Is the 'request_timeout' parameter officially supported and how should I handle timeouts with exponential backoff?

Below is an explanation addressing both points:

---

1. The request_timeout Parameter

• Existence:
  The parameter for setting a custom read timeout (commonly named request_timeout) does exist in practice, and many users have found success by reducing the default wait time. However, note that it’s not prominently featured in the official documentation. This means that while it can work, its behavior hasn’t been fully standardized or documented by OpenAI, so caution is advised.

• Usage Considerations:
  Since the default behavior can lead to very long wait times (e.g., 600 seconds), many developers prefer to specify a lower timeout value. This approach gives you more control over how long you’ll wait for a response from the API before retrying.

---

2. Backoff Strategy and Timeout Duration

• Problem with Long Timeouts:
  If you’re using the default timeout of 600 seconds, waiting for that maximum duration before retrying isn’t ideal. For example, with an exponential backoff strategy, you do not want each retry to start with a 10‑minute wait if the API isn’t responding.

• Recommended Approach:
  • Set a Lower Request Timeout:
  Instead of waiting 600 seconds, set the timeout to a value that meets your application's requirements (for example, 30 seconds). This way, if a request takes longer than 30 seconds, it will throw a timeout error, allowing your retry/backoff mechanism to kick in sooner.

  • Implementing Retry with Exponential Backoff:
  When a timeout occurs, catch the exception (which might be a different networking error rather than a RateLimitError) and apply your backoff logic to retry the call after a reasonable delay. This custom backoff function can start at a short delay and exponentially increase the wait time with each subsequent retry, while ensuring that the raw API request never hangs on its default long timeout.

Example Outline with a Custom Timeout

Below is a simplified conceptual approach in Python:

PYTHONCopied
import time
import random
import openai

# Example custom retry function
def retry_with_exponential_backoff(
    func,
    timeout: float = 30,           # use a shorter timeout than default
    initial_delay: float = 1,
    exponential_factor: float = 2,
    max_retries: int = 5,
):
    def wrapper(*args, **kwargs):
        num_retries = 0
        delay = initial_delay

        while num_retries < max_retries:
            try:
                # override the default timeout on the API call
                kwargs["request_timeout"] = timeout
                return func(*args, **kwargs)
            except Exception as e:
                num_retries += 1
                # Optionally check the exception type before retrying
                if num_retries >= max_retries:
                    raise Exception("Maximum number of retries exceeded.") from e
                # Add jitter to the delay
                sleep_time = delay * (1 + random.random())
                time.sleep(sleep_time)
                delay *= exponential_factor  # increase delay exponentially
    return wrapper

@retry_with_exponential_backoff
def call_openai_api(**kwargs):
    # Replace with your API call; ensure your client supports request_timeout
    return openai.Completion.create(**kwargs)

# Example usage:
response = call_openai_api(model="gpt-4o-mini", prompt="Once upon a time,")
print(response)

In this example:

• The wrapper sets a shorter timeout (30 seconds) for each call.
• If a timeout (or another network-related exception) is raised, the function waits for an exponentially increasing delay before retrying.
• This prevents you from having a 600‑second delay before retrying the next attempt.

---

Summary

1. Yes, the request_timeout parameter is in use by some developers even though it isn’t well-documented.
2. It is advisable to set a custom, shorter timeout (e.g., 30 seconds) so that your exponential backoff strategy doesn’t wait as long as 600 seconds.
   In your custom backoff implementation, you can catch timeout exceptions early and manage the retry intervals to better suit your application’s needs.

This approach helps your application recover faster from delayed responses while still leveraging exponential backoff to handle transient errors.