Add knative ce operator

docs/en/installation/ai-cluster.mdx

@@ -8,7 +8,7 @@ weight: 30
 
 To begin, you will need to deploy the **Alauda AI Operator**. This is the core engine for all Alauda AI products. By default, it uses the **KServe** `Raw Deployment` mode for the inference backend, which is particularly recommended for resource-intensive generative workloads. This mode provides a straightforward way to deploy models and offers robust, customizable deployment capabilities by leveraging foundational Kubernetes functionalities.
 
-If your use case requires `Serverless` functionality, which enables advanced features like **scaling to zero on demand** for cost optimization, you can optionally install the **Alauda AI Model Serving Operator**. This operator is not part of the default installation and can be added at any time to enable `Serverless` functionality.
+If your use case requires `Serverless` functionality, which enables advanced features like **scaling to zero on demand** for cost optimization, you can optionally install the **Knative Operator**. This operator is not part of the default installation and can be added at any time to enable `Serverless` functionality.
 
 
 
@@ -26,19 +26,19 @@ If your use case requires `Serverless` functionality, which enables advanced fea
 
   _Download package: aml-operator.xxx.tgz_
 
-- **Alauda AI Model Serving Operator**
+- **Knative Operator**
 
-  Alauda AI Model Serving Operator provides serverless model inference.
+  Knative Operator provides serverless model inference.
 
-  _Download package: kserveless-operator.xxx.tgz_
+  _Download package: knative-operator.ALL.v1.x.x-yymmdd.tgz_
 
 :::info
-You can download the app named 'Alauda AI' and 'Alauda AI Model Serving' from the Marketplace on the Customer Portal website.
+You can download the app named 'Alauda AI' and 'Knative Operator' from the Marketplace on the Customer Portal website.
 :::
 
 ## Uploading
 
-We need to upload both `Alauda AI` and `Alauda AI Model Serving` to the cluster where Alauda AI is to be used.
+We need to upload both `Alauda AI` and `Knative Operator` to the cluster where Alauda AI is to be used.
 
 <Steps>
 
@@ -66,7 +66,7 @@ export PLATFORM_ADMIN_PASSWORD=<admin-password>  # [!code callout]
 export CLUSTER=<cluster-name>  # [!code callout]
 
 export AI_CLUSTER_OPERATOR_NAME=<path-to-aml-operator-tarball>  # [!code callout]
-export KSERVELESS_OPERATOR_PKG_NAME=<path-to-kserveless-operator-tarball>  # [!code callout]
+export KNATIVE_OPERATOR_PKG_NAME=<path-to-knative-operator-tarball>  # [!code callout]
 
 VIOLET_EXTRA_ARGS=()
 IS_EXTERNAL_REGISTRY=
@@ -97,9 +97,9 @@ violet push \
     ${VIOLET_EXTRA_ARGS[@]}
 
 # [!code highlight]
-# Push **KServeless** operator package to destination cluster
+# Push **Knative Operator** package to destination cluster
 violet push \
-    ${KSERVELESS_OPERATOR_PKG_NAME} \
+    ${KNATIVE_OPERATOR_PKG_NAME} \
     --platform-address=${PLATFORM_ADDRESS} \
     --platform-username=${PLATFORM_ADMIN_USER} \
     --platform-password=${PLATFORM_ADMIN_PASSWORD} \
@@ -114,14 +114,14 @@ violet push \
 3. `${PLATFORM_ADMIN_PASSWORD}` is the password of the ACP platform admin.
 4. `${CLUSTER}` is the name of the cluster to install the Alauda AI components into.
 5. `${AI_CLUSTER_OPERATOR_NAME}` is the path to the Alauda AI Cluster Operator package tarball.
-6. `${KSERVELESS_OPERATOR_PKG_NAME}` is the path to the KServeless Operator package tarball.
+6. `${KNATIVE_OPERATOR_PKG_NAME}` is the path to the Knative CE Operator package tarball.
 7. `${REGISTRY_ADDRESS}` is the address of the external registry.
 8. `${REGISTRY_USERNAME}` is the username of the external registry.
 9. `${REGISTRY_PASSWORD}` is the password of the external registry.
 
 </Callouts>
 
-After configuration, execute the script file using `bash ./uploading-ai-cluster-packages.sh` to upload both `Alauda AI` and `Alauda AI Model Serving` operator.
+After configuration, execute the script file using `bash ./uploading-ai-cluster-packages.sh` to upload both `Alauda AI` and `Knative Operator`.
 
 </Steps>
 
@@ -137,7 +137,7 @@ In **Administrator** view:
 2. At the top of the console, from the **Cluster**  dropdown list, select the destination cluster where you want to install Alauda AI.
 3. Select **Alauda AI**, then click **Install**.
 
