DLightHouse

Introduction Why Structured Output? Notes & Code Walkthrough Conclusion Introduction When building applications that interact with Large Language Models (LLMs), you often need more than just a plain text response. Structured outputs, such as JSON, make it much easier to integrate your AI-powered app with external systems or display specific data on the frontend. In this post, we'll explore how to achieve that using LangChain. We'll also walk through some personal notes on how we've set up our React components, and finally we'll dive into our code to see it all in action. Why Structured Output? If you've ever tried to build an API around an LLM, you may have run into a significant challenge: the LL...

This post shows how to create a simple React application that communicates with an LLM using LangChain. It includes steps for installing dependencies, building a prompt, integrating with OpenAI’s GPT-3.5-turbo, and extracting country, state, and ZIP code information. Project Setup Dependencies llm.js AddressInformationExtractor.jsx App.jsx Running the App Project Setup Unix-like Shells pnpm create vite my-llm-app --template react Mac pnpm create vite my-llm-app --template react Windows Command Prompt (cmd) ...