logical_reasoner.py

import dspy
import os
from dotenv import load_dotenv

load_dotenv()

llm = dspy.OpenAI(
    model='gpt-4',
    api_key=os.environ['OPENAI_API_KEY'],
    max_tokens=100
)

dspy.settings.configure(lm=llm)

class check_logic(dspy.Signature):
    """Given the premises and the conclusions of an argument, check if the conclusions are logical."""
    premises = dspy.InputField(desc="Premises of the argument")
    conclusions = dspy.InputField(desc="Conclusion of the argument")
    sound = dspy.InputField(desc="The premises are sound or not")
    logical = dspy.OutputField(desc="Given the premises, the conclusion is logical or not")


class check_premises(dspy.Signature):
    """Given the premises of an argument, check if the premises are sound."""
    premises = dspy.InputField(desc="Premises of the argument")
    sound = dspy.OutputField(desc="The premises are sound or not")

class logical_reasoner(dspy.Module):
    def __init__(self):
        super().__init__()
        self.logical_reasoner = dspy.Predict("text -> premises, conclusions")
        self.check_prems = dspy.Predict(check_premises)
        self.checker = dspy.ChainOfThought(check_logic)

    def forward(self,text):
        prediction = self.logical_reasoner(text=text)
        sound = self.check_prems(premises=prediction.premises)
        result = self.checker(premises=prediction.premises, conclusions=prediction.conclusions, sound=sound.sound)
        return result

text="If it is raining, then the grass is wet. The grass is wet. Therefore, it is raining."

lr = logical_reasoner()

print(lr(text=text))