diff --git a/src/document_extract.py b/src/document_extract.py
new file mode 100644
index 0000000..1285ac2
--- /dev/null
+++ b/src/document_extract.py
@@ -0,0 +1,106 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import re
+from google.cloud import vision
+from google.cloud import storage
+
+
+def async_document_extract(
+    bucket: str,
+    name: str,
+    timeout: int = 420,
+):
+    """Perform OCR with PDF/TIFF as source files on GCS.
+
+    Original sample is here:
+    https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/vision/snippets/detect/detect.py#L806
+
+    Note: This function can cause the IOPub data rate to be exceeded on a
+    Jupyter server. This rate can be changed by setting the variable
+    `--ServerApp.iopub_data_rate_limit
+
+    Args:
+        bucket (str): GCS URI of the bucket containing the PDF/TIFF files.
+        name (str): Name of the PDF/TIFF file.
+        timeout (int): Timeout in seconds for the request.
+
+
+    Returns:
+        tuple: (text, gcs_output_path)
+    """
+
+    gcs_source_uri = f'gs://{bucket}/{name}'
+    file_stem = name.split('.')
+    gcs_destination_uri = f'gs://{bucket}/{file_stem[-2]}/'
+    gcs_output_path = f'gs://{bucket}/{file_stem[-2]}/complete_text.txt'
+    mime_type = 'application/pdf'
+    batch_size = 2
+
+    # Perform Vision OCR
+    client = vision.ImageAnnotatorClient()
+
+    feature = vision.Feature(
+        type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)
+
+    gcs_source = vision.GcsSource(uri=gcs_source_uri)
+    input_config = vision.InputConfig(
+        gcs_source=gcs_source, mime_type=mime_type)
+
+    gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
+    output_config = vision.OutputConfig(
+        gcs_destination=gcs_destination, batch_size=batch_size)
+
+    async_request = vision.AsyncAnnotateFileRequest(
+        features=[feature], input_config=input_config,
+        output_config=output_config)
+
+    operation = client.async_batch_annotate_files(
+        requests=[async_request])
+
+    print('Waiting for the operation to finish.')
+    operation.result(timeout=timeout)
+
+    # Once the request has completed and the output has been
+    # written to GCS, we can list all the output files.
+    storage_client = storage.Client()
+
+    match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
+    bucket_name = match.group(1)
+    prefix = match.group(2)
+
+    bucket = storage_client.get_bucket(bucket_name)
+
+    # List objects with the given prefix, filtering out folders.
+    blob_list = [blob for blob in list(bucket.list_blobs(
+        prefix=prefix)) if not blob.name.endswith('/')]
+
+    # Concatenate all text from the blobs
+    complete_text = ""
+    for output in blob_list:
+
+        json_string = output.download_as_bytes().decode("utf-8")
+        response = json.loads(json_string)
+
+        # The actual response for the first page of the input file.
+        page_response = response['responses'][0]
+        annotation = page_response['fullTextAnnotation']
+
+        complete_text = complete_text + annotation['text']
+
+    blob = bucket.blob(gcs_output_path)
+    blob.upload_from_string(complete_text)
+
+    return (complete_text, gcs_output_path)