chore(kgo): update generated crd and cli references

app/_src/gateway-operator/reference/cli-arguments/1.4.x.md

@@ -45,6 +45,7 @@ and not CLI flags.
 | `--enable-controller-konnect` | `bool` | Enable the Konnect controllers. | `false` |
 | `--enable-validating-webhook` | `bool` | Enable the validating webhook. | `true` |
 | `--health-probe-bind-address` | `string` | The address the probe endpoint binds to. | `:8081` |
+| `--konnect-controller-max-concurrent-reconciles` | `string` | Maximum number of concurrent reconciles for Konnect entities. | `8` |
 | `--konnect-sync-period` | `string` | Sync period for Konnect entities. After a successful reconciliation of Konnect entities the controller will wait this duration before enforcing configuration on Konnect once again. | `1m0s` |
 | `--kubeconfig` | `string` | Path to the kubeconfig file. | `""` |
 | `--metrics-bind-address` | `string` | The address the metric endpoint binds to. | `:8080` |

app/_src/gateway-operator/reference/custom-resources/1.4.x.md

@@ -1763,30 +1763,30 @@ Machine Learning models such as Large Language Models (LLM).<br /><br />
 The underlying technology for the AIGateway is the Kong Gateway configured
 with a variety of plugins which provide the the AI featureset.<br /><br />
 This is a list of the plugins, which are available in Kong Gateway v3.6.x+:<br /><br />
-  - ai-proxy (https://github.com/kong/kong/tree/master/kong/plugins/ai-proxy)
-  - ai-request-transformer (https://github.com/kong/kong/tree/master/kong/plugins/ai-request-transformer)
-  - ai-response-transformers (https://github.com/kong/kong/tree/master/kong/plugins/ai-response-transformer)
-  - ai-prompt-template (https://github.com/kong/kong/tree/master/kong/plugins/ai-prompt-template)
-  - ai-prompt-guard-plugin (https://github.com/kong/kong/tree/master/kong/plugins/ai-prompt-guard)
-  - ai-prompt-decorator-plugin (https://github.com/kong/kong/tree/master/kong/plugins/ai-prompt-decorator)<br /><br />
-So effectively the AIGateway resource provides a bespoke Gateway resource
-(which it owns and manages) with the gateway, consumers and plugin
-configurations automated and configurable via Kubernetes APIs.<br /><br />
-The current iteration only supports the proxy itself, but the API is being
-built with room for future growth in several dimensions. For instance:<br /><br />
-  - Supporting auxiliary functions (e.g. decorator, guard, templater, token-rate-limit)
-  - Supporting request/response transformers
-  - Supporting more than just LLMs (e.g. CCNs, GANs, e.t.c.)
-  - Supporting more hosting options for LLMs (e.g. self hosted)
-  - Supporting more AI cloud providers
-  - Supporting more AI cloud provider features<br /><br />
-The validation rules throughout are set up to ensure at least one
-cloud-provider-based LLM is specified, but in the future when we have more
-model types and more hosting options for those types so we may want to look
-into using CEL validation to ensure that at least one model configuration is
-provided. We may also want to use CEL to validate things like identifier
-unique-ness, e.t.c.<br /><br />
-See: https://kubernetes.io/docs/reference/using-api/cel/
+- ai-proxy (https://github.com/kong/kong/tree/master/kong/plugins/ai-proxy)
+- ai-request-transformer (https://github.com/kong/kong/tree/master/kong/plugins/ai-request-transformer)
+- ai-response-transformers (https://github.com/kong/kong/tree/master/kong/plugins/ai-response-transformer)
+- ai-prompt-template (https://github.com/kong/kong/tree/master/kong/plugins/ai-prompt-template)
+- ai-prompt-guard-plugin (https://github.com/kong/kong/tree/master/kong/plugins/ai-prompt-guard)
+- ai-prompt-decorator-plugin (https://github.com/kong/kong/tree/master/kong/plugins/ai-prompt-decorator)<br /><br />
+  So effectively the AIGateway resource provides a bespoke Gateway resource
+  (which it owns and manages) with the gateway, consumers and plugin
+  configurations automated and configurable via Kubernetes APIs.<br /><br />
+  The current iteration only supports the proxy itself, but the API is being
+  built with room for future growth in several dimensions. For instance:<br /><br />
+- Supporting auxiliary functions (e.g. decorator, guard, templater, token-rate-limit)
+- Supporting request/response transformers
+- Supporting more than just LLMs (e.g. CCNs, GANs, e.t.c.)
+- Supporting more hosting options for LLMs (e.g. self hosted)
+- Supporting more AI cloud providers
+- Supporting more AI cloud provider features<br /><br />
+  The validation rules throughout are set up to ensure at least one
+  cloud-provider-based LLM is specified, but in the future when we have more
+  model types and more hosting options for those types so we may want to look
+  into using CEL validation to ensure that at least one model configuration is
+  provided. We may also want to use CEL to validate things like identifier
+  unique-ness, e.t.c.<br /><br />
+  See: https://kubernetes.io/docs/reference/using-api/cel/
 
