PageIndex: A New RAG Architecture Hits 98.7% Accuracy on Financial Documents — Without Vectors, Chunking, or a Database
Researchers released an open-source retrieval system that replaces the entire vector-database-and-chunking pipeline with document hierarchy and LLM reasoning, posting near-perfect accuracy on a financial benchmark.
The standard retrieval-augmented generation pipeline — chunk documents, embed them, store vectors, retrieve by similarity — has become so ubiquitous that it's easy to forget it was always a series of compromises. Now a research team has released PageIndex, an open-source alternative that discards vectors, chunking, and database infrastructure entirely, as reported by @akshay_pachaar. The system achieved 98.7% accuracy on a financial document benchmark, a domain where precision is not optional.
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