PGVector and Spring Boot: Building Java AI Applications

Modern enterprise applications increasingly rely on artificial intelligence, making vector databases essential for managing complex semantic data. This text-based course guides you through integrating PGVector with Spring Boot to store and query vector embeddings efficiently. You will transition from understanding basic database concepts to building Java-based AI backend systems that leverage similarity search for advanced data retrieval. What you'll learn: Understand the fundamentals of vector databases, embeddings, and the PGVector extension for PostgreSQL; Configure Spring Boot applications to connect securely to a PGVector store; Implement semantic similarity search queries using modern Spring AI frameworks; Store, update, and manage high-dimensional vector data alongside relational tables; Apply Retrieval-Augmented Generation (RAG) patterns to enhance AI-driven application logic; Practice writing clean, modern Java code to handle embedding generation and metadata filtering. The course begins with foundational concepts of vector spaces and database extensions, then moves into step-by-step configuration and practical Java implementation. You will read clear explanations, analyze production-ready code snippets, and complete written exercises to solidify your knowledge. This course is designed for Java developers and database enthusiasts who want to enter the world of AI engineering. No prior experience with vector databases or AI is required, though a basic understanding of Spring Boot is helpful. Start building modern, AI-enabled enterprise Java applications today.