AWS SDK for Java 2.x - Amazon Bedrock

Overview

Invokes foundation models through AWS SDK for Java 2.x. Configures clients, builds model-specific JSON payloads, handles streaming responses with error recovery, creates embeddings for RAG, integrates generative AI into Spring Boot applications, and implements exponential backoff for resilience.

When to Use

• Invoke Claude, Llama, Titan, or Stable Diffusion for text/image generation

• Configure BedrockClient and BedrockRuntimeClient instances

• Build and parse model-specific payloads (Claude, Titan, Llama formats)

• Stream real-time AI responses with async handlers and error recovery

• Create embeddings for retrieval-augmented generation

• Integrate generative AI into Spring Boot microservices

• Handle throttling with exponential backoff retry logic

Quick Start

Dependencies

<!-- Bedrock (model management) --> <dependency> <groupId>software.amazon.awssdk</groupId> <artifactId>bedrock</artifactId> </dependency>

<!-- Bedrock Runtime (model invocation) --> <dependency> <groupId>software.amazon.awssdk</groupId> <artifactId>bedrockruntime</artifactId> </dependency>

<!-- For JSON processing --> <dependency> <groupId>org.json</groupId> <artifactId>json</artifactId> <version>20231013</version> </dependency>

Client Setup

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.bedrock.BedrockClient; import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

// Model management client BedrockClient bedrockClient = BedrockClient.builder() .region(Region.US_EAST_1) .build();

// Model invocation client BedrockRuntimeClient bedrockRuntimeClient = BedrockRuntimeClient.builder() .region(Region.US_EAST_1) .build();

Instructions

Follow these steps for production-ready Bedrock integration:

• Configure AWS Credentials - Set up IAM roles with Bedrock permissions (avoid access keys)

• Enable Model Access - Request access to specific foundation models in AWS Console

• Initialize Clients - Create reusable BedrockClient and BedrockRuntimeClient instances

• Validate Model Availability - Test with a simple invocation before production use

• Build Payloads - Create model-specific JSON payloads with proper format

• Handle Responses - Parse response structure and extract content

• Implement Streaming - Use response stream handlers for real-time generation

• Add Error Handling - Implement retry logic with exponential backoff

Validation Checkpoint: Always test with a simple prompt (e.g., "Hello") before production use to verify model access and response parsing.

Examples

Text Generation with Claude

public String generateWithClaude(BedrockRuntimeClient client, String prompt) { JSONObject payload = new JSONObject() .put("anthropic_version", "bedrock-2023-05-31") .put("max_tokens", 1000) .put("messages", new JSONObject[]{ new JSONObject().put("role", "user").put("content", prompt) });

InvokeModelResponse response = client.invokeModel(InvokeModelRequest.builder()
    .modelId("anthropic.claude-sonnet-4-5-20250929-v1:0")
    .body(SdkBytes.fromUtf8String(payload.toString()))
    .build());

JSONObject responseBody = new JSONObject(response.body().asUtf8String());
return responseBody.getJSONArray("content")
    .getJSONObject(0)
    .getString("text");

}

Model Discovery

import software.amazon.awssdk.services.bedrock.model.*;

public List<FoundationModelSummary> listFoundationModels(BedrockClient bedrockClient) { return bedrockClient.listFoundationModels().modelSummaries(); }

Multi-Model Invocation

public String invokeModel(BedrockRuntimeClient client, String modelId, String prompt) { JSONObject payload = createPayload(modelId, prompt);

InvokeModelResponse response = client.invokeModel(request -> request
    .modelId(modelId)
    .body(SdkBytes.fromUtf8String(payload.toString())));

return extractTextFromResponse(modelId, response.body().asUtf8String());

}

private JSONObject createPayload(String modelId, String prompt) { if (modelId.startsWith("anthropic.claude")) { return new JSONObject() .put("anthropic_version", "bedrock-2023-05-31") .put("max_tokens", 1000) .put("messages", new JSONObject[]{ new JSONObject().put("role", "user").put("content", prompt) }); } else if (modelId.startsWith("amazon.titan")) { return new JSONObject() .put("inputText", prompt) .put("textGenerationConfig", new JSONObject() .put("maxTokenCount", 512) .put("temperature", 0.7)); } else if (modelId.startsWith("meta.llama")) { return new JSONObject() .put("prompt", "[INST] " + prompt + " [/INST]") .put("max_gen_len", 512) .put("temperature", 0.7); } throw new IllegalArgumentException("Unsupported model: " + modelId); }

Streaming Response with Error Handling

public String streamResponseWithRetry(BedrockRuntimeClient client, String modelId, String prompt, int maxRetries) { int attempt = 0; while (attempt < maxRetries) { try { JSONObject payload = createPayload(modelId, prompt); StringBuilder fullResponse = new StringBuilder();

        InvokeModelWithResponseStreamRequest request = InvokeModelWithResponseStreamRequest.builder()
            .modelId(modelId)
            .body(SdkBytes.fromUtf8String(payload.toString()))
            .build();

        client.invokeModelWithResponseStream(request,
            InvokeModelWithResponseStreamResponseHandler.builder()
                .onEventStream(stream -> stream.forEach(event -> {
                    if (event instanceof PayloadPart) {
                        String chunk = ((PayloadPart) event).bytes().asUtf8String();
                        fullResponse.append(chunk);
                    }
                }))
                .onError(e -> System.err.println("Stream error: " + e.getMessage()))
                .build());

        return fullResponse.toString();
    } catch (Exception e) {
        attempt++;
        if (attempt >= maxRetries) {
            throw new RuntimeException("Stream failed after " + maxRetries + " attempts", e);
        }
        try {
            Thread.sleep((long) Math.pow(2, attempt) * 1000); // Exponential backoff
        } catch (InterruptedException ie) {
            Thread.currentThread().interrupt();
            throw new RuntimeException("Interrupted during retry", ie);
        }
    }
}
throw new RuntimeException("Unexpected error in streaming");

