Course Outline
Core concepts introduced include:
- vectors
- AI vector embeddings
- popular AI embedding models
- semantic search
- distance measures
An examination of vector indexing methods covers:
- IVFFlat index
- HNSW index
Specifics of the PgVector extension for PostgreSQL encompass:
- installation procedures
- storing and querying high-dimensional vectors
- distance measures
- using vector indexes
Learning objective: Upon completion, students will possess a solid understanding of leading AI-driven PostgreSQL extensions. They will have acquired hands-on experience in integrating large language models (LLMs) and vector search capabilities into real-world applications.
Requirements
Foundational proficiency in SQL and basic working experience with PostgreSQL
Lab setup: DaDesktops running Linux virtual machines (Provided by NobleProg)
Target audience: database application developers, system architects, and data analysts
Testimonials (3)
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