PageIndex

I have a great appreciation for PageIndex paper about vectorless RAG. https://github.com/VectifyAI/PageIndex

Similar effort with PageIndex [1], which basically creates a table of contents like tree. Then an LLM traverses the tree to figure out which chunks are relevant for the context in the prompt. 1: https://github.com/VectifyAI/PageIndex

In modern document question answering (QA) systems, Optical Character Recognition (OCR) serves an important role by converting PDF pages into text that can be processed by Large Language Models (LLMs). The resulting text can provide contextual input that enables LLMs to perform question answering over document content. Traditional OCR systems typically use a two-stage process that first detects the layout of a PDF — dividing it into text, tables, and images — and then recognizes and converts these elements into plain text. With the rise of vision-language models (VLMs) (such as Qwen-VL and GPT-4.1), new end-to-end OCR models like DeepSeek-OCR have emerged. These models jointly understand visual and textual information, enabling direct interpretation of PDFs without an explicit layout detection step. However, this paradigm shift raises an

We discuss the limitations of the classic OCR pipeline and provide a pure vision-based RAG system for document analysis (https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vi...) Any feedback is welcome!

Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning about where information lives. That clarity can help teams trust outputs and debug workflows more effectively. The broader implication is that retrieval doesn't need to scale endlessly in vectors to be powerful. By leaning on document structure and reason

github repo: github.com/VectifyAI/PageIndex

Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans actually read: navigating through sections and context rather than relying on embedding similarity. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning about where information lives. That clarity can help teams trust outputs and debug workflows more effectively. The broader implication is that retrieval doesn't need to scale endlessly in vectors to be powerful. By leaning on document structure a

Hey, we use PageIndex to generate "Table of Contents" to do retreival without Vector DBs. Github version can be found: https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pa... Any feedbacks are welcome!

Thanks, that’s a good point. Yeah, it makes retrieval look like function calling or tool selection, which I guess makes the idea more generic and better suited to current AI systems like MCP. For the ToC generation quality, you can try our API: https://docs.pageindex.ai/ or the open-sourced version: https://github.com/VectifyAI/PageIndex. I didn’t realize other people were working on similar ideas or had similar packages. It would be great if you could share the links to the AI agent you mentioned. Thanks!

Github repo: https://github.com/VectifyAI/PageIndex Would love feedback and suggestions.

Github repo: https://github.com/VectifyAI/PageIndex Would love feedback and suggestions!