-    **Install Alauda AI** window will popup.
+    **Install Alauda AI** window will pop up.
 
 4. Then in the **Install Alauda AI** window.
 5. Leave **Channel** unchanged.
@@ -195,9 +195,6 @@ In **Administrator** view:
 
     By default, the configuration uses `SelfSigned` certificate type for securing ingress traffic to your cluster, the certificate is
     stored in the `knative-serving-cert` secret that is specified in the **Domain Certificate Secret** field.
-
-    To use certificate provided by your own, store the certificate secret in the `istio-system` namespace, then update the value of the
-    **Domain Certificate Secret** field, and change the value of the **Domain Certificate Secret** field to `Provided`.
     :::
 
 11. In the **Serverless Configuration** section, set **Knative Serving Provider** to **Operator**; leave all other parameters blank.
@@ -230,115 +227,106 @@ Now, the core capabilities of Alauda AI have been successfully deployed. If you
 
 Serverless functionality is an optional capability that requires an additional operator and instance to be deployed.
 
-### 1. Installing the Alauda AI Model Serving Operator
+### 1. Installing the Knative Operator
 
 
 <Steps>
 
-### Prerequisites
-
-The `Serverless` capability relies on the **Istio** `Gateway` for its networking. Please install the **Service Mesh** first by following the [documentation](./pre-configuration.mdx#deploy-service-mesh).
+:::info
+Starting from **Knative CE Operator**, the Knative networking layer switches to **Kourier**, so installing **Istio** is no longer required.
+:::
 
-### Procedure
+#### Procedure
 
 In **Administrator** view:
 
 1. Click **Marketplace / OperatorHub**.
 2. At the top of the console, from the **Cluster**  dropdown list, select the destination cluster where you want to install.
-3. Select **Alauda AI Model Serving**, then click **Install**.
+3. Search for and select **Knative Operator**, then click **Install**.
 
-    **Install Alauda AI Model Serving** window will popup.
+    **Install Knative Operator** window will pop up.
 
-4. Then in the **Install Alauda AI Model Serving** window.
+4. Then in the **Install Knative Operator** window.
 5. Leave **Channel** unchanged.
-6. Check whether the **Version** matches the **Alauda AI Model Serving** version you want to install.
-7. Leave **Installation Location** unchanged, it should be `kserveless-operator` by default.
+6. Check whether the **Version** matches the **Knative Operator** version you want to install.
+7. Leave **Installation Location** unchanged.
 8. Select **Manual** for **Upgrade Strategy**.
 9. Click **Install**.
 
-### Verification
+#### Verification
 
-Confirm that the **Alauda AI Model Serving** tile shows one of the following states:
+Confirm that the **Knative Operator** tile shows one of the following states:
 
 - `Installing`: installation is in progress; wait for this to change to `Installed`.
 - `Installed`: installation is complete.
 
 </Steps>
 
 
-### 2. Creating Alauda AI Model Serving Instance
-
-Once **Alauda AI Model Serving Operator** is installed, you can create an instance. There are two ways to do this:
+### 2. Creating Knative Serving Instance
 
-####  **Automated Creation (Recommended)**
+Once **Knative Operator** is installed, you need to create the `KnativeServing` instance manually.
 
-You can have the instance automatically created and managed by the `AmlCluster` by editing its parameters.
 <Steps>
 
-In **Administrator** view:
-
-1. Click **Marketplace / OperatorHub**.
-2. At the top of the console, from the **Cluster**  dropdown list, select the destination cluster where you previously installed the `AmlCluster`.
-3. Select **Alauda AI**, then **Click**.
-4. In the **Alauda AI** page, click **All Instances** from the tab.
-5. Click name **default**.
-6. Locate **Actions** dropdown list and select update.
+#### Procedure
+
+1. Create the `knative-serving` namespace.
+
+   ```bash
+   kubectl create ns knative-serving
+   ```
+
+2. In the **Administrator** view, navigate to **Operators** -> **Installed Operators**.
+3. Select the **Knative CE Operator**.
+4. Under **Provided APIs**, locate **KnativeServing** and click **Create Instance**.
+5. Switch to **YAML view**.
+6. Replace the content with the following YAML:
+7. Click **Create**.
+
+   ```yaml
+   apiVersion: operator.knative.dev/v1beta1
+   kind: KnativeServing
+   metadata:
+     name: knative-serving
+     namespace: knative-serving
+   spec:
+     config:
+       deployment:
+         registries-skipping-tag-resolving: kind.local,ko.local,dev.local,private-registry # [!code callout]
+       domain:
+         example.com: ""
+       features:
+         kubernetes.podspec-affinity: enabled
+         kubernetes.podspec-hostipc: enabled
+         kubernetes.podspec-hostnetwork: enabled
+         kubernetes.podspec-init-containers: enabled
+         kubernetes.podspec-nodeselector: enabled
+         kubernetes.podspec-persistent-volume-claim: enabled
+         kubernetes.podspec-persistent-volume-write: enabled
+         kubernetes.podspec-securitycontext: enabled
+         kubernetes.podspec-tolerations: enabled
+         kubernetes.podspec-volumes-emptydir: enabled
+         queueproxy.resource-defaults: enabled
+       network:
+         domain-template: '{{.Name}}.{{.Namespace}}.{{.Domain}}'
+         ingress-class: kourier.ingress.networking.knative.dev
+     ingress:
+       kourier:
+         enabled: true
+   ```
 
