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Course Outline

Foundations of:

  • vectors
  • AI vector embeddings
  • leading AI embedding models
  • semantic search
  • distance metrics

Insights into vector indexing strategies:

  • IVFFlat index
  • HNSW index

The PgVector extension for PostgreSQL:

  • deployment
  • managing and retrieving high-dimensional vectors
  • distance metrics
  • utilizing vector indexes

 Learning Outcomes: Upon completion, participants will have a comprehensive understanding of widely adopted AI-driven PostgreSQL extensions. They will acquire practical expertise in implementing large language models (LLMs) and vector search capabilities within real-world applications.

 

Requirements

Foundational proficiency in SQL and basic experience working with PostgreSQL

Lab setup: DaDesktops Linux virtual machines (supplied by NobleProg)

Target audience: database application developers, system architects, and data analysts

 7 Hours

Number of participants


Price per participant

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Provisional Upcoming Courses (Require 5+ participants)

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