2025.10.28
This commit is contained in:
2287551746@qq.com
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import os
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from dotenv import load_dotenv
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from openai import OpenAI
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import json
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load_dotenv()
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def create_client():
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api_key = os.getenv("OPENAI_API_KEY")
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return OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
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def get_weather(location):
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# 模拟获得天气信息
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return f"{location}当前天气：23℃，晴，风力2级"
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# 以下格式为固定写法，一般仅需改description与name
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get_weather_func = {
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"name": "get_weather", # 函数名称
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"description": "获取指定城市的天气情况", # 对该函数的描述
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string", # 该参数类型
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"description": "城市名称，如北京、上海等" # 对该参数的描述
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}
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},
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"required": ["location"] # 声明必填
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}
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}
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# 固定写法，有多少个tool就往里追加多少个
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tools = [
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{
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"type": "function",
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"function": get_weather_func
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}
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]
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def chat_loop(agent_client, tools):
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messages = [
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{"role": "system",
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"content": "你是一个善解人意会热心回答人问题的助手。如果你感觉你回答不了当前问题，就会调用函数来回答。"},
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{"role": "user", "content": "北京今天的天气怎么样?"}
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]
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response = agent_client.chat.completions.create(
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model="deepseek-chat",
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messages=messages,
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tools=tools, # 调用工具
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tool_choice="auto" # 模型自主选择是否调用工具
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)
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message = response.choices[0].message
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# 如果有该参数，证明ai调用了工具
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if message.tool_calls:
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# 对每个可能要调用的工具进行循环
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for tool_call in message.tool_calls:
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if tool_call.function.name == "get_weather":
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# 解析参数
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args = json.loads(tool_call.function.arguments) # 获取用户关键词的参数
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location = args.get("location", "未知地点")
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# 调用真实参数
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weather_info = get_weather(location)
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# 将函数执行结果以"tool"角色传给模型，等待后面二次调用
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messages.append(message) # 先添加模型的原始响应
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_call.function.name,
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"content": weather_info
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})
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# 第二次调用，让模型基于工具返回的结果再生成最终答案
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final_res = agent_client.chat.completions.create(
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model="deepseek-chat",
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messages=messages
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)
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print('已调用工具...')
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print(f'回答:{final_res.choices[0].message.content}')
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else:
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# 模型没有要调用工具，直接返回
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print('未调用工具...')
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print(f'回答:{response.choices[0].message.content}')
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if __name__ == '__main__':
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client = create_client()
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chat_loop(client, tools)
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@@ -0,0 +1,83 @@
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from dotenv import load_dotenv
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from openai import OpenAI
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import json
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import os
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import requests
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load_dotenv()
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def create_client():
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api_key = os.getenv("OPENAI_API_KEY")
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return OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
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# 通过api调用获得当前ip与位置
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def get_addr():
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res_ip = requests.get('https://ipapi.co/ip/').text
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res_city = requests.get('https://ipapi.co/city/').text
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return f'ip地址:{res_ip},所在城市:{res_city}'
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get_addr_func = {
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"name": "get_addr", # 函数名称
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"description": "获取用户的ip地址与城市", # 对该函数的描述
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"parameters": {
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"type": "object",
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"properties": {}, # 参数为空，那么这两个地方也是空的
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"required": []
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}
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}
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tools = [
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{
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"type":"function",
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"function":get_addr_func
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}
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]
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def chat_loop(agent_client,tools):
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messages = [
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{"role": "system","content": "你是一个善解人意会热心回答人问题的助手。如果你感觉你回答不了当前问题，就会调用函数来回答。"},
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{"role": "user", "content": "我现在在哪个城市，ip地址是多少?"}
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]
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response = agent_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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tool_choice="auto"
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)
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message = response.choices[0].message
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if message.tool_calls:
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for tool_call in message.tool_calls:
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if tool_call.function.name == "get_addr":
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# 解析参数
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# args = json.loads(tool_call.function.arguments) # 获取用户关键词的参数
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# location = args.get("location")
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# 调用真实参数
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# your_info = get_weather(location)
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# 这里无参数，直接调用函数就行
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your_info = get_addr()
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messages.append(message)
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_call.function.name,
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"content": your_info
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})
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final_res = agent_client.chat.completions.create(
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model="deepseek-chat",
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messages=messages
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)
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print('已调用工具...')
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print(f'回答:{final_res.choices[0].message.content}')
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else:
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# 模型没有要调用工具，直接返回
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print('未调用工具...')
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print(f'回答:{response.choices[0].message.content}')
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if __name__ == '__main__':
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client = create_client()
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chat_loop(client, tools)
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@@ -0,0 +1,20 @@
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## 🧩 模块说明：Function Calling 与工具调用
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📌 **核心知识点**：
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Function calling 原理｜工具函数封装｜API 接入｜多轮调用｜Agent 扩展
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---
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### 1. `01_custom_function_calling`（自定义函数调用）
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用一个**假函数**模拟完整流程，重点掌握：
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✅ 如何定义可调用函数
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✅ AI 如何触发函数调用
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---
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### 2. `02_api_invocation`（API 调用）
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接入一个**真实 API**，重点掌握：
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✅ 如何根据 API 文档封装工具
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✅ 让 AI 真正“动手”做事
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💡 强烈建议：自己找一个新 API 测试，看 AI 能否正确调用！
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