Cohere Embeddings

提供 Bedrock Cohere Embedding 客户端。将生成式 AI 功能集成到核心应用程序和工作流中，以改善业务成果。

Provides Bedrock Cohere Embedding client. Integrate generative AI capabilities into essential apps and workflows that improve business outcomes.

AWS Bedrock Cohere Model Page和 Amazon Bedrock User Guide包含有关如何使用 AWS 托管模型的详细信息。

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

Prerequisites

请参阅 Spring AI documentation on Amazon Bedrock 以设置 API 访问。

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

Add Repositories and BOM

Spring AI 工件发布在 Spring Milestone 和 Snapshot 存储库中。有关将这些存储库添加到你的构建系统的说明，请参阅 Repositories 部分。

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

为了帮助进行依赖项管理，Spring AI 提供了一个 BOM（物料清单）以确保在整个项目中使用一致版本的 Spring AI。有关将 Spring AI BOM 添加到你的构建系统的说明，请参阅 Dependency Management 部分。

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

将 spring-ai-bedrock-ai-spring-boot-starter 依赖项添加到项目 Maven 的 pom.xml 文件：

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>

或添加到 Gradle build.gradle 构建文件中。

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock-ai-spring-boot-starter'
}

  1. 参见 Dependency Management 部分，将 Spring AI BOM 添加到你的构建文件中。

Refer to the Dependency Management section to add the Spring AI BOM to your build file.

Enable Cohere Embedding Support

默认情况下，Cohere 模型被禁用。要启用它，请将 spring.ai.bedrock.cohere.embedding.enabled 属性设置为 true。导出环境变量是设置此配置属性的一种方法：

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

export SPRING_AI_BEDROCK_COHERE_EMBEDDING_ENABLED=true

Embedding Properties

spring.ai.bedrock.aws 前缀是配置与 AWS Bedrock 的连接的属性前缀。

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.

-

前缀 spring.ai.bedrock.cohere.embedding （在 BedrockCohereEmbeddingProperties 中定义）是配置 Cohere 嵌入式客户端实现的属性前缀。

The prefix spring.ai.bedrock.cohere.embedding (defined in BedrockCohereEmbeddingProperties) is the property prefix that configures the embedding client implementation for Cohere.

Property

Description

Default

spring.ai.bedrock.cohere.embedding.enabled

Enable or disable support for Cohere

false

spring.ai.bedrock.cohere.embedding.model

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

cohere.embed-multilingual-v3

spring.ai.bedrock.cohere.embedding.options.input-type

Prepends special tokens to differentiate each type from one another. You should not mix different types together, except when mixing types for for search and retrieval. In this case, embed your corpus with the search_document type and embedded queries with type search_query type.

SEARCH_DOCUMENT

spring.ai.bedrock.cohere.embedding.options.truncate

Specifies how the API handles inputs longer than the maximum token length. If you specify LEFT or RIGHT, the model discards the input until the remaining input is exactly the maximum input token length for the model.

NONE

查看 CohereEmbeddingModel 获取其他模型 ID。支持的值为： cohere.embed-multilingual-v3 和 cohere.embed-english-v3。模型 ID 值也可以在 AWS Bedrock documentation for base model IDs 中找到。

Look at the CohereEmbeddingModel for other model IDs. Supported values are: cohere.embed-multilingual-v3 and cohere.embed-english-v3. Model ID values can also be found in the AWS Bedrock documentation for base model IDs.

所有前缀为 spring.ai.bedrock.cohere.embedding.options 的属性可以通过在 EmbeddingRequest 调用中添加一个请求特定的 Embedding Options 来在运行时覆盖。

All properties prefixed with spring.ai.bedrock.cohere.embedding.options can be overridden at runtime by adding a request specific Embedding Options to the EmbeddingRequest call.

Embedding Options

BedrockCohereEmbeddingOptions.java 提供模型配置，例如 input-type 或 truncate。

The BedrockCohereEmbeddingOptions.java provides model configurations, such as input-type or truncate.

在启动时，可以使用 BedrockCohereEmbeddingClient(api, options) 构造函数或 spring.ai.bedrock.cohere.embedding.options.* 属性配置默认选项。

On start-up, the default options can be configured with the BedrockCohereEmbeddingClient(api, options) constructor or the spring.ai.bedrock.cohere.embedding.options.* properties.

