<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LangChain on ByF (BaiFan) · Daily Deep Think</title><link>https://blog.baifan.site/en/tags/langchain/</link><description>Recent content in LangChain on ByF (BaiFan) · Daily Deep Think</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>blog@baifan.site (ByF (BaiFan))</managingEditor><webMaster>blog@baifan.site (ByF (BaiFan))</webMaster><copyright>CC BY-NC 4.0</copyright><lastBuildDate>Sun, 20 Sep 2026 13:00:00 +0800</lastBuildDate><atom:link href="https://blog.baifan.site/en/tags/langchain/index.xml" rel="self" type="application/rss+xml"/><item><title>The Classifier Is the Savior Again</title><link>https://blog.baifan.site/en/the-classifier-returns/</link><pubDate>Sun, 20 Sep 2026 13:00:00 +0800</pubDate><author>blog@baifan.site (ByF (BaiFan))</author><guid>https://blog.baifan.site/en/the-classifier-returns/</guid><description>&lt;p&gt;In any enterprise tech team that claims to be “fully AI-powered,” the bill nobody wants to look at closely is usually not production inference — it’s the &lt;strong&gt;evaluation pipelines&lt;/strong&gt; we run to deceive ourselves.&lt;/p&gt;&#10;&lt;!-- more --&gt;&#10;&lt;p&gt;Over the past three years, the biggest, most sacred, and least defensible totem in the enterprise stack has been the “private enterprise knowledge base” — first repackaged as RAG, then upgraded again as GraphRAG with a knowledge-graph label. Every architect, when drawing the final architecture diagram for the CTO, dutifully adds the last box: “Closed-loop evaluation and continuous alignment.”&lt;/p&gt;</description></item><item><title>RAG Complete Guide: Build a Local RAG System from Scratch</title><link>https://blog.baifan.site/en/rag-system-complete-guide-langchain-ollama-pgvector/</link><pubDate>Wed, 27 Aug 2025 14:00:00 +0800</pubDate><author>blog@baifan.site (ByF (BaiFan))</author><guid>https://blog.baifan.site/en/rag-system-complete-guide-langchain-ollama-pgvector/</guid><description>&lt;h1 class="relative group"&gt;Building a Local RAG with LangChain + Ollama + pgvector: A Complete Hands-On Walkthrough from 0 to 1 (with uv Dependency Management and an Interview Guide)&#10; &lt;div id="building-a-local-rag-with-langchain--ollama--pgvector-a-complete-hands-on-walkthrough-from-0-to-1-with-uv-dependency-management-and-an-interview-guide" class="anchor"&gt;&lt;/div&gt;&#10; &#10; &lt;span&#10; class="absolute top-0 w-6 transition-opacity opacity-0 -start-6 not-prose group-hover:opacity-100 select-none"&gt;&#10; &lt;a class="text-primary-300 dark:text-neutral-700 !no-underline" href="#building-a-local-rag-with-langchain--ollama--pgvector-a-complete-hands-on-walkthrough-from-0-to-1-with-uv-dependency-management-and-an-interview-guide" aria-label="Anchor"&gt;#&lt;/a&gt;&#10; &lt;/span&gt;&#10; &#10;&lt;/h1&gt;&#10;&lt;blockquote&gt;&lt;p&gt;This post is a directly actionable document. Follow it top to bottom and you will build a local RAG (retrieval-augmented generation) system from scratch and understand the key concepts and code. All core scripts come with comments to make learning and interview review easier.&lt;/p&gt;</description></item></channel></rss>