Course Outline
An introductory look at:
- Vectors
- AI vector embeddings
- Leading AI embedding models
- Semantic search
- Distance metrics
A general view of vector indexing methods:
- IVFFlat index
- HNSW index
The PgVector extension for PostgreSQL:
- Setup process
- Managing and retrieving high-dimensional vectors
- Distance metrics
- Applying vector indexes
Course goals: Upon completion, participants will have a solid grasp of prominent AI-driven PostgreSQL extensions. They will also acquire practical experience in integrating large language models (LLMs) and vector search into real-world applications.
Requirements
Essential understanding of SQL and foundational experience with PostgreSQL
Lab setup: Desktops operating Linux virtual machines (Supplied by NobleProg)
Target audience: Database application developers, system architects, and data analysts
Testimonials (3)
Tables indexes and qq reperforming & tuning in order to improve them
Roberto Esposito
Course - PostgreSQL AI for Developers
A good first contact with a new database technology
Cont Florian - Banca Transilvania Cluj
Course - PostgreSQL Server Administration
Tuning strategies.
