Managing Document AI processors with Python

1. Overview

What is Document AI?

Document AI is a platform that lets you extract insights from your documents. At heart, it offers a growing list of document processors (also called parsers or splitters, depending on their functionality).

There are two ways you can manage Document AI processors:

• manually, from the web console;
• programmatically, using the Document AI API.

Here is an example screenshot showing your processor list, both from the web console and from Python code:

In this lab, you will focus on managing Document AI processors programmatically with the Python client library.

What you'll see

• How to set up your environment
• How to fetch processor types
• How to create processors
• How to list project processors
• How to use processors
• How to enable/disable processors
• How to manage processor versions
• How to delete processors

What you'll need

• A Google Cloud project
• A browser, such as Chrome or Firefox
• Familiarity using Python

Survey

How will you use this tutorial?

Read it through only Read it and complete the exercises

How would you rate your experience with Python?

Novice Intermediate Proficient

How would you rate your experience with Google Cloud services?

Novice Intermediate Proficient

2. Setup and requirements

Self-paced environment setup

1. Sign-in to the Google Cloud Console and create a new project or reuse an existing one. If you don't already have a Gmail or Google Workspace account, you must create one.

• The Project name is the display name for this project's participants. It is a character string not used by Google APIs. You can always update it.
• The Project ID is unique across all Google Cloud projects and is immutable (cannot be changed after it has been set). The Cloud Console auto-generates a unique string; usually you don't care what it is. In most codelabs, you'll need to reference your Project ID (typically identified as PROJECT_ID). If you don't like the generated ID, you might generate another random one. Alternatively, you can try your own, and see if it's available. It can't be changed after this step and remains for the duration of the project.
• For your information, there is a third value, a Project Number, which some APIs use. Learn more about all three of these values in the documentation.

Caution: A project ID is globally unique and can't be used by anyone else after you've selected it. You are the only user of that ID. Even if a project is deleted, the ID can't be used again

Note: If you use a Gmail account, you can leave the default location set to No organization. If you use a Google Workspace account, choose a location that makes sense for your organization.

2. Next, you'll need to enable billing in the Cloud Console to use Cloud resources/APIs. Running through this codelab won't cost much, if anything at all. To shut down resources to avoid incurring billing beyond this tutorial, you can delete the resources you created or delete the project. New Google Cloud users are eligible for the $300 USD Free Trial program.

Start Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this lab you are using Cloud Shell, a command line environment running in the Cloud.

Activate Cloud Shell

1. From the Cloud Console, click Activate Cloud Shell .

If this is your first time starting Cloud Shell, you're presented with an intermediate screen describing what it is. If you were presented with an intermediate screen, click Continue.

It should only take a few moments to provision and connect to Cloud Shell.

This virtual machine is loaded with all the development tools needed. It offers a persistent 5 GB home directory and runs in Google Cloud, greatly enhancing network performance and authentication. Much, if not all, of your work in this codelab can be done with a browser.

Once connected to Cloud Shell, you should see that you are authenticated and that the project is set to your project ID.

2. Run the following command in Cloud Shell to confirm that you are authenticated:

gcloud auth list

Command output

 Credentialed Accounts
ACTIVE  ACCOUNT
*       <my_account>@<my_domain.com>

To set the active account, run:
    $ gcloud config set account `ACCOUNT`

Note: The gcloud command-line tool is the powerful and unified command-line tool in Google Cloud. It comes preinstalled in Cloud Shell. You will notice its support for tab completion. For more information, see gcloud command-line tool overview.

3. Run the following command in Cloud Shell to confirm that the gcloud command knows about your project:

gcloud config list project

Command output

[core]
project = <PROJECT_ID>

If it is not, you can set it with this command:

gcloud config set project <PROJECT_ID>

Command output

Updated property [core/project].

Note: If you're completing this tutorial outside of Cloud Shell, follow Set up Application Default Credentials.

3. Environment setup

Before you can begin using Document AI, run the following command in Cloud Shell to enable the Document AI API:

gcloud services enable documentai.googleapis.com

You should see something like this:

Operation "operations/..." finished successfully.

Now, you can use Document AI!

Navigate to your home directory:

cd ~

Create a Python virtual environment to isolate the dependencies:

virtualenv venv-docai

Activate the virtual environment:

source venv-docai/bin/activate

To stop using the virtual environment and go back to your system Python version, you can use the deactivate command.

