Adds E5 model docs (#2601) (#2607)

Co-authored-by: István Zoltán Szabó <[email protected]>

docs/en/stack/ml/nlp/index.asciidoc

@@ -6,7 +6,10 @@ include::ml-nlp-search-compare.asciidoc[leveloffset=+2]
 include::ml-nlp-deploy-models.asciidoc[leveloffset=+1]
 include::ml-nlp-inference.asciidoc[leveloffset=+1]
 include::ml-nlp-apis.asciidoc[leveloffset=+1]
-include::ml-nlp-elser.asciidoc[leveloffset=+1]
+include::ml-nlp-built-in-models.asciidoc[leveloffset=+1]
+include::ml-nlp-elser.asciidoc[leveloffset=+2]
+include::ml-nlp-e5.asciidoc[leveloffset=+2]
+include::ml-nlp-lang-ident.asciidoc[leveloffset=+2]
 include::ml-nlp-model-ref.asciidoc[leveloffset=+1]
 include::ml-nlp-examples.asciidoc[leveloffset=+1]
 include::ml-nlp-ner-example.asciidoc[leveloffset=+2]

docs/en/stack/ml/nlp/ml-nlp-built-in-models.asciidoc

@@ -0,0 +1,11 @@
+[[ml-nlp-built-in-models]]
+= Built-in NLP models
+
+There are {nlp} models that are available for use in every cluster 
+out-of-the-box. These models are pre-trained which means they don't require 
+fine-tuning on your own data, making it adaptable for various use cases out of 
+the box. The following models are available:
+
+* <<ml-nlp-elser>> trained by Elastic
+* <<ml-nlp-e5>>
+* <<ml-nlp-lang-ident>>

docs/en/stack/ml/nlp/ml-nlp-classify-text.asciidoc

@@ -13,7 +13,13 @@ label unstructured input text:
 * <<ml-nlp-text-classification>>
 * <<ml-nlp-zero-shot>>
 
-include::ml-nlp-lang-ident.asciidoc[]
+
+[discrete]
+== {lang-ident-cap}
+
+The {lang-ident} model is provided out-of-the box in your {es} cluster. You can 
+find the documentation of the model on the <<ml-nlp-lang-ident>> page under the 
+Built-in models section.
 
 
 [discrete]

