alauda · fyuan1316 · Apr 22, 2026 · Apr 24, 2026 · Apr 24, 2026
diff --git a/.gitignore b/.gitignore
@@ -13,3 +13,4 @@
 
 .claude
 CLAUDE.md
+.omx/
diff --git a/docs/en/dify/install.mdx b/docs/en/dify/install.mdx
@@ -161,9 +161,7 @@ ingress:
         - <dify.example.com>
 ```
 
-<a id="storage-s3-and-pvc"></a>
-
-### Storage (S3 and PVC)
+### Storage (S3 and PVC) \{#storage-s3-and-pvc}
 
 **PVC (default):** API and plugin daemon each use a PVC when enabled. Override storage class and size as needed.
 

diff --git a/docs/en/installation/ai-cluster.mdx b/docs/en/installation/ai-cluster.mdx
@@ -16,9 +16,7 @@ If your use case requires `Knative` functionality, which enables advanced featur
     [Recommended deployment option](https://kserve.github.io/website/docs/admin-guide/overview#generative-inference): For generative inference workloads, the **Standard** approach (previously known as RawKubernetes Deployment) is recommended as it provides the most control over resource allocation and scaling.
 :::
 
-<a id="downloading"></a>
-
-## Downloading
+## Downloading \{#downloading}
 
 **Operator Components**:
 
@@ -38,9 +36,7 @@ If your use case requires `Knative` functionality, which enables advanced featur
 You can download the app named 'Alauda AI' and 'Knative Operator' from the Marketplace on the Customer Portal website.
 :::
 
-<a id="uploading"></a>
-
-## Uploading
+## Uploading \{#uploading}
 
 We need to upload both `Alauda AI` and `Knative Operator` to the cluster where Alauda AI is to be used.
 
@@ -163,9 +159,7 @@ Confirm that the **Alauda AI** tile shows one of the following states:
 
 For detailed installation steps, see [Install KServe](../kserve/install.mdx) in Alauda Build of KServe.
 
-<a id="enabling-knative-functionality"></a>
-
-## Enabling Knative Functionality
+## Enabling Knative Functionality \{#enabling-knative-functionality}
 
 Knative functionality is an optional capability that requires an additional operator and instance to be deployed.
 

diff --git a/docs/en/kserve/install.mdx b/docs/en/kserve/install.mdx
@@ -26,9 +26,7 @@ Before installing **Alauda Build of KServe**, you need to ensure the following d
 1. **Required Dependencies**: All required dependencies must be installed before installing Alauda Build of KServe.
 2. **GIE Integration**: GIE is bundled and enabled by default. If your environment already has GIE installed separately, set `gie.builtIn` to `false` in the operator configuration to disable the built-in installation.
 
-<a id="upload-operator"></a>
-
-## Upload Operator
+## Upload Operator \{#upload-operator}
 
 Download the Alauda Build of KServe Operator installation file (e.g., `kserve-operator.ALL.xxxx.tgz`).
 
@@ -137,9 +135,7 @@ kubectl get kserve default-kserve -n kserve-operator
 
 The instance is ready when the status shows `DEPLOYED: True`.
 
-<a id="envoy-gateway-configuration"></a>
-
-### Envoy Gateway Configuration
+### Envoy Gateway Configuration \{#envoy-gateway-configuration}
 
 | Field | Description | Default |
 |-------|-------------|---------|
@@ -148,18 +144,14 @@ The instance is ready when the status shows `DEPLOYED: True`.
 | `preset.envoy_gateway.create_instance` | Create an Envoy Gateway instance to manage inference traffic with bundled extensions. | `true` |
 | `preset.envoy_gateway.instance_name` | Name of the Envoy Gateway instance to create. | `aieg` |
 
-<a id="envoy-ai-gateway-configuration"></a>
-
-### Envoy AI Gateway Configuration
+### Envoy AI Gateway Configuration \{#envoy-ai-gateway-configuration}
 
 | Field | Description | Default |
 |-------|-------------|---------|
 | `preset.envoy_ai_gateway.service` | Kubernetes service name for Envoy AI Gateway. | `ai-gateway-controller` |
 | `preset.envoy_ai_gateway.port` | Port number used by Envoy AI Gateway. | `1063` |
 
-<a id="kserve-gateway-configuration"></a>
-
-### KServe Gateway Configuration
+### KServe Gateway Configuration \{#kserve-gateway-configuration}
 
 | Field | Description | Default |
 |-------|-------------|---------|
@@ -169,9 +161,7 @@ The instance is ready when the status shows `DEPLOYED: True`.
 | `preset.kserve_gateway.gateway_class` | Optional custom GatewayClass name. If empty, derived as `{namespace}-{name}`. | `""` |
 | `preset.kserve_gateway.port` | Port number used by the KServe Gateway. | `80` |
 
-<a id="gie-gateway-api-inference-extension-configuration"></a>
-
-### GIE (gateway-api-inference-extension) Configuration
+### GIE (gateway-api-inference-extension) Configuration \{#gie-gateway-api-inference-extension-configuration}
 
 | Field | Description | Default |
 |-------|-------------|---------|

diff --git a/docs/en/label_studio/install.mdx b/docs/en/label_studio/install.mdx
@@ -185,9 +185,7 @@ redirectURIs:
 
 ### 4. Configure User Management
 
-<a id="41-disable-user-registration"></a>
-
-#### 4.1 Disable User Registration
+#### 4.1 Disable User Registration \{#41-disable-user-registration}
 
 User registration can be disabled by setting the following fields:
 

diff --git a/docs/en/llama_stack/quickstart.mdx b/docs/en/llama_stack/quickstart.mdx
@@ -30,9 +30,7 @@ The notebook demonstrates:
 
 ## FAQ
 
-<a id="how-to-prepare-python-312-in-notebook"></a>
-
-### How to prepare Python 3.12 in Notebook
+### How to prepare Python 3.12 in Notebook \{#how-to-prepare-python-312-in-notebook}
 
 1. Download the pre-compiled Python installation package:
 

diff --git a/docs/en/model_inference/inference_service/functions/inference_service.mdx b/docs/en/model_inference/inference_service/functions/inference_service.mdx
@@ -57,9 +57,7 @@ The core definition of the inference service feature is to deploy trained machin
 - Automatically generates Swagger documentation to facilitate user integration and invocation of inference services.
 - Provides real-time monitoring and alarm features to ensure stable service operation.
 
-<a id="create-inference-service"></a>
-
-## Create inference service
+## Create inference service \{#create-inference-service}
 
 <Steps>
 

diff --git a/docs/en/model_inference/inference_service/how_to/vllm_expert_parallel.mdx b/docs/en/model_inference/inference_service/how_to/vllm_expert_parallel.mdx
@@ -213,9 +213,7 @@ Multi-node EP deployments require additional distributed runtime and networking
 This page focuses on the single-node configuration pattern. If you need multi-node EP, refer to the official vLLM guide and adapt the deployment model to your cluster topology and runtime environment.
 :::
 
-<a id="references"></a>
-
-## References
+## References \{#references}
 
 - [Expert Parallel Deployment - vLLM](https://docs.vllm.ai/en/latest/serving/expert_parallel_deployment/)
 - [Data Parallel Deployment - vLLM](https://docs.vllm.ai/en/latest/serving/data_parallel_deployment/)