Updated the document with model configuration

content/patterns/rag-llm-gitops/_index.md

@@ -28,6 +28,8 @@ seamless provisioning of all operators and applications. It deploys a Chatbot
 application that harnesses the power of Large Language Models (LLMs) combined
 with the Retrieval-Augmented Generation (RAG) framework.
 
+The pattern uses the [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) to deploy and serve LLM models at scale.
+
 The application uses either the [EDB Postgres for Kubernetes operator](https://catalog.redhat.com/software/container-stacks/detail/5fb41c88abd2a6f7dbe1b37b)
 (default), or Redis, to store embeddings of Red Hat product documentation, running on Red Hat
 OpenShift Container Platform to generate project proposals for specific Red Hat products.
@@ -41,17 +43,18 @@ OpenShift Container Platform to generate project proposals for specific Red Hat
 
 ## Demo Description & Architecture
 
-The goal of this demo is to demonstrate a Chatbot LLM application augmented with data from Red Hat product documentation running on Red Hat OpenShift. It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. The application generates a project proposal for a Red Hat product
+The goal of this demo is to demonstrate a Chatbot LLM application augmented with data from Red Hat product documentation running on [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai). It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM.
+The application generates a project proposal for a Red Hat product.
 
 ### Key Features
 
+- Leveraging [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) to deploy and serve LLM models powered by NVIDIA GPU accelerator.
 - LLM Application augmented with content from Red Hat product documentation.
 - 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).
 
-
 ![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_

content/patterns/rag-llm-gitops/getting-started.md

@@ -42,11 +42,10 @@ _Figure 4. Schematic diagram for Ingestion of data for RAG._
 
 _Figure 5. Schematic diagram for RAG demo augmented query._
 
-In Figure 5, we can see RAG augmented query. The Mistral-7B model is used for
-language processing. LangChain is used to integrate different tools of the LLM-based
+In Figure 5, we can see RAG augmented query. Community version of [Mistral-7B-Instruct](https://huggingface.co/mistral-community/Mistral-7B-Instruct-v0.3) model is used for language processing. LangChain is used to integrate different tools of the LLM-based
 application together and to process the PDF files and web pages. A vector
 database provider such as EDB Postgres for Kubernetes (or Redis), is used to
-store vectors. HuggingFace TGI is used to serve the Mistral-7B model. Gradio is
+store vectors. [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) to serve the [Mistral-7B-Instruct](https://huggingface.co/mistral-community/Mistral-7B-Instruct-v0.3) 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 Container Platform cluster.
@@ -62,7 +61,7 @@ _Figure 6. Proposed demo architecture with OpenShift AI_
 
 ### Components deployed
 
-- **Hugging Face Text Generation Inference Server:** The pattern deploys a Hugging Face TGIS server. The server deploys `mistral-community/Mistral-7B-v0.2` model. The server will require a GPU node.
+- **vLLM Text Generation Inference Server:** The pattern deploys a vLLM Inference Server. The server deploys and serves `mistral-community/Mistral-7B-Instruct-v0.3` model. The server will require a GPU node.
 - **EDB Postgres for Kubernetes / Redis Server:** A Vector Database server is deployed to store vector embeddings created from Red Hat product documentation.
 - **Populate VectorDb Job:** The job creates the embeddings and populates the vector database.
 - **LLM Application:** This is a Chatbot application that can generate a project proposal by augmenting the LLM with the Red Hat product documentation stored in vector db.
@@ -71,21 +70,52 @@ _Figure 6. Proposed demo architecture with OpenShift AI_
 
 ## Deploying the demo
 
-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) repo 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.
+
+```sh
+oc login --token=<token> --server=<api_server_url> # login to Openshift cluster
+```
 
 ### Cloning repository
 
 ```sh
 git clone https://github.com/<<your-username>>/rag-llm-gitops.git
 cd rag-llm-gitops
-oc login --token=<> --server=<> # login to Openshift cluster
-podman machine start
+```
+
+### Configuring model
+
+This pattern deploys community version of [Mistral-7B-Instruct](https://huggingface.co/mistral-community/Mistral-7B-Instruct-v0.3) 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.
 # You should never check-in these files
 # Add secrets to the values-secret.yaml that needs to be added to the vault.
 cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml
 ```
 
+To deploy a non-community [Mistral-7b-Instruct](https://huggingface.co/mistralai/) model, grab the [Hugging Face token](https://huggingface.co/settings/tokens) and accept the terms and conditions on the model page. Edit ~/values-secret-rag-llm-gitops.yaml to replace the `model Id` and the `Hugging Face` token.
+
+```sh
+secrets:
+  - name: hfmodel
+    fields:
+    - name: hftoken
+      value: null
+    - name: modelId
+      value: "mistral-community/Mistral-7B-Instruct-v0.3"
+  - name: minio
+    fields:
+    - name: MINIO_ROOT_USER
+      value: minio
+    - name: MINIO_ROOT_PASSWORD
+      value: null
+      onMissingValue: generate
+```
+
 ### Provision GPU MachineSet
 
 As a pre-requisite to deploy the application using the validated pattern, GPU nodes should be provisioned along with Node Feature Discovery Operator and NVIDIA GPU operator. To provision GPU Nodes