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We would love for you to contribute to Instructor.


We invite you to contribute evals in pytest as a way to monitor the quality of the openai models and the instructor library. To get started check out the jxnl/instructor/tests/llm/test_openai/evals and contribute your own evals in the form of pytest tests. These evals will be run once a week and the results will be posted.


If you find a bug, please file an issue on our issue tracker on GitHub.

To help us reproduce the bug, please provide a minimal reproducible example, including a code snippet and the full error message.

  1. The response_model you are using.
  2. The messages you are using.
  3. The model you are using.

Pull Requests

We welcome pull requests! There is plenty to do, and we are happy to discuss any contributions you would like to make.

If it is not a small change, please start by filing an issue first.

If you need ideas, you can check out the help wanted or good first issue labels.

Grit is used to enforce best practices. You can run grit check to check your code before submitting a pull request.


Additional Resources

To enhance your understanding of the documentation, here are some useful references:

Learn more in the mkdocs serve documentation and make sure to install the necessary dependencies listed in requirements-doc.txt before running the command.

  • mkdocs serve: The mkdocs serve command is used to preview your documentation locally during the development phase. When you run this command in your terminal, MkDocs starts a development server, allowing you to view and interact with your documentation in a web browser. This is helpful for checking how your changes look before publishing the documentation.

  • hl_lines in Code Blocks: The hl_lines feature in code blocks allows you to highlight specific lines within the code block. This is useful for drawing attention to particular lines of code when explaining examples or providing instructions. You can specify the lines to highlight using the hl_lines option in your code block configuration. For more details and examples, you can refer to the hl_lines documentation.

  • Admonitions: Admonitions are a way to visually emphasize or call attention to certain pieces of information in your documentation. They come in various styles, such as notes, warnings, tips, etc. Admonitions provide a structured and consistent way to present important content. For usage examples and details on incorporating admonitions into your documentation, you can refer to the admonitions documentation.

For more details about the documentation structure and features, refer to the MkDocs Material documentation.

Thank you for your contributions, and happy coding!