Amazon Bedrock provides access to AI language models from AI companies (like Anthropic, Meta, Mistral, and Amazon). By adding an LLM credential for Amazon Bedrock to Validio, you can leverage the capabilities of selected models to generate SQL queries for custom validation and filtering.

Prerequisites

Before setting up your Amazon Bedrock credential, ensure you have:

• Enabled the Allow LLM credentials setting in your Validio Workspace (requires Global Admin)
• Appropriate permissions to create credentials in your Validio namespace (requires Editor)
• An active AWS account with an IAM identity that has permissions to use the Amazon Bedrock console.
• An Amazon Bedrock API key (you can generate one from the Amazon Bedrock console). Refer to Generate an Amazon Bedrock API key in AWS documentation.

Add an AWS Bedrock Credential

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Because each Amazon Bedrock credential is tied to one language model, you should add one credential for each model you want to use in Validio.

To add an AWS Bedrock credential,

1. Navigate to Credentials and click + New Credential.

2. Namespace: Select the namespace where the resources will be created.

3. Credential Type: Select AWS Bedrock from the dropdown.

4. Configuration: Complete the required parameters.

   
   

   Parameter	Description
   Name	A unique identifier for this credential.
   Model	Select language model from the list, or enter a custom model ID or inference profile ARN. Models prefixed with Global route requests across all AWS Bedrock supported regions, while US models route only to North American regions (ca-central-1, us-east-1, us-east-2, us-west-1, us-west-2).
   AWS region	Select your valid AWS region.
   Authentication type	Select how to authenticate your identity when making requests to the Amazon Bedrock API.
   API key	Enter a valid Amazon Bedrock API key.
   Custom base URL	(Optional) Custom base URL for the Amazon Bedrock API. Only needed if using a proxy or custom endpoint.

   
   

5. (Optional) Click Test credential to validate that Validio can successfully connect to Anthropic using your configuration. If the test fails, verify your API key is correct.

6. Click Create credential to save your configuration.

Next Steps

With your Amazon Bedrock credential configured, you can leverage AI models across various Validio features:

• Custom SQL validators: Generate complex validation queries or fix errors in your SQL queries.
• SQL filters: Create sophisticated filtering conditions and data selection criteria.
• Validator recommendations: Profile your data and generate recommendations to quickly set up monitoring