Hydra-Task-Master

The following is a revival of my last project, which can be found on the main branch.

Due to changes from OPENAI, including the introduction of new features like assistants, threads, and files in their API, I have decided to revisit this project and give it a breath of fresh air.

That being said, this project is very much a work in progress. Feel free to join me on this ride of insanity as I try to out-develop the AI overlords that are OpenAI.

Side note: The entire application will be built with my old Hydra prototype, as I will be testing the "memory" or contextual limits of the new GPT-4o model.

Project Explanation

Have you ever wanted to do something far too complex to tackle on your own? Well, boy, do I have news for you! In theory, by making several API calls to multiple self-defined and created assistants, we can simulate the process of creation.

Much like the mythological Hydra this project is named after, the application should be able to spawn a head that is created solely for the completion of a task. For example, if you want to write an application, you would need a project manager. The project manager should be able to list all the resources or skillsets required to complete the task. The concept of Hydra is to allow the user to simply input a task, and the application should be able to define all the points needed to achieve this goal. The application will be able to create the assistants or specialists required to complete those tasks, allowing you to simply oversee the process it has planned so you can interject or review whenever you need to.

Please note that this will require the user to have an OPENAI API key. Due to the nature of this approach, it will be EXTREMELY expensive! Sure, the application will attempt to use the Batch function of the API to ensure lower-cost calls are made, but the nature of this application is to unleash the FULL POWER of GPT-4Turbo/GPT-4o and GPT-3.5 Turbo by breaking all requests into thousands of smaller queries.

Now, are there teething issues? Why yes, thousands of them! Hahaha, this is a project that no single person should ever attempt to tackle, but luckily for me, I have my older version of Hydra to assist me.

High-Level Overview

1. Hydra Core:

   • Task Intake: The user inputs a task description.
   • Task Analysis: The system analyzes the task and maps out the required steps.
   • Roadmap Generation: A roadmap is created and presented to the user for approval.
   • Assistant Creation: Upon approval, the Master-Hydra-Assistant creates specialized sub-assistants to handle each task.

2. Assistant Management:

   • Task Execution: Sub-assistants execute their assigned tasks.
   • Progress Monitoring: Users can view the progress of each assistant.
   • Flowchart Visualization: Generate a visual representation of how the assistants interact.

3. User Interface:

   • GUI Design: A simple, intuitive interface for users to interact with the application.
   • Control Panel: Allows users to control requests, view active requests, and manage the project.
   • Database Integration: Store projects to ensure continuity and allow for retrieval if the application is terminated or crashes.

4. Cost Management:

   • Batch Processing: Implement OpenAI API's batch function to queue and process tasks cost-efficiently.

Detailed Plan

1. Initial Setup and Task Intake:

   • User Input: Create a text input field where users can describe their task.
   • Task Analysis: Use natural language processing to break down the task into actionable steps.

2. Roadmap Generation:

   • Step Mapping: Develop an algorithm to map out all necessary steps based on the task description.
   • User Approval: Display the roadmap to the user for review and approval.

3. Assistant Creation:

   • Master-Hydra-Assistant: Develop the core assistant that oversees the creation and management of sub-assistants.
   • Sub-Assistants: Create specialized assistants for each step of the roadmap.

4. Assistant Interaction and Visualization:

   • Flowchart Generation: Use a visualization library to create dynamic flowcharts that show assistant interactions.
   • Progress Tracking: Implement a tracking system to monitor the progress of each assistant.

5. User Interface Design:

   • Simple GUI: Design a clean and user-friendly interface using a framework like Tkinter or PyQt.
   • Control Panel: Provide users with controls to manage tasks, view active requests, and handle project storage.

6. Database Integration:

   • Project Storage: Implement a database system (e.g., SQLite, PostgreSQL) to store project data.
   • Continuity: Ensure the application can recover and continue tasks after interruptions.

7. Cost Management:

   • Batch Functionality: Utilize the OpenAI API's batch function to manage costs by queueing and processing tasks efficiently.