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Short summary

A clear technical explainer on how vector databases avoid checking every vector during search. It covers embeddings, cosine similarity, why traditional B-tree indexes fail in high-dimensional space, and how HNSW (Hierarchical Navigable Small World) solves this with layered graphs — sparse long-jump layers on top, dense precise layers at the bottom — letting a query touch only a tiny fraction of stored data. Includes working Python code with hnswlib.

• •Embeddings convert 'similar meaning' into 'nearest point' in high-dimensional space, enabling vector search
• •Brute-force search is O(N×d); B-tree indexes fail because similarity isn't a sortable range
• •HNSW uses layered navigable graphs for approximate nearest-neighbor search, trading tunable recall for massive speedup

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