Locy Syntax Cheatsheet¶

Rule¶

CREATE RULE name [PRIORITY n] AS
MATCH ...
[WHERE ...]                         -- pre-aggregation filter
[ALONG x = expr]
[FOLD agg = aggregate(expr)]
[WHERE agg_condition]               -- post-FOLD filter (HAVING)
[BEST BY expr ASC|DESC]
YIELD KEY a, value AS alias, prob_expr AS PROB
-- OR, for graph mutation rules (edge/node props are inline maps, no SET):
DERIVE (src)-[:TYPE {prop: expr}]->(dst)

The second WHERE (after FOLD) filters on aggregated values — equivalent to SQL's HAVING. It can reference FOLD output columns and KEY columns.

FOLD Aggregators¶

Operator	Semantics	Use In Recursion?
`COUNT(*)` / `COUNT(expr)`	Row count	Non-recursive only
`SUM(expr)`	Arithmetic sum	Non-recursive only
`AVG(expr)`	Arithmetic mean	Non-recursive only
`MIN(expr)`	Minimum value	Non-recursive only
`MAX(expr)`	Maximum value	Non-recursive only
`COLLECT(expr)`	Collect into list	Non-recursive only
`MSUM(expr)`	Monotonic sum (non-decreasing)	Safe in recursion
`MMAX(expr)`	Monotonic maximum	Safe in recursion
`MMIN(expr)`	Monotonic minimum	Safe in recursion
`MCOUNT(expr)`	Monotonic count	Safe in recursion
`MNOR(expr)`	Noisy-OR probability: `1 − ∏(1 − pᵢ)`	Safe in recursion
`MPROD(expr)`	Product probability: `∏ pᵢ`	Safe in recursion

Standard aggregators (SUM, MAX, etc.) can decrease between fixpoint iterations, which violates the monotonicity required for safe recursive evaluation. Use the M-prefixed monotonic variants in recursive strata.

Goal Query¶

QUERY name [WHERE ...] [RETURN ...]

Derive Command¶

DERIVE name [WHERE ...]

Explain¶

EXPLAIN RULE name [WHERE ...]

Assume¶

ASSUME { <cypher mutations> } THEN { <locy/cypher body> }

Abduce¶

ABDUCE [NOT] name [WHERE ...] [RETURN ...]

Neural Predicates¶

CREATE MODEL name AS
  INPUT (binding [: Label])
  [FEATURES feature_expr (, feature_expr)*]
  [FEATURES (subject, column) FROM source_rule]
  OUTPUT (PROB | SCORE | LABEL | VECTOR) result_name
  USING xervo('provider_alias' [, embedder = 'embed_alias'])
  [CALIBRATION (platt_scaling | isotonic_regression | temperature_scaling | beta_calibration | dirichlet | conformal | conformal(alpha) | none)]
  [VERSION 'string']

CALIBRATE model_name ON MATCH pattern [WHERE expr] TARGET expr METHOD calibration_method [HOLDOUT n]
VALIDATE  model_name ON MATCH pattern [WHERE expr] TARGET expr METRICS metric (, metric)*

metric ∈ brier_score | ece | debiased_ece | accuracy | log_loss | auc. The classifier-registry key is the CREATE MODEL <name>, not the USING xervo('alias') provider hint. The feature dict the callable receives is keyed by the INPUT binding name; values are the evaluated argument expressions at the call site. See Neural Predicates.

Modules¶

MODULE my.module
USE shared.rules