[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2183":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":37,"readmeContent":38,"aiSummary":39,"trendingCount":16,"starSnapshotCount":16,"syncStatus":40,"lastSyncTime":41,"discoverSource":42},2183,"PageIndex","VectifyAI\u002FPageIndex","VectifyAI","📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG","https:\u002F\u002Fpageindex.ai",null,"Python",32919,2859,138,88,0,75,386,2241,302,45,"MIT License",false,"main",[26,27,28,29,30,31,32,33,34,35,36],"agentic-ai","agents","ai","ai-agents","context-engineering","llm","rag","reasoning","retrieval","retrieval-augmented-generation","vector-database","2026-06-12 02:00:38","\u003Cdiv align=\"center\">\n  \n\u003Ca href=\"https:\u002F\u002Fvectify.ai\u002Fpageindex\" target=\"_blank\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F46201e72-675b-43bc-bfbd-081cc6b65a1d\" alt=\"PageIndex Banner\" \u002F>\n\u003C\u002Fa>\n\n\u003Cbr\u002F>\n\u003Cbr\u002F>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F14736\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F14736\" alt=\"VectifyAI%2FPageIndex | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\n# PageIndex: Vectorless, Reasoning-based RAG\n\n\u003Cp align=\"center\">\u003Cb>Reasoning-based RAG  ◦  No Vector DB  ◦  No Chunking  ◦  Human-like Retrieval\u003C\u002Fb>\u003C\u002Fp>\n\n\u003Ch4 align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fvectify.ai\">🌐 Homepage\u003C\u002Fa>  •  \n  \u003Ca href=\"https:\u002F\u002Fchat.pageindex.ai\">🖥️ Chat Platform\u003C\u002Fa>  •  \n  \u003Ca href=\"https:\u002F\u002Fpageindex.ai\u002Fdeveloper\">🔌 MCP & API\u003C\u002Fa>  •  \n  \u003Ca href=\"https:\u002F\u002Fdocs.pageindex.ai\">📖 Docs\u003C\u002Fa>  •  \n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002FVuXuf29EUj\">💬 Discord\u003C\u002Fa>  •  \n  \u003Ca href=\"https:\u002F\u002Fii2abc2jejf.typeform.com\u002Fto\u002FtK3AXl8T\">✉️ Contact\u003C\u002Fa> \n\u003C\u002Fh4>\n  \n\u003C\u002Fdiv>\n\n\n\u003Cdetails open>\n\u003Csummary>\u003Ch2>📢 Updates\u003C\u002Fh2>\u003C\u002Fsummary>\n\n- 🔥 [**Agentic Vectorless RAG**](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fexamples\u002Fagentic_vectorless_rag_demo.py) — A simple *agentic, vectorless RAG* [example](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fexamples\u002Fagentic_vectorless_rag_demo.py) with self-hosted PageIndex, using OpenAI Agents SDK.\n- [**Scale PageIndex to Millions of Documents**](https:\u002F\u002Fpageindex.ai\u002Fblog\u002Fpageindex-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.\n- [PageIndex Chat](https:\u002F\u002Fchat.pageindex.ai) — Human-like document analysis agent [platform](https:\u002F\u002Fchat.pageindex.ai) for professional long documents. Also available via [MCP](https:\u002F\u002Fpageindex.ai\u002Fdeveloper) or [API](https:\u002F\u002Fpageindex.ai\u002Fdeveloper).\n- [PageIndex Framework](https:\u002F\u002Fpageindex.ai\u002Fblog\u002Fpageindex-intro) — Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, human-like retrieval* over long documents.\n\n \u003C!-- **🧪 Cookbooks:**\n- [Vectorless RAG](https:\u002F\u002Fdocs.pageindex.ai\u002Fcookbook\u002Fvectorless-rag-pageindex): A minimal, hands-on example of reasoning-based RAG using PageIndex. No vectors, no chunking, and human-like retrieval.\n- [Vision-based Vectorless RAG](https:\u002F\u002Fdocs.pageindex.ai\u002Fcookbook\u002Fvision-rag-pageindex): OCR-free, vision-only RAG with PageIndex's reasoning-native retrieval workflow that works directly over PDF page images. -->\n\n\u003C\u002Fdetails>\n\n---\n\n# 📑 Introduction to PageIndex\n\nAre 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.\n\nInspired by AlphaGo, we propose **[PageIndex](https:\u002F\u002Fvectify.ai\u002Fpageindex)** — 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**.\nIt 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:\n\n1. Generate a “Table-of-Contents” **tree structure index** of documents\n2. Perform reasoning-based retrieval through **tree search**\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpageindex.ai\u002Fblog\u002Fpageindex-intro\" target=\"_blank\" title=\"The PageIndex Framework\">\n    \u003Cimg src=\"https:\u002F\u002Fdocs.pageindex.ai\u002Fimages\u002Fcookbook\u002Fvectorless-rag.png\" width=\"70%\">\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n### 🎯 Core Features\n\nCompared to traditional vector-based RAG, **PageIndex** features:\n- **No Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector similarity search.