Repository: validatedpatterns/docs
Pull request #567: Add docs to accompany AMD-accelerated OPEA ChatQnA pattern
Author: login: jeremyary
Labels: name: lgtm
name: ok-to-test
name: size/XL
Created: 2025-05-12T14:54:57Z | Merged: 2025-05-16T11:37:45Z

Accompanying pattern code here: https://github.com/validatedpatterns-sandbox/qna-chat-amd

- Adding documentation for new pattern which deploys OPEA's ChatQnA app and enables AMD Instinct hardware acceleration
- similar to existing Intel-contributed pattern for Gaudi accel.
- card/pattern reflect sandbox status indicator



Diff:
diff --git a/content/patterns/amd-rag-chat-qna/_index.adoc b/content/patterns/amd-rag-chat-qna/_index.adoc
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+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+partners:
+- AMD
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/validatedpatterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) and capability reasoning using large language model (LLM). The application is based on the publicly-available https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
+
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure, performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
+
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct] hardware acceleration in an enterprise-ready and distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development and usage based on declarative Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
+
+Key features::
+- AMD Instinct GPU acceleration for high-performance AI inferencing
+- GitOps-based deployment and management through Red Hat Validated Patterns
+- OPEA-based AI/ML pipeline with specialized services for document processing
+- Enterprise-grade security with HashiCorp Vault integration
+- Vector database support for efficient similarity search and retrieval
+
+[id="about-solution"]
+== About the solution
+
+The following solution integrates the https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA ChatQnA example app] with the http://localhost:1313/patterns/multicloud-gitops/[Multicloud GitOps] Validated Pattern to encapsulate every defined component as an  easily trackable resource via OpenShift GitOps dashboard:
+
+Components::
+* AI/ML Services
+** Text Embeddings Inference (TEI)
+** Document Retriever
+** Retriever Service
+** LLM-TGI (Text Generation Inference) from OPEA
+** vLLM accelerated by AMD Instinct GPUs
+** Redis Vector Database
+* Infrastructure
+** Red Hat OpenShift AI (RHOAI)
+** AMD GPU Operator
+** OpenShift Data Foundation (ODF)
+** Kernel Module Management (KMM)
+** Node Feature Discovery (NFD)
+* Security
+** HashiCorp Vault
+** External Secrets Operator
+
+.Overview of the solution
+image::/images/qna-chat-amd/amd-rag-chat-qna-overview.png[alt=OPEA Chat QnA accelerated with AMD Instinct Validated Pattern architecture,65%,65%]
+
+.Overview of application flow
+image::/images/qna-chat-amd/amd-rag-chat-qna-flow.png[OPEA Chat QnA accelerated with AMD Instinct application flow]
+
+[id="about-technology"]
+== About the technology
+
+The following technologies are used in this solution:
+
+https://www.redhat.com/en/technologies/cloud-computing/openshift/try-it[Red Hat OpenShift Platform]::
+An enterprise-ready Kubernetes container platform built for an open hybrid cloud strategy. It provides a consistent application platform to manage hybrid cloud, public cloud, and edge deployments. It delivers a complete application platform for both traditional and cloud-native applications, allowing them to run anywhere. OpenShift has a pre-configured, pre-installed, and self-updating monitoring stack that provides monitoring for core platform components. It also enables the use of external secret management systems, for example, HashiCorp Vault in this case, to securely add secrets into the OpenShift platform.
+
+https://www.redhat.com/en/technologies/cloud-computing/openshift/try-it[Red Hat OpenShift GitOps]::
+A declarative application continuous delivery tool for Kubernetes based on the ArgoCD project. Application definitions, configurations, and environments are declarative and version controlled in Git. It can automatically push the desired application state into a cluster, quickly find out if the application state is in sync with the desired state, and manage applications in multi-cluster environments.
+
+https://www.redhat.com/en/technologies/management/ansible[Red Hat Ansible Automation Platform]::
+Provides an enterprise framework for building and operating IT automation at scale across hybrid clouds including edge deployments. It enables users across an organization to create, share, and manage automation, from development and operations to security and network teams.
+
+https://www.redhat.com/en/technologies/cloud-computing/openshift-data-foundation[Red Hat OpenShift Data Foundation]::
+It is software-defined storage for containers. Red Hat OpenShift Data Foundation helps teams develop and deploy applications quickly and efficiently across clouds.
+
+https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai[Red Hat OpenShift AI]::
+Red Hat® OpenShift® AI is a flexible, scalable artificial intelligence (AI) and machine learning (ML) platform that enables enterprises to create and deliver AI-enabled applications at scale across hybrid cloud environments.
+
+https://www.redhat.com/en/technologies/cloud-computing/openshift/serverless[Red Hat OpenShift Serverless]::
+Red Hat® OpenShift® Serverless simplifies the development of hybrid cloud applications by eliminating the complexities associated with Kubernetes and the infrastructure applications are developed and deployed on. Developers will be able to focus on coding applications instead of managing intricate infrastructure details.
+
+https://www.redhat.com/en/technologies/cloud-computing/openshift/what-is-openshift-service-mesh[Red Hat OpenShift Service Mesh]::
+Red Hat® OpenShift® Service Mesh provides a uniform way to connect, manage, and observe microservices-based applications. It provides behavioral insight into—and control of—the networked microservices in your service mesh.
+
+https://catalog.redhat.com/software/container-stacks/detail/61954b7020da7eae27db0e2a[Hashicorp Vault]::
+Provides a secure centralized store for dynamic infrastructure and applications across clusters, including over low-trust networks between clouds and data centers.
+
+https://github.com/opea-project[OPEA]::
+OPEA is an ecosystem orchestration framework to integrate performant GenAI technologies and workflows leading to quicker GenAI adoption and business value.
+
+[id="next-steps_mcg-index"]
+== Next steps
+
+* link:amd-rag-chat-qna-getting-started[Deploy the pattern].
diff --git a/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc b/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
new file mode 100644
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+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup and configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model setup
+If you prefer to use tools provided with this project for preparation of the prerequisite model, <<install_rhoai,install OpenShift AI>> to leverage a workbench for notebook access.
+
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model and copying to a Minio/S3 bucket, you can do so and proceed to <<deploy_model, Deploy model via Red Hat OpenShift AI [[deploy_model]]>> afterward. OpenShift AI will be installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you can do so and skip to <<clone_pattern_code,Clone code repository>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+[start=0]
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+To install Red Hat OpenShift AI, refer to link: https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[Installing and deploying OpenShift AI]. To deploy the model, complete the following steps:
+
+. Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This opens up Red Hat OpenShift AI in a new tab.
+. In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click the Create project button.
+. Name the new project `chatqna-llm`.
+
+[start=0]
+Create a connection::
+
+To create a connection, complete the following steps. The connection will be used by init-container to fetch the model uploaded in next step when deploying the model for inferencing:
+
+. Click the Create connection button in the Connections tab in your newly created project
+. Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+. Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create workbench::
+
+To upload the model that is needed for this pattern, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+. Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click the Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+. Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open workbench::
+
+Open workbench named `chatqna` by following these steps:
+. Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+. The workbench will open up in a new tab
+.. When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+.. Click `Allow selected permissions` button in this page
+
+Clone code repository:: [[clone_pattern_code]]
+
+Now that the workbench is created and running, follow these steps to set up the project:
+
+. In the open workbench, click the `Terminal` icon in the `Launcher` tab.
+. Clone the following repository in the Terminal by running the following command:
++
+[source,terminal]
+----
+git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+----
+
+[start=0]
+Run Jupyter notebook::
+
+The notebook mentioned in this section is used to download the `meta-llama/Llama-3.1-8B-Instruct` model and upload it to either MinIO or AWS S3.
