Merge pull request #279836 from jcodella/patch-12

Update vectordistance.md

Author: prmerger-automator[bot]

articles/cosmos-db/nosql/query/vectordistance.md

@@ -37,7 +37,7 @@ VECTORDISTANCE(<vector_expr1>, <vector_expr2>, [<bool_expr>], [<obj_expr>])
 | **`bool_expr`** | A boolean specifying how the computed value is used in an ORDER BY expression. If `true`, then brute force is used. A value of `false` leverages any index defined on the vector property, if it exists. Default value is `false`.|
 |**`obj_expr`**| A JSON formatted object literal used to specify options for the vector distance calculation. Valid items include `distanceFunction` and `dataType`.|
 | **`distanceFunction`** | The metric used to compute distance/similarity.
-| **`dataType`** | The data type of the vectors. `float32`, `float16`, `int8`, `uint8` values. Default value is `float32`. |
+| **`dataType`** | The data type of the vectors. `float32`, `int8`, `uint8` values. Default value is `float32`. |
 
 
 Supported metrics for `distanceFunction` are: 
@@ -69,7 +69,7 @@ ORDER BY VectorDistance(c.vector1, c.vector2)
 - This function requires enrollment in the [Azure Cosmos DB NoSQL Vector Search preview feature](../vector-search.md#enroll-in-the-vector-search-preview-feature).
 - This function benefits from a [vector index](../../index-policy.md#vector-indexes)
 - if `false` is given as the optional `bool_expr`, then the vector index defined on the path is used, if one exists. If no index is defined on the vector path, then this reverts to full scan and incurs higher RU charges and higher latency than if using a vector index. 
-- When `VectorDistance` is used in an `ORDER BY` clause, no direction can be specified for the `ORDER BY`, as the results are always sorted in order of most similar (first) to least similar (last) based on the similarity metric used. If a direction such as `ASC` or `DESC` is specified, an error occurs. 
+- When `VectorDistance` is used in an `ORDER BY` clause, no direction needs to be specified for the `ORDER BY` as the results are always sorted in order of most similar (first) to least similar (last) based on the similarity metric used.
 - The result is expressed as a similarity score.
 
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