Course Outline
Foundational concepts introduced include:
- Vectors
- AI vector embeddings
- Leading AI embedding models
- Semantic search
- Distance metrics
Examination of vector indexing methods:
- IVFFlat index
- HNSW index
Working with the PgVector extension for PostgreSQL:
- Installation procedures
- Storing and retrieving high-dimensional vectors
- Applying distance measures
- Leveraging vector indexes
Learning Outcomes: Upon completion, participants will have a thorough understanding of prominent AI-enhanced PostgreSQL extensions. They will also possess practical experience in integrating large language models (LLMs) and vector search into real-world applications.
Requirements
A solid foundation in SQL and basic proficiency with PostgreSQL are required.
Lab Setup: 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.