From 54346716bd92cec65d55c11225b4f6e4d08477f5 Mon Sep 17 00:00:00 2001
From: Ray <mailtangyu@gmail.com>
Date: Mon, 22 Jun 2026 23:28:08 +0800
Subject: [PATCH] edit readme (#335)

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 9bbd701..9f29e66 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@
 
 # PageIndex: Vectorless, Reasoning-based RAG
 
-<p align="center"><b>Reasoning-based RAG&nbsp; ◦ &nbsp;No Vector DB, No Chunking&nbsp; ◦ &nbsp;Context-Aware Retrieval&nbsp; ◦ &nbsp;Human-like</b></p>
+<p align="center"><b>Reasoning-based RAG&nbsp; ◦ &nbsp;No Vector DB, No Chunking&nbsp; ◦ &nbsp;Context-Aware Retrieval&nbsp; ◦ &nbsp;Reads Like Humans</b></p>
 
 <h4 align="center">
   <a href="https://vectify.ai">🌐 Website</a>&nbsp; • &nbsp;
@@ -45,7 +45,9 @@
 
 # 📑 Introduction to PageIndex
 
-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 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.
+**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.
 PageIndex simulates how *human experts* navigate and extract knowledge from complex documents through *tree search*, enabling LLMs to *think* and *reason* their way to the most relevant document sections. It performs retrieval in two steps: