AI Interviewer / HR Assistant Platform

An intelligent, automated interview system powered by VideoSDK agents and advanced AI.

🚀 Features

• AI-Powered Video Interviews: Real-time interviews with AI agents ( voice)
• Intelligent Question Generation: Based on resumes and job descriptions
• Automated Scoring & Feedback: Comprehensive evaluation with detailed insights
• Candidate Pipeline Management: For HR teams to manage screening workflows
• Resume Parsing: AI-powered extraction of structured data from PDFs

🛠 Tech Stack

• Backend: FastAPI (Python 3.10+)
• Frontend: React 18 + Vite
• Databases: MongoDB (primary), Redis (cache)
• Meeting/AI: VideoSDK Agents, LLAMA , Deepgram (STT), Cogli (TTS)
• Infrastructure: Docker, Docker Compose

📋 Prerequisites

• Docker & Docker Compose (v2.0+)
• Node.js 18+ (for local frontend development)
• Python 3.10+ (for local backend development)

🏃 Quick Start

1. Clone the repository

git clone <repo-url>
cd ai-interviewer-platform

2. Set up environment variables

# Copy example env files
cp .env.example .env
cp backend/.env.example backend/.env
cp frontend/.env.example frontend/.env

# Edit .env files with your credentials

3. Start all services with Docker Compose

make up
# OR
docker-compose up -d

4. Verify services are running

make status
# OR
docker-compose ps

5. Access the application

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• MongoDB: localhost:27017
• Redis: localhost:6379

📚 Project Structure

ai-interviewer-platform/
├── backend/          # FastAPI application
│   ├── app/
│   │   ├── api/      # API endpoints
│   │   ├── core/     # Core config & DB
│   │   ├── models/   # Database models
│   │   ├── schemas/  # Pydantic schemas
│   │   ├── services/ # Business logic
│   │   └── utils/    # Utilities
│   └── tests/        # Backend tests
├── frontend/         # React + Vite application
│   └── src/
│       ├── components/
│       ├── pages/
│       ├── services/
│       └── hooks/
└── docker-compose.yml

🔧 Development Workflow

Backend Development

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Frontend Development

cd frontend
npm install
npm run dev

🌐 API Documentation

Once the backend is running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

🔐 Environment Variables

See .env.example files for required configuration:

• MongoDB connection string
• Redis URL
• VideoSDK API keys
• AI service credentials (Claude, Deepgram, ElevenLabs)
• JWT secret key

🤝 Contributing

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