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title: "VectifyAI/PageIndex: 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG"
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source: "https://github.com/VectifyAI/PageIndex"
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author:
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- "[[rejojer]]"
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published:
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created: 2026-01-20
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description: "📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG - VectifyAI/PageIndex"
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tags:
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- "clippings"
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- "webclipper"
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---
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> [!info] Source
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> URL: https://github.com/VectifyAI/PageIndex
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> Title: VectifyAI/PageIndex: 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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> Clipped:
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**[PageIndex](https://github.com/VectifyAI/PageIndex)** Public
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📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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[pageindex.ai](https://pageindex.ai/ "https://pageindex.ai")
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[MIT license](https://github.com/VectifyAI/PageIndex/blob/main/LICENSE)
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[Open in github.dev](https://github.dev/) [Open in a new github.dev tab](https://github.dev/) [Open in codespace](https://github.com/codespaces/new/VectifyAI/PageIndex?resume=1)
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[](https://vectify.ai/pageindex)
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[](https://trendshift.io/repositories/14736)
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**Reasoning-based RAG ◦ No Vector DB ◦ No Chunking ◦ Human-like Retrieval**
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**🔥 Releases:**
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- [**PageIndex Chat**](https://chat.pageindex.ai/): The first human-like document-analysis agent [platform](https://chat.pageindex.ai/) built for professional long documents. Can also be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart) (beta).
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**📝 Articles:**
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- [**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.
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**🧪 Cookbooks:**
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- [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.
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- [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.
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---
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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.
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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:
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1. Generate a “Table-of-Contents” **tree structure index** of documents
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2. Perform reasoning-based retrieval through **tree search**
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[](https://pageindex.ai/blog/pageindex-intro "The PageIndex Framework")
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### 🎯 Features
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Compared to traditional vector-based RAG, **PageIndex** features:
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- **No Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector similarity search.
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- **No Chunking**: Documents are organized into natural sections, not artificial chunks.
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- **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents.
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- **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”).
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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, demonstrating superior performance over vector-based RAG solutions in professional document analysis (see our [blog post](https://vectify.ai/blog/Mafin2.5) for details).
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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.
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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).
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- Self-host — run locally with this open-source repo.
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- 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).
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- *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.
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- 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.
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- Experiment with [*Vision-based Vectorless RAG*](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, reasoning-native RAG pipeline that works directly over page images.
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---
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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.
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Below is an example PageIndex tree structure. Also see more example [documents](https://github.com/VectifyAI/PageIndex/tree/main/tests/pdfs) and generated [tree structures](https://github.com/VectifyAI/PageIndex/tree/main/tests/results).
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You can generate the PageIndex tree structure with this open-source repo, or use our [API](https://docs.pageindex.ai/quickstart)
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---
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You can follow these steps to generate a PageIndex tree from a PDF document.
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```
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pip3 install --upgrade -r requirements.txt
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```
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Create a `.env` file in the root directory and add your API key:
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```
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CHATGPT_API_KEY=your_openai_key_here
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```
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```
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python3 run_pageindex.py --pdf_path /path/to/your/document.pdf
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```
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**Optional parameters**
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You can customize the processing with additional optional arguments:
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```
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--model OpenAI model to use (default: gpt-4o-2024-11-20)
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--toc-check-pages Pages to check for table of contents (default: 20)
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--max-pages-per-node Max pages per node (default: 10)
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--max-tokens-per-node Max tokens per node (default: 20000)
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--if-add-node-id Add node ID (yes/no, default: yes)
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--if-add-node-summary Add node summary (yes/no, default: yes)
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--if-add-doc-description Add doc description (yes/no, default: yes)
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```
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**Markdown support**
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We also provide markdown support for PageIndex. You can use the \`-md\_path\` flag to generate a tree structure for a markdown file.
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```
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python3 run_pageindex.py --md_path /path/to/your/document.md
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```
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> 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.
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---
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[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.
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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.
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Explore the full [benchmark results](https://github.com/VectifyAI/Mafin2.5-FinanceBench) and our [blog post](https://vectify.ai/blog/Mafin2.5) for detailed comparisons and performance metrics.
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[](https://github.com/VectifyAI/Mafin2.5-FinanceBench)
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---
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## 🧭 Resources
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- 🧪 [Cookbooks](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): hands-on, runnable examples and advanced use cases.
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- 📖 [Tutorials](https://docs.pageindex.ai/doc-search): practical guides and strategies, including *Document Search* and *Tree Search*.
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- 📝 [Blog](https://pageindex.ai/blog): technical articles, research insights, and product updates.
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- 🔌 [MCP setup](https://pageindex.ai/mcp#quick-setup) & [API docs](https://docs.pageindex.ai/quickstart): integration details and configuration options.
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---
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Leave us a star 🌟 if you like our project. Thank you!
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[](https://private-user-images.githubusercontent.com/13518252/481667856-eae4ff38-48ae-4a7c-b19f-eab81201d794.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.pSfTU0Lp63eIF6U3IbkEbdIwr2_U4ZL708xLqslt8uQ)
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---
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© 2025 [Vectify AI](https://vectify.ai/)
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## Releases
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No releases published
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## Packages
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No packages published
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## Languages
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- [Python 100.0%](https://github.com/VectifyAI/PageIndex/search?l=python)
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