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迁移处理器版本

本页介绍了将 Document AI 训练的处理器版本从一个项目迁移到另一个项目，以及将数据集架构和示例从源处理器迁移到目标处理器的步骤。这些步骤会自动执行导入处理器版本、部署该版本以及将其设置为目标项目中的默认版本的过程。

准备工作

获取 Google Cloud 项目 ID。
拥有 Document AI 处理器 ID。
拥有 Cloud Storage。
使用 Python：Jupyter 笔记本 (Vertex AI)。
需要有权向源项目和目标项目中的服务账号授予访问权限。

分步操作

以下步骤介绍了此过程。

第 1 步：确定与 Vertex AI 笔记本关联的服务账号

!gcloud config list account

输出：

[core]
account = example@automl-project.iam.gserviceaccount.com

Your active configuration is: [default]

第 2 步：向服务账号授予所需权限

在要迁移到的目标 Google Cloud 项目中，将在上一步中获取的服务账号添加为主账号，并分配以下两个角色：

Document AI Administrator
Storage Admin

如需了解详情，请参阅向服务账号授予角色和客户管理的加密密钥 (CMEK)。

processor-version-migrate-1

为了使迁移正常进行，用于运行此笔记本的服务账号需要具备以下权限：

来源和目标项目中的角色，用于创建数据集存储分区（如果不存在，则创建该存储分区），以及对所有对象的读取和写入权限。
源项目中的 Document AI Editor 角色，如导入处理器版本中所述。

下载服务账号的 JSON 密钥，以便您以服务账号身份进行身份验证和授权。如需了解详情，请参阅服务账号密钥。

下一步：

前往相应服务账号。
选择要执行此任务的服务账号。
前往密钥标签页，点击 Add Key，然后选择创建新密钥。
选择密钥类型（最好是 JSON）。
点击 Create 并下载到特定路径。
更新以下代码段中 service_account_key 变量的路径。

service_account_key='path_to_sa_key.json'

from google.oauth2 import service_account
from google.cloud import storage

# Authenticate the service account
credentials = service_account.Credentials.from_service_account_file(
    service_account_key
)

# pass this credentials variable to all client initializations
# storage_client = storage.Client(credentials=credentials)
# docai_client = documentai.DocumentProcessorServiceClient(credentials=credentials)

第 3 步：导入库

import time
from pathlib import Path
from typing import Optional, Tuple
from google.cloud.documentai_v1beta3.services.document_service import pagers
from google.api_core.client_options import ClientOptions
from google.api_core.operation import Operation
from google.cloud import documentai_v1beta3 as documentai
from google.cloud import storage
from tqdm import tqdm

第 4 步：输入详细信息

source_project_id：提供源项目 ID。
source_location：提供来源处理器位置 (us 或 eu)。
source_processor_id：提供 Google Cloud Document AI 处理器 ID。
source_processor_version_to_import：为训练好的版本提供 Google Cloud Document AI 处理器版本 ID。
migrate_dataset：将此值提供为 True 或 False，如果您想将数据集从源处理器迁移到目标处理器，请提供 True，否则请提供 False。默认值为 False。
source_exported_gcs_path：提供用于存储 JSON 文件的 Cloud Storage 路径。
destination_project_id：提供目标项目 ID。
destination_processor_id：提供目标项目中的 Google Cloud Document AI 处理器 ID（"" 或 processor_id）。

source_project_id = "source-project-id"
source_location = "processor-location"
source_processor_id = "source-processor-id"
source_processor_version_to_import = "source-processor-version-id"
migrate_dataset = False  # Either True or False
source_exported_gcs_path = (
    "gs://bucket/path/to/export_dataset/"
)
destination_project_id = "< destination-project-id >"
# Give an empty string if you wish to create a new processor
destination_processor_id = ""

