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Course Outline

Foundations covering:

  • vector concepts
  • AI vector embeddings
  • leading AI embedding models
  • semantic search principles
  • distance metrics

Insight into vector indexing methodologies:

  • IVFFlat index structure
  • HNSW index structure

Implementing the PgVector extension in PostgreSQL:

  • setup and installation
  • managing high-dimensional vector data storage and retrieval
  • application of distance metrics
  • leveraging vector indexes

 Learning Outcomes: Upon completion, participants will possess a clear understanding of prominent AI-enhanced PostgreSQL extensions. They will also develop practical expertise in integrating large language models (LLMs) and vector search capabilities into production-grade applications.

 

Requirements

 Fundamental SQL proficiency and basic familiarity with PostgreSQL

Lab environment: DaDesktops utilizing Linux virtual machines (supplied by NobleProg)

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

 7 Hours

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