Getting Started

The following section will cover basic concepts and usage patterns in unstructured. After reading this section, you should be able to:

  • Partitioning a document with the partition function.

  • Understand how documents are structured in unstructured.

  • Convert a document to a dictionary and/or save it as a JSON.

The example documents in this section come from the example-docs directory in the unstructured repo.

Before running the code in this make sure you’ve installed the unstructured library and all dependencies using the instructions in the Quick Start section.

Partitioning a document

In this section, we’ll cut right to the chase and get to the most important part of the library: partitioning a document. The goal of document partitioning is to read in a source document, split the document into sections, categorize those sections, and extract the text associated with those sections. Depending on the document type, unstructured uses different methods for partitioning a document. We’ll cover those in a later section. For now, we’ll use the simplest API in the library, the partition function. The partition function will detect the filetype of the source document and route it to the appropriate partitioning function. You can try out the partition function by running the cell below.

from import partition

elements = partition(filename="example-10k.html")

You can also pass in a file as a file-like object using the following workflow:

with open("example-10k.html", "rb") as f:
    elements = partition(file=f)

The partition function uses libmagic for filetype detection. If libmagic is not present and the user passes a filename, partition falls back to detecting the filetype using the file extension. libmagic is required if you’d like to pass a file-like object to partition. We highly recommend installing libmagic and you may observe different file detection behaviors if libmagic is not installed`.

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:

  • Element
    • Text
      • FigureCaption

      • NarrativeText

      • ListItem

      • Title

      • Address

      • Table

      • PageBreak

    • CheckBox

    • Image

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]:

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:

Serializing Elements

The unstructured library includes helper functions for reading and writing a list of Element objects to and from JSON. You can use the following workflow for serializing and deserializing an Element list.

from unstructured.documents.elements import ElementMetadata, Text, Title, FigureCaption
from unstructured.staging.base import elements_to_json, elements_from_json

filename = "my-elements.json"
metadata = ElementMetadata(filename="fake-file.txt")
elements = [
    FigureCaption(text="caption", metadata=metadata, element_id="1"),
    Title(text="title", metadata=metadata, element_id="2"),
    Text(text="title", metadata=metadata, element_id="3"),


elements_to_json(elements, filename=filename)
new_elements = elements_from_json(filename=filename)

# alternatively, one can also serialize/deserialize to/from a string with:
serialized_elements_json = elements_to_json(elements)
new_elements = elements_from_json(text=serialized_elements_json)

Converting elements to a 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


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)

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 import partition
from unstructured.staging.base import elements_to_json

input_filename = "example-10k.html"
output_filename = "outputs.json"

elements = partition(filename=input_filename)
elements_to_json(elements, filename=output_filename)