Add LTR retriever example in the doc. (elastic#2095)

Co-authored-by: Liam Thompson <[email protected]>

Author: afoucret

solutions/search/ranking/learning-to-rank-search-usage.md

@@ -18,11 +18,15 @@ products:
 This feature was introduced in version 8.12.0 and is only available to certain subscription levels. For more information, see {{subscriptions}}.
 ::::
 
+Once your LTR model is trained and deployed in {{es}}, there are two ways to use it with the [search API](../querying-for-search.md) to improve your search results:
+
+1. **As a [rescorer](#learning-to-rank-rescorer)**
+2. **As a [retriever](#learning-to-rank-retriever)**
 
 
 ## Learning To Rank as a rescorer [learning-to-rank-rescorer]
 
-Once your LTR model is trained and deployed in {{es}}, it can be used as a [rescorer](elasticsearch://reference/elasticsearch/rest-apis/filter-search-results.md#rescore) in the [search API](../querying-for-search.md):
+To use your LTR model as a [rescorer](elasticsearch://reference/elasticsearch/rest-apis/filter-search-results.md#rescore) in the [search API](../querying-for-search.md), follow this example:
 
 ```console
 GET my-index/_search
@@ -50,22 +54,60 @@ GET my-index/_search
 3. Named parameters to be passed to the query templates used for feature.
 4. The number of documents that should be examined by the rescorer on each shard.
 
+## Learning To Rank as a retriever [learning-to-rank-retriever]
 
+```{applies_to}
+stack: ga 9.1
+serverless: ga
+```
 
-### Known limitations [learning-to-rank-rescorer-limitations]
+LTR models can also be used as a [retriever](../retrievers-overview.md) in the search pipeline. You can implement this with a [rescorer retriever](elasticsearch://reference/elasticsearch/rest-apis/retrievers.md#rescorer-retriever) as shown in the following example:
 
+```console
+GET my-index/_search
+{
+  "retriever": {
+    "rescorer": {
+      "rescore": {
+        "window_size": 100, <4>
+        "learning_to_rank": {
+          "model_id": "ltr-model", <2>
+          "params": { <3>
+            "query_text": "the quick brown fox"
+          }
+        }
+      },
+      "retrievers": [ <1>
+        {
+          "standard": {
+            "query": {
+              "multi_match": {
+                "fields": ["title", "content"],
+                "query": "the quick brown fox"
+              }
+            }
+          }
+        }
+      ]
+    }
+  }
+}
+```
 
-#### Rescore window size [learning-to-rank-rescorer-limitations-window-size]
+1. First pass retrievers used to retrieve documents to be rescored
+2. The unique identifier of the trained model uploaded to {{es}}.
+3. Named parameters to be passed to the query templates used for feature extraction.
+4. The number of documents that should be examined by the rescorer.
 
-Scores returned by LTR models are usually not comparable with the scores issued by the first pass query and can be lower than the non-rescored score. This can cause the non-rescored result document to be ranked higher than the rescored document. To prevent this, the `window_size` parameter is mandatory for LTR rescorers and should be greater than or equal to `from + size`.
+## Known limitations [learning-to-rank-rescorer-limitations]
 
 
-#### Pagination [learning-to-rank-rescorer-limitations-pagination]
+### Rescore window size [learning-to-rank-rescorer-limitations-window-size]
 
-When exposing pagination to users, `window_size` should remain constant as each page is progressed by passing different `from` values. Changing the `window_size` can alter the top hits causing results to confusingly shift as the user steps through pages.
+Scores returned by LTR models are usually not comparable with the scores issued by the first pass query and can be lower than the non-rescored score. This can cause the non-rescored result document to be ranked higher than the rescored document. To prevent this, the `window_size` parameter is mandatory for LTR rescorers and should be greater than or equal to `from + size`.
 
 
-#### Negative scores [learning-to-rank-rescorer-limitations-negative-scores]
+### Pagination [learning-to-rank-rescorer-limitations-pagination]
 
-Depending on how your model is trained, it’s possible that the model will return negative scores for documents. While negative scores are not allowed from first-stage retrieval and ranking, it is possible to use them in the LTR rescorer.
+When exposing pagination to users, `window_size` should remain constant as each page is progressed by passing different `from` values. Changing the `window_size` can alter the top hits causing results to confusingly shift as the user steps through pages.