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Course Outline

Introduction to:

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

Overview of vector indexing techniques:

  • IVFFlat index
  • HNSW index

PgVector extension for PostgreSQL:

  • Installation process
  • Storing and querying high-dimensional vectors
  • Distance metrics
  • Leveraging vector indexes

 Course Outcome: Upon completion, students will possess a comprehensive understanding of leading AI-enhanced PostgreSQL extensions. They will have acquired hands-on experience in integrating large language models (LLMs) and vector search capabilities into practical, real-world applications.

 

Requirements

 Foundational knowledge of SQL and basic proficiency with PostgreSQL.

Lab Environment: DaDesktops featuring Linux virtual machines (supplied by NobleProg).

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

 7 Hours

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