langchain/docs/ecosystem/unstructured.md
Matt Robinson 63aa28e2a6
feat: allow the unstructured kwargs to be passed in to Unstructured document loaders (#1667)
### Summary

Allows users to pass in `**unstructured_kwargs` to Unstructured document
loaders. Implemented with the `strategy` kwargs in mind, but will pass
in other kwargs like `include_page_breaks` as well. The two currently
supported strategies are `"hi_res"`, which is more accurate but takes
longer, and `"fast"`, which processes faster but with lower accuracy.
The `"hi_res"` strategy is the default. For PDFs, if `detectron2` is not
available and the user selects `"hi_res"`, the loader will fallback to
using the `"fast"` strategy.


### Testing

#### Make sure the `strategy` kwarg works

Run the following in iPython to verify that the `"fast"` strategy is
indeed faster.

```python
from langchain.document_loaders import UnstructuredFileLoader

loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", strategy="fast", mode="elements")
%timeit loader.load()

loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", mode="elements")
%timeit loader.load()
```

On my system I get:

```python
In [3]: from langchain.document_loaders import UnstructuredFileLoader

In [4]: loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", strategy="fast", mode="elements")

In [5]: %timeit loader.load()
247 ms ± 369 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [6]: loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf", mode="elements")

In [7]: %timeit loader.load()
2.45 s ± 31 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

#### Make sure older versions of `unstructured` still work

Run `pip install unstructured==0.5.3` and then verify the following runs
without error:

```python
from langchain.document_loaders import UnstructuredFileLoader

loader = UnstructuredFileLoader("layout-parser-paper-fast.pdf",  mode="elements")
loader.load()
```
2023-03-14 18:15:28 -07:00

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1.8 KiB
Markdown

# Unstructured
This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
ecosystem within LangChain. The `unstructured` package from
[Unstructured.IO](https://www.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`
- `poppler-utils`
- `tesseract-ocr`
- `libreoffice`
- 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`.
## 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.