How does it handle different model payload formats?

The skill includes logic to construct the correct JSON payloads for various providers like Anthropic, Meta, and Amazon through a unified invocation pattern.

Does it handle streaming responses?

Yes, it includes code patterns for using the Bedrock Runtime client's streaming capabilities to provide real-time token generation for a better user experience.

Can I use this with Spring Boot applications?

Absolutely, the skill provides specific configuration patterns for setting up Bedrock clients as Spring Beans and creating injectable AI services.

Does this skill support the latest Claude 4.5 and 3.7 models?

Yes, the skill includes constant definitions and invocation patterns for the newest Anthropic Claude models, including Claude 4.5 Sonnet and 3.7 Sonnet.

Is it compatible with RAG architectures?

Yes, it provides implementation patterns for creating text embeddings using models like Amazon Titan, which is a core component of Retrieval-Augmented Generation.

AWS Bedrock for Java 2.x

Name: AWS Bedrock for Java 2.x
Author: pur3v4d3r

bypur3v4d3r

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Cloud Infrastructure

Integrates Amazon Bedrock foundation models into Java applications using the AWS SDK 2.x for text, image, and embedding generation.

About

This skill provides standardized implementation patterns for Amazon Bedrock using the AWS SDK for Java 2.x, enabling developers to seamlessly integrate advanced generative AI capabilities into their software. It covers a wide range of foundation models including Anthropic Claude, Meta Llama, and Amazon Titan, offering ready-to-use code for model invocation, real-time streaming responses, high-performance embeddings, and native Spring Boot integration. It is designed to help Java developers navigate the complexities of model-specific payloads through a unified implementation approach.

Key Features

High-performance text embedding generation for RAG (Retrieval-Augmented Generation).
1 GitHub stars
Comprehensive model discovery and management patterns for AWS environments.
Unified API patterns for multiple foundation models including Claude, Llama, and Nova.
Real-time streaming response implementation for low-latency interactive applications.
Optimized Bedrock client configuration and Spring Boot bean integration.

Use Cases

Automating image generation workflows using Stable Diffusion or Titan Image Generator.
Implementing vector-based document search using Amazon Titan embeddings and RAG.
Building enterprise-grade AI chatbots and assistants within existing Java/Spring ecosystems.

About

Key Features

High-performance text embedding generation for RAG (Retrieval-Augmented Generation).
1 GitHub stars
Comprehensive model discovery and management patterns for AWS environments.
Unified API patterns for multiple foundation models including Claude, Llama, and Nova.
Real-time streaming response implementation for low-latency interactive applications.
Optimized Bedrock client configuration and Spring Boot bean integration.

Use Cases

Automating image generation workflows using Stable Diffusion or Titan Image Generator.
Implementing vector-based document search using Amazon Titan embeddings and RAG.
Building enterprise-grade AI chatbots and assistants within existing Java/Spring ecosystems.