第 5 步：运行代码

import time
from pathlib import Path
from typing import Optional, Tuple
from google.cloud.documentai_v1beta3.services.document_service import pagers
from google.api_core.client_options import ClientOptions
from google.api_core.operation import Operation
from google.cloud import documentai_v1beta3 as documentai
from google.cloud import storage
from tqdm import tqdm

source_project_id = "source-project-id"
source_location = "processor-location"
source_processor_id = "source-processor-id"
source_processor_version_to_import = "source-processor-version-id"
migrate_dataset = False # Either True or False
source_exported_gcs_path = (
    "gs://bucket/path/to/export_dataset/"
)
destination_project_id = "< destination-project-id >"
# Give empty string if you wish to create a new processor
destination_processor_id = ""

exported_bucket_name = source_exported_gcs_path.split("/")[2]
exported_bucket_path_prefix = "/".join(source_exported_gcs_path.split("/")[3:])
destination_location = source_location

def sample_get_processor(project_id: str, processor_id: str, location: str)->Tuple[str, str]:
    """
    This function returns Processor Display Name and Type of Processor from source project

    Args:
        project_id (str): Project ID
        processor_id (str): Document AI Processor ID
        location (str): Processor Location

    Returns:
        Tuple[str, str]: Returns Processor Display name and type
    """
    client = documentai.DocumentProcessorServiceClient()
    print(
        f"Fetching processor({processor_id}) details from source project ({project_id})"
    )
    name = f"projects/{project_id}/locations/{location}/processors/{processor_id}"
    request = documentai.GetProcessorRequest(
        name=name,
    )
    response = client.get_processor(request=request)
    print(f"Processor Name: {response.name}")
    print(f"Processor Display Name: {response.display_name}")
    print(f"Processor Type: {response.type_}")
    return response.display_name, response.type_

def sample_create_processor(project_id: str, location: str, display_name: str, processor_type: str)->documentai.Processor:
    """It will create Processor in Destination project

    Args:
        project_id (str): Project ID
        location (str): Location fo processor
        display_name (str): Processor Display Name
        processor_type (str): Google Cloud Document AI Processor type

    Returns:
        documentai.Processor: Returns details abouts newly created processor
    """

    client = documentai.DocumentProcessorServiceClient()
    request = documentai.CreateProcessorRequest(
        parent=f"projects/{project_id}/locations/{location}",
        processor={
            "type_": processor_type,
            "display_name": display_name,
        },
    )
    print(f"Creating Processor in project: {project_id} in location: {location}")
    print(f"Display Name: {display_name} & Processor Type: {processor_type}")
    res = client.create_processor(request=request)
    return res

def initialize_dataset(project_id: str, processor_id: str, location: str)-> Operation:
    """It will configure dataset for target processor in destination project

    Args:
        project_id (str): Project ID
        processor_id (str): DocuemntAI Processor ID
        location (str): Processor Location

    Returns:
        Operation: An object representing a long-running operation
    """

    # opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")
    client = documentai.DocumentServiceClient()  # client_options=opts
    dataset = documentai.types.Dataset(
        name=f"projects/{project_id}/locations/{location}/processors/{processor_id}/dataset",
        state=3,
        unmanaged_dataset_config={},
        spanner_indexing_config={},
    )
    request = documentai.types.UpdateDatasetRequest(dataset=dataset)
    print(
        f"Configuring Dataset in  project: {project_id} for processor: {processor_id}"
    )
    response = client.update_dataset(request=request)
    return response

def get_dataset_schema(project_id: str, processor_id: str, location: str)->documentai.DatasetSchema:
    """It helps to fetch processor schema

    Args:
        project_id (str): Project ID
        processor_id (str): DocumentAI Processor ID
        location (str): Processor Location

    Returns:
        documentai.DatasetSchema: Return deails about Processor Dataset Schema
    """

