Sends a batch (asynchronous) processing request to a processor.
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For detailed documentation that includes this code sample, see the following:
Code sample
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.api.gax.longrunning.OperationFuture;
import com.google.api.gax.paging.Page;
import com.google.cloud.documentai.v1.BatchDocumentsInputConfig;
import com.google.cloud.documentai.v1.BatchProcessMetadata;
import com.google.cloud.documentai.v1.BatchProcessRequest;
import com.google.cloud.documentai.v1.BatchProcessResponse;
import com.google.cloud.documentai.v1.Document;
import com.google.cloud.documentai.v1.DocumentOutputConfig;
import com.google.cloud.documentai.v1.DocumentOutputConfig.GcsOutputConfig;
import com.google.cloud.documentai.v1.DocumentProcessorServiceClient;
import com.google.cloud.documentai.v1.DocumentProcessorServiceSettings;
import com.google.cloud.documentai.v1.GcsDocument;
import com.google.cloud.documentai.v1.GcsDocuments;
import com.google.cloud.storage.Blob;
import com.google.cloud.storage.BlobId;
import com.google.cloud.storage.Bucket;
import com.google.cloud.storage.Storage;
import com.google.cloud.storage.StorageOptions;
import com.google.protobuf.util.JsonFormat;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
public class BatchProcessDocument {
public static void batchProcessDocument()
throws IOException, InterruptedException, TimeoutException, ExecutionException {
// 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 processerId = "your-processor-id";
String outputGcsBucketName = "your-gcs-bucket-name";
String outputGcsPrefix = "PREFIX";
String inputGcsUri = "gs://your-gcs-bucket/path/to/input/file.pdf";
batchProcessDocument(
projectId, location, processerId, inputGcsUri, outputGcsBucketName, outputGcsPrefix);
}
public static void batchProcessDocument(
String projectId,
String location,
String processorId,
String gcsInputUri,
String gcsOutputBucketName,
String gcsOutputUriPrefix)
throws IOException, InterruptedException, TimeoutException, ExecutionException {
// 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);
GcsDocument gcsDocument =
GcsDocument.newBuilder().setGcsUri(gcsInputUri).setMimeType("application/pdf").build();
GcsDocuments gcsDocuments = GcsDocuments.newBuilder().addDocuments(gcsDocument).build();
BatchDocumentsInputConfig inputConfig =
BatchDocumentsInputConfig.newBuilder().setGcsDocuments(gcsDocuments).build();
String fullGcsPath = String.format("gs://%s/%s/", gcsOutputBucketName, gcsOutputUriPrefix);
GcsOutputConfig gcsOutputConfig = GcsOutputConfig.newBuilder().setGcsUri(fullGcsPath).build();
DocumentOutputConfig documentOutputConfig =
DocumentOutputConfig.newBuilder().setGcsOutputConfig(gcsOutputConfig).build();
// Configure the batch process request.
BatchProcessRequest request =
BatchProcessRequest.newBuilder()
.setName(name)
.setInputDocuments(inputConfig)
.setDocumentOutputConfig(documentOutputConfig)
.build();
OperationFuture<BatchProcessResponse, BatchProcessMetadata> future =
client.batchProcessDocumentsAsync(request);
// Batch process document using a long-running operation.
// You can wait for now, or get results later.
// Note: first request to the service takes longer than subsequent
// requests.
System.out.println("Waiting for operation to complete...");
future.get();
System.out.println("Document processing complete.");
Storage storage = StorageOptions.newBuilder().setProjectId(projectId).build().getService();
Bucket bucket = storage.get(gcsOutputBucketName);
// List all of the files in the Storage bucket.
Page<Blob> blobs = bucket.list(Storage.BlobListOption.prefix(gcsOutputUriPrefix + "/"));
int idx = 0;
for (Blob blob : blobs.iterateAll()) {
if (!blob.isDirectory()) {
System.out.printf("Fetched file #%d\n", ++idx);
// Read the results
// Download and store json data in a temp file.
File tempFile = File.createTempFile("file", ".json");
Blob fileInfo = storage.get(BlobId.of(gcsOutputBucketName, blob.getName()));
fileInfo.downloadTo(tempFile.toPath());
// Parse json file into Document.
FileReader reader = new FileReader(tempFile);
Document.Builder builder = Document.newBuilder();
JsonFormat.parser().merge(reader, builder);
Document document = builder.build();
// Get all of the document text as one big string.
