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.
🧠 **Reasoning-based RAG** offers a better alternative: enabling LLMs to *think* and *reason* their way to the most relevant document sections. Inspired by AlphaGo, we use *tree search* to perform structured document retrieval.
**[PageIndex](https://vectify.ai/pageindex)** is a *document indexing system* that builds *search tree structures* from long documents, making them ready for reasoning-based RAG. It has been used to develop a RAG system that achieved 98.7% accuracy on [FinanceBench](https://vectify.ai/blog/Mafin2.5), demonstrating state-of-the-art performance in document analysis.
Self-host it with this open-source repo, or try our ☁️ [Cloud service](https://pageindex.vectify.ai/) — no setup required, with advanced features like OCR for complex and scanned PDFs.
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).
Here is an example output. See more [example documents](https://github.com/VectifyAI/PageIndex/tree/main/docs) and [generated trees](https://github.com/VectifyAI/PageIndex/tree/main/results).
Don't want to host it yourself? Try our [hosted API](https://pageindex.vectify.ai/) for PageIndex. The hosted service leverages our custom OCR model for more accurate PDF recognition, delivering better tree structures for complex documents. Ideal for rapid prototyping, production environments, and documents requiring advanced OCR.
[Mafin 2.5](https://vectify.ai/) is a state-of-the-art reasoning-based RAG model designed specifically for financial document analysis. Powered by **PageIndex**, it achieved a market-leading [**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 enabled precise navigation and extraction of relevant content from complex financial reports, such as SEC filings and earnings disclosures.
👉 See 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.
Use PageIndex to build **reasoning-based retrieval systems** without relying on semantic similarity. Great for domain-specific tasks where nuance matters ([more examples](https://pageindex.vectify.ai/examples/rag)).
This project is in its early beta development, and all progress will remain open and transparent. We welcome you to raise issues, reach out with questions, or contribute directly to the project.
Due to the diverse structures of PDF documents, you may encounter instability during usage. For a more accurate and stable version with a leading OCR integration, please try our [hosted API for PageIndex](https://pageindex.vectify.ai/). Leave your email in [this form](https://ii2abc2jejf.typeform.com/to/meB40zV0) to receive 1,000 pages for free.
