Fix the k-NN train API command for disk based vector search

Signed-off-by: Ryan Bogan <[email protected]>

_search-plugins/knn/disk-based-vector-search.md

@@ -167,7 +167,7 @@ GET my-vector-index/_search
 For [model-based indexes]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#building-a-k-nn-index-from-a-model), you can specify the `on_disk` parameter in the training request in the same way that you would specify it during index creation. By default, `on_disk` mode will use the [Faiss IVF method]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index/#supported-faiss-methods) and a compression level of `32x`. To run the training API, send the following request:
 
 ```json
-POST /_plugins/_knn/models/_train/test-model
+POST /_plugins/_knn/models/test-model/_train
 {
     "training_index": "train-index-name",
     "training_field": "train-field-name",