What is Ollama Mcp Redis Vectordb Kubernetes Helm Deployment

The ollama_mcp_redis_vectordb_kubernetes_helm_deployment is a project that implements a Multi-Agent Control System (MCP) utilizing Ollama AI models and Redis Vector DB. It features a commander agent for issuing commands and a coder agent for generating code based on user input, deployed using Docker, Kubernetes, and Helm with Vertical Pod Autoscaler for resource management.

Use cases

Use cases include automated coding assistants, AI-driven command execution systems, and environments where multiple agents need to collaborate and generate outputs based on user inputs.

How to use

To use the ollama_mcp_redis_vectordb_kubernetes_helm_deployment, clone the repository from GitHub, set up the necessary Docker and Kubernetes environments, and deploy the application using Helm charts. Ensure that the required Ollama models and Redis configurations are in place for optimal performance.

Key features

Key features include integration of Ollama AI models for command issuance and code generation, the use of Redis Vector DB for contextual data storage and retrieval, deployment via Docker and Kubernetes, and dynamic resource scaling with Vertical Pod Autoscaler.

Where to use

This system can be used in fields such as software development, AI-driven automation, and multi-agent systems where command and code generation are required based on user interactions.

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

Setup

1. Clone the repository

git clone [https://github.com/your-username/mcp-ollama.git](https://github.com/AI-SoftwareArchitect/ollama_mcp_redis_vectordb_kubernetes_helm_deployment.git)