For fun slack bot markov chain bot.
Reads 100% of channels it is present in, adding to a sqlite3 db stored both as raw messages and counts of n-grams following other n-grams. Then generates messages randomly with start words, chooses up-to-4 grams one at a time to generate text, and then finishes once it hits a stop words.
The goal here was to get back into natural language processing, and to rewrite a
A python3 bot built on the lins05/slackbot core, using a sqlite3 file for persistent data.
./run.py entrypoint kicks off the bot, and contains all handlers. Handlers kick off threads that run asynchronously, so long database calls (the prod version of this is running off a no-shit usb thumb drive as root disk, so sometimes it gets slow) do not block additional tasks. This is also why timeouts for working with the sqlite3 backend are... generous.
Built for deployment on an RPi3- if you need to run a docker container on another system update the
FROM line in the dockerfile to remove arm achitecture preface.
To run this on a rpi, just clone to disk, make an /opt/botman-v3/ folder, a /var/log/botman-v3/ folder, add a slackbot_settings.py file (see underlying bot architecture for details), and run
This jacky hack of a CI script will check github for any changes and automatically cycle out the exsisting container for a new one if any changes are present. Logrotate will break your logs if you don't set up copy/truncate style log rotation.
Can be run / developed natively, with different requirements. Dev requirements include virtualenv, virtualenvwrapper, and a recent version of python (3.5 or newer).
Run the following commands to set up a dev environment:
mkvirtualenv botman-v3 --python=$(which python3) pip install -r requirements.txt
Do also include your slackbot_settings.py file (see underlying bot architecture for details).