Embeddings are useful for working with natural language and code, because they can be readily consumed and compared by other machine learning models and algorithms like clustering or search.

Embeddings that are numerically similar are also semantically similar. For example, the embedding vector of “canine companions say” will be more similar to the embedding vector of “woof” than that of “meow.”

The new endpoint uses neural network models, which are descendants of GPT‑3, to map text and code to a vector representation—“embedding” them in a high-dimensional space. Each dimension captures some aspect of the input.

The new /embeddings⁠(opens in a new window) endpoint in the OpenAI API⁠(opens in a new window) provides text and code embeddings with a few lines of code: