Multimodal RAG

Real documents aren't just prose. They contain diagrams, screenshots, charts, tables, and scanned pages — often carrying the very information a user needs. Multimodal RAG extends retrieval beyond text so these can be searched and reasoned over too.

Why it matters

A wiring diagram, a revenue chart, a UI screenshot, or a spreadsheet may hold the answer a text-only pipeline can never see. Ignore non-text content and you silently drop part of your corpus — often the densest part.

Two main approaches

1. Convert to text, then do normal RAG

Use a vision-capable model to describe each image, chart, or table in words, and index those descriptions as ordinary text. Simple, works with your existing text pipeline, and keeps everything in one searchable modality. The catch: the description is only as good as the model, and fine visual detail can be lost.

// Index time: caption non-text content into searchable text.
for (const img of documentImages) {
  const caption = await visionModel.describe(img, {
    prompt: "Describe this figure in detail, including any data, labels, and trends.",
  });
  await index.add({ text: caption, metadata: { type: "image", src: img.src } });
}

2. Multimodal embeddings

Use a model that embeds images and text into a shared vector space (CLIP-style). A text query can then directly retrieve relevant images, and vice versa, without a captioning step. More powerful for genuinely visual search, but a bigger change to your pipeline and storage.

Tables deserve special care

Tables are neither prose nor pictures. Flattened into text they become unreadable and unsearchable. Extract them as structured data (Markdown or HTML), optionally add an LLM-written summary of what the table shows, and index both — the summary for semantic matching, the structure for accurate answers.

Generation with multimodal context

At answer time you can feed retrieved images directly to a vision-capable LLM alongside the text, so it reasons over the actual figure rather than a lossy description. This closes the loop: multimodal retrieval feeding multimodal generation.

Written by Bipin Singh

Senior Full-Stack Engineer · AI & AWS. I build production RAG and LLM systems for enterprises.

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