Multimodal RAG
Retrieval-augmented generation that works across multiple data types — retrieving and reasoning over text, images, tables, and charts to answer questions that require multimodal understanding.
Why It Matters
Real-world knowledge lives in tables, charts, diagrams, and images — not just text. Multimodal RAG captures information that text-only RAG misses.
Example
A financial analyst chatbot that retrieves relevant charts, tables, and text passages from annual reports to answer questions like 'How did Q3 revenue compare to guidance?'
Think of it like...
Like a research assistant who can pull relevant photos, charts, and text from files — not just written documents — to give you a complete picture.
Related Terms
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
Multimodal AI
AI systems that can process and generate multiple types of data — text, images, audio, video — within a single model. Multimodal models understand the relationships between different data types.
Vision-Language Model
An AI model that can process both visual and textual inputs, understanding images and generating text about them. VLMs combine computer vision with language understanding.
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
Document Processing
AI-powered extraction and understanding of information from documents including PDFs, images, forms, and scanned papers. It combines OCR, NLP, and computer vision.