--- license: bigcode-openrail-m datasets: - HuggingFaceTB/everyday-conversations-llama3.1-2k language: - en metrics: - accuracy new_version: mattshumer/Reflection-Llama-3.1-70B library_name: adapter-transformers tags: - code --- ```markdown # Llama 3.2 Integration Guide This guide provides instructions for integrating the Llama 3.2 model into your React and backend projects. The Llama model can be used to build intelligent chatbots, such as the "Law Buddy" chatbot for legal queries. ## Table of Contents - [Prerequisites](#prerequisites) - [Backend Setup](#backend-setup) - [React Frontend Setup](#react-frontend-setup) - [Testing the Integration](#testing-the-integration) - [Deployment](#deployment) - [Troubleshooting](#troubleshooting) - [Contributing](#contributing) ## Prerequisites Before you begin, ensure you have the following installed: - Node.js (version 14 or later) - npm (Node package manager) - A running instance of the Llama 3.2 model (API endpoint) ## Backend Setup ### 1. Create a Node.js Server 1. **Initialize your project:** ```bash mkdir law-buddy-backend cd law-buddy-backend npm init -y ``` 2. **Install required packages:** ```bash npm install express axios body-parser ``` 3. **Create the server file:** Create a file named `server.js` and add the following code: ```javascript // server.js const express = require('express'); const bodyParser = require('body-parser'); const axios = require('axios'); const app = express(); const PORT = process.env.PORT || 3000; // Middleware app.use(bodyParser.json()); // Endpoint to handle user queries app.post('/lawbuddy', async (req, res) => { const userQuery = req.body.query; try { const response = await axios.post('http://localhost:8000/api/language-model', { prompt: userQuery, maxTokens: 150, temperature: 0.7, }); const answer = response.data.answer; // Adjust based on your Llama API response structure res.json({ answer }); } catch (error) { console.error(error); res.status(500).send('Internal Server Error'); } }); // Start the server app.listen(PORT, () => { console.log(`Server is running on port ${PORT}`); }); ``` 4. **Run your backend server:** ```bash node server.js ``` ### 2. API Endpoint - The API endpoint to handle queries is `/lawbuddy`. It accepts POST requests with a JSON payload containing the user's query. ## React Frontend Setup ### 1. Create a React App 1. **Create a new React app:** ```bash npx create-react-app law-buddy-frontend cd law-buddy-frontend ``` 2. **Install Axios for HTTP requests:** ```bash npm install axios ``` ### 2. Create the Chat Component 1. **Create a new file named `Chat.js` in the `src` directory:** ```javascript // src/Chat.js import React, { useState } from 'react'; import axios from 'axios'; const Chat = () => { const [query, setQuery] = useState(''); const [answers, setAnswers] = useState([]); const handleSend = async () => { try { const response = await axios.post('http://localhost:3000/lawbuddy', { query }); setAnswers([...answers, { user: query, bot: response.data.answer }]); setQuery(''); } catch (error) { console.error('Error:', error); } }; return (