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Course Outline

Foundations of:

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

Exploration of vector indexing methods:

  • IVFFlat index
  • HNSW index

Implementation of the PgVector extension for PostgreSQL:

  • setup and installation
  • managing and querying high-dimensional vectors
  • calculating distance measures
  • utilizing vector indexes

Learning Objectives: Upon completion, participants will have a comprehensive understanding of widely adopted AI-enhanced PostgreSQL extensions. They will also possess the practical competence required to integrate large language models (LLMs) and vector search functionalities into professional application scenarios.

Requirements

Foundational understanding of SQL and basic proficiency with PostgreSQL

Lab Setup: DaDesktops Linux virtual machine instances (supplied by NobleProg)

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

 7 Hours

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