Skip to content

DocVec

docarray.array.doc_vec.doc_vec.DocVec

Bases: AnyDocArray[T_doc]

DocVec is a container of Documents appropriates to perform computation that require batches of data (ex: matrix multiplication, distance calculation, deep learning forward pass)

A DocVec has a similar interface as DocList but with an underlying implementation that is column based instead of row based. Each field of the schema of the DocVec (the .doc_type which is a BaseDoc) will be stored in a column.

If the field is a tensor, the data from all Documents will be stored as a single (torch/np/tf) tensor.

If the tensor field is AnyTensor or a Union of tensor types, the .tensor_type will be used to determine the type of the column.

If the field is another BaseDoc the column will be another DocVec that follows the schema of the nested Document.

If the field is a DocList or DocVec then the column will be a list of DocVec.

For any other type the column is a Python list.

Every Document inside a DocVec is a view into the data columns stored at the DocVec level. The BaseDoc does not hold any data itself. The behavior of this Document "view" is similar to the behavior of view = tensor[i] in numpy/PyTorch.

Note

DocVec supports optional fields. Nevertheless if a field is optional it needs to be homogeneous. This means that if the first document has a None value all of the other documents should have a None value as well.

Note

If one field is Optional the column will be stored * as None if the first doc is as the field as None * as a normal column otherwise that cannot contain None value

Parameters:

Name Type Description Default
docs Sequence[T_doc]

a homogeneous sequence of BaseDoc

required
tensor_type Type[AbstractTensor]

Tensor Class used to wrap the doc_vec tensors. This is useful if the BaseDoc of this DocVec has some undefined tensor type like AnyTensor or Union of NdArray and TorchTensor

NdArray
Source code in docarray/array/doc_vec/doc_vec.py
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
class DocVec(AnyDocArray[T_doc]):
    """
    DocVec is a container of Documents appropriates to perform
    computation that require batches of data (ex: matrix multiplication, distance
    calculation, deep learning forward pass)

    A DocVec has a similar interface as [`DocList`][docarray.array.DocList]
    but with an underlying implementation that is column based instead of row based.
    Each field of the schema of the `DocVec` (the `.doc_type` which is a
    [`BaseDoc`][docarray.BaseDoc]) will be stored in a column.

    If the field is a tensor, the data from all Documents will be stored as a single
    (torch/np/tf) tensor.

    If the tensor field is `AnyTensor` or a Union of tensor types, the
    `.tensor_type` will be used to determine the type of the column.

    If the field is another [`BaseDoc`][docarray.BaseDoc] the column will be another
    `DocVec` that follows the schema of the nested Document.

    If the field is a [`DocList`][docarray.DocList] or `DocVec` then the column will
    be a list of `DocVec`.

    For any other type the column is a Python list.

    Every `Document` inside a `DocVec` is a view into the data columns stored at the
    `DocVec` level. The `BaseDoc` does not hold any data itself. The behavior of
    this Document "view" is similar to the behavior of `view = tensor[i]` in
    numpy/PyTorch.

    !!! note
        DocVec supports optional fields. Nevertheless if a field is optional it needs to
        be homogeneous. This means that if the first document has a None value all of the
        other documents should have a None value as well.
    !!! note
        If one field is Optional the column will be stored
        * as None if the first doc is as the field as None
        * as a normal column otherwise that cannot contain None value

