From 8bc55b4bbca7542b07ba2f88f4becfefed5c3f0a Mon Sep 17 00:00:00 2001
From: Elastic Machine <elasticmachine@users.noreply.github.com>
Date: Mon, 4 Aug 2025 06:06:03 +0000
Subject: [PATCH] Auto-generated API code

---
 elasticsearch/_async/client/esql.py      |  11 +-
 elasticsearch/_async/client/inference.py | 107 ++++++++++++++++++++
 elasticsearch/_async/client/sql.py       |   2 +-
 elasticsearch/_sync/client/esql.py       |  11 +-
 elasticsearch/_sync/client/inference.py  | 107 ++++++++++++++++++++
 elasticsearch/_sync/client/sql.py        |   2 +-
 elasticsearch/dsl/aggs.py                |  20 ++++
 elasticsearch/dsl/field.py               |  26 +++++
 elasticsearch/dsl/types.py               | 123 ++++++++++++++++++++++-
 9 files changed, 400 insertions(+), 9 deletions(-)

diff --git a/elasticsearch/_async/client/esql.py b/elasticsearch/_async/client/esql.py
index bca1e4255..7ecdd5dae 100644
--- a/elasticsearch/_async/client/esql.py
+++ b/elasticsearch/_async/client/esql.py
@@ -107,7 +107,12 @@ async def async_query(
             which has the name of all the columns.
         :param filter: Specify a Query DSL query in the filter parameter to filter the
             set of documents that an ES|QL query runs on.
-        :param format: A short version of the Accept header, for example `json` or `yaml`.
+        :param format: A short version of the Accept header, e.g. json, yaml. `csv`,
+            `tsv`, and `txt` formats will return results in a tabular format, excluding
+            other metadata fields from the response. For async requests, nothing will
+            be returned if the async query doesn't finish within the timeout. The query
+            ID and running status are available in the `X-Elasticsearch-Async-Id` and
+            `X-Elasticsearch-Async-Is-Running` HTTP headers of the response, respectively.
         :param include_ccs_metadata: When set to `true` and performing a cross-cluster
             query, the response will include an extra `_clusters` object with information
             about the clusters that participated in the search along with info such as
@@ -456,7 +461,9 @@ async def query(
             `all_columns` which has the name of all columns.
         :param filter: Specify a Query DSL query in the filter parameter to filter the
             set of documents that an ES|QL query runs on.
-        :param format: A short version of the Accept header, e.g. json, yaml.
+        :param format: A short version of the Accept header, e.g. json, yaml. `csv`,
+            `tsv`, and `txt` formats will return results in a tabular format, excluding
+            other metadata fields from the response.
         :param include_ccs_metadata: When set to `true` and performing a cross-cluster
             query, the response will include an extra `_clusters` object with information
             about the clusters that participated in the search along with info such as
diff --git a/elasticsearch/_async/client/inference.py b/elasticsearch/_async/client/inference.py
index 0083677f4..f14f08142 100644
--- a/elasticsearch/_async/client/inference.py
+++ b/elasticsearch/_async/client/inference.py
@@ -391,6 +391,7 @@ async def put(
           <ul>
           <li>AlibabaCloud AI Search (<code>completion</code>, <code>rerank</code>, <code>sparse_embedding</code>, <code>text_embedding</code>)</li>
           <li>Amazon Bedrock (<code>completion</code>, <code>text_embedding</code>)</li>
+          <li>Amazon SageMaker (<code>chat_completion</code>, <code>completion</code>, <code>rerank</code>, <code>sparse_embedding</code>, <code>text_embedding</code>)</li>
           <li>Anthropic (<code>completion</code>)</li>
           <li>Azure AI Studio (<code>completion</code>, <code>text_embedding</code>)</li>
           <li>Azure OpenAI (<code>completion</code>, <code>text_embedding</code>)</li>
@@ -659,6 +660,112 @@ async def put_amazonbedrock(
             path_parts=__path_parts,
         )
 
