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feat: enhance timeout management and knowledge base handling in CoSTEER components (#1130)

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* feat: enhance timeout management and knowledge base handling in CoSTEER components

* fix a little bug

* fix small bug

* fix a small bug

* Update rdagent/scenarios/data_science/loop.py

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* add scale check

* fix a small bug

* fix CI

* use dynamic chat_token_limit & remove repeated lines

* fix CI

* remove useless comment

* fix small bug

* update draft appendix

* fix prompt

* add code correctness as top priority

---------

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Xu Yang
2025-07-31 13:06:28 +08:00
committed by GitHub
parent ce25bafbf7
commit 1aa5cc2535
26 changed files with 337 additions and 131 deletions
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rdagent/core/evolving_agent.py
+19 -8
View File
@@ -4,11 +4,14 @@ from abc import ABC, abstractmethod
from collections.abc import Generator
from typing import TYPE_CHECKING, Any, Generic, TypeVar
from filelock import FileLock
from tqdm import tqdm
if TYPE_CHECKING:
from rdagent.core.evolving_framework import EvolvableSubjects
from contextlib import nullcontext
from rdagent.core.evaluation import EvaluableObj, Evaluator, Feedback
from rdagent.core.evolving_framework import EvolvingStrategy, EvoStep
from rdagent.log import rdagent_logger as logger
@@ -55,6 +58,8 @@ class RAGEvoAgent(EvoAgent[RAGEvaluator]):
with_knowledge: bool = False,
with_feedback: bool = True,
knowledge_self_gen: bool = False,
enable_filelock: bool = False,
filelock_path: str | None = None,
) -> None:
super().__init__(max_loop, evolving_strategy)
self.rag = rag
@@ -62,6 +67,8 @@ class RAGEvoAgent(EvoAgent[RAGEvaluator]):
self.with_knowledge = with_knowledge
self.with_feedback = with_feedback
self.knowledge_self_gen = knowledge_self_gen
self.enable_filelock = enable_filelock
self.filelock_path = filelock_path
def multistep_evolve(
self,
@@ -70,35 +77,39 @@ class RAGEvoAgent(EvoAgent[RAGEvaluator]):
) -> 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
# 1. 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
# 2. evolve
evo = self.evolving_strategy.evolve(
evo=evo,
evolving_trace=self.evolving_trace,
queried_knowledge=queried_knowledge,
)
# 4. Pack evolve results
# 3. Pack evolve results
es = EvoStep(evo, queried_knowledge)
# 5. Evaluation
# 4. 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
# 5. update trace
self.evolving_trace.append(es)
# 6. knowledge self-evolving
if self.knowledge_self_gen and self.rag is not None:
with FileLock(self.filelock_path) if self.enable_filelock else nullcontext(): # type: ignore[arg-type]
self.rag.load_dumped_knowledge_base()
self.rag.generate_knowledge(self.evolving_trace)
self.rag.dump_knowledge_base()
yield evo # yield the control to caller for process control and logging.
# 7. check if all tasks are completed