What Is Vector Search?
By Karthick Selvam · Published
Vector search finds items by meaning rather than exact keywords, using numerical representations called embeddings.
Embeddings
An embedding model converts text, images or other data into vectors so that similar meanings sit close together in vector space.
How search works
The query is embedded and the system finds stored vectors nearest to it, using similarity measures such as cosine similarity and approximate nearest-neighbour indexes for speed.
Where it is used
Semantic search, recommendations, deduplication and as the retrieval step in RAG systems.
Tooling
Dedicated vector databases exist, and many existing databases (for example PostgreSQL with the pgvector extension) support vector indexes. Hybrid search combines keywords and vectors.
Frequently asked questions
Does vector search replace keyword search?
Often they are combined; keyword search remains better for exact codes, names and IDs.
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