To install, clone this repo and its dependency into the same directory:

git clone https://github.com/patham9/PeTTa.git
git clone https://github.com/rTreutlein/PeTTaChainer.git

Benchmarks

Run the NatDist vs ParticleDist benchmark:

python pettachainer/benchmarks/particle_vs_nat.py --sizes 100,500,1000 --particle-budgets 128,256,512 --repeats 2

Run the simple forward vs backward chaining benchmark:

.venv/bin/python pettachainer/benchmarks/forward_vs_backward.py --depths 10,25,50 --noise-branching 8 --repeats 3

Run the backward materialization benchmark:

.venv/bin/python pettachainer/benchmarks/backward_materialize.py --depths 5,10 --queries 200 --repeats 3

Run the bounded priority queue benchmark:

.venv/bin/python pettachainer/benchmarks/bounded_queue.py --fanouts 2000,8000 --steps 100 --repeats 3

Add --compare-pruning to compare pruning enabled and disabled within the same checkout.

Optional JSON export:

python pettachainer/benchmarks/particle_vs_nat.py --json-out /tmp/particle_bench.json

Profiling MeTTa Runs

Profile a .metta file through the underlying SWI-Prolog invocation that petta uses:

./profile_petta.sh tests/testmining.metta
./profile_petta.sh --mode time tests/testmining.metta
./profile_petta.sh --mode perf benchmarks/demo_benchgen_forward_backward_compare.metta

Relative paths are resolved from pettachainer/metta by default.

Python API: Language Spec String

from pettachainer import get_language_spec

llm_spec = get_language_spec(llm_focused=True)
full_spec = get_language_spec(llm_focused=False)

Python API: Shared PLN Validator

from pettachainer import PeTTaChainer, check_query, check_stmt

handler = PeTTaChainer()

stmt_eval = handler.evaluate_statement("(: s1 (Dog fido) (STV 1.0 1.0))")
check_stmt(stmt_eval)

query_eval = handler.evaluate_query("(: $prf (Dog fido) $tv)")
check_query(query_eval)

Python API: Forward Chaining

from pettachainer import PeTTaChainer

handler = PeTTaChainer()
handler.add_atom("(: edge_ab (Edge A B) (STV 1.0 1.0))")
handler.add_atom("(: edge_bc (Edge B C) (STV 1.0 1.0))")
handler.add_atom(
    "(: edge_to_path (Implication (Premises (Edge $x $y)) (Conclusions (Path $x $y))) (CTV (STV 1.0 1.0) (STV 0.0 1.0)))"
)
handler.add_atom(
    "(: path_step (Implication (Premises (Path $x $y) (Edge $y $z)) (Conclusions (Path $x $z))) (CTV (STV 1.0 1.0) (STV 0.0 1.0)))"
)

seeds = handler.select_facts(["(Edge A B)", "(Edge B C)"])
changed = handler.forward_chain(seeds, steps=50)
result = handler.query("(: $prf (Path A C) $tv)", timeout_sec=0)

# Returned facts directly seed a later run. Selecting the whole KB is the
# full-saturation special case: handler.forward_chain(handler.all_facts()).
handler.forward_chain(changed, steps=1)