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.
An example of results for filtering "Lenovo" products all inserted into the database after "01/01/2020" is shown in the image below.
Another example is in an e-commerce dataset, we can retrieve all products:
- with prices between 200 and 300 dollars
- with the phrase "free return" included in
- that are produced after January 2020
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.
On Relevance AI's platform, advanced filters are often marked with the filter sign or a link as shown in the image below:
There are four different components to set up an advanced filter:
Field to filter(i.e. the data filed in the document you want to apply the filter to)
Filter type(i.e. the type of filter you want to apply - whether it is date/numeric/text etc.)
Condition(i.e. operators such as greater than or equal)
Filter value(dependent on the filter type but decides what value to filter on)
Supported filter types at Relevance AI are listed below.
Relevance AI covers all common conditions/operators as shown in the image below:
We will explain each filter in the next pages starting with Contains.
Updated about 1 month ago