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用SDK于 Java 2.x 的亚马逊 Bedrock 运行时示例
以下代码示例向您展示了如何使用 Amazon SDK for Java 2.x 与 Amazon Bedrock Runtime 配合使用来执行操作和实现常见场景。
场景是向您展示如何通过在一个服务中调用多个函数或与其他 Amazon Web Services 服务结合来完成特定任务的代码示例。
每个示例都包含一个指向完整源代码的链接,您可以在其中找到有关如何在上下文中设置和运行代码的说明。
主题
场景
以下代码示例展示了如何创建平台,通过不同的模式与 Amazon Bedrock 基础模型进行交互。
- SDK适用于 Java 2.x
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Java Foundation Model (FM) Playground 是一款 Spring Boot 示例应用程序,演示了如何将 Amazon Bedrock 与 Java 结合使用。此示例演示 Java 开发人员可如何使用 Amazon Bedrock 来构建支持生成式人工智能的应用程序。您可以使用以下三个操场测试 Amazon Bedrock 基础模型并与之交互:
-
文本操场。
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聊天操场。
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图像操场。
该示例还列出并显示您可以访问的基础模型及其特点。有关源代码和部署说明,请参阅中的项目GitHub
。 本示例中使用的服务
Amazon Bedrock 运行时系统
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AI21《侏罗纪-2》实验室
以下代码示例展示了如何使用 Bedrock 的 Converse 向 AI21 Labs Jurassic-2 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 AI21 Labs Jurassic-2 发送短信。API
// 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(); } }
使用 Bedrock 的 Converse 和异步 Java 客户端,向 AI21 Labs Jurass API ic-2 发送短信。
// 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(); } }
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有关API详细信息,请参阅《Amazon SDK for Java 2.x API参考资料》中的 “Converse”。
-
以下代码示例演示如何使用调用模型向 AI21 Labs Jurassic-2 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// 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(); } }
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有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
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Amazon Titan Image Generator
以下代码示例展示了如何在 Amazon Bedrock 上调用 Amazon Titan Image 来生成图像。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Amazon Titan 图像生成器创建图片。
// 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); } }
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有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
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亚马逊 Titan 文本
以下代码示例展示了如何使用 Bedrock 的 Converse 向 Amazon Titan Text 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Amazon Titan Text 发送短信。API
// 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(); } }
使用 Bedrock 的 Converse 和异步 Java 客户端,向 Amazon Titan Te API xt 发送短信。
// 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(); } }
-
有关API详细信息,请参阅《Amazon SDK for Java 2.x API参考资料》中的 “Converse”。
-
以下代码示例展示了如何使用 Bedrock 的 Converse 向 Amazon Titan Text 发送短信API并实时处理响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Amazon Titan Text 发送短信,API并实时处理响应流。
// 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()); } } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 ConverseStream” 中的。
-
以下代码示例展示了如何使用调用模型API向 Amazon Titan Text 发送短信。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
以下代码示例演示如何使用调用模型向 Amazon Titan Text 模型API发送短信并打印响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Invoke Model API 发送短信并实时处理响应流。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModelWithResponseStream” 中的。
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Amazon Titan Text Embeddings
以下代码示例展示了如何:
开始创建您的第一个嵌入内容。
创建嵌入式,配置维度数量和归一化(仅限 V2)。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Titan Text Embeddings V2 创建你的第一个嵌入内容。
// 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(); } }
调用 Titan Text Embeddings V2,配置维度数量和归一化。
/** * 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; }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
Anthropic Claude
以下代码示例显示了如何使用 Bedrock 的 Converse 向 Anthropic Claude 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 给 Anthropic Claude 发一条短信。API
// 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(); } }
使用 Bedrock 的 Converse 和异步 Java 客户端,向 Anthropic Claude API 发送短信。
// 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(); } }
-
有关API详细信息,请参阅《Amazon SDK for Java 2.x API参考资料》中的 “Converse”。
-
以下代码示例显示了如何使用 Bedrock 的 Converse 向 Anthropic Claude 发送短信API并实时处理响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Anthropic Claude 发送短信,API并实时处理响应流。
// 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()); } } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 ConverseStream” 中的。
-
以下代码示例显示了如何使用调用模型向 Anthropic Claude 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
以下代码示例演示如何使用调用模型向 Anthropic Claude 模型API发送短信并打印响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Invoke Model API 发送短信并实时处理响应流。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModelWithResponseStream” 中的。
-
Cohere Command
以下代码示例显示了如何使用 Bedrock 的 Converse 向 Cohere Command 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Cohere Command 发送短信。API
// 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(); } }
使用 Bedrock 的 Converse 和异步 Java 客户端,向 Cohere API Command 发送短信。
