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Fix docs
Signed-off-by: Felix Wang <wangfelix98@gmail.com>
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‎docs/getting-started/concepts/feature-view.md‎

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@@ -74,16 +74,17 @@ It is suggested that you dynamically specify the new FeatureView name using `.wi
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{% tabs %}
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{% tab title="location_stats_feature_view.py" %}
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```python
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from feast import BigQuerySource, Entity, FeatureView, Field, ValueType
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from feast.types import Int32
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from feast import BigQuerySource, Entity, FeatureView, Field
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from feast.types import Int32, Int64
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location = Entity(name="location", join_keys=["location_id"], value_type=ValueType.INT64)
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location = Entity(name="location", join_keys=["location_id"])
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location_stats_fv= FeatureView(
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name="location_stats",
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entities=["location"],
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schema=[
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Field(name="temperature", dtype=Int32)
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Field(name="temperature", dtype=Int32),
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Field(name="location", dtype=Int64),
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],
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source=BigQuerySource(
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table="feast-oss.demo_data.location_stats"

‎docs/getting-started/quickstart.md‎

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@@ -82,7 +82,7 @@ online_store:
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from datetime import timedelta
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from feast import Entity, FeatureService, FeatureView, Field, FileSource, ValueType
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from feast import Entity, FeatureService, FeatureView, Field, FileSource
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from feast.types import Float32, Int64
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# Read data from parquet files. Parquet is convenient for local development mode. For
@@ -98,7 +98,7 @@ driver_hourly_stats = FileSource(
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# fetch features.
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# Entity has a name used for later reference (in a feature view, eg)
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# and join_key to identify physical field name used in storages
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driver = Entity(name="driver", value_type=ValueType.INT64, join_keys=["driver_id"], description="driver id",)
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driver = Entity(name="driver", join_keys=["driver_id"], description="driver id",)
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# Our parquet files contain sample data that includes a driver_id column, timestamps and
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# three feature column. Here we define a Feature View that will allow us to serve this
@@ -111,6 +111,7 @@ driver_hourly_stats_view = FeatureView(
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Field(name="conv_rate", dtype=Float32),
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Field(name="acc_rate", dtype=Float32),
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Field(name="avg_daily_trips", dtype=Int64),
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Field(name="driver_id", dtype=Int64),
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],
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online=True,
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source=driver_hourly_stats,
@@ -165,7 +166,7 @@ feast apply
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from datetime import timedelta
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from feast import Entity, FeatureView, Field, FileSource, ValueType
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from feast import Entity, FeatureView, Field, FileSource
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from feast.types import Float32, Int64
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# Read data from parquet files. Parquet is convenient for local development mode. For
@@ -181,7 +182,7 @@ driver_hourly_stats = FileSource(
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# fetch features.
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# Entity has a name used for later reference (in a feature view, eg)
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# and join_key to identify physical field name used in storages
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driver = Entity(name="driver", value_type=ValueType.INT64, join_keys=["driver_id"], description="driver id",)
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driver = Entity(name="driver", join_keys=["driver_id"], description="driver id",)
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# Our parquet files contain sample data that includes a driver_id column, timestamps and
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# three feature column. Here we define a Feature View that will allow us to serve this
@@ -194,6 +195,7 @@ driver_hourly_stats_view = FeatureView(
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Field(name="conv_rate", dtype=Float32),
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Field(name="acc_rate", dtype=Float32),
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Field(name="avg_daily_trips", dtype=Int64),
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Field(name="driver_id", dtype=Int64),
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],
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online=True,
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source=driver_hourly_stats,

‎docs/reference/feature-repository.md‎

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@@ -89,8 +89,8 @@ A feature repository can also contain one or more Python files that contain feat
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```python
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from datetime import timedelta
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field, ValueType
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from feast.types import Float32, String
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field
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from feast.types import Float32, Int64, String
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driver_locations_source = BigQuerySource(
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table="rh_prod.ride_hailing_co.drivers",
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driver = Entity(
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name="driver",
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value_type=ValueType.INT64,
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description="driver id",
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)
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@@ -111,6 +110,7 @@ driver_locations = FeatureView(
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schema=[
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Field(name="lat", dtype=Float32),
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Field(name="lon", dtype=String),
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Field(name="driver", dtype=Int64),
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],
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source=driver_locations_source,
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)

‎docs/reference/feature-repository/README.md‎

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@@ -94,8 +94,8 @@ A feature repository can also contain one or more Python files that contain feat
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```python
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from datetime import timedelta
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field, ValueType
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from feast.types import Float32, String
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from feast import BigQuerySource, Entity, Feature, FeatureView, Field
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from feast.types import Float32, Int64, String
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driver_locations_source = BigQuerySource(
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table_ref="rh_prod.ride_hailing_co.drivers",
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driver = Entity(
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name="driver",
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value_type=ValueType.INT64,
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description="driver id",
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)
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@@ -116,6 +115,7 @@ driver_locations = FeatureView(
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schema=[
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Field(name="lat", dtype=Float32),
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Field(name="lon", dtype=String),
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Field(name="driver", dtype=Int64),
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],
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source=driver_locations_source,
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)

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