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Course Outline

Foundations covering:

  • Vector mathematics
  • AI vector embeddings
  • Leading AI embedding models
  • Principles of semantic search
  • Distance metric calculations

Analysis of vector indexing strategies:

  • IVFFlat indexing
  • HNSW indexing

Deep dive into the PgVector extension for PostgreSQL:

  • Setup and installation procedures
  • Managing and querying high-dimensional vector data
  • Application of distance measures
  • Optimization using vector indexes

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

 

Requirements

• Foundational knowledge of SQL and basic practical experience with PostgreSQL

Lab Environment: DaDesktops Linux virtual machines (Provided by NobleProg)

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

 7 Hours

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