hornet-dsl 0.4.1a7

Hornet: An embedded DSL for Logic Programming in Python.

Hornet

Horn clauses via Expression Trees — a Prolog-like embedded DSL for Python ≥ 3.13.

Hornet lets you write logic programs directly in Python. Instead of parsing Prolog strings, it hijacks Python's operator overloading and __call__ syntax to build expression trees, which a resolution engine then solves via unification and backtracking.

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Installation

pip install hornet-dsl

Requires Python 3.13+. Dependencies: toolz, immutables.

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Core Concepts

Terms

Hornet's term algebra mirrors Prolog's:

Hornet	Prolog equivalent
Variable('X') / symbols.X	X (logic variable)
Atom('foo') / symbols.foo	foo (atom)
symbols.foo(X, Y)	foo(X, Y) (compound term)
[1, 2, 3] / promote([1,2,3])	[1,2,3] (list)
[H | T] via BitOr	[H|T] (cons cell)

Import symbols dynamically from hornet.symbols. Names starting with uppercase become Variables; lowercase become Atoms; _ is the anonymous wildcard.

from hornet.symbols import X, Y, parent, mortal, human

Facts and Rules

Facts and rules are built with .when():

db.tell(
    parent('socrates', 'sophroniscus'),   # fact
    human('socrates'),                     # fact
    mortal(X).when(human(X)),             # rule: mortal(X) :- human(X).
)

Queries

from hornet import database
from hornet.symbols import X, mortal

db = database()
db.tell(human('socrates'))
db.tell(mortal(X).when(human(X)))

for subst in db.ask(mortal(X)):
    print(subst[X])   # → socrates

db.ask() returns an iterable of substitutions. Each substitution maps variables to their bound values.

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Built-in Predicates

Hornet ships a standard library of predicates pre-loaded in every database:

Predicate	Description
equal(X, Y)	Unification (X = Y)
unequal(X, Y)	Negation of unification
let(R, Expr)	Arithmetic evaluation (R is Expr)
arithmetic_equal(X, Y)	Arithmetic equality
smaller(A, B) / greater(A, B)	Numeric comparison
call(G)	Call a goal term
once(G)	Call G, commit to first solution
findall(O, G, L)	Collect all solutions
member(X, L)	List membership
append(A, B, C)	List concatenation
reverse(L, R)	List reversal
select(X, L, R)	Select element from list
length(L, N)	List length
maplist(G, L)	Apply goal to each list element
is_var, nonvar, is_atom, is_int, …	Type checks
write, writeln, nl	I/O
cut	Prolog cut (!)
fail	Always fails
true	Always succeeds
repeat	Succeeds infinitely
throw(E)	Raise an exception
ifelse(T, Y, N)	Soft-cut conditional
phrase(G, L)	DCG query

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Arithmetic

Arithmetic expressions are built using Python's operators on symbolic terms and evaluated lazily by let:

from hornet.symbols import X, Y, R, let, arithmetic_equal

# R is X * Y + 1
db.ask(let(R, X * Y + 1))

# supported: + - * / // % ** ~ & | ^ << >>

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Definite Clause Grammars (DCGs)

Hornet supports DCG notation via DCG() and DCGs():

from hornet import DCGs, database
from hornet.symbols import S, NP, VP, noun, verb, det, phrase

db = database()
db.tell(*DCGs(
    S.when(NP, VP),
    NP.when(det, noun),
    VP.when(verb),
    det.when(['the']),
    noun.when(['cat']),
    verb.when(['sleeps']),
))

for subst in db.ask(phrase(S, ['the', 'cat', 'sleeps'])):
    print('parsed!')

DCG rules are automatically expanded to difference lists. The inline(goal) escape hatch lets you embed regular Prolog goals inside a DCG body.

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Extending with Python

Register native Python predicates using the @predicate decorator. This is how ifelse/3 is implemented internally:

from hornet import database, predicate
from hornet.clauses import Database, Environment, Subst
from hornet.combinators import Step, if_then_else
from hornet.clauses import resolve
from hornet.symbols import T, Y, N

db = database()

@db.tell
@predicate(ifelse(T, Y, N))
def _(db: Database, subst: Subst) -> Step[Database, Environment]:
    return if_then_else(
        resolve(subst[T]),
        resolve(subst[Y]),
        resolve(subst[N]),
    )(db, subst.map)

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Architecture

Hornet is built on two main layers:

Term algebra (hornet.terms): Python expressions construct expression trees rather than computing values. Operator overloading (+, *, |, **, …) and __call__ produce nested Symbolic structures — Functor, Atom, Variable, Cons, Operator subclasses — which represent both data and goals. promote() lifts Python primitives (integers, strings, lists) into this algebra transparently.

Resolution engine (hornet.combinators): A triple-barrelled continuation monad drives search. Every goal is a function (ctx, subst) → Step, where a Step takes three continuations — success (emit a substitution and continue), failure (backtrack), and prune (implement cut). The combinators then, choice, prunable, neg, and if_then_else compose goals; trampoline() drives the whole thing iteratively to avoid stack overflow.

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Examples

The examples/ directory includes:

• append.py — list splitting via backtracking
• queens.py — N-queens constraint solver
• fizzbuzz.py — FizzBuzz via DCGs
• symdiff.py — symbolic differentiation and simplification
• parsing.py — natural language parsing with a German grammar
• turing.py — a Turing machine interpreter
• hanoi.py — Towers of Hanoi with Turtle graphics

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License

MIT. See LICENSE.md.