| from sentence_transformers import SentenceTransformer | |
| from messages import keyword_groups, krishna_blessings | |
| import joblib | |
| import os | |
| # Initialize model | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| # Compute embeddings | |
| embeddings_cache = {} | |
| for group, keywords in keyword_groups.items(): | |
| keyword_texts = keywords + [krishna_blessings.get(k, "") for k in keywords if k in krishna_blessings] | |
| embeddings_cache[group] = model.encode(keyword_texts, convert_to_tensor=True) | |
| # Save to file | |
| joblib.dump(embeddings_cache, 'embeddings_cache.joblib') |