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AlgoAce

AlgoAce is an AI-powered DSA (Data Structures and Algorithms) learning platform that provides personalized learning experiences, problem recommendations, detailed explanations and personalized feedback to help users with interview preparation.

Project Structure

AlgoAce/
├── api/                # Backend API (FastAPI)
├── client/            # Frontend (Next.js + shadcn-ui)
└── README.md          # This file

Getting Started

Prerequisites

  • Python version <= 3.11.0
  • Node.js 18 or higher
  • Gemini API key

Backend Setup

  1. Navigate to the backend directory:
cd api
  1. Follow the setup instructions in api/README.md

Frontend Setup

  1. Navigate to the client directory:
cd client
  1. Install dependencies:
npm install
  1. Set up shadcn-ui:
npx shadcn-ui@latest init

When prompted, use these settings:

  • Would you like to use TypeScript (recommended)? Yes
  • Which style would you like to use? Default
  • Which color would you like to use as base color? Slate
  • Where is your global CSS file? src/app/globals.css
  • Do you want to use CSS variables for colors? Yes
  • Where is your tailwind.config.js located? tailwind.config.js
  • Configure the import alias for components? @/components
  • Configure the import alias for utils? @/lib/utils
  1. create a .env.local file with the following values:
NEXT_PUBLIC_SUPABASE_URL=<supabase_url>
NEXT_PUBLIC_SUPABASE_ANON_KEY=<supabase_key>
  1. Run the development server:
npm run dev

The frontend will be available at http://localhost:3000

Features

  • Personalized DSA learning paths
  • AI-powered problem recommendations
  • Detailed problem explanations
  • Progress tracking and analysis
  • Interactive learning experience

Tech Stack

Backend

  • FastAPI
  • CrewAI
  • Supabase
  • Gemini API

Frontend

  • Next.js 14
  • TypeScript
  • Tailwind CSS
  • shadcn-ui
  • React Server Components

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

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

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A Multi-Agent RAG approach to E-Learning

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