// 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(); } }
-
有关API详细信息,请参阅《Amazon SDK for Java 2.x API参考资料》中的 “Converse”。
-
以下代码示例显示了如何使用 Bedrock 的 Converse 向 Cohere Command 发送短信API并实时处理响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Cohere Command 发送短信,API然后实时处理响应流。
// 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()); } } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 ConverseStream” 中的。
-
以下代码示例显示了如何使用调用模型向 Cohere Command R 和 R+ 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
以下代码示例显示了如何使用调用模型API向 Cohere Command 发送短信。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
以下代码示例演示如何使用API带有响应流的调用模型向 Cohere Command 发送短信。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Invoke Model API 发送短信并实时处理响应流。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
以下代码示例演示如何使用API带有响应流的调用模型向 Cohere Command 发送短信。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Invoke Model API 发送短信并实时处理响应流。
// 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(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
Meta Llama
以下代码示例展示了如何使用 Bedrock 的 Converse 向 Meta Llama 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Meta Llama 发送短信。API
// 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(); } }
使用 Bedrock 的 Converse API 和异步 Java 客户端,向 Meta Llama 发送短信。
// 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(); } }
-
有关API详细信息,请参阅《Amazon SDK for Java 2.x API参考资料》中的 “Converse”。
-
以下代码示例展示了如何使用 Bedrock 的 Converse 向 Meta Llama 发送短信API并实时处理响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Meta Llama 发送短信API并实时处理响应流。
// 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()); } } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 ConverseStream” 中的。
-
以下代码示例展示了如何使用调用模型API向 Meta Llama 3 发送短信。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// Use the native inference API to send a text message to Meta Llama 3. import org.json.JSONObject; import org.json.JSONPointer; import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider; import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.core.exception.SdkClientException; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient; public class Llama3_InvokeModel { public static String invokeModel() { // Create a Bedrock Runtime client in the AWS Region you want to use. // Replace the DefaultCredentialsProvider with your preferred credentials provider. var client = BedrockRuntimeClient.builder() .credentialsProvider(DefaultCredentialsProvider.create()) .region(Region.US_WEST_2) .build(); // Set the model ID, e.g., Llama 3 70b Instruct. var modelId = "meta.llama3-70b-instruct-v1:0"; // The InvokeModel API uses the model's native payload. // Learn more about the available inference parameters and response fields at: // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-meta.html var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }"; // Define the prompt for the model. var prompt = "Describe the purpose of a 'hello world' program in one line."; // Embed the prompt in Llama 3's instruction format. var instruction = ( "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n" + "{{prompt}} <|eot_id|>\\n" + "<|start_header_id|>assistant<|end_header_id|>\\n" ).replace("{{prompt}}", prompt); // Embed the instruction in the the native request payload. var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction); try { // Encode and send the request to the Bedrock Runtime. var response = client.invokeModel(request -> request .body(SdkBytes.fromUtf8String(nativeRequest)) .modelId(modelId) ); // Decode the response body. var responseBody = new JSONObject(response.body().asUtf8String()); // Retrieve the generated text from the model's response. var text = new JSONPointer("/generation").queryFrom(responseBody).toString(); System.out.println(text); return text; } catch (SdkClientException e) { System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage()); throw new RuntimeException(e); } } public static void main(String[] args) { invokeModel(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
-
以下代码示例展示了如何使用调用模型API向 Meta Llama 3 发送短信并打印响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Invoke Model API 发送短信并实时处理响应流。
