Version: Next

AI Functions

AI functions in Spice provide direct integration with large language models (LLMs) and embedding models within SQL queries. These functions process text through configured model providers and return generated responses, typed decisions, or vector embeddings.

`ai`

Invokes large language models (LLMs) directly within SQL queries for text generation tasks. This asynchronous function processes prompts through configured model providers and returns generated text responses.

ai(message)
ai(message, model_name)

Arguments

message: String prompt to send to the language model.
model_name (optional): Name of the model to use as configured in your Spicepod. If omitted, the default model is used (only valid when exactly one model is configured).

Return Type

Returns a string containing the generated text response. Returns NULL for NULL input messages or when the model returns an empty response. If the underlying model call fails, the query errors out (errors are logged).

Behavior

Queries execute asynchronously, processing LLM calls in parallel across rows for improved performance. Each invocation issues an asynchronous call to the specified model provider.

Concurrency: When a per-model rate controller is configured in the Spicepod, it manages concurrency and backpressure. Otherwise, concurrency falls back to DataFusion's execution.target_partitions setting. When multiple models with different providers are configured (e.g., OpenAI and Anthropic), each provider's requests are controlled independently.

Limits:

Maximum batch size: 1000 rows per invocation. Larger inputs are split into batches of up to 1000 rows, so a query can process any number of rows.
Maximum input message size: 1,000,000 bytes (1 MB) per message
Maximum model name length: 256 characters

Configuration

Models must be configured in spicepod.yaml under the models section. See Large Language Models for detailed configuration.

models:
  - name: gpt-4o
    from: openai:gpt-4o
    params:
      openai_api_key: ${secrets:openai_key}

Task History

Each ai() function invocation creates an ai span in the task_history table, which tracks execution time, input prompts, model name, and row count. Child ai_completion spans capture per-row model call details.

Examples

Using default model

When only one model is configured, the model name can be omitted:

SELECT
  zone,
  ai(concat_ws(' ', 'Categorize the zone', zone, 'in a single word. Only return the word.')) AS category
FROM taxi_zones
LIMIT 10;

Example output:

+-----------------------+-------------+
| zone                  | category    |
+-----------------------+-------------+
| Newark Airport        | Transport   |
| Jamaica Bay           | Nature      |
| Allerton/Pelham...    | Residential |
| Alphabet City         | Urban       |
| Arden Heights         | Suburban    |
+-----------------------+-------------+

Specifying a model explicitly

When multiple models are configured, specify which model to use:

SELECT
  product_name,
  ai('Summarize this product in 5 words: ' || product_description, 'gpt-4o') AS summary
FROM products
LIMIT 5;

Generating search terms

Use the ai() function to generate search terms for enhanced search functionality:

SELECT
  user_query,
  ai('Generate 3 alternative search terms for: ' || user_query, 'gpt-4o-mini') AS alt_terms
FROM user_searches
WHERE created_at > NOW() - INTERVAL '1 hour';

Batch text processing

Process multiple rows with parallel LLM calls. Each ai() invocation receives at most 1000 rows, and larger inputs are split across invocations. A LIMIT stops work only between batches, so a query can still call the model for more rows than the limit; narrow the rows with selective predicates when you need a strict bound:

SELECT
  customer_id,
  feedback,
  ai('Classify sentiment as positive, negative, or neutral: ' || feedback) AS sentiment
FROM customer_feedback
WHERE processed = false
LIMIT 1000;

Decision functions

Decision functions ask a model a typed question about each row and return a typed answer: a boolean, a probability, a label, a score, or a struct of answers. Any model in the Spicepod can answer, including a decision model such as TypeSafe Jev or any chat model. See Decisions for the full guide, the /v1/decisions HTTP API, and how a chat model answers.

ai_if(input, condition[, model => 'name'][, on_error => 'fail' | 'null'])
ai_probability(input, condition[, model => 'name'][, on_error => 'fail' | 'null'])
ai_classify(input, labels[, instructions => '...'][, model => 'name'][, on_error => 'fail' | 'null'])
ai_score(input, instructions, levels[, model => 'name'][, on_error => 'fail' | 'null'])
ai_decide(input, questions[, model => 'name'][, on_error => 'fail' | 'null'])

Common arguments

input: The value to decide about. Text is sent as text; a struct, map, or list is sent as JSON; binary data is refused and must be cast to text first. A NULL input returns NULL without a model call.
model (optional): The name of a model in the Spicepod. When omitted, Spice uses the only model that can answer or, when there are several, the only decision model among them. Otherwise the query fails and lists the models to choose from.
on_error (optional): 'fail' (the default) stops the query when the model cannot answer a row, after retrying rate limits and transient failures. 'null' returns NULL for that row. When the model declines one question of an ai_decide call, or of calls that share a request, 'null' sets only that question's answer to NULL and keeps the other answers.

Every argument except input must be a constant. Constants are checked when the query is planned, before any model call.

`ai_if`

Returns BOOLEAN: true when the probability that condition holds for input is above 0.5.

