# This file was auto-generated by Fern from our API Definition. import typing import urllib.parse from json.decoder import JSONDecodeError from ...core.api_error import ApiError from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ...core.jsonable_encoder import jsonable_encoder from ...core.remove_none_from_dict import remove_none_from_dict from ...errors.unprocessable_entity_error import UnprocessableEntityError from ...types.classifier_rule import ClassifierRule from ...types.classify_job import ClassifyJob from ...types.classify_job_results import ClassifyJobResults from ...types.classify_parsing_configuration import ClassifyParsingConfiguration from ...types.http_validation_error import HttpValidationError from ...types.paginated_response_classify_job import PaginatedResponseClassifyJob try: import pydantic if pydantic.__version__.startswith("1."): raise ImportError import pydantic.v1 as pydantic # type: ignore except ImportError: import pydantic # type: ignore # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class ClassifierClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def list_classify_jobs( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> PaginatedResponseClassifyJob: """ List classify jobs. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.classifier.list_classify_jobs() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/classifier/jobs"), params=remove_none_from_dict( { "project_id": project_id, "organization_id": organization_id, "page_size": page_size, "page_token": page_token, } ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(PaginatedResponseClassifyJob, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) def create_classify_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, file_ids: typing.List[str], parsing_configuration: typing.Optional[ClassifyParsingConfiguration] = OMIT, rules: typing.List[ClassifierRule], ) -> ClassifyJob: """ Create a classify job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - file_ids: typing.List[str]. The IDs of the files to classify - parsing_configuration: typing.Optional[ClassifyParsingConfiguration]. The configuration for the parsing job - rules: typing.List[ClassifierRule]. The rules to classify the files --- from llama_cloud import ClassifyParsingConfiguration, ParserLanguages from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.classifier.create_classify_job( file_ids=[], parsing_configuration=ClassifyParsingConfiguration( lang=ParserLanguages.AF, ), rules=[], ) """ _request: typing.Dict[str, typing.Any] = {"file_ids": file_ids, "rules": rules} if parsing_configuration is not OMIT: _request["parsing_configuration"] = parsing_configuration _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/classifier/jobs"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ClassifyJob, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) def get_classify_job( self, classify_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ClassifyJob: """ Get a classify job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - classify_job_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.classifier.get_classify_job( classify_job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/classifier/jobs/{classify_job_id}" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ClassifyJob, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) def get_classification_job_results( self, classify_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ClassifyJobResults: """ Get the results of a classify job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - classify_job_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.classifier.get_classification_job_results( classify_job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/classifier/jobs/{classify_job_id}/results" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ClassifyJobResults, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) class AsyncClassifierClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def list_classify_jobs( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> PaginatedResponseClassifyJob: """ List classify jobs. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.classifier.list_classify_jobs() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/classifier/jobs"), params=remove_none_from_dict( { "project_id": project_id, "organization_id": organization_id, "page_size": page_size, "page_token": page_token, } ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(PaginatedResponseClassifyJob, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) async def create_classify_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, file_ids: typing.List[str], parsing_configuration: typing.Optional[ClassifyParsingConfiguration] = OMIT, rules: typing.List[ClassifierRule], ) -> ClassifyJob: """ Create a classify job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - file_ids: typing.List[str]. The IDs of the files to classify - parsing_configuration: typing.Optional[ClassifyParsingConfiguration]. The configuration for the parsing job - rules: typing.List[ClassifierRule]. The rules to classify the files --- from llama_cloud import ClassifyParsingConfiguration, ParserLanguages from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.classifier.create_classify_job( file_ids=[], parsing_configuration=ClassifyParsingConfiguration( lang=ParserLanguages.AF, ), rules=[], ) """ _request: typing.Dict[str, typing.Any] = {"file_ids": file_ids, "rules": rules} if parsing_configuration is not OMIT: _request["parsing_configuration"] = parsing_configuration _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/classifier/jobs"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ClassifyJob, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) async def get_classify_job( self, classify_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ClassifyJob: """ Get a classify job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - classify_job_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.classifier.get_classify_job( classify_job_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/classifier/jobs/{classify_job_id}" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ClassifyJob, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json) async def get_classification_job_results( self, classify_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ClassifyJobResults: """ Get the results of a classify job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - classify_job_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.classifier.get_classification_job_results( classify_job_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/classifier/jobs/{classify_job_id}/results" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ClassifyJobResults, _response.json()) # type: ignore if _response.status_code == 422: raise UnprocessableEntityError(pydantic.parse_obj_as(HttpValidationError, _response.json())) # type: ignore try: _response_json = _response.json() except JSONDecodeError: raise ApiError(status_code=_response.status_code, body=_response.text) raise ApiError(status_code=_response.status_code, body=_response_json)