An approximate-nearest-neighbor index that organizes vectors in layered proximity graphs and searches from coarse upper layers toward detailed lower layers.
Why it matters
It is a common way to make high-recall vector search practical at scales where exhaustive comparison is too slow.
In practice
Tune construction and query parameters against latency, memory, and Recall@K targets, then rebuild the index when embedding versions change.
Common confusion
HNSW is an index algorithm, not a similarity metric, embedding model, or complete vector database.
Related terms
Sources
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