Process documents by using client libraries

This page shows you how to get started with the Document AI API in your favorite programming language.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Make sure that billing is enabled for your Google Cloud project.

Enable the Document AI API.

Enable the API

Create a service account:

In the Google Cloud console, go to the Create service account page.
Go to Create service account
Select your project.
In the Service account name field, enter a name. The Google Cloud console fills in the Service account ID field based on this name.

In the Service account description field, enter a description. For example, Service account for quickstart.
Click Create and continue.
Grant the Project > Owner role to the service account.

To grant the role, find the Select a role list, then select Project > Owner.

Note: The Role field affects which resources the service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. Instead, grant a predefined role or custom role that meets your needs.
Click Continue.
Click Done to finish creating the service account.

Do not close your browser window. You will use it in the next step.

Create a service account key:

In the Google Cloud console, click the email address for the service account that you created.
Click Keys.
Click Add key, and then click Create new key.
Click Create. A JSON key file is downloaded to your computer.
Click Close.

Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the JSON file that contains your credentials. This variable applies only to your current shell session, so if you open a new session, set the variable again.

Example: Linux or macOS

export GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

Replace KEY_PATH with the path of the JSON file that contains your credentials.

For example:

export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/service-account-file.json"

Example: Windows

For PowerShell:

$env:GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

Replace KEY_PATH with the path of the JSON file that contains your credentials.

For example:

$env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\service-account-file.json"

For command prompt:

set GOOGLE_APPLICATION_CREDENTIALS=KEY_PATH

Replace KEY_PATH with the path of the JSON file that contains your credentials.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Make sure that billing is enabled for your Google Cloud project.

Enable the Document AI API.

Enable the API

Create a service account:

In the Google Cloud console, go to the Create service account page.
Go to Create service account
Select your project.
In the Service account name field, enter a name. The Google Cloud console fills in the Service account ID field based on this name.

In the Service account description field, enter a description. For example, Service account for quickstart.
Click Create and continue.
Grant the Project > Owner role to the service account.

To grant the role, find the Select a role list, then select Project > Owner.

Note: The Role field affects which resources the service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles. Instead, grant a predefined role or custom role that meets your needs.
Click Continue.
Click Done to finish creating the service account.

Do not close your browser window. You will use it in the next step.

Create a service account key:

In the Google Cloud console, click the email address for the service account that you created.
Click Keys.
Click Add key, and then click Create new key.
Click Create. A JSON key file is downloaded to your computer.
Click Close.

Example: Linux or macOS

export GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

Replace KEY_PATH with the path of the JSON file that contains your credentials.

For example:

export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/service-account-file.json"

Example: Windows

For PowerShell:

$env:GOOGLE_APPLICATION_CREDENTIALS="KEY_PATH"

Replace KEY_PATH with the path of the JSON file that contains your credentials.

For example:

$env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\service-account-file.json"

For command prompt:

set GOOGLE_APPLICATION_CREDENTIALS=KEY_PATH

Replace KEY_PATH with the path of the JSON file that contains your credentials.

Install the client library

C#

For more on setting up your C# development environment, refer to the C# Development Environment Setup Guide.

Install-Package Google.Cloud.DocumentAI.V1 -Pre

Go

go get cloud.google.com/go/documentai

Java

For more on setting up your Java development environment, refer to the Java Development Environment Setup Guide.

If you are using Maven, add the following to your pom.xml file. For more information about BOMs, see The Google Cloud Platform Libraries BOM.

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>26.50.0</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-document-ai</artifactId>
  </dependency>
</dependencies>

If you are using Gradle, add the following to your dependencies:

implementation 'com.google.cloud:google-cloud-document-ai:2.57.0'

If you are using sbt, add the following to your dependencies:

libraryDependencies += "com.google.cloud" % "google-cloud-document-ai" % "2.57.0"

If you're using Visual Studio Code, IntelliJ, or Eclipse, you can add client libraries to your project using the following IDE plugins:

The plugins provide additional functionality, such as key management for service accounts. Refer to each plugin's documentation for details.

Node.js

For more on setting up your Node.js development environment, refer to the Node.js Development Environment Setup Guide.

npm install @google-cloud/documentai

PHP

composer require google/cloud-document-ai

Python

For more on setting up your Python development environment, refer to the Python Development Environment Setup Guide.

pip install --upgrade google-cloud-documentai

Ruby

For more on setting up your Ruby development environment, refer to the Ruby Development Environment Setup Guide.

gem install google-cloud-document_ai

Document processing

Use Document AI API to request information from a local PDF document. To run the following samples you must first create a processor in the UI.