SELECT id, subject FROM tickets
WHERE status = 'open' AND ai_if(body, 'The customer is asking for a refund');

`ai_probability`

Returns DOUBLE from 0 to 1: the probability that condition holds for input.

SELECT id, ai_probability(body, 'The customer threatens to cancel') AS churn_risk
FROM tickets
ORDER BY churn_risk DESC;

`ai_classify`

Returns VARCHAR: the label that best fits input. A non-NULL result is always one of labels; the result is NULL for a NULL input, and for a row the model refuses or fails under on_error => 'null'. labels is a list of 2 to 255 labels, such as ['billing', 'technical'], or a JSON object of label to description, such as '{"billing": "Payments and refunds", "technical": null}'. Labels cannot be empty or repeated. Optional instructions => '...' adds guidance. Include a fallback label such as 'other' when no label may fit.

SELECT id, ai_classify(body, ['billing', 'technical', 'other']) AS team
FROM tickets;

`ai_score`

Returns DOUBLE from 0 to n−1: the probability-weighted, 0-based index of levels. levels is a list of 2 to 10 level descriptions, lowest first.

SELECT id, ai_score(body, 'How frustrated is the customer?', ['calm', 'annoyed', 'furious']) AS frustration
FROM tickets
ORDER BY frustration DESC;

`ai_decide`

Returns STRUCT with one field per question. questions is a JSON object of question ID to question in TypeSafe's grammar, the same grammar as Databricks' ai_decide: each question has a type of noul, choice, or score, and optional instructions. A choice maps 1 to 255 labels to descriptions in criteria, and a score lists 2 to 10 levels in criteria, lowest first. See Ask several questions with ai_decide for the answer fields.

SELECT id, d['team']['choice'] AS team, d['urgent']['probability'] AS urgency
FROM (
  SELECT id, ai_decide(body, '{
    "team":   {"type": "choice", "instructions": "Which team should handle this?",
               "criteria": {"billing": "Payments and payouts", "technical": "Bugs and outages"}},
    "urgent": {"type": "noul", "instructions": "Does this convey urgency?"}
  }') AS d
  FROM tickets
);

Decision function behavior

Calls to ai_if, ai_probability, ai_classify, and ai_score in one SELECT list or WHERE clause that share the same input, model, and on_error are answered by at most one request per row: identical non-NULL inputs within a batch are asked once, and a NULL input is not asked at all. In WHERE, the predicates a top-level AND joins to the call run first, so the model only sees rows that pass them; a predicate nested under OR does not prefilter. Decision functions are never pushed down to a federated source.

Each batch of calls is recorded in the task_history table as an ai_decide task. The model's max_concurrency and requests_per_minute_limit apply.

`embed`

Generates vector embeddings for text using specified embedding models. Supports both single text strings and arrays of text for batch processing.

embed(text, model_name)

Arguments

text: String or array of strings to generate embeddings for. Utf8 / LargeUtf8 scalars and lists/arrays of strings are supported.
model_name: Name of the embedding model to use (e.g., 'potion_2m', 'xl_embed') as configured in your Spicepod. Required — unlike ai(), embed() does not auto-select a default when only one model is configured.

Return Type

For a scalar string input: List<Float32> — a single embedding vector.
For an array of strings: List<List<Float32>> — one embedding vector per element, preserving the input length. NULL input elements produce NULL output elements.

Configuration

Embedding models must be configured in spicepod.yaml. See Embeddings for configuration details.

embeddings:
  - from: openai:text-embedding-3-small
    name: openai_embed
    params:
      openai_api_key: ${secrets:openai_key}

Examples

Single text embedding

Generate an embedding for a single piece of text:

SELECT embed('hello world', 'potion_2m') AS embedding;

Multiple text embeddings

Generate embeddings for multiple strings in one call:

SELECT embed(['hey', 'there', 'sunshine'], 'potion_2m') AS embeddings;

Embedding for vector search

Generate embeddings to use with vector search:

SELECT
  title,
  embed(content, 'xl_embed') AS content_embedding
FROM documents
WHERE embedding IS NULL
LIMIT 1000;

For more information on configuring and using AI models, see:

Cookbook

A cookbook recipe to invoke LLMs in SQL queries with the ai function. AI SQL Function

ai​

Arguments​

Return Type​

Behavior​

Configuration​

Task History​

Examples​

Using default model​

Specifying a model explicitly​

Generating search terms​

Batch text processing​

Decision functions​

Common arguments​

ai_if​

ai_probability​

ai_classify​

ai_score​

ai_decide​

Decision function behavior​

embed​

Arguments​

Return Type​

Configuration​

Examples​

Single text embedding​

Multiple text embeddings​

Embedding for vector search​

Cookbook​

`ai`

Arguments

Return Type

Behavior

Configuration

Task History

Examples

Using default model

Specifying a model explicitly

Generating search terms

Batch text processing

Decision functions

Common arguments

`ai_if`

`ai_probability`

`ai_classify`

`ai_score`

`ai_decide`

Decision function behavior

`embed`

Arguments

Return Type

Configuration

Examples

Single text embedding

Multiple text embeddings

Embedding for vector search

Cookbook