Scenari AI21 Laboratori Jurassic-2 Amazon Titan Image Generator Testo Amazon Titan Amazon Titan Text Embeddings Anthropic Claude Cohere Command Meta Lama IA Mistral Diffusione stabile

Esempi di Amazon Bedrock Runtime con SDK for Java 2.x

I seguenti esempi di codice mostrano come eseguire azioni e implementare scenari comuni utilizzando AWS SDK for Java 2.x with Amazon Bedrock Runtime.

Gli scenari sono esempi di codice che mostrano come eseguire un'attività specifica richiamando più funzioni all'interno dello stesso servizio o combinate con altri Servizi AWS.

Ogni esempio include un collegamento al codice sorgente completo, dove puoi trovare istruzioni su come configurare ed eseguire il codice nel contesto.

Scenari

Il seguente esempio di codice mostra come creare parchi giochi per interagire con i modelli di base di Amazon Bedrock attraverso diverse modalità.

SDK per Java 2.x

Java Foundation Model (FM) Playground è un'applicazione di esempio Spring Boot che mostra come usare Amazon Bedrock con Java. Questo esempio mostra come gli sviluppatori Java possono utilizzare Amazon Bedrock per creare applicazioni generative abilitate all'intelligenza artificiale. Puoi testare e interagire con i modelli Amazon Bedrock Foundation utilizzando i seguenti tre campi da gioco:

Un parco giochi testuale.
Un parco giochi per le chat.
Un parco giochi di immagini.

L'esempio elenca e visualizza anche i modelli di base a cui avete accesso, insieme alle loro caratteristiche. Per il codice sorgente e le istruzioni di distribuzione, consultate il progetto in GitHub.

Servizi utilizzati in questo esempio

Runtime di Amazon Bedrock

AI21 Laboratori Jurassic-2

Il seguente esempio di codice mostra come inviare un messaggio di testo a AI21 Labs Jurassic-2, utilizzando l'API Converse di Bedrock.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a AI21 Labs Jurassic-2, utilizzando l'API Converse di Bedrock.


// Use the Converse API to send a text message to AI21 Labs Jurassic-2.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

public class Converse {

    public static String converse() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Jurassic-2 Mid.
        var modelId = "ai21.j2-mid-v1";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        try {
            // Send the message with a basic inference configuration.
            ConverseResponse response = client.converse(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)));

            // Retrieve the generated text from Bedrock's response object.
            var responseText = response.output().message().content().get(0).text();
            System.out.println(responseText);

            return responseText;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converse();
    }
}

Invia un messaggio di testo a AI21 Labs Jurassic-2, utilizzando l'API Converse di Bedrock con il client Java asincrono.


// Use the Converse API to send a text message to AI21 Labs Jurassic-2
// with the async Java client.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ConverseAsync {

    public static String converseAsync() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Jurassic-2 Mid.
        var modelId = "ai21.j2-mid-v1";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Send the message with a basic inference configuration.
        var request = client.converse(params -> params
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                        .maxTokens(512)
                        .temperature(0.5F)
                        .topP(0.9F))
        );

        // Prepare a future object to handle the asynchronous response.
        CompletableFuture<String> future = new CompletableFuture<>();

        // Handle the response or error using the future object.
        request.whenComplete((response, error) -> {
            if (error == null) {
                // Extract the generated text from Bedrock's response object.
                String responseText = response.output().message().content().get(0).text();
                future.complete(responseText);
            } else {
                future.completeExceptionally(error);
            }
        });

        try {
            // Wait for the future object to complete and retrieve the generated text.
            String responseText = future.get();
            System.out.println(responseText);

            return responseText;

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converseAsync();
    }
}

Per i dettagli sulle API, consulta Converse in API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo a AI21 Labs Jurassic-2, utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to AI21 Labs Jurassic-2.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Jurassic-2 Mid.
        var modelId = "ai21.j2-mid-v1";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-jurassic2.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/completions/0/data/text").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Amazon Titan Image Generator

Il seguente esempio di codice mostra come richiamare Amazon Titan Image su Amazon Bedrock per generare un'immagine.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Crea un'immagine con Amazon Titan Image Generator.


