refactor(ch9): align filenames and update corresponding README sections

m09_langgraph_advanced/s01_human_approval.py

@@ -0,0 +1,118 @@
+import os
+from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
+from langchain_openai import ChatOpenAI
+from langchain.tools import tool
+from langchain_core.messages import HumanMessage
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.prebuilt import ToolNode
+from langgraph.checkpoint.memory import MemorySaver
+
+# LangSmith调试
+os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
+os.environ["LANGCHAIN_PROJECT"] = "human_approval" # 自定义项目名
+os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
+
+# llm配置
+llm = ChatOpenAI(
+    model="deepseek-chat",
+    api_key=OPENAI_API_KEY,
+    base_url="https://api.deepseek.com"
+)
+
+
+# 定义一个敏感工具：发送邮件(模拟)
+@tool
+def send_email(to, content):
+    """模拟发送邮件"""
+    return f'邮件已发送至{to},内容为：{content}'
+
+
+# 工具绑定到llm
+tools = [send_email]
+llm_with_tools = llm.bind_tools(tools)
+
+# Node函数与Edge节点
+tool_node = ToolNode(tools)
+
+
+def call_model(state: MessagesState):
+    response = llm_with_tools.invoke(state['messages'])
+    return {"messages":[response]}
+
+
+def should_continue(state: MessagesState):
+    last_msg = state['messages'][-1]
+    if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
+        return "tools"
+    return END
+
+
+
+# 构建基础ReAct图
+workflow = StateGraph(MessagesState)
+
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
+workflow.add_edge(START, "agent")
+
+workflow.add_conditional_edges(
+    "agent",
+    should_continue,
+    {
+        "tools": "tools",
+        END: END
+    }
+)
+
+workflow.add_edge("tools", "agent")
+
+app = workflow.compile(
+    # 在内存里做状态持久化
+    checkpointer=MemorySaver(),
+    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
+)
+
+
+if __name__ == '__main__':
+    config = {
+        "configurable":{"thread_id":"user123"}
+    }
+    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。"
+
+    print("用户输入:",user_input)
+    print("\nAgent正在思考...\n")
+
+    # 初识输入
+    inputs = {"messages":[HumanMessage(content=user_input)]}
+
+    while 1:
+        # 触发工作流执行，推进到下一个中断点或自然结束
+        # inputs注入事件;config确定回话id;"values":完整记录每步结果
+        for _ in app.stream(inputs,config,stream_mode="values"): # 流式执行
+            pass   # 必须迭代生成器，才能实际执行工作流
+
+        # 获取当前状态
+        snapshot = app.get_state(config)
+        next_tasks = snapshot.next # 返回下一步要执行的节点名列表
+
+        # 如果没有下一步，说明工作流已结束
+        if not next_tasks:
+            final_msg = snapshot.values['messages'][-1]
+            print(f'\n最终回复:{final_msg.content}')
+            break
+
+        # 如果下一步是需要审批的节点
+        if "tools" in next_tasks:
+            last_msg = snapshot.values['messages'][-1]
+            tool_call = last_msg.tool_calls[0]
+            print(f'\n⚠️ Agent准备执行操作：')
+            print(f'    工具名称：{tool_call["name"]}')
+            print(f'    参数：{tool_call["args"]}')
+
+            approval = input("\n✅ 是否批准执行？(输入 'yes' 继续，其他取消): ").strip().lower()
+            if approval == "yes":
+                print('\n 继续执行...')
+                inputs = None # 表示从断点继续，无新输入
+            else:
+                print("\n❌ 操作已取消，流程终止")
+                break