Vector Search at Scale

ANN algorithms, HNSW, and the rise of vector databases

Once you have millions of embeddings, brute-force cosine similarity is too slow. This module covers approximate nearest neighbor (ANN) algorithms that trade a tiny bit of accuracy for massive speedups. You'll learn locality-sensitive hashing, IVF (inverted file index), HNSW (hierarchical navigable small world graphs), and product quantization. Then see how vector databases (pgvector, Qdrant, Pinecone) wrap these algorithms into production infrastructure for RAG and recommendation systems. Mini-lab: Index 10,000 embeddings with FAISS, compare brute-force vs. IVF vs. HNSW on speed and recall.

Estimated time: 60 minutes

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