7bfb9946
 tangwang
向量化模块
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"""
BGE-M3 local text embedding implementation.
Internal model implementation used by the embedding service.
"""
import threading
from typing import List, Union
import numpy as np
from sentence_transformers import SentenceTransformer
from modelscope import snapshot_download
class BgeTextModel(object):
"""
Thread-safe singleton text encoder using BGE-M3 model (local inference).
"""
_instance = None
_lock = threading.Lock()
def __new__(cls, model_dir: str = "Xorbits/bge-m3"):
with cls._lock:
if cls._instance is None:
cls._instance = super(BgeTextModel, cls).__new__(cls)
cls._instance.model = SentenceTransformer(snapshot_download(model_dir))
return cls._instance
def encode(
self,
sentences: Union[str, List[str]],
normalize_embeddings: bool = True,
device: str = "cuda",
batch_size: int = 32,
) -> np.ndarray:
if device == "gpu":
device = "cuda"
# Try requested device, fallback to CPU if CUDA fails
try:
if device == "cuda":
import torch
if torch.cuda.is_available():
free_memory = (
torch.cuda.get_device_properties(0).total_memory
- torch.cuda.memory_allocated()
)
if free_memory < 1024 * 1024 * 1024: # 1GB
device = "cpu"
else:
device = "cpu"
self.model = self.model.to(device)
embeddings = self.model.encode(
sentences,
normalize_embeddings=normalize_embeddings,
device=device,
show_progress_bar=False,
batch_size=batch_size,
)
return embeddings
except Exception:
if device != "cpu":
self.model = self.model.to("cpu")
embeddings = self.model.encode(
sentences,
normalize_embeddings=normalize_embeddings,
device="cpu",
show_progress_bar=False,
batch_size=batch_size,
)
return embeddings
raise
def encode_batch(self, texts: List[str], batch_size: int = 32, device: str = "cuda") -> np.ndarray:
return self.encode(texts, batch_size=batch_size, device=device)
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