Graph-Based Approximate Nearest Neighbors and HNSW

Graph-Based Approximate Nearest Neighbors and HNSW

Explore graph-based algorithms for approximate nearest neighbor search in this 59-minute workshop led by HNSW author Yury Malkov and Pinecone's James Briggs. Gain a comprehensive understanding of vector search techniques, from exhaustive to approximate methods, with a focus on graph algorithms and their advantages. Delve into Delaunay graphs, Voronoi diagrams, and relative neighborhood graphs before examining HNSW construction and its extensions. Learn about memory-constrained scenarios, coarse quantization, and disk-based approaches. Discover update and deletion strategies, benchmark with SQUAD and MSMARCO datasets, and receive practical advice for implementing these techniques in real-world applications.