To capture and handle errors when making requests to the Bedrock service, start by using structured exception handling in your code. Most AWS SDKs (like the AWS SDK for Python/Boto3 or JavaScript) provide specific exception classes for Bedrock-related errors. Wrap your Bedrock API calls in try-catch blocks to intercept exceptions, and check the error type or code to determine the cause. For example, in Python with Boto3, you might catch a ClientError and inspect its response property to get details like the HTTP status code or error message. Common errors include throttling due to rate limits (ThrottlingException), invalid parameters (ValidationException), or service unavailability.
Next, implement logic to handle specific error types appropriately. For transient errors like throttling or temporary service issues, use retries with exponential backoff. AWS SDKs often include built-in retry mechanisms, which you can configure using the Config object (e.g., setting max_attempts in Boto3). For non-retryable errors (e.g., invalid input), log the error details and notify the user or system. For example, if a request to invoke_model fails with a ValidationException, log the invalid parameters and return a user-friendly message. If the error is due to permissions, check your IAM role policies or credentials.
Finally, ensure robust logging and monitoring. Log error codes, messages, timestamps, and context (e.g., the input sent to Bedrock) to diagnose issues. Use AWS CloudWatch to track metrics like error rates or throttling events, and set up alarms for critical failures. For asynchronous workflows, consider using dead-letter queues to capture failed requests for later analysis. For example, if a batch processing job fails due to a ModelTimeoutException, log the error and route the task to a queue for retries. This structured approach ensures errors are visible, actionable, and minimally disruptive to users.