Bedrock Converse API

Amazon Bedrock Converse API provides a unified interface for conversational AI models with enhanced capabilities including function/tool calling, multimodal inputs, and streaming responses.

The Bedrock Converse API has the following high-level features:

  • Tool/Function Calling: Support for function definitions and tool use during conversations

  • Multimodal Input: Ability to process both text and image inputs in conversations

  • Streaming Support: Real-time streaming of model responses

  • System Messages: Support for system-level instructions and context setting

The Bedrock Converse API provides a unified interface across multiple model providers while handling AWS-specific authentication and infrastructure concerns.

Following the Bedrock recommendations, Spring AI is transitioning to using Amazon Bedrock’s Converse API for all chat conversation implementations in Spring AI. While the existing InvokeModel API supports conversation applications, we strongly recommend adopting the Converse API for several key benefits:

  • Unified Interface: Write your code once and use it with any supported Amazon Bedrock model

  • Model Flexibility: Seamlessly switch between different conversation models without code changes

  • Extended Functionality: Support for model-specific parameters through dedicated structures

  • Tool Support: Native integration with function calling and tool usage capabilities

  • Multimodal Capabilities: Built-in support for vision and other multimodal features

  • Future-Proof: Aligned with Amazon Bedrock’s recommended best practices

The Converse API does not support embedding operations, so these will remain in the current API and the embedding model functionality in the existing InvokeModel API will be maintained

Prerequisites

Refer to Getting started with Amazon Bedrock for setting up API access

  • Obtain AWS credentials: If you don’t have an AWS account and AWS CLI configured yet, this video guide can help you configure it: AWS CLI & SDK Setup in Less Than 4 Minutes!. You should be able to obtain your access and security keys.

  • Enable the Models to use: Go to Amazon Bedrock and from the Model Access menu on the left, configure access to the models you are going to use.

Auto-configuration

Add the spring-ai-bedrock-converse-spring-boot-starter dependency to your project’s Maven pom.xml or Gradle build.gradle build files:

  • Maven

  • Gradle

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-bedrock-converse-spring-boot-starter</artifactId>
</dependency>
dependencies {
    implementation 'org.springframework.ai:spring-ai-bedrock-converse-spring-boot-starter'
}
Refer to the Dependency Management section to add the Spring AI BOM to your build file.

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.

-

spring.ai.bedrock.aws.session-token

AWS session token for temporary credentials.

-

The prefix spring.ai.bedrock.converse.chat is the property prefix that configures the chat model implementation for the Converse API.

Property Description Default

spring.ai.bedrock.converse.chat.enabled

Enable Bedrock Converse chat model.

true

spring.ai.bedrock.converse.chat.options.model

The model ID to use. You can use the Supported models and model features

None. Select your modelId from the AWS Bedrock console.

spring.ai.bedrock.converse.chat.options.temperature

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

0.8

spring.ai.bedrock.converse.chat.options.top-p

The maximum cumulative probability of tokens to consider when sampling.

AWS Bedrock default

spring.ai.bedrock.converse.chat.options.top-k

Number of token choices for generating the next token.

AWS Bedrock default

spring.ai.bedrock.converse.chat.options.max-tokens

Maximum number of tokens in the generated response.

500

Runtime Options

Use the portable ChatOptions or FunctionCallingOptions portable builders to create model configurations, such as temperature, maxToken, topP, etc.

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

At run-time you can override the default options by adding new, request specific, options to the Prompt call:

var options = FunctionCallingOptions.builder()
        .withModel("anthropic.claude-3-5-sonnet-20240620-v1:0")
        .withTemperature(0.6)
        .withMaxTokens(300)
        .withFunctionCallbacks(List.of(FunctionCallback.builder()
            .description("Get the weather in location. Return temperature in 36°F or 36°C format. Use multi-turn if needed.")
            .function("getCurrentWeather", new WeatherService())
            .inputType(WeatherService.Request.class)
            .build()))
        .build();

ChatResponse response = chatModel.call(new Prompt("What is current weather in Amsterdam?", options));

Tool/Function Calling

The Bedrock Converse API supports function calling capabilities, allowing models to use tools during conversations. Here’s an example of how to define and use functions:

@Bean
@Description("Get the weather in location. Return temperature in 36°F or 36°C format.")
public Function<Request, Response> weatherFunction() {
    return new MockWeatherService();
}

String response = ChatClient.create(this.chatModel)
        .prompt("What's the weather like in Boston?")
        .function("weatherFunction")
        .call()
        .content();

Sample Controller

Create a new Spring Boot project and add the spring-ai-bedrock-converse-spring-boot-starter to your dependencies.

Add an application.properties file under src/main/resources:

spring.ai.bedrock.aws.region=eu-central-1
spring.ai.bedrock.aws.timeout=10m
spring.ai.bedrock.aws.access-key=${AWS_ACCESS_KEY_ID}
spring.ai.bedrock.aws.secret-key=${AWS_SECRET_ACCESS_KEY}
# session token is only required for temporary credentials
spring.ai.bedrock.aws.session-token=${AWS_SESSION_TOKEN}

spring.ai.bedrock.converse.chat.options.temperature=0.8
spring.ai.bedrock.converse.chat.options.top-k=15

Here’s an example controller using the chat model:

@RestController
public class ChatController {

    private final ChatClient chatClient;

    @Autowired
    public ChatController(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }

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

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