Public Preview: Knowledge as a Service with Azure Logic Apps.

Most RAG implementations today are complex. Teams must build ingestion pipelines, manage chunking and embeddings, configure vector stores, wire up retrieval layers, and then operate the entire system in production. With Azure Logic Apps, RAG becomes a native workflow capability instead of a custom infrastructure project. Developers can: * Upload documents directly into a workflow * Automatically ingest, chunk, and vectorize content * Use retrieval as a built-in workflow step * Build grounded and personalized AI experiences without managing separate RAG infrastructure This fundamentally changes the developer experience from building and operating RAG pipelines to simply using retrieval within application logic and automation workflows. By collapsing complex RAG orchestration into a few workflow steps, Azure Logic Apps helps developers move faster from prototype to production for grounded AI agents and intelligent business applications.