Rate Limit Exceeded

Question

I am facing below error when I am using azure open ai chat assistance. I am trying to extract values from the uploaded file. I am getting rate limit exceeded error.

Error
"rate_limit_exceeded: Rate limit is exceeded. Try again in 56 seconds. RunId: run_"

Answer 1

Harichandana Vegesna Greetings!

I am facing below error when I am using azure open ai chat assistance. I am trying to extract values from the uploaded file. I am getting rate limit exceeded error. Error "rate_limit_exceeded: Rate limit is exceeded. Try again in 56 seconds. RunId: run_"

The error message is related to rate limits, which is a common practice in APIs to prevent abuse and ensure fair usage.

Did you check if you have exceeded the quota limit for your Azure OpenAI resources?

To give more context, As requests come into the deployment endpoint, the estimated max-processed-token count is added to a running token count of all requests that is reset each minute. If at any time during that minute, the TPM rate limit value is reached, then further requests will receive a 429 response code until the counter resets. For more details, see Understanding rate limits.

Please see Manage Azure OpenAI Service quota for more details.

If you are using Azure AI studio:

You can view your quotas and limits in Azure AI studio Model Quota section.

Did you check if you have exceeded the quota limit for your Azure OpenAI resources? You can view your quotas and limits in Azure AI studio Model Quota section.

Please see Manage and increase quotas for resources with Azure AI Studio for more details.

You could also try increasing the limit on your deployment.

If you are using OpenAI studio:

To view your quota allocations across deployments in a given region, select Shared Resources> Quota in Azure OpenAI studio and click on the link to increase the quota*.*

Also, to minimize issues related to rate limits, it's a good idea to use the following techniques:

• Set max_tokens and best_of to the minimum values that serve the needs of your scenario. For example, don’t set a large max-tokens value if you expect your responses to be small.
• Use quota management to increase TPM on deployments with high traffic, and to reduce TPM on deployments with limited needs.
• Implement retry logic in your application.
• Avoid sharp changes in the workload. Increase the workload gradually.
• Test different load increase patterns.

Hope this helps. Do let me know if you have any further queries.

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