UniFi Tuner

Table of Contents

• Introduction
• Features
• Technologies Used
• Prerequisites
• Installation
• Configuration
• Usage
• Project Structure
• TODO
• Contributing
• License
• Contact

Introduction

UniFi Tuner is an intelligent automation tool built using Python and the Autogen AI Agent framework. It leverages advanced AI agents to automate various tasks, enhancing efficiency and productivity. This project focuses on creating modular and maintainable code to manage and deploy multiple AI agents effectively.

Features

• Modular AI Agents: That assess the network and make recommendations on how to improve the network and also include a human-in-the-loop agent to continue asking clarifying questions on recommendations.
• Environment Configuration: Securely manage API keys and model configurations using a .env file.
• Group Chat Management: Efficiently handle group chat interactions with multiple agents.
• Logging & Error Handling: Comprehensive logging for better debugging and error management.
• Scalable Architecture: Designed for scalability and ease of maintenance.

Technologies Used

• AI Framework: Autogen AI Agent
• Environment Management: python-dotenv
• Version Control: Git

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.12 installed on your machine. You can download it from here.
• Git installed for version control. Download it from here.
• An OpenAI API Key. Sign up and obtain your API key from OpenAI.
• Create a local account on your UniFi Dream Machine or controller and obtain the API credentials.

Installation

Follow these steps to set up the project locally:

1. Clone the Repository

   git clone https://github.com/yourusername/unifi_tuner.git
   cd unifi_tuner

2. Create a Virtual Environment

   It's recommended to use a virtual environment to manage dependencies.

   python3 -m venv myenv
   source myenv/bin/activate  # On Windows, use `myenv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

4. Configure Environment Variables

   Create a .env file in the root directory and add your API keys and configurations.

   touch .env

   Add the following to .env:

   Create a .env file in the root directory of the project and add the following configurations: your unifi controller ip address, your unifi controller username and password, and your openai api key, logging level

5. Logging

   Logs are configured to display informational messages. Monitor the console output to observe interactions and identify any potential issues.

Configuration

(Details about configuration go here.)

Usage

(Instructions on how to use the tool go here.)

Project Structure

(Overview of the project structure goes here.)

TODO

• Monitor LLM Enhancements
  • Update the monitor to track token usage.
  • Implement cost estimation when running the LLM.
• Automate Configuration of Automations
  • Develop scripts to automate the setup and configuration processes.
  • Integrate automated testing for configuration changes.
• enhance security
  • add in IAM capabilities to test out managing and enforcing access of agents
  • testing ideas

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the Repository

2. Create a Feature Branch

   git checkout -b feature/YourFeature

3. Commit Your Changes

   git commit -m "Add some feature"

4. Push to the Branch

   git push origin feature/YourFeature

5. Open a Pull Request

   Describe your changes and submit the pull request for review.

License

This project is licensed under the Apache License 2.0. You are free to use, modify, and distribute it as per the terms of the license. For more details, refer to the Apache License 2.0.

Additional Steps to Apply the Apache License 2.0:

1. Create a LICENSE File

   Add the full text of the Apache License 2.0 to a file named LICENSE in the root directory of your project.

2. Create a NOTICE File (Optional but Recommended)

   If your project includes a NOTICE file as part of its distribution, ensure that it contains the necessary attribution notices.

   Ramda Adjunct
   Copyright 2017, Vladimír Gorej
   

   Refer to the Apache License 2.0 guidelines for more information on the NOTICE file.

Contact

For any questions or suggestions, feel free to contact me:

• Email:
• LinkedIn: linkedin.com/in/damonmcdougald