// Create an image with the Amazon Titan Image Generator.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

import java.math.BigInteger;
import java.security.SecureRandom;

import static com.example.bedrockruntime.libs.ImageTools.displayImage;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Image G1.
        var modelId = "amazon.titan-image-generator-v1";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
        var nativeRequestTemplate = """
                {
                    "taskType": "TEXT_IMAGE",
                    "textToImageParams": { "text": "{{prompt}}" },
                    "imageGenerationConfig": { "seed": {{seed}} }
                }""";

        // Define the prompt for the image generation.
        var prompt = "A stylized picture of a cute old steampunk robot";

        // Get a random 31-bit seed for the image generation (max. 2,147,483,647).
        var seed = new BigInteger(31, new SecureRandom());

        // Embed the prompt and seed in the model's native request payload.
        var nativeRequest = nativeRequestTemplate
                .replace("{{prompt}}", prompt)
                .replace("{{seed}}", seed.toString());

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated image data from the model's response.
            var base64ImageData = new JSONPointer("/images/0").queryFrom(responseBody).toString();

            return base64ImageData;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        System.out.println("Generating image. This may take a few seconds...");

        String base64ImageData = invokeModel();

        displayImage(base64ImageData);
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Testo Amazon Titan

Il seguente esempio di codice mostra come inviare un messaggio di testo ad Amazon Titan Text, utilizzando l'API Converse di Bedrock.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

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


// Use the Converse API to send a text message to Amazon Titan Text.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

public class Converse {

    public static String converse() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Text Premier.
        var modelId = "amazon.titan-text-premier-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();


        try {
            // Send the message with a basic inference configuration.
            ConverseResponse response = client.converse(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)));

            // Retrieve the generated text from Bedrock's response object.
            var responseText = response.output().message().content().get(0).text();
            System.out.println(responseText);

            return responseText;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converse();
    }
}

Invia un messaggio di testo ad Amazon Titan Text utilizzando l'API Converse di Bedrock con il client Java asincrono.


// Use the Converse API to send a text message to Amazon Titan Text
// with the async Java client.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ConverseAsync {

    public static String converseAsync() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Text Premier.
        var modelId = "amazon.titan-text-premier-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Send the message with a basic inference configuration.
        var request = client.converse(params -> params
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                        .maxTokens(512)
                        .temperature(0.5F)
                        .topP(0.9F))
        );

        // Prepare a future object to handle the asynchronous response.
        CompletableFuture<String> future = new CompletableFuture<>();

        // Handle the response or error using the future object.
        request.whenComplete((response, error) -> {
            if (error == null) {
                // Extract the generated text from Bedrock's response object.
                String responseText = response.output().message().content().get(0).text();
                future.complete(responseText);
            } else {
                future.completeExceptionally(error);
            }
        });

        try {
            // Wait for the future object to complete and retrieve the generated text.
            String responseText = future.get();
            System.out.println(responseText);

            return responseText;

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converseAsync();
    }
}

Per i dettagli sull'API, consulta Converse in API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo ad Amazon Titan Text, utilizzando l'API Converse di Bedrock ed elaborare il flusso di risposta in tempo reale.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

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


// Use the Converse API to send a text message to Amazon Titan Text
// and print the response stream.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseStreamResponseHandler;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.ExecutionException;

public class ConverseStream {

    public static void main(String[] args) {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Text Premier.
        var modelId = "amazon.titan-text-premier-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Create a handler to extract and print the response text in real-time.
        var responseStreamHandler = ConverseStreamResponseHandler.builder()
                .subscriber(ConverseStreamResponseHandler.Visitor.builder()
                        .onContentBlockDelta(chunk -> {
                            String responseText = chunk.delta().text();
                            System.out.print(responseText);
                        }).build()
                ).onError(err ->
                        System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage())
                ).build();

        try {
            // Send the message with a basic inference configuration and attach the handler.
            client.converseStream(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)
                    ), responseStreamHandler).get();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
        }
    }
}

Per i dettagli sull'API, consulta la sezione API ConverseStreamReference AWS SDK for Java 2.x .

