Course Outline
Introduction to vectors, AI vector embeddings, leading AI embedding models, semantic search, and distance metrics.
Survey of vector indexing strategies: IVFFlat and HNSW indexes.
PostgreSQL PgVector extension: Installation procedures, methods for storing and querying high-dimensional vectors, distance calculations, and the utilization of vector indexes.
PostgreSQL PgAI extension: Installation steps, embedding generation, implementation of Retrieval-Augmented Generation, and advanced development patterns.
Survey of Text-to-SQL solutions, with a focus on the LangChain framework.
Course Outcome: Upon completion, students will be equipped to design and construct components of AI-driven database applications using PostgreSQL extensions and libraries. They will acquire practical expertise in integrating large language models (LLMs) and vector search into real-world systems, empowering them to create applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Participants should possess foundational knowledge of SQL, hands-on experience with PostgreSQL, and basic proficiency in either 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.
