# PageIndex
**Repository Path**: bytesifter/PageIndex
## Basic Information
- **Project Name**: PageIndex
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-09
- **Last Updated**: 2026-04-09
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# PageIndex: Vectorless, Reasoning-based RAG
Reasoning-based RAG β¦ No Vector DB β¦ No Chunking β¦ Human-like Retrieval
π’ Updates
- π₯ [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) β A simple *agentic, vectorless RAG* [example](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) with self-hosted PageIndex, using OpenAI Agents SDK.
- [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, human-like retrieval* over long documents.
---
# π 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 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.
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**.
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 βTable-of-Contentsβ **tree structure index** of documents
2. Perform reasoning-based retrieval through **tree search**
### π― Core 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.
- **Better Explainability and Traceability**: Retrieval is based on reasoning β traceable and interpretable, with page and section references. No more opaque, approximate vector search (βvibe retrievalβ).
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.
### π 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.
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).
### π οΈ Deployment Options
- Self-host β run locally with this open-source repo.
- Cloud Service β 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.
### π§ͺ Quick Hands-on
- π₯ [**Agentic Vectorless RAG**](examples/agentic_vectorless_rag_demo.py) (**latest**) β a simple but complete **agentic vectorless RAG** [example](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) 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 β 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) β no OCR; a minimal, vision-based & reasoning-native RAG pipeline that works directly over page images.
---
# π² 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.
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).
```jsonc
...
{
"title": "Financial Stability",
"node_id": "0006",
"start_index": 21,
"end_index": 22,
"summary": "The Federal Reserve ...",
"nodes": [
{
"title": "Monitoring Financial Vulnerabilities",
"node_id": "0007",
"start_index": 22,
"end_index": 28,
"summary": "The Federal Reserve's monitoring ..."
},
{
"title": "Domestic and International Cooperation and Coordination",
"node_id": "0008",
"start_index": 28,
"end_index": 31,
"summary": "In 2023, the Federal Reserve collaborated ..."
}
]
}
...
```
You can generate the PageIndex tree structure with this open-source repo, or use our [API](https://pageindex.ai/developer).
---
# βοΈ Package Usage
You can follow these steps to generate a PageIndex tree from a PDF document.
### 1. Install dependencies
```bash
pip3 install --upgrade -r requirements.txt
```
### 2. Set your LLM API key
Create a `.env` file in the root directory with your LLM API key, with multi-LLM support via [LiteLLM](https://docs.litellm.ai/docs/providers):
```bash
OPENAI_API_KEY=your_openai_key_here
```
### 3. Generate PageIndex structure for your PDF
```bash
python3 run_pageindex.py --pdf_path /path/to/your/document.pdf
```
Optional parameters
You can customize the processing with additional optional arguments:
```
--model LLM model to use (default: gpt-4o-2024-11-20)
--toc-check-pages Pages to check for table of contents (default: 20)
--max-pages-per-node Max pages per node (default: 10)
--max-tokens-per-node Max tokens per node (default: 20000)
--if-add-node-id Add node ID (yes/no, default: yes)
--if-add-node-summary Add node summary (yes/no, default: yes)
--if-add-doc-description Add doc description (yes/no, default: yes)
```
Markdown support
We also provide markdown support for PageIndex. You can use the `--md_path` flag to generate a tree structure for a markdown file.
```bash
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 the original hierarchy, to convert the PDF to a markdown file and then use this mode.
## Agentic Vectorless RAG: An Example
For a simple, end-to-end _**agentic vectorless RAG**_ example using PageIndex with OpenAI Agents SDK, see [`examples/agentic_vectorless_rag_demo.py`](examples/agentic_vectorless_rag_demo.py).
```bash
# Install optional dependency
pip3 install openai-agents
# Run the demo
python3 examples/agentic_vectorless_rag_demo.py
```
---
# π 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.
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.
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.
---
# π§ Resources
* π [Blog](https://pageindex.ai/blog): technical articles, research insights, and product updates.
* π§ [Developer](https://pageindex.ai/developer): MCP setup, API docs, and integration guides.
* π§ͺ [Cookbooks](https://docs.pageindex.ai/cookbook): hands-on, runnable examples and advanced use cases.
* π [Tutorials](https://docs.pageindex.ai/tutorials): practical guides and strategies, including *Document Search* and *Tree Search*.
---
# β Support Us
Leave us a star π if you like our project. Thank you!
Please cite this work as:
```
Mingtian Zhang, Yu Tang and PageIndex Team,
"PageIndex: Next-Generation Vectorless, Reasoning-based RAG",
PageIndex Blog, Sep 2025.
```
Or use the BibTeX citation.
```bibtex
@article{zhang2025pageindex,
author = {Mingtian Zhang and Yu Tang and PageIndex Team},
title = {PageIndex: Next-Generation Vectorless, Reasoning-based RAG},
journal = {PageIndex Blog},
year = {2025},
month = {September},
note = {https://pageindex.ai/blog/pageindex-intro},
}
```
### Connect with Us
[](https://x.com/PageIndexAI)
[](https://www.linkedin.com/company/vectify-ai/)
[](https://discord.com/invite/VuXuf29EUj)
[](https://ii2abc2jejf.typeform.com/to/tK3AXl8T)
---
Β© 2026 [Vectify AI](https://vectify.ai) 