+    @_rewrite_parameters(
+        body_fields=(
+            "service",
+            "service_settings",
+            "chunking_settings",
+            "task_settings",
+        ),
+    )
+    async def put_amazonsagemaker(
+        self,
+        *,
+        task_type: t.Union[
+            str,
+            t.Literal[
+                "chat_completion",
+                "completion",
+                "rerank",
+                "sparse_embedding",
+                "text_embedding",
+            ],
+        ],
+        amazonsagemaker_inference_id: str,
+        service: t.Optional[t.Union[str, t.Literal["amazon_sagemaker"]]] = None,
+        service_settings: t.Optional[t.Mapping[str, t.Any]] = None,
+        chunking_settings: t.Optional[t.Mapping[str, t.Any]] = None,
+        error_trace: t.Optional[bool] = None,
+        filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
+        human: t.Optional[bool] = None,
+        pretty: t.Optional[bool] = None,
+        task_settings: t.Optional[t.Mapping[str, t.Any]] = None,
+        timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
+        body: t.Optional[t.Dict[str, t.Any]] = None,
+    ) -> ObjectApiResponse[t.Any]:
+        """
+        .. raw:: html
+
+          <p>Create an Amazon SageMaker inference endpoint.</p>
+          <p>Create an inference endpoint to perform an inference task with the <code>amazon_sagemaker</code> service.</p>
+
+
+        `<https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-amazonsagemaker>`_
+
+        :param task_type: The type of the inference task that the model will perform.
+        :param amazonsagemaker_inference_id: The unique identifier of the inference endpoint.
+        :param service: The type of service supported for the specified task type. In
+            this case, `amazon_sagemaker`.
+        :param service_settings: Settings used to install the inference model. These
+            settings are specific to the `amazon_sagemaker` service and `service_settings.api`
+            you specified.
+        :param chunking_settings: The chunking configuration object.
+        :param task_settings: Settings to configure the inference task. These settings
+            are specific to the task type and `service_settings.api` you specified.
+        :param timeout: Specifies the amount of time to wait for the inference endpoint
+            to be created.
+        """
+        if task_type in SKIP_IN_PATH:
+            raise ValueError("Empty value passed for parameter 'task_type'")
+        if amazonsagemaker_inference_id in SKIP_IN_PATH:
+            raise ValueError(
+                "Empty value passed for parameter 'amazonsagemaker_inference_id'"
+            )
+        if service is None and body is None:
+            raise ValueError("Empty value passed for parameter 'service'")
+        if service_settings is None and body is None:
+            raise ValueError("Empty value passed for parameter 'service_settings'")
+        __path_parts: t.Dict[str, str] = {
+            "task_type": _quote(task_type),
+            "amazonsagemaker_inference_id": _quote(amazonsagemaker_inference_id),
+        }
+        __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["amazonsagemaker_inference_id"]}'
+        __query: t.Dict[str, t.Any] = {}
+        __body: t.Dict[str, t.Any] = body if body is not None else {}
+        if error_trace is not None:
+            __query["error_trace"] = error_trace
+        if filter_path is not None:
+            __query["filter_path"] = filter_path
+        if human is not None:
+            __query["human"] = human
+        if pretty is not None:
+            __query["pretty"] = pretty
+        if timeout is not None:
+            __query["timeout"] = timeout
+        if not __body:
+            if service is not None:
+                __body["service"] = service
+            if service_settings is not None:
+                __body["service_settings"] = service_settings
+            if chunking_settings is not None:
+                __body["chunking_settings"] = chunking_settings
+            if task_settings is not None:
+                __body["task_settings"] = task_settings
+        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]
+            "PUT",
+            __path,
+            params=__query,
+            headers=__headers,
+            body=__body,
+            endpoint_id="inference.put_amazonsagemaker",
+            path_parts=__path_parts,
+        )
+
     @_rewrite_parameters(
         body_fields=(
             "service",
diff --git a/elasticsearch/_async/client/sql.py b/elasticsearch/_async/client/sql.py
index 1763739c5..a744af06c 100644
--- a/elasticsearch/_async/client/sql.py
+++ b/elasticsearch/_async/client/sql.py
@@ -283,7 +283,7 @@ async def query(
         keep_alive: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
         keep_on_completion: t.Optional[bool] = None,
         page_timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
-        params: t.Optional[t.Mapping[str, t.Any]] = None,
+        params: t.Optional[t.Sequence[t.Any]] = None,
         pretty: t.Optional[bool] = None,
         query: t.Optional[str] = None,
         request_timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
diff --git a/elasticsearch/_sync/client/esql.py b/elasticsearch/_sync/client/esql.py
index e34a26fb8..3aa3aed97 100644
--- a/elasticsearch/_sync/client/esql.py
+++ b/elasticsearch/_sync/client/esql.py
@@ -107,7 +107,12 @@ def async_query(
             which has the name of all the columns.
         :param filter: Specify a Query DSL query in the filter parameter to filter the
             set of documents that an ES|QL query runs on.
-        :param format: A short version of the Accept header, for example `json` or `yaml`.
+        :param format: A short version of the Accept header, e.g. json, yaml. `csv`,
+            `tsv`, and `txt` formats will return results in a tabular format, excluding
+            other metadata fields from the response. For async requests, nothing will
+            be returned if the async query doesn't finish within the timeout. The query
+            ID and running status are available in the `X-Elasticsearch-Async-Id` and
+            `X-Elasticsearch-Async-Is-Running` HTTP headers of the response, respectively.
         :param include_ccs_metadata: When set to `true` and performing a cross-cluster
             query, the response will include an extra `_clusters` object with information
             about the clusters that participated in the search along with info such as
@@ -456,7 +461,9 @@ def query(
             `all_columns` which has the name of all columns.
         :param filter: Specify a Query DSL query in the filter parameter to filter the
             set of documents that an ES|QL query runs on.
-        :param format: A short version of the Accept header, e.g. json, yaml.
+        :param format: A short version of the Accept header, e.g. json, yaml. `csv`,
+            `tsv`, and `txt` formats will return results in a tabular format, excluding
+            other metadata fields from the response.
         :param include_ccs_metadata: When set to `true` and performing a cross-cluster
             query, the response will include an extra `_clusters` object with information
             about the clusters that participated in the search along with info such as
diff --git a/elasticsearch/_sync/client/inference.py b/elasticsearch/_sync/client/inference.py
index 1478883e8..e932789b9 100644
--- a/elasticsearch/_sync/client/inference.py
+++ b/elasticsearch/_sync/client/inference.py
@@ -391,6 +391,7 @@ def put(
           <ul>
           <li>AlibabaCloud AI Search (<code>completion</code>, <code>rerank</code>, <code>sparse_embedding</code>, <code>text_embedding</code>)</li>
           <li>Amazon Bedrock (<code>completion</code>, <code>text_embedding</code>)</li>
+          <li>Amazon SageMaker (<code>chat_completion</code>, <code>completion</code>, <code>rerank</code>, <code>sparse_embedding</code>, <code>text_embedding</code>)</li>
           <li>Anthropic (<code>completion</code>)</li>
           <li>Azure AI Studio (<code>completion</code>, <code>text_embedding</code>)</li>
           <li>Azure OpenAI (<code>completion</code>, <code>text_embedding</code>)</li>
@@ -659,6 +660,112 @@ def put_amazonbedrock(
             path_parts=__path_parts,
         )
 
