AI21 Lab Jurassic-2 Generator Gambar Amazon Titan Teks Amazon Titan Embeddings Teks Amazon Titan Antropik Claude Perintah Cohere Meta Llama Mistral AI Skenario Difusi Stabil

Contoh Amazon Bedrock Runtime menggunakan SDK for Java 2.x

Contoh kode berikut menunjukkan cara melakukan tindakan dan mengimplementasikan skenario umum dengan menggunakan Runtime AWS SDK for Java 2.x with Amazon Bedrock.

Tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Meskipun tindakan menunjukkan cara memanggil fungsi layanan individual, Anda dapat melihat tindakan dalam konteks pada skenario terkait dan contoh lintas layanan.

Skenario adalah contoh kode yang menunjukkan cara menyelesaikan tugas tertentu dengan memanggil beberapa fungsi dalam layanan yang sama.

Setiap contoh menyertakan tautan ke GitHub, di mana Anda dapat menemukan petunjuk tentang cara mengatur dan menjalankan kode dalam konteks.

AI21 Lab Jurassic-2

Contoh kode berikut menunjukkan cara mengirim pesan teks ke AI21 Labs Jurassic-2, menggunakan API Converse Bedrock.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke AI21 Labs Jurassic-2, menggunakan API Converse 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();
    }
}

Kirim pesan teks ke AI21 Labs Jurassic-2, menggunakan API Converse Bedrock dengan klien Java async.


// 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();
    }
}

Untuk detail API, lihat Converse di Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke AI21 Labs Jurassic-2, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Generator Gambar Amazon Titan

Contoh kode berikut menunjukkan cara memanggil Amazon Titan Image di Amazon Bedrock untuk menghasilkan gambar.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Buat gambar dengan 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);
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Teks Amazon Titan

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Amazon Titan Text, menggunakan API Converse Bedrock.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Amazon Titan Text, menggunakan API Converse 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();
    }
}

Kirim pesan teks ke Amazon Titan Text, menggunakan API Converse Bedrock dengan klien Java async.


// 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();
    }
}

Untuk detail API, lihat Converse di Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Amazon Titan Text, menggunakan API Converse Bedrock dan memproses aliran respons secara real-time.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Amazon Titan Text, menggunakan API Converse Bedrock dan proses aliran respons secara real-time.


// 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());
        }
    }
}

Untuk detail API, lihat ConverseStreamdi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Amazon Titan Text, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke model Amazon Titan Text, menggunakan Invoke Model API, dan mencetak aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// 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();
    }
}

Untuk detail API, lihat InvokeModelWithResponseStreamdi Referensi AWS SDK for Java 2.x API.

Embeddings Teks Amazon Titan

Contoh kode berikut ini menunjukkan cara:

Mulailah membuat penyematan pertama Anda.
Buat embeddings yang mengonfigurasi jumlah dimensi dan normalisasi (hanya V2).

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Buat penyematan pertama Anda dengan 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();
    }
}

Panggil Titan Text Embeddings V2 yang mengonfigurasi jumlah dimensi dan normalisasi.



    /**
     * 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;
    }

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Antropik Claude

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Anthropic Claude, menggunakan API Converse Bedrock.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Anthropic Claude, menggunakan API Converse 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();
    }
}

Kirim pesan teks ke Anthropic Claude, menggunakan API Converse Bedrock dengan klien Java async.


// 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();
    }
}

Untuk detail API, lihat Converse di Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Anthropic Claude, menggunakan API Converse Bedrock dan memproses aliran respons secara real-time.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Anthropic Claude, menggunakan API Converse Bedrock dan proses aliran respons secara real-time.


// 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());
        }
    }
}

Untuk detail API, lihat ConverseStreamdi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Anthropic Claude, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke model Anthropic Claude, menggunakan Invoke Model API, dan mencetak aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// 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();
    }
}

Untuk detail API, lihat InvokeModelWithResponseStreamdi Referensi AWS SDK for Java 2.x API.

Perintah Cohere

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Cohere Command, menggunakan API Converse Bedrock.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Cohere Command, menggunakan API Converse 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();
    }
}

Kirim pesan teks ke Cohere Command, menggunakan API Converse Bedrock dengan klien Java async.


// 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();
    }
}

Untuk detail API, lihat Converse di Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Cohere Command, menggunakan API Converse Bedrock dan memproses aliran respons secara real-time.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Cohere Command, menggunakan API Converse Bedrock dan proses aliran respons secara real-time.


