Esempi di Amazon Bedrock Runtime con SDK per Kotlin

Invia un messaggio di testo ad Amazon Nova utilizzando l'API Converse di Bedrock.

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)
        }
    }
}

Invia un messaggio di testo ad Amazon Nova utilizzando l'API Converse di Bedrock ed elabora il flusso di risposta in tempo reale.

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()
}

Usa l'API Invoke Model per generare una breve storia.

import aws.sdk.kotlin.services.bedrockruntime.BedrockRuntimeClient
import aws.sdk.kotlin.services.bedrockruntime.model.InvokeModelRequest
import kotlinx.serialization.Serializable
import kotlinx.serialization.json.Json

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

// Data class for parsing the model's response
@Serializable
private data class BedrockResponse(val results: List<Result>) {
    @Serializable
    data class Result(
        val outputText: String,
    )
}

// Initialize JSON parser with relaxed configuration
private val json = Json { ignoreUnknownKeys = true }

suspend fun invokeModel(): 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.titan-text-lite-v1"

        // Create the request payload with optional configuration parameters
        // For detailed parameter descriptions, see:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-text.html
        val prompt = "Describe the purpose of a 'hello world' program in one line."
        val request = """
            {
                "inputText": "$prompt",
                "textGenerationConfig": {
                    "maxTokenCount": 500,
                    "temperature": 0.5
                }
            }
        """.trimIndent()

        // Send the request and process the model's response
        runCatching {
            // Send the request to the model
            val response = client.invokeModel(
                InvokeModelRequest {
                    this.modelId = modelId
                    body = request.toByteArray()
                },
            )

            // Convert the response bytes to a JSON string
            val jsonResponse = response.body.toString(Charsets.UTF_8)

            // Parse the JSON into a Kotlin object
            val parsedResponse = json.decodeFromString<BedrockResponse>(jsonResponse)

            // Extract and return the generated text
            return parsedResponse.results.firstOrNull()!!.outputText
        }.getOrElse { error ->
            error.message?.let { msg ->
                System.err.println("ERROR: Can't invoke '$modelId'. Reason: $msg")
            }
            throw RuntimeException("Failed to generate text with model $modelId", error)
        }
    }
}