+    @_rewrite_parameters(
+        body_fields=(
+            "service",
+            "service_settings",
+            "chunking_settings",
+            "task_settings",
+        ),
+    )
+    def put_amazonsagemaker(
+        self,
+        *,
+        task_type: t.Union[
+            str,
+            t.Literal[
+                "chat_completion",
+                "completion",
+                "rerank",
+                "sparse_embedding",
+                "text_embedding",
+            ],
+        ],
+        amazonsagemaker_inference_id: str,
+        service: t.Optional[t.Union[str, t.Literal["amazon_sagemaker"]]] = None,
+        service_settings: t.Optional[t.Mapping[str, t.Any]] = None,
+        chunking_settings: t.Optional[t.Mapping[str, t.Any]] = None,
+        error_trace: t.Optional[bool] = None,
+        filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
+        human: t.Optional[bool] = None,
+        pretty: t.Optional[bool] = None,
+        task_settings: t.Optional[t.Mapping[str, t.Any]] = None,
+        timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
+        body: t.Optional[t.Dict[str, t.Any]] = None,
+    ) -> ObjectApiResponse[t.Any]:
+        """
+        .. raw:: html
+
+          <p>Create an Amazon SageMaker inference endpoint.</p>
+          <p>Create an inference endpoint to perform an inference task with the <code>amazon_sagemaker</code> service.</p>
+
+
+        `<https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-amazonsagemaker>`_
+
+        :param task_type: The type of the inference task that the model will perform.
+        :param amazonsagemaker_inference_id: The unique identifier of the inference endpoint.
+        :param service: The type of service supported for the specified task type. In
+            this case, `amazon_sagemaker`.
+        :param service_settings: Settings used to install the inference model. These
+            settings are specific to the `amazon_sagemaker` service and `service_settings.api`
+            you specified.
+        :param chunking_settings: The chunking configuration object.
+        :param task_settings: Settings to configure the inference task. These settings
+            are specific to the task type and `service_settings.api` you specified.
+        :param timeout: Specifies the amount of time to wait for the inference endpoint
+            to be created.
+        """
+        if task_type in SKIP_IN_PATH:
+            raise ValueError("Empty value passed for parameter 'task_type'")
+        if amazonsagemaker_inference_id in SKIP_IN_PATH:
+            raise ValueError(
+                "Empty value passed for parameter 'amazonsagemaker_inference_id'"
+            )
+        if service is None and body is None:
+            raise ValueError("Empty value passed for parameter 'service'")
+        if service_settings is None and body is None:
+            raise ValueError("Empty value passed for parameter 'service_settings'")
+        __path_parts: t.Dict[str, str] = {
+            "task_type": _quote(task_type),
+            "amazonsagemaker_inference_id": _quote(amazonsagemaker_inference_id),
+        }
+        __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["amazonsagemaker_inference_id"]}'
+        __query: t.Dict[str, t.Any] = {}
+        __body: t.Dict[str, t.Any] = body if body is not None else {}
+        if error_trace is not None:
+            __query["error_trace"] = error_trace
+        if filter_path is not None:
+            __query["filter_path"] = filter_path
+        if human is not None:
+            __query["human"] = human
+        if pretty is not None:
+            __query["pretty"] = pretty
+        if timeout is not None:
+            __query["timeout"] = timeout
+        if not __body:
+            if service is not None:
+                __body["service"] = service
+            if service_settings is not None:
+                __body["service_settings"] = service_settings
+            if chunking_settings is not None:
+                __body["chunking_settings"] = chunking_settings
+            if task_settings is not None:
+                __body["task_settings"] = task_settings
+        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]
+            "PUT",
+            __path,
+            params=__query,
+            headers=__headers,
+            body=__body,
+            endpoint_id="inference.put_amazonsagemaker",
+            path_parts=__path_parts,
+        )
+
     @_rewrite_parameters(
         body_fields=(
             "service",
diff --git a/elasticsearch/_sync/client/sql.py b/elasticsearch/_sync/client/sql.py
index cde458be5..094a1b016 100644
--- a/elasticsearch/_sync/client/sql.py
