GraphRAG in R2R
Advanced knowledge graph retrieval and generation
Overview
GraphRAG extends traditional RAG by leveraging community detection and summarization within knowledge graphs. This approach provides richer context and more comprehensive answers by understanding how information is clustered and connected across your documents.
Architecture
Understanding Communities
Communities are automatically detected clusters of related information in your knowledge graph. They provide:
- Higher-level understanding of document themes
- Summarized context for related concepts
- Improved retrieval through topic-based organization
Example communities across different domains:
Implementation Guide
1. Prerequisites
Ensure you have:
- Documents ingested into a collection
- Entities and relationships extracted
- Graph synchronized
client.collections.list()
. Refer to the collections cookbook for a deep dive.2. Building Communities
Collections use either user-provided or automatically generated descriptions (derived from document summaries) to establish context for community creation.
The build process:
- Analyzes graph connectivity
- Identifies dense subgraphs
- Generates community summaries
- Creates findings and insights
3. Using GraphRAG
Once communities are built, they’re automatically integrated into search and RAG:
Understanding Results
GraphRAG returns three types of results:
1. Document Chunks
2. Graph Elements
3. Communities
Scaling GraphRAG
Using Orchestration
For large collections, use R2R’s orchestration capabilities:
-
Access Hatchet UI at
http://localhost:7274
- Login: [email protected]
- Password: Admin123!!
-
Monitor:
- Document extraction progress
- Community detection status
- Error handling
- Workflow retries
Best Practices
-
Development
- Start with small document sets
- Test with single documents first
- Scale gradually to larger collections
-
Performance
- Monitor community size and complexity
- Use pagination for large result sets
- Consider breaking very large collections
-
Quality
- Review community summaries
- Validate findings accuracy
- Monitor retrieval relevance
Troubleshooting
Common issues and solutions:
-
Poor Community Quality
- Check entity extraction quality
- Review relationship connections
- Consider adjusting collection scope
-
Performance Issues
- Monitor graph size
- Check community complexity
- Use orchestration for large graphs
-
Integration Problems
- Verify extraction completion
- Check collection synchronization
- Review API configurations
Next Steps
- Explore hybrid search integration
- Learn about collection management
- Set up observability