    # Create a client
    processor_name = (
        f"projects/{project_id}/locations/{location}/processors/{processor_id}"
    )
    client = documentai.DocumentServiceClient()
    request = documentai.GetDatasetSchemaRequest(
        name=processor_name + "/dataset/datasetSchema"
    )
    # Make the request
    print(f"Fetching schema from source processor: {processor_id}")
    response = client.get_dataset_schema(request=request)
    return response

def upload_dataset_schema(schema: documentai.DatasetSchema)->documentai.DatasetSchema:
    """It helps to update the schema in destination processor

    Args:
        schema (documentai.DatasetSchema): Document AI Processor Schema details & Metadata

    Returns:
        documentai.DatasetSchema: Returns Dataset Schema object
    """

    client = documentai.DocumentServiceClient()
    request = documentai.UpdateDatasetSchemaRequest(dataset_schema=schema)
    print("Updating Schema in destination processor")
    res = client.update_dataset_schema(request=request)
    return res

def store_document_as_json(document: str, bucket_name: str, file_name: str)->None:
    """It helps to upload data to Cloud Storage and stores as a blob

    Args:
        document (str): Processor response in json string format
        bucket_name (str): Cloud Storage bucket name
        file_name (str): Cloud Storage blob uri
    """

    print(f"\tUploading file to Cloud Storage gs://{bucket_name}/{file_name}")
    storage_client = storage.Client()
    process_result_bucket = storage_client.get_bucket(bucket_name)
    document_blob = storage.Blob(
        name=str(Path(file_name)), bucket=process_result_bucket
    )
    document_blob.upload_from_string(document, content_type="application/json")

def list_documents(project_id: str, location: str, processor: str, page_size: Optional[int]=100, page_token: Optional[str]="")->pagers.ListDocumentsPager:
    """This function helps to list the samples present in processor dataset

    Args:
        project_id (str): Project ID
        location (str): Processor Location 
        processor (str): DocumentAI Processor ID
        page_size (Optional[int], optional): The maximum number of documents to return. Defaults to 100.
        page_token (Optional[str], optional): A page token, received from a previous ListDocuments call. Defaults to "".

    Returns:
        pagers.ListDocumentsPager: Returns all details about documents present in Processor Dataset
    """
    client = documentai.DocumentServiceClient()
    dataset = (
        f"projects/{project_id}/locations/{location}/processors/{processor}/dataset"
    )
    request = documentai.types.ListDocumentsRequest(
        dataset=dataset,
        page_token=page_token,
        page_size=page_size,
        return_total_size=True,
    )
    print(f"Listingll  documents/Samples present in processor: {processor}")
    operation = client.list_documents(request)
    return operation

def get_document(project_id: str, location: str, processor: str, doc_id: documentai.DocumentId)->documentai.GetDocumentResponse:
    """It will fetch data for individual sample/document present in dataset

    Args:
        project_id (str): Project ID
        location (str): Processor Location
        processor (str): Document AI Processor ID
        doc_id (documentai.DocumentId): Document identifier

    Returns:
        documentai.GetDocumentResponse: Returns data related to doc_id
    """

    client = documentai.DocumentServiceClient()
    dataset = (
        f"projects/{project_id}/locations/{location}/processors/{processor}/dataset"
    )
    request = documentai.GetDocumentRequest(dataset=dataset, document_id=doc_id)
    operation = client.get_document(request)
    return operation

def import_documents(project_id: str, processor_id: str, location: str, gcs_path: str)->Operation:
    """It helps to import samples/docuemnts from Cloud Storage path to processor via API call

    Args:
        project_id (str): Project ID
        processor_id (str): Document AI Processor ID
        location (str): Processor Location
        gcs_path (str): Cloud Storage path uri prefix 

