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https://github.com/NicolasBohn/NexQuant.git
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Linlang
c7cfd397ca
* fix isort & black & toml-sort & sphinx error * fix ci error * fix ci error * add comments * Update Makefile * change sphinx build command * add auto-lint * add black args * format with black * Auto Linting document * fix ci error --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: Young <afe.young@gmail.com>
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any
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from tqdm import tqdm
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if TYPE_CHECKING:
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from rdagent.core.evaluation import Evaluator
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from rdagent.core.evolving_framework import EvolvableSubjects
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from rdagent.core.evaluation import Feedback
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from rdagent.core.evolving_framework import EvolvingStrategy, EvoStep
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from rdagent.log import rdagent_logger as logger
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class EvoAgent(ABC):
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def __init__(self, max_loop: int, evolving_strategy: EvolvingStrategy) -> 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(
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self,
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evo: EvolvableSubjects,
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eva: Evaluator | Feedback,
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filter_final_evo: bool = False,
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) -> EvolvableSubjects:
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...
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@abstractmethod
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def filter_evolvable_subjects_by_feedback(
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self,
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evo: EvolvableSubjects,
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feedback: Feedback | None,
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) -> EvolvableSubjects:
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...
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class RAGEvoAgent(EvoAgent):
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def __init__(
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self,
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max_loop: int,
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evolving_strategy: EvolvingStrategy,
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rag: Any,
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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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) -> 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: list[EvoStep] = []
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self.with_knowledge = with_knowledge
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self.with_feedback = with_feedback
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self.knowledge_self_gen = knowledge_self_gen
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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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filter_final_evo: bool = False,
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) -> EvolvableSubjects:
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for _ in tqdm(range(self.max_loop), "Implementing"):
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# 1. knowledge self-evolving
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if self.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 self.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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# TODO: Due to design issues, we have chosen to ignore this mypy error.
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logger.log_object(evo.sub_workspace_list, tag="evolving code") # type: ignore[attr-defined]
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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 self.with_feedback:
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es.feedback = (
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# TODO: Due to the irregular design of rdagent.core.evaluation.Evaluator,
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# it fails mypy's test here, so we'll ignore this error for now.
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eva
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if isinstance(eva, Feedback)
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else eva.evaluate(evo, queried_knowledge=queried_knowledge) # type: ignore[arg-type, call-arg]
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)
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logger.log_object(es.feedback, tag="evolving feedback")
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# 6. update trace
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self.evolving_trace.append(es)
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if self.with_feedback and filter_final_evo:
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evo = self.filter_evolvable_subjects_by_feedback(evo, self.evolving_trace[-1].feedback)
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return evo
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