69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, ToolMessage
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from langchain_core.tools import tool
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from conf import settings
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# todo: 第一步：定义工具函数
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@tool
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def add(a: int, b: int) -> int:
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"""
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将数字a与数字b相加
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a + b
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@tool
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def multiply(a: int, b: int) -> int:
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"""
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将数字a与数字b相乘
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a * b
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# 定义 JSON 格式的工具 schema
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tools = [add, multiply]
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# todo: 第二步：初始化模型
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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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# 绑定工具，允许模型自动选择工具
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llm_with_tools = llm.bind_tools(tools, tool_choice="auto")
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# todo: 第三步：调用回复
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query = "2+1等于多少？"
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messages = [HumanMessage(query)]
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try:
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# todo: 第一次调用
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ai_msg = llm_with_tools.invoke(messages)
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messages.append(ai_msg)
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print(f"\n第一轮调用后结果：\n{messages}")
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# 处理工具调用
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# 判断消息中是否有tool_calls，以判断工具是否被调用
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if hasattr(ai_msg, 'tool_calls') and ai_msg.tool_calls:
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for tool_call in ai_msg.tool_calls:
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# todo: 处理工具调用
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selected_tool = {"add": add, "multiply": multiply}[tool_call["name"].lower()]
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tool_output = selected_tool.invoke(tool_call["args"]) # 需要使用invoke进行调用
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messages.append(ToolMessage(content=tool_output, tool_call_id=tool_call["id"]))
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print(f"\n第二轮 message中增加tool_output 之后：\n{messages}")
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# todo: 第二次调用，将工具结果传回模型以生成最终回答
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final_response = llm_with_tools.invoke(messages)
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print(f"\n最终模型响应：\n{final_response.content}")
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else:
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print("模型未生成工具调用，直接返回文本:")
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print(ai_msg.content)
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except Exception as e:
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print(f"模型调用失败: {str(e)}")
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