This guide shows you how to add post-processing data actions.
This feature lets you modify the captured text. For eg: Let's say the date on your document was listed as 20th August 2017, you can use post processing to interpret and modify it as 20/08/2017. See full list of available data actions here.
- 1.Open your model
- 2.Go to the Workflow section from the left side navbar
- 3.Navigate to the Data Actions section on the Workflow page
- 4.Click on Add a new step
- 5.On the Add a step modal, search from our data action options available
- 6.Click on the action card to add it to your Workflow
- 7.Complete the steps as mentioned on the block
- 8.Click on Done to save the action
This feature allows you to set up a conditions for a data action to run. For eg: Let's say you want to format dates to MM/DD/YYYY only if a Vendor's address is in the USA. You would set up a condition to check for the keyword 'USA' in the data captured for vendor_address.
This section on a data action block allows you to decide whether to show the post-processed result in the same field or a new field.
Show in original field: Selecting this option will replace the original value for this field with the post-processed (formatted) value
Show in new field: Selecting this option requires you to specify the name of a new field. The post-processed (formatted) value will be displayed in this new field. The original value will remain as is.
This section on a data action block allows you to test the action on your own files without leaving the Workflows section.
How to test an action:
- 1.Select the file to test with
- 2.Click on Run Test
- 3.Check the input tab to verify an input existed on the test file
- 4.Check the output tab to verify that the action worked correctly and the output was as expected.
Convert to Date format: Change text to a selected date format
Dropdown field: Add GL accounts, Vendor names or any custom values
Currency detector: Extracts currency field as a 3 character code (eg: USD)
Remove characters: Remove alphabets, numbers or special characters
Find and Replace: Replace instances of any character or phrase
Keep only one instance: Keep only one of multiple detected results
Combine instances: Concatenate separated values of data into a single string
Change to closest match: Change text to a value that is a close match of captured data
Change case: Change text to uppercase/lowercase or all caps
Convert to integer: Remove decimal places from numbers
Convert to float: Add decimal places to numbers
Remove Currency symbols: Keep only numbers, decimals and commas in amounts
Convert to ASCII: Convert a string into American Standard Code for Information Interchange encoding
Match Regex: Extract the substring matching the regular expression
Create a new field with Regex: Derive fields from captured regex groups and assign to variables
Delete value: Delete values(contents) of the specified field while retaining the label in the final results/output.
Add/Replace value: Add new label or replace value of a label with a combination of strings and other labels.
Lookup: Get a new field from an external source
- Lookup in PostgreSQL
- Lookup in CSV
- Lookup in MySQL
- Lookup in MariaDB
- Find in Nanonets DB
Python code: Create a custom step with our Python code block.
Gmail: Send an email to anyone with extracted data