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: Upon completion, students will be capable of designing and building components of AI-driven database applications using PostgreSQL extensions and libraries. They will gain practical expertise in integrating large language models (LLMs) and vector search into operational systems, allowing them to create applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Prerequisites: Foundational understanding of SQL, basic experience with PostgreSQL, and elementary knowledge of either Python or JavaScript programming.
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.