From 7ce009c36afebc7b3522ed78c870aedb5ec43871 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Thu, 5 Dec 2024 15:29:03 +0000 Subject: [PATCH 01/22] TELCODOCS-2134 updating AI pattern --- content/patterns/rag-llm-gitops/_index.md | 61 ++++- .../rag-llm-gitops/getting-started.md | 245 +++++++----------- 2 files changed, 152 insertions(+), 154 deletions(-) diff --git a/content/patterns/rag-llm-gitops/_index.md b/content/patterns/rag-llm-gitops/_index.md index 62251c4d4..ff826a5eb 100644 --- a/content/patterns/rag-llm-gitops/_index.md +++ b/content/patterns/rag-llm-gitops/_index.md @@ -19,7 +19,7 @@ links: ci: ai --- -# Document Generation Demo with LLM and RAG +# Document generation demo with LLM and RAG ## Introduction @@ -34,13 +34,6 @@ The application uses either the [EDB Postgres for Kubernetes operator](https://c (default), or Redis, to store embeddings of Red Hat product documentation, running on Red Hat OpenShift Container Platform to generate project proposals for specific Red Hat products. -## Pre-requisites - -- Podman -- Red Hat Openshift cluster running in AWS. Supported regions are us-west-2 and us-east-1. -- GPU Node to run Hugging Face Text Generation Inference server on Red Hat OpenShift cluster. -- Create a fork of the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) git repository. - ## Demo Description & Architecture The goal of this demo is to demonstrate a Chatbot LLM application augmented with data from Red Hat product documentation running on [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai). It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. @@ -55,6 +48,58 @@ The application generates a project proposal for a Red Hat product. - Monitoring dashboard to provide key metrics such as ratings. - GitOps setup to deploy e2e demo (frontend / vector database / served models). +<<<<<<< HEAD +======= +#### RAG Demo Workflow + +![Overview of workflow](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-sd.png) + +_Figure 3. Schematic diagram for workflow of RAG demo with Red Hat OpenShift._ + + +#### RAG Data Ingestion + +![ingestion](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-ingress-sd.png) + +_Figure 4. Schematic diagram for Ingestion of data for RAG._ + + +#### RAG Augmented Query + + +![query](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-query-sd.png) + +_Figure 5. Schematic diagram for RAG demo augmented query._ + +In Figure 5, we can see RAG augmented query. The Mistral-7B model is used for +language processing. LangChain is used to integrate different tools of the LLM-based +application together and to process the PDF files and web pages. A vector +database provider such as EDB Postgres for Kubernetes (or Redis), is used to +store vectors. HuggingFace TGI is used to serve the Mistral-7B model. Gradio is +used for user interface and object storage to store language model and other +datasets. Solution components are deployed as microservices in the Red Hat +OpenShift Container Platform cluster. + +#### Download diagrams +View and download all of the diagrams above in our open source tooling site. + +[Open Diagrams](https://www.redhat.com/architect/portfolio/tool/index.html?#gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/diagrams/rag-demo-vp.drawio) + +![Diagram](/images/rag-llm-gitops/diagram-edb.png) + +_Figure 6. Proposed demo architecture with OpenShift AI_ + +### Components deployed + +- **Hugging Face Text Generation Inference Server:** The pattern deploys a Hugging Face TGIS server. The server deploys `mistral-community/Mistral-7B-v0.2` model. The server will require a GPU node. +- **EDB Postgres for Kubernetes / Redis Server:** A Vector Database server is deployed to store vector embeddings created from Red Hat product documentation. +- **Populate VectorDb Job:** The job creates the embeddings and populates the vector database. +- **LLM Application:** This is a Chatbot application that can generate a project proposal by augmenting the LLM with the Red Hat product documentation stored in vector db. +- **Prometheus:** Deploys a prometheus instance to store the various metrics from the LLM application and TGIS server. +- **Grafana:** Deploys Grafana application to visualize the metrics. + + +>>>>>>> 9e55287b (TELCODOCS-2134 updating AI pattern) ![Overview](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/intro-marketectures/rag-demo-vp-marketing-slide.png) _Figure 1. Overview of the validated pattern for RAG Demo with Red Hat OpenShift_ diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index d6b8cdc78..6c9c9efb6 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -4,165 +4,91 @@ weight: 10 aliases: /rag-llm-gitops/getting-started/ --- -## Deploying the demo +## Prerequisites -Following commands will take about 15-20 minutes ->**Validated pattern will be deployed** +- Podman +- Red Hat Openshift cluster running in AWS. Supported regions are `us-west-2` and `us-east-1`. For more information about installing on AWS see, [Installation methods](https://docs.openshift.com/container-platform/latest/installing/installing_aws/preparing-to-install-on-aws.html). +- GPU Node to run Hugging Face Text Generation Inference server on Red Hat OpenShift cluster. -```sh -git clone https://github.com/<>/rag-llm-gitops.git -cd rag-llm-gitops -oc login --token=<> --server=<> # login to Openshift cluster -podman machine start -# Copy values-secret.yaml.template to ~/values-secret-rag-llm-gitops.yaml. -# You should never check these files -# Add secrets to the values-secret.yaml that needs to be added to the vault. -cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml -./pattern.sh make install -``` - -#### RAG Demo Workflow - -![Overview of workflow](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-sd.png) - -_Figure 3. Schematic diagram for workflow of RAG demo with Red Hat OpenShift._ - - -#### RAG Data Ingestion - -![ingestion](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-ingress-sd.png) - -_Figure 4. Schematic diagram for Ingestion of data for RAG._ - - -#### RAG Augmented Query +## Procedure +1. Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) git repository. -![query](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-query-sd.png) +2. Clone the forked repository by running the following command: -_Figure 5. Schematic diagram for RAG demo augmented query._ + ```sh + $ git clone git@github.com:your-username/multicloud-gitops.git + ``` +3. Navigate to your repository: Ensure you are in the root directory of your git repository by using the following command: -In Figure 5, we can see RAG augmented query. Community version of [Mistral-7B-Instruct](https://huggingface.co/mistral-community/Mistral-7B-Instruct-v0.3) model is used for language processing. LangChain is used to integrate different tools of the LLM-based -application together and to process the PDF files and web pages. A vector -database provider such as EDB Postgres for Kubernetes (or Redis), is used to -store vectors. [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) to serve the [Mistral-7B-Instruct](https://huggingface.co/mistral-community/Mistral-7B-Instruct-v0.3) model. Gradio is -used for user interface and object storage to store language model and other -datasets. Solution components are deployed as microservices in the Red Hat -OpenShift Container Platform cluster. - -#### Download diagrams -View and download all of the diagrams above in our open source tooling site. - -[Open Diagrams](https://www.redhat.com/architect/portfolio/tool/index.html?#gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/diagrams/rag-demo-vp.drawio) - -![Diagram](/images/rag-llm-gitops/diagram-edb.png) - -_Figure 6. Proposed demo architecture with OpenShift AI_ - -### Components deployed - -- **vLLM Text Generation Inference Server:** The pattern deploys a vLLM Inference Server. The server deploys and serves `mistral-community/Mistral-7B-Instruct-v0.3` model. The server will require a GPU node. -- **EDB Postgres for Kubernetes / Redis Server:** A Vector Database server is deployed to store vector embeddings created from Red Hat product documentation. -- **Populate VectorDb Job:** The job creates the embeddings and populates the vector database. -- **LLM Application:** This is a Chatbot application that can generate a project proposal by augmenting the LLM with the Red Hat product documentation stored in vector db. -- **Prometheus:** Deploys a prometheus instance to store the various metrics from the LLM application and TGIS server. -- **Grafana:** Deploys Grafana application to visualize the metrics. - -## Deploying the demo - -To run the demo, ensure the Podman is running on your machine.Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops) repo into your organization -### Login to OpenShift cluster - -Replace the token and the api server url in the command below to login to the OpenShift cluster. + ```sh + $ cd rag-llm-gitops + ``` +3. Create a local copy of the secret values file by running the following command: ```sh -oc login --token= --server= # login to Openshift cluster +$ cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml ``` +> **Note:** +>For this demo, editing this file is unnecessary as the default configuration works out of the box upon installation. -### Cloning repository +4. Add the remote upstream repository by running the following command: -```sh -git clone https://github.com/<>/rag-llm-gitops.git -cd rag-llm-gitops -``` - -### Configuring model - -This pattern deploys community version of [Mistral-7B-Instruct](https://huggingface.co/mistral-community/Mistral-7B-Instruct-v0.3) out of box. Run the following command to configure vault with the model Id. + ```sh + $ git remote add -f upstream git@github.com:validatedpatterns/rag-llm-gitops.git + ``` +5. Create a local branch by running the following command: ```sh -# Copy values-secret.yaml.template to ~/values-secret-rag-llm-gitops.yaml. -# You should never check-in these files -# Add secrets to the values-secret.yaml that needs to be added to the vault. -cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml +$ git checkout -b my-test-branch main ``` -To deploy a non-community [Mistral-7b-Instruct](https://huggingface.co/mistralai/) model, grab the [Hugging Face token](https://huggingface.co/settings/tokens) and accept the terms and conditions on the model page. Edit ~/values-secret-rag-llm-gitops.yaml to replace the `model Id` and the `Hugging Face` token. +6. Run the following command to push `my-test-branch` to the origin remote repository: + + ```sh + $ git push origin my-branch + ``` +6. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: + + ```sh + INFO Install complete! + INFO Run 'export KUBECONFIG=/auth/kubeconfig' to manage the cluster with 'oc', the OpenShift CLI. + INFO The cluster is ready when 'oc login -u kubeadmin -p ' succeeds (wait a few minutes). + INFO Access the OpenShift web-console here: https://console-openshift-console.apps.demo1.openshift4-beta-abcorp.com + INFO Login to the console with user: kubeadmin, password: + ``` + +7. Add GPU nodes to your existing cluster deployment by running the following command: + + ```sh + $ ./pattern.sh make create-gpu-machineset + ``` +> **Note:** +> You may need to create a file `config` in your home directory and populate it with the region name. +> 1. Run the following: +> ```sh +> vi ~/.aws/config +> ``` +> 2. Add the following: +> ```sh +> [default] +> region = us-east-1 +> ``` + +8. Adding the GPU nodes should take about 5-10 minutes. You can verify the addition of these `g5.2xlarge` nodes in the OpenShift web console under **Compute** > **Nodes**. + +9. Install the pattern with the demo application by running the following command: ```sh -secrets: - - name: hfmodel - fields: - - name: hftoken - value: null - - name: modelId - value: "mistral-community/Mistral-7B-Instruct-v0.3" - - name: minio - fields: - - name: MINIO_ROOT_USER - value: minio - - name: MINIO_ROOT_PASSWORD - value: null - onMissingValue: generate -``` - -### Provision GPU MachineSet - -As a pre-requisite to deploy the application using the validated pattern, GPU nodes should be provisioned along with Node Feature Discovery Operator and NVIDIA GPU operator. To provision GPU Nodes - -Following command will take about 5-10 minutes. - -```sh -./pattern.sh make create-gpu-machineset -``` - -Wait till the nodes are provisioned and running. - -![Diagram](/images/rag-llm-gitops/nodes.png) - -Alternatively, follow the [instructions](../gpu_provisioning) to manually install GPU nodes, Node Feature Discovery Operator and NVIDIA GPU operator. - -### Deploy application - -**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). - -```yaml ---- -global: - pattern: rag-llm-gitops - options: - useCSV: false - syncPolicy: Automatic - installPlanApproval: Automatic -# Possible value for db.type = [REDIS, EDB] - db: - index: docs - type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment -main: - clusterGroupName: hub - multiSourceConfig: - enabled: true -``` - -Following commands will take about 15-20 minutes +./pattern.sh make install +``` -> **Validated pattern will be deployed** +> **Note:** +> This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. +> -```sh -./pattern.sh make install -``` -### 1: Verify the installation +## Verify the Installation - Login to the OpenShift web console. - Navigate to the Workloads --> Pods. @@ -173,7 +99,7 @@ Following commands will take about 15-20 minutes Note: If the hf-text-generation-server is not running, make sure you have followed the steps to configure a node with GPU from the [instructions](../gpu_provisioning) provided above. -### 2: Launch the application +### Launch the application - Click the `Application box` icon in the header, and select `Retrieval-Augmented-Generation (RAG) LLM Demonstration UI` @@ -183,7 +109,7 @@ Note: If the hf-text-generation-server is not running, make sure you have follow ![Application](/images/rag-llm-gitops/application.png) -### 3: Generate the proposal document +### Generate the proposal document - It will use the default provider and model configured as part of the application deployment. The default provider is a Hugging Face model server running in the OpenShift. The model server is deployed with this validated pattern and requires a node with GPU. - Enter any company name @@ -192,7 +118,7 @@ Note: If the hf-text-generation-server is not running, make sure you have follow ![Routes](/images/rag-llm-gitops/proposal.png) -### 4: Add an OpenAI provider +### Add an OpenAI provider You can optionally add additional providers. The application supports the following providers @@ -204,19 +130,19 @@ Click on the `Add Provider` tab to add a new provider. Fill in the details and c ![Routes](/images/rag-llm-gitops/add_provider.png) -### 5: Generate the proposal document using OpenAI provider +### Generate the proposal document using OpenAI provider Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. ![Routes](/images/rag-llm-gitops/chatgpt.png) -### 6: Rating the provider +### Rating the provider You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. -### 7: Grafana Dashboard +### Grafana Dashboard -By default, Grafana application is deployed in `llm-monitoring` namespace.To launch the Grafana Dashboard, follow the instructions below: +By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: - Grab the credentials of Grafana Application - Navigate to Workloads --> Secrets @@ -228,3 +154,30 @@ By default, Grafana application is deployed in `llm-monitoring` namespace.To lau - Ratings are displayed for each model. ![Routes](/images/rag-llm-gitops/monitoring.png) + + + + +### Deploy application + +**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). + +```yaml +--- +global: + pattern: rag-llm-gitops + options: + useCSV: false + syncPolicy: Automatic + installPlanApproval: Automatic +# Possible value for db.type = [REDIS, EDB] + db: + index: docs + type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment +main: + clusterGroupName: hub + multiSourceConfig: + enabled: true +``` + + From 8c15057c2c33a94ac5ca4634ba8496baa15b3c9b Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Thu, 5 Dec 2024 15:39:20 +0000 Subject: [PATCH 02/22] TELCODOCS-2134 updating AI pattern 2 --- content/patterns/rag-llm-gitops/_index.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/content/patterns/rag-llm-gitops/_index.md b/content/patterns/rag-llm-gitops/_index.md index ff826a5eb..79e4522de 100644 --- a/content/patterns/rag-llm-gitops/_index.md +++ b/content/patterns/rag-llm-gitops/_index.md @@ -48,8 +48,6 @@ The application generates a project proposal for a Red Hat product. - Monitoring dashboard to provide key metrics such as ratings. - GitOps setup to deploy e2e demo (frontend / vector database / served models). -<<<<<<< HEAD -======= #### RAG Demo Workflow ![Overview of workflow](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/schematic-diagrams/rag-demo-vp-sd.png) @@ -99,7 +97,6 @@ _Figure 6. Proposed demo architecture with OpenShift AI_ - **Grafana:** Deploys Grafana application to visualize the metrics. ->>>>>>> 9e55287b (TELCODOCS-2134 updating AI pattern) ![Overview](https://gitlab.com/osspa/portfolio-architecture-examples/-/raw/main/images/intro-marketectures/rag-demo-vp-marketing-slide.png) _Figure 1. Overview of the validated pattern for RAG Demo with Red Hat OpenShift_ From f336c80c24f88ae32f50ee002e16c052c616a9d3 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Fri, 6 Dec 2024 11:55:21 +0000 Subject: [PATCH 03/22] Adding further clarification --- content/patterns/rag-llm-gitops/getting-started.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 6c9c9efb6..3e8fd4af1 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -19,7 +19,7 @@ aliases: /rag-llm-gitops/getting-started/ ```sh $ git clone git@github.com:your-username/multicloud-gitops.git ``` -3. Navigate to your repository: Ensure you are in the root directory of your git repository by using the following command: +3. Go to your repository: Ensure you are in the root directory of your git repository by using the following command: ```sh $ cd rag-llm-gitops @@ -46,7 +46,7 @@ $ git checkout -b my-test-branch main 6. Run the following command to push `my-test-branch` to the origin remote repository: ```sh - $ git push origin my-branch + $ git push origin my-test-branch ``` 6. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: From cf0b225604fba4632b245e6b0020a965ca60df8a Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Tue, 10 Dec 2024 12:15:31 +0000 Subject: [PATCH 04/22] adding corrections --- content/patterns/rag-llm-gitops/getting-started.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 3e8fd4af1..4dec6a7b7 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -90,10 +90,11 @@ $ git checkout -b my-test-branch main ## Verify the Installation -- Login to the OpenShift web console. -- Navigate to the Workloads --> Pods. -- Select the `rag-llm` project from the drop down. -- Following pods should be up and running. +1. In the OpenShift web console go to the **Workloads** > **Pods** menu. + +2. Select the `rag-llm` project from the drop down. + +3. Following pods should be up and running. ![Pods](/images/rag-llm-gitops/rag-llm.png) From 9e0031673478fa841926887c3b4232bc9e180ace Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Tue, 10 Dec 2024 14:31:37 +0000 Subject: [PATCH 05/22] reformatting section --- .../rag-llm-gitops/getting-started.md | 90 +++++++++---------- 1 file changed, 44 insertions(+), 46 deletions(-) diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 4dec6a7b7..d2fbf387f 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -21,72 +21,70 @@ aliases: /rag-llm-gitops/getting-started/ ``` 3. Go to your repository: Ensure you are in the root directory of your git repository by using the following command: - ```sh - $ cd rag-llm-gitops - ``` + ```sh + $ cd rag-llm-gitops + ``` 3. Create a local copy of the secret values file by running the following command: -```sh -$ cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml -``` -> **Note:** ->For this demo, editing this file is unnecessary as the default configuration works out of the box upon installation. + ```sh + $ cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml + ``` + > **Note:** + >For this demo, editing this file is unnecessary as the default configuration works out of the box upon installation. 