Metadata-Version: 2.1
Name: unstructured
Version: 0.5.7
Summary: A library that prepares raw documents for downstream ML tasks.
Home-page: https://github.com/Unstructured-IO/unstructured
Author: Unstructured Technologies
Author-email: devops@unstructuredai.io
License: Apache-2.0
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        <h3 align="center">
          <p>Open-Source Pre-Processing Tools for Unstructured Data</p>
        </h3>
        
        The `unstructured` library provides open-source components for pre-processing text documents
        such as **PDFs**, **HTML** and **Word** Documents. These components are packaged as *bricks* 🧱, which provide
        users the building blocks they need to build pipelines targeted at the documents they care
        about. Bricks in the library fall into three categories:
        
        - :jigsaw: ***Partitioning bricks*** that break raw documents down into standard, structured
          elements.
        - :broom: ***Cleaning bricks*** that remove unwanted text from documents, such as boilerplate and
          sentence
          fragments.
        - :performing_arts: ***Staging bricks*** that format data for downstream tasks, such as ML inference
          and data labeling.
        
        <br></br>
        
        ## :eight_pointed_black_star: Quick Start
        
        Use the following instructions to get up and running with `unstructured` and test your
        installation. NOTE: We do not currently support python 3.11, please use an older version.
        
        - Install the Python SDK with `pip install "unstructured[local-inference]"`
        		- If you do not need to process PDFs or images, you can run `pip install unstructured`
        - 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)
        - If you are parsing PDFs, 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"`
        
        At this point, you should be able to run the following code:
        
        ```python
        from unstructured.partition.auto import partition
        
        elements = partition(filename="example-docs/fake-email.eml")
        print("\n\n".join([str(el) for el in elements]))
        ```
        
        And if you installed with `local-inference`, you should be able to run this as well:
        
        ```python
        from unstructured.partition.auto import partition
        
        elements = partition("example-docs/layout-parser-paper.pdf")
        print("\n\n".join([str(el) for el in elements]))
        ```
        
        ## :dizzy: Instructions for using the docker image
        
        The following instructions are intended to help you get up and running using Docker to interact with `unstructured`.
        See [here](https://docs.docker.com/get-docker/) if you don't already have docker installed on your machine.
        
        NOTE: the image is only supported for x86_64 hardware and known to have issues on Apple silicon.
        
        We build Docker images for all pushes to `main`. We tag each image with the corresponding short commit hash (e.g. `fbc7a69`) and the application version (e.g. `0.5.5-dev1`). We also tag the most recent image with `latest`. To leverage this, `docker pull` from our image repository.
        
        ```bash
        docker pull quay.io/unstructured-io/unstructured:latest
        ```
        
        Once pulled, you can create a container from this image and shell to it.
        
        ```bash
        # create the container
        docker run --platform linux/amd64 -d -t --name unstructured quay.io/unstructured-io/unstructured:latest
        
        # this will drop you into a bash shell where the Docker image is running
        docker exec -it unstructured bash 
        ```
        
        You can also build your own Docker image.
        
        If you only plan on parsing one type of data you can speed up building the image by commenting out some
        of the packages/requirements necessary for other data types. See Dockerfile to know which lines are necessary
        for your use case.
        
        ```bash
        make docker-build
        
        # this will drop you into a bash shell where the Docker image is running
        make docker-start-bash
        ```
        
        Once in the running container, you can try things out directly in Python interpreter's interactive mode.
        ```bash
        # this will drop you into a python console so you can run the below partition functions
        python3
        
        >>> from unstructured.partition.pdf import partition_pdf
        >>> elements = partition_pdf(filename="example-docs/layout-parser-paper-fast.pdf")
        
        >>> from unstructured.partition.text import partition_text
        >>> elements = partition_text(filename="example-docs/fake-text.txt")
        ```
        
        
        ## :coffee: Installation Instructions for Local Development
        
        The following instructions are intended to help you get up and running with `unstructured`
        locally if you are planning to contribute to the project.
        
        * Using `pyenv` to manage virtualenv's is recommended but not necessary
        	* Mac install instructions. See [here](https://github.com/Unstructured-IO/community#mac--homebrew) for more detailed instructions.
        		* `brew install pyenv-virtualenv`
        	  * `pyenv install 3.8.15`
          * Linux instructions are available [here](https://github.com/Unstructured-IO/community#linux).
        
