TLDR: Product Quantization

Date: 2025-12-07 Source: https://arpitbhayani.me/blogs/product-quantization

Overview

Recommendation engines, image retrieval platforms, document matching services, and RAG pipelines all rely on finding the nearest neighbors to a given query vector in high-dimensional space. This is where vector similarity search comes in.

Key Points

• Recommendation engines, image retrieval platforms, document matching services, and RAG pipelines all rely on finding the nearest neighbors to a given query vector in high-dimensional space.
• Memory Problem in Vector Search: Consider a dataset of one million 128-dimensional vectors, each stored as 32-bit floats.
• Vector Quantization: Before diving into Product Quantization, let’s go through vector quantization.
• Product Quantization: Product Quantization resolves this by decomposing the vector space into a Cartesian product of lower-dimensional subspaces - set of all possible combinations.