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How to use PageIndex: what you can do with it

PageIndex is a vectorless, reasoning-based RAG system that builds a tree index of long documents and lets an LLM search it like a human reader, with local and cloud SDK modes.

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What do you want to do?

3 use cases

  • Ask questions of a long PDF without a vector database

    Submit a document, build a tree index, and chat with it using reasoning-based retrieval.

    View prompt
    Using VectifyAI/PageIndex in local mode, submit <report.pdf> and ask <question>. Return the answer with the page or section it came from and verify it in the PDF.

  • Store document indexes in the cloud

    Use cloud mode so indexes are built and stored by PageIndex while you chat with your own model.

    View prompt
    Submit <document> to PageIndex in cloud mode, then ask <question> from a second script using the same doc_id. Confirm both return consistent answers.

  • Compare reasoning-based retrieval with your vector RAG

    Run the same questions through PageIndex and your existing pipeline to compare answers.

    View prompt
    Answer <10 questions> about <document> with PageIndex and with my vector RAG, then report which got more correct with verifiable citations.

Quick start with PageIndex

  1. Check you have what it needs: Python; An OpenAI API key (local mode) or a PageIndex API key (cloud mode).
  2. Open VectifyAI/PageIndex on GitHub and read the README and license.
  3. Install with pip install -U pageindex
  4. Set OPENAI_API_KEY
  5. Create a PageIndexClient and submit_document("report.pdf")
  6. Call client.chat with your question and the doc_id

These steps come from our top use case, Ask questions of a long PDF without a vector database, which also has a copy-paste agent prompt.

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PageIndex alternatives · Best AI repos for Research

About this repo

  • Who it is forDevelopers building question answering over long documents such as financial reports, where chunked vector search loses context.
  • Why it is hot39k stars
  • LanguagePython
  • Sourcegithub.com/VectifyAI/PageIndex · Back to Hot repos

Requirements

  • Python
  • An OpenAI API key (local mode) or a PageIndex API key (cloud mode)

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Questions about PageIndex

What is PageIndex?

PageIndex is a vectorless, reasoning-based RAG system that builds a tree index of long documents and lets an LLM search it like a human reader, with local and cloud SDK modes. It is an open-source project on GitHub: https://github.com/VectifyAI/PageIndex.

Who is PageIndex for?

Developers building question answering over long documents such as financial reports, where chunked vector search loses context.

What can I do with PageIndex?

3 use cases so far, for example: Ask questions of a long PDF without a vector database; Store document indexes in the cloud; Compare reasoning-based retrieval with your vector RAG. Each has steps and a copy-paste agent prompt.

What do I need to run PageIndex?

Python; An OpenAI API key (local mode) or a PageIndex API key (cloud mode).

What are alternatives to PageIndex?

Other hot AI repos in Research: Scientific Agent Skills, Trustworthy Agent Simulation, Kaas, By2 KB, Lecture to Notes.

Is PageIndex safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.