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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: Upon completion of this course, students will be equipped to design and construct components of AI-driven database applications using PostgreSQL extensions and libraries. They will gain practical expertise in integrating large language models (LLMs) and vector search into real-world systems, empowering them to create solutions such as semantic search engines, AI assistants, and natural-language database interfaces.

Requirements

Foundational understanding of SQL, prior experience working with PostgreSQL, and basic proficiency in Python or JavaScript programming.

Audience: database developers, system architects

 14 Hours

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