rag-on-azure-sql

Builds retrieval augmented generation end to end on Azure SQL Database: chunking source text, storing embeddings with the provenance that makes them re-runnable, retrieving with the filter and the permission check inside the same query, and grounding an answer on what came back. Use when someone asks to "build RAG on Azure SQL Database", "chat with my documents", "add semantic search over my data", "keep embeddings in sync when rows change", "re-embed with a new model", or "which chunks should I put in the prompt"; and when a retrieval pipeline returns plausible but wrong context, or returns text the asking user is not allowed to read. This skill owns the pipeline and the schema around it. The vector type, VECTOR_DISTANCE and the query shape are vector-search-azure-sql, and the in-database embedding call is embeddings-and-external-models. Offline local embedding is an application-side workflow.