    :param docs: a homogeneous sequence of `BaseDoc`
    :param tensor_type: Tensor Class used to wrap the doc_vec tensors. This is useful
        if the BaseDoc of this DocVec has some undefined tensor type like
        AnyTensor or Union of NdArray and TorchTensor
    """

    doc_type: Type[T_doc]

    def __init__(
        self: T,
        docs: Sequence[T_doc],
        tensor_type: Type['AbstractTensor'] = NdArray,
    ):

        if not hasattr(self, 'doc_type') or self.doc_type == AnyDoc:
            raise TypeError(
                f'{self.__class__.__name__} does not precise a doc_type. You probably should do'
                f'docs = DocVec[MyDoc](docs) instead of DocVec(docs)'
            )
        self.tensor_type = tensor_type

        tensor_columns: Dict[str, Optional[AbstractTensor]] = dict()
        doc_columns: Dict[str, Optional['DocVec']] = dict()
        docs_vec_columns: Dict[str, Optional[ListAdvancedIndexing['DocVec']]] = dict()
        any_columns: Dict[str, ListAdvancedIndexing] = dict()

        if len(docs) == 0:
            raise ValueError(f'docs {docs}: should not be empty')
        docs = (
            docs
            if isinstance(docs, DocList)
            else DocList.__class_getitem__(self.doc_type)(docs)
        )

        for field_name, field in self.doc_type.__fields__.items():
            # here we iterate over the field of the docs schema, and we collect the data
            # from each document and put them in the corresponding column
            field_type = self.doc_type._get_field_type(field_name)

            is_field_required = self.doc_type.__fields__[field_name].required

            first_doc_is_none = getattr(docs[0], field_name) is None

            def _verify_optional_field_of_docs(docs):

                if is_field_required:
                    if first_doc_is_none:
                        raise ValueError(
                            f'Field {field_name} is None for {docs[0]} even though it is required'
                        )

                if first_doc_is_none:
                    for i, doc in enumerate(docs):
                        if getattr(doc, field_name) is not None:
                            raise ValueError(
                                f'Field {field_name} is put to None for the first doc. This mean that '
                                f'all of the other docs should have this field set to None as well. '
                                f'This is not the case for {doc} at index {i}'
                            )

            def _check_doc_field_not_none(field_name, doc):
                if getattr(doc, field_name) is None:
                    raise ValueError(
                        f'Field {field_name} is None for {doc} even though it is not None for the first doc'
                    )

            if is_tensor_union(field_type):
                field_type = tensor_type
            # all generic tensor types such as AnyTensor, ImageTensor, etc. are subclasses of AbstractTensor.
            # Perform check only if the field_type is not an alias and is a subclass of AbstractTensor
            elif not isinstance(field_type, typingGenericAlias) and issubclass(
                field_type, AbstractTensor
            ):
                # check if the tensor associated with the field_name in the document is a subclass of the tensor_type
                # e.g. if the field_type is AnyTensor but the type(docs[0][field_name]) is ImageTensor,
                # then we change the field_type to ImageTensor, since AnyTensor is a union of all the tensor types
                # and does not override any methods of specific tensor types
                tensor = getattr(docs[0], field_name)
                if issubclass(tensor.__class__, tensor_type):
                    field_type = tensor_type

            if isinstance(field_type, type):
                if tf_available and issubclass(field_type, TensorFlowTensor):
                    # tf.Tensor does not allow item assignment, therefore the
                    # optimized way
                    # of initializing an empty array and assigning values to it
                    # iteratively
                    # does not work here, therefore handle separately.

                    if first_doc_is_none:
                        _verify_optional_field_of_docs(docs)
                        tensor_columns[field_name] = None
                    else:
                        tf_stack = []
                        for i, doc in enumerate(docs):
                            val = getattr(doc, field_name)
                            _check_doc_field_not_none(field_name, doc)
                            tf_stack.append(val.tensor)

                        stacked: tf.Tensor = tf.stack(tf_stack)
                        tensor_columns[field_name] = TensorFlowTensor(stacked)

                elif issubclass(field_type, AbstractTensor):
                    if first_doc_is_none:
                        _verify_optional_field_of_docs(docs)
                        tensor_columns[field_name] = None
                    else:
                        tensor = getattr(docs[0], field_name)
                        column_shape = (
                            (len(docs), *tensor.shape)
                            if tensor is not None
                            else (len(docs),)
                        )
                        tensor_columns[field_name] = field_type._docarray_from_native(
                            field_type.get_comp_backend().empty(
                                column_shape,
                                dtype=tensor.dtype
                                if hasattr(tensor, 'dtype')
                                else None,
                                device=tensor.device
                                if hasattr(tensor, 'device')
                                else None,
                            )
                        )

