MomConnect Intent Classifier

Model that classifies the intent of inbound messages. It is not intended to be exposed to the outside world, we only have it accisble inside the cluster.

Development

This project uses poetry for packaging and dependancy management, so install that first.

Ensure you're also running at least python 3.11, python --version.

Then you can install the dependencies

~ poetry install

To run a local worker, set NLU_USERNAME and NLU_PASSWORD environment variables, then start up the flask worker

~ poetry run flask --app src.application run

To run the autoformatting and linting, run

~ ruff format && ruff check && mypy --install-types

For the test runner, we use pytest:

~ pytest

Regenerating the embeddings json file

1. Delete the json embeddings file in src/data/
2. Update the nlu.yaml with your changes
3. Run the flask app, this should regenerate the embeddings file. poetry run flask --app src.application run

Editor configuration

If you'd like your editor to handle linting and/or formatting for you, here's how to set it up.

Visual Studio Code

1. Install the Python and Ruff extensions
2. In settings, check the "Python > Linting: Mypy Enabled" box
3. In settings, set the "Python > Formatting: Provider" to "black" (apparently "ruff format" isn't supported by the Python extension yet and "black" is probably close enough)
4. If you want to have formatting automatically apply, in settings, check the "Editor: Format On Save" checkbox

Alternatively, add the following to your settings.json:

{
    "python.linting.mypyEnabled": true,
    "python.formatting.provider": "black",
    "editor.formatOnSave": true,
}

Release process

To release a new version, follow these steps:

1. Make sure all relevant PRs are merged and that all necessary QA testing is complete
2. Make sure release notes are up to date and accurate
3. In one commit on the main branch:
   • Update the version number in pyproject.toml to the release version
   • Replace the UNRELEASED header in CHANGELOG.md with the release version and date
4. Tag the release commit with the release version (for example, v0.2.1 for version 0.2.1)
5. Push the release commit and tag
6. In one commit on the main branch:
   • Update the version number in pyproject.toml to the next pre-release version
   • Add a new UNRELEASED header in CHANGELOG.md
7. Push the post-release commit

Running in Production

There is a docker image that can be used to easily run this service. It uses the following environment variables for configuration:

Variable	Description
NLU_USERNAME	The username used for API requests
NLU_PASSWORD	The password used for API requests
SENTRY_DSN	Where to send exceptions to