-    **update default** form will show up.
+<Callouts>
 
-7. In the **Serverless Configuration** section:
-   1. Set **Knative Serving Provider** to `Legacy`.
-   2. Set **BuiltIn Knative Serving** to `Managed`.
+1. `private-registry` is a placeholder for your private registry address. You can find this in the **Administrator** view, then click **Clusters**, select `your cluster`, and check the **Private Registry** value in the **Basic Info** section.
 
-8. Leave all other parameters unchanged. Click **Update**.
+</Callouts>
 
 </Steps>
 
-#### **Manual Creation and Integration**
-
-You can manually create the `KnativeServing (knativeservings.components.aml.dev)` instance.
-
-<Steps>
-
-In **Administrator** view:
-
-1. Click **Marketplace / OperatorHub**.
-2. At the top of the console, from the **Cluster**  dropdown list, select the destination cluster where you want to install.
-3. Select **Alauda AI Model Serving**, then **Click**.
-4. In the **Alauda AI Model Serving** page, click **All Instances** from the tab.
-5. Click **Create**.
-
-    **Select Instance Type** window will pop up.
-
-6. Locate the **KnativeServing** tile in **Select Instance Type** window, then click **Create**.
-
-    **Create KnativeServing** form will show up.
-
-7. Keep `default-knative-serving` unchanged for **Name**.
-8. Keep `knative-serving` unchanged for **Knative Serving Namespace**.
-9. In the **Ingress Gateway** section, configure the following:
-   1. Set the **Ingress Gateway Istio Revision** to a value that corresponds to your Istio version (e.g., `1-22`).
-   2. Set a valid domain for the **Domain** field.
-   3. Set the appropriate **Domain Certificate Type**.
-
-    :::info
-        For details on configuring the domain and certificate type, refer to the [relevant section](#procedure-1).
-    :::
-
-10. In the **Values** section, configure the following:
-    1. Select **Deploy Flavor** from dropdown:
-        1. `single-node` for non HA deployments.
-        2. `ha-cluster` for HA cluster deployments (**Recommended** for production).
-    2. Set **Global Registry Address** to Match Your Cluster
-
-        You can find your cluster's private registry address by following these steps:
-        1. In the Web Console, go to Administrator / Clusters.
-        2. Select your target **cluster**.
-        3. On the **Overview** tab, find the `Private Registry address` value in the **Basic Info** section.
-
-</Steps>
+### 3. Integrate with AmlCluster
 
-Configure the `AmlCluster` instance to integrate with a `KnativeServing` instance.
+Configure the `AmlCluster` instance to integrate with the `KnativeServing` instance.
 
 <Steps>
 
@@ -348,10 +336,10 @@ In the **AmlCluster** instance update window, you will need to fill in the requi
 After the initial installation, you will find that only the **Knative Serving Provider** is set to `Operator`. You will now need to provide values for the following parameters:
 :::
 
-* **APIVersion**: `components.aml.dev/v1alpha1`
+* **APIVersion**: `operator.knative.dev/v1beta1`
 * **Kind**: `KnativeServing`
-* **Name**: `default-knative-serving`
-* Leave **Namespace** blank.
+* **Name**: `knative-serving`
+* **Namespace**: `knative-serving`
 </Steps>
 
