Remove model requirement from hybrid search documentation

_query-dsl/compound/hybrid.md

@@ -12,11 +12,7 @@ You can use a hybrid query to combine relevance scores from multiple queries int
 
 ## Example
 
-Before using a `hybrid` query, you must set up a machine learning (ML) model, ingest documents, and configure a search pipeline with a [`normalization-processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/).
-
-To learn how to set up an ML model, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
-
-Once you set up an ML model, learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search).
+Learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search).
 
 For a comprehensive example, follow the [Neural search tutorial]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search#tutorial).
 

_search-plugins/hybrid-search.md

@@ -12,7 +12,7 @@ Introduced 2.11
 Hybrid search combines keyword and neural search to improve search relevance. To implement hybrid search, you need to set up a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline you'll configure intercepts search results at an intermediate stage and applies the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) to them. The `normalization_processor` normalizes and combines the document scores from multiple query clauses, rescoring the documents according to the chosen normalization and combination techniques. 
 
 **PREREQUISITE**<br>
-Before using hybrid search, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
+To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). If you already generated text embeddings, ingest the embeddings into an index and skip to [Step 4](#step-4-configure-a-search-pipeline).
 {: .note}
 
 ## Using hybrid search