Source code for docarray.document

from typing import overload, Dict, Optional, List, TYPE_CHECKING, Sequence, Any

from import DocumentData
from docarray.document.mixins import AllMixins
from docarray.base import BaseDCType
from docarray.math.ndarray import detach_tensor_if_present

    from docarray.typing import ArrayType, StructValueType, DocumentContentType

[docs]class Document(AllMixins, BaseDCType): """Document is the basic data type in DocArray. A Document is a container for any kind of data, be it text, image, audio, video, or 3D meshes. You can initialize a Document object with given attributes: .. code-block:: python from docarray import Document import numpy d1 = Document(text='hello') d3 = Document(tensor=numpy.array([1, 2, 3])) d4 = Document( uri='', mime_type='text/plain', granularity=1, adjacency=3, tags={'foo': 'bar'}, ) Documents support a :ref:`nested structure <recursive-nested-document>`, which can also be specified during construction: .. code-block:: python d = Document( id='d0', chunks=[Document(id='d1', chunks=Document(id='d2'))], matches=[Document(id='d3')], ) A Document can embed its contents using the :meth:`embed` method and a provided embedding model: .. code-block:: python import torchvision q = ( Document(uri='/Users/usr/path/to/image.jpg') .load_uri_to_image_tensor() .set_image_tensor_normalization() .set_image_tensor_channel_axis(-1, 0) ) model = torchvision.models.resnet50(pretrained=True) q.embed(model) Multiple Documents can be organized into a :class:`~docarray.array.document.DocumentArray`. .. seealso:: For further details, see our :ref:`user guide <document>`. """ _data_class = DocumentData _unresolved_fields_dest = 'tags' _post_init_fields = ( 'text', 'blob', 'tensor', 'content', 'uri', 'mime_type', 'chunks', 'matches', ) @overload def __init__(self): """Create an empty Document.""" ... @overload def __init__(self, _obj: Optional['Document'] = None, copy: bool = False): ... @overload def __init__(self, _obj: Optional[Any] = None): """Create a Document from a `docarray.dataclass` instance""" ... @overload def __init__( self, _obj: Optional[Dict], copy: bool = False, field_resolver: Optional[Dict[str, str]] = None, unknown_fields_handler: str = 'catch', ): ... @overload def __init__(self, blob: Optional[bytes] = None, **kwargs): """Create a Document with binary content.""" ... @overload def __init__(self, tensor: Optional['ArrayType'] = None, **kwargs): """Create a Document with NdArray-like content.""" ... @overload def __init__(self, text: Optional[str] = None, **kwargs): """Create a Document with string content.""" ... @overload def __init__(self, uri: Optional[str] = None, **kwargs): """Create a Document with content from a URI.""" ... @overload def __init__( self, parent_id: Optional[str] = None, granularity: Optional[int] = None, adjacency: Optional[int] = None, blob: Optional[bytes] = None, tensor: Optional['ArrayType'] = None, mime_type: Optional[str] = None, text: Optional[str] = None, content: Optional['DocumentContentType'] = None, weight: Optional[float] = None, uri: Optional[str] = None, tags: Optional[Dict[str, 'StructValueType']] = None, offset: Optional[float] = None, location: Optional[List[float]] = None, embedding: Optional['ArrayType'] = None, modality: Optional[str] = None, evaluations: Optional[Dict[str, Dict[str, 'StructValueType']]] = None, scores: Optional[Dict[str, Dict[str, 'StructValueType']]] = None, chunks: Optional[Sequence['Document']] = None, matches: Optional[Sequence['Document']] = None, ): ... def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def __getstate__(self): state = self.__dict__.copy() for attribute in ['embedding', 'tensor']: if hasattr(self, attribute): setattr( state['_data'], attribute, detach_tensor_if_present(getattr(state['_data'], attribute)), ) return state