// 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());
        }
    }
}

Untuk detail API, lihat ConverseStreamdi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Cohere Command R dan R +, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Cohere Command, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Cohere Command, menggunakan Invoke Model API dengan aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Cohere Command, menggunakan Invoke Model API dengan aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Meta Llama

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Meta Llama, menggunakan API Converse Bedrock.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Meta Llama, menggunakan API Converse 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();
    }
}

Kirim pesan teks ke Meta Llama, menggunakan API Converse Bedrock dengan klien Java async.


// 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();
    }
}

Untuk detail API, lihat Converse di Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Meta Llama, menggunakan API Converse Bedrock dan memproses aliran respons secara real-time.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Meta Llama, menggunakan API Converse Bedrock dan proses aliran respons secara real-time.


// 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());
        }
    }
}

Untuk detail API, lihat ConverseStreamdi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Meta Llama 2, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// Use the native inference API to send a text message to Meta Llama 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 Llama2_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., Llama 2 Chat 13B.
        var modelId = "meta.llama2-13b-chat-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-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 2'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("/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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Meta Llama 3, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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_EAST_1)
                .build();

        // Set the model ID, e.g., Llama 3 8b Instruct.
        var modelId = "meta.llama3-8b-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|>\\n" +
                "<|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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Meta Llama 2, menggunakan Invoke Model API, dan mencetak aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// Use the native inference API to send a text message to Meta Llama 2
// 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 Llama2_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., Llama 2 Chat 13B.
        var modelId = "meta.llama2-13b-chat-v1";

        // 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 2'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("/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();
    }
}

Untuk detail API, lihat InvokeModelWithResponseStreamdi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Meta Llama 3, menggunakan Invoke Model API, dan mencetak aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// 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() 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., Llama 3 8b Instruct.
        var modelId = "meta.llama3-8b-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|>\\n" +
                "<|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();
    }
}

Untuk detail API, lihat InvokeModelWithResponseStreamdi Referensi AWS SDK for Java 2.x API.

Mistral AI

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Mistral, menggunakan API Converse Bedrock.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Mistral, menggunakan API Converse 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();
    }
}

Kirim pesan teks ke Mistral, menggunakan API Converse Bedrock dengan klien Java async.


// 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();
    }
}

Untuk detail API, lihat Converse di Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke Mistral, menggunakan API Converse Bedrock dan memproses aliran respons secara real-time.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Kirim pesan teks ke Mistral, menggunakan API Converse Bedrock dan proses aliran respons secara real-time.


// 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());
        }
    }
}

Untuk detail API, lihat ConverseStreamdi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke model Mistral, menggunakan Invoke Model API.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks.


// 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();
    }
}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

Contoh kode berikut menunjukkan cara mengirim pesan teks ke model AI Mistral, menggunakan API Model Invoke, dan mencetak aliran respons.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Gunakan API Invoke Model untuk mengirim pesan teks dan memproses aliran respons secara real-time.


// 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();
    }
}

Untuk detail API, lihat InvokeModelWithResponseStreamdi Referensi AWS SDK for Java 2.x API.

Skenario

Contoh kode berikut menunjukkan cara membuat taman bermain untuk berinteraksi dengan model dasar Amazon Bedrock melalui modalitas yang berbeda.

SDK untuk Java 2.x

Java Foundation Model (FM) Playground adalah contoh aplikasi Spring Boot yang menampilkan cara menggunakan Amazon Bedrock dengan Java. Contoh ini menunjukkan bagaimana pengembang Java dapat menggunakan Amazon Bedrock untuk membangun aplikasi berkemampuan AI generatif. Anda dapat menguji dan berinteraksi dengan model yayasan Amazon Bedrock dengan menggunakan tiga taman bermain berikut:

Taman bermain teks.
Taman bermain obrolan.
Taman bermain gambar.

Contoh ini juga mencantumkan dan menampilkan model pondasi yang dapat Anda akses, bersama dengan karakteristiknya. Untuk kode sumber dan petunjuk penerapan, lihat proyek di GitHub.

Layanan yang digunakan dalam contoh ini

Runtime Amazon Bedrock

Difusi Stabil

Contoh kode berikut menunjukkan cara memanggil Stability.ai Stable Diffusion XL di Amazon Bedrock untuk menghasilkan gambar.

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Buat gambar dengan Difusi Stabil.


// 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);
    }


}

Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

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