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Abstract

de>.pptx</code>, <code>.jpg</code>, <code>.png</code>, <code>.eml</code>, <code>.html</code>, and <code>.pdf</code> documents. By integrating Unstructured with LangChain, you can easily start building out first-class support for loading documents of all types into a format that LangChains can work with.</p><p id="e684">Two loaders that are powered by Unstructured are particularly noteworthy.</p><p id="64dd">The first is the <code>UnstructuredFileLoader</code>, which has a simple interface. You just need to pass it a file path, and under the hood, Unstructured does smart logic to infer the data type and extract text.</p><p id="3f01">Here’s an example of how to use the <code>UnstructuredFileLoader</code>:</p><div id="31ec"><pre><span class="hljs-keyword">from</span> unstructured <span class="hljs-keyword">import</span> UnstructuredFileLoader

file_path = "path_to_your_file.pdf" <span class="hljs-type">text</span> = UnstructuredFileLoader.<span class="hljs-keyword">load</span>(file_path) print(<span class="hljs-type">text</span>)</pre></div><p id="0754">The second loader is the <code>DirectoryLoader</code>, which also has a simple interface. It takes a path to a directory and an optional regex to glob for files against. Under the hood, it loops over all files and uses the <code>UnstructuredFileLoader</code> to load them, making it possible to load files of all types in a single call.</p><p id="16a4">Here’s an example of how to use the <code>DirectoryLoader</code>:</p><div id="2cbb"><pre><span class="hljs-built_in">from</span> unstructured im

Options

port DirectoryLoader

directory_path = <span class="hljs-string">"path_to_your_directory"</span> file_texts = DirectoryLoader.<span class="hljs-built_in">load</span>(directory_path) <span class="hljs-keyword">for</span> <span class="hljs-built_in">file</span>, <span class="hljs-keyword">text</span> <span class="hljs-keyword">in</span> file_texts.<span class="hljs-keyword">items</span>(): print(<span class="hljs-built_in">file</span>, <span class="hljs-keyword">text</span>)</pre></div><p id="20b9">By integrating Unstructured with LangChain, you can transform raw data into clean text, making it incredibly easy to combine language models with your data, no matter what form it’s in.</p><div id="79fe" class="link-block"> <a href="https://readmedium.com/langchain-async-api-9561b7af87c0"> <div> <div> <h2>LANGCHAIN — Async API</h2> <div><h3>The function of good software is to make the complex appear to be simple. — Grady Booch</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*nu7ZXSdSXeo6aCLEJYoZpg.jpeg)"></div> </div> </div> </a> </div><p id="c167">In conclusion, the Unstructured Python package provides a seamless way to prepare your data for use with LangChain, and its integration can significantly enhance the capabilities of your natural language processing workflows.</p></article></body>

LANGCHAIN — Can You Provide Unstructured Langchain Information?

I’m not a great programmer; I’m just a good programmer with great habits. — Kent Beck.

LANGCHAIN — Can GPT-3 be Used for Chat Over Your Data?

Computers are good at following instructions, but not at reading your mind. — Donald Knuth

medium.com

LangChain is a powerful platform for combining Large Language Models with your own text data. However, before using text data with LangChain, it’s essential to get the data into a text form. This can be tricky due to the multitude of different formats that exist. Luckily, Unstructured provides an open-source Python package that can help with this.

The Unstructured Python package supports many different types of file extensions, including .txt, .docx, .pptx, .jpg, .png, .eml, .html, and .pdf documents. By integrating Unstructured with LangChain, you can easily start building out first-class support for loading documents of all types into a format that LangChains can work with.

Two loaders that are powered by Unstructured are particularly noteworthy.

The first is the UnstructuredFileLoader, which has a simple interface. You just need to pass it a file path, and under the hood, Unstructured does smart logic to infer the data type and extract text.

Here’s an example of how to use the UnstructuredFileLoader:

from unstructured import UnstructuredFileLoader

file_path = "path_to_your_file.pdf"
text = UnstructuredFileLoader.load(file_path)
print(text)

The second loader is the DirectoryLoader, which also has a simple interface. It takes a path to a directory and an optional regex to glob for files against. Under the hood, it loops over all files and uses the UnstructuredFileLoader to load them, making it possible to load files of all types in a single call.

Here’s an example of how to use the DirectoryLoader:

from unstructured import DirectoryLoader

directory_path = "path_to_your_directory"
file_texts = DirectoryLoader.load(directory_path)
for file, text in file_texts.items():
    print(file, text)

By integrating Unstructured with LangChain, you can transform raw data into clean text, making it incredibly easy to combine language models with your data, no matter what form it’s in.

LANGCHAIN — Async API

The function of good software is to make the complex appear to be simple. — Grady Booch

medium.com

In conclusion, the Unstructured Python package provides a seamless way to prepare your data for use with LangChain, and its integration can significantly enhance the capabilities of your natural language processing workflows.