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 Duration 14 hours (2 days)

Course Outline

Foundational concepts including:

  • vector mathematics
  • AI vector embedding methodologies
  • standard AI embedding models
  • semantic search principles
  • distance metric calculations

Summary of vector indexing approaches for government applications:

  • IVFFlat index architecture
  • HNSW index architecture

PgVector extension implementation for PostgreSQL:

  • deployment procedures
  • storage and retrieval of high-dimensional vectors
  • application of distance metrics
  • utilization of vector indexes

PgAI extension implementation for PostgreSQL:

  • deployment procedures
  • embedding generation workflows
  • implementation of Retrieval-Augmented Generation
  • advanced development patterns

Overview of Text-to-SQL solutions: LangChain framework

Course objectives: Upon completion, participants will be able to:

  • design and construct components of AI-driven database solutions using PostgreSQL extensions and libraries suitable for government operations.
  • acquire practical proficiency in integrating large language models (LLMs) and vector search into production systems, facilitating the development of semantic search engines, AI assistants, and natural-language database interfaces for public sector use.

Requirements

Foundational proficiency in SQL, baseline experience with PostgreSQL, and basic competency in Python or JavaScript programming languages

Target Audience: database developers, system architects

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