[View code on GitHub](https://github.com/creatorrr/turbo-chat/blob/master/turbo_chat/utils/retries.py)

This code defines a retry mechanism for handling API calls in the `turbo-chat` project. The main purpose of this code is to provide a way to automatically retry API calls when certain exceptions occur, such as timeouts, API errors, connection errors, rate limit errors, and service unavailability errors. This can help improve the reliability and robustness of the project when interacting with external APIs.

The code imports necessary modules and defines two main components: `create_retry_decorator` function and `with_retries` decorator.

The `create_retry_decorator` function takes three optional arguments: `min_seconds`, `max_seconds`, and `max_retries`. It returns a retry decorator that can be applied to any function or method. The decorator will retry the function or method when any of the specified exceptions occur, with an exponential backoff strategy. The backoff starts with a minimum wait time of `min_seconds` (default is 4 seconds) and increases exponentially up to a maximum of `max_seconds` (default is 10 seconds). The maximum number of retries is specified by `max_retries` (default is 5).

Here's an example of how the `create_retry_decorator` function can be used:

```python
custom_retry_decorator = create_retry_decorator(min_seconds=2, max_seconds=8, max_retries=3)

@custom_retry_decorator
def make_api_call():
    # Code to make an API call
    pass
```

The `with_retries` decorator is a default retry decorator created using the `create_retry_decorator` function with default arguments. It can be applied to any function or method to automatically retry the function or method when any of the specified exceptions occur, with the default exponential backoff strategy.

Here's an example of how the `with_retries` decorator can be used:

```python
@with_retries
def make_api_call():
    # Code to make an API call
    pass
```

In the larger project, this retry mechanism can be applied to any function or method that makes API calls, ensuring that temporary issues like timeouts or rate limits are handled gracefully and do not cause the entire process to fail.
## Questions: 
 1. **Question:** What is the purpose of the `create_retry_decorator` function and how does it work?

   **Answer:** The `create_retry_decorator` function is used to create a retry decorator that can be applied to other functions. It takes in parameters for minimum and maximum wait times between retries, and the maximum number of retries. It uses the `tenacity` library to create a retry decorator that will retry the function if any of the specified exceptions from the `openai` library are raised.

2. **Question:** What is the purpose of the `with_retries` variable?

   **Answer:** The `with_retries` variable is a default retry decorator created using the `create_retry_decorator` function with its default parameters. It can be used to easily apply the retry functionality to other functions without having to create a new retry decorator each time.

3. **Question:** How can the `with_retries` decorator be used with other functions in the code?

   **Answer:** To use the `with_retries` decorator with other functions, simply add the `@with_retries` decorator above the function definition. This will apply the retry functionality to the function, causing it to retry if any of the specified exceptions from the `openai` library are raised, according to the parameters set in the `create_retry_decorator` function.