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78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
from openai import OpenAI
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import os
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import json
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import requests
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from dotenv import load_dotenv
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load_dotenv()
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client = OpenAI(
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api_key=os.environ.get("DEEPSEEK_API_KEY"),
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base_url="https://api.deepseek.com",
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)
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# 1. 定义一个获取天气的工具
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "获取指定城市的天气",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "城市名，如杭州"},
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},
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"required": ["location"],
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},
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},
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}
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]
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# 2. 用户提问
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messages = [{"role": "user", "content": "杭州天气怎么样？"}]
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# 第一次调用模型：模型会返回 tool_calls
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response = client.chat.completions.create(
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model="deepseek-chat", # 换成你实际可用的模型名，如 deepseek-v4-pro
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messages=messages,
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tools=tools, # 传入工具列表
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)
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# 获取模型的回复（通常包含 tool_calls）
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assistant_msg = response.choices[0].message
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print("模型第一次回复：", assistant_msg)
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# 3. 将模型的回复加入对话历史（必须转换为字典，否则 API 会报错）
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messages.append(assistant_msg.model_dump()) # 或 dict(assistant_msg)
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def get_weather(location: str) -> str:
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"""调用高德地图天气API查询真实天气"""
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url = "https://restapi.amap.com/v3/weather/weatherInfo"
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params = {
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"key": os.getenv("GAODE_API_KEY"),
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"city": location,
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"extensions": "all",
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"output": "json"
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}
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response = requests.get(url, params=params)
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data = response.json()
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return json.dumps(data, ensure_ascii=False, indent=2)
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# 4. 执行工具调用
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if assistant_msg.tool_calls:
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tool_call = assistant_msg.tool_calls[0]
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args = json.loads(tool_call.function.arguments)
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result = get_weather(args["location"])
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tool_result = {"role": "tool", "tool_call_id": tool_call.id, "content": result}
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messages.append(tool_result)
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# 5. 第二次调用模型，将工具返回的结果发给模型，生成最终回答
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final_response = client.chat.completions.create(
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model="deepseek-chat",
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messages=messages,
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tools=tools,
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)
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final_answer = final_response.choices[0].message.content
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print("最终回答：", final_answer) |