Course Outline
Introduction to the following concepts:
- Vectors
- AI vector embeddings
- Popular AI embedding models
- Semantic search
- Distance measures
Overview of vector indexing techniques, including:
- IVFFlat index
- HNSW index
The PgVector extension for PostgreSQL, covering:
- Installation
- Storing and querying high-dimensional vectors
- Distance measures
- Utilizing vector indexes
Course outcome: Upon completion, students will have a solid understanding of popular AI-driven PostgreSQL extensions. They will also possess practical experience in integrating large language models (LLMs) and vector search capabilities into real-world applications.
Requirements
Foundational proficiency in SQL and basic familiarity with PostgreSQL are required.
Lab environment: DaDesktops running Linux virtual machines (Provided by NobleProg)
Target audience: Database application developers, system architects, and data analysts.
Testimonials (2)
CTE and indexes. Maybe more likely to use partitioning, explain analyze and vacuum on daily-base work
Lorenzo Antonelli - Innova S.P.A
Course - PostgreSQL Fundamentals
The patiance and the style of teaching of Michał was nice.