[DOCS] [8.17] Adds new default inference endpoint information

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

@@ -12,13 +12,14 @@ 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.
+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 `inference_id` field defaults to `.elser-2-elasticsearch`, a preconfigured endpoint for the ELSER 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.
 
-If you use the ELSER service, you can set up `semantic_text` with the following API request:
+If you use the preconfigured `.elser-2-elasticsearch` endpoint, you can set up `semantic_text` with the following API request:
 
 [source,console]
 ------------------------------------------------------------
@@ -34,7 +35,7 @@ PUT my-index-000001
 }
 ------------------------------------------------------------
 
-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:
+To use a custom {infer} endpoint instead of the default `.elser-2-elasticsearch`, you must <<put-inference-api>> and specify its `inference_id` when setting up the `semantic_text` field type.
 
 [source,console]
 ------------------------------------------------------------
@@ -53,8 +54,7 @@ PUT my-index-000002
 // 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.
+The recommended way to use `semantic_text` is by having dedicated {infer} endpoints for ingestion and search.
 This ensures that search speed remains unaffected by ingestion workloads, and vice versa.
 After creating dedicated {infer} endpoints for both, you can reference them using the `inference_id` and `search_inference_id` parameters when setting up the index mapping for an index that uses the `semantic_text` field.
 
@@ -82,10 +82,11 @@ PUT my-index-000003
 
 `inference_id`::
 (Required, string)
-{infer-cap} endpoint that will be used to generate the embeddings for the field.
+{infer-cap} endpoint that will be used to generate embeddings for the field.
+By default, `.elser-2-elasticsearch` is used.
 This parameter cannot be updated.
 Use the <<put-inference-api>> to create the endpoint.
-If `search_inference_id` is specified, the {infer} endpoint defined by `inference_id` will only be used at index time.
+If `search_inference_id` is specified, the {infer} endpoint will only be used at index time.
 
 `search_inference_id`::
 (Optional, string)
@@ -129,7 +130,7 @@ Once a document is ingested, a `semantic_text` field will have the following str
 "inference_field": {
   "text": "these are not the droids you're looking for", <1>
   "inference": {
-    "inference_id": "my-elser-endpoint", <2>
+    "inference_id": ".elser-2-elasticsearch", <2>
     "model_settings": { <3>
       "task_type": "sparse_embedding"
     },
@@ -208,7 +209,7 @@ PUT test-index
         "properties": {
             "infer_field": {
                 "type": "semantic_text",
-                "inference_id": "my-elser-endpoint"
+                "inference_id": ".elser-2-elasticsearch"
             },
             "source_field": {
                 "type": "text",

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

@@ -14,15 +14,15 @@ You don't need to define model related settings and parameters, or create {infer
 The recommended way to use <<semantic-search,semantic search>> in the {stack} is following the `semantic_text` workflow.
 When you need more control over indexing and query settings, you can still use the complete {infer} workflow (refer to  <<semantic-search-inference,this tutorial>> to review the process).
 
-This tutorial uses the <<inference-example-elser,`elser` service>> for demonstration, but you can use any service and their supported models offered by the {infer-cap} API.
+This tutorial uses the <<infer-service-elasticsearch,`elasticsearch` service>> for demonstration, but you can use any service and their supported models offered by the {infer-cap} API.
 
 
 [discrete]
 [[semantic-text-requirements]]
 ==== Requirements
 
-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>>.
+This tutorial uses the <<infer-service-elasticsearch,`elasticsearch` service>> for demonstration, which is created automatically as needed. 
+To use the `semantic_text` field type with an {infer} service other than `elasticsearch` service, you must create an inference endpoint using the <<put-inference-api>>.
 
 
 [discrete]
@@ -48,7 +48,7 @@ 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.
-Since no `inference_id` is provided, the <<infer-service-elser,ELSER service>> is used by default.
+Since no `inference_id` is provided, the default endpoint `.elser-2-elasticsearch` for the <<infer-service-elasticsearch,`elasticsearch` service>> is used.
 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]

docs/reference/search/search-your-data/semantic-text-hybrid-search

@@ -8,47 +8,12 @@ This tutorial demonstrates how to perform hybrid search, combining semantic sear
 
 In hybrid search, semantic search retrieves results based on the meaning of the text, while full-text search focuses on exact word matches. By combining both methods, hybrid search delivers more relevant results, particularly in cases where relying on a single approach may not be sufficient.
 
-The recommended way to use hybrid search in the {stack} is following the `semantic_text` workflow. This tutorial uses the <<inference-example-elser,`elser` service>> for demonstration, but you can use any service and its supported models offered by the {infer-cap} API.
-
-[discrete]
-[[semantic-text-hybrid-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.
-====
+The recommended way to use hybrid search in the {stack} is following the `semantic_text` workflow. 
+This tutorial uses the <<infer-service-elasticsearch,`elasticsearch` service>> for demonstration, but you can use any service and their supported models offered by the {infer-cap} API.
 
 [discrete]
 [[hybrid-search-create-index-mapping]]
-==== Create an index mapping for hybrid search
+==== Create an index mapping
 
 The destination index will contain both the embeddings for semantic search and the original text field for full-text search. This structure enables the combination of semantic search and full-text search.
 
@@ -60,21 +25,19 @@ PUT semantic-embeddings
     "properties": {
       "semantic_text": { <1>
         "type": "semantic_text", 
-        "inference_id": "my-elser-endpoint" <2>
       },
-      "content": { <3>
+      "content": { <2>
         "type": "text",
-        "copy_to": "semantic_text" <4>
+        "copy_to": "semantic_text" <3>
       }
     }
   }
 }
 ------------------------------------------------------------
 // TEST[skip:TBD]
 <1> The name of the field to contain the generated embeddings for semantic search.
-<2> The identifier of the inference endpoint that generates the embeddings based on the input text.
-<3> The name of the field to contain the original text for lexical search.
-<4> The textual data stored in the `content` field will be copied to `semantic_text` and processed by the {infer} endpoint.
+<2> The name of the field to contain the original text for lexical search.
+<3> The textual data stored in the `content` field will be copied to `semantic_text` and processed by the {infer} endpoint.
 
 [NOTE]
 ====