+. After the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
+. Run this notebook by running each cell one by one
+. When prompted for a HuggingFace token, provide the prerequisite User Access Token and click `Login`
++
+image::/images/qna-chat-amd/rhoai-upload-model.png[Red Hat OpenShift AI - Run Jupyter notebook - Hugging Face token prompt, 50%, 50%]
++
+. When the notebook successfully runs, llama model should have been uploaded to either MinIO or AWS S3 under Llama-3.1-8B-Instruct directory in models bucket
++
+[NOTE]
+====
+By default, this notebook will upload the model to MinIO. To choose AWS S3, modify the last cell in the notebook by changing the value of XFER_LOCATION to AWS as shown below:
+[source,python]
+----
+XFER_LOCATION = 'MINIO'  <= current value
+XFER_LOCATION = 'AWS'    <= modified to upload to AWS S3
+----
+====
+
+=== Deploy model via Red Hat OpenShift AI [[deploy_model]]
+
+Once the initial notebook has run successfully and the model is uploaded, you can deploy the model by following these steps:
+
+. In the chatqna-llm data science project, select Models tab and click the Deploy model button and fill in the following fields as shown below:
+* Model name: `llama-31b`
+* Serving runtime: `vLLM AMD GPU ServingRuntime for KServe`
+* Model framework: `vLLM`
+* Deployment mode: `Advanced`
+* Model server size: `Small`
+* Accelerator: `AMD`
+* Model route: Enable `Make deployed models available through an external route` checkbox
+* Source Model location: Select `Existing connection` option
++
+--
+** Name: `model-store` (this is the name we used when we created the connection in `Create Connection` step)
+** Path: `Llama-3.1-8B-Instruct`
+*** Location where the model was copied to in the previous step
+--
++
+. Click the Deploy to deploy this model.
+. Once the model is successfully deployed, copy the inference endpoint to use it in the ChatQnA application (it will take a few minutes to deploy the model).
++
+[WARNING]
+====
+Make sure the model name is set to `llama-31b` as this is the value used in the deployment of llm microservice that invokes the inference endpoint.
+====
+
+=== Deploy ChatQnA application
+
+This section provides details on installing the ChatQnA application as well as verifying the deployment and configuration by querying the application.
+
+Install ChatQnA app::
+
+Once all the prerequisites are met, install the ChatQnA application
+. Clone the repository by running the following commands:
++
+[source,terminal]
+----
+git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+cd qna-chat-amd
+----
+. Configure secrets for Hugging Face and inference endpoint
++
+[source,terminal]
+----
+cp values-secret.yaml.template ~/values-secret-qna-chat-amd.yaml
+----
++
+. Modify the `value` field in `~/values-secret-qna-chat-amd.yaml` file
++
+[source,yaml]
+----
+secrets:
+- name: huggingface
+  fields:
+  - name: token
+    value: null  <- CHANGE THIS TO YOUR HUGGING_FACE TOKEN
+    vaultPolicy: validatePatternDefaultPolicy
+- name: rhoai_model
+  fields:
+  - name: inference_endpoint
+    value: null  <- CHANGE THIS TO YOUR MODEL'S INFERENCE ENDPOINT
+----
++
+. Deploy the application
++
+[source,terminal]
+----
+./pattern.sh make install
+----
++
+The above command will install the application by deploying the ChatQnA megaservice along with the following required microservices:
+
+** Dataprep
+** LLM text generation
+** Retriever
+** Hugging Face Text Embedding Inference
+*** Embedding service
+*** Reranker service
+** ChatQnA backend
+** ChatQnA UI
++
+[NOTE]
+====
+Processes for the build and installation of all the required services can take some time to complete. To monitor progress via the ArgoCD application dashboard, follow these steps:
+
+* Open ArgoCD dashboard, in a browser, using the URI returned
+
+[source,terminal]
+----
+echo https://$(oc get route hub-gitops-server -n qna-chat-amd-hub -o jsonpath="{.spec.host}")
+----
+* Get the password
+
+[source,terminal]
+----
+echo $(oc get secret hub-gitops-cluster -n qna-chat-amd-hub -o jsonpath="{.data['admin\.password']}" | base64 -d)
+----
+
+* Login to the ArgoCD dashboard
+** Username: `admin`
+** Password: _password from the previous step_
+====
+
+Verify ChatQnA app::
+Once the application is installed, we can add the knowledge base using a PDF and then query against that data by following these steps:
+. Run the following command to get the ChatQnA UI URI:
++
+[source,terminal]
+----
+echo https://$(oc get route chatqna-ui-secure -n amd-llm -o jsonpath="{.spec.host}")
+----
+. Open ChatQnA UI, in a browser, by using the URI returned from above command
+
+Query ChatQnA without RAG::
++
+. Type the following query at the prompt:
++
+[source,text]
+----
+What is the revenue of Nike in 2023?
+----
++
+Since we have not yet provided any external knowledge base regarding the above query to the application, it does not the correct answer to this query and returns a generic response:
++
+
+image::/images/qna-chat-amd/chatqna-ui-no-rag.png[ChatQnA UI - response without RAG,40%,40%]
+
+Query ChatQnA with RAG - add external knowledge base::
+
+In the ChatQnA UI, follow the steps given below to add an external knowledge base (Nike PDF) to perform the above query using RAG:
+. Click the upload icon in the top right corner
++
+image::/images/qna-chat-amd/chatqna-upload-icon.jpeg[ChatQnA UI - upload icon,40%,40%]
++
+. Click the Choose File button and select `nke-10k-2023.pdf` from _scripts_ directory. When you select the pdf and close the dialog box, the upload will start automatically.
++
+image::/images/qna-chat-amd/chatqna-upload-file.jpeg[ChatQnA UI - Upload File,40%,40%]
++
+. Allow a few minutes for the file to be ingested and processed, and uploaded to the Redis vector database
+. Refresh the page after a few minutes to verify the file has been uploaded
+. Type the following query at the prompt:
++
+[source,text]
+----
+What is the revenue of Nike inc in 2023?
+----
++
+The response for this query makes use of the Nike knowledge base, added in previous step, and is shown in the figure below:
++
+image::/images/qna-chat-amd/chatqna-request-response.png[ChatQnA UI - request and response,50%,50%]
++
+Query ChatQnA - remove external knowledge base::
+
+Follow the steps given in this section to remove the external knowledge base that was added to the app:
+
+. Click the upload icon in the top right corner
+. Move your cursor on top of the file in the Data Source section and click the trashcan icon that pops up in the top right corner of the file icon.
+. Select `Yes, I'm sure` when prompted with `"Confirm file deletion?"` dialog
++
+image::/images/qna-chat-amd/chatqna-delete-file.png[ChatQnA UI - delete knowledge base,40%,40%]
++
+
+Query ChatQnA - general questions::
+
+* When the knowledge base is not added to the app, you can also use the application to ask general questions, such as:
+** Tell me more about Red Hat
+** What services does red hat provide?
+** What is Deep Learning?
+** What is a Neural Network?
++
+image::/images/qna-chat-amd/chatqna-long-response.png[ChatQnA UI - response for general questions,50%,50%]
+
+=== Conclusion
+
+In this article, we deployed Open Platform for Enterprise AI’s ChatQnA megaservice in Red Hat OpenShift Container Platform using Red Hat OpenShift AI and AMD hardware acceleration.
+
+The ChatQnA application makes use of OPEA’s microservices to return RAG responses using external knowledge base (Nike pdf) as well as invoke Llama LLM when there is no external knowledge base present.
+
+Installing and setting up the application was made easy with the use of a Validated Pattern that in turn uses ArgoCD for the CI/CD pipeline to deploy various components of the application as well as to keep them in sync with the git repository in case of any config changes.
+
+Follow the links in the references section to learn more about the various technologies used in this article.
