Core Components

This guide covers the installation of the main AI/Run CodeMie application components that provide the core functionality of the platform.

Overview

The core components consist of four services:

• MCP Connect - Model Context Protocol connector enabling integration with AI Assistants and MCP servers
• Mermaid Server - Diagram rendering service for visualizing workflows and diagrams
• CodeMie UI - Frontend web application providing the user interface
• CodeMie API - Backend REST API handling business logic, data processing, and AI orchestration

MCP Connect Installation

MCP Connect implements the Model Context Protocol, enabling CodeMie to integrate with AI models and external tooling.

Step 1: Install MCP Connect Helm Chart

Deploy MCP Connect:

helm upgrade --install codemie-mcp-connect-service \
  oci://europe-west3-docker.pkg.dev/or2-msq-epmd-edp-anthos-t1iylu/helm-charts/codemie-mcp-connect-service \
  --version x.y.z \
  --namespace codemie \
  -f ./codemie-mcp-connect-service/values.yaml \
  --wait \
  --timeout 600s

Version Number

Use the same version number you retrieved in the Getting Started section.

Step 2: Verify MCP Connect Deployment

Check that MCP Connect is running:

# Check pod status
kubectl get pods -n codemie | grep mcp-connect

# Check deployment
kubectl get deployment -n codemie codemie-mcp-connect-service

# Check logs
kubectl logs -n codemie deployment/codemie-mcp-connect-service --tail=50

Expected output:

• Pod should be in Running state
• Deployment should show ready replicas
• Logs should indicate successful startup

Mermaid Server Installation

Mermaid Server provides diagram rendering capabilities for visualizing workflows, architecture diagrams, and process flows.

Step 1: Install Mermaid Server Helm Chart

Deploy Mermaid Server:

helm upgrade --install mermaid-server \
  oci://europe-west3-docker.pkg.dev/or2-msq-epmd-edp-anthos-t1iylu/helm-charts/mermaid-server \
  --version x.y.z \
  --namespace codemie \
  -f ./mermaid-server/values.yaml \
  --wait \
  --timeout 600s

Step 2: Verify Mermaid Server Deployment

Check that Mermaid Server is running:

# Check pod status
kubectl get pods -n codemie | grep mermaid-server

# Check deployment
kubectl get deployment -n codemie mermaid-server

# Check logs
kubectl logs -n codemie deployment/mermaid-server --tail=50

Expected output:

• Pod should be in Running state
• Deployment should show ready replicas
• Logs should indicate successful HTTP server startup

CodeMie UI Installation

CodeMie UI provides the web-based user interface for interacting with AI assistants and managing workflows.

Step 1: Configure UI Values

The domain configuration should already be set from the Getting Started section. Verify the values in codemie-ui/values-azure.yaml are correct.

Step 2: Install CodeMie UI Helm Chart

Deploy CodeMie UI:

helm upgrade --install codemie-ui \
oci://europe-west3-docker.pkg.dev/or2-msq-epmd-edp-anthos-t1iylu/helm-charts/codemie-ui \
--version x.y.z \
--namespace codemie \
-f ./codemie-ui/values-azure.yaml \
--wait \
--timeout 180s

Step 3: Verify CodeMie UI Deployment

Check that CodeMie UI is running:

# Check pod status
kubectl get pods -n codemie | grep codemie-ui

# Check deployment
kubectl get deployment -n codemie codemie-ui

# Check service
kubectl get service -n codemie codemie-ui

Expected output:

• Pod should be in Running state
• Deployment should show ready replicas
• Service should be available

CodeMie API Installation

CodeMie API is the backend service that handles all business logic, AI orchestration, and data processing.

Step 1: Configure API Values

The domain configuration should already be set from the Getting Started section. Verify the values in codemie-api/values-azure.yaml are correct:

• %%DOMAIN%% should be replaced with your domain (e.g., example.com)

Domain Configuration

If you followed the Getting Started steps, these replacements should already be done.

Step 2: Copy Elasticsearch Credentials

CodeMie API needs access to Elasticsearch. Copy the credentials to the codemie namespace:

kubectl get secret elasticsearch-master-credentials -n elastic -o yaml | \
  sed '/namespace:/d' | \
  kubectl apply -n codemie -f -

Step 3: Install CodeMie API Helm Chart

Deploy CodeMie API:

helm upgrade --install codemie-api \
oci://europe-west3-docker.pkg.dev/or2-msq-epmd-edp-anthos-t1iylu/helm-charts/codemie \
--version x.y.z \
--namespace codemie \
-f ./codemie-api/values-azure.yaml \
--wait \
--timeout 600s

Step 4: Verify CodeMie API Deployment

Check that CodeMie API is running:

# Check pod status
kubectl get pods -n codemie | grep codemie-api

# Check deployment
kubectl get deployment -n codemie codemie-api

# Check logs
kubectl logs -n codemie deployment/codemie-api --tail=100

# Check API health endpoint
kubectl exec -n codemie deployment/codemie-api -- curl -s http://localhost:8080/health

Expected output:

• Pod should be in Running state
• Deployment should show ready replicas
• Logs should show successful connections to all dependencies
• Health endpoint should return healthy status

Access CodeMie Application

Once all core components are deployed, access the CodeMie UI:

https://codemie.example.com

User Creation Required

You'll be redirected to Keycloak for authentication, but no users exist yet. You must complete the Configuration guide to create users before you can log in to the application.

Post-Installation Validation

After completing all core component installations, verify the following:

# All core components are running
kubectl get pods -n codemie | grep -E "(mcp-connect|mermaid|codemie-ui|codemie-api)"

# Check all deployments
kubectl get deployments -n codemie

# Check all services
kubectl get services -n codemie

# Verify ingress is configured
kubectl get ingress -n codemie

# Check overall namespace status
kubectl get all -n codemie

All checks should return successful results before proceeding.

Next Steps

Once core components are deployed and verified, proceed to Observability installation to deploy logging and monitoring infrastructure.