Does this skill support the latest Claude models?

Yes, it includes model IDs and invocation patterns for Claude 3, 3.5, 4, and 4.5 Sonnet and Haiku.

Is it limited to text generation?

No, it also covers patterns for creating text embeddings for vector databases and generating images with models like Stable Diffusion.

Does it support streaming responses for real-time UI?

Yes, it includes boilerplate code for Bedrock's event stream API to handle real-time text generation chunks.

Can I use this with Spring Boot applications?

Absolutely, the skill provides specific @Configuration and @Service patterns tailored for Spring Boot environments.

What version of the AWS SDK is required?

This skill is specifically optimized for AWS SDK for Java 2.x, utilizing the latest Bedrock and BedrockRuntime clients.

Amazon Bedrock for Java SDK v2

Name: Amazon Bedrock for Java SDK v2
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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Infraestructura en la Nube

Integrates Amazon Bedrock foundation models into Java applications using standardized AWS SDK 2.x implementation patterns.

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This skill provides a comprehensive set of patterns and best practices for developers building generative AI features with the AWS SDK for Java 2.x. It streamlines the process of discovering and invoking foundation models like Claude, Llama, and Titan, while offering ready-to-use templates for text generation, image creation, and embeddings. Designed for production environments, it includes specific configurations for Spring Boot integration, secure IAM practices, and efficient client management to ensure high-performance AI implementations in the Java ecosystem.

Características Principales

Real-time streaming response implementations for interactive AI applications
Unified patterns for invoking Claude, Llama, Titan, and Nova models
34 GitHub stars
Standardized text embedding generation for RAG and semantic search
Pre-configured Spring Boot integration and bean management
Best practices for IAM security, client reuse, and error handling

Casos de Uso

Implementing Retrieval-Augmented Generation (RAG) using Titan embeddings
Building generative AI chatbots and assistants within enterprise Java backends
Automating multi-model workflows for text and image generation

Acerca de

Características Principales

Real-time streaming response implementations for interactive AI applications
Unified patterns for invoking Claude, Llama, Titan, and Nova models
34 GitHub stars
Standardized text embedding generation for RAG and semantic search
Pre-configured Spring Boot integration and bean management
Best practices for IAM security, client reuse, and error handling

Casos de Uso

Implementing Retrieval-Augmented Generation (RAG) using Titan embeddings
Building generative AI chatbots and assistants within enterprise Java backends
Automating multi-model workflows for text and image generation