chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,310 @@
|
||||
# Copyright 2025-present the zvec project
|
||||
#
|
||||
# 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
|
||||
#
|
||||
# http://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 __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import Any, Optional, Union
|
||||
|
||||
from zvec._zvec.schema import _FieldSchema
|
||||
from zvec.model.param import (
|
||||
FlatIndexParam,
|
||||
FtsIndexParam,
|
||||
HnswIndexParam,
|
||||
HnswRabitqIndexParam,
|
||||
InvertIndexParam,
|
||||
IVFIndexParam,
|
||||
)
|
||||
from zvec.typing import DataType
|
||||
|
||||
__all__ = [
|
||||
"FieldSchema",
|
||||
"VectorSchema",
|
||||
]
|
||||
|
||||
SUPPORT_VECTOR_DATA_TYPE = [
|
||||
DataType.VECTOR_FP16,
|
||||
DataType.VECTOR_FP32,
|
||||
DataType.VECTOR_FP64,
|
||||
DataType.VECTOR_INT8,
|
||||
DataType.SPARSE_VECTOR_FP16,
|
||||
DataType.SPARSE_VECTOR_FP32,
|
||||
]
|
||||
|
||||
SUPPORT_SCALAR_DATA_TYPE = [
|
||||
DataType.INT32,
|
||||
DataType.INT64,
|
||||
DataType.UINT32,
|
||||
DataType.UINT64,
|
||||
DataType.FLOAT,
|
||||
DataType.DOUBLE,
|
||||
DataType.STRING,
|
||||
DataType.BOOL,
|
||||
DataType.ARRAY_INT32,
|
||||
DataType.ARRAY_INT64,
|
||||
DataType.ARRAY_UINT32,
|
||||
DataType.ARRAY_UINT64,
|
||||
DataType.ARRAY_FLOAT,
|
||||
DataType.ARRAY_DOUBLE,
|
||||
DataType.ARRAY_STRING,
|
||||
DataType.ARRAY_BOOL,
|
||||
]
|
||||
|
||||
|
||||
class FieldSchema:
|
||||
"""Represents a scalar (non-vector) field in a collection schema.
|
||||
|
||||
A `FieldSchema` defines the name, data type, nullability, and optional
|
||||
inverted index configuration for a regular field (e.g., ID, timestamp, category).
|
||||
|
||||
Args:
|
||||
name (str): Name of the field. Must be unique within the collection.
|
||||
data_type (DataType): Data type of the field (e.g., INT64, STRING).
|
||||
nullable (bool, optional): Whether the field can contain null values.
|
||||
Defaults to False.
|
||||
index_param (Optional[Union[InvertIndexParam, FtsIndexParam]], optional):
|
||||
Index parameters for this field. Use ``InvertIndexParam`` for scalar
|
||||
inverted indexing, or ``FtsIndexParam`` for full-text search indexing
|
||||
on STRING fields. Defaults to None.