 <!-- ai_gateway description placeholder -->
 
@@ -2947,11 +2947,11 @@ _Underlying type:_ `string`
 
 PromotionStrategy is the type of promotion strategy consts.<br /><br />
 Allowed values:<br /><br />
-  - `BreakBeforePromotion` is a promotion strategy which will ensure all new
-    resources are ready and then break, to enable manual inspection.
-    The user must indicate manually when they want the promotion to continue.
-    That can be done by annotating the `DataPlane` object with
-    `"gateway-operator.konghq.com/promote-when-ready": "true"`.
+- `BreakBeforePromotion` is a promotion strategy which will ensure all new
+  resources are ready and then break, to enable manual inspection.
+  The user must indicate manually when they want the promotion to continue.
+  That can be done by annotating the `DataPlane` object with
+  `"gateway-operator.konghq.com/promote-when-ready": "true"`.
 
 
 
@@ -2997,11 +2997,11 @@ _Underlying type:_ `string`
 RolloutResourcePlanDeployment is the type that holds the resource plan for
 managing the Deployment objects during and after a rollout.<br /><br />
 Allowed values:<br /><br />
-  - `ScaleDownOnPromotionScaleUpOnRollout` is a rollout
-    resource plan for Deployment which makes the operator scale down
-    the Deployment to 0 when the rollout is not initiated by a spec change
-    and then to scale it up when the rollout is initiated (the owner resource
-    like a DataPlane is patched or updated).
+- `ScaleDownOnPromotionScaleUpOnRollout` is a rollout
+  resource plan for Deployment which makes the operator scale down
+  the Deployment to 0 when the rollout is not initiated by a spec change
+  and then to scale it up when the rollout is initiated (the owner resource
+  like a DataPlane is patched or updated).
 
 
 
@@ -3261,7 +3261,7 @@ KonnectGatewayControlPlaneSpec defines the desired state of KonnectGatewayContro
 | --- | --- |
 | `name` _string_ | The name of the control plane. |
 | `description` _string_ | The description of the control plane in Konnect. |
-| `cluster_type` _[ClusterType](#clustertype)_ | The ClusterType value of the cluster associated with the Control Plane. |
+| `cluster_type` _[CreateControlPlaneRequestClusterType](#createcontrolplanerequestclustertype)_ | The ClusterType value of the cluster associated with the Control Plane. |
 | `auth_type` _[AuthType](#authtype)_ | The auth type value of the cluster associated with the Runtime Group. |
 | `cloud_gateway` _boolean_ | Whether this control-plane can be used for cloud-gateways. |
 | `proxy_urls` _[ProxyURL](#proxyurl) array_ | Array of proxy URLs associated with reaching the data-planes connected to a control-plane. |