Document Generation Demo with LLM and RAG

Introduction

This deployment is based on validated pattern framework that uses GitOps to easily provision all operators and apps. It deploys a Chatbot application that leverages the power of Large Language Models (LLMs) in conjunction with the Retrieval-Augmented Generation (RAG) framework running on Red Hat OpenShift to generate a project proposal for a given Red Hat product.

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 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. 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

LLM Application augmented with content from Red Hat product documentation.
Multiple LLM providers (OpenAI, Hugging Face, NVIDIA)
Vector Database, such as PGVECTOR or REDIS, to store embeddings of RedHat product documentation.
Monitoring dashboard to provide key metrics such as ratings
GitOps setup to deploy e2e demo (frontend / vector database / served models)

Figure 1. Overview of the validated pattern for RAG Demo with Red Hat OpenShift

Figure 2. Logical diagram of the RAG Demo with Red Hat OpenShift.

RAG Demo Workflow

Figure 3. Schematic diagram for workflow of RAG demo with Red Hat OpenShift.

RAG Data Ingestion

Figure 4. Schematic diagram for Ingestion of data for RAG.

RAG Augmented Query

Figure 5. Schematic diagram for RAG demo augmented query.

In Figure 5, we can see RAG augmented query. Llama 2 model is used for for language processing, LangChain to integrate different tools of the LLM-based application together and to process the PDF files and web pages, vector database such as PGVECTOR or REDIS, is used to store vectors, HuggingFace TGI is used to serve the Llama 2 model, Gradio is used for user interface and object storage to store language model and other datasets. Solution components are deployed as microservices in the Red Hat OpenShift cluster.

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View and download all of the diagrams above in our open source tooling site.

Open Diagrams

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 (PGVECTOR) / 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

Cloning repository

git clone https://github.com/<<your-username>>/rag-llm-gitops.git
cd rag-llm-gitops
oc login --token=<> --server=<> # login to Openshift cluster
podman machine start
# 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

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.

./pattern.sh make create-gpu-machineset

Wait till the nodes are provisioned and running.

Alternatiely, follow the instructions to manually install GPU nodes, Node Feature Discovery Operator and NVIDIA GPU operator.

Deploy application

*Note:: This pattern supports two types of vector databases, PGVECTOR and REDIS. By default the pattern will deploy PGVECTOR as a vector DB. To deploy REDIS, change the global.db.type to REDIS in values-global.yaml.

---
global:
  pattern: rag-llm-gitops
  options:
    useCSV: false
    syncPolicy: Automatic
    installPlanApproval: Automatic
# Possible value for db.type = [REDIS, PGVECTOR]
  db:
    index: docs
    type: PGVECTOR  <--- Default is PGVECTOR, Change the db type to REDIS for REDIS deployment
main:
  clusterGroupName: hub
  multiSourceConfig:
    enabled: true

Following commands will take about 15-20 minutes

Validated pattern will be deployed

./pattern.sh make install

1: 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.

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 provided above.

2: Launch the application

Click the Application box icon in the header, and select Retrieval-Augmented-Generation (RAG) LLM Demonstration UI

It should launch the application

3: 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 valdiated pattern and requires a node with GPU.
Enter any company name
Enter the product as RedHat OpenShift
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.

4: 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 uder Chatbot tab.

5: Generate the proposal document using OpenAI provider

Follow the instructions in step 3 to generate the proposal document using the OpenAI provider.

6: 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 prodcution.

7: Grafana Dashboard

By default, Grafana application is deployed in llm-monitoring namespace.To launch the Grafana Dashboard, follow the instructions below:

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
- Enter the Grafana admin credentials.
- Ratings are displayed for each model.

Test Plan

GOTO: Test Plan

validatedpatterns / rag-llm-gitops