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Document AI-Clientbibliotheken

Auf dieser Seite werden die ersten Schritte mit den Cloud-Clientbibliotheken für die Document AI API beschrieben. Clientbibliotheken erleichtern den Zugriff aufGoogle Cloud APIs mit einer unterstützten Sprache. Sie könnenGoogle Cloud APIs zwar direkt verwenden, indem Sie Rohanfragen an den Server senden, aber Clientbibliotheken bieten Vereinfachungen, die den zu schreibenden Code erheblich reduzieren.

Weitere Informationen zu den Cloud-Clientbibliotheken und den älteren Google API-Clientbibliotheken finden Sie unter Erläuterung zu Clientbibliotheken.

Clientbibliothek installieren

C++

Weitere Informationen zu den Anforderungen und Abhängigkeiten der Clientbibliothek finden Sie unter C++ Entwicklungsumgebung einrichten.

C#

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

Weitere Informationen finden Sie unter .NET-Entwicklungsumgebung einrichten.

Go

go get cloud.google.com/go/documentai

Weitere Informationen finden Sie unter Go-Entwicklungsumgebung einrichten.

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>26.53.0</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:2.60.0'

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

libraryDependencies += "com.google.cloud" % "google-cloud-document-ai" % "2.60.0"

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.

Weitere Informationen finden Sie unter Java-Entwicklungsumgebung einrichten.

Node.js

npm install @google-cloud/documentai

Weitere Informationen finden Sie unter Node.js-Entwicklungsumgebung einrichten.

PHP

composer require google/cloud-document-ai

Weitere Informationen finden Sie unter PHP auf Google Cloud verwenden.

Python

pip install --upgrade google-cloud-documentai

Weitere Informationen finden Sie unter Python-Entwicklungsumgebung einrichten.

Ruby

gem install google-cloud-document_ai

Weitere Informationen finden Sie unter Ruby-Entwicklungsumgebung einrichten.

Authentifizierung einrichten

Zur Authentifizierung von Aufrufen an Google Cloud APIs unterstützen Clientbibliotheken Standardanmeldedaten für Anwendungen (Application Default Credentials, ADC). Die Bibliotheken suchen nach Anmeldedaten an einer Reihe von definierten Standorten und verwenden diese Anmeldedaten, um Anfragen an die API zu authentifizieren. Mit ADC können Sie Anmeldedaten für Ihre Anwendung in verschiedenen Umgebungen bereitstellen, z. B. in der lokalen Entwicklung oder Produktion, ohne den Anwendungscode ändern zu müssen.

In Produktionsumgebungen hängt die Art der Einrichtung von ADC vom Dienst und Kontext ab. Weitere Informationen finden Sie unter Standardanmeldedaten für Anwendungen einrichten.

Für eine lokale Entwicklungsumgebung können Sie ADC mit den Anmeldedaten einrichten, die Ihrem Google-Konto zugeordnet sind:

Install the Google Cloud CLI, then initialize it by running the following command:
```
gcloud init
```
If you're using a local shell, then create local authentication credentials for your user account:
```
gcloud auth application-default login
```
You don't need to do this if you're using Cloud Shell.

Ein Anmeldebildschirm wird angezeigt. Nach der Anmeldung werden Ihre Anmeldedaten in der lokalen Anmeldedatendatei für ADC gespeichert.

Clientbibliothek verwenden

Das folgende Beispiel zeigt die Verwendung der Clientbibliothek.

C++


#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;
}

C#


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;
    }
}

Go

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

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

/**
 * 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

# 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


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

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

Weitere Informationen

C++

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für C++:

C#

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für C#:

Go

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für Go:

Java

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für Java:

Node.js

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für Node.js:

PHP

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für PHP:

Python

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für Python:

Ruby

Die folgende Liste enthält Links zu weiteren Ressourcen im Zusammenhang mit der Clientbibliothek für Ruby:

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Beispiel für Prozessorausgabe