Course Outline
Introduction to the following concepts:
- Vectors
- AI vector embeddings
- Popular AI embedding models
- Semantic search
- Distance measures
An overview of vector indexing techniques, including:
- IVFFlat index
- HNSW index
The PgVector extension for PostgreSQL, covering:
- Installation procedures
- Storing and querying high-dimensional vectors
- Applying distance measures
- Leveraging vector indexes
Course Outcomes: Upon completion, students will have a comprehensive understanding of leading AI-powered PostgreSQL extensions. They will also possess practical experience in integrating Large Language Models (LLMs) and vector search capabilities into real-world applications.
Requirements
Foundational knowledge of SQL and basic proficiency with PostgreSQL
Lab Environment: DaDesktops running Linux virtual machines (provided 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.