ClassroomLM

An open-source platform that enables students and teachers to interact with classroom materials through Retrieval-Augmented Generation (RAG). Upload documents, chat with course content, and streamline academic Q&A using powerful language models—all in one collaborative interface.

Tech Stack

• Framework: Next.js 15 with App Router
• Language: TypeScript
• Styling: Tailwind CSS with shadcn/ui components
• Testing: Vitest with React Testing Library
• Database: Supabase
• Deployment: Docker and Kubernetes support

Features

• Classroom-Style Structure
  Teachers can create classrooms, upload documents (PDFs, slides, handwritten notes), and invite students.

• Classroom-Specific AI Assistants
  Each classroom has its own RAG-based LLM assistant trained on uploaded materials.

• Collaborative AI Chats
  Group chat support where the AI can participate with full chat context.

Prerequisites

• Node.js (LTS version)
• pnpm (recommended package manager)
• Docker (for containerized development)
• Kubernetes (for deployment)

Getting Started

1. Clone the repository:

   git clone https://github.com/TechAtNYU/dev-team-spring-25.git
   cd dev-team-spring-25

2. Install dependencies:

   pnpm install

3. Set up environment variables: Create a .env.local file in the root directory with the necessary environment variables.

   cp .env.example .env

   and update the appropriate variables

4. Start the development server:

   pnpm dev

   The application will be available at http://localhost:8080

Available Scripts

• pnpm dev - Start development server with Turbopack
• pnpm build - Build the application for production
• pnpm start - Start the production server
• pnpm test - Run tests in watch mode
• pnpm test:run - Run tests once
• pnpm test:ui - Run tests with UI
• pnpm coverage - Generate test coverage report
• pnpm lint - Run ESLint
• pnpm lint:fix - Fix ESLint issues
• pnpm format - Format code with Prettier
• pnpm format:check - Check code formatting

Development

• The project uses the Next.js App Router for routing
• Components are styled using Tailwind CSS with shadcn/ui
• TypeScript ensures type safety throughout the application
• Git hooks (via Husky) ensure code quality before commits
• Prettier and ESLint maintain consistent code style

Testing

The project uses Vitest for testing with React Testing Library. Tests can be run in watch mode or as a single run. Coverage reports can be generated to ensure comprehensive testing.

Deployment

The application can be deployed using Docker and Kubernetes. The project includes:

• Dockerfile for containerization
• Kubernetes manifests in the k8s directory
• Tekton pipelines for CI/CD

Contributing

1. Create a new branch for your feature
2. Make your changes
3. Run tests and ensure they pass
4. Submit a pull request