The ability of a Retrieve-Augmented Generation (RAG) system to provide accurate and relevant responses depends significantly on its first stage: retrieving the right information. This chapter concentrates on that retrieval mechanism.

You will learn how text is converted into numerical representations, known as vector embeddings, which capture semantic meaning. We will examine how similarity search techniques, often using metrics like cosine similarity ($cos(\theta)$), are applied to find the embeddings most relevant to a user's query within a large collection. This naturally leads to the concept of vector databases, specialized systems built for storing and efficiently querying these high-dimensional vectors. We will also cover common models used for creating embeddings, considerations for selecting an appropriate vector database, and conclude with a hands-on exercise for generating text embeddings yourself.