String text = document.getText();
// Read the text recognition output from the processor
System.out.println("The document contains the following paragraphs:");
Document.Page page1 = document.getPages(0);
List<Document.Page.Paragraph> paragraphList = page1.getParagraphsList();
for (Document.Page.Paragraph paragraph : paragraphList) {
String paragraphText = getText(paragraph.getLayout().getTextAnchor(), text);
System.out.printf("Paragraph text:%s\n", paragraphText);
}
// Form parsing provides additional output about
// form-formatted PDFs. You must create a form
// processor in the Cloud Console to see full field details.
System.out.println("The following form key/value pairs were detected:");
for (Document.Page.FormField field : page1.getFormFieldsList()) {
String fieldName = getText(field.getFieldName().getTextAnchor(), text);
String fieldValue = getText(field.getFieldValue().getTextAnchor(), text);
System.out.println("Extracted form fields pair:");
System.out.printf("\t(%s, %s))", fieldName, fieldValue);
}
// Clean up temp file.
tempFile.deleteOnExit();
}
}
}
}
// 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';
// const gcsInputUri = 'YOUR_SOURCE_PDF';
// const gcsOutputUri = 'YOUR_STORAGE_BUCKET';
// const gcsOutputUriPrefix = 'YOUR_STORAGE_PREFIX';
// Imports the Google Cloud client library
const {DocumentProcessorServiceClient} =
require('@google-cloud/documentai').v1;
const {Storage} = require('@google-cloud/storage');
// Instantiates Document AI, Storage clients
const client = new DocumentProcessorServiceClient();
const storage = new Storage();
const {default: PQueue} = require('p-queue');
async function batchProcessDocument() {
const name = `projects/${projectId}/locations/${location}/processors/${processorId}`;
// Configure the batch process request.
const request = {
name,
inputDocuments: {
gcsDocuments: {
documents: [
{
gcsUri: gcsInputUri,
mimeType: 'application/pdf',
},
],
},
},
documentOutputConfig: {
gcsOutputConfig: {
gcsUri: `${gcsOutputUri}/${gcsOutputUriPrefix}/`,
},
},
};
// Batch process document using a long-running operation.
// You can wait for now, or get results later.
// Note: first request to the service takes longer than subsequent
// requests.
const [operation] = await client.batchProcessDocuments(request);
// Wait for operation to complete.
await operation.promise();
console.log('Document processing complete.');
// Query Storage bucket for the results file(s).
const query = {
prefix: gcsOutputUriPrefix,
};
console.log('Fetching results ...');
// List all of the files in the Storage bucket
const [files] = await storage.bucket(gcsOutputUri).getFiles(query);
// Add all asynchronous downloads to queue for execution.
const queue = new PQueue({concurrency: 15});
const tasks = files.map((fileInfo, index) => async () => {
// Get the file as a buffer
const [file] = await fileInfo.download();
console.log(`Fetched file #${index + 1}:`);
// The results stored in the output Storage location
// are formatted as a document object.
const document = JSON.parse(file.toString());
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${paragraphText}`);
}
// Form parsing provides additional output about
// form-formatted PDFs. You must create a form
// processor in the Cloud Console to see full field details.
console.log('\nThe following form key/value pairs were detected:');
const {formFields} = page1;
for (const field of formFields) {
const fieldName = getText(field.fieldName.textAnchor);
const fieldValue = getText(field.fieldValue.textAnchor);
console.log('Extracted key value pair:');
console.log(`\t(${fieldName}, ${fieldValue})`);
}
});
await queue.addAll(tasks);
}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.
