relevanceai.api.endpoints.cluster
Module Contents
Classes
Base class for all relevanceai client utilities |
- class relevanceai.api.endpoints.cluster.ClusterClient(project, api_key)
Bases:
relevanceai.base._BaseBase class for all relevanceai client utilities
- aggregate(self, dataset_id: str, vector_fields: list, metrics: list = [], groupby: list = [], filters: list = [], page_size: int = 20, page: int = 1, asc: bool = False, flatten: bool = True, alias: str = 'default')
Takes an aggregation query and gets the aggregate of each cluster in a collection. This helps you interpret each cluster and what is in them. It can only can be used after a vector field has been clustered.
For more information about aggregations check out services.aggregate.aggregate.
- Parameters
dataset_id (string) – Unique name of dataset
vector_fields (list) – The vector field that was clustered on
metrics (list) – Fields and metrics you want to calculate
groupby (list) – Fields you want to split the data into
filters (list) – Query for filtering the search results
page_size (int) – Size of each page of results
page (int) – Page of the results
asc (bool) – Whether to sort results by ascending or descending order
flatten (bool) – Whether to flatten
alias (string) – Alias used to name a vector field. Belongs in field_{alias}vector
- facets(self, dataset_id: str, facets_fields: list = [], page_size: int = 20, page: int = 1, asc: bool = False, date_interval: str = 'monthly')
Takes a high level aggregation of every field and every cluster in a collection. This helps you interpret each cluster and what is in them.
Only can be used after a vector field has been clustered.
- Parameters
dataset_id (string) – Unique name of dataset
facets_fields (list) – Fields to include in the facets, if [] then all
page_size (int) – Size of each page of results
page (int) – Page of the results
asc (bool) – Whether to sort results by ascending or descending order
date_interval (string) – Interval for date facets