Update Semantic Search Tutorial to Use Elasticsearch Service and Remove References to Deprecated ELSER Service (#117826)

* Update tutorial to use elasticsearch service

---------

Co-authored-by: Max Hniebergall <[email protected]>

Author: eshaffer321

docs/changelog/117826.yaml

@@ -0,0 +1,5 @@
+pr: 117826
+summary: "Switch ELSER service to elasticsearch service in semantic search tutorial"
+area: Docs
+type: doc
+issues: ["117829"]

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

@@ -14,7 +14,7 @@ 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 <<inference-example-elasticsearch-elser,`elasticsearch` service>> for demonstration, but you can use any service and their supported models offered by the {infer-cap} API.
 
 
 [discrete]
@@ -34,24 +34,28 @@ Create an inference endpoint by using the <<put-inference-api>>:
 ------------------------------------------------------------
 PUT _inference/sparse_embedding/my-elser-endpoint <1>
 {
-  "service": "elser", <2>
+  "service": "elasticsearch", <2>
   "service_settings": {
     "adaptive_allocations": { <3>
       "enabled": true,
       "min_number_of_allocations": 3,
       "max_number_of_allocations": 10
     },
-    "num_threads": 1
+    "num_threads": 1,
+    "model_id": ".elser_model_2" <4>
   }
 }
 ------------------------------------------------------------
 // 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.
+<2> The `elasticsearch` 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.
+<4> The `model_id` must be the ID of one of the built-in ELSER models.
+Valid values are `.elser_model_2` and `.elser_model_2_linux-x86_64`.
+For further details, refer to the {ml-docs}/ml-nlp-elser.html[ELSER model documentation].
 
 [NOTE]
 ====
@@ -282,4 +286,4 @@ query from the `semantic-embedding` index:
 
 * If you want to use `semantic_text` in hybrid search, refer to https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/search/09-semantic-text.ipynb[this notebook] for a step-by-step guide.
 * For more information on how to optimize your ELSER endpoints, refer to {ml-docs}/ml-nlp-elser.html#elser-recommendations[the ELSER recommendations] section in the model documentation.
-* To learn more about model autoscaling, refer to the {ml-docs}/ml-nlp-auto-scale.html[trained model autoscaling] page.
+* To learn more about model autoscaling, refer to the {ml-docs}/ml-nlp-auto-scale.html[trained model autoscaling] page.