法院裁定特朗普政府非法将 Anthropic 列入黑名单

pip install openai python-dotenv

import os
import time
import logging
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

class ResilientLLMClient:
    def __init__(self):
        # 使用 n1n.ai 的统一端点初始化客户端
        self.client = OpenAI(
            base_url="https://api.n1n.ai/v1",
            api_key=os.getenv("N1N_API_KEY")
        )
        # 定义容灾切换的模型管道
        self.model_pipeline = [
            "claude-3-5-sonnet",
            "openai-o3-mini",
            "deepseek-v3"
        ]

    def generate_completion(self, messages, max_tokens=1000, temperature=0.7):
        for model in self.model_pipeline:
            try:
                logging.info(f"正在尝试使用模型: {model}")
                start_time = time.time()
                
                response = self.client.chat.completions.create(
                    model=model,
                    messages=messages,
                    max_tokens=max_tokens,
                    temperature=temperature
                )
                
                latency = time.time() - start_time
                logging.info(f"成功调用 {model}。延迟: {latency:.2f} 秒")
                return {
                    "success": True,
                    "model": model,
                    "content": response.choices[0].message.content,
                    "usage": response.usage.model_dump()
                }
            except Exception as e:
                logging.warning(f"模型 {model} 调用失败。错误: {str(e)}。正在尝试下一个模型...")
                continue
        
        logging.error("管道中的所有模型均未能响应。")
        return {
            "success": False,
            "error": "所有备用模型均已尝试，但均未成功。"
        }

# 使用示例
if __name__ == "__main__":
    # 确保在环境变量中设置了 N1N_API_KEY
    if not os.getenv("N1N_API_KEY"):
        print("请设置 N1N_API_KEY 环境变量。")
        exit(1)

    router = ResilientLLMClient()
    payload = [
        {"role": "system", "content": "你是一位精通法律与技术分析的专业助手。"},
        {"role": "user", "content": "分析联邦法院裁决对行政部门采购政策的影响。"}
    ]
    
    result = router.generate_completion(payload)
    if result["success"]:
        print(f"\n[当前活动模型]: {result['model']}")
        print(f"[响应内容]:\n{result['content'][:300]}...")
    else:
        print(f"\n[错误]: {result['error']}")