在运行时，你可以通过将新的特定于请求的选项添加到 EmbeddingRequest 调用来覆盖默认选项。例如，要覆盖特定请求的默认温度：

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 = embeddingClient.call(
    new EmbeddingRequest(List.of("Hello World", "World is big and salvation is near"),
        BedrockCohereEmbeddingOptions.builder()
        	.withInputType(InputType.SEARCH_DOCUMENT)
        .build()));

Sample Controller (Auto-configuration)

Create一个新的 Spring Boot 项目，并将 `spring-ai-bedrock-ai-spring-boot-starter`添加到您的 pom（或 gradle）依赖项。

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

在 src/main/resources 目录下添加一个 application.properties 文件，以启用和配置 Cohere Embedding 客户端：

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

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.cohere.embedding.enabled=true
spring.ai.bedrock.cohere.embedding.options.input-type=search-document

将 regions、access-key 和 secret-key 替换为 AWS 凭证。

replace the regions, access-key and secret-key with your AWS credentials.

这将创建一个 BedrockCohereEmbeddingClient 实现，你可以将其注入到你的类中。这是一个使用聊天客户端进行文本生成的简单 @Controller 类的示例：

This will create a BedrockCohereEmbeddingClient 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.

@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

BedrockCohereEmbeddingClient实现`EmbeddingClient`，并且使用Low-level CohereEmbeddingBedrockApi Client连接到Bedrock Cohere服务。

The BedrockCohereEmbeddingClient implements the EmbeddingClient and uses the Low-level CohereEmbeddingBedrockApi Client to connect to the Bedrock Cohere service.

将 spring-ai-bedrock 依赖项添加到项目的 Maven pom.xml 文件：

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>

或添加到 Gradle build.gradle 构建文件中。

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock'
}

  1. 参见 Dependency Management 部分，将 Spring AI BOM 添加到你的构建文件中。

Refer to the Dependency Management section to add the Spring AI BOM to your build file.

接下来，创建一个 BedrockCohereEmbeddingClient 并在其中使用文本嵌入：

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

var cohereEmbeddingApi =new CohereEmbeddingBedrockApi(
		CohereEmbeddingModel.COHERE_EMBED_MULTILINGUAL_V1.id(),
		EnvironmentVariableCredentialsProvider.create(), Region.US_EAST_1.id(), new ObjectMapper());


var embeddingClient = new BedrockCohereEmbeddingClient(cohereEmbeddingApi);

EmbeddingResponse embeddingResponse = embeddingClient
	.embedForResponse(List.of("Hello World", "World is big and salvation is near"));

Low-level CohereEmbeddingBedrockApi Client

CohereEmbeddingBedrockApi 提供轻量级 Java 客户端，在 AWS Bedrock Cohere Command models 上。

The CohereEmbeddingBedrockApi provides is lightweight Java client on top of AWS Bedrock Cohere Command models.

以下类图说明了 CohereEmbeddingBedrockApi 接口和构建块：

Following class diagram illustrates the CohereEmbeddingBedrockApi interface and building blocks:

bedrock cohere embedding low level api

CohereEmbeddingBedrockApi 支持 cohere.embed-english-v3 和 cohere.embed-multilingual-v3 模型用于单个和批处理嵌入计算。

The CohereEmbeddingBedrockApi supports the cohere.embed-english-v3 and cohere.embed-multilingual-v3 models for single and batch embedding computation.

下面是一个简单的片段，说明如何以编程方式使用 API：

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

CohereEmbeddingBedrockApi api = new CohereEmbeddingBedrockApi(
		CohereEmbeddingModel.COHERE_EMBED_MULTILINGUAL_V1.id(),
		EnvironmentVariableCredentialsProvider.create(),
		Region.US_EAST_1.id(), new ObjectMapper());

CohereEmbeddingRequest request = new CohereEmbeddingRequest(
		List.of("I like to eat apples", "I like to eat oranges"),
		CohereEmbeddingRequest.InputType.search_document,
		CohereEmbeddingRequest.Truncate.NONE);

CohereEmbeddingResponse response = api.embedding(request);