Auto-generated API code

Author: elasticmachine

elasticsearch/_async/client/__init__.py

@@ -1121,12 +1121,17 @@ async def create(
         error_trace: t.Optional[bool] = None,
         filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
         human: t.Optional[bool] = None,
+        if_primary_term: t.Optional[int] = None,
+        if_seq_no: t.Optional[int] = None,
         include_source_on_error: t.Optional[bool] = None,
+        op_type: t.Optional[t.Union[str, t.Literal["create", "index"]]] = None,
         pipeline: t.Optional[str] = None,
         pretty: t.Optional[bool] = None,
         refresh: t.Optional[
             t.Union[bool, str, t.Literal["false", "true", "wait_for"]]
         ] = None,
+        require_alias: t.Optional[bool] = None,
+        require_data_stream: t.Optional[bool] = None,
         routing: t.Optional[str] = None,
         timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
         version: t.Optional[int] = None,
@@ -1204,8 +1209,18 @@ async def create(
         :param id: A unique identifier for the document. To automatically generate a
             document ID, use the `POST /<target>/_doc/` request format.
         :param document:
+        :param if_primary_term: Only perform the operation if the document has this primary
+            term.
+        :param if_seq_no: Only perform the operation if the document has this sequence
+            number.
         :param include_source_on_error: True or false if to include the document source
             in the error message in case of parsing errors.
+        :param op_type: Set to `create` to only index the document if it does not already
+            exist (put if absent). If a document with the specified `_id` already exists,
+            the indexing operation will fail. The behavior is the same as using the `<index>/_create`
+            endpoint. If a document ID is specified, this paramater defaults to `index`.
+            Otherwise, it defaults to `create`. If the request targets a data stream,
+            an `op_type` of `create` is required.
         :param pipeline: The ID of the pipeline to use to preprocess incoming documents.
             If the index has a default ingest pipeline specified, setting the value to
             `_none` turns off the default ingest pipeline for this request. If a final
@@ -1214,6 +1229,9 @@ async def create(
         :param refresh: If `true`, Elasticsearch refreshes the affected shards to make
             this operation visible to search. If `wait_for`, it waits for a refresh to
             make this operation visible to search. If `false`, it does nothing with refreshes.
+        :param require_alias: If `true`, the destination must be an index alias.
+        :param require_data_stream: If `true`, the request's actions must target a data
+            stream (existing or to be created).
         :param routing: A custom value that is used to route operations to a specific
             shard.
         :param timeout: The period the request waits for the following operations: automatic
@@ -1254,14 +1272,24 @@ async def create(
             __query["filter_path"] = filter_path
         if human is not None:
             __query["human"] = human
+        if if_primary_term is not None:
+            __query["if_primary_term"] = if_primary_term
+        if if_seq_no is not None:
+            __query["if_seq_no"] = if_seq_no
         if include_source_on_error is not None:
             __query["include_source_on_error"] = include_source_on_error
+        if op_type is not None:
+            __query["op_type"] = op_type
         if pipeline is not None:
             __query["pipeline"] = pipeline
         if pretty is not None:
             __query["pretty"] = pretty
         if refresh is not None:
             __query["refresh"] = refresh
+        if require_alias is not None:
+            __query["require_alias"] = require_alias
+        if require_data_stream is not None:
+            __query["require_data_stream"] = require_data_stream
         if routing is not None:
             __query["routing"] = routing
         if timeout is not None:

elasticsearch/_async/client/inference.py

@@ -321,6 +321,104 @@ async def put(
             path_parts=__path_parts,
         )
 
