Bulk Editor - Custom rules/conditions

On this page, we will explain how to build up your desired conditions and modifications. Don't forget to review Data type and conditions and Edit types explained at the bottom of this page. To start, select a dataset and go to Bulk Editor.

Relevance AI - Access to Bulk EditorRelevance AI - Access to Bulk Editor

Relevance AI - Access to Bulk Editor

Search for data

No matter what condition you have in mind, you need to follow three steps under Search for data where section:

  1. Select the source field
  2. Select the applicable rule/condition to the data type in the selected field
  3. Type in what values you have in mind (if applicable)

followed with AND/OR, repeat the above when using compound or mixed conditionals.

Relevance AI - Bulk Editor, search for dataRelevance AI - Bulk Editor, search for data

Relevance AI - Bulk Editor, search for data

You can see the matching documents immediately appear on the page. For instance, in the example below, we looked for documents whose "Content" field is left untagged and the text under the "Content" field is similar in meaning to "Useless". We intend to introduce a new tag "Useless app" to label these documents in the next step.

Note that you can select all documents or a subset of them using checkboxes on the left.

Relevance AI - Bulk Editor, matching resultsRelevance AI - Bulk Editor, matching results

Relevance AI - Bulk Editor, matching results

Bulk edit

Under "Set up bulk edits" click on "Add bulk edit". A menu will open on which you can select

  1. the field to bulk edit
  2. type of edit (applicable to the selected field)
Relevance AI - defining bulk editsRelevance AI - defining bulk edits

Relevance AI - defining bulk edits

The image below shows a bulk update where a new tag is to be added to selected entries. Simply click on the Update matching or Update the selected matching documents to apply the changes. You will receive a warning that the changes are not easily reversible.

Relevance AI - Bulk update sampleRelevance AI - Bulk update sample

Relevance AI - Bulk update sample

Note1: Using "And", it is possible to apply more than one update to selected documents

Note2: Check the selected documents under "Selected" on top

Note3: Check all applied changes under "History" on top

Note4: Check recommendations - on top right - which suggests you more edit options based on your selected conditions and documents.

Note5: Adding new tag(s) to a [No Tag] value will automatically removes [No Tag]

Note6: Use "clear all" on Search or Edit section to start over.

Note7: Clicking on values shown on the left menu will add/remove them from the condition/rule under search.

Overview of conditions and edit types

Data type and conditions

Values in a dataset are of various types such as strings (e.g. name and text responses), numbers (e.g. prices), and Tag analysis results ( a list of string values). Automatically, rules and conditions applied to different data types become different from one another. For example, when dealing with numbers, we look for values that are greater than another number. Whereas, when working with response sentences greater or smaller that does not make sense. We might want to find sentences containing a specific word or be similar in meaning to another phrase.
Parameters for defining conditions are self-explanatory. The following table summarizes the available condition parameters.

ConditionDescriptionExample
containsentries in a dataset whose selected field contain a substring"Conditional Edit" contains "Edit"
fuzzy containsentries in a dataset whose selected field contain parts of a substring"Conditional Edit" contains "Edt"
doesn't containentries in a dataset whose selected field does not contain a substring"Conditional Edit" contains "AI"
isentries in a dataset whose selected field is equal to ...
isn'tentries in a dataset whose selected field is not equal to ...
has any valueentries in a dataset that contain a specific field
is emptyentries in a dataset that contain a specific field but the field does not have any value
is similar in meaning toonly available for vectorized fields and looks for contextual similarity
contains one ofentries in a dataset that contain at least one of ..."App" exists in [App, UI, useful]
doesn't contain any ofentries in a dataset that contain none of ...
is greater thanentries in a dataset whose values under a selected field is greater than ...
is less thanentries in a dataset whose values under a selected field is smaller than ...
isentries in a dataset whose values under a selected field is equal to ...

Mix conditionals

It is possible to combine conditions and rules with two main parameters, [AND, OR].
Example1: X greater than 5 AND X smaller than 10
Example2: X contains the word "customer service" OR X is similar in meaning to "response"

  • AND: for the whole mixed conditional/rule to be valid, every single condition must match. So, X = 7 sets the first example above to True but not X = 11.
  • OR: for the whole mixed conditional/rule to be valid, one value single condition is enough. So, X = Very quick reply will activate the condition in Example2. Since even though X does not contain "customer service", X = Very quick reply is similar in meaning to "response".

Edit types

Depending on the type of values in the selected field for bulk edit, different modification/updates are available on the Bulk Editor. Updates are self explanatory. The following table summarizes the available updates.

change value tochanges an existing value to another value in matching entries
add tagsadds a new tag to matching entries
remove tagsremoves an existing tag from matching entries
merge tagscombines two or more existing tags under a new tag or another existing tag in matching entries
rename tagsrenames one or more existing tags to a new tag in matching entries
update sentiment tochanges sentiment labels in matching entries