Spring AI implements API clients for the Bedrock models along with implementations for the ChatClient, StreamingChatClient and the EmbeddingClient.

The API clients provide structured, type-safe implementation for the Bedrock models, while the ChatClient, StreamingChatClient and the EmbeddingClient implementations provide Chat and Embedding clients compliant with the Spring-AI API. Later can be used interchangeably with the other (e.g. OpenAI, Azure OpenAI, Ollama) model clients.

Also Spring-AI provides Spring Auto-Configurations and Boot Starters for all clients, making it easy to bootstrap and configure for the Bedrocks models.

Prerequisite

AWS credentials.

If you dont have AWS account and AWS Cli configured yet then this video guide can help you to configure it: AWS CLI & SDK Setup in Less Than 4 Minutes!. You should be able to obtain your access and security keys.
Enable Bedrock models to use

Go to Amazon Bedrock and from the Model Access menu on the left configure the access to the models you are going to use.

Quick start

Add the spring-ai-bedrock-ai-spring-boot-starter dependency to your project POM:

<dependency>
 <artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
 <groupId>org.springframework.ai</groupId>
    <version>0.8.0-SNAPSHOT</version>
</dependency>

Connect to AWS Bedrock

Use the BedrockAwsConnectionProperties to configure the AWS credentials and region:

spring.ai.bedrock.aws.region=us-east-1

spring.ai.bedrock.aws.access-key=YOUR_ACCESS_KEY
spring.ai.bedrock.aws.secret-key=YOUR_SECRET_KEY

The region property is compulsory.

The AWS credentials are resolved in the following this order:

Spring-AI Bedrock spring.ai.bedrock.aws.access-key and spring.ai.bedrock.aws.secret-key properties.
Java System Properties - aws.accessKeyId and aws.secretAccessKey
Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
Web Identity Token credentials from system properties or environment variables
Credential profiles file at the default location (~/.aws/credentials) shared by all AWS SDKs and the AWS CLI
Credentials delivered through the Amazon EC2 container service if AWS_CONTAINER_CREDENTIALS_RELATIVE_URI" environment variable is set and security manager has permission to access the variable,
Instance profile credentials delivered through the Amazon EC2 metadata service or set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables.

Enable selected Bedrock model

Note

: By default all models are disabled. You have to enable the chosen Bedrock models explicitly, using the spring.ai.bedrock.<model>.<chat|embedding>.enabled=true property.

Here are the supported <model> and <chat|embedding> combinations:

Model	Chat	Chat Streaming	Embedding
llama2	Yes	Yes	No
cohere	Yes	Yes	Yes
anthropic	Yes	Yes	No
jurassic2	Yes	No	No
titan	Yes	Yes	Yes (no batch mode!)

For example to enable the bedrock Llama2 Chat client you need to set the spring.ai.bedrock.llama2.chat.enabled=true.

Next you can use the spring.ai.bedrock.<model>.<chat|embedding>.* properties to configure each model as provided in its documentation:

Spring AI Bedrock Llama2 Chat - spring.ai.bedrock.llama2.chat.enabled=true
Spring AI Bedrock Cohere Chat - spring.ai.bedrock.cohere.chat.enabled=true
Spring AI Bedrock Cohere Embedding - spring.ai.bedrock.cohere.embedding.enabled=true
Spring AI Bedrock Anthropic Chat - spring.ai.bedrock.anthropic.chat.enabled=true
Spring AI Bedrock Titan Chat - spring.ai.bedrock.titan.chat.enabled=true
Spring AI Bedrock Titan Embedding - spring.ai.bedrock.titan.embedding.enabled=true
(WIP) Spring AI Bedrock Ai21 Jurassic2 Chat - spring.ai.bedrock.jurassic2.chat.enabled=true