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# DEPENDENCIES
import time
import numpy as np
from typing import List
from typing import Optional
from numpy.typing import NDArray
from config.models import DocumentChunk
from config.settings import get_settings
from config.models import EmbeddingRequest
from config.models import EmbeddingResponse
from config.logging_config import get_logger
from utils.error_handler import handle_errors
from utils.error_handler import EmbeddingError
from embeddings.model_loader import get_model_loader
from embeddings.batch_processor import BatchProcessor
# Setup Settings and Logging
settings = get_settings()
logger = get_logger(__name__)
class BGEEmbedder:
"""
BGE (BAAI General Embedding) model wrapper: Optimized for BAAI/bge models with proper normalization and batching
"""
def __init__(self, model_name: Optional[str] = None, device: Optional[str] = None):
"""
Initialize BGE embedder
Arguments:
----------
model_name { str } : BGE model name (default from settings)
device { str } : Device to run on
"""
self.logger = logger
self.model_name = model_name or settings.EMBEDDING_MODEL
self.device = device
# Initialize components
self.model_loader = get_model_loader()
self.batch_processor = BatchProcessor()
# Load model
self.model = self.model_loader.load_model(model_name = self.model_name,
device = self.device,
)
# Get model info
self.embedding_dim = self.model.get_sentence_embedding_dimension()
self.supports_batch = True
self.logger.info(f"Initialized BGEEmbedder: model={self.model_name}, dim={self.embedding_dim}, device={self.model.device}")
@handle_errors(error_type = EmbeddingError, log_error = True, reraise = True)
def embed_text(self, text: str, normalize: bool = True) -> NDArray:
"""
Embed single text string
Arguments:
----------
text { str } : Input text
normalize { bool } : Normalize embeddings to unit length
Returns:
--------
{ NDArray } : Embedding vector
"""
if not text or not text.strip():
raise EmbeddingError("Cannot embed empty text")
try:
# Encode single text
embedding = self.model.encode([text],
normalize_embeddings = normalize,
show_progress_bar = False,
)
# Return single vector
return embedding[0]
except Exception as e:
self.logger.error(f"Failed to embed text: {repr(e)}")
raise EmbeddingError(f"Text embedding failed: {repr(e)}")
@handle_errors(error_type = EmbeddingError, log_error = True, reraise = True)
def embed_texts(self, texts: List[str], batch_size: Optional[int] = None, normalize: bool = True) -> List[NDArray]:
"""
Embed multiple texts with batching
Arguments:
----------
texts { list } : List of text strings
batch_size { int } : Batch size (default from settings)
normalize { bool } : Normalize embeddings
Returns:
--------
{ list } : List of embedding vectors
"""
if not texts:
return []
# Filter empty texts
valid_texts = [t for t in texts if t and t.strip()]
if (len(valid_texts) != len(texts)):
self.logger.warning(f"Filtered {len(texts) - len(valid_texts)} empty texts")
if not valid_texts:
return []
batch_size = batch_size or settings.EMBEDDING_BATCH_SIZE
try:
# Use batch processing for efficiency
embeddings = self.batch_processor.process_embeddings_batch(model = self.model,
texts = valid_texts,
batch_size = batch_size,
normalize = normalize,
)
self.logger.debug(f"Generated {len(embeddings)} embeddings for {len(texts)} texts")
return embeddings
except Exception as e:
self.logger.error(f"Batch embedding failed: {repr(e)}")
raise EmbeddingError(f"Batch embedding failed: {repr(e)}")
@handle_errors(error_type = EmbeddingError, log_error = True, reraise = True)
def embed_chunks(self, chunks: List[DocumentChunk], batch_size: Optional[int] = None, normalize: bool = True) -> List[DocumentChunk]:
"""
Embed document chunks and update them with embeddings
Arguments:
----------
chunks { list } : List of DocumentChunk objects