                        for i, doc in enumerate(docs):
                            _check_doc_field_not_none(field_name, doc)
                            val = getattr(doc, field_name)
                            cast(AbstractTensor, tensor_columns[field_name])[i] = val

                elif issubclass(field_type, BaseDoc):
                    if first_doc_is_none:
                        _verify_optional_field_of_docs(docs)
                        doc_columns[field_name] = None
                    else:
                        if is_field_required:
                            doc_columns[field_name] = getattr(
                                docs, field_name
                            ).to_doc_vec(tensor_type=self.tensor_type)
                        else:
                            doc_columns[field_name] = DocList.__class_getitem__(
                                field_type
                            )(getattr(docs, field_name)).to_doc_vec(
                                tensor_type=self.tensor_type
                            )

                elif issubclass(field_type, AnyDocArray):
                    if first_doc_is_none:
                        _verify_optional_field_of_docs(docs)
                        doc_columns[field_name] = None
                    else:
                        docs_list = list()
                        for doc in docs:
                            docs_nested = getattr(doc, field_name)
                            _check_doc_field_not_none(field_name, doc)
                            if isinstance(docs_nested, DocList):
                                docs_nested = docs_nested.to_doc_vec(
                                    tensor_type=self.tensor_type
                                )
                            docs_list.append(docs_nested)
                        docs_vec_columns[field_name] = ListAdvancedIndexing(docs_list)
                else:
                    any_columns[field_name] = ListAdvancedIndexing(
                        getattr(docs, field_name)
                    )
            else:
                any_columns[field_name] = ListAdvancedIndexing(
                    getattr(docs, field_name)
                )

        self._storage = ColumnStorage(
            tensor_columns,
            doc_columns,
            docs_vec_columns,
            any_columns,
            tensor_type,
        )

    @classmethod
    def from_columns_storage(cls: Type[T], storage: ColumnStorage) -> T:
        """
        Create a DocVec directly from a storage object
        :param storage: the underlying storage.
        :return: a DocVec
        """
        docs = cls.__new__(cls)
        docs.tensor_type = storage.tensor_type
        docs._storage = storage
        return docs

    @classmethod
    def validate(
        cls: Type[T],
        value: Union[T, Iterable[T_doc]],
        field: 'ModelField',
        config: 'BaseConfig',
    ) -> T:
        if isinstance(value, cls):
            return value
        elif isinstance(value, DocList.__class_getitem__(cls.doc_type)):
            return cast(T, value.to_doc_vec())
        elif isinstance(value, Sequence):
            return cls(value)
        elif isinstance(value, Iterable):
            return cls(list(value))
        else:
            raise TypeError(f'Expecting an Iterable of {cls.doc_type}')

    def to(self: T, device: str) -> T:
        """Move all tensors of this DocVec to the given device

        :param device: the device to move the data to
        """
        for field, col_tens in self._storage.tensor_columns.items():
            if col_tens is not None:
                self._storage.tensor_columns[
                    field
                ] = col_tens.get_comp_backend().to_device(col_tens, device)

        for field, col_doc in self._storage.doc_columns.items():
            if col_doc is not None:
                self._storage.doc_columns[field] = col_doc.to(device)
        for _, col_da in self._storage.docs_vec_columns.items():
            if col_da is not None:
                for docs in col_da:
                    docs.to(device)

        return self

    ################################################
    # Accessing data : Indexing / Getitem related  #
    ################################################

    @overload
    def __getitem__(self: T, item: int) -> T_doc:
        ...

