将 Document AI 与来自 google 的 python 和来自 google 代码实验室的代码一起使用 returns 错误或空结果

Question

我尝试了 codelabs.developers.google.com 中的以下代码：

import pandas as pd
from google.cloud import documentai_v1 as documentai


def online_process(
    project_id: str,
    location: str,
    processor_id: str,
    file_path: str,
    mime_type: str,
) -> documentai.Document:
    """
    Processes a document using the Document AI Online Processing API.
    """

    opts = {"api_endpoint": f"{location}-documentai.googleapis.com"}

    # Instantiates a client
    documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts)

    # The full resource name of the processor, e.g.:
    # projects/project-id/locations/location/processor/processor-id
    # You must create new processors in the Cloud Console first
    resource_name = documentai_client.processor_path(project_id, location, processor_id)

    # Read the file into memory
    with open(file_path, "rb") as file:
        file_content = file.read()

    # Load Binary Data into Document AI RawDocument Object
    raw_document = documentai.RawDocument(content=file_content, mime_type=mime_type)

    # Configure the process request
    request = documentai.ProcessRequest(name=resource_name, raw_document=raw_document)

    # Use the Document AI client to process the sample form
    result = documentai_client.process_document(request=request)

    return result.document


PROJECT_ID = "YOUR_PROJECT_ID"
LOCATION = "YOUR_PROJECT_LOCATION"  # Format is 'us' or 'eu'
PROCESSOR_ID = "PROCUREMENT_SPLITTER_ID"  # Create processor in Cloud Console

# The local file in your current working directory
FILE_PATH = "procurement_multi_document.pdf"
# Refer to https://cloud.google.com/document-ai/docs/processors-list
# for supported file types
MIME_TYPE = "application/pdf"

document = online_process(
    project_id=PROJECT_ID,
    location=LOCATION,
    processor_id=PROCESSOR_ID,
    file_path=FILE_PATH,
    mime_type=MIME_TYPE,
)

print("Document processing complete.")

types = []
confidence = []
pages = []

# Each Document.entity is a classification
for entity in document.entities:
    classification = entity.type_
    types.append(classification)
    confidence.append(f"{entity.confidence:.0%}")

    # entity.page_ref contains the pages that match the classification
    pages_list = []
    for page_ref in entity.page_anchor.page_refs:
        pages_list.append(page_ref.page)
    pages.append(pages_list)

# Create a Pandas Dataframe to print the values in tabular format.
df = pd.DataFrame({"Classification": types, "Confidence": confidence, "Pages": pages})

print(df)

预期输出（类似这样）：

$ python3 classification.py
Document processing complete.
         Classification Confidence Pages
0     invoice_statement       100%   [0]
1     receipt_statement        98%   [1]
2                 other        81%   [2]
3     utility_statement       100%   [3]
4  restaurant_statement       100%   [4]

结果输出：

Document processing complete.
Empty DataFrame
Columns: [Classification, Confidence, Pages]
Index: []

我检查了 google documentAI 的所有其他输入，其他一切似乎都很好。但是，我可以毫无问题地使用 document.text 获取文本 (ocr)。也许我的 documentAI 配额已经用完了？任何其他原因或如何解决这个问题（如果可能）？谢谢。

Answer 1

Document AI 是一种文档理解解决方案，它使用机器学习使非结构化数据更易于理解和分析。从您使用的代码中可以看出 Specialized Processor Procurement DocAI which takes input as invoice or receipts or utility statements which are unstructured documents and procure into structured data. The file types which are supported in Document AI are listed in this document. The problem seems to be the type of file you are using. Try to use the file type listed in the documentation. For more information you can check this link .

我已经尝试了下面的代码来获得预期的输出。

代码

import pandas as pd
from google.cloud import documentai_v1 as documentai


def online_process(
   project_id: "project",
   location: "us",
   processor_id: "processor-id",
   file_path: "file_path",
   mime_type: "mime_type",
) -> documentai.Document:
   
   opts = {"api_endpoint": f"{location}-documentai.googleapis.com"}

   # Instantiates a client
   documentai_client = documentai.DocumentProcessorServiceClient(client_options=opts)

   resource_name = documentai_client.processor_path(project_id, location, processor_id)

   # Read the file into memory
   with open(file_path, "rb") as file:
       file_content = file.read()

   # Load Binary Data into Document AI RawDocument Object
   raw_document = documentai.RawDocument(content=file_content, mime_type=mime_type)

   # Configure the process request
   request = documentai.ProcessRequest(name=resource_name, raw_document=raw_document)

   # Use the Document AI client to process the sample form
   result = documentai_client.process_document(request=request)

   return result.document


PROJECT_ID = "project"
LOCATION = "us" 
PROCESSOR_ID = "processor-id"  

# The local file in your current working directory
FILE_PATH = "file_path"
# Refer to https://cloud.google.com/document-ai/docs/processors-list
# for supported file types
MIME_TYPE = "mime_type"

document = online_process(
   project_id=PROJECT_ID,
   location=LOCATION,
   processor_id=PROCESSOR_ID,
   file_path=FILE_PATH,
   mime_type=MIME_TYPE,
)

print("Document processing complete.")

types = []
confidence = []
pages = []

# Each Document.entity is a classification
for entity in document.entities:
   classification = entity.type_
   types.append(classification)
   confidence.append(f"{entity.confidence:.0%}")

   # entity.page_ref contains the pages that match the classification
   pages_list = []
   for page_ref in entity.page_anchor.page_refs:
       pages_list.append(page_ref.page)
   pages.append(pages_list)

# Create a Pandas Dataframe to print the values in tabular format.
df = pd.DataFrame({"Classification": types, "Confidence": confidence, "Pages": pages})

print(df)

不同文件的输出 :

procurement_multi_document.pdf
invoice.pdf
sample.pdf - pdf 文件只包含一个段落
kitten.png - 包含单个图像

将 Document AI 与来自 google 的 python 和来自 google 代码实验室的代码一起使用 returns 错误或空结果

Using Document AI with python from google and code from google codelabs returns wrong or empty result

python

cloud-document-ai