Simple definition
A vector database stores numerical embeddings and supports similarity searches for semantic retrieval and AI applications.
Purpose and use
A vector database stores vectors and searches for neighbors using a distance or similarity measure.
How does it work?
Indexes can accelerate search, sometimes approximately, by trading recall, latency, memory and construction cost.
Example and verification
Choose configuration by measuring retrieved-neighbor quality and latency under expected volume and filtering.
Points to watch
Top results are not automatically the most useful to the application. Approximate indexes can miss neighbors that exact search would return.
In short
The database supports similarity retrieval; relevance depends on representations, metric and index settings.