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Introducing text and code embeddings | OpenAI

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We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification. Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. Our embeddings outperform top models in 3 standard benchmarks, including a 20% relative improvement in code search. 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 repres

January 25, 2022 Product Introducing text and code embeddings We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification. Read documentation (opens in a new window) Read paper (opens in a new window) Loading… Share Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. Our embeddings outperform top models in 3 standard benchmarks, including a 2

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