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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, students will be capable of designing and building 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 operational systems, allowing them to create applications such as semantic search engines, AI assistants, and natural-language database interfaces.

Requirements

Prerequisites: Foundational understanding of SQL, basic experience with PostgreSQL, and elementary knowledge of either Python or JavaScript programming.

Audience: Database developers and system architects.

 14 Hours

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