from rdagent.core.evolving_framework import Feedback, EvolvableSubjects, Evaluator, EvoStep
from rdagent.core.evolving_framework import EvoAgent
from tqdm import tqdm

class RAGEvoAgent(EvoAgent):
    def __init__(self, max_loop, evolving_strategy, rag) -> None:
        super().__init__(max_loop, evolving_strategy)
        self.rag = rag
        self.evolving_trace = []

    def multistep_evolve(
        self, 
        evo: EvolvableSubjects,
        eva: Evaluator | Feedback, 
        *,
        with_knowledge: bool = False,
        with_feedback: bool = True,
        knowledge_self_gen: bool = False) -> EvolvableSubjects:

        for _ in tqdm(range(self.max_loop), "Implementing factors"):
            # 1. knowledge self-evolving
            if knowledge_self_gen and self.rag is not None:
                self.rag.generate_knowledge(self.evolving_trace)
            # 2. 检索需要的Knowledge
            queried_knowledge = None
            if with_knowledge and self.rag is not None:
                # TODO: 这里放了evolving_trace实际上没有作用
                queried_knowledge = self.rag.query(evo, self.evolving_trace)

            # 3. evolve
            evo = self.evolving_strategy.evolve(
                    evo=evo,
                    evolving_trace=self.evolving_trace,
                    queried_knowledge=queried_knowledge,
                )
            
            # 4. 封装Evolve结果
            es = EvoStep(evo, queried_knowledge)

            # 5. 环境评测反馈
            if with_feedback:
                es.feedback = eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)

            # 6. 更新trace
            self.evolving_trace.append(es)
            for index, feedback in enumerate(es.feedback):
                if feedback is not None:
                    evo.evolve_trace[evo.target_factor_tasks[index].factor_name][-1].feedback = feedback

        return evo