Google Cloud Document AI

Document AI is a document understanding platform from Google Cloud to transform unstructured data from documents into structured data, making it easier to understand, analyze, and consume.

Learn more:

• Document AI overview
• Document AI videos and labs
• Try it!

The module contains a PDF parser based on DocAI from Google Cloud.

You need to install two libraries to use this parser:

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

First, you need to set up a Google Cloud Storage (GCS) bucket and create your own Optical Character Recognition (OCR) processor as described here: https://cloud.google.com/document-ai/docs/create-processor

The GCS_OUTPUT_PATH should be a path to a folder on GCS (starting with gs://) and a PROCESSOR_NAME should look like projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID or projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID/processorVersions/PROCESSOR_VERSION_ID. You can get it either programmatically or copy from the Prediction endpoint section of the Processor details tab in the Google Cloud Console.

GCS_OUTPUT_PATH = "gs://BUCKET_NAME/FOLDER_PATH"
PROCESSOR_NAME = "projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID"

from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers import DocAIParser

API Reference:

• Blob
• DocAIParser

Now, create a DocAIParser.

parser = DocAIParser(
    location="us", processor_name=PROCESSOR_NAME, gcs_output_path=GCS_OUTPUT_PATH
)

For this example, you can use an Alphabet earnings report that’s uploaded to a public GCS bucket.

2022Q1_alphabet_earnings_release.pdf

Pass the document to the lazy_parse() method to

blob = Blob(
    path="gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs/2022Q1_alphabet_earnings_release.pdf"
)

We’ll get one document per page, 11 in total:

docs = list(parser.lazy_parse(blob))
print(len(docs))

11

You can run end-to-end parsing of a blob one-by-one. If you have many documents, it might be a better approach to batch them together and maybe even detach parsing from handling the results of parsing.

operations = parser.docai_parse([blob])
print([op.operation.name for op in operations])

['projects/543079149601/locations/us/operations/16447136779727347991']

You can check whether operations are finished:

parser.is_running(operations)

True

And when they’re finished, you can parse the results:

parser.is_running(operations)

False

results = parser.get_results(operations)
print(results[0])

DocAIParsingResults(source_path='gs://vertex-pgt/examples/goog-exhibit-99-1-q1-2023-19.pdf', parsed_path='gs://vertex-pgt/test/run1/16447136779727347991/0')

And now we can finally generate Documents from parsed results:

docs = list(parser.parse_from_results(results))

print(len(docs))

11