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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
  • using vector indexes

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

Requirements

Prerequisites include a foundational understanding of SQL and basic experience working with PostgreSQL

Lab Environment: DadeDesktops running Linux virtual machines (Provided by NobleProg)

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

 7 Hours

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