fix mappings

Signed-off-by: Mingshi Liu <mingshl@amazon.com>

Author: mingshl

_query-dsl/specialized/script-score.md

@@ -390,15 +390,14 @@ This approach enables fine-grained semantic matching between queries and documen
 
 The vector field must be mapped as either an `object` (recommended) or `float` type.
 
-We recommend mapping the vector field as an `object` with `"enabled": false` because it stores raw vectors without parsing, improving performance:
+We recommend mapping the vector field as an `object` because it stores raw vectors without parsing, improving performance:
 
 ```json
 {
   "mappings": {
     "properties": {
       "my_vector": {
-        "type": "object",
-        "enabled": false
+        "type": "object"
       }
     }
   }

_search-plugins/search-relevance/rerank-by-field-late-interaction.md

@@ -118,7 +118,7 @@ Note the `model_id` from the response; you'll use it in subsequent steps.
 
 ## Step 2: Create an index
 
-Create an index with mappings optimized for storing both multi-vectors and single k-NN vectors. The `colbert_vectors` field is stored as an `object` with `enabled: false` for optimal performance. The `knn_vector` field is configured for the `hnsw` algorithm, with the number of dimensions matching the model's number of dimensions. The `image` field stores Base64-encoded image data as a `keyword`:
+Create an index with mappings optimized for storing both multi-vectors and single k-NN vectors. The `colbert_vectors` field is stored as an `object`. The `knn_vector` field is configured for the `hnsw` algorithm, with the number of dimensions matching the model's number of dimensions. The `image` field stores Base64-encoded image data as a `keyword`:
 
 
 ```json
@@ -140,8 +140,7 @@ PUT /multimodal_docs
         "type": "keyword"
       },
       "colbert_vectors": {
-        "type": "object",
-        "enabled": false
+        "type": "object"
       },
       "knn_vector": { 
         "type": "knn_vector",