PageIndex addresses the limitations of traditional vector-based RAG for long, professional documents by enabling reasoning-based retrieval. It transforms lengthy documents into hierarchical, semantic tree structures, allowing LLMs to navigate and extract information more precisely than similarity-based methods. This is ideal for users working with complex documents like financial reports, legal texts, or technical manuals where accuracy and relevance are paramount.

How It Works

PageIndex creates a semantic tree structure from documents, akin to an LLM-optimized table of contents. It avoids arbitrary chunking by segmenting nodes based on the document's natural structure, with each node containing a summary and precise page references. This hierarchical approach, inspired by tree search algorithms, allows LLMs to traverse documents logically, facilitating multi-step reasoning and pinpoint retrieval, which is advantageous for domain-specific tasks requiring nuanced understanding.

Quick Start & Requirements

• Install dependencies: pip3 install -r requirements.txt
• Set OpenAI API key: Create a .env file with CHATGPT_API_KEY=your_openai_key_here.
• Run PageIndex: python3 run_pageindex.py --pdf_path /path/to/your/document.pdf
• Prerequisites: Python 3, OpenAI API key.
• Documentation: https://github.com/VectifyAI/PageIndex

Highlighted Details

• Achieved 98.7% accuracy on the FinanceBench benchmark when integrated with the Mafin 2.5 RAG model.
• Generates hierarchical tree structures with precise page referencing, enabling logical document traversal.
• Supports massive documents (hundreds or thousands of pages) without arbitrary chunking.
• Offers a cloud-hosted API with advanced OCR for complex PDFs.

Maintenance & Community

• Early beta development; welcomes contributions and issue reporting.
• Contact: Discord server and email contact available.

Licensing & Compatibility

• License: Not explicitly stated in the README.
• Compatibility: Designed for use with LLMs; commercial use implications are unclear due to the unstated license.

Limitations & Caveats

The project is in early beta, and users may encounter instability due to the diverse structures of PDF documents. For a more stable and accurate experience, especially with complex PDFs, the README recommends their hosted API. The license is not specified, which may impact commercial adoption.