Incorporated Ben's comments and Gabe's comments

Author: pabel-rh

modules/openshift-ai-connector-for-rhdh/proc-setting-up-openshift-ai-connector-for-rhdh-with-rhoai.adoc

@@ -25,25 +25,25 @@ For more details, see {rhoai-docs-link}/enabling_the_model_registry_component/in
 
 . Configure {rhoai-short}-related RBAC and credentials.
 A Kubernetes `ServiceAccount` and a `service-account-token` Secret are required for the connector to retrieve data from {rhoai-short}. The following resources must be created, replacing namespace names (`ai-rhdh` for {product-very-short}, `rhoai-model-registries` for {rhoai-short}) as needed:
-** `ServiceAccount` (`rhdh-rhoai-bridge`). For example:
+** `ServiceAccount` (`rhdh-rhoai-connector`). For example:
 +
 [source,yaml]
 ----
 apiVersion: v1
 kind: ServiceAccount
 metadata:
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
   namespace: ai-rhdh
 ----
-** `ClusterRole` and `ClusterRoleBinding` (`rhdh-rhoai-bridge`) to allow access to OCP resources like `routes`, `services`, and `inferenceservices`. For example:
+** `ClusterRole` and `ClusterRoleBinding` (`rhdh-rhoai-connector`) to allow access to OCP resources like `routes`, `services`, and `inferenceservices`. For example:
 +
 [source,yaml]
 ----
 # Example for `ClusterRole`
 apiVersion: rbac.authorization.k8s.io/v1
 kind: ClusterRole
 metadata:
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
   annotations:
     argocd.argoproj.io/sync-wave: "0"
 rules:
@@ -81,14 +81,14 @@ rules:
 apiVersion: rbac.authorization.k8s.io/v1
 kind: ClusterRoleBinding
 metadata:
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
 roleRef:
   apiGroup: rbac.authorization.k8s.io
   kind: ClusterRole
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
 subjects:
   - kind: ServiceAccount
-    name: rhdh-rhoai-bridge
+    name: rhdh-rhoai-connector
     namespace: ai-rhdh
 ----
 ** `Role` and `RoleBinding` to allow ConfigMap updates within the {product-very-short} namespace. For example:
@@ -99,7 +99,7 @@ subjects:
 apiVersion: rbac.authorization.k8s.io/v1
 kind: Role
 metadata:
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
   namespace: ai-rhdh
 rules:
   - apiGroups: [""]
@@ -131,28 +131,28 @@ subjects:
 apiVersion: rbac.authorization.k8s.io/v1
 kind: RoleBinding
 metadata:
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
   namespace: ai-rhdh
 roleRef:
   apiGroup: rbac.authorization.k8s.io
   kind: Role
-  name: rhdh-rhoai-bridge
+  name: rhdh-rhoai-connector
 subjects:
   - kind: ServiceAccount
-    name: rhdh-rhoai-bridge
+    name: rhdh-rhoai-connector
     namespace: ai-rhdh
 ----
-** Secret (`rhdh-rhoai-bridge-token`) of type `kubernetes.io/service-account-token` that goes along with the `rhdh-rhoai-bridge` `ServiceAccount`.
+** Secret (`rhdh-rhoai-connector-token`) of type `kubernetes.io/service-account-token` that goes along with the `rhdh-rhoai-connector` `ServiceAccount`.
 +
 [source,yaml]
 ----
 apiVersion: v1
 kind: Secret
 metadata:
-  name: rhdh-rhoai-bridge-token
+  name: rhdh-rhoai-connector-token
   namespace: ai-rhdh
   annotations:
-    kubernetes.io/service-account.name: rhdh-rhoai-bridge
+    kubernetes.io/service-account.name: rhdh-rhoai-connector
 type: kubernetes.io/service-account-token
 ----
 
@@ -173,7 +173,7 @@ plugins:
 ** If {product-very-short} was installed using the Operator, modify your {product-very-short} custom resource (CR) instance.
 ** If {product-very-short} was installed using the Helm charts, modify the *Deployment* specification.
 
-. The system relies on three sidecar containers ({openshift-ai-connector-name}) running alongside the `backstage-backend` container. Add these sidecar containers to your configuration referencing the `rhdh-rhoai-bridge-token` Secret:
+. The system relies on three sidecar containers ({openshift-ai-connector-name}) running alongside the `backstage-backend` container. Add these sidecar containers to your configuration referencing the `rhdh-rhoai-connector-token` Secret:
 ** `location`: Provides the REST API for {product-very-short} plugins to fetch model metadata.
 ** `storage-rest`: Maintains a cache of AI Model metadata in a ConfigMap called `bac-import-model`.
 ** `rhoai-normalizer`: Acts as a Kubernetes controller and {rhoai-short} client, normalizing {rhoai-short} metadata for the connector. The following code block is an example:
@@ -198,7 +198,7 @@ spec:
                   fieldPath: metadata.namespace
           envFrom:
             - secretRef:
-                name: rhdh-rhoai-bridge-token
+                name: rhdh-rhoai-connector-token
           image: quay.io/redhat-ai-dev/model-catalog-location-service@sha256:4f6ab6624a29f627f9f861cfcd5d18177d46aa2c67a81a75a1502c49bc2ff012
 
           imagePullPolicy: Always
@@ -228,7 +228,7 @@ spec:
                   fieldPath: metadata.namespace
           envFrom:
             - secretRef:
-                name: rhdh-rhoai-bridge-token
+                name: rhdh-rhoai-connector-token
           image: quay.io/redhat-ai-dev/model-catalog-storage-rest@sha256:398095e7469e86d84b1196371286363f4b7668aa3e26370b4d78cb8d4ace1dc9
 
           imagePullPolicy: Always
@@ -250,7 +250,7 @@ spec:
                   fieldPath: metadata.namespace
           envFrom:
             - secretRef:
-                name: rhdh-rhoai-bridge-token
+                name: rhdh-rhoai-connector-token
           image: quay.io/redhat-ai-dev/model-catalog-rhoai-normalizer@sha256:fe6c05d57495d6217c4d584940ec552c3727847ff60f39f5d04f94be024576d8
 
           imagePullPolicy: Always

modules/openshift-ai-connector-for-rhdh/ref-model-to-entity-mapping.adoc

@@ -3,7 +3,7 @@
 [id="ref-model-to-entity-mapping_{context}"]
 = Model-to-Entity mapping
 
-{openshift-ai-connector-name-short} integrates with {openshift-ai-connector-name-short}, the model catalog, and KServe-based Model Deployments (InferenceServices). This integration automatically converts your AI/ML artifacts into familiar {backstage} entities, simplifying management and providing a unified view of your models.
+Model-to-Entity mapping integrates with {openshift-ai-connector-name-short}, the model catalog, and KServe-based Model Deployments (InferenceServices). This integration automatically converts your AI/ML artifacts into familiar {backstage} entities, simplifying management and providing a unified view of your available AI models to your developer teams.
 
 This offering interfaces with the {openshift-ai-connector-name-short}, model catalog, and KServe-based Model Deployments (InferenceServices) to create familiar {backstage} entities.