+++ b/elasticsearch/_sync/client/sql.py
@@ -283,7 +283,7 @@ def query(
         keep_alive: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
         keep_on_completion: t.Optional[bool] = None,
         page_timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
-        params: t.Optional[t.Mapping[str, t.Any]] = None,
+        params: t.Optional[t.Sequence[t.Any]] = None,
         pretty: t.Optional[bool] = None,
         query: t.Optional[str] = None,
         request_timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
diff --git a/elasticsearch/dsl/aggs.py b/elasticsearch/dsl/aggs.py
index 3b7a8e8ba..7f1019b56 100644
--- a/elasticsearch/dsl/aggs.py
+++ b/elasticsearch/dsl/aggs.py
@@ -372,6 +372,12 @@ class Boxplot(Agg[_R]):
     :arg compression: Limits the maximum number of nodes used by the
         underlying TDigest algorithm to `20 * compression`, enabling
         control of memory usage and approximation error.
+    :arg execution_hint: The default implementation of TDigest is
+        optimized for performance, scaling to millions or even billions of
+        sample values while maintaining acceptable accuracy levels (close
+        to 1% relative error for millions of samples in some cases). To
+        use an implementation optimized for accuracy, set this parameter
+        to high_accuracy instead. Defaults to `default` if omitted.
     :arg field: The field on which to run the aggregation.
     :arg missing: The value to apply to documents that do not have a
         value. By default, documents without a value are ignored.
@@ -384,6 +390,9 @@ def __init__(
         self,
         *,
         compression: Union[float, "DefaultType"] = DEFAULT,
+        execution_hint: Union[
+            Literal["default", "high_accuracy"], "DefaultType"
+        ] = DEFAULT,
         field: Union[str, "InstrumentedField", "DefaultType"] = DEFAULT,
         missing: Union[str, int, float, bool, "DefaultType"] = DEFAULT,
         script: Union["types.Script", Dict[str, Any], "DefaultType"] = DEFAULT,
@@ -391,6 +400,7 @@ def __init__(
     ):
         super().__init__(
             compression=compression,
+            execution_hint=execution_hint,
             field=field,
             missing=missing,
             script=script,
@@ -1897,6 +1907,12 @@ class MedianAbsoluteDeviation(Agg[_R]):
         underlying TDigest algorithm to `20 * compression`, enabling
         control of memory usage and approximation error. Defaults to
         `1000` if omitted.
+    :arg execution_hint: The default implementation of TDigest is
+        optimized for performance, scaling to millions or even billions of
+        sample values while maintaining acceptable accuracy levels (close
+        to 1% relative error for millions of samples in some cases). To
+        use an implementation optimized for accuracy, set this parameter
+        to high_accuracy instead. Defaults to `default` if omitted.
     :arg format:
     :arg field: The field on which to run the aggregation.
     :arg missing: The value to apply to documents that do not have a
@@ -1910,6 +1926,9 @@ def __init__(
         self,
         *,
         compression: Union[float, "DefaultType"] = DEFAULT,
+        execution_hint: Union[
+            Literal["default", "high_accuracy"], "DefaultType"
+        ] = DEFAULT,
         format: Union[str, "DefaultType"] = DEFAULT,
         field: Union[str, "InstrumentedField", "DefaultType"] = DEFAULT,
         missing: Union[str, int, float, bool, "DefaultType"] = DEFAULT,
@@ -1918,6 +1937,7 @@ def __init__(
     ):
         super().__init__(
             compression=compression,
+            execution_hint=execution_hint,
             format=format,
             field=field,
             missing=missing,
diff --git a/elasticsearch/dsl/field.py b/elasticsearch/dsl/field.py
index 1aa7a4bca..c33261458 100644
--- a/elasticsearch/dsl/field.py
+++ b/elasticsearch/dsl/field.py
@@ -3849,6 +3849,14 @@ class SemanticText(Field):
         by using the Update mapping API. Use the Create inference API to
         create the endpoint. If not specified, the inference endpoint
         defined by inference_id will be used at both index and query time.
+    :arg index_options: Settings for index_options that override any
+        defaults used by semantic_text, for example specific quantization
+        settings.
+    :arg chunking_settings: Settings for chunking text into smaller
+        passages. If specified, these will override the chunking settings
+        sent in the inference endpoint associated with inference_id. If
+        chunking settings are updated, they will not be applied to
+        existing documents until they are reindexed.
     """
 
