# Access Attributes#

Use . expression to get/set the value of an attribute as you would with any Python object:

from docarray import Document

d = Document()
d.text = 'hello world'

print(d.text)

hello world


To unset attribute, simply assign it to None:

d.text = None


or use pop():

d.pop('text')


One can unset multiple attributes with .pop():

d.pop('text', 'id', 'mime_type')


You can check which attributes are set by .non_empty_fields.

## Content attributes#

Among all attributes, content attributes, namely .text, .tensor, and .blob, are super important. They contain the actual content.

They correspond to string-like data (e.g. for natural language), ndarray-like data (e.g. for image/audio/video data), and binary data for general purpose, respectively.

Attribute Accept type Use case
doc.text Python string Contain text
doc.tensor A Python (nested) list/tuple of numbers, Numpy ndarray, SciPy sparse matrix (spmatrix), TensorFlow dense & sparse tensor, PyTorch dense & sparse tensor, PaddlePaddle dense tensor Contain image/video/audio
doc.blob Binary string Contain intermediate IO buffer

Each Document can contain only one type of content. That means these three attributes are mutually exclusive. Let’s see an example:

import numpy as np
from docarray import Document

d = Document(text='hello')
d.tensor = np.array([1, 2, 3])

print(d)

<Document ('id', 'tensor', 'mime_type') at 7623808c6d6211ec9cf21e008a366d49>


As you can see, the text field is reset to empty.

But what if you want to represent more than one kind of information? Say, to fully represent a PDF page you need to store both image and text. In this case, you can use nested Documents and store image in one sub-Document, and text in another sub-Document.

from docarray import Document

d = Document(chunks=[Document(tensor=...), Document(text=...)])


The principle is: each Document contains only one modality of information. In practice, this principle makes your full solution more clear and easier to maintain.

There is also a .content getter/setter of the content fields. The content will be automatically grabbed or assigned to either text, blob, or tensor field, based on the given type.

from docarray import Document

d = Document(content='hello')
print(d)

<Document ('id', 'mime_type', 'text') at b4d675466d6211ecae8d1e008a366d49>

d.content = [1, 2, 3]
print(d)

<Document ('id', 'tensor', 'mime_type') at 2808eeb86d6311ecaddb1e008a366d49>


You can also check which content field is set by .content_type.

A common pattern is loading content from a URI instead of assigning it directly in the code.

This can easily be done with .uri attribute. The value of .uri can point to either a local URI, remote URI or data URI.

from docarray import Document

print(d1.content_type, d1.content)

tensor [[[255 255 255]
[255 255 255]
[255 255 255]
...

from docarray import Document

print(d1.content_type, d1.content)

text ﻿The Project Gutenberg eBook of Pride and Prejudice, by Jane Austen

This eBook is for the use of anyone anywhere in the United States and
most other parts of the wor

from docarray import Document

d1 = Document(
uri='''data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUA
AAAFCAYAAACNbyblAAAAHElEQVQI12P4//8/w38GIAXDIBKE0DHxgljNBAAO
9TXL0Y4OHwAAAABJRU5ErkJggg==
'''

print(d1.content_type, d1.content)

tensor [[[255 255 255]
[255   0   0]
[255   0   0]
[255   0   0]
[255 255 255]]
...


There are more .load_uri_to_* functions that allow you to read text, image, video, 3D mesh, audio and tabular data.

Convert content to data URI

Inline data URI is helpful when you need a quick visualization in HTML, as it embeds all resources directly into that HTML.

You can convert a URI to a data URI using doc.load_uri_to_datauri(). This will fetch the resource and make it inline.