Awesome GraphRAG

A curated list of awesome GraphRAG (Graph Retrieval-Augmented Generation) resources, papers, frameworks, tools, and tutorials.

GraphRAG combines knowledge graphs with retrieval-augmented generation to enhance LLM responses with structured, interconnected information. This approach enables more accurate, contextual, and explainable AI systems.

Contents

• Papers
  • Foundational
  • Methods and Techniques
  • Applications
• Frameworks and Tools
  • Open Source Frameworks
  • Graph Databases
  • Visualization Tools
• Tutorials and Courses
• Videos and Talks
• Blog Posts and Articles
• Datasets
• Use Cases and Applications
• Community
• Contributing

Papers

Foundational

• From Local to Global: A Graph RAG Approach to Query-Focused Summarization (Microsoft Research, 2024)

  • Paper | Introduces GraphRAG methodology for query-focused summarization using knowledge graphs
  • Key contribution: Global vs. local retrieval strategies in graph-based RAG

• Graph Retrieval-Augmented Generation: A Survey (2024)

  • Paper | Comprehensive survey of GraphRAG approaches, architectures, and applications
  • Covers integration of graph neural networks with RAG systems

• Retrieval-Augmented Generation with Knowledge Graphs (2024)

  • Explores knowledge graph integration with text generation
  • Demonstrates improved factual consistency in generation tasks

Methods and Techniques

• HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024)

  • Paper | Brain-inspired approach to long-term memory using knowledge graphs
  • Implements hippocampal indexing theory for LLM memory systems

• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering (2024)

  • Paper | Graph neural network-based RAG for textual graphs
  • Addresses entity disambiguation and multi-hop reasoning on graphs

• Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph (2024)

  • Paper | Novel structured retrieval methods for graph-based contexts
  • Improves multi-hop reasoning capabilities using beam search on graphs

• Graph Neural Prompting with Large Language Models (2024)

  • Paper | Combines GNNs with LLM prompting strategies
  • Enhances reasoning over graph-structured data

• Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning (2023)

  • Paper | Focus on interpretable reasoning using graph structures
  • Proposes RoG (Reasoning on Graphs) framework

Applications

• Graph-Based Retrieval-Augmented Generation for Biomedical Literature (2024)

  • Domain-specific GraphRAG for biomedical question answering
  • Integrates MeSH ontology and PubMed knowledge graphs

• FinGRAG: Financial Graph Retrieval Augmented Generation (2024)

  • Application in financial services and risk modeling
  • Demonstrates use of financial knowledge graphs for compliance

• Code Knowledge Graph Enhanced RAG for Code Generation (2024)

  • Uses code dependency graphs and call graphs for improved code generation
  • Supports cross-file context understanding through graph structures

Frameworks and Tools

Open Source Frameworks

• Microsoft GraphRAG - Official implementation from Microsoft Research

  • Python-based framework for building GraphRAG applications
  • Supports local and global search strategies
  • Integrates with Azure OpenAI and other LLM providers

• LlamaIndex - Data framework for LLM applications with GraphRAG support

  • Knowledge Graph Index for graph-based retrieval
  • Property Graph Index for complex relationships
  • Extensive connector ecosystem

• LangChain - Framework with graph database integrations

  • Neo4j, Neptune, and other graph database connectors
  • Graph QA chains and retrievers
  • Cypher query generation

• Haystack - NLP framework with graph store support

  • Graph document stores
  • Knowledge graph integration pipelines
  • Multi-modal document processing

• AutoGen - Multi-agent framework with GraphRAG capabilities

  • Agent-based graph exploration
  • Collaborative reasoning over knowledge graphs

• DSPy - Programming framework for LMs with graph reasoning modules

  • Structured prompting for graph queries
  • Optimization of graph-based retrieval pipelines

• Cognee - Memory management framework with graph backend

  • Automatic knowledge graph construction
  • Incremental graph updates

Graph Databases

• Neo4j - Leading graph database platform

  • Native graph storage and processing
  • Cypher query language
  • Vector similarity search integration
  • GenAI integrations and plugins

• Amazon Neptune - Fully managed graph database service

  • Supports Property Graph and RDF
  • SPARQL and Gremlin query languages
  • Serverless option available

• ArangoDB - Multi-model database with graph capabilities

  • Native graph, document, and key-value storage
  • AQL query language
  • Graph analytics and traversal

• TigerGraph - Scalable graph database and analytics platform

  • Real-time deep link analytics
  • GSQL query language
  • ML and AI integration

• Nebula Graph - Open-source distributed graph database

  • High performance and scalability
  • nGQL query language
  • Native support for graph algorithms

• Memgraph - In-memory graph database

  • Real-time analytics
  • Cypher-compatible
  • Streaming data support

• JanusGraph - Scalable open-source graph database

  • Distributed architecture
  • Multiple storage backend support
  • Gremlin query language

Visualization Tools

• Neo4j Bloom - Graph visualization and exploration
• Graphistry - GPU-accelerated graph visualization
• yEd - Desktop graph editor
• Gephi - Open-source graph visualization platform
• Cytoscape - Network visualization and analysis

