From 293730afbd4319a683fe4aee439360a2b21b8c3c Mon Sep 17 00:00:00 2001 From: Ray Date: Tue, 23 Jun 2026 03:56:30 +0800 Subject: [PATCH] edit readme (#337) --- README.md | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index e20b754..594b70e 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ # PageIndex: Vectorless, Reasoning-based RAG -

Reasoning-based RAG  ◦  No Vector DB, No Chunking  ◦  Context-Aware Retrieval  ◦  Reads Like Humans

+

Reasoning-based RAG  ◦  No Vector DB, No Chunking  ◦  Context-Aware Retrieval  ◦  Reads Like a Human

🌐 Website  •   @@ -30,8 +30,8 @@

📢 Updates

 -- 🔥 [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) — A simple agentic, vectorless RAG [example](#agentic-vectorless-rag-an-example) with *self-hosted PageIndex*, using OpenAI Agents SDK. -- [**Scale PageIndex to Millions of Documents**](https://pageindex.ai/blog/pageindex-filesystem) — *PageIndex File System* is a file-level tree layer that lets PageIndex reason over an entire corpus, not just a single document, enabling massive-scale document search. +- 🔥 [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) — A simple agentic, vectorless RAG [example](#-agentic-vectorless-rag-an-example) with *self-hosted PageIndex*, using OpenAI Agents SDK. +- [**Scale PageIndex to Millions of Documents**](https://pageindex.ai/blog/pageindex-filesystem) — *PageIndex File System* is a file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document, enabling massive-scale document search. - [PageIndex Chat](https://chat.pageindex.ai) — Human-like document analysis agent [platform](https://chat.pageindex.ai) for professional long documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). - [PageIndex Framework](https://pageindex.ai/blog/pageindex-intro) — Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, context-aware retrieval* over long documents. @@ -45,13 +45,13 @@ # 📑 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 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. +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. +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: 1. Generate a “Table-of-Contents” **tree structure index** of documents -2. Perform reasoning-based retrieval through **tree search** +2. Perform (agentic) reasoning-based retrieval through **tree search**
@@ -61,14 +61,16 @@ PageIndex simulates how *human experts* navigate and extract knowledge from comp ### 🎯 Core Features +> 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. + Compared to traditional vector-based RAG, **PageIndex** features: - **No Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector similarity search. - **No Chunking**: Documents are organized into natural sections, not artificial chunks. - **Better Traceability & Explainability**: Retrieval is reasoning-driven and grounded in explicit page and section references, making every result traceable and interpretable — no more “vibe retrieval” with opaque, approximate vector search. - **Context-Aware Retrieval**: Retrieval depends on your full context (e.g., conversation history and domain knowledge), and easily incorporates new context. -- **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents. +- **Human-like Retrieval**: Mirrors how human experts navigate and extract knowledge from complex documents. -PageIndex powers a reasoning-based RAG system that achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench, vastly outperforming vector-based RAG solutions on professional document analysis ([blog post](https://vectify.ai/blog/Mafin2.5)). +PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector RAG solutions on professional document analysis ([blog post](https://vectify.ai/blog/Mafin2.5)). ### 📍 Explore PageIndex @@ -83,7 +85,7 @@ The PageIndex service is available as a ChatGPT-style [chat platform](https://ch ### 🧪 Quick Hands-on -- 🔥 [**Agentic Vectorless RAG**](examples/agentic_vectorless_rag_demo.py) (**latest**) — a simple but complete **agentic vectorless RAG** [example](#agentic-vectorless-rag-an-example) with *self-hosted* PageIndex, using OpenAI Agents SDK. +- 🔥 [**Agentic Vectorless RAG**](examples/agentic_vectorless_rag_demo.py) *(latest)* — a simple but complete **agentic vectorless RAG** [example](#-agentic-vectorless-rag-an-example) with *self-hosted* PageIndex, using OpenAI Agents SDK. - Try the [Vectorless RAG](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb) notebook — a *minimal*, hands-on example of reasoning-based RAG using PageIndex. - Check out [Vision-based Vectorless RAG](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, vision-based & reasoning-native RAG pipeline that works directly over page images. @@ -105,7 +107,7 @@ The PageIndex service is