     name = "semantic_text"
@@ -3859,6 +3867,12 @@ def __init__(
         meta: Union[Mapping[str, str], "DefaultType"] = DEFAULT,
         inference_id: Union[str, "DefaultType"] = DEFAULT,
         search_inference_id: Union[str, "DefaultType"] = DEFAULT,
+        index_options: Union[
+            "types.SemanticTextIndexOptions", Dict[str, Any], "DefaultType"
+        ] = DEFAULT,
+        chunking_settings: Union[
+            "types.ChunkingSettings", Dict[str, Any], "DefaultType"
+        ] = DEFAULT,
         **kwargs: Any,
     ):
         if meta is not DEFAULT:
@@ -3867,6 +3881,10 @@ def __init__(
             kwargs["inference_id"] = inference_id
         if search_inference_id is not DEFAULT:
             kwargs["search_inference_id"] = search_inference_id
+        if index_options is not DEFAULT:
+            kwargs["index_options"] = index_options
+        if chunking_settings is not DEFAULT:
+            kwargs["chunking_settings"] = chunking_settings
         super().__init__(*args, **kwargs)
 
 
@@ -4063,6 +4081,9 @@ def __init__(
 class SparseVector(Field):
     """
     :arg store:
+    :arg index_options: Additional index options for the sparse vector
+        field that controls the token pruning behavior of the sparse
+        vector field.
     :arg meta: Metadata about the field.
     :arg properties:
     :arg ignore_above:
@@ -4081,6 +4102,9 @@ def __init__(
         self,
         *args: Any,
         store: Union[bool, "DefaultType"] = DEFAULT,
+        index_options: Union[
+            "types.SparseVectorIndexOptions", Dict[str, Any], "DefaultType"
+        ] = DEFAULT,
         meta: Union[Mapping[str, str], "DefaultType"] = DEFAULT,
         properties: Union[Mapping[str, Field], "DefaultType"] = DEFAULT,
         ignore_above: Union[int, "DefaultType"] = DEFAULT,
@@ -4095,6 +4119,8 @@ def __init__(
     ):
         if store is not DEFAULT:
             kwargs["store"] = store
+        if index_options is not DEFAULT:
+            kwargs["index_options"] = index_options
         if meta is not DEFAULT:
             kwargs["meta"] = meta
         if properties is not DEFAULT:
diff --git a/elasticsearch/dsl/types.py b/elasticsearch/dsl/types.py
index 2e616f644..3d39a8023 100644
--- a/elasticsearch/dsl/types.py
+++ b/elasticsearch/dsl/types.py
@@ -170,6 +170,48 @@ def __init__(
         super().__init__(kwargs)
 
