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Course Outline

An introductory look at:

  • Vectors
  • AI vector embeddings
  • Leading AI embedding models
  • Semantic search
  • Distance metrics

A general view of vector indexing methods:

  • IVFFlat index
  • HNSW index

The PgVector extension for PostgreSQL:

  • Setup process
  • Managing and retrieving high-dimensional vectors
  • Distance metrics
  • Applying vector indexes

Course goals: Upon completion, participants will have a solid grasp of prominent AI-driven PostgreSQL extensions. They will also acquire practical experience in integrating large language models (LLMs) and vector search into real-world applications.

Requirements

Essential understanding of SQL and foundational experience with PostgreSQL

Lab setup: Desktops operating Linux virtual machines (Supplied by NobleProg)

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

 7 Hours

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