How do you implement RAG with multi-modal content including images and tables?

Multi-modal RAG extends text-based retrieval to handle images, tables, charts, and diagrams. Document processing: extract images and tables during ingestion using document parsing libraries (Unstructured, LlamaParse, or custom PyMuPDF pipelines). Image handling: generate text descriptions of images using vision models (GPT-4V, LLaVA), embed descriptions alongside the image reference, and during generation include both the description and image URL/path in the context. Table handling: extract tables into structured formats (markdown, CSV, or HTML), generate natural language summaries of table content, embed both the summary and structured data, and implement specialized table querying (text-to-SQL for structured databases, or pass markdown tables directly to the LLM). Chart and diagram handling: use vision models to describe chart contents (axes, trends, key values), extract data points where possible, and store both visual description and extracted data. Multi-modal embedding: use models like CLIP or SigLIP that embed images and text into a shared vector space, enabling cross-modal retrieval (text query retrieves relevant images). Implementation: store multi-modal chunks with type metadata (text, image, table, chart), retrieve across all modalities during search, and format the augmented prompt to include appropriate content types. Retrieval strategy: retrieve text chunks for textual queries, include nearby images and tables from the same document section, and use multi-modal embeddings when the query might be best answered by visual content. Challenges: table structure preservation during chunking, mathematical notation rendering, and context window management when including image descriptions alongside text.