Auto-generated API code

Author: elasticmachine

elasticsearch/_async/client/logstash.py

@@ -141,7 +141,9 @@ async def put_pipeline(
 
         `<https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-logstash-put-pipeline>`_
 
-        :param id: An identifier for the pipeline.
+        :param id: An identifier for the pipeline. Pipeline IDs must begin with a letter
+            or underscore and contain only letters, underscores, dashes, hyphens and
+            numbers.
         :param pipeline:
         """
         if id in SKIP_IN_PATH:

elasticsearch/_sync/client/logstash.py

@@ -141,7 +141,9 @@ def put_pipeline(
 
         `<https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-logstash-put-pipeline>`_
 
-        :param id: An identifier for the pipeline.
+        :param id: An identifier for the pipeline. Pipeline IDs must begin with a letter
+            or underscore and contain only letters, underscores, dashes, hyphens and
+            numbers.
         :param pipeline:
         """
         if id in SKIP_IN_PATH:

elasticsearch/dsl/aggs.py

@@ -2960,6 +2960,14 @@ class SignificantTerms(Bucket[_R]):
         the foreground sample with a term divided by the number of
         documents in the background with the term.
     :arg script_heuristic: Customized score, implemented via a script.
+    :arg p_value: Significant terms heuristic that calculates the p-value
+        between the term existing in foreground and background sets.  The
+        p-value is the probability of obtaining test results at least as
+        extreme as the results actually observed, under the assumption
+        that the null hypothesis is correct. The p-value is calculated
+        assuming that the foreground set and the background set are
+        independent https://en.wikipedia.org/wiki/Bernoulli_trial, with
+        the null hypothesis that the probabilities are the same.
     :arg shard_min_doc_count: Regulates the certainty a shard has if the
         term should actually be added to the candidate list or not with
         respect to the `min_doc_count`. Terms will only be considered if
@@ -3013,6 +3021,9 @@ def __init__(
         script_heuristic: Union[
             "types.ScriptedHeuristic", Dict[str, Any], "DefaultType"
         ] = DEFAULT,
+        p_value: Union[
+            "types.PValueHeuristic", Dict[str, Any], "DefaultType"
+        ] = DEFAULT,
         shard_min_doc_count: Union[int, "DefaultType"] = DEFAULT,
         shard_size: Union[int, "DefaultType"] = DEFAULT,
         size: Union[int, "DefaultType"] = DEFAULT,
@@ -3031,6 +3042,7 @@ def __init__(
             mutual_information=mutual_information,
             percentage=percentage,
             script_heuristic=script_heuristic,
+            p_value=p_value,
             shard_min_doc_count=shard_min_doc_count,
             shard_size=shard_size,
             size=size,

elasticsearch/dsl/types.py

@@ -2673,6 +2673,31 @@ def __init__(
         super().__init__(kwargs)
 
 
+class PValueHeuristic(AttrDict[Any]):
+    """
+    :arg background_is_superset:
+    :arg normalize_above: Should the results be normalized when above the
+        given value. Allows for consistent significance results at various
+        scales. Note: `0` is a special value which means no normalization
+    """
+
+    background_is_superset: Union[bool, DefaultType]
+    normalize_above: Union[int, DefaultType]
+
+    def __init__(
+        self,
+        *,
+        background_is_superset: Union[bool, DefaultType] = DEFAULT,
+        normalize_above: Union[int, DefaultType] = DEFAULT,
+        **kwargs: Any,
+    ):
+        if background_is_superset is not DEFAULT:
+            kwargs["background_is_superset"] = background_is_superset
+        if normalize_above is not DEFAULT:
+            kwargs["normalize_above"] = normalize_above
+        super().__init__(kwargs)
+
+
 class PercentageScoreHeuristic(AttrDict[Any]):
     pass
 
@@ -3853,24 +3878,25 @@ def __init__(
 
 class TextEmbedding(AttrDict[Any]):
     """
-    :arg model_id: (required)
     :arg model_text: (required)
+    :arg model_id: Model ID is required for all dense_vector fields but
+        may be inferred for semantic_text fields
     """
 
-    model_id: Union[str, DefaultType]
     model_text: Union[str, DefaultType]
+    model_id: Union[str, DefaultType]
 
     def __init__(
         self,
         *,
-        model_id: Union[str, DefaultType] = DEFAULT,
         model_text: Union[str, DefaultType] = DEFAULT,
+        model_id: Union[str, DefaultType] = DEFAULT,
         **kwargs: Any,
     ):
-        if model_id is not DEFAULT:
-            kwargs["model_id"] = model_id
         if model_text is not DEFAULT:
             kwargs["model_text"] = model_text
+        if model_id is not DEFAULT:
+            kwargs["model_id"] = model_id
         super().__init__(kwargs)