langchain-tutorials

This repository is a collection of tutorials and code examples for LangChain, a library that helps developers build applications powered by large language models. LangChain provides building blocks for tasks like asking questions over your own documents, summarizing text, extracting structured data from unstructured sources, and building chatbot-style interfaces. This repository is aimed at developers who want to learn how to use LangChain through practical examples rather than just reading the official documentation.

The recommended learning path in the README starts with two cookbook notebooks. The first covers seven core concepts in LangChain, and the second covers nine practical use cases. Each notebook has a matching YouTube video. There are also links to prompt engineering resources for those who are new to writing effective instructions for AI models.

Beyond the cookbooks, the repository includes a gallery of community-submitted projects organized by difficulty level (beginner, intermediate, and advanced) and by category. The categories covered include summarization, question answering over documents, data extraction, evaluation of AI outputs, chatbots, and AI agents. Most entries in the gallery link to separate repositories with the full code for each project.

The tutorials use Jupyter Notebooks, which are files that combine code, explanatory text, and output in a single document that can be run interactively in the browser through tools like Google Colab. The README includes a link to a beginner video explaining how to use notebooks if that format is unfamiliar.

The repository is maintained by Greg Kamradt and accepts contributions via pull request. It accompanies a YouTube channel and email newsletter focused on practical applications of AI tools for developers.