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.

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 multimodal rag

• 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.

FAQ

Related terms

• Retrieval-augmented generation (RAG) — Retrieval-augmented generation (RAG) injects relevant documents into the prompt at query time so the model answers from your data instead of its training memory.
• Embeddings — Embeddings are dense numeric vectors that represent the meaning of text, images, or other data, allowing similarity to be measured as vector distance.
• Multimodal model — A multimodal model accepts more than one input type — text plus images, audio, or video — and reasons across them in a single forward pass.
• Semantic search — Semantic search finds documents by meaning rather than keyword match, using embedding similarity in a vector space.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/multimodal-rag.md.