ollama_mcp_redis_vectordb_kubernetes_helm_deployment

# Multi-Agent Control System (MCP) with Ollama & Redis ## Project Overview This project is designed to create a **Multi-Agent Control System (MCP)** using **Ollama** AI models and **Redis Vector DB**. It includes a **commander agent** that issues commands and a **coder agent** that generates code based on user input. The system is deployed using **Docker**, **Kubernetes**, and **Helm**, with **Vertical Pod Autoscaler (VPA)** in place for automatic resource scaling. ## Key Components ### 1. **Ollama AI Integration** - Two **Ollama agents**: one for issuing commands and the other for generating code. - Ollama models used: `deepseek-coder:latest`, `qwen2.5-coder:3b`, etc. ### 2. **Redis Vector DB** - **Redis** is used to store and retrieve contextual data for the agent’s decision-making process. - The Redis setup is integrated to handle **embedding** and inference operations. ### 3. **FastAPI & Docker** - **FastAPI** is used to create the API for communication between agents. - Docker is used to containerize the entire application for deployment. ### 4. **Kubernetes & Helm** - The project is deployed on **Kubernetes** with **Helm** charts to manage the deployment and scalability. - **Vertical Pod Autoscaler (VPA)** ensures dynamic resource allocation based on demand. ## Architecture .png) ## Setup ### 1. Clone the repository ```bash git clone [https://github.com/your-username/mcp-ollama.git](https://github.com/AI-SoftwareArchitect/ollama_mcp_redis_vectordb_kubernetes_helm_deployment.git)