Basic RAG works for text-in, text-out. But real-world data includes images, tables, PDFs with charts, and relational data. Advanced architectures handle these complexities.
Multimodal RAG
Embed and retrieve across modalities: text documents, images, diagrams, and tables. Vision-language models (like CLIP or GPT-4V) can embed images into the same vector space as text, enabling cross-modal retrieval.
Federated Retrieval
Enterprise data lives in multiple systems: Confluence, SharePoint, databases, GitHub, email. Federated RAG queries multiple sources in parallel, merges results, and generates answers from the combined context.
Personalized Retrieval
Different users need different answers to the same question. A junior developer asking "how do I deploy?" needs a tutorial. A senior architect needs a reference. Personalized RAG uses user profile, role, and history to weight retrieval.
Graph RAG
When data has relationships (org charts, dependencies, knowledge graphs), graph RAG traverses edges to find connected information that flat vector search would miss.