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Course Outline

Foundational concepts introduced include:

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

Examination of vector indexing methods:

  • IVFFlat index
  • HNSW index

Working with the PgVector extension for PostgreSQL:

  • Installation procedures
  • Storing and retrieving high-dimensional vectors
  • Applying distance measures
  • Leveraging vector indexes

Learning Outcomes: Upon completion, participants will have a thorough understanding of prominent AI-enhanced PostgreSQL extensions. They will also possess practical experience in integrating large language models (LLMs) and vector search into real-world applications.

Requirements

A solid foundation in SQL and basic proficiency with PostgreSQL are required.

Lab Setup: DaDesktops running Linux virtual machines (provided by NobleProg).

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

 7 Hours

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