docarray/docarray/base_doc/doc.py at main · docarray/docarray · GitHub

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import os

import warnings

from typing import (

TYPE_CHECKING,

AbstractSet,

Any,

Callable,

Dict,

List,

Literal,

Mapping,

Optional,

Tuple,

Type,

TypeVar,

Union,

cast,

no_type_check,

)

import orjson

import typing_extensions

from pydantic import BaseModel, Field

from pydantic.fields import FieldInfo

from typing_inspect import get_args, is_optional_type

from docarray.utils._internal.pydantic import is_pydantic_v2

if not is_pydantic_v2:

from pydantic.main import ROOT_KEY

from rich.console import Console

from docarray.base_doc.base_node import BaseNode

from docarray.base_doc.io.json import orjson_dumps_and_decode

from docarray.base_doc.mixins import IOMixin, UpdateMixin

from docarray.typing import ID

from docarray.typing.tensor.abstract_tensor import AbstractTensor

from docarray.utils._internal._typing import safe_issubclass

if TYPE_CHECKING:

from pydantic import Protocol

from pydantic.types import StrBytes

from pydantic.typing import AbstractSetIntStr, DictStrAny, MappingIntStrAny

from docarray.array.doc_vec.column_storage import ColumnStorageView

if is_pydantic_v2:

IncEx: typing_extensions.TypeAlias = (

'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'

)

from pydantic import ConfigDict

_console: Console = Console()

T = TypeVar('T', bound='BaseDoc')

T_update = TypeVar('T_update', bound='UpdateMixin')

ExcludeType = Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']]

class BaseDocWithoutId(BaseModel, IOMixin, UpdateMixin, BaseNode):

"""

BaseDocWoId is the class behind BaseDoc, it should not be used directly unless you know what you are doing.

It is basically a BaseDoc without the ID field.

!!! warning

This class cannot be used with DocumentIndex. Only BaseDoc is compatible

"""

if is_pydantic_v2:

class ConfigDocArray(ConfigDict):

_load_extra_fields_from_protobuf: bool

model_config = ConfigDocArray(

validate_assignment=True,

_load_extra_fields_from_protobuf=False,

json_encoders={AbstractTensor: lambda x: x},

)

else:

class Config:

json_loads = orjson.loads

json_dumps = orjson_dumps_and_decode

# `DocArrayResponse` is able to handle tensors by itself.

# Therefore, we stop FastAPI from doing any transformations

# on tensors by setting an identity function as a custom encoder.

json_encoders = {AbstractTensor: lambda x: x}

validate_assignment = True

_load_extra_fields_from_protobuf = False

if is_pydantic_v2:

## pydantic v2 handle view and shallow copy a bit differently. We need to update different fields

@classmethod

def from_view(cls: Type[T], storage_view: 'ColumnStorageView') -> T:

doc = cls.__new__(cls)

object.__setattr__(doc, '__dict__', storage_view)

object.__setattr__(doc, '__pydantic_fields_set__', set(storage_view.keys()))

object.__setattr__(doc, '__pydantic_extra__', {})

if cls.__pydantic_post_init__:

doc.model_post_init(None)

else:

# Note: if there are any private attributes, cls.__pydantic_post_init__ would exist

# Since it doesn't, that means that `__pydantic_private__` should be set to None

object.__setattr__(doc, '__pydantic_private__', None)

return doc

@classmethod

def _shallow_copy(cls: Type[T], doc_to_copy: T) -> T:

"""

perform a shallow copy, the new doc share the same data with the original doc

"""

doc = cls.__new__(cls)

object.__setattr__(doc, '__dict__', doc_to_copy.__dict__)

object.__setattr__(

doc, '__pydantic_fields_set__', doc_to_copy.__pydantic_fields_set__

)

object.__setattr__(doc, '__pydantic_extra__', {})

if cls.__pydantic_post_init__:

doc.model_post_init(None)

else:

# Note: if there are any private attributes, cls.__pydantic_post_init__ would exist

# Since it doesn't, that means that `__pydantic_private__` should be set to None

object.__setattr__(doc, '__pydantic_private__', None)

return doc

else:

@classmethod

def from_view(cls: Type[T], storage_view: 'ColumnStorageView') -> T:

doc = cls.__new__(cls)

object.__setattr__(doc, '__dict__', storage_view)

object.__setattr__(doc, '__fields_set__', set(storage_view.keys()))

doc._init_private_attributes()

return doc

@classmethod

def _shallow_copy(cls: Type[T], doc_to_copy: T) -> T:

"""

perform a shallow copy, the new doc share the same data with the original doc

"""

doc = cls.__new__(cls)

object.__setattr__(doc, '__dict__', doc_to_copy.__dict__)

object.__setattr__(doc, '__fields_set__', set(doc_to_copy.__fields_set__))

doc._init_private_attributes()

return doc

@classmethod

def _docarray_fields(cls) -> Dict[str, FieldInfo]:

"""

Returns a dictionary of all fields of this document.

"""

if is_pydantic_v2:

return cls.model_fields

else:

return cls.__fields__

@classmethod

def _get_field_annotation(cls, field: str) -> Type:

"""

Accessing annotation associated with the field in the schema

:param field: name of the field

:return:

"""

if is_pydantic_v2:

annotation = cls._docarray_fields()[field].annotation

if is_optional_type(

annotation

): # this is equivalent to `outer_type_` in pydantic v1

return get_args(annotation)[0]

else:

return annotation

else:

return cls._docarray_fields()[field].outer_type_

@classmethod

def _get_field_inner_type(cls, field: str) -> Type:

"""

Accessing typed associated with the field in the schema

:param field: name of the field

:return:

"""

if is_pydantic_v2:

annotation = cls._docarray_fields()[field].annotation

if is_optional_type(

annotation

): # this is equivalent to `outer_type_` in pydantic v1

return get_args(annotation)[0]

elif annotation == Tuple:

if len(get_args(annotation)) == 0:

return Any

else:

get_args(annotation)[0]

else:

return annotation

else:

return cls._docarray_fields()[field].type_

def __str__(self) -> str:

content: Any = None

if self.is_view():

attr_str = ", ".join(

f"{field}={self.__getattr__(field)}" for field in self.__dict__.keys()

)

content = f"{self.__class__.__name__}({attr_str})"

else:

content = self

with _console.capture() as capture:

_console.print(content)

return capture.get().strip()

def summary(self) -> None:

"""Print non-empty fields and nested structure of this Document object."""

from docarray.display.document_summary import DocumentSummary

DocumentSummary(doc=self).summary()

@classmethod

def schema_summary(cls) -> None:

"""Print a summary of the Documents schema."""

from docarray.display.document_summary import DocumentSummary

DocumentSummary.schema_summary(cls)

def _ipython_display_(self) -> None:

"""Displays the object in IPython as a summary"""

self.summary()

def is_view(self) -> bool:

from docarray.array.doc_vec.column_storage import ColumnStorageView

return isinstance(self.__dict__, ColumnStorageView)

def __getattr__(self, item) -> Any:

if item in self._docarray_fields().keys():

return self.__dict__[item]

else:

return super().__getattribute__(item)

def __setattr__(self, field, value) -> None:

if not self.is_view():

super().__setattr__(field, value)

else:

# here we first validate with pydantic

# Then we apply the value to the remote dict,

# and we change back the __dict__ value to the remote dict

dict_ref = self.__dict__

super().__setattr__(field, value)

for key, val in self.__dict__.items():

dict_ref[key] = val

object.__setattr__(self, '__dict__', dict_ref)

def __eq__(self, other) -> bool:

if not isinstance(other, BaseDoc):

return False

if self._docarray_fields().keys() != other._docarray_fields().keys():

return False

for field_name in self._docarray_fields():

value1 = getattr(self, field_name)

value2 = getattr(other, field_name)

if field_name == 'id':

continue

if isinstance(value1, AbstractTensor) and isinstance(

value2, AbstractTensor

comp_be1 = value1.get_comp_backend()

comp_be2 = value2.get_comp_backend()

if comp_be1.shape(value1) != comp_be2.shape(value2):

return False

if (

not (comp_be1.to_numpy(value1) == comp_be2.to_numpy(value2))

.all()

.item()

return False

else:

if value1 != value2:

return False

return True

def __ne__(self, other) -> bool:

return not (self == other)

def _docarray_to_json_compatible(self) -> Dict:

"""

Convert itself into a json compatible object

:return: A dictionary of the BaseDoc object

"""

return self.dict()

def _exclude_doclist(

self, exclude: ExcludeType

) -> Tuple[ExcludeType, ExcludeType, List[str]]:

"""

This function exclude the doclist field from the list. It is used in the model dump function because we give a special treatment to DocList during seriliaztion and therefore we want pydantic to ignore this field and let us handle it.

