From 42aa80533950072d06becdcc585b5e2efaab323c Mon Sep 17 00:00:00 2001 From: Ray Date: Tue, 23 Jun 2026 01:11:20 +0800 Subject: [PATCH] edit readme (#336) --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 9f29e66..e20b754 100644 --- a/README.md +++ b/README.md @@ -45,8 +45,6 @@ # 📑 Introduction to PageIndex -**PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, without vector databases or chunking.** - Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic *similarity* rather than true *relevance*. But **similarity ≠ relevance** — what we truly need in retrieval is **relevance**, and that requires **reasoning**. When working with professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search often falls short — missing what's relevant but not similar, and returning what's similar yet not relevant. Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/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**. The retrieval is *traceable* and *explainable*, with no vector DBs or chunking.