Document Elements
Introduction
The unstructured
library aims to simplify and streamline the preprocessing of structured and unstructured documents for downstream tasks. And what that means is no matter where your data is and no matter what format that data is in, Unstructured’s toolkit will transform and preprocess that data into an easily digestable and usable format.
Document elements
When we partition a document, the output is a list of document Element
objects.
These element objects represent different components of the source document. Currently, the unstructured
library supports the following element types:
type
FigureCaption
NarrativeText
ListItem
Title
Address
Table
PageBreak
Header
Footer
UncategorizedText
Image
Formula
element_id
metadata
- see: Metadata pagetext
Other element types that we will add in the future include tables and figures.
Different partitioning functions use different methods for determining the element type and extracting the associated content.
Document elements have a str
representation. You can print them using the snippet below.
elements = partition(filename="example-10k.html")
for element in elements[:5]:
print(element)
print("\n")
One helpful aspect of document elements is that they allow you to cut a document down to the elements that you need for your particular use case. For example, if you’re training a summarization model you may only want to include narrative text for model training. You’ll notice that the output above includes a lot of titles and other content that may not be suitable for a summarization model. The following code shows how you can limit your output to only narrative text with at least two sentences. As you can see, the output now only contains narrative text.
from unstructured.documents.elements import NarrativeText
from unstructured.partition.text_type import sentence_count
for element in elements[:100]:
if isinstance(element, NarrativeText) and sentence_count(element.text) > 2:
print(element)
print("\n")
Tables
For Table
elements, the raw text of the table will be stored in the text
attribute for the Element, and HTML representation
of the table will be available in the element metadata under element.metadata.text_as_html
. For most documents where
table extraction is available, the partition
function will extract tables automatically if they are present.
For PDFs and images, table extraction requires a relatively expensive call to a table recognition model, and so for those
document types table extraction is an option you need to enable. If you would like to extract tables for PDFs or images,
pass in infer_table_structure=True
. Here is an example (Note: this example requires the pdf
extra. This can be installed with pip install "unstructured[pdf]"
):
from unstructured.partition.pdf import partition_pdf
filename = "example-docs/layout-parser-paper.pdf"
elements = partition_pdf(filename=filename, infer_table_structure=True)
tables = [el for el in elements if el.category == "Table"]
print(tables[0].text)
print(tables[0].metadata.text_as_html)
The text will look like this:
Dataset Base Model1 Large Model Notes PubLayNet [38] F / M M Layouts of modern scientific documents PRImA [3] M - Layouts of scanned modern magazines and scientific reports Newspaper [17] F - Layouts of scanned US newspapers from the 20th century TableBank [18] F F Table region on modern scientific and business document HJDataset [31] F / M - Layouts of history Japanese documents
And the text_as_html
metadata will look like this:
<table><thead><th>Dataset</th><th>| Base Model’</th><th>| Notes</th></thead><tr><td>PubLayNet</td><td>[38] F/M</td><td>Layouts of modern scientific documents</td></tr><tr><td>PRImA [3]</td><td>M</td><td>Layouts of scanned modern magazines and scientific reports</td></tr><tr><td>Newspaper</td><td>F</td><td>Layouts of scanned US newspapers from the 20th century</td></tr><tr><td>TableBank</td><td>F</td><td>Table region on modern scientific and business document</td></tr><tr><td>HJDataset [31]</td><td>F/M</td><td>Layouts of history Japanese documents</td></tr></table>
Converting Elements to Dictionary or JSON
The final step in the process for most users is to convert the output to JSON.
You can convert a list of document elements to a list of dictionaries using the convert_to_dict
function.
The workflow for using convert_to_dict
appears below.
from unstructured.staging.base import convert_to_dict
convert_to_dict(elements)
The unstructured
library also includes utilities for saving a list of elements to JSON and reading
a list of elements from JSON, as seen in the snippet below
from unstructured.staging.base import elements_to_json, elements_from_json
filename = "outputs.json"
elements_to_json(elements, filename=filename)
elements = elements_from_json(filename=filename)
Unique Element IDs
By default, the element ID is a SHA-256 hash of the element text. This is to ensure that
the ID is deterministic. One downside is that the ID is not guaranteed to be unique.
Different elements with the same text will have the same ID, and there could also
be hash collisions. To use UUIDs in the output instead, you can pass
unique_element_ids=True
into any of the partition functions. This can be helpful
if you’d like to use the IDs as a primary key in a database, for example.
from unstructured.partition.text import partition_text
elements = partition_text(text="Here is some example text.", unique_element_ids=True)
elements[0].id
Wrapping it all up
To conclude, the basic workflow for reading in a document and converting it to a JSON in unstructured
looks like the following:
from unstructured.partition.auto import partition
from unstructured.staging.base import elements_to_json
input_filename = "example-docs/example-10k.html"
output_filename = "outputs.json"
elements = partition(filename=input_filename)
elements_to_json(elements, filename=output_filename)