4. Add the remote upstream repository by running the following command: - ```sh - $ git remote add -f upstream git@github.com:validatedpatterns/rag-llm-gitops.git - ``` + ```sh + $ git remote add -f upstream git@github.com:validatedpatterns/rag-llm-gitops.git + ``` 5. Create a local branch by running the following command: -```sh -$ git checkout -b my-test-branch main -``` + ```sh + $ git checkout -b my-test-branch main + ``` 6. Run the following command to push `my-test-branch` to the origin remote repository: - ```sh - $ git push origin my-test-branch - ``` + ```sh + $ git push origin my-test-branch + ``` 6. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: - ```sh - INFO Install complete! - INFO Run 'export KUBECONFIG=/auth/kubeconfig' to manage the cluster with 'oc', the OpenShift CLI. - INFO The cluster is ready when 'oc login -u kubeadmin -p ' succeeds (wait a few minutes). - INFO Access the OpenShift web-console here: https://console-openshift-console.apps.demo1.openshift4-beta-abcorp.com - INFO Login to the console with user: kubeadmin, password: - ``` - + ```sh + INFO Install complete! + INFO Run 'export KUBECONFIG=/auth/kubeconfig' to manage the cluster with 'oc', the OpenShift CLI. + INFO The cluster is ready when 'oc login -u kubeadmin -p ' succeeds (wait a few minutes). + INFO Access the OpenShift web-console here: https://console-openshift-console.apps.demo1.openshift4-beta-abcorp.com + INFO Login to the console with user: kubeadmin, password: + ``` 7. Add GPU nodes to your existing cluster deployment by running the following command: - ```sh - $ ./pattern.sh make create-gpu-machineset - ``` -> **Note:** -> You may need to create a file `config` in your home directory and populate it with the region name. -> 1. Run the following: -> ```sh -> vi ~/.aws/config -> ``` -> 2. Add the following: -> ```sh -> [default] -> region = us-east-1 -> ``` + ```sh + $ ./pattern.sh make create-gpu-machineset + ``` + > **Note:** + > You may need to create a file `config` in your home directory and populate it with the region name. + > 1. Run the following: + > ```sh + > vi ~/.aws/config + > ``` + > 2. Add the following: + > ```sh + > [default] + > region = us-east-1 + > ``` 8. Adding the GPU nodes should take about 5-10 minutes. You can verify the addition of these `g5.2xlarge` nodes in the OpenShift web console under **Compute** > **Nodes**. 9. Install the pattern with the demo application by running the following command: -```sh -./pattern.sh make install -``` - -> **Note:** -> This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. -> + ```sh + $ ./pattern.sh make install + ``` + > **Note:** + > This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. + > ## Verify the Installation From 6193652b5a1b0daab100d7f5b5dd0fc139d530bd Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Wed, 11 Dec 2024 15:13:01 +0000 Subject: [PATCH 06/22] splitting out content --- .../rag-llm-gitops/getting-started.md | 9 ++++----- static/images/rag-llm-gitops/application.png | Bin 268595 -> 56379 bytes .../rag-llm-gitops/launch-application.png | Bin 94542 -> 41019 bytes static/images/rag-llm-gitops/rag-llm.png | Bin 240661 -> 112360 bytes 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index d2fbf387f..66faac1af 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -96,8 +96,6 @@ aliases: /rag-llm-gitops/getting-started/ ![Pods](/images/rag-llm-gitops/rag-llm.png) -Note: If the hf-text-generation-server is not running, make sure you have followed the steps to configure a node with GPU from the [instructions](../gpu_provisioning) provided above. - ### Launch the application - Click the `Application box` icon in the header, and select `Retrieval-Augmented-Generation (RAG) LLM Demonstration UI` @@ -110,9 +108,10 @@ Note: If the hf-text-generation-server is not running, make sure you have follow ### Generate the proposal document -- It will use the default provider and model configured as part of the application deployment. The default provider is a Hugging Face model server running in the OpenShift. The model server is deployed with this validated pattern and requires a node with GPU. -- Enter any company name -- Enter the product as `RedHat OpenShift` +- It will use the default provider and model configured as part of the application deployment. The default provider is `Mistral-7B-Instruct` a model available on Hugging Face. It is a fine-tuned version of the base `Mistral-7B` model. The model server is deployed with this validated pattern and requires a node with GPU. + +- Enter any company name for example Microsoft. +- Enter the product as `RedHat OpenShift AI` - Click the `Generate` button, a project proposal should be generated. The project proposal also contains the reference of the RAG content. 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zuehv0&U~fr{i7rD`1zAtf?wz0FZ)l60rWih!bbb%}t@DK;l~xBzWkHYwMUZ6bt^GjP1VG3<~)AT7&_in$+G zIsRVP(BA>j-+9Kr5!uarmSEv4q(;*-TQ?@Y51^TyceZU{7(jm{dBB1(#DX0g6E^`c zzi+3=-)s1Bs1``Tf>4Bm)W*c40r!~fRah_lKNAN4l#tVwK6UVoiSGw^J?8ky&-br~ zuT!Fc1xb@(xDAQ}Bm?~|e!U()6DP1d#`g{2f3Vdzfd9cDK)`$h_@9~d8-xD^lYV3H q&+Pi2$^ILI|2b6%{l7_3ctR$M(a+FGg9Dp@pE1(ZAYcFd^?w2@g%_Xz From efff8b32dcfeddfea719673ea68ea005b963165d Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Wed, 11 Dec 2024 15:13:14 +0000 Subject: [PATCH 07/22] splitting out content --- .../rag-llm-gitops/customize-demo-app.md | 69 +++++++++++++++++++ .../rag-llm-gitops/deploying-different-db.md | 29 ++++++++ .../rag-llm-gitops/rating-the-provider.md | 51 ++++++++++++++ .../rag-llm-gitops/rating_the-provider.md | 69 +++++++++++++++++++ 4 files changed, 218 insertions(+) create mode 100644 content/patterns/rag-llm-gitops/customize-demo-app.md create mode 100644 content/patterns/rag-llm-gitops/deploying-different-db.md create mode 100644 content/patterns/rag-llm-gitops/rating-the-provider.md create mode 100644 content/patterns/rag-llm-gitops/rating_the-provider.md diff --git a/content/patterns/rag-llm-gitops/customize-demo-app.md b/content/patterns/rag-llm-gitops/customize-demo-app.md new file mode 100644 index 000000000..a20cf6df2 --- /dev/null +++ b/content/patterns/rag-llm-gitops/customize-demo-app.md @@ -0,0 +1,69 @@ +--- +title: Customize the demo application +weight: 11 +aliases: /rag-llm-gitops/getting-started/ +--- + +# Add an OpenAI provider + +You can optionally add additional providers. The application supports the following providers + +- Hugging Face Text Generation Inference Server +- OpenAI +- NVIDIA + +Click on the `Add Provider` tab to add a new provider. Fill in the details and click `Add Provider` button. The provider should be added in the `Providers` dropdown under `Chatbot` tab. + +![Routes](/images/rag-llm-gitops/add_provider.png) + +## Generate the proposal document using OpenAI provider + +Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. + +![Routes](/images/rag-llm-gitops/chatgpt.png) + +### Rating the provider + +You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. + +### Grafana Dashboard + +By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: + +- Grab the credentials of Grafana Application + - Navigate to Workloads --> Secrets + - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD +- Launch Grafana Dashboard + - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` + ![Launch Application](/images/rag-llm-gitops/launch-application.png) + - Enter the Grafana admin credentials. + - Ratings are displayed for each model. + +![Routes](/images/rag-llm-gitops/monitoring.png) + + + + +### Deploy application + +**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). + +```yaml +--- +global: + pattern: rag-llm-gitops + options: + useCSV: false + syncPolicy: Automatic + installPlanApproval: Automatic +# Possible value for db.type = [REDIS, EDB] + db: + index: docs + type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment +main: + clusterGroupName: hub + multiSourceConfig: + enabled: true +``` + + diff --git a/content/patterns/rag-llm-gitops/deploying-different-db.md b/content/patterns/rag-llm-gitops/deploying-different-db.md new file mode 100644 index 000000000..40088f7b2 --- /dev/null +++ b/content/patterns/rag-llm-gitops/deploying-different-db.md @@ -0,0 +1,29 @@ +--- +title: Deploying a different database +weight: 12 +aliases: /rag-llm-gitops/getting-started/ +--- + +# Deploying a different database + +**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). + +```yaml +--- +global: + pattern: rag-llm-gitops + options: + useCSV: false + syncPolicy: Automatic + installPlanApproval: Automatic +# Possible value for db.type = [REDIS, EDB] + db: + index: docs + type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment +main: + clusterGroupName: hub + multiSourceConfig: + enabled: true +``` + + diff --git a/content/patterns/rag-llm-gitops/rating-the-provider.md b/content/patterns/rag-llm-gitops/rating-the-provider.md new file mode 100644 index 000000000..ee88af16a --- /dev/null +++ b/content/patterns/rag-llm-gitops/rating-the-provider.md @@ -0,0 +1,51 @@ +--- +title: Rating the provider +weight: 11 +aliases: /rag-llm-gitops/getting-started/ +--- + +# Rating the provider + +You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. + +## Grafana Dashboard + +By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: + +- Grab the credentials of Grafana Application + - Navigate to Workloads --> Secrets + - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD +- Launch Grafana Dashboard + - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` + ![Launch Application](/images/rag-llm-gitops/launch-application.png) + - Enter the Grafana admin credentials. + - Ratings are displayed for each model. + +![Routes](/images/rag-llm-gitops/monitoring.png) + + + + +### Deploy application + +**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). + +```yaml +--- +global: + pattern: rag-llm-gitops + options: + useCSV: false + syncPolicy: Automatic + installPlanApproval: Automatic +# Possible value for db.type = [REDIS, EDB] + db: + index: docs + type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment +main: + clusterGroupName: hub + multiSourceConfig: + enabled: true +``` + + diff --git a/content/patterns/rag-llm-gitops/rating_the-provider.md b/content/patterns/rag-llm-gitops/rating_the-provider.md new file mode 100644 index 000000000..a20cf6df2 --- /dev/null +++ b/content/patterns/rag-llm-gitops/rating_the-provider.md @@ -0,0 +1,69 @@ +--- +title: Customize the demo application +weight: 11 +aliases: /rag-llm-gitops/getting-started/ +--- + +# Add an OpenAI provider + +You can optionally add additional providers. The application supports the following providers + +- Hugging Face Text Generation Inference Server +- OpenAI +- NVIDIA + +Click on the `Add Provider` tab to add a new provider. Fill in the details and click `Add Provider` button. The provider should be added in the `Providers` dropdown under `Chatbot` tab. + +![Routes](/images/rag-llm-gitops/add_provider.png) + +## Generate the proposal document using OpenAI provider + +Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. + +![Routes](/images/rag-llm-gitops/chatgpt.png) + +### Rating the provider + +You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. + +### Grafana Dashboard + +By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: + +- Grab the credentials of Grafana Application + - Navigate to Workloads --> Secrets + - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD +- Launch Grafana Dashboard + - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` + ![Launch Application](/images/rag-llm-gitops/launch-application.png) + - Enter the Grafana admin credentials. + - Ratings are displayed for each model. + +![Routes](/images/rag-llm-gitops/monitoring.png) + + + + +### Deploy application + +**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). + +```yaml +--- +global: + pattern: rag-llm-gitops + options: + useCSV: false + syncPolicy: Automatic + installPlanApproval: Automatic +# Possible value for db.type = [REDIS, EDB] + db: + index: docs + type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment +main: + clusterGroupName: hub + multiSourceConfig: + enabled: true +``` + + From 3a04d89be94ab509d4355756cdb9855de311555a Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Wed, 11 Dec 2024 16:29:10 +0000 Subject: [PATCH 08/22] reformatting section 3 --- .../rag-llm-gitops/deploying-different-db.md | 2 +- .../rag-llm-gitops/getting-started.md | 62 ---------------- .../rag-llm-gitops/rating-the-provider.md | 43 +++-------- .../rag-llm-gitops/rating_the-provider.md | 69 ------------------ .../rag-llm-gitops/launch-application.png | Bin 41019 -> 94640 bytes static/images/rag-llm-gitops/monitoring.png | Bin 284218 -> 111543 bytes 6 files changed, 11 insertions(+), 165 deletions(-) delete mode 100644 content/patterns/rag-llm-gitops/rating_the-provider.md diff --git a/content/patterns/rag-llm-gitops/deploying-different-db.md b/content/patterns/rag-llm-gitops/deploying-different-db.md index 40088f7b2..116a061ba 100644 --- a/content/patterns/rag-llm-gitops/deploying-different-db.md +++ b/content/patterns/rag-llm-gitops/deploying-different-db.md @@ -6,7 +6,7 @@ aliases: /rag-llm-gitops/getting-started/ # Deploying a different database -**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). +This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). ```yaml --- diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 66faac1af..936e2748b 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -116,66 +116,4 @@ aliases: /rag-llm-gitops/getting-started/ ![Routes](/images/rag-llm-gitops/proposal.png) -### Add an OpenAI provider - -You can optionally add additional providers. The application supports the following providers - -- Hugging Face Text Generation Inference Server -- OpenAI -- NVIDIA - -Click on the `Add Provider` tab to add a new provider. Fill in the details and click `Add Provider` button. The provider should be added in the `Providers` dropdown under `Chatbot` tab. - -![Routes](/images/rag-llm-gitops/add_provider.png) - -### Generate the proposal document using OpenAI provider - -Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. - -![Routes](/images/rag-llm-gitops/chatgpt.png) - -### Rating the provider - -You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. - -### Grafana Dashboard - -By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: - -- Grab the credentials of Grafana Application - - Navigate to Workloads --> Secrets - - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD -- Launch Grafana Dashboard - - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` - ![Launch Application](/images/rag-llm-gitops/launch-application.png) - - Enter the Grafana admin credentials. - - Ratings are displayed for each model. - -![Routes](/images/rag-llm-gitops/monitoring.png) - - - - -### Deploy application - -**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). - -```yaml ---- -global: - pattern: rag-llm-gitops - options: - useCSV: false - syncPolicy: Automatic - installPlanApproval: Automatic -# Possible value for db.type = [REDIS, EDB] - db: - index: docs - type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment -main: - clusterGroupName: hub - multiSourceConfig: - enabled: true -``` - diff --git a/content/patterns/rag-llm-gitops/rating-the-provider.md b/content/patterns/rag-llm-gitops/rating-the-provider.md index ee88af16a..a6a2c7713 100644 --- a/content/patterns/rag-llm-gitops/rating-the-provider.md +++ b/content/patterns/rag-llm-gitops/rating-the-provider.md @@ -6,46 +6,23 @@ aliases: /rag-llm-gitops/getting-started/ # Rating the provider -You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. +You can provide rating to the model by clicking on the `Rate the model` radio button. The rating are captured as part of the metrics and can help the company decide which model to deploy in production. ## Grafana Dashboard -By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: - -- Grab the credentials of Grafana Application - - Navigate to Workloads --> Secrets - - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD -- Launch Grafana Dashboard - - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` - ![Launch Application](/images/rag-llm-gitops/launch-application.png) - - Enter the Grafana admin credentials. - - Ratings are displayed for each model. - -![Routes](/images/rag-llm-gitops/monitoring.png) +By default, the Grafana application is deployed in the `llm-monitoring` namespace. You can track the ratings by logging in to the Grafana Dashboard by following the steps below. +1. In the OpenShift web console go to **Workloads** > **Secrets**. +2. Click on the `ai-llm-grafana-admin-credentials` scroll down. +3. Launch Grafana Dashboard by clicking the `Application box` icon in the header, and select `Grafana UI for LLM ratings`. -### Deploy application - -**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). +4. In the top right hand corner click `Sign in` + ![Launch Application](/images/rag-llm-gitops/launch-application.png) -```yaml ---- -global: - pattern: rag-llm-gitops - options: - useCSV: false - syncPolicy: Automatic - installPlanApproval: Automatic -# Possible value for db.type = [REDIS, EDB] - db: - index: docs - type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment -main: - clusterGroupName: hub - multiSourceConfig: - enabled: true -``` +5. Enter the Grafana admin credentials. Copy the `GF_SECURITY_ADMIN_USER`, `GF_SECURITY_ADMIN_PASSWORD` from `ai-llm-grafana-admin-credentials` screen in the OpenShift web console. +6. Ratings are displayed for each model. +![Routes](/images/rag-llm-gitops/monitoring.png) \ No newline at end of file diff --git a/content/patterns/rag-llm-gitops/rating_the-provider.md b/content/patterns/rag-llm-gitops/rating_the-provider.md deleted file mode 100644 index a20cf6df2..000000000 --- a/content/patterns/rag-llm-gitops/rating_the-provider.md +++ /dev/null @@ -1,69 +0,0 @@ ---- -title: Customize the demo application -weight: 11 -aliases: /rag-llm-gitops/getting-started/ ---- - -# Add an OpenAI provider - -You can optionally add additional providers. The application supports the following providers - -- Hugging Face Text Generation Inference Server -- OpenAI -- NVIDIA - -Click on the `Add Provider` tab to add a new provider. Fill in the details and click `Add Provider` button. The provider should be added in the `Providers` dropdown under `Chatbot` tab. - -![Routes](/images/rag-llm-gitops/add_provider.png) - -## Generate the proposal document using OpenAI provider - -Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. - -![Routes](/images/rag-llm-gitops/chatgpt.png) - -### Rating the provider - -You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. - -### Grafana Dashboard - -By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: - -- Grab the credentials of Grafana Application - - Navigate to Workloads --> Secrets - - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD -- Launch Grafana Dashboard - - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` - ![Launch Application](/images/rag-llm-gitops/launch-application.png) - - Enter the Grafana admin credentials. - - Ratings are displayed for each model. - -![Routes](/images/rag-llm-gitops/monitoring.png) - - - - -### Deploy application - -**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). - -```yaml ---- -global: - pattern: rag-llm-gitops - options: - useCSV: false - syncPolicy: Automatic - installPlanApproval: Automatic -# Possible value for db.type = [REDIS, EDB] - db: - index: docs - type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment -main: - clusterGroupName: hub - multiSourceConfig: - enabled: true -``` - - diff --git a/static/images/rag-llm-gitops/launch-application.png b/static/images/rag-llm-gitops/launch-application.png index 94595ac621fc24b457c0c5704b05857b5d9e51ef..72c35eb1ecd08962fe842d1bb48d562b2d881bb3 100644 GIT binary patch literal 94640 zcmV)NK)1h%P)ZgXgFbngSdJ^%n907*naRCt{1 zy?M}W$x$Ep%ewdNulLuRy4Bhm2}vypwGcw!VbNe=8*DbSP5^_q3EN>sFm`yE1uK{_ zLwEp(0|+nU@Qg9W1Tc0Cn`|=}CRiGSjgZiagg|OFy{Ws^+grZxJ5}??sXA4am08QV z_j}zE)a%>%MtSFFFH*2^Jd=EF267lXEacaL$1Y!C4D32Cxn= 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z&H(IJsMj9x{ZghOPzovr;JMqR1M^8G_FF-9c7Ptwb`$C;5xCT7%(_wk_RO+UdR`3}Kre)pgEV04pqVu;ylz73;J z_^#vd4@fQb*i%DspCeRNtQAl#Z2ZForI2$}*<6!6u=|MMJwJRkOP`&}ky-^3?UoO< zz;0~a&(*e39S!