Botify - Dockerized Django Project

This project is a Django application that uses PostgreSQL with pgvector extension for vector similarity search capabilities.

Prerequisites

• Docker (version 20.10.0 or higher)
• Docker Compose (version 2.0.0 or higher)
• Git (optional, for cloning the repository)

Project Structure

botify/
├── api/                 # API endpoints
├── config/             # Django project configuration
├── staticfiles/        # Static files
├── .env                # Environment variables
├── requirements.txt    # Python dependencies
├── manage.py          # Django management script
├── Dockerfile         # Docker configuration
└── docker-compose.yml # Docker Compose configuration

Getting Started

1. Clone the repository (if you haven't already):

   git clone <repository-url>
   cd botify

2. Create environment variables:

   • Copy the .env.example file to .env:

   cp .env.example .env

   • Update the .env file with your desired configuration

3. Build and start the containers:

   docker-compose up --build

   This will:

   • Build the Django application container
   • Start the PostgreSQL database with pgvector
   • Run the migration
   • Run the Django development server

4. Create a superuser (optional):

   docker-compose exec api python manage.py createsuperuser

Accessing the Application

• Django Application: http://localhost:8000
• Admin Interface: http://localhost:8000/admin
• API Endpoints: http://localhost:8000/api/

Database Configuration

The PostgreSQL database is configured with the following defaults:

• Host: db
• Port: 5432
• Database: botify
• User: postgres
• Password: postgres

Development Workflow

1. Making changes to the code:

   • The application code is mounted as a volume, so changes will be reflected immediately
   • No need to rebuild the container for code changes

2. Installing new dependencies:

   • Add new packages to requirements.txt
   • Rebuild the containers:

   docker-compose up --build

3. Viewing logs:

   docker-compose logs -f

Common Commands

• Stop the containers:

  docker-compose down

• Stop and remove volumes (including database data):

  docker-compose down -v

• Run management commands:

  docker-compose exec api python manage.py <command>

• Access the database:

  docker-compose exec db psql -U postgres -d botify

Troubleshooting

1. Port conflicts:

   • If port 8000 or 5432 is already in use, modify the ports in docker-compose.yml

2. Database issues:

   • If the database isn't starting properly, try:

   docker-compose down -v
   docker-compose up --build

3. Container issues:

   • To rebuild a specific service:

   docker-compose up --build api

Production Deployment

For production deployment, you should:

1. Set appropriate environment variables
2. Use a production-grade WSGI server (Gunicorn is already configured)
3. Configure proper security settings
4. Set up proper static file serving
5. Use a production-grade database backup strategy