<?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 Ghafoor's Personal Blog</title><link>http://ghafoorsblog.com/categories/langchain/</link><description>Recent content in Langchain on Ghafoor's Personal Blog</description><generator>Hugo</generator><language>en</language><managingEditor>noreply@example.com (AG Sayyed)</managingEditor><webMaster>noreply@example.com (AG Sayyed)</webMaster><copyright>Copyright © 2024-2026 AG Sayyed. All Rights Reserved.</copyright><lastBuildDate>Fri, 15 May 2026 13:20:20 +0100</lastBuildDate><atom:link href="http://ghafoorsblog.com/categories/langchain/index.xml" rel="self" type="application/rss+xml"/><item><title>LCEL Chaining Method</title><link>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/02-module/003-langchain-chains-methods/</link><pubDate>Fri, 21 Nov 2025 13:19:01 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/02-module/003-langchain-chains-methods/</guid><description>&lt;p class="lead text-primary"&gt;
This document explores LangChain Expression Language (LCEL), a modern pattern for building composable chains using the pipe operator to connect components. It covers prompt template structuring with variables, runnable composition primitives including sequential and parallel execution, type coercion mechanisms, and practical implementation patterns for developing reusable AI applications with enhanced readability and flexibility.
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&lt;h2 id="introduction-to-lcel"&gt;Introduction to LCEL&lt;/h2&gt;
&lt;p&gt;LangChain Expression Language (LCEL) is a pattern for building LangChain applications that utilizes the pipe operator to connect components. This approach ensures a clean, readable flow of data from input to output.&lt;/p&gt;</description></item><item><title>LangChain Chains and Agents for Budilding LLM Applications</title><link>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/02-module/002-langchain-chains/</link><pubDate>Fri, 21 Nov 2025 02:42:46 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/02-module/002-langchain-chains/</guid><description>&lt;p class="lead text-primary"&gt;
This document explores LangChain chains as sequences of calls that create seamless information flows, demonstrates sequential chain implementation through practical examples, explains memory storage mechanisms for preserving conversation context, and introduces agents as dynamic systems that leverage language models with external tools to autonomously fulfill complex user requests.
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&lt;h2 id="langchain-for-building-applications"&gt;LangChain for Building Applications&lt;/h2&gt;
&lt;p&gt;LangChain is a platform embedded with APIs to develop applications, empowering them to infuse language processing capabilities. Developers find LangChain suitable for building applications due to its comprehensive toolset and integration capabilities.&lt;/p&gt;</description></item><item><title>LangChain Core Concepts</title><link>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/02-module/001-langchain-core-conepts/</link><pubDate>Fri, 21 Nov 2025 02:34:38 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/02-module/001-langchain-core-conepts/</guid><description>&lt;p class="lead text-primary"&gt;
This document provides a comprehensive overview of LangChain's core components that enable efficient application development using large language models. It covers language models for text generation, chat models for conversational interfaces, various chat message types, prompt templates for instruction formatting, example selectors for optimizing few-shot prompts, and output parsers for transforming LLM responses into structured data formats.
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&lt;h2 id="what-is-langchain"&gt;What is LangChain&lt;/h2&gt;
&lt;p&gt;LangChain is an open-source interface that simplifies the application development process using large language models (LLMs). It facilitates a structured way to integrate language models into various use cases, including Natural Language Processing (NLP) and data retrieval.&lt;/p&gt;</description></item><item><title>LangChain Expression Language</title><link>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/01-module/008-lcel/</link><pubDate>Fri, 21 Nov 2025 02:20:39 +0000</pubDate><author>noreply@example.com (AG Sayyed)</author><guid>http://ghafoorsblog.com/courses/ibm/rag-agentic-ai-content/rag-agentic-ai-pcert/01-develop-genai-apps/01-module/008-lcel/</guid><description>&lt;p class="lead text-primary"&gt;
This document explores LangChain Expression Language (LCEL), a modern pattern for building LangChain applications using the pipe operator to connect components. It covers the fundamentals of creating flexible chains, structuring prompts with templates, understanding runnable composition primitives, and leveraging type coercion to develop reusable patterns for various AI applications with improved composability and data flow visualization.
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&lt;h2 id="introduction-to-lcel"&gt;Introduction to LCEL&lt;/h2&gt;
&lt;p&gt;LangChain Expression Language (LCEL) is a pattern for building LangChain applications that utilizes the pipe operator to connect components. This approach ensures a clean, readable flow of data from input to output.&lt;/p&gt;</description></item></channel></rss>