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Course Outline

Key concepts covered in this course include:

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

We will also examine vector indexing strategies, specifically:

  • IVFFlat index
  • HNSW index

Furthermore, the curriculum details the PgVector extension for PostgreSQL, covering:

  • installation procedures
  • storage and retrieval of high-dimensional vectors
  • application of distance metrics
  • utilizing vector indexes

Course Outcome: Upon completion, participants will possess a solid understanding of prominent AI-enabled PostgreSQL extensions. They will also have acquired hands-on experience in integrating large language models (LLMs) and vector search capabilities into practical, real-world scenarios.

 

Requirements

Participants should possess foundational SQL knowledge and basic familiarity with PostgreSQL.

Lab Environment: Access to DaDesktops Linux virtual machines (provided by NobleProg).

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

 7 Hours

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