+
+=== References
+
+* https://github.com/validatedpatterns-sandbox/qna-chat-amd.git[Config repository]
+* https://www.redhat.com/en/products/ai/openshift-ai[Red Hat OpenShift AI]
+* https://www.redhat.com/en/technologies/cloud-computing/openshift/container-platform[Red Hat OpenShift Container Platform]
+* https://validatedpatterns.io/[Validated Patterns]
+* https://opea.dev/[Open Platform for Enterprise AI (OPEA)]
+** https://github.com/opea-project/GenAIComps[Various microservices]
+** https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[ChatQnA application]
+* https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[Hugging Face Llama-3.1-8B-Instruct model]
diff --git a/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-required-hardware.adoc b/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-required-hardware.adoc
new file mode 100644
index 0000000000..0e6ea7f128
--- /dev/null
+++ b/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-required-hardware.adoc
@@ -0,0 +1,41 @@
+---
+title: Required hardware
+weight: 30
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-required-hardware/
+---
+
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+== Required hardware
+
+:_content-type: CONCEPT
+:imagesdir: ../../images
+
+[id="about-openshift"]
+== Red Hat OpenShift Container Platform
+
+OpenShift Container Platform is a cloud-based Kubernetes container platform. The foundation of OpenShift Container Platform is based on Kubernetes and therefore shares the same technology. It is
+designed to allow applications and the data centers that support them to expand from just a few machines and applications to thousands of machines that serve millions of clients. OpenShift Container Platform enables you to do the following:
+
+* Provide developers and IT organizations with cloud application platforms that can be used for deploying applications on secure and scalable resources.
+* Require minimal configuration and management overhead.
+* Bring the Kubernetes platform to customer data centers and cloud.
+* Meet security, privacy, compliance, and governance requirements.
+
+With its foundation in Kubernetes, OpenShift Container Platform incorporates the same technology that serves as the engine for massive telecommunications, streaming video, gaming, banking, and other applications.
+Its implementation in open Red Hat technologies lets you extend your containerized applications beyond a single cloud to on-premise and multi-cloud environments. For more information including hardware requirements,
+visit https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/getting_started/openshift-overview[Red Hat's product documentation].
+
+[id="about-amd-instinct"]
+== AMD Instinct™
+
+Generative AI is transforming the way enterprise customers operate. In fact, AI is quickly becoming a part of nearly every business process that it supports, from customer service to data analytics,
+and that deepening integration is only going to grow. However, AI is a relatively new workload, added to existing infrastructure and putting strain on current hardware configurations.
+
+If customers want to enjoy seamless AI experiences and productivity gains immediately and over the long-term, they need help evolving their IT infrastructure. That’s where AMD technologies come in,
+offering enterprises the performance and efficiency to operate existing workflows alongside the new possibilities that AI brings to the table.
+
+For more information, visit the link:https://www.amd.com/en/products/accelerators/instinct.html#overview[AMD Instinct] website.
diff --git a/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-troubleshooting.adoc b/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-troubleshooting.adoc
new file mode 100644
index 0000000000..ef552bbbe1
--- /dev/null
+++ b/content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-troubleshooting.adoc
@@ -0,0 +1,35 @@
+---
+title: Troubleshooting
+weight: 40
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-troubleshooting/
+---
+
+:toc:
+:imagesdir: /images
+:_content-type: REFERENCE
+include::modules/comm-attributes.adoc[]
+
+[id="troubleshooting-the-pattern-deployment"]
+=== Troubleshooting common pattern deployment issues
+
+'''
+Problem:: Validated Pattern installation process is stuck on deploying Vault
+
+Solution:: Most common reason of this is that prerequisites are not satisfied. Please refer to section `Getting started -> Prerequisites` and make sure all is done before proceeding to pattern deployment.
+
+
+'''
+
+Problem:: Downloading AI model `Llama-3.1-8B-Instruct` using supplied Jupyter notebook is failing or deployment fails after model is downloaded
+
+Solution:: Most often this is due to some network errors while downloading the model. If not sure if whole model was downloaded, please clear the storage bucket and repeat the download process.
+
+
+'''
+
+Problem:: Builds of OPEA chat resources are failing, i.e. cannot pull images
+
+Solution:: If all Build resources are failing due to Image Pull errors, ensure there is no proxy issue. Refer to section `Getting started -> Procedure`. Also make sure that Image Registry is properly set up.
+
+
+'''
\ No newline at end of file
diff --git a/modules/comm-attributes.adoc b/modules/comm-attributes.adoc
index af1a142bc1..0660662a2b 100644
--- a/modules/comm-attributes.adoc
+++ b/modules/comm-attributes.adoc
@@ -33,6 +33,7 @@
 :amx-rhoai-mcg-pattern: Intel AMX accelerated Multicloud GitOps pattern with Openshift AI
 :qat-mcg-pattern: Intel QAT accelerated Multicloud GitOps pattern
 :gcqna-pattern: OPEA QnA chat accelerated with Intel Gaudi pattern
+:amdqna-pattern: OPEA QnA chat accelerated with AMD Instinct pattern
 :mcg: multicloud GitOps
 :med-pattern: Medical Diagnosis pattern
 :amx-med-pattern: Intel AMX accelerated Medical Diagnosis pattern
@@ -186,3 +187,5 @@
 :oai: OpenShift Data Science
 // Icons
 :grid: grid.png
+// AMD
+:amd-instinct: AMD Instinct
\ No newline at end of file
diff --git a/static/images/qna-chat-amd/amd-rag-chat-qna-flow.png b/static/images/qna-chat-amd/amd-rag-chat-qna-flow.png
new file mode 100644
index 0000000000..a224ac4ac1
Binary files /dev/null and b/static/images/qna-chat-amd/amd-rag-chat-qna-flow.png differ
diff --git a/static/images/qna-chat-amd/amd-rag-chat-qna-overview.png b/static/images/qna-chat-amd/amd-rag-chat-qna-overview.png
new file mode 100644
index 0000000000..41302e083f
Binary files /dev/null and b/static/images/qna-chat-amd/amd-rag-chat-qna-overview.png differ
diff --git a/static/images/qna-chat-amd/chatqna-delete-file.png b/static/images/qna-chat-amd/chatqna-delete-file.png
new file mode 100644
index 0000000000..4c31c28035
Binary files /dev/null and b/static/images/qna-chat-amd/chatqna-delete-file.png differ
diff --git a/static/images/qna-chat-amd/chatqna-long-response.png b/static/images/qna-chat-amd/chatqna-long-response.png
new file mode 100644
index 0000000000..a553c87179
Binary files /dev/null and b/static/images/qna-chat-amd/chatqna-long-response.png differ
diff --git a/static/images/qna-chat-amd/chatqna-request-response.png b/static/images/qna-chat-amd/chatqna-request-response.png
new file mode 100644
index 0000000000..2127845abb
Binary files /dev/null and b/static/images/qna-chat-amd/chatqna-request-response.png differ
diff --git a/static/images/qna-chat-amd/chatqna-ui-no-rag.png b/static/images/qna-chat-amd/chatqna-ui-no-rag.png
new file mode 100644
index 0000000000..4d8b109ec2
Binary files /dev/null and b/static/images/qna-chat-amd/chatqna-ui-no-rag.png differ
diff --git a/static/images/qna-chat-amd/chatqna-upload-file.jpeg b/static/images/qna-chat-amd/chatqna-upload-file.jpeg
new file mode 100644
index 0000000000..7f7c12e8fc
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diff --git a/static/images/qna-chat-amd/chatqna-upload-icon.jpeg b/static/images/qna-chat-amd/chatqna-upload-icon.jpeg
new file mode 100644
index 0000000000..16b5642c52
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diff --git a/static/images/qna-chat-amd/rhoai-create-connection-2.png b/static/images/qna-chat-amd/rhoai-create-connection-2.png
new file mode 100644
index 0000000000..987b2fa72f
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diff --git a/static/images/qna-chat-amd/rhoai-create-workbench.png b/static/images/qna-chat-amd/rhoai-create-workbench.png
new file mode 100644
index 0000000000..9ad7623d4f
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diff --git a/static/images/qna-chat-amd/rhoai-upload-model.png b/static/images/qna-chat-amd/rhoai-upload-model.png
new file mode 100644
index 0000000000..4dc4436999
Binary files /dev/null and b/static/images/qna-chat-amd/rhoai-upload-model.png differ


Reviews:
user: login: abhatt-rh
body: Hi @jeremyary 
Thank you for submitting this PR. On a cursory look, I have a added a couple of style-related comments based IBM Style and Red Hat Supplementary Style guides.

