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Module 14: Advanced RAG Architectures Slides

Slide walkthrough for Module 14 of Production-Grade RAG Systems Engineering: Multimodal RAG, federated retrieval, personalized retrieval, and graph-based...

This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.

Slide Outline

  1. Advanced RAG Architectures - Multimodal RAG, federated retrieval, personalized retrieval, and graph-based architectures
  2. Learning Objectives - 4 outcomes for this module
  3. Why This Module Matters - Real-world enterprise data is not just text. It is PDFs with charts, databases with relationships, images with annotatio
  4. Multimodal RAG - Lesson section from the full module
  5. Federated Retrieval - Lesson section from the full module
  6. Personalized Retrieval - Lesson section from the full module
  7. Graph RAG - Lesson section from the full module
  8. Hands-On Labs - 2 hands-on labs
  9. Key Takeaways - 5 points to remember

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 Module 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.

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

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

Read the full module | Back to course curriculum