Rerank
Rerank models sort text inputs by semantic relevance to a specified query. They are often used to sort search results returned from an existing search solution. Learn more about using Rerank in the best practices guide.
| Latest Model | Description | Max Tokens | Endpoints |
|---|---|---|---|
rerank-english-v3.0 | A model that allows for re-ranking English Language documents and semi-structured data (JSON). This model has a context length of 4096 tokens.. | N/A | Rerank |
rerank-multilingual-v3.0 | A model for documents and semi-structure data (JSON) that are not in English. Supports the same languages as embed-multilingual-v3.0. This model has a context length of 4096 tokens. | N/A | Rerank |
rerank-english-v2.0 | A model that allows for re-ranking English language documents. This model has a context length of 512 tokens. | N/A | Rerank |
rerank-multilingual-v2.0 | A model for documents that are not in English. Supports the same languages as embed-multilingual-v3.0. This model has a context length of 512 tokens. | N/A | Rerank |
Rerank accepts full strings and than tokens, so the token limit works a little differently. Rerank will automatically chunk documents longer than 4096 tokens, and there is therefore no explicit limit to how long a document can be when using rerank. See our best practice guide for more info about formatting documents for the Rerank endpoint.
Updated 19 days ago