chunk_content: ddings_array)
  Get the query embeddings and nearest neighbors:
  
  
  	Copied
  def get_query_embeddings(query, model, tokenizer, device):
      inputs = tokenizer(query, padding="max_length", max_length=70, truncation=True, return_tensors="pt")
      model.eval()
      with torch.no_grad():
          query_embs = model(**{k:v.to(device) for k, v in inputs.items()}).detach().cpu()
      return query_embs[0]
  
      
  def get_nearest_neighbours(k, search_index, query