        * Create a virtualenv to work in and activate it, e.g. for one named `unstructured`:
        
        	`pyenv  virtualenv 3.8.15 unstructured` <br />
        	`pyenv activate unstructured`
        
        * Run `make install`
        
        * Optional:
          * To install models and dependencies for processing images and PDFs locally, run `make install-local-inference`.
          * For processing image files, `tesseract` is required. See [here](https://tesseract-ocr.github.io/tessdoc/Installation.html) for installation instructions.
          * For processing PDF files, `tesseract` and `poppler` are required. The [pdf2image docs](https://pdf2image.readthedocs.io/en/latest/installation.html) have instructions on installing `poppler` across various platforms.
        
        Additionally, if you're planning to contribute to `unstructured`, we provide you an optional `pre-commit` configuration
        file to ensure your code matches the formatting and linting standards used in `unstructured`.
        If you'd prefer not having code changes auto-tidied before every commit, you can use  `make check` to see
        whether any linting or formatting changes should be applied, and `make tidy` to apply them.
        
        If using the optional `pre-commit`, you'll just need to install the hooks with `pre-commit install` since the
        `pre-commit` package is installed as part of `make install` mentioned above. Finally, if you decided to use `pre-commit`
        you can also uninstall the hooks with `pre-commit uninstall`.
        
        ## :clap: Quick Tour
        
        You can run this [Colab notebook](https://colab.research.google.com/drive/1U8VCjY2-x8c6y5TYMbSFtQGlQVFHCVIW) to run the examples below.
        
        The following examples show how to get started with the `unstructured` library.
        You can parse **TXT**, **HTML**, **PDF**, **EML**, **EPUB**, **DOC**, **DOCX**, **PPT**, **PPTX**, **JPG**,
        and **PNG** documents with one line of code!
        <br></br>
        See our [documentation page](https://unstructured-io.github.io/unstructured) for a full description
        of the features in the library.
        
        ### Document Parsing
        
        The easiest way to parse a document in unstructured is to use the `partition` brick. If you
        use `partition` brick, `unstructured` will detect the file type and route it to the appropriate
        file-specific partitioning brick.
        If you are using the `partition` brick, you may need to install additional parameters via `pip install unstructured[local-inference]`. Ensure you first install `libmagic` using the
        instructions outlined [here](https://unstructured-io.github.io/unstructured/installing.html#filetype-detection)
        `partition` will always apply the default arguments. If you need
        advanced features, use a document-specific brick. The `partition` brick currently works for
        `.txt`, `.doc`, `.docx`, `.ppt`, `.pptx`, `.jpg`, `.png`, `.eml`, `.html`, and `.pdf` documents.
        
        ```python
        from unstructured.partition.auto import partition
        
        elements = partition("example-docs/layout-parser-paper.pdf")
        ```
        
        Run `print("\n\n".join([str(el) for el in elements]))` to get a string representation of the
        output, which looks like:
        
        ```
        
        LayoutParser : A Uniﬁed Toolkit for Deep Learning Based Document Image Analysis
        
        Zejiang Shen 1 ( (cid:0) ), Ruochen Zhang 2 , Melissa Dell 3 , Benjamin Charles Germain Lee 4 , Jacob Carlson 3 , and
        Weining Li 5
        
        Abstract. Recent advances in document image analysis (DIA) have been primarily driven by the application of neural
        networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation.
        However, various factors like loosely organized codebases and sophisticated model conﬁgurations complicate the easy
        reuse of im- portant innovations by a wide audience. Though there have been on-going eﬀorts to improve reusability and
        simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none
        of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA
        is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper
        introduces LayoutParser , an open-source library for streamlining the usage of DL in DIA research and applica- tions.
        The core LayoutParser library comes with a set of simple and intuitive interfaces for applying and customizing DL models
        for layout de- tection, character recognition, and many other document processing tasks. To promote extensibility,
        LayoutParser also incorporates a community platform for sharing both pre-trained models and full document digiti- zation
        pipelines. We demonstrate that LayoutParser is helpful for both lightweight and large-scale digitization pipelines in
        real-word use cases. The library is publicly available at https://layout-parser.github.io
        
        Keywords: Document Image Analysis · Deep Learning · Layout Analysis · Character Recognition · Open Source library ·
        Toolkit.
        
