Document Bedrock Titan Embedding API and clients
- Remove outdated Bedrock README fiels and add references to the main docs.
This commit is contained in:
Christian Tzolov
@@ -2,7 +2,6 @@
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[Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) is a managed service that provides foundation models from various AI providers, available through a unified API.
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Spring AI implements `API` clients for the [Bedrock models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html) along with implementations for the `ChatClient`, `StreamingChatClient` and the `EmbeddingClient`.
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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,
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@@ -21,64 +20,13 @@ Also Spring-AI provides Spring Auto-Configurations and Boot Starters for all cli
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Go to [Amazon Bedrock](https://us-east-1.console.aws.amazon.com/bedrock/home) and from the [Model Access](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess) menu on the left configure the access to the models you are going to use.
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## Quick start
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## User guides
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Add the `spring-ai-bedrock-ai-spring-boot-starter` dependency to your project POM:
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[Amazon Bedrock Overview](https://docs.spring.io/spring-ai/reference/api/bedrock.html)
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```xml
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<dependency>
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<artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
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<groupId>org.springframework.ai</groupId>
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<version>0.8.0-SNAPSHOT</version>
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</dependency>
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```
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### Connect to AWS Bedrock
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Use the `BedrockAwsConnectionProperties` to configure the AWS credentials and region:
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```shell
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spring.ai.bedrock.aws.region=us-east-1
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spring.ai.bedrock.aws.access-key=YOUR_ACCESS_KEY
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spring.ai.bedrock.aws.secret-key=YOUR_SECRET_KEY
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```
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The `region` property is compulsory.
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The AWS credentials are resolved in the following this order:
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* Spring-AI Bedrock `spring.ai.bedrock.aws.access-key` and `spring.ai.bedrock.aws.secret-key` properties.
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* Java System Properties - `aws.accessKeyId` and `aws.secretAccessKey`
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* Environment Variables - `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`
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* Web Identity Token credentials from system properties or environment variables
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* Credential profiles file at the default location (`~/.aws/credentials`) shared by all AWS SDKs and the AWS CLI
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* 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,
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* Instance profile credentials delivered through the Amazon EC2 metadata service or set the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment variables.
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### Enable selected Bedrock model
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> **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.
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Here are the supported `<model>` and `<chat|embedding>` combinations:
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| Model | Chat | Chat Streaming | Embedding |
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| ------------- | ------------- | ------------- | ------------- |
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| llama2 | Yes | Yes | No |
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| cohere | Yes | Yes | Yes |
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| anthropic | Yes | Yes | No |
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| jurassic2 | Yes | No | No |
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| titan | Yes | Yes | Yes (no batch mode!) |
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For example to enable the bedrock Llama2 Chat client you need to set the
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`spring.ai.bedrock.llama2.chat.enabled=true`.
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Next you can use the `spring.ai.bedrock.<model>.<chat|embedding>.*` properties to configure each model as provided in its documentation:
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* [Spring AI Bedrock Llama2 Chat](./README_LLAMA2_CHAT.md) - `spring.ai.bedrock.llama2.chat.enabled=true`
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* [Spring AI Bedrock Cohere Chat](./README_COHERE_CHAT.md) - `spring.ai.bedrock.cohere.chat.enabled=true`
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* [Spring AI Bedrock Cohere Embedding](./README_COHERE_EMBEDDING.md) - `spring.ai.bedrock.cohere.embedding.enabled=true`
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* [Spring AI Bedrock Anthropic Chat](./README_ANTHROPIC_CHAT.md) - `spring.ai.bedrock.anthropic.chat.enabled=true`
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* [Spring AI Bedrock Titan Chat](./README_TITAN_CHAT.md) - `spring.ai.bedrock.titan.chat.enabled=true`
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* [Spring AI Bedrock Titan Embedding](./README_TITAN_EMBEDDING.md) - `spring.ai.bedrock.titan.embedding.enabled=true`
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* (WIP) [Spring AI Bedrock Ai21 Jurassic2 Chat](./README_JURASSIC2_CHAT.md) - `spring.ai.bedrock.jurassic2.chat.enabled=true`
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- [Anthropic Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-anthropic.html).
