From 1cc1b11980513610b38f8c7309ce52233401655e Mon Sep 17 00:00:00 2001 From: Elastic Machine Date: Mon, 11 Aug 2025 06:03:18 +0000 Subject: [PATCH] Auto-generated API code --- elasticsearch/_async/client/__init__.py | 3 +- elasticsearch/_async/client/cluster.py | 9 +- elasticsearch/_async/client/esql.py | 11 +- elasticsearch/_async/client/indices.py | 2 +- elasticsearch/_async/client/inference.py | 283 ++++++++++++++++++++++- elasticsearch/_async/client/sql.py | 2 +- elasticsearch/_sync/client/__init__.py | 3 +- elasticsearch/_sync/client/cluster.py | 9 +- elasticsearch/_sync/client/esql.py | 11 +- elasticsearch/_sync/client/indices.py | 2 +- elasticsearch/_sync/client/inference.py | 283 ++++++++++++++++++++++- elasticsearch/_sync/client/sql.py | 2 +- 12 files changed, 598 insertions(+), 22 deletions(-) diff --git a/elasticsearch/_async/client/__init__.py b/elasticsearch/_async/client/__init__.py index 0874e120f..ccadbb952 100644 --- a/elasticsearch/_async/client/__init__.py +++ b/elasticsearch/_async/client/__init__.py @@ -608,6 +608,7 @@ async def bulk(
  • JavaScript: Check out client.helpers.*
  • .NET: Check out BulkAllObservable
  • PHP: Check out bulk indexing.
    • +
  • Ruby: Check out Elasticsearch::Helpers::BulkHelper

    • Submitting bulk requests with cURL

    If you're providing text file input to curl, you must use the --data-binary flag instead of plain -d. @@ -5659,7 +5660,7 @@ 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, diff --git a/elasticsearch/_async/client/cluster.py b/elasticsearch/_async/client/cluster.py index 91956f7c4..a6efa8529 100644 --- a/elasticsearch/_async/client/cluster.py +++ b/elasticsearch/_async/client/cluster.py @@ -374,8 +374,13 @@ async def get_settings( ``_ :param flat_settings: If `true`, returns settings in flat format. - :param include_defaults: If `true`, returns default cluster settings from the - local node. + :param include_defaults: If `true`, also returns default values for all other + cluster settings, reflecting the values in the `elasticsearch.yml` file of + one of the nodes in the cluster. If the nodes in your cluster do not all + have the same values in their `elasticsearch.yml` config files then the values + returned by this API may vary from invocation to invocation and may not reflect + the values that Elasticsearch uses in all situations. Use the `GET _nodes/settings` + API to fetch the settings for each individual node in your cluster. :param master_timeout: Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error. diff --git a/elasticsearch/_async/client/esql.py b/elasticsearch/_async/client/esql.py index 9999f1db1..cb8b423fd 100644 --- a/elasticsearch/_async/client/esql.py +++ b/elasticsearch/_async/client/esql.py @@ -111,7 +111,12 @@ 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 @@ -539,7 +544,9 @@ 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/indices.py b/elasticsearch/_async/client/indices.py index 2a40027c2..ec8d9d9c3 100644 --- a/elasticsearch/_async/client/indices.py +++ b/elasticsearch/_async/client/indices.py @@ -4179,7 +4179,7 @@ async def put_mapping(

  • Change a field's mapping using reindexing
  • Rename a field using a field alias
    • -

    Learn how to use the update mapping API with practical examples in the Update mapping API examples guide.

    +

    Learn how to use the update mapping API with practical examples in the Update mapping API examples guide.

    ``_ diff --git a/elasticsearch/_async/client/inference.py b/elasticsearch/_async/client/inference.py index 58b51a72e..686d99d27 100644 --- a/elasticsearch/_async/client/inference.py +++ b/elasticsearch/_async/client/inference.py @@ -389,23 +389,26 @@ async def put( However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

    The following integrations are available through the inference API. You can find the available task types next to the integration name:

    @@ -461,6 +464,86 @@ async def put( path_parts=__path_parts, ) + @_rewrite_parameters( + body_fields=("service", "service_settings"), + ) + async def put_ai21( + self, + *, + task_type: t.Union[str, t.Literal["chat_completion", "completion"]], + ai21_inference_id: str, + service: t.Optional[t.Union[str, t.Literal["ai21"]]] = None, + service_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, + 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 + +

    Create a AI21 inference endpoint.

    +

    Create an inference endpoint to perform an inference task with the ai21 service.

