NodeRAG, a graph-centric framework introducing heterogeneous graph structures that enable the seamless and holistic integration of graph-based methodologies into the RAG workflow. Here is an example of using NodeRAG on Harry Potter and the Sorcerer’s Stone.
Enhancing Graph Structure for RAG
NodeRAG introduces a heterogeneous graph structure that strengthens the foundation of graph-based Retrieval-Augmented Generation (RAG).
Fine-Grained and Explainable Retrieval
NodeRAG leverages HeteroGraphs to enable functionally distinct nodes, ensuring precise and context-aware retrieval while improving interpretability.
A Unified Information Retrieval
Instead of treating extracted insights and raw data as separate layers, NodeRAG integrates them as interconnected nodes, creating a seamless and adaptable retrieval system.
Optimized Performance and Speed
NodeRAG achieves faster graph construction and retrieval speeds through unified algorithms and optimized implementations.
Incremental Graph Updates
NodeRAG supports incremental updates within heterogeneous graphs using graph connectivity mechanisms.
Visualization and User Interface
NodeRAG offers a user-friendly visualization system. Coupled with a fully developed web UI, users can explore, analyze, and manage the graph structure with ease.
Install from PyPI
NodeRAG is available on PyPI for simple and quick installation. Welcome to use!
Contributions welcome!
We do a Pull Request contributions workflow on GitHub. New users are always welcome!