Auto-generated API code

Author: elasticmachine

elasticsearch/_async/client/__init__.py

@@ -700,6 +700,7 @@ async def bulk(
           <li>JavaScript: Check out <code>client.helpers.*</code></li>
           <li>.NET: Check out <code>BulkAllObservable</code></li>
           <li>PHP: Check out bulk indexing.</li>
+          <li>Ruby: Check out <code>Elasticsearch::Helpers::BulkHelper</code></li>
           </ul>
           <p><strong>Submitting bulk requests with cURL</strong></p>
           <p>If you're providing text file input to <code>curl</code>, you must use the <code>--data-binary</code> flag instead of plain <code>-d</code>.
@@ -6010,7 +6011,7 @@ async def termvectors(
         doc: t.Optional[t.Mapping[str, t.Any]] = None,
         error_trace: t.Optional[bool] = None,
         field_statistics: t.Optional[bool] = None,
-        fields: t.Optional[t.Union[str, t.Sequence[str]]] = None,
+        fields: t.Optional[t.Sequence[str]] = None,
         filter: t.Optional[t.Mapping[str, t.Any]] = None,
         filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
         human: t.Optional[bool] = None,

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

elasticsearch/_async/client/inference.py

@@ -391,21 +391,23 @@ 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>
           <li>Cohere (<code>completion</code>, <code>rerank</code>, <code>text_embedding</code>)</li>
-          <li>DeepSeek (<code>completion</code>, <code>chat_completion</code>)</li>
+          <li>DeepSeek (<code>chat_completion</code>, <code>completion</code>)</li>
           <li>Elasticsearch (<code>rerank</code>, <code>sparse_embedding</code>, <code>text_embedding</code> - this service is for built-in models and models uploaded through Eland)</li>
           <li>ELSER (<code>sparse_embedding</code>)</li>
           <li>Google AI Studio (<code>completion</code>, <code>text_embedding</code>)</li>
-          <li>Google Vertex AI (<code>rerank</code>, <code>text_embedding</code>)</li>
+          <li>Google Vertex AI (<code>chat_completion</code>, <code>completion</code>, <code>rerank</code>, <code>text_embedding</code>)</li>
           <li>Hugging Face (<code>chat_completion</code>, <code>completion</code>, <code>rerank</code>, <code>text_embedding</code>)</li>
+          <li>JinaAI (<code>rerank</code>, <code>text_embedding</code>)</li>
+          <li>Llama (<code>chat_completion</code>, <code>completion</code>, <code>text_embedding</code>)</li>
           <li>Mistral (<code>chat_completion</code>, <code>completion</code>, <code>text_embedding</code>)</li>
           <li>OpenAI (<code>chat_completion</code>, <code>completion</code>, <code>text_embedding</code>)</li>
-          <li>VoyageAI (<code>text_embedding</code>, <code>rerank</code>)</li>
+          <li>VoyageAI (<code>rerank</code>, <code>text_embedding</code>)</li>
           <li>Watsonx inference integration (<code>text_embedding</code>)</li>
-          <li>JinaAI (<code>text_embedding</code>, <code>rerank</code>)</li>
           </ul>
 
 
@@ -659,6 +661,112 @@ 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",

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,

elasticsearch/_sync/client/__init__.py

@@ -698,6 +698,7 @@ def bulk(
           <li>JavaScript: Check out <code>client.helpers.*</code></li>
           <li>.NET: Check out <code>BulkAllObservable</code></li>
           <li>PHP: Check out bulk indexing.</li>
+          <li>Ruby: Check out <code>Elasticsearch::Helpers::BulkHelper</code></li>
           </ul>
           <p><strong>Submitting bulk requests with cURL</strong></p>
           <p>If you're providing text file input to <code>curl</code>, you must use the <code>--data-binary</code> flag instead of plain <code>-d</code>.
@@ -6008,7 +6009,7 @@ def termvectors(
         doc: t.Optional[t.Mapping[str, t.Any]] = None,
         error_trace: t.Optional[bool] = None,
         field_statistics: t.Optional[bool] = None,
-        fields: t.Optional[t.Union[str, t.Sequence[str]]] = None,
+        fields: t.Optional[t.Sequence[str]] = None,
         filter: t.Optional[t.Mapping[str, t.Any]] = None,
         filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
         human: t.Optional[bool] = None,

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