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Document AI Toolbox 用戶端程式庫

本頁說明如何開始使用 Document AI Toolbox API 適用的 Cloud 用戶端程式庫。用戶端程式庫可讓您從支援的語言輕鬆存取Google Cloud API。雖然您可以直接向伺服器發出原始要求來使用Google Cloud API，但用戶端程式庫提供簡化功能，可大幅減少您需要編寫的程式碼數量。

如要進一步瞭解 Cloud 用戶端程式庫和舊版 Google API 用戶端程式庫，請參閱用戶端程式庫說明。

安裝用戶端程式庫

Python

pip install --upgrade google-cloud-documentai-toolbox

詳情請參閱「設定 Python 開發環境」。

設定驗證方法

為驗證對 Google Cloud API 的呼叫，用戶端程式庫支援應用程式預設憑證 (ADC)；程式庫會在定義的一組位置中尋找憑證，並使用這些憑證驗證對 API 的要求。使用 ADC，您可以在各種環境 (例如本機開發或正式版) 中，為應用程式提供憑證，不必修改應用程式程式碼。

在實際工作環境中，設定 ADC 的方式取決於服務和環境。詳情請參閱「設定應用程式預設憑證」。

在本地開發環境中，您可以使用與 Google 帳戶相關聯的憑證設定 ADC：

Install the Google Cloud CLI. After installation, initialize the Google Cloud CLI by running the following command:
```
gcloud init
```
If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.
If you're using a local shell, then create local authentication credentials for your user account:
```
gcloud auth application-default login
```
You don't need to do this if you're using Cloud Shell.

If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

畫面上會顯示登入畫面。登入後，您的憑證會儲存在 ADC 使用的本機憑證檔案中。

使用用戶端程式庫

Document AI Toolbox 是 Python 適用的 SDK，提供公用函式，可用於管理、操控及擷取文件回應中的資訊。這個方法會從 Cloud Storage 中的 JSON 檔案、本機 JSON 檔案，或直接從 process_document() 方法的輸出內容，建立「包裝」的文件物件。

可執行下列動作：

將批次處理產生的 Document JSON 檔案片段合併為單一「包裝」文件。

將分片匯出為統一的 Document。

從下列位置取得 Document 輸出內容：
存取 Pages、Lines、Paragraphs、FormFields 和 Tables 中的文字，不必處理 Layout 資訊。
搜尋包含目標字串或符合規則運算式的 Pages。
依名稱搜尋 FormFields。
依類型搜尋 Entities。
將 Tables 轉換為 Pandas DataFrame 或 CSV。
將 Entities 和 FormFields 插入 BigQuery 資料表。
根據分割器/分類器處理器的輸出內容分割 PDF 檔案。
從Document 定界框中擷取圖片 Entities。
將 Documents 轉換為常用格式，或從常用格式轉換：
- Cloud Vision API AnnotateFileResponse
- hOCR
- 第三方文件處理格式
從 Cloud Storage 資料夾建立要處理的文件批次。

程式碼範例

下列程式碼範例說明如何使用 Document AI Toolbox。

快速入門導覽課程

from typing import Optional

from google.cloud import documentai
from google.cloud.documentai_toolbox import document, gcs_utilities

# TODO(developer): Uncomment these variables before running the sample.
# Given a Document JSON or sharded Document JSON in path gs://bucket/path/to/folder
# gcs_bucket_name = "bucket"
# gcs_prefix = "path/to/folder"

# Or, given a Document JSON in path gs://bucket/path/to/folder/document.json
# gcs_uri = "gs://bucket/path/to/folder/document.json"

# Or, given a Document JSON in path local/path/to/folder/document.json
# document_path = "local/path/to/folder/document.json"

# Or, given a Document object from Document AI
# documentai_document = documentai.Document()

# Or, given a BatchProcessMetadata object from Document AI
# operation = client.batch_process_documents(request)
# operation.result(timeout=timeout)
# batch_process_metadata = documentai.BatchProcessMetadata(operation.metadata)

# Or, given a BatchProcessOperation name from Document AI
# batch_process_operation = "projects/project_id/locations/location/operations/operation_id"


def quickstart_sample(
    gcs_bucket_name: Optional[str] = None,
    gcs_prefix: Optional[str] = None,
    gcs_uri: Optional[str] = None,
    document_path: Optional[str] = None,
    documentai_document: Optional[documentai.Document] = None,
    batch_process_metadata: Optional[documentai.BatchProcessMetadata] = None,
    batch_process_operation: Optional[str] = None,
) -> document.Document:
    if gcs_bucket_name and gcs_prefix:
        # Load from Google Cloud Storage Directory
        print("Document structure in Cloud Storage")
        gcs_utilities.print_gcs_document_tree(
            gcs_bucket_name=gcs_bucket_name, gcs_prefix=gcs_prefix
        )

        wrapped_document = document.Document.from_gcs(
            gcs_bucket_name=gcs_bucket_name, gcs_prefix=gcs_prefix
        )
    elif gcs_uri:
        # Load a single Document from a Google Cloud Storage URI
        wrapped_document = document.Document.from_gcs_uri(gcs_uri=gcs_uri)
    elif document_path:
        # Load from local `Document` JSON file
        wrapped_document = document.Document.from_document_path(document_path)
    elif documentai_document:
        # Load from `documentai.Document` object
        wrapped_document = document.Document.from_documentai_document(
            documentai_document
        )
    elif batch_process_metadata:
        # Load Documents from `BatchProcessMetadata` object
        wrapped_documents = document.Document.from_batch_process_metadata(
            metadata=batch_process_metadata
        )
        wrapped_document = wrapped_documents[0]
    elif batch_process_operation:
        wrapped_documents = document.Document.from_batch_process_operation(
            location="us", operation_name=batch_process_operation
        )
        wrapped_document = wrapped_documents[0]
    else:
        raise ValueError("No document source provided.")

    # For all properties and methods, refer to:
    # https://cloud.google.com/python/docs/reference/documentai-toolbox/latest/google.cloud.documentai_toolbox.wrappers.document.Document

    print("Document Successfully Loaded!")
    print(f"\t Number of Pages: {len(wrapped_document.pages)}")
    print(f"\t Number of Entities: {len(wrapped_document.entities)}")

    for page in wrapped_document.pages:
        print(f"Page {page.page_number}")
        for block in page.blocks:
            print(block.text)
        for paragraph in page.paragraphs:
            print(paragraph.text)
        for line in page.lines:
            print(line.text)
        for token in page.tokens:
            print(token.text)

        # Only supported with Form Parser processor
        # https://cloud.google.com/document-ai/docs/form-parser
        for form_field in page.form_fields:
            print(f"{form_field.field_name} : {form_field.field_value}")

        # Only supported with Enterprise Document OCR version `pretrained-ocr-v2.0-2023-06-02`
        # https://cloud.google.com/document-ai/docs/process-documents-ocr#enable_symbols
        for symbol in page.symbols:
            print(symbol.text)

        # Only supported with Enterprise Document OCR version `pretrained-ocr-v2.0-2023-06-02`
        # https://cloud.google.com/document-ai/docs/process-documents-ocr#math_ocr
        for math_formula in page.math_formulas:
            print(math_formula.text)

    # Only supported with Entity Extraction processors
    # https://cloud.google.com/document-ai/docs/processors-list
    for entity in wrapped_document.entities:
        print(f"{entity.type_} : {entity.mention_text}")
        if entity.normalized_text:
            print(f"\tNormalized Text: {entity.normalized_text}")

    # Only supported with Layout Parser
    for chunk in wrapped_document.chunks:
        print(f"Chunk {chunk.chunk_id}: {chunk.content}")

    for block in wrapped_document.document_layout_blocks:
        print(f"Document Layout Block {block.block_id}")

        if block.text_block:
            print(f"{block.text_block.type_}: {block.text_block.text}")
        if block.list_block:
            print(f"{block.list_block.type_}: {block.list_block.list_entries}")
        if block.table_block:
            print(block.table_block.header_rows, block.table_block.body_rows)

資料表


from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a local document.proto or sharded document.proto in path
# document_path = "path/to/local/document.json"
# output_file_prefix = "output/table"


def table_sample(document_path: str, output_file_prefix: str) -> None:
    wrapped_document = document.Document.from_document_path(document_path=document_path)

    print("Tables in Document")
    for page in wrapped_document.pages:
        for table_index, table in enumerate(page.tables):
            # Convert table to Pandas Dataframe
            # Refer to https://pandas.pydata.org/docs/reference/frame.html for all supported methods
            df = table.to_dataframe()
            print(df)

            output_filename = f"{output_file_prefix}-{page.page_number}-{table_index}"

