Graph RAG

GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when perform RAG-based tasks.

Resources

• Neo4j LLM Knowledge Graph Builder - Extract Nodes and Relationships from Unstructured Text - Neo4j Labs
  • The Neo4j LLM Knowledge Graph Builder is an online application for turning unstructured text into a knowledge graph, it provides a magical text to graph experience.
  • GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem - YouTube
• What is Graph RAG | Ontotext Fundamentals
• Welcome - GraphRAG
• Introducing GraphRAG with LangChain and Neo4j | by Valentina Alto | Microsoft Azure | Medium

Code

• #CODE Knowledge Graph RAG Query Engine - LlamaIndex

References

• #PAPER From Local to Global: A Graph RAG Approach to Query-Focused Summarization (2024)
  • #CODE GrapRAG
  • GraphRAG: Unlocking LLM discovery on narrative private data - Microsoft Research
  • GraphRAG: New tool for complex data discovery now on GitHub - Microsoft Research
  • GraphRAG: LLM-Derived Knowledge Graphs for RAG - YouTube
• #PAPER RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models (2024)
  • How To Do RAG Without Vector Databases | Towards AI
  • #CODE https://github.com/cisco-open/DeepVision/tree/main/recallm
  • Novel architecture designed to enhance Large Language Models (LLMs) by integrating an adaptable and updatable long-term memory mechanism.
  • Unlike traditional methods that rely on vector databases, RecallM employs a graph database, enabling the capture and updating of complex relationships between concepts efficiently. This neuro-symbolic architecture enhances belief updating and temporal understanding of the knowledge provided to the model.