mirror of
https://github.com/NicolasBohn/NexQuant.git
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50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
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from rdagent.core.evolving_framework import Feedback, EvolvableSubjects, Evaluator, EvoStep
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from rdagent.core.evolving_framework import EvoAgent
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from tqdm import tqdm
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class RAGEvoAgent(EvoAgent):
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def __init__(self, max_loop, evolving_strategy, rag) -> None:
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super().__init__(max_loop, evolving_strategy)
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self.rag = rag
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self.evolving_trace = []
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def multistep_evolve(
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self,
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evo: EvolvableSubjects,
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eva: Evaluator | Feedback,
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*,
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with_knowledge: bool = False,
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with_feedback: bool = True,
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knowledge_self_gen: bool = False) -> EvolvableSubjects:
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for _ in tqdm(range(self.max_loop), "Implementing factors"):
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# 1. knowledge self-evolving
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if knowledge_self_gen and self.rag is not None:
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self.rag.generate_knowledge(self.evolving_trace)
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# 2. 检索需要的Knowledge
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queried_knowledge = None
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if with_knowledge and self.rag is not None:
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# TODO: 这里放了evolving_trace实际上没有作用
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queried_knowledge = self.rag.query(evo, self.evolving_trace)
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# 3. evolve
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evo = self.evolving_strategy.evolve(
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evo=evo,
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evolving_trace=self.evolving_trace,
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queried_knowledge=queried_knowledge,
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)
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# 4. 封装Evolve结果
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es = EvoStep(evo, queried_knowledge)
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# 5. 环境评测反馈
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if with_feedback:
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es.feedback = eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
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# 6. 更新trace
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self.evolving_trace.append(es)
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for index, feedback in enumerate(es.feedback):
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if feedback is not None:
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evo.evolve_trace[evo.target_factor_tasks[index].factor_name][-1].feedback = feedback
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return evo
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