search_engine.py

import numpy as np
from sentence_transformers import SentenceTransformer

def search_relevant_chunks(query, index, documents, model, top_k=5):
    query_embedding = model.encode(query, convert_to_tensor=True)
    distances, indices = index.search(np.array([query_embedding]), top_k)
    return [documents[i]["content"] for i in indices[0]]