"""
LLM-based follow-up question generator.

This implementation uses an LLM to intelligently reformulate follow-up
questions based on the previous research context.
"""

from typing import Dict, List
from loguru import logger
from .base_followup_question import BaseFollowUpQuestionGenerator


class LLMFollowUpQuestionGenerator(BaseFollowUpQuestionGenerator):
    """
    LLM-based follow-up question generator.

    This generator uses an LLM to reformulate follow-up questions
    based on the previous research context, creating more targeted
    and effective search queries.

    NOTE: This is a placeholder for future implementation.
    Currently falls back to simple concatenation.
    """

    def generate_contextualized_query(
        self,
        follow_up_query: str,
        original_query: str,
        past_findings: str,
        **kwargs,
    ) -> str:
        """
        Generate a contextualized query using LLM reformulation.

        Future implementation will:
        1. Analyze the follow-up query in context of past findings
        2. Identify information gaps
        3. Reformulate for more effective searching
        4. Generate multiple targeted search questions

        Args:
            follow_up_query: The user's follow-up question
            original_query: The original research query
            past_findings: The findings from previous research
            **kwargs: Additional context parameters

        Returns:
            str: An LLM-reformulated contextualized query
        """
        # TODO: Implement LLM-based reformulation
        # For now, fall back to simple concatenation
        logger.warning(
            "LLM-based follow-up question generation not yet implemented, "
            "falling back to simple concatenation"
        )

        from .simple_followup_question import SimpleFollowUpQuestionGenerator

        simple_generator = SimpleFollowUpQuestionGenerator(self.model)
        return simple_generator.generate_contextualized_query(
            follow_up_query, original_query, past_findings, **kwargs
        )

    def generate_questions(
        self,
        current_knowledge: str,
        query: str,
        questions_per_iteration: int,
        questions_by_iteration: Dict[int, List[str]],
    ) -> List[str]:
        """
        Generate multiple targeted questions for follow-up research.

        Future implementation will generate multiple specific questions
        based on the follow-up query and context.

        Args:
            current_knowledge: The accumulated knowledge so far
            query: The research query
            questions_per_iteration: Number of questions to generate
            questions_by_iteration: Previous questions

        Returns:
            List[str]: List of targeted follow-up questions
        """
        # TODO: Implement multi-question generation
        # For now, return single contextualized query
        return super().generate_questions(
            current_knowledge,
            query,
            questions_per_iteration,
            questions_by_iteration,
        )
