We’re excited to announce a brand new embedding mannequin which is considerably extra succesful, price efficient, and easier to make use of. The brand new mannequin, text-embedding-ada-002, replaces 5 separate fashions for textual content search, textual content similarity, and code search, and outperforms our earlier most succesful mannequin, Davinci, at most duties, whereas being priced 99.8% decrease.

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Embeddings are numerical representations of ideas transformed to quantity sequences, which make it simple for computer systems to know the relationships between these ideas. For the reason that preliminary launch of the OpenAI /embeddings endpoint, many functions have integrated embeddings to personalize, advocate, and search content material.

You may question the /embeddings endpoint for the brand new mannequin with two traces of code utilizing our OpenAI Python Library, similar to you may with earlier fashions:

import openai
response = openai.Embedding.create(
  enter="porcine buddies say",

Mannequin Enhancements

Stronger efficiency. text-embedding-ada-002 outperforms all of the outdated embedding fashions on textual content search, code search, and sentence similarity duties and will get comparable efficiency on textual content classification. For every job class, we consider the fashions on the datasets utilized in outdated embeddings.

Unification of capabilities. We’ve considerably simplified the interface of the /embeddings endpoint by merging the 5 separate fashions proven above (text-similarity, text-search-query, text-search-doc, code-search-text and code-search-code) right into a single new mannequin. This single illustration performs higher than our earlier embedding fashions throughout a various set of textual content search, sentence similarity, and code search benchmarks.

Longer context. The context size of the brand new mannequin is elevated by an element of 4, from 2048 to 8192, making it extra handy to work with lengthy paperwork.

Smaller embedding dimension. The brand new embeddings have solely 1536 dimensions, one-eighth the dimensions of davinci-001 embeddings, making the brand new embeddings less expensive in working with vector databases.

Lowered worth. We’ve diminished the worth of latest embedding fashions by 90% in comparison with outdated fashions of the identical dimension. The brand new mannequin achieves higher or related efficiency because the outdated Davinci fashions at a 99.8% lower cost.

Total, the brand new embedding mannequin is a way more highly effective software for pure language processing and code duties. We’re excited to see how our prospects will use it to create much more succesful functions of their respective fields.


The brand new text-embedding-ada-002 mannequin isn’t outperforming text-similarity-davinci-001 on the SentEval linear probing classification benchmark. For duties that require coaching a light-weighted linear layer on prime of embedding vectors for classification prediction, we propose evaluating the brand new mannequin to text-similarity-davinci-001 and selecting whichever mannequin offers optimum efficiency.

Verify the Limitations & Dangers part within the embeddings documentation for common limitations of our embedding fashions.

Examples of Embeddings API in Motion

Kalendar AI is a gross sales outreach product that makes use of embeddings to match the appropriate gross sales pitch to the appropriate prospects out of a dataset containing 340M profiles. This automation depends on similarity between embeddings of buyer profiles and sale pitches to rank up most fitted matches, eliminating 40–56% of undesirable concentrating on in comparison with their outdated method.

Notion, the web workspace firm, will use OpenAI’s new embeddings to enhance Notion search past at this time’s key phrase matching techniques.

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