Tag: vector-database
4 items with this tag.
Embeddings
An embedding is a list of numbers, learned during training, that represents a piece of text so that similar meanings get similar numbers. Embeddings let AI find material by meaning rather than by matching words, but they record that ideas are associated, not how they are related.
Vector database
A database that stores embeddings, so that it can find content by meaning instead of by matching words. It's the storage layer behind most RAG systems.
Knowledge graph
How a knowledge graph differs from a vector database: it stores entities and the typed relationships between them, so AI can reason across connections.
Beyond document management: Graph infrastructure for professional education curricula
Professional curricula are extensively documented but not systematically queryable, creating artificial information scarcity that makes compliance reporting and quality assurance labour-intensive. This essay proposes a three-layer architecture — graph databases as the source of truth for curriculum structure, vector databases for semantic content retrieval, and a Model Context Protocol layer for stakeholder access — that transforms documentation into operational infrastructure. The architecture incorporates temporal versioning for longitudinal evidence, role-based access controls for multi-stakeholder environments, and internal quality audit against institutional policy alongside external regulatory compliance, enabling verification in hours rather than weeks.