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artifacts/attributes.adoc

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:openshift-ai-connector-for-rhdh-title: Integrate {product} with {openshift-ai-connector-name} to leverage AI models
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:openshift-ai-connector-name: OpenShift AI Connector for {product}
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:openshift-ai-connector-name-short: OpenShift AI Connector for {product-very-short}
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:openshift-ai-connector-name-short: OpenShift AI Connector for {product-very-short}
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:rhoai-docs-link: link:https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.25/html-single

modules/openshift-ai-connector-for-rhdh/con-understand-how-ai-assets-map-to-rhdh-catalog.adoc

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The {openshift-ai-connector-name} ({openshift-ai-connector-name-short}) serves as a crucial link, enabling the discovery and accessibility of AI assets managed within the {rhoai-brand-name} offering directly within your {product-very-short} instance.
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For more information on model registry components, see https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.25/html/enabling_the_model_registry_component/overview-of-model-registries_model-registry-config[Overview of model registries and model catalog].
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For more information on model registry components, see {rhoai-docs-link}/enabling_the_model_registry_component/index#overview-of-model-registries_model-registry-config[Overview of model registries and model catalog].

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

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* For the rest of the features, version 2.20 or later suffices. Enabling model registry and its associated dashboard allows for a user experience that more directly allows for customizing AI Model metadata.
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For best overall experience, {rhoai-short} 2.25 is recommended.
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For more details, see link:https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.25/html-single/enabling_the_model_registry_component/index[Enabling the model registry component].
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For more details, see {rhoai-docs-link}/enabling_the_model_registry_component/index[Enabling the model registry component].
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.Procedure
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kind: Group
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name: system:serviceaccounts:ai-rhdh
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----
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** `RoleBinding` in the {rhoai-short} namespace to grant the {product-very-short} `ServiceAccount` read permissions to the Model Registry data (binding to `registry-user-modelregistry-public`).
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** `RoleBinding` in the {rhoai-short} namespace to grant the {product-very-short} `ServiceAccount` read permissions to the model registry data (binding to `registry-user-modelregistry-public`).
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[source,yaml]
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----

modules/openshift-ai-connector-for-rhdh/proc-troubleshooting-connector-functionality.adoc

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[id="proc-troubleshooting-connector-functionality_{context}"]
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= Troubleshooting Connector functionality
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The connector system consists of the two dynamic plugins and the three Model Catalog Bridge sidecar containers. Generally speaking, the logs collected should be provided to {company-name} Support for analysis.
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The connector system consists of the two dynamic plugins and the three model catalog bridge sidecar containers. Generally speaking, the logs collected must be provided to {company-name} Support for analysis.
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The actual contents of the diagnostic data are not part of any product guaranteed specification, and can change at any time.
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== Checking Dynamic Plugins status
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Validate that the dynamic plugins have been successfully installed into your {product-very-short} project Pod by using the following command:
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[source,bash]
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[source,bash,subs=+attributes]
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----
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oc logs -c install-dynamic-plugins deployment/<your RHDH deployment>
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oc logs -c install-dynamic-plugins deployment/<your {product-very-short} deployment>
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----
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The `install-dynamic-plugin` logs allow you to check the following installation logs for successful logs:
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. Check Location Service API: Confirm the location service is providing data to the {product-very-short} Entity Provider.
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[source,bash]
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[source,bash,subs=+attributes]
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----
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oc rsh -c backstage-backend deployment/<your RHDH deployment>
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oc rsh -c backstage-backend deployment/<your {product-very-short} deployment>
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curl http://localhost:9090/list
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. Check Sidecar Container Logs:
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[source,bash]
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oc logs -c rhoai-normalizer deployment/<your RHDH deployment>
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oc logs -c storage-rest deployment/<your RHDH deployment>
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oc logs -c location deployment/<your RHDH deployment>
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oc logs -c rhoai-normalizer deployment/<your {product-very-short} deployment>
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oc logs -c storage-rest deployment/<your {product-very-short} deployment>
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oc logs -c location deployment/<your {product-very-short} deployment>
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modules/openshift-ai-connector-for-rhdh/ref-enrich-ai-model-metadata.adoc

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[id="ref-enrich-ai-model-metadata_{context}"]
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= Enrich AI model metadata for enhanced {product} experience
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While {rhoai-short} provides essential data, an AI platform engineer using {rhoai-short} can enrich the {backstage}/{product-very-short} experience by adding custom properties to the `ModelVersion` or `RegisteredModel` (or annotations to the `KServe InferenceService` if the model registry is not used) so that the {openshift-ai-connector-name} can add the information to the {product-very-short} entities it creates. For more details, see https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.25/html/working_with_model_registries/working-with-model-registries_model-registry#editing-model-version-metadata-in-a-model-registry_model-registry[Editing model version metadata in a model registry].
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While {rhoai-short} provides essential data, an AI platform engineer using {rhoai-short} can enrich the {backstage}/{product-very-short} experience by adding custom properties to the `ModelVersion` or `RegisteredModel` (or annotations to the `KServe InferenceService` if the model registry is not used) so that the {openshift-ai-connector-name} can add the information to the {product-very-short} entities it creates. For more details, see {rhoai-docs-link}/working_with_model_registries/index#editing-model-version-metadata-in-a-model-registry_model-registry[Editing model version metadata in a model registry].
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|===
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|Property Key |Entity Field Populated |Description

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

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|Model Server (InferenceService)
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|Component
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|`model-server`
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|Represents a running, accessible AI model endpoint. See https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.25/html/configuring_your_model-serving_platform/index[Configuring your model-serving platform].
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|Represents a running, accessible AI model endpoint. See {rhoai-docs-link}/configuring_your_model-serving_platform/index[Configuring your model-serving platform].
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|AI Model (Model Registry Version)
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|Resource
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|Model Cards
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|TechDocs
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|N/A
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|Model cards from the {rhoai-short} model catalog are associated with the Component and Resource entities. See https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.25/html/working_with_the_model_catalog/registering-a-model-from-the-model-catalog_working-model-catalog[Registering a model from the model catalog].
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|Model cards from the {rhoai-short} model catalog are associated with the Component and Resource entities. See {rhoai-docs-link}/working_with_the_model_catalog/registering-a-model-from-the-model-catalog_working-model-catalog#registering-a-model-from-the-model-catalog_working-model-catalog[Registering a model from the model catalog].
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|===
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Once the {openshift-ai-connector-name-short} is installed and connected with {rhoai-short}, the transfer of information commences automatically.

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