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
Course outcome: Upon completion, students will have a clear understanding of leading AI-powered PostgreSQL extensions. They will acquire hands-on experience in integrating large language models (LLMs) and vector search functionalities into practical, real-world applications.
Requirements
Fundamental understanding of SQL and basic proficiency with PostgreSQL
Lab environment: DaDesktops running Linux virtual machines (Provided by NobleProg)
Audience: database application developers, system architects, and data analysts
Testimonials (2)
Tuning strategies.
Jeffrey Zieg - Matrix Consulting
Course - PostgreSQL Performance Tuning
Logging behaviour when the instance is under stress, and the hierarchy/nomenclature of instances, databases, files, etc.