Once you convert text into embeddings, you need a way to store and search those vectors efficiently. This is where vector databases come in.

A vector database is designed to store high-dimensional vectors and quickly find the ones that are most similar to a given query. Instead of searching for exact matches like a traditional database, vector databases perform similarity search, retrieving items whose embeddings are closest in vector space.

This capability is essential for many modern AI systems. Applications like semantic search, recommendation engines, and retrieval-augmented generation (RAG) rely on finding relevant pieces of information based on meaning rather than exact keywords.

In this chapter, we will explore what vector databases are, how they work, and why they are a critical component of embedding-based systems.