Skip to content

microsoft/microsoft-sql

v1.0.0

Build and operate Azure SQL Database applications, from provisioning and secure connections through schema design, deployment, data movement, performance tuning, vector search, RAG, and local development with the Azure SQL Database container.

embeddings-and-external-models

Generates embeddings and chunks inside Azure SQL Database with CREATE EXTERNAL MODEL, AI_GENERATE_EMBEDDINGS, AI_GENERATE_CHUNKS and sp_invoke_external_rest_endpoint, covering the database scoped credential naming rule, the permissions, the dimension budget that decides which embedding model fits, and the outbound allowlist. Use when someone asks to "create an external model", "call AI_GENERATE_EMBEDDINGS", "embed text in T-SQL", "chunk text in the database", or "call an Azure OpenAI endpoint from SQL"; when such a call fails on the credential secret, managed identity, permissions, HTTPS or a blocked domain; and when embedding a whole table in one statement runs for hours. This skill owns producing the vector and calling out of the engine; storing and searching it is vector-search-azure-sql, the pipeline around it is rag-on-azure-sql, and offline embedding from application code is outside this in-engine workflow.

Read SKILL.md at the source

Pinned to revision eeb1c6867c2d, so it is the text this page describes rather than whatever the author pushed since.

Files

Every link opens the file at its source, pinned to the revision this page describes.