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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import faiss
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') # 64-dim *nb queries
xb[:, 0] += np.arange(nb) / 1000.
xq = np.random.random((nq, d)).astype('float32')
xq[:, 0] += np.arange(nq) / 1000.
m = 8 # 8 specifies that the number of sub-vector is 8
k = 4 # number of dimension in etracted vector
n_bit = 4 # 4 specifies that each sub-vector is encoded as 4 bits
bbs = 32 # build block size ( bbs % 32 == 0 ) for PQ
index = faiss.IndexPQFastScan(d, m, n_bit, faiss.METRIC_L2, bbs)
# construct FastScan Index
assert not index.is_trained
index.train(xb) # Train vectors data index within mockup database
assert index.is_trained
index.add(xb)
D, I = index.search(xb[:5], k) # sanity check
print(I)
print(D)
index.nprobe = 10 # make comparable with experiment above
D, I = index.search(xq, k) # search
print(I[-5:]) # neighbors of the 5 last queries
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