Vector Search in Azure Database for PostgreSQL with pgvector

As modern applications increasingly rely on artificial intelligence, developers need efficient ways to store and query high-dimensional vector embeddings. Discover how to leverage your existing database infrastructure to power semantic search and generative AI applications. This text-based course guides you through the fundamentals of vector databases, teaching you how to configure, query, and optimize vector data directly within Azure Database for PostgreSQL. You will transition from understanding basic relational data to implementing advanced similarity searches and Retrieval-Augmented Generation (RAG) patterns. What you will learn: - Understand the foundational concepts of vector embeddings and semantic similarity. - Configure the pgvector extension on Azure Database for PostgreSQL. - Store and query high-dimensional vectors using standard SQL. - Apply advanced indexing techniques like IVFFlat and HNSW to optimize search performance. - Build the database foundation for modern Retrieval-Augmented Generation (RAG) pipelines. - Implement practical semantic search and recommendation workflows. The course starts with essential definitions and core concepts behind vector search before moving into hands-on SQL configurations and indexing strategies. You will progress through realistic database scenarios, reading structured explanations and analyzing clear code snippets. This course is designed for database administrators, backend developers, and data engineers who are new to vector databases, with no prior AI or machine learning experience required. Start reading today to unlock the power of semantic search in your cloud database.