DOC-765 | Cosine similarity fix for vector indexes

site/content/3.12/aql/functions/vector.md

@@ -67,9 +67,9 @@ be found depends on the data as well as the search effort (see the `nProbe` opti
 Retrieve the approximate angular similarity using the cosine metric, accelerated
 by a matching vector index.
 
-The higher the cosine similarity value is, the more similar the two vectors
-are. The closer it is to 0, the more different they are. The value can also
-be negative, indicating that the vectors are not similar and point in opposite
+The closer the cosine similarity value is to 1, the more similar the two vectors
+are. The closer it is to 0, the more different they are. The value can also be
+negative up to -1, indicating that the vectors are not similar and point in opposite
 directions. You need to sort in descending order so that the most similar
 documents come first, which is what a vector index using the `cosine` metric
 can provide.

site/content/3.12/index-and-search/indexing/working-with-indexes/vector-indexes.md

@@ -114,7 +114,6 @@ centroids and the quality of vector search thus degrades.
 {{< tabs "interfaces" >}}
 
 {{< tab "Web interface" >}}
-{{< comment >}}TODO: Only in v3.12.6+
 1. In the **Collections** section, click the name or row of the desired collection.
 2. Go to the **Indexes** tab.
 3. Click **Add index**.
@@ -124,8 +123,6 @@ centroids and the quality of vector search thus degrades.
    under `param`.
 7. Optionally give the index a user-defined name.
 8. Click **Create**.
-{{< /comment >}}
-The web interface does not support vector indexes yet.
 {{< /tab >}}
 
 {{< tab "arangosh" >}}

site/content/3.12/release-notes/version-3.12/incompatible-changes-in-3-12.md

@@ -900,6 +900,17 @@ the following steps.
 4. Restore the dump to the new deployment. You can directly move from any
    3.11 or 3.12 version to 3.12.4 (or later) this way.
 
+## Cosine similarity fix for vector indexes
+
+<small>Introduced in: v3.12.6</small>
+
+A normalization issue has been addressed for the experimental vector index type.
+It was possible for the cosine similarity value returned by `APPROX_NEAR_COSINE()`
+to be outside the expected range of `[-1, 1]`.
+
+It is recommended to recreate all vector indexes that use the `cosine` metric
+after upgrading to v3.12.6 or later.
+
 ## HTTP RESTful API
 
 ### JavaScript-based traversal using `/_api/traversal` removed

site/content/3.13/aql/functions/vector.md

@@ -67,9 +67,9 @@ be found depends on the data as well as the search effort (see the `nProbe` opti
 Retrieve the approximate angular similarity using the cosine metric, accelerated
 by a matching vector index.
 
-The higher the cosine similarity value is, the more similar the two vectors
-are. The closer it is to 0, the more different they are. The value can also
-be negative, indicating that the vectors are not similar and point in opposite
+The closer the cosine similarity value is to 1, the more similar the two vectors
+are. The closer it is to 0, the more different they are. The value can also be
+negative up to -1, indicating that the vectors are not similar and point in opposite
 directions. You need to sort in descending order so that the most similar
 documents come first, which is what a vector index using the `cosine` metric
 can provide.

site/content/3.13/release-notes/version-3.12/incompatible-changes-in-3-12.md

@@ -900,6 +900,17 @@ the following steps.
 4. Restore the dump to the new deployment. You can directly move from any
    3.11 or 3.12 version to 3.12.4 (or later) this way.
 
+## Cosine similarity fix for vector indexes
+
+<small>Introduced in: v3.12.6</small>
+
+A normalization issue has been addressed for the experimental vector index type.
+It was possible for the cosine similarity value returned by `APPROX_NEAR_COSINE()`
+to be outside the expected range of `[-1, 1]`.
+
+It is recommended to recreate all vector indexes that use the `cosine` metric
+after upgrading to v3.12.6 or later.
+
 ## HTTP RESTful API
 
 ### JavaScript-based traversal using `/_api/traversal` removed