Tukma is a research project designed to filter and select suitable job applicants.

The Tukma application provides a platform for:

• Recruiters to post job opportunities and review applicant resumes
• Applicants to search for jobs and submit their resumes
• AI-powered resume analysis to match applicants with job requirements
• Automated interviewing capabilities

The application includes maintenance endpoints for database management:

• Resume duplicate cleanup: /api/v1/resume/cleanup-duplicates - Removes duplicate resume entries while preserving the most recent version

The API is organized into several logical sections:

• Authentication API - User registration, login, and status
• Jobs API - Job creation, management, search, and retrieval
• Questions API - Interview question management for jobs
• Resume API - Resume uploads, analysis, and retrieval
• Interviewer API - Real-time interviewing via WebSockets
• Survey API - Survey question management for feedback collection
• General Information - Authentication, models, and common patterns

• Backend: Spring Boot 3.4.1
• Database: PostgreSQL
• Authentication: JWT (JSON Web Tokens)
• Cache: Redis
• External APIs: OpenAI for text and audio processing
• Deployment: Docker

• Java 17+
• Maven 3.9+
• PostgreSQL
• Redis
• OpenAI API key

1. Clone the repository:

git clone https://github.com/your-username/tukma-backend.git

2. Configure environment variables: Create a src/main/resources/application.properties file with the following content:

# Database Configuration
spring.datasource.url=jdbc:postgresql://localhost:5432/tukma
spring.datasource.username=your_username
spring.datasource.password=your_password
spring.jpa.hibernate.ddl-auto=update
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.PostgreSQLDialect

# Redis Configuration
spring.data.redis.url=redis://localhost:6379

# Server Configuration
server.port=8080

# OpenAI API Key
openai.key=your_openai_api_key

3. Build the application:

./mvnw clean package

4. Run the application:

./mvnw spring-boot:run

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

src/main/java/org/tukma/
├── auth/                    # Authentication components
│   ├── controllers/         # REST controllers for auth endpoints
│   ├── dtos/                # Data transfer objects
│   ├── models/              # Entity models
│   ├── repositories/        # Data access layer
│   └── services/            # Business logic
├── config/                  # Configuration classes
├── globals/                 # Global constants and utilities
├── interviewer/             # Interviewer components
│   ├── controller/          # REST controllers
│   ├── dto/                 # Data transfer objects
│   ├── models/              # Entity models
│   ├── repositories/        # Data access layer
│   └── services/            # Business logic
├── jobs/                    # Job management components
│   ├── controllers/         # REST controllers
│   ├── dtos/                # Data transfer objects
│   ├── models/              # Entity models
│   ├── repositories/        # Data access layer
│   └── services/            # Business logic
├── resume/                  # Resume processing components
│   ├── controllers/         # REST controllers
│   ├── dtos/                # Data transfer objects
│   ├── models/              # Entity models
│   ├── repositories/        # Data access layer
│   ├── services/            # Business logic
│   └── utils/               # Utility classes
└── utils/                   # Common utilities

To build and run with Docker:

# Build the image
docker buildx build --platform linux/amd64 -t tukma-backend .

# Run the container
docker run -p 8080:8080 \
  -e SPRING_DATASOURCE_URL=jdbc:postgresql://host.docker.internal:5432/tukma \
  -e SPRING_DATASOURCE_USERNAME=your_username \
  -e SPRING_DATASOURCE_PASSWORD=your_password \
  -e SPRING_DATA_REDIS_URL=redis://host.docker.internal:6379 \
  -e OPENAI_KEY=your_openai_key \
  tukma-backend

For production deployment, configure with environment variables or bind-mount a properties file.

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

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature-name
3. Commit your changes: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature/your-feature-name
5. Create a pull request

• Special thanks to all contributors to this research project
• OpenAI for providing AI capabilities