# This file was auto-generated by Fern from our API Definition. import typing import urllib.parse from json.decoder import JSONDecodeError from ...core.api_error import ApiError from ...core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ...core.jsonable_encoder import jsonable_encoder from ...core.remove_none_from_dict import remove_none_from_dict from ...errors.unprocessable_entity_error import UnprocessableEntityError from ...types.extract_agent import ExtractAgent from ...types.extract_config import ExtractConfig from ...types.extract_job import ExtractJob from ...types.extract_resultset import ExtractResultset from ...types.extract_run import ExtractRun from ...types.extract_schema_generate_response import ExtractSchemaGenerateResponse from ...types.extract_schema_validate_response import ExtractSchemaValidateResponse from ...types.file_data import FileData from ...types.http_validation_error import HttpValidationError from ...types.paginated_extract_runs_response import PaginatedExtractRunsResponse from ...types.webhook_configuration import WebhookConfiguration from .types.extract_agent_create_data_schema import ExtractAgentCreateDataSchema from .types.extract_agent_update_data_schema import ExtractAgentUpdateDataSchema from .types.extract_job_create_batch_data_schema_override import ExtractJobCreateBatchDataSchemaOverride from .types.extract_job_create_data_schema_override import ExtractJobCreateDataSchemaOverride from .types.extract_job_create_priority import ExtractJobCreatePriority from .types.extract_schema_generate_request_data_schema import ExtractSchemaGenerateRequestDataSchema from .types.extract_schema_validate_request_data_schema import ExtractSchemaValidateRequestDataSchema from .types.extract_stateless_request_data_schema import ExtractStatelessRequestDataSchema 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 LlamaExtractClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def list_extraction_agents( self, *, include_default: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> typing.List[ExtractAgent]: """ Parameters: - include_default: typing.Optional[bool]. Whether to include default agents in the results - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.list_extraction_agents() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents"), params=remove_none_from_dict( {"include_default": include_default, "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.List[ExtractAgent], _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_extraction_agent( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, config: ExtractConfig, data_schema: ExtractAgentCreateDataSchema, name: str, ) -> ExtractAgent: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - config: ExtractConfig. The configuration parameters for the extraction agent. - data_schema: ExtractAgentCreateDataSchema. The schema of the data. - name: str. The name of the extraction schema --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.create_extraction_agent( config=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), name="string", ) """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"config": config, "data_schema": data_schema, "name": name}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractAgent, _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_extraction_agent_by_name( self, name: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractAgent: """ Parameters: - name: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.get_extraction_agent_by_name( name="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/by-name/{name}" ), 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(ExtractAgent, _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_or_create_default_extraction_agent( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractAgent: """ Get or create a default extraction agent for the current project. The default agent has an empty schema and default configuration. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.get_or_create_default_extraction_agent() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents/default" ), 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(ExtractAgent, _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 generate_extraction_schema( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, data_schema: typing.Optional[ExtractSchemaGenerateRequestDataSchema] = OMIT, file_id: typing.Optional[str] = OMIT, prompt: typing.Optional[str] = OMIT, ) -> ExtractSchemaGenerateResponse: """ Generates or refines an extraction agent's schema definition from a file, natural language prompt, or existing schema. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - data_schema: typing.Optional[ExtractSchemaGenerateRequestDataSchema]. Optional schema to validate, refine, or extend during generation - file_id: typing.Optional[str]. - prompt: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.generate_extraction_schema() """ _request: typing.Dict[str, typing.Any] = {} if data_schema is not OMIT: _request["data_schema"] = data_schema if file_id is not OMIT: _request["file_id"] = file_id if prompt is not OMIT: _request["prompt"] = prompt _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents/schema/generate" ), 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(ExtractSchemaGenerateResponse, _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 validate_extraction_schema( self, *, data_schema: ExtractSchemaValidateRequestDataSchema ) -> ExtractSchemaValidateResponse: """ Validates an extraction agent's schema definition. Returns the normalized and validated schema if valid, otherwise raises an HTTP 400. Parameters: - data_schema: ExtractSchemaValidateRequestDataSchema. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.validate_extraction_schema() """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents/schema/validation" ), json=jsonable_encoder({"data_schema": data_schema}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractSchemaValidateResponse, _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_extraction_agent(self, extraction_agent_id: str) -> ExtractAgent: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.get_extraction_agent( extraction_agent_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/{extraction_agent_id}" ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractAgent, _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_extraction_agent( self, extraction_agent_id: str, *, config: ExtractConfig, data_schema: ExtractAgentUpdateDataSchema ) -> ExtractAgent: """ Parameters: - extraction_agent_id: str. - config: ExtractConfig. The configuration parameters for the extraction agent. - data_schema: ExtractAgentUpdateDataSchema. The schema of the data --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.update_extraction_agent( extraction_agent_id="string", config=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), ) """ _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/{extraction_agent_id}" ), json=jsonable_encoder({"config": config, "data_schema": data_schema}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractAgent, _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_extraction_agent(self, extraction_agent_id: str) -> typing.Any: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.delete_extraction_agent( extraction_agent_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/{extraction_agent_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_jobs(self, *, extraction_agent_id: str) -> typing.List[ExtractJob]: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.list_jobs( extraction_agent_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/jobs"), params=remove_none_from_dict({"extraction_agent_id": extraction_agent_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(typing.List[ExtractJob], _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 run_job( self, *, from_ui: typing.Optional[bool] = None, config_override: typing.Optional[ExtractConfig] = OMIT, data_schema_override: typing.Optional[ExtractJobCreateDataSchemaOverride] = OMIT, extraction_agent_id: str, file_id: str, priority: typing.Optional[ExtractJobCreatePriority] = OMIT, webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> ExtractJob: """ Parameters: - from_ui: typing.Optional[bool]. - config_override: typing.Optional[ExtractConfig]. - data_schema_override: typing.Optional[ExtractJobCreateDataSchemaOverride]. The data schema to override the extraction agent's data schema with - extraction_agent_id: str. The id of the extraction agent - file_id: str. The id of the file - priority: typing.Optional[ExtractJobCreatePriority]. - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractJobCreatePriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.run_job( config_override=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), extraction_agent_id="string", file_id="string", priority=ExtractJobCreatePriority.LOW, ) """ _request: typing.Dict[str, typing.Any] = {"extraction_agent_id": extraction_agent_id, "file_id": file_id} if config_override is not OMIT: _request["config_override"] = config_override if data_schema_override is not OMIT: _request["data_schema_override"] = data_schema_override if priority is not OMIT: _request["priority"] = priority 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/extraction/jobs"), params=remove_none_from_dict({"from_ui": from_ui}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractJob, _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 run_batch_jobs( self, *, from_ui: typing.Optional[bool] = None, config_override: typing.Optional[ExtractConfig] = OMIT, data_schema_override: typing.Optional[ExtractJobCreateBatchDataSchemaOverride] = OMIT, extraction_agent_id: str, file_ids: typing.List[str], webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> typing.List[ExtractJob]: """ Parameters: - from_ui: typing.Optional[bool]. - config_override: typing.Optional[ExtractConfig]. - data_schema_override: typing.Optional[ExtractJobCreateBatchDataSchemaOverride]. The data schema to override the extraction agent's data schema with - extraction_agent_id: str. The id of the extraction agent - file_ids: typing.List[str]. The ids of the files - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.run_batch_jobs( config_override=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), extraction_agent_id="string", file_ids=[], ) """ _request: typing.Dict[str, typing.Any] = {"extraction_agent_id": extraction_agent_id, "file_ids": file_ids} if config_override is not OMIT: _request["config_override"] = config_override if data_schema_override is not OMIT: _request["data_schema_override"] = data_schema_override 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/extraction/jobs/batch"), params=remove_none_from_dict({"from_ui": from_ui}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(typing.List[ExtractJob], _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 run_job_on_file( self, *, from_ui: typing.Optional[bool] = None, config_override: typing.Optional[str] = None, data_schema_override: typing.Optional[str] = None, extraction_agent_id: str, file: typing.IO, ) -> ExtractJob: """ Parameters: - from_ui: typing.Optional[bool]. - config_override: typing.Optional[str]. - data_schema_override: typing.Optional[str]. - extraction_agent_id: str. - file: typing.IO. """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/jobs/file"), params=remove_none_from_dict({"from_ui": from_ui}), data=jsonable_encoder( { "config_override": config_override, "data_schema_override": data_schema_override, "extraction_agent_id": extraction_agent_id, } ), files={"file": file}, headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractJob, _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_job(self, job_id: str) -> ExtractJob: """ Parameters: - job_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.get_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/extraction/jobs/{job_id}"), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractJob, _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_job_result( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractResultset: """ 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.llama_extract.get_job_result( job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/jobs/{job_id}/result"), 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(ExtractResultset, _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 extract_stateless( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, config: ExtractConfig, data_schema: ExtractStatelessRequestDataSchema, file: typing.Optional[FileData] = OMIT, file_id: typing.Optional[str] = OMIT, text: typing.Optional[str] = OMIT, webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> ExtractJob: """ Stateless extraction endpoint that uses a default extraction agent in the user's default project. Requires data_schema, config, and either file_id, text, or base64 encoded file data. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - config: ExtractConfig. The configuration parameters for the extraction - data_schema: ExtractStatelessRequestDataSchema. The schema of the data to extract - file: typing.Optional[FileData]. - file_id: typing.Optional[str]. - text: typing.Optional[str]. - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, FileData, PublicModelName, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.extract_stateless( config=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), file=FileData( data="string", mime_type="string", ), ) """ _request: typing.Dict[str, typing.Any] = {"config": config, "data_schema": data_schema} if file is not OMIT: _request["file"] = file if file_id is not OMIT: _request["file_id"] = file_id if text is not OMIT: _request["text"] = text 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/extraction/run"), 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(ExtractJob, _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_extract_runs( self, *, extraction_agent_id: str, skip: typing.Optional[int] = None, limit: typing.Optional[int] = None ) -> PaginatedExtractRunsResponse: """ Parameters: - extraction_agent_id: str. - skip: typing.Optional[int]. - limit: typing.Optional[int]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.list_extract_runs( extraction_agent_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/runs"), params=remove_none_from_dict({"extraction_agent_id": extraction_agent_id, "skip": skip, "limit": limit}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(PaginatedExtractRunsResponse, _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_run_by_job_id( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractRun: """ 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.llama_extract.get_run_by_job_id( job_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/runs/by-job/{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(ExtractRun, _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_latest_run_from_ui(self, *, extraction_agent_id: str) -> typing.Optional[ExtractRun]: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.get_latest_run_from_ui( extraction_agent_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/runs/latest-from-ui"), params=remove_none_from_dict({"extraction_agent_id": extraction_agent_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(typing.Optional[ExtractRun], _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_run( self, run_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractRun: """ Parameters: - run_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.get_run( run_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/runs/{run_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(ExtractRun, _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_extraction_run( self, run_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.Any: """ Parameters: - run_id: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.llama_extract.delete_extraction_run( run_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/runs/{run_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) class AsyncLlamaExtractClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def list_extraction_agents( self, *, include_default: typing.Optional[bool] = None, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, ) -> typing.List[ExtractAgent]: """ Parameters: - include_default: typing.Optional[bool]. Whether to include default agents in the results - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.list_extraction_agents() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents"), params=remove_none_from_dict( {"include_default": include_default, "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.List[ExtractAgent], _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_extraction_agent( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, config: ExtractConfig, data_schema: ExtractAgentCreateDataSchema, name: str, ) -> ExtractAgent: """ Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - config: ExtractConfig. The configuration parameters for the extraction agent. - data_schema: ExtractAgentCreateDataSchema. The schema of the data. - name: str. The name of the extraction schema --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.create_extraction_agent( config=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), name="string", ) """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents"), params=remove_none_from_dict({"project_id": project_id, "organization_id": organization_id}), json=jsonable_encoder({"config": config, "data_schema": data_schema, "name": name}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractAgent, _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_extraction_agent_by_name( self, name: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractAgent: """ Parameters: - name: str. - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.get_extraction_agent_by_name( name="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/by-name/{name}" ), 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(ExtractAgent, _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_or_create_default_extraction_agent( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractAgent: """ Get or create a default extraction agent for the current project. The default agent has an empty schema and default configuration. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.get_or_create_default_extraction_agent() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents/default" ), 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(ExtractAgent, _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 generate_extraction_schema( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, data_schema: typing.Optional[ExtractSchemaGenerateRequestDataSchema] = OMIT, file_id: typing.Optional[str] = OMIT, prompt: typing.Optional[str] = OMIT, ) -> ExtractSchemaGenerateResponse: """ Generates or refines an extraction agent's schema definition from a file, natural language prompt, or existing schema. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - data_schema: typing.Optional[ExtractSchemaGenerateRequestDataSchema]. Optional schema to validate, refine, or extend during generation - file_id: typing.Optional[str]. - prompt: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.generate_extraction_schema() """ _request: typing.Dict[str, typing.Any] = {} if data_schema is not OMIT: _request["data_schema"] = data_schema if file_id is not OMIT: _request["file_id"] = file_id if prompt is not OMIT: _request["prompt"] = prompt _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents/schema/generate" ), 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(ExtractSchemaGenerateResponse, _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 validate_extraction_schema( self, *, data_schema: ExtractSchemaValidateRequestDataSchema ) -> ExtractSchemaValidateResponse: """ Validates an extraction agent's schema definition. Returns the normalized and validated schema if valid, otherwise raises an HTTP 400. Parameters: - data_schema: ExtractSchemaValidateRequestDataSchema. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.validate_extraction_schema() """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/extraction-agents/schema/validation" ), json=jsonable_encoder({"data_schema": data_schema}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractSchemaValidateResponse, _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_extraction_agent(self, extraction_agent_id: str) -> ExtractAgent: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.get_extraction_agent( extraction_agent_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/{extraction_agent_id}" ), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractAgent, _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_extraction_agent( self, extraction_agent_id: str, *, config: ExtractConfig, data_schema: ExtractAgentUpdateDataSchema ) -> ExtractAgent: """ Parameters: - extraction_agent_id: str. - config: ExtractConfig. The configuration parameters for the extraction agent. - data_schema: ExtractAgentUpdateDataSchema. The schema of the data --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.update_extraction_agent( extraction_agent_id="string", config=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), ) """ _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/{extraction_agent_id}" ), json=jsonable_encoder({"config": config, "data_schema": data_schema}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractAgent, _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_extraction_agent(self, extraction_agent_id: str) -> typing.Any: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.delete_extraction_agent( extraction_agent_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin( f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/extraction-agents/{extraction_agent_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_jobs(self, *, extraction_agent_id: str) -> typing.List[ExtractJob]: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.list_jobs( extraction_agent_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/jobs"), params=remove_none_from_dict({"extraction_agent_id": extraction_agent_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(typing.List[ExtractJob], _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 run_job( self, *, from_ui: typing.Optional[bool] = None, config_override: typing.Optional[ExtractConfig] = OMIT, data_schema_override: typing.Optional[ExtractJobCreateDataSchemaOverride] = OMIT, extraction_agent_id: str, file_id: str, priority: typing.Optional[ExtractJobCreatePriority] = OMIT, webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> ExtractJob: """ Parameters: - from_ui: typing.Optional[bool]. - config_override: typing.Optional[ExtractConfig]. - data_schema_override: typing.Optional[ExtractJobCreateDataSchemaOverride]. The data schema to override the extraction agent's data schema with - extraction_agent_id: str. The id of the extraction agent - file_id: str. The id of the file - priority: typing.Optional[ExtractJobCreatePriority]. - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractJobCreatePriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.run_job( config_override=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), extraction_agent_id="string", file_id="string", priority=ExtractJobCreatePriority.LOW, ) """ _request: typing.Dict[str, typing.Any] = {"extraction_agent_id": extraction_agent_id, "file_id": file_id} if config_override is not OMIT: _request["config_override"] = config_override if data_schema_override is not OMIT: _request["data_schema_override"] = data_schema_override if priority is not OMIT: _request["priority"] = priority 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/extraction/jobs"), params=remove_none_from_dict({"from_ui": from_ui}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractJob, _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 run_batch_jobs( self, *, from_ui: typing.Optional[bool] = None, config_override: typing.Optional[ExtractConfig] = OMIT, data_schema_override: typing.Optional[ExtractJobCreateBatchDataSchemaOverride] = OMIT, extraction_agent_id: str, file_ids: typing.List[str], webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> typing.List[ExtractJob]: """ Parameters: - from_ui: typing.Optional[bool]. - config_override: typing.Optional[ExtractConfig]. - data_schema_override: typing.Optional[ExtractJobCreateBatchDataSchemaOverride]. The data schema to override the extraction agent's data schema with - extraction_agent_id: str. The id of the extraction agent - file_ids: typing.List[str]. The ids of the files - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, PublicModelName, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.run_batch_jobs( config_override=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), extraction_agent_id="string", file_ids=[], ) """ _request: typing.Dict[str, typing.Any] = {"extraction_agent_id": extraction_agent_id, "file_ids": file_ids} if config_override is not OMIT: _request["config_override"] = config_override if data_schema_override is not OMIT: _request["data_schema_override"] = data_schema_override 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/extraction/jobs/batch"), params=remove_none_from_dict({"from_ui": from_ui}), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(typing.List[ExtractJob], _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 run_job_on_file( self, *, from_ui: typing.Optional[bool] = None, config_override: typing.Optional[str] = None, data_schema_override: typing.Optional[str] = None, extraction_agent_id: str, file: typing.IO, ) -> ExtractJob: """ Parameters: - from_ui: typing.Optional[bool]. - config_override: typing.Optional[str]. - data_schema_override: typing.Optional[str]. - extraction_agent_id: str. - file: typing.IO. """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/jobs/file"), params=remove_none_from_dict({"from_ui": from_ui}), data=jsonable_encoder( { "config_override": config_override, "data_schema_override": data_schema_override, "extraction_agent_id": extraction_agent_id, } ), files={"file": file}, headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractJob, _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_job(self, job_id: str) -> ExtractJob: """ Parameters: - job_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.get_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/extraction/jobs/{job_id}"), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(ExtractJob, _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_job_result( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractResultset: """ 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.llama_extract.get_job_result( 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/extraction/jobs/{job_id}/result"), 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(ExtractResultset, _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 extract_stateless( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, config: ExtractConfig, data_schema: ExtractStatelessRequestDataSchema, file: typing.Optional[FileData] = OMIT, file_id: typing.Optional[str] = OMIT, text: typing.Optional[str] = OMIT, webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]] = OMIT, ) -> ExtractJob: """ Stateless extraction endpoint that uses a default extraction agent in the user's default project. Requires data_schema, config, and either file_id, text, or base64 encoded file data. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - config: ExtractConfig. The configuration parameters for the extraction - data_schema: ExtractStatelessRequestDataSchema. The schema of the data to extract - file: typing.Optional[FileData]. - file_id: typing.Optional[str]. - text: typing.Optional[str]. - webhook_configurations: typing.Optional[typing.List[WebhookConfiguration]]. --- from llama_cloud import ( DocumentChunkMode, ExtractConfig, ExtractConfigPriority, ExtractMode, ExtractModels, ExtractTarget, FileData, PublicModelName, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.extract_stateless( config=ExtractConfig( chunk_mode=DocumentChunkMode.PAGE, extract_model=ExtractModels.OPENAI_GPT_4_1, extraction_mode=ExtractMode.FAST, extraction_target=ExtractTarget.PER_DOC, parse_model=PublicModelName.OPENAI_GPT_4_O, priority=ExtractConfigPriority.LOW, ), file=FileData( data="string", mime_type="string", ), ) """ _request: typing.Dict[str, typing.Any] = {"config": config, "data_schema": data_schema} if file is not OMIT: _request["file"] = file if file_id is not OMIT: _request["file_id"] = file_id if text is not OMIT: _request["text"] = text 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/extraction/run"), 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(ExtractJob, _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_extract_runs( self, *, extraction_agent_id: str, skip: typing.Optional[int] = None, limit: typing.Optional[int] = None ) -> PaginatedExtractRunsResponse: """ Parameters: - extraction_agent_id: str. - skip: typing.Optional[int]. - limit: typing.Optional[int]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.list_extract_runs( extraction_agent_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/runs"), params=remove_none_from_dict({"extraction_agent_id": extraction_agent_id, "skip": skip, "limit": limit}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(PaginatedExtractRunsResponse, _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_run_by_job_id( self, job_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractRun: """ 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.llama_extract.get_run_by_job_id( 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/extraction/runs/by-job/{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(ExtractRun, _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_latest_run_from_ui(self, *, extraction_agent_id: str) -> typing.Optional[ExtractRun]: """ Parameters: - extraction_agent_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.llama_extract.get_latest_run_from_ui( extraction_agent_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/extraction/runs/latest-from-ui"), params=remove_none_from_dict({"extraction_agent_id": extraction_agent_id}), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(typing.Optional[ExtractRun], _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_run( self, run_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> ExtractRun: """ Parameters: - run_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.llama_extract.get_run( run_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/runs/{run_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(ExtractRun, _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_extraction_run( self, run_id: str, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.Any: """ Parameters: - run_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.llama_extract.delete_extraction_run( run_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/extraction/runs/{run_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)