Document AI: Node.js Client

release level npm version codecov

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

Before you begin

  1. Select or create a Cloud Platform project.
  2. Enable billing for your project.
  3. Enable the Document AI API.
  4. Set up authentication with a service account 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.

SampleSource CodeTry it
Batch-parse-form.v1beta2source codeOpen in Cloud Shell
Batch-parse-table.v1beta2source codeOpen in Cloud Shell
Batch-process-documentsource codeOpen in Cloud Shell
Parse-form.v1beta2source codeOpen in Cloud Shell
Parse-table.v1beta2source codeOpen in Cloud Shell
Parse-with-model.v1beta2source codeOpen in Cloud Shell
Process-document-formsource codeOpen in Cloud Shell
Process-document-ocrsource codeOpen in Cloud Shell
Process-document-qualitysource codeOpen in Cloud Shell
Process-document-specializedsource codeOpen in Cloud Shell
Process-document-splittersource codeOpen in Cloud Shell
Process-documentsource codeOpen in Cloud Shell
Quickstartsource codeOpen in Cloud Shell
Set-endpoint.v1beta2source codeOpen in Cloud Shell

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.

Client libraries targeting some end-of-life versions of Node.js are available, and can be installed via npm dist-tags. The dist-tags follow the naming convention legacy-(version).

Legacy Node.js versions are supported as a best effort:

  • Legacy versions will not be tested in continuous integration.
  • Some security patches may not be able to be backported.
  • Dependencies will not be kept up-to-date, and features will not be backported.

Legacy tags available

  • legacy-8: install client libraries from this dist-tag for versions compatible with Node.js 8.

Versioning

This library follows Semantic Versioning.

This library is considered to be General Availability (GA). This means it is 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 GA 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