OpenAI API timeout issue: HTTPSConnectionPool read timeout error

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1

I’m running into a persistent timeout problem when trying to use OpenAI’s API. The error message I get is:

HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=600)

Here’s my code that’s causing the issue:

def fetch_openai_completion(prompt_text, debug_mode=False):
    """
    Send request to OpenAI API and retrieve completion
    :param prompt_text: str - input text for the API
    :param debug_mode: bool - whether to show raw response
    """
    time.sleep(3)
    response = openai.Completion.create(
        model='text-davinci-003',
        temperature=0.7,
        prompt=prompt_text,
        max_tokens=200,
        n=1,
        stop=None,
    )
    
    if debug_mode:
        print(response)
    
    return response.choices[0].text

data_frame['AI_Response'] = data_frame['Survey_Answer'].apply(lambda response: 
                          fetch_openai_completion(
                          "Based on this survey response [{}], 
                           categorize it into broad themes like support, product, 
                           pricing or similar categories. 
                           Return format: [Category: your_category] 
                           for: '{}'".format(response, response)))

# Clean up results
data_frame['AI_Response'] = data_frame['AI_Response'].apply(lambda result: result.split(':')[1].replace(']',''))

I’ve tried adjusting different settings but the timeout keeps happening. Has anyone dealt with this before? Any suggestions would be helpful.

2

Your timeout issue is likely due to processing large datasets row by row, which can overwhelm the API. I’ve encountered this problem with survey data previously. You’re making sequential API calls without implementing error handling or retry logic, which significantly hampers performance. Consider using exponential backoff for retries and batch your requests instead of processing each row individually. Additionally, increase the timeout in your OpenAI client beyond the default 600 seconds, or adopt an asynchronous approach if working with substantial data. I found that dividing the dataframe into smaller segments and incorporating breaks between batches helped alleviate the timeout problem.