LangGraph(四)——加入人机交互控制

作者：CSDN博客
目录

1. 引言2. 添加Human Assistance工具3. 编译状态图4. 提示聊天机器人5. 恢复执行参考

1. 引言

  智能体可能不可靠，甚至需要人工输入才能完成任务。同样，对于某些操作，你可能需要在运行前获得人工批准，以保证一切按预期运行。
  LangGraph的持久层支持人机交互工作流，允许根据用户反馈暂停和恢复执行。此功能的主要接口是interrupt函数。在节点内部调用Interrupt将暂停执行。可以通过传入command来interrupt执行，并接收新的人工输入。interrupt在人机工程学上类似于Python的内置input()，但也有一些注意事项。
2. 添加Human Assistance工具

  初始化聊天模型：

1. from langchain.chat_models import init_chat_model
3. llm = init_chat_model("deepseek:deepseek-chat")

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  使用附加工具将human assistance附加到状态图中：

1. from typing import Annotated
3. from langchain_tavily import TavilySearch
4. from langchain_core.tools import tool
5. from typing_extensions import TypedDict
7. from langgraph.checkpoint.memory import MemorySaver
8. from langgraph.graph import StateGraph, START, END
9. from langgraph.graph.message import add_messages
10. from langgraph.prebuilt import ToolNode, tools_condition
12. from langgraph.types import Command, interrupt
14. classState(TypedDict):
15.     messages: Annotated[list, add_messages]
17. graph_builder = StateGraph(State)@tooldefhuman_assistance(query:str)->str:"""Request assistance from a human."""
18.     human_response = interrupt({"query": query})return human_response["data"]
20. tool = TavilySearch(max_results=2)
21. tools =[tool, human_assistance]
22. llm_with_tools = llm.bind_tools(tools)defchatbot(state: State):
23.     message = llm_with_tools.invoke(state["messages"])# Because we will be interrupting during tool execution,# we disable parallel tool calling to avoid repeating any# tool invocations when we resume.assertlen(message.tool_calls)<=1return{"messages":[message]}
25. graph_builder.add_node("chatbot", chatbot)
27. tool_node = ToolNode(tools=tools)
28. graph_builder.add_node("tools", tool_node)
30. graph_builder.add_conditional_edges("chatbot",
31.     tools_condition,)
32. graph_builder.add_edge("tools","chatbot")
33. graph_builder.add_edge(START,"chatbot")

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3. 编译状态图

  使用检查点编译状态图：

1. memory = MemorySaver()
3. graph = graph_builder.compile(checkpointer=memory)

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4. 提示聊天机器人

  向聊天机器人提出一个问题，该问题将使用human assistance工具：

1. user_input ="I need some expert guidance for building an AI agent. Could you request assistance for me?"
2. config ={"configurable":{"thread_id":"1"}}
4. events = graph.stream({"messages":[{"role":"user","content": user_input}]},
5.     config,
6.     stream_mode="values",)for event in events:if"messages"in event:
7.         event["messages"][-1].pretty_print()

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  运行结果为：

  聊天机器人生成了一个工具调用，但随后执行被中断。如果你检查状态图，会发现它在工具节点处停止了：

1. snapshot = graph.get_state(config)
2. snapshot.next

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  运行结果为：

1. ('tools',)

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5. 恢复执行

  要恢复执行需要传递一个包含工具所需数据的Command对象。此数据的格式可根据需要自定义。在本例中，使用一个带有键”data"字典：

1. human_response =("We, the experts are here to help! We'd recommend you check out LangGraph to build your agent."" It's much more reliable and extensible than simple autonomous agents.")
3. human_command = Command(resume={"data": human_response})
5. events = graph.stream(human_command, config, stream_mode="values")for event in events:if"messages"in event:
6.         event["messages"][-1].pretty_print()

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  运行结果为：

1. ================================== Ai Message ==================================
2. Tool Calls:
3.   human_assistance (call_0_cee258cf-15db-49d4-8495-46761c7ddc65)
4. Call ID: call_0_cee258cf-15db-49d4-8495-46761c7ddc65
5.   Args:
6.     query: I need expert guidance for building an AI agent.================================= Tool Message =================================
7. Name: human_assistance
9. We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.================================== Ai Message ==================================
11. Great! It seems the experts recommend using **LangGraph**for building your AI agent,as it is more reliable and extensible compared to simple autonomous agents.
13. If you'd like, I can provide more details about LangGraph or assist you with specific steps to get started. Let me know how you'd like to proceed!

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参考

https://langchain-ai.github.io/langgraph/tutorials/get-started/4-human-in-the-loop/

原文地址：https://blog.csdn.net/qq_51180928/article/details/148014677