document-understanding
2020.10
false
DEPRECATED
Document Understanding User Guide
Automation CloudAutomation Cloud Public SectorAutomation SuiteStandalone
Last updated Jul 29, 2024

Configure Data Manager

You must first create a working folder for holding your ML data. This is referenced in all commands documented below.

Note: Run the configuration steps below before launching Data Manager. If later on, you need to change the configuration (e.g., the OCR engine, or a user password), you need to stop Data Manager using the Docker stop command, run the configuration commands, and then launch Data Manager again. See here the Docker cheat sheet.

Adding Users (only When Running the Standalone Docker Container)

An admin user with the admin username and admin password is created by default.

To create new users, stop the Data Manager container if it is running, use the following command, and then start the Data Manager container again:

docker run --rm -it -p <port_number>:80 -v "<path_to_working_folder>:/app/data" aiflprodweacr.azurecr.io/datamanager:latest --license-agreement accept --user <username> --passw <password>docker run --rm -it -p <port_number>:80 -v "<path_to_working_folder>:/app/data" aiflprodweacr.azurecr.io/datamanager:latest --license-agreement accept --user <username> --passw <password>

Each user can also modify their password from the Settings -> Password view accessible through the button at the top right of the screen.

Enabling SSL Encryption (https)

This is not necessary when running Data Manager on your own machine or on a secure office network.

However, if you plan to run Data Manager on a remote server open to the Internet, then we strongly suggest you enable SSL encryption.

In order to do this you need to obtain the DNS name of the remote server and to generate an https certificate in PEM format (.crt file) and key (.key file) for that domain name, and place them in a folder called certs on the remote server.

Then you need to launch the Data Manager using the following command:

docker run -d -p <port_number>:80 -v "<path_to_working_folder>:/app/data" -v "<path_to_certs_folder>:/certs" aiflprodweacr.azurecr.io/datamanager:latest --license-agreement accept --https-certificate /certs/<cert_filename.crt> --https-private-key /certs/<key_filename.key>docker run -d -p <port_number>:80 -v "<path_to_working_folder>:/app/data" -v "<path_to_certs_folder>:/certs" aiflprodweacr.azurecr.io/datamanager:latest --license-agreement accept --https-certificate /certs/<cert_filename.crt> --https-private-key /certs/<key_filename.key>

In this command, <cert_filename.crt> refers to the name of the .crt file, and <key_filename.key> refers to the name of the .key file which you have placed in the certs folder.

Using a Predefined Schema

To use the retraining capability in AI Center, you need to use a set of fields based on the fields already extracted by the out-of-the-box pre-trained ML Packages offered by UiPath. This list of fields is called a schema.

To make it easier to get started, we are providing below the schemas for the out-of-the-box ML Packages. These are .zip files that you can import into Data Manager just like you would import a dataset.

The schemas for the pre-trained ML Packages provided by UiPath are available at the links in the table below:

Pre-trained ML Package

Schema

Invoices

InvoicesAustralia

InvoicesIndia

InvoicesJapan

Receipts

PurchaseOrders

UtilityBills

Important: The InvoicesJapan ML Package only supports Google Cloud Vision OCR.
After downloading a .zip file from the table above, you can import the schema in Data Manager by clicking on the Import button at the top of the screen, and then select the .zip file from the dialog box. Data Manager detects that it is a new schema and imports it directly.

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