docs/en/stack/ml/nlp/ml-nlp-e5.asciidoc

@@ -0,0 +1,159 @@
+[[ml-nlp-e5]]
+= E5 – EmbEddings from bidirEctional Encoder rEpresentations
+++++
+<titleabbrev>E5</titleabbrev>
+++++
+
+:frontmatter-description: E5 is a multilingual dense vector model.
+:frontmatter-tags-products: [ml] 
+:frontmatter-tags-content-type: [how-to] 
+:frontmatter-tags-user-goals: [analyze]
+
+EmbEddings from bidirEctional Encoder rEpresentations - or E5 -  is a {nlp} 
+model that enables you to perform multi-lingual semantic search by using dense 
+vector representations. This model is recommended for non-English language 
+documents and queries. If you want to perform semantic search on English 
+language documents, use the <<ml-nlp-elser>> model.
+
+{ref}/semantic-search.html[Semantic search] provides you search results based on 
+contextual meaning and user intent, rather than exact keyword matches.
+
+E5 has two versions: one cross-platform version which runs on any hardware 
+and one version which is optimized for Intel® silicon. The 
+**Model Management** > **Trained Models** page shows you which version of E5 is 
+recommended to deploy based on your cluster's hardware.
+
+The supported model version of E5 is `multilingual-e5-small`, refer to 
+<<ml-nlp-e5-limit, this page>> for more information.
+
+Refer to the model cards of the 
+https://huggingface.co/elastic/multilingual-e5-small[multilingual-e5-small] and 
+the 
+https://huggingface.co/elastic/multilingual-e5-small-optimized[multilingual-e5-small-optimized]
+models on HuggingFace for further information including licensing.
+
+
+[discrete]
+[[e5-req]]
+== Requirements
+
+To use E5, you must have the {subscriptions}[appropriate subscription] level 
+for semantic search or the trial period activated.
+
+
+[discrete]
+[[download-deploy-e5]]
+== Download and deploy E5
+
+You can download and deploy the E5 model either from 
+**{ml-app}** > **Trained Models**, from **Search** > **Indices**, or by using 
+the Dev Console.
+
+
+[discrete]
+[[trained-model-e5]]
+=== Using the Trained Models page
+
+1. In {kib}, navigate to **{ml-app}** > **Trained Models**. E5 can be found in 
+the list of trained models. There are two versions available: one portable 
+version which runs on any hardware and one version which is optimized for Intel® 
+silicon. You can see which model is recommended to use based on your hardware 
+configuration.
+2. Click the **Add trained model** button. Select the E5 model version you want 
+to use in the opening modal window. The model that is recommended for you based 
+on your hardware configuration is highlighted. Click **Download**. You can check 
+the download status on the **Notifications** page.
++
+--
+[role="screenshot"]
+image::images/ml-nlp-e5-download.png[alt="Downloading E5",align="center"]
+--
++
+--
+Alternatively, click the **Download model** button under **Actions** in the 
+trained model list.
+--
+3. After the download is finished, start the deployment by clicking the 
+**Start deployment** button.
+4. Provide a deployment ID, select the priority, and set the number of 
+allocations and threads per allocation values.
++
+--
+[role="screenshot"]
+image::images/ml-nlp-deployment-id-e5.png[alt="Deploying ELSER",align="center"]
+--
+5. Click Start.
+
+
+[discrete]
+[[elasticsearch-e5]]
+=== Using the search indices UI
+
+Alternatively, you can download and deploy the E5 model to an {infer} pipeline 
+using the search indices UI.
+
+1. In {kib}, navigate to **Search** > **Indices**.
+2. Select the index from the list that has an {infer} pipeline in which you want 
+to use E5.
+3. Navigate to the **Pipelines** tab.
+4. Under **{ml-app} {infer-cap} Pipelines**, click the **Deploy** button in the 
+**Improve your results with E5** section to begin downloading the E5 model. This 
+may take a few minutes depending on your network. 
++
+--
+[role="screenshot"]
+image::images/ml-nlp-deploy-e5-es.png[alt="Deploying E5 in Elasticsearch",align="center"]
+--
+5. Once the model is downloaded, click the **Start single-threaded** button to 
+start the model with basic configuration or select the **Fine-tune performance** 
+option to navigate to the **Trained Models** page where you can configure the 
+model deployment.
++
+--
+[role="screenshot"]
+image::images/ml-nlp-start-e5-es.png[alt="Start E5 in Elasticsearch",align="center"]
+--
+
+When your E5 model is deployed and started, it is ready to be used in a 
+pipeline.
+
+
+[discrete]
+[[dev-console-e5]]
+=== Using the Dev Console
+
+1. In {kib}, navigate to the **Dev Console**.
+2. Create the E5 model configuration by running the following API call:
++
+--
+[source,console]
+----------------------------------
+PUT _ml/trained_models/.multilingual-e5-small
+{
+  "input": {
+	"field_names": ["text_field"]
+  }
+}
+----------------------------------
+
+The API call automatically initiates the model download if the model is not 
+downloaded yet.
+--
+3. Deploy the model by using the 
+{ref}/start-trained-model-deployment.html[start trained model deployment API] 
+with a delpoyment ID:
++
+--
+[source,console]
+----------------------------------
+POST _ml/trained_models/.multilingual-e5-small/deployment/_start?deployment_id=for_search
+----------------------------------
+--
+
+[discrete]
+[[air-gapped-install-e5]]
+== Deploy the E5 model in an air-gapped environment
+
+If you want to deploy the E5 model in a restricted or closed network, follow the 
+instructions 
+https://www.elastic.co/guide/en/elasticsearch/client/eland/current/machine-learning.html#ml-nlp-pytorch-air-gapped[in the Eland client documentation].

docs/en/stack/ml/nlp/ml-nlp-lang-ident.asciidoc

@@ -1,6 +1,5 @@
-[discrete]
 [[ml-nlp-lang-ident]]
-== {lang-ident-cap}
+= {lang-ident-cap}
 
 {lang-ident-cap} enables you to determine the language of text.
 

docs/en/stack/ml/nlp/ml-nlp-limitations.asciidoc

@@ -18,3 +18,11 @@ each field of the ingested documents that ELSER is applied to are taken into
 account for the search process. If your data set contains long documents, divide 
 them into smaller segments before ingestion if you need the full text to be 
 searchable.
+
+
+[discrete]
+[[ml-nlp-e5-limit]]
+== The `multilingual-e5-small` model is supported
+
+From the list of E5 models, currently only the `multilingual-e5-small` is 
+supported.