mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
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
|
|
|
|
from abc import ABC, abstractmethod
|
|
from typing import TYPE_CHECKING, Any
|
|
|
|
from tqdm import tqdm
|
|
|
|
if TYPE_CHECKING:
|
|
from rdagent.core.evaluation import Evaluator
|
|
from rdagent.core.evolving_framework import EvolvableSubjects
|
|
|
|
from rdagent.core.evaluation import Feedback
|
|
from rdagent.core.evolving_framework import EvolvingStrategy, EvoStep
|
|
from rdagent.log import rdagent_logger as logger
|
|
|
|
|
|
class EvoAgent(ABC):
|
|
def __init__(self, max_loop: int, evolving_strategy: EvolvingStrategy) -> None:
|
|
self.max_loop = max_loop
|
|
self.evolving_strategy = evolving_strategy
|
|
|
|
@abstractmethod
|
|
def multistep_evolve(
|
|
self,
|
|
evo: EvolvableSubjects,
|
|
eva: Evaluator | Feedback,
|
|
filter_final_evo: bool = False,
|
|
) -> EvolvableSubjects:
|
|
...
|
|
|
|
@abstractmethod
|
|
def filter_evolvable_subjects_by_feedback(
|
|
self,
|
|
evo: EvolvableSubjects,
|
|
feedback: Feedback | None,
|
|
) -> EvolvableSubjects:
|
|
...
|
|
|
|
|
|
class RAGEvoAgent(EvoAgent):
|
|
def __init__(
|
|
self,
|
|
max_loop: int,
|
|
evolving_strategy: EvolvingStrategy,
|
|
rag: Any,
|
|
with_knowledge: bool = False,
|
|
with_feedback: bool = True,
|
|
knowledge_self_gen: bool = False,
|
|
) -> None:
|
|
super().__init__(max_loop, evolving_strategy)
|
|
self.rag = rag
|
|
self.evolving_trace: list[EvoStep] = []
|
|
self.with_knowledge = with_knowledge
|
|
self.with_feedback = with_feedback
|
|
self.knowledge_self_gen = knowledge_self_gen
|
|
|
|
def multistep_evolve(
|
|
self,
|
|
evo: EvolvableSubjects,
|
|
eva: Evaluator | Feedback,
|
|
filter_final_evo: bool = False,
|
|
) -> EvolvableSubjects:
|
|
for _ in tqdm(range(self.max_loop), "Implementing"):
|
|
# 1. knowledge self-evolving
|
|
if self.knowledge_self_gen and self.rag is not None:
|
|
self.rag.generate_knowledge(self.evolving_trace)
|
|
# 2. RAG
|
|
queried_knowledge = None
|
|
if self.with_knowledge and self.rag is not None:
|
|
# TODO: Putting the evolving trace in here doesn't actually work
|
|
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,
|
|
)
|
|
# TODO: Due to design issues, we have chosen to ignore this mypy error.
|
|
logger.log_object(evo.sub_workspace_list, tag="evolving code") # type: ignore[attr-defined]
|
|
|
|
# 4. Pack evolve results
|
|
es = EvoStep(evo, queried_knowledge)
|
|
|
|
# 5. Evaluation
|
|
if self.with_feedback:
|
|
es.feedback = (
|
|
# TODO: Due to the irregular design of rdagent.core.evaluation.Evaluator,
|
|
# it fails mypy's test here, so we'll ignore this error for now.
|
|
eva
|
|
if isinstance(eva, Feedback)
|
|
else eva.evaluate(evo, queried_knowledge=queried_knowledge) # type: ignore[arg-type, call-arg]
|
|
)
|
|
logger.log_object(es.feedback, tag="evolving feedback")
|
|
|
|
# 6. update trace
|
|
self.evolving_trace.append(es)
|
|
if self.with_feedback and filter_final_evo:
|
|
evo = self.filter_evolvable_subjects_by_feedback(evo, self.evolving_trace[-1].feedback)
|
|
return evo
|