add code review bot

[bacongobbler]

https://github.com/bacongobbler/code-review-bot

[github-actions]

This pull request introduces automation for conducting code reviews using OpenAI's GPT model, which is a unique and innovative way to leverage AI in improving code quality. However, there are a few considerations and potential improvements to address:

Security Considerations:

1. Secure Secret Handling: The workflow uses an OpenAI API key (via ${{ secrets.OPENAI_API_KEY }}). It’s crucial to ensure this secret is appropriately guarded in the GitHub Secrets management and limited to only those actions and individuals that require access.

2. Input Validation and Escaping: When constructing the request to OpenAI's API, the diff is escaped and included in the payload. While the current method of escaping and inclusion seems methodologically sound, it's important to continuously review how external input (like a PR diff) is handled to mitigate injection attacks or unintended API usage.

3. Dependency Trustworthiness: The use of third-party actions such as GrantBirki/git-diff-action@v2.6.0 and actions/github-script@v7 should be continuously evaluated for trustworthiness, security updates, and maintenance status. Preferring official actions or well-maintained community actions with a good security track record is advisable.

Code Improvement Suggestions:

1. Error Handling: The current script assumes successful execution at every step, especially for the API calls to OpenAI and GitHub. Implementing comprehensive error handling (for example, checking HTTP response codes, and handling API rate limiting or errors gracefully) would make the workflow more robust.

2. API Rate Limiting Considerations: Depending on the number of pull requests and the size of diffs, the interaction with the OpenAI API might hit rate limits or incur significant costs. It would be beneficial to add logic to pre-screen or limit the number of requests sent to the OpenAI API based on certain criteria (e.g., PR size, number of files changed).

3. Output Sanitization: Before posting comments back to the GitHub pull request, ensure that the script sanitizes the output from OpenAI to avoid inadvertently introducing formatted markdown or special characters that could affect the comment's integrity or security.

4. Model Selection: The script uses the "gpt-4-turbo-preview" model. Given the rapid development in AI models, ensure that this model choice remains optimal for the task in terms of cost, speed, and accuracy. It may be worth parameterizing the model to easily adjust it based on future evaluations.

Additional Enhancements:

• Feedback Loop Integration: Consider incorporating a method for developers to provide feedback on the AI-generated code reviews directly within the workflow. This feedback could help refine the model or the parameters used for the review.

• Customization Options: Provide a way for developers or teams to customize the instructions sent to OpenAI for reviewing the code. Different projects or teams might have different focus areas or standards that could be reflected in the review.

Overall, this is an ambitious and innovative step towards automating code reviews. With attention to security, error handling, and continuous evaluation of tools and services used, this approach has the potential to significantly aid code quality checks.