325eec03
 tangwang
1. 日志、配置基础设施，使用优化
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"""
Text embedding encoder using BGE-M3 model.
Generates 1024-dimensional vectors for text using the BGE-M3 multilingual model.
"""
import sys
import torch
from sentence_transformers import SentenceTransformer
import time
import threading
from modelscope import snapshot_download
from transformers import AutoModel
import os
import numpy as np
from typing import List, Union
class BgeEncoder:
"""
Singleton text encoder using BGE-M3 model.
Thread-safe singleton pattern ensures only one model instance exists.
"""
_instance = None
_lock = threading.Lock()
def __new__(cls, model_dir='Xorbits/bge-m3'):
with cls._lock:
if cls._instance is None:
cls._instance = super(BgeEncoder, cls).__new__(cls)
print(f"[BgeEncoder] Creating a new instance with model directory: {model_dir}")
cls._instance.model = SentenceTransformer(snapshot_download(model_dir))
print("[BgeEncoder] New instance has been created")
return cls._instance
def encode(
self,
sentences: Union[str, List[str]],
normalize_embeddings: bool = True,
device: str = 'cuda',
batch_size: int = 32
) -> np.ndarray:
"""
Encode text into embeddings.
Args:
sentences: Single string or list of strings to encode
normalize_embeddings: Whether to normalize embeddings
device: Device to use ('cuda' or 'cpu')
batch_size: Batch size for encoding
Returns:
numpy array of shape (n, 1024) containing embeddings
"""
# Move model to specified device
if device == 'gpu':
device = 'cuda'
# Try requested device, fallback to CPU if CUDA fails
try:
if device == 'cuda':
# Check CUDA memory first
import torch
if torch.cuda.is_available():
# Check if we have enough memory (at least 1GB free)
free_memory = torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated()
if free_memory < 1024 * 1024 * 1024: # 1GB
print(f"[BgeEncoder] CUDA memory insufficient ({free_memory/1024/1024:.1f}MB free), falling back to CPU")
device = 'cpu'
else:
print(f"[BgeEncoder] CUDA not available, using CPU")
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 as e:
print(f"[BgeEncoder] Device {device} failed: {e}")
if device != 'cpu':
print(f"[BgeEncoder] Falling back to CPU")
try:
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
except Exception as e2:
print(f"[BgeEncoder] CPU also failed: {e2}")
raise
else:
raise
def encode_batch(
self,
texts: List[str],
batch_size: int = 32,
device: str = 'cuda'
) -> np.ndarray:
"""
Encode a batch of texts efficiently.
Args:
texts: List of texts to encode
batch_size: Batch size for processing
device: Device to use
Returns:
numpy array of embeddings
"""
return self.encode(texts, batch_size=batch_size, device=device)
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