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Course Outline

Core concepts introduced include:

  • vectors
  • AI vector embeddings
  • popular AI embedding models
  • semantic search
  • distance measures

An examination of vector indexing methods covers:

  • IVFFlat index
  • HNSW index

Specifics of the PgVector extension for PostgreSQL encompass:

  • installation procedures
  • storing and querying high-dimensional vectors
  • distance measures
  • using vector indexes

Learning objective: Upon completion, students will possess a solid understanding of leading AI-driven PostgreSQL extensions. They will have acquired hands-on experience in integrating large language models (LLMs) and vector search capabilities into real-world applications.

Requirements

Foundational proficiency in SQL and basic working experience with PostgreSQL

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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