Course Outline
Fundamentals covered in the introduction include:
- vectors
- AI vector embeddings
- widely used AI embedding models
- semantic search
- distance measurement metrics
A review of vector indexing methodologies:
- IVFFlat index
- HNSW index
Utilizing the PgVector extension in PostgreSQL:
- deployment procedures
- management and retrieval of high-dimensional vectors
- application of distance metrics
- leveraging vector indexes
Learning outcomes: Upon completion of the course, participants will possess a comprehensive understanding of prominent AI-enabled PostgreSQL extensions. They will also have gained practical proficiency in integrating Large Language Models (LLMs) and vector search capabilities into production-level applications.
Requirements
A foundational understanding of SQL, along with basic experience using PostgreSQL
Lab setup: DaDesktops equipped with 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.
