Several update on the repo (see desc) (#76)

* ignore result csv file

* fix app scripts

* rename taskgenerator to developer and generate to develop

* fix a config bug in coder

* fix a small bug in factor coder evaluators

* remove a single logger in factor coder evaluators

* fix a small bug in model coder main.py

* rename Implementation to Workspace

* move the prepare the inject_code into FBWorkspace to align all the behavior

* fix a small bug in model feedback

* remove debug lines for multi processing and simplify evaluators multi proc

* add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging

* make hypothesisgen a abc class

* use Qlib***Experiment

* fix a small bug

* rename Imp to Ws

* rename sub_implementations to sub_workspace_list

* fix a bug in feedback not presented as content in prompts

* move proposal pys to proposal folder

* reformat the folder

* align factor and model qlib workspace and use template to handle the workspace

* add a filter to evoagent to filter out false evo

* align multi_proc_n into RDAGENT seeting

* handle when runner gets empty experiment

* fix logger merge remaining problems

* fix black and isort automatically

rdagent/components/coder/factor_coder/CoSTEER/__init__.py

@@ -9,6 +9,9 @@ from rdagent.components.coder.factor_coder.CoSTEER.evaluators import (
 from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
     FactorEvolvingItem,
 )
+from rdagent.components.coder.factor_coder.CoSTEER.evolving_agent import (
+    FactorRAGEvoAgent,
+)
 from rdagent.components.coder.factor_coder.CoSTEER.evolving_strategy import (
     FactorEvolvingStrategyWithGraph,
 )
@@ -18,18 +21,19 @@ from rdagent.components.coder.factor_coder.CoSTEER.knowledge_management import (
     FactorKnowledgeBaseV1,
 )
 from rdagent.components.coder.factor_coder.factor import FactorExperiment
+from rdagent.core.developer import Developer
 from rdagent.core.evolving_agent import RAGEvoAgent
 from rdagent.core.scenario import Scenario
-from rdagent.core.task_generator import TaskGenerator
 
 
-class FactorCoSTEER(TaskGenerator[FactorExperiment]):
+class FactorCoSTEER(Developer[FactorExperiment]):
     def __init__(
         self,
         *args,
         with_knowledge: bool = True,
         with_feedback: bool = True,
         knowledge_self_gen: bool = True,
+        filter_final_evo: bool = True,
         **kwargs,
     ) -> None:
         super().__init__(*args, **kwargs)
@@ -47,6 +51,7 @@ class FactorCoSTEER(TaskGenerator[FactorExperiment]):
         self.with_knowledge = with_knowledge
         self.with_feedback = with_feedback
         self.knowledge_self_gen = knowledge_self_gen
+        self.filter_final_evo = filter_final_evo
         self.evolving_strategy = FactorEvolvingStrategyWithGraph(scen=self.scen)
         # declare the factor evaluator
         self.factor_evaluator = FactorMultiEvaluator(FactorEvaluatorForCoder(scen=self.scen), scen=self.scen)
@@ -72,7 +77,7 @@ class FactorCoSTEER(TaskGenerator[FactorExperiment]):
             )
         return factor_knowledge_base
 
-    def generate(self, exp: FactorExperiment) -> FactorExperiment:
+    def develop(self, exp: FactorExperiment) -> FactorExperiment:
         # init knowledge base
         factor_knowledge_base = self.load_or_init_knowledge_base(
             former_knowledge_base_path=self.knowledge_base_path,
@@ -84,7 +89,9 @@ class FactorCoSTEER(TaskGenerator[FactorExperiment]):
         # init intermediate items
         factor_experiment = FactorEvolvingItem(sub_tasks=exp.sub_tasks)
 
-        self.evolve_agent = RAGEvoAgent(max_loop=self.max_loop, evolving_strategy=self.evolving_strategy, rag=self.rag)
+        self.evolve_agent = FactorRAGEvoAgent(
+            max_loop=self.max_loop, evolving_strategy=self.evolving_strategy, rag=self.rag
+        )
 
         factor_experiment = self.evolve_agent.multistep_evolve(
             factor_experiment,
@@ -92,11 +99,11 @@ class FactorCoSTEER(TaskGenerator[FactorExperiment]):
             with_knowledge=self.with_knowledge,
             with_feedback=self.with_feedback,
             knowledge_self_gen=self.knowledge_self_gen,
+            filter_final_evo=self.filter_final_evo,
         )
 
