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165 lines (145 loc) · 5.54 KB
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# Copyright 2022 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Optional
from typeguard import typechecked
from feast.feature import Feature
from feast.protos.feast.core.Feature_pb2 import FeatureSpecV2 as FieldProto
from feast.types import FeastType, from_value_type
from feast.value_type import ValueType
@typechecked
class Field:
"""
A Field represents a set of values with the same structure.
Attributes:
name: The name of the field.
dtype: The type of the field, such as string or float.
description: A human-readable description.
tags: User-defined metadata in dictionary form.
vector_index: If set to True the field will be indexed for vector similarity search.
vector_length: The length of the vector if the vector index is set to True.
vector_search_metric: The metric used for vector similarity search.
"""
name: str
dtype: FeastType
description: str
tags: Dict[str, str]
vector_index: bool
vector_length: int
vector_search_metric: Optional[str]
def __init__(
self,
*,
name: str,
dtype: FeastType,
description: str = "",
tags: Optional[Dict[str, str]] = None,
vector_index: bool = False,
vector_length: int = 0,
vector_search_metric: Optional[str] = None,
):
"""
Creates a Field object.
Args:
name: The name of the field.
dtype: The type of the field, such as string or float.
description (optional): A human-readable description.
tags (optional): User-defined metadata in dictionary form.
vector_index (optional): If set to True the field will be indexed for vector similarity search.
vector_search_metric (optional): The metric used for vector similarity search.
"""
self.name = name
self.dtype = dtype
self.description = description
self.tags = tags or {}
self.vector_index = vector_index
self.vector_length = vector_length
self.vector_search_metric = vector_search_metric
def __eq__(self, other):
if type(self) != type(other):
return False
if (
self.name != other.name
or self.dtype != other.dtype
or self.description != other.description
or self.tags != other.tags
or self.vector_length != other.vector_length
# or self.vector_index != other.vector_index
# or self.vector_search_metric != other.vector_search_metric
):
return False
return True
def __hash__(self):
return hash((self.name, hash(self.dtype)))
def __lt__(self, other):
return self.name < other.name
def __repr__(self):
return (
f"Field(\n"
f" name={self.name!r},\n"
f" dtype={self.dtype!r},\n"
f" description={self.description!r},\n"
f" tags={self.tags!r}\n"
f" vector_index={self.vector_index!r}\n"
f" vector_length={self.vector_length!r}\n"
f" vector_search_metric={self.vector_search_metric!r}\n"
f")"
)
def __str__(self):
return f"Field(name={self.name}, dtype={self.dtype}, tags={self.tags})"
def to_proto(self) -> FieldProto:
"""Converts a Field object to its protobuf representation."""
value_type = self.dtype.to_value_type()
vector_search_metric = self.vector_search_metric or ""
return FieldProto(
name=self.name,
value_type=value_type.value,
description=self.description,
tags=self.tags,
vector_index=self.vector_index,
vector_length=self.vector_length,
vector_search_metric=vector_search_metric,
)
@classmethod
def from_proto(cls, field_proto: FieldProto):
"""
Creates a Field object from a protobuf representation.
Args:
field_proto: FieldProto protobuf object
"""
value_type = ValueType(field_proto.value_type)
vector_search_metric = getattr(field_proto, "vector_search_metric", "")
vector_index = getattr(field_proto, "vector_index", False)
vector_length = getattr(field_proto, "vector_length", 0)
return cls(
name=field_proto.name,
dtype=from_value_type(value_type=value_type),
tags=dict(field_proto.tags),
description=field_proto.description,
vector_index=vector_index,
vector_length=vector_length,
vector_search_metric=vector_search_metric,
)
@classmethod
def from_feature(cls, feature: Feature):
"""
Creates a Field object from a Feature object.
Args:
feature: Feature object to convert.
"""
return cls(
name=feature.name,
dtype=from_value_type(feature.dtype),
description=feature.description,
tags=feature.labels,
)