similarity-search

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.

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.

Dive into the world of vector databases, specialized systems designed to store and query high-dimensional vector embeddings efficiently. Learn how these databases power semantic search, recommendation systems, and large language model applications by finding semantically similar data points at scale.