You can query the /embeddings⁠(opens in a new window) endpoint for the new model with two lines of code using our OpenAI Python Library⁠(opens in a new window), just like you could with previous models:

Stronger performance. text-embedding-ada-002 outperforms all the old embedding models on text search, code search, and sentence similarity tasks and gets comparable performance on text classification. For each task category, we evaluate the models on the datasets used in old embeddings⁠(opens in a new window).

Unification of capabilities. We have significantly simplified the interface of the /embeddings⁠(opens in a new window) endpoint by merging the five separate models shown above (text-similarity, text-search-query, text-search-doc, code-search-text and code-search-code) into a single new model. This single representation performs better than our previous embedding models across a diverse set of text search, sentence similarity, and code search benchmarks.

Longer context. The context length of the new model is increased by a factor of four, from 2048 to 8192, making it more convenient to work with long documents.