Amazon Bedrock Runtime-Beispiele mit SDK für Kotlin

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova.

import aws.sdk.kotlin.services.bedrockruntime.BedrockRuntimeClient
import aws.sdk.kotlin.services.bedrockruntime.model.ContentBlock
import aws.sdk.kotlin.services.bedrockruntime.model.ConversationRole
import aws.sdk.kotlin.services.bedrockruntime.model.ConverseRequest
import aws.sdk.kotlin.services.bedrockruntime.model.Message

/**
 * This example demonstrates how to use the Amazon Nova foundation models to generate text.
 * It shows how to:
 * - Set up the Amazon Bedrock runtime client
 * - Create a message
 * - Configure and send a request
 * - Process the response
 */
suspend fun main() {
    converse().also { println(it) }
}

suspend fun converse(): String {
    // Create and configure the Bedrock runtime client
    BedrockRuntimeClient { region = "us-east-1" }.use { client ->

        // Specify the model ID. For the latest available models, see:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
        val modelId = "amazon.nova-lite-v1:0"

        // Create the message with the user's prompt
        val prompt = "Describe the purpose of a 'hello world' program in one line."
        val message = Message {
            role = ConversationRole.User
            content = listOf(ContentBlock.Text(prompt))
        }

        // Configure the request with optional model parameters
        val request = ConverseRequest {
            this.modelId = modelId
            messages = listOf(message)
            inferenceConfig {
                maxTokens = 500 // Maximum response length
                temperature = 0.5F // Lower values: more focused output
                // topP = 0.8F // Alternative to temperature
            }
        }

        // Send the request and process the model's response
        runCatching {
            val response = client.converse(request)
            return response.output!!.asMessage().content.first().asText()
        }.getOrElse { error ->
            error.message?.let { e -> System.err.println("ERROR: Can't invoke '$modelId'. Reason: $e") }
            throw RuntimeException("Failed to generate text with model $modelId", error)
        }
    }
}

Senden Sie mithilfe der Converse-API von Bedrock eine Textnachricht an Amazon Nova und verarbeiten Sie den Antwortstream in Echtzeit.

import aws.sdk.kotlin.services.bedrockruntime.BedrockRuntimeClient
import aws.sdk.kotlin.services.bedrockruntime.model.ContentBlock
import aws.sdk.kotlin.services.bedrockruntime.model.ConversationRole
import aws.sdk.kotlin.services.bedrockruntime.model.ConverseStreamOutput
import aws.sdk.kotlin.services.bedrockruntime.model.ConverseStreamRequest
import aws.sdk.kotlin.services.bedrockruntime.model.Message

/**
 * This example demonstrates how to use the Amazon Nova foundation models
 * to generate streaming text responses.
 * It shows how to:
 * - Set up the Amazon Bedrock runtime client
 * - Create a message with a prompt
 * - Configure a streaming request with parameters
 * - Process the response stream in real time
 */
suspend fun main() {
    converseStream()
}

suspend fun converseStream(): String {
    // A buffer to collect the complete response
    val completeResponseBuffer = StringBuilder()

    // Create and configure the Bedrock runtime client
    BedrockRuntimeClient { region = "us-east-1" }.use { client ->

        // Specify the model ID. For the latest available models, see:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
        val modelId = "amazon.nova-lite-v1:0"

        // Create the message with the user's prompt
        val prompt = "Describe the purpose of a 'hello world' program in a paragraph."
        val message = Message {
            role = ConversationRole.User
            content = listOf(ContentBlock.Text(prompt))
        }

        // Configure the request with optional model parameters
        val request = ConverseStreamRequest {
            this.modelId = modelId
            messages = listOf(message)
            inferenceConfig {
                maxTokens = 500 // Maximum response length
                temperature = 0.5F // Lower values: more focused output
                // topP = 0.8F // Alternative to temperature
            }
        }

        // Process the streaming response
        runCatching {
            client.converseStream(request) { response ->
                response.stream?.collect { chunk ->
                    when (chunk) {
                        is ConverseStreamOutput.ContentBlockDelta -> {
                            // Process each text chunk as it arrives
                            chunk.value.delta?.asText()?.let { text ->
                                print(text)
                                System.out.flush() // Ensure immediate output
                                completeResponseBuffer.append(text)
                            }
                        }
                        else -> {} // Other output block types can be handled as needed
                    }
                }
            }
        }.onFailure { error ->
            error.message?.let { e -> System.err.println("ERROR: Can't invoke '$modelId'. Reason: $e") }
            throw RuntimeException("Failed to generate text with model $modelId: $error", error)
        }
    }

    return completeResponseBuffer.toString()
}