04 中间件

01_Agent.py

@@ -0,0 +1,26 @@
+from langchain.agents import create_agent
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_core.tools import tool
+
+@tool(description="查询天气")
+def get_weather():
+    return "晴天"
+
+
+agent = create_agent(
+    model=ChatTongyi(model="qwen3-max"),
+    tools=[get_weather],
+    system_prompt="你是一个聊天助手，可以回答用户问题"
+)
+
+res = agent.invoke(
+    {
+        "messages": [
+            {"role": "user", "content": "明天深圳的天气如何"},
+        ]
+    }
+)
+
+for msg in res["messages"]:
+    print(f"{type(msg).__name__}: {msg.content}")
+ 

02_Stream.py

@@ -0,0 +1,37 @@
+from langchain.agents import create_agent
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_core.tools import tool
+
+
+@tool(description="查询股票价格")
+def get_price(name: str) -> str:
+    return f"股票{name}的价格是20元"
+
+
+@tool(description="查询股票信息")
+def get_info(name: str) -> str:
+    return f"股票{name}是一家A股上市公司"
+
+
+agent = create_agent(
+    model=ChatTongyi(model="qwen3-max"),
+    tools=[get_price, get_info],
+    system_prompt="你是一个智能助手，可以回答股票相关问题，请告知我思考过程，让我知道你为什么调用某个工具"
+)
+
+res = agent.stream(
+    {"messages": [{
+        "role": "user", "content": "kk公司股价多少，并介绍一下"
+    }]},
+    stream_mode="values"
+)
+
+for chunk in res:
+    last_msg = chunk["messages"][-1]
+    if last_msg.content:
+        print(type(last_msg).__name__, last_msg.content)
+
+    if type(last_msg).__name__ == "AIMessage":
+        if last_msg.tool_calls:
+            tool_list = [tc['name'] for tc in last_msg.tool_calls]
+            print(f"工具调佣 {tool_list}")

03_ReAct.py

@@ -0,0 +1,39 @@
+from langchain.agents import create_agent
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_core.tools import tool
+
+
+@tool(description="获取体重，返回值整数，单位千克")
+def get_weight() -> int:
+    return 93
+
+
+@tool(description="获取身高，返回值整数，单位厘米")
+def get_height() -> int:
+    return 185
+
+
+agent = create_agent(
+    model=ChatTongyi(model="qwen3-max"),
+    tools=[get_height, get_weight],
+    system_prompt="""你是严格遵循ReAct框架的智能体，必须按[思考，行动，观察，再思考]的流程解决问题
+    每轮仅能思考并调用1个工具，禁止单词调用多个工具。并告知我你的思考过程，工具调用的原因，按思考、行动
+    、观察三个结构告知我"""
+)
+
+res = agent.stream(
+    {"messages": [{
+        "role": "user", "content": "计算我的BMI"
+    }]},
+    stream_mode="values"
+)
+
+for chunk in res:
+    last_msg = chunk["messages"][-1]
+    if last_msg.content:
+        print(type(last_msg).__name__, last_msg.content)
+
+    if type(last_msg).__name__ == "AIMessage":
+        if last_msg.tool_calls:
+            tool_list = [tc['name'] for tc in last_msg.tool_calls]
+            print(f"工具调佣 {tool_list}")

04_middle_ware.py

@@ -0,0 +1,70 @@
+"""
+agent 前后
+model 前后
+工具 中
+模型 中
+"""
+
+from langchain.agents import create_agent, AgentState
+from langchain.agents.middleware import before_agent, after_agent, before_model, after_model, wrap_model_call, \
+    wrap_tool_call
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_core.tools import tool
+from langgraph.runtime import Runtime
+
+
+@tool(description="查询天气, 传入城市名称字符串，返回字符串天气信息")
+def get_weather(city: str) -> str:
+    return f"{city} : 晴天"
+
+
+@before_agent
+def log_before_agent(state: AgentState, runtime: Runtime) -> None:
+    print(f"before agent: info_num: {len(state["messages"])}")
+
+
+@after_agent
+def log_after_agent(state: AgentState, runtime: Runtime) -> None:
+    print(f"after agent: info_num: {len(state["messages"])}")
+
+
+@before_model
+def log_before_model(state: AgentState, runtime: Runtime) -> None:
+    print(f"before model: info_num: {len(state["messages"])}")
+
+
+@after_model
+def log_after_model(state: AgentState, runtime: Runtime) -> None:
+    print(f"after model: info_num: {len(state["messages"])}")
+
+
+@wrap_model_call
+def model_call_hook(request, handler):
+    print(f"model call: {request}")
+    return handler(request)
+
+
+@wrap_tool_call
+def model_tool_hook(request, handler):
+    print(f"model tool: {request.tool_call['name']}")
+    print(f"args: {request.tool_call['args']}")
+    return handler(request)
+
+agent = create_agent(
+    model=ChatTongyi(model="qwen3-max"),
+    tools=[get_weather],
+    middleware=[model_call_hook, model_tool_hook, log_before_model,
+                log_after_model, log_before_agent, log_after_agent],
+    system_prompt="""你是严格遵循ReAct框架的智能体，必须按[思考，行动，观察，再思考]的流程解决问题
+    每轮仅能思考并调用1个工具，禁止单词调用多个工具。并告知我你的思考过程，工具调用的原因，按思考、行动
+    、观察三个结构告知我"""
+)
+
+res = agent.stream(
+    {"messages": [{
+        "role": "user", "content": "查询北京的天气"
+    }]},
+    stream_mode="values"
+)
+for chunk in res:
+    print(chunk["messages"][-1].content)