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from transformers import pipeline
# A second pipeline for self-critique (using a lighter model for demonstration)
CRITIQUE_MODEL = "gpt2" # This can be replaced with another model as needed
critique_pipeline = pipeline("text-generation", model=CRITIQUE_MODEL)
def generate_sub_questions(main_query: str):
"""
Naively generates sub-questions for the given main query.
"""
return [
f"1) What are common causes of {main_query}?",
f"2) Which medications are typically used for {main_query}?",
f"3) What are non-pharmacological approaches to {main_query}?"
]
def self_critique_and_refine(query: str, initial_answer: str, docs: list):
"""
Critiques the initial answer and refines it if necessary.
"""
# Step 1: Generate a critique using a critique prompt
critique_prompt = (
f"The following is an answer to the question '{query}'. "
"Evaluate its correctness, clarity, and completeness. "
"List any missing details or inaccuracies.\n\n"
f"ANSWER:\n{initial_answer}\n\n"
"CRITIQUE:"
)
critique_gen = critique_pipeline(critique_prompt, max_new_tokens=80, truncation=True)
if critique_gen and isinstance(critique_gen, list):
critique_text = critique_gen[0]["generated_text"]
else:
critique_text = "No critique generated."
# Step 2: If the critique suggests issues, refine the answer using the original QA pipeline.
if any(word in critique_text.lower() for word in ["missing", "incomplete", "incorrect", "lacks"]):
refine_prompt = (
f"Question: {query}\n"
f"Current Answer: {initial_answer}\n"
f"Critique: {critique_text}\n"
"Refine the answer by adding missing or corrected information. "
"Use the context below if needed:\n\n"
+ "\n\n".join(docs)
+ "\nREFINED ANSWER:"
)
# Import the qa_pipeline from backend to reuse it (local import to avoid circular dependencies)
from backend import qa_pipeline
refined_gen = qa_pipeline(refine_prompt, max_new_tokens=120, truncation=True)
if refined_gen and isinstance(refined_gen, list):
refined_answer = refined_gen[0]["generated_text"]
else:
refined_answer = initial_answer
else:
refined_answer = initial_answer
return refined_answer, critique_text
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