87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
from openai import OpenAI
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import os
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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": "你好,杭州和深圳的天气怎么样"}]
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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-v4-pro", # 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(assistant_msg)
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print(assistant_msg.model_dump_json(indent=2))
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print(assistant_msg.content)
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print("模型第一次回复：", assistant_msg.content)
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if assistant_msg.tool_calls:
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name=assistant_msg.tool_calls[0].function.name
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print(name)
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import json
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r=json.loads(assistant_msg.tool_calls[0].function.arguments)
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print(r)
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arg=r.get('location')
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print(arg)
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tool_id=assistant_msg.tool_calls[0].id
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print(tool_id)
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name2=assistant_msg.tool_calls[1].function.name
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print(name2)
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import json
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r2=json.loads(assistant_msg.tool_calls[1].function.arguments)
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print(r2)
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arg2=r2.get('location')
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print(arg2)
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tool_id2=assistant_msg.tool_calls[1].id
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print(tool_id2)
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def get_weather(location: str) -> str:
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return f"{location}的天气为 880℃"
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tools_map={'get_weather':get_weather}
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tools_result=tools_map.get(name)(**r)
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tools_result2=tools_map.get(name2)(**r2)
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print(tools_result)
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messages.append(assistant_msg)
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messages.append({'role':"tool","tool_call_id": tool_id,'content':tools_result})
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messages.append({'role':"tool","tool_call_id": tool_id2,'content':tools_result2})
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response = client.chat.completions.create(
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model="deepseek-v4-pro", # 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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print("模型第二次回复：", response.choices[0].message.content)
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