mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
Xu Yang
a126c84c92
* refine CI script * refine all the code to higher quality * refine the script to factor extraction and implementation * add task loader interface * add a task loader interface && move pdf analysis to pdf task loader * change the name to global variables --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
from rdagent.core.evaluation import Evaluator
|
|
from rdagent.core.evolving_framework import Feedback, EvolvableSubjects, EvoStep
|
|
from tqdm import tqdm
|
|
from abc import ABC, abstractmethod
|
|
from typing import Any
|
|
|
|
|
|
class EvoAgent(ABC):
|
|
def __init__(self, max_loop, evolving_strategy) -> None:
|
|
self.max_loop = max_loop
|
|
self.evolving_strategy = evolving_strategy
|
|
|
|
@abstractmethod
|
|
def multistep_evolve(self, evo: EvolvableSubjects, eva: Evaluator | Feedback, **kwargs: Any) -> EvolvableSubjects:
|
|
pass
|
|
|
|
|
|
class RAGEvoAgent(EvoAgent):
|
|
def __init__(self, max_loop, evolving_strategy, rag) -> None:
|
|
super().__init__(max_loop, evolving_strategy)
|
|
self.rag = rag
|
|
self.evolving_trace = []
|
|
|
|
def multistep_evolve(
|
|
self,
|
|
evo: EvolvableSubjects,
|
|
eva: Evaluator | Feedback,
|
|
*,
|
|
with_knowledge: bool = False,
|
|
with_feedback: bool = True,
|
|
knowledge_self_gen: bool = False,
|
|
) -> EvolvableSubjects:
|
|
|
|
for _ in tqdm(range(self.max_loop), "Implementing factors"):
|
|
# 1. knowledge self-evolving
|
|
if knowledge_self_gen and self.rag is not None:
|
|
self.rag.generate_knowledge(self.evolving_trace)
|
|
# 2. RAG
|
|
queried_knowledge = None
|
|
if 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 with_feedback:
|
|
es.feedback = (
|
|
eva if isinstance(eva, Feedback) else eva.evaluate(evo, queried_knowledge=queried_knowledge)
|
|
)
|
|
|
|
# 6. update trace
|
|
self.evolving_trace.append(es)
|
|
|
|
return evo
|