Are you interested in adding an AI-powered chatbot to your website to provide instant assistance and enhance user engagement? In this article, I will guide you through the process of creating a chatbot using the power of OpenAI’s GPT-3.5, combined with the capabilities of the Llama Index for document retrieval. We’ll implement this chatbot using Python and FastAPI framework. This guide is designed to help you integrate a chatbot into your website and provide a seamless interactive experience for your users.
Combining GPT-3.5 and Llama Index
Now in a more serious tone, we will build our chatbot by combining the strengths of two technologies: 1) GPT-3.5, perhaps the most well-known LLM model on planet earth, you can, of course, use other LLMs. 2) Llama Index, a powerful tool for document retrieval. This hybrid approach enables the chatbot to provide accurate and relevant responses to user queries by leveraging both pre-trained language understanding and document-based knowledge.
Getting Started
To start building your chatbot, follow these steps:
- Setting Up Environment and Dependencies: Install the necessary libraries and tools. You’ll need
llama_index,openai, andfastapi, as well as any other dependencies of your FastAPI application. - Loading Documents: Depending on your use case, you can load documents from various sources such as PDF files, webpages, or other data repositories. For more loaders for Llama Index, check out Llama Hub.
- Building the Llama Index: Create a Vector Store Index using the loaded documents. This index will facilitate quick and accurate retrieval of relevant information.
from llama_index import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()- Creating the Chatbot Interface: Now, let’s implement the interface for user interaction. We will define an API using FastAPI that receives user queries and returns chatbot responses.
from fastapi import FastAPI
app = FastAPI()
@app.post("/query")
def query_chatbot(query: str):
response = query_engine.query(query)
return {"response": response}- Handling CORS: To allow communication between your frontend and backend (API), you’ll need to configure CORS settings. This ensures that the API can be accessed from your website.
from fastapi.middleware.cors import CORSMiddleware
origins = ["https://your-website.com"] # Replace with your website's URL
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)- Deploying the Application: You can deploy your FastAPI application using various platforms like Heroku, AWS Lightsail, or others. I personally recommend Heroku for personal projects, as it is super easy to setup. The deployment process might differ for each platform, so refer to their documentation for specific instructions.
Improving the Chatbot
To enhance your chatbot’s capabilities, consider exploring additional resources and tools:
- LangChain Agent: This tool allows you to create a virtual assistant. You can define a set of tools available to the agent. The agent, powered by LLMs, will use reasoning to determine what is the best approach to answer your request. I highly recommend this research paper: ReAct: Synergizing Reasoning and Acting in Language Models (arxiv.org)
- Persistent Memory: If you want to enable persistent memory for your chatbot, consider integrating it with a database solution like MongoDB Atlas or even better a vector database such as Pinecone. This allows your chatbot to remember user interactions and provide context-aware responses.
Integrating the Chatbot into Your Website’s Frontend
Now that we have the backend set up, let’s integrate the chatbot into your website’s frontend using React. This will allow users to interact with the chatbot interface directly on your website.
Setting Up the Chatbot Component
Begin by creating a React component for the chatbot. We’ll name it Chatbot and import the necessary dependencies:
import React, { useState } from 'react';
import { FaComment, FaTimes } from "react-icons/fa";
import axios from 'axios';
import './chatbot.scss'; // Apply your preferred styling Inside the Chatbot component, set up the required state variables using the useState hook:
const Chatbot = () => {
const [inputValue, setInputValue] = useState('');
const [messages, setMessages] = useState([]);
const [isLoading, setIsLoading] = useState(false);
const [expanded, setExpanded] = useState(false);
// ...Handling User Input
Implement the handleChange function to update the inputValue state as the user types in the input field:
const handleChange = (e) => {
setInputValue(e.target.value);
};Submitting User Queries
Define the handleSubmit function to handle user queries. When the user submits a query, the function will send a request to the backend API using the axios library:
const handleSubmit = async (e) => {
e.preventDefault();
if (inputValue.trim() === '') return;
setIsLoading(true);
try {
const response = await axios.get(`YOUR_BACKEND_API_URL/${inputValue}`);
const botReply = response.data.response;
setMessages([...messages, { text: inputValue, sender: 'user' }, { text: botReply, sender: 'bot' }]);
setInputValue('');
} catch (error) {
console.error('Error fetching chatbot response:', error);
} finally {
setIsLoading(false);
}
};Toggling the Chatbot Interface
Implement the toggleChatbot function to expand or collapse the chatbot interface:
const toggleChatbot = () => {
setExpanded(!expanded);
};Rendering the Chatbot Interface
Finally, render the chatbot interface based on the expanded state. When expanded, show the chatbot messages, input field, and loading indicator. When collapsed, show a button to expand the interface:
return (
<div className="chatbot-container">
{!expanded &&
<div className="chatbot-button" onClick={toggleChatbot}>
<FaComment className="chatbot-button-icon"/>
</div>
}
{expanded &&
<div className="chatbot-content">
<div className="chatbot-button" onClick={toggleChatbot}>
<FaTimes className="chatbot-button-icon"/>
</div>
<div className="chatbot-messages">
{messages.map((message, index) => (
<div key={index} className={`message ${message.sender}`}>
{message.text}
</div>
))}
</div>
<form className="chatbot-form" onSubmit={handleSubmit}>
<input className="chatbot-form-input" type="text" value={inputValue} onChange={handleChange} placeholder='Ask me about Anything' />
<button className="chatbot-form-button rn-button-style--2 btn-solid" type="submit">Ask Chatbot</button>
</form>
{isLoading && <div className="loading-indicator">Thinking...</div>}
</div>
}
</div>
);Adding the Chatbot Component to Your Website
You can include the Chatbot component in your website's layout to allow users to interact with the chatbot. Remember to replace YOUR_BACKEND_API_URL with the actual URL of your deployed backend API.
Now, users can easily ask questions and receive responses from the GPT-3.5 powered chatbot directly on your website’s interface.
Conclusion
Brace yourselves, because we’ve just gone on a wild ride through the creation of an AI-powered chatbot for your website, and trust me, this isn’t your grandma’s chatbot. Armed with the forces of GPT-3.5 and the Llama Index, we’ve summoned a digital wizardry that delivers responses so spot-on, Sherlock Holmes would be impressed. Whether you’re looking to up your customer support game, mesmerize your users, or drop knowledge bombs like a boss, this chatbot isn’t just a feature — it’s a turbocharged upgrade to your website’s user experience.
