diff --git a/docs/src/main/sphinx/functions/array.md b/docs/src/main/sphinx/functions/array.md
index cb60ef8dcc5e..c0222f48a99a 100644
--- a/docs/src/main/sphinx/functions/array.md
+++ b/docs/src/main/sphinx/functions/array.md
@@ -410,15 +410,6 @@ SELECT dot_product(ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]);
 ```
 :::
 
-:::{function} cosine_distance(array(double), array(double)) -> double
-Calculates the cosine distance:
-
-```sql
-SELECT cosine_distance(ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]);
--- 0.01613008990009257
-```
-:::
-
 :::{function} zip(array1, array2[, ...]) -> array(row)
 Merges the given arrays, element-wise, into a single array of rows. The M-th element of
 the N-th argument will be the N-th field of the M-th output element.
diff --git a/docs/src/main/sphinx/functions/list-by-topic.md b/docs/src/main/sphinx/functions/list-by-topic.md
index 9e61e1aed278..233bf3e580c5 100644
--- a/docs/src/main/sphinx/functions/list-by-topic.md
+++ b/docs/src/main/sphinx/functions/list-by-topic.md
@@ -370,6 +370,7 @@ For more details, see {doc}`math`
 - {func}`ceil`
 - {func}`cos`
 - {func}`cosh`
+- {func}`cosine_distance`
 - {func}`cosine_similarity`
 - {func}`degrees`
 - {func}`e`
diff --git a/docs/src/main/sphinx/functions/list.md b/docs/src/main/sphinx/functions/list.md
index 2301be603a3e..fef1ade7672a 100644
--- a/docs/src/main/sphinx/functions/list.md
+++ b/docs/src/main/sphinx/functions/list.md
@@ -102,6 +102,7 @@
 - {func}`corr`
 - {func}`cos`
 - {func}`cosh`
+- {func}`cosine_distance`
 - {func}`cosine_similarity`
 - {func}`count`
 - {func}`count_if`
diff --git a/docs/src/main/sphinx/functions/math.md b/docs/src/main/sphinx/functions/math.md
index 584dfc3fee09..58823823e668 100644
--- a/docs/src/main/sphinx/functions/math.md
+++ b/docs/src/main/sphinx/functions/math.md
@@ -194,6 +194,36 @@ Returns the tangent of `x`.
 Returns the hyperbolic tangent of `x`.
 :::
 
+## Geometric functions
+
+:::{function} cosine_distance(array(double), array(double)) -> double
+Calculates the cosine distance between two dense vectors:
+
+```sql
+SELECT cosine_distance(ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]);
+-- 0.01613008990009257
+```
+:::
+
+:::{function} cosine_similarity(array(double), array(double)) -> double
+Calculates the cosine similarity of two dense vectors:
+
+```sql
+SELECT cosine_similarity(ARRAY[1.0, 2.0], ARRAY[3.0, 4.0]);
+-- 0.9838699100999074
+```
+:::
+
+:::{function} cosine_similarity(x, y) -> double
+:no-index:
+Calculates the cosine similarity of two sparse vectors:
+
+```sql
+SELECT cosine_similarity(MAP(ARRAY['a'], ARRAY[1.0]), MAP(ARRAY['a'], ARRAY[2.0]));
+-- 1.0
+```
+:::
+
 ## Floating point functions
 
 :::{function} infinity() -> double
@@ -228,14 +258,6 @@ Returns the base-`radix` representation of `x`.
 
 ## Statistical functions
 
-:::{function} cosine_similarity(x, y) -> double
-Returns the cosine similarity between the sparse vectors `x` and `y`:
-
-```
-SELECT cosine_similarity(MAP(ARRAY['a'], ARRAY[1.0]), MAP(ARRAY['a'], ARRAY[2.0])); -- 1.0
-```
-:::
-
 :::{function} t_pdf(x, df) -> double
 Computes the Student's t-distribution probability density function for given x and
 degrees of freedom (df). The x must be a real value and degrees of freedom must be