Building RAG Applications with Azure Database for PostgreSQL

Integrating generative AI with your organization's private data is essential for building highly context-aware systems. This text-based course guides you step-by-step through the process of designing and implementing Retrieval-Augmented Generation (RAG) applications using Azure Database for PostgreSQL. You will transition from understanding basic database concepts to constructing robust, Python-based RAG pipelines. By mastering vector search extensions and modern AI integration patterns, you will be equipped to build intelligent search solutions that deliver accurate, context-rich answers. What you'll learn: - Understand the foundational architecture of Retrieval-Augmented Generation (RAG) and vector databases. - Configure Azure Database for PostgreSQL and enable the pgvector extension for storing high-dimensional embeddings. - Develop a Python application to generate, store, and query vector embeddings using clean, modern development practices. - Implement semantic search queries and optimize database performance using advanced indexing strategies like HNSW. - Explore next-generation RAG patterns, including hybrid search concepts and the fundamentals of GraphRAG. The course begins with core definitions of vector search and embeddings before moving into practical database configuration and Python integration. You will study comprehensive text explanations, review structured code snippets, and complete written conceptual exercises designed to solidify your understanding of modern AI database architecture. This course is built for software developers, database administrators, and aspiring AI engineers who are new to vector databases and RAG systems. No prior experience with AI models is required, though a basic familiarity with PostgreSQL and Python is recommended. Start reading today to unlock the power of vector search in your database applications.