import os import requests from typing import Tuple, Union import time class LLMModel: # def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs): def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs): self.model_name = model_name self.params = kwargs self.api_key = os.getenv("SILICONFLOW_KEY") self.base_url = os.getenv("SILICONFLOW_BASE_URL") def generate_response(self, prompt: str) -> Tuple[str, str]: """根据输入的提示生成模型的响应""" headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json" } # 构建请求体 data = { "model": self.model_name, "messages": [{"role": "user", "content": prompt}], "temperature": 0.5, **self.params } # 发送请求到完整的chat/completions端点 api_url = f"{self.base_url.rstrip('/')}/chat/completions" max_retries = 3 base_wait_time = 15 # 基础等待时间(秒) for retry in range(max_retries): try: response = requests.post(api_url, headers=headers, json=data) if response.status_code == 429: wait_time = base_wait_time * (2 ** retry) # 指数退避 print(f"遇到请求限制(429),等待{wait_time}秒后重试...") time.sleep(wait_time) continue response.raise_for_status() # 检查其他响应状态 result = response.json() if "choices" in result and len(result["choices"]) > 0: content = result["choices"][0]["message"]["content"] reasoning_content = result["choices"][0]["message"].get("reasoning_content", "") return content, reasoning_content return "没有返回结果", "" except requests.exceptions.RequestException as e: if retry < max_retries - 1: # 如果还有重试机会 wait_time = base_wait_time * (2 ** retry) print(f"请求失败,等待{wait_time}秒后重试... 错误: {str(e)}") time.sleep(wait_time) else: return f"请求失败: {str(e)}", "" return "达到最大重试次数,请求仍然失败", ""