mini_ladder.py

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