forked from Archives/langchain
c51dec5101
### Summary
Adds `UnstructuredAPIFileLoaders` and `UnstructuredAPIFIleIOLoaders`
that partition documents through the Unstructured API. Defaults to the
URL for hosted Unstructured API, but can switch to a self hosted or
locally running API using the `url` kwarg. Currently, the Unstructured
API is open and does not require an API, but it will soon. A note was
added about that to the Unstructured ecosystem page.
### Testing
```python
from langchain.document_loaders import UnstructuredAPIFileIOLoader
filename = "fake-email.eml"
with open(filename, "rb") as f:
loader = UnstructuredAPIFileIOLoader(file=f, file_filename=filename)
docs = loader.load()
docs[0]
```
```python
from langchain.document_loaders import UnstructuredAPIFileLoader
filename = "fake-email.eml"
loader = UnstructuredAPIFileLoader(file_path=filename, mode="elements")
docs = loader.load()
docs[0]
```
59 lines
2.7 KiB
Markdown
59 lines
2.7 KiB
Markdown
# Unstructured
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This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
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ecosystem within LangChain. The `unstructured` package from
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[Unstructured.IO](https://www.unstructured.io/) extracts clean text from raw source documents like
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PDFs and Word documents.
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This page is broken into two parts: installation and setup, and then references to specific
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`unstructured` wrappers.
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## Installation and Setup
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If you are using a loader that runs locally, use the following steps to get `unstructured` and
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its dependencies running locally.
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- Install the Python SDK with `pip install "unstructured[local-inference]"`
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- Install the following system dependencies if they are not already available on your system.
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Depending on what document types you're parsing, you may not need all of these.
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- `libmagic-dev` (filetype detection)
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- `poppler-utils` (images and PDFs)
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- `tesseract-ocr`(images and PDFs)
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- `libreoffice` (MS Office docs)
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- `pandoc` (EPUBs)
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- If you are parsing PDFs using the `"hi_res"` strategy, run the following to install the `detectron2` model, which
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`unstructured` uses for layout detection:
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- `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@e2ce8dc#egg=detectron2"`
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- If `detectron2` is not installed, `unstructured` will fallback to processing PDFs
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using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
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`detectron2`.
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If you want to get up and running with less set up, you can
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simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or
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`UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API.
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Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require
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an API. The [Unstructured documentation page](https://unstructured-io.github.io/) will have
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instructions on how to generate an API key once they're available. Check out the instructions
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[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image)
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if you'd like to self-host the Unstructured API or run it locally.
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## Wrappers
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### Data Loaders
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The primary `unstructured` wrappers within `langchain` are data loaders. The following
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shows how to use the most basic unstructured data loader. There are other file-specific
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data loaders available in the `langchain.document_loaders` module.
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```python
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from langchain.document_loaders import UnstructuredFileLoader
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loader = UnstructuredFileLoader("state_of_the_union.txt")
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loader.load()
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```
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If you instantiate the loader with `UnstructuredFileLoader(mode="elements")`, the loader
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will track additional metadata like the page number and text type (i.e. title, narrative text)
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when that information is available.
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