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Course Outline

Foundational concepts covered include:

  • Vector mathematics
  • AI-driven vector embeddings
  • Leading AI embedding models
  • Semantic search mechanisms
  • Distance metrics

An examination of vector indexing methodologies:

  • IVFFlat indexing
  • HNSW indexing

Implementation of the PgVector extension in PostgreSQL:

  • Deployment procedures
  • Management and retrieval of high-dimensional vectors
  • Application of distance metrics
  • Leveraging vector indexes for performance

Learning outcomes: Upon completion, students will possess a comprehensive understanding of prominent AI-enabled PostgreSQL extensions. They will also gain practical proficiency in integrating large language models (LLMs) and vector search capabilities into production-grade applications.

 

Requirements

 Solid foundation in SQL and prior experience working with PostgreSQL

Practical setup: DaDesktops equipped with Linux virtual machines (supplied by NobleProg)

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

 7 Hours

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