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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 outcomes: Upon completion, students will be able to:

  • design and construct components of AI-driven database applications utilizing PostgreSQL extensions and libraries.
  • acquire practical experience in integrating large language models (LLMs) and vector search into real-world systems, empowering them to build solutions such as semantic search engines, AI assistants, and natural-language database interfaces.

Requirements

Prerequisites include foundational knowledge of SQL, practical experience with PostgreSQL, and basic proficiency in either Python or JavaScript.

Audience: database developers and system architects

 14 Hours

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