Enterprise-grade Human Resource Management System powered by cutting-edge AI technologies
- 🌟 Overview
- ✨ Key Features
- 🏗️ Architecture
- 🚀 Quick Start
- 💻 Tech Stack
- 📖 API Documentation
- 🔧 Configuration
- 🚢 Deployment
- 🤝 Contributing
A short product demo video is available to help you quickly understand AuraHR's core features (recruitment, AI screening, performance dashboards, and development plans).
AuraHR Demo Video.mp4:- View Demo Video
- Hosted demo (Google Drive): https://drive.google.com/file/d/1D5WMNyfPaUsWGriTvKjdA-IWPIJPngoh/view?usp=sharing
Comprehensive admin dashboard with system-wide analytics, user management, and KPI tracking
AI-powered recruitment dashboard with candidate screening and matching insights
Manager dashboard showing team performance metrics and department insights
Recruiter job management interface with posting creation and candidate pipeline
Conversational AI screening interface for automated candidate interviews
Admin oversight of recruitment processes with system-wide metrics
Admin view of employee management with detailed profiles and analytics
Advanced team analytics with performance trends and skill gap analysis
AI-powered learning and development planning interface
Employee performance tracking with AI-generated insights and recommendations
Personal employee dashboard with performance metrics and career goals
Employee document management and file handling interface
AuraHR is a comprehensive, production-ready Human Resource Management System that leverages cutting-edge AI technologies to automate recruitment processes, foster personalized employee growth, and provide data-driven insights for modern enterprises.
- 🤖 AI-Powered Recruitment: Automated resume screening with 90%+ accuracy using semantic matching
- 📊 Smart Analytics: Real-time performance insights and predictive analytics
- 🎓 Personalized L&D: AI-generated learning and development plans for each employee
- 💬 Conversational AI: Interactive candidate screening through natural language processing
- 🔍 Vector Search: Advanced candidate matching using ChromaDB embeddings
- 🚀 Production Ready: Enterprise-grade security, scalability, and performance
- Smart Resume Screening: Automatic PDF/DOCX parsing with NLP-based entity extraction
- Semantic Candidate Matching: HuggingFace Sentence Transformers for precise job-candidate matching
- AI Interview Screening: Conversational AI with contextual follow-up questions
- Automated Ranking: ML-powered candidate scoring and recommendation engine
- 360° Performance Tracking: Comprehensive performance metrics and analytics
- Skills Gap Analysis: AI-driven identification of skill gaps and improvement areas
- Career Path Planning: Personalized development roadmaps using Google Gemini AI
- Real-time Insights: Dynamic dashboards for HR managers and employees
- JWT Authentication: Secure token-based authentication system
- Role-Based Access: Granular permissions for Admin, Manager, Recruiter, and Employee roles
- Data Encryption: bcrypt password hashing and secure data transmission
- Audit Trails: Complete activity logging for compliance and security
- Performance Dashboards: Real-time KPIs and metrics visualization
- Predictive Analytics: Turnover prediction and retention insights
- Team Performance: Department-wide and team-level performance tracking
- Business Intelligence: Data-driven decision making tools
┌─────────────────────────────────────────────────────────────────┐
│ AuraHR Architecture │
├─────────────────────────────────────────────────────────────────┤
│ Frontend (React + Vite) │
│ ├── UI Components (Material-UI + Radix UI) │
│ ├── State Management (Zustand) │
│ ├── API Integration (React Query) │
│ └── Routing (React Router) │
├─────────────────────────────────────────────────────────────────┤
│ Backend (FastAPI) │
│ ├── REST API Endpoints │
│ ├── Authentication & Authorization │
│ ├── Business Logic Services │
│ └── AI Integration Layer │
├─────────────────────────────────────────────────────────────────┤
│ AI Services │
│ ├── HuggingFace Transformers (Resume Analysis) │
│ ├── Sentence Transformers (Semantic Matching) │
│ ├── Google Gemini (Conversational AI) │
│ ├── spaCy (Entity Extraction) │
│ └── ChromaDB (Vector Database) │
├─────────────────────────────────────────────────────────────────┤
│ Data Layer │
│ ├── MongoDB (Primary Database) │
│ ├── ChromaDB (Vector Embeddings) │
│ └── File Storage (Resume Upload) │
└─────────────────────────────────────────────────────────────────┘