    @overload
    def __getitem__(self: T, item: IndexIterType) -> T:
        ...

    def __getitem__(self: T, item: Union[int, IndexIterType]) -> Union[T_doc, T]:
        if item is None:
            return self  # PyTorch behaviour
        # multiple docs case
        if isinstance(item, (slice, Iterable)):
            return self.__class__.from_columns_storage(self._storage[item])
        # single doc case
        return self.doc_type.from_view(ColumnStorageView(item, self._storage))

    def _get_data_column(
        self: T,
        field: str,
    ) -> Union[MutableSequence, 'DocVec', AbstractTensor, None]:
        """Return one column of the data

        :param field: name of the fields to extract
        :return: Returns a list of the field value for each document
        in the array like container
        """
        if field in self._storage.any_columns.keys():
            return self._storage.any_columns[field]
        elif field in self._storage.docs_vec_columns.keys():
            return self._storage.docs_vec_columns[field]
        elif field in self._storage.columns.keys():
            return self._storage.columns[field]
        else:
            raise ValueError(f'{field} does not exist in {self}')

    ####################################
    # Updating data : Setitem related  #
    ####################################

    @overload
    def __setitem__(self: T, key: int, value: T_doc):
        ...

    @overload
    def __setitem__(self: T, key: IndexIterType, value: T):
        ...

    @no_type_check
    def __setitem__(self: T, key, value):
        # single doc case
        if not isinstance(key, (slice, Iterable)):
            if not isinstance(value, self.doc_type):
                raise ValueError(f'{value} is not a {self.doc_type}')

            for field, value in value.dict().items():
                self._storage.columns[field][key] = value  # todo we might want to
                # define a safety mechanism in someone put a wrong value
        else:
            # multiple docs case
            self._set_data_and_columns(key, value)

    def _set_data_and_columns(
        self: T,
        index_item: Union[Tuple, Iterable, slice],
        value: Union[T, DocList[T_doc]],
    ) -> None:
        """Delegates the setting to the data and the columns.

        :param index_item: the key used as index. Needs to be a valid index for both
            DocList (data) and column types (torch/tensorflow/numpy tensors)
        :value: the value to set at the `key` location
        """
        if isinstance(index_item, tuple):
            index_item = list(index_item)

        # set data and prepare columns
        processed_value: T
        if isinstance(value, DocList):
            if not issubclass(value.doc_type, self.doc_type):
                raise TypeError(
                    f'{value} schema : {value.doc_type} is not compatible with '
                    f'this DocVec schema : {self.doc_type}'
                )
            processed_value = cast(
                T, value.to_doc_vec(tensor_type=self.tensor_type)
            )  # we need to copy data here

        elif isinstance(value, DocVec):
            if not issubclass(value.doc_type, self.doc_type):
                raise TypeError(
                    f'{value} schema : {value.doc_type} is not compatible with '
                    f'this DocVec schema : {self.doc_type}'
                )
            processed_value = value
        else:
            raise TypeError(f'Can not set a DocVec with {type(value)}')

        for field, col in self._storage.columns.items():
            col[index_item] = processed_value._storage.columns[field]

    def _set_data_column(
        self: T,
        field: str,
        values: Union[
            Sequence[DocList[T_doc]],
            Sequence[Any],
            T,
            DocList,
            AbstractTensor,
            None,
        ],
    ) -> None:
        """Set all Documents in this DocList using the passed values

        :param field: name of the fields to set
        :values: the values to set at the DocList level
        """
        if values is None:
            if field in self._storage.tensor_columns.keys():
                self._storage.tensor_columns[field] = values
            elif field in self._storage.doc_columns.keys():
                self._storage.doc_columns[field] = values
            elif field in self._storage.docs_vec_columns.keys():
                self._storage.docs_vec_columns[field] = values
            elif field in self._storage.any_columns.keys():
                raise ValueError(
                    f'column {field} cannot be set to None, try to pass '
                    f'a list of None instead'
                )
            else:
                raise ValueError(f'{field} does not exist in {self}')

        else:
            if len(values) != len(self._storage):
                raise ValueError(
                    f'{values} has not the right length, expected '
                    f'{len(self._storage)} , got {len(values)}'
                )
            if field in self._storage.tensor_columns.keys():

                col = self._storage.tensor_columns[field]
                if col is not None:
                    validation_class = col.__unparametrizedcls__ or col.__class__
                else:
                    validation_class = self.doc_type.__fields__[field].type_

