AI Agent

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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):
Get full code

Frequently Asked Questions

Content Creation - Use the agent to help generate and review content following brand guidelines Q2: How does the team management feature benefit organizations?

The AI Agent's team management system provides several key benefits: - Domain-based Access: Add entire company domains (e.g., "@yourcompany.com") to automatically grant access to all employees - Individual Control: Add or remove specific team members regardless of their email domain - Role Management: Designate admins who can modify the agent's behavior and settings - Security: Block specific users while maintaining domain-wide access for others - Audit Trail: Track who makes changes to the agent's configuration

Q3: What makes this template more secure than using a public AI chatbot? A: The AI Agent template offers several security advantages:

Team Oversight: Administrators can monitor and adjust usage patterns Q4: How can I modify the temperature setting for different use cases? Can you show an example?

The AI Agent allows dynamic temperature adjustment through the settings API. Here's a code example:

```python @app.route("/api/update_settings", methods=["POST"]) def update_settings(): try: settings = ChatbotSettings.query.first() if not settings: settings = ChatbotSettings() db.session.add(settings)

    # Set lower temperature (0.3) for more focused, consistent responses
    # Set higher temperature (0.8) for more creative responses
    settings.temperature = float(request.form.get("temperature", 0.7))

    db.session.commit()
    return jsonify({"status": "success"})
except Exception as e:
    return jsonify({"status": "error", "message": str(e)})

```

Q5: How can I add custom validation for allowed email domains? Can you provide a code example? A: You can extend the AI Agent's domain validation by adding custom rules. Here's an example:

```python @app.route("/api/allowed_email_endings/create", methods=["POST"]) def api_create_allowed_email_ending(): email_ending = request.json.get('email_ending')

# Custom validation rules
if not email_ending:
    return jsonify({"status": "error", "message": "Email ending required"})

# Remove '@' if included
email_ending = email_ending.lstrip('@')

# Validate domain format
if not re.match(r'^[a-zA-Z0-9][a-zA-Z0-9-]{1,61}[a-zA-Z0-9]\.[a-zA-Z]{2,}$', email_ending):
    return jsonify({"status": "error", "message": "Invalid domain format"})

try:
    new_ending = AllowedEmailEndings(email_ending=email_ending)
    db.session.add(new_ending)
    db.session.commit()
    return jsonify({"status": "success"})
except Exception as e:
    return jsonify({"status": "error", "message": str(e)})

```

Created: | Last Updated:

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.

Here's how to use this template to create a customizable AI agent with team management:

Introduction

This template provides a web-based dashboard for configuring and testing an AI agent that can be customized with specific instructions, model selection, and response parameters. It includes team management features to control access through email domains and individual permissions.

Getting Started

  • Click "Start with this Template" to begin
  • This will create your instance of the AI agent dashboard

Test the Application

  • Click the "Test" button to deploy your instance
  • The application will launch and provide you with a server link to access the dashboard

Using the Dashboard

Configure AI Settings

  • On the main dashboard page:
  • Enter custom instructions for your AI agent in the "Chatbot Instructions" field
  • Choose an AI model from the dropdown (GPT-4, Claude, Gemini options)
  • Adjust the temperature slider to control response creativity (0-2)
  • Click "Save Changes" to update the configuration

Manage Team Access

  • Navigate to the "Team" page to:
  • Add allowed email domains (e.g., "company.com")
  • Add individual admin email addresses
  • Block/unblock specific users
  • View current team members

Test the AI Agent

  • Click "Open Simulator" or go to the "AI Chat" page
  • Enter test messages to see how your agent responds
  • The agent will use your saved configuration settings
  • Clear the chat history using the "Clear This Chat" button

The dashboard provides real-time testing of your AI agent's behavior based on your configuration. You can iteratively adjust the settings and test responses until you achieve the desired interaction style for your use case.

This template serves as a foundation for creating AI agents that can be integrated into other applications through the API endpoints. The team management features ensure controlled access to the configuration interface.



Template Benefits

  1. Customizable AI Response Control - Organizations can fine-tune their AI agent's behavior through detailed instructions and temperature settings, ensuring responses align with company voice, policies, and compliance requirements.

  2. Enterprise-Grade Access Management - Built-in team management features allow organizations to control access through domain-level permissions and individual user management, making it ideal for corporate deployments.

  3. Multi-Model Flexibility - Support for multiple AI models (GPT-4, Claude, Gemini) gives businesses the freedom to choose the best model for their needs based on performance, cost, or specific capabilities.

  4. Real-Time Testing Environment - The integrated simulator allows teams to test and validate AI responses before deployment, reducing the risk of inappropriate or incorrect responses in production.

  5. Scalable Team Collaboration - The platform enables multiple team members to collaborate on AI agent configuration, with role-based access control and the ability to block/unblock users, making it suitable for growing organizations.

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Similar templates

Open Source LLM based Web Chat Interface

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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