chore(kgo-ee): update API reference for v1.4.0

Signed-off-by: Jintao Zhang <zhangjintao9020@gmail.com>

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).