                # TODO shape check should be handle by the tensor validation

                values = parse_obj_as(validation_class, values)
                self._storage.tensor_columns[field] = values

            elif field in self._storage.doc_columns.keys():
                values_ = parse_obj_as(
                    DocVec.__class_getitem__(self.doc_type._get_field_type(field)),
                    values,
                )
                self._storage.doc_columns[field] = values_

            elif field in self._storage.docs_vec_columns.keys():
                values_ = cast(Sequence[DocList[T_doc]], values)
                # TODO here we should actually check if this is correct
                self._storage.docs_vec_columns[field] = values_
            elif field in self._storage.any_columns.keys():
                # TODO here we should actually check if this is correct
                values_ = cast(Sequence, values)
                self._storage.any_columns[field] = values_
            else:
                raise KeyError(f'{field} is not a valid field for this DocList')

    ####################
    # Deleting data    #
    ####################

    def __delitem__(self, key: Union[int, IndexIterType]) -> None:
        raise NotImplementedError(
            f'{self.__class__.__name__} does not implement '
            f'__del_item__. You are trying to delete an element'
            f'from {self.__class__.__name__} which is not '
            f'designed for this operation. Please `unstack`'
            f' before doing the deletion'
        )

    ####################
    # Sequence related #
    ####################
    def __iter__(self):
        for i in range(len(self)):
            yield self[i]

    def __len__(self):
        return len(self._storage)

    ####################
    # IO related       #
    ####################

    @classmethod
    def from_protobuf(cls: Type[T], pb_msg: 'DocVecProto') -> T:
        """create a Document from a protobuf message"""
        storage = ColumnStorage(
            pb_msg.tensor_columns,
            pb_msg.doc_columns,
            pb_msg.docs_vec_columns,
            pb_msg.any_columns,
        )

        return cls.from_columns_storage(storage)

    def to_protobuf(self) -> 'DocVecProto':
        """Convert DocVec into a Protobuf message"""
        from docarray.proto import (
            DocListProto,
            DocVecProto,
            ListOfAnyProto,
            ListOfDocArrayProto,
            NdArrayProto,
        )

        da_proto = DocListProto()
        for doc in self:
            da_proto.docs.append(doc.to_protobuf())

        doc_columns_proto: Dict[str, DocVecProto] = dict()
        tensor_columns_proto: Dict[str, NdArrayProto] = dict()
        da_columns_proto: Dict[str, ListOfDocArrayProto] = dict()
        any_columns_proto: Dict[str, ListOfAnyProto] = dict()

        for field, col_doc in self._storage.doc_columns.items():
            doc_columns_proto[field] = (
                col_doc.to_protobuf() if col_doc is not None else None
            )
        for field, col_tens in self._storage.tensor_columns.items():
            tensor_columns_proto[field] = (
                col_tens.to_protobuf() if col_tens is not None else None
            )
        for field, col_da in self._storage.docs_vec_columns.items():
            list_proto = ListOfDocArrayProto()
            if col_da:
                for docs in col_da:
                    list_proto.data.append(docs.to_protobuf())
            da_columns_proto[field] = list_proto
        for field, col_any in self._storage.any_columns.items():
            list_proto = ListOfAnyProto()
            for data in col_any:
                list_proto.data.append(_type_to_protobuf(data))
            any_columns_proto[field] = list_proto

        return DocVecProto(
            doc_columns=doc_columns_proto,
            tensor_columns=tensor_columns_proto,
            docs_vec_columns=da_columns_proto,
            any_columns=any_columns_proto,
        )

    def to_doc_list(self: T) -> DocList[T_doc]:
        """Convert DocVec into a DocList.