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- [Cohere Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-cohere.html).
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- [Cohere Embedding Documentation](https://docs.spring.io/spring-ai/reference/api/embeddings/bedrock-cohere-embedding.html).
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- [Llama2 Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-llama2.html).
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- [Titan Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-titan.html).
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- [Titan Embedding Documentation](https://docs.spring.io/spring-ai/reference/api/embeddings/bedrock-titan-embedding.html).
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# Bedrock Anthropic
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Provides Bedrock Anthropic Chat API and Spring-AI chat clients.
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Visit the Spring AI [Bedrock Anthropic Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-anthropic.html).
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# Bedrock Cohere Chat
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Provides Bedrock Cohere Chat clients.
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Visit the Spring AI [Bedrock Cohere Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-cohere.html).
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# Bedrock Cohere Embedding
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Visit the Spring AI [Bedrock Cohere Embedding Documentation](https://docs.spring.io/spring-ai/reference/api/embeddings/bedrock-cohere-embedding.html).
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# Bedrock Llama2
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Provides Bedrock Llama2 Chat API and Spring-AI chat clients.
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Visit the Spring AI [Bedrock Llama2 Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-llama2.html).
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# Bedrock Titan Chat
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Visit the Spring AI [Bedrock Titan Chat Documentation](https://docs.spring.io/spring-ai/reference/api/clients/bedrock/bedrock-titan.html).
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@@ -1,95 +0,0 @@
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# 1. Bedrock Titan Embedding
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Use the [TitanEmbeddingBedrockApi.java](src/main/java/org/springframework/ai/bedrock/titan/api/TitanEmbeddingBedrockApi.java) Bedrock Embedding client to implement `EmbeddingClient`.
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Consult the the existing Cohere embedding client implementation.
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Mind that Titan doesn't support batch embedding. You have to either emulate it (could be very expensive) or throw a not supported exception.
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## 1.1 TitanEmbeddingBedrockApi
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[TitanEmbeddingBedrockApi](./src/main/java/org/springframework/ai/bedrock/titan/api/TitanEmbeddingBedrockApi.java) provides is lightweight Java client on top of AWS Bedrock [Titan Embedding models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html).
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> TitanEmbeddingBedrockApi supports Text and Image embedding.
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> **NOTE:** TitanEmbeddingBedrockApi does NOT support batch embedding.
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Following class diagram illustrates the Llama2ChatBedrockApi interface and building blocks:
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The CohereEmbeddingBedrockApi supports the `amazon.titan-embed-image-v1` and `amazon.titan-embed-image-v1` models for single and batch embedding computation.
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Here is a simple snippet how to use the api programmatically:
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```java
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TitanEmbeddingBedrockApi titanEmbedApi = new TitanEmbeddingBedrockApi(
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TitanEmbeddingModel.TITAN_EMBED_TEXT_V1.id(), Region.US_EAST_1.id());
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TitanEmbeddingRequest request = TitanEmbeddingRequest.builder()
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.withInputText("I like to eat apples.")
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.build();
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TitanEmbeddingResponse response = titanEmbedApi.embedding(request);
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```
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To embed an image you need to convert it into base64 format:
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```java
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TitanEmbeddingBedrockApi titanEmbedApi = new TitanEmbeddingBedrockApi(
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TitanEmbeddingModel.TITAN_EMBED_IMAGE_V1.id(), Region.US_EAST_1.id());
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byte[] image = new DefaultResourceLoader()
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.getResource("classpath:/spring_framework.png")
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.getContentAsByteArray();
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TitanEmbeddingRequest request = TitanEmbeddingRequest.builder()
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.withInputImage(Base64.getEncoder().encodeToString(image))
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.build();
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TitanEmbeddingResponse response = titanEmbedApi.embedding(request);
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```
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## 1.2 BedrockTitanEmbeddingClient
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[BedrockTitanEmbeddingClient](./src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanEmbeddingClient.java) implements the Spring-Ai `EmbeddingClient` on top of the `TitanEmbeddingBedrockApi`.
