Course Outline
Introduction to:
- Vectors
- AI vector embeddings
- Leading AI embedding models
- Semantic search
- Distance metrics
Overview of vector indexing methodologies:
- IVFFlat index
- HNSW index
PgVector extension for PostgreSQL:
- Installation procedures
- Managing and querying high-dimensional vectors
- Applying distance metrics
- Leveraging vector indexes
Course Outcomes: Upon completion, students will have a comprehensive understanding of prominent AI-driven PostgreSQL extensions. They will also acquire hands-on experience in integrating Large Language Models (LLMs) and vector search capabilities into practical, real-world applications.
Requirements
Prerequisites: Foundational knowledge of SQL and basic proficiency with PostgreSQL
Lab environment: DaDesktops operating Linux virtual machines (Facilitated 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.