EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909675460493312 |
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| author | Zhang, Fangyuan Huang, Zhengjun Zhou, Yingli Guo, Qintian Li, Zhixun Luo, Wensheng Jiang, Di Fang, Yixiang Zhou, Xiaofang |
| author_facet | Zhang, Fangyuan Huang, Zhengjun Zhou, Yingli Guo, Qintian Li, Zhixun Luo, Wensheng Jiang, Di Fang, Yixiang Zhou, Xiaofang |
| contents | Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our method leverages hyperplane-based Locality-Sensitive Hashing (LSH) to partition and organize the original corpus into hierarchical graph structures, enabling efficient and localized insertions of new data without disrupting the existing topology. The design eliminates the need for retraining or costly recomputation while preserving high retrieval accuracy and low latency. Experiments on large-scale benchmarks demonstrate that EraRag achieves up to an order of magnitude reduction in update time and token consumption compared to existing Graph-RAG systems, while providing superior accuracy performance. This work offers a practical path forward for RAG systems that must operate over continually growing corpora, bridging the gap between retrieval efficiency and adaptability. Our code and data are available at https://github.com/EverM0re/EraRAG-Official. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Zhang, Fangyuan Huang, Zhengjun Zhou, Yingli Guo, Qintian Li, Zhixun Luo, Wensheng Jiang, Di Fang, Yixiang Zhou, Xiaofang Information Retrieval Machine Learning Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our method leverages hyperplane-based Locality-Sensitive Hashing (LSH) to partition and organize the original corpus into hierarchical graph structures, enabling efficient and localized insertions of new data without disrupting the existing topology. The design eliminates the need for retraining or costly recomputation while preserving high retrieval accuracy and low latency. Experiments on large-scale benchmarks demonstrate that EraRag achieves up to an order of magnitude reduction in update time and token consumption compared to existing Graph-RAG systems, while providing superior accuracy performance. This work offers a practical path forward for RAG systems that must operate over continually growing corpora, bridging the gap between retrieval efficiency and adaptability. Our code and data are available at https://github.com/EverM0re/EraRAG-Official. |
| title | EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2506.20963 |