         # save new knowledge base
         if self.new_knowledge_base_path is not None:
             pickle.dump(factor_knowledge_base, open(self.new_knowledge_base_path, "wb"))
-        self.knowledge_base = factor_knowledge_base
-        factor_experiment.based_experiments = exp.based_experiments
-        return factor_experiment
+        exp.sub_workspace_list = factor_experiment.sub_workspace_list
+        return exp

rdagent/components/coder/factor_coder/CoSTEER/evaluators.py

@@ -8,7 +8,6 @@ from typing import List, Tuple
 import pandas as pd
 from jinja2 import Environment, StrictUndefined
 
-from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
 from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
     FactorEvolvingItem,
 )
@@ -16,10 +15,10 @@ from rdagent.components.coder.factor_coder.factor import FactorTask
 from rdagent.core.conf import RD_AGENT_SETTINGS
 from rdagent.core.evaluation import Evaluator
 from rdagent.core.evolving_framework import Feedback, QueriedKnowledge
-from rdagent.core.experiment import Implementation, Task
-from rdagent.log import rdagent_logger as logger
+from rdagent.core.experiment import Task, Workspace
 from rdagent.core.prompts import Prompts
 from rdagent.core.utils import multiprocessing_wrapper
+from rdagent.log import rdagent_logger as logger
 from rdagent.oai.llm_utils import APIBackend
 
 evaluate_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
@@ -33,8 +32,8 @@ class FactorEvaluator(Evaluator):
     def evaluate(
         self,
         target_task: Task,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
         **kwargs,
     ) -> Tuple[str, object]:
         """You can get the dataframe by
@@ -53,7 +52,7 @@ class FactorEvaluator(Evaluator):
         """
         raise NotImplementedError("Please implement the `evaluator` method")
 
-    def _get_df(self, gt_implementation: Implementation, implementation: Implementation):
+    def _get_df(self, gt_implementation: Workspace, implementation: Workspace):
         if gt_implementation is not None:
             _, gt_df = gt_implementation.execute()
             if isinstance(gt_df, pd.Series):
@@ -78,10 +77,10 @@ class FactorCodeEvaluator(FactorEvaluator):
     def evaluate(
         self,
         target_task: FactorTask,
-        implementation: Implementation,
+        implementation: Workspace,
         execution_feedback: str,
         factor_value_feedback: str = "",
-        gt_implementation: Implementation = None,
+        gt_implementation: Workspace = None,
         **kwargs,
     ):
         factor_information = target_task.get_task_information()
@@ -130,8 +129,8 @@ class FactorCodeEvaluator(FactorEvaluator):
 class FactorSingleColumnEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         _, gen_df = self._get_df(gt_implementation, implementation)
 
@@ -147,8 +146,8 @@ class FactorSingleColumnEvaluator(FactorEvaluator):
 class FactorOutputFormatEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         gt_df, gen_df = self._get_df(gt_implementation, implementation)
         if gen_df is None:
@@ -184,8 +183,8 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
 class FactorDatetimeDailyEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str | object]:
         _, gen_df = self._get_df(gt_implementation, implementation)
         if gen_df is None:
@@ -214,8 +213,8 @@ class FactorDatetimeDailyEvaluator(FactorEvaluator):
 class FactorRowCountEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         gt_df, gen_df = self._get_df(gt_implementation, implementation)
 
@@ -231,8 +230,8 @@ class FactorRowCountEvaluator(FactorEvaluator):
 class FactorIndexEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         gt_df, gen_df = self._get_df(gt_implementation, implementation)
 
@@ -248,8 +247,8 @@ class FactorIndexEvaluator(FactorEvaluator):
 class FactorMissingValuesEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         gt_df, gen_df = self._get_df(gt_implementation, implementation)
 
