Digital Lexicon Hub

Dense numerical vectors that represent text, images or other data so that items with similar meaning sit close together in vector space.

Embeddings let systems find content by meaning rather than exact keywords, which improves internal search, support deflection and AI assistants that answer from company documents, often without training a custom model.

A support team splits 4,000 help articles into 500-token chunks, embeds each with text-embedding-3-small (1,536 dimensions), stores them in a vector database and returns the five closest chunks by cosine similarity to ground chatbot answers.

Embeddings reflect the training data of the model that produced them, including gaps and bias, and vectors from different models cannot be mixed. Similarity is not correctness: close vectors can still retrieve outdated or contradictory passages.

An embedding model maps an input to a fixed-length vector, often hundreds or thousands of dimensions, learned so that semantic relationships become geometric distances. Similarity is usually measured with cosine similarity or dot product. Unlike sparse one-hot encodings, embeddings are dense and compact. They power semantic search, clustering and classification, and form the retrieval layer of Retrieval-Augmented Generation (RAG), with vectors stored and searched in a Vector Database.