databricks-ai-functions — Claude Code skill: AI functions in SQL

What databricks-ai-functions does

The skill documents Databricks built-in functions: classification, field extraction, summarization, PII masking, translation, grammar fixing, generation, sentiment analysis, semantic similarity, document parsing, search prep, arbitrary endpoint queries and time series forecasting. Your agent gets a reference per function — input types, output shape, extra prerequisites.

It also ships task patterns and call templates. Classification and extraction return VARIANT, so you read fields with the : operator, for example :response[0]::STRING. In PySpark the same calls go inside expr(...). A separate section walks through a RAG pipeline: parse a document, chunk it, index, then query.

Baseline requirements are shared: DBR 15.1+ in notebooks or 15.4 ML LTS for batch, not SQL Warehouse Classic, and a region that supports AI Functions. Document parsing needs DBR 17.3+, search prep needs 18.2+. ai_query and ai_forecast add their own: a Pro or Serverless warehouse and CAN QUERY on the endpoint. Every call is a model inference billed per token and it is slow, so materialize the result into a Delta table once and query that cheap output afterwards. That part matters — re-invoking the function on every downstream query is how token bills get out of hand.