Java Spring AI: Knowledge Graph RAG with Neo4J

Modern AI applications require more than just a large language model; they need access to structured, specialized data to provide accurate and relevant answers. This course teaches you how to bridge the gap between your data and AI using the Java ecosystem. You will learn how to build sophisticated Retrieval-Augmented Generation (RAG) systems that leverage the power of graph databases to provide deeper context than standard vector searches alone. By the end of this course, you will be able to architect Java-based AI solutions that understand complex data relationships. What you'll learn: - Understand the core principles of Retrieval-Augmented Generation (RAG) and its role in modern AI. - Configure Spring AI to communicate with OpenAI and manage large language model interactions. - Implement Neo4J to store and query data using Knowledge Graph structures. - Combine vector similarity search with graph-based retrieval for high-precision data fetching. - Apply prompt engineering basics to refine and optimize AI responses within your Java code. - Practice building reliable workflows for data ingestion and processing. The course begins with foundational definitions of AI integration and graph theory before moving into practical implementation patterns within the Spring framework. You will read through detailed explanations and study code examples designed to illustrate modern AI architecture. This course is designed for Java developers who are new to AI integration and want to learn how to use graph technologies to enhance their applications. No prior experience with AI or Neo4J is required. Start building context-aware AI applications with Java today.