Course Outline
Introductory concepts covering:
- Vector structures
- AI vector embeddings
- Leading AI embedding models
- Semantic search mechanics
- Metric measures for distance
An examination of vector indexing strategies:
- IVFFlat index implementation
- HNSW index implementation
Utilizing the PgVector extension within PostgreSQL:
- Deployment and installation
- Management and retrieval of high-dimensional vectors
- Application of distance metrics
- Implementation of vector indexes
Learning Outcomes: Upon completion, students will possess a comprehensive understanding of prominent AI-enabled PostgreSQL extensions. They will acquire hands-on experience in integrating Large Language Models (LLMs) and vector search capabilities into practical application scenarios.
Requirements
Fundamental proficiency in SQL and basic familiarity with the PostgreSQL platform
Lab environment: DaDesktops operating on Linux virtual machines (supplied by NobleProg)
Intended 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.