    + + + ``_ + + :param task_type: The type of the inference task that the model will perform. + :param ai21_inference_id: The unique identifier of the inference endpoint. + :param service: The type of service supported for the specified task type. In + this case, `ai21`. + :param service_settings: Settings used to install the inference model. These + settings are specific to the `ai21` service. + :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 ai21_inference_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'ai21_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), + "ai21_inference_id": _quote(ai21_inference_id), + } + __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["ai21_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 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_ai21", + path_parts=__path_parts, + ) + @_rewrite_parameters( body_fields=( "service", @@ -659,6 +742,112 @@ 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 + +

    Create an Amazon SageMaker inference endpoint.

    +

    Create an inference endpoint to perform an inference task with the amazon_sagemaker service.

    + + + ``_ + + :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", @@ -1887,6 +2076,92 @@ async def put_jinaai( path_parts=__path_parts, ) + @_rewrite_parameters( + body_fields=("service", "service_settings", "chunking_settings"), + ) + async def put_llama( + self, + *, + task_type: t.Union[ + str, t.Literal["chat_completion", "completion", "text_embedding"] + ], + llama_inference_id: str, + service: t.Optional[t.Union[str, t.Literal["llama"]]] = 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, + 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 + +

    Create a Llama inference endpoint.

    +

    Create an inference endpoint to perform an inference task with the llama service.

    + + + ``_ + + :param task_type: The type of the inference task that the model will perform. + :param llama_inference_id: The unique identifier of the inference endpoint. + :param service: The type of service supported for the specified task type. In + this case, `llama`. + :param service_settings: Settings used to install the inference model. These + settings are specific to the `llama` service. + :param chunking_settings: The chunking configuration object. + :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 llama_inference_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'llama_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), + "llama_inference_id": _quote(llama_inference_id), + } + __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["llama_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 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_llama", + path_parts=__path_parts, + ) + @_rewrite_parameters( body_fields=("service", "service_settings", "chunking_settings"), ) diff --git a/elasticsearch/_async/client/sql.py b/elasticsearch/_async/client/sql.py index 3eb37a6cc..de423ea66 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/__init__.py b/elasticsearch/_sync/client/__init__.py index 5f7a4313d..891887af9 100644 --- a/elasticsearch/_sync/client/__init__.py +++ b/elasticsearch/_sync/client/__init__.py @@ -606,6 +606,7 @@ def bulk(
  • JavaScript: Check out client.helpers.*
  • .NET: Check out BulkAllObservable
  • PHP: Check out bulk indexing.
    • +
  • Ruby: Check out Elasticsearch::Helpers::BulkHelper

    • Submitting bulk requests with cURL

    If you're providing text file input to curl, you must use the --data-binary flag instead of plain -d. @@ -5657,7 +5658,7 @@ 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, diff --git a/elasticsearch/_sync/client/cluster.py b/elasticsearch/_sync/client/cluster.py index a56892d54..46cb6059f 100644 --- a/elasticsearch/_sync/client/cluster.py +++ b/elasticsearch/_sync/client/cluster.py @@ -374,8 +374,13 @@ def get_settings( ``_ :param flat_settings: If `true`, returns settings in flat format. - :param include_defaults: If `true`, returns default cluster settings from the - local node. + :param include_defaults: If `true`, also returns default values for all other + cluster settings, reflecting the values in the `elasticsearch.yml` file of + one of the nodes in the cluster. If the nodes in your cluster do not all + have the same values in their `elasticsearch.yml` config files then the values + returned by this API may vary from invocation to invocation and may not reflect + the values that Elasticsearch uses in all situations. Use the `GET _nodes/settings` + API to fetch the settings for each individual node in your cluster. :param master_timeout: Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error. diff --git a/elasticsearch/_sync/client/esql.py b/elasticsearch/_sync/client/esql.py index 0744a81bd..e8d0a690f 100644 --- a/elasticsearch/_sync/client/esql.py +++ b/elasticsearch/_sync/client/esql.py @@ -111,7 +111,12 @@ 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 @@ -539,7 +544,9 @@ 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/indices.py b/elasticsearch/_sync/client/indices.py index 8697d971b..4fb65847f 100644 --- a/elasticsearch/_sync/client/indices.py +++ b/elasticsearch/_sync/client/indices.py @@ -4179,7 +4179,7 @@ def put_mapping(

  • Change a field's mapping using reindexing
  • Rename a field using a field alias
    • -

    Learn how to use the update mapping API with practical examples in the Update mapping API examples guide.

    +

    Learn how to use the update mapping API with practical examples in the Update mapping API examples guide.

