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](https://pageindex.ai)** 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, which simulates how **human experts** navigate and extract knowledge from long documents.
**[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.
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.
You can also upload PDFs from your browser and explore results visually with our [Dashboard](https://pageindex.vectify.ai/overview) — no coding needed.
This repo is designed for generating PageIndex tree structure with text input, but many real-world use cases involve PDFs that require OCR to convert them into Markdown. 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.
To address this, we introduced PageIndex OCR — the first OCR system 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.
- Experience next-level OCR quality with PageIndex OCR at our [Dashboard](https://dash.pageindex.ai).
- Integrate seamlessly PageIndex OCR into your stack via our [API](https://docs.pageindex.ai/quickstart).
[Mafin 2.5](https://vectify.ai/mafin) 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)).
