app/faiss_utils.py

import numpy as np
import faiss
from sentence_transformers import SentenceTransformer

# Load global embedding model once
model = SentenceTransformer("multi-qa-mpnet-base-dot-v1")

def build_index(keywords):
    """Builds FAISS index dynamically from keywords."""
    enriched = [f"{k} category" for k in keywords]
    embeddings = model.encode(enriched).astype("float32")
    embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
    dim = embeddings.shape[1]

    index = faiss.IndexFlatIP(dim)
    index.add(embeddings)

    return index, embeddings