- Python 3.11
- pip
- python-setuptools
- pycord 2.4.1
To install all Python dependencies you can use pip. Just enter
pip install -r requirements.txt in the project directory.
Note: We use Python 3.11+ so if your system does have multiple versions
installed, you may have to specify which installation to use e.g.
python3.11 and pip3.11.
To build a Docker image, simply invoke:
docker image build --tag mxtress_ai:latest .Running the Discord bot in a Docker container is simple, though care must be
given to expose the Discord API key and bot database to the runtime. Assuming
the ai.db file is present in the current working directory, run the following:
docker run \
--name HiveMxtressAI \
--detach \
--restart always \
--env DISCORD_ACCESS_TOKEN=(bot token) \
--volume /absolute/path/to/the/repo/ai.db:/var/opt/HexCorpDiscordAI/ai.db \
mxtress_ai:latestTo start the bot you can enter the following command in the project root:
python3.11 main.py <access_token>To update the current production instance of the AI you have to:
- Kill the running process
- Navigate into the project repo
git fetchgit checkout <NEW_VERSION>cd ..sh start_ai.sh
To develop in a container, download the git repository and then run code .
to launch Visual Studio Code. Select "Reopen in Container" when prompted.
This will build a Docker container with Python, Git, the required Python packages, and VSCode extensions.
The project code is mounted from the host into: /workspaces/HexCorpDiscordAI/
The Discord bot uses an SQLite3 database to persist runtime data. A graphical database client is recommended to easily view its contents.
When performing database schema changes, create a new SQL file in the
res/db/migrate/ directory. The schema filename should adhere to the naming
convention therein, with an incremented four-digit sequence number. Any schema
files which have not been applied yet will be applied in ordered sequence when
starting the bot.
In the event of database corruption, simply remove the ai.db file; the
database will be recreated with the res/db/migrate/ schema files the next time
the bot is started.
The global log object provides functions for each of the standard log levels:
- debug
- info
- warning
- error
- critical
They accept an error message as the first parameter, followed by arbitrary positional and keyword arguments to be appended to the log message.
Internally the logger maintains a per-execution-context stack of logging contexts. Entering a new logging context will cause all subsequent log messages within that context to be prefixed with the context's description.
Logging contexts are stored per execution context, and so are safe to use with async functions.
Basic usage is:
# Import the global logging instance.
import log from src.Log
# Log a basic message with associated data.
log.error('Something went wrong', example_data='test')To add additional context information use:
import LoggingContext from src.Log
# Start a new logging context with additional information.
with LoggingContext('Doing a thing...'):
do_thing()The Python tool flake8 is used to lint the codebase. To perform linting
locally, one may install the tool using pip3 install flake8; invoking it is
as simple as running flake8 in the project root.
If using an IDE for development, it is highly recommended to set up flake8 to benefit from syntax highlighting.
All commits pushed to the repository upstream pass through GitHub's continuous
integration pipeline, as per the file .github/workflows/continuous-integration.yml.
The project will go through the following stages in the pipeline:
- The codebase will be linted with
flake8. - Unit tests will be invoked with the
run_tests_with_coverage.shscript. - The unit test coverage metrics from the above step will be measured; 60% coverage of the codebase must be met .
All steps must pass in order for a commit to be accepted.
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