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Course Outline

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

Overview of vector indexing techniques: IVFFlat and HNSW indexes.

PgVector extension for PostgreSQL: installation, storage and querying of high-dimensional vectors, distance metrics, and utilizing vector indexes.

PgAI extension for PostgreSQL: installation, embedding generation, implementing Retrieval-Augmented Generation, and exploring advanced development patterns.

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

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

Requirements

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

Target Audience: Database developers and system architects

 14 Hours

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