AI Web Chatbot

Here's a step-by-step guide on how to use the AI Web Chatbot template:

Introduction

This template provides a web-based AI chatbot interface with real-time interaction, LLM responses, and session history, styled with Tailwind CSS. It's perfect for creating a customizable chatbot for your website or application.

Getting Started

1. Click "Start with this Template" to begin using the AI Web Chatbot template in the Lazy Builder interface.

2. Review the pre-populated code in the Lazy Builder. You'll see files for the HTML template, JavaScript, Python backend, and database setup.

Test the Application

1. Click the "Test" button to deploy the application and launch the Lazy CLI.

2. Wait for the deployment process to complete. The Lazy CLI will provide you with a dedicated server link to access your chatbot interface.

Using the Chatbot Interface

1. Open the provided server link in your web browser to access the chatbot interface.

2. You'll see a simple, clean interface with a chat window and an input field at the bottom.

3. Type your message in the input field and press "Enter" or click the "Send" button to interact with the chatbot.

4. The chatbot will respond using the configured LLM (GPT-4 in this case), and the conversation will be displayed in the chat window.

Customizing the Chatbot

1. To customize the chatbot's appearance or behavior, you can modify the following files in the Lazy Builder:

2. template.html: Adjust the HTML structure and Tailwind CSS classes for styling.

3. script.js: Modify the JavaScript to change the chat interaction behavior.

4. main.py: Update the Python backend to alter the chatbot's response logic or integrate different LLM models.

5. If you want to change the chatbot's name or branding, update the relevant text in the template.html file:

html <span class="ml-3 text-xl">Your Chatbot</span>

1. To adjust the conversation history length, modify the following line in main.py:

python if len(session['history']) > 10: session['history'] = session['history'][-10:]

1. If you need to change the LLM model or its parameters, update this line in main.py:

python response = llm(prompt=conversation_history, response_schema={"type": "object", "properties": {"response": {"type": "string"}}}, model="gpt-4o", temperature=0.7)["response"]

Integrating the Chatbot

1. To integrate this chatbot into your existing website or application, you'll need to:

2. Copy the HTML structure from template.html into your desired page.

3. Include the Tailwind CSS link in your HTML head: html <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
4. Copy the contents of script.js into your JavaScript file or a <script> tag in your HTML.

5. Ensure your backend can handle the /send_message route and respond with JSON containing the chatbot's message.

6. Remember to update the fetch URL in script.js to match your server's endpoint:

javascript fetch('/send_message', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ message: userText }), })

By following these steps, you'll have a fully functional AI Web Chatbot up and running, which you can further customize to fit your specific needs.

Here are 5 key business benefits for this AI Web Chatbot template:

Template Benefits

1. Enhanced Customer Support: Implement a 24/7 AI-powered customer service solution, reducing response times and improving customer satisfaction while lowering support costs.

2. Lead Generation and Qualification: Use the chatbot as a front-line tool to engage website visitors, qualify leads, and gather initial information before human intervention, streamlining the sales process.

3. Personalized User Experience: Leverage the session history feature to provide context-aware, personalized interactions, improving user engagement and potentially increasing conversion rates.

4. Scalable Information Dissemination: Efficiently handle frequently asked questions and provide instant access to product information, reducing the workload on human staff and ensuring consistent messaging.

5. Data Collection and Insights: Gather valuable user data and conversation logs to gain insights into customer needs, preferences, and pain points, informing business strategy and product development.