[DOCS] Documents that ELSER is the default service for semantic_text.

Author: szabosteve

docs/reference/mapping/types/semantic-text.asciidoc

@@ -13,25 +13,45 @@ Long passages are <<auto-text-chunking, automatically chunked>> to smaller secti
 The `semantic_text` field type specifies an inference endpoint identifier that will be used to generate embeddings.
 You can create the inference endpoint by using the <<put-inference-api>>.
 This field type and the <<query-dsl-semantic-query,`semantic` query>> type make it simpler to perform semantic search on your data.
+If you don't specify an inference endpoint, the <<infer-service-elser,ELSER serive>> is used by default.
 
 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.
 
+If you use the ELSER service, you can set up `semantic_text` with the following API request:
+
 [source,console]
 ------------------------------------------------------------
 PUT my-index-000001
+{
+  "mappings": {
+    "properties": {
+      "inference_field": {
+        "type": "semantic_text"
+      }
+    }
+  }
+}
+------------------------------------------------------------
+
+If you use a service other than ELSER, you must create an {infer} endpoint using the <<put-inference-api>> and reference it when setting up `semantic_text` as the following example demonstrates:
+
+[source,console]
+------------------------------------------------------------
+PUT my-index-000002
 {
   "mappings": {
     "properties": {
       "inference_field": {
         "type": "semantic_text",
-        "inference_id": "my-elser-endpoint"
+        "inference_id": "my-openai-endpoint" <1>
       }
     }
   }
 }
 ------------------------------------------------------------
 // TEST[skip:Requires inference endpoint]
+<1> The `inference_id` of the {infer} endpoint you created previously.
 
 
 The recommended way to use semantic_text is by having dedicated {infer} endpoints for ingestion and search.
@@ -40,7 +60,7 @@ After creating dedicated {infer} endpoints for both, you can reference them usin
 
 [source,console]
 ------------------------------------------------------------
-PUT my-index-000002
+PUT my-index-000003
 {
   "mappings": {
     "properties": {

docs/reference/search/search-your-data/semantic-search-semantic-text.asciidoc

@@ -21,45 +21,9 @@ This tutorial uses the <<inference-example-elser,`elser` service>> for demonstra
 [[semantic-text-requirements]]
 ==== Requirements
 
-To use the `semantic_text` field type, you must have an {infer} endpoint deployed in
-your cluster using the <<put-inference-api>>.
+To use the `semantic_text` field type with an {infer} service other than ELSER, you must have an {infer} endpoint deployed in your cluster using the <<put-inference-api>>.
+If you use ELSER, the {infer} endpoint will be created automatically, when you set up the index with the `semantic_text` field.
 
-[discrete]
-[[semantic-text-infer-endpoint]]
-==== Create the {infer} endpoint
-
-Create an inference endpoint by using the <<put-inference-api>>:
-
-[source,console]
-------------------------------------------------------------
-PUT _inference/sparse_embedding/my-elser-endpoint <1>
-{
-  "service": "elser", <2>
-  "service_settings": {
-    "adaptive_allocations": { <3>
-      "enabled": true,
-      "min_number_of_allocations": 3,
-      "max_number_of_allocations": 10
-    },
-    "num_threads": 1
-  }
-}
-------------------------------------------------------------
-// TEST[skip:TBD]
-<1> The task type is `sparse_embedding` in the path as the `elser` service will
-be used and ELSER creates sparse vectors. The `inference_id` is
-`my-elser-endpoint`.
-<2> The `elser` service is used in this example.
-<3> This setting enables and configures adaptive allocations.
-Adaptive allocations make it possible for ELSER to automatically scale up or down resources based on the current load on the process.
-
-[NOTE]
-====
-You might see a 502 bad gateway error in the response when using the {kib} Console.
-This error usually just reflects a timeout, while the model downloads in the background.
-You can check the download progress in the {ml-app} UI.
-If using the Python client, you can set the `timeout` parameter to a higher value.
-====
 
 [discrete]
 [[semantic-text-index-mapping]]
@@ -75,8 +39,7 @@ PUT semantic-embeddings
   "mappings": {
     "properties": {
       "content": { <1>
-        "type": "semantic_text", <2>
-        "inference_id": "my-elser-endpoint" <3>
+        "type": "semantic_text" <2>
       }
     }
   }
@@ -85,18 +48,14 @@ PUT semantic-embeddings
 // TEST[skip:TBD]
 <1> The name of the field to contain the generated embeddings.
 <2> The field to contain the embeddings is a `semantic_text` field.
-<3> The `inference_id` is the inference endpoint you created in the previous step.
-It will be used to generate the embeddings based on the input text.
-Every time you ingest data into the related `semantic_text` field, this endpoint will be used for creating the vector representation of the text.
+As there is no `inference_id` provided, the <<infer-service-elser,ELSER serive>> will be used by default.
+If you use a different {infer} service, you must create an {infer} endpoint first and then reference it in the `semantic_text` field mapping.
 
 [NOTE]
 ====
-If you're using web crawlers or connectors to generate indices, you have to
-<<indices-put-mapping,update the index mappings>> for these indices to
-include the `semantic_text` field. Once the mapping is updated, you'll need to run
-a full web crawl or a full connector sync. This ensures that all existing
-documents are reprocessed and updated with the new semantic embeddings,
-enabling semantic search on the updated data.
+If you're using web crawlers or connectors to generate indices, you have to <<indices-put-mapping,update the index mappings>> for these indices to include the `semantic_text` field.
+Once the mapping is updated, you'll need to run a full web crawl or a full connector sync.
+This ensures that all existing documents are reprocessed and updated with the new semantic embeddings, enabling semantic search on the updated data.
 ====