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Course Outline

Introduction to the following concepts:

  • vectors
  • AI vector embeddings
  • popular AI embedding models
  • semantic search
  • distance measures

Overview of vector indexing techniques:

  • IVFFlat index
  • HNSW index

The PgVector extension for PostgreSQL:

  • installation
  • storing and querying high-dimensional vectors
  • distance measures
  • utilising vector indexes

 Course outcome: Upon completion, students will possess a comprehensive understanding of leading AI-enabled PostgreSQL extensions. They will also acquire practical expertise in integrating Large Language Models (LLMs) and vector search into real-world applications.

 

Requirements

A foundational understanding of SQL and basic experience working with PostgreSQL

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

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

 7 Hours

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