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LightRAG: Simple and Fast Retrieval-Augmented Generation
Open accessAbstract
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex interdependencies.To address these challenges, we propose Ligh-tRAG, a novel framework that incorporates graph structures into text indexing and retrieval processes.This innovative approach employs a dual-level retrieval system that enhances comprehensive information retrieval from both lowand high-level knowledge discovery.Additionally, the integration of graph structures with vector representations facilitates efficient retrieval of related entities and their relationships, significantly improving response times while maintaining contextual relevance.This capability is further enhanced by an incremental update algorithm that ensures the timely integration of new data, allowing the system to remain effective and responsive in rapidly changing data environments.Extensive experimental validation demonstrates considerable improvements in retrieval accuracy and efficiency compared to existing approaches.
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“Additionally, while current research on LLM-generated knowledge graphs has primarily operated without formal ontologies [9][4][17][19][26], domain-specific ontologies present significant potential.”
From Zep: A Temporal Knowledge Graph Architecture for Agent Memory (2025) · cites this paper as [17]
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“Knowledge graph-based methods (Edge et al., 2025; Guo et al., 2025; Zhu et al., 2025) enhance semantic connectivity through structured representations, but their partial graph construction prevents establishing relationships between temporally distant nodes across conversation sessions.”
From Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025) · cites this paper as Guo et al., 2025
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“While developed independently, our community search approach parallels the high-level key search methodology in LightRAG [17].”
From Zep: A Temporal Knowledge Graph Architecture for Agent Memory (2025) · cites this paper as [17]
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Research works printed but not linked: 21
- William Brannon, Suyash Fulay, Hang Jiang, Won- june Kang, Brandon Roy, Jad Kabbara, and Deb Roy. 2023. Congrat: Self-supervised contrastive pre- training for joint graph and text embeddings. arXiv preprint arXiv:2305.14321. · arXiv
- Chi-Min Chan, Chunpu Xu, Ruibin Yuan, Hongyin Luo, Wei Xue, Yike Guo, and Jie Fu. 2024. Rq-rag: Learn- ing to refine queries for retrieval augmented genera- tion. arXiv preprint arXiv:2404.00610. · arXiv
- Runjin Chen, Tong Zhao, AJAY KUMAR JAISWAL, Neil Shah, and Zhangyang Wang. 2024. Llaga: Large language and graph assistant. In ICML.
- Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. 2024. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130. · arXiv
- Shahul Es, Jithin James, Luis Espinosa Anke, and Steven Schockaert. 2024. Ragas: Automated evalua- tion of retrieval augmented generation. In EACL.
- Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. 2024. A survey on rag meeting llms: Towards retrieval-augmented large language models. In KDD.
- Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022. Precise zero-shot dense retrieval without rele- vance labels. arXiv preprint arXiv:2212.10496. · arXiv
- Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997. · arXiv
- Yichuan Li, Kaize Ding, and Kyumin Lee. 2023. Grenade: Graph-centric language model for self- supervised representation learning on text-attributed graphs. In EMNLP.
- Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, and Muhan Zhang. 2024. One for all: Towards training one graph model for all classification tasks. In ICLR.
- Yuanjie Lyu, Zhiyu Li, Simin Niu, Feiyu Xiong, Bo Tang, Wenjin Wang, Hao Wu, Huanyong Liu, Tong Xu, and Enhong Chen. 2024. Crud-rag: A comprehensive chinese benchmark for retrieval- augmented generation of large language models. arXiv preprint arXiv:2401.17043. · arXiv
- Hongjin Qian, Peitian Zhang, Zheng Liu, Kelong Mao, and Zhicheng Dou. 2024. Memorag: Moving to- wards next-gen rag via memory-inspired knowledge discovery. arXiv preprint arXiv:2409.05591. · arXiv
- Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. In-context retrieval-augmented lan- guage models. TACL, 11:1316-1331.
- Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2022. Recipe for a general, powerful, scal- able graph transformer. NeurIPS, 35:14501-14515.
- Alireza Salemi et al. 2024. Evaluating retrieval quality in retrieval-augmented generation. In SIGIR.
- Viju Sudhi, Sinchana Ramakanth Bhat, Max Rudat, et al. 2024. Rag-ex: A generic framework for explaining retrieval augmented generation. In SIGIR.
- Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. 2024. Graphgpt: Graph instruction tuning for large lan- guage models. In SIGIR.
- Shangqing Tu, Yuanchun Wang, Jifan Yu, Yuyang Xie, Yaran Shi, Xiaozhi Wang, Jing Zhang, Lei Hou, and Juanzi Li. 2024. R-eval: A unified toolkit for evaluat- ing domain knowledge of retrieval augmented large language models. In KDD.
- Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N Ioannidis, Xiang Song, Qing Ping, et al. 2023. Graph- aware language model pre-training on a large graph corpus can help multiple graph applications. In KDD.
- Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, and Bryan Catanzaro. 2024. Rankrag: Unifying context ranking with retrieval-augmented generation in llms. arXiv preprint arXiv:2407.02485. · arXiv
- Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui. 2024. Retrieval- augmented generation for ai-generated content: A survey. arXiv preprint arXiv:2402.19473. · arXiv
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Yang, M., Ren, Y., Wang, H. B., Hou, W. Z., Li, W., Chun, Z., Zheng, W., & Ren, D. G. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3426-4_5
Extract, Align, Reflect: An LLM-Based Framework for Knowledge Graph Construction
Huang, H., & Zhao, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3426-4_13
Jin, L., Ju, L., Zhang, X., Lu, Z., Wang, T., Zhou, J., & Liu, K. (2026) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling7040148
GraphContainer: A Unified Platform for Comparing and Debugging Graph RAG Methods
An, S., Hyun, C., & Kim, M.-S. (2026) · Proceedings of the VLDB Endowment · doi:10.14778/3827998.3828063
Security and Privacy Concerns in Retrieval‐Augmented Generation
Singh, R., Prabha, C., Balusamy, B., & Al-Khasawneh, M. A. (2026) · Wiley eBooks · doi:10.1002/9781394336128.ch5
Long-Term Memory Mechanism of Large Language Models for Personalized Medical Inquiry Service
Xu, F. (2026) · ITM Web of Conferences · doi:10.1051/itmconf/20268801023
Nguyen, T. N.-B., & Phan, T. H. V. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967261468810
Pandora: Leveraging Code-Driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Chen, Y., He, J., Fu, L., Zhang, S., Jin, R., Dai, X., Li, J., Min, D., Hu, N., Zhang, Y., et al. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3718049 · Cited by 1
Zhang, T., Wu, J., & Kang, Y. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01077-0
Agentology: Ontology-Driven Operational Environments for Multi-Agent SystemsPreprint
Ortaç, A., Tosun, E., Özbek, A. K., Terzioglu, F., & Bayraktar, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6919461
Potluri, M. (2026) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v11i3s.15185
Dillan, T., Isa, S. M., Girsang, A. S., & Suhartono, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-63525-9
URAG 2.0: An Agentic Dual Retrieval Framework for Enhanced Reasoning in RAG-Based QA Systems
Nguyen, L., Vo, Q. N. T., Nguyen, T. T., & Quan, T. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-2584-2_37
Lin, Q. G., Xian, G., Zhijie, H. U., Wu, D., Xin, Z., Zhang, X., & Sun, T. (2026) · Systems · doi:10.3390/systems14080934
Top-r semantically important community search on semantic-rich heterogeneous graphs
Zhang, D., Wang, Y., Gu, C., Ke, X., Xu, X., & Fang, Q. (2026) · The Knowledge Engineering Review · doi:10.48130/ker-0026-0008
Knowledge Discovery with Knowledge Graphs: From Structural Embeddings to Generative Reasoning
Whang, J. J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3816462
MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
Wu, C., Xiang, Z., Tang, Y., Chen, Z., Zhang, Q., & Su, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818074 · Cited by 3
Metadata
- Authors
- Guo, Z., Xia, L., Yu, Y., Ao, T., & Huang, C.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- University of Hong Kong (HK)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2025
- Published
- 2025
- Pages
- 10746-10761
- OpenAlex
- W4416034797
- Cited by
- 208
- References
- 0
- Citations by year
- 2026: 159 · 2025: 49
- FWCI
- 198.29
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (U22B2019)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- retrieval-augmented generation, vector representation, large language models, external knowledge integration, context awareness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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