mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Linlang
6da6fb5ee5
* refactor: Remove unused code and update documentation in CoSTEER module * fix mypy error * fix black error --------- Co-authored-by: Young <afe.young@gmail.com>
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
from __future__ import annotations
|
|
|
|
from abc import ABC, abstractmethod
|
|
from collections.abc import Generator
|
|
from typing import TYPE_CHECKING, Any, Generic, TypeVar
|
|
|
|
from tqdm import tqdm
|
|
|
|
if TYPE_CHECKING:
|
|
from rdagent.core.evolving_framework import EvolvableSubjects
|
|
|
|
from rdagent.core.evaluation import EvaluableObj, Evaluator, Feedback
|
|
from rdagent.core.evolving_framework import EvolvingStrategy, EvoStep
|
|
from rdagent.log import rdagent_logger as logger
|
|
|
|
ASpecificEvaluator = TypeVar("ASpecificEvaluator", bound=Evaluator)
|
|
|
|
|
|
class EvoAgent(ABC, Generic[ASpecificEvaluator]):
|
|
|
|
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: ASpecificEvaluator | Feedback,
|
|
) -> Generator[EvolvableSubjects, None, None]:
|
|
"""
|
|
yield EvolvableSubjects for caller for easier process control and logging.
|
|
"""
|
|
|
|
|
|
class RAGEvaluator(Evaluator):
|
|
|
|
@abstractmethod
|
|
def evaluate(
|
|
self,
|
|
eo: EvaluableObj,
|
|
queried_knowledge: object = None,
|
|
) -> Feedback:
|
|
raise NotImplementedError
|
|
|
|
|
|
class RAGEvoAgent(EvoAgent[RAGEvaluator]):
|
|
|
|
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: RAGEvaluator | Feedback,
|
|
) -> Generator[EvolvableSubjects, None, None]:
|
|
for evo_loop_id in tqdm(range(self.max_loop), "Implementing"):
|
|
with logger.tag(f"evo_loop_{evo_loop_id}"):
|
|
# 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,
|
|
)
|
|
|
|
# 4. Pack evolve results
|
|
es = EvoStep(evo, queried_knowledge)
|
|
|
|
# 5. Evaluation
|
|
if self.with_feedback:
|
|
es.feedback = (
|
|
eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
|
|
)
|
|
logger.log_object(es.feedback, tag="evolving feedback")
|
|
|
|
# 6. update trace
|
|
self.evolving_trace.append(es)
|
|
|
|
yield evo # yield the control to caller for process control and logging.
|
|
|
|
# 7. check if all tasks are completed
|
|
if self.with_feedback and es.feedback is not None and es.feedback.finished():
|
|
logger.info("All tasks in evolving subject have been completed.")
|
|
break
|