Multimodal RAG
Extending RAG to retrieve and process multiple data types including text, images, tables, and audio.
Overview
Multimodal RAG extends traditional retrieval-augmented generation to work with multiple data types. Instead of retrieving only text documents, multimodal RAG can retrieve and process images, tables, charts, PDFs with complex layouts, audio transcripts, and video frames alongside text.
Approaches
Techniques include using vision-language models to embed images alongside text, table-aware parsing and retrieval, ColPali-style late interaction for document images, and multimodal embedding models that map different modalities to a shared vector space. This enables AI systems to answer questions that require understanding visual and textual information together.