零成本构建高质量RAG，性能超越传统知识库方法
ScalableRAG: High-Quality RAG at Zero Ingestion Cost
- 用动态文档集和值集实现无需预处理的即时推理
- 六组数据中三组超越基线，平均准确率高出7.36%
- 适合追求低成本高效率RAG落地的开发者
近期RAG研究为提升性能付出高昂的知识摄入成本：构建知识图谱或提取SQL表格。本文展示，这些知识库操作可零摄入成本实现（甚至无需向量数据库）。所提出的零摄入可扩展RAG（Zero-Ingestion ScalableRAG）在六个语料库中的三组上显著优于所有基线（包括知识图谱方法），其余三组仅略逊于最优表现，全数据集平均准确率比第二优基线高出7.36%。其核心是维护可读写的文档集与值集工作区，支持在主键与文档子集一一对应时的实时聚合推理。通过限制大模型调用次数为与语料规模无关的常数，我们进一步提出有限摄入可扩展RAG（Limited-Ingestion ScalableRAG），引入最小向量数据库及文档样本自动模式发现机制，在规模化场景下进一步提升精度。代码已开源：https://github.com/cohesity/ScalableRAG。
原文摘要 · Abstract (English)
Recent advances in RAG aim to optimize for performance by paying high ingestion costs for knowledge ingestion: building knowledge graphs or extracting SQL tables. In this work we show that the operations that such knowledge bases allow can be replicated with zero ingestion costs (not even a vector database); in fact our solution, Zero-Ingestion ScalableRAG, handily out-performs all baselines (including knowledge graph approaches) in three out of the six corpora considered here, and only marginally missing maximum performance on the other three, with average accuracy across all six datasets 7.36% above the next most competitive baseline. It achieves this by keeping a workspace of document sets and values sets that it can write into and read from, allowing for on-the-fly aggregative reasoning in all situations where grouping is required on a primary key that is in one to one correspondence with a subset of the total document set. Capping the number of LLM calls by a constant independent of the corpus size, we also introduce Limited-Ingestion ScalableRAG, which does use a minimal vector database as well as an automated pattern discovery from a sample of documents, to further improve accuracy at scale. Our code is available at https://github.com/cohesity/ScalableRAG .
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