Acadira AI

Your AI-powered study companion — Transform documents into interactive learning experiences with flashcards, quizzes, and smart summaries.

What is Acadira AI?

Acadira AI is an intelligent learning platform that turns your study materials into an interactive AI-powered experience. Built as a sophomore year project, it combines modern web technologies with cutting-edge AI to help students learn more effectively.

The Problem: Reading PDFs and documents is passive and boring.
The Solution: Upload your notes, textbooks, or papers and instantly get AI-powered chat, flashcards with spaced repetition, auto-generated quizzes, and smart summaries.

Features

Core Learning Tools

AI Chat with RAG — Ask questions about your documents and get contextual answers with source citations
Smart Flashcards — Auto-generate flashcards with spaced repetition algorithm (SM-2) for optimal retention
Quiz Generator — Create multiple-choice, true/false, and short-answer quizzes from any document
Smart Summaries — Get brief overviews, detailed explanations, or bullet-point summaries instantly
Key Takeaways — Automatically extract the most important points from your materials

Document Support

Multi-format Upload — PDF, DOCX, PPTX, TXT, Markdown, and RTF files
Batch Processing — Upload multiple documents at once with progress tracking
Highlights Sync — Highlights are synced in Convex

User Experience

Keyboard-First Workflows — Study flashcards using arrow keys and number shortcuts
Dark/Light Themes — System-aware theme switching with persistent preferences
Smooth Animations — 3D flip effects, scroll reveals, and polished transitions
Responsive Design — Works seamlessly on desktop, tablet, and mobile
Clerk Auth — Secure authentication with Google OAuth and hosted UI

Tech Stack

Frontend

React 19 — Latest React with concurrent features
TypeScript — Type-safe development
Vite — Lightning-fast build tool and HMR
Tailwind CSS — Utility-first styling
React Markdown — Rich markdown rendering with LaTeX math support

Backend

FastAPI — High-performance Python web framework
Google Gemini 2.5 Flash — State-of-the-art language model for AI features
Grok (xAI) — Alternative AI model support
RAG Implementation — Retrieval-Augmented Generation for context-aware responses
Clerk JWT — Backend authentication

AI & Document Processing

PyPDF2 & PyMuPDF — PDF text extraction and annotation handling
python-docx — DOCX file processing
pdfjs-dist — Client-side PDF rendering
Sentence Embeddings — Document chunking and semantic search

Infrastructure

Clerk Authentication — User management
Convex Database — Highlights and metadata
CORS Middleware — Secure cross-origin requests
Rate Limiting — API protection

Getting Started

Prerequisites

Node.js 18+ and npm
Python 3.12+ (for backend)
Google Gemini API Key (Get one free)
Clerk Project (required for auth)
Convex Project (required for database)

Installation

1. Clone the Repository

git clone https://github.com/Ronak-IIITD/acadria_AI.git
cd acadria_AI
npm install

2. Frontend Setup

# Install dependencies
npm install

# Create environment file
cp .env.example .env.local

# Edit .env.local and add your API keys:
# VITE_API_KEY=your_gemini_api_key
# VITE_CLERK_PUBLISHABLE_KEY=your_clerk_key
# VITE_CONVEX_URL=your_convex_url
# ... (see Configuration section below)

3. Backend Setup

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create environment file
cp .env.example .env

# Edit .env and add:
# GEMINI_API_KEY=your_gemini_api_key
# GROK_API_KEY=your_grok_api_key (optional)

4. Run the Application

Terminal 1 - Frontend:

npm run dev

Vite dev server starts at http://localhost:5173

Terminal 2 - Backend:

cd backend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

FastAPI server starts at http://localhost:8000

Configuration

Frontend Environment Variables

Create .env.local in the project root:

# Google Gemini API
VITE_API_KEY=your_gemini_api_key_here

# Firebase Configuration
VITE_FIREBASE_API_KEY=your_firebase_api_key
VITE_FIREBASE_AUTH_DOMAIN=your-project.firebaseapp.com
VITE_FIREBASE_PROJECT_ID=your-project-id
VITE_FIREBASE_STORAGE_BUCKET=your-project.appspot.com
VITE_FIREBASE_MESSAGING_SENDER_ID=123456789
VITE_FIREBASE_APP_ID=1:123456789:web:abcdef

Backend Environment Variables

Create backend/.env:

# AI Models
GEMINI_API_KEY=your_gemini_api_key
GROK_API_KEY=your_grok_api_key  # Optional

# Firebase Admin (optional)
FIREBASE_PROJECT_ID=your-project-id

# CORS (for production)
ALLOWED_ORIGINS=https://your-frontend-domain.com,http://localhost:5173

Project Structure

acadira-ai/
├── src/                          # Frontend source code
│   ├── components/               # React components (36 files)
│   │   ├── Dashboard.tsx         # Main app dashboard
│   │   ├── CalmChatWindow.tsx    # AI chat interface
│   │   ├── FlashcardDeck.tsx     # Flashcard study mode
│   │   ├── FlashcardGenerator.tsx
│   │   ├── QuizGenerator.tsx
│   │   ├── QuizTaker.tsx
│   │   ├── SummaryViewer.tsx
│   │   ├── KeyTakeawaysPanel.tsx
│   │   ├── FileUpload.tsx
│   │   └── ...
│   ├── services/                 # Business logic
│   │   ├── geminiService.ts      # AI integration
│   │   ├── uploadService.ts      # File handling
│   │   └── studyToolsService.ts  # Flashcard/quiz generation
│   ├── contexts/                 # React contexts
│   │   └── ThemeContext.tsx
│   ├── utils/                    # Helper functions
│   └── types/                    # TypeScript definitions
│
├── backend/                      # Python FastAPI backend
│   ├── app/
│   │   ├── main.py               # FastAPI application
│   │   ├── routes/               # API endpoints
│   │   │   ├── chat.py           # Chat with RAG
│   │   │   ├── upload.py         # Document upload
│   │   │   └── models.py         # AI model management
│   │   ├── services/             # Business logic
│   │   │   ├── rag_service.py    # RAG implementation
│   │   │   ├── embedding_service.py
│   │   │   ├── document_service.py
│   │   │   └── groq_service.py
│   │   ├── middleware/           # Custom middleware
│   │   └── models/               # Data schemas
│   └── requirements.txt
│
├── data/                         # Document storage
├── docs/                         # Documentation
├── public/                       # Static assets
└── dist/                         # Production build

Keyboard Shortcuts

Flashcard Study Mode

Key	Action
`←`	Previous card
`→`	Next card (after revealing answer)
`↑` / `Space` / `Enter`	Flip current card
`↓`	Skip to next card
`0` - `5`	Grade your confidence (SM-2 algorithm)

Key Technical Features

1. RAG (Retrieval-Augmented Generation)

The backend implements a custom RAG system that:

Chunks documents into semantic segments
Stores embeddings for fast similarity search
Retrieves relevant context before generating AI responses
Provides source citations for transparency

See backend/app/services/rag_service.py

2. Spaced Repetition Algorithm (SM-2)

Flashcards use the SuperMemo SM-2 algorithm to optimize review intervals:

Tracks difficulty, repetitions, and ease factor for each card
Calculates optimal review dates based on performance
Adapts to individual learning patterns

See src/services/studyToolsService.ts:111

3. Multi-Format Document Processing

Supports 6 different file formats with specialized parsers:

PDF: Text extraction + annotation support
DOCX: Full document structure preservation
PPTX: Slide-by-slide extraction using JSZip
TXT/MD/RTF: Direct text processing

See src/services/uploadService.ts

4. Real-Time Chat with Context

AI chat maintains conversation history and document context:

Streaming responses for better UX
Context-aware follow-up questions
Markdown rendering with LaTeX math support

See src/components/CalmChatWindow.tsx

Building for Production

# Frontend build
npm run build

# Preview the production build
npm run preview

# Backend (use gunicorn for production)
cd backend
gunicorn app.main:app -w 4 -k uvicorn.workers.UvicornWorker

The frontend build outputs to dist/ and can be hosted on:

Vercel (recommended)
Netlify
Firebase Hosting
Any static file host

Testing

Run the test suite (Vitest):

npm test

# With UI
npm run test:ui

# With coverage
npm run test:coverage

Roadmap

Check out our detailed feature roadmap for planned features:

Coming Soon:

Mind map visualization
Audio transcription support
YouTube video processing
AI-generated podcasts from documents
Progress tracking dashboard
Mobile apps

Why I Built This

As a sophomore at IIIT-Delhi, I found myself drowning in PDFs and lecture slides. I wanted a tool that would make studying more active and engaging, not just passive reading. This project combines my interests in AI, full-stack development, and education technology.

What I Learned:

Building production-ready React applications with TypeScript
Implementing RAG systems and working with LLMs
FastAPI and async Python patterns
Firebase authentication and cloud storage
Spaced repetition algorithms
Document parsing and text processing

Contributing

Contributions are welcome! Whether it's:

Bug fixes
New features
Documentation improvements
UI/UX enhancements

Steps to contribute:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Test thoroughly
Commit with clear messages (git commit -m 'Add amazing feature')
Push to your fork (git push origin feature/amazing-feature)
Open a Pull Request

License

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

Acknowledgments

Google Gemini for the incredible AI capabilities
Firebase for hassle-free backend infrastructure
Vercel for inspiration on clean, modern UI design
YouLearn.ai for feature inspiration
All the open-source libraries that made this possible

Contact & Links

GitHub: @Ronak-IIITD
Repository: Acadira AI
Issues: Report a bug

If you find this project useful, please consider giving it a ⭐️!

Made for Learners by a Learner ❤️

Name		Name	Last commit message	Last commit date
Latest commit History 77 Commits
backend		backend
convex		convex
data		data
docs		docs
public		public
src		src
.env.example		.env.example
.gitignore		.gitignore
ARCHITECTURE.md		ARCHITECTURE.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
convex.json		convex.json
index.html		index.html
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
tailwind.config.js		tailwind.config.js
tsconfig.json		tsconfig.json
vite.config.ts		vite.config.ts

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Acadira AI

What is Acadira AI?

Features

Core Learning Tools

Document Support

User Experience

Tech Stack

Frontend

Backend

AI & Document Processing

Infrastructure

Getting Started

Prerequisites

Installation

1. Clone the Repository

2. Frontend Setup

3. Backend Setup

4. Run the Application

Configuration

Frontend Environment Variables

Backend Environment Variables

Project Structure

Keyboard Shortcuts

Flashcard Study Mode

Key Technical Features

1. RAG (Retrieval-Augmented Generation)

2. Spaced Repetition Algorithm (SM-2)

3. Multi-Format Document Processing

4. Real-Time Chat with Context

Building for Production

Testing

Roadmap

Why I Built This

Contributing

License

Acknowledgments

Contact & Links

About

Resources

License

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