synthetic-prompt-optimization-dspy-only.py

# Synthetic Prompt Optimization
from dspy.teleprompt import BootstrapFewShot
from dspy.evaluate import answer_exact_match

text = "Barack Obama was not President of the USA"

# define the fact as input to the lie detector
trainset = [x.with_inputs('fact') for x in few_shot_examples]

# define the signature to be used in by the lie detector module
# for the evaluation, you need to define an answer field
class Veracity(dspy.Signature):
  "Evaluate the veracity of a statement"
  fact = dspy.InputField(desc="a statement")
  answer = dspy.OutputField(desc="an assessment of the veracity of the statement")

class lie_detector(dspy.Module):
  def __init__(self):
    super().__init__()
    self.lie_identification = dspy.ChainOfThought(Veracity)

  def forward(self, fact):
    return self.lie_identification(fact=fact)

teleprompter = BootstrapFewShot(metric=answer_exact_match)

compiled_lie_detector = teleprompter.compile(lie_detector(), trainset=trainset)

response = compiled_lie_detector(fact=text)

print(f"The statement '{text}' is  {response.answer}.")