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Course Outline

Introductory concepts covering:

  • Vector structures
  • AI vector embeddings
  • Leading AI embedding models
  • Semantic search mechanics
  • Metric measures for distance

An examination of vector indexing strategies:

  • IVFFlat index implementation
  • HNSW index implementation

Utilizing the PgVector extension within PostgreSQL:

  • Deployment and installation
  • Management and retrieval of high-dimensional vectors
  • Application of distance metrics
  • Implementation of vector indexes

 Learning Outcomes: Upon completion, students will possess a comprehensive understanding of prominent AI-enabled PostgreSQL extensions. They will acquire hands-on experience in integrating Large Language Models (LLMs) and vector search capabilities into practical application scenarios.

 

Requirements

 Fundamental proficiency in SQL and basic familiarity with the PostgreSQL platform

Lab environment: DaDesktops operating on Linux virtual machines (supplied by NobleProg)

Intended for: database application developers, system architects, and data analysts

 7 Hours

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