"""

doclist_exclude_fields = []

for field in self._docarray_fields().keys():

from docarray.array.any_array import AnyDocArray

type_ = self._get_field_annotation(field)

if is_pydantic_v2:

# Conservative when touching pydantic v1 logic

if safe_issubclass(type_, AnyDocArray):

doclist_exclude_fields.append(field)

else:

if isinstance(type_, type) and safe_issubclass(type_, AnyDocArray):

doclist_exclude_fields.append(field)

original_exclude = exclude

if exclude is None:

exclude = set(doclist_exclude_fields)

elif isinstance(exclude, AbstractSet):

exclude = set([*exclude, *doclist_exclude_fields])

elif isinstance(exclude, Mapping):

exclude = dict(**exclude)

exclude.update({field: ... for field in doclist_exclude_fields})

return (

exclude,

original_exclude,

doclist_exclude_fields,

)

if not is_pydantic_v2:

def json(

self,

include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,

by_alias: bool = False,

skip_defaults: Optional[bool] = None,

exclude_unset: bool = False,

exclude_defaults: bool = False,

exclude_none: bool = False,

encoder: Optional[Callable[[Any], Any]] = None,

models_as_dict: bool = True,

**dumps_kwargs: Any,

) -> str:

"""

Generate a JSON representation of the model, `include` and `exclude`

arguments as per `dict()`.

`encoder` is an optional function to supply as `default` to json.dumps(),

other arguments as per `json.dumps()`.

"""

exclude, original_exclude, doclist_exclude_fields = self._exclude_docarray(

exclude=exclude

)

# this is copy from pydantic code

if skip_defaults is not None:

warnings.warn(

f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated and replaced by "exclude_unset"',

DeprecationWarning,

)

exclude_unset = skip_defaults

encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)

# We don't directly call `self.dict()`, which does exactly this with `to_dict=True`

# because we want to be able to keep raw `BaseModel` instances and not as `dict`.

# This allows users to write custom JSON encoders for given `BaseModel` classes.

data = dict(

self._iter(

to_dict=models_as_dict,

by_alias=by_alias,

include=include,

exclude=exclude,

exclude_unset=exclude_unset,

exclude_defaults=exclude_defaults,

exclude_none=exclude_none,

)

# this is the custom part to deal with DocList

for field in doclist_exclude_fields:

# we need to do this because pydantic will not recognize DocList correctly

original_exclude = original_exclude or {}

if field not in original_exclude:

data[field] = getattr(

self, field

) # here we need to keep doclist as doclist otherwise if a user want to have a special json config it will not work

# this is copy from pydantic code

if self.__custom_root_type__:

data = data[ROOT_KEY]

return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)

def dict(

self,

include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,

by_alias: bool = False,

skip_defaults: Optional[bool] = None,

exclude_unset: bool = False,

exclude_defaults: bool = False,

exclude_none: bool = False,

) -> 'DictStrAny':

"""

Generate a dictionary representation of the model, optionally specifying

which fields to include or exclude.

"""

exclude, original_exclude, doclist_exclude_fields = self._exclude_doclist(

exclude=exclude

)

data = super().dict(

include=include,

exclude=exclude,

by_alias=by_alias,

skip_defaults=skip_defaults,

exclude_unset=exclude_unset,

exclude_defaults=exclude_defaults,

exclude_none=exclude_none,

)

for field in doclist_exclude_fields:

# we need to do this because pydantic will not recognize DocList correctly

original_exclude = original_exclude or {}

if field not in original_exclude:

val = getattr(self, field)

data[field] = (

[doc.dict() for doc in val] if val is not None else None

)

return data

else:

def _copy_view_pydantic_v2(self: T) -> T:

