Course Outline
Introduction to:
- Vectors
- AI vector embeddings
- Popular AI embedding models
- Semantic search
- Distance measures
Overview of vector indexing techniques:
- IVFFlat index
- HNSW index
PgVector extension for PostgreSQL:
- Installation
- Storing and querying high-dimensional vectors
- Distance measures
- Utilizing vector indexes
Course Outcome: By the completion of this course, participants will have a comprehensive understanding of popular AI-powered PostgreSQL extensions. They will develop practical proficiency in integrating large language models (LLMs) and vector search capabilities into real-world applications.
Requirements
Foundational knowledge of SQL and basic proficiency with PostgreSQL.
Lab environment: 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.