#hnsw

3 articles

01 ·RAG & Retrieval·★ MEMBER·PAPER·13 min read Build Your Own Vector DB — From Brute Force to HNSW Assemble a vector search engine step by step, starting from a 20-line brute-force scan. The curse of dimensionality, IVF partitioning, HNSW graph traversal and quantization, all viewed through one lens: the trade between recall and speed. 02 ·Data Structures·FREE·PAPER·9 min read Hashing and Nearest-Neighbor Search — The Groundwork Under Vector Search Two inventions that made looking things up fast — the exact-match hash table, and LSH and HNSW for searching by meaning — from zero assumed knowledge. What is actually running underneath RAG and every vector database. 03 ·Search & Optimization·FREE·PAPER·8 min read Graph Algorithms from Scratch — Shortest Paths and Where They Lead From transit apps to vector search, the world runs on dots and lines. We build up BFS, Dijkstra, and A* assuming zero background, then follow one unbroken thread all the way to HNSW — the graph search powering retrieval in the LLM era.