        Note this destroys the arguments and returns a new DocList
        """

        unstacked_doc_column: Dict[str, Optional[DocList]] = dict()
        unstacked_da_column: Dict[str, Optional[List[DocList]]] = dict()
        unstacked_tensor_column: Dict[str, Optional[List[AbstractTensor]]] = dict()
        unstacked_any_column = self._storage.any_columns

        for field, doc_col in self._storage.doc_columns.items():
            unstacked_doc_column[field] = doc_col.to_doc_list() if doc_col else None

        for field, da_col in self._storage.docs_vec_columns.items():

            unstacked_da_column[field] = (
                [docs.to_doc_list() for docs in da_col] if da_col else None
            )

        for field, tensor_col in list(self._storage.tensor_columns.items()):
            # list is needed here otherwise we cannot delete the column
            if tensor_col is not None:
                tensors = list()
                for tensor in tensor_col:
                    tensor_copy = tensor.get_comp_backend().copy(tensor)
                    tensors.append(tensor_copy)

                unstacked_tensor_column[field] = tensors
            del self._storage.tensor_columns[field]

        unstacked_column = ChainMap(  # type: ignore
            unstacked_any_column,  # type: ignore
            unstacked_tensor_column,  # type: ignore
            unstacked_da_column,  # type: ignore
            unstacked_doc_column,  # type: ignore
        )  # type: ignore

        docs = []

        for i in range(len(self)):
            data = {field: col[i] for field, col in unstacked_column.items()}
            docs.append(self.doc_type.construct(**data))

        del self._storage

        return DocList.__class_getitem__(self.doc_type).construct(docs)

    def traverse_flat(
        self,
        access_path: str,
    ) -> Union[List[Any], 'TorchTensor', 'NdArray']:
        nodes = list(AnyDocArray._traverse(node=self, access_path=access_path))
        flattened = AnyDocArray._flatten_one_level(nodes)

        cls_to_check = (NdArray, TorchTensor) if TorchTensor is not None else (NdArray,)

        if len(flattened) == 1 and isinstance(flattened[0], cls_to_check):
            return flattened[0]
        else:
            return flattened

from_columns_storage(storage) classmethod

Create a DocVec directly from a storage object

Parameters:

Name Type Description Default
storage ColumnStorage

the underlying storage.

required

Returns:

Type Description
T

a DocVec

Source code in docarray/array/doc_vec/doc_vec.py
@classmethod
def from_columns_storage(cls: Type[T], storage: ColumnStorage) -> T:
    """
    Create a DocVec directly from a storage object
    :param storage: the underlying storage.
    :return: a DocVec
    """
    docs = cls.__new__(cls)
    docs.tensor_type = storage.tensor_type
    docs._storage = storage
    return docs

from_protobuf(pb_msg) classmethod

create a Document from a protobuf message

Source code in docarray/array/doc_vec/doc_vec.py
@classmethod
def from_protobuf(cls: Type[T], pb_msg: 'DocVecProto') -> T:
    """create a Document from a protobuf message"""
    storage = ColumnStorage(
        pb_msg.tensor_columns,
        pb_msg.doc_columns,
        pb_msg.docs_vec_columns,
        pb_msg.any_columns,
    )

    return cls.from_columns_storage(storage)

to(device)

Move all tensors of this DocVec to the given device

Parameters:

Name Type Description Default
device str

the device to move the data to

required
Source code in docarray/array/doc_vec/doc_vec.py
def to(self: T, device: str) -> T:
    """Move all tensors of this DocVec to the given device

    :param device: the device to move the data to
    """
    for field, col_tens in self._storage.tensor_columns.items():
        if col_tens is not None:
            self._storage.tensor_columns[
                field
            ] = col_tens.get_comp_backend().to_device(col_tens, device)

    for field, col_doc in self._storage.doc_columns.items():
        if col_doc is not None:
            self._storage.doc_columns[field] = col_doc.to(device)
    for _, col_da in self._storage.docs_vec_columns.items():
        if col_da is not None:
            for docs in col_da:
                docs.to(device)

    return self

to_doc_list()

Convert DocVec into a DocList.

