Course Outline
Introduction to vectors, AI vector embeddings, widely used embedding models, semantic search, and distance metrics.
Overview of vector indexing techniques: IVFFlat and HNSW indexes.
PgVector extension for PostgreSQL: installation, storage and querying of high-dimensional vectors, distance metrics, and utilizing vector indexes.
PgAI extension for PostgreSQL: installation, embedding generation, implementing Retrieval-Augmented Generation, and exploring advanced development patterns.
Overview of Text-to-SQL solutions: 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 real-world systems, empowering them to build applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Foundational knowledge of SQL, practical experience with PostgreSQL, and basic proficiency in Python or JavaScript.
Target 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.