\n- **No Chunking**: Documents are organized into natural sections, not artificial chunks.\n- **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents.\n- **Better Explainability and Traceability**: Retrieval is based on reasoning — traceable and interpretable, with page and section references. No more opaque, approximate vector search (“vibe retrieval”).\n\nPageIndex powers a reasoning-based RAG system that achieved **state-of-the-art** [98.7% accuracy](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FMafin2.5-FinanceBench) on FinanceBench, demonstrating superior performance over vector-based RAG solutions in professional document analysis. See our [blog post](https:\u002F\u002Fvectify.ai\u002Fblog\u002FMafin2.5) for details.\n\n### 📍 Explore PageIndex\n\nTo learn more, please see a detailed introduction to the [PageIndex framework](https:\u002F\u002Fpageindex.ai\u002Fblog\u002Fpageindex-intro). Check out this GitHub repo for open-source code, and the [cookbooks](https:\u002F\u002Fdocs.pageindex.ai\u002Fcookbook), [tutorials](https:\u002F\u002Fdocs.pageindex.ai\u002Ftutorials), and [blog](https:\u002F\u002Fpageindex.ai\u002Fblog) for additional usage guides and examples.\n\nThe PageIndex service is available as a ChatGPT-style [chat platform](https:\u002F\u002Fchat.pageindex.ai), or can be integrated via [MCP](https:\u002F\u002Fpageindex.ai\u002Fdeveloper) or [API](https:\u002F\u002Fpageindex.ai\u002Fdeveloper).\n\n### 🛠️ Deployment Options\n- Self-host — run locally with this open-source repo (using standard PDF parsing).\n- Cloud Service — production-grade pipeline with enhanced OCR, tree building, and retrieval for best results. Try instantly with our [Chat Platform](https:\u002F\u002Fchat.pageindex.ai\u002F), or integrate via [MCP](https:\u002F\u002Fpageindex.ai\u002Fdeveloper) or [API](https:\u002F\u002Fpageindex.ai\u002Fdeveloper).\n- _Enterprise_ — private or on-prem deployment. [Contact us](https:\u002F\u002Fii2abc2jejf.typeform.com\u002Fto\u002FtK3AXl8T) or [book a demo](https:\u002F\u002Fcalendly.com\u002Fpageindex\u002Fmeet) for more details.\n\n### 🧪 Quick Hands-on\n\n- 🔥 [**Agentic Vectorless RAG**](examples\u002Fagentic_vectorless_rag_demo.py) (**latest**) — a simple but complete **agentic vectorless RAG** [example](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fexamples\u002Fagentic_vectorless_rag_demo.py) with *self-hosted* PageIndex, using OpenAI Agents SDK.\n- Try the [Vectorless RAG](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fcookbook\u002Fpageindex_RAG_simple.ipynb) notebook — a *minimal*, hands-on example of reasoning-based RAG using PageIndex.\n- Check out [Vision-based Vectorless RAG](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fcookbook\u002Fvision_RAG_pageindex.ipynb) — no OCR; a minimal, vision-based & reasoning-native RAG pipeline that works directly over page images.\n  \n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fexamples\u002Fagentic_vectorless_rag_demo.py\" target=\"_blank\" rel=\"noopener\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FView_on_GitHub-Agentic_Vectorless_RAG-blue?style=for-the-badge&logo=github\" alt=\"View on GitHub: Agentic Vectorless RAG\" \u002F>\n  \u003C\u002Fa>\n  \u003Cbr\u002F>\n  \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fcookbook\u002Fpageindex_RAG_simple.ipynb\" target=\"_blank\" rel=\"noopener\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpen_In_Colab-Vectorless_RAG-orange?style=for-the-badge&logo=googlecolab\" alt=\"Open in Colab: Vectorless RAG\" \u002F>\n  \u003C\u002Fa>\n    \n  \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FVectifyAI\u002FPageIndex\u002Fblob\u002Fmain\u002Fcookbook\u002Fvision_RAG_pageindex.ipynb\" target=\"_blank\" rel=\"noopener\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpen_In_Colab-Vision_RAG-orange?style=for-the-badge&logo=googlecolab\" alt=\"Open in Colab: Vision RAG\" \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n---\n\n# 🌲 PageIndex Tree Structure\n\nPageIndex 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.