fabric-ai-functionslisted

# Fabric AI Functions One-line, LLM-powered transformations applied to whole pandas or PySpark DataFrames in Fabric notebooks. Fabric handles the model endpoint, auth, request orchestration, batching, and retries — you call a DataFrame method and get an enriched column back. Nine prebuilt functions cover sentiment, classification, extraction, embeddings, grammar, custom prompts, similarity, summarization, and translation. ## When to use vs not Use AI Functions to enrich, classify, extract, summarize, translate, or embed **tabular data at scale** — thousands to millions of rows — with minimal code, letting Fabric manage concurrency (200 rows in parallel by default) and the built-in endpoint. This is the fastest path to apply an LLM across a column. Skip them when you need **low-level control** over a single prompt/response, custom orchestration, function-calling loops, or a conversational agent — use the [Azure OpenAI Python SDK](https://learn.microsoft.com/fabric/data-science/ai-services/how-to-use-openai-python-sdk) or [SynapseML](https://learn.microsoft.com/fabric/data-science/ai-services/how-to-use-openai-synapse-ml) instead. For a governed natural-language-to-data experience over your semantic models/lakehouses, that's a Data Agent (`fabric-data-agent`), not AI Functions. ## Prerequisites - **Paid capacity** — F2 or higher, or any P edition. Not available on trial/Free. - **Fabric Runtime 1.3+** — earlier runtimes can't run AI Functions. The `+` is upstream's own wo