<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Extraction Schema on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/extraction-schema/</link><description>Recent content in Extraction Schema 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/extraction-schema/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 7: The LangExtract API: Core Functions and Parameters</title><link>https://ai-blog.noorshomelab.dev/langextract-guide-2026/07-api-functions/</link><pubDate>Mon, 05 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/langextract-guide-2026/07-api-functions/</guid><description>&lt;h2 id="introduction-to-the-langextract-api"&gt;Introduction to the LangExtract API&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid data explorer! In our previous chapters, we laid the groundwork for using LangExtract by setting up your environment and understanding how to define extraction tasks using schemas. Now, it&amp;rsquo;s time to get to the heart of the matter: the LangExtract API itself.&lt;/p&gt;
&lt;p&gt;This chapter will guide you through the core functions that empower you to perform structured information extraction. We&amp;rsquo;ll focus primarily on the star of the show: the &lt;code&gt;langextract.extract()&lt;/code&gt; function. You&amp;rsquo;ll learn how to use its various parameters to precisely control your extraction tasks, from specifying your input text to selecting the underlying Large Language Model (LLM) and fine-tuning performance.&lt;/p&gt;</description></item></channel></rss>