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This guide explains how to configure Amazon Bedrock as the Large Language Model (LLM) provider for Atoti Intelligence.

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Prerequisites

Before configuring Amazon Bedrock, ensure the following requirements are met:
  • An AWS account with Amazon Bedrock access with appropriate permissions
  • The AWS region where Bedrock models are available

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Add the dependency

Add the Spring AI Amazon Bedrock dependency to the project. Add the following to pom.xml: 
<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter-model-bedrock-converse</artifactId>
</dependency>

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Configure the model

Configure the Amazon Bedrock model in the application configuration file. This example assumes that AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are passed as JVM arguments or environment variables. Add the following to application.yaml: 
spring:
  ai:
    bedrock:
      aws:
        access-key: ${AWS_ACCESS_KEY_ID}
        secret-key: ${AWS_SECRET_ACCESS_KEY}
        region: "your-aws-region"
      converse:
        chat:
          options:
            temperature: 0.
            maxTokens: 5000
            topP: 0.8
            model: eu.mistral.pixtral-large-2502-v1:0
Replace the following placeholders:
  • your-aws-region: The AWS region where Bedrock is available
  • Model name: Choose an available Bedrock model
Please refer to the Spring AI documentation for any additional configuration options.

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Verify the configuration

After completing the configuration, verify that the LLM connection works:
  1. Start the Atoti application
  2. Check the application logs for successful LLM initialization
  3. Test an AI feature such as Visualize This or Auto-Explain
After configuring Amazon Bedrock, proceed to set up AI features: