The retrieval component, discussed previously, needs access to well-structured information to function effectively. Raw documents, whether they are PDFs, text files, or web pages, require specific preparation before they can be indexed and searched efficiently. This chapter focuses on the practical steps involved in transforming your knowledge sources into a format suitable for a Retrieve-Augmented Generation system.

You will learn methods for ingesting documents from different origins and the rationale behind splitting large documents into smaller, more manageable pieces, a process known as chunking. We will cover various chunking strategies, from simple fixed-size splits to more content-aware techniques. Additionally, you will understand how to associate meaningful metadata with these chunks and the process for storing the final processed data, along with its vector embeddings, within a vector database, making it ready for the retriever component. Practical exercises will guide you through implementing document loading and chunking using common libraries.