Course Outline
Key concepts introduced in this session include:
- Vectors
- AI vector embeddings
- Leading AI embedding models
- Semantic search
- Distance metrics
An examination of vector indexing strategies, specifically:
- IVFFlat index
- HNSW index
Detailed coverage of the PgVector extension for PostgreSQL, covering:
- Installation procedures
- Storing and querying high-dimensional vectors
- Applying distance measures
- Leveraging vector indexes
Learning outcomes: Upon completion, participants will possess a comprehensive understanding of widely adopted AI-enhanced PostgreSQL extensions. They will also acquire hands-on experience in integrating large language models (LLMs) and vector search capabilities into production-grade applications.
Requirements
A foundational understanding of SQL and basic hands-on experience with PostgreSQL are required.
Practical sessions will be conducted using DaDesktops, which provide access to Linux virtual machines (facilities supplied by NobleProg).
This course is tailored for 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.