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internal-bot

This repository is home to the internal bot we use on the EPS server.

Overview

The bot's main goal is to make life easier for the conference teams by streamlining workflows and integrating with other services.

Design Decisions

  • One Codebase, Multiple Entry Points

    • HTTP Endpoint: Handles webhooks.
    • Discord Bot: Lets us communicate back and forth with Discord.
      • It can post scheduled messages and respond to messages, commands, and events on the server.
    • Background Worker: Mainly processes HTTP requests but can also be used within the bot itself.

  • Small-Scale Setup

    • Built to be easily extendable with new features, but designed for low-traffic environments like the EPS Organizers server.

Tech Stack and Implementation Choices

  • Django

    • Handles the HTTP layer, database, and general framework duties.
    • Why Django?
      • It's popular and easy to work with.
      • Comes with great built-in tools like ORM, migrations, and Django admin for quick access to data.
      • We're already using it for other EPS projects.

  • PostgreSQL

    • Both development and production setups can run using Docker Compose.

  • discord.py

    • Handles the Discord integration.
    • Runs in the same environment as Django, so we can reuse code, models, and the database.
    • The bot runs as a Django management command.

  • django-tasks

    • Keeps background task management simple by storing tasks in the database—no need for extra tools like Redis or RabbitMQ.

  • Deployment

    • Deployed on a dedicated VPS just for the bot.
    • Packaged as a single Docker image with multiple entry points.
    • Deployed using Ansible to match the deployment setup we use for other EPS projects.
      • We use separate playbooks for setting up a VPS and deploying the app.
    • Uses Docker Compose for simplicity.
    • Separate playbooks for Nginx (for the HTTP endpoint) and the app.

  • Dependency Management

    • Managed with uv. All Makefiles, Docker images, and related configs are set up to use uv.

  • CI/CD

    • Built with GitHub Actions.

Local Development

FAQ

Using M1 Mac, and psycopg doesn't work?

Make sure that your Python installation is compiled with lipbq correctly. In case of using brew and pyenv this could help:

$ brew link --force libpq
$ export PATH="/opt/homebrew/opt/libpq/bin:$PATH"
$ pyenv install 3.12

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Contributing

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Operations

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Deployment

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