New Framework for idea proposal and implementation on RD-Agent (#34)

* Commit init framework * Co-authored-by: Yuante Li (FESCO Adecco Human Resources) <v-yuanteli@microsoft.com> Co-authored-by: XianBW <XianBW@users.noreply.github.com> * add an import * refine the whole framework * benchmark related framework * fix black and isort errors * move requirements to folder * fix black again --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: xuyang1 <xuyang1@microsoft.com>
2026-08-02 09:57:44 +00:00 · 2024-06-28 11:45:23 +08:00
parent bc8d96e96c
commit 6b626eb56d
62 changed files with 654 additions and 1120 deletions
@@ -0,0 +1,77 @@
+import json
+from pathlib import Path
+
+from jinja2 import Template
+
+from rdagent.components.task_implementation.factor_implementation.evolving.factor import (
+    FactorEvovlingItem,
+)
+from rdagent.components.task_implementation.factor_implementation.share_modules.factor_implementation_utils import (
+    get_data_folder_intro,
+)
+from rdagent.core.conf import RD_AGENT_SETTINGS
+from rdagent.core.log import RDAgentLog
+from rdagent.core.prompts import Prompts
+from rdagent.oai.llm_utils import APIBackend
+
+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,
+    )
+
+    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):
+    tasks = []
+    for i in to_be_finished_task_index:
+        # 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]))
+
+    system_prompt = Template(
+        scheduler_prompts["select_implementable_factor_system"],
+    ).render(
+        data_info=get_data_folder_intro(),
+    )
+
+    session = APIBackend(use_chat_cache=False).build_chat_session(
+        session_system_prompt=system_prompt,
+    )
+
+    while True:
+        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,
+            )
+            < RD_AGENT_SETTINGS.chat_token_limit
+        ):
+            break
+
+    response = session.build_chat_completion(
+        user_prompt=user_prompt,
+        json_mode=True,
+    )
+    try:
+        selection = json.loads(response)["selected_factor"]
+        if not isinstance(selection, list):
+            return to_be_finished_task_index
+        selection_index = [x for x in selection if isinstance(x, int)]
+    except:
+        return to_be_finished_task_index
+
+    return selection_index