utils.py

import faiss
import numpy as np

def generate_faiss_index(embeddings):
    index = faiss.IndexFlatL2(768)  # Assuming 768-dimensional embeddings for a model like MiniLM
    index.add(np.array(embeddings))
    return index

def load_faiss_index_to_gpu(index):
    res = faiss.StandardGpuResources()
    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)  # Load into GPU (assuming GPU 0 is available)
    return gpu_index

def query_faiss_index(query_embedding, gpu_index):
    distances, indices = gpu_index.search(np.array([query_embedding]), 1)
    return indices, distances