Having established how data can be transformed into meaningful vector representations in the previous chapter, we now address the practical question: how do we store, manage, and efficiently search through these high-dimensional vectors? Standard relational or NoSQL databases are generally not optimized for the primary operation required here: finding vectors closest to a query vector in a high-dimensional space using similarity metrics like $cosine\_similarity$ or Euclidean distance ($d(p, q) = \sqrt{\sum_{i=1}^{n}(p_i - q_i)^2}$).

This chapter introduces vector databases, systems specifically designed to handle these unique requirements. We will examine their core architectural components, understanding how they differ from traditional databases. You will learn about common data models used for vectors and their associated metadata, how basic Create, Read, Update, and Delete (CRUD) operations function in this context, and the capability of combining vector similarity search with filtering based on metadata attributes. We will also touch upon fundamental considerations for scaling these databases to handle large volumes of vector data. By the chapter's end, you'll have a foundational grasp of vector database concepts and their operational principles.