Fix merge issue

Author: sauagarwa

.github/linters/.markdown-lint.yml

@@ -2,5 +2,6 @@
     "default": true,
     "MD003": false,
     "MD013": false,
-    "MD033": false
-}
+    "MD033": false,
+    "MD036": false
+}

.github/workflows/ansible-lint.yml

@@ -21,17 +21,22 @@ jobs:
       # Run Linter against code base #
       ################################
       - name: Lint Code Base
-        uses: github/super-linter/slim@v6
+        uses: super-linter/super-linter/slim@v7
         env:
           VALIDATE_ALL_CODEBASE: true
           DEFAULT_BRANCH: main
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           # These are the validation we disable atm
           VALIDATE_BASH: false
+          VALIDATE_CHECKOV: false
           VALIDATE_JSCPD: false
           VALIDATE_KUBERNETES_KUBECONFORM: false
           VALIDATE_YAML: false
           VALIDATE_ANSIBLE: false
+          VALIDATE_PYTHON_PYLINT: false
+          VALIDATE_MARKDOWN_PRETTIER: false
+          VALIDATE_SHELL_SHFMT: false
+          VALIDATE_YAML_PRETTIER: false
           # VALIDATE_DOCKERFILE_HADOLINT: false
           # VALIDATE_MARKDOWN: false
           # VALIDATE_NATURAL_LANGUAGE: false

.github/workflows/jsonschema.yaml

@@ -43,11 +43,18 @@ spec:
 
 Use `kubectl` or `oc` command line to create new machineset `oc apply -f gpu_machineset.yaml`
 
-Depending on type of EC2 instance creation of the new machines make take some time. Please note that all nodes with GPU will have labels(`node-role.kubernetes.io/odh-notebook`in our case) and taints (`odh-notebook `) that we have specified in machineset applied automatically
+Depending on type of EC2 instance creation of the new machines make take some
+time. Please note that all nodes with GPU will have
+labels(`node-role.kubernetes.io/odh-notebook` in our case) and taints
+(`odh-notebook`) that we have specified in machineset applied automatically
 
 ## Install Node Feature Operator
 
-From OperatorHub install `Node Feature Discovery Operator` , accepting defaults . Once Operator has been installed , create `NodeFeatureDiscovery`instance . Use default entries unless you something specific is needed . Node Feature Discovery Operator will add labels to nodes based on available hardware resources
+From OperatorHub install `Node Feature Discovery Operator` , accepting
+defaults. Once Operator has been installed, create
+`NodeFeatureDiscovery`instance. Use default entries unless you something
+specific is needed. Node Feature Discovery Operator will add labels to nodes
+based on available hardware resources.
 
 ![Diagram](images/node-feature-discovery.png)
 

.github/workflows/linter.yml

@@ -16,7 +16,7 @@ install: operator-deploy post-install ## installs the pattern and loads the secr
 	@echo "Installed"
 
 .PHONY: create-gpu-machineset
-create-gpu-machineset:
+create-gpu-machineset: ## Creates a gpu machineset
 	ansible-playbook ansible/playbooks/create-gpu-machine-set.yaml
 
 .PHONY: post-install

.github/workflows/superlinter.yml

@@ -18,7 +18,7 @@ OpenShift to generate project proposals for specific Red Hat products.
 - Podman
 - Red Hat Openshift cluster running in AWS. Supported regions are : us-east-1 us-east-2 us-west-1 us-west-2 ca-central-1 sa-east-1 eu-west-1 eu-west-2 eu-west-3 eu-central-1 eu-north-1 ap-northeast-1 ap-northeast-2 ap-northeast-3 ap-southeast-1 ap-southeast-2 ap-south-1.
 - GPU Node to run Hugging Face Text Generation Inference server on Red Hat OpenShift cluster.
-- Create a fork of the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) git repository.
+- Create a fork of the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) Git repository.
 
 ## Demo Description & Architecture
 
@@ -33,52 +33,45 @@ The application generates a project proposal for a Red Hat product.
 - Multiple LLM providers (OpenAI, Hugging Face, NVIDIA).
 - Vector Database, such as EDB Postgres for Kubernetes or Redis, to store embeddings of Red Hat product documentation.
 - Monitoring dashboard to provide key metrics such as ratings.
-- GitOps setup to deploy e2e demo (frontend / vector database / served models).
+- GitOps setup to deploy end-to-end demo (frontend / vector database / served models).
 
 ![Overview](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/intro-marketectures/rag-demo-vp-marketing-slide.png)
 
 _Figure 1. Overview of the validated pattern for RAG Demo with Red Hat OpenShift_
 
-
 ![Logical](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/logical-diagrams/rag-demo-vp-ld.png)
 
 _Figure 2. Logical diagram of the RAG Demo with Red Hat OpenShift._
 
-
 #### RAG Demo Workflow
 
 ![Overview of workflow](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-sd.png)
 
 _Figure 3. Schematic diagram for workflow of RAG demo with Red Hat OpenShift._
 
-
 #### RAG Data Ingestion
 
 ![ingestion](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-ingress-sd.png)
 
 _Figure 4. Schematic diagram for Ingestion of data for RAG._
 
-
 #### RAG Augmented Query
 
-
 ![query](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-query-sd.png)
 
 _Figure 5. Schematic diagram for RAG demo augmented query._
 
-
 In Figure 5, we can see RAG augmented query. [IBM Granite 3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) model is used for language processing, LangChain to
 integrate different tools of the LLM-based application together and to process the PDF
 files and web pages, vector database provider such as EDB Postgres for Kubernetes or Redis, is used to store vectors, and [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) to serve the [IBM Granite 3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) model, Gradio is used for user interface and object storage to store language model and other datasets.
 Solution components are deployed as microservices in the Red Hat OpenShift cluster.
 
