AI & Machine Learning
Vector Embeddings
Mathematical representations of text that capture semantic meaning in multi-dimensional space.
Definition
Vector embeddings are numerical representations of words, phrases, or entire documents in a high-dimensional space where semantically similar items are positioned closer together. Modern AI systems use embeddings (like OpenAI's text-embedding-3-small or Google's embeddings) to understand and compare the meaning of text. This technology powers semantic search, content recommendations, and AI-driven tools like RankVectors that can identify related content for internal linking based on meaning rather than just keyword matches.