SAG2: Embedding-Free Multi-Hop Retrieval with BM25 and JEV

NLP RAG CPMSRR
2026年10月15日
强大的多跳检索系统应当具备实用的可部署性，无需昂贵的语料库预处理，也无需复杂的数据库基础设施。我们提出了SAG2——一种不依赖嵌入表示（embedding-free）的检索架构，其核心仅由一个SQLite数据库、BM25算法以及JEV模型提供的上下文证据判定能力构成。SAG2延续了SAG（SQL增强型检索与生成，即SQL-Retrieval Augmented Generation）所倡导的“查询时关联”（query-time association）原则，但摒弃了SAG中基于事件-实体的关系索引和稠密向量索引，转而采用对原始段落构建的纯词法索引（lexical index）。在检索过程中，已选中的段落为其后续检索提供扩展关键词，而新检索到的证据又反过来引导下一步的判定决策。这一设计将语义层面的证据筛选工作完全迁移至查询执行阶段，从而彻底消除了离线嵌入计算、依赖大语言模型（LLM）的事件-实体抽取，以及专用数据库服务器的需求。最终实现的系统兼具轻量级预处理开销与卓越的检索准确率：在MuSiQue、2WikiMultiHopQA和HotpotQA三个数据集各含1,000个问题的子集上，SAG2在所有对比方法中取得了最高的Recall@5与Recall@10指标，其中Recall@10分别达到86.62%、97.43%和99.00%；在包含511,962份文档的EnterpriseRAG-Bench企业级评测基准上，其文档级Recall@10亦达81.10%。此外，对MuSiQue全部11,656个段落完成索引构建仅需2.36秒，生成的SQLite数据库体积仅为25.87 MiB，且全程无需调用任何机器学习模型。SAG2表明，高精度的多跳证据检索完全可依托紧凑、快速构建的基础设施予以支撑，因而成为知识检索类应用一项切实可行的基础技术方案。
Strong multi-hop retrieval should be practical to deploy without expensive corpus preparation or complex database infrastructure. We present SAG2, an embedding-free retrieval architecture built around a single SQLite database, BM25, and contextual evidence decisions from JEV. SAG2 follows the query-time association principle of SAG (SQL-Retrieval Augmented Generation), while replacing its event-entity and dense indexes with a lexical index over original passages. Selected passages supply terms for further retrieval, and newly retrieved evidence guides subsequent decisions. This moves semantic evidence selection to query time, eliminating offline embedding computation, LLM-based event-entity extraction, and dedicated database servers. The resulting system combines lightweight preparation with strong retrieval accuracy: on 1,000-question subsets of MuSiQue, 2WikiMultiHopQA, and HotpotQA, SAG2 achieves the highest Recall@5 and Recall@10 among the compared methods, with Recall@10 of 86.62%, 97.43%, and 99.00%. It also reaches 81.10% document Recall@10 over the 511,962-document EnterpriseRAG-Bench corpus. Indexing all 11,656 MuSiQue passages takes 2.36 seconds, produces a 25.87 MiB database, and requires no model calls. SAG2 demonstrates that accurate multi-hop evidence retrieval can be supported by compact, rapidly prepared infrastructure, making it a practical foundation for knowledge retrieval applications.
许愿