| # app/embeddings.py | |
| from sentence_transformers import SentenceTransformer | |
| import numpy as np | |
| class EmbeddingManager: | |
| def __init__(self, model_name="all-MiniLM-L6-v2"): | |
| self.model = SentenceTransformer(model_name) | |
| print(f"✅ Loaded embedding model: {model_name}") | |
| def generate_embeddings(self, texts): | |
| """Generate embeddings for a list of text chunks""" | |
| # Returns a numpy array of embeddings | |
| return self.model.encode(texts, convert_to_numpy=True, show_progress_bar=True) | |