cache_embeddings.py

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')