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
Fundamentals of:
- Vectors
- AI vector embeddings
- Popular AI embedding models
- Semantic search
- Distance measures
Techniques for vector indexing:
- 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
Exploring 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 using PostgreSQL extensions and libraries.
- Apply practical techniques for integrating Large Language Models (LLMs) and vector search into real-world systems, enabling the creation of applications such as semantic search engines, AI assistants, and natural language database interfaces.
Requirements
Familiarity with basic SQL concepts, foundational experience with PostgreSQL, and working knowledge of either Python or JavaScript.
Audience: Database developers and system architects.
Testimonials (2)
Tuning strategies.
Jeffrey Zieg - Matrix Consulting
Course - PostgreSQL Performance Tuning
Logging behaviour when the instance is under stress, and the hierarchy/nomenclature of instances, databases, files, etc.