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You can use like this:
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```java
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@Bean
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public TitanEmbeddingBedrockApi titanEmbeddingApi() {
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return new TitanEmbeddingBedrockApi(
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TitanEmbeddingModel.TITAN_EMBED_IMAGE_V1.id(), Region.US_EAST_1.id());
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}
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@Bean
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public BedrockTitanEmbeddingClient titanEmbedding(TitanEmbeddingBedrockApi titanEmbeddingApi) {
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return new BedrockTitanEmbeddingClient(titanEmbeddingApi);
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}
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```
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or you can leverage the `spring-ai-bedrock-ai-spring-boot-starter` Boot starter. For this add the following dependency:
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```xml
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<dependency>
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<artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
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<groupId>org.springframework.ai</groupId>
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<version>0.8.0-SNAPSHOT</version>
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</dependency>
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```
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**NOTE:** You have to enable the Bedrock Titan embedding client with `spring.ai.bedrock.titan.embedding.enabled=true`.
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By default the client is disabled.
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Use the `BedrockTitanEmbeddingProperties` to configure the Bedrock Titan embedding client:
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| Property | Description | Default |
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| ------------- | ------------- | ------------- |
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| spring.ai.bedrock.aws.region | AWS region to use. | us-east-1 |
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| spring.ai.bedrock.aws.accessKey | AWS credentials access key. | |
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| spring.ai.bedrock.aws.secretKey | AWS credentials secret key. | |
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| spring.ai.bedrock.titan.embedding.enable | Enable Bedrock Titan embedding client. Disabled by default | false |
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| spring.ai.bedrock.titan.embedding.model | The model id to use. See the `TitanEmbeddingModel` for the supported models. | amazon.titan-embed-image-v1 |
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| spring.ai.bedrock.titan.embedding.inputType | Titan Embedding API input types. Could be either text or image (encoded in base64). | TEXT |
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@@ -10,6 +10,7 @@
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*** xref:api/embeddings/postgresml-embeddings.adoc[]
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*** xref:api/bedrock.adoc[Amazon Bedrock Embedding]
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**** xref:api/embeddings/bedrock-cohere-embedding.adoc[]
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**** xref:api/embeddings/bedrock-titan-embedding.adoc[]
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** xref:api/chatclient.adoc[]
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*** xref:api/clients/openai-chat.adoc[]
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*** xref:api/clients/azure-openai-chat.adoc[]
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@@ -74,7 +74,7 @@ Here are the supported `<model>` and `<chat|embedding>` combinations:
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| llama2 | Yes | Yes | No
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| cohere | Yes | Yes | Yes
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| anthropic | Yes | Yes | No
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| jurassic2 | Yes | No | No
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| jurassic2 (WIP) | Yes | No | No
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| titan | Yes | Yes | Yes (however, no batch support)
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|====
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@@ -89,5 +89,6 @@ For more information, refer to the documentation below for each supported model.
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* xref:api/clients/bedrock/bedrock-cohere.adoc[Spring AI Bedrock Cohere Chat]: `spring.ai.bedrock.cohere.chat.enabled=true`
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* xref:api/embeddings/bedrock-cohere-embedding.adoc[Spring AI Bedrock Cohere Embeddings]: `spring.ai.bedrock.cohere.embedding.enabled=true`
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* xref:api/clients/bedrock/bedrock-titan.adoc[Spring AI Bedrock Titan Chat]: `spring.ai.bedrock.titan.chat.enabled=true`
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* xref:api/embeddings/bedrock-titan-embedding.adoc[Spring AI Bedrock Titan Embeddings]: `spring.ai.bedrock.titan.embedding.enabled=true`
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// * xref:api/clients/bedrock/bedrock-jurassic2-chat.adoc[(WIP)Spring AI Bedrock Jurassic Chat]: `spring.ai.bedrock.jurassic2.chat.enabled=true`
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@@ -156,3 +156,4 @@ Internally the various `EmbeddingClient` implementations use different low-level
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* xref:api/embeddings/onnx.adoc[Spring AI Transformers (ONNX) Embeddings]
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* xref:api/embeddings/postgresml-embeddings.adoc[Spring AI PostgresML Embeddings]
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* xref:api/embeddings/bedrock-cohere-embedding.adoc[Spring AI Bedrock Cohere Embeddings]
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* xref:api/embeddings/bedrock-titan-embedding.adoc[Spring AI Bedrock Titan Embeddings]
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@@ -0,0 +1,199 @@
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= Titan Embedding
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Provides Bedrock Titan Embedding client.
