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Course Outline

Foundations of:

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

Introduction to vector indexing methods:

  • IVFFlat index
  • HNSW index

The PgVector extension for PostgreSQL:

  • Installation procedures
  • Managing and querying high-dimensional vectors
  • Applying distance metrics
  • Leveraging vector indexes

Course outcomes: Upon completion, students will possess a solid understanding of popular AI-driven PostgreSQL extensions. They will also have gained practical proficiency in integrating large language models (LLMs) and vector search capabilities into real-world applications.

Requirements

Basic familiarity with SQL and fundamental experience working with PostgreSQL

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

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

 7 Hours

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