Il seguente esempio di codice mostra come inviare un messaggio di testo ad Amazon Titan Text utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to Amazon Titan Text.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Text Premier.
        var modelId = "amazon.titan-text-premier-v1:0";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-text.html
        var nativeRequestTemplate = "{ \"inputText\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/results/0/outputText").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo ai modelli Amazon Titan Text, utilizzando l'API Invoke Model, e stampare il flusso di risposta.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// Use the native inference API to send a text message to Amazon Titan Text
// and print the response stream.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() throws ExecutionException, InterruptedException {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Text Premier.
        var modelId = "amazon.titan-text-premier-v1:0";

        // The InvokeModelWithResponseStream API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-text.html
        var nativeRequestTemplate = "{ \"inputText\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    // Extract and print the text from the model's native response.
                    var response = new JSONObject(chunk.bytes().asUtf8String());
                    var text = new JSONPointer("/outputText").queryFrom(response);
                    System.out.print(text);

                    // Append the text to the response text buffer.
                    completeResponseTextBuffer.append(text);
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModelWithResponseStream AWS SDK for Java 2.xAPI Reference.

Amazon Titan Text Embeddings

L'esempio di codice seguente mostra come:

Inizia a creare il tuo primo incorporamento.
Crea incorporamenti configurando il numero di dimensioni e la normalizzazione (solo V2).

SDK per Java 2.x

Nota

C'è altro da fare. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Crea il tuo primo incorporamento con Titan Text Embeddings V2.


// Generate and print an embedding with Amazon Titan Text Embeddings.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Text Embeddings V2.
        var modelId = "amazon.titan-embed-text-v2:0";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-embed-text.html
        var nativeRequestTemplate = "{ \"inputText\": \"{{inputText}}\" }";

        // The text to convert into an embedding.
        var inputText = "Please recommend books with a theme similar to the movie 'Inception'.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{inputText}}", inputText);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/embedding").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Richiama Titan Text Embeddings V2 per configurare il numero di dimensioni e la normalizzazione.



    /**
     * Invoke Amazon Titan Text Embeddings V2 with additional inference parameters.
     *
     * @param inputText  - The text to convert to an embedding.
     * @param dimensions - The number of dimensions the output embeddings should have.
     *                   Values accepted by the model: 256, 512, 1024.
     * @param normalize  - A flag indicating whether or not to normalize the output embeddings.
     * @return The {@link JSONObject} representing the model's response.
     */
    public static JSONObject invokeModel(String inputText, int dimensions, boolean normalize) {

        // Create a Bedrock Runtime client in the AWS Region of your choice.
        var client = BedrockRuntimeClient.builder()
                .region(Region.US_WEST_2)
                .build();

        // Set the model ID, e.g., Titan Embed Text v2.0.
        var modelId = "amazon.titan-embed-text-v2:0";

        // Create the request for the model.
        var nativeRequest = """
                {
                    "inputText": "%s",
                    "dimensions": %d,
                    "normalize": %b
                }
                """.formatted(inputText, dimensions, normalize);

        // Encode and send the request.
        var response = client.invokeModel(request -> {
            request.body(SdkBytes.fromUtf8String(nativeRequest));
            request.modelId(modelId);
        });

        // Decode the model's response.
        var modelResponse = new JSONObject(response.body().asUtf8String());

        // Extract and print the generated embedding and the input text token count.
        var embedding = modelResponse.getJSONArray("embedding");
        var inputTokenCount = modelResponse.getBigInteger("inputTextTokenCount");
        System.out.println("Embedding: " + embedding);
        System.out.println("\nInput token count: " + inputTokenCount);

        // Return the model's native response.
        return modelResponse;
    }

Per i dettagli sull'API, consulta la sezione API Reference. InvokeModelAWS SDK for Java 2.x

Anthropic Claude

Il seguente esempio di codice mostra come inviare un messaggio di testo a Anthropic Claude, utilizzando l'API Converse di Bedrock.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Anthropic Claude, utilizzando l'API Converse di Bedrock.


// Use the Converse API to send a text message to Anthropic Claude.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

public class Converse {

    public static String converse() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Claude 3 Haiku.
        var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();


        try {
            // Send the message with a basic inference configuration.
            ConverseResponse response = client.converse(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)));

            // Retrieve the generated text from Bedrock's response object.
            var responseText = response.output().message().content().get(0).text();
            System.out.println(responseText);

            return responseText;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converse();
    }
}

Invia un messaggio di testo a Anthropic Claude, utilizzando l'API Converse di Bedrock con il client Java asincrono.


// Use the Converse API to send a text message to Anthropic Claude
// with the async Java client.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ConverseAsync {

    public static String converseAsync() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Claude 3 Haiku.
        var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Send the message with a basic inference configuration.
        var request = client.converse(params -> params
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                        .maxTokens(512)
                        .temperature(0.5F)
                        .topP(0.9F))
        );

        // Prepare a future object to handle the asynchronous response.
        CompletableFuture<String> future = new CompletableFuture<>();

        // Handle the response or error using the future object.
        request.whenComplete((response, error) -> {
            if (error == null) {
                // Extract the generated text from Bedrock's response object.
                String responseText = response.output().message().content().get(0).text();
                future.complete(responseText);
            } else {
                future.completeExceptionally(error);
            }
        });

        try {
            // Wait for the future object to complete and retrieve the generated text.
            String responseText = future.get();
            System.out.println(responseText);

            return responseText;

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converseAsync();
    }
}

Per i dettagli sulle API, consulta Converse in API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo ad Anthropic Claude, utilizzando l'API Converse di Bedrock ed elaborare il flusso di risposta in tempo reale.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Anthropic Claude utilizzando l'API Converse di Bedrock ed elabora il flusso di risposta in tempo reale.


// Use the Converse API to send a text message to Anthropic Claude
// and print the response stream.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseStreamResponseHandler;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.ExecutionException;

public class ConverseStream {

    public static void main(String[] args) {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Claude 3 Haiku.
        var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Create a handler to extract and print the response text in real-time.
        var responseStreamHandler = ConverseStreamResponseHandler.builder()
                .subscriber(ConverseStreamResponseHandler.Visitor.builder()
                        .onContentBlockDelta(chunk -> {
                            String responseText = chunk.delta().text();
                            System.out.print(responseText);
                        }).build()
                ).onError(err ->
                        System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage())
                ).build();

        try {
            // Send the message with a basic inference configuration and attach the handler.
            client.converseStream(request -> request.modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)
                    ), responseStreamHandler).get();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
        }
    }
}

Per i dettagli sulle API, consulta ConverseStreamla sezione API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo a Anthropic Claude, utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to Anthropic Claude.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Claude 3 Haiku.
        var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
        var nativeRequestTemplate = """
                {
                    "anthropic_version": "bedrock-2023-05-31",
                    "max_tokens": 512,
                    "temperature": 0.5,
                    "messages": [{
                        "role": "user",
                        "content": "{{prompt}}"
                    }]
                }""";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/content/0/text").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo ai modelli Anthropic Claude, utilizzando l'API Invoke Model, e stampare il flusso di risposta.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// Use the native inference API to send a text message to Anthropic Claude
// and print the response stream.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.Objects;
import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() throws ExecutionException, InterruptedException {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Claude 3 Haiku.
        var modelId = "anthropic.claude-3-haiku-20240307-v1:0";

        // The InvokeModelWithResponseStream API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
        var nativeRequestTemplate = """
                {
                    "anthropic_version": "bedrock-2023-05-31",
                    "max_tokens": 512,
                    "temperature": 0.5,
                    "messages": [{
                        "role": "user",
                        "content": "{{prompt}}"
                    }]
                }""";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    var response = new JSONObject(chunk.bytes().asUtf8String());

                    // Extract and print the text from the content blocks.
                    if (Objects.equals(response.getString("type"), "content_block_delta")) {
                        var text = new JSONPointer("/delta/text").queryFrom(response);
                        System.out.print(text);

                        // Append the text to the response text buffer.
                        completeResponseTextBuffer.append(text);
                    }
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModelWithResponseStream AWS SDK for Java 2.xAPI Reference.

Cohere Command

Il seguente esempio di codice mostra come inviare un messaggio di testo a Cohere Command, utilizzando l'API Converse di Bedrock.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Cohere Command, utilizzando l'API Converse di Bedrock.


// Use the Converse API to send a text message to Cohere Command.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

public class Converse {

    public static String converse() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command R.
        var modelId = "cohere.command-r-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();


        try {
            // Send the message with a basic inference configuration.
            ConverseResponse response = client.converse(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)));

            // Retrieve the generated text from Bedrock's response object.
            var responseText = response.output().message().content().get(0).text();
            System.out.println(responseText);

            return responseText;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converse();
    }
}

Invia un messaggio di testo a Cohere Command, utilizzando l'API Converse di Bedrock con il client Java asincrono.


// Use the Converse API to send a text message to Cohere Command
// with the async Java client.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ConverseAsync {

    public static String converseAsync() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command R.
        var modelId = "cohere.command-r-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Send the message with a basic inference configuration.
        var request = client.converse(params -> params
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                        .maxTokens(512)
                        .temperature(0.5F)
                        .topP(0.9F))
        );

        // Prepare a future object to handle the asynchronous response.
        CompletableFuture<String> future = new CompletableFuture<>();

        // Handle the response or error using the future object.
        request.whenComplete((response, error) -> {
            if (error == null) {
                // Extract the generated text from Bedrock's response object.
                String responseText = response.output().message().content().get(0).text();
                future.complete(responseText);
            } else {
                future.completeExceptionally(error);
            }
        });

        try {
            // Wait for the future object to complete and retrieve the generated text.
            String responseText = future.get();
            System.out.println(responseText);

            return responseText;

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converseAsync();
    }
}

Per i dettagli sulle API, consulta Converse in API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo a Cohere Command, utilizzando l'API Converse di Bedrock ed elaborare il flusso di risposta in tempo reale.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Cohere Command, utilizzando l'API Converse di Bedrock ed elabora il flusso di risposta in tempo reale.


// Use the Converse API to send a text message to Cohere Command
// and print the response stream.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseStreamResponseHandler;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.ExecutionException;

public class ConverseStream {

    public static void main(String[] args) {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command R.
        var modelId = "cohere.command-r-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Create a handler to extract and print the response text in real-time.
        var responseStreamHandler = ConverseStreamResponseHandler.builder()
                .subscriber(ConverseStreamResponseHandler.Visitor.builder()
                        .onContentBlockDelta(chunk -> {
                            String responseText = chunk.delta().text();
                            System.out.print(responseText);
                        }).build()
                ).onError(err ->
                        System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage())
                ).build();

        try {
            // Send the message with a basic inference configuration and attach the handler.
            client.converseStream(request -> request.modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)
                    ), responseStreamHandler).get();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
        }
    }
}

Per i dettagli sull'API, consulta la sezione API ConverseStreamReference AWS SDK for Java 2.x .

Il seguente esempio di codice mostra come inviare un messaggio di testo a Cohere Command R e R+, utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to Cohere Command R.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class Command_R_InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command R.
        var modelId = "cohere.command-r-v1:0";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-command-r-plus.html
        var nativeRequestTemplate = "{ \"message\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/text").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo a Cohere Command, utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to Cohere Command.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class Command_InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command Light.
        var modelId = "cohere.command-light-text-v14";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-command.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/generations/0/text").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo a Cohere Command, utilizzando l'API Invoke Model con un flusso di risposta.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// Use the native inference API to send a text message to Cohere Command R
// and print the response stream.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class Command_R_InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() throws ExecutionException, InterruptedException {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command R.
        var modelId = "cohere.command-r-v1:0";

        // The InvokeModelWithResponseStream API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-command-r-plus.html
        var nativeRequestTemplate = "{ \"message\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    // Extract and print the text from the model's native response.
                    var response = new JSONObject(chunk.bytes().asUtf8String());
                    var text = new JSONPointer("/text").queryFrom(response);
                    System.out.print(text);

                    // Append the text to the response text buffer.
                    completeResponseTextBuffer.append(text);
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo a Cohere Command, utilizzando l'API Invoke Model con un flusso di risposta.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// Use the native inference API to send a text message to Cohere Command
// and print the response stream.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class Command_InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() throws ExecutionException, InterruptedException {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Command Light.
        var modelId = "cohere.command-light-text-v14";

        // The InvokeModelWithResponseStream API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-command.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{prompt}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in the model's native request payload.
        String nativeRequest = nativeRequestTemplate.replace("{{prompt}}", prompt);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    // Extract and print the text from the model's native response.
                    var response = new JSONObject(chunk.bytes().asUtf8String());
                    var text = new JSONPointer("/generations/0/text").queryFrom(response);
                    System.out.print(text);

                    // Append the text to the response text buffer.
                    completeResponseTextBuffer.append(text);
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Meta Llama

Il seguente esempio di codice mostra come inviare un messaggio di testo a Meta Llama, utilizzando l'API Converse di Bedrock.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Meta Llama utilizzando l'API Converse di Bedrock.


// Use the Converse API to send a text message to Meta Llama.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

public class Converse {

    public static String converse() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Llama 3 8b Instruct.
        var modelId = "meta.llama3-8b-instruct-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();


        try {
            // Send the message with a basic inference configuration.
            ConverseResponse response = client.converse(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)));

            // Retrieve the generated text from Bedrock's response object.
            var responseText = response.output().message().content().get(0).text();
            System.out.println(responseText);

            return responseText;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converse();
    }
}

Invia un messaggio di testo a Meta Llama, utilizzando l'API Converse di Bedrock con il client Java asincrono.


// Use the Converse API to send a text message to Meta Llama
// with the async Java client.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ConverseAsync {

    public static String converseAsync() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Llama 3 8b Instruct.
        var modelId = "meta.llama3-8b-instruct-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Send the message with a basic inference configuration.
        var request = client.converse(params -> params
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                        .maxTokens(512)
                        .temperature(0.5F)
                        .topP(0.9F))
        );

        // Prepare a future object to handle the asynchronous response.
        CompletableFuture<String> future = new CompletableFuture<>();

        // Handle the response or error using the future object.
        request.whenComplete((response, error) -> {
            if (error == null) {
                // Extract the generated text from Bedrock's response object.
                String responseText = response.output().message().content().get(0).text();
                future.complete(responseText);
            } else {
                future.completeExceptionally(error);
            }
        });

        try {
            // Wait for the future object to complete and retrieve the generated text.
            String responseText = future.get();
            System.out.println(responseText);

            return responseText;

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converseAsync();
    }
}

Per i dettagli sulle API, consulta Converse in API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo a Meta Llama, utilizzando l'API Converse di Bedrock ed elaborare il flusso di risposta in tempo reale.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Meta Llama utilizzando l'API Converse di Bedrock ed elabora il flusso di risposta in tempo reale.


// Use the Converse API to send a text message to Meta Llama
// and print the response stream.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseStreamResponseHandler;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.ExecutionException;

public class ConverseStream {

    public static void main(String[] args) {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Llama 3 8b Instruct.
        var modelId = "meta.llama3-8b-instruct-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Create a handler to extract and print the response text in real-time.
        var responseStreamHandler = ConverseStreamResponseHandler.builder()
                .subscriber(ConverseStreamResponseHandler.Visitor.builder()
                        .onContentBlockDelta(chunk -> {
                            String responseText = chunk.delta().text();
                            System.out.print(responseText);
                        }).build()
                ).onError(err ->
                        System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage())
                ).build();

        try {
            // Send the message with a basic inference configuration and attach the handler.
            client.converseStream(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)
                    ), responseStreamHandler).get();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
        }
    }
}

Per i dettagli sull'API, consulta la sezione API ConverseStreamReference AWS SDK for Java 2.x .

Il seguente esempio di codice mostra come inviare un messaggio di testo a Meta Llama 3, utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to Meta Llama 3.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class Llama3_InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_WEST_2)
                .build();

        // Set the model ID, e.g., Llama 3 70b Instruct.
        var modelId = "meta.llama3-70b-instruct-v1:0";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-meta.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in Llama 3's instruction format.
        var instruction = (
                "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n" +
                "{{prompt}} <|eot_id|>\\n" +
                "<|start_header_id|>assistant<|end_header_id|>\\n"
        ).replace("{{prompt}}", prompt);

        // Embed the instruction in the the native request payload.
        var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/generation").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo a Meta Llama 3, utilizzando l'API Invoke Model, e stampare il flusso di risposta.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// Use the native inference API to send a text message to Meta Llama 3
// and print the response stream.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class Llama3_InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_WEST_2)
                .build();

        // Set the model ID, e.g., Llama 3 70b Instruct.
        var modelId = "meta.llama3-70b-instruct-v1:0";

        // The InvokeModelWithResponseStream API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-meta.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in Llama 3's instruction format.
        var instruction = (
                "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n" +
                "{{prompt}} <|eot_id|>\\n" +
                "<|start_header_id|>assistant<|end_header_id|>\\n"
        ).replace("{{prompt}}", prompt);

        // Embed the instruction in the the native request payload.
        var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    // Extract and print the text from the model's native response.
                    var response = new JSONObject(chunk.bytes().asUtf8String());
                    var text = new JSONPointer("/generation").queryFrom(response);
                    System.out.print(text);

                    // Append the text to the response text buffer.
                    completeResponseTextBuffer.append(text);
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModelWithResponseStream AWS SDK for Java 2.xAPI Reference.

IA Mistral

Il seguente esempio di codice mostra come inviare un messaggio di testo a Mistral, utilizzando l'API Converse di Bedrock.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Invia un messaggio di testo a Mistral utilizzando l'API Converse di Bedrock.


// Use the Converse API to send a text message to Mistral.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseResponse;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

public class Converse {

    public static String converse() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Mistral Large.
        var modelId = "mistral.mistral-large-2402-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();


        try {
            // Send the message with a basic inference configuration.
            ConverseResponse response = client.converse(request -> request
                    .modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)));

            // Retrieve the generated text from Bedrock's response object.
            var responseText = response.output().message().content().get(0).text();
            System.out.println(responseText);

            return responseText;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }

    }

    public static void main(String[] args) {
        converse();
    }
}

Invia un messaggio di testo a Mistral, utilizzando l'API Converse di Bedrock con il client Java asincrono.


// Use the Converse API to send a text message to Mistral
// with the async Java client.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public class ConverseAsync {

    public static String converseAsync() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Mistral Large.
        var modelId = "mistral.mistral-large-2402-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Send the message with a basic inference configuration.
        var request = client.converse(params -> params
                .modelId(modelId)
                .messages(message)
                .inferenceConfig(config -> config
                        .maxTokens(512)
                        .temperature(0.5F)
                        .topP(0.9F))
        );

        // Prepare a future object to handle the asynchronous response.
        CompletableFuture<String> future = new CompletableFuture<>();

        // Handle the response or error using the future object.
        request.whenComplete((response, error) -> {
            if (error == null) {
                // Extract the generated text from Bedrock's response object.
                String responseText = response.output().message().content().get(0).text();
                future.complete(responseText);
            } else {
                future.completeExceptionally(error);
            }
        });

        try {
            // Wait for the future object to complete and retrieve the generated text.
            String responseText = future.get();
            System.out.println(responseText);

            return responseText;

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        converseAsync();
    }
}

Per i dettagli sull'API, consulta Converse in API Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo a Mistral, utilizzando l'API Converse di Bedrock ed elaborare il flusso di risposta in tempo reale.

SDK per Java 2.x

Nota

C'è altro su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

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


// Use the Converse API to send a text message to Mistral
// and print the response stream.

import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.ContentBlock;
import software.amazon.awssdk.services.bedrockruntime.model.ConversationRole;
import software.amazon.awssdk.services.bedrockruntime.model.ConverseStreamResponseHandler;
import software.amazon.awssdk.services.bedrockruntime.model.Message;

import java.util.concurrent.ExecutionException;

public class ConverseStream {

    public static void main(String[] args) {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Mistral Large.
        var modelId = "mistral.mistral-large-2402-v1:0";

        // Create the input text and embed it in a message object with the user role.
        var inputText = "Describe the purpose of a 'hello world' program in one line.";
        var message = Message.builder()
                .content(ContentBlock.fromText(inputText))
                .role(ConversationRole.USER)
                .build();

        // Create a handler to extract and print the response text in real-time.
        var responseStreamHandler = ConverseStreamResponseHandler.builder()
                .subscriber(ConverseStreamResponseHandler.Visitor.builder()
                        .onContentBlockDelta(chunk -> {
                            String responseText = chunk.delta().text();
                            System.out.print(responseText);
                        }).build()
                ).onError(err ->
                        System.err.printf("Can't invoke '%s': %s", modelId, err.getMessage())
                ).build();

        try {
            // Send the message with a basic inference configuration and attach the handler.
            client.converseStream(request -> request.modelId(modelId)
                    .messages(message)
                    .inferenceConfig(config -> config
                            .maxTokens(512)
                            .temperature(0.5F)
                            .topP(0.9F)
                    ), responseStreamHandler).get();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
        }
    }
}

Per i dettagli sull'API, consulta ConverseStreamAPI Reference.AWS SDK for Java 2.x

Il seguente esempio di codice mostra come inviare un messaggio di testo ai modelli Mistral, utilizzando l'API Invoke Model.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Usa l'API Invoke Model per inviare un messaggio di testo.


// Use the native inference API to send a text message to Mistral.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Mistral Large.
        var modelId = "mistral.mistral-large-2402-v1:0";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral-text-completion.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in Mistral's instruction format.
        var instruction = "<s>[INST] {{prompt}} [/INST]\\n".replace("{{prompt}}", prompt);

        // Embed the instruction in the the native request payload.
        var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated text from the model's response.
            var text = new JSONPointer("/outputs/0/text").queryFrom(responseBody).toString();
            System.out.println(text);

            return text;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        invokeModel();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

Il seguente esempio di codice mostra come inviare un messaggio di testo ai modelli Mistral AI, utilizzando l'API Invoke Model, e stampare il flusso di risposta.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Utilizza l'API Invoke Model per inviare un messaggio di testo ed elaborare il flusso di risposta in tempo reale.