@@ -265,8 +264,8 @@ class FactorMissingValuesEvaluator(FactorEvaluator):
 class FactorEqualValueCountEvaluator(FactorEvaluator):
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         gt_df, gen_df = self._get_df(gt_implementation, implementation)
 
@@ -296,8 +295,8 @@ class FactorCorrelationEvaluator(FactorEvaluator):
 
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
     ) -> Tuple[str, object]:
         gt_df, gen_df = self._get_df(gt_implementation, implementation)
 
@@ -327,11 +326,10 @@ class FactorCorrelationEvaluator(FactorEvaluator):
 
 
 class FactorValueEvaluator(FactorEvaluator):
-
     def evaluate(
         self,
-        implementation: Implementation,
-        gt_implementation: Implementation,
+        implementation: Workspace,
+        gt_implementation: Workspace,
         **kwargs,
     ) -> Tuple:
         conclusions = []
@@ -508,8 +506,8 @@ class FactorEvaluatorForCoder(FactorEvaluator):
     def evaluate(
         self,
         target_task: FactorTask,
-        implementation: Implementation,
-        gt_implementation: Implementation = None,
+        implementation: Workspace,
+        gt_implementation: Workspace = None,
         queried_knowledge: QueriedKnowledge = None,
         **kwargs,
     ) -> FactorSingleFeedback:
@@ -603,41 +601,21 @@ class FactorMultiEvaluator(Evaluator):
         queried_knowledge: QueriedKnowledge = None,
         **kwargs,
     ) -> FactorMultiFeedback:
-        multi_implementation_feedback = FactorMultiFeedback()
-
-        # for index in range(len(evo.sub_tasks)):
-        #     corresponding_implementation = evo.sub_implementations[index]
-        #     corresponding_gt_implementation = (
-        #         evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None
-        #     )
-
-        #     multi_implementation_feedback.append(
-        #         self.single_factor_implementation_evaluator.evaluate(
-        #             target_task=evo.sub_tasks[index],
-        #             implementation=corresponding_implementation,
-        #             gt_implementation=corresponding_gt_implementation,
-        #             queried_knowledge=queried_knowledge,
-        #         )
-        #     )
-
-        calls = []
-        for index in range(len(evo.sub_tasks)):
-            corresponding_implementation = evo.sub_implementations[index]
-            corresponding_gt_implementation = (
-                evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None
-            )
-            calls.append(
+        multi_implementation_feedback = multiprocessing_wrapper(
+            [
                 (
                     self.single_factor_implementation_evaluator.evaluate,
                     (
                         evo.sub_tasks[index],
-                        corresponding_implementation,
-                        corresponding_gt_implementation,
+                        evo.sub_workspace_list[index],
+                        evo.sub_gt_implementations[index] if evo.sub_gt_implementations is not None else None,
                         queried_knowledge,
                     ),
-                ),
-            )
-        multi_implementation_feedback = multiprocessing_wrapper(calls, n=FACTOR_IMPLEMENT_SETTINGS.evo_multi_proc_n)
+                )
+                for index in range(len(evo.sub_tasks))
+            ],
+            n=RD_AGENT_SETTINGS.multi_proc_n,
+        )
 
         final_decision = [
             None if single_feedback is None else single_feedback.final_decision

rdagent/components/coder/factor_coder/CoSTEER/evolvable_subjects.py

@@ -1,7 +1,7 @@
 from rdagent.components.coder.factor_coder.factor import (
     FactorExperiment,
+    FactorFBWorkspace,
     FactorTask,
-    FileBasedFactorImplementation,
 )
 from rdagent.core.evolving_framework import EvolvableSubjects
 from rdagent.log import rdagent_logger as logger
@@ -15,7 +15,7 @@ class FactorEvolvingItem(FactorExperiment, EvolvableSubjects):
     def __init__(
         self,
         sub_tasks: list[FactorTask],
-        sub_gt_implementations: list[FileBasedFactorImplementation] = None,
+        sub_gt_implementations: list[FactorFBWorkspace] = None,
     ):
         FactorExperiment.__init__(self, sub_tasks=sub_tasks)
         self.corresponding_selection: list = None

