Get in Touch

Course Outline

Overview of Core Concepts:

  • Fundamentals of vectors
  • AI vector embeddings
  • Prevalent AI embedding models
  • Semantic search methodologies
  • Distance measurement metrics

Review of vector indexing methodologies:

  • Inverted File Flat (IVFFlat) index
  • Hierarchical Navigable Small World (HNSW) index

PgVector extension for PostgreSQL:

  • Installation procedures
  • Storage and querying of high-dimensional vectors
  • Application of distance measures
  • Utilization of vector indexes

PgAI extension for PostgreSQL:

  • Installation procedures
  • Embedding generation
  • Implementation of Retrieval-Augmented Generation (RAG)
  • Advanced development patterns

Survey of Text-to-SQL solutions utilizing the LangChain framework

Learning Objectives: Upon completion of this course, participants will be able to:

  • Design and construct components of AI-enabled database applications using PostgreSQL extensions and libraries for government systems.
  • Acquire practical proficiency in integrating large language models (LLMs) and vector search technologies into operational environments, facilitating the development of semantic search engines, AI assistants, and natural-language database interfaces suitable for public sector needs.

Requirements

Prerequisites include foundational proficiency in SQL, practical familiarity with PostgreSQL, and elementary competency in Python or JavaScript programming. This curriculum is intended for government agencies seeking to upskill database developers and system architects in alignment with federal technology standards for government operations.
 14 Hours

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories