Learn how FAISS powers fast vector search using Flat, IVF, and HNSW indexes, balancing recall and speed for large-scale retrieval.

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FAISS enables fast vector search at scale through Approximate Nearest Neighbor (ANN) algorithms. The tutorial implements and benchmarks three index types: Flat (exact but slow), HNSW (graph-based with near-perfect recall), and IVF-Flat (cluster-based with speed-accuracy tradeoffs). Includes complete Python code for building indexes, measuring recall@k and query latency, and persisting indexes for production use. Part of a 3-lesson RAG series connecting embeddings to retrieval systems.

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