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# file: vector/embeddings.py
from sentence_transformers import SentenceTransformer
import numpy as np
from app.config import EMBEDDING_MODEL, EMBEDDING_DIM
class EmbeddingModel:
"""Manages sentence transformer embeddings"""
def __init__(self):
self.model = None
self._load_model()
def _load_model(self):
"""Load the embedding model"""
try:
self.model = SentenceTransformer(EMBEDDING_MODEL)
except Exception as e:
print(f"Warning: Could not load embedding model: {e}")
# Fallback to random embeddings for testing
self.model = None
def encode(self, texts):
"""Encode texts to embeddings"""
if self.model:
embeddings = self.model.encode(texts, normalize_embeddings=True)
return embeddings
else:
# Fallback: random embeddings
return np.random.randn(len(texts), EMBEDDING_DIM).astype(np.float32)
# Singleton
_embedding_model = None
def get_embedding_model():
global _embedding_model
if _embedding_model is None:
_embedding_model = EmbeddingModel()
return _embedding_model |