ProofOfThought

LLM-based reasoning using Z3 theorem proving.

Quick Start

from openai import OpenAI
from z3dsl.reasoning import ProofOfThought

client = OpenAI(api_key="...")
pot = ProofOfThought(llm_client=client)

result = pot.query("Would Nancy Pelosi publicly denounce abortion?")
print(result.answer)  # False

Batch Evaluation

from z3dsl.reasoning import EvaluationPipeline

evaluator = EvaluationPipeline(pot, output_dir="results/")
result = evaluator.evaluate(
    dataset="strategyqa_train.json",
    max_samples=10
)
print(f"Accuracy: {result.metrics.accuracy:.2%}")

Installation

pip install z3-solver openai scikit-learn numpy

Architecture

The system has two layers:

1. High-level API (z3dsl.reasoning) - Simple Python interface for reasoning tasks
2. Low-level DSL (z3dsl) - JSON-based Z3 theorem prover interface

Most users should use the high-level API.

Examples

See examples/ directory for complete examples including Azure OpenAI support.