Install IPython, the Document AI client library, and python-tabulate (which you'll use to pretty-print the request results):

pip install ipython google-cloud-documentai tabulate

You should see something like this:

...
Installing collected packages: ..., tabulate, ipython, google-cloud-documentai
Successfully installed ... google-cloud-documentai-2.15.0 ...

Now, you're ready to use the Document AI client library!

If you're setting up your own Python development environment outside of Cloud Shell, follow these guidelines.

Set the following environment variables:

export PROJECT_ID=$(gcloud config get-value core/project)

# Choose "us" or "eu"
export API_LOCATION="us"

• Both environment variables will be used in your application.
• API_LOCATION lets you choose where the requests will take place ("us" for the United States, "eu" for the European Union). You can use processors in multiple locations, but you'll use a single location throughout this lab to keep it simple.

From now on, all steps should be completed in the same session.

If you start a new session, you'll need to restart from here after restoring your environment with the following specific parts from this section:

• Activate your virtual environment
• Set and check your environment variables

Make sure your environment variables are properly defined:

echo $PROJECT_ID

echo $API_LOCATION

In the next steps, you'll use an interactive Python interpreter called IPython, which you just installed. Start a session by running ipython in Cloud Shell:

ipython

You should see something like this:

Python 3.9.2 (default, Feb 28 2021, 17:03:44)
Type 'copyright', 'credits' or 'license' for more information
IPython 8.14.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]:

If needed, you can quit your IPython session with the exit command.

Copy the following code into your IPython session:

import os
from typing import Iterator, MutableSequence, Optional, Sequence, Tuple

import google.cloud.documentai_v1 as docai
from tabulate import tabulate

PROJECT_ID = os.getenv("PROJECT_ID", "")
API_LOCATION = os.getenv("API_LOCATION", "")

assert PROJECT_ID, "PROJECT_ID is undefined"
assert API_LOCATION in ("us", "eu"), "API_LOCATION is incorrect"

# Test processors
document_ocr_display_name = "document-ocr"
form_parser_display_name = "form-parser"

test_processor_display_names_and_types = (
    (document_ocr_display_name, "OCR_PROCESSOR"),
    (form_parser_display_name, "FORM_PARSER_PROCESSOR"),
)

def get_client() -> docai.DocumentProcessorServiceClient:
    client_options = {"api_endpoint": f"{API_LOCATION}-documentai.googleapis.com"}
    return docai.DocumentProcessorServiceClient(client_options=client_options)

def get_parent(client: docai.DocumentProcessorServiceClient) -> str:
    return client.common_location_path(PROJECT_ID, API_LOCATION)

def get_client_and_parent() -> Tuple[docai.DocumentProcessorServiceClient, str]:
    client = get_client()
    parent = get_parent(client)
    return client, parent
    

• This uses the PROJECT_ID and API_LOCATION environment variables defined earlier.
• The get_*() utility functions will be used in the next steps for processor requests.

You're ready to make your first request and fetch the processor types.

4. Fetching processor types

Before creating a processor in the next step, fetch the available processor types. You can retrieve this list with fetch_processor_types.

Add the following functions into your IPython session:

def fetch_processor_types() -> MutableSequence[docai.ProcessorType]:
    client, parent = get_client_and_parent()
    response = client.fetch_processor_types(parent=parent)

    return response.processor_types

def print_processor_types(processor_types: Sequence[docai.ProcessorType]):
    def sort_key(pt):
        return (not pt.allow_creation, pt.category, pt.type_)

    sorted_processor_types = sorted(processor_types, key=sort_key)
    data = processor_type_tabular_data(sorted_processor_types)
    headers = next(data)
    colalign = next(data)

    print(tabulate(data, headers, tablefmt="pretty", colalign=colalign))
    print(f"→ Processor types: {len(sorted_processor_types)}")

def processor_type_tabular_data(
    processor_types: Sequence[docai.ProcessorType],
) -> Iterator[Tuple[str, str, str, str]]:
    def locations(pt):
        return ", ".join(sorted(loc.location_id for loc in pt.available_locations))

    yield ("type", "category", "allow_creation", "locations")
    yield ("left", "left", "left", "left")
    if not processor_types:
        yield ("-", "-", "-", "-")
        return
    for pt in processor_types:
        yield (pt.type_, pt.category, f"{pt.allow_creation}", locations(pt))
        

