Filters help us find what we need.

Filters are great way to retrieve a subset of documents that matches certain criteria. This allows us to have a more fine-grained overview of the data.
For instance, 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

Filters can be used in many different places such as retrieving documents, search, cluster, transformations, etc.

Simple filters

For python we provide a simpler interface to filter data that is very similar to pandas.

Category filter (exact match)

from relevanceai import Client
client = Client(token=YOUR_ACTIVATION_TOKEN)
ds = client.Dataset("quickstart")

filters = ds["title"] == "Apple IPhone 13 Pro"


Numeric filter

filters = (ds["price"] >= 200) + (ds["price"] < 300)


Exists filter

filters = ds['sold'].exists()


or to filter by not exist

filters = ds['sold'].not_exists()


Contains substring

To filter by a substring of a text.

filters = ds['title'].contains("Apple")


Date filtering

To filter by data matching an exact date.

filters = ds["_insert_date"].date("2020-07-01")


There are more filters options and ways to customize filters that are explored in the following sections.

The full list of filters we support are: exists, ids, numeric, date, contains, exact_match, word_match, categories, regex