MCP Annotations in Spring AI

Spring AI introduces first-class support for the Model Context Protocol (MCP), enabling AI models to discover tools, data, and prompts…

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Building Semantic Caching with Spring AI

Traditional caching relies on exact key matching. This approach works well for deterministic inputs but fails when dealing with natural…

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Building AI-Powered Applications with Spring AI and LangChain4j

1. Introduction: Java Enters the AI Arena For years, Python dominated AI development while Java—the backbone of enterprise systems—remained on…

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Managing Multiple LLM Integrations with Spring AI

Integrating multiple Large Language Models (LLMs) in a single Spring Boot application has become a sought-after architectural pattern for AI-driven…

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Designing Intelligent Agents with Spring AI

Spring AI is a powerful framework that seamlessly integrates AI capabilities into Spring Boot applications. It abstracts complex AI workflows…

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Real-Time Streaming with Spring AI ChatClient

Streaming response in Spring AI ChatClient enables progressive delivery of AI-generated content, allowing clients to receive partial data as it…

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MCP Authorization With Spring AI and OAuth2

As AI systems evolve, integrating external tools securely has become a critical concern. MCP (Model Context Protocol) enables AI applications…

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Integrating the OpenAI Moderation Model in Spring AI

When building applications that handle user input, such as forums, chatbots, or social platforms, it is essential to protect users…

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Spring AI with Oracle Vector DB

This guide walks you through building a Retrieval-Augmented Generation (RAG) chatbot using Spring Boot, Spring AI, and Oracle Database 23ai.…

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