// Use the native inference API to send a text message to Meta Llama 3 // and print the response stream. import org.json.JSONObject; import org.json.JSONPointer; import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider; import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeAsyncClient; import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamRequest; import software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler; import java.util.concurrent.ExecutionException; import static software.amazon.awssdk.services.bedrockruntime.model.InvokeModelWithResponseStreamResponseHandler.Visitor; public class Llama3_InvokeModelWithResponseStream { public static String invokeModelWithResponseStream() { // Create a Bedrock Runtime client in the AWS Region you want to use. // Replace the DefaultCredentialsProvider with your preferred credentials provider. var client = BedrockRuntimeAsyncClient.builder() .credentialsProvider(DefaultCredentialsProvider.create()) .region(Region.US_WEST_2) .build(); // Set the model ID, e.g., Llama 3 70b Instruct. var modelId = "meta.llama3-70b-instruct-v1:0"; // The InvokeModelWithResponseStream API uses the model's native payload. // Learn more about the available inference parameters and response fields at: // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-meta.html var nativeRequestTemplate = "{ \"prompt\": \"{{instruction}}\" }"; // Define the prompt for the model. var prompt = "Describe the purpose of a 'hello world' program in one line."; // Embed the prompt in Llama 3's instruction format. var instruction = ( "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n" + "{{prompt}} <|eot_id|>\\n" + "<|start_header_id|>assistant<|end_header_id|>\\n" ).replace("{{prompt}}", prompt); // Embed the instruction in the the native request payload. var nativeRequest = nativeRequestTemplate.replace("{{instruction}}", instruction); // Create a request with the model ID and the model's native request payload. var request = InvokeModelWithResponseStreamRequest.builder() .body(SdkBytes.fromUtf8String(nativeRequest)) .modelId(modelId) .build(); // Prepare a buffer to accumulate the generated response text. var completeResponseTextBuffer = new StringBuilder(); // Prepare a handler to extract, accumulate, and print the response text in real-time. var responseStreamHandler = InvokeModelWithResponseStreamResponseHandler.builder() .subscriber(Visitor.builder().onChunk(chunk -> { // Extract and print the text from the model's native response. var response = new JSONObject(chunk.bytes().asUtf8String()); var text = new JSONPointer("/generation").queryFrom(response); System.out.print(text); // Append the text to the response text buffer. completeResponseTextBuffer.append(text); }).build()).build(); try { // Send the request and wait for the handler to process the response. client.invokeModelWithResponseStream(request, responseStreamHandler).get(); // Return the complete response text. return completeResponseTextBuffer.toString(); } catch (ExecutionException | InterruptedException e) { System.err.printf("Can't invoke '%s': %s", modelId, e.getCause().getMessage()); throw new RuntimeException(e); } } public static void main(String[] args) throws ExecutionException, InterruptedException { invokeModelWithResponseStream(); } }
-
有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModelWithResponseStream” 中的。
-
Mistral AI
以下代码示例显示了如何使用 Bedrock 的 Converse 向 Mistral 发送短信。API
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Mistral 发送短信。API
// 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(); } }
使用 Bedrock 的 Converse API 和异步 Java 客户端,向 Mistral 发送短信。
// 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(); } }
-
有关API详细信息,请参阅《Amazon SDK for Java 2.x API参考资料》中的 “Converse”。
-
以下代码示例显示了如何使用 Bedrock 的 Converse 向 Mistral 发送短信API并实时处理响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Bedrock 的 Converse 向 Mistral 发送短信API并实时处理响应流。
// 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()); } } }
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有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 ConverseStream” 中的。
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以下代码示例显示了如何使用调用模型向 Mistral 模型发送短信。API
- SDK适用于 Java 2.x
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注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用调用模型API发送短信。
// 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(); } }
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有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
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以下代码示例演示如何使用调用模型向 Mistral AI 模型API发送短信并打印响应流。
- SDK适用于 Java 2.x
-
注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 Invoke Model API 发送短信并实时处理响应流。
// 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(); } }
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有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModelWithResponseStream” 中的。
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Stable Diffusion
以下代码示例展示了如何在 Amazon Bedrock 上调用 Stability.ai Stable Diffusion XL 来生成图像。
- SDK适用于 Java 2.x
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注意
还有更多相关信息 GitHub。查找完整示例,学习如何在 Amazon 代码示例存储库
中进行设置和运行。 使用 “稳定扩散” 创建图像。
// 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); } }
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有关API详细信息,请参阅 “Amazon SDK for Java 2.x API参考 InvokeModel” 中的。
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