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Course Outline

Introduction to:

  • vectors
  • AI vector embeddings
  • prevalent AI embedding models
  • semantic search capabilities
  • distance measurement methodologies

Overview of vector indexing techniques:

  • IVFFlat index implementation
  • HNSW index implementation

PgVector extension for PostgreSQL:

  • deployment and installation procedures
  • storage and retrieval of high-dimensional vectors
  • application of distance measures
  • utilization of vector indexes

 Course outcomes: Upon completion, participants will possess a comprehensive understanding of AI-enabled PostgreSQL extensions for government. They will acquire practical proficiency in integrating large language models (LLMs) and vector search into operational applications. 

 

Requirements

 Fundamental SQL knowledge and basic proficiency with PostgreSQL environments

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

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

 7 Hours

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