import logging import requests from typing import List, Optional import threading import os import sys # 添加项目根目录到路径 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')) if project_root not in sys.path: sys.path.insert(0, project_root) try: import utils.config_util as cfg CONFIG_UTIL_AVAILABLE = True except ImportError as e: CONFIG_UTIL_AVAILABLE = False cfg = None # 使用统一日志配置 from bionicmemory.utils.logging_config import get_logger logger = get_logger(__name__) if not CONFIG_UTIL_AVAILABLE: logger.warning("无法导入 config_util，将使用环境变量配置") class ApiEmbeddingService: """API Embedding服务 - 单例模式，调用 OpenAI 兼容的 API""" _instance = None _lock = threading.Lock() _initialized = False def __new__(cls): if cls._instance is None: with cls._lock: if cls._instance is None: cls._instance = super().__new__(cls) return cls._instance def __init__(self): if not self._initialized: with self._lock: if not self._initialized: self._initialize_config() ApiEmbeddingService._initialized = True def _initialize_config(self): """初始化配置，只执行一次""" try: # 优先从 system.conf 读取配置 api_base_url = None api_key = None model_name = None if CONFIG_UTIL_AVAILABLE and cfg: try: # 确保配置已加载 if cfg.config is None: cfg.load_config() # 从 config_util 获取配置（自动复用 LLM 配置） api_base_url = cfg.embedding_api_base_url api_key = cfg.embedding_api_key model_name = cfg.embedding_api_model logger.info(f"从 system.conf 读取配置:") logger.info(f" - embedding_api_model: {model_name}") logger.info(f" - embedding_api_base_url: {api_base_url}") logger.info(f" - embedding_api_key: {'已配置' if api_key else '未配置'}") except Exception as e: logger.warning(f"从 system.conf 读取配置失败: {e}") # 验证必需配置并提供更好的错误提示 if not api_base_url: api_base_url = os.getenv('EMBEDDING_API_BASE_URL') if not api_base_url: error_msg = ("未配置 embedding_api_base_url！\n" "请确保 system.conf 中配置了 gpt_base_url，" "或设置环境变量 EMBEDDING_API_BASE_URL") logger.error(error_msg) raise ValueError(error_msg) logger.warning(f"使用环境变量配置: base_url={api_base_url}") if not api_key: api_key = os.getenv('EMBEDDING_API_KEY') if not api_key: error_msg = ("未配置 embedding_api_key！\n" "请确保 system.conf 中配置了 gpt_api_key，" "或设置环境变量 EMBEDDING_API_KEY") logger.error(error_msg) raise ValueError(error_msg) logger.warning("使用环境变量配置: api_key") if not model_name: model_name = os.getenv('EMBEDDING_API_MODEL', 'text-embedding-ada-002') logger.warning(f"未配置 embedding_api_model，使用默认值: {model_name}") # 保存配置信息 self.api_base_url = api_base_url.rstrip('/') # 移除末尾的斜杠 self.api_key = api_key self.model_name = model_name self.embedding_dim = None # 将在首次调用时动态获取 self.timeout = 60 # API 请求超时时间（秒），默认 60 秒 self.max_retries = 2 # 最大重试次数 logger.info(f"API Embedding 服务初始化完成") logger.info(f"模型: {self.model_name}") logger.info(f"API 地址: {self.api_base_url}") except Exception as e: logger.error(f"API Embedding 服务初始化失败: {e}") raise def encode_text(self, text: str) -> List[float]: """编码单个文本（带重试机制）""" import time last_error = None for attempt in range(self.max_retries + 1): try: # 调用 API 进行编码 url = f"{self.api_base_url}/embeddings" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}" } payload = { "model": self.model_name, "input": text } # 记录请求信息 text_preview = text[:50] + "..." if len(text) > 50 else text logger.info(f"发送 embedding 请求 (尝试 {attempt + 1}/{self.max_retries + 1}): 文本长度={len(text)}, 预览='{text_preview}'") response = requests.post(url, json=payload, headers=headers, timeout=self.timeout) response.raise_for_status() result = response.json() embedding = result['data'][0]['embedding'] # 首次调用时获取实际维度 if self.embedding_dim is None: self.embedding_dim = len(embedding) logger.info(f"动态获取 embedding 维度: {self.embedding_dim}") logger.info(f"embedding 生成成功") return embedding except requests.exceptions.Timeout as e: last_error = e logger.warning(f"请求超时 (尝试 {attempt + 1}/{self.max_retries + 1}): {e}") if attempt < self.max_retries: wait_time = 2 ** attempt # 指数退避: 1s, 2s, 4s logger.info(f"等待 {wait_time} 秒后重试...") time.sleep(wait_time) else: logger.error(f"所有重试均失败，文本长度: {len(text)}") raise except Exception as e: last_error = e logger.error(f"文本编码失败 (尝试 {attempt + 1}/{self.max_retries + 1}): {e}") if attempt < self.max_retries: wait_time = 2 ** attempt logger.info(f"等待 {wait_time} 秒后重试...") time.sleep(wait_time) else: raise def encode_texts(self, texts: List[str]) -> List[List[float]]: """批量编码文本""" try: # 调用 API 进行批量编码 url = f"{self.api_base_url}/embeddings" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}" } payload = { "model": self.model_name, "input": texts } # 批量请求使用更长的超时时间 batch_timeout = self.timeout * 2 # 批量请求超时时间加倍 logger.info(f"发送批量 embedding 请求: 文本数={len(texts)}, 超时={batch_timeout}秒") response = requests.post(url, json=payload, headers=headers, timeout=batch_timeout) response.raise_for_status() result = response.json() embeddings = [item['embedding'] for item in result['data']] return embeddings except Exception as e: logger.error(f"批量文本编码失败: {e}") raise def get_model_info(self) -> dict: """获取模型信息""" return { "model_name": self.model_name, "embedding_dim": self.embedding_dim, "api_base_url": self.api_base_url, "initialized": self._initialized, "service_type": "api" } # 全局实例 _global_embedding_service = None def get_embedding_service() -> ApiEmbeddingService: """获取全局embedding服务实例""" global _global_embedding_service if _global_embedding_service is None: _global_embedding_service = ApiEmbeddingService() return _global_embedding_service