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Course Outline

Introduction to the following concepts:

  • Vectors
  • AI vector embeddings
  • Popular AI embedding models
  • Semantic search
  • Distance measures

An overview of vector indexing techniques, including:

  • IVFFlat index
  • HNSW index

The PgVector extension for PostgreSQL, covering:

  • Installation procedures
  • Storing and querying high-dimensional vectors
  • Applying distance measures
  • Leveraging vector indexes

 Course Outcomes: Upon completion, students will have a comprehensive understanding of leading AI-powered PostgreSQL extensions. They will also possess practical experience 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

 7 Hours

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