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import time
import logging
import requests

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

class ResilientAIClient:
    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_completion(self, prompt: str, fallback_chain: list) -> dict:
        """
        尝试通过 n1n.ai 使用模型链生成文本。
        """
        for model in fallback_chain:
            logging.info(f"正在尝试使用模型: {model}")
            payload = {
                "model": model,
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0.7
            }
            
            try:
                start_time = time.time()
                response = requests.post(
                    f"{self.base_url}/chat/completions",
                    headers=self.headers,
                    json=payload,
                    timeout=30.0
                )
                latency = time.time() - start_time
                
                if response.status_code == 200:
                    data = response.json()
                    logging.info(f"成功调用 {model} | 延迟: {latency:.2f}秒")
                    return {
                        "status": "success",
                        "model": model,
                        "text": data["choices"][0]["message"]["content"],
                        "latency": latency
                    }
                else:
                    logging.warning(f"模型 {model} 失败，状态码: {response.status_code} | 错误信息: {response.text}")
            
            except requests.exceptions.RequestException as e:
                logging.error(f"模型 {model} 出现网络或超时错误: {str(e)}")
            
            # 可选：在尝试下一个模型前添加退避延迟
            time.sleep(1.0)
            
        raise RuntimeError("备用链中的所有模型均未能响应。")

# 示例用法
if __name__ == "__main__":
    # 请替换为您实际的 n1n.ai API 密钥
    N1N_API_KEY = "your_n1n_api_key_here"
    
    client = ResilientAIClient(api_key=N1N_API_KEY)
    
    user_prompt = "分析在联邦软件系统中使用开源与商业 LLM 的安全影响。"
    
    # 定义备用顺序：首选 -> 次选 -> 备选
    models_to_try = [
        "claude-3-5-sonnet",
        "gpt-4o",
        "deepseek-v3"
    ]
    
    try:
        result = client.generate_completion(prompt=user_prompt, fallback_chain=models_to_try)
        print("\n--- 执行结果 ---")
        print(f"解析模型: {result['model']}")
        print(f"延迟: {result['latency']:.2f} 秒")
        print(f"响应内容: {result['text'][:200]}...")
    except Exception as error:
        print(f"严重错误: {str(error)}")