Fabric ai functions

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 or SynapseML 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.
• Tenant switch — an admin must enable Copilot and other features powered by Azure OpenAI. Depending on region you may also need the cross-geo processing tenant setting (the built-in endpoint isn't in every region).
• Prompts, input data, and outputs are not logged or stored by AI Functions.

Setup — imports differ by engine

The single most common mistake is the import path. It is not the same for the two engines, and the API shapes differ too (see next section).

Engine	Import	Runs
pandas	import synapse.ml.aifunc as aifunc	single node (driver)
PySpark	import synapse.ml.spark.aifunc as aifunc	distributed across the Spark cluster

# pandas
import synapse.ml.aifunc as aifunc
import pandas as pd

# PySpark  (SparkSession `spark` is pre-provisioned)
import synapse.ml.spark.aifunc as aifunc

Choose PySpark for large-scale datasets (work distributes across workers); pandas runs on a single node and is fine for smaller frames or ad-hoc work.

Dependencies

Runtime	What to install
pandas on the Python runtime	synapseml_internal + synapseml_core wheels (https://aka.ms/fabric-aifunctions-whl, https://aka.ms/fabric-synapseml-core-whl)
pandas on the PySpark runtime	nothing for most usage
PySpark on the PySpark runtime	nothing

As of GA, the openai package is no longer a required dependency — install openai>=1.99.5 only if you need SDK-native client behavior or Pydantic response_format examples. Keep it out of the install to stay lightweight.

The nine functions

Function	Does	pandas (Series accessor)	PySpark (df.ai, keyword args)
ai.analyze_sentiment	positive/negative/mixed/neutral (or custom labels)	df["c"].ai.analyze_sentiment()	df.ai.analyze_sentiment(input_col="c", output_col="s")
ai.classify	categorize into your labels	df["c"].ai.classify("a","b","other")	df.ai.classify(labels=["a","b"], input_col="c", output_col="cat")
ai.embed	vector embeddings	df["c"].ai.embed()	df.ai.embed(input_col="c", output_col="e")
ai.extract	pull fields/entities into new columns	df["c"].ai.extract("name","city")	df.ai.extract(labels=["name","city"], input_col="c")
ai.fix_grammar	correct spelling/grammar/punctuation	df["c"].ai.fix_grammar()	df.ai.fix_grammar(input_col="c", output_col="fixed")
ai.generate_response	custom prompt over row data	df.ai.generate_response("<prompt>")	df.ai.generate_response(prompt="…", output_col="r")
ai.similarity	semantic similarity, -1…1	df["a"].ai.similarity(df["b"])	df.ai.similarity(input_col="a", other_col="b", output_col="sim")
ai.summarize	summarize text/columns/row/files	df["c"].ai.summarize()	df.ai.summarize(input_col="c", output_col="sum")
ai.translate	translate to a language	df["c"].ai.translate("spanish")	df.ai.translate(to_lang="spanish", input_col="c", output_col="t")

API-shape gotcha (pandas vs PySpark)

They look similar but aren't interchangeable:

• pandas attaches .ai to a Series and returns a Series you assign back (df["out"] = df["in"].ai.classify(...)). ai.extract returns a new DataFrame of the extracted columns.
• PySpark attaches .ai to the DataFrame, takes input_col/output_col keyword args, and returns a new DataFrame with the output column appended.

# pandas — Series in, Series out
df["sentiment"] = df["reviews"].ai.analyze_sentiment()

# PySpark — DataFrame in, DataFrame out
result = df.ai.analyze_sentiment(input_col="reviews", output_col="sentiment")

Structured extraction & responses

• ai.extract accepts ExtractLabel for typed, schema-driven output — JSON Schema with typed fields, enums, arrays, nested objects, nullable/required properties, and additionalProperties=false. You can author a Pydantic model and convert it to JSON Schema.
• ai.generate_response accepts response_format for structured output (JSON object, JSON Schema, or Pydantic model).

Chaining (PySpark)

PySpark results keep the df.ai accessor bound to the new schema, so transformations chain without materializing intermediates:

output = (df
    .ai.summarize(input_col="review_text", output_col="summary")
    .ai.classify(labels=["service","cleanliness","location","other"],
                 input_col="summary", output_col="category"))

Configuration

Defaults: model gpt-5-mini, reasoning_effort="low", temperature unset (GPT-5-series accepts only the model default — setting it is ignored), embeddings via text-embedding-ada-002, concurrency 200. This changed at GA — pandas/PySpark Python functions previously defaulted to the GPT-4 series.

The config objects differ by engine — don't copy pandas config into a PySpark notebook:

pandas — set attributes on aifunc.Conf / aifunc.default_conf, or pass a per-call conf=:

aifunc.default_conf.model_deployment_name = "gpt-5.1"
aifunc.default_conf.reasoning_effort = "medium"      # minimal|low|medium|high
aifunc.default_conf.verbosity = "low"                # low|medium|high
aifunc.default_conf.progress_bar_mode = "stats"      # basic|stats|disable

PySpark — use OpenAIDefaults via set_* / get_* / reset_*; per-call params are camelCase without the set_ prefix:

aifunc.default_conf.set_deployment_name("gpt-5.1")
aifunc.default_conf.set_reasoning_effort("medium")
# per-call override:
df.ai.translate(to_lang="spanish", input_col="text", output_col="out",
                deploymentName="gpt-5.1", concurrency=200)

Key params (full table in references/REFERENCE.md): deployment_name/model_deployment_name, reasoning_effort, verbosity, seed (pandas — reproducibility), temperature, top_p, timeout (pandas), concurrency (PySpark: per-call only, per worker, up to 1000), api_type (PySpark: responses default, set chat_completions for non-OpenAI Foundry models), embedding_deployment_name, URL + subscription_key (custom endpoints).

