黄庄三号
451e9a12ed
能力1: Function Call - LangGraph ToolNode 能力2: MCP - langchain-mcp-adapters + 确定性路由 能力3: 思考模式 - think_node + CoT推理链 能力4: Skill - 自建SkillRegistry注册机制 模型: GLM-4.5-air (智谱)
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
"""
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Step 1: 最简单的 LangGraph Agent + GLM-4.5-air + 工具调用
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只验证核心能力：Function Call
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"""
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import os
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from langgraph.prebuilt import create_react_agent
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# ── 模型配置 ──
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llm = ChatOpenAI(
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base_url="https://open.bigmodel.cn/api/paas/v4",
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api_key="2259e33a1357460abe17919aaf81e73d.K44a8LPQTmFM5PKm",
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model="glm-4.5-air",
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)
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# ── 定义工具 ──
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@tool
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def get_weather(city: str) -> str:
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"""查询指定城市的天气信息"""
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# 模拟天气数据
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weather_data = {
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"北京": "晴天，气温22°C，北风3级",
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"上海": "多云，气温25°C，东风2级",
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"深圳": "阵雨，气温28°C，南风4级",
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"黄庄": "晴转多云，气温23°C，微风",
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}
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return weather_data.get(city, f"暂无{city}的天气数据")
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@tool
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def calculate(expression: str) -> str:
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"""计算数学表达式，输入如 '2+3*4'"""
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try:
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result = eval(expression, {"__builtins__": {}}, {})
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return f"计算结果: {expression} = {result}"
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except Exception as e:
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return f"计算错误: {e}"
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@tool
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def search_knowledge(query: str) -> str:
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"""搜索知识库（模拟）"""
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kb = {
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"黄庄三号": "黄庄三号是AI助手，定位为严肃、认真、听话、聪明的AI助手",
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"LangGraph": "LangGraph是LangChain团队推出的Agent框架，支持状态图、循环、持久化",
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"MCP": "MCP是Model Context Protocol，AI工具互操作标准协议",
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}
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for key, val in kb.items():
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if key in query:
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return val
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return f"知识库中未找到关于'{query}'的信息"
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# ── 创建 Agent ──
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tools = [get_weather, calculate, search_knowledge]
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agent = create_react_agent(llm, tools)
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# ── 运行测试 ──
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if __name__ == "__main__":
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import asyncio
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async def test():
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print("=" * 50)
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print("Step 1: LangGraph + GLM-4.5-air + FC 工具调用")
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print("=" * 50)
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# 测试1: 天气查询
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print("\n[测试1] 天气查询")
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result = await agent.ainvoke({"messages": [("user", "黄庄今天天气怎么样？")]})
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last_msg = result["messages"][-1]
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print(f"回复: {last_msg.content}")
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# 测试2: 数学计算
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print("\n[测试2] 数学计算")
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result = await agent.ainvoke({"messages": [("user", "帮我算一下 123 * 456 + 789")]})
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last_msg = result["messages"][-1]
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print(f"回复: {last_msg.content}")
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# 测试3: 知识搜索
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print("\n[测试3] 知识搜索")
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result = await agent.ainvoke({"messages": [("user", "LangGraph是什么？")]})
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last_msg = result["messages"][-1]
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print(f"回复: {last_msg.content}")
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print("\n" + "=" * 50)
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print("Step 1 完成！FC 工具调用正常工作")
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asyncio.run(test())
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