- Overview
- Document Processing Contracts
- Release notes
- About the Document Processing Contracts
- Box Class
- IPersistedActivity interface
- PrettyBoxConverter Class
- IClassifierActivity Interface
- IClassifierCapabilitiesProvider Interface
- ClassifierDocumentType Class
- ClassifierResult Class
- ClassifierCodeActivity Class
- ClassifierNativeActivity Class
- ClassifierAsyncCodeActivity Class
- ClassifierDocumentTypeCapability Class
- ExtractorAsyncCodeActivity Class
- ExtractorCodeActivity Class
- ExtractorDocumentType Class
- ExtractorDocumentTypeCapabilities Class
- ExtractorFieldCapability Class
- ExtractorNativeActivity Class
- ExtractorResult Class
- ICapabilitiesProvider Interface
- IExtractorActivity Interface
- ExtractorPayload Class
- DocumentActionPriority Enum
- DocumentActionData Class
- DocumentActionStatus Enum
- DocumentActionType Enum
- DocumentClassificationActionData Class
- DocumentValidationActionData Class
- UserData Class
- Document Class
- DocumentSplittingResult Class
- DomExtensions Class
- Page Class
- PageSection Class
- Polygon Class
- PolygonConverter Class
- Metadata Class
- WordGroup Class
- Word Class
- ProcessingSource Enum
- ResultsTableCell Class
- ResultsTableValue Class
- ResultsTableColumnInfo Class
- ResultsTable Class
- Rotation Enum
- SectionType Enum
- WordGroupType Enum
- IDocumentTextProjection Interface
- ClassificationResult Class
- ExtractionResult Class
- ResultsDocument Class
- ResultsDocumentBounds Class
- ResultsDataPoint Class
- ResultsValue Class
- ResultsContentReference Class
- ResultsValueTokens Class
- ResultsDerivedField Class
- ResultsDataSource Enum
- ResultConstants Class
- SimpleFieldValue Class
- TableFieldValue Class
- DocumentGroup Class
- DocumentTaxonomy Class
- DocumentType Class
- Field Class
- FieldType Enum
- LanguageInfo Class
- MetadataEntry Class
- TextType Enum
- TypeField Class
- ITrackingActivity Interface
- ITrainableActivity Interface
- ITrainableClassifierActivity Interface
- ITrainableExtractorActivity Interface
- TrainableClassifierAsyncCodeActivity Class
- TrainableClassifierCodeActivity Class
- TrainableClassifierNativeActivity Class
- TrainableExtractorAsyncCodeActivity Class
- TrainableExtractorCodeActivity Class
- TrainableExtractorNativeActivity Class
- Document Understanding Digitizer
- Document Understanding ML
- Document Understanding OCR Local Server
- Document Understanding
- Release notes
- About the Document Understanding activity package
- Project compatibility
- Set PDF Password
- Merge PDFs
- Get PDF Page Count
- Extract PDF Text
- Extract PDF Images
- Extract PDF Page Range
- Extract Document Data
- Create Validation Task and Wait
- Wait for Validation Task and Resume
- Create Validation Task
- Classify Document
- Create Classification Validation Task
- Create Classification Validation Task and Wait
- Wait for Classification Validation Task and Resume
- Intelligent OCR
- Release notes
- About the IntelligentOCR activity package
- Project compatibility
- Configuring Authentication
- Load Taxonomy
- Digitize Document
- Classify Document Scope
- Keyword Based Classifier
- Document Understanding Project Classifier
- Intelligent Keyword Classifier
- Create Document Classification Action
- Wait For Document Classification Action And Resume
- Train Classifiers Scope
- Keyword Based Classifier Trainer
- Intelligent Keyword Classifier Trainer
- Data Extraction Scope
- Document Understanding Project Extractor
- RegEx Based Extractor
- Form Extractor
- Intelligent Form Extractor
- Present Validation Station
- Create Document Validation Action
- Wait For Document Validation Action And Resume
- Train Extractors Scope
- Export Extraction Results
- ML Services
- OCR
- OCR Contracts
- Release notes
- About the OCR Contracts
- Project compatibility
- IOCRActivity Interface
- OCRAsyncCodeActivity Class
- OCRCodeActivity Class
- OCRNativeActivity Class
- Character Class
- OCRResult Class
- Word Class
- FontStyles Enum
- OCRRotation Enum
- OCRCapabilities Class
- OCRScrapeBase Class
- OCRScrapeFactory Class
- ScrapeControlBase Class
- ScrapeEngineUsages Enum
- ScrapeEngineBase
- ScrapeEngineFactory Class
- ScrapeEngineProvider Class
- OmniPage
- PDF
- [Unlisted] Abbyy
- [Unlisted] Abbyy Embedded
Extract Document Data
UiPath.IntelligentOCR.StudioWeb.Activities.ExtractDocumentDataWithDocumentData<UiPath.IntelligentOCR.StudioWeb.Activities.DataExtraction.ExtendedExtractionResultForDocumentData>
Extracts data from an input file or Document Data object, and stores the results into a Document Data object.
