@Fábio Santos ,
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Azure Function's timer trigger might be failing due to discrepancies between its execution environment and that of the runOnStartup
setting. While runOnStartup
executes the function after full initialization of resources, network, and dependencies, the timer trigger can encounter issues like cold starts, network initialization delays, or resource limitations, potentially causing timeouts when connecting to the OpenAI API.
Here are some potential solutions:
- Cold Starts: B3 service plan cold starts can introduce significant latency. Consider upgrading to a Premium Plan for more consistent resource allocation and reduced cold start times.
- Network Initialization: Delays in network initialization can hinder API calls. Implement retry logic and ensure proper network initialization within your function.
- Resource Constraints: The B3 plan's resource limitations might impact performance. Monitor resource usage and consider scaling up if necessary.
- Dependency Initialization: Ensure all dependencies are fully initialized before the timer trigger attempts the API call to prevent errors.
- Timeout Settings: Adjust timeout settings to accommodate potentially longer-running OpenAI API calls.
- Application Insights: Enable Application Insights for detailed logs and metrics to aid in troubleshooting.
For more details, you can refer to the official Timer trigger documentation.
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