feast/sdk/python/feast/field.py at v0.63.0 · feast-dev/feast · GitHub

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# 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.

import json

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, Struct, from_value_type

from feast.value_type import ValueType

STRUCT_SCHEMA_TAG = "feast:struct_schema"

NESTED_COLLECTION_INNER_TYPE_TAG = "feast:nested_inner_type"

@typechecked

class Field:

"""

A Field represents a set of values with the same structure.

Attributes:

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.

"""

dtype: FeastType

description: str

tags: Dict[str, str]

vector_index: bool

vector_length: int

vector_search_metric: Optional[str]

def __init__(

self,

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:

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."""

from feast.types import Array, Set

value_type = self.dtype.to_value_type()

vector_search_metric = self.vector_search_metric or ""

tags = dict(self.tags)

# Persist Struct field schema in tags

if isinstance(self.dtype, Struct):

tags[STRUCT_SCHEMA_TAG] = _serialize_struct_schema(self.dtype)

elif isinstance(self.dtype, Array) and isinstance(self.dtype.base_type, Struct):

tags[STRUCT_SCHEMA_TAG] = _serialize_struct_schema(self.dtype.base_type)

# Persist nested collection type info in tags

if isinstance(self.dtype, (Array, Set)) and isinstance(

self.dtype.base_type, (Array, Set)

tags[NESTED_COLLECTION_INNER_TYPE_TAG] = _feast_type_to_str(self.dtype)

return FieldProto(

name=self.name,

value_type=value_type.value, # type: ignore[arg-type]

description=self.description,

tags=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)

tags = dict(field_proto.tags)

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)

# Reconstruct Struct type from persisted schema in tags

from feast.types import Array

internal_tags = {STRUCT_SCHEMA_TAG, NESTED_COLLECTION_INNER_TYPE_TAG}

dtype: FeastType

if value_type == ValueType.STRUCT and STRUCT_SCHEMA_TAG in tags:

dtype = _deserialize_struct_schema(tags[STRUCT_SCHEMA_TAG])

user_tags = {k: v for k, v in tags.items() if k not in internal_tags}

elif value_type == ValueType.STRUCT_LIST and STRUCT_SCHEMA_TAG in tags:

inner_struct = _deserialize_struct_schema(tags[STRUCT_SCHEMA_TAG])

dtype = Array(inner_struct)

user_tags = {k: v for k, v in tags.items() if k not in internal_tags}

elif (

value_type in (ValueType.VALUE_LIST, ValueType.VALUE_SET)

and NESTED_COLLECTION_INNER_TYPE_TAG in tags

dtype = _str_to_feast_type(tags[NESTED_COLLECTION_INNER_TYPE_TAG])

user_tags = {k: v for k, v in tags.items() if k not in internal_tags}

else:

dtype = from_value_type(value_type=value_type)

user_tags = {k: v for k, v in tags.items() if k not in internal_tags}

return cls(

name=field_proto.name,

dtype=dtype,

tags=user_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,

)

def _feast_type_to_str(feast_type: FeastType) -> str:

"""Convert a FeastType to a string representation for serialization."""

from feast.types import (

Array,

PrimitiveFeastType,

Set,

)

if isinstance(feast_type, PrimitiveFeastType):

return feast_type.name

elif isinstance(feast_type, Struct):

nested = {

}

return json.dumps({"__struct__": nested})

elif isinstance(feast_type, Array):

return f"Array({_feast_type_to_str(feast_type.base_type)})"

elif isinstance(feast_type, Set):

return f"Set({_feast_type_to_str(feast_type.base_type)})"

else:

return str(feast_type)

def _str_to_feast_type(type_str: str) -> FeastType:

"""Convert a string representation back to a FeastType."""

from feast.types import (

Array,

PrimitiveFeastType,

Set,

)

# Check if it's an Array type

if type_str.startswith("Array(") and type_str.endswith(")"):

inner = type_str[6:-1]

base_type = _str_to_feast_type(inner)

return Array(base_type)

# Check if it's a Set type

if type_str.startswith("Set(") and type_str.endswith(")"):

inner = type_str[4:-1]

base_type = _str_to_feast_type(inner)

return Set(base_type)

# Check if it's a nested Struct (JSON encoded)

if type_str.startswith("{"):

try:

parsed = json.loads(type_str)

if "__struct__" in parsed:

fields = {

for name, ft_str in parsed["__struct__"].items()

}

return Struct(fields)

except (json.JSONDecodeError, TypeError):

pass

# Must be a PrimitiveFeastType name

try:

return PrimitiveFeastType[type_str]

except KeyError:

raise ValueError(

f"Unknown FeastType: {type_str!r}. "

f"Valid primitive types: {[t.name for t in PrimitiveFeastType]}"

)

def _serialize_struct_schema(struct_type: Struct) -> str:

"""Serialize a Struct's field schema to a JSON string for tag storage."""

schema_dict = {}

for name, feast_type in struct_type.fields.items():

schema_dict[name] = _feast_type_to_str(feast_type)

return json.dumps(schema_dict)

def _deserialize_struct_schema(schema_str: str) -> Struct:

"""Deserialize a JSON string from tags back to a Struct type."""

schema_dict = json.loads(schema_str)

fields = {}

for name, type_str in schema_dict.items():

fields[name] = _str_to_feast_type(type_str)

return Struct(fields)

feast/sdk/python/feast/field.py at v0.63.0 · feast-dev/feast · GitHub

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