Vector Database
A database built to store embeddings and find the nearest ones fast.
Vector databases index high-dimensional vectors so that ‘find the most similar items’ runs in milliseconds across millions of records. They are the storage layer under most RAG systems. Whether you need a dedicated one or just a vector extension on your existing database depends entirely on scale.
In practice: Storing every paragraph of your docs as a vector so a support bot can retrieve the right three.