167 lines
6.5 KiB
Python
167 lines
6.5 KiB
Python
from typing import Optional
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import requests
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import time
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from config_manager import get_config_manager
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from logger_manager import get_logger
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config_manager = get_config_manager()
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logger = get_logger()
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class LLMManager:
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"""LLM管理器，负责加载API配置并提供与LLM交互的功能"""
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_instance = None # 单例实例
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def __init__(self):
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self._api_key = None
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self._api_base = None
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self._model = None
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self._temperature = None
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self._config = None
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def __new__(cls):
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"""实现单例模式"""
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if cls._instance is None:
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cls._instance = super(LLMManager, cls).__new__(cls)
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cls._instance._config = None
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cls._instance._api_key = None
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cls._instance._api_base = None
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cls._instance._model = None
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cls._instance._provider = None
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return cls._instance
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def initialize(self):
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"""初始化LLM配置"""
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llm_config = config_manager.get('llm', {})
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# 加载LLM配置
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self._api_key = llm_config.get('api_key', '')
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self._api_base = llm_config.get('api_base', 'https://api.openai.com/v1')
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self._model = llm_config.get('model', 'gpt-3.5-turbo')
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self._temperature = llm_config.get('temperature', 0.7)
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self._config = llm_config
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if not self._api_key:
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logger.warning("警告: LLM API密钥未配置，请在config.yaml中设置。")
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def chat(self, content: str, prompt: Optional[str] = None, max_retries: int = 5) -> str:
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"""
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与LLM进行对话，包含自动重试机制
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Args:
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content: 用户输入内容
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prompt: 可选的系统提示词
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max_retries: 最大重试次数，默认为5
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Returns:
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LLM的回复内容
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"""
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if self._config is None:
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self.initialize()
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headers = {
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"Authorization": f"Bearer {self._api_key}",
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"Content-Type": "application/json"
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}
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messages = []
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# 添加系统提示(如果有)
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if prompt:
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messages.append({"role": "system", "content": prompt})
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# 添加用户消息
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messages.append({"role": "user", "content": content})
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payload = {
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"model": self._model,
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"messages": messages,
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"temperature": self._temperature,
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}
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logger.debug(f"请求数据: {payload}")
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# 实现重试机制
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attempts = 0
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last_exception = None
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while attempts < max_retries:
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try:
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# 尝试直接不使用代理
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if attempts > 0:
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logger.info(f"尝试不使用代理进行请求 (尝试 {attempts + 1}/{max_retries})")
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response = requests.post(
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f"{self._api_base}/chat/completions",
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headers=headers,
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json=payload,
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proxies=None, # 明确不使用代理
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timeout=30 # 设置超时时间
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)
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else:
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# 第一次尝试使用默认设置
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response = requests.post(
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f"{self._api_base}/chat/completions",
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headers=headers,
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json=payload,
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timeout=30 # 设置超时时间
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)
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if response.status_code == 200:
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return response.json()["choices"][0]["message"]["content"]
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elif response.status_code == 429:
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# 限流错误，需要等待更长时间
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logger.warning(
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f"API请求限流 (尝试 {attempts + 1}/{max_retries}): {response.status_code}, {response.text}")
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attempts += 1
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wait_time = 10 * (2 ** attempts) # 指数退避：10s, 20s, 40s...
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logger.info(f"等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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else:
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# 其他服务器错误
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logger.warning(
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f"请求失败 (尝试 {attempts + 1}/{max_retries}): {response.status_code}, {response.text}")
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attempts += 1
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wait_time = 2 * (2 ** attempts) # 指数退避：4s, 8s, 16s...
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logger.info(f"等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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except requests.exceptions.ProxyError as e:
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# 代理错误处理
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last_exception = e
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attempts += 1
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logger.warning(f"代理连接错误 (尝试 {attempts}/{max_retries}): {str(e)}")
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if attempts >= max_retries:
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break
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wait_time = 2 * attempts # 线性退避：2s, 4s, 6s...
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logger.info(f"等待 {wait_time} 秒后尝试不使用代理重试...")
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time.sleep(wait_time)
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except (requests.exceptions.ConnectionError,
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requests.exceptions.Timeout,
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requests.exceptions.ReadTimeout) as e:
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# 连接错误和超时
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last_exception = e
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attempts += 1
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logger.warning(f"连接错误或超时 (尝试 {attempts}/{max_retries}): {str(e)}")
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if attempts >= max_retries:
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break
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wait_time = 3 * (2 ** attempts) # 指数退避：6s, 12s, 24s...
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logger.info(f"等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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except Exception as e:
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# 其他未预期的错误
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last_exception = e
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attempts += 1
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logger.error(f"未预期的错误 (尝试 {attempts}/{max_retries}): {str(e)}")
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if attempts >= max_retries:
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break
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wait_time = 2 * attempts # 线性退避
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logger.info(f"等待 {wait_time} 秒后重试...")
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time.sleep(wait_time)
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# 所有重试都失败后
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logger.error(f"达到最大重试次数 ({max_retries})，请求失败。最后错误: {str(last_exception)}")
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return f"抱歉，请求遇到网络问题，无法获取分析结果。请检查网络设置或稍后再试。\n错误信息: {str(last_exception)}"
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# 提供简单的访问函数
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def get_llm_manager() -> LLMManager:
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"""获取LLM管理器实例"""
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return LLMManager() |