How can I test the 128k context window on gpt-4-1106-preview without hitting rate limits?

Below is some guidance on how to proceed when testing a model like gpt-4-1106-preview that has an exceptionally large context window but is still subject to rate limits:

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Understanding the Rate Limit Error

The error indicates that your request exceeded the allowed tokens-per-minute threshold (in this case, 40,000 tokens per minute for gpt-4-1106-preview). Rate limits are in place to manage overall system usage and to ensure fair access. Even though the model supports a large context window, the rate limit may restrict how much data you can send in a given time period.

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Strategies to Work Within the Limit

• Reduce Request Size:
  Consider breaking your large chunk of text into smaller parts. This allows you to process the text without exceeding the token per minute limit.

• Batch or Delay Requests:
  Instead of one large request, you might spread the workload over time. For example, you can process portions sequentially with a slight delay between requests to avoid bursts that exceed the per-minute threshold.

• Monitor Your Usage:
  Check your account settings for your specific limits. The rate limits can vary depending on your plan and individual settings.

• Contact Support:
  If you need to regularly test the model with larger inputs, you might consider reaching out to OpenAI support. They may be able to offer guidance or adjustments based on your use case.

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Example Approach

Here’s a pseudocode example to demonstrate splitting a large input:

PYTHONCopied
import time

def process_chunk(chunk):
    # Your API call here
    response = call_openai_api(prompt=chunk)
    return response

large_text = "..."  # Your large input text
token_limit_per_request = 30000  # Stay under the per-minute rate limit

# Split your text into smaller chunks based on token count (implementation depends on your tokenizer)
chunks = split_text_into_chunks(large_text, token_limit_per_request)

for chunk in chunks:
    response = process_chunk(chunk)
    # Do something with the response
    time.sleep(1)  # Adjust sleep to ensure you don't exceed 40000 tokens/min

In this example, the large text is divided so that each request respects a token threshold. The sleep between requests further helps to avoid reaching the per-minute cap.

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Final Note

It's important to remember that while the model’s context window is large, testing its limits requires managing the throughput of tokens sent over time. Adjusting your usage pattern or discussing your needs with support can help ensure you can fully evaluate the model’s capabilities.

Let me know if you need further details on how to implement these strategies or have additional questions on the OpenAI API usage!