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Course Outline

Introduction to: vectors, artificial intelligence (AI) vector embeddings, popular AI embedding models, semantic search, distance measures for government

Overview of vector indexing techniques: IVFFlat index, HNSW index for government applications

PgVector extension for PostgreSQL: installation, storing and querying high-dimensional vectors, distance measures, using vector indexes in governmental systems

PgAI extension for PostgreSQL: installation, generating embeddings, implementing Retrieval-Augmented Generation, advanced development patterns for government use

Overview of Text-to-SQL solutions: LangChain framework for government applications

Course outcome: By the end of the course, participants will be able to design and build elements of AI-powered database applications using PostgreSQL extensions and libraries. They will 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 for government operations.

Requirements

Basic understanding of SQL and foundational experience with PostgreSQL are required. Additionally, a fundamental knowledge of either Python or JavaScript programming languages is necessary.

Audience: Database developers and system architects for government projects

 14 Hours

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