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Vector similarity search is a technique for finding semantically similar items in high-dimensional vector spaces. It combines vector representations (e.g., embeddings) with efficient index structures for nearest-neighbor queries. Common applications include semantic search, recommendations, and deduplication. Choice of index and distance metric affects perfo…
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Vector similarity search compares vector representations and finds content whose features are close in the numeric space.
The approach grew from nearest-neighbor search and became practical for large vector collections through methods such as HNSW.
A query is converted into a vector. The search measures its distance from stored vectors and returns the nearest results.
Content is made comparable as vectors.
A distance measures similarity between two vectors.
The most similar results appear first.
Vector similarity search enables semantic search, recommendations, and retrieval-augmented applications.
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