From 5a18553284ed8ab0d891db27057ef7827558851b Mon Sep 17 00:00:00 2001 From: Ray Date: Sat, 6 Jun 2026 06:07:59 +0800 Subject: [PATCH] update readme --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index a91737b..5e4397c 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@

πŸ“’ Updates

 -- πŸ”₯ [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) β€” A simple *agentic, vectorless RAG* [example](#agentic-vectorless-rag-an-example) with self-hosted PageIndex, using OpenAI Agents SDK. +- πŸ”₯ [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) β€” 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) β€” *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. - [PageIndex Chat](https://chat.pageindex.ai) β€” 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) β€” Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, context-aware retrieval* over long documents. @@ -47,7 +47,7 @@ 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 β‰  relevance** β€” 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 β€” 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)** β€” 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)** β€” 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 β€œTable-of-Contents” **tree structure index** of documents @@ -72,14 +72,14 @@ PageIndex powers a reasoning-based RAG system that achieved **state-of-the-art** ### πŸ“ Explore PageIndex -To learn more, please see a detailed introduction to 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. +To learn more, please see a detailed introduction to the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out [our GitHub](https://docs.pageindex.ai/open-source) 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 more usage guides and examples. -The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or can be integrated via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). +The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or can be integrated via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer), with [enterprise](https://pageindex.ai/enterprise) deployment available. ### πŸ› οΈ Deployment Options -- Self-host β€” run locally with this open-source repo (using standard PDF parsing). -- Cloud Service β€” production-grade pipeline with enhanced OCR, tree building, and retrieval for best results. Try instantly with our [Chat Platform](https://chat.pageindex.ai/), or integrate via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). -- _Enterprise_ β€” 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. +- **Self-host** β€” run locally with this open-source repo (using standard PDF parsing). +- **Cloud Service** β€” production-grade pipeline with enhanced OCR, tree building, and retrieval for best results. Try instantly on our [Chat Platform](https://chat.pageindex.ai/), or integrate via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). +- **Enterprise** β€” dedicated or private deployment (VPC, on-prem). [Contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet) to learn more. ### πŸ§ͺ Quick Hands-on @@ -197,7 +197,7 @@ python3 run_pageindex.py --md_path /path/to/your/document.md ## 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). +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). ```bash # Install optional dependency @@ -282,7 +282,7 @@ PageIndex Blog, Sep 2025. ### 🌐 Ecosystem -Other open-source projects from the PageIndex ecosystem: [OpenKB](https://github.com/VectifyAI/OpenKB) is an LLM knowledge base that compiles documents into an interlinked wiki. [ChatIndex](https://github.com/VectifyAI/ChatIndex) brings tree indexing and retrieval to long conversational histories. [ConDB](https://github.com/VectifyAI/ConDB) is a KV-cache native context database for tree-based retrieval. [PageIndex MCP](https://github.com/VectifyAI/pageindex-mcp) is PageIndex's MCP server. +Other [open-source projects](https://docs.pageindex.ai/open-source) from the PageIndex ecosystem: [OpenKB](https://github.com/VectifyAI/OpenKB) is an LLM knowledge base that compiles documents into an interlinked wiki. [ChatIndex](https://github.com/VectifyAI/ChatIndex) brings tree indexing and retrieval to long conversational histories. [ConDB](https://github.com/VectifyAI/ConDB) is a KV-cache native context database for tree-based retrieval. [PageIndex MCP](https://github.com/VectifyAI/pageindex-mcp) is PageIndex's MCP server. ### Connect with Us