List the processor types:

processor_types = fetch_processor_types()
print_processor_types(processor_types)

You should get something like the following:

+---------------------------------+-------------+----------------+------------+
| type                            | category    | allow_creation | locations  |
+---------------------------------+-------------+----------------+------------+
| CUSTOM_CLASSIFICATION_PROCESSOR | CUSTOM      | True           | eu, us...  |
| CUSTOM_EXTRACTION_PROCESSOR     | CUSTOM      | True           | eu, us...  |
| FORM_PARSER_PROCESSOR           | GENERAL     | True           | eu, us...  |
| OCR_PROCESSOR                   | GENERAL     | True           | eu, us...  |
| EXPENSE_PROCESSOR               | SPECIALIZED | True           | eu, us...  |
...
+---------------------------------+-------------+----------------+------------+
→ Processor types: 40

• The type column shows the processor type string to use when creating a processor.
• The category column shows whether the processor is general, specialized, or a custom one.
• The allow_creation column shows whether the processor is allowed for your project. To use a restricted processor type, you need to request access from the console and be approved beforehand.

If you get a PermissionDenied error, type exit to quit IPython, and return to the Setup and requirements step.

Now, you have all the info needed to create processors in the next step.

5. Creating processors

To create a processor, call create_processor with a display name and a processor type.

Add the following function:

def create_processor(display_name: str, type: str) -> docai.Processor:
    client, parent = get_client_and_parent()
    processor = docai.Processor(display_name=display_name, type_=type)

    return client.create_processor(parent=parent, processor=processor)
    

At the project level, you identify processors by unique display names.

Create the test processors:

separator = "=" * 80
for display_name, type in test_processor_display_names_and_types:
    print(separator)
    print(f"Creating {display_name} ({type})...")
    try:
        create_processor(display_name, type)
    except Exception as err:
        print(err)
print(separator)
print("Done")

You should get the following:

================================================================================
Creating document-ocr (OCR_PROCESSOR)...
================================================================================
Creating form-parser (FORM_PARSER_PROCESSOR)...
================================================================================
Done

You have created new processors!

By default, upon creation, a processor is enabled and can be used right away. To prevent a processor from being used (for example, while setting up an architecture), you'll see how to disable a processor in a later step.

Next, see how to list the processors.

6. Listing project processors

list_processors returns the list of all the processors belonging to your project.

Add the following functions:

def list_processors() -> MutableSequence[docai.Processor]:
    client, parent = get_client_and_parent()
    response = client.list_processors(parent=parent)

    return list(response.processors)

def print_processors(processors: Optional[Sequence[docai.Processor]] = None):
    def sort_key(processor):
        return processor.display_name

    if processors is None:
        processors = list_processors()
    sorted_processors = sorted(processors, key=sort_key)
    data = processor_tabular_data(sorted_processors)
    headers = next(data)
    colalign = next(data)

    print(tabulate(data, headers, tablefmt="pretty", colalign=colalign))
    print(f"→ Processors: {len(sorted_processors)}")

def processor_tabular_data(
    processors: Sequence[docai.Processor],
) -> Iterator[Tuple[str, str, str]]:
    yield ("display_name", "type", "state")
    yield ("left", "left", "left")
    if not processors:
        yield ("-", "-", "-")
        return
    for processor in processors:
        yield (processor.display_name, processor.type_, processor.state.name)
        

Call the functions:

processors = list_processors()
print_processors(processors)

You should get the following:

+--------------+-----------------------+---------+
| display_name | type                  | state   |
+--------------+-----------------------+---------+
| document-ocr | OCR_PROCESSOR         | ENABLED |
| form-parser  | FORM_PARSER_PROCESSOR | ENABLED |
+--------------+-----------------------+---------+
→ Processors: 2

To retrieve a processor by its display name, add the following function:

def get_processor(
    display_name: str,
    processors: Optional[Sequence[docai.Processor]] = None,
) -> Optional[docai.Processor]:
    if processors is None:
        processors = list_processors()
    for processor in processors:
        if processor.display_name == display_name:
            return processor
    return None
    

Test the function:

processor = get_processor(document_ocr_display_name, processors)

assert processor is not None
print(processor)

You should see something like this:

name: "projects/PROJECT_NUM/locations/LOCATION/processors/PROCESSOR_ID"
type_: "OCR_PROCESSOR"
display_name: "document-ocr"
state: ENABLED
...

