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databricks-ai-functions

Skill

Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing mod

0Writing

Install

git clone https://github.com/databricks/databricks-agent-skills.git ~/.claude/skills/databricks-ai-functions

What is databricks-ai-functions?

Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~104 tokens of context used while enabled, before you invoke anything

Documentation

README · ~7 min read

Databricks AI Functions

Official Docs: https://docs.databricks.com/large-language-models/ai-functions Individual function reference: https://docs.databricks.com/sql/language-manual/functions/

Overview

Databricks AI Functions are built-in SQL and PySpark functions that call Foundation Model APIs directly from your data pipelines — no model endpoint setup, no API keys, no boilerplate. They operate on table columns as naturally as UPPER() or LENGTH(), and are optimized for batch inference at scale.

Always prefer a task-specific function over ai_query. Reach for ai_query only when no task function fits (custom/external endpoints, multimodal, or JSON beyond ai_extract's limits). Every function below shares a baseline: DBR 15.1+ (notebooks) / 15.4 ML LTS (batch), not on SQL Warehouse Classic, and region must support AI Functions — the Prereqs column lists only what's additional.

Cost & speed — each call is an LLM inference (slow and billed per token). Run a function once per row and persist the result to a Delta table; never re-invoke it on every downstream query. In demos, avoid generating tables with millions of rows — sample the input when needed so the demo runs quickly. Materialize once, then query the cheap Delta output.

The Function column links to the in-repo deep reference (full options, schemas, examples); Docs links to the official page.

FunctionTaskInputOutputExtra prereqsDocs

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