Similarity Search Explained: kNN, IVF & Faiss (Part 1)

Welcome to Part 1 of our series on vector similarity search, where we go under the hood to explore how high-dimensional search actually works. In this episode, we break down the foundational concepts behind k-Nearest Neighbors (kNN) and the Inverted File Index (IVF). You'll see step-by-step code examples using the Faiss library to show how these indexes scale search speed. Subscribe to follow along as we explore more advanced similarity search techniques in upcoming parts! Timestamps: 00:00 - Intro 00:38 - Motivation & Idea 02:02 - Similarity metrics 02:29 - kNN 03:07 - kNN performance 04:12 - kNN application scope 04:38 - Faiss implementation (kNN) 06:50 - Improving kNN 07:37 - Inverted file index (IVF) 08:51 - Voronoï diagram 10:25 - Inference 11:06 - Edge problem 12:07 - IVF application 13:16 - IVF performance 13:48 - Faiss implementation (IVF) 15:57 - Outro Prefer reading? I've written a similarity search article series: https://shorturl.at/4Dt2M Connect with me: LinkedIn:   / vyacheslav-efimov   Medium:   / slavahead   GitHub: https://github.com/slavastar