A traditional database finds rows that match a value exactly. A vector database is built to find items that are merely similar, by comparing the number lists, or embeddings, that represent their meaning and returning the nearest ones.
Doing this quickly across millions of items needs specialised indexes that approximate nearest neighbours rather than checking every entry. The pay-off is search that returns relevant results even when no keyword matches, which is why these stores sit behind many meaning-based features.