 ## Replace GitLab Service After Installation

docs/en/installation/ai-generative.mdx

@@ -0,0 +1,105 @@
+---
+weight: 35
+---
+
+# Install Alauda AI Generative
+
+**Alauda AI Generative** is a cloud-native component built on **KServe** for serving generative AI models. As an extension of the Alauda AI ecosystem, it specifically optimizes for **Large Language Models (LLMs)**, offering essential features such as inference orchestration, streaming responses, and resource-based auto-scaling for generative workloads.
+
+---
+
+## Prerequisites
+
+Before installing **Alauda AI Generative**, you need to ensure the following dependencies are installed:
+
+### Required Dependencies
+
+| Dependency | Type | Description |
+|------------|------|-------------|
+| Alauda build of Envoy Gateway <ExternalSiteLink name="acp" href="/networking/operators/envoy_gateway_operator.mdx" children="install_envoy_gateway_operator" /> | Operator | Provides the underlying gateway functionality for AI services |
+| Envoy AI Gateway | Cluster Plugin | Provides AI-specific gateway capabilities |
+| [Alauda Build of LeaderWorkerSet](../../lws/install.mdx) | Cluster Plugin | Provides leader-worker set functionality for AI workloads |
+
+:::info
+`Alauda build of Envoy Gateway` is natively integrated into ACP 4.2. For environments running earlier versions (including ACP 4.0 and 4.1), please contact Customer Support for compatibility and installation guidance.
+:::
+
+### Optional Dependencies
+
+| Dependency | Type | Description |
+|------------|------|-------------|
+| GIE | Built-in | Integrated GIE (gateway-api-inference-extension) for enhanced AI capabilities. Can be enabled through the Alauda AI Generative UI. |
+| Alauda AI | Operator | Required only if you need to use KServe Predictive AI functionality. Can be disabled if you only need LLM Generative AI functionality. |
+
+### Installation Notes
+
+1. **Required Dependencies**: All three required dependencies must be installed before installing Alauda AI Generative.
+2. **GIE Integration**: If you want to use GIE, you can enable it during the installation process by selecting the "Integrated GIE" option in the Alauda AI Generative UI.
+3. **Alauda AI Integration**: If you don't need KServe Predictive AI functionality and only want to use LLM Generative AI, you can disable the "Integrated With Alauda AI" option during installation.
+
+## Downloading Cluster Plugin
+
+:::info
+
+`Alauda AI Generative` cluster plugin can be retrieved from Customer Portal.
+
+Please contact Consumer Support for more information.
+
+:::
+
+## Uploading the Cluster Plugin
+
+For more information on uploading the cluster plugin, please refer to <ExternalSiteLink name="acp" href="ui/cli_tools/index.html#uploading-cluster-plugins" children="Uploading Cluster Plugins" />
+
+## Installing Alauda AI Generative
+
+1. Go to the `Administrator` -> `Marketplace` -> `Cluster Plugin` page, switch to the target cluster, and then deploy the `Alauda AI Generative` Cluster plugin.
+
+2. In the deployment form, configure the following parameters as needed:
+
+### Envoy Gateway Configuration
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| **ServiceAccount Name** | The name of the service account used by Envoy Gateway. | envoy-gateway |
+| **ServiceAccount Namespace** | The namespace where the service account is located. | envoy-gateway-system |
+| **Create Instance** | Create an Envoy Gateway instance to manage inference traffic with bundled extensions. | Enabled |
+| **Instance Name** | The name of the Envoy Gateway instance to be created. | aieg |
+
+### Envoy AI Gateway Configuration
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| **Service Name** | The Kubernetes service name for Envoy AI Gateway. | ai-gateway-controller |
+| **Port Number** | The port number used by Envoy AI Gateway. | 1063 |
+
+### KServe Gateway Configuration
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| **Enabled** | Install a KServe Gateway Instance for inferenceservices functionality. | Enabled |
+| **Gateway Name** | The name of the KServe Gateway. | kserve-ingress-gateway |
+| **Gateway Namespace** | The namespace where the KServe Gateway is deployed. | kserve |
+| **GatewayClass** | Optional. The custom name for the GatewayClass. If left empty, the system will automatically derive it following the "\{Namespace\}-\{Name\}" pattern. | (Empty) |
+| **Port Number** | The port number used by KServe Gateway. | 80 |
+
+### GIE(gateway-api-inference-extension) Configuration
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| **BuiltIn** | Install with the bundled gateway-api-inference-extension v0.5.1 dependencies for enhanced AI capabilities. | Enabled |
+
+### Alauda AI Integration
+
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| **Integrated** | Enable integration with Alauda AI core plugin to reuse existing configurations. | Disabled |
+
+3. Click **Install** to begin the installation process.
+
+4. Verify result. You can see the status of "Installed" in the UI.
+
+## Upgrading Alauda AI Generative
+
+1. Upload the new version for package of **Alauda AI Generative** plugin to ACP.
+2. Go to the `Administrator` -> `Clusters` -> `Target Cluster` -> `Functional Components` page, then click the `Upgrade` button, and you will see the `Alauda AI Generative` can be upgraded.

docs/en/lws/index.mdx

@@ -0,0 +1,6 @@
+---
+weight: 90
+---
+# Alauda Build of LeaderWorkerSet
+
+<Overview />