chunk_content: ry = "deep learning books"
  k = 10
  query_embeddings = get_query_embeddings(query, model, tokenizer, device)
  search_results = get_nearest_neighbours(k, product_search_index, query_embeddings, ids_to_products_dict, threshold=0.7)
  
  print(f"{query=}") 
  for product, cosine_sim_score in search_results:
      print(f"cosine_sim_score={round(cosine_sim_score,2)} {product=}")
  Output:
  
  
  	Copied
  query='deep learning books'
  cosine_sim_score=0.95 product='Deep