Term Stats documentation (#115933) (#116167)

* Term Stats documentation

* Update docs/reference/reranking/learning-to-rank-model-training.asciidoc

Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>

* Fix query example.

---------

Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
(cherry picked from commit 0416812456af0a763a5f43f9ab6813221ea6e4d8)

Co-authored-by: Aurélien FOUCRET <aurelien.foucret@gmail.com>

docs/reference/query-dsl/script-score-query.asciidoc

@@ -62,10 +62,17 @@ multiplied by `boost` to produce final documents' scores. Defaults to `1.0`.
 ===== Use relevance scores in a script
 
 Within a script, you can
-{ref}/modules-scripting-fields.html#scripting-score[access] 
+{ref}/modules-scripting-fields.html#scripting-score[access]
 the `_score` variable which represents the current relevance score of a
 document.
 
+[[script-score-access-term-statistics]]
+===== Use term statistics in a script
+
+Within a script, you can
+{ref}/modules-scripting-fields.html#scripting-term-statistics[access]
+the `_termStats` variable which provides statistical information about the terms used in the child query of the `script_score` query.
+
 [[script-score-predefined-functions]]
 ===== Predefined functions
 You can use any of the available {painless}/painless-contexts.html[painless
@@ -147,7 +154,7 @@ updated since update operations also update the value of the `_seq_no` field.
 
 [[decay-functions-numeric-fields]]
 ====== Decay functions for numeric fields
-You can read more about decay functions 
+You can read more about decay functions
 {ref}/query-dsl-function-score-query.html#function-decay[here].
 
 * `double decayNumericLinear(double origin, double scale, double offset, double decay, double docValue)`
@@ -233,7 +240,7 @@ The `script_score` query calculates the score for
 every matching document, or hit. There are faster alternative query types that
 can efficiently skip non-competitive hits:
 
-* If you want to boost documents on some static fields, use the 
+* If you want to boost documents on some static fields, use the
  <<query-dsl-rank-feature-query, `rank_feature`>> query.
  * If you want to boost documents closer to a date or geographic point, use the
  <<query-dsl-distance-feature-query, `distance_feature`>> query.

docs/reference/reranking/learning-to-rank-model-training.asciidoc

@@ -38,11 +38,21 @@ Feature extractors are defined using templated queries. https://eland.readthedoc
 from eland.ml.ltr import QueryFeatureExtractor
 
 feature_extractors=[
-    # We want to use the score of the match query for the title field as a feature:
+    # We want to use the BM25 score of the match query for the title field as a feature:
     QueryFeatureExtractor(
         feature_name="title_bm25",
         query={"match": {"title": "{{query}}"}}
     ),
+    # We want to use the the number of matched terms in the title field as a feature:
+    QueryFeatureExtractor(
+        feature_name="title_matched_term_count",
+        query={
+            "script_score": {
+                "query": {"match": {"title": "{{query}}"}},
+                "script": {"source": "return _termStats.matchedTermsCount();"},
+            }
+        },
+    ),
     # We can use a script_score query to get the value
     # of the field rating directly as a feature:
     QueryFeatureExtractor(
@@ -54,19 +64,13 @@ feature_extractors=[
             }
         },
     ),
-    # We can execute a script on the value of the query
-    # and use the return value as a feature:
-    QueryFeatureExtractor(
-        feature_name="query_length",
+    # We extract the number of terms in the query as feature.
+   QueryFeatureExtractor(
+        feature_name="query_term_count",
         query={
             "script_score": {
-                "query": {"match_all": {}},
-                "script": {
-                    "source": "return params['query'].splitOnToken(' ').length;",
-                    "params": {
-                        "query": "{{query}}",
-                    }
-                },
+                "query": {"match": {"title": "{{query}}"}},
+                "script": {"source": "return _termStats.uniqueTermsCount();"},
             }
         },
     ),
@@ -74,6 +78,15 @@ feature_extractors=[
 ----
 // NOTCONSOLE
 
+[NOTE]
+.Tern statistics as features
+===================================================
+
+It is very common for an LTR model to leverage raw term statistics as features.
+To extract this information, you can use the {ref}/modules-scripting-fields.html#scripting-term-statistics[term statistics feature] provided as part of the  <<query-dsl-script-score-query,`script_score`>> query.
+
+===================================================
+
 Once the feature extractors have been defined, they are wrapped in an `eland.ml.ltr.LTRModelConfig` object for use in later training steps:
 
 [source,python]

docs/reference/reranking/learning-to-rank-search-usage.asciidoc

@@ -61,10 +61,3 @@ When exposing pagination to users, `window_size` should remain constant as each
 ====== Negative scores
 
 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.
-
-[discrete]
-[[learning-to-rank-rescorer-limitations-term-statistics]]
-====== Term statistics as features
-
-We do not currently support term statistics as features, however future releases will introduce this capability.
-

docs/reference/scripting/fields.asciidoc

@@ -80,6 +80,79 @@ GET my-index-000001/_search
 }
 -------------------------------------
 
+[discrete]
+[[scripting-term-statistics]]
+=== Accessing term statistics of a document within a script
+
+Scripts used in a <<query-dsl-script-score-query,`script_score`>> query have access to the `_termStats` variable which provides statistical information about the terms in the child query.
+
+In the following example, `_termStats` is used within a <<query-dsl-script-score-query,`script_score`>> query to retrieve the average term frequency for the terms `quick`, `brown`, and `fox` in the `text` field:
+
+[source,console]
+-------------------------------------
+PUT my-index-000001/_doc/1?refresh
+{
+  "text": "quick brown fox"
+}
+
+PUT my-index-000001/_doc/2?refresh
+{
+  "text": "quick fox"
+}
+
+GET my-index-000001/_search
+{
+  "query": {
+    "script_score": {
+      "query": { <1>
+        "match": {
+          "text": "quick brown fox"
+        }
+      },
+      "script": {
+        "source": "_termStats.termFreq().getAverage()" <2>
+      }
+    }
+  }
+}
+-------------------------------------
+
+<1> Child query used to infer the field and the terms considered in term statistics.
+
+<2> The script calculates the average document frequency for the terms in the query using `_termStats`.
+
+`_termStats` provides access to the following functions for working with term statistics:
+
+- `uniqueTermsCount`: Returns the total number of unique terms in the query. This value is the same across all documents.
+- `matchedTermsCount`: Returns the count of query terms that matched within the current document.
+- `docFreq`: Provides document frequency statistics for the terms in the query, indicating how many documents contain each term. This value is consistent across all documents.
+- `totalTermFreq`: Provides the total frequency of terms across all documents, representing how often each term appears in the entire corpus. This value is consistent across all documents.
+- `termFreq`: Returns the frequency of query terms within the current document, showing how often each term appears in that document.
+
+[NOTE]
+.Functions returning aggregated statistics
+===================================================
+
+The `docFreq`, `termFreq` and `totalTermFreq` functions return objects that represent statistics across all terms of the child query.
+
+Statistics provides support for the following methods:
+
+`getAverage()`: Returns the average value of the metric.
+`getMin()`: Returns the minimum value of the metric.
+`getMax()`: Returns the maximum value of the metric.
+`getSum()`: Returns the sum of the metric values.
+`getCount()`: Returns the count of terms included in the metric calculation.
+
+===================================================
+
+
+[NOTE]
+.Painless language required
+===================================================
+
+The `_termStats` variable is only available when using the <<modules-scripting-painless, Painless>> scripting language.
+
+===================================================
 
 [discrete]
 [[modules-scripting-doc-vals]]