About This Architecture

Enterprise RAG (Retrieval-Augmented Generation) architecture built with Spring AI on Azure App Service integrates Azure OpenAI Service for LLM inference and embeddings. The RAG Pipeline Component orchestrates document retrieval from Azure Storage Account, vector search via Azure Database for PostgreSQL with PGVector extension, and function calling through Azure SQL Database. Application Insights monitors performance while Key Vault secures API keys and connection strings, demonstrating production-ready AI application patterns for Spring developers. Fork this diagram on Diagrams.so to customize embedding strategies, add caching layers, or integrate Azure Cognitive Search for hybrid retrieval.

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How do I build a RAG application with Spring AI and Azure OpenAI Service using PGVector for embeddings?

This diagram shows a production RAG architecture where Spring AI on Azure App Service orchestrates document retrieval from Azure Storage, generates embeddings via Azure OpenAI Embedding Service, performs vector search in PostgreSQL with PGVector, and executes function calls through Azure SQL Database.