f8-45fe-a777-91f42d48b3a3-0', usage_metadata={'input_tokens': 68, 'output_tokens': 20, 'total_tokens': 88})]

请记住，在删除消息时，你需要确保剩余的消息列表仍然有效。这个消息列表**实际上可能无效** —— 这是因为它当前以一条 AI 消息开头，而有些模型不允许这样。

如何删除消息¶

配置¶

构建代理¶

手动删除消息¶

以编程方式删除消息¶

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