diff --git a/pgml-cms/docs/api/sql-extension/pgml.rank.md b/pgml-cms/docs/api/sql-extension/pgml.rank.md
new file mode 100644
index 000000000..897f13993
--- /dev/null
+++ b/pgml-cms/docs/api/sql-extension/pgml.rank.md
@@ -0,0 +1,40 @@
+---
+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
+
+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.
+
+By default we provide the following ranking models:
+
+* `mixedbread-ai/mxbai-rerank-base-v1`