Cohere Chat

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

The AWS Bedrock Cohere 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 Cohere Chat Support

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

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.timeout

AWS timeout to use.

5m

spring.ai.bedrock.aws.access-key

AWS access key.

-

spring.ai.bedrock.aws.secret-key

AWS secret key.

-

The prefix spring.ai.bedrock.cohere.chat is the property prefix that configures the chat model 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

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.

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

Runtime Options

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

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

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 = chatModel.call(
    new Prompt(
        "Generate the names of 5 famous pirates.",
        BedrockCohereChatOptions.builder()
            .withTemperature(0.4)
        .build()
    ));
In addition to the model specific BedrockCohereChatOptions you can use a portable ChatOptions instance, created with the ChatOptionsBuilder#builder().

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 Cohere chat model:

spring.ai.bedrock.aws.region=eu-central-1
spring.ai.bedrock.aws.timeout=1000ms
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
replace the regions, access-key and secret-key with your AWS credentials.

This will create a BedrockCohereChatModel 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 ChatController {

    private final BedrockCohereChatModel chatModel;

    @Autowired
    public ChatController(BedrockCohereChatModel chatModel) {
        this.chatModel = chatModel;
    }

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

Manual Configuration

The BedrockCohereChatModel implements the ChatModel and StreamingChatModel and uses the Low-level CohereChatBedrockApi Client to connect to the Bedrock Cohere 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 BedrockCohereChatModel and use it for text generations:

CohereChatBedrockApi api = new CohereChatBedrockApi(CohereChatModel.COHERE_COMMAND_V14.id(),
		EnvironmentVariableCredentialsProvider.create(),
		Region.US_EAST_1.id(),
		new ObjectMapper(),
		Duration.ofMillis(1000L));

BedrockCohereChatModel chatModel = new BedrockCohereChatModel(this.api,
	    BedrockCohereChatOptions.builder()
					.withTemperature(0.6)
					.withTopK(10)
					.withTopP(0.5)
					.withMaxTokens(678)
					.build());

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

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

Low-level CohereChatBedrockApi Client

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

Following class diagram illustrates the CohereChatBedrockApi interface and building blocks:

bedrock cohere chat low level api

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.

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(),
	Duration.ofMillis(1000L));

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

CohereChatResponse response = this.cohereChatApi.chatCompletion(this.request);

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

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