    Returns:
        Operation: An object representing a long-running operation
    """

    client = documentai.DocumentServiceClient()
    dataset = (
        f"projects/{project_id}/locations/{location}/processors/{processor_id}/dataset"
    )
    request = documentai.ImportDocumentsRequest(
        dataset=dataset,
        batch_documents_import_configs=[
            {
                "dataset_split": "DATASET_SPLIT_TRAIN",
                "batch_input_config": {
                    "gcs_prefix": {"gcs_uri_prefix": gcs_path + "train/"}
                },
            },
            {
                "dataset_split": "DATASET_SPLIT_TEST",
                "batch_input_config": {
                    "gcs_prefix": {"gcs_uri_prefix": gcs_path + "test/"}
                },
            },
            {
                "dataset_split": "DATASET_SPLIT_UNASSIGNED",
                "batch_input_config": {
                    "gcs_prefix": {"gcs_uri_prefix": gcs_path + "unassigned/"}
                },
            },
        ],
    )
    print(
        f"Importing Documents/samples from {gcs_path} to corresponding tran_test_unassigned sections"
    )
    response = client.import_documents(request=request)
    return response

def import_processor_version(source_processor_version_name: str, destination_processor_name: str)->Operation:
    """It helps to import processor version from source processor to destanation processor

    Args:
        source_processor_version_name (str): source processor name in this format projects/{project}/locations/{location}/processors/{processor}
        destination_processor_name (str): destination processor name in this format projects/{project}/locations/{location}/processors/{processor}

    Returns:
        Operation: An object representing a long-running operation
    """

    from google.cloud import documentai_v1beta3

    # provide the source version(to copy) processor details in the following format
    client = documentai_v1beta3.DocumentProcessorServiceClient()
    # provide the new processor name in the parent variable in format 'projects/{project_number}/locations/{location}/processors/{new_processor_id}'
    import google.cloud.documentai_v1beta3 as documentai

    op_import_version_req = (
        documentai.types.document_processor_service.ImportProcessorVersionRequest(
            processor_version_source=source_processor_version_name,
            parent=destination_processor_name,
        )
    )

    print("Importing processor from source to destination")
    print(f"\tSource: {source_processor_version_name}")
    print(f"\tDestination: {destination_processor_name}")
    # copying the processor
    operation = client.import_processor_version(request=op_import_version_req)
    print(operation.metadata)
    print("Waitin for operation to complete...")
    operation.result()
    return operation

def deploy_and_set_default_processor_version(
    project_id: str, location: str, processor_id: str, processor_version_id: str
)->None:
    """It helps to deploy to imported processor version and set it as default version

    Args:
        project_id (str): Project ID
        location (str): Processor Location
        processor_id (str): Document AI Processor ID
        processor_version_id (str): Document AI Processor Version ID
    """

    # Construct the resource name of the processor version
    processor_name = (
        f"projects/{project_id}/locations/{location}/processors/{processor_id}"
    )
    default_processor_version_name = f"projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}"

    # Initialize the Document AI client
    client_options = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")
    client = documentai.DocumentProcessorServiceClient(client_options=client_options)

    # Deploy the processor version
    operation = client.deploy_processor_version(name=default_processor_version_name)
    print(f"Deploying processor version: {operation.operation.name}")
    print("Waiting for operation to complete...")
    result = operation.result()
    print("Processor version deployed")
    # Set the deployed version as the default version
    request = documentai.SetDefaultProcessorVersionRequest(
        processor=processor_name,
        default_processor_version=default_processor_version_name,
    )
    operation = client.set_default_processor_version(request=request)
    print(f"Setting default processor version: {operation.operation.name}")
    operation.result()
    print(f"Default processor version set {default_processor_version_name}")

def main(destination_processor_id: str, migrate_dataset: bool = False)->None:
    """Entry function to perform Processor Migration from Source Project to Destination project

