The RAG pattern enables businesses to use the reasoning capabilities of LLMs, using their existing models to process and generate responses based on new data. RAG facilitates periodic data updates without the need for fine-tuning, thereby streamlining the integration of LLMs into businesses.
The Enterprise RAG Solution Accelerator (GPT-RAG) offers a robust architecture tailored for enterprise-grade deployment of the RAG pattern. It ensures grounded responses and is built on Zero-trust security and Responsible AI, ensuring availability, scalability, and auditability. Ideal for organizations transitioning from exploration and PoC stages to full-scale production and MVPs.
- Discord Channel Enterprise RAG - Connected Community
GPT-RAG follows a modular approach, consisting of three components, each with a specific function.
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Data Ingestion - Optimizes data chunking and indexing for the RAG retrieval step.
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Orchestrator - Orchestrates LLMs and knowledge bases calls to generate optimal responses for users.
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App Front-End - Uses the Backend for Front-End pattern to provide a scalable and efficient web interface.
If you want to learn more about the RAG Pattern and GPT-RAG architecture.
This guide will walk you through the deployment process of Enterprise RAG. There are two deployment options available, Basic Architecture and Zero Trust Architecture. Before beginning the deployment, please ensure you have prepared all the necessary tools and services as outlined in the Pre-requisites section.
Pre-requisites
- Azure Developer CLI: Download azd for Windows, Other OS's.
- Powershell 7+ with AZ module (Windows only): Powershell, AZ Module
- Git: Download Git
- Node.js 16+ windows/mac linux/wsl
- Python 3.11: Download Python
- Initiate an Azure AI services creation and agree to the Responsible AI terms **
** If you have not created an Azure AI service resource in the subscription before
Note: If you implement the Zero-trust architecture described below, you will only need Node.js and Python for the second part of the procedure, which you will carry out on the VM created during the deployment process of this architecture.
For quick demonstrations or proof-of-concept projects without network isolation requirements, you can deploy the accelerator using its basic architecture.
The deployment procedure is quite simple, just install the prerequisites and follow these four steps using Azure Developer CLI (azd) in a terminal:
1 Download the Repository:
azd init -t azure/gpt-rag2 Login to Azure:
azd auth login3 Start Building the infrastructure and components deployment:
azd up4 Add source documents to object storage
Upload your documents to the 'documents' folder located in the storage account. The name of this account should start with 'strag'. This is the default storage account, as shown in the sample image below.
For more secure and isolated deployments, you can opt for the Zero Trust architecture. This architecture is ideal for production environments where network isolation and stringent security measures are highly valued.
Deploying the Zero Trust architecture follows a similar procedure to the Basic Architecture deployment, but includes some additional steps. Refer to the instructions below for a detailed guide on deploying this option:
1 Download the Repository
azd init -t azure/gpt-rag2 Enable network isolation
azd env set AZURE_NETWORK_ISOLATION true 3 Login to Azure:
azd auth login4 Start Building the infrastructure and components deployment:
azd provision5 Next, you will use the Virtual Machine with the Bastion connection (created during step 4) to continue the deployment.
Log into the created VM with the user gptrag and authenticate with the password stored in the keyvault, similar to the figure below:
6 Upon accessing Windows, install Powershell, as the other prerequisites are already installed on the VM.
7 Open the command prompt and run the following command to update azd to the latest version:
choco upgrade azd
After updating azd, simply close and reopen the terminal.
8 Create a new directory, for example, deploy then enter the created directory.
mkdir deploy
cd deploy
To finalize the procedure, execute the subsequent commands in the command prompt to successfully complete the deployment:
azd init -t azure/gpt-rag
azd auth login
azd env refresh
azd package
azd deploy
Note: when running the
azd init ...andazd env refresh, use the same environment name, subscription, and region used in the initial provisioning of the infrastructure.
Done! Zero trust deployment is completed.
The deployment process outlined in the Getting Started section sets up Azure resources and deploys the accelerator components with a standard configuration. For those looking to tailor the deployment more closely to their specific requirements, the Custom Deployment section offers further customization possibilities.
If you're looking to expand your data retrieval capabilities by adding new data sources, consider integrating Bing Custom Search, SQL Server, and Teradata. For more information, refer to the AI Integration Hub page.
Look at the Troubleshooting page in case you face some error in the deployment process.
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