PostgreSQL AI: Introduction to semantic search Training Course
This course explores the core functionalities of PostgreSQL server extensions designed to incorporate artificial intelligence capabilities into database architectures. It places a strong emphasis on similarity and semantic search leveraging embedding vectors. Learners will acquire the skills to install PgVector, generate and ingest vector embeddings, construct vector indexes, and execute semantic search queries. Through a blend of live demonstrations and practical exercises, the curriculum reinforces fundamental concepts and hands-on proficiency.
This course is available as onsite live training in Argentina 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
- using vector indexes
Course Outcome: Upon completion, students will possess a solid understanding of leading AI-driven PostgreSQL extensions. They will have gained practical expertise in integrating large language models (LLMs) and vector search capabilities into real-world applications.
Requirements
Prerequisites include a foundational understanding of SQL and basic experience working with PostgreSQL
Lab Environment: DadeDesktops running Linux virtual machines (Provided by NobleProg)
Target 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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