Most RAG systems focus on text. They retrieve documents, paragraphs, or code snippets and use them as context for a language model. But in many real-world applications, important information is not limited to text. It may exist in images, diagrams, PDFs, tables, audio, or videos.

Multimodal RAG extends the RAG framework to work with multiple data modalities. Instead of retrieving only text, the system can search and incorporate information from images, charts, screenshots, documents, and other structured or unstructured formats.

In this chapter, we will explore how to design RAG systems that handle multiple data types, including the challenges of indexing multimodal data, retrieving relevant context, and integrating it effectively into model prompts.