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README.md
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@ -33,7 +33,7 @@
<p>Open-Source Pre-Processing Tools for Unstructured Data</p>
</h2>
The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and [many more](https://unstructured-io.github.io/unstructured/bricks.html#partitioning). The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular bricks and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and is efficient in transforming unstructured data into structured outputs.
The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and [many more](https://unstructured-io.github.io/unstructured/bricks.html#partitioning). The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular bricks and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.
<h3 align="center">
<p>API Announcement!</p>
@ -205,14 +205,14 @@ 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
reuse of important innovations by a wide audience. Though there have been ongoing 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.
introduces LayoutParser, an open-source library for streamlining the usage of DL in DIA research and applications.
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
for layout detection, 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 digitization
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

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docs/source/index.rst
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@ -2,7 +2,7 @@ Unstructured Core Library
=========================
The ``unstructured`` library is designed to help preprocess structure unstructured text documents
for use in downstream machine learning tasks. Examples of documents that can be processes
for use in downstream machine learning tasks. Examples of documents that can be processed
using the ``unstructured`` library include PDFs, XML and HTML documents.
Library Documentation
@ -18,7 +18,7 @@ Library Documentation
Learn more about partitioning, cleaning, and staging bricks, including advanced usage patterns.
:doc:`upstream_connectors`
Connect to your favortite data storage platforms for an efortless batch processing of your files.
Connect to your favorite data storage platforms for an effortless batch processing of your files.
:doc:`metadata`
Learn more about how metadata is tracked in the ``unstructured`` library.

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docs/source/integrations.rst
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@ -60,13 +60,13 @@ Our integration with `LangChain <https://github.com/hwchase17/langchain>`_ makes
loader.load()
Checkout the `LangChain docs <https://python.langchain.com/en/latest/modules/indexes/document_loaders.html>`_ for more
examples about how to use Unstructured data loders.
examples about how to use Unstructured data loaders.
``Integration with LlamaIndex``
--------------------------------
To use ``Unstructured.io File Loader`` you will need to have `LlamaIndex <https://github.com/jerryjliu/llama_index>`_ 🦙 (GPT Index) installed in your environment. Just ``pip install llama-index`` and then pass in a ``Path`` to a local file. Optionally, you may specify split_documents if you want each element generated by ``unstructured`` to be placed in a separate document. Here is a simple example on how to use it:
To use ``Unstructured.io File Loader`` you will need to have `LlamaIndex <https://github.com/jerryjliu/llama_index>`_ 🦙 (GPT Index) installed in your environment. Just ``pip install llama-index`` and then pass in a ``Path`` to a local file. Optionally, you may specify split_documents if you want each element generated by ``unstructured`` to be placed in a separate document. Here is a simple example of how to use it:
.. code:: python