[DOCS] Resolves conflict. (#115503)

Author: szabosteve

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

@@ -13,25 +13,47 @@ 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 service>> 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"
+      }
+    }
+  }
+}
+------------------------------------------------------------
+
+NOTE: In Serverless, you must create an {infer} endpoint using the <<put-inference-api>> and reference it when setting up `semantic_text` even if you use the ELSER service. 
+
+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 to use to generate embeddings.
 
 
 The recommended way to use semantic_text is by having dedicated {infer} endpoints for ingestion and search.
@@ -40,7 +62,7 @@ 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 +21,11 @@ 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>>.
+This tutorial uses the <<infer-service-elser,ELSER service>> for demonstration, which is created automatically as needed. 
+To use the `semantic_text` field type with an {infer} service other than ELSER, you must create an inference endpoint using the <<put-inference-api>>.
 
-[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 {ml-docs}/ml-nlp-auto-scale.html#nlp-model-adaptive-allocations[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: In Serverless, you must create an {infer} endpoint using the <<put-inference-api>> and reference it when setting up `semantic_text` even if you use the ELSER service. 
 
-[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 +41,7 @@ PUT semantic-embeddings
   "mappings": {
     "properties": {
       "content": { <1>
-        "type": "semantic_text", <2>
-        "inference_id": "my-elser-endpoint" <3>
+        "type": "semantic_text" <2>
       }
     }
   }
@@ -85,9 +50,16 @@ 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.
+Since no `inference_id` is provided, the <<infer-service-elser,ELSER service>> is used by default.
+To use a different {infer} service, you must create an {infer} endpoint first using the <<put-inference-api>> and then specify it in the `semantic_text` field mapping using the `inference_id` parameter.
+
+
+[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.
+====
 
 
 [discrete]