This page covers how to use the unstructured ecosystem within LangChain. The unstructured package from Unstructured.IO extracts clean text from raw source documents like PDFs and Word documents.

This page is broken into two parts: installation and setup, and then references to specific unstructured wrappers.

Installation and Setup#

  • Install the Python SDK with pip install "unstructured[local-inference]"

  • Install the following system dependencies if they are not already available on your system. Depending on what document types you’re parsing, you may not need all of these.

    • libmagic-dev (filetype detection)

    • poppler-utils (images and PDFs)

    • tesseract-ocr(images and PDFs)

    • libreoffice (MS Office docs)

    • pandoc (EPUBs)

  • If you are parsing PDFs using the "hi_res" strategy, run the following to install the detectron2 model, which unstructured uses for layout detection:

    • pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"

    • If detectron2 is not installed, unstructured will fallback to processing PDFs using the "fast" strategy, which uses pdfminer directly and doesn’t require detectron2.


Data Loaders#

The primary unstructured wrappers within langchain are data loaders. The following shows how to use the most basic unstructured data loader. There are other file-specific data loaders available in the langchain.document_loaders module.

from langchain.document_loaders import UnstructuredFileLoader

loader = UnstructuredFileLoader("state_of_the_union.txt")

If you instantiate the loader with UnstructuredFileLoader(mode="elements"), the loader will track additional metadata like the page number and text type (i.e. title, narrative text) when that information is available.