Multimodal RAG is a retrieval-augmented generation approach that retrieves and grounds a generative model using multiple data modalities such as text, images, tables, charts, audio, or video, rather than text-only documents.
What is Multimodal RAG?
In multimodal RAG, the retrieval stage indexes heterogeneous assets and returns the most relevant items for a query that may itself be multimodal. For example, a user can ask a question about a product manual page image, a chart in a PDF, or a screenshot of an application. The system encodes the query and candidate items into comparable representations using multimodal embedding models, sometimes with separate encoders per modality and a shared embedding space. Retrieved items are then provided to a multimodal generative model that can attend to both text and visual inputs, or to a pipeline that converts non-text assets into text via OCR, captioning, or table extraction. Compared with text-only RAG, multimodal RAG requires careful handling of layout, resolution, and attribution. It also needs evaluation that checks whether the answer is grounded in the retrieved visual or structured evidence.
Where it is used and why it matters
Multimodal RAG is used in enterprise document assistants, technical support for screenshots, medical or industrial workflows involving images, and analytics copilots that must interpret tables and plots. It matters because many real knowledge bases are PDFs, slide decks, and images where key facts live in figures and tables. Without multimodal grounding, systems either ignore that evidence or hallucinate. Multimodal RAG also introduces security and privacy concerns, such as extracting sensitive information from images and the risk of prompt injection through embedded text in documents.
Types
- OCR-first RAG: convert images to text, then run standard text RAG.
- Embedding-based multimodal RAG: retrieve using multimodal embeddings and feed images plus text to a multimodal model.
- Hybrid RAG: combine text retrieval, image retrieval, and re-ranking before generation.
FAQs
- Do you need a multimodal LLM for multimodal RAG?
Not always. OCR and captioning can work, but a multimodal model is often better for charts, spatial layout, and visual reasoning. - What are key evaluation metrics?
Retrieval recall for each modality, grounding faithfulness, citation accuracy, and correctness on visual question answering tasks. - How is chunking handled for images and PDFs?
You often chunk by page regions, figure boundaries, and table blocks, not just by token count. - What are common failure modes?
Bad OCR, retrieving the wrong figure, losing layout context, and the model over-trusting irrelevant visual artifacts.
