# Multimodal RAG

**Source:** https://promtable.com/glossary/multimodal-rag

> Multimodal RAG retrieves images, audio, video, or tables alongside (or instead of) text, embedding each modality with a compatible encoder so they can be searched and ranked together.

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Multimodal RAG retrieves images, audio, video, or tables alongside (or instead of) text, embedding each modality with a compatible encoder so they can be searched and ranked together.

Standard RAG searches text. Multimodal RAG handles document corpora where the answer might live in a chart, an embedded image, a video frame, or a table. Approaches in 2026: image-text co-embedding (CLIP successors, Voyage-multimodal-3), table-aware text representations, audio embedding (Wav2Vec, Whisper-derived). Retrieved non-text artefacts are inserted into the prompt as base64 or rendered images for the multimodal LLM to read. The pipeline is more complex than text RAG — chunking, indexing, and retrieval all need modality awareness — but unlocks documents (PDF reports, technical manuals, slide decks) that text-only RAG misses.

## When to use

- RAG over PDFs with charts and tables.
- Technical manuals with diagrams.
- Video summarisation pipelines.

## Common mistakes

- Mixing modality-specific embeddings in the same index without explicit modality tagging.
- Sending the model 4K images when 512px would suffice — token costs explode.

## Related terms

- [rag](https://promtable.com/glossary/rag)
- [embeddings](https://promtable.com/glossary/embeddings)
- [multimodal](https://promtable.com/glossary/multimodal)
- [semantic-search](https://promtable.com/glossary/semantic-search)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/multimodal-rag
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