Document AI: Node.js Client

Document AI client for Node.js

A comprehensive list of changes in each version may be found in the CHANGELOG.

Read more about the client libraries for Cloud APIs, including the older Google APIs Client Libraries, in Client Libraries Explained.

Table of contents:

Quickstart
Samples
Versioning
Contributing
License

Quickstart

Before you begin

Select or create a Cloud Platform project.
Enable billing for your project.
Enable the Document AI API.
Set up authentication so you can access the API from your local workstation.

Installing the client library

npm install @google-cloud/documentai

Using the client library

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION'; // Format is 'us' or 'eu'
// const processorId = 'YOUR_PROCESSOR_ID'; // Create processor in Cloud Console
// const filePath = '/path/to/local/pdf';

const {DocumentProcessorServiceClient} =
  require('@google-cloud/documentai').v1;

// Instantiates a client
// apiEndpoint regions available: eu-documentai.googleapis.com, us-documentai.googleapis.com (Required if using eu based processor)
// const client = new DocumentProcessorServiceClient({apiEndpoint: 'eu-documentai.googleapis.com'});
const client = new DocumentProcessorServiceClient();

async function quickstart() {
  // 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
  const name = `projects/${projectId}/locations/${location}/processors/${processorId}`;

  // Read the file into memory.
  const fs = require('fs').promises;
  const imageFile = await fs.readFile(filePath);

  // Convert the image data to a Buffer and base64 encode it.
  const encodedImage = Buffer.from(imageFile).toString('base64');

  const request = {
    name,
    rawDocument: {
      content: encodedImage,
      mimeType: 'application/pdf',
    },
  };

  // Recognizes text entities in the PDF document
  const [result] = await client.processDocument(request);
  const {document} = result;

  // Get all of the document text as one big string
  const {text} = document;

  // Extract shards from the text field
  const getText = textAnchor => {
    if (!textAnchor.textSegments || textAnchor.textSegments.length === 0) {
      return '';
    }

    // First shard in document doesn't have startIndex property
    const startIndex = textAnchor.textSegments[0].startIndex || 0;
    const endIndex = textAnchor.textSegments[0].endIndex;

    return text.substring(startIndex, endIndex);
  };

  // Read the text recognition output from the processor
  console.log('The document contains the following paragraphs:');
  const [page1] = document.pages;
  const {paragraphs} = page1;

  for (const paragraph of paragraphs) {
    const paragraphText = getText(paragraph.layout.textAnchor);
    console.log(`Paragraph text:\n${paragraphText}`);
  }
}

Samples

Samples are in the samples/ directory. Each sample's README.md has instructions for running its sample.

Sample	Source Code	Try it
Document_processor_service.batch_process_documents	source code
Document_processor_service.create_processor	source code
Document_processor_service.delete_processor	source code
Document_processor_service.delete_processor_version	source code
Document_processor_service.deploy_processor_version	source code
Document_processor_service.disable_processor	source code
Document_processor_service.enable_processor	source code
Document_processor_service.evaluate_processor_version	source code
Document_processor_service.fetch_processor_types	source code
Document_processor_service.get_evaluation	source code
Document_processor_service.get_processor	source code
Document_processor_service.get_processor_type	source code
Document_processor_service.get_processor_version	source code
Document_processor_service.list_evaluations	source code
Document_processor_service.list_processor_types	source code
Document_processor_service.list_processor_versions	source code
Document_processor_service.list_processors	source code
Document_processor_service.process_document	source code
Document_processor_service.review_document	source code
Document_processor_service.set_default_processor_version	source code
Document_processor_service.train_processor_version	source code
Document_processor_service.undeploy_processor_version	source code
Document_understanding_service.batch_process_documents	source code
Document_understanding_service.batch_process_documents	source code
Document_understanding_service.process_document	source code
Document_processor_service.batch_process_documents	source code
Document_processor_service.create_processor	source code
Document_processor_service.delete_processor	source code
Document_processor_service.delete_processor_version	source code
Document_processor_service.deploy_processor_version	source code
Document_processor_service.disable_processor	source code
Document_processor_service.enable_processor	source code
Document_processor_service.evaluate_processor_version	source code
Document_processor_service.fetch_processor_types	source code
Document_processor_service.get_evaluation	source code
Document_processor_service.get_processor	source code
Document_processor_service.get_processor_type	source code
Document_processor_service.get_processor_version	source code
Document_processor_service.import_processor_version	source code
Document_processor_service.list_evaluations	source code
Document_processor_service.list_processor_types	source code
Document_processor_service.list_processor_versions	source code
Document_processor_service.list_processors	source code
Document_processor_service.process_document	source code
Document_processor_service.review_document	source code
Document_processor_service.set_default_processor_version	source code
Document_processor_service.train_processor_version	source code
Document_processor_service.undeploy_processor_version	source code
Document_service.batch_delete_documents	source code
Document_service.get_dataset_schema	source code
Document_service.get_document	source code
Document_service.import_documents	source code
Document_service.list_documents	source code
Document_service.update_dataset	source code
Document_service.update_dataset_schema	source code
Quickstart	source code

The Document AI Node.js Client API Reference documentation also contains samples.

Supported Node.js Versions

Our client libraries follow the Node.js release schedule. Libraries are compatible with all current active and maintenance versions of Node.js. If you are using an end-of-life version of Node.js, we recommend that you update as soon as possible to an actively supported LTS version.

Google's client libraries support legacy versions of Node.js runtimes on a best-efforts basis with the following warnings:

Legacy versions are not tested in continuous integration.
Some security patches and features cannot be backported.
Dependencies cannot be kept up-to-date.

Client libraries targeting some end-of-life versions of Node.js are available, and can be installed through npm dist-tags. The dist-tags follow the naming convention legacy-(version). For example, npm install @google-cloud/documentai@legacy-8 installs client libraries for versions compatible with Node.js 8.

Versioning

This library follows Semantic Versioning.

This library is considered to be stable. The code surface will not change in backwards-incompatible ways unless absolutely necessary (e.g. because of critical security issues) or with an extensive deprecation period. Issues and requests against stable libraries are addressed with the highest priority.

More Information: Google Cloud Platform Launch Stages

Contributing

Contributions welcome! See the Contributing Guide.

Please note that this README.md, the samples/README.md, and a variety of configuration files in this repository (including .nycrc and tsconfig.json) are generated from a central template. To edit one of these files, make an edit to its templates in directory.

License

Apache Version 2.0

See LICENSE