This guide explains LangChainGo’s Chain abstraction, detailing the core Chain interface, the LLMChain implementation, various chain types such as SequentialChain and RetrievalQA, and provides a complete walkthrough of setting up prompts, creating chains, and executing them with example Go code.
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 tutorial walks through the concept of PromptTemplate in LangChain, demonstrates how to build chat prompt templates, use message placeholders, apply Few‑Shot prompting and ExampleSelector techniques, and shows concrete code and output examples that help mitigate large‑language‑model hallucinations.
This tutorial walks through installing LangChain, creating an LLM (via own GPU resources or third‑party APIs), designing parameterized prompt templates, implementing a custom output parser for structured results, and running a complete Python example that generates culturally specific names.
This article demonstrates how to use LangChain, LLMs, and SerpAPI to create a Weibo influencer‑search tool that extracts UID numbers, scrapes profile data, filters Chinese content, and prepares the information for automated marketing outreach.
This article introduces LangChain—a Python framework for building LLM‑driven applications—explains its core components such as models, indexes, chains, memory, and agents, and provides practical code examples for document summarization, retrieval‑augmented QA, and future development directions.
This article explains what LangChain is, outlines its core components such as Models, Indexes, Chains, Memory and Agents, and demonstrates how to build LLM‑driven applications with detailed Python code snippets, visual diagrams, and future development suggestions.