~O4Z*fSj#W0Te)=DY8y2j$trnC~{3hIc6`&t(?`RE@WWoow*9Q&< zz$)7#?IM?&uc)WMO~_e;S6anaP+&l=omA%t4=!X}@@I!5e278DRK+~E*NTPY_bl2k z1BKVJg>o0Ja{fGR%3ZM2bjja>P&#S+`Yu4<_;HOs(eM$rna>i80=(p8l%>lg4gLNP D?~8;P From e3d9bf5d069c6dc410ec2ba3da9dbd670bb96189 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Wed, 11 Dec 2024 17:11:16 +0000 Subject: [PATCH 09/22] reformatting section 4 --- .../rag-llm-gitops/GPU_provisioning.md | 155 +----------------- .../rag-llm-gitops/customize-demo-app.md | 48 +----- 2 files changed, 10 insertions(+), 193 deletions(-) diff --git a/content/patterns/rag-llm-gitops/GPU_provisioning.md b/content/patterns/rag-llm-gitops/GPU_provisioning.md index 2eabf13f0..2346b20b9 100644 --- a/content/patterns/rag-llm-gitops/GPU_provisioning.md +++ b/content/patterns/rag-llm-gitops/GPU_provisioning.md @@ -1,155 +1,18 @@ --- -title: GPU provisioning +title: Customize GPU provisioning nodes weight: 20 aliases: /rag-llm-gitops/gpuprovisioning/ --- -# GPU provisioning +# Customizing GPU provisioning nodes -Use the instructions to add nodes with GPU in OpenShift cluster running in AWS cloud. Nodes with GPU will be tainted to allow only pods that required GPU to be scheduled to these nodes +By default the GPU nodes deployed are of instance type `g5.2xlarge`. If for some reason you want to change this maybe due to performance issues carry out the following steps: -More details can be found in following documents [Openshift AI](https://ai-on-openshift.io/odh-rhoai/nvidia-gpus/), [NVIDIA on OpenShift](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/index.html) +1. In your local branch of the `rag-llm-gitops` git repository change to the `ansible/playbooks/templates` directory. -## Add machineset +2. Edit the file `gpu-machine-sets.j2` changing the `instanceType` to for example `g5.4xlarge`. Save and exit. -The easiest way is to use existing machineset manifest and update certain elements. Use worker machineset manifest and modify some of the entries (naming conventions provided as reference only, use own if required.), keep other entries as is: +3. Push the changes to the origin remote repository by running the following command: -```yaml -apiVersion: machine.openshift.io/v1beta1 -kind: MachineSet -metadata: - name: -gpu- -.............. -spec: - replicas: 1 - selector: - matchLabels: - ................ - machine.openshift.io/cluster-api-machineset: -gpu- - template: - metadata: - labels: - ........ - machine.openshift.io/cluster-api-machineset: -gpu- - spec: - ................... - metadata: - labels: - node-role.kubernetes.io/odh-notebook: '' <--- Put your label if needed - providerSpec: - value: - ........................ - instanceType: g5.2xlarge <---- Change vm type if needed - ............. - taints: - - effect: NoSchedule - key: odh-notebook <--- Use own taint name or skip all together - value: 'true' -``` - -Use `kubectl` or `oc` command line to create new machineset `oc apply -f gpu_machineset.yaml` - -Depending on type of EC2 instance creation of the new machines make take some time. Please note that all nodes with GPU will have labels(`node-role.kubernetes.io/odh-notebook`in our case) and taints (`odh-notebook `) that we have specified in machineset applied automatically - -## Install Node Feature Operator - -From OperatorHub install Node Feature Discovery Operator , accepting defaults . Once Operator has been installed , create `NodeFeatureDiscovery`instance . Use default entries unless you something specific is needed . Node Feature Discovery Operator will add labels to nodes based on available hardware resources - -## Install NVIDIA GPU Operator - -NVIDIA GPU Operator will provision daemonsets with drivers for the GPU to be used by workload running on these nodes . Detailed instructions are available in NVIDIA Documentation [NVIDIA on OpenShift](https://docs.nvidia.com/datacenter/cloud-native/openshift/latest/index.html) . Following simplified steps for specific setup : - -- Install NVIDIA GPU Operator from OperatorHub -- Once operator is ready create `ClusterPolicy` custom resource. Unless required you can use default settings with adding `tolerations` if machineset in first section has been created with taint. Failing to add `tolerations` will prevent drivers to be installed on GPU enabled node : - -```yaml -apiVersion: nvidia.com/v1 -kind: ClusterPolicy -metadata: - name: gpu-cluster-policy -spec: - vgpuDeviceManager: - enabled: true - migManager: - enabled: true - operator: - defaultRuntime: crio - initContainer: {} - runtimeClass: nvidia - use_ocp_driver_toolkit: true - dcgm: - enabled: true - gfd: - enabled: true - dcgmExporter: - config: - name: '' - enabled: true - serviceMonitor: - enabled: true - driver: - certConfig: - name: '' - enabled: true - kernelModuleConfig: - name: '' - licensingConfig: - configMapName: '' - nlsEnabled: false - repoConfig: - configMapName: '' - upgradePolicy: - autoUpgrade: true - drain: - deleteEmptyDir: false - enable: false - force: false - timeoutSeconds: 300 - maxParallelUpgrades: 1 - maxUnavailable: 25% - podDeletion: - deleteEmptyDir: false - force: false - timeoutSeconds: 300 - waitForCompletion: - timeoutSeconds: 0 - virtualTopology: - config: '' - devicePlugin: - config: - default: '' - name: '' - enabled: true - mig: - strategy: single - sandboxDevicePlugin: - enabled: true - validator: - plugin: - env: - - name: WITH_WORKLOAD - value: 'false' - nodeStatusExporter: - enabled: true - daemonsets: - rollingUpdate: - maxUnavailable: '1' - tolerations: - - effect: NoSchedule - key: odh-notebook - value: 'true' - updateStrategy: RollingUpdate - sandboxWorkloads: - defaultWorkload: container - enabled: false - gds: - enabled: false - vgpuManager: - enabled: false - vfioManager: - enabled: true - toolkit: - enabled: true - installDir: /usr/local/nvidia -``` - -Provisioning NVIDIA daemonsets and compiling drivers may take some time (5-10 minutes) + ```sh + $ git push origin my-test-branch + ``` diff --git a/content/patterns/rag-llm-gitops/customize-demo-app.md b/content/patterns/rag-llm-gitops/customize-demo-app.md index a20cf6df2..b4d6e1b9d 100644 --- a/content/patterns/rag-llm-gitops/customize-demo-app.md +++ b/content/patterns/rag-llm-gitops/customize-demo-app.md @@ -20,50 +20,4 @@ Click on the `Add Provider` tab to add a new provider. Fill in the details and c Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. -![Routes](/images/rag-llm-gitops/chatgpt.png) - -### Rating the provider - -You can provide rating to the model by clicking on the `Rate the model` radio button. The rating will be captured as part of the metrics and can help the company which model to deploy in production. - -### Grafana Dashboard - -By default, Grafana application is deployed in `llm-monitoring` namespace. Launch the Grafana Dashboard, by running the following instructions: - -- Grab the credentials of Grafana Application - - Navigate to Workloads --> Secrets - - Click on the grafana-admin-credentials and copy the GF_SECURITY_ADMIN_USER, GF_SECURITY_ADMIN_PASSWORD -- Launch Grafana Dashboard - - Click the `Application box` icon in the header, and select `Grafana UI for LLM ratings` - ![Launch Application](/images/rag-llm-gitops/launch-application.png) - - Enter the Grafana admin credentials. - - Ratings are displayed for each model. - -![Routes](/images/rag-llm-gitops/monitoring.png) - - - - -### Deploy application - -**Note**: This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). - -```yaml ---- -global: - pattern: rag-llm-gitops - options: - useCSV: false - syncPolicy: Automatic - installPlanApproval: Automatic -# Possible value for db.type = [REDIS, EDB] - db: - index: docs - type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment -main: - clusterGroupName: hub - multiSourceConfig: - enabled: true -``` - - +![Routes](/images/rag-llm-gitops/chatgpt.png) \ No newline at end of file From 1d74e5d36b938d5be16ec5c0064194374bcd88f6 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Thu, 12 Dec 2024 10:08:35 +0000 Subject: [PATCH 10/22] aliging various things --- .../rag-llm-gitops/getting-started.md | 80 +++++++++++++++---- .../rag-llm-gitops/rating-the-provider.md | 5 +- 2 files changed, 67 insertions(+), 18 deletions(-) diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 936e2748b..91277618a 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -7,24 +7,72 @@ aliases: /rag-llm-gitops/getting-started/ ## Prerequisites - Podman +- You have the OpenShift Container Platform installation program and the pull secret for your cluster. You can get these from [Install OpenShift on AWS with installer-provisioned infrastructure](https://console.redhat.com/openshift/install/aws/installer-provisioned). - Red Hat Openshift cluster running in AWS. Supported regions are `us-west-2` and `us-east-1`. For more information about installing on AWS see, [Installation methods](https://docs.openshift.com/container-platform/latest/installing/installing_aws/preparing-to-install-on-aws.html). -- GPU Node to run Hugging Face Text Generation Inference server on Red Hat OpenShift cluster. ## Procedure -1. Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) git repository. +1. Create the installation configuration file using the steps described in [Creating the installation configuration file](https://docs.openshift.com/container-platform/4.17/installing/installing_aws/ipi/installing-aws-customizations.html#installation-initializing_installing-aws-customizations). -2. Clone the forked repository by running the following command: + > **Note:** + > This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. + > + +2. Customize the generated `install-config.yaml` creating one control plane node with instance type `m5a.2xlarge` and 3 worker nodes with instance type `p3.2xlarge`. A sample YAML file is shown here: + ```yaml + additionalTrustBundlePolicy: Proxyonly + apiVersion: v1 + baseDomain: aws.validatedpatterns.io + compute: + - architecture: amd64 + hyperthreading: Enabled + name: worker + platform: + aws: + type: p3.2xlarge + replicas: 3 + controlPlane: + architecture: amd64 + hyperthreading: Enabled + name: master + platform: + aws: + type: m5a.2xlarge + replicas: 1 + metadata: + creationTimestamp: null + name: kevstestcluster + networking: + clusterNetwork: + - cidr: 10.128.0.0/14 + hostPrefix: 23 + machineNetwork: + - cidr: 10.0.0.0/16 + networkType: OVNKubernetes + serviceNetwork: + - 172.30.0.0/16 + platform: + aws: + region: us-east-1 + publish: External + pullSecret: '{"auths":{"cloud.openshift.com":{"auth":"V9azMZJ1JRl3WlLkmNVclTNMOJN0DjOicnQ6WETOYVZpkUGblb2DjOUD5JjDjRpFgM0Z0zYZKRRl34b0jZSbUsVMDUDDM2zRlpZNgOTdTYUlhBUl3hJJNGGMVhRDDOW222ZyoBPiQfYO2DTkYNFRWBNMtYNRl=4R=WVGVz9ZOccGEX1N","email":"someuser@redhat.com"},"quay.io":{"auth":"V9azMZJ1JRl3WlLkmNVclTNMOJN0DjOicnQ6WETOYVZpkUGblb2DjOUD5JjDjRpFgM0Z0zYZKRRl34b0jZSbUsVMDUDDM2zRlpZNgOTdTYUlhBUl3hJJNGGMVhRDDOW222ZyoBPiQfYO2DTkYNFRWBNMtYNRl=4R=WVGVz9ZOccGEX1N","email":"someuser@redhat.com"},"registry.connect.redhat.com":{"auth":"xlz4pNTRkRTRmJZpvqJRCdPYlmZMmAFhWziTZUSk4lNZpzTnUR9cWk11NDVGJ4QaFckxpMNzlZNxhW0mbjhRe4Ye10V5RSVZUTUVMZVTkR2NBJdlexSzapkHTTQyrTS9InzpVeOvXdYQOy2UhhmbwBtYxcSWm2Xd4n3tUWtYpYEcjtEnpW4mYNB9dElmWlPahcZxlalfO2alQLTHWB0NURUmZhVpkislTHqNo1wjRovii0zDz1TFXGUKm1N3YRs1WbJSNzGZQFtRpBNRhmp00EkRWUD3zFB2RxVEXMTTwJEdaaoNS1XhxdmNXJhYbkNJQwlblGaX1ScklTZXFPVoF2R1cewHIq1k34p0jQNhW2IEpL4kxQ9UJNJEmWNZQdHjdNVhULzTNBbBFs2WHDV3h1ZF1eJRRaZZjVnD9qx4JSLDXlUZaDSldSVWMj16OybTdNMMmpUkeHTkHMF2LZk5bZU1pdF3VX2RTcNGwUlhHWRXoXX=zNElV121iIcVZdcR3RWiNBMTVTdTaxUN0ZUUpVTbJ3KhG0UTBmkNEYVZden1FVyG1eZSNXQGlQBW5TdF6XbR4zk2WcTmUEYJ9yNkml0G51IHtZRZUDVV3NBZdb2TVZJpTLtdVVHESWk80LXm1jRKRjFNokdFkWcGtNdF3RNYXlI5hUDVUXTTbaRZWTVURjLGlQnIS3TTVYjUXLNXrdUQW11acAqx0QX2pQJSB30GemblkNUMYskY5nahQWZpXNUMVmXVz3GUUZRpQRSVN2ycUZ5hYTDbWKLGZ2N10lTIZpadr1Hl2kZVXhMN3Q0VCdN3WFFVheltLmwEzVQVFO3s4DoNbCZQPOhUQwDOkTotYsJBeht4=0NNdZekVSVRV9qKaYbDVTWHnYpMMZRJlhkMMRVMF1XF0VShZGUUWZ3TJZNd5azG3FyEye3ZSLSslZFxMSuyXdSEmFSJB8y2ThQZGEOlc4ko5VN0l3VV0bUmlEShpRVpj4iVYNdBj20aSegUd2VNeEMrOiSNRlODFlSblWn015aaijUIHNwfCRMzlE5RUdRxUDTMZn1UTBkJU1SDNzlMD0U","email":"someuser@redhat.com"},"registry.redhat.io":{"auth":"GQd1FBJQkXRB3U2NJsxTdT1eUmVXGUXrXOBETWKVWQNN2V3Bnjset3dSJI8pj8XLy6RiMEkDahYZDT5UVU2BWURxp0leVt31KYoycuoBRlDNTwGFXzxlNmO0ETZJlD1nQ4TqFVhh1T3iZRRcZZ2ro3myzflTW3ZN0V6EeNSDpRRSMk2mZHk2WVF1oXp2NoIxZlNVd=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","email":"someuser@redhat.com"}}}' + sshKey: | + ssh-ed25519 aCAyJUTgG/NeknKEiqAqBhAAMDq1C29lAEwAmNFTCBi315ZXZ0bTgzI5IqwAAEfKMujN someuser@redhat.com + ``` + +3. Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) git repository. + +4. Clone the forked repository by running the following command: ```sh $ git clone git@github.com:your-username/multicloud-gitops.git ``` -3. Go to your repository: Ensure you are in the root directory of your git repository by using the following command: +5. Go to your repository: Ensure you are in the root directory of your git repository by using the following command: ```sh $ cd rag-llm-gitops ``` -3. Create a local copy of the secret values file by running the following command: +6. Create a local copy of the secret values file by running the following command: ```sh $ cp values-secret.yaml.template ~/values-secret-rag-llm-gitops.yaml @@ -32,23 +80,23 @@ aliases: /rag-llm-gitops/getting-started/ > **Note:** >For this demo, editing this file is unnecessary as the default configuration works out of the box upon installation. -4. Add the remote upstream repository by running the following command: +7. Add the remote upstream repository by running the following command: ```sh $ git remote add -f upstream git@github.com:validatedpatterns/rag-llm-gitops.git ``` -5. Create a local branch by running the following command: +8. Create a local branch by running the following command: ```sh $ git checkout -b my-test-branch main ``` -6. Run the following command to push `my-test-branch` to the origin remote repository: +9. Run the following command to push `my-test-branch` to the origin remote repository: ```sh $ git push origin my-test-branch ``` -6. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: +10. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: ```sh INFO Install complete! @@ -57,7 +105,7 @@ aliases: /rag-llm-gitops/getting-started/ INFO Access the OpenShift web-console here: https://console-openshift-console.apps.demo1.openshift4-beta-abcorp.com INFO Login to the console with user: kubeadmin, password: ``` -7. Add GPU nodes to your existing cluster deployment by running the following command: +11. Add GPU nodes to your existing cluster deployment by running the following command: ```sh $ ./pattern.sh make create-gpu-machineset @@ -74,9 +122,9 @@ aliases: /rag-llm-gitops/getting-started/ > region = us-east-1 > ``` -8. Adding the GPU nodes should take about 5-10 minutes. You can verify the addition of these `g5.2xlarge` nodes in the OpenShift web console under **Compute** > **Nodes**. +12. Adding the GPU nodes should take about 5-10 minutes. You can verify the addition of these `g5.2xlarge` nodes in the OpenShift web console under **Compute** > **Nodes**. -9. Install the pattern with the demo application by running the following command: +13. Install the pattern with the demo application by running the following command: ```sh $ ./pattern.sh make install @@ -94,13 +142,13 @@ aliases: /rag-llm-gitops/getting-started/ 3. Following pods should be up and running. -![Pods](/images/rag-llm-gitops/rag-llm.png) + ![Pods](/images/rag-llm-gitops/rag-llm.png) ### Launch the application - Click the `Application box` icon in the header, and select `Retrieval-Augmented-Generation (RAG) LLM Demonstration UI` -![Launch Application](/images/rag-llm-gitops/launch-application.png) + ![Launch Application](/images/rag-llm-gitops/launch-application.png) - It should launch the application @@ -108,9 +156,9 @@ aliases: /rag-llm-gitops/getting-started/ ### Generate the proposal document -- It will use the default provider and model configured as part of the application deployment. The default provider is `Mistral-7B-Instruct` a model available on Hugging Face. It is a fine-tuned version of the base `Mistral-7B` model. The model server is deployed with this validated pattern and requires a node with GPU. +- The demo which generates a proposal document uses the default provider `Mistral-7B-Instruct` a model available on Hugging Face. It is a fine-tuned version of the base `Mistral-7B` model. -- Enter any company name for example Microsoft. +- Enter any company name for example `Microsoft`. - Enter the product as `RedHat OpenShift AI` - Click the `Generate` button, a project proposal should be generated. The project proposal also contains the reference of the RAG content. The project proposal document can be Downloaded in the form of a PDF document. diff --git a/content/patterns/rag-llm-gitops/rating-the-provider.md b/content/patterns/rag-llm-gitops/rating-the-provider.md index a6a2c7713..81ae0c840 100644 --- a/content/patterns/rag-llm-gitops/rating-the-provider.md +++ b/content/patterns/rag-llm-gitops/rating-the-provider.md @@ -19,10 +19,11 @@ By default, the Grafana application is deployed in the `llm-monitoring` namespac 3. Launch Grafana Dashboard by clicking the `Application box` icon in the header, and select `Grafana UI for LLM ratings`. 4. In the top right hand corner click `Sign in` - ![Launch Application](/images/rag-llm-gitops/launch-application.png) + + ![Launch Application](/images/rag-llm-gitops/launch-application.png) 5. Enter the Grafana admin credentials. Copy the `GF_SECURITY_ADMIN_USER`, `GF_SECURITY_ADMIN_PASSWORD` from `ai-llm-grafana-admin-credentials` screen in the OpenShift web console. 6. 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zEgcDxj(eU9p>^-s>dGa0D9-K2HM$Z^?{I)6t<#)3S)b%Zi_US4jq11UZxN>b{g+9E zyD6m(*V+qTk!_snkMz_K*LOGMAVx+{IbO6u4&r}XgNLtexH3vRLjGWW;0uo`EI9mF J+ZShl_&?U->1zN0 literal 0 HcmV?d00001 From b7bb9d026fcf7c4bf520559d46bb3af6fe8b43b0 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Wed, 22 Jan 2025 16:10:35 +0000 Subject: [PATCH 22/22] Updating based on peer review --- content/patterns/rag-llm-gitops/GPU_provisioning.md | 2 +- content/patterns/rag-llm-gitops/_index.md | 4 ++-- content/patterns/rag-llm-gitops/getting-started.