import re
from typing import Optional
from google.api_core.client_options import ClientOptions
from google.api_core.exceptions import InternalServerError
from google.api_core.exceptions import RetryError
from google.cloud import documentai # type: ignore
from google.cloud import storage
# TODO(developer): Uncomment these variables before running the sample.
# project_id = "YOUR_PROJECT_ID"
# location = "YOUR_PROCESSOR_LOCATION" # Format is "us" or "eu"
# processor_id = "YOUR_PROCESSOR_ID" # Create processor before running sample
# gcs_output_uri = "YOUR_OUTPUT_URI" # Must end with a trailing slash `/`. Format: gs://bucket/directory/subdirectory/
# processor_version_id = "YOUR_PROCESSOR_VERSION_ID" # Optional. Example: pretrained-ocr-v1.0-2020-09-23
# TODO(developer): You must specify either `gcs_input_uri` and `mime_type` or `gcs_input_prefix`
# gcs_input_uri = "YOUR_INPUT_URI" # Format: gs://bucket/directory/file.pdf
# input_mime_type = "application/pdf"
# gcs_input_prefix = "YOUR_INPUT_URI_PREFIX" # Format: gs://bucket/directory/
# field_mask = "text,entities,pages.pageNumber" # Optional. The fields to return in the Document object.
def batch_process_documents(
project_id: str,
location: str,
processor_id: str,
gcs_output_uri: str,
processor_version_id: Optional[str] = None,
gcs_input_uri: Optional[str] = None,
input_mime_type: Optional[str] = None,
gcs_input_prefix: Optional[str] = None,
field_mask: Optional[str] = None,
timeout: int = 400,
) -> None:
# 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)
if gcs_input_uri:
# Specify specific GCS URIs to process individual documents
gcs_document = documentai.GcsDocument(
gcs_uri=gcs_input_uri, mime_type=input_mime_type
)
# Load GCS Input URI into a List of document files
gcs_documents = documentai.GcsDocuments(documents=[gcs_document])
input_config = documentai.BatchDocumentsInputConfig(gcs_documents=gcs_documents)
else:
# Specify a GCS URI Prefix to process an entire directory
gcs_prefix = documentai.GcsPrefix(gcs_uri_prefix=gcs_input_prefix)
input_config = documentai.BatchDocumentsInputConfig(gcs_prefix=gcs_prefix)
# Cloud Storage URI for the Output Directory
gcs_output_config = documentai.DocumentOutputConfig.GcsOutputConfig(
gcs_uri=gcs_output_uri, field_mask=field_mask
)
# Where to write results
output_config = documentai.DocumentOutputConfig(gcs_output_config=gcs_output_config)
if processor_version_id:
# The full resource name of the processor version, e.g.:
# projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}
name = client.processor_version_path(
project_id, location, processor_id, processor_version_id
)
else:
# The full resource name of the processor, e.g.:
# projects/{project_id}/locations/{location}/processors/{processor_id}
name = client.processor_path(project_id, location, processor_id)
request = documentai.BatchProcessRequest(
name=name,
input_documents=input_config,
document_output_config=output_config,
)
# BatchProcess returns a Long Running Operation (LRO)
operation = client.batch_process_documents(request)
# Continually polls the operation until it is complete.
# This could take some time for larger files
# Format: projects/{project_id}/locations/{location}/operations/{operation_id}
try:
print(f"Waiting for operation {operation.operation.name} to complete...")
operation.result(timeout=timeout)
# Catch exception when operation doesn't finish before timeout
except (RetryError, InternalServerError) as e:
print(e.message)
# NOTE: Can also use callbacks for asynchronous processing
#
# def my_callback(future):
# result = future.result()
#
# operation.add_done_callback(my_callback)
# After the operation is complete,
# get output document information from operation metadata
metadata = documentai.BatchProcessMetadata(operation.metadata)
if metadata.state != documentai.BatchProcessMetadata.State.SUCCEEDED:
raise ValueError(f"Batch Process Failed: {metadata.state_message}")
storage_client = storage.Client()
print("Output files:")
# One process per Input Document
for process in list(metadata.individual_process_statuses):
# output_gcs_destination format: gs://BUCKET/PREFIX/OPERATION_NUMBER/INPUT_FILE_NUMBER/
# The Cloud Storage API requires the bucket name and URI prefix separately
matches = re.match(r"gs://(.*?)/(.*)", process.output_gcs_destination)
if not matches:
print(
"Could not parse output GCS destination:",
process.output_gcs_destination,
)
continue
output_bucket, output_prefix = matches.groups()
# Get List of Document Objects from the Output Bucket
output_blobs = storage_client.list_blobs(output_bucket, prefix=output_prefix)
# Document AI may output multiple JSON files per source file
for blob in output_blobs:
# Document AI should only output JSON files to GCS
if blob.content_type != "application/json":
print(
f"Skipping non-supported file: {blob.name} - Mimetype: {blob.content_type}"
)
continue
# Download JSON File as bytes object and convert to Document Object
print(f"Fetching {blob.name}")
document = documentai.Document.from_json(
blob.download_as_bytes(), ignore_unknown_fields=True
)
# For a full list of Document object attributes, please reference this page:
# https://cloud.google.com/python/docs/reference/documentai/latest/google.cloud.documentai_v1.types.Document
# Read the text recognition output from the processor
print("The document contains the following text:")
print(document.text)
What's next
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