            # Write Dataframe to CSV file
            df.to_csv(f"{output_filename}.csv", index=False)

            # Write Dataframe to HTML file
            df.to_html(f"{output_filename}.html", index=False)

            # Write Dataframe to Markdown file
            df.to_markdown(f"{output_filename}.md", index=False)

BigQuery 匯出內容


from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a document.proto or sharded document.proto in path gs://bucket/path/to/folder
# gcs_bucket_name = "bucket"
# gcs_prefix = "path/to/folder"
# dataset_name = "test_dataset"
# table_name = "test_table"
# project_id = "YOUR_PROJECT_ID"


def entities_to_bigquery_sample(
    gcs_bucket_name: str,
    gcs_prefix: str,
    dataset_name: str,
    table_name: str,
    project_id: str,
) -> None:
    wrapped_document = document.Document.from_gcs(
        gcs_bucket_name=gcs_bucket_name, gcs_prefix=gcs_prefix
    )

    job = wrapped_document.entities_to_bigquery(
        dataset_name=dataset_name, table_name=table_name, project_id=project_id
    )

    # Also supported:
    # job = wrapped_document.form_fields_to_bigquery(
    #     dataset_name=dataset_name, table_name=table_name, project_id=project_id
    # )

    print("Document entities loaded into BigQuery")
    print(f"Job ID: {job.job_id}")
    print(f"Table: {job.destination.path}")

分割 PDF


from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a local document.proto or sharded document.proto from a splitter/classifier in path
# document_path = "path/to/local/document.json"
# pdf_path = "path/to/local/document.pdf"
# output_path = "resources/output/"


def split_pdf_sample(document_path: str, pdf_path: str, output_path: str) -> None:
    wrapped_document = document.Document.from_document_path(document_path=document_path)

    output_files = wrapped_document.split_pdf(
        pdf_path=pdf_path, output_path=output_path
    )

    print("Document Successfully Split")
    for output_file in output_files:
        print(output_file)

圖片擷取


from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a local document.proto or sharded document.proto from an identity processor in path
# document_path = "path/to/local/document.json"
# output_path = "resources/output/"
# output_file_prefix = "exported_photo"
# output_file_extension = "png"


def export_images_sample(
    document_path: str,
    output_path: str,
    output_file_prefix: str,
    output_file_extension: str,
) -> None:
    wrapped_document = document.Document.from_document_path(document_path=document_path)

    output_files = wrapped_document.export_images(
        output_path=output_path,
        output_file_prefix=output_file_prefix,
        output_file_extension=output_file_extension,
    )
    print("Images Successfully Exported")
    for output_file in output_files:
        print(output_file)

影像轉換


from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a document.proto or sharded document.proto in path gs://bucket/path/to/folder
# gcs_bucket_name = "bucket"
# gcs_prefix = "path/to/folder"


def convert_document_to_vision_sample(
    gcs_bucket_name: str,
    gcs_prefix: str,
) -> None:
    wrapped_document = document.Document.from_gcs(
        gcs_bucket_name=gcs_bucket_name, gcs_prefix=gcs_prefix
    )

    # Converting wrapped_document to vision AnnotateFileResponse
    annotate_file_response = (
        wrapped_document.convert_document_to_annotate_file_response()
    )

    print("Document converted to AnnotateFileResponse!")
    print(
        f"Number of Pages : {len(annotate_file_response.responses[0].full_text_annotation.pages)}"
    )

hOCR 轉換


from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a document.proto or sharded document.proto in path gs://bucket/path/to/folder
# document_path = "path/to/local/document.json"
# document_title = "your-document-title"


def convert_document_to_hocr_sample(document_path: str, document_title: str) -> str:
    wrapped_document = document.Document.from_document_path(document_path=document_path)

