DEV Community

Jaypal Brahmbhatt
Jaypal Brahmbhatt

Posted on

AI Chatbot Using LLAMA for Intelligent Conversations

How I Built an AI Chatbot Using LLAMA for Intelligent Conversations

πŸ“… By Jaypalkumar Brahmbhatt

Introduction

Artificial Intelligence (AI) chatbots are revolutionizing how businesses interact with customers. With the advancement of Large Language Models (LLMs), AI-powered chatbots can now understand context, generate human-like responses, and provide real-time assistance.

In this blog, I’ll walk you through how I built an AI chatbot using LLAMA that leverages NLP and machine learning to deliver intelligent, engaging, and efficient conversations.


Why Build an AI Chatbot? πŸ€–

Chatbots have multiple real-world applications:

βœ” Customer Support Automation – Reduce response time and improve efficiency.

βœ” Personal Assistants – Automate tasks like setting reminders and answering queries.

βœ” E-Commerce Assistance – Help users find products and make recommendations.

βœ” Lead Generation – Qualify potential customers before connecting them with sales teams.

I wanted to build a chatbot that could:

βœ… Understand user intent

βœ… Respond in natural language

βœ… Learn and improve over time

βœ… Be easily integrated into applications


Tech Stack & Tools Used πŸ› οΈ

To build my AI chatbot, I used the following technologies:

Component Technology Used
LLM Model LLAMA
Backend Python, Flask
Frontend React.js (optional)
Database MongoDB / PostgreSQL
Deployment Docker, AWS

LLAMA is a powerful, open-source large language model (LLM) that provides high-quality, human-like text generation, making it ideal for chatbots.


Step-by-Step Guide to Building the Chatbot

1️⃣ Setting Up the Project

First, we create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate  # Mac/Linux
venv\Scripts\activate  # Windows
pip install flask llama-cpp-python
Enter fullscreen mode Exit fullscreen mode

2️⃣ Building the Backend with Flask

Flask provides a lightweight framework to handle chatbot requests.

from flask import Flask, request, jsonify
from llama_cpp import Llama

app = Flask(__name__)

# Load LLAMA Model
llm = Llama(model_path="path/to/llama_model.bin")

@app.route("/chat", methods=["POST"])
def chat():
    user_input = request.json.get("message")
    response = llm(user_input)
    return jsonify({"response": response})

if __name__ == "__main__":
    app.run(debug=True)
Enter fullscreen mode Exit fullscreen mode

3️⃣ Creating the Frontend (Optional)

A simple React.js frontend for user interaction:

import { useState } from "react";

function Chatbot() {
  const [message, setMessage] = useState("");
  const [response, setResponse] = useState("");

  const sendMessage = async () => {
    const res = await fetch("/chat", {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({ message }),
    });
    const data = await res.json();
    setResponse(data.response);
  };

  return (
    <div>
      <input value={message} onChange={(e) => setMessage(e.target.value)} />
      <button onClick={sendMessage}>Send</button>
      <p>Response: {response}</p>
    </div>
  );
}

export default Chatbot;
Enter fullscreen mode Exit fullscreen mode

4️⃣ Deploying the Chatbot on AWS ☁️

To make the chatbot available globally, we deploy it using Docker & AWS.

Dockerfile for Deployment

FROM python:3.9
WORKDIR /app
COPY . .
RUN pip install flask llama-cpp-python
CMD ["python", "app.py"]
Enter fullscreen mode Exit fullscreen mode

Deploy to AWS EC2

docker build -t chatbot .
docker run -p 5000:5000 chatbot
Enter fullscreen mode Exit fullscreen mode

Final Thoughts & Future Enhancements

This chatbot is just the beginning! πŸš€ In the future, I plan to:

βœ” Integrate Speech-to-Text & Voice Support

βœ” Train LLAMA on Custom Datasets for domain-specific conversations

βœ” Deploy on WhatsApp, Slack, and Telegram

πŸ’‘ Interested in AI & Chatbots? Check out my GitHub repository: πŸ”— GitHub Repo

πŸ“© Let’s connect on LinkedIn if you’re working on AI projects!


Conclusion

In this blog, I shared how I built an AI chatbot using LLAMA with Python, Flask, and React. The project demonstrates how LLMs can be used to create intelligent, context-aware bots.

πŸš€ If you found this helpful, consider starring the GitHub repository or leaving a comment!

πŸ”— πŸ”— View Full Project on GitHub

πŸš€ GitHub Repo demo image: https://github.com/jaypal0111/AI-chat-boat-LLAMA/blob/main/AIChatBoat.png

Top comments (0)

πŸ‘‹ Kindness is contagious

Please leave a ❀️ or a friendly comment on this post if you found it helpful!

Okay