o iJ@sddlZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z dd lmZmZdd lmZdd lmZdd lmZdd lmZeejdZGdddZGdddZdS)N)core)AsyncClientWrapperSyncClientWrapper)RequestOptions) DatasetType)DatasetValidationStatus)AsyncRawDatasetsClientRawDatasetsClient)DatasetsCreateResponse)DatasetsGetResponse)DatasetsGetUsageResponse)DatasetsListResponse.c@eZdZdefddZedefddZddddddddd ej e d ej e j d ej e j d ej e d ej e dej edej edefddZddddddeddde dedejdej edej edej eje eje fdej eje eje fdej e dej e dej ejdej edefddZdddej edefd d!Zddd"e dej edefd#d$Zddd"e dej edeje ej ej ffd%d&Z!dS)'DatasetsClientclient_wrappercCt|d|_dSN)r)r _raw_clientselfrr\/var/www/html/karishye-ai-python/venv/lib/python3.10/site-packages/cohere/datasets/client.py__init__zDatasetsClient.__init__returncC|jS)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawDatasetsClient rrrrrwith_raw_response z DatasetsClient.with_raw_responseN dataset_typebeforeafterlimitoffsetvalidation_statusrequest_optionsr#r$r%r&r'r(r)c Cs |jj|||||||d}|jS)a* List datasets that have been created. Parameters ---------- dataset_type : typing.Optional[str] optional filter by dataset type before : typing.Optional[dt.datetime] optional filter before a date after : typing.Optional[dt.datetime] optional filter after a date limit : typing.Optional[float] optional limit to number of results offset : typing.Optional[float] optional offset to start of results validation_status : typing.Optional[DatasetValidationStatus] optional filter by validation status request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsListResponse A successful response. Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.datasets.list() r"rlistdata rr#r$r%r&r'r(r) _responserrrr+$s4 zDatasetsClient.listkeep_original_fileskip_malformed_input keep_fieldsoptional_fieldstext_separator csv_delimiter eval_datar)nametyper,r0r1r2r3r4r5r6c Cs(|jj||||||||| | | d } | jS)a Create a dataset by uploading a file. See ['Dataset Creation'](https://docs.cohere.com/docs/datasets#dataset-creation) for more information. Parameters ---------- name : str The name of the uploaded dataset. type : DatasetType The dataset type, which is used to validate the data. The only valid type is `embed-input` used in conjunction with the Embed Jobs API. data : core.File See core.File for more documentation keep_original_file : typing.Optional[bool] Indicates if the original file should be stored. skip_malformed_input : typing.Optional[bool] Indicates whether rows with malformed input should be dropped (instead of failing the validation check). Dropped rows will be returned in the warnings field. keep_fields : typing.Optional[typing.Union[str, typing.Sequence[str]]] List of names of fields that will be persisted in the Dataset. By default the Dataset will retain only the required fields indicated in the [schema for the corresponding Dataset type](https://docs.cohere.com/docs/datasets#dataset-types). For example, datasets of type `embed-input` will drop all fields other than the required `text` field. If any of the fields in `keep_fields` are missing from the uploaded file, Dataset validation will fail. optional_fields : typing.Optional[typing.Union[str, typing.Sequence[str]]] List of names of fields that will be persisted in the Dataset. By default the Dataset will retain only the required fields indicated in the [schema for the corresponding Dataset type](https://docs.cohere.com/docs/datasets#dataset-types). For example, Datasets of type `embed-input` will drop all fields other than the required `text` field. If any of the fields in `optional_fields` are missing from the uploaded file, Dataset validation will pass. text_separator : typing.Optional[str] Raw .txt uploads will be split into entries using the text_separator value. csv_delimiter : typing.Optional[str] The delimiter used for .csv uploads. eval_data : typing.Optional[core.File] See core.File for more documentation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsCreateResponse A successful response. Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.datasets.create( name="name", type="embed-input", ) r7r8r,r0r1r2r3r4r5r6r)rcreater, rr7r8r,r0r1r2r3r4r5r6r)r.rrrr;csG zDatasetsClient.creater)cCs|jj|d}|jS)a[ View the dataset storage usage for your Organization. Each Organization can have up to 10GB of storage across all their users. Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsGetUsageResponse A successful response. Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.datasets.get_usage() r=r get_usager,rr)r.rrrr?szDatasetsClient.get_usageidcC|jj||d}|jS)ai Retrieve a dataset by ID. See ['Datasets'](https://docs.cohere.com/docs/datasets) for more information. Parameters ---------- id : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsGetResponse A successful response. Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.datasets.get( id="id", ) r=rgetr,rrAr)r.rrrrDszDatasetsClient.getcCrB)a Delete a dataset by ID. Datasets are automatically deleted after 30 days, but they can also be deleted manually. Parameters ---------- id : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Dict[str, typing.Optional[typing.Any]] A successful response. Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.datasets.delete( id="id", ) r=rdeleter,rErrrrGszDatasetsClient.delete)"__name__ __module__ __qualname__rrpropertyr r typingOptionalstrdtdatetimefloatrrrr+OMITrrFileboolUnionSequencer r;rr?r rDDictAnyrGrrrrrs     E      V" rc@r)'AsyncDatasetsClientrcCrr)r rrrrrrrzAsyncDatasetsClient.__init__rcCr)z Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawDatasetsClient rrrrrr r!z%AsyncDatasetsClient.with_raw_responseNr"r#r$r%r&r'r(r)c s(|jj|||||||dIdH}|jS)a List datasets that have been created. Parameters ---------- dataset_type : typing.Optional[str] optional filter by dataset type before : typing.Optional[dt.datetime] optional filter before a date after : typing.Optional[dt.datetime] optional filter after a date limit : typing.Optional[float] optional limit to number of results offset : typing.Optional[float] optional offset to start of results validation_status : typing.Optional[DatasetValidationStatus] optional filter by validation status request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsListResponse A successful response. Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.datasets.list() asyncio.run(main()) r"Nr*r-rrrr+$s<  zAsyncDatasetsClient.listr/r7r8r,r0r1r2r3r4r5r6c s0|jj||||||||| | | d IdH} | jS)a/ Create a dataset by uploading a file. See ['Dataset Creation'](https://docs.cohere.com/docs/datasets#dataset-creation) for more information. Parameters ---------- name : str The name of the uploaded dataset. type : DatasetType The dataset type, which is used to validate the data. The only valid type is `embed-input` used in conjunction with the Embed Jobs API. data : core.File See core.File for more documentation keep_original_file : typing.Optional[bool] Indicates if the original file should be stored. skip_malformed_input : typing.Optional[bool] Indicates whether rows with malformed input should be dropped (instead of failing the validation check). Dropped rows will be returned in the warnings field. keep_fields : typing.Optional[typing.Union[str, typing.Sequence[str]]] List of names of fields that will be persisted in the Dataset. By default the Dataset will retain only the required fields indicated in the [schema for the corresponding Dataset type](https://docs.cohere.com/docs/datasets#dataset-types). For example, datasets of type `embed-input` will drop all fields other than the required `text` field. If any of the fields in `keep_fields` are missing from the uploaded file, Dataset validation will fail. optional_fields : typing.Optional[typing.Union[str, typing.Sequence[str]]] List of names of fields that will be persisted in the Dataset. By default the Dataset will retain only the required fields indicated in the [schema for the corresponding Dataset type](https://docs.cohere.com/docs/datasets#dataset-types). For example, Datasets of type `embed-input` will drop all fields other than the required `text` field. If any of the fields in `optional_fields` are missing from the uploaded file, Dataset validation will pass. text_separator : typing.Optional[str] Raw .txt uploads will be split into entries using the text_separator value. csv_delimiter : typing.Optional[str] The delimiter used for .csv uploads. eval_data : typing.Optional[core.File] See core.File for more documentation request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsCreateResponse A successful response. Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.datasets.create( name="name", type="embed-input", ) asyncio.run(main()) r9Nr:r<rrrr;ksO  zAsyncDatasetsClient.creater=cs|jj|dIdH}|jS)a View the dataset storage usage for your Organization. Each Organization can have up to 10GB of storage across all their users. Parameters ---------- request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsGetUsageResponse A successful response. Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.datasets.get_usage() asyncio.run(main()) r=Nr>r@rrrr?s zAsyncDatasetsClient.get_usagerAc|jj||dIdH}|jS)a Retrieve a dataset by ID. See ['Datasets'](https://docs.cohere.com/docs/datasets) for more information. Parameters ---------- id : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- DatasetsGetResponse A successful response. Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.datasets.get( id="id", ) asyncio.run(main()) r=NrCrErrrrDs$zAsyncDatasetsClient.getcrZ)a Delete a dataset by ID. Datasets are automatically deleted after 30 days, but they can also be deleted manually. Parameters ---------- id : str request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- typing.Dict[str, typing.Optional[typing.Any]] A successful response. Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.datasets.delete( id="id", ) asyncio.run(main()) r=NrFrErrrrGs&zAsyncDatasetsClient.delete)"rHrIrJrrrKr r rLrMrNrOrPrQrrrr+rRrrrSrTrUrVr r;rr?r rDrWrXrGrrrrrYs     M      ^"#(rY)rPrOrLrcore.client_wrapperrrcore.request_optionsrtypes.dataset_typertypes.dataset_validation_statusr raw_clientr r types.datasets_create_responser types.datasets_get_responser !types.datasets_get_usage_responsertypes.datasets_list_responsercastrXrRrrYrrrrs"