chore: rename multiple files to improve importability and module structure
This commit is contained in:
Annyfee
@@ -0,0 +1,119 @@
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import os
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from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
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from langchain_openai import ChatOpenAI
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from langchain.tools import tool
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from langchain_core.messages import HumanMessage, SystemMessage
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from langgraph.graph import StateGraph, MessagesState, START, END
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from langgraph.prebuilt import ToolNode
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from typing import TypedDict
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# LangSmith调试
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os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
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os.environ["LANGCHAIN_PROJECT"] = "graph_as_tool" # 自定义项目名
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os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
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# llm配置
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 构建子工作流
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# 1.子任务状态
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class RetryState(TypedDict):
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query: str
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attempt: int
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result: str
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# 2.子图逻辑 -- 模拟一个可能失败，需重试的API调用
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def call_unstable_api(state:RetryState):
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"""模拟偶发性的外部服务，偶发失败"""
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attempt = state["attempt"]
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if attempt == 1:
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# 第一次故意失败
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return {"result":"ERROR：服务暂时不可用","attempt":attempt+1}
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else:
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# 第二次成功
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return {"result":f"SUCCESS：成功处理请求：{state['query']}","attempt":attempt+1}
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def should_retry(state:RetryState):
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if "ERROR" in state["result"] and state["attempt"] <= 2: # 出现报错且重试次数小于2，重连
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return "call_api"
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return END
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# 3.构建子图工作流
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retry_workflow = StateGraph(RetryState)
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retry_workflow.add_node("call_api",call_unstable_api)
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retry_workflow.add_edge(START,"call_api")
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retry_workflow.add_conditional_edges(
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"call_api",
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should_retry,
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{"call_api":"call_api",END:END}
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)
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retry_app = retry_workflow.compile()
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# 4.封装为tool(Graph-as-a-Tool)
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@tool
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def create_order(query:str) -> str:
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"""创建新订单，自动重试保障成功率"""
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result = retry_app.invoke({"query":query,"attempt":1,"result":""})
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return result["result"]
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# 5.主graph
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tools = [create_order]
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llm_with_tools = llm.bind_tools(tools)
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tool_node = ToolNode(tools)
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def agent_node(state:MessagesState):
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response = llm_with_tools.invoke(state["messages"])
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return {"messages":[response]}
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def should_continue(state:MessagesState):
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last_msg = state["messages"][-1]
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if hasattr(last_msg,"tool_calls") and last_msg.tool_calls:
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return "tools"
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return END
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# 构建主工作流
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workflow = StateGraph(MessagesState)
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workflow.add_node("agent",agent_node)
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workflow.add_node("tools",tool_node)
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workflow.add_edge(START,"agent")
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workflow.add_conditional_edges(
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"agent",
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should_continue,
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{
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"tools": "tools",
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END: END
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}
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)
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workflow.add_edge("tools","agent")
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app = workflow.compile()
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# 运行
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if __name__ == '__main__':
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user_input = "请创建一个新订单：购买三本书"
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print('用户输入:',user_input)
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inputs = {"messages":[
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SystemMessage(content="你是一个任务执行助手。当用户提出任何需要处理、操作或执行的请求时，必须调用 create_order 工具来完成，不要自行回答细节"),
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HumanMessage(content=user_input)
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]}
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result = app.invoke(inputs)
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tool_result = None
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# 在主工作流的消息历史中，查找最近的工具执行结果
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for msg in reversed(result["messages"]):
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if msg.type == "tool": # 找到ToolMessage类型消息
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tool_result = msg.content
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break
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if tool_result:
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print(f"\n✅ 直接获取子图返回值:\n{tool_result}")
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else:
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print("\n❌ 未执行任何工具")
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final_reply = result["messages"][-1]
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print(f'\n最终回复:\n{final_reply}')
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@@ -0,0 +1,118 @@
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import os
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from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
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from langchain_openai import ChatOpenAI
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from langchain.tools import tool
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from langchain_core.messages import HumanMessage
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from langgraph.graph import StateGraph, MessagesState, START, END
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from langgraph.prebuilt import ToolNode
