LangChain Labs 101 - Part 1 - (v 0.2 based)
Lang* technologies were the first to bring LLMs to developers...
Kicking off a three part tutorial on LangChain and tooling, I will be covering LangGraph and LangSmith in parts 2 and 3.
Audience
This is intended for those that are somewhat new to python and LangChain. The first two exercises get you started and support Jupyter notebooks - intended to get you your very first Hello World implementation of LangChain.
The initial agentic sample is the basic tool usage available in LangChain.
LangChain in a nutshell
LangChain is an open-source framework designed to facilitate the development and deployment of applications leveraging large language models (LLMs). Its modular architecture allows developers to create sophisticated AI applications by combining pre-built components and custom code. Key features include the LangChain Expression Language (LCEL) for composing chains, LangGraph for defining cognitive architectures, and LangServe for deploying these chains as REST APIs. This modularity and ease of integration have significantly accelerated the development process and improved productivity for developers working with LLMs (SlashDev) (howtouselinux).
