Get in Touch

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

Prerequisites include basic knowledge of SQL, foundational experience with PostgreSQL, and a fundamental understanding of either Python or JavaScript programming languages.

Audience: Database developers and system architects

 14 Hours

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories