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Course Outline

Key concepts introduced in this session include:

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

An examination of vector indexing strategies, specifically:

  • IVFFlat index
  • HNSW index

Detailed coverage of the PgVector extension for PostgreSQL, covering:

  • Installation procedures
  • Storing and querying high-dimensional vectors
  • Applying distance measures
  • Leveraging vector indexes

Learning outcomes: Upon completion, participants will possess a comprehensive understanding of widely adopted AI-enhanced PostgreSQL extensions. They will also acquire hands-on experience in integrating large language models (LLMs) and vector search capabilities into production-grade applications.

Requirements

A foundational understanding of SQL and basic hands-on experience with PostgreSQL are required.

Practical sessions will be conducted using DaDesktops, which provide access to Linux virtual machines (facilities supplied by NobleProg).

This course is tailored for database application developers, system architects, and data analysts.

 7 Hours

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Price per participant

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