Course Outline
Introduction to: vectors, AI vector embeddings, popular AI embedding models, semantic search, distance measures
Overview of vector indexing techniques: IVFFlat index, HNSW index
PgVector extension for PostgreSQL: installation, storing and querying high-dimensional vectors, distance measures, using vector indexes
PgAI extension for PostgreSQL: installation, generating embeddings, implementing Retrieval-Augmented Generation, advanced development patterns
Overview of Text-to-SQL solutions: LangChain framework
Course outcome: By the end of the course, students will be able to design and build elements of AI-powered database applications using PostgreSQL extensions and libraries. They will gain practical experience with techniques for integrating large language models (LLMs) and vector search into real-world systems, enabling them to develop applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Foundational understanding of SQL and basic experience with PostgreSQL. Familiarity with either Python or JavaScript programming languages is required.
Audience: Database developers and system architects
Testimonials (2)
The provided examples and labs
Christophe OSTER - EU Lisa
Course - PostgreSQL Advanced DBA
1. A very well-structured training program 2. The warm atmosphere the trainer created, along with his outstanding personal professionalism 3. That the trainer explained everything as if he were talking to a complete beginner, without slipping into any technical jargon.
