Pipeline API client for Document Grounding.
 
This module provides the PipelineAPIClient class for managing document vectorization
pipelines. Pipelines automate the process of fetching documents from data repositories,
preprocessing and chunking content, generating semantic embeddings, and storing them
in HANA Vector Store.
 
Supported data repositories:
    - Microsoft SharePoint
    - AWS S3
    - SFTP
 
API Reference: https://api.sap.com/api/DOCUMENT_GROUNDING_API/resource/Pipelines

Modules
		humps requests

Classes
		builtins.object PipelineAPIClient   class PipelineAPIClient(builtins.object)     PipelineAPIClient(proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)   The Pipelines API creates and manages vector stores based on documents from user data repositories: S3, SFTP, and Microsoft SharePoint. Each pipeline represents a configured end-to-end process including the following steps:   - Fetches documents from a supported data source   - Preprocesses and chunks the document content, and generates semantic embeddings.    Semantic embeddings are multidimensional representations of textual information.   - Stores semantic embeddings into the HANA Vector Store     The Pipeline API is compatible with the following data repositories:   - Microsoft SharePoint   - AWS S3   - SFTP   See https://api.sap.com/api/DOCUMENT_GROUNDING_API/resource/Pipelines     Methods defined here: __init__(self, proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None) Initializes the PipelineAPIClient   :param proxy_client: proxy client to use for requests, defaults to None :type proxy_client: Optional[GenAIHubProxyClient], optional create_pipeline(self, pipeline_request: Union[gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineCreateRequest, gen_ai_hub.document_grounding.models.pipeline.S3PipelineCreateRequest, gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineCreateRequest]) -> gen_ai_hub.document_grounding.models.pipeline.PipelineIdResponse Create a document vectorization pipeline   :param pipeline_request: The object containing the pipeline configuration. :type pipeline_request: CreatePipelineRequest :return: ID of the created pipeline :rtype: PipelineIdResponse delete_pipeline_by_id(self, pipeline_id: str) -> requests.models.Response Delete a pipeline by pipeline id   :param pipeline_id: ID of the pipeline to delete :type pipeline_id: str :return: Response of the delete operation :rtype: requests.Response get_execution_document_by_id(self, pipeline_id: str, execution_id: str, document_id: str) -> gen_ai_hub.document_grounding.models.pipeline.Document Get Document by ID for a Pipeline Execution   :return: Document for the Pipeline Execution :rtype: Document get_execution_documents(self, pipeline_id: str, execution_id: str, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse Get Documents for a Pipeline Execution   :param pipeline_id: Pipeline ID :type pipeline_id: str :param execution_id: Execution ID :type execution_id: str :param top: the maximum number of documents to return, defaults to None :type top: Optional[int], optional :param skip: number of documents to skip, defaults to None :type skip: Optional[int], optional :param count: flag to include count of total documents, defaults to None :type count: Optional[bool], optional :return: Documents for the Pipeline Execution :rtype: DocumentsStatusResponse get_pipeline_by_id(self, pipeline_id: str) -> gen_ai_hub.document_grounding.models.pipeline.BasePipelineResponse Get details of a pipeline by pipeline id.   :param pipeline_id: Pipeline ID :type pipeline_id: str :return: Details of the pipeline :rtype: BasePipelineResponse get_pipeline_document_by_id(self, pipeline_id: str, document_id: str) -> gen_ai_hub.document_grounding.models.pipeline.Document Get Document by ID for a Pipeline   :param pipeline_id: Pipeline ID :type pipeline_id: str :param document_id: Document ID :type document_id: str :return: Document for the Pipeline :rtype: Document get_pipeline_documents(self, pipeline_id: str, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse Get Documents for a Pipeline   :param pipeline_id: Pipeline ID :type pipeline_id: str :param top: the maximum number of documents to return, defaults to None :type top: Optional[int], optional :param skip: number of documents to skip, defaults to None :type skip: Optional[int], optional :param count: flag to include count of total documents, defaults to None :type count: Optional[bool], optional :return: Documents for the Pipeline :rtype: DocumentsStatusResponse get_pipeline_execution_by_id(self, pipeline_id: str, execution_id: str) -> gen_ai_hub.document_grounding.models.pipeline.PipelineExecution Get Pipeline Execution by ID   :param pipeline_id: Pipeline ID :type pipeline_id: str :param execution_id: Execution ID :type execution_id: str :return: Pipeline Execution :rtype: PipelineExecution get_pipeline_executions(self, pipeline_id: str, last_execution: Optional[bool] = None, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.GetPipelineExecutionsResponse Get Pipeline Executions   :param pipeline_id: Pipeline ID :type pipeline_id: str :param last_execution: flag to get only the last execution, defaults to None :type last_execution: Optional[bool], optional :param top: number of executions to retrieve, defaults to None :type top: Optional[int], optional :param skip: number of executions to skip, defaults to None :type skip: Optional[int], optional :param count: flag to include count of total executions, defaults to None :type count: Optional[bool], optional :return: Pipeline Executions :rtype: GetPipelineExecutionsResponse get_pipeline_status(self, pipeline_id: str) -> gen_ai_hub.document_grounding.models.pipeline.GetPipelineStatusResponse Get pipeline status by pipeline id   :param pipeline_id: Pipeline ID :type pipeline_id: str :return: Status of the pipeline :rtype: GetPipelineStatusResponse get_pipelines(self, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.GetPipelinesResponse Get all pipelines.   :return: Get all pipelines :rtype: GetPipelinesResponse search_pipelines(self, body: gen_ai_hub.document_grounding.models.pipeline.SearchPipelineRequest) -> gen_ai_hub.document_grounding.models.pipeline.SearchPipelinesResponse Pipeline Search by Metadata   :param body: The search request object containing metadata filters. :type body: SearchPipelineRequest :return: Search results containing matching pipelines. :rtype: SearchPipelinesResponse trigger_pipeline(self, request: gen_ai_hub.document_grounding.models.pipeline.ManualPipelineTrigger) -> requests.models.Response Trigger Pipeline Manually   :param request: The manual trigger request object. :type request: ManualPipelineTrigger :return: Response of the trigger operation :rtype: requests.Response Data descriptors defined here: __dict__ dictionary for instance variables (if defined) __weakref__ list of weak references to the object (if defined)

Data
		CreatePipelineRequest = typing.Union[gen_ai_hub.document_grounding.model...unding.models.pipeline.SFTPPipelineCreateRequest] Optional = typing.Optional PATH_DOCUMENT_GROUNDING = '/lm/document-grounding/pipelines' PATH_DOCUMENT_GROUNDING_PIPELINES = '/lm/document-grounding/pipelines'