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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.
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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.
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Use them as is or privately customize them with your own data.
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NOTE: Bedrock Titan Embedding supports Text and Image embedding.
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NOTE: Bedrock Titan Embedding does NOT support batch embedding.
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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.
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== Prerequisites
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Refer to the xref:api/bedrock.adoc[Spring AI documentation on Amazon Bedrock] for setting up API access.
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== Auto-configuration
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Add the `spring-ai-bedrock-ai-spring-boot-starter` dependency to your project's Maven `pom.xml` file:
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[source,xml]
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----
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-bedrock-ai-spring-boot-starter</artifactId>
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<version>0.8.0-SNAPSHOT</version>
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</dependency>
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----
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or to your Gradle `build.gradle` build file.
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[source,gradle]
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----
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dependencies {
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implementation 'org.springframework.ai:spring-ai-bedrock-ai-spring-boot-starter:0.8.0-SNAPSHOT'
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}
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----
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TIP: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories to your build file.
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=== Enable Titan Embedding Support
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By default the Titan embedding model is disabled.
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To enable it set the `spring.ai.bedrock.titan.embedding.enabled` property to `true`.
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Exporting environment variable is one way to set this configuration property:
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[source,shell]
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----
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export SPRING_AI_BEDROCK_TITAN_EMBEDDING_ENABLED=true
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----
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=== Embedding Properties
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The prefix `spring.ai.bedrock.aws` is the property prefix to configure the connection to AWS Bedrock.
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[cols="3,4,1"]
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|====
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| Property | Description | Default
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| spring.ai.bedrock.aws.region | AWS region to use. | us-east-1
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| spring.ai.bedrock.aws.access-key | AWS access key. | -
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| spring.ai.bedrock.aws.secret-key | AWS secret key. | -
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|====
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The prefix `spring.ai.bedrock.titan.embedding` (defined in `BedrockTitanEmbeddingProperties`) is the property prefix that configures the embedding client implementation for Titan.
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[cols="3,4,1"]
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|====
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| Property | Description | Default
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| spring.ai.bedrock.titan.embedding.enabled | Enable or disable support for Titan embedding | false
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| spring.ai.bedrock.titan.embedding.model | The model id to use. See the `TitanEmbeddingModel` for the supported models. | amazon.titan-embed-image-v1
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|====
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Supported values are: `amazon.titan-embed-image-v1` and `amazon.titan-embed-text-v1`.
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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].
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=== Sample Controller (Auto-configuration)
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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.
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Add a `application.properties` file, under the `src/main/resources` directory, to enable and configure the Titan Embedding client:
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[source]
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----
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spring.ai.bedrock.aws.region=eu-central-1
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spring.ai.bedrock.aws.access-key=${AWS_ACCESS_KEY_ID}
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spring.ai.bedrock.aws.secret-key=${AWS_SECRET_ACCESS_KEY}
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spring.ai.bedrock.titan.embedding.enabled=true
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----
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TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
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This will create a `EmbeddingController` implementation that you can inject into your class.
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Here is an example of a simple `@Controller` class that uses the chat client for text generations.
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[source,java]
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----
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@RestController
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public class EmbeddingController {
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private final EmbeddingClient embeddingClient;
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@Autowired
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public EmbeddingController(EmbeddingClient embeddingClient) {
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this.embeddingClient = embeddingClient;
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}
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@GetMapping("/ai/embedding")
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public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
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EmbeddingResponse embeddingResponse = this.embeddingClient.embedForResponse(List.of(message));
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return Map.of("embedding", embeddingResponse);
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}
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}
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----
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== Manual Configuration
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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.
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Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
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[source,xml]
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||||
----
|
||||
<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);
|
||||
----
|
||||
Reference in New Issue
Block a user