Resume Upload → Text Extraction → Entity Recognition →
Embedding Generation → Similarity Calculation →
Candidate Ranking → AI Screening → Interview Summary
- Node.js 18+ and npm
- Python 3.10+
- MongoDB 6.0+ (Atlas or local)
- Git
git clone https://github.com/pkparthk/AuraHR---The-Next-Generation-AI-Powered-HRMS.git
cd "AuraHR - The Next-Generation AI-Powered HRMS"cd backend
# Create virtual environment
python -m venv venv
venv\Scripts\activate # Windows
# source venv/bin/activate # Linux/Mac
# Install dependencies
pip install -r requirements.txt
# Setup environment variables
copy .env.example .env
# Edit .env with your configuration
# Start backend server
uvicorn app.main:app --reloadcd frontend
# Install dependencies
npm install
# Start development server
npm run dev- Frontend: http://localhost:3000
- Backend API: http://localhost:8000
- API Documentation: http://localhost:8000/docs
Admin: admin@example.com / admin123
Manager: manager@example.com / manager123
Recruiter: recruiter@example.com / recruiter123
Employee: employee@example.com / employee123
- Framework: React 18 with TypeScript
- Build Tool: Vite (Lightning-fast builds)
- UI Framework: Radix UI + Tailwind CSS
- State Management: Zustand
- HTTP Client: React Query + Axios
- Form Handling: React Hook Form with Zod validation
- Icons: Lucide React
- Framework: FastAPI (High-performance Python API)
- Database: MongoDB with Motor (Async driver)
- Authentication: JWT with passlib + bcrypt
- Validation: Pydantic v2 with custom validators
- File Processing: PyPDF2, python-docx, Pillow
- API Documentation: OpenAPI 3.0 (Swagger)
- Embedding Model: HuggingFace Sentence Transformers
- Vector Database: ChromaDB for similarity search
- NLP Processing: spaCy for entity extraction
- Conversational AI: Google Gemini API
- ML Framework: scikit-learn for classification
- Caching: In-memory + file-based embedding cache
- Containerization: Docker with multi-stage builds
- Development: Hot reload, auto-restart
- Production: Gunicorn + Uvicorn workers
- Monitoring: Structured logging with Python logging
POST /api/v1/auth/login # User login
POST /api/v1/auth/register # User registration
POST /api/v1/auth/refresh # Token refreshGET /api/v1/jobs # List all jobs
POST /api/v1/jobs # Create new job
POST /api/v1/jobs/{id}/upload-resume # Upload candidate resume
GET /api/v1/jobs/{id}/candidates # Get job candidates (AI-ranked)GET /api/v1/candidates/{id}/ai-score # AI resume scoring
POST /api/v1/candidates/{id}/start-screening # AI interview screening
GET /api/v1/employees/{id}/development-plan # AI learning planGET /api/v1/employees # List employees
POST /api/v1/employees # Create employee
PUT /api/v1/employees/{id} # Update employee
DELETE /api/v1/employees/{id} # Delete employeeVisit http://localhost:8000/docs for complete interactive API documentation with request/response examples.
# Database
DATABASE_URL=your-mongodb-url
# Security
SECRET_KEY=your-super-secret-jwt-key-here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
# AI Configuration
GOOGLE_API_KEY=your-gemini-api-key-here
EMBEDDING_MODEL_NAME=sentence-transformers/msmarco-distilbert-base-v4
SPACY_MODEL_NAME=en_core_web_sm# AI Features (Enable/Disable)
INIT_AI_ON_STARTUP=true
ENABLE_SPACY_PROCESSING=true
ENABLE_GEMINI_FALLBACK=false
ENABLE_ML_CLASSIFIER=true
ENABLE_ENTITY_EXTRACTION=true
# Performance
CHROMA_PERSIST_DIRECTORY=./chroma_db
MAX_FILE_SIZE=10485760
UPLOAD_DIR=./uploads
# Development
DEBUG=false
LOG_LEVEL=INFO
ENABLE_DOCS=true- Backend: Deploy to Render using
render.yaml - Frontend: Deploy to Vercel using
vercel.json - Database: MongoDB Atlas (Free tier available)
# Build and run with Docker Compose
docker-compose up --build
# Production deployment
docker-compose -f docker-compose.prod.yml up -dWe welcome contributions! Please see our Contributing Guidelines for details.
- Fork the repository
- Create feature branch (
git checkout -b feature/amazing-feature) - Commit changes (
git commit -m 'Add amazing feature') - Push to branch (
git push origin feature/amazing-feature) - Open Pull Request
- ✅ Production Ready: Enterprise-grade security and performance
- 🤖 AI-First: 5+ AI models integrated for intelligent automation
- 📱 Responsive: Works seamlessly on desktop, tablet, and mobile
- 🔒 Secure: JWT authentication, role-based access, encrypted data
- ⚡ Fast: Optimized queries, caching, and lazy loading
- 📈 Scalable: Microservices-ready architecture
⭐ Star this repo if you find it helpful!
Built with ❤️ by Parth Kothari