What is vector search?
A similarity-search method that represents queries and stored items as numeric embeddings, then retrieves the nearest vectors according to a distance or similarity metric.
Why is this important?
Vector search can find conceptually related text, images, audio, products, or records even when they share few exact keywords. It is a common retrieval layer for semantic search, recommendations, and RAG.
How it works
An embedding model converts each item and query into vectors. A vector index uses exact or approximate nearest-neighbor algorithms to return the closest candidates, often followed by filtering or reranking.
Technical example
A support question about cancelling a subscription retrieves documentation chunks about account termination even though the query and source use different words.
Implementation notes
Match the embedding model and distance metric, measure recall and latency, choose chunking carefully, preserve metadata filters, handle model migrations, and consider hybrid search when exact terms matter.
