feat(kaggle): several update in kaggle scenarios (#476)

* udpate plot

* log and reduce token

* trace tag

* add simple_background parameter to get_scenario_all_desc

* update trace

* update first version code

* chat model map

* add annotation for stack index

* add annotation

* reformatted by black

* several update on kaggle scenarios

* update some new change

* fix CI

* fix CI

* fix a bug

* fix bugs in graph RAG

---------

Co-authored-by: Tim <illking@foxmail.com>

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

@@ -8,6 +8,7 @@ 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,
 )
@@ -92,7 +93,11 @@ class FactorCodeEvaluator(FactorEvaluator):
             .from_string(evaluate_prompts["evaluator_code_feedback_v1_system"])
             .render(
                 scenario=(
-                    self.scen.get_scenario_all_desc(target_task)
+                    self.scen.get_scenario_all_desc(
+                        target_task,
+                        filtered_tag="feature",
+                        simple_background=FACTOR_IMPLEMENT_SETTINGS.simple_background,
+                    )
                     if self.scen is not None
                     else "No scenario description."
                 )
@@ -190,7 +195,7 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
             )
         buffer = io.StringIO()
         gen_df.info(buf=buffer)
-        gen_df_info_str = f"The use is currently working on a feature related task.\nThe output dataframe info is:\n{buffer.getvalue()}"
+        gen_df_info_str = f"The user is currently working on a feature related task.\nThe output dataframe info is:\n{buffer.getvalue()}"
         system_prompt = (
             Environment(undefined=StrictUndefined)
             .from_string(
@@ -198,7 +203,7 @@ class FactorOutputFormatEvaluator(FactorEvaluator):
             )
             .render(
                 scenario=(
-                    self.scen.get_scenario_all_desc(implementation.target_task)
+                    self.scen.get_scenario_all_desc(implementation.target_task, filtered_tag="feature")
                     if self.scen is not None
                     else "No scenario description."
                 )
@@ -512,7 +517,7 @@ class FactorFinalDecisionEvaluator(Evaluator):
             .from_string(evaluate_prompts["evaluator_final_decision_v1_system"])
             .render(
                 scenario=(
-                    self.scen.get_scenario_all_desc(target_task)
+                    self.scen.get_scenario_all_desc(target_task, filtered_tag="feature")
                     if self.scen is not None
                     else "No scenario description."
                 )

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

@@ -234,7 +234,7 @@ class FactorEvolvingStrategyWithGraph(MultiProcessEvolvingStrategy):
                     implement_prompts["evolving_strategy_factor_implementation_v1_system"],
                 )
                 .render(
-                    scenario=self.scen.get_scenario_all_desc(target_task),
+                    scenario=self.scen.get_scenario_all_desc(target_task, filtered_tag="feature"),
                     queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
                 )
             )

rdagent/components/coder/factor_coder/config.py

@@ -35,6 +35,9 @@ class FactorImplementSettings(BaseSettings):
     v2_error_summary: bool = False
     v2_knowledge_sampler: float = 1.0
 
+    simple_background: bool = False
+    """Whether to use simple background information for code feedback"""
+
     file_based_execution_timeout: int = 120
     """Timeout in seconds for each factor implementation execution"""