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Inspired by AlphaGo, we propose PageIndex — a vectorless, reasoning-based RAG system that builds a hierarchical tree index from long documents and uses LLMs to reason over that index for agentic, context-aware retrieval.
PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read documents. Achieve 98.7% accuracy on FinanceBench with traceable, explainable retrieval.
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Mar 1, 2026 · PageIndex is a vectorless RAG architecture that retrieves information by reasoning over document structure instead of performing semantic search. Rather than treating a document as a flat pile of...
Mar 9, 2026 · PageIndex is a reasoning-based, vectorless RAG framework that performs retrieval in two steps: Vector-based RAG retrieves information using semantic embeddings and similarity search over chunked text sto...
Mar 1, 2026 · To turn a complex document — like a Game of Thrones script or a Federal Reserve report — into a reasoning-ready index, the Java PageIndexAgent follows a strict two-phase pipeline. This article walks thro...
While this notebook highlights a minimal workflow, the PageIndex framework is built to support far more advanced use cases. In upcoming tutorials, we will introduce: Multi-Node Reasoning with...
Mar 2, 2026 · In this article, I am explaining a new approach by Mingtian Zhang and Yu Tang who have created “PageIndex”. Its a a reasoning-based retrieval system that replaces vector similarity search with LLM reason...
Sep 19, 2025 · To address these challenges, we introduce PageIndex, a reasoning-based retrieval framework that enables LLMs to dynamically navigate document structures and infer which sections are genuinely relevant, ...
Jan 25, 2026 · PageIndex is an open-source RAG framework that eliminates vector embeddings and chunking, replacing them with hierarchical document trees for reasoning-based retrieval. Created by Vectify AI, it achieve...