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from langgraph.checkpoint.memory import MemorySaver
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# LangSmith调试
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os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
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os.environ["LANGCHAIN_PROJECT"] = "human_approval" # 自定义项目名
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os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
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# llm配置
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 定义一个敏感工具：发送邮件(模拟)
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@tool
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def send_email(to, content):
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"""模拟发送邮件"""
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return f'邮件已发送至{to},内容为：{content}'
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# 工具绑定到llm
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tools = [send_email]
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llm_with_tools = llm.bind_tools(tools)
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# Node函数与Edge节点
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tool_node = ToolNode(tools)
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def call_model(state: MessagesState):
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response = llm_with_tools.invoke(state['messages'])
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return {"messages":[response]}
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def should_continue(state: MessagesState):
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last_msg = state['messages'][-1]
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if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
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return "tools"
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return END
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# 构建基础ReAct图
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workflow = StateGraph(MessagesState)
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workflow.add_node("agent", call_model)
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workflow.add_node("tools", tool_node)
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workflow.add_edge(START, "agent")
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workflow.add_conditional_edges(
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"agent",
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should_continue,
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{
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"tools": "tools",
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END: END
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}
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)
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workflow.add_edge("tools", "agent")
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app = workflow.compile(
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# 在内存里做状态持久化
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checkpointer=MemorySaver(),
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interrupt_before=["tools"] # 选择要人工审批的节点 -- 负责在哪里停，之后的代码负责停了之后怎么办
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)
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if __name__ == '__main__':
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config = {
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"configurable":{"thread_id":"user123"}
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}
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user_input = "请帮我给 boss@example.com 发一封邮件，内容是：会议推迟到明天下午3点。"
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print("用户输入:",user_input)
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print("\nAgent正在思考...\n")
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# 初识输入
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inputs = {"messages":[HumanMessage(content=user_input)]}
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while 1:
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# 触发工作流执行，推进到下一个中断点或自然结束
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# inputs注入事件;config确定回话id;"values":完整记录每步结果
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for _ in app.stream(inputs,config,stream_mode="values"): # 流式执行
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pass # 必须迭代生成器，才能实际执行工作流
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# 获取当前状态
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snapshot = app.get_state(config)
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next_tasks = snapshot.next # 返回下一步要执行的节点名列表
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# 如果没有下一步，说明工作流已结束
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if not next_tasks:
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final_msg = snapshot.values['messages'][-1]
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print(f'\n最终回复:{final_msg.content}')
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break
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# 如果下一步是需要审批的节点
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if "tools" in next_tasks:
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last_msg = snapshot.values['messages'][-1]
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tool_call = last_msg.tool_calls[0]
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print(f'\n⚠️ Agent准备执行操作：')
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print(f' 工具名称：{tool_call["name"]}')
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print(f' 参数：{tool_call["args"]}')
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approval = input("\n✅ 是否批准执行？(输入 'yes' 继续，其他取消): ").strip().lower()
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if approval == "yes":
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print('\n 继续执行...')
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inputs = None # 表示从断点继续，无新输入
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else:
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print("\n❌ 操作已取消，流程终止")
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break
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@@ -0,0 +1,167 @@
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import os
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from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
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from langchain_openai import ChatOpenAI
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from langchain.tools import tool
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from langchain_core.messages import HumanMessage, SystemMessage
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from langgraph.graph import StateGraph, START, END
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from langgraph.prebuilt import ToolNode
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from typing import TypedDict # 定义数据类型
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from typing import Annotated # 注释说明细节
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from langgraph.graph.message import add_messages
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# LangSmith调试
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os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
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os.environ["LANGCHAIN_PROJECT"] = "multi_agent_orchestration" # 自定义项目名
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os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
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# llm配置
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 模拟工具
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@tool
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def search_internal_docs(query:str):
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"""搜索公司内部文档获取政策信息"""
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return "根据公司手册，年假为15天"
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@tool
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def search_web(query:str):
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"""通过搜索引擎获取最新的公开信息"""
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return "据TechCrunch报道，LangGraph 0.6已支持持久化记忆"
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@tool
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def generate_code(requirement:str):
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"""根据需求生成可运行的Python代码"""
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return "python\nprint('Hello from Code writer!')"