Prerequisites
The Extract Document Data activity requires input objects of type Document Data or File. A possible use case for using this activity is to precede it with a Classify Document activity, that generates an object of type Document Data.
Input options
- Document Data - from the Classify Document activity
- File - from Get File/Folder or Get Newest Email activities
Supported languages for generative models
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, visit the OCR Supported languages page.
Models used by the activity
The Extract Document Data activity uses the following:- Public endpoints for out-of-the-box models.
- Custom ML models deployed in Document Understanding App projects.
- Generative extraction model.
Designer panel
- Project - Requires you to select
your Document Understanding project from the dropdown list. The available options are:
- Predefined - The default project
- You can create a custom project by going to Document Understanding.
Note: If you have created more than 500 projects on your tenant and use the Extract Document Data activity, UiPath Studio or Studio Web will not display any projects beyond the initial 500. Therefore, those projects cannot be used.
- Extractor - Select an extractor
from your current project.
- For the Predefined project, you have two choices:
- Select an ML package. Visit Out-of-the-box models for a list of
pre-trained models that you can use.
Note: The Extract Document Data activity overrides the document type with the selected extractor. This is not applicable for generative models.
- Select the Generative
extractor.
Note: The information sent to the Generative Extractor goes to an LLM Model instance. This instance isn't publicly available, doesn't store the data sent, and doesn't use it for training purposes.Important:
This feature is currently part of an audit process and is not to be considered part of the FedRAMP Authorization until the review is finalized. See here the full list of features currently under review.
- Select an ML package. Visit Out-of-the-box models for a list of
pre-trained models that you can use.
- For the Predefined project, you have two choices:
- 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. The response, extraction result, also called Completion, has a word limit of 700. This is limited to 700 words. This means that you can't extract more than 700 words from a single prompt. If your extraction requirements exceed this limit, you can divide the document into multiple pages, process them individually, and then merge the results afterwards.
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).
Important: The maximum numbers of pages a file can have is 500. Files exceeding this limit fail to extract.
Properties panel
Input
- 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.
- 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.
- Generate Data Type - If set to
True, indicates that the output should be generated based on the selected extractor, resulting in anIDocumentData<ExtractorType>object. Alternatively, if set toFalse, indicates that the data generation should be skipped, resulting in a genericIDocumentData<DictionaryData>object.Visit Document Data for additional details and limitations available for the two object types.
Output
- 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.
To quickly get started with the generative capabilities of the Extract Document Data activity, perform the following steps:
- Add an Extract Document Data activity.
- From the Project dropdown list, select Predefined.
- For Extractor, select
Generative Extractor.
The Prompt property appears in the body of the activity.
- 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. Examples of key-value pairs for the generative extractor prompt Field name Generative 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?" Figure 1. Key-value pairs prompt for the generative extractor
