# Unstructured >The `unstructured` package from [Unstructured.IO](https://www.unstructured.io/) extracts clean text from raw source documents like PDFs and Word documents. This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured) ecosystem within LangChain. ## Installation and Setup If you are using a loader that runs locally, use the following steps to get `unstructured` and its dependencies running locally. - 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@e2ce8dc#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`. If you want to get up and running with less set up, you can simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or `UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API. Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require an API. The [Unstructured documentation page](https://unstructured-io.github.io/) will have instructions on how to generate an API key once they're available. Check out the instructions [here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image) if you'd like to self-host the Unstructured API or run it locally. ## Wrappers ### 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. ```python from langchain.document_loaders import UnstructuredFileLoader loader = UnstructuredFileLoader("state_of_the_union.txt") loader.load() ``` 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.