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 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.

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