class_docstr: Indexer for Dense, HNSW FLAT Index.
  
  High retrieval accuracy comes at the cost of High RAM Usage.
docstr: Add an additional dimension to the query vectors to account for cosine
  similarity.
  
  :param query_vectors:
      query vectors of dimension [n_search, q_dim]
  
  :return search_vectors:
      search vectors of dimension [n_search, q_dim + 1]
docstr_tok: ["Add","an","additional","dimension","to","the","query","vectors","to","account","for","cosine","similarity",".",":","param","query_vectors",":","query","vectors","of","dimension","[","n_search",",","q_dim","]",":","return","search_vectors",":","search","vectors","of","dimension","[","n_search",",","q_dim","+","1","]"]
code: def get_search_vectors(self, query_vectors: np.array) -> np.array:
      """
      Add an additional dimension to the query vectors to account for cosine
      similarity.
  
      :param query_vectors:
          query vectors of dimension [n_search, q_dim]
  
      :return search_vectors:
          search vectors of dimension [n_search, q_dim + 1]
      """
      aux_dim = np.zeros(len(query_vectors), dtype="float32")
      query_hnsw_vectors = np.hstack(
          (query_vectors, aux_dim.reshape(-1, 1))
      )
      return query_hnsw_vectors
  
imports: from parlai.core.build_data import modelzoo_path
  from parlai.core.opt import Opt
  import parlai.utils.logging
  from abc import ABC, abstractmethod
  import math
  import numpy
  import os
  import random
  import time
  import torch
  import torch.cuda
  from typing import List, Tuple, Optional
  from parlai.agents.rag.args import WIKIPEDIA_COMPRESSED_INDEX, WIKIPEDIA_EXACT_INDEX
label_desc: Use image node_id 3 for calling the DenseHNSWFlatIndexer obj's underlying member method code with example usage: obj.get_search_vectors(query_vectors) and returns: query_hnsw_vectors