mirror of
https://github.com/xszyou/Fay.git
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200 lines
7.5 KiB
Python
200 lines
7.5 KiB
Python
import logging
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import numpy as np
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from typing import List, Optional
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from sentence_transformers import SentenceTransformer
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import torch
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import hashlib
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import threading
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import os
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import sys
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from dotenv import load_dotenv,find_dotenv
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# 设置离线模式，避免访问Hugging Face
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os.environ['TRANSFORMERS_OFFLINE'] = '1'
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os.environ['HF_HUB_OFFLINE'] = '1'
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os.environ['HF_DATASETS_OFFLINE'] = '1'
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# 设置国内 Hugging Face 镜像站点（作为备用）
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os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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# 加载环境变量
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load_dotenv()
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# 导入配置工具
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# 添加项目根目录到路径
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
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if project_root not in sys.path:
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sys.path.insert(0, project_root)
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try:
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import utils.config_util as cfg
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CONFIG_UTIL_AVAILABLE = True
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except ImportError as e:
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CONFIG_UTIL_AVAILABLE = False
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cfg = None
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# 使用统一日志配置
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from bionicmemory.utils.logging_config import get_logger
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logger = get_logger(__name__)
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if not CONFIG_UTIL_AVAILABLE:
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logger.warning("无法导入 config_util，将使用 .env 配置")
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class LocalEmbeddingService:
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"""本地Embedding服务 - 单例模式，模型驻留内存"""
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_instance = None
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_lock = threading.Lock()
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_initialized = False
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def __new__(cls):
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if cls._instance is None:
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with cls._lock:
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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if not self._initialized:
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with self._lock:
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if not self._initialized:
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self._initialize_model()
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LocalEmbeddingService._initialized = True
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def _initialize_model(self):
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"""初始化模型，只执行一次"""
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try:
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# 优先从 system.conf 读取配置
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user_model_name = None
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cache_dir_config = None
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if CONFIG_UTIL_AVAILABLE and cfg:
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try:
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# 确保配置已加载
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if cfg.config is None:
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cfg.load_config()
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# 从 config_util 获取配置
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user_model_name = cfg.embedding_model
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cache_dir_config = cfg.embedding_cache_dir
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if user_model_name:
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logger.info(f"从 system.conf 读取配置: embedding_model={user_model_name}")
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if cache_dir_config:
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logger.info(f"从 system.conf 读取配置: embedding_cache_dir={cache_dir_config}")
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except Exception as e:
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logger.warning(f"从 system.conf 读取配置失败: {e}")
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# 降级到 .env 或默认值
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if not user_model_name:
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user_model_name = os.getenv('LOCAL_EMBEDDING_MODEL', 'Qwen/Qwen3-Embedding-0.6B')
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logger.info(f"使用 .env 或默认配置: embedding_model={user_model_name}")
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if not cache_dir_config:
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cache_dir_config = os.getenv('LOCAL_EMBEDDING_CACHE_DIR', 'models/embeddings')
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logger.info(f"使用 .env 或默认配置: embedding_cache_dir={cache_dir_config}")
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# 处理相对路径
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if not os.path.isabs(cache_dir_config):
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cache_dir = os.path.join(os.getcwd(), cache_dir_config)
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else:
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cache_dir = cache_dir_config
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cache_dir_abs = os.path.abspath(cache_dir)
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# 按规则拼成路径
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model_path = os.path.join(cache_dir_abs, f"models--{user_model_name.replace('/', '--')}", "snapshots",
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"c54f2e6e80b2d7b7de06f51cec4959f6b3e03418")
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# 转换为绝对路径
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model_name_abs = os.path.abspath(model_path)
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logger.info(f"用户设置的模型名称: {user_model_name}")
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logger.info(f"按规则拼成的模型路径: {model_path}")
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logger.info(f"程序实际使用的模型绝对路径: {model_name_abs}")
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logger.info(f"程序实际使用的缓存绝对路径: {cache_dir_abs}")
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logger.info(f"模型路径是否存在: {os.path.exists(model_name_abs)}")
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logger.info(f"缓存路径是否存在: {os.path.exists(cache_dir_abs)}")
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# 检查路径是否存在，如果不存在则自动下载
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if not os.path.exists(model_name_abs):
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logger.info(f"模型路径不存在: {model_name_abs}")
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logger.info("开始自动下载模型...")
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# 确保缓存目录存在
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os.makedirs(cache_dir_abs, exist_ok=True)
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# 使用 SentenceTransformer 自动下载模型
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logger.info(f"正在下载模型: {user_model_name}")
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self.model = SentenceTransformer(user_model_name, cache_folder=cache_dir_abs)
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logger.info("模型下载完成！")
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else:
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logger.info(f"使用本地模型: {model_name_abs}")
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# 使用绝对路径
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self.model = SentenceTransformer(model_name_abs, cache_folder=cache_dir_abs)
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# 设置为评估模式
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self.model.eval()
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# 如果支持GPU，使用GPU
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if torch.cuda.is_available():
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self.model = self.model.cuda()
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logger.info("使用GPU加速")
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else:
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logger.info("使用CPU")
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logger.info(f"{model_name_abs}模型加载完成")
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logger.info(f"模型缓存路径: {cache_dir_abs}")
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# 保存配置信息
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self.model_name = user_model_name
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self.cache_dir = cache_dir
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except Exception as e:
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logger.error(f"{model_name_abs}模型加载失败: {e}")
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raise
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def encode_text(self, text: str) -> List[float]:
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"""编码单个文本"""
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try:
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# 使用驻留的模型进行编码
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embedding = self.model.encode(text, convert_to_numpy=True)
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return embedding.tolist() # 转换为list
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except Exception as e:
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logger.error(f"文本编码失败: {e}")
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raise
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def encode_texts(self, texts: List[str]) -> List[List[float]]:
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"""批量编码文本"""
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try:
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# 使用驻留的模型进行批量编码
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embeddings = self.model.encode(texts, convert_to_numpy=True)
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return embeddings.tolist() # 转换为list
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except Exception as e:
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logger.error(f"批量文本编码失败: {e}")
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raise
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def get_model_info(self) -> dict:
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"""获取模型信息"""
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return {
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"model_name": getattr(self, 'model_name', 'Qwen/Qwen3-Embedding-0.6B'),
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"embedding_dim": 1024,
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"device": "cuda" if torch.cuda.is_available() else "cpu",
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"initialized": self._initialized,
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"cache_dir": getattr(self, 'cache_dir', os.path.join(os.getcwd(), "ChromaWithForgetting", "models", "embeddings"))
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}
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# 导入 API Embedding 服务
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from bionicmemory.services.api_embedding_service import ApiEmbeddingService
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# 全局实例
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_global_embedding_service = None
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def get_embedding_service() -> ApiEmbeddingService:
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"""获取全局embedding服务实例（现在返回 API 服务）"""
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global _global_embedding_service
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if _global_embedding_service is None:
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_global_embedding_service = ApiEmbeddingService()
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return _global_embedding_service
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