From d938ce1c016bb3e41a71e718f7c72242e9d85026 Mon Sep 17 00:00:00 2001 From: Ray Date: Mon, 3 Nov 2025 00:00:44 +0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d910be4..d562ce9 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ 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. -Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)**, a **reasoning-based RAG** system that simulates how **human experts** navigate and extract knowledge from long documents through **tree search**, enabling LLMs to *think* and *reason* their way to the most relevant document sections. It performs retrieval in two steps: +Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)**, a **reasoning-based RAG** system that builds a tree index over long documents and reasons over that index for retrieval. It simulates how **human experts** navigate and extract knowledge from long documents through **tree search**, enabling LLMs to *think* and *reason* their way to the most relevant document sections. It performs retrieval in two steps: 1. Generate a "Table-of-Contents" **tree structure index** of documents 2. Perform reasoning-based retrieval through **tree search**