text: 
  # Copyright (c) 2015-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the CC-by-NC license found in the
  # LICENSE file in the root directory of this source tree.
  
  import numpy as np
  d = 64                           # dimension
  nb = 100000                      # database size
  nq = 10000                       # nb of queries
  np.random.seed(1234)             # make reproducible
  xb = np.random.random((nb, d)).astype('float32')
  xb[:, 0] += np.arange(nb) / 1000.
  xq = np.random.random((nq, d)).astype('float32')
  xq[:, 0] += np.arange(nq) / 1000.
  
  import faiss                   # make faiss available
  index = faiss.IndexFlatL2(d)   # build the index
  print index.is_trained
  index.add(xb)                  # add vectors to the index
  print index.ntotal
  
  k = 4                          # we want to see 4 nearest neighbors
  D, I = index.search(xb[:5], k) # sanity check
  print I
  print D
  D, I = index.search(xq, k)     # actual search
  print I[:5]                    # neighbors of the 5 first queries
  print I[-5:]                   # neighbors of the 5 last queries
  
  
  