OpenAI在万亿估值背景下再获巨额融资本地化解析

import os
import requests
from typing import Dict, Any

class LLMGatewayClient:
    def __init__(self, api_key: str, base_url: str = "https://api.n1n.ai/v1"):
        self.api_key = api_key
        self.base_url = base_url
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def generate_response(self, model: str, prompt: str, max_tokens: int = 1000) -> Dict[str, Any]:
        endpoint = f"{self.base_url}/chat/completions"
        payload = {
            "model": model,
            "messages": [
                {"role": "system", "content": "你是一个协助企业级开发者的全能AI助手。"},
                {"role": "user", "content": prompt}
            ],
            "max_tokens": max_tokens,
            "temperature": 0.7
        }
        
        try:
            response = requests.post(endpoint, json=payload, headers=self.headers, timeout=30)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            # 在此处实现自动降级或备用模型路由逻辑
            print(f"连接AI网关时发生错误: {e}")
            return {"error": str(e)}

# 使用示例
if __name__ == "__main__":
    # 从环境变量安全获取API密钥
    api_key = os.getenv("N1N_API_KEY", "your_n1n_api_key_here")
    client = LLMGatewayClient(api_key=api_key)
    
    # 无需修改客户端代码结构即可自由切换底层模型
    result = client.generate_response(
        model="openai/gpt-4o",
        prompt="请详细解释在分布式系统中使用LLM API聚合网关的性能优势。"
    )
    
    if "choices" in result:
        print(result["choices"][0]["message"]["content"])
    else:
        print("未能成功获取模型输出。")