[DOCS] Adds inline applies_to tags to semantic text docs

docs/reference/elasticsearch/mapping-reference/semantic-text.md

@@ -2,6 +2,9 @@
 navigation_title: "Semantic text"
 mapped_pages:
   - https://www.elastic.co/guide/en/elasticsearch/reference/current/semantic-text.html
+applies_to:
+  stack: ga 9.0
+  serverless: ga
 ---
 
 # Semantic text field type [semantic-text]
@@ -29,7 +32,7 @@ service.
 Using `semantic_text`, you won’t need to specify how to generate embeddings for
 your data, or how to index it. The {{infer}} endpoint automatically determines
 the embedding generation, indexing, and query to use.
-Newly created indices with `semantic_text` fields using dense embeddings will be
+{applies_to}`stack: ga 9.1` Newly created indices with `semantic_text` fields using dense embeddings will be
 [quantized](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-quantization)
 to `bbq_hnsw` automatically.
 
@@ -117,13 +120,13 @@ for the field. Currently, `dense_vector` index options are supported.
 For text embeddings, `index_options` may match any allowed
 [dense_vector index options](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-index-options).
 
-`chunking_settings` {applies_to}`stack: ga 9.1`
+`chunking_settings`
 :   (Optional, object) Settings for chunking text into smaller passages.
 If specified, these will override the chunking settings set in the {{infer-cap}}
 endpoint associated with `inference_id`.
 If chunking settings are updated, they will not be applied to existing documents
 until they are reindexed.
-To completely disable chunking, use the `none` chunking strategy.
+{applies_to}`stack: ga 9.1` To completely disable chunking, use the `none` chunking strategy.
 
     **Valid values for `chunking_settings`**:
 
@@ -182,6 +185,8 @@ For more details on chunking and how to configure chunking settings,
 see [Configuring chunking](https://www.elastic.co/docs/api/doc/elasticsearch/group/endpoint-inference)
 in the Inference API documentation.
 
+{applies_to}`stack: ga 9.1`
+
 You can pre-chunk the input by sending it to Elasticsearch as an array of
 strings.
 Example:
@@ -295,6 +300,8 @@ specified. It enables you to quickstart your semantic search by providing
 automatic {{infer}} and a dedicated query so you don’t need to provide further
 details.
 
+{applies_to}`stack: ga 9.1`
+
 If you want to override those defaults and customize the embeddings that
 `semantic_text` indexes, you can do so by
 modifying [parameters](#semantic-text-params):
@@ -328,6 +335,21 @@ PUT my-index-000004
 }
 ```
 
+{applies_to}`stack: ga 9.0`
+
+In case you want to customize data indexing, use the
+[`sparse_vector`](/reference/elasticsearch/mapping-reference/sparse-vector.md)
+or [`dense_vector`](/reference/elasticsearch/mapping-reference/dense-vector.md)
+field types and create an ingest pipeline with an 
+[{{infer}} processor](/reference/enrich-processor/inference-processor.md) to
+generate the embeddings.
+[This tutorial](docs-content://solutions/search/semantic-search/semantic-search-inference.md)
+walks you through the process. In these cases - when you use `sparse_vector` or
+`dense_vector` field types instead of the `semantic_text` field type to
+customize indexing - using the
+[`semantic_query`](/reference/query-languages/query-dsl/query-dsl-semantic-query.md)
+is not supported for querying the field data.
+
 ## Updates to `semantic_text` fields [update-script]
 
 For indices containing `semantic_text` fields, updates that use scripts have the