This sample demonstrates the use of Dedoc in combination with LangChain as a DocumentLoader.

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Overview

Dedoc is an open-source library/service that extracts texts, tables, attached files and document structure (e.g., titles, list items, etc.) from files of various formats. Dedoc supports DOCX, XLSX, PPTX, EML, HTML, PDF, images and more. Full list of supported formats can be found here.

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Integration details

ClassPackageLocalSerializableJS support
DedocFileLoaderlangchain_community❌beta❌
DedocPDFLoaderlangchain_community❌beta❌
DedocAPIFileLoaderlangchain_community❌beta❌

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Loader features

Methods for lazy loading and async loading are available, but in fact, document loading is executed synchronously.
SourceDocument Lazy LoadingAsync Support
DedocFileLoader❌❌
DedocPDFLoader❌❌
DedocAPIFileLoader❌❌

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Setup

  • To access DedocFileLoader and DedocPDFLoader document loaders, you’ll need to install the dedoc integration package.
  • To access DedocAPIFileLoader, you’ll need to run the Dedoc service, e.g. Docker container (please see the documentation for more details):
docker pull dedocproject/dedoc
docker run -p 1231:1231
Dedoc installation instruction is given here. 
# Install package
%pip install --quiet "dedoc[torch]"

Note: you may need to restart the kernel to use updated packages.

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Instantiation

from langchain_community.document_loaders import DedocFileLoader

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

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Load

docs = loader.load()
docs[0].page_content[:100]

'\nMadam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and t'

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Lazy Load

docs = loader.lazy_load()

for doc in docs:
    print(doc.page_content[:100])
    break

Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and t

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API reference

For detailed information on configuring and calling Dedoc loaders, please see the API references:

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Loading any file

For automatic handling of any file in a supported format, DedocFileLoader can be useful. The file loader automatically detects the file type with a correct extension. File parsing process can be configured through dedoc_kwargs during the DedocFileLoader class initialization. Here the basic examples of some options usage are given, please see the documentation of DedocFileLoader and dedoc documentation to get more details about configuration parameters.

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Basic example

from langchain_community.document_loaders import DedocFileLoader

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

docs = loader.load()

docs[0].page_content[:400]

'\nMadam 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\n\n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\n\n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\n\n\nWith a duty to one another to the American people to '

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Modes of split

DedocFileLoader supports different types of document splitting into parts (each part is returned separately). For this purpose, split parameter is used with the following options:
  • document (default value): document text is returned as a single langchain Document object (don’t split);
  • page: split document text into pages (works for PDF, DJVU, PPTX, PPT, ODP);
  • node: split document text into Dedoc tree nodes (title nodes, list item nodes, raw text nodes);
  • line: split document text into textual lines.
loader = DedocFileLoader(
    "./example_data/layout-parser-paper.pdf",
    split="page",
    pages=":2",
)

docs = loader.load()

len(docs)

2

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Handling tables

DedocFileLoader supports tables handling when with_tables parameter is set to True during loader initialization (with_tables=True by default). Tables are not split - each table corresponds to one langchain Document object. For tables, Document object has additional metadata fields type="table" and text_as_html with table HTML representation. 
loader = DedocFileLoader("./example_data/mlb_teams_2012.csv")

docs = loader.load()

docs[1].metadata["type"], docs[1].metadata["text_as_html"][:200]

('table',
 '<table border="1" style="border-collapse: collapse; width: 100%;">\n<tbody>\n<tr>\n<td colspan="1" rowspan="1">Team</td>\n<td colspan="1" rowspan="1"> &quot;Payroll (millions)&quot;</td>\n<td colspan="1" r')

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Handling attached files

DedocFileLoader supports attached files handling when with_attachments is set to True during loader initialization (with_attachments=False by default). Attachments are split according to the split parameter. For attachments, langchain Document object has an additional metadata field type="attachment". 
loader = DedocFileLoader(
    "./example_data/fake-email-attachment.eml",
    with_attachments=True,
)

docs = loader.load()

docs[1].metadata["type"], docs[1].page_content

('attachment',
 '\nContent-Type\nmultipart/mixed; boundary="0000000000005d654405f082adb7"\nDate\nFri, 23 Dec 2022 12:08:48 -0600\nFrom\nMallori Harrell <mallori@unstructured.io>\nMIME-Version\n1.0\nMessage-ID\n<CAPgNNXSzLVJ-d1OCX_TjFgJU7ugtQrjFybPtAMmmYZzphxNFYg@mail.gmail.com>\nSubject\nFake email with attachment\nTo\nMallori Harrell <mallori@unstructured.io>')

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Loading PDF file

If you want to handle only PDF documents, you can use DedocPDFLoader with only PDF support. The loader supports the same parameters for document split, tables and attachments extraction. Dedoc can extract PDF with or without a textual layer, as well as automatically detect its presence and correctness. Several PDF handlers are available, you can use pdf_with_text_layer parameter to choose one of them. Please see parameters description to get more details. For PDF without a textual layer, Tesseract OCR and its language packages should be installed. In this case, the instruction can be useful. 
from langchain_community.document_loaders import DedocPDFLoader

loader = DedocPDFLoader(
    "./example_data/layout-parser-paper.pdf", pdf_with_text_layer="true", pages="2:2"
)

docs = loader.load()

docs[0].page_content[:400]

'\n2\n\nZ. Shen et al.\n\n37], layout detection [38, 22], table detection [26], and scene text detection [4].\n\nA generalized learning-based framework dramatically reduces the need for the\n\nmanual specification of complicated rules, which is the status quo with traditional\n\nmethods. DL has the potential to transform DIA pipelines and benefit a broad\n\nspectrum of large-scale document digitization projects.\n'

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Dedoc API

If you want to get up and running with less set up, you can use Dedoc as a service. DedocAPIFileLoader can be used without installation of dedoc library. The loader supports the same parameters as DedocFileLoader and also automatically detects input file types. To use DedocAPIFileLoader, you should run the Dedoc service, e.g. Docker container (please see the documentation for more details): 
docker pull dedocproject/dedoc
docker run -p 1231:1231
Please do not use our demo URL https://dedoc-readme.hf.space in your code. 
from langchain_community.document_loaders import DedocAPIFileLoader

loader = DedocAPIFileLoader(
    "./example_data/state_of_the_union.txt",
    url="https://dedoc-readme.hf.space",
)

docs = loader.load()

docs[0].page_content[:400]

'\nMadam 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\n\n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\n\n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\n\n\nWith a duty to one another to the American people to '