---
title: "Step 6: Advanced RAG Architectures - Hierarchical, Graph-Based, and Specialized Patterns"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Step 6: Advanced [RAG Architectures](https://humanloop.com/blog/rag-architectures) - Hierarchical, Graph-Based, and Specialized Patterns

- Branched RAG. ...
- HyDe (Hypothetical Document Embedding) ...
- Adaptive RAG. ...
- Corrective RAG (CRAG) ...
- Self-RAG. ...
- Agentic RAG.

## Learning Objectives
By the end of this module, you will be able to:
- Implement hierarchical retrieval for large document collections
- Build graph-based RAG systems for interconnected information
- Design specialized architectures for different use cases
- Understand when to use each architectural pattern
- Optimize retrieval for specific domain requirements

## Theoretical Foundation

### Beyond Simple Vector Search

**Limitations of Flat Search:**
- **Scale issues**: Performance degrades with millions of documents
- **Context loss**: No understanding of document relationships
- **Rigid structure**: One-size-fits-all approach doesn't suit all use cases
- **Poor precision**: High-level queries return scattered results

**Need for Advanced Architectures:**
- **Hierarchical organization**: Navigate from general to specific
- **Relationship awareness**: Understand how information connects
- **Domain specialization**: Optimize for specific content types
- **Multi-stage reasoning**: Complex queries need sophisticated retrieval

## Hierarchical RAG Architecture

### Concept: Search Tree Structure

**How it Works:**
1. **Document Hierarchy**: Organize content in tree structure
2. **Top-down Search**: Start with high-level categories
3. **Progressive Refinement**: Drill down to specific content
4. **Context Preservation**: Maintain document structure throughout

## Success Criteria
- Implement at least two advanced architectures
- Demonstrate improved retrieval for specific use cases
- Show measurable benefits over flat vector search
- Handle complex, multi-stage queries effectively
- Build adaptive system that selects appropriate architecture

---

**Next Step**: In Step 7, we'll integrate everything into an MCP (Model Context Protocol) server, enabling AI agents to use our advanced RAG system as a tool.