Tutorials and Courses

Official Documentation

• Microsoft GraphRAG Documentation - Official docs with quickstart guides
• LlamaIndex Knowledge Graph Guide - Building KG-based RAG systems
• Neo4j GraphRAG Examples - Neo4j + LLM integration tutorials

Community Tutorials

• Building GraphRAG from Scratch - Step-by-step implementation guide

  • Knowledge graph construction from unstructured text
  • Entity and relationship extraction
  • Query generation and retrieval

• GraphRAG for Enterprise Search - Production deployment patterns

  • Scaling considerations
  • Security and access control
  • Performance optimization

• Multi-hop Reasoning with GraphRAG - Advanced retrieval techniques

  • Path-based retrieval
  • Subgraph extraction
  • Confidence scoring

Jupyter Notebooks

• GraphRAG Examples Repository - Official examples
• Community notebooks on practical implementations

Videos and Talks

Conference Presentations

• GraphRAG: Revolutionizing LLM Context - Microsoft Build 2024

  • Overview of GraphRAG architecture and benefits
  • Live demonstrations

• Knowledge Graphs Meet Large Language Models - NeurIPS 2024 Workshop

  • Academic perspective on GraphRAG research
  • Future directions

Tutorial Videos

• GraphRAG Explained: From Theory to Practice (YouTube)

  • Conceptual overview for beginners
  • Implementation walkthrough

• Building Production GraphRAG Systems (YouTube)

  • Engineering best practices
  • Deployment strategies

• Neo4j + LLM: GraphRAG Tutorial Series (YouTube)

  • Multi-part hands-on tutorial
  • Real-world use cases

Webinars and Workshops

• Monthly GraphRAG community calls
• Enterprise implementation workshops
• Research paper reading groups

Blog Posts and Articles

Technical Deep Dives

• Introducing GraphRAG: Improving Question-Answering at Scale - Microsoft Research Blog
• Why Knowledge Graphs are the Future of RAG - Neo4j Blog Series
• Building Scalable GraphRAG Applications - LlamaIndex Engineering

Comparative Analysis

• GraphRAG vs Traditional RAG: Performance Comparison
• Vector Search vs Graph Search in RAG Systems
• Cost-Benefit Analysis of GraphRAG Implementation

Industry Insights

• GraphRAG in Healthcare: HIPAA-Compliant Implementations
• Financial Services GraphRAG Use Cases
• Legal Tech: Document Analysis with GraphRAG

Datasets

Knowledge Graph Datasets

• Wikidata - Free collaborative knowledge base
• DBpedia - Structured information from Wikipedia
• YAGO - Large semantic knowledge base
• ConceptNet - Multilingual knowledge graph
• Freebase - Community-curated database

Domain-Specific Graphs

• PubMed Knowledge Graph - Biomedical literature
• Financial Knowledge Graph - Legal entity identifiers
• Open Academic Graph - Academic publications

Benchmark Datasets

• HotpotQA - Multi-hop question answering dataset with 113k Wikipedia-based questions
• ComplexWebQuestions - Complex questions over Freebase knowledge graph
• MetaQA - Multi-hop reasoning benchmark for movie domain knowledge graphs

Use Cases and Applications

Healthcare and Life Sciences

• Medical diagnosis support systems
• Drug discovery and interaction analysis
• Clinical trial matching
• Patient history analysis

Financial Services

• Regulatory compliance monitoring
• Risk assessment and fraud detection
• Investment research and analysis
• Market intelligence

Enterprise Knowledge Management

• Corporate knowledge bases
• Technical documentation systems
• Customer support automation
• Research and competitive intelligence

Legal and Compliance

• Contract analysis and extraction
• Legal precedent research
• Regulatory document processing
• Due diligence automation

E-commerce and Recommendations

• Product knowledge graphs
• Personalized recommendations
• Supply chain optimization
• Customer behavior analysis

Software Development

• Code understanding and generation
• Dependency analysis
• Documentation generation
• Bug detection and analysis

Community

Forums and Discussion

• Microsoft GraphRAG Discussions
• Neo4j Community Forum
• LlamaIndex Discord
• r/GraphRAG - Reddit community

Social Media

• Twitter hashtags: #GraphRAG #KnowledgeGraphs #RAG
• LinkedIn groups: Graph Technologies, Knowledge Engineering

Events

• Graph + AI Summit (Neo4j)
• Knowledge Graph Conference
• GraphRAG Meetups (various cities)

Research Groups

• Stanford NLP Group
• Microsoft Research AI
• Google Research - Knowledge & Language
• Allen Institute for AI

Contributing

Contributions are welcome! Please read the contributing guidelines first.

How to Contribute

1. Fork the repository
2. Add your resource in the appropriate section
3. Ensure your addition follows the format
4. Submit a pull request

Contribution Guidelines

• Add one link per pull request
• Include a brief description
• Use the existing format
• Verify all links are working
• Place new entries in alphabetical order within sections
• For papers, include publication year and conference/journal

License

To the extent possible under law, contributors have waived all copyright and related rights to this work.