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Nano Product Quantization (nanopq): a vanilla implementation of Product Quantization (PQ) and Optimized Product Quantization (OPQ) written in pure python without any third party dependencies.


You can install the package via pip. This library works with Python 3.5+ on linux.

pip install nanopq



import nanopq
import numpy as np

N, Nt, D = 10000, 2000, 128
X = np.random.random((N, D)).astype(np.float32)  # 10,000 128-dim vectors to be indexed
Xt = np.random.random((Nt, D)).astype(np.float32)  # 2,000 128-dim vectors for training
query = np.random.random((D,)).astype(np.float32)  # a 128-dim query vector

# Instantiate with M=8 sub-spaces
pq = nanopq.PQ(M=8)

# Train codewords

# Encode to PQ-codes
X_code = pq.encode(X)  # (10000, 8) with dtype=np.uint8

# Results: create a distance table online, and compute Asymmetric Distance to each PQ-code 
dists = pq.dtable(query).adist(X_code)  # (10000, ) 



  • @Hiroshiba fixed a bug of importlib (#3)
  • @calvinmccarter implemented parametric initialization for OPQ (#14)
  • @de9uch1 exntended the interface to the faiss so that OPQ can be handled (#19)
  • @mpskex implemented (1) initialization of clustering and (2) dot-product for computation (#24)
  • @lsb fixed a typo (#26)