Document Bedrock Titan Embedding API and clients

- Remove outdated Bedrock README fiels and add references to the main docs.

spring-ai-docs/src/main/antora/modules/ROOT/nav.adoc

@@ -10,6 +10,7 @@
 *** xref:api/embeddings/postgresml-embeddings.adoc[]
 *** xref:api/bedrock.adoc[Amazon Bedrock Embedding]
 **** xref:api/embeddings/bedrock-cohere-embedding.adoc[]
+**** xref:api/embeddings/bedrock-titan-embedding.adoc[]
 ** xref:api/chatclient.adoc[]
 *** xref:api/clients/openai-chat.adoc[]
 *** xref:api/clients/azure-openai-chat.adoc[]

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/bedrock.adoc

@@ -74,7 +74,7 @@ Here are the supported `<model>` and `<chat|embedding>` combinations:
 | llama2     | Yes  | Yes            | No
 | cohere     | Yes  | Yes            | Yes
 | anthropic  | Yes  | Yes            | No
-| jurassic2  | Yes  | No             | No
+| jurassic2 (WIP)  | Yes  | No             | No
 | titan      | Yes  | Yes            | Yes (however, no batch support)
 |====
 
@@ -89,5 +89,6 @@ For more information, refer to the documentation below for each supported model.
 * xref:api/clients/bedrock/bedrock-cohere.adoc[Spring AI Bedrock Cohere Chat]: `spring.ai.bedrock.cohere.chat.enabled=true`
 * xref:api/embeddings/bedrock-cohere-embedding.adoc[Spring AI Bedrock Cohere Embeddings]: `spring.ai.bedrock.cohere.embedding.enabled=true`
 * xref:api/clients/bedrock/bedrock-titan.adoc[Spring AI Bedrock Titan Chat]: `spring.ai.bedrock.titan.chat.enabled=true`
+* xref:api/embeddings/bedrock-titan-embedding.adoc[Spring AI Bedrock Titan Embeddings]: `spring.ai.bedrock.titan.embedding.enabled=true`
 
 // * xref:api/clients/bedrock/bedrock-jurassic2-chat.adoc[(WIP)Spring AI Bedrock Jurassic Chat]: `spring.ai.bedrock.jurassic2.chat.enabled=true`

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings.adoc

@@ -156,3 +156,4 @@ Internally the various `EmbeddingClient` implementations use different low-level
 * xref:api/embeddings/onnx.adoc[Spring AI Transformers (ONNX) Embeddings]
 * xref:api/embeddings/postgresml-embeddings.adoc[Spring AI PostgresML Embeddings]
 * xref:api/embeddings/bedrock-cohere-embedding.adoc[Spring AI Bedrock Cohere Embeddings]
+* xref:api/embeddings/bedrock-titan-embedding.adoc[Spring AI Bedrock Titan Embeddings]

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/bedrock-titan-embedding.adoc

@@ -0,0 +1,199 @@
+= Titan Embedding
+
+Provides Bedrock Titan Embedding client.
+link:https://aws.amazon.com/bedrock/titan/[Amazon Titan] foundation models (FMs) provide customers with a breadth of high-performing image, multimodal, 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.
+
+NOTE: Bedrock Titan Embedding supports Text and Image embedding.
+
+NOTE: Bedrock Titan Embedding does NOT support batch embedding.
+
+The https://aws.amazon.com/bedrock/titan/[AWS Bedrock Titan Model Page] and https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html[Amazon Bedrock User Guide] contains detailed information on how to use the AWS hosted model.
+
+== Prerequisites
+
+Refer to the xref:api/bedrock.adoc[Spring AI documentation on Amazon Bedrock] for setting up API access.
+
+== Auto-configuration
+
+Add the `spring-ai-bedrock-ai-spring-boot-starter` dependency to your project's Maven `pom.xml` file:
+
+[source,xml]
+----
+<dependency>
+  <groupId>org.springframework.ai</groupId>
+  <artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
+  <version>0.8.0-SNAPSHOT</version>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,gradle]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-bedrock-ai-spring-boot-starter:0.8.0-SNAPSHOT'
+}
+----
+
+TIP: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories 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:
+
+[source,shell]
+----
+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.
+
+[cols="3,4,1"]
+|====
+| 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 client implementation for Titan.
+
+[cols="3,4,1"]
+|====
+| 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` and `amazon.titan-embed-text-v1`.
+Model ID values can also be found in the https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html[AWS Bedrock documentation for base model IDs].
+
+
+=== Sample Controller (Auto-configuration)
+
+https://start.spring.io/[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 client:
+
+[source]
+----
+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
+----
+
+TIP: 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 client for text generations.
+
+[source,java]
+----
+@RestController
+public class EmbeddingController {
+
+    private final EmbeddingClient embeddingClient;
+
+    @Autowired
+    public EmbeddingController(EmbeddingClient embeddingClient) {
+        this.embeddingClient = embeddingClient;
+    }
+
+    @GetMapping("/ai/embedding")
+    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
+        EmbeddingResponse embeddingResponse = this.embeddingClient.embedForResponse(List.of(message));
+        return Map.of("embedding", embeddingResponse);
+    }
+}
+----
+
+== Manual Configuration
+
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanEmbeddingClient.java[BedrockTitanEmbeddingClient] implements the `EmbeddingClient` and uses the <<low-level-api>> to connect to the Bedrock Titan service.
+
+Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
+
+[source,xml]
+----
+<dependency>
+    <groupId>org.springframework.ai</groupId>
+    <artifactId>spring-ai-bedrock</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
+</dependency>
+----
+
+or to your Gradle `build.gradle` build file.
+
+[source,gradle]
+----
+dependencies {
+    implementation 'org.springframework.ai:spring-ai-bedrock:0.8.0-SNAPSHOT'
+}
+----
+
+TIP: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories to your build file.
+
+Next, create an https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanEmbeddingClient.java[BedrockTitanEmbeddingClient] and use it for text embeddings:
+
+[source,java]
+----
+var titanEmbeddingApi = new TitanEmbeddingBedrockApi(
+	TitanEmbeddingModel.TITAN_EMBED_IMAGE_V1.id(), Region.US_EAST_1.id());
+
+var embeddingClient  new BedrockTitanEmbeddingClient(titanEmbeddingApi);
+
+EmbeddingResponse embeddingResponse = embeddingClient
+	.embedForResponse(List.of("Hello World")); // NOTE titan does not support batch embedding.
+----
+
+== Low-level TitanEmbeddingBedrockApi Client [[low-level-api]]
+
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/api/TitanEmbeddingBedrockApi.java[TitanEmbeddingBedrockApi] provides is lightweight Java client on top of AWS Bedrock https://docs.aws.amazon.com/bedrock/latest/userguide/titan-multiemb-models.html[Titan Embedding models].
+
+Following class diagram illustrates the TitanEmbeddingBedrockApi interface and building blocks:
+
+image::bedrock/bedrock-titan-embedding-low-level-api.jpg[align="center", width="500px"]
+
+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:
+
+[source,java]
+----
+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 = titanEmbedApi.embedding(request);
+----
+
+To embed an image you need to convert it into `base64` format:
+
+[source,java]
+----
+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(image))
+	.build();
+
+TitanEmbeddingResponse response = titanEmbedApi.embedding(request);
+----