Add score normalization and combination documentation

_ml-commons-plugin/semantic-search.md

@@ -38,7 +38,7 @@
 
 ## Prerequisites
 
-For this simple example, you'll use an OpenSearch-provided machine learning (ML) model and a cluster with no dedicated ML nodes. To ensure that this basic local setup works, send the following request to update ML-related cluster settings:
+For this simple setup, you'll use an OpenSearch-provided machine learning (ML) model and a cluster with no dedicated ML nodes. To ensure that this basic local setup works, send the following request to update ML-related cluster settings:
 
 ```json
 PUT _cluster/settings
@@ -61,7 +61,7 @@
 For a more advanced setup, note the following requirements:
 
 - To register a custom model, you need to specify an additional `"allow_registering_model_via_url": "true"` cluster setting. 
-- On clusters with dedicated ML nodes, you may want to specify `"only_run_on_ml_node": "true"` for improved performance. 
+- In production, it's best practice to separate the workloads by having dedicated ML nodes. On clusters with dedicated ML nodes, specify `"only_run_on_ml_node": "true"` for improved performance. 
 
 For more information about ML-related cluster settings, see [ML Commons cluster settings]({{site.url}}{{site.baseurl}}/ml-commons-plugin/cluster-settings/).
 
@@ -297,7 +297,7 @@
 }
 ```
 
-The response contains the model information, including the `model_state` (`REGISTERED`) and the number of chunks into which it was split `total_chunks` (27).
+The response contains the model information. You can see that the `model_state` is `REGISTERED`. Additionally, the model was split into 27 chunks, as shown in the `total_chunks` field.
 </details>
 
 #### Advanced: Registering a custom model
@@ -427,7 +427,7 @@
 
 ### Step 2(a): Create an ingest pipeline for neural search
 
-The first step in setting up [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) is to create an [ingest pipeline]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/). The ingest pipeline will contain one processor: a task that transforms document fields. For neural search, you'll need to set up a `text_embedding` processor that takes in text and creates vector embeddings from that text. You'll need the `model_id` of the model you set up in the previous section and a `field_map`, which specifies the name of the field from which to take the text (`text`) and the name of the field in which to record embeddings (`passage_embedding`):
+Now that you have deployed a model, you can use this model to configure [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/). First, you need to create an [ingest pipeline]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/) that contains one processor: a task that transforms document fields before documents are ingested into an index. For neural search, you'll set up a `text_embedding` processor that creates vector embeddings from text. You'll need the `model_id` of the model you set up in the previous section and a `field_map`, which specifies the name of the field from which to take the text (`text`) and the name of the field in which to record embeddings (`passage_embedding`):
 
 ```json
 PUT /_ingest/pipeline/nlp-ingest-pipeline
@@ -588,6 +588,37 @@
 ```
 {% include copy-curl.html %}
 
+When the documents are ingested into the index, the `text_embedding` processor creates an additional field that contains vector embeddings and adds that field to the document. To see an example document that is indexed, search for document 1:
+
+```json
+GET /my-nlp-index/_search/1
+```
+{% include copy-curl.html %}
+
+The response shows the document `_source` containing the original `text` and `id` fields and the added `passage_embeddings` field:
+
+```json
+{
+ "_index": "my-nlp-index",
+ "_id": "1",
+ "_version": 1,
+ "_seq_no": 0,
+ "_primary_term": 1,
+ "found": true,
+ "_source": {
+ "passage_embedding": [
+ 0.04491629,
+ -0.34105563,
+ 0.036822468,
+ -0.14139028,
+ ...
+ ],
+ "text": "A West Virginia university women 's basketball team , officials , and a small gathering of fans are in a West Virginia arena .",
+ "id": "4319130149.jpg"
+ }
+}
+```
+
 ## Step 3: Search the data
 
 Now you'll search the index using keyword search, neural search, and a combination of the two.
@@ -785,7 +816,7 @@
 
 ### Search using a combined keyword search and neural search
 
 To combine keyword search and neural 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. 
 
 #### Step 1: Configure a search pipeline
 
@@ -943,6 +974,10 @@
 
 You can now experiment with different weights, normalization techniques, and combination techniques. For more information, see the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) and [`hybrid` query]({{site.url}}{{site.baseurl}}/query-dsl/compound/hybrid/) documentation.
 
+#### Advanced
+
+You can parametrize the search by using search templates, hiding implementation details and reducing the number of nested levels and thus the query complexity. For more information, see [search templates]({{site.url}}{{site.baseurl}}/search-plugins/search-template/).
+
 ### Clean up
 
 After you're done, delete the components you've created in tutorial from the cluster: