Vibemeter Backend

Overview

Vibemeter is a conversational bot platform designed for employee engagement analysis and feedback management. This FastAPI-based backend provides real-time interaction capabilities, comprehensive data analysis, and reporting features to help organizations better understand and improve employee satisfaction and engagement.

Features

• Conversational Interface: Real-time chat interactions via WebSockets
• Sentiment Analysis: ML-powered analysis of employee feedback
• Report Generation: Automated generation of insights and trends
• REST API: Comprehensive endpoints for data access and management
• Scalable Architecture: Docker-ready deployment with configurable environments

Project Structure

vibemeter-backend-2025/
├── app/                      # Main application package
│   ├── __init__.py
│   ├── config.py             # Configuration settings
│   ├── main.py               # Application entry point
│   ├── socket.py             # WebSocket implementation
│   ├── api/                  # API package
│   │   ├── __init__.py
│   │   └── endpoints/        # REST API endpoints
│   ├── data/                 # Data storage and management
│   ├── ml/                   # Machine learning modules
│   ├── models/               # Data models
│   └── utils/                # Utility functions
├── .env                      # Environment variables
├── .gitignore                # Git ignore patterns
├── docker-compose.yml        # Docker compose configuration
├── Dockerfile                # Docker container definition
├── Makefile                  # Make commands for common tasks
├── pyproject.toml            # Project metadata and dependencies
├── README.md                 # Project documentation
├── report-gen-fresh.py       # Report generation script
├── reportgen.py              # Report generation module
└── requirements.txt          # Python dependencies

Prerequisites

• Python 3.8 or later
• pip (Python package manager)
• Docker and Docker Compose (for containerized deployment)

Running the Application

There are two ways to set up and run the Vibemeter backend:

1. Local Development Setup

1. Clone the repository:

git clone https://github.com/your-organization/vibemeter-backend-2025.git
cd vibemeter-backend-2025

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env file with your configuration

5. Start the development server:

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

2. Using Docker

1. Clone the repository:

git clone https://github.com/your-organization/vibemeter-backend-2025.git
cd vibemeter-backend-2025

2. Set up environment variables:

cp .env.example .env
# Edit .env file with your configuration

3. Build and run with Docker Compose:

# Build and start the containers
docker-compose up --build

# Or run in background
docker-compose up -d

4. Alternatively, you can use the provided Makefile:

# Build and start the application
make build
make run

API Documentation

Once the application is running, access the automatically generated API documentation:

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

WebSocket Interface

The application provides WebSocket connections for real-time communication. Connect to:

ws://localhost:8000/ws/chat

Report Generation

Generate employee engagement reports using:

python reportgen.py [options]

Development

Environment Variables

Key configuration options in .env:

• DATABASE_URL: Database connection string
• API_KEY: API authentication key
• DEBUG: Enable/disable debug mode
• MODEL_PATH: Path to ML models

Adding New Endpoints

Place new endpoints in app/api/endpoints/ directory following the FastAPI router pattern.

ML Model Updates

Machine learning models are stored in the app/ml/ directory. Update models by:

1. Placing new model files in appropriate subdirectories
2. Updating configuration in app/config.py

Deployment

The application can be deployed using:

Docker Deployment

docker build -t vibemeter-backend .
docker run -p 8000:8000 vibemeter-backend

CI/CD Pipeline

The project includes GitHub Actions workflows in .github/workflows/:

• ci.yml: Runs tests and linting on pull requests
• deploy.yml: Handles deployment to production environments

Contributing

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

Support

For questions and support, please open an issue or contact the development team.