Binary Quantization - Andrey Vasnetsov | Vector Space Talks - Qdrant

What this post added

This post details the implementation and benefits of binary quantization for vector embeddings in Qdrant. It explains how binary quantization approximates dot product/cosine similarity using Hamming distance, leading to significant speedups (over 30x) and storage reduction by converting float vectors to binary representations (0 or 1). The post highlights its effectiveness with OpenAI models and introduces oversampling as a technique to maintain search precision in real-time. It also touches upon the challenges of storing large vectors and the necessity of quantization for efficient HNSW index operation.