Update vector-search.md

fixed typo where nProbes appeared in index definition rather than a parameter of the search

Author: jcodella

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

@@ -41,7 +41,6 @@ To create a vector index, use the following `createIndexes` template:
       "cosmosSearchOptions": {
         "kind": "vector-ivf",
         "numLists": <integer_value>,
-        "nProbes": <integer_value>,
         "similarity": "<string_value>",
         "dimensions": <integer_value>
       }
@@ -56,7 +55,6 @@ To create a vector index, use the following `createIndexes` template:
 | `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).|
 | `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. |
-| `nProbes` | integer | This integer controls the number of nearby clusters that are inspected in each search. A higher value may improve accuracy, however the search will be slower as a result. This is an optional parameter, with a default value of 1.  |
 | `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`. |
 
@@ -92,7 +90,6 @@ db.runCommand({
       cosmosSearchOptions: {
         kind: 'vector-ivf',
         numLists: 3,
-        nProbes: 1
         similarity: 'COS',
         dimensions: 3
       }
@@ -142,7 +139,8 @@ To retrieve the similarity score (`searchScore`) along with the documents found
 
 ### Query vectors and vector distances (aka similarity scores) using $search"
 
-Continuing with the last example, create another vector, `queryVector`. Vector search measures the distance between `queryVector` and the vectors in the `vectorContent` path of your documents. You can set the number of results that the search returns by setting the parameter `k`, which is set to `2` here.
+Continuing with the last example, create another vector, `queryVector`. Vector search measures the distance between `queryVector` and the vectors in the `vectorContent` path of your documents. You can set the number of results that the search returns by setting the parameter `k`, which is set to `2` here. You can also set `nProbes`, which is an integer that controls the number of nearby clusters that are inspected in each search. A higher value may improve accuracy, however the search will be slower as a result. This is an optional parameter with a default value of 1 and cannot be larger than the `numLists` value specified in the vector index. 
+
 
 ```javascript
 const queryVector = [0.52, 0.28, 0.12];