# 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.configurable_data_source_names import ConfigurableDataSourceNames from ...types.data_source import DataSource from ...types.data_source_create import DataSourceCreate from ...types.http_validation_error import HttpValidationError from .types.data_source_update_component import DataSourceUpdateComponent from .types.data_source_update_custom_metadata_value import DataSourceUpdateCustomMetadataValue 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 DataSourcesClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._client_wrapper = client_wrapper def list_data_sources( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.List[DataSource]: """ List data sources for a given project. If project_id is not provided, uses the default project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.data_sources.list_data_sources() """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/data-sources"), 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.List[DataSource], _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_data_source( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: DataSourceCreate, ) -> DataSource: """ Create a new data source. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: DataSourceCreate. --- from llama_cloud import ConfigurableDataSourceNames, DataSourceCreate from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.data_sources.create_data_source( request=DataSourceCreate( name="string", source_type=ConfigurableDataSourceNames.S_3, ), ) """ _response = self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/data-sources"), 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(DataSource, _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_data_source( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: DataSourceCreate, ) -> DataSource: """ Upserts a data source. Updates if a data source with the same name and project_id already exists. Otherwise, creates a new data source. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: DataSourceCreate. --- from llama_cloud import ConfigurableDataSourceNames, DataSourceCreate from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.data_sources.upsert_data_source( request=DataSourceCreate( name="string", source_type=ConfigurableDataSourceNames.S_3, ), ) """ _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/data-sources"), 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(DataSource, _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_data_source(self, data_source_id: str) -> DataSource: """ Get a data source by ID. Parameters: - data_source_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.data_sources.get_data_source( data_source_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/data-sources/{data_source_id}"), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DataSource, _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_data_source( self, data_source_id: str, *, name: typing.Optional[str] = OMIT, source_type: ConfigurableDataSourceNames, custom_metadata: typing.Optional[typing.Dict[str, typing.Optional[DataSourceUpdateCustomMetadataValue]]] = OMIT, component: typing.Optional[DataSourceUpdateComponent] = OMIT, ) -> DataSource: """ Update a data source by ID. Parameters: - data_source_id: str. - name: typing.Optional[str]. - source_type: ConfigurableDataSourceNames. - custom_metadata: typing.Optional[typing.Dict[str, typing.Optional[DataSourceUpdateCustomMetadataValue]]]. - component: typing.Optional[DataSourceUpdateComponent]. Component that implements the data source --- from llama_cloud import ConfigurableDataSourceNames from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.data_sources.update_data_source( data_source_id="string", source_type=ConfigurableDataSourceNames.S_3, ) """ _request: typing.Dict[str, typing.Any] = {"source_type": source_type} if name is not OMIT: _request["name"] = name if custom_metadata is not OMIT: _request["custom_metadata"] = custom_metadata if component is not OMIT: _request["component"] = component _response = self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/data-sources/{data_source_id}"), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DataSource, _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_data_source(self, data_source_id: str) -> None: """ Delete a data source by ID. Parameters: - data_source_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.data_sources.delete_data_source( data_source_id="string", ) """ _response = self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/data-sources/{data_source_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) class AsyncDataSourcesClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._client_wrapper = client_wrapper async def list_data_sources( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None ) -> typing.List[DataSource]: """ List data sources for a given project. If project_id is not provided, uses the default project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.data_sources.list_data_sources() """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/data-sources"), 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.List[DataSource], _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_data_source( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: DataSourceCreate, ) -> DataSource: """ Create a new data source. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: DataSourceCreate. --- from llama_cloud import ConfigurableDataSourceNames, DataSourceCreate from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.data_sources.create_data_source( request=DataSourceCreate( name="string", source_type=ConfigurableDataSourceNames.S_3, ), ) """ _response = await self._client_wrapper.httpx_client.request( "POST", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/data-sources"), 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(DataSource, _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_data_source( self, *, project_id: typing.Optional[str] = None, organization_id: typing.Optional[str] = None, request: DataSourceCreate, ) -> DataSource: """ Upserts a data source. Updates if a data source with the same name and project_id already exists. Otherwise, creates a new data source. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: DataSourceCreate. --- from llama_cloud import ConfigurableDataSourceNames, DataSourceCreate from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.data_sources.upsert_data_source( request=DataSourceCreate( name="string", source_type=ConfigurableDataSourceNames.S_3, ), ) """ _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", "api/v1/data-sources"), 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(DataSource, _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_data_source(self, data_source_id: str) -> DataSource: """ Get a data source by ID. Parameters: - data_source_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.data_sources.get_data_source( data_source_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "GET", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/data-sources/{data_source_id}"), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DataSource, _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_data_source( self, data_source_id: str, *, name: typing.Optional[str] = OMIT, source_type: ConfigurableDataSourceNames, custom_metadata: typing.Optional[typing.Dict[str, typing.Optional[DataSourceUpdateCustomMetadataValue]]] = OMIT, component: typing.Optional[DataSourceUpdateComponent] = OMIT, ) -> DataSource: """ Update a data source by ID. Parameters: - data_source_id: str. - name: typing.Optional[str]. - source_type: ConfigurableDataSourceNames. - custom_metadata: typing.Optional[typing.Dict[str, typing.Optional[DataSourceUpdateCustomMetadataValue]]]. - component: typing.Optional[DataSourceUpdateComponent]. Component that implements the data source --- from llama_cloud import ConfigurableDataSourceNames from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.data_sources.update_data_source( data_source_id="string", source_type=ConfigurableDataSourceNames.S_3, ) """ _request: typing.Dict[str, typing.Any] = {"source_type": source_type} if name is not OMIT: _request["name"] = name if custom_metadata is not OMIT: _request["custom_metadata"] = custom_metadata if component is not OMIT: _request["component"] = component _response = await self._client_wrapper.httpx_client.request( "PUT", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/data-sources/{data_source_id}"), json=jsonable_encoder(_request), headers=self._client_wrapper.get_headers(), timeout=60, ) if 200 <= _response.status_code < 300: return pydantic.parse_obj_as(DataSource, _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_data_source(self, data_source_id: str) -> None: """ Delete a data source by ID. Parameters: - data_source_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.data_sources.delete_data_source( data_source_id="string", ) """ _response = await self._client_wrapper.httpx_client.request( "DELETE", urllib.parse.urljoin(f"{self._client_wrapper.get_base_url()}/", f"api/v1/data-sources/{data_source_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)