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# 共享状态定义
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class AgentState(TypedDict):
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messages: Annotated[list,add_messages] # 自动累积对话历史
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next_speaker: str
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# 专家节点
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def rag_expert(state:AgentState):
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prompt = "你是公司知识库专家，只基于内部文档回答问题。回答应简洁明了，直接给出最终结论。请在回答的最后一行加上：(任务已完成)"
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messages = [SystemMessage(content=prompt)]+state['messages']
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tools = [search_internal_docs]
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response = llm.bind_tools(tools).invoke(messages)
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return {'messages':[response]}
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def web_research(state:AgentState):
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prompt = "你是互联网研究员，擅长用搜索引擎获取最新公开信息。回答应简洁明了，直接给出最终结论。请在回答的最后一行加上：(任务已完成)"
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messages = [SystemMessage(content=prompt)]+state['messages']
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tools = [search_web]
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response = llm.bind_tools(tools).invoke(messages)
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return {'messages':[response]}
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def code_writer(state:AgentState):
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prompt = "你是python工程师，只生成可运行代码，不解释。回答应简洁明了，直接给出最终结论。请在回答的最后一行加上：(任务已完成)"
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messages = [SystemMessage(content=prompt)]+state['messages']
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tools = [generate_code]
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response = llm.bind_tools(tools).invoke(messages)
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return {'messages':[response]}
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# 总控节点
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def supervisor(state:AgentState):
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supervisor_prompt = """
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你是一个任务协调员。你的目标是管理专家来解决用户的问题。
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当前对话需要以下专家参与：
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- rag_expert：涉及公司政策、内部流程
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- web_research：涉及外部新闻、公开数据
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- code_writer：需要生成代码
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【决策逻辑】
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1. **检查历史记录**：先看上一个回复是否已经完整回答了用户的初始问题。
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2. **如果已经回答完毕**：必须输出 'FINISH'。
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3. **如果尚未回答或需要补充**：根据当前缺少的步骤，选择下一个最合适的专家。
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请只输出专家名字或 'FINISH'，不要输出任何其他解释。
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"""
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messages = [SystemMessage(content=supervisor_prompt)]+state['messages']
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response = llm.invoke(messages)
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next_speaker = response.content.strip()
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return {"next_speaker":next_speaker}
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# 路由函数定义
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def route_supervisor(state:AgentState):
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if state["next_speaker"]=="FINISH":
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return END
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return state["next_speaker"]
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def should_continue(state:AgentState):
