NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
Retrieval-augmented generation (RAG) empowers large language models to access
external and private corpus, enabling factually consistent responses in
specific domains. By exploiting the inherent structure of the corpus,
graph-based RAG methods further enrich this process by building a knowledge
graph index and leveraging the structural nature of graphs. However, current
graph-based RAG approaches seldom prioritize the design of graph structures.
Inadequately designed graph not only impede the seamless integration of diverse
graph algorithms but also result in workflow inconsistencies and degraded
performance. To further unleash the potential of graph for RAG, we propose
NodeRAG, a graph-centric framework introducing heterogeneous graph structures
that enable the seamless and holistic integration of graph-based methodologies
into the RAG workflow. By aligning closely with the capabilities of LLMs, this
framework ensures a fully cohesive and efficient end-to-end process. Through
extensive experiments, we demonstrate that NodeRAG exhibits performance
advantages over previous methods, including GraphRAG and LightRAG, not only in
indexing time, query time, and storage efficiency but also in delivering
superior question-answering performance on multi-hop benchmarks and open-ended
head-to-head evaluations with minimal retrieval tokens. Our GitHub repository
could be seen at https://github.com/Terry-Xu-666/NodeRAG.
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Tianyang Xu
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Haojie Zheng
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Haojie Z
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Chengze Li
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Chengze Li
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Haoxiang Chen
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Yixin Liu
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liu
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Ruoxi Chen
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Lichao Sun
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
Retrieval-augmented generation (RAG) empowers large language models to access
external and private corpus, enabling factually consistent responses in
specific domains. By exploiting the inherent structure of the corpus,
graph-based RAG methods further enrich this process by building a knowledge
graph index and leveraging the structural nature of graphs. However, current
graph-based RAG approaches seldom prioritize the design of graph structures.
Inadequately designed graph not only impede the seamless integration of diverse
graph algorithms but also result in workflow inconsistencies and degraded
performance. To further unleash the potential of graph for RAG, we propose
NodeRAG, a graph-centric framework introducing heterogeneous graph structures
that enable the seamless and holistic integration of graph-based methodologies
into the RAG workflow. By aligning closely with the capabilities of LLMs, this
framework ensures a fully cohesive and efficient end-to-end process. Through
extensive experiments, we demonstrate that NodeRAG exhibits performance
advantages over previous methods, including GraphRAG and LightRAG, not only in
indexing time, query time, and storage efficiency but also in delivering
superior question-answering performance on multi-hop benchmarks and open-ended
head-to-head evaluations with minimal retrieval tokens. Our GitHub repository
could be seen at https://github.com/Terry-Xu-666/NodeRAG.
NodeRAG, a graph-centric framework with heterogeneous graph structures, enhances Retrieval-augmented Generation (RAG) by improving integration and performance in indexing, querying, and question-answering.
Retrieval-augmented generation NodeRAG graph-based RAG heterogeneous graph structures graph-centric framework knowledge graph index graph-based methodologies LLMs indexing time query time storage efficiency multi-hop benchmarks open-ended evaluations retrieval tokens