What is the difference between RAG and multimodal RAG?

Standard RAG retrieves text passages. Multimodal RAG also retrieves images, diagrams, tables, scanned pages, audio, or video, either by converting them to text descriptions or by embedding them directly with a multimodal model. The answering model then reads the retrieved mix, which requires it to handle images when those are included.

Do I need a multimodal embedding model?

Not necessarily. Converting images, tables, and recordings into text descriptions lets you use a standard text embedding model and existing pipelines. Direct multimodal embeddings preserve more visual detail and layout. Many systems start with text conversion and add direct image retrieval for document types where descriptions lose too much.

How does multimodal RAG handle video?

Usually by transcribing the audio, sampling key frames, and describing or embedding those frames, then indexing both with timestamps. A question can retrieve the relevant transcript passage or frame, and the answer can point to the exact moment in the recording. Chunking by topic rather than fixed intervals improves retrieval.

Is multimodal RAG more expensive?

Generally, yes. Describing images and transcribing recordings adds processing at indexing time, and answering with image inputs costs more per query than text alone. Limit costs by indexing only content that carries information, such as diagrams, charts, and scans, and by retrieving a small number of the most relevant items per question.

What Is Multimodal RAG?

Multimodal RAG extends retrieval augmented generation beyond text to images, diagrams, tables, scanned pages, audio, and video. Content is either converted into text descriptions or embedded directly with a multimodal embedding model, then retrieved by similarity to the question. A model that can read images and text answers from the retrieved mix, citing the source item.

Why text-only RAG misses so much

A standard RAG pipeline extracts text from documents, chunks it, and retrieves passages. Anything that is not text is lost or mangled along the way: the architecture diagram in a design document, the chart in a quarterly report, the scanned contract, the whiteboard photo, the recorded meeting where a decision was made.

For many teams, those are exactly the sources that hold the answers. An engineer asking which services write to a database may find the answer only in a diagram. A text-only system will answer from whatever prose happens to mention it, or not at all.

Two ways to build it

The main decision is whether non-text content becomes text before indexing, or is indexed as itself.

Convert everything to text

Run each non-text item through a model that describes it: captions for images, structured text for tables, transcripts for audio and video, OCR for scans. Then index those descriptions like any other text. This works with existing text pipelines and makes everything keyword searchable. The weakness is that the description is only as good as the model that wrote it, and fine visual detail is often lost.

Embed each modality directly

Use a multimodal embedding model that places images and text in the same vector space, so a text question can retrieve an image directly. Page-level approaches embed whole document pages as images, which preserves layout, tables, and figures together. This keeps visual information intact, but it needs a multimodal model at answer time and the retrieved items are harder to inspect than text.

Design choices that matter

Keep the original linked. Whichever approach you use, store a reference to the source image, page, or timestamp alongside each indexed item. The answering model and the reader should both be able to see the original, not only a description of it.

Handle tables deliberately. Tables are where both approaches fail most often. Text conversion scrambles rows and columns; image embeddings retrieve the right page but the model may misread a dense table. Extract tables into structured form where possible, and keep the page image as a fallback.

Chunk recordings by topic. Split transcripts at topic changes rather than fixed time windows, and keep timestamps so answers can point to the moment something was said.

Mind the cost. Describing every image with a model and answering with image inputs both cost more than text. Index visual content that carries information, such as diagrams, charts, and scans, and skip decorative images, logos, and repeated page furniture that add cost without adding answers.

Where to start

Inventory where answers actually live before building anything. If most questions can be answered from prose, with the occasional diagram, text conversion of the important images is enough. If key documents are slide decks, scanned forms, or reports built around charts, page-level image retrieval is worth the extra cost.

Start with one document type that text-only retrieval clearly fails on, index it both ways on a small sample, and compare answers against a set of real questions. The better approach for your content will be obvious quickly, and the comparison costs far less than indexing everything twice.

Where it breaks

Dense charts. Models misread exact values from charts. If numbers matter, retrieve the underlying data too.
Handwriting and poor scans. OCR errors propagate into every answer that relies on them.
Diagrams without labels. A diagram whose meaning depends on context outside the image retrieves poorly, since nothing in it matches the question's words.
Mixed-language content. Captions and transcripts in one language, questions in another, can reduce retrieval quality unless the embedding model handles both.
Stale visuals. Diagrams are updated less often than text. An old architecture drawing retrieved confidently can contradict the current prose, so date visual sources and prefer the newer one.

Frequently asked questions

What is the difference between RAG and multimodal RAG?: Standard RAG retrieves text passages. Multimodal RAG also retrieves images, diagrams, tables, scanned pages, audio, or video, either by converting them to text descriptions or by embedding them directly with a multimodal model. The answering model then reads the retrieved mix, which requires it to handle images when those are included.
Do I need a multimodal embedding model?: Not necessarily. Converting images, tables, and recordings into text descriptions lets you use a standard text embedding model and existing pipelines. Direct multimodal embeddings preserve more visual detail and layout. Many systems start with text conversion and add direct image retrieval for document types where descriptions lose too much.
How does multimodal RAG handle video?: Usually by transcribing the audio, sampling key frames, and describing or embedding those frames, then indexing both with timestamps. A question can retrieve the relevant transcript passage or frame, and the answer can point to the exact moment in the recording. Chunking by topic rather than fixed intervals improves retrieval.
Is multimodal RAG more expensive?: Generally, yes. Describing images and transcribing recordings adds processing at indexing time, and answering with image inputs costs more per query than text alone. Limit costs by indexing only content that carries information, such as diagrams, charts, and scans, and by retrieving a small number of the most relevant items per question.