state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-05-15T15:51:08Z
user: login: jeremyary
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-15T19:39:45Z
user: login: abhatt-rh
body: Hi @jeremyary, 
Thank you for addressing the comments. Please squash all the commits to one. I have added a comment for AMD to show up under our _Partners_ filter like shown here:
![image](https://github.com/user-attachments/assets/975459c9-b35b-4c42-b089-094325cba76c)

state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-05-16T10:01:47Z
user: login: jeremyary
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-05-16T11:26:25Z
user: login: abhatt-rh
body: /lgtm
Proceeding with merge
state: COMMENTED
author_association: COLLABORATOR
submitted_at: 2025-05-16T11:37:18Z

Review comments:
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: ```suggestion
  help: https://groups.google.com/g/validatedpatterns
```
created_at: 2025-05-14T17:53:27Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: ```suggestion
This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) and capability reasoning using large language model (LLM). The application is based on the publicly-available
```
created_at: 2025-05-14T17:54:25Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
+https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
++
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the
+implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure,
+performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
++
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct]
+hardware acceleration in an enterprise-ready & distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development & usage based on declarative
+Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: Remove hard line breaks
created_at: 2025-05-14T17:55:38Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
+https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
++
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the
+implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure,
+performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
++
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct]
+hardware acceleration in an enterprise-ready & distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development & usage based on declarative
+Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
+
+Key Features::
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: ```suggestion
Key features::
```
created_at: 2025-05-14T17:56:02Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
+https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
++
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the
+implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure,
+performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
++
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct]
+hardware acceleration in an enterprise-ready & distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development & usage based on declarative
+Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
+
+Key Features::
+- AMD Instinct GPU acceleration for high-performance AI inferencing
+- GitOps-based deployment and management through Red Hat Validated Patterns
+- OPEA-based AI/ML pipeline with specialized services for document processing
+- Enterprise-grade security with HashiCorp Vault integration
+- Vector database support for efficient similarity search and retrieval
+
+[id="about-solution"]
+== About the solution
+
+The following solution integrates the https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA ChatQnA example app] with the http://localhost:1313/patterns/multicloud-gitops/[Multicloud GitOps] Validated Pattern to encapsulate every defined component as an
+easily trackable resource via OpenShift GitOps dashboard:
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: Remove hard line break
created_at: 2025-05-14T17:56:31Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
+https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
++
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the
+implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure,
+performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
++
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct]
+hardware acceleration in an enterprise-ready & distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development & usage based on declarative
+Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
+
+Key Features::
+- AMD Instinct GPU acceleration for high-performance AI inferencing
+- GitOps-based deployment and management through Red Hat Validated Patterns
+- OPEA-based AI/ML pipeline with specialized services for document processing
+- Enterprise-grade security with HashiCorp Vault integration
+- Vector database support for efficient similarity search and retrieval
+
+[id="about-solution"]
+== About the solution
+
+The following solution integrates the https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA ChatQnA example app] with the http://localhost:1313/patterns/multicloud-gitops/[Multicloud GitOps] Validated Pattern to encapsulate every defined component as an
+easily trackable resource via OpenShift GitOps dashboard:
+
+Components::
+* AI/ML Services
+** Text Embeddings Inference (TEI)
+** Document Retriever
+** Retriever Service
+** LLM-TGI (Text Generation Inference) from OPEA
+** vLLM accelerated by AMD Instinct GPUs
+** Redis Vector Database
+* Infrastructure
+** Red Hat OpenShift AI (RHOAI)
+** AMD GPU Operator
+** OpenShift Data Foundation (ODF)
+** Kernel Module Management (KMM)
+** Node Feature Discovery (NFD)
+* Security
+** HashiCorp Vault
+** External Secrets Operator
+
+{empty} +
+
+.Overview of the solution
+image::/images/qna-chat-amd/amd-rag-chat-qna-overview.png[alt=OPEA Chat QnA accelerated with AMD Instinct Validated Pattern architecture,65%,65%]
+
+{empty} +
+
+.Overview of application flow
+image::/images/qna-chat-amd/amd-rag-chat-qna-flow.png[OPEA Chat QnA accelerated with AMD Instinct application flow]
+
+{empty} +
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: ```suggestion

```
created_at: 2025-05-14T17:57:32Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
+https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
++
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the
+implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure,
+performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
++
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct]
+hardware acceleration in an enterprise-ready & distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development & usage based on declarative
+Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
+
+Key Features::
+- AMD Instinct GPU acceleration for high-performance AI inferencing
+- GitOps-based deployment and management through Red Hat Validated Patterns
+- OPEA-based AI/ML pipeline with specialized services for document processing
+- Enterprise-grade security with HashiCorp Vault integration
+- Vector database support for efficient similarity search and retrieval
+
+[id="about-solution"]
+== About the solution
+
+The following solution integrates the https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA ChatQnA example app] with the http://localhost:1313/patterns/multicloud-gitops/[Multicloud GitOps] Validated Pattern to encapsulate every defined component as an
+easily trackable resource via OpenShift GitOps dashboard:
+
+Components::
+* AI/ML Services
+** Text Embeddings Inference (TEI)
+** Document Retriever
+** Retriever Service
+** LLM-TGI (Text Generation Inference) from OPEA
+** vLLM accelerated by AMD Instinct GPUs
+** Redis Vector Database
+* Infrastructure
+** Red Hat OpenShift AI (RHOAI)
+** AMD GPU Operator
+** OpenShift Data Foundation (ODF)
+** Kernel Module Management (KMM)
+** Node Feature Discovery (NFD)
+* Security
+** HashiCorp Vault
+** External Secrets Operator
+
+{empty} +
+
+.Overview of the solution
+image::/images/qna-chat-amd/amd-rag-chat-qna-overview.png[alt=OPEA Chat QnA accelerated with AMD Instinct Validated Pattern architecture,65%,65%]
+
+{empty} +
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: ```suggestion

```
created_at: 2025-05-14T17:57:42Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,121 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
+industries:
+- General
+aliases: /amd-rag-chat-qna/
+#pattern_logo: amd-rag-chat-qna.png
+links:
+  install: amd-rag-chat-qna-getting-started
+  help: https://groups.google.com/g/hybrid-cloud-patterns
+  bugs: https://github.com/validatedpatterns-sandbox/
+---
+:toc:
+:imagesdir: /images
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+
+[id="about-amd-rag-chat-qna-pattern"]
+= About {amdqna-pattern}
+
+Background::
+This Validated Pattern implements an enterprise-ready question-and-answer chatbot utilizing retrieval-augmented generation (RAG) & capability reasoning via large language model (LLM). The application is based on the publicly-available
+https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA Chat QnA] example application created by the https://opea.dev/[Open Platform for Enterprise AI (OPEA)] community.
++
+OPEA provides a framework that enables the creation and evaluation of open, multi-provider, robust, and composable generative AI (GenAI) solutions. It harnesses the best innovations across the ecosystem while keeping enterprise-level needs front and center. It simplifies the
+implementation of enterprise-grade composite GenAI solutions, starting with a focus on Retrieval Augmented Generative AI (RAG). The platform is designed to facilitate efficient integration of secure,
+performant, and cost-effective GenAI workflows into business systems and manage its deployments, leading to quicker GenAI adoption and business value.
