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databricks-ai-functions

Run AI inference directly in SQL and PySpark pipelines.

1|Updated Mar 17, 2026
Search Tags:#ai#sql#nlp#pyspark#document-processing#databricks#ai-functions
One-click install
npx skills add https://github.com/leary-poken/ai-dev-kit --skill databricks-ai-functions
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-ai-functions
Source: https://github.com/leary-poken/ai-dev-kit/tree/main/databricks-skills/databricks-ai-functions
Command: npx skills add https://github.com/leary-poken/ai-dev-kit --skill databricks-ai-functions

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Databricks AI Functions provide built-in AI capabilities within SQL and PySpark pipelines, eliminating the need to manage external model endpoints or custom inference deployments.

Core Features & Use Cases

  • Task-specific functions for common analytics tasks (ai_classify, ai_extract, ai_summarize, ai_translate, ai_parse_document, ai_query, ai_forecast, ai_analyze_sentiment, ai_mask, ai_gen, ai_similarity) that operate directly on table columns and scale in batch workloads.
  • Document ingestion and RAG-style patterns: parse PDFs and office documents with ai_parse_document, then enrich with targeted extractions or structured JSON via ai_query when necessary.
  • End-to-end data workflows: apply AI functions to support ticket analysis, invoice processing, and time-series forecasting without managing model endpoints.

Quick Start

Run an example: classify a snippet of text using ai_classify and extract fields with ai_extract.

Frequently Asked Questions about databricks-ai-functions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run AI-powered text classification and extraction directly in SQL?▼

You can run AI-powered text classification and extraction directly in SQL using Databricks AI Functions like ai_classify and ai_extract on table columns without managing external model endpoints.

What are Databricks AI Functions and how do they work for data analytics?▼

Databricks AI Functions are built-in capabilities that apply AI-powered inference directly to SQL and PySpark data analytics workloads, supporting tasks like sentiment analysis, translation, and document parsing without external endpoints.

Can I parse PDF documents and extract structured data in PySpark without external model endpoints?▼

Yes, you can parse PDF and office documents in PySpark using ai_parse_document, then enrich the output with targeted extractions or structured JSON via ai_query without managing any external model endpoints.

Do I need to set up external inference endpoints to use AI functions for batch workloads?▼

No, you do not need external inference endpoints. Databricks AI Functions support scalable, zero-endpoint workflows for batch processing by operating directly on table columns within your existing SQL and PySpark pipelines.

What is the best way to perform sentiment analysis and time-series forecasting in Spark?▼

The best way to perform sentiment analysis and time-series forecasting in Spark is using Databricks AI Functions like ai_analyze_sentiment and ai_forecast, which scale directly across batch and streaming data workflows.

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