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Course Outline

Foundations covered include:

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

An examination of vector indexing strategies:

  • IVFFlat index
  • HNSW index

Implementation details for the PgVector extension in PostgreSQL:

  • installation procedures
  • managing high-dimensional vectors (storage and retrieval)
  • application of distance measures
  • utilization of vector indexes

 Learning Outcomes: Upon completion, students will possess a comprehensive understanding of leading AI-driven PostgreSQL extensions. They will have developed practical competence in integrating large language models (LLMs) and vector search capabilities into production-ready applications.

 

Requirements

 Foundational proficiency in SQL and basic working knowledge of PostgreSQL

Lab environment: DaDesktops hosting Linux virtual machines (provided by NobleProg)

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

 7 Hours

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