Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to the following concepts:
- vectors
- AI vector embeddings
- popular AI embedding models
- semantic search
- distance measures
Overview of vector indexing techniques:
- IVFFlat index
- HNSW index
The PgVector extension for PostgreSQL:
- installation
- storing and querying high-dimensional vectors
- distance measures
- utilizing vector indexes
The PgAI extension for PostgreSQL:
- installation
- embedding generation
- implementing Retrieval-Augmented Generation
- advanced development patterns
Overview of Text-to-SQL solutions: the LangChain framework
Course outcomes: Upon completion, students will be equipped to:
- design and construct components of AI-driven database applications leveraging PostgreSQL extensions and libraries.
- apply practical techniques for integrating large language models (LLMs) and vector search into real-world systems, allowing them to build applications such as semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
Foundational knowledge of SQL, prior experience working with PostgreSQL, and basic proficiency in either Python or JavaScript
Target Audience: database developers, system architects
Testimonials (1)
Tables indexes and qq reperforming & tuning in order to improve them
