如何删除消息¶
图的一个常见状态是消息列表。通常情况下，您只会向该状态添加消息。然而，在某些情况下，您可能希望从该状态中移除消息（可以通过直接修改状态或作为图的一部分来实现）。为此，您可以使用RemoveMessage修饰符。本指南将介绍如何做到这一点。
关键思想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，而该键的reducer接受这些RemoveMessage修饰符。然后，该reducer使用这些RemoveMessage来从该键中删除消息。
因此请注意，仅仅因为您的图状态有一个表示消息列表的键，并不意味着这个RemoveMessage修饰符会起作用。您还需要定义一个知道如何处理这种操作的reducer。
注意：许多模型期望消息列表遵循特定规则。例如，有些模型期望消息列表以user消息开头，而另一些则期望所有包含工具调用的消息后面都跟随着一条工具消息。在删除消息时，请确保不会违反这些规则。
设置¶
首先，让我们构建一个使用消息的简单图。请注意，它使用了包含所需reducer的MessagesState。
接下来，我们需要为Anthropic（我们将使用的大型语言模型）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")
为LangGraph开发设置LangSmith
注册LangSmith可以快速发现并解决您的LangGraph项目中的问题，并提高其性能。通过使用跟踪数据，您可以调试、测试和监控使用LangGraph构建的LLM应用程序——更多关于如何开始的信息，请参阅这里。
构建代理¶
现在让我们构建一个简单的ReAct风格的代理。
API Reference: tool | MemorySaver | StateGraph | START | END | ToolNode
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: HumanMessage
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()
================================ Human Message =================================
hi! I'm bob
================================== Ai Message ==================================
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.
================================ Human Message =================================
what's my name?
================================== Ai Message ==================================
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。让我们看一下当前线程的状态：
[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。这将会删除该消息。
API Reference: RemoveMessage
from langchain_core.messages import RemoveMessage
app.update_state(config, {"messages": RemoveMessage(id=messages[0].id)})
{'configurable': {'thread_id': '2',
'checkpoint_ns': '',
'checkpoint_id': '1ef75157-f251-6a2a-8005-82a86a6593a0'}}
如果我们现在查看消息，可以验证第一个消息已被删除。
[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})]
通过编程删除消息¶
我们也可以从图内部通过编程方式删除消息。在这里，我们将修改图以在每次图运行结束时删除任何过时的消息（超过3条消息之前的）。
API Reference: RemoveMessage | END
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)
我们现在可以尝试一下。我们可以调用图两次，然后检查状态
API Reference: HumanMessage
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"]])
[('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.')]
[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})]
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**可能实际上已经无效**，这是因为当前它以一条AI消息开头，而某些模型不允许这种情况。