<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PDF on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/pdf/</link><description>Recent content in PDF on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 05 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/pdf/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 6: Handling Different Document Types – Text, HTML, PDF</title><link>https://ai-blog.noorshomelab.dev/langextract-guide-2026/06-document-types/</link><pubDate>Mon, 05 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/langextract-guide-2026/06-document-types/</guid><description>&lt;h2 id="introduction-beyond-plain-text--embracing-diverse-documents"&gt;Introduction: Beyond Plain Text – Embracing Diverse Documents&lt;/h2&gt;
&lt;p&gt;Welcome back, future data alchemist! In our previous chapters, you&amp;rsquo;ve mastered the fundamentals of setting up LangExtract, defining extraction schemas, and pulling structured data from plain text. That&amp;rsquo;s a fantastic start, but let&amp;rsquo;s be honest: the real world isn&amp;rsquo;t always neatly packaged in plain &lt;code&gt;.txt&lt;/code&gt; files.&lt;/p&gt;
&lt;p&gt;Imagine needing to extract key clauses from a legal contract (often a PDF), product details from an e-commerce webpage (HTML), or specific figures from a research report. These diverse document types present unique challenges.&lt;/p&gt;</description></item></channel></rss>