Configurable AI Chatbot

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import logging
from gunicorn.app.base import BaseApplication
from app_init import create_initialized_flask_app

# Flask app creation should be done by create_initialized_flask_app to avoid circular dependency problems.
app = create_initialized_flask_app()

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class StandaloneApplication(BaseApplication):
    def __init__(self, app, options=None):
        self.application = app
        self.options = options or {}
        super().__init__()

    def load_config(self):
        # Apply configuration to Gunicorn
        for key, value in self.options.items():
            if key in self.cfg.settings and value is not None:
                self.cfg.set(key.lower(), value)

    def load(self):
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The AI Agent is a versatile web application that lets you create and manage your own AI-powered chatbot. You can customize how the agent responds by providing specific instructions, choosing from different AI models (like GPT-4, Claude, or Gemini), and adjusting the creativity level of responses. The app includes team management features, allowing you to control who can access and configure the agent by managing email domains and individual users. Test your agent's behavior in real-time using the built-in simulator to ensure it meets your needs.

Certainly! I'll create a step-by-step guide for using the Configurable AI Chatbot template in Lazy. Here's the article in Markdown format:

How to Create a Configurable AI Chatbot with Lazy

This guide will walk you through setting up and using the Configurable AI Chatbot template in Lazy. This template allows you to create a customizable AI-powered chatbot with team management features and a built-in simulator.

Getting Started

To begin using the Configurable AI Chatbot template, follow these steps:

  1. Click "Start with this Template" to initialize the project in Lazy.

  2. Press the "Test" button to deploy the application and launch the Lazy CLI.

Setting Up Your Chatbot

After deployment, you'll need to configure your chatbot:

  1. Access the home page of your chatbot application.

  2. In the "Chatbot Settings" section, you can customize:

  3. Chatbot Instructions: Enter specific guidelines for your AI assistant.
  4. AI Model: Choose from options like GPT-4, Claude, or Gemini.
  5. AI Response Temperature: Adjust the creativity level of the AI's responses.

  6. Click "Save Changes" to update your settings.

Managing Team Access

To control who can access and configure your chatbot:

  1. Navigate to the "Team" page in your application.

  2. Under "Domain Access":

  3. Add new email domains to allow access for entire organizations.
  4. Remove domains to restrict access.

  5. Under "Company Admins":

  6. Add individual email addresses for specific admin access.
  7. Block or unblock admin accounts as needed.

Testing Your Chatbot

Use the built-in simulator to test your chatbot's behavior:

  1. Go to the "AI Chat Simulator" page.

  2. Type a message in the chat input and press send.

  3. Observe how the AI responds based on your configured settings.

  4. Use the "Clear This Chat" button to start a new conversation.

Using Your Chatbot

Your Configurable AI Chatbot is now ready to use! You can:

  • Integrate it into your customer support systems.
  • Use it for internal knowledge management.
  • Deploy it as a standalone chat interface for your users.

Remember to regularly review and update your chatbot's settings to ensure it continues to meet your needs and provides accurate, helpful responses.



Here are the top 5 business benefits of this AI Agent template:

Template Benefits

  1. Customizable AI Chatbot: Businesses can create a tailored AI assistant that aligns with their brand voice and specific needs by providing custom instructions and selecting from various AI models.

  2. Team Collaboration: The team management features allow companies to control access and permissions, enabling collaborative development and management of the AI agent across different departments or roles.

  3. Real-time Testing: The built-in simulator allows businesses to test and refine their chatbot's responses immediately, ensuring quality and appropriateness before deployment to customers.

  4. Flexible Integration: With its web-based interface, this AI agent can be easily integrated into existing business systems, websites, or customer service platforms.

  5. Cost-effective AI Solution: By providing a ready-to-use template with advanced features, businesses can quickly implement AI capabilities without the need for extensive development resources or expertise.

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This app will be a web interface that allows the user to send prompts to open source LLMs. It requires to enter the openrouter API key for it to work. This api key is free to get on openrouter.ai and there are a bunch of free opensource models on openrouter.ai so you can make a free chatbot. The user will be able to choose from a list of models and have a conversation with the chosen model. The conversation history will be displayed in chronological order, with the oldest message on top and the newest message below. The app will indicate who said each message in the conversation. The app will show a loader and block the send button while waiting for the model's response. The chat bar will be displayed as a sticky bar at the bottom of the page, with 10 pixels of padding below it. The input field will be 3 times wider than the default size, but it will not exceed the width of the page. The send button will be on the right side of the input field and will always fit on the page. The user will be able to press enter to send the message in addition to pressing the send button. The send button will have padding on the right side to match the left side. The message will be cleared from the input bar after pressing send. The last message will now be displayed above the sticky input block, and the conversation div will have a height of 80% to leave space for the model selection and input fields. There will be some space between the messages, and the user messages will be colored in green while the model messages will be colored in grey. The input will be blocked when waiting for the model's response, and a spinner will be displayed on the send button during this time.

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