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This paper introduces ScalableRAG, a retrieval-augmented generation method that achieves high accuracy without any ingestion costs (no vector database or knowledge graph) by using regex-based set creation and aggregative reasoning. It outperforms baselines on multiple datasets and also presents a limited-ingestion variant for further accuracy improvements.