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Module 14: Advanced RAG Architectures

Multimodal RAG, federated retrieval, personalized retrieval, and graph-based architectures

3.5 hours. 2 hands-on labs. Free course module.

Learning Objectives

  • Build multimodal RAG with text + images
  • Design federated retrieval across multiple sources
  • Implement personalized retrieval based on user context
  • Architect graph-based retrieval for relational data

Why This Matters

Real-world enterprise data is not just text. It is PDFs with charts, databases with relationships, images with annotations, and knowledge scattered across 10 systems. Advanced RAG architectures handle the messiness of real data.

ADVANCED RAG ARCHITECTURESMultimodal RAGtext + images + tablesFederated RAGmultiple data sourcesPersonalized RAGuser-context awareGraph RAGrelationship traversalThese architectures solve specific problems that basic RAG cannot handle.Choose based on your data type, query complexity, and user needs.
Architecture diagram for Module 14: Advanced RAG Architectures.

Lesson Content

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.

Key Terms

Multimodal RAG
RAG that retrieves across text, images, and other modalities
Federated Retrieval
Querying multiple data sources in parallel and merging results
CLIP
Vision-language model that embeds images and text in the same space

Hands-On Labs

  1. Build a Multimodal Retrieval Pipeline

    Retrieve across text and images.

    35 min - Advanced

    • Embed text and images with CLIP
    • Store both in the same vector collection
    • Query with text and retrieve relevant images
    • Generate answers that reference visual content

    View lab files on GitHub

  2. Graph-Based Retrieval

    Build a knowledge graph for relationship-aware RAG.

    40 min - Advanced

    • Extract entities and relationships from documents
    • Build a knowledge graph
    • Query with multi-hop graph traversal
    • Combine graph results with vector search

    View lab files on GitHub

Key Takeaways

  • Multimodal RAG handles text + images + tables in a unified vector space
  • Federated retrieval queries multiple data sources and merges results
  • Personalization uses user context to weight retrieval for relevance
  • Graph RAG enables multi-hop reasoning across entity relationships
  • Choose the architecture based on your data type and query complexity