Update vector-search.md

fixed links and removed hnsw preview mentions

Author: jcodella

articles/cosmos-db/mongodb/vcore/vector-search.md

@@ -55,7 +55,7 @@ You can create (Hierarchical Navigable Small World) indexes on M40 cluster tiers
 
 |Field    |Type     |Description  |
 |---------|---------|---------|
-| `kind` | string | Type of vector index to create. Type of vector index to create. Primarily, `vector-ivf` is supported. `vector-hnsw` is available as a preview feature that requires enablement via [Azure Feature Enablement Control](../../../azure-resource-manager/management/preview-features.md).|
+| `kind` | string | Type of vector index to create. Type of vector index to create. `vector-hnsw` is available on M40 cluster tiers and higher.|
 |`m`        |integer    |The max number of connections per layer (`16` by default, minimum value is `2`, maximum value is `100`). Higher m is suitable for datasets with high dimensionality and/or high accuracy requirements.    |
 |`efConstruction` |integer    |the size of the dynamic candidate list for constructing the graph (`64` by default, minimum value is `4`, maximum value is `1000`). Higher `efConstruction` will result in better index quality and higher accuracy, but it will also increase the time required to build the index. `efConstruction` has to be at least `2 * m`    |
 |`similarity`     |string     |Similarity metric to use with the index. Possible options are `COS` (cosine distance), `L2` (Euclidean distance), and `IP` (inner product).    |
@@ -113,7 +113,7 @@ To create a vector index using the IVF (Inverted File) algorithm, use the follow
 | --- | --- | --- |
 | `index_name` | string | Unique name of the index. |
 | `path_to_property` | string | Path to the property that contains the vector. This path can be a top-level property or a dot notation path to the property. If a dot notation path is used, then all the nonleaf elements can't be arrays. Vectors must be a `number[]` to be indexed and return in vector search results.|
-| `kind` | string | Type of vector index to create. Primarily, `vector-ivf` is supported. `vector-hnsw` is available as a preview feature that requires enablement via [Azure Feature Enablement Control](../../../azure-resource-manager/management/preview-features.md).|
+| `kind` | string | Type of vector index to create. IVF, `vector-ivf`, is shown in this example. |
 | `numLists` | integer | This integer is the number of clusters that the inverted file (IVF) index uses to group the vector data. We recommend that `numLists` is set to `documentCount/1000` for up to 1 million documents and to `sqrt(documentCount)` for more than 1 million documents. Using a `numLists` value of `1` is akin to performing brute-force search, which has limited performance. |
 | `similarity` | string | Similarity metric to use with the IVF index. Possible options are `COS` (cosine distance), `L2` (Euclidean distance), and `IP` (inner product). |
 | `dimensions` | integer | Number of dimensions for vector similarity. The maximum number of supported dimensions is `2000`. |
@@ -272,7 +272,7 @@ In this example, `vectorIndex` is returned with all the `cosmosSearch` parameter
 ```
 
 ## Filtered vector search (preview)
-You can now execute vector searches with any supported query filter such as `$lt, $lte, $eq, $neq, $gte, $gt, $in, $nin, and $regex`. Enable the "filtering vector search" feature in the "Preview Features" tab of your Azure Subscription. Learn more about preview features [here](../../../azure-resource-manager/management/preview-features?tabs=azure-portal).
+You can now execute vector searches with any supported query filter such as `$lt, $lte, $eq, $neq, $gte, $gt, $in, $nin, and $regex`. Enable the "filtering vector search" feature in the "Preview Features" tab of your Azure Subscription. Learn more about preview features [here](../../../azure-resource-manager/management/preview-features.md).
 
 First, you'll need to define an index for your filter in addition to a vector index. For example, you can define the filter index on a property