wehub-resource-sync
199 lines
8.3 KiB
Python
199 lines
8.3 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 contextlib import nullcontext
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from typing import Generic, TypeVar, cast
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from filelock import FileLock
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from tqdm import tqdm
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from rdagent.core.evaluation import EvaluableObj, Evaluator, Feedback
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from rdagent.core.evolving_framework import (
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EvolvableSubjects,
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EvolvingStrategy,
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EvoStep,
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IterEvaluator,
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RAGStrategy,
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)
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from rdagent.core.exception import EvaluatorDidNotTerminateError
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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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ASpecificEvolvableSubjects = TypeVar("ASpecificEvolvableSubjects", bound=EvolvableSubjects)
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class EvoAgent(ABC, Generic[ASpecificEvaluator, ASpecificEvolvableSubjects]):
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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: ASpecificEvolvableSubjects,
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eva: ASpecificEvaluator,
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) -> Generator[ASpecificEvolvableSubjects, 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(IterEvaluator):
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@abstractmethod
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def evaluate_iter(
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self,
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queried_knowledge: object | None = None,
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evolving_trace: list[EvoStep] | None = None,
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) -> Generator[Feedback, EvaluableObj | None, Feedback]:
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"""
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1) It will yield a evaluation for each implement part and yield the feedback for that part.
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2) And finally, it will get the summarize all the feedback and return a overall feedback.
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Sending a None feedback will stop the evaluation chain and just return the overall feedback.
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Assumptions:
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- The evaluation process will make modifications on evo in-place.
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A typical implementation of this method is:
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.. code-block:: python
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evo = yield Feedback() # it will receive the evo first, so the first yield is for get the sent evo instead of generate useful feedback
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assert evo is not None
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for partial_eval_func in self.evaluate_func_iter():
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partial_fb = partial_eval_func(evo, queried_knowledge, evolving_trace)
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# return the partial feedback and receive the evolved solution for next iteration
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yield partial_fb
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final_fb = get_final_fb(...)
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return final_fb
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"""
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class RAGEvoAgent(EvoAgent[RAGEvaluator, ASpecificEvolvableSubjects], Generic[ASpecificEvolvableSubjects]):
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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: RAGStrategy,
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*,
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with_knowledge: bool = False,
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knowledge_self_gen: bool = False,
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enable_filelock: bool = False,
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filelock_path: str | None = None,
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stop_eval_chain_on_fail: bool = False,
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) -> None:
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"""
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Initialize a Retrieval-Augmented Generation (RAG) based evolutionary agent.
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Args:
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max_loop (int): Maximum number of evolution loops to execute.
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evolving_strategy (EvolvingStrategy): Strategy defining how the subjects evolve each step.
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rag (RAGStrategy): Retrieval-Augmented Generation strategy instance used for knowledge querying and/or creation.
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with_knowledge (bool, optional): If True, retrieves knowledge from RAG for each evolution step. Defaults to False.
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knowledge_self_gen (bool, optional): If True, enable RAG to load, generate, dump new knowledge from evolving trace. Defaults to False.
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enable_filelock (bool, optional): If True, enables file-based lock when accessing/modifying the RAG knowledge base. Defaults to False.
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filelock_path (str | None, optional): Path to the lock file when enable_filelock is True. Defaults to None.
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This class coordinates the multi-step evolution process with optional:
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- Knowledge retrieval before evolving.
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- Feedback collection after evolving.
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- Self-generation and persisting of knowledge base updates.
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Evolving trace is maintained across steps for adaptive strategies and knowledge generation.
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"""
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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[ASpecificEvolvableSubjects]] = []
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self.with_knowledge = with_knowledge
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self.knowledge_self_gen = knowledge_self_gen
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self.enable_filelock = enable_filelock
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self.filelock_path = filelock_path
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self.stop_eval_chain_on_fail = stop_eval_chain_on_fail
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def _get_overall_feedback(
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self,
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eva_iter: Generator[Feedback, EvaluableObj | None, Feedback],
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evo: EvolvableSubjects,
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eval_failed_happened: bool,
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) -> Feedback:
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"""get overall feedback from eva_iter"""
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try:
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if self.stop_eval_chain_on_fail and eval_failed_happened:
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fb = eva_iter.send(
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None,
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) # send the signal to skip the rest partial evaluation and return the overall feedback directly
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else:
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fb = eva_iter.send(evo)
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if not fb:
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eval_failed_happened = True
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raise EvaluatorDidNotTerminateError
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except StopIteration as e:
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return cast("Feedback", e.value)
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def multistep_evolve(
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self,
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evo: ASpecificEvolvableSubjects,
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eva: RAGEvaluator,
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) -> Generator[ASpecificEvolvableSubjects, 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. 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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# 2. evolve:
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# A compelete solution of an evo can be break down into multiple evolving steps.
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# Each evolving step can be evaluated separately.
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# Assumptions:
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# - if we want to stop on some point of the implementation, we must have a according evaluator (Otherwise, It is meaningless to stop)
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evo_iter = self.evolving_strategy.evolve_iter(
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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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eva_iter = eva.evaluate_iter(
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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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next(eva_iter) # kick off the first iteration
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eval_failed_happened = False
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for evolved_evo in evo_iter:
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step_feedback = eva_iter.send(evolved_evo)
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if not step_feedback:
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eval_failed_happened = True
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if self.stop_eval_chain_on_fail:
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break
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overall_feedback = self._get_overall_feedback(eva_iter, evolved_evo, eval_failed_happened)
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# 3. Pack evolve results
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es = EvoStep[ASpecificEvolvableSubjects](evolved_evo, queried_knowledge, overall_feedback)
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# 4. Evaluation
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logger.log_object(es.feedback, tag="evolving feedback")
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# 5. update trace
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self.evolving_trace.append(es)
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# 6. knowledge self-evolving
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if self.knowledge_self_gen and self.rag is not None:
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with FileLock(self.filelock_path) if self.enable_filelock else nullcontext(): # type: ignore[arg-type]
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self.rag.load_dumped_knowledge_base()
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self.rag.generate_knowledge(self.evolving_trace)
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self.rag.dump_knowledge_base()
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yield evo # yield the control to caller for process control and logging.
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# 7. check if all tasks are completed
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if es.feedback is not None and es.feedback.finished():
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logger.info("All tasks in evolving subject have been completed.")
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break
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