Amazon SageMaker templates
The Amazon SageMaker CloudFormation templates define multiple AWS resources in order to set up the neural plugin and semantic search for you.
Begin by using the Integration with text embedding models through Amazon SageMaker template to deploy a text embedding model in SageMaker Runtime as a server. If you don't provide a model endpoint, CloudFormation creates an IAM role that allows SageMaker Runtime to download model artifacts from Amazon S3 and deploy them to the server. If you provide an endpoint, CloudFormation creates an IAM role that allows the Lambda function to access the OpenSearch Service domain or, if the role already exists, updates and reuses the role. The endpoint serves the remote model that is used for the ML connector with the ML Commons plugin.
Then, use the Integration with Sparse Encoders through Amazon SageMaker template to create a Lambda function that has your domain set up remote inference connectors. After the connector is created in OpenSearch Service, the remote inference can run semantic search using the remote model in SageMaker Runtime. The template returns the model ID in your domain back to you to so you can start searching.
To use the Amazon SageMaker AI CloudFormation templates
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Open the Amazon OpenSearch Service console
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In the left navigation pane, choose Integrations.
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Under each of the Amazon SageMaker AI templates, choose Configure domain, Configure public domain.
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Follow the prompt in the CloudFormation console to provision your stack and set up a model.
Note
OpenSearch Service also provides a separate template to configure VPC domain. If you use this template, you need to provide the VPC ID for the Lambda function.
