69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from langchain.agents import AgentExecutor, create_tool_calling_agent, create_react_agent
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from conf import settings
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# 创建模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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# 2.定义工具
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@tool
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def multiply(a: int, b: int) -> int:
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"""用于计算两个整数的乘积。"""
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print(f"正在执行乘法: {a} * {b}")
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return a * b
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@tool
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def search_weather(city: str) -> str:
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"""用于查询指定城市的实时天气。"""
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print(f"正在查询天气: {city}")
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if "北京" in city:
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return "北京今天是晴天，气温25摄氏度。"
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elif "上海" in city:
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return "上海今天是阴天，有小雨，气温22摄氏度。"
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else:
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return f"抱歉，我没有'{city}'的天气信息。"
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# 将工具列表放入一个变量
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tools = [multiply, search_weather]
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# 3.定义一个提示模板，用于控制Agent的思考过程和工具调用
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tool_use_prompt = ChatPromptTemplate.from_messages([
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("system", "你是一个强大的AI助手，可以访问和使用各种工具来回答问题。请根据用户的问题，决定是否需要调用工具。当需要调用工具时，请使用正确的JSON格式。"),
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("user", "{input}"),
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("placeholder", "{agent_scratchpad}") # 这个占位符用于保存 Agent 的思考过程和工具调用历史
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])
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# 4.创建一个 LLM 能够识别和使用的 Agent
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# 使用 create_tool_calling_agent 函数，它能让 LLM 自动判断何时以及如何调用工具
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tool_calling_agent = create_tool_calling_agent(llm, tools, tool_use_prompt)
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# 5.创建 Agent Executor
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# AgentExecutor 负责 Agent 和工具之间的协调
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tool_use_executor = AgentExecutor(
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agent=tool_calling_agent,
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tools=tools,
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verbose=True # 开启 verbose 模式，可以打印详细的执行过程
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)
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# 6.通用的执行函数，用于运行agent并打印结果
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def run_agent_and_print(agent_executor, query):
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"""一个通用函数，用于运行Agent并打印结果。"""
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print(f"--- 运行Agent，查询: {query} ---")
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response = agent_executor.invoke({"input": query})
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print(f"\n--- Agent响应: ---")
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print(response.get("output", "没有找到输出。"))
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print("-" * 30 + "\n")
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if __name__ == "__main__":
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run_agent_and_print(tool_use_executor, "上海今天的天气怎么样？")
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run_agent_and_print(tool_use_executor, "30乘以5等于多少？ 上海天气怎么样")
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