Multimodal RAG
Retrieval-augmented generation that combines text, images, and other media types.
Definition
Multimodal RAG extends traditional retrieval-augmented generation to incorporate multiple content types—text, images, audio, video, and documents—into AI responses. The system can retrieve and reason across diverse media, enabling richer and more accurate outputs.
This approach is particularly powerful for enterprises with mixed-media knowledge bases, allowing AI to answer questions by combining information from PDFs, presentations, diagrams, and textual documents simultaneously.
Why It Matters
Enterprise knowledge isn't just text—it includes charts, diagrams, screenshots, and presentations. Multimodal RAG unlocks AI capabilities across your complete knowledge base, not just documents.
For businesses, this means AI assistants that can truly understand and utilize all organizational knowledge, dramatically increasing their usefulness.
Examples in Practice
A technical support AI retrieves relevant product diagrams alongside documentation to help customers troubleshoot visual issues.
An investment research assistant analyzes charts, financial reports, and news articles together to provide comprehensive market analysis.
A manufacturing AI references equipment photos, maintenance videos, and service manuals to guide repair procedures.