chunk_content: osest elements (returns 2 numpy arrays)
      labels, distances = search_index.knn_query(query_embeddings, k = k)
      
      return [(ids_to_products_dict[label], (1-distance)) for label, distance in zip(labels[0], distances[0]) if (1-distance)>=threshold]
  Let’s test it out with the query deep learning books:
  
  
  	Copied
  query = "deep learning books"
  k = 10
  query_embeddings = get_query_embeddings(query, model, tokenizer, device)
  search_results = get_