Building a Production-Ready RAG Architecture on Azure | React, AI Search & OpenAI

Описание: In this video, I demonstrate a scalable, end-to-end RAG (Retrieval-Augmented Generation) architecture built entirely on Azure. This isn't just a demo; it’s a production-style setup designed to process unstructured data securely and deliver context-aware AI responses in real-time.

🏗️ Architecture Overview: We break down how to move from raw unstructured documents to a fully functional AI application using semantic and vector search.

🛠️ The Tech Stack:

Frontend: ReactJS (Desktop Application) for a seamless user experience.

Backend: Azure Container Apps running cloud-native microservices.

Storage: Azure Blob Storage (Documents) & Cosmos DB (Metadata).

Search & Vectors: Azure AI Search as our high-performance vector store.

AI Engine: Azure OpenAI for LLM orchestration and embeddings.

Key Features Covered: ✅ Event-driven workflows for document processing. ✅ Secure handling of unstructured data. ✅ Implementation of Semantic Search for better accuracy. ✅ Scalable microservices architecture.

If you're looking to build enterprise-grade AI applications, this walkthrough of the system design and data modeling will give you a solid blueprint.

#Azure #RAG #GenerativeAI #SystemDesign #OpenAI #CloudComputing #SoftwareArchitecture #ReactJS #VectorSearch