Advanced filters

Filters are great tools to retrieve a subset of documents whose data match the criteria specified in the filter.
This can be applied to different types of data (text, numerics, dates) as well as different fields.
For example in an e-commerce dataset, we can retrieve all products:

with prices between 200 and 300 dollars
with the phrase "free return" included in description field
that are produced after January 2020

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Filters help us find what we need.
Filters are great tools to retrieve a subset of documents whose data match certain criteria. This allows us to have a more fine-grained overview of the data since only documents that meet the filtering condition(s) will be displayed.

How to form an advanced filter on Relevance AI?

On Relevance AI's platform, advanced filters are often marked with the filter sign or a link as shown in the image below:

1404 — Relevance AI - how to find the advanced filter setup

How to form an advanced filter

When setting up an advance filter, technically we explain what subset up data to be retrieved via conditions. Parameters affecting these conditions are

Field to filter - the data filed in the document to which you wish to apply the filter
Data type - Depending what the data type is - whether it is date/numeric/text etc. - different conditions are available. For instance, "is greater that" applies to numeric values whereas "contains" or "is similar in meaning" applies to text/string values.
Condition or the filter- the filtering operators such as "greater than"

Table below shows the common filters available at Relevance AI, their description, the appropriate field type for them.

Condition/Filter	Description	Data type	Example
contains	entries in a dataset whose selected field contain a substring	String	"Conditional Edit" contains "Edit"
fuzzy contains	entries in a dataset whose selected field contain parts of a substring	String	"Conditional Edit" contains "Edit"
doesn't contain	entries in a dataset whose selected field does not contain a substring	String	"Conditional Edit" contains "AI"
is	entries in a dataset whose selected field is equal to ...	All types
isn't	entries in a dataset whose selected field is not equal to ...	All types
has any value	entries in a dataset that contain a specific field	All types
is empty	entries in a dataset that contain a specific field but the field does not have any value	All types
is similar in meaning to	only available for vectorized fields and looks for contextual similarity	String
contains one of	entries in a dataset that contain at least one of ...	List	"App" exists in [App, UI, useful]
doesn't contain any of	entries in a dataset that contain none of ...	List
doesn't just contain any of	entries in a dataset that does not just contain a value	List
is less than	entries in a dataset whose values under a selected field is smaller than ...	Numeric
is greater than	entries in a dataset whose values under a selected field is greater than ...	Numeric

No matter what condition you have in mind, you need to follow three steps:

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

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