RAG & Retrieval
Multimodal RAG
An extension of RAG that retrieves and processes not just text but also images, tables, code snippets, diagrams, and other non-text content.
In depth
An extension of RAG that retrieves and processes not just text but also images, tables, code snippets, diagrams, and other non-text content. A multimodal RAG system might embed images alongside text, retrieve relevant diagrams when answering questions about architecture, or extract data from tables in PDF documents. As models become more capable of understanding multiple modalities, multimodal RAG closes the gap between what a model can process and what a knowledge base contains.
Example
In practice, developers reach for Multimodal RAG when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
Common questions
What is Multimodal RAG?
An extension of RAG that retrieves and processes not just text but also images, tables, code snippets, diagrams, and other non-text content.
Why does Multimodal RAG matter for AI developers?
Multimodal RAG sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Multimodal RAG?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Multimodal RAG. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
RAG & Retrieval
MTEB
The Massive Text Embedding Benchmark, a suite for comparing embedding models across many tasks instead of a single similarity dataset.
RAG & Retrieval
Multi-Head Latent Attention (MLA)
Multi-head latent attention is an attention design introduced by DeepSeek that compresses keys and values into a small shared latent vector, so the model caches that latent instead of full per-head keys and values.
RAG & Retrieval
Knowledge Cutoff
The date after which a model has no training data.
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