Geometric RAG Engine

About

This project offers an advanced Retrieval-Augmented Generation (RAG) engine built upon the principles of Information Geometry and Statistical Manifolds. It transforms business data into statistical varieties, enabling AI to navigate contexts through precise metric and spatial relationships. Designed for industrial deployment, it integrates Docker, FastAPI, and the Model Context Protocol (MCP) for robust, scalable operations and high-precision vector alignment.

Key Features

• Fundamented in Information Geometry, Tensor Geometry, and Manifold Analysis
• High-precision vector alignment using 384-dimension embeddings and Cosine Metric
• Industrial deployment with Docker, FastAPI, and persistent ChromaDB (HNSW indexing)
• Integration with Model Context Protocol (MCP) for standardized dialogue
• Scalable architecture designed to integrate with Data Lakes, Azure, and Clusters
• 0 GitHub stars

Use Cases

• Integrating advanced RAG capabilities into large-scale data lakes and cloud environments
• Enhancing AI context navigation and semantic understanding in complex business data
• Deploying robust and scalable RAG systems for real-time data transposition