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Course Outline

Key concepts introduced:

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

Insights into vector indexing methods:

  • IVFFlat index
  • HNSW index

Deep dive into the PgVector extension for PostgreSQL:

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

 Learning outcomes: Upon completion, students will have a solid grasp of popular AI-enhanced PostgreSQL extensions. They will also acquire practical skills in integrating large language models (LLMs) and vector search capabilities into real-world applications.

 

Requirements

Foundational knowledge of SQL and basic 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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