NodeRAG is an open source Retrieval Augmented Generation (RAG) system hosted on GitHub by developer Terry-Xu-666. It optimizes information retrieval and generation through heterogeneous graph structures, significantly improving retrieval accuracy and contextual relevance. The system supports local deployment and provides user-friendly interface and visualization tools for academic research, knowledge management and data analysis.
Core features include:
- isomorphic structure: Support multiple node types (e.g., document, entity, keyword) to improve retrieval accuracy.
- Precision Search: Support for multi-hop reasoning and context-sensitive queries through graph decomposition, augmentation, enrichment and search.
- data visualization: Provide interactive graph structure visualization for easy understanding of complex data relationships.
- Local Deployment Interface: Supports local operation and provides an intuitive user interaction experience.
- Cross-platform installation: Supports Conda, Docker and PyPI installations, compatible with a wide range of environments.
- incremental update: Supports dynamic updating of the graph structure without the need to rebuild the entire graph database.
- High Performance Optimization: Fast indexing and querying for large-scale dataset processing.
- open file: Provides detailed tutorials, sample code and academic papers for easy learning.
This answer comes from the articleNodeRAG: A Heterogeneous Graph-Based Tool for Accurate Information Retrieval and GenerationThe
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