        Introduction
        
        Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of document image analysis (DIA) tasks
        including document image classiﬁcation [11,
        ```
        
        ### HTML Parsing
        
        You can parse an HTML document using the following workflow:
        
        ```python
        from unstructured.partition.html import partition_html
        
        elements = partition_html("example-docs/example-10k.html")
        print("\n\n".join([str(el) for el in elements[:5]]))
        ```
        
        The print statement will show the following text:
        ```
        UNITED STATES
        
        SECURITIES AND EXCHANGE COMMISSION
        
        Washington, D.C. 20549
        
        FORM 10-K
        
        ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934
        ```
        
        And `elements` will be a list of elements in the HTML document, similar to the following:
        
        ```python
        [<unstructured.documents.elements.Title at 0x169cbe820>,
         <unstructured.documents.elements.NarrativeText at 0x169cbe8e0>,
         <unstructured.documents.elements.NarrativeText at 0x169cbe3a0>]
        ```
        
        ### PDF Parsing
        
        You can use the following workflow to parse PDF documents.
        
        ```python
        from unstructured.partition.pdf import partition_pdf
        
        elements = partition_pdf("example-docs/layout-parser-paper.pdf")
        ```
        
        The output will look the same as the example from the document parsing section above.
        
        
        ### E-mail Parsing
        
        The `partition_email` function within `unstructured` is helpful for parsing `.eml` files. Common
        e-mail clients such as Microsoft Outlook and Gmail support exporting e-mails as `.eml` files.
        `partition_email` accepts filenames, file-like object, and raw text as input. The following
        three snippets for parsing `.eml` files are equivalent:
        
        ```python
        from unstructured.partition.email import partition_email
        
        elements = partition_email(filename="example-docs/fake-email.eml")
        
        with open("example-docs/fake-email.eml", "r") as f:
          elements = partition_email(file=f)
        
        with open("example-docs/fake-email.eml", "r") as f:
          text = f.read()
        elements = partition_email(text=text)
        ```
        
        The `elements` output will look like the following:
        
        ```python
        [<unstructured.documents.html.HTMLNarrativeText at 0x13ab14370>,
        <unstructured.documents.html.HTMLTitle at 0x106877970>,
        <unstructured.documents.html.HTMLListItem at 0x1068776a0>,
        <unstructured.documents.html.HTMLListItem at 0x13fe4b0a0>]
        ```
        
        Run `print("\n\n".join([str(el) for el in elements]))` to get a string representation of the
        output, which looks like:
        
        ```python
        This is a test email to use for unit tests.
        
        Important points:
        
        Roses are red
        
        Violets are blue
        ```
        
        ### Text Document Parsing
        
        The `partition_text` function within `unstructured` can be used to parse simple
        text files into elements.
        
        `partition_text` accepts filenames, file-like object, and raw text as input. The following three snippets are for parsing text files:
        
        ```python
        from unstructured.partition.text import partition_text
        
        elements = partition_text(filename="example-docs/fake-text.txt")
        
        with open("example-docs/fake-text.txt", "r") as f:
          elements = partition_text(file=f)
        
        with open("example-docs/fake-text.txt", "r") as f:
          text = f.read()
        elements = partition_text(text=text)
        ```
        
        The `elements` output will look like the following:
        
        ```python
        [<unstructured.documents.html.HTMLNarrativeText at 0x13ab14370>,
        <unstructured.documents.html.HTMLTitle at 0x106877970>,
        <unstructured.documents.html.HTMLListItem at 0x1068776a0>,
        <unstructured.documents.html.HTMLListItem at 0x13fe4b0a0>]
        ```
        
        Run `print("\n\n".join([str(el) for el in elements]))` to get a string representation of the
        output, which looks like:
        
        ```python
        This is a test document to use for unit tests.
        
        Important points:
        
        Hamburgers are delicious
        
        Dogs are the best
        
        I love fuzzy blankets
        ```
        
        
        ## :guardsman: Security Policy
        
        See our [security policy](https://github.com/Unstructured-IO/unstructured/security/policy) for
        information on how to report security vulnerabilities.
        
        ## :books: Learn more
        
        | Section | Description |
        |-|-|
        | [Company Website](https://unstructured.io) | Unstructured.io product and company info |
        | [Documentation](https://unstructured-io.github.io/unstructured) | Full API documentation |
        | [Batch Processing](Ingest.md) | Ingesting batches of documents through Unstructured |
        
Keywords: NLP PDF HTML CV XML parsing preprocessing
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Provides-Extra: huggingface
Provides-Extra: local-inference
Provides-Extra: s3
Provides-Extra: azure
Provides-Extra: github
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