mirror of
https://github.com/NicolasBohn/NexQuant.git
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you-n-g
5baed909e7
* refactor: Update type annotations and remove unused class in evolving modules * refactor: Simplify evolving agent and feedback handling in CoSTEER module * lint & CI * mypy * ruff for core * mypy * refactor: remove unnecessary comments and update feedback handling logic * refactor: Add prev_task_feedback parameter to evolving strategies * feat: Clear folder before extracting zip file in DockerEnv * fix: Correct retrieval of last experiment from history
107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
from __future__ import annotations
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from abc import ABC, abstractmethod
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from collections.abc import Generator
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from typing import TYPE_CHECKING, Any, Generic, TypeVar
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from tqdm import tqdm
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if TYPE_CHECKING:
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from rdagent.core.evolving_framework import EvolvableSubjects
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from rdagent.core.evaluation import EvaluableObj, Evaluator, 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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ASpecificEvaluator = TypeVar("ASpecificEvaluator", bound=Evaluator)
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class EvoAgent(ABC, Generic[ASpecificEvaluator]):
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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: ASpecificEvaluator | Feedback,
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) -> Generator[EvolvableSubjects, None, None]:
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"""
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yield EvolvableSubjects for caller for easier process control and logging.
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"""
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class RAGEvaluator(Evaluator):
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@abstractmethod
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def evaluate(
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self,
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eo: EvaluableObj,
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queried_knowledge: object = None,
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) -> Feedback:
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raise NotImplementedError
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class RAGEvoAgent(EvoAgent[RAGEvaluator]):
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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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*,
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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: RAGEvaluator | Feedback,
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) -> Generator[EvolvableSubjects, None, None]:
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for evo_loop_id in tqdm(range(self.max_loop), "Implementing"):
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with logger.tag(f"evo_loop_{evo_loop_id}"):
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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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yield evo # yield the control to caller for process control and logging.
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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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eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
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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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# 7. check if all tasks are completed
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if self.with_feedback and es.feedback:
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logger.info("All tasks in evolving subject have been completed.")
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break
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