mv module integrations docs (#8101)

2026-06-05 23:00:18 +00:00 · 2023-07-23 23:23:16 -07:00
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+# 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 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.
+
+
+The Unstructured API requires API keys to make requests.
+You can generate a free API key [here](https://www.unstructured.io/api-key) and start using it today!
+Checkout the README [here](https://github.com/Unstructured-IO/unstructured-api) here to get started making API calls.
+We'd love to hear your feedback, let us know how it goes in our [community slack](https://join.slack.com/t/unstructuredw-kbe4326/shared_invite/zt-1x7cgo0pg-PTptXWylzPQF9xZolzCnwQ).
+And stay tuned for improvements to both quality and performance!
+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.