Model migration

The GPT-4.1 series is being retired. If you pinned pipelines:

Pinned to	Migrate to
gpt-4.1	gpt-5.1
gpt-4.1-mini	gpt-5-mini (the new default)

For higher-quality/complex transformations, opt into gpt-5.1 and/or raise reasoning_effort. AI Functions in Warehouse/SQL and Dataflow Gen2 receive the same model upgrade by end of June 2026.

Custom Azure OpenAI / Foundry endpoints

Point at your own resource with set_URL + set_subscription_key (PySpark) or the pandas equivalents. Foundry (non-OpenAI) models — Qwen, Grok, Llama, Mistral, Kimi — require api_type="chat_completions" and must support response_format with JSON schema. ai.embed and ai.similarity are not supported on Foundry resources (embeddings need the OpenAI-compatible endpoint). Reset to the built-in endpoint with reset_URL() / reset_subscription_key().

Usage stats & error handling

ai.stats reports per-run metrics — pandas: on the returned Series/DataFrame; PySpark: df.ai.stats on the result. Fields include num_successful, num_exceptions, num_unevaluated, num_harmful, cached_tokens, input_tokens, output_tokens, reasoning_tokens, model.

Non-successful rows surface as typed sentinels, not silent nulls:

Sentinel	Meaning
aifunc.ExceptionResult	row raised an exception
aifunc.NotEvaluatedResult	skipped because an earlier exception halted evaluation
aifunc.FilterResult	blocked by the Azure OpenAI content filter
aifunc.CapacityExceededResult	hit a capacity limit — retry after capacity frees up

In pandas, aifunc.split_results separates successful outputs from non-results so you can inspect and retry the failures. During execution, progress_bar_mode="stats" shows live token counts and CU-hour estimates.

Multimodal input

Most functions accept file paths (images, PDFs, text) instead of literal text: column_type="path" in pandas, or input_col_type/col_types="path" in PySpark. Supported: JPG/PNG/GIF/WebP, PDF, MD/TXT/CSV/TSV/JSON/XML/PY. Helpers: aifunc.load (ingest a folder to a table), aifunc.list_file_paths (enumerate URLs/paths), ai.infer_schema (derive an extraction schema from file contents for ai.extract).

Billing

AI Functions bill against the Copilot and AI meter on your capacity — separate from the Spark meter that covers the notebook/cluster compute. A PySpark ai.classify over millions of rows therefore shows up as two line items: notebook compute (Spark meter) + model token usage (AI Functions on the Copilot-&-AI meter). From 2026-03-17 the Capacity Metrics app reports AI Functions and AI Services as separate operations (reporting change only; rates unchanged). See fabric-monitoring for the metrics app.

Gotchas

Symptom	Cause	Fix
ModuleNotFoundError / .ai accessor missing	wrong import for the engine	pandas → synapse.ml.aifunc; PySpark → synapse.ml.spark.aifunc
pandas config keys rejected in PySpark (or vice-versa)	different config objects	pandas: aifunc.Conf/default_conf attrs; PySpark: OpenAIDefaults set_*
temperature has no effect	GPT-5-series accepts only the model default	omit it; steer with reasoning_effort / verbosity instead
per-call PySpark param ignored	wrong casing	per-call args are camelCase w/o set_ (deploymentName, not set_deployment_name)
ai.embed/ai.similarity fail on a custom endpoint	Foundry resource	embeddings need the OpenAI-compatible endpoint; keep the built-in for embed/similarity
results are ExceptionResult/CapacityExceededResult objects	per-row failure / capacity limit	check ai.stats; use aifunc.split_results (pandas) and retry
function errors on trial capacity	needs F2+/P	move to a paid capacity
pipeline broke after model upgrade	pinned to retiring gpt-4.1*	migrate gpt-4.1→gpt-5.1, gpt-4.1-mini→gpt-5-mini
unexpectedly high AI cost	model token usage on Copilot-&-AI meter, not Spark	monitor the AI Functions op in Capacity Metrics; lower reasoning_effort, batch fewer rows

Other surfaces (brief)

Same capability, different engines — not covered in depth here:

• Warehouse / SQL analytics endpoint — T-SQL functions ai_summarize, ai_classify, ai_generate_response (see fabric-warehouse).
• Dataflow Gen2 — the AI Prompt transform adds AI-generated columns in Power Query.

Reference

• Microsoft Learn: AI Functions: Transform data at scale with AI (overview)
• Microsoft Learn: Customize AI Functions with pandas · with PySpark
• Microsoft Learn: Billing for AI Functions
• Starter notebooks: https://aka.ms/fabric-aifunctions-starter-notebooks · Eval notebooks: https://aka.ms/fabric-aifunctions-eval-notebooks
• Full config-parameter tables, multimodal helpers, and the complete MS Learn link bundle: references/REFERENCE.md

See also

• fabric-spark — the broader PySpark-in-Fabric surface; AI Functions are one consumer of it
• fabric-data-agent — governed NL-to-data over semantic models/lakehouses (different tool, different job)
• fabric-warehouse — the T-SQL ai_* scalar functions
• fabric-monitoring — Capacity Metrics app and the Copilot-&-AI meter
• coding-python rule — conventions for the pandas/PySpark you'll write around these calls