---
title: "Multimodal RAG for Text, Images, and Tables"
description: "Index each modality with provenance, retrieve at the right granularity, and cite original evidence."
language: en
canonical: https://navinresearch.com/blog/multimodal/multimodal-rag.md
topics: ["multimodal RAG", "image retrieval augmented generation", "document retrieval AI"]
---

# Multimodal RAG for Text, Images, and Tables

Index each modality with provenance, retrieve at the right granularity, and cite original evidence.

**Short answer:** Do not flatten every modality into one lossy text summary. Store extracted text, page or image regions, captions, table structure, and source metadata. Retrieve candidates across modalities, rerank for the question, and provide the original evidence to the model.

## Implementation steps

1. Choose chunks based on document structure and visual boundaries.
2. Link derived text and embeddings to immutable source locations.
3. Retrieve separately by modality, then rerank jointly.
4. Return citations that a user can open at the relevant page or region.

## Validation checklist

- Evaluate retrieval recall before generation quality.
- Test questions answerable only from charts or tables.
- Check that citations support the exact claim.

## Common mistakes

- OCR-only indexing of layout-heavy documents.
- Embedding whole files as one chunk.
- Citing a document title without a page or region.

## Practical decision

Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.

## Official sources

- [Google, Gemini image understanding](https://ai.google.dev/gemini-api/docs/image-understanding)
- [Google, Gemini API long context](https://ai.google.dev/gemini-api/docs/long-context)

---

Reviewed: 2026-08-24. This concise engineering note is educational, not a claim that Navin Research created or independently verified the cited provider technology.