Updating notebook to address version differences for rerank endpoint

Author: Samiul-TheSoccerFan

notebooks/search/12-semantic-reranking-elastic-rerank.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "In this notebook you'll learn how to implement semantic reranking in Elasticsearch using the built-in [Elastic Rerank model](https://www.elastic.co/guide/en/machine-learning/master/ml-nlp-rerank.html). You'll also learn about the `retriever` abstraction, a simpler syntax for crafting queries and combining different search operations.\n",
     "\n",
-    "You will query your data using the `text_similarity_rerank` retriever, which leverages the Elastic Rerank model to boost the relevance of your search results."
+    "You will query your data using the `text_similarity_rerank` retriever, and the Elastic Rerank model to boost the relevance of your search results."
    ]
   },
   {
@@ -381,10 +381,7 @@
    "source": [
     "## Semantic reranker\n",
     "\n",
-    "In the following `retriever` syntax, we wrap our standard `match` query retriever in a `text_similarity_reranker`. This allows us to leverage the [Elastic rerank model](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-rerank.html) we deployed to Elasticsearch to rerank the results based on the phrase \"flesh-eating bad guy\".\n",
-    "\n",
-    "⚠️ When you deploy your model, you might need to sync your ML saved objects in the Kibana (or Serverless) UI.\n",
-    "Go to **Trained Models** and select **Synchronize saved objects**."
+    "In the following `retriever` syntax, we wrap our standard `match` query retriever in a `text_similarity_reranker`. This allows us to leverage the [Elastic rerank model](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-rerank.html) to rerank the results based on the phrase \"flesh-eating bad guy\"."
    ]
   },
   {
@@ -463,7 +460,7 @@
     "\n",
     "Semantic reranking enables semantic search in a few steps, without the need for generating and storing embeddings. This a great tool for testing and building hybrid search systems in Elasticsearch.\n",
     "\n",
-    "*Note* The `inference_id` field is optional and if not provided, it defaults to `.rerank-v1-elasticsearch`. However, If you prefer to manage your own endpoint, you can set up a custom `rerank` inference endpoint using the [create inference API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put)."
+    "*Note* Starting with Elasticsearch version `8.18`, The `inference_id` field is optional. If not specified, it defaults to `.rerank-v1-elasticsearch`. If you are using an earlier version or prefer to manage your own endpoint, you can set up a custom `rerank` inference endpoint using the [create inference API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put)."
    ]
   },
   {