Course Outline
Introduction to the following concepts:
- vectors
- AI vector embeddings
- popular AI embedding models
- semantic search
- distance measures
Overview of vector indexing techniques:
- IVFFlat index
- HNSW index
The PgVector extension for PostgreSQL:
- installation
- storing and querying high-dimensional vectors
- distance measures
- utilising vector indexes
Course outcome: Upon completion, students will possess a comprehensive understanding of leading AI-enabled PostgreSQL extensions. They will also acquire practical expertise in integrating Large Language Models (LLMs) and vector search into real-world applications.
Requirements
A foundational understanding of SQL and basic experience working with PostgreSQL
Lab environment: DaDesktops operating Linux virtual machines (supplied by NobleProg)
Target 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.