chore: rename multiple files to improve importability and module structure

m08_langgraph_basics/agent_with_memory.py

@@ -0,0 +1,108 @@
+import os
+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
+from langchain_core.messages import SystemMessage
+from langgraph.checkpoint.memory import MemorySaver
+
+os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
+os.environ["LANGCHAIN_PROJECT"] = "demo02" # 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"
+)
+
+# Prompt配置
+sys_prompt = "你是一个强大的助手，能查天气，也能回答一般问题。请使用中文回答。"
+
+# 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):
+    # 构造带system prompt 的完整消息列表(仅用于本次LLM调用)
+    message_for_llm = [SystemMessage(content=sys_prompt)]+state["messages"]
+    response = llm_with_tools.invoke(message_for_llm)
+    # 此处只会返回新生成的消息，不包含prompt，防止污染历史
+    return {"messages":[response]} # 新消息追加到状态
+
+
+# ReAct Step2-3:Action + Observation
+tool_node = ToolNode(tools)
+
+# 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")
+
+
+
+
+
+# 编译时启用记忆
+app = workflow.compile(checkpointer=MemorySaver())
+
+
+
+if __name__ == '__main__':
+    session_id = "user123"
+    config = {
+        "configurable":{"thread_id":session_id}
+    }
+    while 1:
+        user_input = input('\n你：')
+        if user_input.strip().lower() == 'quit':
+            break
+
+        result = app.invoke(
+            {'messages':[HumanMessage(content=user_input)]},
+            config=config
+        )
+
+        ai_msg = result["messages"][-1]
+        print(f'AI：{ai_msg.content}')
+
+
+
+
+
+

m08_langgraph_basics/conditional_router.py

@@ -0,0 +1,81 @@
+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)

m08_langgraph_basics/langsmith_trace.py

@@ -0,0 +1,43 @@
+from config import LANGCHAIN_API_KEY
+from typing import TypedDict
+from langgraph.graph import StateGraph,END,START
+import os
+
+os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
+os.environ["LANGCHAIN_PROJECT"] = "my_demo" # 自定义项目名
+os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
+
+
+# 1. 定义State(状态) -- 白板上只有一个字段"count"
+class State(TypedDict):
+    count:int
+
+# 2. 编写Node(节点) -- 两个"工人"
+def node_a(state:State):
+    # 接收当前State状态，返回要更新的部分
+    print(f'[Node A]收到状态：{state}')
+    return {"count":state["count"]+1}
+
+def node_b(state:State):
+    print(f'[Node B]收到状态：{state}')
+    return {"count":state["count"]+1}
+
+
+# 3. 添加Node到图中
+workflow = StateGraph(State) # 创建画布
+workflow.add_node("A",node_a) # 添加节点A
+workflow.add_node("B",node_b) # 添加节点B
+
+# 4. 用Edge连线
+workflow.add_edge(START,"A") # START -> A
+workflow.add_edge("A","B") # A -> B
+workflow.add_edge("B",END) # B -> END
+
+# 编译成可运行应用
+app = workflow.compile()
+
+# 传入初始状态，执行工作流
+print("---开始执行---")
+result = app.invoke({"count":1})
+print("最终状态:",result) # 输出{'count':4}
+

m08_langgraph_basics/state_node_edge.py

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+from typing import TypedDict # 定义数据类型
+from langgraph.graph import StateGraph,END,START
+
+
+# 1. 定义State(状态) -- 白板上只有一个字段"count"
+class State(TypedDict):
+    count:int
+
+# 2. 编写Node(节点) -- 两个"工人"
+def node_a(state:State):
+    # 接收当前State状态，返回要更新的部分
+    print(f'[Node A]收到状态：{state}')
+    return {"count":state["count"]+1}
+
+def node_b(state:State):
+    print(f'[Node B]收到状态：{state}')
+    return {"count":state["count"]+1}
+
+
+# 3. 添加Node到图中
+workflow = StateGraph(State) # 创建画布
+workflow.add_node("A",node_a) # 添加节点A
+workflow.add_node("B",node_b) # 添加节点B
+
+# 4. 用Edge连线
+workflow.add_edge(START,"A") # START -> A
+workflow.add_edge("A","B") # A -> B
+workflow.add_edge("B",END) # B -> END
+
+# 编译成可运行应用
+app = workflow.compile()
+
+# 传入初始状态，执行工作流
+print("---开始执行---")
+result = app.invoke({"count":1})
+print("最终状态:",result) # 输出{'count':4}
+
+
+# 保存可视化架构图
+with open('workflow.png', 'wb') as f:
+    f.write(app.get_graph().draw_mermaid_png())
+print("图表已保存为 workflow.png")