rdagent/components/coder/factor_coder/CoSTEER/evolving_agent.py

@@ -0,0 +1,19 @@
+from rdagent.components.coder.factor_coder.CoSTEER.evaluators import FactorMultiFeedback
+from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
+    FactorEvolvingItem,
+)
+from rdagent.core.evaluation import Feedback
+from rdagent.core.evolving_agent import RAGEvoAgent
+from rdagent.core.evolving_framework import EvolvableSubjects
+
+
+class FactorRAGEvoAgent(RAGEvoAgent):
+    def filter_evolvable_subjects_by_feedback(self, evo: EvolvableSubjects, feedback: Feedback) -> EvolvableSubjects:
+        assert isinstance(evo, FactorEvolvingItem)
+        assert isinstance(feedback, list)
+        assert len(evo.sub_workspace_list) == len(feedback)
+
+        for index in range(len(evo.sub_workspace_list)):
+            if not feedback[index].final_decision:
+                evo.sub_workspace_list[index].clear()
+        return evo

rdagent/components/coder/factor_coder/CoSTEER/evolving_strategy.py

@@ -16,14 +16,11 @@ from rdagent.components.coder.factor_coder.CoSTEER.scheduler import (
     LLMSelect,
     RandomSelect,
 )
-from rdagent.components.coder.factor_coder.factor import (
-    FactorTask,
-    FileBasedFactorImplementation,
-)
+from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
 from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
 from rdagent.core.conf import RD_AGENT_SETTINGS
 from rdagent.core.evolving_framework import EvolvingStrategy, QueriedKnowledge
-from rdagent.core.experiment import Implementation
+from rdagent.core.experiment import Workspace
 from rdagent.core.prompts import Prompts
 from rdagent.core.utils import multiprocessing_wrapper
 from rdagent.oai.llm_utils import APIBackend
@@ -43,7 +40,7 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
         self,
         target_task: FactorTask,
         queried_knowledge: QueriedKnowledge = None,
-    ) -> Implementation:
+    ) -> Workspace:
         raise NotImplementedError
 
     def evolve(
@@ -53,15 +50,12 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
         queried_knowledge: FactorQueriedKnowledge | None = None,
         **kwargs,
     ) -> FactorEvolvingItem:
-        self.num_loop += 1
-        new_evo = deepcopy(evo)
-
         # 1.找出需要evolve的factor
         to_be_finished_task_index = []
-        for index, target_factor_task in enumerate(new_evo.sub_tasks):
+        for index, target_factor_task in enumerate(evo.sub_tasks):
             target_factor_task_desc = target_factor_task.get_task_information()
             if target_factor_task_desc in queried_knowledge.success_task_to_knowledge_dict:
-                new_evo.sub_implementations[index] = queried_knowledge.success_task_to_knowledge_dict[
+                evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
                     target_factor_task_desc
                 ].implementation
             elif (
@@ -87,30 +81,27 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
                 to_be_finished_task_index = LLMSelect(
                     to_be_finished_task_index,
                     implementation_factors_per_round,
-                    new_evo,
+                    evo,
                     queried_knowledge.former_traces,
                     self.scen,
                 )
 
         result = multiprocessing_wrapper(
             [
-                (self.implement_one_factor, (new_evo.sub_tasks[target_index], queried_knowledge))
+                (self.implement_one_factor, (evo.sub_tasks[target_index], queried_knowledge))
                 for target_index in to_be_finished_task_index
             ],
-            n=FACTOR_IMPLEMENT_SETTINGS.evo_multi_proc_n,
+            n=RD_AGENT_SETTINGS.multi_proc_n,
         )
 
         for index, target_index in enumerate(to_be_finished_task_index):
-            new_evo.sub_implementations[target_index] = result[index]
+            if evo.sub_workspace_list[target_index] is None:
+                evo.sub_workspace_list[target_index] = FactorFBWorkspace(target_task=evo.sub_tasks[target_index])
+            evo.sub_workspace_list[target_index].inject_code(**{"factor.py": result[index]})
 
