Course Outline
Introduction to vectors, AI vector embeddings, leading AI embedding models, semantic search, and distance metrics
Overview of vector indexing techniques, including IVFFlat and HNSW indexes
PostgreSQL PgVector extension: installation procedures, storage and querying of high-dimensional vectors, distance metrics, and utilization of vector indexes
PostgreSQL PgAI extension: installation procedures, embedding generation, implementation of Retrieval-Augmented Generation, and advanced development patterns
Overview of Text-to-SQL solutions, focusing on the LangChain framework
Course outcomes: Upon completion, students will be equipped to design and construct components of AI-driven database applications using PostgreSQL extensions and libraries. Participants will gain practical expertise in integrating large language models (LLMs) and vector search into production systems, empowering them to build applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Essential prerequisites include a foundational understanding of SQL, practical experience with PostgreSQL, and basic proficiency in either Python or JavaScript.
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.
