In plain English
Most RAG systems only read text. When you ingest a PDF, the pipeline extracts the words and quietly throws away everything else — the bar chart that shows the trend, the architecture diagram, the screenshot, the table of prices. But in real documents, the answer often lives in those visuals. A financial report's key number is in a table. A product spec's wiring is in a diagram. Strip the figures and you've deleted the part the user actually needs.
Multimodal RAG fixes this by retrieving images, tables, and text together, then handing the relevant visuals to a vision-capable model so it can read them when it answers. "Multimodal" just means more than one kind of data — here, pixels and words living in the same search index.
Think of a librarian helping you research. Plain RAG is a librarian who can only photocopy the paragraphs of a book — every chart, map, and photo comes back blank. Multimodal RAG is a librarian who photocopies the whole page, charts included, and can also describe what each picture shows. When your question is "what does the revenue chart on page 12 tell us?", only the second librarian is any use.
Why it matters
The documents people most want to query are rarely plain text. Annual reports, research papers, slide decks, invoices, manuals, and dashboards are dense with tables, charts, and diagrams. A text-only pipeline silently loses all of it, and the failure is invisible: retrieval still returns something, the model still answers fluently, and nobody notices the answer skipped the one figure that mattered.
- The answer is in the picture. A chart's headline number, a diagram's connections, or a screenshot's error message can be the entire point. Text extraction returns an empty space where the image was.
- Tables get mangled. When a PDF table is flattened to text, columns and rows scramble together. "Q3 | 42 | up" becomes a soup of numbers with no structure, and the model can't tell which figure belongs to which heading.
- Scanned and image-only pages vanish. A photographed contract or a slide exported as an image has no extractable text at all. To a text-only ingester, the page is blank.
- Layout carries meaning. Captions, callout boxes, and the position of a label relative to a bar all encode information that disappears when a page becomes a flat string.
Who cares? Anyone building search or chat over visual-heavy documents: financial analysts querying reports, support tools that answer from screenshot-laden guides, research assistants over scientific papers, and "chat with your PDF" products that promise to read the whole document, not just its prose. If your users complain "it ignored the chart," you have a multimodal RAG problem.
How it works
Multimodal RAG keeps the same four-step shape as ordinary RAG — ingest, retrieve, augment, generate — but each step has to handle visuals. The big design decision is at ingestion: how do you make an image searchable? There are two main strategies, and most of the article's choices flow from which one you pick.
Strategy A — caption everything into text
The first approach turns every visual into text, then runs a normal text pipeline. During ingestion you send each image, chart, or table to a vision model and ask it to write a description or summary: "Bar chart showing quarterly revenue: Q1 $4.2M, Q2 $5.1M, Q3 $6.8M, rising each quarter." You embed that text like any other chunk and store it in your vector database. The original image is kept alongside, linked to its summary.
At query time you do plain semantic search over those text summaries. When a summary is retrieved, you can pass the original image (not just its caption) into the prompt so a vision-capable model reads the real pixels when generating. The summary is the searchable handle; the image is the ground truth.
Strategy B — embed the image directly
The second approach skips captioning. A multimodal embedding model maps both images and text into the same vector space, so a picture of a cat and the word "cat" land near each other. You embed each image straight into a vector and store it. At query time you embed the user's text question into that same shared space and search — the closest vectors might be images, text chunks, or both. No captioning step, and you never lose detail to a summary that missed something.
Whichever strategy you use, the generation step is the same and it is the crucial one: the retrieved images must reach a vision-capable model. You build a prompt that interleaves the text question, any retrieved text chunks, and the actual retrieved images, then ask a model that can see to answer. If you retrieve a chart but only feed the model its caption, you're back to text-only RAG with extra steps.
Caption-to-text vs direct image embedding
The two ingestion strategies trade off in predictable ways. Many production systems combine them — embed images directly for recall, and also keep generated summaries for keyword matching and citations.
| Aspect | Caption-to-text | Direct image embedding |
|---|---|---|
| What you index | A model-written text summary of each visual | The image itself, as a vector |
| Search index | Reuse your existing text vector store | Needs a multimodal embedding model |
| Ingestion cost | A vision-model call per image (slower, pricier) | One embedding call per image (cheaper) |
| Detail loss | Whatever the summary omits is gone from search | Nothing dropped — the pixels are the index |
| Hybrid + keyword search | Easy — it's just text | Harder — vectors aren't words |
| Best when | You also want readable captions and citations | Fine visual detail matters and captions miss it |
Handling tables specifically
Tables deserve their own treatment because they fail in a special way. Flattened to plain text, a table loses its grid: column headers detach from their numbers, merged cells scramble, and the model can no longer tell that "6.8" sits under "Q3 Revenue." You have two good options, and they mirror the two strategies above.
- Convert the table to structured text. Extract it into a clean format that preserves rows and columns — Markdown, HTML, or CSV — and embed that as a chunk. The model sees a real grid, not a number soup, and can read across rows correctly.
