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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: Upon completion, students will have a solid understanding of leading AI-driven PostgreSQL extensions. They will also possess the practical expertise to integrate Large Language Models (LLMs) and vector search capabilities into real-world applications.

 

Requirements

 Foundational knowledge of SQL and basic experience working with PostgreSQL

Lab environment: DaDesktops running Linux virtual machines (Provided by NobleProg)

Audience: Database application developers, system architects, and data analysts

 7 Hours

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