ScalableRAG: High-Quality Retrieval at Zero Ingestion Cost Without Vector DBs
_reachsumit · x · 2026-07-30
This article introduces ScalableRAG, a novel RAG framework designed to solve the high ingestion cost problem of traditional RAG (e.g., building knowledge graphs or extracting SQL tables).
- Core Mechanism: By maintaining a workspace of document sets and value sets that it can write into and read from, it enables on-the-fly aggregative reasoning without pre-building knowledge bases.
- Performance: It outperforms all baselines (including knowledge graph approaches) in 3 out of 6 tested corpora, with an average accuracy 7.36% higher than the next best competitor.
- Extended Version: Introduces Limited-Ingestion ScalableRAG, which uses a minimal vector database and automated pattern discovery to further improve accuracy at scale.
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