Building AI Applications with Azure Database for PostgreSQL

Modern applications require intelligent search capabilities that go beyond simple keyword matching. By leveraging Azure Database for PostgreSQL, you can store data and power advanced AI features directly within your relational database. This text-based course guides you from relational database basics to building fully functional AI-driven search solutions. You will learn how to store, query, and manage high-dimensional vector embeddings to support semantic search and Retrieval-Augmented Generation (RAG) pipelines. What you'll learn: - Understand the core concepts of vector databases and how semantic search differs from traditional querying. - Configure and enable the pgvector extension on Azure Database for PostgreSQL. - Store and index high-dimensional vector embeddings efficiently using modern indexing techniques. - Build Retrieval-Augmented Generation (RAG) pipelines to connect your database with large language models. - Apply similarity search operators to retrieve highly relevant context for AI prompts. - Implement security and performance best practices for production-ready database AI solutions. You will start with the fundamental terminology of embeddings and vector databases before diving into practical SQL exercises, configuration steps, and real-world implementation strategies. This course is designed for software developers, database administrators, and aspiring AI engineers who are new to vector search and want to build intelligent applications using PostgreSQL. No prior experience with AI or machine learning is required. Start reading today to unlock the power of semantic search in your PostgreSQL databases.