Refine all the implementation code to higher quality for release (#29)

* 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>

rdagent/factor_implementation/evolving/scheduler.py

@@ -1,27 +1,32 @@
 from rdagent.oai.llm_utils import APIBackend
 from jinja2 import Template
-from rdagent.factor_implementation.share_modules.factor_implementation_config import FactorImplementSettings
 import json
 from rdagent.factor_implementation.share_modules.factor_implementation_utils import get_data_folder_intro
 from rdagent.factor_implementation.evolving.factor import FactorEvovlingItem
 from rdagent.core.prompts import Prompts
+from rdagent.core.log import RDAgentLog
+from rdagent.core.conf import RD_AGENT_SETTINGS
 from pathlib import Path
 
 scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
 
+
 def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
     import random
+
     to_be_finished_task_index = random.sample(
         to_be_finished_task_index,
         implementation_factors_per_round,
     )
-    print("The random selection is:",to_be_finished_task_index)
+
+    RDAgentLog().info(f"The random selection is: {to_be_finished_task_index}")
     return to_be_finished_task_index
 
-def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:FactorEvovlingItem, former_trace):
+
+def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo: FactorEvovlingItem, former_trace):
     tasks = []
     for i in to_be_finished_task_index:
-    # find corresponding former trace for each task
+        # find corresponding former trace for each task
         target_factor_task_information = evo.target_factor_tasks[i].get_factor_information()
         if target_factor_task_information in former_trace:
             tasks.append((i, evo.target_factor_tasks[i], former_trace[target_factor_task_information]))
@@ -37,20 +42,17 @@ def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:F
     )
 
     while True:
-        user_prompt = (
-            Template(
-                scheduler_prompts["select_implementable_factor_user"],
-            )
-            .render(
-                factor_num = implementation_factors_per_round,
-                target_factor_tasks=tasks,
-            )
+        user_prompt = Template(
+            scheduler_prompts["select_implementable_factor_user"],
+        ).render(
+            factor_num=implementation_factors_per_round,
+            target_factor_tasks=tasks,
         )
         if (
             session.build_chat_completion_message_and_calculate_token(
                 user_prompt,
             )
-            < FactorImplementSettings().chat_token_limit
+            < RD_AGENT_SETTINGS.chat_token_limit
         ):
             break
 
@@ -65,5 +67,5 @@ def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:F
         selection_index = [x for x in selection if isinstance(x, int)]
     except:
         return to_be_finished_task_index
-    
+
     return selection_index