In this video, I’ll walk you through setting up and using local models with LangChain, making AI development more private, cost-effective, and flexible. We’ll start by installing Ollama, a tool that simplifies running local language models, and then set up a local LLM using Llama 3.2. Once the model is running, I’ll show you how to interact with it and even turn off your Wi-Fi to demonstrate its true offline capabilities.
Next, we’ll dive into integrating this local model with LangChain and LangGraph, making it easy to build AI-powered applications without relying on cloud-based APIs. I’ll guide you through serving the model on localhost and writing a simple script to interact with it using LangChain. By the end of this tutorial, you’ll have a fully functional local AI setup that you can extend further—whether by connecting it to an AI agent, using retrieval-augmented generation (RAG), or building more advanced workflows.
Plus, you’ll enjoy the benefits of full data privacy and reduced costs.
👨💻Full code here: https://github.com/daniel-jscraft/Javascript-CSS-demos/tree/main/demos/%F0%9F%A6%9C_langchain/23-local-llm
📖 Neural Networks from Scratch - Presale
I'm writing a book about the timeless foundational concepts of neural networks for JavaScript developers. Go from if-else to weights and biases by building tiny AI models from scratch!
📖 Neural Networks from Scratch - Presale
I'm writing a book about the timeless foundational concepts of neural networks for JavaScript developers. Go from if-else to weights and biases by building tiny AI models from scratch!