How can I optimize GPT-3.5 API performance for batch processing in Python?

Below is some guidance to help troubleshoot and optimize performance when calling the API:

Check Your Code Logic

It looks like your provided sample simply simulates a delay using time.sleep(30). Ensure that your actual API call isn’t being blocked by unnecessary delays. If your script uses blocking calls, consider switching to asynchronous requests so that you can process multiple prompts concurrently rather than sequentially.

Use Asynchronous Requests

Instead of calling your API in a loop sequentially, you might try using Python’s async features (e.g., using the asyncio module or libraries like aiohttp) so that you don’t wait for one request to complete before starting the next. For example:

PYTHONCopied
import asyncio
import aiohttp

async def fetch_response(session, prompt):
    # Replace with your actual API endpoint and request details
    async with session.post("https://api.openai.com/v1/your_endpoint", json={"prompt": prompt}) as response:
        return await response.json()

async def main(prompts):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_response(session, prompt) for prompt in prompts]
        responses = await asyncio.gather(*tasks)
        for response in responses:
            print(response)

prompts = ["Prompt 1", "Prompt 2"]
asyncio.run(main(prompts))

Compare API and Chat UI Performance

The ChatGPT UI may seem more responsive because it uses optimizations and streaming that aren’t automatically enabled when you make API calls. Additionally:

• Connection Routing and Caching: The UI might benefit from internal optimizations such as caching or specialized server routing.
• Request Overhead: Each API call includes network latency, and if you’re making several sequential calls, those delays accumulate.
• Timeout Settings: Make sure your code or network isn’t imposing strict timeout limits that cause premature termination of requests.

Network and Infrastructure Considerations

• Cloudflare Errors: The Cloudflare error message may indicate that some intermediary (like a proxy or the API gateway) is enforcing timeouts or rate limits. Check the production best practices to ensure you meet requirements (using TLS 1.2+ and handling rate limits appropriately).
• Rate Limits and Back-off: The API may dynamically slow down if you exceed a certain number of requests in a short time. Using throttling or rate-limiting mechanisms in your client code can help.

Evaluate Model Choice and Request Composition

• Model Selection: If your tasks are straightforward, using a fine-tuned or optimized GPT-3.5 variant might improve latency over GPT-4. However, balance speed and response quality according to your needs.
• Token Processing: Both the number and type of tokens processed can affect response times. If your structured responses don’t require GPT-4-level reasoning, using GPT-3.5 might offer benefits in speed.

Summary

1. Replace synchronous blocking (e.g., time.sleep) with asynchronous API calls.
2. Evaluate your network connection and see if intermediary timeouts or rate limits are being triggered.
3. Compare the model you’re using—if your tasks are well defined, a fine-tuned GPT-3.5 might be faster.
4. Consult production best practices for handling API endpoints to reduce latency and avoid errors.

By reviewing these areas and optimizing your request logic, you should be able to reduce the observed delays and avoid timeouts when processing larger batches of entries.