SQL for AI: Implementing Models and Vector Embeddings

Modern applications require databases that can handle more than just standard text and numbers; they need to understand semantic meaning through artificial intelligence. This text-only course guides you through bringing machine learning capabilities directly to your SQL database, enabling you to store, query, and manage vector embeddings efficiently. You will start with key terminology and foundational definitions before progressing to practical SQL implementations. Throughout this course, you will learn to: 1. Understand the core concepts of vector embeddings and semantic search within relational databases. 2. Store and manage high-dimensional vector data types directly in SQL tables. 3. Generate embeddings from text using integrated AI model endpoints and built-in database functions. 4. Write SQL queries to perform cosine similarity and nearest-neighbor vector searches. 5. Implement maintenance patterns to optimize vector index performance and storage. 6. Apply Retrieval-Augmented Generation (RAG) patterns to connect your SQL data with large language models. This comprehensive text-only guide walks you through database schema design, vector query syntax, and performance tuning strategies. Designed specifically for beginners, this course requires no prior machine learning experience—only a basic familiarity with standard SQL. Start reading today to bridge the gap between relational databases and modern artificial intelligence.