 
+class ChunkingSettings(AttrDict[Any]):
+    """
+    :arg strategy: (required) The chunking strategy: `sentence` or `word`.
+        Defaults to `sentence` if omitted.
+    :arg max_chunk_size: (required) The maximum size of a chunk in words.
+        This value cannot be higher than `300` or lower than `20` (for
+        `sentence` strategy) or `10` (for `word` strategy). Defaults to
+        `250` if omitted.
+    :arg overlap: The number of overlapping words for chunks. It is
+        applicable only to a `word` chunking strategy. This value cannot
+        be higher than half the `max_chunk_size` value. Defaults to `100`
+        if omitted.
+    :arg sentence_overlap: The number of overlapping sentences for chunks.
+        It is applicable only for a `sentence` chunking strategy. It can
+        be either `1` or `0`. Defaults to `1` if omitted.
+    """
+
+    strategy: Union[str, DefaultType]
+    max_chunk_size: Union[int, DefaultType]
+    overlap: Union[int, DefaultType]
+    sentence_overlap: Union[int, DefaultType]
+
+    def __init__(
+        self,
+        *,
+        strategy: Union[str, DefaultType] = DEFAULT,
+        max_chunk_size: Union[int, DefaultType] = DEFAULT,
+        overlap: Union[int, DefaultType] = DEFAULT,
+        sentence_overlap: Union[int, DefaultType] = DEFAULT,
+        **kwargs: Any,
+    ):
+        if strategy is not DEFAULT:
+            kwargs["strategy"] = strategy
+        if max_chunk_size is not DEFAULT:
+            kwargs["max_chunk_size"] = max_chunk_size
+        if overlap is not DEFAULT:
+            kwargs["overlap"] = overlap
+        if sentence_overlap is not DEFAULT:
+            kwargs["sentence_overlap"] = sentence_overlap
+        super().__init__(kwargs)
+
+
 class ClassificationInferenceOptions(AttrDict[Any]):
     """
     :arg num_top_classes: Specifies the number of top class predictions to
@@ -3119,6 +3161,26 @@ def __init__(
         super().__init__(kwargs)
 
 
+class SemanticTextIndexOptions(AttrDict[Any]):
+    """
+    :arg dense_vector:
+    """
+
+    dense_vector: Union["DenseVectorIndexOptions", Dict[str, Any], DefaultType]
+
+    def __init__(
+        self,
+        *,
+        dense_vector: Union[
+            "DenseVectorIndexOptions", Dict[str, Any], DefaultType
+        ] = DEFAULT,
+        **kwargs: Any,
+    ):
+        if dense_vector is not DEFAULT:
+            kwargs["dense_vector"] = dense_vector
+        super().__init__(kwargs)
+
+
 class ShapeFieldQuery(AttrDict[Any]):
     """
     :arg indexed_shape: Queries using a pre-indexed shape.
@@ -3196,10 +3258,15 @@ def __init__(
 
 class SourceFilter(AttrDict[Any]):
     """
-    :arg excludes:
-    :arg includes:
+    :arg exclude_vectors: If `true`, vector fields are excluded from the
+        returned source.  This option takes precedence over `includes`:
+        any vector field will remain excluded even if it matches an
+        `includes` rule.
+    :arg excludes: A list of fields to exclude from the returned source.
+    :arg includes: A list of fields to include in the returned source.
     """
 
+    exclude_vectors: Union[bool, DefaultType]
     excludes: Union[
         Union[str, InstrumentedField],
         Sequence[Union[str, InstrumentedField]],
@@ -3214,6 +3281,7 @@ class SourceFilter(AttrDict[Any]):
     def __init__(
         self,
         *,
+        exclude_vectors: Union[bool, DefaultType] = DEFAULT,
         excludes: Union[
             Union[str, InstrumentedField],
             Sequence[Union[str, InstrumentedField]],
@@ -3226,6 +3294,8 @@ def __init__(
         ] = DEFAULT,
         **kwargs: Any,
     ):
+        if exclude_vectors is not DEFAULT:
+            kwargs["exclude_vectors"] = exclude_vectors
         if excludes is not DEFAULT:
             kwargs["excludes"] = str(excludes)
         if includes is not DEFAULT:
@@ -3675,6 +3745,38 @@ def __init__(
         super().__init__(kwargs)
 
 
+class SparseVectorIndexOptions(AttrDict[Any]):
+    """
+    :arg prune: Whether to perform pruning, omitting the non-significant
+        tokens from the query to improve query performance. If prune is
+        true but the pruning_config is not specified, pruning will occur
+        but default values will be used. Default: false
+    :arg pruning_config: Optional pruning configuration. If enabled, this
+        will omit non-significant tokens from the query in order to
+        improve query performance. This is only used if prune is set to
+        true. If prune is set to true but pruning_config is not specified,
+        default values will be used.
+    """
+
+    prune: Union[bool, DefaultType]
+    pruning_config: Union["TokenPruningConfig", Dict[str, Any], DefaultType]
+
+    def __init__(
+        self,
+        *,
+        prune: Union[bool, DefaultType] = DEFAULT,
+        pruning_config: Union[
+            "TokenPruningConfig", Dict[str, Any], DefaultType
+        ] = DEFAULT,
+        **kwargs: Any,
+    ):
+        if prune is not DEFAULT:
+            kwargs["prune"] = prune
+        if pruning_config is not DEFAULT:
+            kwargs["pruning_config"] = pruning_config
+        super().__init__(kwargs)
+
+
 class SuggestContext(AttrDict[Any]):
     """
     :arg name: (required)
@@ -3713,15 +3815,30 @@ class TDigest(AttrDict[Any]):
     :arg compression: Limits the maximum number of nodes used by the
         underlying TDigest algorithm to `20 * compression`, enabling
         control of memory usage and approximation error.
+    :arg execution_hint: The default implementation of TDigest is
+        optimized for performance, scaling to millions or even billions of
+        sample values while maintaining acceptable accuracy levels (close
+        to 1% relative error for millions of samples in some cases). To
+        use an implementation optimized for accuracy, set this parameter
+        to high_accuracy instead. Defaults to `default` if omitted.
     """
 
     compression: Union[int, DefaultType]
+    execution_hint: Union[Literal["default", "high_accuracy"], DefaultType]
 
     def __init__(
-        self, *, compression: Union[int, DefaultType] = DEFAULT, **kwargs: Any
+        self,
+        *,
+        compression: Union[int, DefaultType] = DEFAULT,
+        execution_hint: Union[
+            Literal["default", "high_accuracy"], DefaultType
+        ] = DEFAULT,
+        **kwargs: Any,
     ):
         if compression is not DEFAULT:
             kwargs["compression"] = compression
+        if execution_hint is not DEFAULT:
+            kwargs["execution_hint"] = execution_hint
         super().__init__(kwargs)