Course Outline
Introduction to vectors, AI vector embeddings, prevalent AI embedding models, semantic search, and distance metrics
Overview of vector indexing techniques, including IVFFlat and HNSW indexes
PgVector extension for PostgreSQL: installation, storing and querying high-dimensional vectors, distance measures, and utilising vector indexes
PgAI extension for PostgreSQL: installation, generating embeddings, implementing Retrieval-Augmented Generation, and advanced development patterns
Overview of Text-to-SQL solutions, focusing on the LangChain framework
Course outcome: By the conclusion of the course, students will be equipped to design and construct components of AI-powered database applications using PostgreSQL extensions and libraries. They will acquire practical experience integrating large language models (LLMs) and vector search into real-world systems, enabling them to develop applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Participants should possess a foundational understanding of SQL, practical experience with PostgreSQL, and basic proficiency in either Python or JavaScript.
Audience: Database developers, system architects
Testimonials (2)
The provided examples and labs
Christophe OSTER - EU Lisa
Course - PostgreSQL Advanced DBA
1. A very well-structured training program 2. The warm atmosphere the trainer created, along with his outstanding personal professionalism 3. That the trainer explained everything as if he were talking to a complete beginner, without slipping into any technical jargon.
