>>> import numpy as np
>>> d = 64                           # dimension
>>> nb = 10000                      # database size
>>> nq = 1                       # 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.IndexFlat(d)   # build the index
>>> print index.is_trained
True
>>> index.add(xb)                  # add vectors to the index
>>> print index.ntotal
10000
>>>
>>> lims, D, I = index.range_search(xq, 0.0000000000000000000004)
>>> D
array([ 18.48775864,  18.66941452,  18.37726593, ...,  22.67871475,
        24.99220467,  22.61538315], dtype=float32)
>>> I
array([   0,    1,    2, ..., 9997, 9998, 9999])
>>> len(I)
10000
>>> lims, D, I = index.range_search(xq, 20)
>>> len(I)
5927
>>> lims, D, I = index.range_search(xq, 200)
>>> len(I)
0
>>>