-        # for target_index in to_be_finished_task_index:
-        #     new_evo.sub_implementations[target_index] = self.implement_one_factor(
-        #         new_evo.sub_tasks[target_index], queried_knowledge
-        #     )
+        evo.corresponding_selection = to_be_finished_task_index
 
-        new_evo.corresponding_selection = to_be_finished_task_index
-
-        return new_evo
+        return evo
 
 
 class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
@@ -118,7 +109,7 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
         self,
         target_task: FactorTask,
         queried_knowledge: FactorQueriedKnowledgeV1 = None,
-    ) -> Implementation:
+    ) -> str:
         factor_information_str = target_task.get_task_information()
 
         if queried_knowledge is not None and factor_information_str in queried_knowledge.success_task_to_knowledge_dict:
@@ -149,7 +140,7 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
                     queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
                 )
             )
-            session = APIBackend(use_chat_cache=False).build_chat_session(
+            session = APIBackend(use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache).build_chat_session(
                 session_system_prompt=system_prompt,
             )
 
@@ -185,14 +176,8 @@ class FactorEvolvingStrategy(MultiProcessEvolvingStrategy):
                     json_mode=True,
                 ),
             )["code"]
-            # ast.parse(code)
-            factor_implementation = FileBasedFactorImplementation(
-                target_task,
-            )
-            factor_implementation.prepare()
-            factor_implementation.inject_code(**{"factor.py": code})
 
-            return factor_implementation
+            return code
 
 
 class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
@@ -205,7 +190,7 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
         self,
         target_task: FactorTask,
         queried_knowledge,
-    ) -> Implementation:
+    ) -> str:
         error_summary = FACTOR_IMPLEMENT_SETTINGS.v2_error_summary
         # 1. 提取因子的背景信息
         target_factor_task_information = target_task.get_task_information()
@@ -249,7 +234,7 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
                 )
             )
 
-            session = APIBackend(use_chat_cache=False).build_chat_session(
+            session = APIBackend(use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache).build_chat_session(
                 session_system_prompt=system_prompt,
             )
 
@@ -276,7 +261,9 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
                         )
                         .strip("\n")
                     )
-                    session_summary = APIBackend(use_chat_cache=False).build_chat_session(
+                    session_summary = APIBackend(
+                        use_chat_cache=FACTOR_IMPLEMENT_SETTINGS.coder_use_cache
+                    ).build_chat_session(
                         session_system_prompt=error_summary_system_prompt,
                     )
                     for _ in range(10):  # max attempt to reduce the length of error_summary_user_prompt
@@ -335,7 +322,4 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
                 json_mode=True,
             )
             code = json.loads(response)["code"]
-            factor_implementation = FileBasedFactorImplementation(target_task)
-            factor_implementation.prepare()
-            factor_implementation.inject_code(**{"factor.py": code})
-            return factor_implementation
+            return code

rdagent/components/coder/factor_coder/CoSTEER/knowledge_management.py

@@ -27,9 +27,9 @@ from rdagent.core.evolving_framework import (
     QueriedKnowledge,
     RAGStrategy,
 )
-from rdagent.core.experiment import Implementation
-from rdagent.log import rdagent_logger as logger
+from rdagent.core.experiment import Workspace
 from rdagent.core.prompts import Prompts
+from rdagent.log import rdagent_logger as logger
 from rdagent.oai.llm_utils import (
     APIBackend,
     calculate_embedding_distance_between_str_list,
@@ -40,7 +40,7 @@ class FactorKnowledge(Knowledge):
     def __init__(
         self,
         target_task: FactorTask,
-        implementation: Implementation,
+        implementation: Workspace,
         feedback: FactorSingleFeedback,
     ) -> None:
         """
@@ -53,7 +53,7 @@ class FactorKnowledge(Knowledge):
             None
         """
         self.target_task = target_task
-        self.implementation = implementation
+        self.implementation = implementation.copy()
         self.feedback = feedback
 
