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
PgAI extension for PostgreSQL:
- installation
- generating embeddings
- implementing Retrieval-Augmented Generation
- advanced development patterns
Overview of Text-to-SQL solutions: LangChain framework
Course outcome: By the end of the course, students will be able to:
- design and build elements of AI-powered database applications using PostgreSQL extensions and libraries.
- gain practical experience with techniques for 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
basic knowledge of SQL, basic experience with PostgreSQL, basic knowledge of Python or JavaScript programming languages
Audience: database developers, system architects
Testimonials (3)
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.
Piotr Romer - Asseco Poland S.A
Course - PostgreSQL Administration, Optimization and Replication
advance topics hands on + discussion like timescaleDB and hypertable , trainer's knowledge on the subject :)
Shivam - Paessler LLC
Course - PostgreSQL Fundamentals
The patiance and the style of teaching of Michał was nice.
