Bridging Knowledge and Action: How RAG and MCP Power the Next Era of AI Agents
As Large Language Models (LLMs) continue to evolve, two fundamental limitations persist: knowledge cutoffs and isolation from execution environments. An AI model may possess impressive reasoning capabilities, but without direct…
Integrating Drools with a Spring Boot Microservice
This blog post will guide you through integrating the Drools rule engine with your Spring Boot microservice. We’ll cover the necessary dependencies, configuration, and an example service to demonstrate its…
Mastering Retrieval-Augmented Generation (RAG) with Spring AI
Retrieval-Augmented Generation (RAG) has become the gold standard architecture for extending the capabilities of Large Language Models (LLMs) with enterprise domain knowledge. By combining the natural language understanding of LLMs…
Spring Data REST: Simplify RESTful API Development
Spring Data REST is a Spring module that automatically creates RESTful APIs for Spring Data repositories. It eliminates boilerplate code, allowing you to focus on your application’s core logic. Benefits:…
Integrating Model Context Protocol (MCP) Tooling with Spring AI
The Model Context Protocol (MCP), originally introduced by Anthropic, is an open standard designed to standardize how Large Language Models (LLMs) connect with external context sources—such as databases, local file…
Choosing the Right Circuit Breaker: A Comparison of Implementations
Spring Cloud Circuit Breaker provides a facade over popular circuit breaker implementations, giving developers flexibility and a consistent API. However, the level of support for Spring Cloud Circuit Breaker interfaces…
Anomaly Detection in Spring Boot Gateway with AI and DL4J: Unsupervised Learning Approach
In this article, we’ll focus on using unsupervised learning with DL4J to detect anomalies in data traffic passing through your Spring Boot Gateway. This is especially useful when you don’t…