A deep dive into vector DB essentials
To fully comprehend RAG, it’s imperative to understand vector DBs because RAG relies heavily on its efficient data retrieval for query resolution. A vector DB is a database designed to store and efficiently query highly dimensional vectors and is often used in similarity searches and machine learning tasks. The design and mechanics of vector DBs directly influence the effectiveness and accuracy of RAG answers.
In this section, we will cover the fundamental components of vector DBs (vectors and vector embeddings), and in the next section, we will dive deeper into the important characteristics of vector DBs that enable a RAG-based generative AI solution. We will also explain how it differs from regular databases and then tie it all back to explain RAG.
Vectors and vector embeddings
A vector is a mathematical object that has both magnitude and direction and can be represented by an ordered list of numbers. In a more general sense,...