Console

In the Google Cloud console, in the Document AI section, go to the Processors page.

Go to the Processors page
Select Create processor.
Click on the processor type from the list you want to create.

Note: Access is restricted to certain processors. If a processor is restricted access you must request access and be approved to be able to create the processor.
In the side Create processor window specify a processor name.
Select your Region from the list.
Click Create to create your processor.

After you have created a processor and have the processor ID, run the following code to request individual document processing:

C#

For more information, see the Document AI C# API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


using Google.Cloud.DocumentAI.V1;
using Google.Protobuf;
using System;
using System.IO;

public class QuickstartSample
{
    public Document Quickstart(
        string projectId = "your-project-id",
        string locationId = "your-processor-location",
        string processorId = "your-processor-id",
        string localPath = "my-local-path/my-file-name",
        string mimeType = "application/pdf"
    )
    {
        // Create client
        var client = new DocumentProcessorServiceClientBuilder
        {
            Endpoint = $"{locationId}-documentai.googleapis.com"
        }.Build();

        // Read in local file
        using var fileStream = File.OpenRead(localPath);
        var rawDocument = new RawDocument
        {
            Content = ByteString.FromStream(fileStream),
            MimeType = mimeType
        };

        // Initialize request argument(s)
        var request = new ProcessRequest
        {
            Name = ProcessorName.FromProjectLocationProcessor(projectId, locationId, processorId).ToString(),
            RawDocument = rawDocument
        };

        // Make the request
        var response = client.ProcessDocument(request);

        var document = response.Document;
        Console.WriteLine(document.Text);
        return document;
    }
}

C++

For more information, see the Document AI C++ API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


#include "google/cloud/documentai/v1/document_processor_client.h"
#include "google/cloud/location.h"
#include <fstream>
#include <iostream>
#include <string>

int main(int argc, char* argv[]) try {
  if (argc != 5) {
    std::cerr << "Usage: " << argv[0]
              << " project-id location-id processor-id filename (PDF only)\n";
    return 1;
  }

  std::string const location_id = argv[2];
  if (location_id != "us" && location_id != "eu") {
    std::cerr << "location-id must be either 'us' or 'eu'\n";
    return 1;
  }
  auto const location = google::cloud::Location(argv[1], location_id);

  namespace documentai = ::google::cloud::documentai_v1;
  auto client = documentai::DocumentProcessorServiceClient(
      documentai::MakeDocumentProcessorServiceConnection(
          location.location_id()));

  google::cloud::documentai::v1::ProcessRequest req;
  req.set_name(location.FullName() + "/processors/" + argv[3]);
  req.set_skip_human_review(true);
  auto& doc = *req.mutable_raw_document();
  doc.set_mime_type("application/pdf");
  std::ifstream is(argv[4]);
  doc.set_content(std::string{std::istreambuf_iterator<char>(is), {}});

  auto resp = client.ProcessDocument(std::move(req));
  if (!resp) throw std::move(resp).status();
  std::cout << resp->document().text() << "\n";

  return 0;
} catch (google::cloud::Status const& status) {
  std::cerr << "google::cloud::Status thrown: " << status << "\n";
  return 1;
}

Go

For more information, see the Document AI Go API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import (
	"context"
	"flag"
	"fmt"
	"os"

	documentai "cloud.google.com/go/documentai/apiv1"
	"cloud.google.com/go/documentai/apiv1/documentaipb"
	"google.golang.org/api/option"
)

func main() {
	projectID := flag.String("project_id", "PROJECT_ID", "Cloud Project ID")
	location := flag.String("location", "us", "The Processor location")
	// Create a Processor before running sample
	processorID := flag.String("processor_id", "aaaaaaaa", "The Processor ID")
	filePath := flag.String("file_path", "invoice.pdf", "The path to the file to parse")
	mimeType := flag.String("mime_type", "application/pdf", "The mimeType of the file")
	flag.Parse()

	ctx := context.Background()

	endpoint := fmt.Sprintf("%s-documentai.googleapis.com:443", *location)
	client, err := documentai.NewDocumentProcessorClient(ctx, option.WithEndpoint(endpoint))
	if err != nil {
		fmt.Println(fmt.Errorf("error creating Document AI client: %w", err))
	}
	defer client.Close()

	// Open local file.
	data, err := os.ReadFile(*filePath)
	if err != nil {
		fmt.Println(fmt.Errorf("os.ReadFile: %w", err))
	}

	req := &documentaipb.ProcessRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/processors/%s", *projectID, *location, *processorID),
		Source: &documentaipb.ProcessRequest_RawDocument{
			RawDocument: &documentaipb.RawDocument{
				Content:  data,
				MimeType: *mimeType,
			},
		},
	}
	resp, err := client.ProcessDocument(ctx, req)
	if err != nil {
		fmt.Println(fmt.Errorf("processDocument: %w", err))
	}

	// Handle the results.
	document := resp.GetDocument()
	fmt.Printf("Document Text: %s", document.GetText())
}

Java

For more information, see the Document AI Java API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.cloud.documentai.v1.Document;
import com.google.cloud.documentai.v1.DocumentProcessorServiceClient;
import com.google.cloud.documentai.v1.DocumentProcessorServiceSettings;
import com.google.cloud.documentai.v1.ProcessRequest;
import com.google.cloud.documentai.v1.ProcessResponse;
import com.google.cloud.documentai.v1.RawDocument;
import com.google.protobuf.ByteString;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeoutException;

public class QuickStart {
  public static void main(String[] args)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String location = "your-project-location"; // Format is "us" or "eu".
    String processorId = "your-processor-id";
    String filePath = "path/to/input/file.pdf";
    quickStart(projectId, location, processorId, filePath);
  }

  public static void quickStart(
      String projectId, String location, String processorId, String filePath)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs
    // to be created
    // once, and can be reused for multiple requests. After completing all of your
    // requests, call
    // the "close" method on the client to safely clean up any remaining background
    // resources.
    String endpoint = String.format("%s-documentai.googleapis.com:443", location);
    DocumentProcessorServiceSettings settings =
        DocumentProcessorServiceSettings.newBuilder().setEndpoint(endpoint).build();
    try (DocumentProcessorServiceClient client = DocumentProcessorServiceClient.create(settings)) {
      // 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
      String name =
          String.format("projects/%s/locations/%s/processors/%s", projectId, location, processorId);

      // Read the file.
      byte[] imageFileData = Files.readAllBytes(Paths.get(filePath));

      // Convert the image data to a Buffer and base64 encode it.
      ByteString content = ByteString.copyFrom(imageFileData);

      RawDocument document =
          RawDocument.newBuilder().setContent(content).setMimeType("application/pdf").build();

      // Configure the process request.
      ProcessRequest request =
          ProcessRequest.newBuilder().setName(name).setRawDocument(document).build();

      // Recognizes text entities in the PDF document
      ProcessResponse result = client.processDocument(request);
      Document documentResponse = result.getDocument();

      // Get all of the document text as one big string
      String text = documentResponse.getText();

      // Read the text recognition output from the processor
      System.out.println("The document contains the following paragraphs:");
      Document.Page firstPage = documentResponse.getPages(0);
      List<Document.Page.Paragraph> paragraphs = firstPage.getParagraphsList();

      for (Document.Page.Paragraph paragraph : paragraphs) {
        String paragraphText = getText(paragraph.getLayout().getTextAnchor(), text);
        System.out.printf("Paragraph text:\n%s\n", paragraphText);
      }
    }
  }

  // Extract shards from the text field
  private static String getText(Document.TextAnchor textAnchor, String text) {
    if (textAnchor.getTextSegmentsList().size() > 0) {
      int startIdx = (int) textAnchor.getTextSegments(0).getStartIndex();
      int endIdx = (int) textAnchor.getTextSegments(0).getEndIndex();
      return text.substring(startIdx, endIdx);
    }
    return "[NO TEXT]";
  }
}

Node.js

For more information, see the Document AI Node.js API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * 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}`);
  }
}

PHP

For more information, see the Document AI PHP API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

# Includes the autoloader for libraries installed with composer
require __DIR__ . '/vendor/autoload.php';

# Imports the Google Cloud client library
use Google\Cloud\DocumentAI\V1\DocumentProcessorServiceClient;
use Google\Cloud\DocumentAI\V1\RawDocument;

$projectId = 'YOUR_PROJECT_ID'; # Your Google Cloud Platform project ID
$location = 'us'; # Your Processor Location
$processor = 'YOUR_PROCESSOR_ID'; # Your Processor ID

