Multimodal RAG Architecture

Multimodal RAG Architecture

Use this skill when a knowledge base contains more than plain text. Treat images, tables, equations, layout, captions, and cross-page structure as first-class evidence instead of stripping everything into text chunks.

When Text-Only RAG Fails

Switch to multimodal RAG when:

• Important answers live in tables, charts, screenshots, diagrams, figures, or equations
• PDFs have layout-dependent meaning such as forms, invoices, manuals, or scientific papers
• The same concept appears across text, image, and table regions
• Long documents cause retrieval to miss sparse visual evidence
• Users ask for answers that require comparing visual and textual context

Architecture

1. Parse by modality

   • Text blocks
   • Tables
   • Figures and images
   • Equations
   • Captions
   • Page and section layout

2. Create multimodal evidence nodes

   • Preserve source document, page, bounding box, modality, caption, extracted text, and raw asset pointer.
   • Attach normalized text summaries for retrieval.
   • Keep original media accessible for answer verification.

3. Build relationships

   • Figure-to-caption
   • Table-to-section
   • Equation-to-explanation
   • Cross-page continuation
   • Visual element-to-text mention

4. Retrieve in stages

   • Query rewrite into text, table, and visual intents.
   • Hybrid lexical/vector retrieval over summaries and extracted text.
   • Graph traversal to pull adjacent evidence.
   • Optional visual reranking for image-heavy answers.

5. Generate with provenance

   • Cite document, page, modality, and region.
   • Distinguish extracted facts from model interpretation.
   • Re-open raw assets when the answer depends on visual detail.

Design Rules

• Do not OCR everything and discard layout.
• Do not embed raw image summaries without retaining the image.
• Do not answer from captions alone when the figure itself matters.
• Prefer smaller modality-specific indexes over one overloaded index.
• Keep chunk boundaries aligned to document structure, not fixed token counts.
• Record extraction confidence for OCR, table parsing, and visual descriptions.

Helper Script

Use rag_modality_audit.py to scan a folder and estimate whether a corpus needs multimodal handling:

python scripts/rag_modality_audit.py ./docs

References

Read architecture-checklist.md when implementing or reviewing a multimodal RAG pipeline.

External grounding:

• RAG-Anything arXiv paper
• RAG-Anything GitHub repository
• VimRAG HuggingFace paper page
• Alibaba-NLP/VRAG GitHub repository
• HM-RAG arXiv paper