Note this destroys the arguments and returns a new DocList

Source code in docarray/array/doc_vec/doc_vec.py
def to_doc_list(self: T) -> DocList[T_doc]:
    """Convert DocVec into a DocList.

    Note this destroys the arguments and returns a new DocList
    """

    unstacked_doc_column: Dict[str, Optional[DocList]] = dict()
    unstacked_da_column: Dict[str, Optional[List[DocList]]] = dict()
    unstacked_tensor_column: Dict[str, Optional[List[AbstractTensor]]] = dict()
    unstacked_any_column = self._storage.any_columns

    for field, doc_col in self._storage.doc_columns.items():
        unstacked_doc_column[field] = doc_col.to_doc_list() if doc_col else None

    for field, da_col in self._storage.docs_vec_columns.items():

        unstacked_da_column[field] = (
            [docs.to_doc_list() for docs in da_col] if da_col else None
        )

    for field, tensor_col in list(self._storage.tensor_columns.items()):
        # list is needed here otherwise we cannot delete the column
        if tensor_col is not None:
            tensors = list()
            for tensor in tensor_col:
                tensor_copy = tensor.get_comp_backend().copy(tensor)
                tensors.append(tensor_copy)

            unstacked_tensor_column[field] = tensors
        del self._storage.tensor_columns[field]

    unstacked_column = ChainMap(  # type: ignore
        unstacked_any_column,  # type: ignore
        unstacked_tensor_column,  # type: ignore
        unstacked_da_column,  # type: ignore
        unstacked_doc_column,  # type: ignore
    )  # type: ignore

    docs = []

    for i in range(len(self)):
        data = {field: col[i] for field, col in unstacked_column.items()}
        docs.append(self.doc_type.construct(**data))

    del self._storage

    return DocList.__class_getitem__(self.doc_type).construct(docs)

to_protobuf()

Convert DocVec into a Protobuf message

Source code in docarray/array/doc_vec/doc_vec.py
def to_protobuf(self) -> 'DocVecProto':
    """Convert DocVec into a Protobuf message"""
    from docarray.proto import (
        DocListProto,
        DocVecProto,
        ListOfAnyProto,
        ListOfDocArrayProto,
        NdArrayProto,
    )

    da_proto = DocListProto()
    for doc in self:
        da_proto.docs.append(doc.to_protobuf())

    doc_columns_proto: Dict[str, DocVecProto] = dict()
    tensor_columns_proto: Dict[str, NdArrayProto] = dict()
    da_columns_proto: Dict[str, ListOfDocArrayProto] = dict()
    any_columns_proto: Dict[str, ListOfAnyProto] = dict()

    for field, col_doc in self._storage.doc_columns.items():
        doc_columns_proto[field] = (
            col_doc.to_protobuf() if col_doc is not None else None
        )
    for field, col_tens in self._storage.tensor_columns.items():
        tensor_columns_proto[field] = (
            col_tens.to_protobuf() if col_tens is not None else None
        )
    for field, col_da in self._storage.docs_vec_columns.items():
        list_proto = ListOfDocArrayProto()
        if col_da:
            for docs in col_da:
                list_proto.data.append(docs.to_protobuf())
        da_columns_proto[field] = list_proto
    for field, col_any in self._storage.any_columns.items():
        list_proto = ListOfAnyProto()
        for data in col_any:
            list_proto.data.append(_type_to_protobuf(data))
        any_columns_proto[field] = list_proto

    return DocVecProto(
        doc_columns=doc_columns_proto,
        tensor_columns=tensor_columns_proto,
        docs_vec_columns=da_columns_proto,
        any_columns=any_columns_proto,
    )