Now, you know how to list your project processors and retrieve them by their display names. Next, see how to use a processor.

7. Using processors

Documents can be processed in two ways:

• Synchronously: Call process_document to analyze a single document and directly use the results.
• Asynchronously: Call batch_process_documents to launch a batch processing on multiple or large documents.

Your test document ( PDF) is a scanned questionnaire completed with handwritten answers. Download it into your working directory, directly from your IPython session:

!gsutil cp gs://cloud-samples-data/documentai/form.pdf .

Check the content of your working directory:

!ls

You should have the following:

...  form.pdf  ...  venv-docai  ...

You can use the synchronous process_document method to analyze a local file. Add the following function:

def process_file(
    processor: docai.Processor,
    file_path: str,
    mime_type: str,
) -> docai.Document:
    client = get_client()
    with open(file_path, "rb") as document_file:
        document_content = document_file.read()
    document = docai.RawDocument(content=document_content, mime_type=mime_type)
    request = docai.ProcessRequest(raw_document=document, name=processor.name)

    response = client.process_document(request)

    return response.document
    

The API returns a document object containing the insights extracted by the chosen processor.

Because your document is a questionnaire, choose the form parser. In addition to extracting the text (printed and handwritten), which all processors do, this general processor detects form fields.

Analyze the document:

processor = get_processor(form_parser_display_name)
assert processor is not None

file_path = "./form.pdf"
mime_type = "application/pdf"

document = process_file(processor, file_path, mime_type)

All processors run an Optical Character Recognition (OCR) first pass on the document. Review the text detected by the OCR pass:

document.text.split("\n")

You should see something like the following:

['FakeDoc M.D.',
 'HEALTH INTAKE FORM',
 'Please fill out the questionnaire carefully. The information you provide will be used to complete',
 'your health profile and will be kept confidential.',
 'Date:',
 'Sally',
 'Walker',
 'Name:',
 '9/14/19',
 'DOB: 09/04/1986',
 'Address: 24 Barney Lane City: Towaco State: NJ Zip: 07082',
 'Email: Sally, [e-mail] Phone #: [phone]',
 'Gender: F',
 'Single Occupation: Software Engineer',
 'Referred By: None',
 'Emergency Contact: Eva Walker Emergency Contact Phone: [phone]',
 'Marital Status:',
  ...
]

Add the following functions to print out the detected form fields:

def print_form_fields(document: docai.Document):
    sorted_form_fields = form_fields_sorted_by_ocr_order(document)
    data = form_field_tabular_data(sorted_form_fields, document)
    headers = next(data)
    colalign = next(data)

    print(tabulate(data, headers, tablefmt="pretty", colalign=colalign))
    print(f"→ Form fields: {len(sorted_form_fields)}")

def form_field_tabular_data(
    form_fields: Sequence[docai.Document.Page.FormField],
    document: docai.Document,
) -> Iterator[Tuple[str, str, str]]:
    yield ("name", "value", "confidence")
    yield ("right", "left", "right")
    if not form_fields:
        yield ("-", "-", "-")
        return
    for form_field in form_fields:
        name_layout = form_field.field_name
        value_layout = form_field.field_value
        name = text_from_layout(name_layout, document)
        value = text_from_layout(value_layout, document)
        confidence = value_layout.confidence
        yield (name, value, f"{confidence:.1%}")
        

Also add these utility functions:

def form_fields_sorted_by_ocr_order(
    document: docai.Document,
) -> MutableSequence[docai.Document.Page.FormField]:
    def sort_key(form_field):
        # Sort according to the field name detected position
        text_anchor = form_field.field_name.text_anchor
        return text_anchor.text_segments[0].start_index if text_anchor else 0

    fields = (field for page in document.pages for field in page.form_fields)

    return sorted(fields, key=sort_key)


def text_from_layout(
    layout: docai.Document.Page.Layout,
    document: docai.Document,
) -> str:
    full_text = document.text
    segs = layout.text_anchor.text_segments
    text = "".join(full_text[seg.start_index : seg.end_index] for seg in segs)
    if text.endswith("\n"):
        text = text[:-1]

    return text
    

Print the detected form fields:

print_form_fields(document)

