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Course Outline

Introduction to:

  • vectors
  • AI vector embeddings
  • popular AI embedding models
  • semantic search
  • distance measures

Overview of vector indexing techniques:

  • IVFFlat index
  • HNSW index

PgVector extension for PostgreSQL:

  • installation
  • storing and querying high-dimensional vectors
  • distance measures
  • using vector indexes

PgAI extension for PostgreSQL:

  • installation
  • generating embeddings
  • implementing Retrieval-Augmented Generation
  • advanced development patterns

Overview of Text-to-SQL solutions: LangChain framework

Course outcome: By the end of the course, students will be able to:

  • design and build elements of AI-powered database applications using PostgreSQL extensions and libraries.
  • gain practical experience with techniques for integrating large language models (LLMs) and vector search into real-world systems, enabling them to develop applications such as semantic search engines, AI assistants, and natural-language database interfaces.

Requirements

basic knowledge of SQL, basic experience with PostgreSQL, basic knowledge of Python or JavaScript programming languages

Audience: database developers, system architects

 14 Hours

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