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)
- the style and manner in which the facilitator communicates with us
Kamil Gabrek - Santander Consumer Bank S.A.
Course - PostgreSQL Administration and Development
Machine Translated
The provided examples and labs
Christophe OSTER - EU Lisa
Course - PostgreSQL Advanced DBA
The knowledge and manner of conducting the training by the instructor, answering all questions, and the approach to participants.
Anna Knap - Intel Technology Poland sp. z o.o.
Course - PostgreSQL 16 for Developers and Administrators
Machine Translated