Processe documentos através de bibliotecas de cliente

Esta página mostra como começar a usar a Document AI API na sua linguagem de programação favorita.

Antes de começar

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.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Document AI API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

Create a service account:

1. Ensure that you have the Create Service Accounts IAM role (roles/iam.serviceAccountCreator). Learn how to grant roles.

2. In the Google Cloud console, go to the Create service account page.

   Go to Create service account
3. Select your project.

4. 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.

5. Click Create and continue.

6. 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.
7. Click Continue.

8. 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:

1. In the Google Cloud console, click the email address for the service account that you created.
2. Click Keys.
3. Click Add key, and then click Create new key.
4. Click Create. A JSON key file is downloaded to your computer.
5. 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.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Document AI API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

Create a service account:

1. Ensure that you have the Create Service Accounts IAM role (roles/iam.serviceAccountCreator). Learn how to grant roles.

2. In the Google Cloud console, go to the Create service account page.

   Go to Create service account
3. Select your project.

4. 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.

5. Click Create and continue.

6. 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.
7. Click Continue.

8. 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:

1. In the Google Cloud console, click the email address for the service account that you created.
2. Click Keys.
3. Click Add key, and then click Create new key.
4. Click Create. A JSON key file is downloaded to your computer.
5. 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.

Instale a biblioteca cliente

C#

Para mais informações sobre a configuração do ambiente de programação C#, consulte o guia de configuração do ambiente de programação C#.

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

Go

go get cloud.google.com/go/documentai

Java

Para mais informações sobre a configuração do seu ambiente de programação Java, consulte o guia de configuração do ambiente de programação Java.

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>26.70.0</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:2.81.0'

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

libraryDependencies += "com.google.cloud" % "google-cloud-document-ai" % "2.81.0"

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

• Cloud Code for VS Code
• Cloud Code for IntelliJ
• Cloud Tools for Eclipse

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

Note: Cloud Java client libraries do not currently support Android.

Node.js

Para mais informações sobre a configuração do seu ambiente de programação Node.js, consulte o guia de configuração do ambiente de programação Node.js.

npm install @google-cloud/documentai

PHP

composer require google/cloud-document-ai

Python

Para mais informações sobre como configurar o ambiente de programação Python, consulte o guia de configuração do ambiente de programação Python.

pip install --upgrade google-cloud-documentai

Ruby

Para mais informações sobre como configurar o seu ambiente de programação Ruby, consulte o guia de configuração do ambiente de programação Ruby.

gem install google-cloud-document_ai

Processamento de documentos

Use a API Document AI para pedir informações de um documento PDF local. Para executar os seguintes exemplos, tem de criar primeiro um processador na IU.

Consola

1. Na Google Cloud consola, na secção Document AI, aceda à página Processadores.

   Aceda à página Processadores

2. Selecione Criar processador.

3. Clique no tipo de processador da lista que quer criar.

   Nota: o acesso está restrito a determinados processadores. Se um processador tiver acesso restrito, tem de pedir acesso e receber aprovação para poder criar o processador.

4. Na janela lateral Criar processador, especifique um nome para o processador.

5. Selecione a sua Região na lista.

6. Clique em Criar para criar o processador.

Depois de criar um processador e ter o ID do processador, execute o seguinte código para pedir o processamento de documentos individuais:

C#

Para mais informações, consulte a documentação de referência da API C# Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

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++

Para mais informações, consulte a documentação de referência da API C++ Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

#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

Para mais informações, consulte a documentação de referência da API Go Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

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

Para mais informações, consulte a documentação de referência da API Java Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

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

Para mais informações, consulte a documentação de referência da API Node.js Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

/**
 * 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

Para mais informações, consulte a documentação de referência da API PHP Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

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

# Import the Google Cloud client library.
use Google\Cloud\DocumentAI\V1\Client\DocumentProcessorServiceClient;
use Google\Cloud\DocumentAI\V1\RawDocument;
use Google\Cloud\DocumentAI\V1\ProcessRequest;

# TODO(developer): Update the following lines before running the sample.
# Your Google Cloud Platform project ID.
$projectId = 'YOUR_PROJECT_ID';

# Your Processor Location.
$location = 'us';

# Your Processor ID as hexadecimal characters.
# Not to be confused with the Processor Display Name.
$processorId = 'YOUR_PROCESSOR_ID';

# Path for the file to read.
$documentPath = 'resources/invoice.pdf';

# Create Client.
$client = new DocumentProcessorServiceClient();

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

# Load file contents into a RawDocument.
$rawDocument = (new RawDocument())
    ->setContent($contents)
    ->SetMimeType('application/pdf');

# Get the Fully-qualified Processor Name.
$fullProcessorName = $client->processorName($projectId, $location, $processorId);

# Send a ProcessRequest and get a ProcessResponse.
$request = (new ProcessRequest())
    ->setName($fullProcessorName)
    ->setRawDocument($rawDocument);

$response = $client->processDocument($request);

# Show the text found in the document.
printf('Document Text: %s', $response->getDocument()->getText());

Python

Para mais informações, consulte a documentação de referência da API Python Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

from google.api_core.client_options import ClientOptions
from google.cloud import documentai_v1

# TODO(developer): Create a processor of type "OCR_PROCESSOR".

# TODO(developer): Update and uncomment these variables before running the sample.
# project_id = "MY_PROJECT_ID"

# Processor ID as hexadecimal characters.
# Not to be confused with the Processor Display Name.
# processor_id = "MY_PROCESSOR_ID"

# Processor location. For example: "us" or "eu".
# location = "MY_PROCESSOR_LOCATION"

# Path for file to process.
# file_path = "/path/to/local/pdf"

# Set `api_endpoint` if you use a location other than "us".
opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")

# Initialize Document AI client.
client = documentai_v1.DocumentProcessorServiceClient(client_options=opts)

# Get the Fully-qualified Processor path.
full_processor_name = client.processor_path(project_id, location, processor_id)

# Get a Processor reference.
request = documentai_v1.GetProcessorRequest(name=full_processor_name)
processor = client.get_processor(request=request)

# `processor.name` is the full resource name of the processor.
# For example: `projects/{project_id}/locations/{location}/processors/{processor_id}`
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.
# For supported MIME types, refer to https://cloud.google.com/document-ai/docs/file-types
raw_document = documentai_v1.RawDocument(
    content=image_content,
    mime_type="application/pdf",
)

# Send a request and get the processed document.
request = documentai_v1.ProcessRequest(name=processor.name, raw_document=raw_document)
result = client.process_document(request=request)
document = result.document

# Read the text recognition output from the processor.
# For a full list of `Document` object attributes, reference this page:
# https://cloud.google.com/document-ai/docs/reference/rest/v1/Document
print("The document contains the following text:")
print(document.text)

Ruby

Para mais informações, consulte a documentação de referência da API Ruby Document AI.

Para se autenticar no Document AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

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

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