PostgreSQL for Vector Search: Build AI-Powered Recommendation Systems

Modern AI applications rely heavily on vector embeddings to power semantic search and recommendation systems. PostgreSQL, equipped with the pgvector extension, allows you to store and query these vectors efficiently without needing a separate database. In this text-based course, you will transition from understanding relational databases to leveraging PostgreSQL as a robust vector database. You will learn the mechanics of vector storage, similarity metrics, and how to construct a practical recommendation engine. What you'll learn: • Understand the fundamentals of vector embeddings, similarity metrics, and how AI models represent data. • Configure PostgreSQL with the pgvector extension to store high-dimensional vectors. • Query vector data using cosine distance, L2 distance, and inner product search techniques. • Optimize search performance by implementing IVFFlat and HNSW vector indexes. • Build a semantic recommendation workflow, such as a personalized movie recommendation system. • Apply vector databases within modern Retrieval-Augmented Generation (RAG) patterns for AI agents. The course begins with core definitions and essential mathematical concepts behind vector search, progressing through practical SQL queries, indexing strategies, and real-world recommendation workflows. This course is designed for software developers, database administrators, and aspiring AI engineers who want to extend their SQL skills into the world of AI. No prior experience with vector databases is required. Start reading today to unlock the potential of vector search inside your database.