# 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.batch import Batch from ...types.batch_paginated_list import BatchPaginatedList from ...types.batch_public_output import BatchPublicOutput from ...types.http_validation_error import HttpValidationError from ...types.llama_parse_parameters import LlamaParseParameters 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 BetaClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def list_batches( self, *, limit: typing.Optional[int] = None, offset: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> BatchPaginatedList: """ Parameters: - limit: typing.Optional[int]. - offset: typing.Optional[int]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_batches() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batches"), params=remove_none_from_dict( {"limit": limit, "offset": offset, "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(BatchPaginatedList, _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_batch( self, *, organization_id: typing.Optional[str] = None, project_id: typing.Optional[str] = None, tool: str, tool_data: typing.Optional[LlamaParseParameters] = OMIT, input_type: str, input_id: str, output_type: typing.Optional[str] = OMIT, output_id: typing.Optional[str] = OMIT, batch_create_project_id: str, external_id: str, completion_window: typing.Optional[int] = OMIT, ) -> Batch: """ Parameters: - organization_id: typing.Optional[str]. - project_id: typing.Optional[str]. - tool: str. The tool to be used for all requests in the batch. - tool_data: typing.Optional[LlamaParseParameters]. - input_type: str. The type of input file. Currently only 'datasource' is supported. - input_id: str. The ID of the input file for the batch. - output_type: typing.Optional[str]. - output_id: typing.Optional[str]. - batch_create_project_id: str. The ID of the project to which the batch belongs - external_id: str. A developer-provided ID for the batch. This ID will be returned in the response. - completion_window: typing.Optional[int]. The time frame within which the batch should be processed. Currently only 24h is supported. --- from llama_cloud import FailPageMode, LlamaParseParameters, ParsingMode from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_batch( tool="string", tool_data=LlamaParseParameters( parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, replace_failed_page_mode=FailPageMode.RAW_TEXT, ), input_type="string", input_id="string", batch_create_project_id="string", external_id="string", ) """ _request: typing.Dict[str, typing.Any] = { "tool": tool, "input_type": input_type, "input_id": input_id, "project_id": batch_create_project_id, "external_id": external_id, } if tool_data is not OMIT: _request["tool_data"] = tool_data if output_type is not OMIT: _request["output_type"] = output_type if output_id is not OMIT: _request["output_id"] = output_id if completion_window is not OMIT: _request["completion_window"] = completion_window _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batches"), params=remove_none_from_dict({"organization_id": organization_id, "project_id": project_id}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(Batch, _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_batch(self, batch_id: str, *, organization_id: typing.Optional[str] = None) -> BatchPublicOutput: """ Parameters: - batch_id: str. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_batch( batch_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batches/{batch_id}"), params=remove_none_from_dict({"organization_id": organization_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(BatchPublicOutput, _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 AsyncBetaClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def list_batches( self, *, limit: typing.Optional[int] = None, offset: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> BatchPaginatedList: """ Parameters: - limit: typing.Optional[int]. - offset: typing.Optional[int]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_batches() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batches"), params=remove_none_from_dict( {"limit": limit, "offset": offset, "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(BatchPaginatedList, _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_batch( self, *, organization_id: typing.Optional[str] = None, project_id: typing.Optional[str] = None, tool: str, tool_data: typing.Optional[LlamaParseParameters] = OMIT, input_type: str, input_id: str, output_type: typing.Optional[str] = OMIT, output_id: typing.Optional[str] = OMIT, batch_create_project_id: str, external_id: str, completion_window: typing.Optional[int] = OMIT, ) -> Batch: """ Parameters: - organization_id: typing.Optional[str]. - project_id: typing.Optional[str]. - tool: str. The tool to be used for all requests in the batch. - tool_data: typing.Optional[LlamaParseParameters]. - input_type: str. The type of input file. Currently only 'datasource' is supported. - input_id: str. The ID of the input file for the batch. - output_type: typing.Optional[str]. - output_id: typing.Optional[str]. - batch_create_project_id: str. The ID of the project to which the batch belongs - external_id: str. A developer-provided ID for the batch. This ID will be returned in the response. - completion_window: typing.Optional[int]. The time frame within which the batch should be processed. Currently only 24h is supported. --- from llama_cloud import FailPageMode, LlamaParseParameters, ParsingMode from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_batch( tool="string", tool_data=LlamaParseParameters( parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, replace_failed_page_mode=FailPageMode.RAW_TEXT, ), input_type="string", input_id="string", batch_create_project_id="string", external_id="string", ) """ _request: typing.Dict[str, typing.Any] = { "tool": tool, "input_type": input_type, "input_id": input_id, "project_id": batch_create_project_id, "external_id": external_id, } if tool_data is not OMIT: _request["tool_data"] = tool_data if output_type is not OMIT: _request["output_type"] = output_type if output_id is not OMIT: _request["output_id"] = output_id if completion_window is not OMIT: _request["completion_window"] = completion_window _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batches"), params=remove_none_from_dict({"organization_id": organization_id, "project_id": project_id}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(Batch, _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_batch(self, batch_id: str, *, organization_id: typing.Optional[str] = None) -> BatchPublicOutput: """ Parameters: - batch_id: str. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.get_batch( batch_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batches/{batch_id}"), params=remove_none_from_dict({"organization_id": organization_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(BatchPublicOutput, _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)