Add openai-automated to python

[nicolewhite]

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Name	Status	Preview	Comments	Updated (UTC)
autoblocks-examples	✅ Ready (Inspect)	Visit Preview	💬 Add feedback	Oct 23, 2023 11:20pm

[review-agent-prime]

The PR looks good overall, but there are a few suggestions that could improve the code quality:

1. In main.py, it's a good practice to handle exceptions, especially when making API calls. This can help to debug issues more easily. Here is a suggestion:

try:
    openai.ChatCompletion.create(
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant. You answer questions about a software product named Acme."
            },
            {
                "role": "user",
                "content": "How do I sign up?"
            }
        ],
        model="gpt-3.5-turbo",
        temperature=0.7,
    )
except Exception as e:
    print(f"An error occurred while calling OpenAI: {e}")

2. In main.py, consider using a logger instead of print statements for better logging management. Here is a suggestion:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

...

logger.info("Automatically tracing all calls to OpenAI...")

3. In README.md, consider adding a section about how to set up the environment variables required by the script. This will make it easier for new users to get started.

4. In pyproject.toml, consider specifying the exact versions of the dependencies to ensure the reproducibility of the environment. This can help to avoid potential issues caused by version updates of the dependencies.
   It's hard to provide specific feedback without seeing the actual code changes. However, here are some general suggestions:

5. Ensure that the new code line added in the PR follows the coding standards and conventions of the project. This includes proper indentation, use of variable names, and overall code structure.

6. If the new code line is a function or method, make sure it's not too long or complex. If it is, consider breaking it down into smaller, more manageable functions.

7. Check if the new code line has any potential performance issues. For example, if it's a loop that could potentially run for a long time, consider if there's a more efficient way to achieve the same result.

8. If the new code line is a call to an external service or API, make sure error handling is properly implemented. This includes handling network errors, timeouts, and unexpected response data.

9. If the new code line involves any form of user input, ensure that proper input validation and sanitization is implemented to prevent potential security vulnerabilities.

10. Finally, make sure the new code line is covered by unit tests. This will help catch any potential bugs or issues before they make it into the production code.