-
 #### Download diagrams
+
 View and download all of the diagrams above in our open source tooling site.
 
 [Open Diagrams](https://www.redhat.com/architect/portfolio/tool/index.html?#gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/diagrams/rag-demo-vp.drawio)
 
-
 ![Diagram](images/diagram.png)
 _Figure 6. Proposed demo architecture with OpenShift AI_
 
@@ -93,11 +86,11 @@ _Figure 6. Proposed demo architecture with OpenShift AI_
 
 ## Deploying the demo
 
-To run the demo, ensure the Podman is running on your machine.Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops) repo into your organization
+To run the demo, ensure the Podman is running on your machine.Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops) repository into your organization
 
 ### Login to OpenShift cluster
 
-Replace the token and the api server url in the command below to login to the OpenShift cluster.
+Replace the token and the API server URL in the command below to login to the OpenShift cluster.
 
 ```sh
 oc login --token=<token> --server=<api_server_url> # login to Openshift cluster
@@ -112,7 +105,7 @@ cd rag-llm-gitops
 
 ### Configuring model
 
-This pattern deploys [IBM Granite 3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) out of box. Run the following command to configure vault with the model Id.
+This pattern deploys [IBM Granite 3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) out of box. Run the following command to configure vault with the model ID.
 
 ```sh
 # Copy values-secret.yaml.template to ~/values-secret-rag-llm-gitops.yaml.
@@ -254,10 +247,10 @@ By default, Grafana application is deployed in `llm-monitoring` namespace.To lau
 ![Routes](images/monitoring.png)
 
 ## Test Plan
+
 GOTO: [Test Plan](./TESTPLAN.md)
 
 ## Licenses
 
 EDB Postgres for Kubernetes is distributed under the EDB Limited Usage License
 Agreement, available at [enterprisedb.com/limited-use-license](https://www.enterprisedb.com/limited-use-license).
-

GPU_provisioning.md

@@ -1,10 +1,11 @@
-# Test Cases for E2E Demo.
+# Test Cases for End-to-End Demo
 
 - [Provisioning of GPU Node](https://github.com/validatedpatterns-sandbox/rag-llm-gitops/blob/main/GPU_provisioning.md)
 
   - MachineSet is created.
-    - Name of the machine set <clustername>-gpu-<AWSregion>. This should not be a hard requirement though. 
+    - Name of the machine set <clustername>-gpu-<AWSregion>. This should not be a hard requirement though.
     - Machine set has taint section
+
       ```yaml
       taints:
         - effect: NoSchedule
@@ -13,13 +14,15 @@
       ```
 
     - MachineSet has a label
+
       ```yaml
        metadata:
          labels:
            node-role.kubernetes.io/odh-notebook: '' <--- Put your label if needed
       ```
 
     - MachineSet instance type
+
       ```yaml
        providerSpec:
          value:
@@ -40,16 +43,16 @@
     - Click on the Node Feature Discovery Operator. Under NodeFeatureDiscovery an instance should be created. Status should be Available.
       ![nfd instance](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nfd-instance.png)
 
-
   - Verify NVIDIA GPU Operator is installed.
     - NVIDIA GPU Operator is installed
-      
+
       ![nvidia operator](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nvidia-operator.png)
 
     - Click on the NVIDIA GPU Operator and click on ClusterPolicy. A gpu-cluster-policy should exist
-      ![nvidia clusterpolicies](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nvidia-clusterpolicies.png)    
+      ![nvidia clusterpolicies](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-nvidia-clusterpolicies.png)
 
     - Click on the gpu-cluster-policy and click on the YAML tab. The YAML should contain the tolerations
+
       ```yaml
        tolerations:
          - effect: NoSchedule
@@ -59,13 +62,13 @@
 
 - Application provisioned correctly.
   - Click on the rag-llm namespace
-  
+
     - By Default, EDB Operator will be deployed, which will deploy PGVECTOR vector database, 6 pods should be running
       ![ragllm pgvector pods](https://validatedpatterns.io/images/rag-llm-gitops/rag-llm-pgvector.png)
 
     - If the global.db.type is set to REDIS in the values-global.yaml, four pods should be running
       ![ragllm pods](https://validatedpatterns.io/images/rag-llm-gitops/rag-llm.png)
-  
+
     - Click on Networking → Routes from the left Navigation panel. An llm-ui route should exist
       ![llm-ui route](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-application_route.png)
 
@@ -88,10 +91,5 @@
       - Enter the Grafana admin credentials.
       - Ratings are displayed for each model
     - Grafana Dashboard is displayed
-      
-      ![llm-ui grafana](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-grafana.png)
-
-
-
-	
 
+      ![llm-ui grafana](https://validatedpatterns.io/images/rag-llm-gitops/ragllm-grafana.png)