Get in Touch

Course Outline

Introduction to:

  • Vectors
  • AI vector embeddings
  • Leading AI embedding models
  • Semantic search
  • Distance metrics

Overview of vector indexing methodologies:

  • IVFFlat index
  • HNSW index

PgVector extension for PostgreSQL:

  • Installation procedures
  • Managing and querying high-dimensional vectors
  • Applying distance metrics
  • Leveraging vector indexes

Course Outcomes: Upon completion, students will have a comprehensive understanding of prominent AI-driven PostgreSQL extensions. They will also acquire hands-on experience in integrating Large Language Models (LLMs) and vector search capabilities into practical, real-world applications.

Requirements

Prerequisites: Foundational knowledge of SQL and basic proficiency with PostgreSQL

Lab environment: DaDesktops operating Linux virtual machines (Facilitated by NobleProg)

Target audience: Database application developers, system architects, and data analysts

 7 Hours

Testimonials (2)

Upcoming Courses

Related Categories