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/content/patterns/rag-llm-gitops/GPU_provisioning.md b/content/patterns/rag-llm-gitops/GPU_provisioning.md index 2346b20b9..1e3b3e41d 100644 --- a/content/patterns/rag-llm-gitops/GPU_provisioning.md +++ b/content/patterns/rag-llm-gitops/GPU_provisioning.md @@ -5,7 +5,7 @@ aliases: /rag-llm-gitops/gpuprovisioning/ --- # Customizing GPU provisioning nodes -By default the GPU nodes deployed are of instance type `g5.2xlarge`. If for some reason you want to change this maybe due to performance issues carry out the following steps: +By default, GPU nodes use the instance type `g5.2xlarge`. If you need to change the instance type—such as to address performance requirements, carry out these steps: 1. In your local branch of the `rag-llm-gitops` git repository change to the `ansible/playbooks/templates` directory. diff --git a/content/patterns/rag-llm-gitops/_index.md b/content/patterns/rag-llm-gitops/_index.md index 79e4522de..8c83051eb 100644 --- a/content/patterns/rag-llm-gitops/_index.md +++ b/content/patterns/rag-llm-gitops/_index.md @@ -2,7 +2,7 @@ title: AI Generation with LLM and RAG date: 2024-07-25 tier: tested -summary: The goal of this demo is to demonstrate a Chatbot LLM application augmented with data from Red Hat product documentation running on Red Hat OpenShift. It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. The application generates a project proposal for a Red Hat product. +summary: The goal of this demo is to showcase a Chatbot LLM application augmented with data from Red Hat product documentation running on Red Hat OpenShift. It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. The application generates a project proposal for a Red Hat product. rh_products: - Red Hat OpenShift Container Platform - Red Hat OpenShift GitOps @@ -36,7 +36,7 @@ OpenShift Container Platform to generate project proposals for specific Red Hat ## Demo Description & Architecture -The goal of this demo is to demonstrate a Chatbot LLM application augmented with data from Red Hat product documentation running on [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai). It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. +The goal of this demo is to showcase a Chatbot LLM application augmented with data from Red Hat product documentation running on [Red Hat OpenShift AI](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai). It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. The application generates a project proposal for a Red Hat product. ### Key Features diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 41e4c6dc2..a9aa0ea41 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -6,7 +6,7 @@ aliases: /rag-llm-gitops/getting-started/ ## Prerequisites -- Podman +- Podman is installed on your system. - You have the OpenShift Container Platform installation program and the pull secret for your cluster. You can get these from [Install OpenShift on AWS with installer-provisioned infrastructure](https://console.redhat.com/openshift/install/aws/installer-provisioned). - Red Hat Openshift cluster running in AWS.

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zo0me^VsgciZ6rQhgLIVjMalU{lhGM|TUEyBmcROHM+vK0U+a`rm$)Bx-5VZ=lxG89 z_x|v188LKkB|sJlKNs z1H`j~PPfEU|MW4oT^AZl#9Um?d*MA8s9c}ZazCl-$hXAV2*qHwNfNf4(BJq;-IFol zFt2(W-i;E`j8Il9>nNXhy%EIaMlCwuS{vXiU%pNK<OdyJ@)9DUscAirp z4!i|73Uu22eIxp~DRu_?H4^13Y9+rWtMhLo{7?SrQlF1+v!(`qOYpZ{2}W4r6xE^w z5{sXi1@4NU#CJ9G6s^8oAu1>8e1Zms7f-{)Z-zVKl4rB z=C6C6Kl^6o^-{6MiAbY``dqj%ax_u+5o8*L=Y{Z9`_SuM1z}*`XJMD_Q-1U0)q4j2 z^*v3!h#hExoMSu1+9x6(wm>6KIFXI3BfH%kLUWIp7|jHKj}4iB5MEU6ctj%%;CKBmfgoUh$oV>$lg`{27ZjoF*3 zpg%Cg&0hMiea8jjZ0cM~+}-}L9AosdA|9Mls3c^Sf)m9!pFyb3xdi~a{1+7z&P2FA zu0kccXo{cq=$jbC^O)c*_$1 zautHZFE-zrwEk-w|J_TtYEkFrCQZD{`RhyOfB$A08u3wu2CU?F{M8 zknXI~|J6+X@)!Pp8h-0Wckz)|yLRp3A;(YV3z0s&oo;WoZ~xH?F1rW_iTD1!Ul#WX znyvB6N&S<*g*%o?mHHX*2dn(;`~G}dCqf^UlG=kb|Loz9^3a$Lgi^2s{eu@r?&uon z!rWrQ_v!Br+HX%=L(Gt;{rJnte>Cz(FZ8hXe4cBUI`j8uZR-K}2qaV5GH$9X9VDa-lnH-VTxb8^yH4;s50k*g^O^2>GsX Chq^le From b2442dac792d3efad9def24f96cfab2d6b8bcf8c Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Thu, 12 Dec 2024 10:25:21 +0000 Subject: [PATCH 13/22] removing hackded key --- content/patterns/rag-llm-gitops/getting-started.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 91277618a..1d8a67abe 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -55,9 +55,9 @@ aliases: /rag-llm-gitops/getting-started/ aws: region: us-east-1 publish: External - pullSecret: '{"auths":{"cloud.openshift.com":{"auth":"V9azMZJ1JRl3WlLkmNVclTNMOJN0DjOicnQ6WETOYVZpkUGblb2DjOUD5JjDjRpFgM0Z0zYZKRRl34b0jZSbUsVMDUDDM2zRlpZNgOTdTYUlhBUl3hJJNGGMVhRDDOW222ZyoBPiQfYO2DTkYNFRWBNMtYNRl=4R=WVGVz9ZOccGEX1N","email":"someuser@redhat.com"},"quay.io":{"auth":"V9azMZJ1JRl3WlLkmNVclTNMOJN0DjOicnQ6WETOYVZpkUGblb2DjOUD5JjDjRpFgM0Z0zYZKRRl34b0jZSbUsVMDUDDM2zRlpZNgOTdTYUlhBUl3hJJNGGMVhRDDOW222ZyoBPiQfYO2DTkYNFRWBNMtYNRl=4R=WVGVz9ZOccGEX1N","email":"someuser@redhat.com"},"registry.connect.redhat.com":{"auth":"xlz4pNTRkRTRmJZpvqJRCdPYlmZMmAFhWziTZUSk4lNZpzTnUR9cWk11NDVGJ4QaFckxpMNzlZNxhW0mbjhRe4Ye10V5RSVZUTUVMZVTkR2NBJdlexSzapkHTTQyrTS9InzpVeOvXdYQOy2UhhmbwBtYxcSWm2Xd4n3tUWtYpYEcjtEnpW4mYNB9dElmWlPahcZxlalfO2alQLTHWB0NURUmZhVpkislTHqNo1wjRovii0zDz1TFXGUKm1N3YRs1WbJSNzGZQFtRpBNRhmp00EkRWUD3zFB2RxVEXMTTwJEdaaoNS1XhxdmNXJhYbkNJQwlblGaX1ScklTZXFPVoF2R1cewHIq1k34p0jQNhW2IEpL4kxQ9UJNJEmWNZQdHjdNVhULzTNBbBFs2WHDV3h1ZF1eJRRaZZjVnD9qx4JSLDXlUZaDSldSVWMj16OybTdNMMmpUkeHTkHMF2LZk5bZU1pdF3VX2RTcNGwUlhHWRXoXX=zNElV121iIcVZdcR3RWiNBMTVTdTaxUN0ZUUpVTbJ3KhG0UTBmkNEYVZden1FVyG1eZSNXQGlQBW5TdF6XbR4zk2WcTmUEYJ9yNkml0G51IHtZRZUDVV3NBZdb2TVZJpTLtdVVHESWk80LXm1jRKRjFNokdFkWcGtNdF3RNYXlI5hUDVUXTTbaRZWTVURjLGlQnIS3TTVYjUXLNXrdUQW11acAqx0QX2pQJSB30GemblkNUMYskY5nahQWZpXNUMVmXVz3GUUZRpQRSVN2ycUZ5hYTDbWKLGZ2N10lTIZpadr1Hl2kZVXhMN3Q0VCdN3WFFVheltLmwEzVQVFO3s4DoNbCZQPOhUQwDOkTotYsJBeht4=0NNdZekVSVRV9qKaYbDVTWHnYpMMZRJlhkMMRVMF1XF0VShZGUUWZ3TJZNd5azG3FyEye3ZSLSslZFxMSuyXdSEmFSJB8y2ThQZGEOlc4ko5VN0l3VV0bUmlEShpRVpj4iVYNdBj20aSegUd2VNeEMrOiSNRlODFlSblWn015aaijUIHNwfCRMzlE5RUdRxUDTMZn1UTBkJU1SDNzlMD0U","email":"someuser@redhat.com"},"registry.redhat.io":{"auth":"GQd1FBJQkXRB3U2NJsxTdT1eUmVXGUXrXOBETWKVWQNN2V3Bnjset3dSJI8pj8XLy6RiMEkDahYZDT5UVU2BWURxp0leVt31KYoycuoBRlDNTwGFXzxlNmO0ETZJlD1nQ4TqFVhh1T3iZRRcZZ2ro3myzflTW3ZN0V6EeNSDpRRSMk2mZHk2WVF1oXp2NoIxZlNVd=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","email":"someuser@redhat.com"}}}' + pullSecret: '' sshKey: | - ssh-ed25519 aCAyJUTgG/NeknKEiqAqBhAAMDq1C29lAEwAmNFTCBi315ZXZ0bTgzI5IqwAAEfKMujN someuser@redhat.com + ssh-ed25519 someuser@redhat.com ``` 3. Fork the [rag-llm-gitops](https://github.com/validatedpatterns/rag-llm-gitops.git) git repository. From 1f08bda646210445ad6eb6907fe6915911bfadd5 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Thu, 12 Dec 2024 11:04:23 +0000 Subject: [PATCH 14/22] adding links --- .../rag-llm-gitops/deploying-different-db.md | 7 +- .../rag-llm-gitops/getting-started.md | 82 ++++++++++--------- 2 files changed, 48 insertions(+), 41 deletions(-) diff --git a/content/patterns/rag-llm-gitops/deploying-different-db.md b/content/patterns/rag-llm-gitops/deploying-different-db.md index 116a061ba..edec8a4a4 100644 --- a/content/patterns/rag-llm-gitops/deploying-different-db.md +++ b/content/patterns/rag-llm-gitops/deploying-different-db.md @@ -1,7 +1,7 @@ --- title: Deploying a different database weight: 12 -aliases: /rag-llm-gitops/getting-started/ +aliases: /rag-llm-gitops/deploy-different-db/ --- # Deploying a different database @@ -19,7 +19,10 @@ global: # Possible value for db.type = [REDIS, EDB] db: index: docs - type: EDB # <--- Default is EDB, Change the db type to REDIS for Redis deployment + type: EDB +# Add for model ID + model: + modelId: mistral-community/Mistral-7B-Instruct-v0.3 main: clusterGroupName: hub multiSourceConfig: diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 1d8a67abe..b3a985d68 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -8,14 +8,14 @@ aliases: /rag-llm-gitops/getting-started/ - Podman - You have the OpenShift Container Platform installation program and the pull secret for your cluster. You can get these from [Install OpenShift on AWS with installer-provisioned infrastructure](https://console.redhat.com/openshift/install/aws/installer-provisioned). -- Red Hat Openshift cluster running in AWS. Supported regions are `us-west-2` and `us-east-1`. For more information about installing on AWS see, [Installation methods](https://docs.openshift.com/container-platform/latest/installing/installing_aws/preparing-to-install-on-aws.html). +- Red Hat Openshift cluster running in AWS. ## Procedure 1. Create the installation configuration file using the steps described in [Creating the installation configuration file](https://docs.openshift.com/container-platform/4.17/installing/installing_aws/ipi/installing-aws-customizations.html#installation-initializing_installing-aws-customizations). > **Note:** - > This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. + > Supported regions are `us-west-2` and `us-east-1`. For more information about installing on AWS see, [Installation methods](https://docs.openshift.com/container-platform/latest/installing/installing_aws/preparing-to-install-on-aws.html). > 2. Customize the generated `install-config.yaml` creating one control plane node with instance type `m5a.2xlarge` and 3 worker nodes with instance type `p3.2xlarge`. A sample YAML file is shown here: @@ -91,48 +91,52 @@ aliases: /rag-llm-gitops/getting-started/ $ git checkout -b my-test-branch main ``` -9. Run the following command to push `my-test-branch` to the origin remote repository: +9. By default the pattern deploys the EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in your local branch in `values-global.yaml`. For more information see, [Deploying a different databases](/rag-llm-gitops/deploy-different-db/) to change the vector database. - ```sh - $ git push origin my-test-branch - ``` -10. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: +10. By default instance types for the GPU nodes are `g5.2xlarge`. Follow the [Customize GPU provisioning nodes](/rag-llm-gitops/gpuprovisioning) to change the GPU instance types. - ```sh - INFO Install complete! - INFO Run 'export KUBECONFIG=/auth/kubeconfig' to manage the cluster with 'oc', the OpenShift CLI. - INFO The cluster is ready when 'oc login -u kubeadmin -p ' succeeds (wait a few minutes). - INFO Access the OpenShift web-console here: https://console-openshift-console.apps.demo1.openshift4-beta-abcorp.com - INFO Login to the console with user: kubeadmin, password: - ``` -11. Add GPU nodes to your existing cluster deployment by running the following command: +11. Run the following command to push `my-test-branch` (including any changes) to the origin remote repository: ```sh - $ ./pattern.sh make create-gpu-machineset + $ git push origin my-test-branch ``` - > **Note:** - > You may need to create a file `config` in your home directory and populate it with the region name. - > 1. Run the following: - > ```sh - > vi ~/.aws/config - > ``` - > 2. Add the following: - > ```sh - > [default] - > region = us-east-1 - > ``` - -12. Adding the GPU nodes should take about 5-10 minutes. You can verify the addition of these `g5.2xlarge` nodes in the OpenShift web console under **Compute** > **Nodes**. - -13. Install the pattern with the demo application by running the following command: - - ```sh - $ ./pattern.sh make install - ``` - - > **Note:** - > This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. - > +12. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. For example: + + ```sh + INFO Install complete! + INFO Run 'export KUBECONFIG=/auth/kubeconfig' to manage the cluster with 'oc', the OpenShift CLI. + INFO The cluster is ready when 'oc login -u kubeadmin -p ' succeeds (wait a few minutes). + INFO Access the OpenShift web-console here: https://console-openshift-console.apps.demo1.openshift4-beta-abcorp.com + INFO Login to the console with user: kubeadmin, password: + ``` +13. Add GPU nodes to your existing cluster deployment by running the following command: + + ```sh + $ ./pattern.sh make create-gpu-machineset + ``` + > **Note:** + > You may need to create a file `config` in your home directory and populate it with the region name. + > 1. Run the following: + > ```sh + > vi ~/.aws/config + > ``` + > 2. Add the following: + > ```sh + > [default] + > region = us-east-1 + > ``` + +14. Adding the GPU nodes should take about 5-10 minutes. You can verify the addition of these `g5.2xlarge` nodes in the OpenShift web console under **Compute** > **Nodes**. + +15. Install the pattern with the demo application by running the following command: + + ```sh + $ ./pattern.sh make install + ``` + + > **Note:** + > This deploys everything you need to run the demo application including the Nividia GPU Operator and the Node Feature Discovery Operator used to determine your GPU nodes. + > ## Verify the Installation From 89fadc1c9aa173501a01aa19d0d555d40e1bfa70 Mon Sep 17 00:00:00 2001 From: Kevin Quinn Date: Thu, 12 Dec 2024 12:01:06 +0000 Subject: [PATCH 15/22] adding links 2 --- content/patterns/rag-llm-gitops/deploying-different-db.md | 2 +- content/patterns/rag-llm-gitops/getting-started.md | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/content/patterns/rag-llm-gitops/deploying-different-db.md b/content/patterns/rag-llm-gitops/deploying-different-db.md index edec8a4a4..167addc5e 100644 --- a/content/patterns/rag-llm-gitops/deploying-different-db.md +++ b/content/patterns/rag-llm-gitops/deploying-different-db.md @@ -6,7 +6,7 @@ aliases: /rag-llm-gitops/deploy-different-db/ # Deploying a different database -This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in [values-global.yaml](./values-global.yaml). +This pattern supports two types of vector databases, EDB Postgres for Kubernetes, and Redis. By default the pattern will deploy EDB Postgres for Kubernetes as a vector database. To deploy change the global.db.type parameter to the REDIS value in your local branch in `values-global.yaml`. ```yaml --- diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index b3a985d68..360d89526 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -93,13 +93,13 @@ aliases: /rag-llm-gitops/getting-started/ 9. By default the pattern deploys the EDB Postgres for Kubernetes as a vector database. To deploy Redis, change the `global.db.type` parameter to the `REDIS` value in your local branch in `values-global.yaml`. For more information see, [Deploying a different databases](/rag-llm-gitops/deploy-different-db/) to change the vector database. -10. By default instance types for the GPU nodes are `g5.2xlarge`. Follow the [Customize GPU provisioning nodes](/rag-llm-gitops/gpuprovisioning) to change the GPU instance types. +10. By default instance types for the GPU nodes are `g5.2xlarge`. Follow the [Customize GPU provisioning nodes](/rag-llm-gitops/gpuprovisioning/) to change the GPU instance types. 11. Run the following command to push `my-test-branch` (including any changes) to the origin remote repository: - ```sh - $ git push origin my-test-branch - ``` + ```sh + $ git push origin my-test-branch + ``` 12. Ensure you have logged in to the cluster at both command line and the console by using the login credentials presented to you when you installed the cluster. 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The application supports the following providers -- Hugging Face Text Generation Inference Server +- Hugging Face - OpenAI - NVIDIA -Click on the `Add Provider` tab to add a new provider. Fill in the details and click `Add Provider` button. The provider should be added in the `Providers` dropdown under `Chatbot` tab. +## Procedure -![Routes](/images/rag-llm-gitops/add_provider.png) +1. Click the `Application box` icon in the header, and select `Retrieval-Augmented-Generation (RAG) LLM Demonstration UI` -## Generate the proposal document using OpenAI provider + ![Launch Application](/images/rag-llm-gitops/launch-application-main_menu.png) + +- It should launch the application + + ![Application](/images/rag-llm-gitops/application.png) + +2. Click the `Configuration` tab to add a new provider. + +3. Click the `Add Provider` button. + + ![Addprovider](/images/rag-llm-gitops/add_provider.png) -Follow the instructions in step 3 to generate the proposal document using the OpenAI provider. +4. Complete the details and click the `Add` button. + + ![Addprovider](/images/rag-llm-gitops/add_provider-1.png) + +The provider is now available to select in the `Providers` dropdown under the `Chatbot` tab. + +![Routes](/images/rag-llm-gitops/add_provider-3.png) + +## Generate the proposal document using OpenAI provider -![Routes](/images/rag-llm-gitops/chatgpt.png) \ No newline at end of file +Follow the instructions in the section "Generate the proposal document" in [Getting Started](/rag-llm-gitops/getting-started/) to generate the proposal document using the OpenAI provider. \ No newline at end of file diff --git a/content/patterns/rag-llm-gitops/getting-started.md b/content/patterns/rag-llm-gitops/getting-started.md index 171a0a4fa..41e4c6dc2 100644 --- a/content/patterns/rag-llm-gitops/getting-started.md +++ b/content/patterns/rag-llm-gitops/getting-started.md @@ -65,7 +65,7 @@ aliases: /rag-llm-gitops/getting-started/ 4. Clone the forked repository by running the following command: ```sh - $ git clone git@github.com:your-username/multicloud-gitops.git + $ git clone git@github.com:your-username/rag-llm-gitops.git ``` 5. Go to your repository: Ensure you are in the root directory of your git repository by using the following command: @@ -160,7 +160,7 @@ aliases: /rag-llm-gitops/getting-started/ ### Generate the proposal document -- The demo which generates a proposal document uses the default provider `Mistral-7B-Instruct` a model available on Hugging Face. It is a fine-tuned version of the base `Mistral-7B` model. +- The demo generates a proposal document using the default provider `Mistral-7B-Instruct`; a model available on Hugging Face. 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z10_&44g-!g9s5Um+JV5Cs>s9(*rH`ue_3h3^D@b%OGLcA8?n3KefyZ2Jxj&w9yj z?0VpWqSw@ac_U*wM3g%bC;?Jdp#hbb?J3+P5cphDPOlM8Lr3f!2RJo6`$C2ZoTT*4 z(Jc|NiK@P38A2KJxpRD{BJM$$61d{>C=v{``eDfDTMQC z Date: Thu, 12 Dec 2024 16:46:56 +0000 Subject: [PATCH 20/22] correcting links etc. --- .../rag-llm-gitops/rating-the-provider.md | 2 +- .../images/rag-llm-gitops/add_provider-1.png | Bin 24893 -> 41546 bytes static/images/rag-llm-gitops/add_provider.png | Bin 42599 -> 41063 bytes 3 files changed, 1 insertion(+), 1 deletion(-) diff --git a/content/patterns/rag-llm-gitops/rating-the-provider.md b/content/patterns/rag-llm-gitops/rating-the-provider.md index 81ae0c840..cf8270b90 100644 --- a/content/patterns/rag-llm-gitops/rating-the-provider.md +++ b/content/patterns/rag-llm-gitops/rating-the-provider.md @@ -1,7 +1,7 @@ --- title: Rating the provider weight: 11 -aliases: /rag-llm-gitops/getting-started/ +aliases: /rag-llm-gitops/rating-provider/ --- # Rating the provider diff --git 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The application supports the follow 3. Click the `Add Provider` button. - ![Addprovider](/images/rag-llm-gitops/add_provider.png) + ![Addprovider](/images/rag-llm-gitops/provider-1.png) 4. Complete the details and click the `Add` button. - ![Addprovider](/images/rag-llm-gitops/add_provider-1.png) + ![Addprovider](/images/rag-llm-gitops/provider-2.png) The provider is now available to select in the `Providers` dropdown under the `Chatbot` tab. diff --git a/static/images/rag-llm-gitops/provider-1.png b/static/images/rag-llm-gitops/provider-1.png new file mode 100644 index 0000000000000000000000000000000000000000..ff45fef3c748124b691d85c876f3346ddf2b73a9 GIT binary patch literal 24699 zcmb@uWmp_t(=IxMAVGp#umFMJ?wa84?ykYzA-F?;;O_439)j!O?(QzT`QClL=h^$m zzOHl5?jJBP)2q8{)vCJdu6s?0th5Ls+$T5?2!tpmDku*EL4kqSQ?Ss$PlFc9b>P=~ zdwwxRSm5x0H4Fy+#&Hl*bx^Q2c5v3SGXj}dSz8*>*c;dx8CltzT05LVcJP8gpFv`R z-xXa_kCq)((KQHR&Q7c0sXyz+V*b>3Hh4!Fim&-t3_fggavrE_X}O-U2!o|`()E28 zDo&oa#v&URd5ffLNa}=zDfsM_9<-~sJ3N}2#IZX(Y(18WvcHE*>KFXafu4WU#`I0_ zzmKaNJ)5+D-WC-@C`Uv2?}H+w%aQn>gOs6B`oGT<%>4ol4*BQQxG#gp?`q*O_+#`* z$ub@jN~{5zjQAY zL6L#aWUSC1K70V#wX*+n>Fm>Q??VLN73HI=tIK;l+(BgdLd<)U6m(tNVNgO6vj_?c 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