available as a ChatGPT-style [chat platform](https://ch # 🌲 PageIndex Tree Structure -PageIndex can transform lengthy PDF documents into a semantic **tree structure**, similar to a _“table of contents”_ but optimized for use with Large Language Models (LLMs). It's ideal for: financial reports, regulatory filings, academic textbooks, legal or technical manuals, and any document that exceeds LLM context limits. +PageIndex can transform lengthy PDF documents into a semantic **tree structure**, similar to a _“table of contents”_ but optimized for use with LLMs and AI agents. It's ideal for: financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks, and any long, complex professional documents. Below is an example PageIndex tree structure. Also see more example [documents](https://github.com/VectifyAI/PageIndex/tree/main/examples/documents) and generated [tree structures](https://github.com/VectifyAI/PageIndex/tree/main/examples/documents/results). @@ -137,7 +139,7 @@ Below is an example PageIndex tree structure. Also see more example [documents]( ... ``` -You can generate the PageIndex tree structure with this open-source repo; or use our [API](https://pageindex.ai/developer) for higher-quality results powered by our enhanced OCR and tree building pipeline. +You can generate PageIndex tree structures with this open-source repo. Or use our [API](https://pageindex.ai/developer) for higher-quality results powered by our enhanced OCR and tree building pipeline. --- @@ -195,7 +197,7 @@ python3 run_pageindex.py --md_path /path/to/your/document.md > Note: in this mode, we use "#" to determine node headings and their levels. For example, "##" is level 2, "###" is level 3, etc. Make sure your markdown file is formatted correctly. If your Markdown file was converted from a PDF or HTML, we don't recommend using this mode, since most existing conversion tools cannot preserve the original hierarchy. Instead, use our [PageIndex OCR](https://pageindex.ai/blog/ocr), which is designed to preserve it, to convert the PDF to a markdown file and then use this mode.
-## Agentic Vectorless RAG: An Example +## 🚀 Agentic Vectorless RAG: An Example For a simple, end-to-end **agentic vectorless RAG** example using **self-hosted PageIndex** (with OpenAI Agents SDK), see [`examples/agentic_vectorless_rag_demo.py`](examples/agentic_vectorless_rag_demo.py). @@ -226,7 +228,7 @@ To address this, we introduced PageIndex OCR — the first long-context OCR mode # 📈 Case Study: PageIndex Leads Finance QA Benchmark -[Mafin 2.5](https://vectify.ai/mafin) is a reasoning-based RAG system for financial document analysis, powered by **PageIndex**. It achieved a state-of-the-art [**98.7% accuracy**](https://vectify.ai/blog/Mafin2.5) on the [FinanceBench](https://arxiv.org/abs/2311.11944) benchmark, significantly outperforming traditional vector-based RAG systems. +[Mafin 2.5](https://vectify.ai/mafin) is a reasoning-based RAG system for financial document analysis, powered by **PageIndex**. It achieved a state-of-the-art [**98.7% accuracy**](https://vectify.ai/blog/Mafin2.5) on [FinanceBench](https://arxiv.org/abs/2311.11944) (financial document QA benchmark), significantly outperforming traditional vector-based RAG systems. PageIndex's hierarchical indexing and reasoning-driven retrieval enable precise navigation and extraction of relevant context from complex financial reports, such as SEC filings and earnings disclosures. @@ -282,7 +284,7 @@ PageIndex Blog, Sep 2025. ### 🌐 Open-Source Ecosystem -PageIndex anchors a growing open-source [ecosystem](https://docs.pageindex.ai/open-source) of **long-context AI infra** — [OpenKB](https://github.com/VectifyAI/OpenKB) is an LLM knowledge base that compiles documents into an interlinked wiki. [ChatIndex](https://github.com/VectifyAI/ChatIndex) provides tree indexing and retrieval for long conversational histories and memory. [ConDB](https://github.com/VectifyAI/ConDB) is a KV-cache native context database for tree-based retrieval at scale. [PageIndex MCP](https://github.com/VectifyAI/pageindex-mcp) is PageIndex's MCP server. +[PageIndex](https://github.com/VectifyAI/PageIndex) anchors a growing open-source [ecosystem](https://docs.pageindex.ai/open-source) of **long-context AI infra** — [OpenKB](https://github.com/VectifyAI/OpenKB) is an LLM knowledge base that compiles documents into an interlinked wiki. [ChatIndex](https://github.com/VectifyAI/ChatIndex) provides tree indexing and retrieval for long conversational histories and memory. [ConDB](https://github.com/VectifyAI/ConDB) is a KV-cache native context database for tree-based retrieval at scale. [PageIndex MCP](https://github.com/VectifyAI/pageindex-mcp) is PageIndex's MCP server. ### Connect with Us