# This file was auto-generated by Fern from our API Definition. import datetime as dt import typing import urllib.parse from json.decoder import JSONDecodeError import typing_extensions 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.agent_data import AgentData from ...types.batch_file_status import BatchFileStatus from ...types.batch_item_list_response import BatchItemListResponse from ...types.batch_job_cancel_response import BatchJobCancelResponse from ...types.batch_job_query_response import BatchJobQueryResponse from ...types.batch_job_response import BatchJobResponse from ...types.batch_job_status import BatchJobStatus from ...types.batch_job_status_response import BatchJobStatusResponse from ...types.batch_job_type import BatchJobType from ...types.delete_response import DeleteResponse from ...types.directory_file_query_response import DirectoryFileQueryResponse from ...types.directory_file_response import DirectoryFileResponse from ...types.directory_query_response import DirectoryQueryResponse from ...types.directory_response import DirectoryResponse from ...types.directory_sync_job_response import DirectorySyncJobResponse from ...types.file import File from ...types.file_create import FileCreate from ...types.file_filter import FileFilter from ...types.file_query_response import FileQueryResponse from ...types.filter_operation import FilterOperation from ...types.http_validation_error import HttpValidationError from ...types.item_processing_results_response import ItemProcessingResultsResponse from ...types.llama_parse_parameters import LlamaParseParameters from ...types.paginated_response_agent_data import PaginatedResponseAgentData from ...types.paginated_response_aggregate_group import PaginatedResponseAggregateGroup from ...types.paginated_response_quota_configuration import PaginatedResponseQuotaConfiguration from ...types.paginated_response_spreadsheet_job import PaginatedResponseSpreadsheetJob from ...types.parse_configuration import ParseConfiguration from ...types.parse_configuration_query_response import ParseConfigurationQueryResponse from ...types.parse_job_query_response import ParseJobQueryResponse from ...types.parse_job_response import ParseJobResponse from ...types.parse_job_result_response import ParseJobResultResponse from ...types.presigned_url import PresignedUrl from ...types.split_category import SplitCategory from ...types.split_document_input import SplitDocumentInput from ...types.split_job_query_response import SplitJobQueryResponse from ...types.split_job_response import SplitJobResponse from ...types.split_strategy import SplitStrategy from ...types.spreadsheet_job import SpreadsheetJob from ...types.spreadsheet_parsing_config import SpreadsheetParsingConfig from ...types.spreadsheet_result_type import SpreadsheetResultType from ...types.src_parse_schemas_workflow_parse_job_config import SrcParseSchemasWorkflowParseJobConfig from ...types.usage_metric import UsageMetric from ...types.usage_metric_query_response import UsageMetricQueryResponse from ...types.webhook_configuration import WebhookConfiguration from .types.batch_job_create_request_job_config import BatchJobCreateRequestJobConfig 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 create_agent_data( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, data: typing.Dict[str, typing.Any], deployment_name: str, ) -> AgentData: """ Create new agent data. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. - data: typing.Dict[str, typing.Any]. - deployment_name: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_agent_data( data={"string": {}}, deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"data": data, "deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data"), 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(AgentData, _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 aggregate_agent_data_api_v_1_beta_agent_data_aggregate_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, count: typing.Optional[bool] = OMIT, deployment_name: str, filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]] = OMIT, first: typing.Optional[bool] = OMIT, group_by: typing.Optional[typing.List[str]] = OMIT, offset: typing.Optional[int] = OMIT, order_by: typing.Optional[str] = OMIT, page_size: typing.Optional[int] = OMIT, page_token: typing.Optional[str] = OMIT, ) -> PaginatedResponseAggregateGroup: """ Aggregate agent data with grouping and optional counting/first item retrieval. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. The logical agent data collection to aggregate data for - count: typing.Optional[bool]. - deployment_name: str. The agent deployment's name to aggregate data for - filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]]. - first: typing.Optional[bool]. - group_by: typing.Optional[typing.List[str]]. - offset: typing.Optional[int]. - order_by: 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.beta.aggregate_agent_data_api_v_1_beta_agent_data_aggregate_post( deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection if count is not OMIT: _request["count"] = count if filter is not OMIT: _request["filter"] = filter if first is not OMIT: _request["first"] = first if group_by is not OMIT: _request["group_by"] = group_by if offset is not OMIT: _request["offset"] = offset if order_by is not OMIT: _request["order_by"] = order_by if page_size is not OMIT: _request["page_size"] = page_size if page_token is not OMIT: _request["page_token"] = page_token _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data/:aggregate"), 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(PaginatedResponseAggregateGroup, _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 delete_agent_data_by_query_api_v_1_beta_agent_data_delete_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, deployment_name: str, filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]] = OMIT, ) -> DeleteResponse: """ Bulk delete agent data by query (deployment_name, collection, optional filters). Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. The logical agent data collection to delete from - deployment_name: str. The agent deployment's name to delete data for - filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.delete_agent_data_by_query_api_v_1_beta_agent_data_delete_post( deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection if filter is not OMIT: _request["filter"] = filter _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data/:delete"), 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(DeleteResponse, _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 search_agent_data_api_v_1_beta_agent_data_search_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, deployment_name: str, filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]] = OMIT, include_total: typing.Optional[bool] = OMIT, offset: typing.Optional[int] = OMIT, order_by: typing.Optional[str] = OMIT, page_size: typing.Optional[int] = OMIT, page_token: typing.Optional[str] = OMIT, ) -> PaginatedResponseAgentData: """ Search agent data with filtering, sorting, and pagination. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. The logical agent data collection to search within - deployment_name: str. The agent deployment's name to search within - filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]]. - include_total: typing.Optional[bool]. Whether to include the total number of items in the response - offset: typing.Optional[int]. - order_by: 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.beta.search_agent_data_api_v_1_beta_agent_data_search_post( deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection if filter is not OMIT: _request["filter"] = filter if include_total is not OMIT: _request["include_total"] = include_total if offset is not OMIT: _request["offset"] = offset if order_by is not OMIT: _request["order_by"] = order_by if page_size is not OMIT: _request["page_size"] = page_size if page_token is not OMIT: _request["page_token"] = page_token _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data/:search"), 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(PaginatedResponseAgentData, _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_agent_data( self, item_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> AgentData: """ Get agent data by ID. Parameters: - item_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_agent_data( item_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/agent-data/{item_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(AgentData, _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 update_agent_data( self, item_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, data: typing.Dict[str, typing.Any], ) -> AgentData: """ Update agent data by ID (overwrites). Parameters: - item_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - data: typing.Dict[str, typing.Any]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.update_agent_data( item_id="string", data={"string": {}}, ) """ _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/agent-data/{item_id}"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"data": data}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(AgentData, _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 delete_agent_data( self, item_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.Dict[str, str]: """ Delete agent data by ID. Parameters: - item_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.delete_agent_data( item_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/agent-data/{item_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(typing.Dict[str, str], _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 list_batch_jobs( self, *, directory_id: typing.Optional[str] = None, job_type: typing.Optional[BatchJobType] = None, status: typing.Optional[BatchJobStatus] = None, limit: typing.Optional[int] = None, offset: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> BatchJobQueryResponse: """ List all batch processing jobs for a project with optional filtering. Returns a paginated list of batch jobs with filters for directory, job type, and status. Useful for viewing job history, monitoring progress, and finding specific jobs. Parameters: - directory_id: typing.Optional[str]. Filter by directory ID - job_type: typing.Optional[BatchJobType]. Filter by job type (PARSE, EXTRACT, CLASSIFY) - status: typing.Optional[BatchJobStatus]. Filter by job status (PENDING, RUNNING, COMPLETED, FAILED, CANCELLED) - limit: typing.Optional[int]. Maximum number of jobs to return - offset: typing.Optional[int]. Number of jobs to skip for pagination - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import BatchJobStatus, BatchJobType from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_batch_jobs( job_type=BatchJobType.PARSE, status=BatchJobStatus.PENDING, ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batch-processing"), params=remove_none_from_dict( { "directory_id": directory_id, "job_type": job_type, "status": status, "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(BatchJobQueryResponse, _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_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, continue_as_new_threshold: typing.Optional[int] = OMIT, directory_id: typing.Optional[str] = OMIT, item_ids: typing.Optional[typing.List[str]] = OMIT, job_config: BatchJobCreateRequestJobConfig, page_size: typing.Optional[int] = OMIT, temporal_namespace: typing.Optional[str] = None, ) -> BatchJobResponse: """ Create a new batch processing job for a directory. Processes all files in the specified directory according to the job configuration. The job runs asynchronously and you can monitor progress using the job status endpoint. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - continue_as_new_threshold: typing.Optional[int]. - directory_id: typing.Optional[str]. - item_ids: typing.Optional[typing.List[str]]. - job_config: BatchJobCreateRequestJobConfig. Job configuration for batch processing. Can be BatchParseJobRecordCreate or ClassifyJob. - page_size: typing.Optional[int]. Number of files to fetch per batch from the directory (only used in directory mode) - temporal_namespace: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_batch_job() """ _request: typing.Dict[str, typing.Any] = {"job_config": job_config} if continue_as_new_threshold is not OMIT: _request["continue_as_new_threshold"] = continue_as_new_threshold if directory_id is not OMIT: _request["directory_id"] = directory_id if item_ids is not OMIT: _request["item_ids"] = item_ids if page_size is not OMIT: _request["page_size"] = page_size _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batch-processing"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder(_request), headers=remove_none_from_dict( {**self._client_wrapper.get_headers(), "temporal-namespace": temporal_namespace} ), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(BatchJobResponse, _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_item_processing_results( self, item_id: str, *, job_type: typing.Optional[BatchJobType] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ItemProcessingResultsResponse: """ Get all processing results for a specific item (lineage query). Shows complete processing history including what operations have been performed, with what parameters, and where outputs are stored. Useful for understanding what processing has already been done to avoid redundant work. Optionally filter by job type to see only specific processing operations. Parameters: - item_id: str. - job_type: typing.Optional[BatchJobType]. Filter results by job type - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import BatchJobType from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_item_processing_results( item_id="string", job_type=BatchJobType.PARSE, ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/items/{item_id}/processing-results", ), params=remove_none_from_dict( {"job_type": job_type, "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(ItemProcessingResultsResponse, _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_job_status( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> BatchJobStatusResponse: """ Get detailed status of a batch processing job. Returns current progress, file counts, and estimated completion time. Parameters: - 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.beta.get_batch_job_status( job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/{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(BatchJobStatusResponse, _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 cancel_batch_job( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, reason: typing.Optional[str] = OMIT, temporal_namespace: typing.Optional[str] = None, ) -> BatchJobCancelResponse: """ Cancel a running batch processing job. Stops processing and marks all pending items as cancelled. Items currently being processed may complete depending on their state. Parameters: - job_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - reason: typing.Optional[str]. - temporal_namespace: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.cancel_batch_job( job_id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if reason is not OMIT: _request["reason"] = reason _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/{job_id}/cancel" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder(_request), headers=remove_none_from_dict( {**self._client_wrapper.get_headers(), "temporal-namespace": temporal_namespace} ), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(BatchJobCancelResponse, _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 list_batch_job_items( self, job_id: str, *, status: typing.Optional[BatchFileStatus] = None, limit: typing.Optional[int] = None, offset: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> BatchItemListResponse: """ List items in a batch job with optional status filtering. Useful for finding failed items, viewing completed items, or debugging issues. Results are paginated for performance with configurable limit and offset parameters. Parameters: - job_id: str. - status: typing.Optional[BatchFileStatus]. Filter items by status - limit: typing.Optional[int]. Maximum number of items to return - offset: typing.Optional[int]. Number of items to skip - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import BatchFileStatus from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_batch_job_items( job_id="string", status=BatchFileStatus.PENDING, ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/{job_id}/items" ), params=remove_none_from_dict( { "status": status, "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(BatchItemListResponse, _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 list_directories( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: typing.Optional[str] = None, data_source_id: typing.Optional[str] = None, include_deleted: typing.Optional[bool] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> DirectoryQueryResponse: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: typing.Optional[str]. - data_source_id: typing.Optional[str]. - include_deleted: typing.Optional[bool]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_directories() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/directories"), params=remove_none_from_dict( { "project_id": project_id, "organization_id": organization_id, "name": name, "data_source_id": data_source_id, "include_deleted": include_deleted, "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(DirectoryQueryResponse, _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_directory( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, data_source_id: typing.Optional[str] = OMIT, description: typing.Optional[str] = OMIT, name: str, ) -> DirectoryResponse: """ Create a new directory within the specified project. If data_source_id is provided, validates that the data source exists and belongs to the same project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - data_source_id: typing.Optional[str]. - description: typing.Optional[str]. - name: str. Human-readable name for the directory. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_directory( name="string", ) """ _request: typing.Dict[str, typing.Any] = {"name": name} if data_source_id is not OMIT: _request["data_source_id"] = data_source_id if description is not OMIT: _request["description"] = description _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/directories"), 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(DirectoryResponse, _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_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> DirectoryResponse: """ Retrieve a directory by its identifier. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_directory( directory_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_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(DirectoryResponse, _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 delete_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> None: """ Permanently delete a directory. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.delete_directory( directory_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_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 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 update_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, description: typing.Optional[str] = OMIT, name: typing.Optional[str] = OMIT, ) -> DirectoryResponse: """ Update directory metadata. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - description: typing.Optional[str]. - name: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.update_directory( directory_id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if description is not OMIT: _request["description"] = description if name is not OMIT: _request["name"] = name _response = self._client_wrapper.httpx_client.request( "PATCH", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}"), 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(DirectoryResponse, _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 list_directory_files( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = None, display_name_contains: typing.Optional[str] = None, unique_id: typing.Optional[str] = None, file_id: typing.Optional[str] = None, include_deleted: typing.Optional[bool] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> DirectoryFileQueryResponse: """ List all files within the specified directory with optional filtering and pagination. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - display_name_contains: typing.Optional[str]. - unique_id: typing.Optional[str]. - file_id: typing.Optional[str]. - include_deleted: typing.Optional[bool]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_directory_files( directory_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files" ), params=remove_none_from_dict( { "project_id": project_id, "organization_id": organization_id, "display_name": display_name, "display_name_contains": display_name_contains, "unique_id": unique_id, "file_id": file_id, "include_deleted": include_deleted, "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(DirectoryFileQueryResponse, _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 add_directory_file( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = OMIT, file_id: str, unique_id: typing.Optional[str] = OMIT, ) -> DirectoryFileResponse: """ Create a new file within the specified directory. The directory must exist and belong to the project passed in. The file_id must be provided and exist in the project. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - file_id: str. File ID for the storage location (required). - unique_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.add_directory_file( directory_id="string", file_id="string", ) """ _request: typing.Dict[str, typing.Any] = {"file_id": file_id} if display_name is not OMIT: _request["display_name"] = display_name if unique_id is not OMIT: _request["unique_id"] = unique_id _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files" ), 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(DirectoryFileResponse, _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 upload_file_to_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = None, external_file_id: typing.Optional[str] = None, unique_id: typing.Optional[str] = None, upload_file: typing.IO, ) -> DirectoryFileResponse: """ Upload a file directly to a directory. Uploads a file and creates a directory file entry in a single operation. If unique_id or display_name are not provided, they will be derived from the file metadata. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - external_file_id: typing.Optional[str]. - unique_id: typing.Optional[str]. - upload_file: typing.IO. """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/upload" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), data=jsonable_encoder( {"display_name": display_name, "external_file_id": external_file_id, "unique_id": unique_id} ), files={"upload_file": upload_file}, headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DirectoryFileResponse, _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_directory_file( self, directory_id: str, directory_file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> DirectoryFileResponse: """ Get a file by its directory_file_id within the specified directory. If you're trying to get a file by its unique_id, use the list endpoint with a filter instead. Parameters: - directory_id: str. - directory_file_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_directory_file( directory_id="string", directory_file_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/{directory_file_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(DirectoryFileResponse, _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 delete_directory_file( self, directory_id: str, directory_file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> None: """ Delete a file from the specified directory. Note: This endpoint uses directory_file_id (the internal ID). If you're trying to delete a file by its unique_id, use the list endpoint with a filter to find the directory_file_id first. Parameters: - directory_id: str. - directory_file_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.delete_directory_file( directory_id="string", directory_file_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/{directory_file_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 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 update_directory_file( self, directory_id: str, directory_file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = OMIT, unique_id: typing.Optional[str] = OMIT, ) -> DirectoryFileResponse: """ Update file metadata within the specified directory. Note: This endpoint uses directory_file_id (the internal ID). If you're trying to update a file by its unique_id, use the list endpoint with a filter to find the directory_file_id first. Parameters: - directory_id: str. - directory_file_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - unique_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.update_directory_file( directory_id="string", directory_file_id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if display_name is not OMIT: _request["display_name"] = display_name if unique_id is not OMIT: _request["unique_id"] = unique_id _response = self._client_wrapper.httpx_client.request( "PATCH", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/{directory_file_id}", ), 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(DirectoryFileResponse, _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_directory_sync_job_api_v_1_beta_directory_sync_jobs_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, directory_id: str, ) -> DirectorySyncJobResponse: """ Create a new directory sync job. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - directory_id: str. Directory being processed --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_directory_sync_job_api_v_1_beta_directory_sync_jobs_post( directory_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/directory-sync-jobs"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"directory_id": directory_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DirectorySyncJobResponse, _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_directory_sync_job( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> DirectorySyncJobResponse: """ Get a directory sync job by ID. Parameters: - 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.beta.get_directory_sync_job( job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directory-sync-jobs/{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(DirectorySyncJobResponse, _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_file( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: FileCreate, ) -> File: """ Create a new file in the project. Args: file_create: File creation data project: Validated project from dependency db: Database session Returns: The created file Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: FileCreate. --- from llama_cloud import FileCreate from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_file( request=FileCreate( name="string", ), ) """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/files"), 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(File, _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 upsert_file( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: FileCreate, ) -> File: """ Upsert a file (create or update if exists) in the project. Args: file_create: File creation/update data project: Validated project from dependency db: Database session Returns: The upserted file Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: FileCreate. --- from llama_cloud import FileCreate from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.upsert_file( request=FileCreate( name="string", ), ) """ _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/files"), 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(File, _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 query_files( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, filter: typing.Optional[FileFilter] = OMIT, order_by: typing.Optional[str] = OMIT, page_size: typing.Optional[int] = OMIT, page_token: typing.Optional[str] = OMIT, ) -> FileQueryResponse: """ Query files with flexible filtering and pagination. Args: request: The query request with filters and pagination project: Validated project from dependency db: Database session Returns: Paginated response with files Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - filter: typing.Optional[FileFilter]. - order_by: typing.Optional[str]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud import FileFilter from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.query_files( filter=FileFilter(), ) """ _request: typing.Dict[str, typing.Any] = {} if filter is not OMIT: _request["filter"] = filter if order_by is not OMIT: _request["order_by"] = order_by if page_size is not OMIT: _request["page_size"] = page_size if page_token is not OMIT: _request["page_token"] = page_token _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/files/query"), 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(FileQueryResponse, _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 delete_file( self, file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> None: """ Delete a single file from the project. Args: file_id: The ID of the file to delete project: Validated project from dependency db: Database session Returns: None (204 No Content on success) Parameters: - file_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.delete_file( file_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/files/{file_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 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 list_parse_configurations( self, *, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, name: typing.Optional[str] = None, creator: typing.Optional[str] = None, version: typing.Optional[str] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ParseConfigurationQueryResponse: """ List parse configurations for the current project. Args: project: Validated project from dependency user: Current user db: Database session page_size: Number of items per page page_token: Token for pagination name: Filter by configuration name creator: Filter by creator version: Filter by version Returns: Paginated response with parse configurations Parameters: - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. - name: typing.Optional[str]. - creator: typing.Optional[str]. - version: typing.Optional[str]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_parse_configurations() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parse-configurations"), params=remove_none_from_dict( { "page_size": page_size, "page_token": page_token, "name": name, "creator": creator, "version": version, "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(ParseConfigurationQueryResponse, _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_parse_configuration( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, creator: typing.Optional[str] = OMIT, name: str, parameters: LlamaParseParameters, source_id: typing.Optional[str] = OMIT, source_type: typing.Optional[str] = OMIT, version: str, ) -> ParseConfiguration: """ Create a new parse configuration. Args: config_create: Parse configuration creation data project: Validated project from dependency user: Current user db: Database session Returns: The created parse configuration Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - creator: typing.Optional[str]. - name: str. Name of the parse configuration - parameters: LlamaParseParameters. LlamaParseParameters configuration - source_id: typing.Optional[str]. - source_type: typing.Optional[str]. - version: str. Version of the configuration --- from llama_cloud import ( FailPageMode, LlamaParseParameters, LlamaParseParametersPriority, ParsingMode, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_parse_configuration( name="string", parameters=LlamaParseParameters( parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, priority=LlamaParseParametersPriority.LOW, replace_failed_page_mode=FailPageMode.RAW_TEXT, ), version="string", ) """ _request: typing.Dict[str, typing.Any] = {"name": name, "parameters": parameters, "version": version} if creator is not OMIT: _request["creator"] = creator if source_id is not OMIT: _request["source_id"] = source_id if source_type is not OMIT: _request["source_type"] = source_type _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parse-configurations"), 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(ParseConfiguration, _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_parse_configuration( self, config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ParseConfiguration: """ Get a parse configuration by ID. Args: config_id: The ID of the parse configuration project: Validated project from dependency user: Current user db: Database session Returns: The parse configuration Parameters: - config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_parse_configuration( config_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parse-configurations/{config_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(ParseConfiguration, _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 update_parse_configuration( self, config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, parameters: typing.Optional[LlamaParseParameters] = OMIT, ) -> ParseConfiguration: """ Update a parse configuration. Args: config_id: The ID of the parse configuration to update config_update: Update data project: Validated project from dependency user: Current user db: Database session Returns: The updated parse configuration Parameters: - config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - parameters: typing.Optional[LlamaParseParameters]. --- from llama_cloud import ( FailPageMode, LlamaParseParameters, LlamaParseParametersPriority, ParsingMode, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.update_parse_configuration( config_id="string", parameters=LlamaParseParameters( parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, priority=LlamaParseParametersPriority.LOW, replace_failed_page_mode=FailPageMode.RAW_TEXT, ), ) """ _request: typing.Dict[str, typing.Any] = {} if parameters is not OMIT: _request["parameters"] = parameters _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parse-configurations/{config_id}" ), 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(ParseConfiguration, _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 delete_parse_configuration( self, config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> None: """ Delete a parse configuration. Args: config_id: The ID of the parse configuration to delete project: Validated project from dependency user: Current user db: Database session Parameters: - config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.delete_parse_configuration( config_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parse-configurations/{config_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 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 list_parse_jobs( self, *, status: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ParseJobQueryResponse: """ List parse jobs with optional filtering and pagination. Parameters: - status: typing.Optional[str]. Filter by job status (pending, running, completed, failed, cancelled) - page_size: typing.Optional[int]. Maximum number of jobs to return - page_token: typing.Optional[str]. Page token for cursor-based pagination - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_parse_jobs() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parsing-temporal"), params=remove_none_from_dict( { "status": status, "page_size": page_size, "page_token": page_token, "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(ParseJobQueryResponse, _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_parse_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, correlation_id: typing.Optional[str] = OMIT, created_at: typing.Optional[dt.datetime] = OMIT, job_name: typing_extensions.Literal["parse_raw_file_job"], parameters: typing.Optional[SrcParseSchemasWorkflowParseJobConfig] = OMIT, parent_job_execution_id: typing.Optional[str] = OMIT, partitions: typing.Dict[str, str], parse_job_record_create_project_id: typing.Optional[str] = OMIT, session_id: typing.Optional[str] = OMIT, user_id: typing.Optional[str] = OMIT, webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> ParseJobResponse: """ Create a new parse job. Processes a document asynchronously. Use the job ID to check status and retrieve results. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - correlation_id: typing.Optional[str]. - created_at: typing.Optional[dt.datetime]. Creation datetime - job_name: typing_extensions.Literal["parse_raw_file_job"]. - parameters: typing.Optional[SrcParseSchemasWorkflowParseJobConfig]. - parent_job_execution_id: typing.Optional[str]. - partitions: typing.Dict[str, str]. The partitions for this execution. Used for determining where to save job output. - parse_job_record_create_project_id: typing.Optional[str]. - session_id: typing.Optional[str]. - user_id: typing.Optional[str]. - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( FailPageMode, ParsingMode, SrcParseSchemasWorkflowParseJobConfig, SrcParseSchemasWorkflowParseJobConfigPriority, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_parse_job( job_name="parse_raw_file_job", parameters=SrcParseSchemasWorkflowParseJobConfig( file_key="string", file_name="string", lang="string", original_file_name="string", parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, priority=SrcParseSchemasWorkflowParseJobConfigPriority.LOW, replace_failed_page_mode=FailPageMode.RAW_TEXT, type="parse", ), partitions={"string": "string"}, ) """ _request: typing.Dict[str, typing.Any] = {"job_name": job_name, "partitions": partitions} if correlation_id is not OMIT: _request["correlation_id"] = correlation_id if created_at is not OMIT: _request["created_at"] = created_at if parameters is not OMIT: _request["parameters"] = parameters if parent_job_execution_id is not OMIT: _request["parent_job_execution_id"] = parent_job_execution_id if parse_job_record_create_project_id is not OMIT: _request["project_id"] = parse_job_record_create_project_id if session_id is not OMIT: _request["session_id"] = session_id if user_id is not OMIT: _request["user_id"] = user_id if webhook_configurations is not OMIT: _request["webhook_configurations"] = webhook_configurations _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parsing-temporal"), 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(ParseJobResponse, _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_parse_job( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ParseJobResponse: """ Get a parse job by ID. Parameters: - 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.beta.get_parse_job( job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parsing-temporal/{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(ParseJobResponse, _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_parse_job_results( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.Optional[ParseJobResultResponse]: """ Get results for a parse job. Returns a single result object containing the full OutputFiles JSON with all output types (full_text, md, json_output, job_info, xlsx, output_pdf, structured, images, charts). Parameters: - 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.beta.get_parse_job_results( job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parsing-temporal/{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(typing.Optional[ParseJobResultResponse], _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 stream_parse_job_result_content( self, job_id: str, result_type: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> typing.Any: """ Stream parse result content from storage. The result_type should be one of: fullText, md, json, jobInfo, xlsx, outputPDF, structured. For images and charts, use the index-based approach (e.g., images[0], charts[1]). Parameters: - job_id: str. - result_type: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.stream_parse_job_result_content( job_id="string", result_type="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parsing-temporal/{job_id}/results/{result_type}/content", ), 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(typing.Any, _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 list_quota_configurations( self, *, source_type: typing_extensions.Literal["organization"], source_id: str, page: typing.Optional[int] = None, page_size: typing.Optional[int] = None, ) -> PaginatedResponseQuotaConfiguration: """ Retrieve a paginated list of quota configurations with optional filtering. Parameters: - source_type: typing_extensions.Literal["organization"]. - source_id: str. - page: typing.Optional[int]. - page_size: typing.Optional[int]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_quota_configurations( source_type="organization", source_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/quota-management"), params=remove_none_from_dict( {"source_type": source_type, "source_id": source_id, "page": page, "page_size": page_size} ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(PaginatedResponseQuotaConfiguration, _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 list_spreadsheet_jobs( self, *, include_results: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> PaginatedResponseSpreadsheetJob: """ List spreadsheet parsing jobs. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - include_results: typing.Optional[bool]. - 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.beta.list_spreadsheet_jobs() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/sheets/jobs"), params=remove_none_from_dict( { "include_results": include_results, "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(PaginatedResponseSpreadsheetJob, _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_spreadsheet_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, config: typing.Optional[SpreadsheetParsingConfig] = OMIT, file_id: str, ) -> SpreadsheetJob: """ Create a spreadsheet parsing 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]. - config: typing.Optional[SpreadsheetParsingConfig]. Configuration for the parsing job - file_id: str. The ID of the file to parse --- from llama_cloud import SpreadsheetParsingConfig from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_spreadsheet_job( config=SpreadsheetParsingConfig(), file_id="string", ) """ _request: typing.Dict[str, typing.Any] = {"file_id": file_id} if config is not OMIT: _request["config"] = config _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/sheets/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(SpreadsheetJob, _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_spreadsheet_job( self, spreadsheet_job_id: str, *, include_results: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> SpreadsheetJob: """ Get a spreadsheet parsing job. When include_results=True (default), the response will include extracted regions and results if the job is complete, eliminating the need for a separate /results call. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - spreadsheet_job_id: str. - include_results: typing.Optional[bool]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_spreadsheet_job( spreadsheet_job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/sheets/jobs/{spreadsheet_job_id}" ), params=remove_none_from_dict( {"include_results": include_results, "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(SpreadsheetJob, _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 delete_spreadsheet_job( self, spreadsheet_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> typing.Any: """ Delete a spreadsheet parsing job and its associated data. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - spreadsheet_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.beta.delete_spreadsheet_job( spreadsheet_job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/sheets/jobs/{spreadsheet_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(typing.Any, _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_result_region( self, spreadsheet_job_id: str, region_id: str, region_type: SpreadsheetResultType, *, expires_at_seconds: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> PresignedUrl: """ Generate a presigned URL to download a specific extracted region. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - spreadsheet_job_id: str. - region_id: str. - region_type: SpreadsheetResultType. - expires_at_seconds: typing.Optional[int]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import SpreadsheetResultType from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_result_region( spreadsheet_job_id="string", region_id="string", region_type=SpreadsheetResultType.TABLE, ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/sheets/jobs/{spreadsheet_job_id}/regions/{region_id}/result/{region_type}", ), params=remove_none_from_dict( {"expires_at_seconds": expires_at_seconds, "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(PresignedUrl, _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 list_split_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, ) -> SplitJobQueryResponse: """ List document split 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.beta.list_split_jobs() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/split/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(SplitJobQueryResponse, _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_split_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, categories: typing.List[SplitCategory], document_input: SplitDocumentInput, splitting_strategy: typing.Optional[SplitStrategy] = OMIT, ) -> SplitJobResponse: """ Create a document split 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]. - categories: typing.List[SplitCategory]. Categories to split the document into. - document_input: SplitDocumentInput. Document to be split. - splitting_strategy: typing.Optional[SplitStrategy]. Strategy for splitting the document. --- from llama_cloud import SplitDocumentInput, SplitStrategy from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.create_split_job( categories=[], document_input=SplitDocumentInput( type="string", value="string", ), splitting_strategy=SplitStrategy(), ) """ _request: typing.Dict[str, typing.Any] = {"categories": categories, "document_input": document_input} if splitting_strategy is not OMIT: _request["splitting_strategy"] = splitting_strategy _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/split/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(SplitJobResponse, _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_split_job( self, split_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> SplitJobResponse: """ Get a document split job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - split_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.beta.get_split_job( split_job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/split/jobs/{split_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(SplitJobResponse, _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 list_usage_metrics( self, *, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, include_total: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, user_id: typing.Optional[str] = None, event_types: typing.Optional[typing.Union[str, typing.List[str]]] = None, days: typing.Optional[typing.Union[str, typing.List[str]]] = None, day_on_or_before: typing.Optional[str] = None, day_on_or_after: typing.Optional[str] = None, event_aggregation_type: typing.Optional[str] = None, event_aggregation_key: typing.Optional[str] = None, organization_id: str, ) -> UsageMetricQueryResponse: """ List usage metrics with filtering and pagination. Parameters: - page_size: typing.Optional[int]. Number of items per page - page_token: typing.Optional[str]. Token for pagination - include_total: typing.Optional[bool]. Include total count in response - project_id: typing.Optional[str]. Filter by project ID - user_id: typing.Optional[str]. Filter by user ID - event_types: typing.Optional[typing.Union[str, typing.List[str]]]. Filter by event types - days: typing.Optional[typing.Union[str, typing.List[str]]]. Filter by specific days (YYYY-MM-DD) - day_on_or_before: typing.Optional[str]. Filter by days on or before this date (YYYY-MM-DD) - day_on_or_after: typing.Optional[str]. Filter by days on or after this date (YYYY-MM-DD) - event_aggregation_type: typing.Optional[str]. Filter by event aggregation type - event_aggregation_key: typing.Optional[str]. Filter by event aggregation key - organization_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.list_usage_metrics( organization_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/usage-metrics"), params=remove_none_from_dict( { "page_size": page_size, "page_token": page_token, "include_total": include_total, "project_id": project_id, "user_id": user_id, "event_types": event_types, "days": days, "day_on_or_before": day_on_or_before, "day_on_or_after": day_on_or_after, "event_aggregation_type": event_aggregation_type, "event_aggregation_key": event_aggregation_key, "organization_id": organization_id, } ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(UsageMetricQueryResponse, _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_usage_metric(self, usage_metric_id: str) -> UsageMetric: """ Get a usage metric by ID. Parameters: - usage_metric_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.beta.get_usage_metric( usage_metric_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/usage-metrics/{usage_metric_id}" ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(UsageMetric, _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 create_agent_data( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, data: typing.Dict[str, typing.Any], deployment_name: str, ) -> AgentData: """ Create new agent data. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. - data: typing.Dict[str, typing.Any]. - deployment_name: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_agent_data( data={"string": {}}, deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"data": data, "deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data"), 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(AgentData, _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 aggregate_agent_data_api_v_1_beta_agent_data_aggregate_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, count: typing.Optional[bool] = OMIT, deployment_name: str, filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]] = OMIT, first: typing.Optional[bool] = OMIT, group_by: typing.Optional[typing.List[str]] = OMIT, offset: typing.Optional[int] = OMIT, order_by: typing.Optional[str] = OMIT, page_size: typing.Optional[int] = OMIT, page_token: typing.Optional[str] = OMIT, ) -> PaginatedResponseAggregateGroup: """ Aggregate agent data with grouping and optional counting/first item retrieval. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. The logical agent data collection to aggregate data for - count: typing.Optional[bool]. - deployment_name: str. The agent deployment's name to aggregate data for - filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]]. - first: typing.Optional[bool]. - group_by: typing.Optional[typing.List[str]]. - offset: typing.Optional[int]. - order_by: 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.beta.aggregate_agent_data_api_v_1_beta_agent_data_aggregate_post( deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection if count is not OMIT: _request["count"] = count if filter is not OMIT: _request["filter"] = filter if first is not OMIT: _request["first"] = first if group_by is not OMIT: _request["group_by"] = group_by if offset is not OMIT: _request["offset"] = offset if order_by is not OMIT: _request["order_by"] = order_by if page_size is not OMIT: _request["page_size"] = page_size if page_token is not OMIT: _request["page_token"] = page_token _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data/:aggregate"), 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(PaginatedResponseAggregateGroup, _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 delete_agent_data_by_query_api_v_1_beta_agent_data_delete_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, deployment_name: str, filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]] = OMIT, ) -> DeleteResponse: """ Bulk delete agent data by query (deployment_name, collection, optional filters). Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. The logical agent data collection to delete from - deployment_name: str. The agent deployment's name to delete data for - filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.delete_agent_data_by_query_api_v_1_beta_agent_data_delete_post( deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection if filter is not OMIT: _request["filter"] = filter _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data/:delete"), 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(DeleteResponse, _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 search_agent_data_api_v_1_beta_agent_data_search_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, collection: typing.Optional[str] = OMIT, deployment_name: str, filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]] = OMIT, include_total: typing.Optional[bool] = OMIT, offset: typing.Optional[int] = OMIT, order_by: typing.Optional[str] = OMIT, page_size: typing.Optional[int] = OMIT, page_token: typing.Optional[str] = OMIT, ) -> PaginatedResponseAgentData: """ Search agent data with filtering, sorting, and pagination. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - collection: typing.Optional[str]. The logical agent data collection to search within - deployment_name: str. The agent deployment's name to search within - filter: typing.Optional[typing.Dict[str, typing.Optional[FilterOperation]]]. - include_total: typing.Optional[bool]. Whether to include the total number of items in the response - offset: typing.Optional[int]. - order_by: 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.beta.search_agent_data_api_v_1_beta_agent_data_search_post( deployment_name="string", ) """ _request: typing.Dict[str, typing.Any] = {"deployment_name": deployment_name} if collection is not OMIT: _request["collection"] = collection if filter is not OMIT: _request["filter"] = filter if include_total is not OMIT: _request["include_total"] = include_total if offset is not OMIT: _request["offset"] = offset if order_by is not OMIT: _request["order_by"] = order_by if page_size is not OMIT: _request["page_size"] = page_size if page_token is not OMIT: _request["page_token"] = page_token _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/agent-data/:search"), 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(PaginatedResponseAgentData, _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_agent_data( self, item_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> AgentData: """ Get agent data by ID. Parameters: - item_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.beta.get_agent_data( item_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/agent-data/{item_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(AgentData, _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 update_agent_data( self, item_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, data: typing.Dict[str, typing.Any], ) -> AgentData: """ Update agent data by ID (overwrites). Parameters: - item_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - data: typing.Dict[str, typing.Any]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.update_agent_data( item_id="string", data={"string": {}}, ) """ _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/agent-data/{item_id}"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"data": data}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(AgentData, _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 delete_agent_data( self, item_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.Dict[str, str]: """ Delete agent data by ID. Parameters: - item_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.beta.delete_agent_data( item_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/agent-data/{item_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(typing.Dict[str, str], _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 list_batch_jobs( self, *, directory_id: typing.Optional[str] = None, job_type: typing.Optional[BatchJobType] = None, status: typing.Optional[BatchJobStatus] = None, limit: typing.Optional[int] = None, offset: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> BatchJobQueryResponse: """ List all batch processing jobs for a project with optional filtering. Returns a paginated list of batch jobs with filters for directory, job type, and status. Useful for viewing job history, monitoring progress, and finding specific jobs. Parameters: - directory_id: typing.Optional[str]. Filter by directory ID - job_type: typing.Optional[BatchJobType]. Filter by job type (PARSE, EXTRACT, CLASSIFY) - status: typing.Optional[BatchJobStatus]. Filter by job status (PENDING, RUNNING, COMPLETED, FAILED, CANCELLED) - limit: typing.Optional[int]. Maximum number of jobs to return - offset: typing.Optional[int]. Number of jobs to skip for pagination - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import BatchJobStatus, BatchJobType from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_batch_jobs( job_type=BatchJobType.PARSE, status=BatchJobStatus.PENDING, ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batch-processing"), params=remove_none_from_dict( { "directory_id": directory_id, "job_type": job_type, "status": status, "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(BatchJobQueryResponse, _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_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, continue_as_new_threshold: typing.Optional[int] = OMIT, directory_id: typing.Optional[str] = OMIT, item_ids: typing.Optional[typing.List[str]] = OMIT, job_config: BatchJobCreateRequestJobConfig, page_size: typing.Optional[int] = OMIT, temporal_namespace: typing.Optional[str] = None, ) -> BatchJobResponse: """ Create a new batch processing job for a directory. Processes all files in the specified directory according to the job configuration. The job runs asynchronously and you can monitor progress using the job status endpoint. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - continue_as_new_threshold: typing.Optional[int]. - directory_id: typing.Optional[str]. - item_ids: typing.Optional[typing.List[str]]. - job_config: BatchJobCreateRequestJobConfig. Job configuration for batch processing. Can be BatchParseJobRecordCreate or ClassifyJob. - page_size: typing.Optional[int]. Number of files to fetch per batch from the directory (only used in directory mode) - temporal_namespace: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_batch_job() """ _request: typing.Dict[str, typing.Any] = {"job_config": job_config} if continue_as_new_threshold is not OMIT: _request["continue_as_new_threshold"] = continue_as_new_threshold if directory_id is not OMIT: _request["directory_id"] = directory_id if item_ids is not OMIT: _request["item_ids"] = item_ids if page_size is not OMIT: _request["page_size"] = page_size _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/batch-processing"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder(_request), headers=remove_none_from_dict( {**self._client_wrapper.get_headers(), "temporal-namespace": temporal_namespace} ), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(BatchJobResponse, _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_item_processing_results( self, item_id: str, *, job_type: typing.Optional[BatchJobType] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ItemProcessingResultsResponse: """ Get all processing results for a specific item (lineage query). Shows complete processing history including what operations have been performed, with what parameters, and where outputs are stored. Useful for understanding what processing has already been done to avoid redundant work. Optionally filter by job type to see only specific processing operations. Parameters: - item_id: str. - job_type: typing.Optional[BatchJobType]. Filter results by job type - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import BatchJobType from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.get_item_processing_results( item_id="string", job_type=BatchJobType.PARSE, ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/items/{item_id}/processing-results", ), params=remove_none_from_dict( {"job_type": job_type, "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(ItemProcessingResultsResponse, _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_job_status( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> BatchJobStatusResponse: """ Get detailed status of a batch processing job. Returns current progress, file counts, and estimated completion time. Parameters: - 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.beta.get_batch_job_status( 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/beta/batch-processing/{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(BatchJobStatusResponse, _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 cancel_batch_job( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, reason: typing.Optional[str] = OMIT, temporal_namespace: typing.Optional[str] = None, ) -> BatchJobCancelResponse: """ Cancel a running batch processing job. Stops processing and marks all pending items as cancelled. Items currently being processed may complete depending on their state. Parameters: - job_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - reason: typing.Optional[str]. - temporal_namespace: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.cancel_batch_job( job_id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if reason is not OMIT: _request["reason"] = reason _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/{job_id}/cancel" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder(_request), headers=remove_none_from_dict( {**self._client_wrapper.get_headers(), "temporal-namespace": temporal_namespace} ), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(BatchJobCancelResponse, _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 list_batch_job_items( self, job_id: str, *, status: typing.Optional[BatchFileStatus] = None, limit: typing.Optional[int] = None, offset: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> BatchItemListResponse: """ List items in a batch job with optional status filtering. Useful for finding failed items, viewing completed items, or debugging issues. Results are paginated for performance with configurable limit and offset parameters. Parameters: - job_id: str. - status: typing.Optional[BatchFileStatus]. Filter items by status - limit: typing.Optional[int]. Maximum number of items to return - offset: typing.Optional[int]. Number of items to skip - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import BatchFileStatus from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_batch_job_items( job_id="string", status=BatchFileStatus.PENDING, ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/batch-processing/{job_id}/items" ), params=remove_none_from_dict( { "status": status, "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(BatchItemListResponse, _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 list_directories( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, name: typing.Optional[str] = None, data_source_id: typing.Optional[str] = None, include_deleted: typing.Optional[bool] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> DirectoryQueryResponse: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - name: typing.Optional[str]. - data_source_id: typing.Optional[str]. - include_deleted: typing.Optional[bool]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_directories() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/directories"), params=remove_none_from_dict( { "project_id": project_id, "organization_id": organization_id, "name": name, "data_source_id": data_source_id, "include_deleted": include_deleted, "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(DirectoryQueryResponse, _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_directory( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, data_source_id: typing.Optional[str] = OMIT, description: typing.Optional[str] = OMIT, name: str, ) -> DirectoryResponse: """ Create a new directory within the specified project. If data_source_id is provided, validates that the data source exists and belongs to the same project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - data_source_id: typing.Optional[str]. - description: typing.Optional[str]. - name: str. Human-readable name for the directory. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_directory( name="string", ) """ _request: typing.Dict[str, typing.Any] = {"name": name} if data_source_id is not OMIT: _request["data_source_id"] = data_source_id if description is not OMIT: _request["description"] = description _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/directories"), 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(DirectoryResponse, _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_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> DirectoryResponse: """ Retrieve a directory by its identifier. Parameters: - directory_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.beta.get_directory( directory_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/directories/{directory_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(DirectoryResponse, _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 delete_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> None: """ Permanently delete a directory. Parameters: - directory_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.beta.delete_directory( directory_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_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 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 update_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, description: typing.Optional[str] = OMIT, name: typing.Optional[str] = OMIT, ) -> DirectoryResponse: """ Update directory metadata. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - description: typing.Optional[str]. - name: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.update_directory( directory_id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if description is not OMIT: _request["description"] = description if name is not OMIT: _request["name"] = name _response = await self._client_wrapper.httpx_client.request( "PATCH", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}"), 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(DirectoryResponse, _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 list_directory_files( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = None, display_name_contains: typing.Optional[str] = None, unique_id: typing.Optional[str] = None, file_id: typing.Optional[str] = None, include_deleted: typing.Optional[bool] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> DirectoryFileQueryResponse: """ List all files within the specified directory with optional filtering and pagination. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - display_name_contains: typing.Optional[str]. - unique_id: typing.Optional[str]. - file_id: typing.Optional[str]. - include_deleted: typing.Optional[bool]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_directory_files( directory_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/directories/{directory_id}/files" ), params=remove_none_from_dict( { "project_id": project_id, "organization_id": organization_id, "display_name": display_name, "display_name_contains": display_name_contains, "unique_id": unique_id, "file_id": file_id, "include_deleted": include_deleted, "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(DirectoryFileQueryResponse, _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 add_directory_file( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = OMIT, file_id: str, unique_id: typing.Optional[str] = OMIT, ) -> DirectoryFileResponse: """ Create a new file within the specified directory. The directory must exist and belong to the project passed in. The file_id must be provided and exist in the project. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - file_id: str. File ID for the storage location (required). - unique_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.add_directory_file( directory_id="string", file_id="string", ) """ _request: typing.Dict[str, typing.Any] = {"file_id": file_id} if display_name is not OMIT: _request["display_name"] = display_name if unique_id is not OMIT: _request["unique_id"] = unique_id _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files" ), 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(DirectoryFileResponse, _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 upload_file_to_directory( self, directory_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = None, external_file_id: typing.Optional[str] = None, unique_id: typing.Optional[str] = None, upload_file: typing.IO, ) -> DirectoryFileResponse: """ Upload a file directly to a directory. Uploads a file and creates a directory file entry in a single operation. If unique_id or display_name are not provided, they will be derived from the file metadata. Parameters: - directory_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - external_file_id: typing.Optional[str]. - unique_id: typing.Optional[str]. - upload_file: typing.IO. """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/upload" ), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), data=jsonable_encoder( {"display_name": display_name, "external_file_id": external_file_id, "unique_id": unique_id} ), files={"upload_file": upload_file}, headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DirectoryFileResponse, _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_directory_file( self, directory_id: str, directory_file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> DirectoryFileResponse: """ Get a file by its directory_file_id within the specified directory. If you're trying to get a file by its unique_id, use the list endpoint with a filter instead. Parameters: - directory_id: str. - directory_file_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.beta.get_directory_file( directory_id="string", directory_file_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/directories/{directory_id}/files/{directory_file_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(DirectoryFileResponse, _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 delete_directory_file( self, directory_id: str, directory_file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> None: """ Delete a file from the specified directory. Note: This endpoint uses directory_file_id (the internal ID). If you're trying to delete a file by its unique_id, use the list endpoint with a filter to find the directory_file_id first. Parameters: - directory_id: str. - directory_file_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.beta.delete_directory_file( directory_id="string", directory_file_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/{directory_file_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 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 update_directory_file( self, directory_id: str, directory_file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, display_name: typing.Optional[str] = OMIT, unique_id: typing.Optional[str] = OMIT, ) -> DirectoryFileResponse: """ Update file metadata within the specified directory. Note: This endpoint uses directory_file_id (the internal ID). If you're trying to update a file by its unique_id, use the list endpoint with a filter to find the directory_file_id first. Parameters: - directory_id: str. - directory_file_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - display_name: typing.Optional[str]. - unique_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.update_directory_file( directory_id="string", directory_file_id="string", ) """ _request: typing.Dict[str, typing.Any] = {} if display_name is not OMIT: _request["display_name"] = display_name if unique_id is not OMIT: _request["unique_id"] = unique_id _response = await self._client_wrapper.httpx_client.request( "PATCH", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/directories/{directory_id}/files/{directory_file_id}", ), 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(DirectoryFileResponse, _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_directory_sync_job_api_v_1_beta_directory_sync_jobs_post( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, directory_id: str, ) -> DirectorySyncJobResponse: """ Create a new directory sync job. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - directory_id: str. Directory being processed --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_directory_sync_job_api_v_1_beta_directory_sync_jobs_post( directory_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/directory-sync-jobs"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"directory_id": directory_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DirectorySyncJobResponse, _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_directory_sync_job( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> DirectorySyncJobResponse: """ Get a directory sync job by ID. Parameters: - 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.beta.get_directory_sync_job( 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/beta/directory-sync-jobs/{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(DirectorySyncJobResponse, _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_file( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: FileCreate, ) -> File: """ Create a new file in the project. Args: file_create: File creation data project: Validated project from dependency db: Database session Returns: The created file Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: FileCreate. --- from llama_cloud import FileCreate from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_file( request=FileCreate( name="string", ), ) """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/files"), 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(File, _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 upsert_file( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: FileCreate, ) -> File: """ Upsert a file (create or update if exists) in the project. Args: file_create: File creation/update data project: Validated project from dependency db: Database session Returns: The upserted file Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: FileCreate. --- from llama_cloud import FileCreate from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.upsert_file( request=FileCreate( name="string", ), ) """ _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/files"), 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(File, _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 query_files( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, filter: typing.Optional[FileFilter] = OMIT, order_by: typing.Optional[str] = OMIT, page_size: typing.Optional[int] = OMIT, page_token: typing.Optional[str] = OMIT, ) -> FileQueryResponse: """ Query files with flexible filtering and pagination. Args: request: The query request with filters and pagination project: Validated project from dependency db: Database session Returns: Paginated response with files Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - filter: typing.Optional[FileFilter]. - order_by: typing.Optional[str]. - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. --- from llama_cloud import FileFilter from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.query_files( filter=FileFilter(), ) """ _request: typing.Dict[str, typing.Any] = {} if filter is not OMIT: _request["filter"] = filter if order_by is not OMIT: _request["order_by"] = order_by if page_size is not OMIT: _request["page_size"] = page_size if page_token is not OMIT: _request["page_token"] = page_token _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/files/query"), 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(FileQueryResponse, _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 delete_file( self, file_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> None: """ Delete a single file from the project. Args: file_id: The ID of the file to delete project: Validated project from dependency db: Database session Returns: None (204 No Content on success) Parameters: - file_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.beta.delete_file( file_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/files/{file_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 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 list_parse_configurations( self, *, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, name: typing.Optional[str] = None, creator: typing.Optional[str] = None, version: typing.Optional[str] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ParseConfigurationQueryResponse: """ List parse configurations for the current project. Args: project: Validated project from dependency user: Current user db: Database session page_size: Number of items per page page_token: Token for pagination name: Filter by configuration name creator: Filter by creator version: Filter by version Returns: Paginated response with parse configurations Parameters: - page_size: typing.Optional[int]. - page_token: typing.Optional[str]. - name: typing.Optional[str]. - creator: typing.Optional[str]. - version: typing.Optional[str]. - 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_parse_configurations() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parse-configurations"), params=remove_none_from_dict( { "page_size": page_size, "page_token": page_token, "name": name, "creator": creator, "version": version, "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(ParseConfigurationQueryResponse, _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_parse_configuration( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, creator: typing.Optional[str] = OMIT, name: str, parameters: LlamaParseParameters, source_id: typing.Optional[str] = OMIT, source_type: typing.Optional[str] = OMIT, version: str, ) -> ParseConfiguration: """ Create a new parse configuration. Args: config_create: Parse configuration creation data project: Validated project from dependency user: Current user db: Database session Returns: The created parse configuration Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - creator: typing.Optional[str]. - name: str. Name of the parse configuration - parameters: LlamaParseParameters. LlamaParseParameters configuration - source_id: typing.Optional[str]. - source_type: typing.Optional[str]. - version: str. Version of the configuration --- from llama_cloud import ( FailPageMode, LlamaParseParameters, LlamaParseParametersPriority, ParsingMode, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_parse_configuration( name="string", parameters=LlamaParseParameters( parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, priority=LlamaParseParametersPriority.LOW, replace_failed_page_mode=FailPageMode.RAW_TEXT, ), version="string", ) """ _request: typing.Dict[str, typing.Any] = {"name": name, "parameters": parameters, "version": version} if creator is not OMIT: _request["creator"] = creator if source_id is not OMIT: _request["source_id"] = source_id if source_type is not OMIT: _request["source_type"] = source_type _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parse-configurations"), 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(ParseConfiguration, _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_parse_configuration( self, config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ParseConfiguration: """ Get a parse configuration by ID. Args: config_id: The ID of the parse configuration project: Validated project from dependency user: Current user db: Database session Returns: The parse configuration Parameters: - config_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.beta.get_parse_configuration( config_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/parse-configurations/{config_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(ParseConfiguration, _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 update_parse_configuration( self, config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, parameters: typing.Optional[LlamaParseParameters] = OMIT, ) -> ParseConfiguration: """ Update a parse configuration. Args: config_id: The ID of the parse configuration to update config_update: Update data project: Validated project from dependency user: Current user db: Database session Returns: The updated parse configuration Parameters: - config_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - parameters: typing.Optional[LlamaParseParameters]. --- from llama_cloud import ( FailPageMode, LlamaParseParameters, LlamaParseParametersPriority, ParsingMode, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.update_parse_configuration( config_id="string", parameters=LlamaParseParameters( parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, priority=LlamaParseParametersPriority.LOW, replace_failed_page_mode=FailPageMode.RAW_TEXT, ), ) """ _request: typing.Dict[str, typing.Any] = {} if parameters is not OMIT: _request["parameters"] = parameters _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parse-configurations/{config_id}" ), 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(ParseConfiguration, _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 delete_parse_configuration( self, config_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> None: """ Delete a parse configuration. Args: config_id: The ID of the parse configuration to delete project: Validated project from dependency user: Current user db: Database session Parameters: - config_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.beta.delete_parse_configuration( config_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parse-configurations/{config_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 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 list_parse_jobs( self, *, status: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> ParseJobQueryResponse: """ List parse jobs with optional filtering and pagination. Parameters: - status: typing.Optional[str]. Filter by job status (pending, running, completed, failed, cancelled) - page_size: typing.Optional[int]. Maximum number of jobs to return - page_token: typing.Optional[str]. Page token for cursor-based pagination - 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_parse_jobs() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parsing-temporal"), params=remove_none_from_dict( { "status": status, "page_size": page_size, "page_token": page_token, "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(ParseJobQueryResponse, _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_parse_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, correlation_id: typing.Optional[str] = OMIT, created_at: typing.Optional[dt.datetime] = OMIT, job_name: typing_extensions.Literal["parse_raw_file_job"], parameters: typing.Optional[SrcParseSchemasWorkflowParseJobConfig] = OMIT, parent_job_execution_id: typing.Optional[str] = OMIT, partitions: typing.Dict[str, str], parse_job_record_create_project_id: typing.Optional[str] = OMIT, session_id: typing.Optional[str] = OMIT, user_id: typing.Optional[str] = OMIT, webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> ParseJobResponse: """ Create a new parse job. Processes a document asynchronously. Use the job ID to check status and retrieve results. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - correlation_id: typing.Optional[str]. - created_at: typing.Optional[dt.datetime]. Creation datetime - job_name: typing_extensions.Literal["parse_raw_file_job"]. - parameters: typing.Optional[SrcParseSchemasWorkflowParseJobConfig]. - parent_job_execution_id: typing.Optional[str]. - partitions: typing.Dict[str, str]. The partitions for this execution. Used for determining where to save job output. - parse_job_record_create_project_id: typing.Optional[str]. - session_id: typing.Optional[str]. - user_id: typing.Optional[str]. - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( FailPageMode, ParsingMode, SrcParseSchemasWorkflowParseJobConfig, SrcParseSchemasWorkflowParseJobConfigPriority, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_parse_job( job_name="parse_raw_file_job", parameters=SrcParseSchemasWorkflowParseJobConfig( file_key="string", file_name="string", lang="string", original_file_name="string", parse_mode=ParsingMode.PARSE_PAGE_WITHOUT_LLM, priority=SrcParseSchemasWorkflowParseJobConfigPriority.LOW, replace_failed_page_mode=FailPageMode.RAW_TEXT, type="parse", ), partitions={"string": "string"}, ) """ _request: typing.Dict[str, typing.Any] = {"job_name": job_name, "partitions": partitions} if correlation_id is not OMIT: _request["correlation_id"] = correlation_id if created_at is not OMIT: _request["created_at"] = created_at if parameters is not OMIT: _request["parameters"] = parameters if parent_job_execution_id is not OMIT: _request["parent_job_execution_id"] = parent_job_execution_id if parse_job_record_create_project_id is not OMIT: _request["project_id"] = parse_job_record_create_project_id if session_id is not OMIT: _request["session_id"] = session_id if user_id is not OMIT: _request["user_id"] = user_id if webhook_configurations is not OMIT: _request["webhook_configurations"] = webhook_configurations _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/parsing-temporal"), 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(ParseJobResponse, _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_parse_job( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ParseJobResponse: """ Get a parse job by ID. Parameters: - 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.beta.get_parse_job( 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/beta/parsing-temporal/{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(ParseJobResponse, _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_parse_job_results( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.Optional[ParseJobResultResponse]: """ Get results for a parse job. Returns a single result object containing the full OutputFiles JSON with all output types (full_text, md, json_output, job_info, xlsx, output_pdf, structured, images, charts). Parameters: - 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.beta.get_parse_job_results( 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/beta/parsing-temporal/{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(typing.Optional[ParseJobResultResponse], _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 stream_parse_job_result_content( self, job_id: str, result_type: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> typing.Any: """ Stream parse result content from storage. The result_type should be one of: fullText, md, json, jobInfo, xlsx, outputPDF, structured. For images and charts, use the index-based approach (e.g., images[0], charts[1]). Parameters: - job_id: str. - result_type: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.stream_parse_job_result_content( job_id="string", result_type="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/parsing-temporal/{job_id}/results/{result_type}/content", ), 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(typing.Any, _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 list_quota_configurations( self, *, source_type: typing_extensions.Literal["organization"], source_id: str, page: typing.Optional[int] = None, page_size: typing.Optional[int] = None, ) -> PaginatedResponseQuotaConfiguration: """ Retrieve a paginated list of quota configurations with optional filtering. Parameters: - source_type: typing_extensions.Literal["organization"]. - source_id: str. - page: typing.Optional[int]. - page_size: typing.Optional[int]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_quota_configurations( source_type="organization", source_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/quota-management"), params=remove_none_from_dict( {"source_type": source_type, "source_id": source_id, "page": page, "page_size": page_size} ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(PaginatedResponseQuotaConfiguration, _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 list_spreadsheet_jobs( self, *, include_results: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, ) -> PaginatedResponseSpreadsheetJob: """ List spreadsheet parsing jobs. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - include_results: typing.Optional[bool]. - 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.beta.list_spreadsheet_jobs() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/sheets/jobs"), params=remove_none_from_dict( { "include_results": include_results, "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(PaginatedResponseSpreadsheetJob, _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_spreadsheet_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, config: typing.Optional[SpreadsheetParsingConfig] = OMIT, file_id: str, ) -> SpreadsheetJob: """ Create a spreadsheet parsing 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]. - config: typing.Optional[SpreadsheetParsingConfig]. Configuration for the parsing job - file_id: str. The ID of the file to parse --- from llama_cloud import SpreadsheetParsingConfig from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_spreadsheet_job( config=SpreadsheetParsingConfig(), file_id="string", ) """ _request: typing.Dict[str, typing.Any] = {"file_id": file_id} if config is not OMIT: _request["config"] = config _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/sheets/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(SpreadsheetJob, _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_spreadsheet_job( self, spreadsheet_job_id: str, *, include_results: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> SpreadsheetJob: """ Get a spreadsheet parsing job. When include_results=True (default), the response will include extracted regions and results if the job is complete, eliminating the need for a separate /results call. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - spreadsheet_job_id: str. - include_results: typing.Optional[bool]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.get_spreadsheet_job( spreadsheet_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/beta/sheets/jobs/{spreadsheet_job_id}" ), params=remove_none_from_dict( {"include_results": include_results, "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(SpreadsheetJob, _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 delete_spreadsheet_job( self, spreadsheet_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> typing.Any: """ Delete a spreadsheet parsing job and its associated data. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - spreadsheet_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.beta.delete_spreadsheet_job( spreadsheet_job_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/sheets/jobs/{spreadsheet_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(typing.Any, _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_result_region( self, spreadsheet_job_id: str, region_id: str, region_type: SpreadsheetResultType, *, expires_at_seconds: typing.Optional[int] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> PresignedUrl: """ Generate a presigned URL to download a specific extracted region. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - spreadsheet_job_id: str. - region_id: str. - region_type: SpreadsheetResultType. - expires_at_seconds: typing.Optional[int]. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud import SpreadsheetResultType from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.get_result_region( spreadsheet_job_id="string", region_id="string", region_type=SpreadsheetResultType.TABLE, ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/beta/sheets/jobs/{spreadsheet_job_id}/regions/{region_id}/result/{region_type}", ), params=remove_none_from_dict( {"expires_at_seconds": expires_at_seconds, "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(PresignedUrl, _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 list_split_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, ) -> SplitJobQueryResponse: """ List document split 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.beta.list_split_jobs() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/split/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(SplitJobQueryResponse, _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_split_job( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, categories: typing.List[SplitCategory], document_input: SplitDocumentInput, splitting_strategy: typing.Optional[SplitStrategy] = OMIT, ) -> SplitJobResponse: """ Create a document split 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]. - categories: typing.List[SplitCategory]. Categories to split the document into. - document_input: SplitDocumentInput. Document to be split. - splitting_strategy: typing.Optional[SplitStrategy]. Strategy for splitting the document. --- from llama_cloud import SplitDocumentInput, SplitStrategy from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.create_split_job( categories=[], document_input=SplitDocumentInput( type="string", value="string", ), splitting_strategy=SplitStrategy(), ) """ _request: typing.Dict[str, typing.Any] = {"categories": categories, "document_input": document_input} if splitting_strategy is not OMIT: _request["splitting_strategy"] = splitting_strategy _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/split/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(SplitJobResponse, _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_split_job( self, split_job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> SplitJobResponse: """ Get a document split job. Experimental: This endpoint is not yet ready for production use and is subject to change at any time. Parameters: - split_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.beta.get_split_job( split_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/beta/split/jobs/{split_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(SplitJobResponse, _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 list_usage_metrics( self, *, page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None, include_total: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, user_id: typing.Optional[str] = None, event_types: typing.Optional[typing.Union[str, typing.List[str]]] = None, days: typing.Optional[typing.Union[str, typing.List[str]]] = None, day_on_or_before: typing.Optional[str] = None, day_on_or_after: typing.Optional[str] = None, event_aggregation_type: typing.Optional[str] = None, event_aggregation_key: typing.Optional[str] = None, organization_id: str, ) -> UsageMetricQueryResponse: """ List usage metrics with filtering and pagination. Parameters: - page_size: typing.Optional[int]. Number of items per page - page_token: typing.Optional[str]. Token for pagination - include_total: typing.Optional[bool]. Include total count in response - project_id: typing.Optional[str]. Filter by project ID - user_id: typing.Optional[str]. Filter by user ID - event_types: typing.Optional[typing.Union[str, typing.List[str]]]. Filter by event types - days: typing.Optional[typing.Union[str, typing.List[str]]]. Filter by specific days (YYYY-MM-DD) - day_on_or_before: typing.Optional[str]. Filter by days on or before this date (YYYY-MM-DD) - day_on_or_after: typing.Optional[str]. Filter by days on or after this date (YYYY-MM-DD) - event_aggregation_type: typing.Optional[str]. Filter by event aggregation type - event_aggregation_key: typing.Optional[str]. Filter by event aggregation key - organization_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.list_usage_metrics( organization_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/beta/usage-metrics"), params=remove_none_from_dict( { "page_size": page_size, "page_token": page_token, "include_total": include_total, "project_id": project_id, "user_id": user_id, "event_types": event_types, "days": days, "day_on_or_before": day_on_or_before, "day_on_or_after": day_on_or_after, "event_aggregation_type": event_aggregation_type, "event_aggregation_key": event_aggregation_key, "organization_id": organization_id, } ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(UsageMetricQueryResponse, _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_usage_metric(self, usage_metric_id: str) -> UsageMetric: """ Get a usage metric by ID. Parameters: - usage_metric_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.beta.get_usage_metric( usage_metric_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/usage-metrics/{usage_metric_id}" ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(UsageMetric, _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)