<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Private Cloud on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/private-cloud/</link><description>Recent content in Private Cloud 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/private-cloud/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 13: Custom LLM Providers and Integrations</title><link>https://ai-blog.noorshomelab.dev/langextract-guide-2026/13-custom-llm-providers/</link><pubDate>Mon, 05 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/langextract-guide-2026/13-custom-llm-providers/</guid><description>&lt;h2 id="introduction-to-custom-llm-providers"&gt;Introduction to Custom LLM Providers&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid data explorer! In previous chapters, we&amp;rsquo;ve seen how LangExtract brilliantly orchestrates Large Language Models (LLMs) to extract structured information from unstructured text. We&amp;rsquo;ve used its default integrations, which are fantastic for getting started. But what if your needs are a bit more unique?&lt;/p&gt;
&lt;p&gt;Perhaps you&amp;rsquo;re working with a highly specialized, fine-tuned LLM running on your company&amp;rsquo;s private cloud. Maybe you want to experiment with a bleeding-edge open-source model that just got released on Hugging Face, or you need to integrate with a less common commercial LLM API. This is where the power of LangExtract&amp;rsquo;s custom LLM provider interface shines!&lt;/p&gt;</description></item></channel></rss>