Contributing 🌻

Contributing 🌻#

Setup#

If you want to contribute to Feedback Forensics, there are two options to set up the development environment:

Option 1: Standard development setup#

  1. Clone this repository

  2. Install the package with development dependencies:

    pip install -e ".[dev]"

Option 2: Development container#

For a consistent development environment, this repository includes a VS Code dev container configuration:

  1. Install the Remote - Containers extension

  2. Open the repository in VS Code

  3. Click “Reopen in Container” when prompted

Running test cases#

To run the tests for the package, run:

pytest ./src

Creating a PR#

First create a PR to the staging branch, from there the work will then be merged with the main branch. A merge (and push) in the staging branch will allow you to view the staged online version of Feedback Forensics app at https://rdnfn-ff-dev.hf.space.

Creating a new release#

Ensure that the current branch is up-to-date with main, and then bump the version (using patch, minor, or major):

bump-my-version bump patch

Then on the GitHub website create a new release named after the new version (e.g. “v0.1.2”). As part of this release in the GitHub interface, create a new tag with the updated version. This release will trigger a GitHub action to build and upload the PyPI package.

Creating docs locally#

If you want to test and compile the docs locally, run:

jupyter-book build docs/

Update HF Model Personality dataset#

The HuggingFace dataset rdnfn/ff-model-personality is updated via a GitHub action in the Feedback Forensics repo. By configuring the action script, you can add new models to the dataset - e.g. following a new model release. All models available via openrouter are supported. The following steps are required to add a new model:

  1. Add the new model to the .github/workflows/update-model-comparison.yml config file under the env.MODELS parameter. Note that models need to be separated by a space (not a comma).

  2. Merge the change onto the main branch (i.e. by opening and merging a PR). Once a new FF version is released, the new model will be added to the dataset. Alternatively, you can request an FF maintainer to manually trigger a dataset update ahead of the next release.

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