++
+This pattern aims to leverage the strengths of OPEA's framework in combination with other OpenShift-centric toolings in order to deploy a modern, LLM-backed reasoning application stack capable of leveraging https://www.amd.com/en/products/accelerators/instinct.html[AMD's Instinct]
+hardware acceleration in an enterprise-ready & distributed manner. The pattern utilizes https://www.redhat.com/en/technologies/cloud-computing/openshift/gitops[Red Hat OpenShift GitOps] to bring a continuous delivery approach to the application's development & usage based on declarative
+Git-driven workflows, backed by a centralized, single-source-of-truth git repository.
+
+Key Features::
+- AMD Instinct GPU acceleration for high-performance AI inferencing
+- GitOps-based deployment and management through Red Hat Validated Patterns
+- OPEA-based AI/ML pipeline with specialized services for document processing
+- Enterprise-grade security with HashiCorp Vault integration
+- Vector database support for efficient similarity search and retrieval
+
+[id="about-solution"]
+== About the solution
+
+The following solution integrates the https://github.com/opea-project/GenAIExamples/tree/main/ChatQnA[OPEA ChatQnA example app] with the http://localhost:1313/patterns/multicloud-gitops/[Multicloud GitOps] Validated Pattern to encapsulate every defined component as an
+easily trackable resource via OpenShift GitOps dashboard:
+
+Components::
+* AI/ML Services
+** Text Embeddings Inference (TEI)
+** Document Retriever
+** Retriever Service
+** LLM-TGI (Text Generation Inference) from OPEA
+** vLLM accelerated by AMD Instinct GPUs
+** Redis Vector Database
+* Infrastructure
+** Red Hat OpenShift AI (RHOAI)
+** AMD GPU Operator
+** OpenShift Data Foundation (ODF)
+** Kernel Module Management (KMM)
+** Node Feature Discovery (NFD)
+* Security
+** HashiCorp Vault
+** External Secrets Operator
+
+{empty} +
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: ```suggestion

```
created_at: 2025-05-14T17:57:51Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
* If you prefer to use tools provided with this project for preparation of the prerequisite model, <<install_rhoai,install OpenShift AI>> to leverage a workbench for notebook access.
```
created_at: 2025-05-14T17:59:13Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model and copying to a Minio/S3 bucket, you may do so and proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be installed later during pattern execution.
```
created_at: 2025-05-14T18:00:17Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: Remove hard line breaks
```suggestion

```
created_at: 2025-05-14T18:00:37Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
If you prefer to run the provided notebooks in another environment, you can do so and skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
```
created_at: 2025-05-14T18:01:25Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: Remove the hard line break
created_at: 2025-05-14T18:01:47Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This opens Red Hat OpenShift AI in a new tab.
```
created_at: 2025-05-14T18:03:08Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click the Create project button.
```
created_at: 2025-05-14T18:05:53Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
* Name the new project `chatqna-llm`. 
```
created_at: 2025-05-14T18:06:06Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
Create a connection::
```
created_at: 2025-05-15T14:35:53Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
To install Red Hat OpenShift AI, refer to link: https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[Installing and deploying OpenShift AI]
```
created_at: 2025-05-15T15:06:16Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
To deploy the model, complete the following steps:
```
created_at: 2025-05-15T15:10:41Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: Global comment: Use ordered list in procedures
```suggestion
. Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
```
created_at: 2025-05-15T15:11:26Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
To create a connection, complete the following steps. The connection will be used by init-container to fetch the model uploaded in next step when deploying the model for inferencing:
```
created_at: 2025-05-15T15:38:35Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: Use ordered list for procedures
```suggestion
. Click on the Create connection button in the Connections tab in your newly created project
```
created_at: 2025-05-15T15:39:05Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
Create workbench::
```
created_at: 2025-05-15T15:39:24Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: By _article_, did you mean this _pattern_?
created_at: 2025-05-15T15:41:15Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
=== Upload model using Red Hat OpenShift AI [[notebook_model_prep]]
```
created_at: 2025-05-15T15:41:38Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
To serve the model, download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
```
created_at: 2025-05-15T15:42:08Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
. Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
```
created_at: 2025-05-15T15:42:27Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+* The workbench will open up in a new tab
+** When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+** Click `Allow selected permissions` button in this page
+
+Clone Code Repository:: [[clone_pattern_code]]
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
Clone code repository:: [[clone_pattern_code]]
```
created_at: 2025-05-15T15:42:38Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+* The workbench will open up in a new tab
+** When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+** Click `Allow selected permissions` button in this page
+
+Clone Code Repository:: [[clone_pattern_code]]
+
+Now that the workbench is created and running, follow these steps to setup the project:
+
+* In the open workbench, click on the `Terminal` icon in the `Launcher` tab.
+* Clone the following repository in the Terminal by running the following command:
+
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+
+Run Jupyter notebook::
+
+The notebook mentioned in this section is used to download the `meta-llama/Llama-3.1-8B-Instruct` model and upload it to either MinIO or AWS S3.
+* Once the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
* After the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
```
created_at: 2025-05-15T15:42:58Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+* The workbench will open up in a new tab
+** When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+** Click `Allow selected permissions` button in this page
+
+Clone Code Repository:: [[clone_pattern_code]]
+
+Now that the workbench is created and running, follow these steps to setup the project:
+
+* In the open workbench, click on the `Terminal` icon in the `Launcher` tab.
+* Clone the following repository in the Terminal by running the following command:
+
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+
+Run Jupyter notebook::
+
+The notebook mentioned in this section is used to download the `meta-llama/Llama-3.1-8B-Instruct` model and upload it to either MinIO or AWS S3.
+* Once the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
+* Run this notebook by running each cell one by one
+** When prompted for a HuggingFace token, provide the prerequisite User Access Token and click `Login`
++
+image::/images/qna-chat-amd/rhoai-upload-model.png[Red Hat OpenShift AI - Run Jupyter notebook - Hugging Face token prompt, 50%, 50%]
++
+** When the notebook successfully runs, llama model should have been uploaded to either MinIO or AWS S3 under Llama-3.1-8B-Instruct directory in models bucket
++
+[NOTE]
+====
+By default, this notebook will upload the model to MinIO. To choose AWS S3, modify the last cell in the notebook by changing the value of XFER_LOCATION to AWS as shown below:
+----
+    XFER_LOCATION = 'MINIO'  <= current value
+    XFER_LOCATION = 'AWS'    <= modified to upload to AWS S3
+----
+====
+
+=== Deploy Model via Red Hat OpenShift AI
+
+Once the initial notebook has run successfully and the model is uploaded, you can deploy the model by following these steps:
+
+In the chatqna-llm data science project, select Models tab and click on Deploy model button and fill in the following fields as shown below:
+
+* Model name: `llama-31b`
+* Serving runtime: `vLLM AMD GPU ServingRuntime for KServe`
+* Model framework: `vLLM`
+* Deployment mode: `Advanced`
+* Model server size: `Small`
+* Accelerator: `AMD`
+* Model route: Enable `Make deployed models available through an external route` checkbox
+* Source Model location: Select `Existing connection` option
++
+--
+** Name: `model-store` (this is the name we used when we created the connection in `Create Connection` step)
+** Path: `Llama-3.1-8B-Instruct`
+*** Location where the model was copied to in the previous step
+--
++
+Click on Deploy to deploy this model. Once the model is successfully deployed, copy the inference endpoint to use it in the ChatQnA application (it will take a few minutes to deploy the model).
++
+[WARNING]
+====
+Make sure the model name is set to `llama-31b` as this is the value used in the deployment of llm micro-service that invokes the inference endpoint.
+====
+
+=== Deploy ChatQnA application
+
+This section provides details on installing the ChatQnA application as well as verifying the deployment and configuration by querying the application.
+
+Install ChatQnA app::
+
+Once all the prerequisites are met, install the ChatQnA application
+* Clone the repository by running the following commands:
++
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: Global comment: Use appropriate code blacks for commands and code snippets
```suggestion
    [source,terminal]
    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
```
created_at: 2025-05-15T15:44:19Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+* The workbench will open up in a new tab
+** When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+** Click `Allow selected permissions` button in this page
+
+Clone Code Repository:: [[clone_pattern_code]]
+
+Now that the workbench is created and running, follow these steps to setup the project:
+
+* In the open workbench, click on the `Terminal` icon in the `Launcher` tab.