}

Exponential Backoff for Throttling

import software.amazon.awssdk.awscore.exception.AwsServiceException;

public <T> T invokeWithRetry(Supplier<T> invocation, int maxRetries) { int attempt = 0; while (attempt < maxRetries) { try { return invocation.get(); } catch (AwsServiceException e) { if (e.statusCode() == 429 || e.statusCode() >= 500) { attempt++; if (attempt >= maxRetries) throw e; long delayMs = Math.min(1000 * (1L << attempt) + (long) (Math.random() * 1000), 30000); Thread.sleep(delayMs); } else { throw e; } } } throw new IllegalStateException("Should not reach here"); }

Text Embeddings

public double[] createEmbeddings(BedrockRuntimeClient client, String text) { String modelId = "amazon.titan-embed-text-v1";

JSONObject payload = new JSONObject().put("inputText", text);

InvokeModelResponse response = client.invokeModel(request -> request
    .modelId(modelId)
    .body(SdkBytes.fromUtf8String(payload.toString())));

JSONObject responseBody = new JSONObject(response.body().asUtf8String());
JSONArray embeddingArray = responseBody.getJSONArray("embedding");

double[] embeddings = new double[embeddingArray.length()];
for (int i = 0; i < embeddingArray.length(); i++) {
    embeddings[i] = embeddingArray.getDouble(i);
}
return embeddings;

}

Spring Boot Integration

@Configuration public class BedrockConfiguration {

@Bean
public BedrockClient bedrockClient() {
    return BedrockClient.builder()
        .region(Region.US_EAST_1)
        .build();
}

@Bean
public BedrockRuntimeClient bedrockRuntimeClient() {
    return BedrockRuntimeClient.builder()
        .region(Region.US_EAST_1)
        .build();
}

}

@Service public class BedrockAIService {

private final BedrockRuntimeClient bedrockRuntimeClient;
private final ObjectMapper mapper;

@Value("${bedrock.default-model-id:anthropic.claude-sonnet-4-5-20250929-v1:0}")
private String defaultModelId;

public BedrockAIService(BedrockRuntimeClient bedrockRuntimeClient, ObjectMapper mapper) {
    this.bedrockRuntimeClient = bedrockRuntimeClient;
    this.mapper = mapper;
}

public String generateText(String prompt) {
    Map<String, Object> payload = Map.of(
        "anthropic_version", "bedrock-2023-05-31",
        "max_tokens", 1000,
        "messages", List.of(Map.of("role", "user", "content", prompt))
    );

    InvokeModelResponse response = bedrockRuntimeClient.invokeModel(
        InvokeModelRequest.builder()
            .modelId(defaultModelId)
            .body(SdkBytes.fromUtf8String(mapper.writeValueAsString(payload)))
            .build());

    return extractText(response.body().asUtf8String());
}

}

See examples directory for comprehensive usage patterns.

Best Practices

Model Selection

• Claude 4.5 Sonnet: Complex reasoning, analysis, and creative tasks

• Claude 4.5 Haiku: Fast and affordable for real-time applications

• Llama 3.1: Open-source alternative for general tasks

• Titan: AWS native, cost-effective for simple text generation

Performance

• Reuse client instances (avoid creating new clients per request)

• Use async clients for I/O operations

• Implement streaming for long responses

• Cache foundation model lists

Security

• Never log sensitive prompt data

• Use IAM roles for authentication

• Sanitize user inputs to prevent prompt injection

• Implement rate limiting for public applications

Constraints and Warnings

• Cost Management: Bedrock API calls incur charges per token; implement usage monitoring and budget alerts.

• Model Access: Foundation models must be enabled in AWS Console; verify region availability.

• Rate Limits: Implement exponential backoff for throttling; check per-model limits.

• Payload Size: Maximum payload size varies by model; use chunking for large documents.

• Streaming Complexity: Handle partial content and error recovery carefully.

• Data Privacy: Prompts and responses may be logged by AWS; review data policies.

• Credentials: Never embed credentials in code; use IAM roles for EC2/Lambda.

Common Model IDs

• Claude Sonnet 4.5: anthropic.claude-sonnet-4-5-20250929-v1:0

• Claude Haiku 4.5: anthropic.claude-haiku-4-5-20251001-v1:0

• Llama 3.1 70B: meta.llama3-1-70b-instruct-v1:0

• Titan Embeddings: amazon.titan-embed-text-v1

See Model Reference for complete list.

References

• Advanced Topics - Multi-model patterns, advanced error handling

• Model Reference - Detailed specifications, payload formats

• Testing Strategies - Unit testing, LocalStack integration

• AWS Bedrock User Guide

• AWS SDK Examples

• Supported Models

Related Skills

• aws-sdk-java-v2-core

• Core AWS SDK patterns

• langchain4j-ai-services-patterns

• LangChain4j integration

• spring-boot-dependency-injection

• Spring DI patterns