Course Outline
An introductory overview covering:
- vectors
- AI vector embeddings
- prevalent AI embedding models
- semantic search
- distance metrics
A review of vector indexing methods:
- IVFFlat index
- HNSW index
The PgVector extension for PostgreSQL:
- installation procedures
- storing and querying high-dimensional vectors
- distance metrics
- utilisation of vector indexes
The PgAI extension for PostgreSQL:
- installation procedures
- embedding generation
- implementation of Retrieval-Augmented Generation
- advanced development patterns
An introduction to Text-to-SQL solutions using the LangChain framework
Learning outcomes: Upon completion, participants 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 production environments, facilitating the creation of semantic search engines, AI assistants, and natural-language database interfaces.
Requirements
A foundational understanding of SQL, basic familiarity with PostgreSQL, and introductory knowledge of either Python or JavaScript are required.
Audience: Database developers and system architects
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customised Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from £3200 + VAT*
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Testimonials (1)
Tables indexes and qq reperforming & tuning in order to improve them