+* Clone the following repository in the Terminal by running the following command:
+
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+
+Run Jupyter notebook::
+
+The notebook mentioned in this section is used to download the `meta-llama/Llama-3.1-8B-Instruct` model and upload it to either MinIO or AWS S3.
+* Once the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
+* Run this notebook by running each cell one by one
+** When prompted for a HuggingFace token, provide the prerequisite User Access Token and click `Login`
++
+image::/images/qna-chat-amd/rhoai-upload-model.png[Red Hat OpenShift AI - Run Jupyter notebook - Hugging Face token prompt, 50%, 50%]
++
+** When the notebook successfully runs, llama model should have been uploaded to either MinIO or AWS S3 under Llama-3.1-8B-Instruct directory in models bucket
++
+[NOTE]
+====
+By default, this notebook will upload the model to MinIO. To choose AWS S3, modify the last cell in the notebook by changing the value of XFER_LOCATION to AWS as shown below:
+----
+    XFER_LOCATION = 'MINIO'  <= current value
+    XFER_LOCATION = 'AWS'    <= modified to upload to AWS S3
+----
+====
+
+=== Deploy Model via Red Hat OpenShift AI
+
+Once the initial notebook has run successfully and the model is uploaded, you can deploy the model by following these steps:
+
+In the chatqna-llm data science project, select Models tab and click on Deploy model button and fill in the following fields as shown below:
+
+* Model name: `llama-31b`
+* Serving runtime: `vLLM AMD GPU ServingRuntime for KServe`
+* Model framework: `vLLM`
+* Deployment mode: `Advanced`
+* Model server size: `Small`
+* Accelerator: `AMD`
+* Model route: Enable `Make deployed models available through an external route` checkbox
+* Source Model location: Select `Existing connection` option
++
+--
+** Name: `model-store` (this is the name we used when we created the connection in `Create Connection` step)
+** Path: `Llama-3.1-8B-Instruct`
+*** Location where the model was copied to in the previous step
+--
++
+Click on Deploy to deploy this model. Once the model is successfully deployed, copy the inference endpoint to use it in the ChatQnA application (it will take a few minutes to deploy the model).
++
+[WARNING]
+====
+Make sure the model name is set to `llama-31b` as this is the value used in the deployment of llm micro-service that invokes the inference endpoint.
+====
+
+=== Deploy ChatQnA application
+
+This section provides details on installing the ChatQnA application as well as verifying the deployment and configuration by querying the application.
+
+Install ChatQnA app::
+
+Once all the prerequisites are met, install the ChatQnA application
+* Clone the repository by running the following commands:
++
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+    cd qna-chat-amd
++
+* Configure secrets for Hugging Face and inference endpoint
++
+    cp values-secret.yaml.template ~/values-secret-qna-chat-amd.yaml
++
+* Modify the `value` field in `~/values-secret-qna-chat-amd.yaml` file
++
+----
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
[source,yaml]
----
```
created_at: 2025-05-15T15:45:20Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+* The workbench will open up in a new tab
+** When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+** Click `Allow selected permissions` button in this page
+
+Clone Code Repository:: [[clone_pattern_code]]
+
+Now that the workbench is created and running, follow these steps to setup the project:
+
+* In the open workbench, click on the `Terminal` icon in the `Launcher` tab.
+* Clone the following repository in the Terminal by running the following command:
+
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+
+Run Jupyter notebook::
+
+The notebook mentioned in this section is used to download the `meta-llama/Llama-3.1-8B-Instruct` model and upload it to either MinIO or AWS S3.
+* Once the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
+* Run this notebook by running each cell one by one
+** When prompted for a HuggingFace token, provide the prerequisite User Access Token and click `Login`
++
+image::/images/qna-chat-amd/rhoai-upload-model.png[Red Hat OpenShift AI - Run Jupyter notebook - Hugging Face token prompt, 50%, 50%]
++
+** When the notebook successfully runs, llama model should have been uploaded to either MinIO or AWS S3 under Llama-3.1-8B-Instruct directory in models bucket
++
+[NOTE]
+====
+By default, this notebook will upload the model to MinIO. To choose AWS S3, modify the last cell in the notebook by changing the value of XFER_LOCATION to AWS as shown below:
+----
+    XFER_LOCATION = 'MINIO'  <= current value
+    XFER_LOCATION = 'AWS'    <= modified to upload to AWS S3
+----
+====
+
+=== Deploy Model via Red Hat OpenShift AI
+
+Once the initial notebook has run successfully and the model is uploaded, you can deploy the model by following these steps:
+
+In the chatqna-llm data science project, select Models tab and click on Deploy model button and fill in the following fields as shown below:
+
+* Model name: `llama-31b`
+* Serving runtime: `vLLM AMD GPU ServingRuntime for KServe`
+* Model framework: `vLLM`
+* Deployment mode: `Advanced`
+* Model server size: `Small`
+* Accelerator: `AMD`
+* Model route: Enable `Make deployed models available through an external route` checkbox
+* Source Model location: Select `Existing connection` option
++
+--
+** Name: `model-store` (this is the name we used when we created the connection in `Create Connection` step)
+** Path: `Llama-3.1-8B-Instruct`
+*** Location where the model was copied to in the previous step
+--
++
+Click on Deploy to deploy this model. Once the model is successfully deployed, copy the inference endpoint to use it in the ChatQnA application (it will take a few minutes to deploy the model).
++
+[WARNING]
+====
+Make sure the model name is set to `llama-31b` as this is the value used in the deployment of llm micro-service that invokes the inference endpoint.
+====
+
+=== Deploy ChatQnA application
+
+This section provides details on installing the ChatQnA application as well as verifying the deployment and configuration by querying the application.
+
+Install ChatQnA app::
+
+Once all the prerequisites are met, install the ChatQnA application
+* Clone the repository by running the following commands:
++
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+    cd qna-chat-amd
++
+* Configure secrets for Hugging Face and inference endpoint
++
+    cp values-secret.yaml.template ~/values-secret-qna-chat-amd.yaml
++
+* Modify the `value` field in `~/values-secret-qna-chat-amd.yaml` file
++
+----
+    secrets:
+    - name: huggingface
+      fields:
+      - name: token
+        value: null  <- CHANGE THIS TO YOUR HUGGING_FACE TOKEN
+        vaultPolicy: validatePatternDefaultPolicy
+
+    - name: rhoai_model
+      fields:
+      - name: inference_endpoint
+        value: null  <- CHANGE THIS TO YOUR MODEL'S INFERENCE ENDPOINT
+----
++
+* Deploy the application
++
+    ./pattern.sh make install
++
+The above command will install the application by deploying the ChatQnA megaservice along with the following required microservices:
+
+** Dataprep
+** LLM text generation
+** Retriever
+** Hugging Face Text Embedding Inference
+*** Embedding service
+*** Reranker service
+** ChatQnA backend
+** ChatQnA UI
++
+[NOTE]
+====
+Processes for the build and installation of all the required services can take some time to complete. To monitor progress via the ArgoCD application dashboard, follow these steps:
+
+* Open ArgoCD dashboard, in a browser, using the URI returned
+----
+    echo https://$(oc get route hub-gitops-server -n qna-chat-amd-hub -o jsonpath="{.spec.host}")
+----
+* Get the password
+----
+    echo $(oc get secret hub-gitops-cluster -n qna-chat-amd-hub -o jsonpath="{.data['admin\.password']}" | base64 -d)
+----
+* Login to the ArgoCD dashboard
+** Username: `admin`
+** Password: _password from the previous step_
+====
+
+Verify ChatQnA app::
+
+Once the application is installed, we can add the knowledge base using a PDF and then query against that data by following these steps:
+
+* Run the following command to get the ChatQnA UI URI:
++
+    echo https://$(oc get route chatqna-ui-secure -n amd-llm -o jsonpath="{.spec.host}")
++
+* Open ChatQnA UI, in a browser, by using the URI returned from above command
+
+Query ChatQnA without RAG::
+
+* Type the following query at the prompt:
++
+    What is the revenue of Nike in 2023?
++
++
+Since we have not yet provided any external knowledge base regarding the above query to the application, it does not the correct answer to this query and returns a generic response:
++
+
+image::/images/qna-chat-amd/chatqna-ui-no-rag.png[ChatQnA UI - response without RAG,40%,40%]
+
+Query ChatQnA with RAG - add external knowledge base::
+
+In the ChatQnA UI, follow the steps given below to add an external knowledge base (Nike PDF) to perform the above query using RAG:
+* Click on the upload icon in the top right corner
++
+image::/images/qna-chat-amd/chatqna-upload-icon.jpeg[ChatQnA UI - upload icon,40%,40%]
++
+* Click on Choose File button and select `nke-10k-2023.pdf` from _scripts_ directory. When you select the pdf and close the dialog box, the upload will start automatically.
++
+image::/images/qna-chat-amd/chatqna-upload-file.jpeg[ChatQnA UI - Upload File,40%,40%]
++
+* Allow a few minutes for the file to be ingested and processed, and uploaded to the Redis vector database
+* Refresh the page after a few minutes to verify the file has been uploaded
+* Type the following query at the prompt:
++
+----
+What is the revenue of Nike inc in 2023?
+----
++
+The response for this query makes use of the Nike knowledge base, added in previous step, and is shown in the figure below:
++
+image::/images/qna-chat-amd/chatqna-request-response.png[ChatQnA UI - request and response,50%,50%]
++
+Query ChatQnA - remove external knowledge base::
+
+Follow the steps given in this section to remove the external knowledge base that was added to the app:
+
+* Click on the upload icon in the top right corner
+* Move your cursor on top of the file in the Data Source section and click on the trashcan icon that pops up in the top right corner of the file icon.
+* Select `Yes, I'm sure` when prompted with `"Confirm file deletion?"` dialog
++
+image::/images/qna-chat-amd/chatqna-delete-file.png[ChatQnA UI - delete knowledge base,40%,40%]
++
+
+Query ChatQnA - general questions::
+
+* When the knowledge base is not added to the app, you can also use the application to ask general questions, such as:
+** Tell me more about Red Hat
+** What services does red hat provide?
+** What is Deep Learning?
+** What is a Neural Network?
++
+image::/images/qna-chat-amd/chatqna-long-response.png[ChatQnA UI - response for general questions,50%,50%]
+
+=== Wrap Up
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: abhatt-rh
body: ```suggestion
=== Conclusion
```
created_at: 2025-05-15T15:46:11Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,41 @@
+---
+title: Required Hardware
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-required-hardware.adoc
user: login: abhatt-rh
body: Global comment: Write all titles and headings, in sentence-style capitalization.
```suggestion
title: Required hardware
```
created_at: 2025-05-15T15:46:34Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,35 @@
+---
+title: Troubleshooting
+weight: 40
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-troubleshooting/
+---
+
+:toc:
+:imagesdir: /images
+:_content-type: REFERENCE
+include::modules/comm-attributes.adoc[]
+
+[id="troubleshooting-the-pattern-deployment"]
+=== Troubleshooting common Pattern Deployment issues
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-troubleshooting.adoc
user: login: abhatt-rh
body: ```suggestion
=== Troubleshooting common pattern deployment issues
```
created_at: 2025-05-15T15:48:09Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,313 @@
+---
+title: Getting started
+weight: 10
+aliases: /amd-rag-chat-qna/amd-rag-chat-qna-getting-started/
+---
+
+:toc:
+:_content-type: ASSEMBLY
+include::modules/comm-attributes.adoc[]
+
+[id="deploying-amdqna-pattern"]
+= Deploying the {amdqna-pattern}
+
+=== Prerequisites
+
+* An {ocp} (4.16-4.18) cluster. If you do not have a running Red Hat OpenShift cluster, you can start one on a public or private cloud by visiting https://console.redhat.com/[Red Hat Hybrid Cloud Console].
+ ** select *Services \-> Containers \-> Create cluster*.
+ ** link:../../amd-rag-chat-qna/amd-rag-chat-qna-required-hardware[Required hardware].
+* {ocp} Cluster must have a configured https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/registry/setting-up-and-configuring-the-registry[Image Registry].
+* A GitHub account and https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens[Personal Access Token] with permissions to read/write to forks.
+* A HuggingFace account and https://huggingface.co/docs/hub/en/security-tokens[User Access Token], which allows to download AI models.
+* Contact information https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct[shared] in order to access Meta's `Llama-3.1-8B-Instruct` model with your HuggingFace account.
+* Access to an S3 or Minio bucket for model storage purposes.
+ ** This guide does not go into details regarding setup, please refer to https://min.io/docs/minio/kubernetes/upstream/operations/installation.html[Minio] or
+https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html[S3] documentation for further information on bucket setup & configuration.
+* Install pattern https://validatedpatterns.io/learn/quickstart/[tooling dependencies].
+
+
+=== Model Setup
+
+* If you prefer to utilize tools provided with this project for preparation of the prerequisite model, we'll first <<install_rhoai,install OpenShift AI>> in order to leverage a workbench for notebook access.
++
+[NOTE]
+====
+If you are familiar with the steps necessary for downloading the `Llama-3.1-8B-Instruct` model & copying to a Minio/S3 bucket, you may do so & proceed to <<clone_pattern_code,Clone Code Repository>> below. OpenShift AI will be
+installed later during pattern execution.
+
+If you prefer to run the provided notebooks in another environment, you may do so & skip to <<notebook_model_prep, Upload Model using Red Hat OpenShift AI>> below for further instructions. OpenShift AI will be installed later during pattern execution.
+====
+
+Install Red Hat OpenShift AI:: [[install_rhoai]]
+
+If not already installed on the cluster, follow this https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/2.13/html-single/installing_and_uninstalling_openshift_ai_self-managed/index#installing-and-deploying-openshift-ai_install[link]
+to install Red Hat OpenShift AI. Follow the given steps to deploy the model for this article:
+
+* Open up Red Hat OpenShift AI by selecting it from the OpenShift Application Launcher. This will open up Red Hat OpenShift AI in a new tab.
+* In the Red Hat OpenShift AI window, select Data Science projects in the sidebar and click on the Create project button.
+* Name the new project `chatqna-llm`. +
+
+[start=0]
+Create Connection::
+
+Follow the steps given below to create a Connection that will be used by the init-container to fetch the model uploaded in next step when deploying the mode for inferencing:
+
+* Click on the Create connection button in the Connections tab in your newly created project
+* Select `S3 compatible object storage - v1` in the Connection type dropdown
++
+image::/images/qna-chat-amd/rhoai-create-connection.png[Red Hat OpenShift AI - Create Connection - Connection type, 60%, 60%]
++
+* Use the following values for this data connection:
+** Connection name: `model-store`
+** Connection description: `Connection that points to the model store` (_you can provide any relevant description here_)
+** Access key: MinIO username if using MinIO, else use AWS credentials
+** Secret key: MinIO password if using MinIO, else use AWS credentials
+** Endpoint: minio-api route location, from OCP cluster, if using MinIO, else use AWS S3 endpoint that is in the format of `https://s3.<REGION>.amazonaws.com`
+** Region: `us-east-1` if using MinIO, otherwise use the correct AWS region
+** Bucket: `models`
++
+--
+*** This bucket will be created by the Jupyter notebook, if it does not exist, when uploading the model
+*** If using AWS S3 and the bucket does not exist, make sure correct permissions are assigned to the IAM user to be able to create the bucket
++
+{empty} +
+--
++
+image::/images/qna-chat-amd/rhoai-create-connection-2.png[Red Hat OpenShift AI - Create connection]
+
+[start=0]
+Create Workbench::
+
+To upload the model that is needed for this article, you need to create a workbench first. In the chatqna-llm data science project, create a new workbench by clicking the Create workbench button in the Workbenches tab.
