Add Konveyor deployment and usage guide

docs/en/solutions/How_to_Use_Konveyor.md

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+---
+kind:
+   - Solution
+products:
+  - Alauda Application Services
+ProductsVersion:
+   - 4.x
+---
+
+# How to Deploy and Use Konveyor
+
+## Overview
+
+Konveyor is a CNCF (Cloud Native Computing Foundation) project that provides a modular platform for application modernization. It supports the entire lifecycle of modernization: discovery, assessment, analysis, and execution. This guide covers deploying the Konveyor Hub (Tackle) platform and its core components.
+
+## Prerequisites
+
+- Kubernetes cluster with kubectl access
+- StorageClass that supports ReadWriteMany (RWX) access mode
+- StorageClass for RWO volumes (for databases)
+- (Optional) LoadBalancer or Ingress Controller for external access
+
+## Install Konveyor Operator
+
+Download the Konveyor Operator plugin from [Alauda Cloud Console](https://cloud.alauda.io/) Marketplace, and install it using the [violet tool](https://docs.alauda.io/container_platform/4.1/configure/backup/install.html).
+
+## Deploy Konveyor Hub (Tackle)
+
+### Create Tackle Instance
+
+Deploy the Tackle platform by creating a Tackle CR. The Tackle instance must be deployed in the same namespace as the konveyor-operator.
+
+```yaml
+cat << EOF | kubectl create -f -
+apiVersion: tackle.konveyor.io/v1alpha1
+kind: Tackle
+metadata:
+  name: tackle
+  namespace: konveyor-tackle
+spec:
+  feature_auth_required: true
+  feature_isolate_namespace: true
+  feature_analysis_archiver: true
+  hub_database_volume_size: 5Gi
+  hub_bucket_volume_size: 100Gi
+  rwx_supported: true
+  hub_bucket_storage_class: nfs        # Replace with your RWX StorageClass
+  rwo_storage_class: sc-topolvm         # Replace with your RWO StorageClass
+EOF
+```
+
+### Verify Deployment
+
+Check the status of the pods in the `konveyor-tackle` namespace:
+
+```bash
+kubectl get pods -n konveyor-tackle
+```
+
+Ensure all pods are in `Running` or `Completed` state before proceeding.
+
+> [!WARNING]
+> The Tackle instance must be in the same namespace as the konveyor-operator. Otherwise, resources may not be cleaned up properly when deleted.
+
+### Configuration Options
+
+| Name | Default | Description |
+| --- | --- | --- |
+| `spec.feature_auth_required` | `true` | Enable Keycloak authentication (set `false` for single user/no auth) |
+| `spec.feature_isolate_namespace` | `true` | Enable namespace isolation via network policies |
+| `spec.feature_analysis_archiver` | `true` | Automatically archive old analysis reports when a new one is created |
+| `spec.rwx_supported` | `true` | Whether RWX volumes are supported in the cluster |
+| `spec.hub_database_volume_size` | `5Gi` | Size requested for Hub database volume |
+| `spec.hub_bucket_volume_size` | `100Gi` | Size requested for Hub bucket volume |
+| `spec.keycloak_database_data_volume_size` | `1Gi` | Size requested for Keycloak DB volume |
+| `spec.cache_data_volume_size` | `100Gi` | Size requested for Tackle Cache volume |
+| `spec.cache_storage_class` | N/A | StorageClass requested for Tackle Cache volume |
+| `spec.hub_bucket_storage_class` | N/A | StorageClass requested for Tackle Hub Bucket volume (RWX) |
+| `spec.rwo_storage_class` | N/A | StorageClass requested for RWO database volumes |
+
+## Access Tackle UI
+
+### Quick Access via Port-Forward
+
+1. Set up port forwarding:
+
+   ```bash
+   kubectl -n konveyor-tackle port-forward services/tackle-ui 8080:8080
+   ```
+
+2. Open [http://127.0.0.1:8080](http://127.0.0.1:8080) in your browser.
+
+### Initialize Admin Account
+
+The built-in Keycloak generates a random password on startup. This is the Keycloak root password, stored in the `tackle-keycloak-sso` secret.
+
+1. Retrieve Keycloak admin credentials:
+
+   ```bash
+   # Get username (default: admin)
+   kubectl -n konveyor-tackle get secret tackle-keycloak-sso -o jsonpath='{.data.username}' | base64 -d
+
+   # Get password
+   kubectl -n konveyor-tackle get secret tackle-keycloak-sso -o jsonpath='{.data.password}' | base64 -d
+   ```
+
+2. Login to Keycloak admin console at [http://127.0.0.1:8080/auth/admin/](http://127.0.0.1:8080/auth/admin/)
+
+3. Reset the Tackle admin password:
+   - Select the **tackle** Realm from the dropdown (not Master Realm)
+   - Click **Users** in the left menu
+   - Find and select the **admin** user
+   - Click the **Credentials** tab
+   - Enter a new password (e.g., `admin@123`)
+   - Disable **Temporary** toggle
+   - Click **Reset Password**
+
+4. Login to Tackle at [http://127.0.0.1:8080](http://127.0.0.1:8080) using the admin user and new password.
+
+### Secure Access via Ingress (Production)
+
+Port-forward is only for temporary access. For production, configure Ingress with TLS.
+
+#### Ingress Prerequisites
+
+- A domain name (e.g., `tackle.example.com`)
+- LoadBalancer service deployed (see [ALB deployment guide](https://docs.alauda.io/container_platform/4.1/configure/networking/how_to/alb/deploy_alb.html))
+- cert-manager installed
+
+#### Create TLS Certificate
+
+```yaml
+apiVersion: cert-manager.io/v1
+kind: Certificate
+metadata:
+  name: tackle-ssl-cert
+  namespace: konveyor-tackle
+spec:
+  commonName: tackle.example.com
+  dnsNames:
+    - tackle.example.com
+  issuerRef:
+    kind: ClusterIssuer
+    name: cpaas-ca              # Replace with your Issuer
+  secretName: tackle-tls-secret
+  usages:
+    - server auth
+    - client auth
+```
+
+#### Create Ingress
+
+```yaml
+apiVersion: networking.k8s.io/v1
+kind: Ingress
+metadata:
+  annotations:
+    nginx.ingress.kubernetes.io/backend-protocol: HTTP
+    nginx.ingress.kubernetes.io/ssl-redirect: "true"
+  name: tackle-ui-tls-ingress
+  namespace: konveyor-tackle
+spec:
+  ingressClassName: nginx    # Replace with your Ingress Class
+  rules:
+    - host: tackle.example.com
+      http:
+        paths:
+          - backend:
+              service:
+                name: tackle-ui
+                port:
+                  number: 8080
+            path: /
+            pathType: Prefix
+  tls:
+    - hosts:
+        - tackle.example.com
+      secretName: tackle-tls-secret
+```
+
+> [!NOTE]
+> Replace `tackle.example.com` with your actual domain.
+
+Access Tackle at `https://tackle.example.com`.
+
+## Enable KAI (Konveyor AI)
+
+KAI uses AI services to provide AI-powered code migration assistance. It supports multiple providers and models.
+
+### Supported Providers and Models
+
+| Provider (`kai_llm_provider`) | Model (`kai_llm_model`) |
+| --- | --- |
+| `openai` | `gpt-4`, `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo` |
+| `azure_openai` | `gpt-4`, `gpt-35-turbo` |
+| `bedrock` | `anthropic.claude-3-5-sonnet-20241022-v2:0`, `meta.llama3-1-70b-instruct-v1:0` |
+| `google` | `gemini-2.0-flash-exp`, `gemini-1.5-pro` |
+| `ollama` | `llama3.1`, `codellama`, `mistral` |
+| `groq` | `llama-3.1-70b-versatile`, `mixtral-8x7b-32768` |
+| `anthropic` | `claude-3-5-sonnet-20241022`, `claude-3-haiku-20240307` |
+
+### Enable KAI in Tackle
+
+1. Update the Tackle configuration:
+
+   ```yaml
+   apiVersion: tackle.konveyor.io/v1alpha1
+   kind: Tackle
+   metadata:
+     name: tackle
+     namespace: konveyor-tackle
+   spec:
+     kai_solution_server_enabled: true
+     kai_llm_provider: openai              # Choose your provider
+     kai_llm_model: gpt-4o-mini            # Choose your model
+   ```
+
+2. Create API credentials secret:
+
+   **For OpenAI:**
+
+   ```bash
+   kubectl create secret generic kai-api-keys -n konveyor-tackle \
+     --from-literal=OPENAI_API_BASE='https://api.openai.com/v1' \
+     --from-literal=OPENAI_API_KEY='<YOUR_OPENAI_KEY>'
+   ```
+
+   **For Google:**
+
+   ```bash
+   kubectl create secret generic kai-api-keys -n konveyor-tackle \
+     --from-literal=GOOGLE_API_KEY='<YOUR_GOOGLE_API_KEY>'
+   ```
+
+3. Force the operator to reconcile and pick up the new credentials:
+
+   ```bash
+   kubectl patch tackle tackle -n konveyor-tackle --type=merge -p \
+     '{"metadata":{"annotations":{"konveyor.io/force-reconcile":"'"$(date +%s)"'"}}}'
+   ```
+
+## Konveyor Components Overview
+
+Konveyor provides a modular architecture for application modernization:
+
+| Component | Description |
+| --- | --- |
+| **Konveyor Hub** | Central control plane providing unified application inventory, assessment module (risk evaluation), and analysis module (static code analysis). Implements RBAC with Administrator, Architect, and Migrator roles. |
+| **Kantra & Analyzer-LSP** | CLI tool for offline static analysis. Analyzer-LSP integrates into IDEs (VSCode) via Language Server Protocol for real-time migration issue detection. |
+| **Konveyor AI (KAI)** | RAG-based AI assistant for automated code remediation. Uses Solved Incident Store for context-aware code patch generation. |
+| **Move2Kube** | Automates conversion from Cloud Foundry/OpenShift to Kubernetes. Three phases: Collect, Plan, Transform. Generates Dockerfiles, K8s manifests, Helm Charts, and Tekton Pipelines. |
+| **Forklift** | VM migration tool for moving VMs from VMware vSphere, oVirt, or OpenStack to KubeVirt. |
+| **Crane** | Kubernetes-to-Kubernetes migration tool for cluster upgrades or cross-distribution migrations. Handles PV data sync with Restic or VolSync. (Note: Community version is currently inactive). |
+
+## Reference
+
+- [Konveyor Official Documentation](https://konveyor.io/docs/konveyor/)
+- [Konveyor Admin Tasks](https://konveyor.io/docs/konveyor/admintasks/)
+- [Konveyor Operator Repository](https://github.com/konveyor/operator)