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Course Outline

Introduction to vectors, AI vector embeddings, prevalent AI embedding models, semantic search, and distance metrics.

Survey of vector indexing strategies: IVFFlat and HNSW indexes.

PgVector extension for PostgreSQL: setup, storage and retrieval of high-dimensional vectors, distance calculations, and utilization of vector indexes.

PgAI extension for PostgreSQL: installation, embedding generation, RAG implementation, and advanced development patterns.

Survey of Text-to-SQL solutions: The LangChain framework.

Course outcome: Upon completion, learners will be equipped to design and construct components of AI-driven database applications leveraging PostgreSQL extensions and libraries. They will obtain hands-on experience with techniques for integrating large language models (LLMs) and vector search into practical systems, empowering them to build applications such as semantic search engines, AI assistants, and natural-language database interfaces.

Requirements

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

Audience: Database developers, system architects

 14 Hours

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