    ``_ diff --git a/elasticsearch/_sync/client/inference.py b/elasticsearch/_sync/client/inference.py index b7bab7f1c..47fc86fd5 100644 --- a/elasticsearch/_sync/client/inference.py +++ b/elasticsearch/_sync/client/inference.py @@ -389,23 +389,26 @@ def put( However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.

    The following integrations are available through the inference API. You can find the available task types next to the integration name:

      +
    • AI21 (chat_completion, completion)
    • AlibabaCloud AI Search (completion, rerank, sparse_embedding, text_embedding)
    • Amazon Bedrock (completion, text_embedding)
      • +
    • Amazon SageMaker (chat_completion, completion, rerank, sparse_embedding, text_embedding)
    • Anthropic (completion)
    • Azure AI Studio (completion, 'rerank', text_embedding)
    • Azure OpenAI (completion, text_embedding)
    • Cohere (completion, rerank, text_embedding)
      • -
    • DeepSeek (completion, chat_completion)
      • +
    • DeepSeek (chat_completion, completion)
    • Elasticsearch (rerank, sparse_embedding, text_embedding - this service is for built-in models and models uploaded through Eland)
    • ELSER (sparse_embedding)
    • Google AI Studio (completion, text_embedding)
      • -
    • Google Vertex AI (rerank, text_embedding)
      • +
    • Google Vertex AI (chat_completion, completion, rerank, text_embedding)
    • Hugging Face (chat_completion, completion, rerank, text_embedding)
      • +
    • JinaAI (rerank, text_embedding)
      • +
    • Llama (chat_completion, completion, text_embedding)
    • Mistral (chat_completion, completion, text_embedding)
    • OpenAI (chat_completion, completion, text_embedding)
      • -
    • VoyageAI (text_embedding, rerank)
      • +
    • VoyageAI (rerank, text_embedding)
    • Watsonx inference integration (text_embedding)
      • -
    • JinaAI (text_embedding, rerank)
    @@ -461,6 +464,86 @@ def put( path_parts=__path_parts, ) + @_rewrite_parameters( + body_fields=("service", "service_settings"), + ) + def put_ai21( + self, + *, + task_type: t.Union[str, t.Literal["chat_completion", "completion"]], + ai21_inference_id: str, + service: t.Optional[t.Union[str, t.Literal["ai21"]]] = None, + service_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, + 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 + +

    Create a AI21 inference endpoint.

    +

    Create an inference endpoint to perform an inference task with the ai21 service.

    + + + ``_ + + :param task_type: The type of the inference task that the model will perform. + :param ai21_inference_id: The unique identifier of the inference endpoint. + :param service: The type of service supported for the specified task type. In + this case, `ai21`. + :param service_settings: Settings used to install the inference model. These + settings are specific to the `ai21` service. + :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 ai21_inference_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'ai21_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), + "ai21_inference_id": _quote(ai21_inference_id), + } + __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["ai21_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 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_ai21", + path_parts=__path_parts, + ) + @_rewrite_parameters( body_fields=( "service", @@ -659,6 +742,112 @@ 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 + +

    Create an Amazon SageMaker inference endpoint.

    +

    Create an inference endpoint to perform an inference task with the amazon_sagemaker service.

    + + + ``_ + + :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", @@ -1887,6 +2076,92 @@ def put_jinaai( path_parts=__path_parts, ) + @_rewrite_parameters( + body_fields=("service", "service_settings", "chunking_settings"), + ) + def put_llama( + self, + *, + task_type: t.Union[ + str, t.Literal["chat_completion", "completion", "text_embedding"] + ], + llama_inference_id: str, + service: t.Optional[t.Union[str, t.Literal["llama"]]] = 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, + 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 + +

    Create a Llama inference endpoint.

    +

    Create an inference endpoint to perform an inference task with the llama service.

    + + + ``_ + + :param task_type: The type of the inference task that the model will perform. + :param llama_inference_id: The unique identifier of the inference endpoint. + :param service: The type of service supported for the specified task type. In + this case, `llama`. + :param service_settings: Settings used to install the inference model. These + settings are specific to the `llama` service. + :param chunking_settings: The chunking configuration object. + :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 llama_inference_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'llama_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), + "llama_inference_id": _quote(llama_inference_id), + } + __path = f'/_inference/{__path_parts["task_type"]}/{__path_parts["llama_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 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_llama", + path_parts=__path_parts, + ) + @_rewrite_parameters( body_fields=("service", "service_settings", "chunking_settings"), ) diff --git a/elasticsearch/_sync/client/sql.py b/elasticsearch/_sync/client/sql.py index 90cb01681..b2750ede1 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,