NeurAIGraph is a cutting-edge artificial intelligence system that combines the power of Large Language Models (LLMs), Graph Neural Networks (GNNs), and advanced 3D visualization techniques to create, analyze, and represent complex knowledge graphs. This system is designed to extract semantic information and relationships from text, embed them in multidimensional spaces, and provide intuitive visual representations of ontologies and ground truth.

• Local LLM Integration: Utilizes a carefully selected, efficient language model for semantic extraction, optimized for performance on high-end consumer hardware.
• Graph Neural Network Processing: Employs state-of-the-art GNN architectures to process and enrich the extracted knowledge graphs.
• 3D Visualization Interface: Features a custom-built 3D rendering engine for intuitive exploration of complex knowledge structures.
• Neurosymbolic Architecture: Bridges the gap between symbolic AI and neural networks, enabling more robust and interpretable AI systems.
• Ontology Embedding: Implements novel techniques for embedding ontologies in multidimensional spaces, enhancing the system's ability to represent and reason about complex relationships.

• Language Model: [Specific LLM to be determined based on performance requirements]
• Graph Processing: PyTorch Geometric
• Visualization: Three.js / WebGL
• Backend: Python with FastAPI
• Frontend: React with TypeScript

[Instructions for setting up and running the project locally]

This project builds on cutting-edge research in neurosymbolic AI, knowledge representation, and multidimensional embeddings. Key inspirations include:

• [Relevant paper 1]
• [Relevant paper 2]
• [Relevant paper 3]

We welcome contributions from the AI research community. Please see our CONTRIBUTING.md file for guidelines on how to get involved.

A persistent dreamer with a knack for changing the world for the better with technology

This project is licensed under the MIT License - see the LICENSE.md file for details.