Unstructured File Loader #

This notebook covers how to use Unstructured to load files of many types. Unstructured currently supports loading of text files, powerpoints, html, pdfs, images, and more.

# # Install package
!pip install "unstructured[local-inference]"
!pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"
!pip install layoutparser[layoutmodels,tesseract]

# # Install other dependencies
# # https://github.com/Unstructured-IO/unstructured/blob/main/docs/source/installing.rst
# !brew install libmagic
# !brew install poppler
# !brew install tesseract
# # If parsing xml / html documents:
# !brew install libxml2
# !brew install libxslt

# import nltk
# nltk.download('punkt')

from langchain.document_loaders import UnstructuredFileLoader

loader = UnstructuredFileLoader("./example_data/state_of_the_union.txt")

docs = loader.load()

docs[0].page_content[:400]

'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.\n\nLast year COVID-19 kept us apart. This year we are finally together again.\n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.\n\nWith a duty to one another to the American people to the Constit'

Retain Elements #

Under the hood, Unstructured creates different “elements” for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements" .

loader = UnstructuredFileLoader("./example_data/state_of_the_union.txt", mode="elements")

docs = loader.load()

docs[:5]

[Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
 Document(page_content='Last year COVID-19 kept us apart. This year we are finally together again.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
 Document(page_content='Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
 Document(page_content='With a duty to one another to the American people to the Constitution.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
 Document(page_content='And with an unwavering resolve that freedom will always triumph over tyranny.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]

Define a Partitioning Strategy #

Unstructured document loader allow users to pass in a strategy parameter that lets unstructured know how to partition the document. Currently supported strategies are "hi_res" (the default) and "fast" . Hi res partitioning strategies are more accurate, but take longer to process. Fast strategies partition the document more quickly, but trade-off accuracy. Not all document types have separate hi res and fast partitioning strategies. For those document types, the strategy kwarg is ignored. In some cases, the high res strategy will fallback to fast if there is a dependency missing (i.e. a model for document partitioning). You can see how to apply a strategy to an UnstructuredFileLoader below.

from langchain.document_loaders import UnstructuredFileLoader

loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", strategy="fast", mode="elements")

docs = loader.load()

docs[:5]

[Document(page_content='1', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
 Document(page_content='2', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
 Document(page_content='0', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
 Document(page_content='2', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'UncategorizedText'}, lookup_index=0),
 Document(page_content='n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.pdf', 'filename': 'layout-parser-paper-fast.pdf', 'page_number': 1, 'category': 'Title'}, lookup_index=0)]

PDF Example #

Processing PDF documents works exactly the same way. Unstructured detects the file type and extracts the same types of elements .

!wget https://raw.githubusercontent.com/Unstructured-IO/unstructured/main/example-docs/layout-parser-paper.pdf -P "../../"

loader = UnstructuredFileLoader("./example_data/layout-parser-paper.pdf", mode="elements")

docs = loader.load()

docs[:5]

[Document(page_content='LayoutParser : A Uniﬁed Toolkit for Deep Learning Based Document Image Analysis', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
 Document(page_content='Zejiang Shen 1 ( (ea)\n ), Ruochen Zhang 2 , Melissa Dell 3 , Benjamin Charles Germain Lee 4 , Jacob Carlson 3 , and Weining Li 5', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
 Document(page_content='Allen Institute for AI shannons@allenai.org', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
 Document(page_content='Brown University ruochen zhang@brown.edu', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0),
 Document(page_content='Harvard University { melissadell,jacob carlson } @fas.harvard.edu', lookup_str='', metadata={'source': '../../layout-parser-paper.pdf'}, lookup_index=0)]

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