Improving OpenAI API Usage in Rust with Exponential Backoff - Episode 171
3In this video, I dive deep into optimizing our project’s functionality by addressing rate limiting for OpenAI's API and improving error handling within the AWS Lambda functions. Here's a quick overview of what I worked on and discussed:
I began by reviewing and committing updates to the stream management components. A significant task was integrating a template for the stream title and keeping the stream counter synced for better organization while working on backend infrastructure. Later, I transitioned to improving the stream ingestion process by tackling some persistent issues with metadata extraction and automatic transcription storage in DynamoDB.
The core focus of the session was on handling rate limits triggered by OpenAI's API when processing multiple transcription tasks. I explained the limitations of token usage, explored OpenAI's rate-limiting guidelines, and brainstormed solutions, including exponential backoff with jitter to gracefully retry API calls. Additionally, we explored the option for batching requests to OpenAI's servers and discussed how this might scale better for long-term goals.
I also walked through the implementation of better error reporting in the Lambda function handling transcriptions. Instead of allowing the function to panic on API failures, I set up structured error responses that communicate the nature of issues back to the AWS Step Functions workflow. This allows for smarter error handling and smoother processing without abrupt stops.
Lastly, I touched on enhancements to our infrastructure, such as debugging the integration of stream metadata processing with S3 and DynamoDB. Future improvements include implementing retry logic and enhancing user interfaces for better integration between the streaming dashboard and OBS tools.
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