StudyRAG 🚀

StudyRAG is a premium, AI-powered study assistant designed to help students master their subjects through Retrieval-Augmented Generation (RAG). It transforms your static notes into an interactive knowledge base with automated flashcards, quizzes, and visual analytics.

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✨ Key Features

🧠 Subject-Aware AI Chat (RAG)

• Ask questions directly to your uploaded notes.
• "Exam Mode" for strict, syllabus-focused tutoring.
• Asymmetric, modern chat UI for intuitive interaction.

🃏 AI Flashcard Generation

• Automatically generate flashcards from PDFs and images.
• Spaced Repetition System (SRS) for long-term retention.
• Tinder-style swipe interface for active recall.

📝 Interactive AI Quizzes

• Dynamic MCQ generation based on note content.
• Real-time scoring and performance summary.
• Progressive difficulty scaling.

📊 Study Analytics

• Weekly activity tracking with interactive bar charts.
• Retention trend lines to visualize your progress.
• Mastery stats for every subject.

📅 Intelligent Exam Planner

• Automated notification system for upcoming exams.
• Color-coded urgency badges (Red/Orange/Green).

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🎨 UI/UX Design

Built with a Premium Glassmorphism aesthetic:

• Sleek Dark Mode: Deep slate and neon violet palette.
• Floating Navigation: Custom frosted-glass bottom navbar.
• Smooth Animations: Staggered list entries and micro-interactions powered by flutter_animate.

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🛠 Technology Stack

Frontend (Flutter)

• State Management: flutter_riverpod (MVVM Architecture)
• Navigation: Custom Floating Glass Navbar
• Database: Hive (High-speed local persistence)
• Charts: fl_chart
• Typography: Google Fonts (Inter)

Backend (Python/FastAPI)

• Core: FastAPI for high-performance async requests.
• LLM: OpenAI/LangChain for RAG and content generation.
• OCR: Automated text extraction from images and PDFs.

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🚀 Getting Started

Prerequisites

• Flutter SDK
• Python 3.10+

Installation

1. Clone the repository:

   git clone https://github.com/dj2313/StudyRAG.git
   cd StudyRAG

2. Setup Backend:

   cd studyrag/backend
   python -m venv venv
   source venv/bin/activate # or venv\Scripts\activate on Windows
   pip install -r requirements.txt
   # Configure your .env file with API keys
   uvicorn app.main:app --reload

3. Setup Frontend:

   cd ../../persona
   flutter pub get
   flutter run

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🏗 MVVM Clean Architecture

The project follows a strict separation of concerns:

• Models: Data entities and schemas.
• Views: UI components and screens.
• ViewModels: Riverpod providers managing state and business logic.
• Services: API and storage handlers.

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

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Made with ❤️ by dj2313