Ror
What is Ror
RoR, or RewrittenOnRust, is an advanced AI development stack that leverages popular methodologies such as RAG and MCP, all rewritten in Rust to enhance performance and efficiency.
Use cases
Use cases for RoR include building AI models that require fast inference, developing applications that utilize vector databases for enhanced data retrieval, and integrating advanced generation features in AI-driven solutions.
How to use
To use RoR, developers can integrate it into their projects by utilizing its high-performance RAG system, which includes local inference through Ollama and advanced generation via the Gemini API.
Key features
Key features of RoR include its high-performance architecture, local inference capabilities with Ollama, integration with Qdrant for vector databases, and future support for Rust MCP integration.
Where to use
RoR can be used in fields such as AI development, machine learning applications, and any domain requiring efficient data processing and inference.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Ror
RoR, or RewrittenOnRust, is an advanced AI development stack that leverages popular methodologies such as RAG and MCP, all rewritten in Rust to enhance performance and efficiency.
Use cases
Use cases for RoR include building AI models that require fast inference, developing applications that utilize vector databases for enhanced data retrieval, and integrating advanced generation features in AI-driven solutions.
How to use
To use RoR, developers can integrate it into their projects by utilizing its high-performance RAG system, which includes local inference through Ollama and advanced generation via the Gemini API.
Key features
Key features of RoR include its high-performance architecture, local inference capabilities with Ollama, integration with Qdrant for vector databases, and future support for Rust MCP integration.
Where to use
RoR can be used in fields such as AI development, machine learning applications, and any domain requiring efficient data processing and inference.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
RoR / Rag-On-Rust
A high-performance RAG system fully built in Rust, Offering Ollama for local inference and Gemini API Call for advanced generation. Designed for Speed & Efficiency
WIP [Work In Progress]
- Qdrant based VectorDB []
- Ollama based LLM Model & Embedding Model []
Future Checkpoint
- Rust MCP Integration []
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
