title: NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
summary: 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.
paper_authors._id: 6804ca9fd8538baa1c39ca93
paper_authors.name: Tianyang Xu
paper_authors._id: 6804ca9fd8538baa1c39ca94
paper_authors.name: Haojie Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T10:13:14.818Z
paper_authors._id: 6806619b9d5a7dc142bcd9a3
paper_authors.avatarUrl: /avatars/de568500427c3ac40022237e54931c9f.svg
paper_authors.fullname: Haojie Z
paper_authors.type: user
paper_authors.user: Hav0cc
paper_authors._id: 6804ca9fd8538baa1c39ca95
paper_authors.name: Chengze Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T10:13:11.802Z
paper_authors._id: 6806f6f5c4e909c4b4e408cf
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paper_authors.fullname: Chengze Li
paper_authors.type: user
paper_authors.user: cl4460
paper_authors._id: 6804ca9fd8538baa1c39ca96
paper_authors.name: Haoxiang Chen
paper_authors._id: 6804ca9fd8538baa1c39ca97
paper_authors.name: Yixin Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-28T12:54:17.128Z
paper_authors._id: 62b88694bd3b8cc946078cb5
paper_authors.avatarUrl: /avatars/c7251e4426890095f7567e26e9262580.svg
paper_authors.fullname: liu
paper_authors.type: user
paper_authors.user: yixin
paper_authors._id: 6804ca9fd8538baa1c39ca98
paper_authors.name: Ruoxi Chen
paper_authors._id: 6804ca9fd8538baa1c39ca99
paper_authors.name: Lichao Sun
paper_title: NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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