大模型学习记录（五）-------调用大模型API接口

小女孩真可爱2025-11-25 22:38

torch环境：

复制代码

pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu126

运行需要先申请API秘钥，通过load_dotenv来获取api秘钥。在系统环境变量中的创建DEEPSEEK_API_KEY.再将它的秘钥复制过去。

python 复制代码

import os
from dotenv import load_dotenv
load_dotenv()
# 从环境变量中获取API_KEY
API_KEY = os.getenv("DEEPSEEK_API_KEY")
# print("API_KEY:", API_KEY)

# 调用Deepseek API
# 方法一、使用Python的requests库
def requests_connect_deepseek(api_key):
    import requests
    # 定义API endpoint
    url = "https://api.deepseek.com/v1/chat/completions"
    # 定义请求头
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    data = {
        "model": "deepseek-chat",                        # 模型名称
        "messages": [
            {"role": "user", "content": "你好"}          # 消息内容
        ],
        "max_tokens": 10                                # 回答内容最大token数
    }

    # 发送POST请求
    response = requests.post(url, headers=headers, json=data)

    # 打印响应
    print("requests_connect_deepseek回复:", response.json()["choices"][0]["message"]["content"])

# 方法二、使用OpenAI Python库
def openai_connect_deepseek(api_key):
    from openai import OpenAI
    client = OpenAI(
        api_key=api_key,                                # API密钥 
        base_url="https://api.deepseek.com/v1",         # API基础URL
    )   
    response = client.chat.completions.create(
        model="deepseek-chat",  
                      # 模型名称
        messages=[        # 系统消息
            {"role": "user", "content": "你好"}          # 消息内容
        ],
        max_tokens=10                                   # 回答内容最大token数
    )
    print("openai_connect_deepseek回复:", response.choices[0].message.content)


if __name__ == "__main__":
    # requests_connect_deepseek(API_KEY)
    openai_connect_deepseek(API_KEY)