\n\nBelow is an example PageIndex tree structure. Also see more example [documents](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Ftree\u002Fmain\u002Fexamples\u002Fdocuments) and generated [tree structures](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FPageIndex\u002Ftree\u002Fmain\u002Fexamples\u002Fdocuments\u002Fresults).\n\n```jsonc\n...\n{\n  \"title\": \"Financial Stability\",\n  \"node_id\": \"0006\",\n  \"start_index\": 21,\n  \"end_index\": 22,\n  \"summary\": \"The Federal Reserve ...\",\n  \"nodes\": [\n    {\n      \"title\": \"Monitoring Financial Vulnerabilities\",\n      \"node_id\": \"0007\",\n      \"start_index\": 22,\n      \"end_index\": 28,\n      \"summary\": \"The Federal Reserve's monitoring ...\"\n    },\n    {\n      \"title\": \"Domestic and International Cooperation and Coordination\",\n      \"node_id\": \"0008\",\n      \"start_index\": 28,\n      \"end_index\": 31,\n      \"summary\": \"In 2023, the Federal Reserve collaborated ...\"\n    }\n  ]\n}\n...\n```\n\nYou can generate the PageIndex tree structure with this open-source repo; or use our [API](https:\u002F\u002Fpageindex.ai\u002Fdeveloper) for higher-quality results powered by our enhanced OCR and tree building pipeline.\n\n---\n\n# ⚙️ Package Usage\n\n> **Note:** This package uses standard PDF parsing. For use cases with complex PDFs, our [Cloud Service](https:\u002F\u002Fpageindex.ai\u002Fdeveloper) (via MCP and API) offers enhanced OCR, tree building, and retrieval.\n\nYou can follow these steps to generate a PageIndex tree from a PDF document.\n\n### 1. Install dependencies\n\n```bash\npip3 install --upgrade -r requirements.txt\n```\n\n### 2. Set your LLM API key\n\nCreate a `.env` file in the root directory with your LLM API key, with multi-LLM support via [LiteLLM](https:\u002F\u002Fdocs.litellm.ai\u002Fdocs\u002Fproviders):\n\n```bash\nOPENAI_API_KEY=your_openai_key_here\n```\n\n### 3. Generate PageIndex structure for your PDF\n\n```bash\npython3 run_pageindex.py --pdf_path \u002Fpath\u002Fto\u002Fyour\u002Fdocument.pdf\n```\n\n\u003Cdetails>\n\u003Csummary>Optional parameters\u003C\u002Fsummary>\n\u003Cbr>\nYou can customize the processing with additional optional arguments:\n\n```\n--model                 LLM model to use (default: gpt-4o-2024-11-20)\n--toc-check-pages       Pages to check for table of contents (default: 20)\n--max-pages-per-node    Max pages per node (default: 10)\n--max-tokens-per-node   Max tokens per node (default: 20000)\n--if-add-node-id        Add node ID (yes\u002Fno, default: yes)\n--if-add-node-summary   Add node summary (yes\u002Fno, default: yes)\n--if-add-doc-description Add doc description (yes\u002Fno, default: yes)\n```\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>Markdown support\u003C\u002Fsummary>\n\u003Cbr>\nWe also provide markdown support for PageIndex. You can use the `--md_path` flag to generate a tree structure for a markdown file.\n\n```bash\npython3 run_pageindex.py --md_path \u002Fpath\u002Fto\u002Fyour\u002Fdocument.md\n```\n\n> 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:\u002F\u002Fpageindex.ai\u002Fblog\u002Focr), which is designed to preserve the original hierarchy, to convert the PDF to a markdown file and then use this mode.\n\u003C\u002Fdetails>\n\n## Agentic Vectorless RAG: An Example\n\nFor a simple, end-to-end _**agentic vectorless RAG**_ example using PageIndex with OpenAI Agents SDK, see [`examples\u002Fagentic_vectorless_rag_demo.py`](examples\u002Fagentic_vectorless_rag_demo.py).\n\n```bash\n# Install optional dependency\npip3 install openai-agents\n\n# Run the demo\npython3 examples\u002Fagentic_vectorless_rag_demo.py\n```\n\n\u003C!--\n# ☁️ Improved Tree Generation with PageIndex OCR\n\nThis repo is designed for generating PageIndex tree structure for simple PDFs, but many real-world use cases involve complex PDFs that are hard to parse by classic Python tools. However, extracting high-quality text from PDF documents remains a non-trivial challenge. Most OCR tools only extract page-level content, losing the broader document context and hierarchy.