References

1. Hervé Jégou, Matthijs Douze, Cordelia Schmid. Product Quantization for Nearest Neighbor Search. IEEE TPAMI, 2011. — Introduces PQ, ADC, and the IVFADC system this book reproduces.

2. Tiezheng Ge, Kaiming He, Qifa Ke, Jian Sun. Optimized Product Quantization. CVPR 2013. — OPQ: a learned rotation before PQ that improves recall (OPQ in FAISS).

3. Stuart P. Lloyd. Least squares quantization in PCM. IEEE Trans. Information Theory, 1982 (work from 1957). — The k-means algorithm at the core of every codebook here.

4. Jeff Johnson, Matthijs Douze, Hervé Jégou. Billion-scale similarity search with GPUs. IEEE Big Data, 2019. — The FAISS library and its GPU implementations.

5. Ruiqi Guo et al. Accelerating Large-Scale Inference with Anisotropic Vector Quantization (ScaNN). ICML 2020. — Quantization tuned for maximum inner-product search.

Tools & systems

• FAISS — IndexFlat, IndexIVFFlat, IndexPQ, IndexIVFPQ, IndexScalarQuantizer, IndexHNSWFlat, and the index factory.
• ScaNN (Google) — anisotropic quantization for MIPS.
• Vector databases — Qdrant, Weaviate, Milvus, Pinecone (IVF/PQ/HNSW indexes).

Related books here

• HNSW from Scratch — the graph-navigation side of vector search.
• KTS from Scratch — kernel temporal segmentation.

This book's code

• code/ivfpq.py — k-means, IVF, PQ, IVFPQ.
• code/demo.py — recall vs. memory vs. work comparison.
• code/pqtool.py — the trade-off estimator CLI.

All depend only on NumPy and the standard library.