LangChain-Chinese-Getting-Started-Guide

This repository is a Chinese-language beginner's guide to LangChain, a software library for building applications powered by large language models. The guide is written in Markdown and accompanied by runnable code examples hosted on Google Colab. A formatted version of the same content is published as a GitBook for easier reading.

LangChain is described in the guide as a framework that lets developers connect AI language models to outside data sources and interact with those models programmatically. The README explains that using a language model on its own through an API has limitations, such as a cap on how much text can be processed at once, and no ability to search the web or read documents in real time. LangChain is presented as the solution that handles these problems.

The guide walks through the core concepts step by step. Loaders bring data into the system from sources like PDF files, CSV files, YouTube videos, and websites. Text Splitters divide long documents into smaller pieces so they fit within the model's input limits. Vector stores convert text into numerical representations that allow the system to search for relevant passages by meaning rather than keyword. Chains string together multiple processing steps into a single workflow. Agents let the system decide at runtime which tools to call, such as a web search, to answer a question.

Each section includes Python code examples. The practical exercises cover tasks like asking a single question to an AI model, having the model search Google before answering, summarizing a very long text document, and building a custom question-answering system based on a private document set.

A note in the README warns that some code examples may be out of date because the LangChain library updates frequently. The author also published a book based on this material. The project is a documentation and tutorial repository with no application to install or run directly.