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AWS SDK for Java 2.x - Amazon Bedrock

AWS SDK for Java 2.x - Amazon Bedrock

When to Use

适用场景

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
本技能适用于以下场景：
  • 列举和查看Amazon Bedrock上的基础模型
  • 调用基础模型进行文本生成（Claude、Llama、Titan）
  • 使用AI模型生成图像（Stable Diffusion）
  • 为RAG应用创建文本嵌入向量
  • 实现实时生成的流式响应
  • 通过统一API对接多个AI供应商
  • 将生成式AI集成到Spring Boot应用中
  • 构建AI驱动的聊天机器人和助手

Overview

概述

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.
Amazon Bedrock通过统一API提供来自领先AI供应商的基础模型访问权限。本技能涵盖了使用AWS SDK for Java 2.x与Claude、Llama、Titan和Stability Diffusion等各类模型交互的模式。

Quick Start

快速开始

Dependencies

依赖项

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>
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

基础客户端配置

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();
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

核心模式

Model Discovery

模型发现

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

public List<FoundationModelSummary> listFoundationModels(BedrockClient bedrockClient) {
    return bedrockClient.listFoundationModels().modelSummaries();
}
java
import software.amazon.awssdk.services.bedrock.model.*;
import java.util.List;

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

Model Invocation

模型调用

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);
}
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

流式响应

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());
}
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

文本嵌入向量

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;
}
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

Spring Boot集成

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());
    }
}
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

基础使用示例

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);
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

最佳实践

Model Selection

模型选择

  • 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
  • Claude 4.5 Sonnet：最适合复杂推理、分析和创意任务
  • Claude 4.5 Haiku：速度快且经济实惠，适用于实时应用
  • Claude 3.7 Sonnet：具备最先进的推理能力
  • Llama 3.1：最新一代开源替代方案，适合通用任务
  • Titan：AWS原生模型，成本效益高，适用于简单文本生成

Performance Optimization

性能优化

  • 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
  • 复用客户端实例（不要为每个请求创建新客户端）
  • 对I/O操作使用异步客户端
  • 为长响应实现流式处理
  • 缓存基础模型列表

Security

安全规范

  • 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
  • 切勿记录敏感的提示词数据
  • 使用IAM角色进行身份验证（绝不要使用访问密钥）
  • 为公共应用实现速率限制
  • 对用户输入进行清理，防止提示词注入

Error Handling

错误处理

  • Implement retry logic for throttling (exponential backoff)
  • Handle model-specific validation errors
  • Validate responses before processing
  • Use proper exception handling for different error types
  • 为限流实现重试逻辑（指数退避）
  • 处理模型特定的验证错误
  • 处理前先验证响应
  • 针对不同错误类型使用适当的异常处理

Cost Optimization

成本优化

  • Use appropriate max_tokens limits
  • Choose cost-effective models for simple tasks
  • Cache embeddings when possible
  • Monitor usage and set budget alerts
  • 设置合理的max_tokens限制
  • 为简单任务选择性价比高的模型
  • 尽可能缓存嵌入向量
  • 监控使用情况并设置预算警报

Common Model IDs

常用模型ID

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";
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

示例

See the examples directory for comprehensive usage patterns.
查看示例目录获取全面的使用模式。

Advanced Topics

进阶主题

See the Advanced Topics for:
  • Multi-model service patterns
  • Advanced error handling with retries
  • Batch processing strategies
  • Performance optimization techniques
  • Custom response parsing
查看进阶主题了解：
  • 多模型服务模式
  • 带重试的高级错误处理
  • 批处理策略
  • 性能优化技巧
  • 自定义响应解析

Model Reference

模型参考

See the Model Reference for:
  • Detailed model specifications
  • Payload/response formats for each provider
  • Performance characteristics
  • Model selection guidelines
  • Configuration templates
查看模型参考了解：
  • 详细的模型规格
  • 各供应商的请求/响应格式
  • 性能特征
  • 模型选择指南
  • 配置模板

Testing Strategies

测试策略

See the Testing Strategies for:
  • Unit testing with mocked clients
  • Integration testing with LocalStack
  • Performance testing
  • Streaming response testing
  • Test data management
查看测试策略了解：
  • 使用模拟客户端进行单元测试
  • 结合LocalStack进行集成测试
  • 性能测试
  • 流式响应测试
  • 测试数据管理

Related Skills

相关技能

  • 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
  • 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

参考资料

  • 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 Bedrock用户指南
  • AWS SDK for Java 2.x文档
  • Bedrock API参考
  • AWS SDK示例
  • 官方AWS示例
  • 支持的模型
  • 运行时示例