# Create Client
$client = new DocumentProcessorServiceClient();

# Local File Path
$documentPath = 'resources/invoice.pdf';

# Read in File Contents
$handle = fopen($documentPath, 'rb');
$contents = fread($handle, filesize($documentPath));
fclose($handle);

# Load File Contents into RawDocument
$rawDocument = new RawDocument([
    'content' => $contents,
    'mime_type' => 'application/pdf'
]);

# Fully-qualified Processor Name
$name = $client->processorName($projectId, $location, $processor);

# Make Processing Request
$response = $client->processDocument($name, [
    'rawDocument' => $rawDocument
]);

# Print Document Text
printf('Document Text: %s', $response->getDocument()->getText());

Python

For more information, see the Document AI Python API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


from google.api_core.client_options import ClientOptions
from google.cloud import documentai  # type: ignore

# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_PROCESSOR_LOCATION"  # Format is "us" or "eu"
# file_path = "/path/to/local/pdf"
# processor_display_name = "YOUR_PROCESSOR_DISPLAY_NAME" # Must be unique per project, e.g.: "My Processor"


def quickstart(
    project_id: str,
    location: str,
    file_path: str,
    processor_display_name: str = "My Processor",
):
    # You must set the `api_endpoint`if you use a location other than "us".
    opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")

    client = documentai.DocumentProcessorServiceClient(client_options=opts)

    # The full resource name of the location, e.g.:
    # `projects/{project_id}/locations/{location}`
    parent = client.common_location_path(project_id, location)

    # Create a Processor
    processor = client.create_processor(
        parent=parent,
        processor=documentai.Processor(
            type_="OCR_PROCESSOR",  # Refer to https://cloud.google.com/document-ai/docs/create-processor for how to get available processor types
            display_name=processor_display_name,
        ),
    )

    # Print the processor information
    print(f"Processor Name: {processor.name}")

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

    # Load binary data
    raw_document = documentai.RawDocument(
        content=image_content,
        mime_type="application/pdf",  # Refer to https://cloud.google.com/document-ai/docs/file-types for supported file types
    )

    # Configure the process request
    # `processor.name` is the full resource name of the processor, e.g.:
    # `projects/{project_id}/locations/{location}/processors/{processor_id}`
    request = documentai.ProcessRequest(name=processor.name, raw_document=raw_document)

    result = client.process_document(request=request)

    # For a full list of `Document` object attributes, reference this page:
    # https://cloud.google.com/document-ai/docs/reference/rest/v1/Document
    document = result.document

    # Read the text recognition output from the processor
    print("The document contains the following text:")
    print(document.text)

Ruby

For more information, see the Document AI Ruby API reference documentation.

To authenticate to Document AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

require "google/cloud/document_ai/v1"

##
# Document AI quickstart
#
# @param project_id [String] Your Google Cloud project (e.g. "my-project")
# @param location_id [String] Your Processor Location (e.g. "us")
# @param processor_id [String] Your Processor ID (e.g. "a14dae8f043b60bd")
# @param file_path [String] Path to Local File (e.g. "invoice.pdf")
# @param mime_type [String] Refer to https://cloud.google.com/document-ai/docs/file-types (e.g. "application/pdf")
#
def quickstart project_id:, location_id:, processor_id:, file_path:, mime_type:
  # Create the Document AI client.
  client = ::Google::Cloud::DocumentAI::V1::DocumentProcessorService::Client.new do |config|
    config.endpoint = "#{location_id}-documentai.googleapis.com"
  end

  # Build the resource name from the project.
  name = client.processor_path(
    project: project_id,
    location: location_id,
    processor: processor_id
  )

  # Read the bytes into memory
  content = File.binread file_path

  # Create request
  request = Google::Cloud::DocumentAI::V1::ProcessRequest.new(
    skip_human_review: true,
    name: name,
    raw_document: {
      content: content,
      mime_type: mime_type
    }
  )

  # Process document
  response = client.process_document request

  # Handle response
  puts response.document.text
end

Congratulations! You've sent your first request to Document AI.

How did it go?

Clean up

To avoid incurring charges to your Google Account for the resources used in this quickstart:

Use the Google Cloud console to delete your project if you do not need it.

What's next

Find out more about our Document AI API Client Libraries.

Create and manage processors

Send a process request