Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts.

Embeddings that are numerically similar are also semantically similar.

Text similarity models provide embeddings that capture the semantic similarity of pieces of text. These models are useful for many tasks including clustering, data visualization, and classification.

Text search models provide embeddings that enable large-scale search tasks, like finding a relevant document among a collection of documents given a text query.

OpenAI’s embeddings significantly improved the task of finding textbook content based on learning objectives. Achieving a top-5 accuracy of 89.1%, OpenAI’s text-search-curie embeddings model outperformed previous approaches like Sentence-BERT (64.5%).