AWS SDK for Java 2.x - Amazon Bedrock

When to Use

Use this skill when:

Listing and inspecting foundation models on Amazon Bedrock
Invoking foundation models for text generation (Claude, Llama, Titan)
Generating images with AI models (Stable Diffusion)
Creating text embeddings for RAG applications
Implementing streaming responses for real-time generation
Working with multiple AI providers through unified API
Integrating generative AI into Spring Boot applications
Building AI-powered chatbots and assistants

Overview

Amazon Bedrock provides access to foundation models from leading AI providers through a unified API. This skill covers patterns for working with various models including Claude, Llama, Titan, and Stability Diffusion using AWS SDK for Java 2.x.

Quick Start

Dependencies

xml

<!-- 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>

Basic Client Setup

java

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();

Core Patterns

Model Discovery

java

import software.amazon.awssdk.services.bedrock.model.*;
import java.util.List;

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

Model Invocation

java

import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.services.bedrockruntime.model.*;
import org.json.JSONObject;

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 Responses

java

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

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

    client.invokeModelWithResponseStream(streamRequest,
        InvokeModelWithResponseStreamResponseHandler.builder()
            .onEventStream(stream -> {
                stream.forEach(event -> {
                    if (event instanceof PayloadPart) {
                        PayloadPart payloadPart = (PayloadPart) event;
                        String chunk = payloadPart.bytes().asUtf8String();
                        processChunk(modelId, chunk);
                    }
                });
            })
            .build());
}

Text Embeddings

java

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

java

@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;

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

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

    public String generateText(String prompt) {
        return generateText(prompt, defaultModelId);
    }

    public String generateText(String prompt, String modelId) {
        Map<String, Object> payload = createPayload(modelId, prompt);
        String payloadJson = new ObjectMapper().writeValueAsString(payload);

        InvokeModelResponse response = bedrockRuntimeClient.invokeModel(
            request -> request
                .modelId(modelId)
                .body(SdkBytes.fromUtf8String(payloadJson)));

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

Basic Usage Example

java

BedrockRuntimeClient client = BedrockRuntimeClient.builder()
    .region(Region.US_EAST_1)
    .build();

String prompt = "Explain quantum computing in simple terms";
String response = invokeModel(client, "anthropic.claude-sonnet-4-5-20250929-v1:0", prompt);
System.out.println(response);

Best Practices

Model Selection

Claude 4.5 Sonnet: Best for complex reasoning, analysis, and creative tasks
Claude 4.5 Haiku: Fast and affordable for real-time applications
Claude 3.7 Sonnet: Most advanced reasoning capabilities
Llama 3.1: Latest generation open-source alternative, good for general tasks
Titan: AWS native, cost-effective for simple text generation

Performance Optimization

Reuse client instances (don't create new clients for each 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 (never access keys)
Implement rate limiting for public applications
Sanitize user inputs to prevent prompt injection

Error Handling

Implement retry logic for throttling (exponential backoff)
Handle model-specific validation errors
Validate responses before processing
Use proper exception handling for different error types

Cost Optimization

Use appropriate max_tokens limits
Choose cost-effective models for simple tasks
Cache embeddings when possible
Monitor usage and set budget alerts

Common Model IDs

java

// Claude Models
public static final String CLAUDE_SONNET_4_5 = "anthropic.claude-sonnet-4-5-20250929-v1:0";
public static final String CLAUDE_HAIKU_4_5 = "anthropic.claude-haiku-4-5-20251001-v1:0";
public static final String CLAUDE_OPUS_4_1 = "anthropic.claude-opus-4-1-20250805-v1:0";
public static final String CLAUDE_3_7_SONNET = "anthropic.claude-3-7-sonnet-20250219-v1:0";
public static final String CLAUDE_OPUS_4 = "anthropic.claude-opus-4-20250514-v1:0";
public static final String CLAUDE_SONNET_4 = "anthropic.claude-sonnet-4-20250514-v1:0";
public static final String CLAUDE_3_5_SONNET_V2 = "anthropic.claude-3-5-sonnet-20241022-v2:0";
public static final String CLAUDE_3_5_HAIKU = "anthropic.claude-3-5-haiku-20241022-v1:0";
public static final String CLAUDE_3_OPUS = "anthropic.claude-3-opus-20240229-v1:0";

// Llama Models
public static final String LLAMA_3_3_70B = "meta.llama3-3-70b-instruct-v1:0";
public static final String LLAMA_3_2_90B = "meta.llama3-2-90b-instruct-v1:0";
public static final String LLAMA_3_2_11B = "meta.llama3-2-11b-instruct-v1:0";
public static final String LLAMA_3_2_3B = "meta.llama3-2-3b-instruct-v1:0";
public static final String LLAMA_3_2_1B = "meta.llama3-2-1b-instruct-v1:0";
public static final String LLAMA_4_MAV_17B = "meta.llama4-maverick-17b-instruct-v1:0";
public static final String LLAMA_4_SCOUT_17B = "meta.llama4-scout-17b-instruct-v1:0";
public static final String LLAMA_3_1_405B = "meta.llama3-1-405b-instruct-v1:0";
public static final String LLAMA_3_1_70B = "meta.llama3-1-70b-instruct-v1:0";
public static final String LLAMA_3_1_8B = "meta.llama3-1-8b-instruct-v1:0";
public static final String LLAMA_3_70B = "meta.llama3-70b-instruct-v1:0";
public static final String LLAMA_3_8B = "meta.llama3-8b-instruct-v1:0";

// Amazon Titan Models
public static final String TITAN_TEXT_EXPRESS = "amazon.titan-text-express-v1";
public static final String TITAN_TEXT_LITE = "amazon.titan-text-lite-v1";
public static final String TITAN_EMBEDDINGS = "amazon.titan-embed-text-v1";
public static final String TITAN_IMAGE_GENERATOR = "amazon.titan-image-generator-v1";

// Stable Diffusion
public static final String STABLE_DIFFUSION_XL = "stability.stable-diffusion-xl-v1";

// Mistral AI Models
public static final String MISTRAL_LARGE_2407 = "mistral.mistral-large-2407-v1:0";
public static final String MISTRAL_LARGE_2402 = "mistral.mistral-large-2402-v1:0";
public static final String MISTRAL_SMALL_2402 = "mistral.mistral-small-2402-v1:0";
public static final String MISTRAL_PIXTRAL_2502 = "mistral.pixtral-large-2502-v1:0";
public static final String MISTRAL_MIXTRAL_8X7B = "mistral.mixtral-8x7b-instruct-v0:1";
public static final String MISTRAL_7B = "mistral.mistral-7b-instruct-v0:2";

// Amazon Nova Models
public static final String NOVA_PREMIER = "amazon.nova-premier-v1:0";
public static final String NOVA_PRO = "amazon.nova-pro-v1:0";
public static final String NOVA_LITE = "amazon.nova-lite-v1:0";
public static final String NOVA_MICRO = "amazon.nova-micro-v1:0";
public static final String NOVA_CANVAS = "amazon.nova-canvas-v1:0";
public static final String NOVA_REEL = "amazon.nova-reel-v1:1";

// Other Models
public static final String COHERE_COMMAND = "cohere.command-text-v14";
public static final String DEEPSEEK_R1 = "deepseek.r1-v1:0";
public static final String DEEPSEEK_V3_1 = "deepseek.v3-v1:0";

Examples

See the examples directory for comprehensive usage patterns.

Advanced Topics

See the Advanced Topics for:

Multi-model service patterns
Advanced error handling with retries
Batch processing strategies
Performance optimization techniques
Custom response parsing

Model Reference

See the Model Reference for:

Detailed model specifications
Payload/response formats for each provider
Performance characteristics
Model selection guidelines
Configuration templates

Testing Strategies

See the Testing Strategies for:

Unit testing with mocked clients
Integration testing with LocalStack
Performance testing
Streaming response testing
Test data management

Related Skills

```
aws-sdk-java-v2-core
```
- Core AWS SDK patterns
```
langchain4j-ai-services-patterns
```
- LangChain4j integration
```
spring-boot-dependency-injection
```
- Spring DI patterns
```
spring-boot-test-patterns
```
- Spring testing patterns

References

AWS Bedrock User Guide
AWS SDK for Java 2.x Documentation
Bedrock API Reference
AWS SDK Examples
Official AWS Examples
Supported Models
Runtime Examples

aws-sdk-java-v2-bedrock

NPX Install

Tags

SKILL.md Content

AWS SDK for Java 2.x - Amazon Bedrock

When to Use

Overview

Quick Start

Dependencies

Basic Client Setup

Core Patterns

Model Discovery

Model Invocation

Streaming Responses

Text Embeddings

Spring Boot Integration

Basic Usage Example

Best Practices

Model Selection

Performance Optimization

Security

Error Handling

Cost Optimization

Common Model IDs

Examples

Advanced Topics

Model Reference

Testing Strategies

Related Skills

References