A newer version of this model is available:
mattshumer/Reflection-Llama-3.1-70B
# 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
Install required packages:
npm install express axios body-parser
Create the server file:
Create a file named
server.js
and add the following code:// 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}`); });
Run your backend server:
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
Create a new React app:
npx create-react-app law-buddy-frontend cd law-buddy-frontend
Install Axios for HTTP requests:
npm install axios
2. Create the Chat Component
Create a new file named
Chat.js
in thesrc
directory:// 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 ( <div> <h1>Law Buddy Chatbot</h1> <div> {answers.map((item, index) => ( <div key={index}> <strong>You:</strong> {item.user}<br /> <strong>Law Buddy:</strong> {item.bot}<br /><br /> </div> ))} </div> <input type="text" value={query} onChange={(e) => setQuery(e.target.value)} placeholder="Ask a legal question..." /> <button onClick={handleSend}>Send</button> </div> ); }; export default Chat;
3. Update App.js
Import and use the Chat
component in App.js
:
// src/App.js
import React from 'react';
import Chat from './Chat';
const App = () => {
return (
<div>
<Chat />
</div>
);
};
export default App;
4. Run Your React App
Start the React application:
npm start
Testing the Integration
- Make sure the backend server is running on
http://localhost:3000
. - Open your React app in a browser (default:
http://localhost:3000
). - Type a legal question in the input field and press "Send".
- The Law Buddy chatbot should respond with the appropriate answer from the Llama 3.2 model.
Deployment
- Backend Deployment: You can deploy your backend to platforms like Heroku, AWS, or DigitalOcean. Ensure the API endpoint for the Llama model is accessible.
- Frontend Deployment: Use services like Vercel, Netlify, or GitHub Pages to deploy your React app.
Troubleshooting
- Ensure that the Llama model API is running and accessible.
- Check the console for any error messages if the chatbot is not responding.
- Verify that CORS is correctly configured on your backend if you're deploying on different origins.
Contributing
Contributions are welcome! If you have suggestions or improvements, feel free to submit a pull request.
License
This project is licensed under the MIT License.
### Usage
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