Auto-generated API code

elasticsearch/_async/client/inference.py

@@ -36,8 +36,10 @@ async def delete(
                 str,
             ]
         ] = None,
+        dry_run: t.Optional[bool] = None,
         error_trace: t.Optional[bool] = None,
         filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
+        force: t.Optional[bool] = None,
         human: t.Optional[bool] = None,
         pretty: t.Optional[bool] = None,
     ) -> ObjectApiResponse[t.Any]:
@@ -48,6 +50,10 @@ async def delete(
 
         :param inference_id: The inference Id
         :param task_type: The task type
+        :param dry_run: When true, the endpoint is not deleted, and a list of ingest
+            processors which reference this endpoint is returned
+        :param force: When true, the inference endpoint is forcefully deleted even if
+            it is still being used by ingest processors or semantic text fields
         """
         if inference_id in SKIP_IN_PATH:
             raise ValueError("Empty value passed for parameter 'inference_id'")
@@ -64,10 +70,14 @@ async def delete(
         else:
             raise ValueError("Couldn't find a path for the given parameters")
         __query: t.Dict[str, t.Any] = {}
+        if dry_run is not None:
+            __query["dry_run"] = dry_run
         if error_trace is not None:
             __query["error_trace"] = error_trace
         if filter_path is not None:
             __query["filter_path"] = filter_path
+        if force is not None:
+            __query["force"] = force
         if human is not None:
             __query["human"] = human
         if pretty is not None:

elasticsearch/_async/client/ml.py

@@ -5088,7 +5088,11 @@ async def update_trained_model_deployment(
         if not __body:
             if number_of_allocations is not None:
                 __body["number_of_allocations"] = number_of_allocations
-        __headers = {"accept": "application/json", "content-type": "application/json"}
+        if not __body:
+            __body = None  # type: ignore[assignment]
+        __headers = {"accept": "application/json"}
+        if __body is not None:
+            __headers["content-type"] = "application/json"
         return await self.perform_request(  # type: ignore[return-value]
             "POST",
             __path,

elasticsearch/_async/client/security.py

@@ -2122,6 +2122,9 @@ async def put_role(
             this operation visible to search, if `wait_for` then wait for a refresh to
             make this operation visible to search, if `false` then do nothing with refreshes.
         :param run_as: A list of users that the owners of this role can impersonate.
+            *Note*: in Serverless, the run-as feature is disabled. For API compatibility,
+            you can still specify an empty `run_as` field, but a non-empty list will
+            be rejected.
         :param transient_metadata: Indicates roles that might be incompatible with the
             current cluster license, specifically roles with document and field level
             security. When the cluster license doesn’t allow certain features for a given

elasticsearch/_sync/client/inference.py

@@ -36,8 +36,10 @@ def delete(
                 str,
             ]
         ] = None,
+        dry_run: t.Optional[bool] = None,
         error_trace: t.Optional[bool] = None,
         filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
+        force: t.Optional[bool] = None,
         human: t.Optional[bool] = None,
         pretty: t.Optional[bool] = None,
     ) -> ObjectApiResponse[t.Any]:
@@ -48,6 +50,10 @@ def delete(
 
         :param inference_id: The inference Id
         :param task_type: The task type
+        :param dry_run: When true, the endpoint is not deleted, and a list of ingest
+            processors which reference this endpoint is returned
+        :param force: When true, the inference endpoint is forcefully deleted even if
+            it is still being used by ingest processors or semantic text fields
         """
         if inference_id in SKIP_IN_PATH:
             raise ValueError("Empty value passed for parameter 'inference_id'")
@@ -64,10 +70,14 @@ def delete(
         else:
             raise ValueError("Couldn't find a path for the given parameters")
         __query: t.Dict[str, t.Any] = {}
+        if dry_run is not None:
+            __query["dry_run"] = dry_run
         if error_trace is not None:
             __query["error_trace"] = error_trace
         if filter_path is not None:
             __query["filter_path"] = filter_path
+        if force is not None:
+            __query["force"] = force
         if human is not None:
             __query["human"] = human
         if pretty is not None:

elasticsearch/_sync/client/ml.py

@@ -5088,7 +5088,11 @@ def update_trained_model_deployment(
         if not __body:
             if number_of_allocations is not None:
                 __body["number_of_allocations"] = number_of_allocations
-        __headers = {"accept": "application/json", "content-type": "application/json"}
+        if not __body:
+            __body = None  # type: ignore[assignment]
+        __headers = {"accept": "application/json"}
+        if __body is not None:
+            __headers["content-type"] = "application/json"
         return self.perform_request(  # type: ignore[return-value]
             "POST",
             __path,

elasticsearch/_sync/client/security.py

@@ -2122,6 +2122,9 @@ def put_role(
             this operation visible to search, if `wait_for` then wait for a refresh to
             make this operation visible to search, if `false` then do nothing with refreshes.
         :param run_as: A list of users that the owners of this role can impersonate.
+            *Note*: in Serverless, the run-as feature is disabled. For API compatibility,
+            you can still specify an empty `run_as` field, but a non-empty list will
+            be rejected.
         :param transient_metadata: Indicates roles that might be incompatible with the
             current cluster license, specifically roles with document and field level
             security. When the cluster license doesn’t allow certain features for a given