You should get a printout like the following:

+--------------+-------------------------+------------+
|         name | value                   | confidence |
+--------------+-------------------------+------------+
|        Date: | 9/14/19                 |     100.0% |
|        Name: | Sally                   |      99.7% |
|              | Walker                  |            |
|         DOB: | 09/04/1986              |     100.0% |
|     Address: | 24 Barney Lane          |      99.9% |
|        City: | Towaco                  |      99.8% |
|       State: | NJ                      |      99.7% |
|         Zip: | 07082                   |      99.5% |
|       Email: | Sally, [e-mail] |      99.6% |
|     Phone #: | [phone]          |     100.0% |
|      Gender: | F                       |     100.0% |
|  Occupation: | Software Engineer       |     100.0% |
| Referred By: | None                    |     100.0% |
...
+--------------+-------------------------+------------+
→ Form fields: 17

Review the field names and values that have been detected ( PDF). Here is the top half of the questionnaire:

You have analyzed a form that contains both printed and handwritten text. You have also detected its fields with high confidence. The result is that your pixels have been transformed into structured data!

8. Enabling and disabling processors

With disable_processor and enable_processor, you can control whether a processor can be used.

Add the following functions:

def update_processor_state(processor: docai.Processor, enable_processor: bool):
    client = get_client()
    if enable_processor:
        request = docai.EnableProcessorRequest(name=processor.name)
        operation = client.enable_processor(request)
    else:
        request = docai.DisableProcessorRequest(name=processor.name)
        operation = client.disable_processor(request)
    operation.result()  # Wait for operation to complete

def enable_processor(processor: docai.Processor):
    update_processor_state(processor, True)

def disable_processor(processor: docai.Processor):
    update_processor_state(processor, False)
    

Disable the form parser processor, and check the state of your processors:

processor = get_processor(form_parser_display_name)
assert processor is not None

disable_processor(processor)
print_processors()

You should get the following:

+--------------+-----------------------+----------+
| display_name | type                  | state    |
+--------------+-----------------------+----------+
| document-ocr | OCR_PROCESSOR         | ENABLED  |
| form-parser  | FORM_PARSER_PROCESSOR | DISABLED |
+--------------+-----------------------+----------+
→ Processors: 2

The form parser is disabled. If your application tries to use it, the API returns a FailedPrecondition: 400 Processor PROCESSOR_ID is not enabled error.

Re-enable the form parser processor:

enable_processor(processor)
print_processors()

You should get the following:

+--------------+-----------------------+---------+
| display_name | type                  | state   |
+--------------+-----------------------+---------+
| document-ocr | OCR_PROCESSOR         | ENABLED |
| form-parser  | FORM_PARSER_PROCESSOR | ENABLED |
+--------------+-----------------------+---------+
→ Processors: 2

Next, see how to manage processor versions.

9. Managing processor versions

Processors can be available in multiple versions. Check out how to use the list_processor_versions and set_default_processor_version methods.

Add the following functions:

def list_processor_versions(
    processor: docai.Processor,
) -> MutableSequence[docai.ProcessorVersion]:
    client = get_client()
    response = client.list_processor_versions(parent=processor.name)

    return list(response)


def get_sorted_processor_versions(
    processor: docai.Processor,
) -> MutableSequence[docai.ProcessorVersion]:
    def sort_key(processor_version: docai.ProcessorVersion):
        return processor_version.name

    versions = list_processor_versions(processor)

    return sorted(versions, key=sort_key)


def print_processor_versions(processor: docai.Processor):
    versions = get_sorted_processor_versions(processor)
    default_version_name = processor.default_processor_version
    data = processor_versions_tabular_data(versions, default_version_name)
    headers = next(data)
    colalign = next(data)

    print(tabulate(data, headers, tablefmt="pretty", colalign=colalign))
    print(f"→ Processor versions: {len(versions)}")


def processor_versions_tabular_data(
    versions: Sequence[docai.ProcessorVersion],
    default_version_name: str,
) -> Iterator[Tuple[str, str, str]]:
    yield ("version", "display name", "default")
    yield ("left", "left", "left")
    if not versions:
        yield ("-", "-", "-")
        return
    for version in versions:
        mapping = docai.DocumentProcessorServiceClient.parse_processor_version_path(
            version.name
        )
        processor_version = mapping["processor_version"]
        is_default = "Y" if version.name == default_version_name else ""
        yield (processor_version, version.display_name, is_default)
        

