AI Glossarytechniques

Multimodal RAG

An extension of RAG that retrieves and reasons over multiple data types — text, images, tables, charts, and audio — not just text documents.

How It Works

Standard RAG works with text: embed text chunks, retrieve relevant text, generate a text answer. Multimodal RAG extends this to handle real-world documents that contain images, tables, charts, diagrams, and mixed media. A financial report has text, tables, and charts. A medical record has text, images, and lab results. A product catalog has descriptions and photos. Implementation approaches: (1) Visual document parsing — use vision models to extract information from images, tables, and charts, then store as text embeddings alongside the originals. (2) Multi-modal embeddings — use models like CLIP or SigLIP that embed both text and images into the same vector space, enabling cross-modal retrieval. (3) Direct multimodal reasoning — pass retrieved images and text directly to a vision-capable LLM (GPT-5.2, Claude Opus 4.6) and let it reason over both. The third approach is increasingly preferred because modern LLMs handle images well. Instead of converting a chart to text (lossy), pass the chart image directly to the model with the question. This is simpler to implement and preserves more information. The tradeoff is cost — processing images uses more tokens than text.

Common Use Cases

  • 1Financial document analysis with charts
  • 2Medical record processing
  • 3Product catalog search
  • 4Technical documentation with diagrams
  • 5Scientific paper analysis

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Embeddings

Numerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.

Vector Database

A specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.

Multimodal AI

AI models that can process and generate multiple types of data: text, images, audio, video, and code.

Computer Vision

The field of AI that enables machines to interpret and understand visual information from images and video.

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