+
+* Use the following values to create the workbench:
+** Name: `chatqna`
+** Image selection: `ROCm-PyTorch`
+** Version selection: `2025.1`
+** Container size: `Medium`
+** Accelerator: `AMD`
+** Cluster storage: Make sure the storage is at least 50GB
+** Connection: Click on Attach existing connections button and attach the connection named model-store created in the previous step. This will pass on the connection values to the workbench when it is started, which will be used to upload the model.
+* Create the workbench by clicking on the `Create workbench` button. This workbench will be started and will move to `Running` status soon.
++
+image::/images/qna-chat-amd/rhoai-create-workbench.png[Red Hat OpenShift AI - Create Workbench - Attach existing connections]
+
+=== Upload Model using Red Hat OpenShift AI [[notebook_model_prep]]
+
+To serve the model, we will need to first download the model using the workbench created in the previous step as well as upload it to either MinIO or AWS S3, using the connection named model-store created in one of the previous steps. Follow the steps given in this section to serve the model.
+
+Open Workbench::
+
+Open workbench named `chatqna` by following these steps:
+* Once `chatqna` workbench is in `Running` status, open the workbench by clicking on its name, in `Workbenches` tab
+* The workbench will open up in a new tab
+** When the workbench is opened for the first time, you will be shown an _Authorize Access page_
+** Click `Allow selected permissions` button in this page
+
+Clone Code Repository:: [[clone_pattern_code]]
+
+Now that the workbench is created and running, follow these steps to setup the project:
+
+* In the open workbench, click on the `Terminal` icon in the `Launcher` tab.
+* Clone the following repository in the Terminal by running the following command:
+
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+
+Run Jupyter notebook::
+
+The notebook mentioned in this section is used to download the `meta-llama/Llama-3.1-8B-Instruct` model and upload it to either MinIO or AWS S3.
+* Once the repository is cloned, select the folder where you cloned the repository (in the sidebar) and open `scripts/model-setup/upload-model.ipynb` jupyter notebook
+* Run this notebook by running each cell one by one
+** When prompted for a HuggingFace token, provide the prerequisite User Access Token and click `Login`
++
+image::/images/qna-chat-amd/rhoai-upload-model.png[Red Hat OpenShift AI - Run Jupyter notebook - Hugging Face token prompt, 50%, 50%]
++
+** When the notebook successfully runs, llama model should have been uploaded to either MinIO or AWS S3 under Llama-3.1-8B-Instruct directory in models bucket
++
+[NOTE]
+====
+By default, this notebook will upload the model to MinIO. To choose AWS S3, modify the last cell in the notebook by changing the value of XFER_LOCATION to AWS as shown below:
+----
+    XFER_LOCATION = 'MINIO'  <= current value
+    XFER_LOCATION = 'AWS'    <= modified to upload to AWS S3
+----
+====
+
+=== Deploy Model via Red Hat OpenShift AI
+
+Once the initial notebook has run successfully and the model is uploaded, you can deploy the model by following these steps:
+
+In the chatqna-llm data science project, select Models tab and click on Deploy model button and fill in the following fields as shown below:
+
+* Model name: `llama-31b`
+* Serving runtime: `vLLM AMD GPU ServingRuntime for KServe`
+* Model framework: `vLLM`
+* Deployment mode: `Advanced`
+* Model server size: `Small`
+* Accelerator: `AMD`
+* Model route: Enable `Make deployed models available through an external route` checkbox
+* Source Model location: Select `Existing connection` option
++
+--
+** Name: `model-store` (this is the name we used when we created the connection in `Create Connection` step)
+** Path: `Llama-3.1-8B-Instruct`
+*** Location where the model was copied to in the previous step
+--
++
+Click on Deploy to deploy this model. Once the model is successfully deployed, copy the inference endpoint to use it in the ChatQnA application (it will take a few minutes to deploy the model).
++
+[WARNING]
+====
+Make sure the model name is set to `llama-31b` as this is the value used in the deployment of llm micro-service that invokes the inference endpoint.
+====
+
+=== Deploy ChatQnA application
+
+This section provides details on installing the ChatQnA application as well as verifying the deployment and configuration by querying the application.
+
+Install ChatQnA app::
+
+Once all the prerequisites are met, install the ChatQnA application
+* Clone the repository by running the following commands:
++
+    git clone https://github.com/validatedpatterns-sandbox/qna-chat-amd.git
+    cd qna-chat-amd
++
+* Configure secrets for Hugging Face and inference endpoint
++
+    cp values-secret.yaml.template ~/values-secret-qna-chat-amd.yaml
++
+* Modify the `value` field in `~/values-secret-qna-chat-amd.yaml` file
++
+----
path: content/patterns/amd-rag-chat-qna/amd-rag-chat-qna-getting-started.adoc
user: login: jeremyary
body: for some reason it didn't recognize the fix for this one, but it's in there.
created_at: 2025-05-15T19:39:45Z
author_association: CONTRIBUTOR
diff_hunk: @@ -0,0 +1,109 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: abhatt-rh
body: Add the following lines to add AMD as Partner. This will bring up your pattern should anyone filter on AMD:
```
partners:
- AMD
```

created_at: 2025-05-16T09:57:41Z
author_association: COLLABORATOR
diff_hunk: @@ -0,0 +1,109 @@
+---
+title: OPEA Chat QnA accelerated with AMD Instinct
+date: 2025-05-01
+tier: sandbox
+validated: false
+summary: This pattern aids with deployment of OPEA's Chat QnA RAG application, accelerated with AMD Instinct hardware.
+rh_products:
+- Red Hat OpenShift Container Platform
+- Red Hat OpenShift Data Foundation
+- Red Hat OpenShift AI
+- Red Hat OpenShift Serverless
+- Red Hat OpenShift Service Mesh
path: content/patterns/amd-rag-chat-qna/_index.adoc
user: login: jeremyary
body: ooh, solid improvement for search, thank you! Added & commits squashed.
created_at: 2025-05-16T11:26:25Z
author_association: CONTRIBUTOR

PR comments:
user: login: mbaldessari
created_at: 2025-05-12T14:55:09Z
body: <!-- add-pr-comment:add-pr-comment -->

This is an automated message:

You can preview this docs PR at http://567.docs-pr.validatedpatterns.io
Note that they get generated every five minutes, so please wait a bit.
author_association: CONTRIBUTOR
user: login: openshift-ci[bot]
created_at: 2025-05-12T14:55:10Z
body: Hi @jeremyary. Thanks for your PR.

I'm waiting for a [validatedpatterns](https://github.com/orgs/validatedpatterns/people) member to verify that this patch is reasonable to test. If it is, they should reply with `/ok-to-test` on its own line. Until that is done, I will not automatically test new commits in this PR, but the usual testing commands by org members will still work. Regular contributors should [join the org](https://github.com/orgs/validatedpatterns/people) to skip this step.

Once the patch is verified, the new status will be reflected by the `ok-to-test` label.

I understand the commands that are listed [here](https://go.k8s.io/bot-commands?repo=validatedpatterns%2Fdocs).

<details>

Instructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md).  If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes-sigs/prow](https://github.com/kubernetes-sigs/prow/issues/new?title=Prow%20issue:) repository.
</details>

author_association: CONTRIBUTOR
user: login: mbaldessari
created_at: 2025-05-14T14:42:53Z
body: /ok-to-test
author_association: CONTRIBUTOR
user: login: day0hero
created_at: 2025-05-14T22:53:16Z
body: /lgtm

author_association: CONTRIBUTOR
user: login: jeremyary
created_at: 2025-05-15T19:41:13Z
body: Thanks @abhatt-rh & @day0hero, all recommended changes implemented, incl. global comments and a few similar things I caught.
author_association: CONTRIBUTOR