Course Outline
Introduction to:
- Vectors
- AI vector embeddings
- Prevalent AI embedding models
- Semantic search
- Distance metrics
Overview of vector indexing techniques:
- IVFFlat index
- HNSW index
PgVector extension for PostgreSQL:
- Installation process
- Storing and querying high-dimensional vectors
- Distance metrics
- Leveraging vector indexes
Course Outcome: Upon completion, students will possess a comprehensive understanding of leading AI-enhanced PostgreSQL extensions. They will have acquired hands-on experience in integrating large language models (LLMs) and vector search capabilities into practical, real-world applications.
Requirements
Foundational knowledge of SQL and basic proficiency with PostgreSQL.
Lab Environment: DaDesktops featuring 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.