Document Understanding Activities
Last updated Jul 15, 2024

Extract Document Data


Extracts data from an input file or Document Data object, and stores the results into a Document Data object.


The Extract Document Data activity requires an activity that precedes it which can provide a Document Data object (produced as output by other Document Understanding activities, for example Classify Document).

The Extract Document Data activity receives as input one of the following choices:
  • Document Data - from the Classify Document activity
  • File - from Get File/Folder or Get Newest Email activities

The supported languages for the generative models are the same as the OCR engine used, which depends on the project. For Predefined projects, the OCR Engine used is UiPath Document OCR. For more information, check the OCR Supported languages page.

Project compatibility: Cross-platform


  • Project - Requires you to select your Document Understanding project from the drop-down menu. The available options are:
    • Predefined - The default project
    • You can create a custom project by going to Document Understanding.
  • Extractor - Requires you to select the Extractor from the selected project. For the Predefined Project, the available options are:
    • Either one of the ML Packages found here
      Note: The Extract Document Data activity overrides the document type with the selected extractor. This is not applicable for generative models.
    • Generative
  • Prompt - this field appears if you choose the option Generative. Prompt to identify the fields to be extracted, provided as key-value pairs, where the key represents the name of the field and the value a description for it, helping the extractor identify the corresponding value. Click on the field, and you will get a prompt with the following options, provided as pairs:
    • Field name - Requires you to input the field name to be extracted (Ex. Due date) (30-character limit)
    • Generative prompt - Requires you to provide the prompt as input for the Generative Extractor. The maximum number of characters allowed is 1000.
    Tip: For good practices on how to use generative prompts, check the Generative extractor - Good practices page.
  • Input - Requires you to specify the file itself, or Document Data, in case you have used other Document Understanding Activities before in your workflow, (for example, Classify Document).


  • Timeout (seconds) - Maximum execution time (in seconds) for the call to the generative model. If the operation exceeds this timeout, it is automatically terminated to prevent delays or hangs. This property is only displayed if the Generative Extractor is selected as an extractor.


  • Auto-validation - Use this option to enable automatic validation, a capability that helps validate the results obtained for data extraction against a Generative model. The default value for the Auto-validation field is False.
  • Confidence threshold - This field becomes visible once you enable Auto-validation. Extraction results falling below the threshold are compared to the generative extraction model. If they match, the system adjusts the extraction confidence to meet the threshold value. Possible threshold values range from 0 to 100.

    If the value is set to 0, no validation is applied. However, if you set a specific value (from 0 to 100), the system checks all extraction results below this value. For example, if you set a confidence threshold of 80%, the system will apply the generative validation for fields with confidence below 80%.

    Note: Auto-validation is available only for specialized extraction models.
  • Document Data - All the extracted field data from the file. Information can also be received from Classify Document.

    Visit Document data to learn how Document Data works and how to consume the extracted results for single and multi-value fields.

Note: The data sent to the Generative Extractor will be sent to an LLM Model instance which is not publicly available, will not leave it, and once processed, it will not be stored or used for training.
Note: The Extract Document Data activity uses:
  • Public endpoints for out-of-the-box models.
  • Custom ML models deployed in Document Understanding App projects.
  • Generative extraction model.

Using the generative extractor

To quickly get started with the generative capabilities of the Extract Document Data activity, follow these steps:

  1. Add an Extract Document Data activity.
  2. From the Project dropdown list, select Predefined.
  3. For Extractor, select Generative Extractor.

    The Prompt property appears in the body of the activity.

  4. In the Prompt field, provide your instructions as Dictionary key-value pairs, where:
    • Key represents the Field Name (example: email address).
    • Value represents the Generative prompt: The description used by the generative extractor to identify the corresponding value.

      For example, check the following table for a sample of key-value pairs:

    Table 1. Key-value pairs prompt for the generative extractor
    Field nameGenerative prompt
    Name"What is the name of the candidate?"
    Current Job"What is the current job of the candidate?"
    Employer"What is the current employer of the candidate?"

  • Using the generative extractor

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