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# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you 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 typing import Any, ClassVar, Dict, List, Optional, Tuple, Type
from pydantic import BaseModel, Field, PrivateAttr
from typing_extensions import Annotated, Self, dataclass_transform
from .. import dsl
class ESMeta(BaseModel):
"""Metadata items associated with Elasticsearch documents."""
id: str = ""
index: str = ""
primary_term: int = 0
seq_no: int = 0
version: int = 0
score: float = 0
class _BaseModel(BaseModel):
meta: Annotated[ESMeta, dsl.mapped_field(exclude=True)] = Field(
default=ESMeta(),
init=False,
)
class _BaseESModelMetaclass(type(BaseModel)): # type: ignore[misc]
"""Generic metaclass methods for BaseEsModel and AsyncBaseESModel."""
@staticmethod
def process_annotations(
metacls: Type["_BaseESModelMetaclass"], annotations: Dict[str, Any]
) -> Dict[str, Any]:
"""Process Pydantic typing annotations and adapt them so that they can
be used to create the Elasticsearch document.
"""
updated_annotations = {}
for var, ann in annotations.items():
if isinstance(ann, type(BaseModel)):
# an inner Pydantic model is transformed into an Object field
updated_annotations[var] = metacls.make_dsl_class(
metacls, dsl.InnerDoc, ann
)
elif (
hasattr(ann, "__origin__")
and ann.__origin__ in [list, List]
and isinstance(ann.__args__[0], type(BaseModel))
):
# an inner list of Pydantic models is transformed into a Nested field
updated_annotations[var] = List[ # type: ignore[assignment,misc]
metacls.make_dsl_class(metacls, dsl.InnerDoc, ann.__args__[0])
]
else:
updated_annotations[var] = ann
return updated_annotations
@staticmethod
def make_dsl_class(
metacls: Type["_BaseESModelMetaclass"],
dsl_class: type,
pydantic_model: type,
pydantic_attrs: Optional[Dict[str, Any]] = None,
) -> type:
"""Create a DSL document class dynamically, using the structure of a
Pydantic model."""
dsl_attrs = {
attr: value
for attr, value in dsl_class.__dict__.items()
if not attr.startswith("__")
}
pydantic_attrs = {
**(pydantic_attrs or {}),
"__annotations__": metacls.process_annotations(
metacls, pydantic_model.__annotations__
),
}
return type(dsl_class)(
f"_ES{pydantic_model.__name__}",
(dsl_class,),
{
**pydantic_attrs,
**dsl_attrs,
"__qualname__": f"_ES{pydantic_model.__name__}",
},
)
class BaseESModelMetaclass(_BaseESModelMetaclass):
"""Metaclass for the BaseESModel class."""
def __new__(cls, name: str, bases: Tuple[type, ...], attrs: Dict[str, Any]) -> Any:
model = super().__new__(cls, name, bases, attrs)
model._doc = cls.make_dsl_class(cls, dsl.Document, model, attrs)
return model
class AsyncBaseESModelMetaclass(_BaseESModelMetaclass):
"""Metaclass for the AsyncBaseESModel class."""
def __new__(cls, name: str, bases: Tuple[type, ...], attrs: Dict[str, Any]) -> Any:
model = super().__new__(cls, name, bases, attrs)
model._doc = cls.make_dsl_class(cls, dsl.AsyncDocument, model, attrs)
return model
@dataclass_transform(kw_only_default=True, field_specifiers=(Field, PrivateAttr))
class BaseESModel(_BaseModel, metaclass=BaseESModelMetaclass):
_doc: ClassVar[Type[dsl.Document]]
def to_doc(self) -> dsl.Document:
"""Convert this model to an Elasticsearch document."""
data = self.model_dump()
meta = {f"_{k}": v for k, v in data.pop("meta", {}).items() if v}
return self._doc(**meta, **data)
@classmethod
def from_doc(cls, dsl_obj: dsl.Document) -> Self:
"""Create a model from the given Elasticsearch document."""
return cls(meta=ESMeta(**dsl_obj.meta.to_dict()), **dsl_obj.to_dict())
@dataclass_transform(kw_only_default=True, field_specifiers=(Field, PrivateAttr))
class AsyncBaseESModel(_BaseModel, metaclass=AsyncBaseESModelMetaclass):
_doc: ClassVar[Type[dsl.AsyncDocument]]
def to_doc(self) -> dsl.AsyncDocument:
"""Convert this model to an Elasticsearch document."""
data = self.model_dump()
meta = {f"_{k}": v for k, v in data.pop("meta", {}).items() if v}
return self._doc(**meta, **data)
@classmethod
def from_doc(cls, dsl_obj: dsl.AsyncDocument) -> Self:
"""Create a model from the given Elasticsearch document."""
return cls(meta=ESMeta(**dsl_obj.meta.to_dict()), **dsl_obj.to_dict())
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