|
||||
|
||||
Examples:
|
||||
>>> from zvec.typing import DataType
|
||||
>>> from zvec.model.param import InvertIndexParam, FtsIndexParam
|
||||
>>> id_field = FieldSchema(
|
||||
... name="id",
|
||||
... data_type=DataType.INT64,
|
||||
... nullable=False,
|
||||
... index_param=InvertIndexParam(enable_range_optimization=True)
|
||||
... )
|
||||
>>> content_field = FieldSchema(
|
||||
... name="content",
|
||||
... data_type=DataType.STRING,
|
||||
... nullable=False,
|
||||
... index_param=FtsIndexParam(tokenizer_name="standard")
|
||||
... )
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
data_type: DataType,
|
||||
nullable: bool = False,
|
||||
index_param: Optional[Union[InvertIndexParam, FtsIndexParam]] = None,
|
||||
):
|
||||
if name is None or not isinstance(name, str):
|
||||
raise ValueError(
|
||||
f"schema validate failed: field name must be str, got {type(name).__name__}"
|
||||
)
|
||||
|
||||
if data_type not in SUPPORT_SCALAR_DATA_TYPE:
|
||||
raise ValueError(
|
||||
f"schema validate failed: scalar_field's data_type must be one of "
|
||||
f"{', '.join(str(dt) for dt in SUPPORT_SCALAR_DATA_TYPE)}, "
|
||||
f"but field[{name}]'s data_type is {data_type}"
|
||||
)
|
||||
|
||||
self._cpp_obj = _FieldSchema(
|
||||
name=name,
|
||||
data_type=data_type,
|
||||
dimension=0,
|
||||
nullable=nullable,
|
||||
index_param=index_param,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _from_core(cls, core_field_schema: _FieldSchema):
|
||||
if core_field_schema is None:
|
||||
raise ValueError("schema validate failed: field schema is None")
|
||||
inst = cls.__new__(cls)
|
||||
inst._cpp_obj = core_field_schema
|
||||
return inst
|
||||
|
||||
def _get_object(self) -> _FieldSchema:
|
||||
return self._cpp_obj
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
"""str: The name of the field."""
|
||||
return self._cpp_obj.name
|
||||
|
||||
@property
|
||||
def data_type(self) -> DataType:
|
||||
"""DataType: The data type of the field (e.g., INT64, STRING)."""
|
||||
return self._cpp_obj.data_type
|
||||
|
||||
@property
|
||||
def nullable(self) -> bool:
|
||||
"""bool: Whether the field allows null values."""
|
||||
return self._cpp_obj.nullable
|
||||
|
||||
@property
|
||||
def index_param(self) -> Optional[Union[InvertIndexParam, FtsIndexParam]]:
|
||||
"""Optional[Union[InvertIndexParam, FtsIndexParam]]: Index configuration, if any."""
|
||||
return self._cpp_obj.index_param
|
||||
|
||||
def __dict__(self) -> dict[str, Any]:
|
||||
return {
|
||||
"name": self.name,
|
||||
"data_type": (
|
||||
self.data_type.name
|
||||
if hasattr(self.data_type, "name")
|
||||
else str(self.data_type)
|
||||
),
|
||||
"nullable": self.nullable,
|
||||
"index_param": (
|
||||
self.index_param.to_dict() if self.index_param is not None else None
|
||||
),
|
||||
}
|
||||
|
||||
def __repr__(self) -> str:
|
||||
try:
|
||||
schema = self.__dict__()
|
||||
return json.dumps(schema, indent=2, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
return f"<FieldSchema error during repr: {e}>"
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__repr__()
|
||||
|
||||
def __eq__(self, other: object) -> bool:
|
||||
if not isinstance(other, FieldSchema):
|
||||
return False
|
||||
return self._cpp_obj == other._cpp_obj
|
||||
|
||||
def __hash__(self) -> int:
|
||||
return hash((self.name, self.data_type, self.nullable))
|
||||
|
||||
|
||||
class VectorSchema:
|
||||
"""Represents a vector field in a collection schema.
|
||||
|
||||
A `VectorSchema` defines the name, data type, dimensionality, and index
|
||||
configuration for a vector field used in similarity search.
|
||||
|
||||
Args:
|
||||
name (str): Name of the vector field. Must be unique within the collection.
|
||||
data_type (DataType): Vector data type (e.g., VECTOR_FP32, VECTOR_INT8).
|
||||
dimension (int, optional): Dimensionality of the vector. Must be > 0 for dense vectors;
|
||||
may be `None` for sparse vectors.
|
||||
index_param (Union[HnswIndexParam, IVFIndexParam, FlatIndexParam], optional):
|
||||
Index configuration for this vector field. Defaults to
|
||||
``HnswIndexParam()``.
