---
id: google-cloud-documentai
version: "3.15.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# google-cloud-documentai — Google Cloud Documentai API client library

License: permissive · Maintenance: active · Downloads: 3.9M/mo

## What it is and what it does

This is the official Python client for Google Cloud Document AI, a managed service that uses machine learning to extract and classify information from documents. It wraps Google's REST and gRPC APIs, allowing you to send documents to pre-trained or custom Document AI processors and receive structured data back. The library handles authentication, request serialization, and response parsing, so you work with Python objects rather than raw JSON.

The package is built on standard Google Cloud infrastructure (grpcio, protobuf, google-api-core, google-auth) and requires an active Google Cloud project with Document AI enabled and at least one deployed processor. It's intended for developers integrating document processing into applications—extracting invoices, forms, contracts, or other business documents at scale.

Use it for:

- Extract line items, totals, and vendor details from invoices and receipts automatically
- Parse form responses and structured fields from application forms or surveys
- Classify and extract key information from contracts or legal documents
- Batch process large volumes of documents to populate databases or data warehouses
- Build document intake workflows that route documents based on extracted metadata

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Python client library for Google Cloud Document AI, which parses structured information from unstructured or semi-structured documents using Google's natural language, computer vision, and machine learning capabilities.

Yes, if you have a Google Cloud project with Document AI enabled and need to process documents programmatically. The library is production-stable, actively maintained, has no known vulnerabilities, and integrates seamlessly with Google Cloud authentication. The main prerequisite is a working Google Cloud setup and a deployed processor; without those, the library alone is not useful.

## Install

pip install google-cloud-documentai
uv add google-cloud-documentai
poetry add google-cloud-documentai

## Installing google-cloud-documentai

Before you install: Low install friction with a pure-Python wheel distribution. Active maintenance with a release 72 days ago. Depends on standard Google Cloud libraries (grpcio, protobuf, google-api-core, google-auth, proto-plus), all widely used and well-maintained.

License in practice: Apache-2.0 permissive license allows use in commercial and open-source projects with minimal restrictions; you must include a copy of the license and note any modifications.

Quickstart:

pip install google-cloud-documentai

from google.cloud import documentai_v1

client = documentai_v1.DocumentProcessorServiceClient()
name = client.processor_path(project_id, location, processor_id)
response = client.process_document(request={"name": name, "raw_document": raw_document})

Requires Google Cloud project with Document AI enabled, valid credentials (via GOOGLE_APPLICATION_CREDENTIALS or Application Default Credentials), and a deployed Document AI processor. Python >= 3.10 required.

Verify before relying:

- Specific supported document types and formats beyond the general 'unstructured or semi-structured' claim
- Performance characteristics or throughput limits for batch processing
- Whether the library supports all Document AI processor types or only a subset

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 3.9M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

document parsing cloud api, extract data from documents, google document ai client, ocr and document understanding, unstructured document processing, invoice receipt extraction, structured data from documents, document-processing, google-cloud, machine-learning

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