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last_msg = state["messages"][-1]
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if hasattr(last_msg,"tool_calls") and last_msg.tool_calls:
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return "tools"
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return "supervisor"
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def route_after_tool(state:AgentState):
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# 工具执行完后，通过next_speaker知道是谁调用的，路由回去
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return state["next_speaker"]
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# 添加工具节点
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tools = [search_internal_docs,search_web,generate_code]
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tool_node = ToolNode(tools)
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# 构建协作图
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workflow = StateGraph(AgentState)
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# 1. 添加节点
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workflow.add_node("supervisor",supervisor)
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workflow.add_node("rag_expert",rag_expert)
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workflow.add_node("web_research",web_research)
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workflow.add_node("code_writer",code_writer)
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workflow.add_node("tools",tool_node)
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# 2. 总控回路
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workflow.add_edge(START,"supervisor")
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workflow.add_conditional_edges("supervisor",route_supervisor)
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# 3. 专家节点的ReAct循环
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for member in ["rag_expert","web_research","code_writer"]: # 为每个专家添加条件边：决定是去执行工具还是回总控
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workflow.add_conditional_edges(
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member,
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should_continue,
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{"tools":"tools","supervisor":"supervisor"}
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)
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# 4.工具节点闭环
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workflow.add_conditional_edges( # 工具执行完，根据next_speaker路由回原来的专家
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"tools",
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route_after_tool
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)
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app = workflow.compile()
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# 测试运行
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if __name__ == '__main__':
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user_input = "公司年假多少天"
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print("用户提问:",user_input)
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print('\n开始多智能体协作...\n')
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inputs = {"messages":[HumanMessage(content=user_input)]}
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# app是编译好的图，stream()会让图开始运转，并返回一个生成器
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# 图每执行完一个节点，就会产出一个step字典
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for step in app.stream(inputs):
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# 因为step是个字典，所以需要拆包拿到 节点名(Node) 与 输出内容(output)
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for node,output in step.items():
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# 有工具/专家回复
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if "messages" in output:
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msg = output["messages"][-1]
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if hasattr(msg,"tool_calls") and msg.tool_calls:
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call = msg.tool_calls[0]
