Functions
local_stream_feature_view​
Make a local Tecton StreamFeatureView from raw data.Parameters
name
(str
) - The name of the feature view.data
(Any
) - The raw data, can be a pandas DataFrame, a dictionary, or a list of dictionaries.entity_keys
(List
[str
]) - The entity keys for lookup.timestamp_field
(Optional
[str
]) - The timestamp field, defaults to None (will add timestamp field automatically). Default:None
max_rows
(Optional
[int
]) - The maximum number of rows allowed in the data, defaults to None. Default:None
fv_kwargs
(Any
) -
Returns
StreamFeatureView
: A Tecton StreamFeatureView.Example
from tecton_gen_ai.testing import local_stream_feature_view# simplest formfv = local_stream_feature_view("fv", {"user_id": "user1", "age": 30}, ["user_id"])# with a list of dictionariesdata = [{"user_id": "user1", "age": 30},{"user_id": "user2", "age": 40},]fv = local_stream_feature_view("fv", data, ["user_id"])# with a pandas DataFrameimport pandas as pddf = pd.DataFrame(data)fv = local_stream_feature_view("fv", df, ["user_id"])
make_local_batch_feature_view​
Make a local Tecton BatchFeatureView from raw data.Parameters
name
(str
) - The name of the feature view.data
(Any
) - The raw data, can be a pandas DataFrame, a dictionary, or a list of dictionaries.entity_keys
(List
[str
]) - The entity keys for dedup and lookup.timestamp_field
(Optional
[str
]) - The timestamp field, defaults to None (will add timestamp field automatically). Default:None
max_rows
(Optional
[int
]) - The maximum number of rows allowed in the data, defaults to None. Default:None
fv_kwargs
(Any
) -
Returns
BatchFeatureView
: A Tecton BatchFeatureView.Example
from tecton_gen_ai.testing import make_local_batch_feature_view# simplest formmy_fv1 = make_local_batch_feature_view("my_fv1", {"user_id": 1, "name": "Jim"}, ["user_id"])# with a list of dictionariesdata = [{"user_id": 1, "name": "Jim"},{"user_id": 2, "name": "John"},]my_fv2 = make_local_batch_feature_view("my_fv2", data, ["user_id"])# with a pandas DataFrameimport pandas as pddf = pd.DataFrame(data)my_fv3 = make_local_batch_feature_view("my_fv3", df, ["user_id"])
make_local_realtime_feature_view​
Make a local Tecton RealtimeFeatureView from raw data.Parameters
name
(str
) - The name of the feature view.data
(Any
) - The raw data, can be a pandas DataFrame, a dictionary, or a list of dictionaries.entity_keys
(List
[str
]) - The entity keys for lookup.fv_kwargs
(Any
) -
Returns
RealtimeFeatureView
: A Tecton RealtimeFeatureView.Example
from tecton_gen_ai.testing import make_local_realtime_feature_viewfv = make_local_realtime_feature_view("fv", {"user_id": "user1", "age": 30}, ["user_id"])events = pd.DataFrame([{"user_id": "user1"}])odf = fv.get_features_for_events(events).to_pandas()
make_local_source​
Make a local Tecton DataSource from raw data.Parameters
name
(str
) - The name of the source.raw
(Any
) - The raw data, can be a pandas DataFrame, a dictionary, or a list of dictionaries.auto_timestamp
(bool
) - Whether to automatically add a timestamp field to the data when timestamp_field is None, defaults to True. Default:true
timestamp_field
(Optional
[str
]) - The timestamp field, defaults to None (no timestamp field). Default:None
max_rows
(Optional
[int
]) - The maximum number of rows allowed in the data, defaults to 100. Default:None
is_stream
(bool
) - Whether the returned source is a stream source, defaults to False. Default:false
source_kwargs
(Any
) -
Returns
Union
[BatchSource
, StreamSource
]: A Tecton DataSource.Example
from tecton_gen_ai.testing import make_local_sourceimport pandas as pd# simplest formmy_src1 = make_local_source("my_src1", {"user_id": [1, 2]}))# with a list of dictionariesdata = [{"user_id": 1, "event_time": "2024-01-01T00:00:00", "event_type": "click"},{"user_id": 2, "event_time": "2024-01-01T00:00:01", "event_type": "click"},]my_src2 = make_local_source("my_src2", data, timestamp_field="event_time")# with a pandas DataFramedf = pd.DataFrame(data)my_src3 = make_local_source("my_src3", df, timestamp_field="event_time")