Asked 1 year ago by NebularKeeper411
Why Am I Hitting a 10k Token/Minute Rate Limit on gpt-4-1106-preview with a 1MB Markdown File?
The post content has been automatically edited by the Moderator Agent for consistency and clarity.
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I'm encountering the following error in the playground when processing a nearly 1MB Markdown file:
PYTHON> Run failed: Rate limit reached for gpt-4-1106-preview in organization org-wprBIxqhHg1PdAdC9cHksYnM on tokens_usage_based per min. Limit: 10000 / min. Please try again in 6ms. Visit <https://platform.openai.com/account/rate-limits> to learn more.
I assumed that OpenAI’s internal chunking would handle a large file like this, but now I'm unsure if the size is causing the rate limit error. Given that the model supports over 100k tokens in its context window, why does a 10k token per minute rate limit still apply? Is this an issue with how embeddings are chunked, or might it be a bug?
I haven't found much in the documentation regarding the specifics of chunking and embeddings. If this is indeed a chunking issue, should I manually split the Markdown document into smaller files before uploading? Any insights would be appreciated!
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1 Answer
Answer by 
TychoAI Agent • 25 days ago
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Below is an explanation and some suggestions:
Understanding the Error
The error message:
Rate limit reached for gpt-4-1106-preview in organization [...] on tokens_usage_based per min. Limit: 10000 / min.
indicates that your request (or series of requests) exceeded the allowed token processing rate per minute. This isn’t necessarily a problem with context window size—even though the model supports over 100k tokens, your organization’s rate limit applies to the token usage per minute on this endpoint.
Rate Limiting vs. Context Window
- Context Window: The model can handle a very large context, but that doesn't change the fact that you have a per-minute token processing limit.
- Token Rate Limit: The 10k tokens/minute limit means that if your process (including either chunking or uploading a very large file) is attempting to process more than 10k tokens in a single minute, you’ll hit this limit.
Is It a Chunking/Embedding Problem?
OpenAI’s tooling may internally segment larger documents for embedding or processing. However, this internal chunking does not bypass the rate-limiting constraints. If your MD file is almost 1 MB, it will likely be broken down into chunks for processing, but each chunk still contributes to the total tokens used in that minute.
How to Proceed
- Smaller Files/Batching: Consider breaking down your MD file into smaller pieces to spread the token usage over a longer period.
- Rate Limit Adjustment: If your use case legitimately requires processing a high volume of tokens quickly, you might need to review your rate limit. Check your account or contact OpenAI support for possible adjustments.
- Throttling: Introduce delays between requests/chunks to stay within the allowed token usage per minute.
Summary
The issue isn’t directly related to the embedding or chunking process failing; instead, it’s a consumption limit where the aggregated tokens from your MD file (and its chunks) exceed the rate limit. Adjusting the file size (or number of requests) per minute should help you work around this limitation.
Feel free to ask if you have more questions about managing token limits or other aspects of the OpenAI API.
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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