- Summarize the table, keep the original. Ask a model to write a short description ("revenue by quarter, rising from $4.2M to $6.8M") for retrieval, but store the original table image or structured form to pass at generation — the same caption-and-keep pattern used for images.
For complex, multi-page, or scanned tables, the modern shortcut is to skip text extraction entirely: render the table as an image and let a vision model read it directly, exactly as you would any other figure. It is often more reliable than fighting a fragile PDF parser.
| Quarter | Revenue | Change |
| ------- | ------- | ------ |
| Q1 | $4.2M | — |
| Q2 | $5.1M | +21% |
| Q3 | $6.8M | +33% |
Source: 2025 annual report, page 12, revenue summary table.Common pitfalls
Multimodal RAG adds new ways to fail on top of the usual retrieval traps. Most of them come from a visual being retrieved but not actually read by the model.
- Retrieving the caption, forgetting the image. The classic mistake: your summary gets matched, but you only paste the summary into the prompt. The model never sees the chart it was supposed to interpret. Always carry the original visual through to generation.
- Using a text-only model at the end. Retrieval can be perfectly multimodal, but if the generating model can't process images, the pixels are useless. Confirm your model is vision-capable before blaming retrieval.
- Lossy captions. A one-line summary of a dense chart inevitably drops detail. If users ask about a specific data point the caption didn't mention, search can't find it. Either write richer descriptions or embed the image directly.
- Mangled tables. Trusting a naive PDF text extractor on tables produces scrambled numbers that look fine until someone checks. Convert to structured text or treat the table as an image.
- No evaluation on visual questions. Testing only text questions hides the whole problem. Build a test set that explicitly asks about charts, tables, and diagrams, or you'll never know the visuals are being dropped.
Going deeper
Once the basic pipeline works, the interesting questions are about how to make images searchable at scale and how to keep answers trustworthy. A few directions worth knowing.
Page-as-image retrieval. Instead of carefully splitting a page into text, tables, and figures, a newer approach treats every page as a single image and embeds it directly with a vision model — preserving layout, fonts, and figures with zero parsing. It trades heavier storage and compute for skipping the brittle extraction step entirely, and it shines on documents where layout itself carries meaning.
Citations and grounding. With visuals in the mix, citations get more useful and more important — pointing the user to "the chart on page 12" lets them verify a number the model read off pixels. Because vision models can misread a value or mix up two bars, showing the source image is a guardrail, not a nicety.
Agentic multimodal RAG. Rather than one fixed pass, you can give a model retrieval as a tool and let it decide whether to look at a figure, zoom into a table, or search again — the agentic RAG pattern extended to visuals. This helps with multi-step questions like "compare the chart on page 4 with the table on page 9."
Cost and latency. Vision calls are heavier than text calls, on both tokens and time. A practical setup retrieves cheaply (text summaries, small image vectors), filters down to a handful of candidates, and only sends those full-resolution images to the vision model at generation — the same retrieve-broadly-then-narrow discipline that governs ordinary RAG.
The durable lesson carries over from plain RAG: the system is only as good as what its retriever puts in front of the model. Multimodal RAG just widens "what" to include pixels and grids — and adds one new rule, that the visual you retrieve must actually reach a model that can see it. Get those two things right and the rest is tuning.
FAQ
What is multimodal RAG?
Multimodal RAG is retrieval-augmented generation that indexes and retrieves images, tables, and text together, instead of only text. When a question arrives, it can return relevant charts, screenshots, or tables and pass them to a vision-capable model so the answer uses the whole document, not just its prose.
How is multimodal RAG different from regular RAG?
Regular RAG extracts text from documents and throws away figures and charts, so the answer can never use them. Multimodal RAG keeps the visuals — making them searchable by captioning them into text or embedding the images directly — and feeds the relevant ones to a model that can actually read images.
What are the two main approaches to multimodal RAG?
First, caption-to-text: a model writes a description of each image or table, you embed that text, and run a normal text pipeline (keeping the original visual to pass at generation). Second, direct image embedding: a multimodal embedding model puts images and text in one shared vector space so you can search images directly. Many systems combine both.
How do you handle tables in RAG?
Don't trust a naive text extractor — it scrambles columns and rows. Either convert the table into a structured format like Markdown, HTML, or CSV and embed that as a chunk, or render the table as an image and let a vision model read it. For complex or scanned tables, the image route is often more reliable.
Do I need a vision model for multimodal RAG?
Yes, at the generation step. Retrieval can surface images, but only a vision-capable model can actually read a chart or screenshot and answer from it. If your final model is text-only, the retrieved pixels are wasted — confirm the model can process images before building the rest.
What is a multimodal embedding model?
It is a model that maps both images and text into the same vector space, so a picture and a matching word land close together. That shared space lets you embed a text question and search directly against image vectors, retrieving relevant pictures without first turning them into captions.