A vector database is a specialized system that stores and manages data converted into vectors (number sequences) using
embeddings. Unlike traditional databases that search for exact matches, these can find similar elements even if they are not identical.
Embeddings are the key piece of these databases: they transform complex data like texts, images, or sounds into mathematical vectors that capture their meaning and relationships. For example, in a word
embedding, terms like "king" and "monarch" will be represented by very similar vectors, allowing the system to find semantic connections beyond literal word matches.
Vector databases are fundamental to many
AI systems. When a search system needs to find related information, it uses these
embeddings to compare vectors and retrieve conceptually similar content. This applies to recommendation systems, semantic search, document analysis, and many other applications where finding deeper similarities beyond textual matches is of interest.