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Course Outline

Foundations covered include:

  • Vectors
  • AI vector embeddings
  • Widely used AI embedding models
  • Semantic search
  • Distance metrics

An examination of vector indexing methodologies:

  • IVFFlat index
  • HNSW index

Implementation of the PgVector extension for PostgreSQL:

  • Setup and installation
  • Managing high-dimensional vector data
  • Application of distance metrics
  • Leveraging vector indexes

Learning objectives: Upon completion, participants will possess a thorough understanding of leading AI-driven PostgreSQL extensions. They will also achieve hands-on proficiency in integrating large language models (LLMs) and vector search capabilities into practical applications.

Requirements

A solid grasp of SQL fundamentals and prior experience working with PostgreSQL.

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

Target audience: Developers of database applications, system architects, and data analysts.

 7 Hours

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