Numerical representations of text, images, or data that capture semantic meaning in high-dimensional space.
Vector embeddings are dense numerical representations of data — text, images, audio, or structured records — in a high-dimensional vector space. The key property of embeddings is that semantically similar items are positioned close together in this space. The word 'king' minus 'man' plus 'woman' approximately equals 'queen' in embedding space — a famous demonstration that these representations capture genuine semantic relationships, not just surface-level patterns.
Vector embeddings are the foundational technology behind the entire modern AI stack. They power semantic search, recommendation systems, RAG pipelines, and multimodal AI. In 2026, every enterprise AI system relies on embeddings to understand and compare the meaning of data. The quality of embeddings directly determines the quality of AI outputs.
Embeddings are generated by neural networks trained on large corpora of text or other data. The network learns to map inputs to vectors such that similar inputs produce similar vectors. For text, models like OpenAI's text-embedding-3 or Cohere's embed models produce vectors of 768 to 3072 dimensions. These vectors are stored in vector databases and compared using distance metrics like cosine similarity or dot product.
An e-commerce company embeds all their product descriptions using a text embedding model. When a customer searches for 'comfortable office chair for long hours,' the system computes the embedding of that query and finds the products with the most similar embeddings — returning ergonomic chairs, lumbar support cushions, and adjustable desks, even if none of those products used the exact words 'comfortable' or 'long hours.'