Multimodal RAG
Definition
Multimodal RAG extends retrieval-augmented generation to handle images, videos, audio, and documents alongside text, enabling AI to answer questions using visual and other non-text content.
Why It Matters
Most enterprise data isn’t just text - it includes diagrams, charts, product images, PDFs with layouts, and video content. Multimodal RAG unlocks this data for AI applications, enabling questions like “Find products similar to this image” or “What does this chart show about revenue trends?”
Approaches
Vision-Language Embedding:
- Embed images and text in the same vector space
- Retrieve images using text queries and vice versa
- Models: CLIP, ColPali, SigLIP
Document Understanding:
- Process PDFs as images to preserve layout
- Extract text + visual elements together
- Handle tables, charts, and diagrams
Video RAG:
- Extract frames and transcripts
- Enable temporal queries (“What happened at the 5-minute mark?”)
When to Use
Multimodal RAG is essential when: your knowledge base includes visual content, users need to query with images, documents have important visual elements, or you’re building product search/recommendation systems. Start with text RAG and add modalities as your use case requires.