List the available versions for the OCR processor:

processor = get_processor(document_ocr_display_name)
assert processor is not None
print_processor_versions(processor)

You get the processor versions:

+--------------------------------+--------------------------+---------+
| version                        | display name             | default |
+--------------------------------+--------------------------+---------+
| pretrained-ocr-v1.0-2020-09-23 | Google Stable            | Y       |
| pretrained-ocr-v1.1-2022-09-12 | Google Release Candidate |         |
| pretrained-ocr-v1.2-2022-11-10 | Google Release Candidate |         |
+--------------------------------+--------------------------+---------+
→ Processor versions: 3

You might use the Stable version 1.0 in production, use Release Candidate version 1.1 or 1.2 to benefit from new features, or to test a future version.

Now, add a function to change the default processor version:

def set_default_processor_version(processor: docai.Processor, version_name: str):
    client = get_client()
    request = docai.SetDefaultProcessorVersionRequest(
        processor=processor.name,
        default_processor_version=version_name,
    )

    operation = client.set_default_processor_version(request)
    operation.result()  # Wait for operation to complete
    

Switch to the latest processor version:

processor = get_processor(document_ocr_display_name)
assert processor is not None
versions = get_sorted_processor_versions(processor)

new_version = versions[-1]  # Latest version
set_default_processor_version(processor, new_version.name)

# Update the processor info
processor = get_processor(document_ocr_display_name)
assert processor is not None
print_processor_versions(processor)

You get the new version configuration:

+--------------------------------+--------------------------+---------+
| version                        | display name             | default |
+--------------------------------+--------------------------+---------+
| pretrained-ocr-v1.0-2020-09-23 | Google Stable            |         |
| pretrained-ocr-v1.1-2022-09-12 | Google Release Candidate |         |
| pretrained-ocr-v1.2-2022-11-10 | Google Release Candidate | Y       |
+--------------------------------+--------------------------+---------+
→ Processor versions: 3

As a consequence, all document processing requests for this processor will now use the latest release candidate.

And next, the ultimate processor management method (deletion).

10. Deleting processors

Finally, check out how to use the delete_processor method.

Add the following function:

def delete_processor(processor: docai.Processor):
    client = get_client()
    operation = client.delete_processor(name=processor.name)
    operation.result()  # Wait for operation to complete
    

Delete your test processors:

processors_to_delete = [dn for dn, _ in test_processor_display_names_and_types]
print("Deleting processors...")

for processor in list_processors():
    if processor.display_name not in processors_to_delete:
        continue
    print(f"  Deleting {processor.display_name}...")
    delete_processor(processor)

print("Done\n")
print_processors()

You should get the following:

Deleting processors...
  Deleting form-parser...
  Deleting document-ocr...
Done

+--------------+------+-------+
| display_name | type | state |
+--------------+------+-------+
| -            | -    | -     |
+--------------+------+-------+
→ Processors: 0

You've covered all the processor management methods! You're almost done...

11. Congratulations!

You learned how to manage Document AI processors using Python!

Clean up

To clean up your development environment, from Cloud Shell:

• If you're still in your IPython session, go back to the shell: exit
• Stop using the Python virtual environment: deactivate
• Delete your virtual environment folder: cd ~ ; rm -rf ./venv-docai

To delete your Google Cloud project, from Cloud Shell:

• Retrieve your current project ID: PROJECT_ID=$(gcloud config get-value core/project)
• Make sure this is the project you want to delete: echo $PROJECT_ID
• Delete the project: gcloud projects delete $PROJECT_ID

Learn more

• Try Document AI in your browser: https://cloud.google.com/document-ai/docs/drag-and-drop
• Document AI processor details: https://cloud.google.com/document-ai/docs/processors-list
• Python on Google Cloud: https://cloud.google.com/python
• Cloud Client Libraries for Python: https://github.com/googleapis/google-cloud-python

License

This work is licensed under a Creative Commons Attribution 2.0 Generic License.