While UnifyWeaver is great for AI pipelines, it is also a fully capable Logic Compiler. It translates Prolog’s recursive logic into efficient Python code, handling patterns that would normally cause stack overflows.
The Python target supports two compilation modes:
yield).|).p(X) :- q(X) becomes for x in q(): yield x.total, delta) and loops until no new facts are added.Python does not natively support Tail Recursion Optimization. If you write a deep recursive function in Python, you will hit RecursionError.
UnifyWeaver solves this by compiling tail-recursive Prolog rules into Python while loops.
Prolog:
count_down(0).
count_down(N) :- N > 0, N1 is N - 1, count_down(N1).
Generated Python (Simplified):
def count_down(n):
current = n
while current > 0:
# Logic body
current = current - 1
return
This allows you to iterate millions of times without consuming stack frames.
For non-tail recursion (like Fibonacci or Tree traversal), UnifyWeaver uses Memoization.
Prolog:
fib(0, 0).
fib(1, 1).
fib(N, F) :- N > 1, N1 is N-1, N2 is N-2, fib(N1, F1), fib(N2, F2), F is F1+F2.
Generated Python:
import functools
@functools.cache
def _fib_worker(n):
if n == 0: return 0
if n == 1: return 1
return _fib_worker(n-1) + _fib_worker(n-2)
The compiler automatically identifies the “worker” logic and wraps it in functools.cache (or a custom dictionary), ensuring $O(N)$ performance instead of exponential $O(2^N)$.
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