chunk_content: d
      search_index.init_index(max_elements = num_elements, ef_construction = 200, M = 100)
  
      # Element insertion (can be called several times):
      ids = np.arange(num_elements)
      search_index.add_items(data, ids)
  
      return search_index
  
  product_search_index = construct_search_index(d, num_products, product_embeddings_array)
  Get the query embeddings and nearest neighbors:
  
  
  	Copied
  def get_query_embeddings(query, model, tokenizer, device