Real-time vector embedding blueprints - troubleshooting

Review the following troubleshooting topics about real-time vector embedding blueprints. For more information about real-time vector embedding blueprints, see Real-time vector embedding blueprints.

My CloudFormation stack deployment has failed or rolled back. What can I do to fix it?

• Go to your CFN stack and find the reason for the stack failure. It could be related to missing permissions, AWS resource name collisions, among other causes. Fix the root cause of the deployment failure. For more information, see the CloudWatch troubleshooting guide.

• [Optional] There can only be one VPC endpoint per service per VPC. If you deployed multiple real-time vector embedding blueprints to write to the Amazon OpenSearch Service collections in the same VPC, they might be sharing VPC endpoints. These might either already be present in your account for the VPC, or the first real-time vector embedding blueprint stack will create VPC endpoints for Amazon Bedrock and Amazon OpenSearch Service that will be used by all other stacks deployed in your account. If a stack fails, check if that stack created VPC endpoints for Amazon Bedrock and Amazon OpenSearch Service and delete them if they are not used anywhere else in your account. For steps for deleting VPC endpoints, see How do I safely delete my application? (delete).

• There might be other services or applications in your account using the VPC endpoint. Deleting it might create network disruption for other services. Be careful in deleting these endpoints.

I don't want my application to start reading messages from the beginning of the Amazon MSK topics. What do I do?

You must explicitly set source.msk.starting.offset to one of the following values, depending on the desired behavior:

• Earliest offset: The oldest offset in the partition.

• Latest offset: Consumers will read messages from the end of the partition.

• Committed offset: Read from the last message the consumer processed within a partition.

How do I know if there is an issue with my Managed Service for Apache Flink application and how can I debug it?

Use the Managed Service for Apache Flink troubleshooting guide to debug Managed Service for Apache Flink related issues with your application.

What are the key metrics that I should be monitoring for my Managed Service for Apache Flink application?

• All metrics available for a regular Managed Service for Apache Flink application can help you monitor your application. For more information, see Metrics and dimensions in Managed Service for Apache Flink.

• To monitor Amazon Bedrock metrics, see Amazon CloudWatch metrics for Amazon Bedrock.

• We have added two new metrics for monitoring performance of generating embeddings. Find them under the EmbeddingGeneration operation name in CloudWatch. The two metrics are:

  • BedrockTitanEmbeddingTokenCount: Number of tokens present in a single request to Amazon Bedrock.

  • BedrockEmbeddingGenerationLatencyMs: Reports the time taken to send and receive a response from Amazon Bedrock for generating embeddings, in milliseconds.

• For Amazon OpenSearch Service serverless collections, you can use metrics such as IngestionDataRate, IngestionDocumentErrors and others. For more information, see Monitoring OpenSearch Serverless with Amazon CloudWatch.

• For OpenSearch provisioned metrics, see Monitoring OpenSearch cluster metrics with Amazon CloudWatch.