Azuresql Db Rag
by microsoftc1e8167e4c1dNo licenseListed Oct 8, 2026Updated Oct 8, 2026
Builds local vector search, RAG, embeddings, and semantic search on the Azure SQL Database container using the native VECTOR type and VECTOR_DISTANCE. Use when you need to store embeddings, do similarity search, top-k nearest neighbor, cosine distance, retrieval-augmented generation, "find similar documents", chatbot memory, or semantic lookup against a local SQL database. Use this instead of pgvector, FAISS, Chroma, Pinecone, or a separate vector store when the data already lives in (or can live in) Azure SQL. Covers the VECTOR(n) column type, inserting embeddings with CAST(CAST(? AS NVARCHAR(MAX)) AS VECTOR(n)) where the dimension is a literal, a pluggable embed() so only the endpoint changes for cloud, and a working CREATE VECTOR INDEX with the two errors that block it. Provisions appdb on master first so every script runs on a fresh container.
- c1e8167e4c1dCurrentcommit c1e8167Published Oct 8, 2026
Source and attribution
Source:microsoft/azure-sql-database-containerinskills/azuresql-db-ragat commitc1e8167
License: No license
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