|
||||
|
||||
Examples:
|
||||
>>> from zvec.typing import DataType
|
||||
>>> from zvec.model.param import HnswIndexParam
|
||||
>>> emb_field = VectorSchema(
|
||||
... name="embedding",
|
||||
... data_type=DataType.VECTOR_FP32,
|
||||
... dimension=128,
|
||||
... index_param=HnswIndexParam(ef_construction=200, m=16)
|
||||
... )
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
data_type: DataType,
|
||||
dimension: Optional[int] = 0,
|
||||
index_param: Optional[
|
||||
Union[HnswIndexParam, HnswRabitqIndexParam, FlatIndexParam, IVFIndexParam]
|
||||
] = None,
|
||||
):
|
||||
if name is None or not isinstance(name, str):
|
||||
raise ValueError(
|
||||
f"schema validate failed: field name must be str, got {type(name).__name__}"
|
||||
)
|
||||
|
||||
if not isinstance(dimension, int) or dimension < 0:
|
||||
raise ValueError("schema validate failed: vector's dimension must be >= 0")
|
||||
|
||||
if data_type not in SUPPORT_VECTOR_DATA_TYPE:
|
||||
raise ValueError(
|
||||
f"schema validate failed: vector's data_type must be one of "
|
||||
f"{', '.join(str(dt) for dt in SUPPORT_VECTOR_DATA_TYPE)}, "
|
||||
f"but field[{name}]'s data_type is {data_type}"
|
||||
)
|
||||
|
||||
if index_param is None:
|
||||
index_param = FlatIndexParam()
|
||||
|
||||
self._cpp_obj = _FieldSchema(
|
||||
name=name,
|
||||
data_type=data_type,
|
||||
dimension=dimension,
|
||||
nullable=False,
|
||||
index_param=index_param,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _from_core(cls, core_field_schema: _FieldSchema):
|
||||
inst = cls.__new__(cls)
|
||||
inst._cpp_obj = core_field_schema
|
||||
return inst
|
||||
|
||||
def _get_object(self) -> _FieldSchema:
|
||||
return self._cpp_obj
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
"""str: The name of the vector field."""
|
||||
return self._cpp_obj.name
|
||||
|
||||
@property
|
||||
def data_type(self) -> DataType:
|
||||
"""DataType: The vector data type (e.g., VECTOR_FP32)."""
|
||||
return self._cpp_obj.data_type
|
||||
|
||||
@property
|
||||
def dimension(self) -> int:
|
||||
"""int: The dimensionality of the vector."""
|
||||
return self._cpp_obj.dimension
|
||||
|
||||
@property
|
||||
def index_param(
|
||||
self,
|
||||
) -> Union[HnswIndexParam, HnswRabitqIndexParam, IVFIndexParam, FlatIndexParam]:
|
||||
"""Union[HnswIndexParam, HnswRabitqIndexParam, IVFIndexParam, FlatIndexParam]: Index configuration for the vector."""
|
||||
return self._cpp_obj.index_param
|
||||
|
||||
def __dict__(self) -> dict[str, Any]:
|
||||
return {
|
||||
"name": self.name,
|
||||
"data_type": (
|
||||
self.data_type.name
|
||||
if hasattr(self.data_type, "name")
|
||||
else str(self.data_type)
|
||||
),
|
||||
"dimension": self.dimension,
|
||||
"index_param": (
|
||||
self.index_param.to_dict() if self.index_param is not None else None
|
||||
),
|
||||
}
|
||||
|
||||
def __repr__(self) -> str:
|
||||
try:
|
||||
schema = self.__dict__()
|
||||
return json.dumps(schema, indent=2, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
return f"<FieldSchema error during repr: {e}>"
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.__repr__()
|
||||
|
||||
def __eq__(self, other: object) -> bool:
|
||||
if not isinstance(other, VectorSchema):
|
||||
return False
|
||||
return self._cpp_obj == other._cpp_obj
|
||||
|
||||
def __hash__(self) -> int:
|
||||
return hash((self.name, self.data_type, self.dimension))
|
||||
Reference in New Issue
Block a user