PostgreSQL AI: Introduction to semantic search Training Course
This course explores the core principles of PostgreSQL server extensions that enable the integration of artificial intelligence into database environments. It places a particular emphasis on similarity and semantic search powered by embedding vectors. Participants will learn to install PgVector, generate and manage vector embeddings, build efficient vector indexes, and execute semantic search queries. Through a combination of live demonstrations and hands-on exercises, the course reinforces key theoretical concepts while developing practical technical skills.
This course is available as onsite live training in Norway or online live training.Course Outline
Introduction to:
- Vectors
- AI vector embeddings
- Popular AI embedding models
- Semantic search
- Distance measures
Overview of vector indexing techniques:
- IVFFlat index
- HNSW index
PgVector extension for PostgreSQL:
- Installation
- Storing and querying high-dimensional vectors
- Distance measures
- Utilizing vector indexes
Course Outcome: Upon completion, students will have a solid understanding of leading AI-driven PostgreSQL extensions. They will also possess the practical expertise to integrate Large Language Models (LLMs) and vector search capabilities into real-world applications.
Requirements
Foundational knowledge of SQL and basic experience working with PostgreSQL
Lab environment: DaDesktops running Linux virtual machines (Provided by NobleProg)
Audience: Database application developers, system architects, and data analysts
Open Training Courses require 5+ participants.
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Testimonials (2)
Tuning strategies.
Jeffrey Zieg - Matrix Consulting
Course - PostgreSQL Performance Tuning
Logging behaviour when the instance is under stress, and the hierarchy/nomenclature of instances, databases, files, etc.
Daniel Inwood - Sinara Consultants LTD
Course - PostgreSQL Advanced DBA
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