vector-database
3 free lessons tagged vector-database across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Hybrid Search, Tuning, and Running It in Production
Dense vectors miss exact terms, so production retrieval combines them with lexical search. This lesson covers hybrid retrieval and reciprocal rank fusion, a tuning method that starts from a stated recall target, capacity planning, the operational failures that catch teams, and how to choose between running your own index and buying a service.
HNSW, IVF, and Quantization
Three ideas carry almost all production vector search. This lesson covers HNSW as a navigable small-world graph with its M and ef parameters, IVF as coarse partitioning with nprobe, and the quantization schemes that cut memory by an order of magnitude, plus how they compose into the hybrid indexes real systems actually run.
Why Exact Nearest Neighbour Search Does Not Scale
Vector search exists because exact nearest neighbour search is intractable at scale and the curse of dimensionality defeats the classical index structures. This lesson covers distance metrics and when each is right, why brute force costs what it does, why k-d trees fail above a few dozen dimensions, and the recall-latency trade that every approximate index makes.