Titan Embeddings

Provides Bedrock Titan Embedding model. Amazon Titan foundation models (FMs) provide customers with a breadth of high-performing image, multimodal embeddings, and text model choices, via a fully managed API. Amazon Titan models are created by AWS and pretrained on large datasets, making them powerful, general-purpose models built to support a variety of use cases, while also supporting the responsible use of AI. Use them as is or privately customize them with your own data.

Bedrock Titan Embedding supports Text and Image embedding.
Bedrock Titan Embedding does NOT support batch embedding.

The AWS Bedrock Titan Model Page and Amazon Bedrock User Guide contains detailed information on how to use the AWS hosted model.

Prerequisites

Refer to the Spring AI documentation on Amazon Bedrock for setting up API access.

Add Repositories and BOM

Spring AI artifacts are published in Spring Milestone and Snapshot repositories. Refer to the Repositories section to add these repositories to your build system.

To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the Dependency Management section to add the Spring AI BOM to your build system.

Auto-configuration

Add the spring-ai-bedrock-ai-spring-boot-starter dependency to your project’s Maven pom.xml file:

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

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock-ai-spring-boot-starter'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Enable Titan Embedding Support

By default the Titan embedding model is disabled. To enable it set the spring.ai.bedrock.titan.embedding.enabled property to true. Exporting environment variable is one way to set this configuration property:

export SPRING_AI_BEDROCK_TITAN_EMBEDDING_ENABLED=true

Embedding Properties

The prefix spring.ai.bedrock.aws is the property prefix to configure the connection to AWS Bedrock.

Property Description Default

spring.ai.bedrock.aws.region

AWS region to use.

us-east-1

spring.ai.bedrock.aws.access-key

AWS access key.

-

spring.ai.bedrock.aws.secret-key

AWS secret key.

-

The prefix spring.ai.bedrock.titan.embedding (defined in BedrockTitanEmbeddingProperties) is the property prefix that configures the embedding model implementation for Titan.

Property

Description

Default

spring.ai.bedrock.titan.embedding.enabled

Enable or disable support for Titan embedding

false

spring.ai.bedrock.titan.embedding.model

The model id to use. See the TitanEmbeddingModel for the supported models.

amazon.titan-embed-image-v1

Supported values are: amazon.titan-embed-image-v1, amazon.titan-embed-text-v1 and amazon.titan-embed-text-v2:0. Model ID values can also be found in the AWS Bedrock documentation for base model IDs.

Runtime Options

The BedrockTitanEmbeddingOptions.java provides model configurations, such as input-type. On start-up, the default options can be configured with the BedrockTitanEmbeddingModel(api).withInputType(type) method or the spring.ai.bedrock.titan.embedding.input-type properties.

At run-time you can override the default options by adding new, request specific, options to the EmbeddingRequest call. For example to override the default temperature for a specific request:

EmbeddingResponse embeddingResponse = embeddingModel.call(
    new EmbeddingRequest(List.of("Hello World", "World is big and salvation is near"),
        BedrockTitanEmbeddingOptions.builder()
        	.withInputType(InputType.TEXT)
        .build()));

Sample Controller

Create a new Spring Boot project and add the spring-ai-bedrock-ai-spring-boot-starter to your pom (or gradle) dependencies.

Add a application.properties file, under the src/main/resources directory, to enable and configure the Titan Embedding model:

spring.ai.bedrock.aws.region=eu-central-1
spring.ai.bedrock.aws.access-key=${AWS_ACCESS_KEY_ID}
spring.ai.bedrock.aws.secret-key=${AWS_SECRET_ACCESS_KEY}

spring.ai.bedrock.titan.embedding.enabled=true
replace the regions, access-key and secret-key with your AWS credentials.

This will create a EmbeddingController implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the chat model for text generations.

@RestController
public class EmbeddingController {

    private final EmbeddingModel embeddingModel;

    @Autowired
    public EmbeddingController(EmbeddingModel embeddingModel) {
        this.embeddingModel = embeddingModel;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        EmbeddingResponse embeddingResponse = this.embeddingModel.embedForResponse(List.of(message));
        return Map.of("embedding", embeddingResponse);
    }
}

Manual Configuration

The BedrockTitanEmbeddingModel implements the EmbeddingModel and uses the Low-level TitanEmbeddingBedrockApi Client to connect to the Bedrock Titan service.

Add the spring-ai-bedrock dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-bedrock</artifactId>
</dependency>

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Next, create an BedrockTitanEmbeddingModel and use it for text embeddings:

var titanEmbeddingApi = new TitanEmbeddingBedrockApi(
	TitanEmbeddingModel.TITAN_EMBED_IMAGE_V1.id(), Region.US_EAST_1.id());

var embeddingModel = new BedrockTitanEmbeddingModel(this.titanEmbeddingApi);

EmbeddingResponse embeddingResponse = this.embeddingModel
	.embedForResponse(List.of("Hello World")); // NOTE titan does not support batch embedding.

Low-level TitanEmbeddingBedrockApi Client

The TitanEmbeddingBedrockApi provides is lightweight Java client on top of AWS Bedrock Titan Embedding models.

Following class diagram illustrates the TitanEmbeddingBedrockApi interface and building blocks:

bedrock titan embedding low level api

The TitanEmbeddingBedrockApi supports the amazon.titan-embed-image-v1 and amazon.titan-embed-image-v1 models for single and batch embedding computation.

Here is a simple snippet how to use the api programmatically:

TitanEmbeddingBedrockApi titanEmbedApi = new TitanEmbeddingBedrockApi(
		TitanEmbeddingModel.TITAN_EMBED_TEXT_V1.id(), Region.US_EAST_1.id());

TitanEmbeddingRequest request = TitanEmbeddingRequest.builder()
	.withInputText("I like to eat apples.")
	.build();

TitanEmbeddingResponse response = this.titanEmbedApi.embedding(this.request);

To embed an image you need to convert it into base64 format:

TitanEmbeddingBedrockApi titanEmbedApi = new TitanEmbeddingBedrockApi(
		TitanEmbeddingModel.TITAN_EMBED_IMAGE_V1.id(), Region.US_EAST_1.id());

byte[] image = new DefaultResourceLoader()
	.getResource("classpath:/spring_framework.png")
	.getContentAsByteArray();


TitanEmbeddingRequest request = TitanEmbeddingRequest.builder()
	.withInputImage(Base64.getEncoder().encodeToString(this.image))
	.build();

TitanEmbeddingResponse response = this.titanEmbedApi.embedding(this.request);