+    @_rewrite_parameters(
+        body_fields=(
+            "service",
+            "service_settings",
+            "chunking_settings",
+            "task_settings",
+        ),
+    )
+    async def put_openai(
+        self,
+        *,
+        task_type: t.Union[
+            str, t.Literal["chat_completion", "completion", "text_embedding"]
+        ],
+        openai_inference_id: str,
+        service: t.Optional[t.Union[str, t.Literal["openai"]]] = 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,
+        body: t.Optional[t.Dict[str, t.Any]] = None,
+    ) -> ObjectApiResponse[t.Any]:
+        """
+        .. raw:: html
+
+          <p>Create an OpenAI inference endpoint.</p>
+          <p>Create an inference endpoint to perform an inference task with the <code>openai</code> service.</p>
+          <p>When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
+          After creating the endpoint, wait for the model deployment to complete before using it.
+          To verify the deployment status, use the get trained model statistics API.
+          Look for <code>&quot;state&quot;: &quot;fully_allocated&quot;</code> in the response and ensure that the <code>&quot;allocation_count&quot;</code> matches the <code>&quot;target_allocation_count&quot;</code>.
+          Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.</p>
+
+
+        `<https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-openai.html>`_
+
+        :param task_type: The type of the inference task that the model will perform.
+            NOTE: The `chat_completion` task type only supports streaming and only through
+            the _stream API.
+        :param openai_inference_id: The unique identifier of the inference endpoint.
+        :param service: The type of service supported for the specified task type. In
+            this case, `openai`.
+        :param service_settings: Settings used to install the inference model. These
+            settings are specific to the `openai` service.
+        :param chunking_settings: The chunking configuration object.
+        :param task_settings: Settings to configure the inference task. These settings
+            are specific to the task type you specified.
+        """
+        if task_type in SKIP_IN_PATH:
+            raise ValueError("Empty value passed for parameter 'task_type'")
+        if openai_inference_id in SKIP_IN_PATH:
+            raise ValueError("Empty value passed for parameter 'openai_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),
+            "openai_inference_id": _quote(openai_inference_id),
+        }
+        __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["openai_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 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_openai",
+            path_parts=__path_parts,
+        )
+
     @_rewrite_parameters(
         body_fields=("service", "service_settings"),
     )
@@ -341,7 +439,7 @@ async def put_watsonx(
         .. raw:: html
 
           <p>Create a Watsonx inference endpoint.</p>
-          <p>Creates an inference endpoint to perform an inference task with the <code>watsonxai</code> service.
+          <p>Create an inference endpoint to perform an inference task with the <code>watsonxai</code> service.
           You need an IBM Cloud Databases for Elasticsearch deployment to use the <code>watsonxai</code> inference service.
           You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.</p>
           <p>When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.

elasticsearch/_sync/client/__init__.py

@@ -1119,12 +1119,17 @@ def create(
         error_trace: t.Optional[bool] = None,
         filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
         human: t.Optional[bool] = None,
+        if_primary_term: t.Optional[int] = None,
+        if_seq_no: t.Optional[int] = None,
         include_source_on_error: t.Optional[bool] = None,
+        op_type: t.Optional[t.Union[str, t.Literal["create", "index"]]] = None,
         pipeline: t.Optional[str] = None,
         pretty: t.Optional[bool] = None,
         refresh: t.Optional[
             t.Union[bool, str, t.Literal["false", "true", "wait_for"]]
         ] = None,
+        require_alias: t.Optional[bool] = None,
+        require_data_stream: t.Optional[bool] = None,
         routing: t.Optional[str] = None,
         timeout: t.Optional[t.Union[str, t.Literal[-1], t.Literal[0]]] = None,
         version: t.Optional[int] = None,
@@ -1202,8 +1207,18 @@ def create(
         :param id: A unique identifier for the document. To automatically generate a
             document ID, use the `POST /<target>/_doc/` request format.
         :param document:
+        :param if_primary_term: Only perform the operation if the document has this primary
+            term.
+        :param if_seq_no: Only perform the operation if the document has this sequence
+            number.
         :param include_source_on_error: True or false if to include the document source
             in the error message in case of parsing errors.
+        :param op_type: Set to `create` to only index the document if it does not already
+            exist (put if absent). If a document with the specified `_id` already exists,
+            the indexing operation will fail. The behavior is the same as using the `<index>/_create`
+            endpoint. If a document ID is specified, this paramater defaults to `index`.
+            Otherwise, it defaults to `create`. If the request targets a data stream,
+            an `op_type` of `create` is required.
         :param pipeline: The ID of the pipeline to use to preprocess incoming documents.
             If the index has a default ingest pipeline specified, setting the value to
             `_none` turns off the default ingest pipeline for this request. If a final
@@ -1212,6 +1227,9 @@ def create(
         :param refresh: If `true`, Elasticsearch refreshes the affected shards to make
             this operation visible to search. If `wait_for`, it waits for a refresh to
             make this operation visible to search. If `false`, it does nothing with refreshes.
+        :param require_alias: If `true`, the destination must be an index alias.
+        :param require_data_stream: If `true`, the request's actions must target a data
+            stream (existing or to be created).
         :param routing: A custom value that is used to route operations to a specific
             shard.
         :param timeout: The period the request waits for the following operations: automatic
@@ -1252,14 +1270,24 @@ def create(
             __query["filter_path"] = filter_path
         if human is not None:
             __query["human"] = human
+        if if_primary_term is not None:
+            __query["if_primary_term"] = if_primary_term
+        if if_seq_no is not None:
+            __query["if_seq_no"] = if_seq_no
         if include_source_on_error is not None:
             __query["include_source_on_error"] = include_source_on_error
+        if op_type is not None:
+            __query["op_type"] = op_type
         if pipeline is not None:
             __query["pipeline"] = pipeline
         if pretty is not None:
             __query["pretty"] = pretty
         if refresh is not None:
             __query["refresh"] = refresh
+        if require_alias is not None:
+            __query["require_alias"] = require_alias
+        if require_data_stream is not None:
+            __query["require_data_stream"] = require_data_stream
         if routing is not None:
             __query["routing"] = routing
         if timeout is not None:

elasticsearch/_sync/client/inference.py

@@ -321,6 +321,104 @@ def put(
             path_parts=__path_parts,
         )
 
+    @_rewrite_parameters(
+        body_fields=(
+            "service",
+            "service_settings",
+            "chunking_settings",
+            "task_settings",
+        ),
+    )
+    def put_openai(
+        self,
+        *,
+        task_type: t.Union[
+            str, t.Literal["chat_completion", "completion", "text_embedding"]
+        ],
+        openai_inference_id: str,
+        service: t.Optional[t.Union[str, t.Literal["openai"]]] = 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,
+        body: t.Optional[t.Dict[str, t.Any]] = None,
+    ) -> ObjectApiResponse[t.Any]:
+        """
+        .. raw:: html
+
+          <p>Create an OpenAI inference endpoint.</p>
+          <p>Create an inference endpoint to perform an inference task with the <code>openai</code> service.</p>
+          <p>When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
+          After creating the endpoint, wait for the model deployment to complete before using it.
+          To verify the deployment status, use the get trained model statistics API.
+          Look for <code>&quot;state&quot;: &quot;fully_allocated&quot;</code> in the response and ensure that the <code>&quot;allocation_count&quot;</code> matches the <code>&quot;target_allocation_count&quot;</code>.
+          Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.</p>
+
+
+        `<https://www.elastic.co/guide/en/elasticsearch/reference/8.18/infer-service-openai.html>`_
+
+        :param task_type: The type of the inference task that the model will perform.
+            NOTE: The `chat_completion` task type only supports streaming and only through
+            the _stream API.
+        :param openai_inference_id: The unique identifier of the inference endpoint.
+        :param service: The type of service supported for the specified task type. In
+            this case, `openai`.
+        :param service_settings: Settings used to install the inference model. These
+            settings are specific to the `openai` service.
+        :param chunking_settings: The chunking configuration object.
+        :param task_settings: Settings to configure the inference task. These settings
+            are specific to the task type you specified.
+        """
+        if task_type in SKIP_IN_PATH:
+            raise ValueError("Empty value passed for parameter 'task_type'")
+        if openai_inference_id in SKIP_IN_PATH:
+            raise ValueError("Empty value passed for parameter 'openai_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),
+            "openai_inference_id": _quote(openai_inference_id),
+        }
+        __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["openai_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 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_openai",
+            path_parts=__path_parts,
+        )
+
     @_rewrite_parameters(
         body_fields=("service", "service_settings"),
     )
@@ -341,7 +439,7 @@ def put_watsonx(
         .. raw:: html
 
           <p>Create a Watsonx inference endpoint.</p>
-          <p>Creates an inference endpoint to perform an inference task with the <code>watsonxai</code> service.
+          <p>Create an inference endpoint to perform an inference task with the <code>watsonxai</code> service.
           You need an IBM Cloud Databases for Elasticsearch deployment to use the <code>watsonxai</code> inference service.
           You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.</p>
           <p>When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.