Cohere Chat

提供 Bedrock Cohere Chat 客户端。将生成式 AI 功能集成到核心应用程序和提高业务成果的工作流中。

Provides Bedrock Cohere Chat 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 Chat Support

默认情况下，Cohere 模型被禁用。若要启用它，请将 spring.ai.bedrock.cohere.chat.enabled 属性设置为 true。导出环境变量是一种设置此配置属性的方法：

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

export SPRING_AI_BEDROCK_COHERE_CHAT_ENABLED=true

Chat 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.

-

spring.ai.bedrock.cohere.chat 前缀是配置 Cohere 的聊天客户端实现的属性前缀。

The prefix spring.ai.bedrock.cohere.chat is the property prefix that configures the chat client implementation for Cohere.

Property Description Default

spring.ai.bedrock.cohere.chat.enabled

Enable or disable support for Cohere

false

spring.ai.bedrock.cohere.chat.model

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

cohere.command-text-v14

spring.ai.bedrock.cohere.chat.options.temperature

Controls the randomness of the output. Values can range over [0.0,1.0]

0.7

spring.ai.bedrock.cohere.chat.options.topP

The maximum cumulative probability of tokens to consider when sampling.

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.topK

Specify the number of token choices the model uses to generate the next token

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.maxTokens

Specify the maximum number of tokens to use in the generated response.

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.stopSequences

Configure up to four sequences that the model recognizes.

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.returnLikelihoods

The token likelihoods are returned with the response.

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.numGenerations

The maximum number of generations that the model should return.

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.logitBias

Prevents the model from generating unwanted tokens or incentivize the model to include desired tokens.

AWS Bedrock default

spring.ai.bedrock.cohere.chat.options.truncate

Specifies how the API handles inputs longer than the maximum token length

AWS Bedrock default

查看 CohereChatModel以了解其他模型 ID。支持的值包括：cohere.command-light-text-v14`和 `cohere.command-text-v14。还可以从 AWS Bedrock documentation for base model IDs中找到模型 ID 值。

Look at the CohereChatModel for other model IDs. Supported values are: cohere.command-light-text-v14 and cohere.command-text-v14. Model ID values can also be found in the AWS Bedrock documentation for base model IDs.

所有以 spring.ai.bedrock.cohere.chat.options 为前缀的属性都可以通过在 Prompt 调用中添加请求特定的 Chat Options 来在运行时进行覆盖。

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

Chat Options

BedrockCohereChatOptions.java提供模型配置，例如 temperature、topK、topP 等。

The BedrockCohereChatOptions.java provides model configurations, such as temperature, topK, topP, etc.

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

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

在运行时，你可以通过向 Prompt 调用添加新的请求特定选项来覆盖默认选项。例如，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 Prompt call. For example to override the default temperature for a specific request:

ChatResponse response = chatClient.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        BedrockCohereChatOptions.builder()
            .withTemperature(0.4)
        .build()
    ));

  1. 除了模型特定的 BedrockCohereChatOptions 之外，你还可以使用用 ChatOptionsBuilder#builder() 创建的便携式 ChatOptions 实例。

In addition to the model specific BedrockCohereChatOptions you can use a portable ChatOptions instance, created with the ChatOptionsBuilder#builder().

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 Chat 客户端：

Add a application.properties file, under the src/main/resources directory, to enable and configure the Cohere Chat 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.chat.enabled=true
spring.ai.bedrock.cohere.chat.options.temperature=0.8

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.

这将创建一个你可以注入到你的类中的 BedrockCohereChatClient 实现。下面是一个简单的 @Controller 类的示例，它使用聊天客户端来生成文本。

This will create a BedrockCohereChatClient 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 ChatController {

    private final BedrockCohereChatClient chatClient;

    @Autowired
    public ChatController(BedrockCohereChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @GetMapping("/ai/generate")
    public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        return Map.of("generation", chatClient.call(message));
    }

    @GetMapping("/ai/generateStream")
	public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return chatClient.stream(prompt);
    }
}

Manual Configuration

BedrockCohereChatClient实现 ChatClient`和 `StreamingChatClient，并使用 Low-level CohereChatBedrockApi Client连接到 Bedrock Cohere 服务。

The BedrockCohereChatClient implements the ChatClient and StreamingChatClient and uses the Low-level CohereChatBedrockApi 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 BedrockCohereChatClient并使用它进行文本生成：

Next, create an BedrockCohereChatClient and use it for text generations:

CohereChatBedrockApi api = new CohereChatBedrockApi(CohereChatModel.COHERE_COMMAND_V14.id(),
		EnvironmentVariableCredentialsProvider.create(), Region.US_EAST_1.id(), new ObjectMapper());

BedrockCohereChatClient chatClient = new BedrockCohereChatClient(api,
	    BedrockCohereChatOptions.builder()
					.withTemperature(0.6f)
					.withTopK(10)
					.withTopP(0.5f)
					.withMaxTokens(678)
					.build()

ChatResponse response = chatClient.call(
    new Prompt("Generate the names of 5 famous pirates."));

// Or with streaming responses
Flux<ChatResponse> response = chatClient.stream(
    new Prompt("Generate the names of 5 famous pirates."));

Low-level CohereChatBedrockApi Client

CohereChatBedrockApi提供轻量级 Java 客户端，基于 AWS Bedrock Cohere Command models。

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

以下类图说明了 CohereChatBedrockApi 接口及其构建模块：

Following class diagram illustrates the CohereChatBedrockApi interface and building blocks:

bedrock cohere chat low level api

CohereChatBedrockApi 支持 cohere.command-light-text-v14 和 cohere.command-text-v14 模型，用于同步（例如 chatCompletion()）和流式（例如 chatCompletionStream()）请求。

The CohereChatBedrockApi supports the cohere.command-light-text-v14 and cohere.command-text-v14 models for both synchronous (e.g. chatCompletion()) and streaming (e.g. chatCompletionStream()) requests.

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:

CohereChatBedrockApi cohereChatApi = new CohereChatBedrockApi(
	CohereChatModel.COHERE_COMMAND_V14.id(),
	Region.US_EAST_1.id());

var request = CohereChatRequest
	.builder("What is the capital of Bulgaria and what is the size? What it the national anthem?")
	.withStream(false)
	.withTemperature(0.5f)
	.withTopP(0.8f)
	.withTopK(15)
	.withMaxTokens(100)
	.withStopSequences(List.of("END"))
	.withReturnLikelihoods(CohereChatRequest.ReturnLikelihoods.ALL)
	.withNumGenerations(3)
	.withLogitBias(null)
	.withTruncate(Truncate.NONE)
	.build();

CohereChatResponse response = cohereChatApi.chatCompletion(request);

var request = CohereChatRequest
	.builder("What is the capital of Bulgaria and what is the size? What it the national anthem?")
	.withStream(true)
	.withTemperature(0.5f)
	.withTopP(0.8f)
	.withTopK(15)
	.withMaxTokens(100)
	.withStopSequences(List.of("END"))
	.withReturnLikelihoods(CohereChatRequest.ReturnLikelihoods.ALL)
	.withNumGenerations(3)
	.withLogitBias(null)
	.withTruncate(Truncate.NONE)
	.build();

Flux<CohereChatResponse.Generation> responseStream = cohereChatApi.chatCompletionStream(request);
List<CohereChatResponse.Generation> responses = responseStream.collectList().block();