cache.py

import os
from typing import List, Optional
from sentence_transformers import SentenceTransformer
from functools import lru_cache
from dotenv import load_dotenv

@lru_cache(maxsize=1)
def get_sentence_transformer() -> SentenceTransformer:
    """Loads and caches the Sentence Transformer model."""
    try:
        model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
        print("✅ Sentence Transformer Model Loaded")
        return model
    except Exception as e:
        print(f"❌ Error loading Sentence Transformer: {str(e)}")
        raise RuntimeError("Failed to load Sentence Transformer model.")

    
def get_cached_embeddings(text: str, model_type: str) -> Optional[List[float]]:
    pass

def set_cached_embeddings(text: str, model_type: str, embeddings: List[float]):
    pass