GraphRAG - The-Learners-Community/RoadMaps-and-Resources GitHub Wiki

GraphRAG Roadmap

A structured and comprehensive roadmap to master GraphRAG, progressing from beginner to master level. It integrates theoretical concepts, hands-on projects, and in-depth explorations at each stage.

🟢 Beginner Level: Understanding Foundations of GraphRAG

1. Introduction to GraphRAG and Retrieval-Augmented Generation (RAG)

  • Overview of GraphRAG
  • Understanding Retrieval-Augmented Generation (RAG)
  • Key concepts: Retrieval, Augmentation, Generation, and Knowledge Graphs

Project:
Build a simple Retrieval-Augmented Generation pipeline using Hugging Face Transformers.

2. Fundamentals of Knowledge Graphs

  • What is a Knowledge Graph?
  • Graph databases: Neo4j, Amazon Neptune
  • RDF and Semantic Web basics
  • Introduction to graph queries: Cypher/SPARQL basics

Project:
Construct your first Knowledge Graph using Neo4j and query it using Cypher.

3. Vector Embeddings and Semantic Search

  • Basics of embeddings: Word2Vec, GloVe, BERT embeddings
  • Vector similarity (cosine, dot product, Euclidean)
  • Semantic Search Techniques: FAISS, Weaviate, Pinecone

Project:
Implement semantic retrieval of documents using FAISS.

🟡 Intermediate Level: Integrating Retrieval and Knowledge Graphs

4. Graph Neural Networks (GNNs) Fundamentals

  • Introduction to Graph Neural Networks
  • Types: Graph Convolutional Networks (GCN), Graph Attention Networks (GAT)
  • Message passing techniques and neighborhood aggregation methods

Project:
Implement a basic GCN for node classification tasks on popular datasets (Cora/Citeseer).

5. Advanced RAG Techniques and Architecture

  • Dense vs. Sparse retrieval techniques
  • Cross-encoders and Bi-encoders
  • Optimizing Retrieval Accuracy: DPR (Dense Passage Retrieval), BM25, Hybrid retrieval methods

Project:
Build a hybrid retrieval system combining sparse (BM25) and dense retrieval (DPR).

6. Integration of RAG with Graph Databases

  • Connecting embeddings with nodes and edges
  • Enriching retrieval using structured graph data
  • Query planning and execution for RAG

Project:
Develop a simple GraphRAG system: combine embeddings with graph data in Neo4j for document retrieval.

🔵 Advanced Level: GraphRAG Deep Dive

7. In-depth Graph Representation Learning

  • Advanced embedding methods: GraphSAGE, node2vec, TransE, RotatE
  • Graph pre-training and fine-tuning strategies
  • Knowledge Graph completion and link prediction techniques

Project:
Predict missing links in a large-scale knowledge graph using GraphSAGE and node2vec embeddings.

8. Scalable GraphRAG Systems and Infrastructure

  • Efficient indexing of embeddings: HNSW, ANN algorithms, IVF indices
  • Distributed graph databases and scaling retrieval
  • Infrastructure for high-throughput RAG systems (Kubernetes, Docker, cloud solutions)

Project:
Deploy a scalable GraphRAG solution on cloud infrastructure (AWS/Azure/GCP) using Kubernetes, FAISS or Weaviate, and Neo4j cluster.

9. Advanced Graph Query Optimization

  • Advanced Cypher/SPARQL optimization techniques
  • Cost-based and rule-based optimizers
  • Techniques for reducing latency in complex GraphRAG queries

Project:
Optimize a complex GraphRAG query pipeline with performance tuning and analysis.

🧠 Expert Level: Mastering GraphRAG

10. GraphRAG for Reasoning and Complex QA

  • Reasoning over graphs: Logical, symbolic, and hybrid reasoning techniques
  • Complex multi-hop retrieval with graph traversal methods
  • Integrating reasoning engines with GraphRAG (DL-reasoners, symbolic reasoners)

Project:
Implement multi-hop reasoning over knowledge graphs integrated with retrieval-augmented generation to answer complex questions (HotpotQA, ComplexWebQuestions).

11. Robustness and Explainability in GraphRAG

  • Evaluating robustness of retrieval systems
  • Explainability methods in graph-based retrieval (Graph attention visualization)
  • Trustworthiness and bias mitigation in GraphRAG systems

Project:
Develop methods to explain GraphRAG-generated answers, visualize reasoning paths, and detect biases in generated content.

12. Cutting-Edge Topics and Research Trends

  • Graph-based prompt engineering and retrieval-enhanced prompting
  • Few-shot and zero-shot GraphRAG systems
  • Research frontier: GraphRAG with multimodal data (text, images, video graphs)

Project:
Conduct original research or contribute to open-source projects in multimodal GraphRAG retrieval.

🚀 Mastery Level: Pushing Boundaries and Innovation

13. GraphRAG in Production Environments

  • Real-world deployment challenges and solutions
  • Monitoring and observability for production GraphRAG systems
  • Continuous integration and deployment practices for retrieval-based AI systems

Project:
Deploy a production-grade GraphRAG application, complete with monitoring, alerting, and CI/CD practices.

14. Contribution to GraphRAG Open-Source Community

  • Contributing to popular libraries: Hugging Face, PyG, DGL
  • Developing new plugins, extensions, or libraries
  • Open-source maintenance and documentation best practices

Project:
Contribute significant improvements or innovative new features to GraphRAG-related open-source frameworks.

15. Thought Leadership and Research Contributions

  • Publishing GraphRAG research in peer-reviewed conferences and journals
  • Sharing expertise via workshops, tutorials, and conference talks
  • Building and maintaining a GraphRAG research portfolio

Project:
Publish research articles, give industry talks, or organize workshops to share insights and advance the state-of-the-art in GraphRAG.

📅 Suggested Weekly Rhythm

  1. Read one research paper or doc section
  2. Code its core idea in a notebook or PR
  3. Write a brief blog post explaining what you learned
  4. Demo your project to the community for feedback