README
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
π’ News
-
[2025-03-18] π NodeRAG v0.1.0 Released! The first stable version is now available on PyPI. Install it with
pip install NodeRAG. -
[2025-03-18] π Official Website Launched! Visit NodeRAG_web for comprehensive documentation, tutorials, and examples.
π NodeRAG is a heterogeneous graph-based generation and retrieval RAG system that you can install and use in multiple ways. π₯οΈ We also provide a user interface (local deployment) and convenient tools for visualization generation. You can read our paper π to learn more. For experimental discussions, check out our blog posts π.
π Quick Start
π View our official website for comprehensive documentation and tutorials:
π NodeRAG_web
𧩠Workflow
NodeRAG
Conda Setup
Create and activate a virtual environment for NodeRAG:
conda create -n NodeRAG python=3.10
conda activate NodeRAG
Install uv (Optional: Faster Package Installation)
To speed up package installation, use uv:
pip install uv
Install NodeRAG
Install NodeRAG using uv for optimized performance:
uv pip install NodeRAG
Next
For indexing and answering processes, please refer to our website: Indexing and Answering
β¨ Features
π Enhancing Graph Structure for RAG
NodeRAG introduces a heterogeneous graph structure that strengthens the foundation of graph-based Retrieval-Augmented Generation (RAG).
π Fine-Grained and Explainable Retrieval
NodeRAG leverages HeteroGraphs to enable functionally distinct nodes, ensuring precise and context-aware retrieval while improving interpretability.
𧱠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.
β‘ Optimized Performance and Speed
NodeRAG achieves faster graph construction and retrieval speeds through unified algorithms and optimized implementations.
π Incremental Graph Updates
NodeRAG supports incremental updates within heterogeneous graphs using graph connectivity mechanisms.
π 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.
βοΈ Performance
π Benchmark Performance
NodeRAG demonstrates strong performance across multiple benchmark tasks, showcasing efficiency and retrieval quality.
π₯οΈ System Performance
Optimized for speed and scalability, NodeRAG achieves fast indexing and query response times even on large datasets.
Contributors
6 commits
Similar repos
Languages
Python
100.0%
README
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
π’ News
-
[2025-03-18] π NodeRAG v0.1.0 Released! The first stable version is now available on PyPI. Install it with
pip install NodeRAG. -
[2025-03-18] π Official Website Launched! Visit NodeRAG_web for comprehensive documentation, tutorials, and examples.
π NodeRAG is a heterogeneous graph-based generation and retrieval RAG system that you can install and use in multiple ways. π₯οΈ We also provide a user interface (local deployment) and convenient tools for visualization generation. You can read our paper π to learn more. For experimental discussions, check out our blog posts π.
π Quick Start
π View our official website for comprehensive documentation and tutorials:
π NodeRAG_web
𧩠Workflow
NodeRAG
Conda Setup
Create and activate a virtual environment for NodeRAG:
conda create -n NodeRAG python=3.10
conda activate NodeRAG
Install uv (Optional: Faster Package Installation)
To speed up package installation, use uv:
pip install uv
Install NodeRAG
Install NodeRAG using uv for optimized performance:
uv pip install NodeRAG
Next
For indexing and answering processes, please refer to our website: Indexing and Answering
β¨ Features
π Enhancing Graph Structure for RAG
NodeRAG introduces a heterogeneous graph structure that strengthens the foundation of graph-based Retrieval-Augmented Generation (RAG).
π Fine-Grained and Explainable Retrieval
NodeRAG leverages HeteroGraphs to enable functionally distinct nodes, ensuring precise and context-aware retrieval while improving interpretability.
𧱠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.
β‘ Optimized Performance and Speed
NodeRAG achieves faster graph construction and retrieval speeds through unified algorithms and optimized implementations.
π Incremental Graph Updates
NodeRAG supports incremental updates within heterogeneous graphs using graph connectivity mechanisms.
π 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.
βοΈ Performance
π Benchmark Performance
NodeRAG demonstrates strong performance across multiple benchmark tasks, showcasing efficiency and retrieval quality.
π₯οΈ System Performance
Optimized for speed and scalability, NodeRAG achieves fast indexing and query response times even on large datasets.
Similar repos
Contributors
6 commits
Languages
Python
100.0%