> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-kapa-ai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Full Installation

**Basic Usage**

For a complete set of extras catering to every document type, use:

```bash
pip install "unstructured[all-docs]"
```

**Installation for Specific Document Types**

If you’re processing document types beyond the basics, you can install the necessary extras:

```bash
pip install "unstructured[docx,pptx]"

```

*Available document types:*

```bash
"csv", "doc", "docx", "epub", "image", "md", "msg", "odt", "org", "pdf", "ppt", "pptx", "rtf", "rst", "tsv", "xlsx"

```

**Installation for Specific Data Connectors**

To use any of the data connectors, you must install the specific dependency:

```bash
pip install "unstructured[s3]"

```

*Available data connectors:*

```bash
"airtable", "azure", "azure-cognitive-search", "biomed", "box", "confluence", "delta-table", "discord", "dropbox", "elasticsearch", "gcs", "github", "gitlab", "google-drive", "jira", "mongodb", "notion", "opensearch", "onedrive", "outlook", "reddit", "s3", "sharepoint", "salesforce", "slack", "wikipedia"

```

## Installation with `conda` on Windows

You can install and run `unstructured` on Windows with `conda`, but the process involves a few extra steps. This section will help you get up and running.

* Install [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html) on your Windows machine.

* Install Microsoft C++ Build Tools using the instructions in [this Stackoverflow post](https://stackoverflow.com/questions/64261546/how-to-solve-error-microsoft-visual-c-14-0-or-greater-is-required-when-inst). C++ build tools are required for the `pycocotools` dependency.

* Run `conda env create -f environment.yml` using the `environment.yml` file in the `unstructured` repo to create a virtual environment. The environment will be named `unstructured`.

* Run `conda activate unstructured` to activate the virtualenvironment.

* Run `pip install unstructured` to install the `unstructured` library.

### Setting up `unstructured` for local inference

If you need to run model inferences locally, there are a few additional steps you need to take. The main challenge is installing `detectron2` for PDF layout parsing. `detectron2` does not officially support Windows, but it is possible to get it to install on Windows. The installation instructions are based on the instructions LayoutParser provides [here](https://layout-parser.github.io/tutorials/installation#for-windows-users).

* Run `pip install pycocotools-windows` to install a Windows compatible version of `pycocotools`. Alternatively, you can run `pip3 install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"` as outlined in [this GitHub issue](https://github.com/cocodataset/cocoapi/issues/169#issuecomment-462528628).

* Run `git clone https://github.com/ivanpp/detectron2.git`, then `cd detectron2`, then `pip install -e .` to install a Windows compatible version of the `detectron2` library.

* Install the a Windows compatible version of `iopath` using the instructions outlined in [this GitHub issue](https://github.com/Layout-Parser/layout-parser/issues/15#issuecomment-819546751). First, run `git clone https://github.com/facebookresearch/iopath --single-branch --branch v0.1.8`. Then on line 753 in `iopath/iopath/common/file_io.py` change `filename = path.split("/")[-1]` to `filename = parsed_url.path.split("/")[-1]`. After that, navigate to the `iopath` directory and run `pip install -e .`.

* Run `pip install unstructured[local-inference]`. This will install the `unstructured_inference` dependency.

At this point, you can verify the installation by running the following from the root directory of the `unstructured` [repo](https://github.com/Unstructured-IO/unstructured):

```bash
from unstructured.partition.pdf import partition_pdf

partition_pdf("example-docs/layout-parser-paper-fast.pdf", url=None)

```

### Installing PaddleOCR

PaddleOCR is another package that is helpful to use in conjunction with `unstructured`. You can use the following steps to install `paddleocr` in your `unstructured` `conda` environment.

* Run `conda install -c esri paddleocr`

* If you have the Windows version of `detectron2` cloned and installed locally, change the name of `detectron2/tools` to `detectron2/detectron2_tools`. Otherwise, you will hit the module name conflict error described in [this issue](https://github.com/PaddlePaddle/PaddleOCR/issues/1024).

* Set the environment variable `KMP_DUPLICATE_LIB_OK` to `"TRUE"`. This prevents the `libiomp5md.dll` linking issue described [in this issue on GitHub](https://github.com/PaddlePaddle/PaddleOCR/issues/4613).

At this point, you can verify the installation using the following commands. Choose a `.jpg` image that contains text.

```
import numpy as np
from PIL import Image
from paddleocr import PaddleOCR

filename = "path/to/my/image.jpg"
img = np.array(Image.open(filename))
ocr = PaddleOCR(lang="en", use_gpu=False, show_log=False)
result = ocr.ocr(img=img)

```

## Logging

You can set the logging level for the package with the `LOG_LEVEL` environment variable. By default, the log level is set to `WARNING`. For debugging, consider setting the log level to `INFO` or `DEBUG`.

## Note on Older Versions

<Note>
  **For versions earlier than unstructured\<0.9.0, the following installation pattern was recommended:**

  ```
  pip install "unstructured[local-inference]"
  ```

  While “local-inference” remains supported in newer versions for backward compatibility, it might be deprecated in future releases. It’s advisable to transition to the “all-docs” extra for comprehensive support.
</Note>