Faiss - Vector Compression with PQ and IVFPQ (in Python)
So far we’ve worked through the logic behind a simple, readable implementation of product quantization (PQ) in Python for semantic search. Realistically we wouldn’t use this because it is not optimized and we already have excellent implementations elsewhere. Instead, we would use a library like Faiss (Facebook AI Similarity Search) — or a production-ready service like Pinecone.

We’ll take a look at how we can build a PQ index in Faiss, and we’ll even take a look at combining PQ with an Inverted File (IVF) step to improve search speed.

Before we start, we need to get data. We will be using the Sift1M dataset. It can be downloaded and opened using this script:
https://gist.github.com/jamescalam/928a374b85daffa49a565f3dc18d059c#file-get_sift1m-ipynb

🌲 Pinecone article:
https://www.pinecone.io/learn/product-quantization/

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