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Added pgml.rank docs#1514
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| --- | ||
| description: Rank documents against a piece of text using the specified ranking model. | ||
| --- | ||
| # pgml.rank() | ||
| The `pgml.rank()` function is used to compute a relevance score between documents and some text. This function is primarily used as the last step in a search system where the results returned from the initial search are re-ranked by relevance before being used. | ||
| ## API | ||
| ```postgresql | ||
| pgml.rank( | ||
| transformer TEXT, -- transformer name | ||
| query TEXT, -- text to rank against | ||
| documents TEXT[], -- documents to rank | ||
| kwargs JSON -- optional arguments (see below) | ||
| ) | ||
| ``` | ||
| ## Example | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Let's document the difference between these calls. Take a look at examples: There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. How does this look:f9c5ff2 | ||
| Ranking documents is as simple as calling the the function with the documents you want to rank, and text you want to rank against: | ||
| ```postgresql | ||
| SELECT pgml.rank('mixedbread-ai/mxbai-rerank-base-v1', 'test', ARRAY['doc1', 'doc2']); | ||
| ``` | ||
| By default the `pgml.rank()` function will return and rank all of the documents. The function can be configured to only return the relevance score and index of the top k documents by setting `return_documents` to `false` and `top_k` to the number of documents you want returned. | ||
| ```postgresql | ||
| SELECT pgml.rank('mixedbread-ai/mxbai-rerank-base-v1', 'test', ARRAY['doc1', 'doc2'], '{"return_documents": false, "top_k": 10}'::JSONB); | ||
| ``` | ||
| ## Supported ranking models | ||
| We currently support cross-encoders for re-ranking. Check out [Sentence Transformer's documentation](https://sbert.net/examples/applications/cross-encoder/README.html) for more information on how cross-encoders work. | ||
SilasMarvin marked this conversation as resolved. Show resolvedHide resolvedUh oh!There was an error while loading.Please reload this page. | ||
| By default we provide the following ranking models: | ||
| * `mixedbread-ai/mxbai-rerank-base-v1` | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Can I use other models from hugging face? There's no mention here. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Yes you can. If you are using a serverless hosted database you can request for that model specifically. Is there a model you are interested in using that is not here? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Just to make sure that I can use any because embedding and reranking models will get update regularly.
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Those should work. If you are using serverless and want to use them, you will need to request access to them. Let me know if you have any problems and I'm happy to take a look! | ||