Reference implementation of the Retrieval-Augmented Generation (RAG) pattern.
Connectivity Components:
- Azure Virtual Network (vnet) to Secure Data Flow (Isolated, Internal inbound & outbound connections).
- Azure Front Door (LB L7) + Web Application Firewall (WAF) to Secure Internet Facing Components.
- Bastion (RDP/SSH over TLS), secure remote desktop access solution for VMs in the virtual network.
- Jumpbox, a secure jump host to access VMs in private subnets.
AI Workloads:
- Azure Open AI, a managed AI service for running advanced language models like GPT-4.
- Private DNS Zones for name resolution within the virtual network and between VNets.
- Cosmos DB, a globally distributed, multi-model database service to support AI applications.
- Web applications in Azure Web App.
- Azure AI services for building intelligent applications.
- High Availability & Disaster Recovery Ready Solution.
- Audit Logs, Monitoring & Observability (App Insight)
- Continuous Operational Improvement
1 Data ingestion Optimizes data preparation for Azure OpenAI
2 Orchestrator The system's dynamic backbone ensuring scalability and a consistent user experience
3 App Front-End Built with Azure App Services and the Backend for Front-End pattern, offers a smooth and scalable user interface
To deploy this solution you just need to execute the next steps:
1) Provision required Azure services
You can do it by clicking on the following button
or by using Azure Developer CLI (azd) executing the following lines in terminal
azd auth login
azd init -t azure/gpt-rag
azd up
Important: when selecting the target location check here the regions that currently support the Azure OpenAI models you want to use.
2) Ingestion Component
Use Data ingestion repo template to create your data ingestion git repo and execute the steps in its Deploy section.
3) Orchestrator Component
Use Orchestrator repo template to create your orchestrator git repo and execute the steps in its Deploy section.
4) Front-end Component
Use App Front-end repo template to create your own frontend git repo and execute the steps in its Deploy section.
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