    # Converting wrapped_document to hOCR format
    hocr_string = wrapped_document.export_hocr_str(title=document_title)

    print("Document converted to hOCR!")
    return hocr_string

第三方轉換


from google.cloud.documentai_toolbox import converter

# TODO(developer): Uncomment these variables before running the sample.
# This sample will convert external annotations to the Document.json format used by Document AI Workbench for training.
# To process this the external annotation must have these type of objects:
#       1) Type
#       2) Text
#       3) Bounding Box (bounding boxes must be 1 of the 3 optional types)
#
# This is the bare minimum requirement to convert the annotations but for better accuracy you will need to also have:
#       1) Document width & height
#
# Bounding Box Types:
#   Type 1:
#       bounding_box:[{"x":1,"y":2},{"x":2,"y":2},{"x":2,"y":3},{"x":1,"y":3}]
#   Type 2:
#       bounding_box:{ "Width": 1, "Height": 1, "Left": 1, "Top": 1}
#   Type 3:
#       bounding_box: [1,2,2,2,2,3,1,3]
#
#   Note: If these types are not sufficient you can propose a feature request or contribute the new type and conversion functionality.
#
# Given a folders in gcs_input_path with the following structure :
#
# gs://path/to/input/folder
#   ├──test_annotations.json
#   ├──test_config.json
#   └──test.pdf
#
# An example of the config is in sample-converter-configs/Azure/form-config.json
#
# location = "us",
# processor_id = "my_processor_id"
# gcs_input_path = "gs://path/to/input/folder"
# gcs_output_path = "gs://path/to/input/folder"


def convert_external_annotations_sample(
    location: str,
    processor_id: str,
    project_id: str,
    gcs_input_path: str,
    gcs_output_path: str,
) -> None:
    converter.convert_from_config(
        project_id=project_id,
        location=location,
        processor_id=processor_id,
        gcs_input_path=gcs_input_path,
        gcs_output_path=gcs_output_path,
    )

文件批次


from google.cloud import documentai
from google.cloud.documentai_toolbox import gcs_utilities

# TODO(developer): Uncomment these variables before running the sample.
# Given unprocessed documents in path gs://bucket/path/to/folder
# gcs_bucket_name = "bucket"
# gcs_prefix = "path/to/folder"
# batch_size = 50


def create_batches_sample(
    gcs_bucket_name: str,
    gcs_prefix: str,
    batch_size: int = 50,
) -> None:
    # Creating batches of documents for processing
    batches = gcs_utilities.create_batches(
        gcs_bucket_name=gcs_bucket_name, gcs_prefix=gcs_prefix, batch_size=batch_size
    )

    print(f"{len(batches)} batch(es) created.")
    for batch in batches:
        print(f"{len(batch.gcs_documents.documents)} files in batch.")
        print(batch.gcs_documents.documents)

        # Use as input for batch_process_documents()
        # Refer to https://cloud.google.com/document-ai/docs/send-request
        # for how to send a batch processing request
        request = documentai.BatchProcessRequest(
            name="processor_name", input_documents=batch
        )
        print(request)

合併文件分片


from google.cloud import documentai
from google.cloud.documentai_toolbox import document

# TODO(developer): Uncomment these variables before running the sample.
# Given a document.proto or sharded document.proto in path gs://bucket/path/to/folder
# gcs_bucket_name = "bucket"
# gcs_prefix = "path/to/folder"
# output_file_name = "path/to/folder/file.json"


def merge_document_shards_sample(
    gcs_bucket_name: str, gcs_prefix: str, output_file_name: str
) -> None:
    wrapped_document = document.Document.from_gcs(
        gcs_bucket_name=gcs_bucket_name, gcs_prefix=gcs_prefix
    )

    merged_document = wrapped_document.to_merged_documentai_document()

    with open(output_file_name, "w") as f:
        f.write(documentai.Document.to_json(merged_document))

    print(f"Document with {len(wrapped_document.shards)} shards successfully merged.")

其他資源

Python

以下清單包含適用於 Python 的用戶端程式庫相關資源連結：

單一區域與多區域支援

用戶端程式庫

Document AI Toolbox 用戶端程式庫 透過集合功能整理內容 你可以依據偏好儲存及分類內容。

安裝用戶端程式庫

Python

設定驗證方法

使用用戶端程式庫

程式碼範例

快速入門導覽課程

資料表

BigQuery 匯出內容

分割 PDF

圖片擷取

影像轉換

hOCR 轉換

第三方轉換

文件批次

合併文件分片

其他資源

Python

Document AI Toolbox 用戶端程式庫