Multimodal RAG
One index across text, images, and tables.
When to use
When knowledge lives in IMAGES/CHARTS/TABLES/scanned PDFs/slides/video frames (financial reports, technical diagrams, invoices, product photos) where OCR-to-text loses layout and visual meaning. ❌ Not needed if the documents are already clean text: a text pipeline is cheaper and more accurate.
Real-world examples
- Q&A over financial reports: ask about numbers living in a chart/table, not in prose.
- Search internal presentation slides: content is images, diagrams, sparse text.
- Process scanned invoices/receipts: extract info from images while keeping layout.
- Q&A over product catalogs with photos, or reading technical diagrams/blueprints.
Diagram
Illustrative pipeline diagram; see the step-by-step description in the Pipeline flow section below.Pipeline flow
- 1Mixed sources: Text Chunks · Images/Charts · Tables
- 2Shared Multimodal Embedding Model (e.g. CLIP / ColPali)
- 3Unified Vector Index (one shared index)
- 4Retrieval
- 5Multimodal LLM (vision + text) → Answer
In plain words
Like a colleague who can ACTUALLY LOOK at the chart and the photo, instead of only reading a caption "this is a sales chart". Because they can see it, they read the exact numbers on each bar, understand the technical diagram, and answer correctly — what text-only RAG (reading words alone) misses.
Concept A–Z
A lot of knowledge is NOT text: charts in reports, architecture diagrams, data tables, scanned PDFs, invoices. Text-only RAG misses all of it. Multimodal RAG embeds EVERY content type into the SAME vector space using a multimodal embedding model (CLIP for image-text; ColPali/ColQwen embed the document PAGE IMAGE directly, skipping OCR), stored in one Unified Vector Index. At query time (by text), it retrieves both relevant text passages and images/tables, then hands them to a Multimodal LLM (GPT-4o, Gemini, Claude vision) to "look" and answer. Two main approaches: (A) convert everything to text (caption images, parse tables) then do normal RAG; (B) embed images directly (ColPali) — more accurate for visually-rich docs.
How it works
Two embedding strategies
Pick based on how visually-rich your docs are and your budget.
- A — Translate-to-text: use a vision LLM to caption images/charts, parse tables to markdown, then do normal text RAG. Simple, cheap, but LOSES visual detail.
- B — Embed images directly: ColPali/ColQwen embed page images (multi-vector, late interaction) → preserve full layout/charts, skip OCR. High accuracy for complex PDFs/slides.
- In practice often HYBRID: text → text embeddings; visually-rich pages → image embeddings; merged in a unified index.
Retrieval + generation
Query in text, retrieve a text+image mix, then let a vision LLM read it.
- A text query embedded into the same space → retrieves both text chunks and page images.
- Feed the REAL image (not a caption) to the Multimodal LLM so it "sees" the chart/table and reasons.
- Citations: return the source page/image so users can verify numbers in the chart.
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