Multimodal RAG: Definition and Examples

Multimodal RAG (Multimodal Retrieval-Augmented Generation) is an AI architecture that combines information retrieval from heterogeneous databases with the generation capability of a large language model. Unlike classic RAG, which is limited to text, Multimodal RAG can index, search, and reason over documents containing images, charts, tables, audio, or video.

Its operation relies on three key steps. First, multimodal indexing: documents are chunked and transformed into vector embeddings capable of representing different types of content in the same semantic space. Next, retrieval: when a query arrives, the system identifies the most relevant fragments, whether a text paragraph, a technical diagram, or an audio clip. Finally, generation: the multimodal language model synthesizes these varied sources to produce a coherent response.

This approach solves a major issue of traditional RAG systems: the loss of information when processing rich documents. An annual report contains essential charts, a technical manual includes diagrams, a training session involves videos. Multimodal RAG preserves and leverages all this richness instead of limiting itself to raw text.

Use cases are numerous: customer support for physical products (with photos and manuals), medical document analysis (imaging + reports), legal research (scanned documents + annotations), or professional training combining videos, slides, and transcripts.