\n\nTo address this, we introduced PageIndex OCR — the first long-context OCR model designed to preserve the global structure of documents. PageIndex OCR significantly outperforms other leading OCR tools, such as those from Mistral and Contextual AI, in recognizing true hierarchy and semantic relationships across document pages.\n\n- Experience next-level OCR quality with PageIndex OCR at our [Dashboard](https:\u002F\u002Fdash.pageindex.ai\u002F).\n- Integrate PageIndex OCR seamlessly into your stack via our [API](https:\u002F\u002Fdocs.pageindex.ai\u002Fquickstart).\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Feb35d8ae-865c-4e60-a33b-ebbd00c41732\" width=\"80%\">\n\u003C\u002Fp>\n-->\n\n---\n\n# 📈 Case Study: PageIndex Leads Finance QA Benchmark\n\n[Mafin 2.5](https:\u002F\u002Fvectify.ai\u002Fmafin) is a reasoning-based RAG system for financial document analysis, powered by **PageIndex**. It achieved a state-of-the-art [**98.7% accuracy**](https:\u002F\u002Fvectify.ai\u002Fblog\u002FMafin2.5) on the [FinanceBench](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.11944) benchmark, significantly outperforming traditional vector-based RAG systems.\n\nPageIndex'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.\n\nExplore the full [benchmark results](https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FMafin2.5-FinanceBench) and our [blog post](https:\u002F\u002Fvectify.ai\u002Fblog\u002FMafin2.5) for detailed comparisons and performance metrics.\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FVectifyAI\u002FMafin2.5-FinanceBench\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F571aa074-d803-43c7-80c4-a04254b782a3\" width=\"70%\">\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n---\n\n# 🧭 Resources\n\n* 📝 [Blog](https:\u002F\u002Fpageindex.ai\u002Fblog): technical articles, research insights, and product updates.\n* 🔧 [Developer](https:\u002F\u002Fpageindex.ai\u002Fdeveloper): MCP setup, API docs, and integration guides.\n* 🧪 [Cookbooks](https:\u002F\u002Fdocs.pageindex.ai\u002Fcookbook): hands-on, runnable examples and advanced use cases.\n* 📖 [Tutorials](https:\u002F\u002Fdocs.pageindex.ai\u002Ftutorials): practical guides and strategies, including *Document Search* and *Tree Search*.\n\n---\n\n# ⭐ Support Us\n\nLeave us a star 🌟 if you like our project. Thank you!  \n\n\u003Cp>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Feae4ff38-48ae-4a7c-b19f-eab81201d794\" width=\"80%\">\n\u003C\u002Fp>\n\nPlease cite this work as:\n```\nMingtian Zhang, Yu Tang and PageIndex Team,\n\"PageIndex: Next-Generation Vectorless, Reasoning-based RAG\",\nPageIndex Blog, Sep 2025.\n```\n\n\u003Cdetails>\n\u003Csummary>Or use the BibTeX citation.\u003C\u002Fsummary>\n\n```bibtex\n@article{zhang2025pageindex,\n  author = {Mingtian Zhang and Yu Tang and PageIndex Team},\n  title = {PageIndex: Next-Generation Vectorless, Reasoning-based RAG},\n  journal = {PageIndex Blog},\n  year = {2025},\n  month = {September},\n  note = {https:\u002F\u002Fpageindex.ai\u002Fblog\u002Fpageindex-intro},\n}\n```\n\u003C\u002Fdetails>\n\n\n### Connect with Us\n\n\u003Cdiv align=\"center\">\n\n[![Twitter](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTwitter-000000?style=for-the-badge&logo=x&logoColor=white)](https:\u002F\u002Fx.com\u002FPageIndexAI) \n[![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fvectify-ai\u002F) \n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscord-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https:\u002F\u002Fdiscord.com\u002Finvite\u002FVuXuf29EUj) \n[![Contact Us](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FContact_Us-3B82F6?style=for-the-badge&logo=envelope&logoColor=white)](https:\u002F\u002Fii2abc2jejf.typeform.com\u002Fto\u002FtK3AXl8T)\n\n\u003C\u002Fdiv>\n\n---\n\n© 2026 [Vectify AI](https:\u002F\u002Fvectify.ai)\n","PageIndex 是一个基于推理的无向量RAG（检索增强生成）文档索引工具。其核心功能包括无需使用向量数据库、无需对文档进行切分，以及提供类似人类的检索能力。通过构建上下文树形索引，PageIndex 使大型语言模型能够实现基于推理的人类式信息检索，特别适用于处理长篇专业文档或需要在大规模文档库中进行高效精准搜索的场景。该项目采用 Python 开发，并以 MIT 许可证发布，表明了其开源和易于集成的特点。",2,"2026-06-11 02:48:42","top_language"]