// Use the native inference API to send a text message to Mistral
// and print the response stream.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest;
import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler;

import java.util.concurrent.ExecutionException;

import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor;

public class InvokeModelWithResponseStream {

    public static String invokeModelWithResponseStream() throws ExecutionException, InterruptedException {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeAsyncClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Mistral Large.
        var modelId = "mistral.mistral-large-2402-v1:0";

        // The InvokeModelWithResponseStream API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral-text-completion.html
        var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }";

        // Define the prompt for the model.
        var prompt = "Describe the purpose of a 'hello world' program in one line.";

        // Embed the prompt in Mistral's instruction format.
        var instruction = "<s>[INST] {{prompt}} [/INST]\\n".replace("{{prompt}}", prompt);

        // Embed the instruction in the the native request payload.
        var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction);

        // Create a request with the model ID and the model's native request payload.
        var request = InvokeModelWithResponseStreamRequest.builder()
                .body(SdkBytes.fromUtf8String(nativeRequest))
                .modelId(modelId)
                .build();

        // Prepare a buffer to accumulate the generated response text.
        var completeResponseTextBuffer = new StringBuilder();

        // Prepare a handler to extract, accumulate, and print the response text in real-time.
        var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder()
                .subscriber(Visitor.builder().onChunk(chunk -> {
                    // Extract and print the text from the model's native response.
                    var response = new JSONObject(chunk.bytes().asUtf8String());
                    var text = new JSONPointer("/outputs/0/text").queryFrom(response);
                    System.out.print(text);

                    // Append the text to the response text buffer.
                    completeResponseTextBuffer.append(text);
                }).build()).build();

        try {
            // Send the request and wait for the handler to process the response.
            client.invokeModelWithResponseStream(request, responseStreamHandler).get();

            // Return the complete response text.
            return completeResponseTextBuffer.toString();

        } catch (ExecutionException | InterruptedException e) {
            System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        invokeModelWithResponseStream();
    }
}

Per i dettagli sull'API, consulta la sezione InvokeModelWithResponseStream AWS SDK for Java 2.xAPI Reference.

Diffusione stabile

Il seguente esempio di codice mostra come richiamare Stability.ai Stable Diffusion XL su Amazon Bedrock per generare un'immagine.

SDK per Java 2.x

Nota

C'è di più su. GitHub Trova l'esempio completo e scopri di più sulla configurazione e l'esecuzione nel Repository di esempi di codice AWS.

Crea un'immagine con Stable Diffusion.


// Create an image with Stable Diffusion.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

import java.math.BigInteger;
import java.security.SecureRandom;

import static com.example.bedrockruntime.libs.ImageTools.displayImage;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Stable Diffusion XL v1.
        var modelId = "stability.stable-diffusion-xl-v1";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-diffusion-1-0-text-image.html
        var nativeRequestTemplate = """
                {
                    "text_prompts": [{ "text": "{{prompt}}" }],
                    "style_preset": "{{style}}",
                    "seed": {{seed}}
                }""";

        // Define the prompt for the image generation.
        var prompt = "A stylized picture of a cute old steampunk robot";

        // Get a random 32-bit seed for the image generation (max. 4,294,967,295).
        var seed = new BigInteger(31, new SecureRandom());

        // Choose a style preset.
        var style = "cinematic";

        // Embed the prompt, seed, and style in the model's native request payload.
        String nativeRequest = nativeRequestTemplate
                .replace("{{prompt}}", prompt)
                .replace("{{seed}}", seed.toString())
                .replace("{{style}}", style);

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated image data from the model's response.
            var base64ImageData = new JSONPointer("/artifacts/0/base64")
                    .queryFrom(responseBody)
                    .toString();

            return base64ImageData;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        System.out.println("Generating image. This may take a few seconds...");

        String base64ImageData = invokeModel();

        displayImage(base64ImageData);
    }


}

Per i dettagli sull'API, consulta la sezione InvokeModel AWS SDK for Java 2.xAPI Reference.

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