batch_size { int } : Batch size
normalize { bool } : Normalize embeddings
Returns:
--------
{ list } : Chunks with embeddings added
"""
if not chunks:
return []
# Extract texts from chunks
texts = [chunk.text for chunk in chunks]
# Generate embeddings
embeddings = self.embed_texts(texts = texts,
batch_size = batch_size,
normalize = normalize,
)
# Update chunks with embeddings
for chunk, embedding in zip(chunks, embeddings):
# Convert numpy to list for serialization
chunk.embedding = embedding.tolist()
self.logger.info(f"Embedded {len(chunks)} document chunks")
return chunks
def process_embedding_request(self, request: EmbeddingRequest) -> EmbeddingResponse:
"""
Process embedding request from API
Arguments:
----------
request { EmbeddingRequest } : Embedding request
Returns:
--------
{ EmbeddingResponse } : Embedding response
"""
start_time = time.time()
# Generate embeddings
embeddings = self.embed_texts(texts = request.texts,
batch_size = request.batch_size,
normalize = request.normalize,
)
# Convert to milliseconds
processing_time = (time.time() - start_time) * 1000
# Convert to list for serialization
embedding_list = [emb.tolist() for emb in embeddings]
response = EmbeddingResponse(embeddings = embedding_list,
dimension = self.embedding_dim,
num_embeddings = len(embeddings),
processing_time_ms = processing_time,
)
return response
def get_embedding_dimension(self) -> int:
"""
Get embedding dimension
Returns:
--------
{ int } : Embedding vector dimension
"""
return self.embedding_dim
def cosine_similarity(self, emb1: NDArray, emb2: NDArray) -> float:
"""
Calculate cosine similarity between two embeddings
Arguments:
----------
emb1 { NDArray } : First embedding
emb2 { NDArray } : Second embedding
Returns:
--------
{ float } : Cosine similarity (-1 to 1)
"""
# Ensure embeddings are normalized
emb1_norm = emb1 / np.linalg.norm(emb1)
emb2_norm = emb2 / np.linalg.norm(emb2)
return float(np.dot(emb1_norm, emb2_norm))
def validate_embedding(self, embedding: NDArray) -> bool:
"""
Validate embedding vector
Arguments:
----------
embedding { NDArray } : Embedding vector
Returns:
--------
{ bool } : True if valid
"""
if (embedding is None):
return False
if (not isinstance(embedding, np.ndarray)):
return False
if (embedding.shape != (self.embedding_dim,)):
return False
if (np.all(embedding == 0)):
return False
if (np.any(np.isnan(embedding))):
return False
return True
def get_model_info(self) -> dict:
"""
Get embedder information
Returns:
--------
{ dict } : Embedder information
"""
return {"model_name" : self.model_name,
"embedding_dim" : self.embedding_dim,
"device" : str(self.model.device),
"supports_batch" : self.supports_batch,
"normalize_default" : True,
}
# Global embedder instance
_embedder = None
def get_embedder(model_name: Optional[str] = None, device: Optional[str] = None) -> BGEEmbedder:
"""
Get global embedder instance
Arguments:
----------
model_name { str } : Model name
device { str } : Device
Returns:
--------
{ BGEEmbedder } : BGEEmbedder instance
"""
global _embedder
if _embedder is None:
_embedder = BGEEmbedder(model_name, device)
return _embedder
def embed_texts(texts: List[str], **kwargs) -> List[NDArray]:
"""
Convenience function to embed texts
Arguments:
----------
texts { list } : List of texts
**kwargs : Additional arguments for BGEEmbedder
Returns:
--------
{ list } : List of embeddings
"""
embedder = get_embedder()
return embedder.embed_texts(texts, **kwargs)
def embed_chunks(chunks: List[DocumentChunk], **kwargs) -> List[DocumentChunk]:
"""
Convenience function to embed document chunks
Arguments:
----------
chunks { list } : List of DocumentChunk objects
**kwargs : Additional arguments
Returns:
--------
{ list } : Chunks with embeddings
"""
embedder = get_embedder()
return embedder.embed_chunks(chunks, **kwargs) |