228 lines
8.2 KiB
Python
228 lines
8.2 KiB
Python
"""Provide service-layer logic for bge m3 embedder."""
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import numpy as np
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from typing import List, Dict, Optional, Union
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from dataclasses import dataclass, field
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from loguru import logger
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import torch
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import os
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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if 'HF_ENDPOINT' not in os.environ:
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os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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LOCAL_MODEL_PATHS = [
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os.path.expanduser("~/.cache/modelscope/Xorbits/bge-m3"), # Keep service responsibilities explicit so downstream behavior stays predictable.
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os.path.expanduser("~/.cache/huggingface/hub/models--BAAI--bge-m3/snapshots/main"), # Keep service responsibilities explicit so downstream behavior stays predictable.
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]
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@dataclass
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class EmbeddingResult:
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"""Represent the Embedding Result type."""
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dense_embeddings: np.ndarray # Keep service responsibilities explicit so downstream behavior stays predictable.
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sparse_embeddings: List[Dict[int, float]] # Keep service responsibilities explicit so downstream behavior stays predictable.
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texts: List[str]
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dim: int = 1024
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class BGEM3Embedder:
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"""Represent the B G E M3 Embedder type."""
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def __init__(
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self,
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model_name: str = "BAAI/bge-m3",
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use_fp16: bool = True,
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device: Optional[str] = None,
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batch_size: int = 12,
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max_length: int = 8192,
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local_model_path: Optional[str] = None
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):
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"""Initialize the B G E M3 Embedder instance."""
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self.use_fp16 = use_fp16
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self.batch_size = batch_size
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self.max_length = max_length
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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if local_model_path and os.path.exists(local_model_path):
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self.model_path = local_model_path
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self.model_name = "local"
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logger.info(f"使用本地模型路径: {local_model_path}")
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else:
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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found_local = False
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for path in LOCAL_MODEL_PATHS:
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if os.path.exists(path) and os.path.exists(os.path.join(path, "config.json")):
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self.model_path = path
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self.model_name = "local"
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logger.info(f"使用本地模型路径: {path}")
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found_local = True
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break
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if not found_local:
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self.model_path = model_name
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self.model_name = model_name
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logger.info(f"使用远程模型: {model_name}")
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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if device is None:
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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else:
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self.device = device
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logger.info(f"初始化BGE-M3模型, 设备: {self.device}")
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self.model = None
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self._load_model()
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def _load_model(self):
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"""Handle load model for this module for the B G E M3 Embedder instance."""
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try:
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from FlagEmbedding import BGEM3FlagModel
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self.model = BGEM3FlagModel(
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self.model_path,
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use_fp16=self.use_fp16,
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device=self.device
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)
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logger.success(f"BGE-M3模型加载成功")
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except ImportError:
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logger.warning("FlagEmbedding库未安装，请运行: pip install FlagEmbedding")
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raise
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except Exception as e:
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logger.error(f"模型加载失败: {e}")
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raise
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def embed(
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self,
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texts: List[str],
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return_dense: bool = True,
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return_sparse: bool = True,
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return_colbert_vecs: bool = False
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) -> EmbeddingResult:
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"""Handle embed for the B G E M3 Embedder instance."""
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if not texts:
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logger.warning("输入文本列表为空")
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return EmbeddingResult(
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dense_embeddings=np.array([]),
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sparse_embeddings=[],
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texts=[],
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dim=0
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)
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logger.info(f"开始嵌入{len(texts)}个文本块")
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try:
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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embeddings = self.model.encode(
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texts,
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batch_size=self.batch_size,
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max_length=self.max_length,
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return_dense=return_dense,
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return_sparse=return_sparse,
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return_colbert_vecs=return_colbert_vecs
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)
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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dense_embeddings = embeddings.get('dense_vecs', np.array([]))
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sparse_embeddings = embeddings.get('lexical_weights', [])
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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dim = dense_embeddings.shape[1] if len(dense_embeddings) > 0 else 1024
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logger.success(f"嵌入完成，向量维度: {dim}")
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return EmbeddingResult(
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dense_embeddings=dense_embeddings,
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sparse_embeddings=sparse_embeddings,
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texts=texts,
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dim=dim
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)
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except Exception as e:
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logger.error(f"嵌入失败: {e}")
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raise
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def embed_single(self, text: str) -> Dict[str, Union[np.ndarray, Dict]]:
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"""Embed single for the B G E M3 Embedder instance."""
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result = self.embed([text])
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return {
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'dense': result.dense_embeddings[0],
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'sparse': result.sparse_embeddings[0] if result.sparse_embeddings else {},
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'dim': result.dim
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}
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def embed_dense(self, texts: List[str]) -> np.ndarray:
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"""Embed dense for the B G E M3 Embedder instance."""
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result = self.embed(texts, return_sparse=False, return_colbert_vecs=False)
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return result.dense_embeddings
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def embed_sparse(self, texts: List[str]) -> List[Dict[int, float]]:
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"""Embed sparse for the B G E M3 Embedder instance."""
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result = self.embed(texts, return_dense=False, return_colbert_vecs=False)
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return result.sparse_embeddings
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def embed_query(self, query: str) -> Dict:
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"""Embed query for the B G E M3 Embedder instance."""
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return self.embed_single(query)
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def compute_similarity(
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self,
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query_embedding: np.ndarray,
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doc_embeddings: np.ndarray,
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metric: str = "cosine"
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) -> np.ndarray:
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"""Handle compute similarity for the B G E M3 Embedder instance."""
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if metric == "cosine":
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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query_norm = np.linalg.norm(query_embedding)
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doc_norms = np.linalg.norm(doc_embeddings, axis=1)
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similarities = np.dot(doc_embeddings, query_embedding) / (doc_norms * query_norm)
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elif metric == "dot":
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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similarities = np.dot(doc_embeddings, query_embedding)
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else:
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raise ValueError(f"不支持的相似度度量: {metric}")
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return similarities
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def sparse_similarity(
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self,
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query_sparse: Dict[int, float],
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doc_sparse: Dict[int, float]
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) -> float:
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"""Handle sparse similarity for the B G E M3 Embedder instance."""
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# Keep service responsibilities explicit so downstream behavior stays predictable.
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common_keys = set(query_sparse.keys()) & set(doc_sparse.keys())
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score = sum(query_sparse[k] * doc_sparse[k] for k in common_keys)
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return score
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def embed_texts(
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texts: List[str],
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model_name: str = "BAAI/bge-m3",
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**kwargs
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) -> EmbeddingResult:
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"""Embed texts."""
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embedder = BGEM3Embedder(model_name=model_name, **kwargs)
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return embedder.embed(texts)
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def embed_single_text(
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text: str,
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model_name: str = "BAAI/bge-m3",
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**kwargs
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) -> Dict:
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"""Embed single text."""
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embedder = BGEM3Embedder(model_name=model_name, **kwargs)
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return embedder.embed_single(text)
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