---
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 (
           <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`:

```javascript
// 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

1. **Start the React application:**

   ```bash
   npm start
   ```

## Testing the Integration

1. Make sure the backend server is running on `http://localhost:3000`.
2. Open your React app in a browser (default: `http://localhost:3000`).
3. Type a legal question in the input field and press "Send".
4. The Law Buddy chatbot should respond with the appropriate answer from the Llama 3.2 model.

## Deployment

1. **Backend Deployment**: You can deploy your backend to platforms like Heroku, AWS, or DigitalOcean. Ensure the API endpoint for the Llama model is accessible.
2. **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

-