Vithushan Sylvester explains vector databases using HNSW and IVF

This article explains how vector databases work under the hood by breaking them down into five conceptual levels: turning meaning into coordinates, measuring distance, managing the scaling limits of brute force search, using HNSW graphs for approximate nearest neighbor search, and reducing memory usage through IVF and quantization. The author argues that buyers and engineers should evaluate vector databases by testing recall, distance metrics, and quantization on their own queries rather than relying on vendor claims. Open article →