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2 changes: 1 addition & 1 deletion docs/en/dify/install.mdx
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Expand Up @@ -161,7 +161,7 @@ ingress:
- <dify.example.com>
```

### 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.

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6 changes: 3 additions & 3 deletions docs/en/installation/ai-cluster.mdx
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Expand Up @@ -16,7 +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.
:::

## Downloading
## Downloading \{#downloading}

**Operator Components**:

Expand All @@ -36,7 +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.
:::

## Uploading
## Uploading \{#uploading}

We need to upload both `Alauda AI` and `Knative Operator` to the cluster where Alauda AI is to be used.

Expand Down Expand Up @@ -155,7 +155,7 @@ Confirm that the **Alauda AI** tile shows one of the following states:

</Steps>

## 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.

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2 changes: 1 addition & 1 deletion docs/en/label_studio/install.mdx
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Expand Up @@ -185,7 +185,7 @@ redirectURIs:

### 4. Configure User Management

#### 4.1 Disable User Registration
#### 4.1 Disable User Registration \{#41-disable-user-registration}

User registration can be disabled by setting the following fields:

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2 changes: 1 addition & 1 deletion docs/en/llama_stack/quickstart.mdx
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Expand Up @@ -34,7 +34,7 @@ The notebook demonstrates:

## FAQ

### 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:

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Expand Up @@ -57,7 +57,7 @@ The core definition of the inference service feature is to deploy trained machin
* Provides real-time monitoring and alarm features to ensure stable service operation.


## Create inference service
## Create inference service \{#create-inference-service}

<Steps>

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Expand Up @@ -64,6 +64,6 @@ The core definition of the model repository feature is to provide a Git-based ve
- Misconfigured `README.md` metadata may block inference deployment.


## Create Model and Upload Model Files
## Create Model and Upload Model Files \{#create-model-repository}

Refer to [Upload Models Using Notebook](../how_to/upload_models_using_notebook.mdx) for detailed steps on uploading model files to the model repository.
2 changes: 1 addition & 1 deletion docs/en/trustyai/ai-guardrails.mdx
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Expand Up @@ -91,7 +91,7 @@ Change `regex` under `built-in-detector` to the desired algorithm (e.g. `- email

The Guardrails Orchestrator is exposed by a Service named `<orchestrator-name>-service`. Port numbers depend on whether authentication is enabled (annotation `security.opendatahub.io/enable-auth: "true"` on the GuardrailsOrchestrator).

### Ports and roles
### Ports and roles \{#ports-and-roles}

| Port name | Auth disabled | Auth enabled | Role |
|------------------------|---------------|--------------|------|
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