    Args:
        destination_processor_id (str): Either empty string or processor id in desination project
    """
    # Checking processor id of destination project
    if destination_processor_id == "":
        # Fetching Processor Display Name and Type of Processor from source project
        display_name, processor_type = sample_get_processor(
            source_project_id, source_processor_id, source_location
        )
        # Creating Processor in Destination project
        des_processor = sample_create_processor(
            destination_project_id, destination_location, display_name, processor_type
        )
        print(des_processor)
        destination_processor_id = des_processor.name.split("/")[-1]
        # configuring dataset for target processor in destination project
        r = initialize_dataset(
            destination_project_id, destination_processor_id, destination_location
        )
    # fetching processor schema from source processor
    exported_schema = get_dataset_schema(
        source_project_id, source_processor_id, source_location
    )
    exported_schema.name = f"projects/{destination_project_id}/locations/{destination_location}/processors/{destination_processor_id}/dataset/datasetSchema"
    # Copying schema from source processor to desination processor 
    import_schema = upload_dataset_schema(exported_schema)
    if migrate_dataset == True: # to migrate dataset from source to destination processor
        print("Migrating Dataset from source to destination processor")
        # Fetching/listing the samples/JSONs present in source processor dataset
        results = list_documents(source_project_id, source_location, source_processor_id)
        document_list = results.document_metadata
        while len(document_list) != results.total_size:
            page_token = results.next_page_token
            results = list_documents(
                source_project_id,
                source_location,
                source_processor_id,
                page_token=page_token,
            )
            document_list.extend(results.document_metadata)
        print("Exporting Dataset...")
        for doc in tqdm(document_list):
            doc_id = doc.document_id
            split_type = doc.dataset_type
            if split_type == 3:
                split = "unassigned"
            elif split_type == 2:
                split = "test"
            elif split_type == 1:
                split = "train"
            else:
                split = "unknown"
            file_name = doc.display_name
            # fetching/downloading data for individual sample/document present in dataset
            res = get_document(
                source_project_id, source_location, source_processor_id, doc_id
            )
            output_file_name = (
                f"{exported_bucket_path_prefix.strip('/')}/{split}/{file_name}.json"
            )
            # Converting Document AI Proto object to JSON string
            json_data = documentai.Document.to_json(res.document)
            # Uploading JSON data to specified Cloud Storage path
            store_document_as_json(json_data, exported_bucket_name, output_file_name)

        print(f"Importing dataset to {destination_processor_id}")
        gcs_path = source_exported_gcs_path.strip("/") + "/"
        project = destination_project_id
        location = destination_location
        processor = destination_processor_id
        # importing samples/docuemnts from Cloud Storage path to destination processor
        res = import_documents(project, processor, location, gcs_path)
        print(f"Waiting for {len(document_list)*1.5} seconds")
        time.sleep(len(document_list) * 1.5)
    else:
        print("\tSkipping Dataset Migration actions like, exporting source dataset to Cloud Storage and importing dataset to destination processor")

    # Checking for source processor vesion, if id provided then it will be imported to destination processor
    if source_processor_version_to_import != "":
        print(f"Importing Processor Version {source_processor_version_to_import}")
        source_version = f"projects/{source_project_id}/locations/{source_location}/processors/{source_processor_id}/processorVersions/{source_processor_version_to_import}"
        destination_version = f"projects/{destination_project_id}/locations/{destination_location}/processors/{destination_processor_id}"
        # source_version = f"projects/{source_project_id}/locations/us/processors/a82fc086440d7ea1/processorVersions/f1eeed93aad5e317"  # Data for testing
        # Importing processor version from source processor to destanation processor
        operation = import_processor_version(source_version, destination_version)
        name = operation.metadata.common_metadata.resource
        destination_processor_version_id = name.split("/")[-1]
        # deploying newly imported processor version and set it as default version in desination project
        deploy_and_set_default_processor_version(
            destination_project_id,
            destination_location,
            destination_processor_id,
            destination_processor_version_id,
        )

main(destination_processor_id, migrate_dataset)
print("Process Completed!!!")

第 6 步：查看输出详情

前往目标项目，验证处理器的创建情况、数据集可用性，以及新处理器版本是否为默认版本。

processor-version-migrate-3

processor-version-migrate-4