"""

perform a deep copy, the new doc has its own data

"""

data = {}

for key, value in self.__dict__.to_dict().items():

if isinstance(value, BaseDocWithoutId):

data[key] = value._copy_view_pydantic_v2()

else:

data[key] = value

doc = self.__class__.model_construct(**data)

return doc

def model_dump( # type: ignore

self,

mode: Union[Literal['json', 'python'], str] = 'python',

include: IncEx = None,

by_alias: bool = False,

exclude_unset: bool = False,

exclude_defaults: bool = False,

exclude_none: bool = False,

round_trip: bool = False,

warnings: bool = True,

) -> Dict[str, Any]:

def _model_dump(doc):

(

exclude_,

original_exclude,

doclist_exclude_fields,

) = self._exclude_doclist(exclude=exclude)

data = doc.model_dump(

mode=mode,

include=include,

exclude=exclude_,

by_alias=by_alias,

exclude_unset=exclude_unset,

exclude_defaults=exclude_defaults,

exclude_none=exclude_none,

round_trip=round_trip,

warnings=warnings,

)

for field in doclist_exclude_fields:

# we need to do this because pydantic will not recognize DocList correctly

original_exclude = original_exclude or {}

if field not in original_exclude:

val = getattr(self, field)

data[field] = (

[doc.dict() for doc in val] if val is not None else None

)

return data

if self.is_view():

## for some reason use ColumnViewStorage to dump the data is not working with

## pydantic v2, so we need to create a new doc and dump it

new_doc = self._copy_view_pydantic_v2()

return _model_dump(new_doc)

else:

return _model_dump(super())

@no_type_check

@classmethod

def parse_raw(

cls: Type[T],

b: 'StrBytes',

content_type: str = None,

encoding: str = 'utf8',

proto: 'Protocol' = None,

allow_pickle: bool = False,

) -> T:

"""

Parse a raw string or bytes into a base doc

:param b:

:param content_type:

:param encoding: the encoding to use when parsing a string, defaults to 'utf8'

:param proto: protocol to use.

:param allow_pickle: allow pickle protocol

:return: a document

"""

return super(BaseDocWithoutId, cls).parse_raw(

content_type=content_type,

encoding=encoding,

proto=proto,

allow_pickle=allow_pickle,

)

def _exclude_docarray(

self, exclude: ExcludeType

) -> Tuple[ExcludeType, ExcludeType, List[str]]:

docarray_exclude_fields = []

for field in self.__fields__.keys():

from docarray import DocList, DocVec

type_ = self._get_field_annotation(field)

if isinstance(type_, type) and (

safe_issubclass(type_, DocList) or safe_issubclass(type_, DocVec)

docarray_exclude_fields.append(field)

original_exclude = exclude

if exclude is None:

exclude = set(docarray_exclude_fields)

elif isinstance(exclude, AbstractSet):

exclude = set([*exclude, *docarray_exclude_fields])

elif isinstance(exclude, Mapping):

exclude = dict(**exclude)

exclude.update({field: ... for field in docarray_exclude_fields})

return (

exclude,

original_exclude,

docarray_exclude_fields,

)

to_json = BaseModel.model_dump_json if is_pydantic_v2 else json

class BaseDoc(BaseDocWithoutId):

"""

BaseDoc is the base class for all Documents. This class should be subclassed

to create new Document types with a specific schema.

The schema of a Document is defined by the fields of the class.

Example:

```python

from docarray import BaseDoc

from docarray.typing import NdArray, ImageUrl

import numpy as np

class MyDoc(BaseDoc):

embedding: NdArray[512]

image: ImageUrl

doc = MyDoc(embedding=np.zeros(512), image='https://example.com/image.jpg')

```

BaseDoc is a subclass of [pydantic.BaseModel](

https://docs.pydantic.dev/usage/models/) and can be used in a similar way.

"""

id: Optional[ID] = Field(

description='The ID of the BaseDoc. This is useful for indexing in vector stores. If not set by user, it will automatically be assigned a random value',

default_factory=lambda: ID(os.urandom(16).hex()),

example=os.urandom(16).hex(),

)

docarray/docarray/base_doc/doc.py at main · docarray/docarray · GitHub

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