chore: separate yaml files for rhoai demo

Author: Bobbins228

demos/llama-stack-openshift/README.md

@@ -23,7 +23,7 @@ oc create secret generic llama-stack-inference-model-secret \
 
 ## Setup Deployment files
 ### Configuring the `kubeflow-ragas-config` ConfigMap
-Update the [kubeflow-ragas-config](deployment/llama-stack-distribution.yaml) with the following data:
+Update the [kubeflow-ragas-config](deployment/kubeflow-ragas-config.yaml) with the following data:
 ``` bash
 # See project README for more details
 EMBEDDING_MODEL=all-MiniLM-L6-v2
@@ -49,7 +49,7 @@ kubectl create secret generic kubeflow-pipelines-token \
 ```
 
 ## Deploy Llama Stack on OpenShift
-You can now deploy the configuration files and the Llama Stack distribution with `oc apply -f deployment/llama-stack-distribution.yaml`
+You can now deploy the configuration files and the Llama Stack distribution with `oc apply -f deployment/kubeflow-ragas-config.yaml` and `oc apply -f deployment/llama-stack-distribution.yaml`
 
 You should now have a Llama Stack server on OpenShift with the remote ragas eval provider configured.
 You can now follow the [remote_demo.ipynb](../../demos/remote_demo.ipynb) demo but ensure you are running it in a Data Science workbench and use the `LLAMA_STACK_URL` defined earlier. Alternatively you can run it locally if you create a Route.

demos/llama-stack-openshift/deployment/kubeflow-ragas-config.yaml

@@ -0,0 +1,12 @@
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: kubeflow-ragas-config
+data:
+  EMBEDDING_MODEL: "all-MiniLM-L6-v2"
+  KUBEFLOW_LLAMA_STACK_URL: "<your-llama-stack-url>"
+  KUBEFLOW_PIPELINES_ENDPOINT: "<your-kfp-endpoint>"
+  KUBEFLOW_NAMESPACE: "<your-namespace>"
+  KUBEFLOW_BASE_IMAGE: "quay.io/diegosquayorg/my-ragas-provider-image:latest"
+  KUBEFLOW_RESULTS_S3_PREFIX: "s3://my-bucket/ragas-results"
+  KUBEFLOW_S3_CREDENTIALS_SECRET_NAME: "<secret-name>"

demos/llama-stack-openshift/deployment/llama-stack-distribution.yaml

@@ -1,17 +1,3 @@
----
-apiVersion: v1
-kind: ConfigMap
-metadata:
-  name: kubeflow-ragas-config
-data:
-  EMBEDDING_MODEL: "all-MiniLM-L6-v2"
-  KUBEFLOW_LLAMA_STACK_URL: "<your-llama-stack-url>"
-  KUBEFLOW_PIPELINES_ENDPOINT: "<your-kfp-endpoint>"
-  KUBEFLOW_NAMESPACE: "<your-namespace>"
-  KUBEFLOW_BASE_IMAGE: "quay.io/diegosquayorg/my-ragas-provider-image:latest"
-  KUBEFLOW_RESULTS_S3_PREFIX: "s3://my-bucket/ragas-results"
-  KUBEFLOW_S3_CREDENTIALS_SECRET_NAME: "<secret-name>"
----
 apiVersion: llamastack.io/v1alpha1
 kind: LlamaStackDistribution
 metadata: