- 🔥 [**Agentic Vectorless RAG**](https://github.com/VectifyAI/PageIndex/blob/main/examples/agentic_vectorless_rag_demo.py) — 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) — 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) — Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, human-like retrieval* over long documents.
- [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.
- [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. -->
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.
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:
- **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”).
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.
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).
- Cloud Service — 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_ — 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.
- 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.
- Check out [Vision-based Vectorless RAG](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, vision-based & reasoning-native RAG pipeline that works directly over page images.
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).
> 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.
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).
This repo is designed for generating PageIndex tree structure for simple PDFs, but many real-world use cases involve complex PDFs that are hard to parse by classic Python tools. 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 long-context OCR model 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/).
[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.
