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Noto.ai is a local AI-powered PDF assistant that lets you chat with documents, ask questions, and get instant summaries—all offline using LLaMA 3 via Ollama. Built with Python and Kivy, it offers a clean desktop interface for students, researchers, and professionals who want to extract insights from complex PDFs efficiently and privately.

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sagnikdatta2k6/noto-ai-app

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Noto.ai 📝🧠

Your intelligent, AI-powered PDF research companion — powered locally by LLaMA 3 via Ollama.


🌟 Overview

Noto.ai transforms static PDFs into interactive, AI-narrated research companions. Ask questions, summarize content, and interact naturally — all while keeping your AI engine local and private using Meta’s LLaMA 3 via Ollama.


🚀 Features

Feature Description
📄 PDF Reader Upload and parse any PDF
🤖 Chat with PDF Interact with your documents using LLaMA 3
🧠 Summarization AI-generated summaries of sections or pages
📍 Search & Navigate Quickly find terms and content
🌙 Dark Mode Kivy-powered user-friendly interface
🖥️ Works Offline No cloud required – local model inference

🛠️ Tech Stack

  • UI: Python + Kivy + KivyMD
  • AI Backend: LLaMA 3 via Ollama
  • PDF Parsing: PyMuPDF (fitz)
  • Text-to-AI: Local API calls to http://localhost:11434/api/generate

📦 Installation

🐍 Python Environment

git clone https://github.com/sagnikdatta2k6/noto-ai-app.git
cd noto-ai-app
pip install -r requirements.txt

🤖 Ollama Setup (LLaMA 3)

  1. Install Ollama

  2. Pull LLaMA 3 model:

    ollama pull llama3
  3. Run the model:

    ollama run llama3

▶️ Run the App

python main.py

Workflow

diagram


⚙️ .env Configuration (Optional)

If needed, configure .env:

OLLAMA_API_URL=http://localhost:11434
MODEL_NAME=llama3

🤝 Contributing

  1. Fork the repo
  2. git checkout -b feature/improve-ui
  3. Make your changes
  4. Submit a pull request ✨

📜 License

MIT License. See LICENSE.


🙋‍♂️ Author

Sagnik Datta 🌐 GitHub

About

Noto.ai is a local AI-powered PDF assistant that lets you chat with documents, ask questions, and get instant summaries—all offline using LLaMA 3 via Ollama. Built with Python and Kivy, it offers a clean desktop interface for students, researchers, and professionals who want to extract insights from complex PDFs efficiently and privately.

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