change research assistant to experimenter

metagpt/ext/sela/data.yaml

@@ -1,3 +1,3 @@
 datasets_dir: "path/to/datasets" # path to the datasets directory
-work_dir: ../../workspace # path to the workspace directory
+work_dir: ../../../workspace # path to the workspace directory
 role_dir: storage/SELA # path to the role directory

metagpt/ext/sela/experimenter.py

@@ -60,7 +60,7 @@ async def wrapper(self, *args, **kwargs):
     return decorator
 
 
-class ResearchAssistant(DataInterpreter):
+class Experimenter(DataInterpreter):
     node_id: str = "0"
     start_task_id: int = 1
     state_saved: bool = False
@@ -78,7 +78,7 @@ def change_next_instruction(self, new_instruction):
             self.planner.plan.task_map[str(self.start_task_id)].instruction = new_instruction
             self.remap_tasks()
 
-    def update_til_start_task(self, role: ResearchAssistant, backward: bool = True):
+    def update_til_start_task(self, role: Experimenter, backward: bool = True):
         if backward:
             # make sure the previous task instructions are matched
             assert (

metagpt/ext/sela/runner/random_search.py

@@ -1,4 +1,4 @@
-from metagpt.ext.sela.experimenter import ResearchAssistant
+from metagpt.ext.sela.experimenter import Experimenter
 from metagpt.ext.sela.insights.instruction_generator import InstructionGenerator
 from metagpt.ext.sela.runner.runner import Runner
 from metagpt.ext.sela.utils import get_exp_pool_path
@@ -34,9 +34,7 @@ async def run_experiment(self):
 
         results = []
         for i in range(self.args.num_experiments):
-            di = ResearchAssistant(
-                node_id=str(i), use_reflection=self.args.reflection, role_timeout=self.args.role_timeout
-            )
+            di = Experimenter(node_id=str(i), use_reflection=self.args.reflection, role_timeout=self.args.role_timeout)
             di.role_dir = f"{di.role_dir}_{self.args.task}"
             requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i])
             print(requirement)

metagpt/ext/sela/runner/runner.py

@@ -6,7 +6,7 @@
 import pandas as pd
 
 from metagpt.ext.sela.evaluation.evaluation import evaluate_score
-from metagpt.ext.sela.experimenter import ResearchAssistant
+from metagpt.ext.sela.experimenter import Experimenter
 from metagpt.ext.sela.search.tree_search import create_initial_state
 from metagpt.ext.sela.utils import DATA_CONFIG, save_notebook
 
@@ -83,9 +83,7 @@ async def run_experiment(self):
         results = []
 
         for i in range(self.args.num_experiments):
-            di = ResearchAssistant(
-                node_id="0", use_reflection=self.args.reflection, role_timeout=self.args.role_timeout
-            )
+            di = Experimenter(node_id="0", use_reflection=self.args.reflection, role_timeout=self.args.role_timeout)
             score_dict = await self.run_di(di, user_requirement, run_idx=i)
             results.append(
                 {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)}

metagpt/ext/sela/search/tree_search.py

@@ -15,7 +15,7 @@
     get_split_dataset_path,
 )
 from metagpt.ext.sela.evaluation.evaluation import evaluate_score
-from metagpt.ext.sela.experimenter import ResearchAssistant, TimeoutException
+from metagpt.ext.sela.experimenter import Experimenter, TimeoutException
 from metagpt.ext.sela.insights.instruction_generator import InstructionGenerator
 from metagpt.ext.sela.utils import get_exp_pool_path, load_execute_notebook, mcts_logger
 from metagpt.tools.tool_recommend import ToolRecommender
@@ -44,9 +44,9 @@ def initialize_di_root_node(state: dict, reflection: bool = True):
         reflection (bool, optional): Whether to use reflection. Defaults to True.
 
     Returns:
-        tuple: A tuple containing the ResearchAssistant role and the root Node.
+        tuple: A tuple containing the Experimenter role and the root Node.
     """
-    role = ResearchAssistant(
+    role = Experimenter(
         node_id="0",
         start_task_id=state["start_task_id"],
         use_reflection=reflection,
@@ -204,14 +204,14 @@ def load_role(self):
             role_dict["tool_recommender"] = ToolRecommender()
         elif isinstance(role_dict.get("tool_recommender", {}).get("tools"), dict):
             role_dict["tool_recommender"]["tools"] = list(role_dict["tool_recommender"]["tools"].keys())
-        role = ResearchAssistant(**role_dict)
+        role = Experimenter(**role_dict)
         if self.parent is not None:  # TODO: Check this
             parent_role = self.parent.load_role()
             role.update_til_start_task(parent_role, backward=False)
         role.remap_tasks()
         return role
 
-    def save_new_role(self, role: ResearchAssistant):
+    def save_new_role(self, role: Experimenter):
         role.node_id = self.id
         role.start_task_id = self.state["start_task_id"]
         role.state_saved = False
@@ -268,7 +268,7 @@ def evaluate_simulation(self, score_dict):
             self.get_and_move_predictions("test")
         return score_dict
 
-    async def run_node(self, role: ResearchAssistant = None):
+    async def run_node(self, role: Experimenter = None):
         if self.is_terminal() and role is not None:
             if role.state_saved:
                 return self.raw_reward