diff --git a/README.md b/README.md index ebf5a1a..833e723 100644 --- a/README.md +++ b/README.md @@ -30,16 +30,16 @@ **🔥 Releases:** - [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document-analysis agent platform built for professional long documents. Can also be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart) (beta). - + - **✍️ Articles:** + **📝 Articles:** - [**PageIndex Framework**](https://pageindex.ai/blog/pageindex-intro): Introduces the PageIndex framework — an *agentic, in-context* *tree index* that enables LLMs to perform *reasoning-based*, *human-like retrieval* over long documents, without vector DB or chunking. **🧪 Cookbooks:** - [Vectorless RAG](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): A minimal, hands-on example of reasoning-based RAG using PageIndex. No vectors, no chunking, and human-like retrieval. -- [Vision-based Vectorless RAG](https://docs.pageindex.ai/cookbook/vision-rag-pageindex): Experience OCR-free document understanding with PageIndex’s visual retrieval workflow that retrieves and reasons directly over PDF page images. +- [Vision-based Vectorless RAG](https://docs.pageindex.ai/cookbook/vision-rag-pageindex): OCR-free, vision-only RAG with PageIndex's reasoning-native retrieval workflow that works directly over PDF page images. --- @@ -48,7 +48,8 @@ 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 ***vectorless***, **reasoning-based RAG** system that builds a **hierarchical tree index** from long documents and uses LLMs to **reason over that index** for retrieval. It 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. PageIndex performs retrieval in two steps: +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**. +It 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. PageIndex performs retrieval in two steps: 1. Generate a “Table-of-Contents” **tree structure index** of documents 2. Perform reasoning-based retrieval through **tree search** @@ -59,9 +60,9 @@ Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)** -### 🧩 Features +### 🎯 Features -Compared to traditional *vector-based RAG*, **PageIndex** features: +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. - **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents. @@ -71,12 +72,14 @@ PageIndex powers a reasoning-based RAG system that achieved **state-of-the-art** ### 📍 Explore PageIndex -Please see a detailed introduction of the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out this GitHub repo for open-source code, and [cookbooks](https://docs.pageindex.ai/cookbook) and [tutorials](https://docs.pageindex.ai/tutorials) for additional usage guides and examples. The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or could be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart). +To learn more, please see a detailed introduction of the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out this GitHub repo for open-source code, and the [cookbooks](https://docs.pageindex.ai/cookbook), [tutorials](https://docs.pageindex.ai/tutorials), and [blog](https://pageindex.ai/blog) for additional usage guides and examples. -### ⚙️ Deployment Options +The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or can be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart). + +### 🛠️ Deployment Options - Self-host — run locally with this open-source repo. - Cloud Service — try instantly with our [Chat Platform](https://chat.pageindex.ai/), or integrate with [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart). -- Enterprise — private or on-prem deployment. [Contact us](https://ii2abc2jejf.typeform.com/to/tK3AXl8T) or [book a demo](https://calendly.com/pageindex/meet). +- _Enterprise_ — private or on-prem deployment. [Contact us](https://ii2abc2jejf.typeform.com/to/tK3AXl8T) or [book a demo](https://calendly.com/pageindex/meet) for more details. ### 🧪 Quick Hands-on @@ -128,11 +131,11 @@ Below is an example PageIndex tree structure. Also see more example [documents]( ... ``` -You can either generate the PageIndex tree structure with this open-source repo, or try our [API](https://docs.pageindex.ai/quickstart) service. +You can generate the PageIndex tree structure with this open-source repo, or use our [API](https://docs.pageindex.ai/quickstart) --- -# 📦 Package Usage +# ⚙️ Package Usage You can follow these steps to generate a PageIndex tree from a PDF document. @@ -181,7 +184,7 @@ We also provide markdown support for PageIndex. You can use the `-md_path` flag python3 run_pageindex.py --md_path /path/to/your/document.md ``` -> Note: in this function, we use "#" to determine node heading 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 function, 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 the original hierarchy, to convert the PDF to a markdown file and then use this function. +> Note: in this function, we use "#" to determine node heading 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 function, 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 the original hierarchy, to convert the PDF to a markdown file and then use this function.