Course Outline
Introduction to vectors, AI vector embeddings, widely used AI embedding models, semantic search, and distance metrics.
Overview of vector indexing techniques: IVFFlat and HNSW indexes.
PgVector extension for PostgreSQL: installation, storing and querying high-dimensional vectors, distance metrics, and utilizing vector indexes.
PgAI extension for PostgreSQL: installation, generating embeddings, implementing Retrieval-Augmented Generation, and advanced development patterns.
Overview of Text-to-SQL solutions: LangChain framework.
Course Outcome: By the end of the course, students will be capable of designing and building components of AI-driven database applications using PostgreSQL extensions and libraries. They will acquire practical experience in integrating large language models (LLMs) and vector search into real-world systems, empowering them to develop applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Foundational knowledge of SQL, practical experience with PostgreSQL, and basic proficiency in Python or JavaScript programming.
Target Audience: Database developers and 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.
