GoogleCloudContentwarehouseV1GcsIngestPipeline

GoogleApi.ContentWarehouse.V1.Model.GoogleCloudContentwarehouseV1GcsIngestPipeline


Table of Contents ▼

Jump to a specific part of the page:

Description

The configuration of the Cloud Storage Ingestion pipeline.

Attributes List

This module has the following attributes (case-insensitive ascending order):

View Attributes

Attributes

  1. inputPath (type: String.t, default: nil)
    - The input Cloud Storage folder. All files under this folder will be imported to Document Warehouse. Format: gs:///.
  2. pipelineConfig (type: GoogleApi.ContentWarehouse.V1.Model.GoogleCloudContentwarehouseV1IngestPipelineConfig, default: nil)
    - Optional. The config for the Cloud Storage Ingestion pipeline. It provides additional customization options to run the pipeline and can be skipped if it is not applicable.
  3. processorType (type: String.t, default: nil)
    - The Doc AI processor type name. Only used when the format of ingested files is Doc AI Document proto format.
  4. schemaName (type: String.t, default: nil)
    - The Document Warehouse schema resource name. All documents processed by this pipeline will use this schema. Format: projects/{project_number}/locations/{location}/documentSchemas/{document_schema_id}.
  5. skipIngestedDocuments (type: boolean(), default: nil)
    - The flag whether to skip ingested documents. If it is set to true, documents in Cloud Storage contains key "status" with value "status=ingested" in custom metadata will be skipped to ingest.

Type

@type t() :: %GoogleApi.ContentWarehouse.V1.Model.GoogleCloudContentwarehouseV1GcsIngestPipeline{
inputPath: String.t() | nil,
pipelineConfig: GoogleApi.ContentWarehouse.V1.Model.GoogleCloudContentwarehouseV1IngestPipelineConfig.t() | nil,
processorType: String.t() | nil,
schemaName: String.t() | nil,
skipIngestedDocuments: boolean() | nil
}

Function

@spec decode(struct(), keyword()) :: struct()

Data sourced from HexDocs : GoogleApi.ContentWarehouse.V1.Model.GoogleCloudContentwarehouseV1GcsIngestPipeline