refactor(ch8): align filenames and update corresponding README sections

m08_langgraph_basics/s03_conditional_router.py

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+from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, MessagesState, END,START
+from langgraph.prebuilt import ToolNode
+import os
+
+os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
+os.environ["LANGCHAIN_PROJECT"] = "demo01" # 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"
+)
+
+# 工具定义
+@tool
+def get_weather(loaction):
+    """模拟获取天气"""
+    return f'{loaction}当前天气：23℃，晴，风力2级'
+
+
+tools = [get_weather]
+llm_with_tools = llm.bind_tools(tools) # 让llm学会调用工具节点
+
+
+# --- 核心组件:拆解AgentExecutor ---
+# ReAct Step1:Thought(LLM决策)
+def call_model(state:MessagesState):
+    response = llm_with_tools.invoke(state['messages'])
+    return {"messages":[response]} # 新消息追加到状态
+
+
+# ReAct Step2-3:Action + Observation
+tool_node = ToolNode(tools) # 工具节点函数，langgraph已封装
+
+# ReAct Step4:Loop Controller(是否循环)
+def should_continue(state:MessagesState):
+    last_msg = state["messages"][-1]
+    if hasattr(last_msg,"tool_calls") and last_msg.tool_calls:
+        return "tools" # 有工具调用 -> 执行工具
+    return END         # 无工具调用 -> 返回答案
+
+
+# --- 构建 ReAct 循环图---
+workflow = StateGraph(MessagesState)
+
+workflow.add_node("agent",call_model) # Thought
+workflow.add_node("tools",tool_node) # Action + Observation
+
+workflow.add_edge(START,"agent")
+
+# 条件边:Thought -> 决定下一步
+workflow.add_conditional_edges(
+    "agent", # 从哪个节点出发
+    should_continue, # 决定下一步去哪
+    {
+        "tools":"tools", # 如果返回tools，去tools节点
+        END:END          # 如果返回END，直接结束工作流
+    }
+)
+
+workflow.add_edge("tools","agent") # 工具调用的结果再返回给agent节点
+
+app = workflow.compile()
+
+if __name__ == '__main__':
+    # 触发工具
+    result = app.invoke(
+        {"messages":[
+            HumanMessage(content="北京天气如何?")
+        ]}
+    )
+    print('工具调用结果:',result['messages'][-1].content)
+    # 不触发工具
+    result = app.invoke({"messages":HumanMessage(content="你好")})
+    print('直接回答:',result['messages'][-1].content)