This little repo tackles a problem that's surprisingly annoying in day-to-day life: making sense of a stack of PDFs without shipping them off to a paid cloud service. It bundles extraction, classification, summarization, and question-answering for PDF documents, and it handles scanned pages via OCR — all with local, open-source AI models.

What makes this stand out from the sea of "chat with your PDF" projects is the deliberate stance on privacy and cost. The README pitch is refreshingly clear:

• No API keys — nothing to sign up for, nothing to leak.
• No paid services — runs entirely on models you host.
• OCR for scanned pages — many similar tools fall over the moment a document isn't already text-selectable.
• Multi-mode — not just Q&A, but classification and summarization too, which suggests a small pipeline rather than a single-shot LLM prompt.

Who benefits? A few obvious audiences:

• Solo professionals — lawyers, accountants, researchers — who work with confidential documents that can't leave the machine for compliance reasons.
• Homelabbers and self-hosters who already have Ollama or a similar local runtime and want a practical use case beyond chat.
• Students and educators who need to digest scanned lecture notes or historical PDFs on a budget.
• Developers looking for a readable reference implementation of an end-to-end RAG + OCR pipeline they can fork and adapt.

The repo is brand new and unstarred, so it's likely a personal or academic project rather than production-ready software. That's actually part of the appeal — small enough to read cover-to-cover, and a good jumping-off point for anyone wanting to understand how the pieces (PDF parser, OCR engine, embedding store, local LLM) fit together without wading through an over-engineered framework.