mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
43cb4a564b
* Commit init framework * Co-authored-by: Yuante Li (FESCO Adecco Human Resources) <v-yuanteli@microsoft.com> Co-authored-by: XianBW <XianBW@users.noreply.github.com> * add an import * refine the whole framework * benchmark related framework * fix black and isort errors * move requirements to folder * fix black again --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: xuyang1 <xuyang1@microsoft.com>
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
from abc import ABC, abstractmethod
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from typing import Any
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from tqdm import tqdm
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from rdagent.core.evaluation import Evaluator
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from rdagent.core.evolving_framework import EvolvableSubjects, EvoStep, Feedback
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class EvoAgent(ABC):
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def __init__(self, max_loop, evolving_strategy) -> None:
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self.max_loop = max_loop
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self.evolving_strategy = evolving_strategy
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@abstractmethod
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def multistep_evolve(self, evo: EvolvableSubjects, eva: Evaluator | Feedback, **kwargs: Any) -> EvolvableSubjects:
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pass
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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,
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) -> 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. RAG
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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: Putting the evolving trace in here doesn't actually work
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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. Pack evolve results
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es = EvoStep(evo, queried_knowledge)
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# 5. Evaluation
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if with_feedback:
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es.feedback = (
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eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
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)
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# 6. update trace
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self.evolving_trace.append(es)
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return evo
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