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import logging
import asyncio
from typing import List, Optional, Dict, Any
import numpy as np
from sentence_transformers import SentenceTransformer
import torch
import config
logger = logging.getLogger(__name__)
class EmbeddingService:
def __init__(self):
self.config = config.config
self.model_name = self.config.EMBEDDING_MODEL
self.model = None
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load model lazily
self._load_model()
def _load_model(self):
"""Load the embedding model"""
try:
logger.info(f"Loading embedding model: {self.model_name}")
self.model = SentenceTransformer(self.model_name, device=self.device)
logger.info(f"Embedding model loaded successfully on {self.device}")
except Exception as e:
logger.error(f"Failed to load embedding model: {str(e)}")
# Fallback to a smaller model
try:
self.model_name = "all-MiniLM-L6-v2"
self.model = SentenceTransformer(self.model_name, device=self.device)
logger.info(f"Loaded fallback embedding model: {self.model_name}")
except Exception as fallback_error:
logger.error(f"Failed to load fallback model: {str(fallback_error)}")
raise
async def generate_embeddings(self, texts: List[str], batch_size: int = 32) -> List[List[float]]:
"""Generate embeddings for a list of texts"""
if not texts:
return []
if self.model is None:
raise RuntimeError("Embedding model not loaded")
try:
# Filter out empty texts
non_empty_texts = [text for text in texts if text and text.strip()]
if not non_empty_texts:
logger.warning("No non-empty texts provided for embedding")
return []
logger.info(f"Generating embeddings for {len(non_empty_texts)} texts")
# Process in batches to manage memory
all_embeddings = []
for i in range(0, len(non_empty_texts), batch_size):
batch = non_empty_texts[i:i + batch_size]
# Run embedding generation in thread pool to avoid blocking
loop = asyncio.get_event_loop()
batch_embeddings = await loop.run_in_executor(
None,
self._generate_batch_embeddings,
batch
)
all_embeddings.extend(batch_embeddings)
logger.info(f"Generated {len(all_embeddings)} embeddings")
return all_embeddings
except Exception as e:
logger.error(f"Error generating embeddings: {str(e)}")
raise
def _generate_batch_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Generate embeddings for a batch of texts (synchronous)"""
try:
# Generate embeddings
embeddings = self.model.encode(
texts,
convert_to_numpy=True,
normalize_embeddings=True,
batch_size=len(texts)
)
# Convert to list of lists
return embeddings.tolist()
except Exception as e:
logger.error(f"Error in batch embedding generation: {str(e)}")
raise
async def generate_single_embedding(self, text: str) -> Optional[List[float]]:
"""Generate embedding for a single text"""
if not text or not text.strip():
return None
try:
embeddings = await self.generate_embeddings([text])
return embeddings[0] if embeddings else None
except Exception as e:
logger.error(f"Error generating single embedding: {str(e)}")
return None
def get_embedding_dimension(self) -> int:
"""Get the dimension of embeddings produced by the model"""
if self.model is None:
raise RuntimeError("Embedding model not loaded")
return self.model.get_sentence_embedding_dimension()
def compute_similarity(self, embedding1: List[float], embedding2: List[float]) -> float:
"""Compute cosine similarity between two embeddings"""
try:
# Convert to numpy arrays
emb1 = np.array(embedding1)
emb2 = np.array(embedding2)
# Compute cosine similarity
similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
return float(similarity)
except Exception as e:
logger.error(f"Error computing similarity: {str(e)}")
return 0.0
def compute_similarities(self, query_embedding: List[float], embeddings: List[List[float]]) -> List[float]:
"""Compute similarities between a query embedding and multiple embeddings"""
try:
query_emb = np.array(query_embedding)
emb_matrix = np.array(embeddings)
# Compute cosine similarities
similarities = np.dot(emb_matrix, query_emb) / (
np.linalg.norm(emb_matrix, axis=1) * np.linalg.norm(query_emb)
)
return similarities.tolist()
except Exception as e:
logger.error(f"Error computing similarities: {str(e)}")
return [0.0] * len(embeddings)
async def embed_chunks(self, chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Embed a list of chunks and add embeddings to them"""
if not chunks:
return []
try:
# Extract texts
texts = [chunk.get('content', '') for chunk in chunks]
# Generate embeddings
embeddings = await self.generate_embeddings(texts)
# Add embeddings to chunks
embedded_chunks = []
for i, chunk in enumerate(chunks):
if i < len(embeddings):
chunk_copy = chunk.copy()
chunk_copy['embedding'] = embeddings[i]
embedded_chunks.append(chunk_copy)
else:
logger.warning(f"No embedding generated for chunk {i}")
embedded_chunks.append(chunk)
return embedded_chunks
except Exception as e:
logger.error(f"Error embedding chunks: {str(e)}")
raise
def validate_embedding(self, embedding: List[float]) -> bool:
"""Validate that an embedding is properly formatted"""
try:
if not embedding:
return False
if not isinstance(embedding, list):
return False
if len(embedding) != self.get_embedding_dimension():
return False
# Check for NaN or infinite values
emb_array = np.array(embedding)
if np.isnan(emb_array).any() or np.isinf(emb_array).any():
return False
return True
except Exception:
return False
async def get_model_info(self) -> Dict[str, Any]:
"""Get information about the loaded model"""
try:
return {
"model_name": self.model_name,
"device": self.device,
"embedding_dimension": self.get_embedding_dimension(),
"max_sequence_length": getattr(self.model, 'max_seq_length', 'unknown'),
"model_loaded": self.model is not None
}
except Exception as e:
logger.error(f"Error getting model info: {str(e)}")
return {"error": str(e)} |