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print(f"【{node}】调用工具 {call['name']}({call['args']})")
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else:
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print(f"【{node}】回复：{msg.content}")
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# supervisor刚做完决策，确定下个发言人
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elif "next_speaker" in output:
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speaker = output["next_speaker"]
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print(f"【Supervisor】指定下一位发言人：{speaker}")
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@@ -0,0 +1,226 @@
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import os
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from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
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from langchain_openai import ChatOpenAI
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from langchain.tools import tool
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from langchain_core.messages import HumanMessage,SystemMessage
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from langgraph.graph import StateGraph,START,END
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from langgraph.prebuilt import ToolNode
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from langgraph.checkpoint.memory import MemorySaver
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from typing import TypedDict,Annotated
|
||||
from langgraph.graph.message import add_messages
|
||||
|
||||
# LangSmith调试
|
||||
os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
|
||||
os.environ["LANGCHAIN_PROJECT"] = "supervisor_agent_ops_system" # 自定义项目名
|
||||
os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
|
||||
|
||||
llm = ChatOpenAI(
|
||||
model="deepseek-chat",
|
||||
api_key=OPENAI_API_KEY,
|
||||
base_url="https://api.deepseek.com"
|
||||
)
|
||||
|
||||
# === 一、Graph-as-a-Tool ===
|
||||
# === 模拟一个不稳定的SSH日志查询过程 ===
|
||||
|
||||
class SSHState(TypedDict):
|
||||
target_ip: str
|
||||
attempt: int
|
||||
logs: str
|
||||
|
||||
def connect_ssh(state:SSHState):
|
||||
"""模拟SSH连接，第一次连接必定超时"""
|
||||
print(f' [子图]正在尝试连接服务器{state["target_ip"]}(第{state["attempt"]}次)')
|
||||
if state['attempt'] == 1:
|
||||
return {'logs':'ERROR:Connection Timed Out','attempt':state['attempt']+1}
|
||||
return {'logs':'CONNECTED','attempt':state['attempt']+1}
|
||||
|
||||
def grep_system_logs(state:SSHState):
|
||||
"""连接成功后读取日志"""
|
||||
if state["logs"] == "CONNECTED":
|
||||
# 打印查到的结果
|
||||
return {'logs':f'SUCCESS: Retrieved logs from {state["target_ip"]}:[ERROR: OutOfMemory at line 4032]'}
|
||||
return {'logs':state['logs']} # 保持错误状态
|
||||
|
||||
def ssh_routing(state:SSHState):
|
||||
"""路由逻辑：如果连接失败且尝试次数少于3，重试"""
|
||||
if "ERROR" in state['logs'] and state["attempt"] <= 2:
|
||||
return "connect"
|
||||
return "grep"
|
||||
|
||||
# 构建子图
|
||||
ssh_workflow = StateGraph(SSHState)
|
||||
ssh_workflow.add_node("connect",connect_ssh)
|
||||
ssh_workflow.add_node("grep",grep_system_logs)
|
||||
|
||||
ssh_workflow.add_edge(START,"connect")
|
||||
ssh_workflow.add_conditional_edges("connect",ssh_routing,{"connect":"connect","grep":"grep"})
|
||||
ssh_workflow.add_edge("grep",END)
|
||||
|
||||
ssh_app = ssh_workflow.compile()
|
||||
|
||||
# 将子图封装为工具
|
||||
@tool
|
||||
def analyze_server_logs(ip_address:str):
|
||||
"""使用SSH连接服务器并分析最近的错误日志（内含自动重连机制）"""
|
||||
result = ssh_app.invoke({"target_ip":ip_address,"attempt":1,"log":""})
|
||||
return result['logs']
|
||||
|
||||
|
||||
|
||||
# === 二、Human-in-the-Loop ===
|
||||
# === 重启服务，高危操作，需要审批 ===
|
||||
|
||||
@tool
|
||||
def restart_service(service_name:str):
|
||||
"""重启指定的服务器服务"""
|
||||
return f"服务[{service_name}]已成功重启，系统负载已恢复正常"
|
||||
|
||||
|
||||
|
||||
# === 三、Multi-Agent 编排 ===
|
||||
# === 总控调度 + 专家分工 ===
|
||||
|
||||
# 1. 共享状态
|
||||
class AgentState(TypedDict):
|
||||
messages:Annotated[list,add_messages]
|
||||
next_speaker:str
|
||||
|
||||
# 2. 专家节点
|
||||
def log_expert(state:AgentState):
|
||||
prompt = "你是日志分析专家，使用工具分析服务器日志，找出报错原因。回答需简洁。"
|
||||
