标题： EraRAG：适用于不断增长语料库的高效和增量检索增强生成 显示英文标题

标题： EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

摘要： 基于图的检索增强生成（Graph-RAG）通过在外部语料库上进行结构化检索来增强大型语言模型（LLMs）。 然而，现有方法通常假设语料库是静态的，每当新文档到达时需要昂贵的全图重建，这限制了它们在动态、演进环境中的可扩展性。 为解决这些限制，我们引入EraRAG，一种支持高效和可扩展动态更新的新型多层Graph-RAG框架。 我们的方法利用基于超平面的局部敏感哈希（LSH）将原始语料库划分为分层图结构，从而实现无需破坏现有拓扑结构的高效和局部新数据插入。 该设计消除了重新训练或昂贵重新计算的需要，同时保持高检索准确性和低延迟。 在大规模基准测试中的实验表明，与现有的Graph-RAG系统相比，EraRag在更新时间和令牌消耗方面实现了数量级的减少，同时提供了更优的准确性表现。 这项工作为必须在持续增长的语料库上运行的RAG系统提供了一条实用的前进路径，弥合了检索效率和适应性之间的差距。 我们的代码和数据可在https://github.com/EverM0re/EraRAG-Official获取。 显示英文摘要

摘要： 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.