This article introduces LangChain, an open‑source framework for building LLM applications, covering its modular design, supported models, core components such as Prompt templates, Chains, Memory, Agents, and practical Python code examples for each feature.
This article presents a complete LangChain architecture diagram, quick‑reference tables for core modules, a side‑by‑side comparison with LlamaIndex and Haystack, practical interview Q&A covering advantages, RAG optimization, Agent vs Chain differences, token‑cost reduction, and a seven‑day recap with advanced learning paths.
This article explains why LLMs need memory, compares BufferMemory and ConversationSummaryBufferMemory, demonstrates how to build memory‑aware chains with LangChain Expression Language (LCEL), and shows practical examples including translation, RAG, and callbacks for monitoring chain execution.
This article introduces LangChain’s four core components—Model, Prompt, Chain, and Agent—explains their purposes, shows how to install and configure the library, demonstrates building pipelines with LCEL, and provides code examples for agents, parallel execution, and environment setup.
This tutorial walks through LangChain's core concept of Chains, demonstrating how to build simple, composite, and custom node chains, use prompt templates and structured output parsers, and leverage the LCEL syntax for clean, modular LLM applications.
This article walks through the step‑by‑step creation of a JavaScript Promise implementation, explains why true chaining requires returning a new Promise from then, demonstrates mock asynchronous calls, and provides detailed code examples and execution logs to illustrate the complete chain behavior.