messages = [SystemMessage(content=prompt)] + state['messages']
|
||||
# 绑定子图工具
|
||||
tools = [analyze_server_logs]
|
||||
response = llm.bind_tools(tools).invoke(messages)
|
||||
return {"messages":[response]}
|
||||
|
||||
def ops_expert(state:AgentState):
|
||||
prompt = "你是运维专家。当收到修复指令时，请立即调用 'restart_service' 工具进行修复，不要输出任何额外的解释文本。"
|
||||
messages = [SystemMessage(content=prompt)] + state['messages']
|
||||
tools = [restart_service]
|
||||
# 绑定敏感工具
|
||||
response = llm.bind_tools(tools).invoke(messages)
|
||||
return {"messages":[response]}
|
||||
|
||||
# 3. 总控节点（supervisor）
|
||||
def supervisor(state:AgentState):
|
||||
prompt = """
|
||||
你是 IT 运维总指挥。
|
||||
专家列表：
|
||||
- log_expert
|
||||
- ops_expert
|
||||
|
||||
决策逻辑：
|
||||
1. 未知原因 -> log_expert
|
||||
2. 已知原因（如OOM、报错） -> ops_expert
|
||||
3. 修复完成 -> FINISH
|
||||
|
||||
【输出约束】
|
||||
仅输出下一个专家的名字（如 log_expert），不要包含任何其他字符或标点。
|
||||
"""
|
||||
messages = [SystemMessage(content=prompt)] + state['messages']
|
||||
response = llm.invoke(messages)
|
||||
return {"next_speaker":response.content.strip()}
|
||||
|
||||
# 4. 路由逻辑
|
||||
def route_supervisor(state:AgentState):
|
||||
if state['next_speaker'] == "FINISH":
|
||||
return END
|
||||
return state['next_speaker']
|
||||
|
||||
def should_continue(state:AgentState):
|
||||
last_msg = state['messages'][-1]
|
||||
if hasattr(last_msg,"tool_calls") and last_msg.tool_calls:
|
||||
return "tools"
|
||||
return "supervisor"
|
||||
|
||||
def route_after_tool(state:AgentState):
|
||||
return state["next_speaker"]
|
||||
|
||||
|
||||
# === 四、构建主图与集成 ===
|
||||
|
||||
# 工具集合
|
||||
all_tools = [analyze_server_logs,restart_service]
|
||||
tool_node = ToolNode(all_tools)
|
||||
|
||||
# 构建主图
|
||||
workflow = StateGraph(AgentState)
|
||||
|
||||
workflow.add_node("supervisor",supervisor)
|
||||
workflow.add_node("log_expert",log_expert)
|
||||
workflow.add_node("ops_expert",ops_expert)
|
||||
workflow.add_node("tools",tool_node)
|
||||
|
||||
workflow.add_edge(START,"supervisor")
|
||||
workflow.add_conditional_edges("supervisor",route_supervisor)
|
||||
for member in ["log_expert","ops_expert"]:
|
||||
workflow.add_conditional_edges(
|
||||
member,
|
||||
should_continue,
|
||||
{"tools":"tools","supervisor":"supervisor"}
|
||||
)
|
||||
workflow.add_conditional_edges("tools",route_after_tool)
|
||||
|
||||
# 编译图:加入记忆与中断机制(Human-in-the-Loop)
|
||||
# 注: 我们在所有工具执行前都暂停，但在运行时进行逻辑判断
|
||||
app = workflow.compile(
|
||||
checkpointer=MemorySaver(),
|
||||
interrupt_before=["tools"]
|
||||
)
|
||||
|
||||
# === 五、运行时逻辑(模拟生产环境的交互) ===
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 模拟一次完整的故障处理流程
|
||||
user_input = "服务器 192.168.1.100 报警，响应极慢，请处理。"
|
||||
config = {
|
||||
"configurable": {"thread_id": "incident_001"}
|
||||
}
|
||||
|
||||
print(f'收到报警 : {user_input}')
|
||||
inputs = {"messages":[HumanMessage(content=user_input)]}
|
||||
|
||||
# 循环执行，直到任务结束
|
||||
while 1:
|
||||
# 1. 执行图直到中断或结束
|
||||
for _ in app.stream(inputs,config,stream_mode="values"):
|
||||
pass
|
||||
|
||||
# 2. 检查当前状态
|
||||
snapshot = app.get_state(config)
|
||||
next_tasks = snapshot.next
|
||||
|
||||
# 没有下一步，任务结束
|
||||
if not next_tasks:
|
||||
print(f" 最终报告:{snapshot.values['messages'][-1].content}")
|
||||
break
|
||||
|
||||
# 3. 处理中断:判断是哪个工具被调用
|
||||
if "tools" in next_tasks:
|
||||
last_msg = snapshot.values['messages'][-1]
|
||||
tool_call = last_msg.tool_calls[0]
|
||||
tool_name = tool_call["name"]
|
||||
|
||||
print(f'\n[系统暂停] 请求调用工具:{tool_name}')
|
||||
|
||||
# 策略A: 自动放行安全工具(Graph-as-a-Tool)
|
||||
if tool_name == "analyze_server_logs":
|
||||
print(' -> 这是一个查询类工具，系统自行批准。')
|
||||
inputs = None # 继续执行
|
||||
continue
|
||||
|
||||
# 策略B: 拦截高危工具(Human-in-the-Loop)
|
||||
elif tool_name == "restart_service":
|
||||
print(" -> ⚠️ 警告: 这是一个高危操作!")
|
||||
user_approval = input(" -> 请人工审批 (输入 'yes' 允许重启):")
|
||||
|
||||
if user_approval == "yes":
|
||||
print(' -> ✅ 审批通过，正在执行...')
|
||||
inputs = None # 继续执行
|
||||
else:
|
||||
print(' -> ❌️ 审批拒绝，任务终止!')
|
||||
# 实际系统中，应通过 ToolMessage 反馈人工拒绝，使 LLM 能继续响应；
|
||||
# 当前demo为简化，直接退出
|
||||
break
|
||||
Reference in New Issue
Block a user