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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, including:

  • IVFFlat index
  • HNSW index

The PgVector extension for PostgreSQL, covering:

  • Installation
  • Storing and querying high-dimensional vectors
  • Distance measures
  • Utilizing vector indexes

Course outcome: Upon completion, students will have a solid understanding of popular AI-driven PostgreSQL extensions. They will also possess practical experience in integrating large language models (LLMs) and vector search capabilities into real-world applications.

Requirements

Foundational proficiency in SQL and basic familiarity with PostgreSQL are required.

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

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

 7 Hours

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