• Overview
• Release Information
• Repository Structure
• Quick Start
• Python DevOps Scripts
• Documentation
• Support

This repository provides comprehensive resources for deploying and managing IBM Content Cortex (CCx) on Kubernetes and OpenShift platforms. It includes:

• Python DevOps Scripts: Modern CLI tools for deployment automation and lifecycle management
• Deployment Descriptors: YAML manifests for operators and supporting services
• Helm Charts: Available via public Helm repository
• Documentation: Comprehensive guides, examples, and troubleshooting resources

IBM Content Cortex is an enterprise content management platform that centralizes, governs, and activates content across organizations. It provides:

• Content Platform Engine (CPE): High-performance content repository and workflow engine
• Content Navigator (ICN): Modern web-based user interface
• AI Services: Intelligent content processing with watsonx.ai, Azure OpenAI, and other AI providers
• GraphQL & REST APIs: Modern APIs for content access and integration
• Enterprise Records Management: Compliance and governance capabilities
• Advanced Integration: SAP, Microsoft Office, and third-party system connectivity

Note: For iFix releases, detailed component versions, specific fixes, and new features, see the Releases tab.

• Documentation: IBM Content Cortex Docs
• Helm Charts: Available via IBM Entitled Registry and CASE packages

container-samples/
├── README.md                          # This file
├── descriptors/                       # Kubernetes/OpenShift deployment descriptors
│   ├── content-cortex/               # Content Cortex operator manifests
│   │   ├── ai-services/              # AI Services operator and CRs
│   │   ├── op-olm/                   # OLM-based deployment (CatalogSource, Subscription)
│   │   └── turbonomics/              # Turbonomics integration
│   ├── license-service/              # IBM License Service operator
│   └── usage-metering/               # IBM Usage Metering operator
│
└── scripts/                           # Python DevOps automation suite
    ├── README.rst                    # Comprehensive script documentation
    ├── requirements.txt              # Python dependencies
    ├── prerequisites.py              # Deployment preparation and validation
    ├── deploy_operator.py            # Operator deployment automation
    ├── upgrade_deployment.py         # Upgrade workflow automation
    ├── clean_deployment.py           # Cleanup and removal automation
    ├── load_images.py                # Image management for air-gap deployments
    ├── must_gather.py                # Diagnostic data collection
    ├── silent_config/                # Silent mode configuration files
    └── helper_scripts/               # Modular helper libraries
        ├── gather/                   # Prerequisite gathering
        ├── deploy/                   # Deployment orchestration
        ├── helm/                     # Helm integration
        ├── loadimages/               # Image loading utilities
        ├── mustgather/               # Diagnostics collection
        ├── property/                 # Configuration management
        ├── utilities/                # Shared utilities
        └── validate/                 # Validation frameworks

Before you begin, ensure you have:

• Kubernetes/OpenShift Cluster: v1.24+ (Kubernetes) or v4.12+ (OpenShift)
• Helm: v4.x or later (for Helm-based deployments)
• kubectl/oc: Configured with cluster access
• Python: 3.12 or later (for DevOps scripts)
• IBM Entitlement Key: From IBM Container Library

IBM Content Cortex operators are available as Helm charts for streamlined deployment:

# 1. Add Helm repository
helm repo add ibm-content-cortex https://ibm-ecm.github.io/ibm-content-cortex-containers/charts
helm repo update

# 2. Create namespace
kubectl create namespace ibm-content

# 3. Create image pull secret
kubectl create secret docker-registry ibm-entitlement-key \
  --docker-server=cp.icr.io \
  --docker-username=cp \
  --docker-password=<your-entitlement-key> \
  --namespace ibm-content

# 4. Install Content Operator
helm install content-operator ibm-content-cortex/ibm-content-operator \
  --namespace ibm-content

# 5. Install AI Services Operator (optional)
helm install ai-services-operator ibm-content-cortex/ibm-ccx-ai-services-operator \
  --namespace ibm-content

# 6. Verify installation
kubectl get pods -n ibm-content
helm list -n ibm-content

Available Charts:

• ibm-content-operator - Content Cortex operator (CPE, ICN, GraphQL)
• ibm-ccx-ai-services-operator - AI Services operator (Reasoning Service and Core MCP Server)
• ibm-license-service-operator - IBM License Service operator
• ibm-usage-metering-operator - IBM Usage Metering operator

# 1. Navigate to scripts directory
cd scripts/

# 2. Create Python virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 3. Install dependencies
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt

# 4. For air-gap environments, load images first
python3 load_images.py generate
python3 load_images.py push

# 5. Deploy operators (interactive mode)
python3 deploy_operator.py

# 6. Run prerequisite gathering and validation
python3 prerequisites.py gather
python3 prerequisites.py generate
python3 prerequisites.py validate

For OpenShift environments, integrate the Helm repository directly into the Developer Catalog:

Step 1: Create HelmChartRepository Resource

apiVersion: helm.openshift.io/v1beta1
kind: HelmChartRepository
metadata:
  name: ibm-content-cortex
spec:
  connectionConfig:
    url: https://ibm-ecm.github.io/ibm-content-cortex-containers/charts
  name: IBM Content Cortex

Step 2: Apply the Configuration

# Login to OpenShift
oc login --token=<your-token> --server=https://api.your-cluster.com:6443

# Apply HelmChartRepository (requires cluster-admin)
oc apply -f helmchartrepo-content-cortex.yaml

# Verify repository was added
oc get helmchartrepository ibm-content-cortex

Step 3: Install from OpenShift Console

1. Navigate to Developer perspective → +Add → Helm Chart
2. Select IBM Content Cortex repository
3. Choose IBM Content Operator or IBM AI Services Operator
4. Click Install Helm Chart
5. Configure values and click Install

For OLM-based deployment and additional installation methods, see the IBM Content Cortex Documentation.

The scripts/ directory contains a comprehensive suite of Python-based CLI tools for Content Cortex lifecycle management:

• 🎨 Rich Terminal UI: Colored output, progress bars, tables, and panels
• 🤖 Interactive Prompts: Guided workflows with validation
• 🔇 Silent Mode: Configuration-driven automation
• 📝 Comprehensive Logging: Detailed logs for troubleshooting
• 🧪 Dry-Run Support: Preview changes before applying
• ✅ Validation: Pre-flight checks for cluster, storage, databases, LDAP, and more

# Interactive deployment preparation
python3 prerequisites.py gather
python3 prerequisites.py generate
python3 prerequisites.py validate

# Deploy operators 
python3 deploy_operator.py 

# Silent mode deployment (automation)
python3 deploy_operator.py --silent

# Dry-run upgrade preview
python3 upgrade_deployment.py --dryrun

# Collect diagnostics
python3 must_gather.py 

Full Documentation: scripts/README.rst

• Quick Start Guide - Get up and running quickly
• Python Scripts Guide - DevOps automation suite documentation
• IBM Documentation - Complete installation guides

• Air-Gap Deployments - Offline installation guide
• Silent Mode Configuration - Automation setup
• Troubleshooting - Common issues and solutions
• Upgrade Procedures - Lifecycle management

• Product Documentation: IBM Content Cortex Docs

• IBM Support Portal: https://www.ibm.com/mysupport
• GitHub Issues: Report Issues
• Community Forums: IBM ECM Community
• Email: ecm-container-service@ibm.com

• Product Website: IBM Content Cortex
• Container Library: IBM Entitled Registry
• Release Notes: What's New
• Helm Charts: Available via Github Repository or manual download.

When opening a support case, please provide:

1. Environment Details: Kubernetes/OpenShift version, cluster configuration
2. Deployment Method: Helm, Python scripts, or OLM
3. Logs: Operator logs, pod logs, and must-gather output
4. Configuration: Helm values files or CR YAML (sanitized)
5. Error Messages: Complete error output and stack traces

Use must_gather.py to collect comprehensive diagnostic data:

python3 must_gather.py --verbose

Licensed Materials - Property of IBM

© Copyright IBM Corp. 2026. All Rights Reserved.

US Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.

Repository: ibm-ecm/ibm-content-cortex-containers Version: 26.0.0 Last Updated: 2026-06-16