     def get_implementation_and_feedback_str(self) -> str:
@@ -115,7 +115,7 @@ class FactorRAGStrategyV1(RAGStrategy):
                 for task_index in range(len(implementations.sub_tasks)):
                     target_task = implementations.sub_tasks[task_index]
                     target_task_information = target_task.get_task_information()
-                    implementation = implementations.sub_implementations[task_index]
+                    implementation = implementations.sub_workspace_list[task_index]
                     single_feedback = feedback[task_index]
                     if single_feedback is None:
                         continue
@@ -149,9 +149,9 @@ class FactorRAGStrategyV1(RAGStrategy):
         for target_factor_task in evo.sub_tasks:
             target_factor_task_information = target_factor_task.get_task_information()
             if target_factor_task_information in self.knowledgebase.success_task_info_set:
-                queried_knowledge.success_task_to_knowledge_dict[target_factor_task_information] = (
-                    self.knowledgebase.implementation_trace[target_factor_task_information][-1]
-                )
+                queried_knowledge.success_task_to_knowledge_dict[
+                    target_factor_task_information
+                ] = self.knowledgebase.implementation_trace[target_factor_task_information][-1]
             elif (
                 len(
                     self.knowledgebase.implementation_trace.setdefault(
@@ -163,12 +163,14 @@ class FactorRAGStrategyV1(RAGStrategy):
             ):
                 queried_knowledge.failed_task_info_set.add(target_factor_task_information)
             else:
-                queried_knowledge.working_task_to_former_failed_knowledge_dict[target_factor_task_information] = (
-                    self.knowledgebase.implementation_trace.setdefault(
-                        target_factor_task_information,
-                        [],
-                    )[-v1_query_former_trace_limit:]
-                )
+                queried_knowledge.working_task_to_former_failed_knowledge_dict[
+                    target_factor_task_information
+                ] = self.knowledgebase.implementation_trace.setdefault(
+                    target_factor_task_information,
+                    [],
+                )[
+                    -v1_query_former_trace_limit:
+                ]
 
                 knowledge_base_success_task_list = list(
                     self.knowledgebase.success_task_info_set,
@@ -189,9 +191,9 @@ class FactorRAGStrategyV1(RAGStrategy):
                     )[-1]
                     for index in similar_indexes
                 ]
-                queried_knowledge.working_task_to_similar_successful_knowledge_dict[target_factor_task_information] = (
-                    similar_successful_knowledge
-                )
+                queried_knowledge.working_task_to_similar_successful_knowledge_dict[
+                    target_factor_task_information
+                ] = similar_successful_knowledge
         return queried_knowledge
 
 
@@ -234,7 +236,7 @@ class FactorGraphRAGStrategy(RAGStrategy):
                     single_feedback = feedback[task_index]
                     target_task = implementations.sub_tasks[task_index]
                     target_task_information = target_task.get_task_information()
-                    implementation = implementations.sub_implementations[task_index]
+                    implementation = implementations.sub_workspace_list[task_index]
                     single_feedback = feedback[task_index]
                     if single_feedback is None:
                         continue
@@ -425,9 +427,9 @@ class FactorGraphRAGStrategy(RAGStrategy):
                     else:
                         current_index += 1
 
-                factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information] = (
-                    former_trace_knowledge[-v2_query_former_trace_limit:]
-                )
+                factor_implementation_queried_graph_knowledge.former_traces[
+                    target_factor_task_information
+                ] = former_trace_knowledge[-v2_query_former_trace_limit:]
             else:
                 factor_implementation_queried_graph_knowledge.former_traces[target_factor_task_information] = []
 

rdagent/components/coder/factor_coder/CoSTEER/scheduler.py

@@ -9,9 +9,9 @@ from rdagent.components.coder.factor_coder.CoSTEER.evolvable_subjects import (
 )
 from rdagent.components.coder.factor_coder.utils import get_data_folder_intro
 from rdagent.core.conf import RD_AGENT_SETTINGS
-from rdagent.log import rdagent_logger as logger
 from rdagent.core.prompts import Prompts
 from rdagent.core.scenario import Scenario
+from rdagent.log import rdagent_logger as logger
 from rdagent.oai.llm_utils import APIBackend
 
 scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")