Building Agents with AWS: Complete Tutorial (Java, Spring AI, Amazon Bedrock & MCP)

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Discover how Java developers can leverage the power of Spring AI, Amazon Bedrock, and the Model Context Protocol (MCP) to build an AI-powered dog adoption service. This step-by-step tutorial covers everything from using AI models to integrating with external data & services.

In this hands-on tutorial, Josh and James demonstrate how Java developers can create an AI-powered dog adoption application using Spring AI, Amazon Bedrock, and Model Context Protocol (MCP). They walk through the entire process, starting with setting up the development environment and configuring the necessary dependencies.

The video covers integrating large language models (LLMs) like Amazon’s Nova Pro model for natural language processing and the Cohere model for text embeddings. You’ll learn how to use system prompts, user prompts, and retrieval-augmented generation (RAG) to provide contextual responses.

Josh and James also dive into vector stores, showcasing how to store and retrieve relevant data using semantic similarity searches. They demonstrate the integration of tools and remote services using the Model Context Protocol (MCP), enabling seamless communication between the AI assistant and external systems.

Throughout the tutorial, you’ll gain insights into advanced AI engineering techniques, such as agentic systems and iterative model interactions. By the end, you’ll have a solid understanding of how to build AI-powered applications using Java and Spring AI, opening up new possibilities for enhancing user experiences and streamlining business processes.

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