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USTCKevinF
ebb659a018
* reformat factor implement process * move some code to more reasonable place * fix the bug * add test function in factor_extract_and_implement.py * change select factor number to ratio , add some factor implement setting and fix some bug while using knowledgebase * change evoagent * add abstract class EvoAgent * add benchmark workflow * fix some bug in llm_utils * run wenjun's code * fix the knowledgebase instance check --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com>
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
from rdagent.oai.llm_utils import APIBackend
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from jinja2 import Template
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from rdagent.factor_implementation.share_modules.factor_implementation_config import FactorImplementSettings
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import json
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from rdagent.factor_implementation.share_modules.factor_implementation_utils import get_data_folder_intro
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from rdagent.factor_implementation.evolving.factor import FactorEvovlingItem
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from rdagent.core.prompts import Prompts
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from pathlib import Path
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scheduler_prompts = Prompts(file_path=Path(__file__).parent.parent / "prompts.yaml")
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def RandomSelect(to_be_finished_task_index, implementation_factors_per_round):
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import random
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to_be_finished_task_index = random.sample(
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to_be_finished_task_index,
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implementation_factors_per_round,
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)
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print("The random selection is:",to_be_finished_task_index)
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return to_be_finished_task_index
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def LLMSelect(to_be_finished_task_index, implementation_factors_per_round, evo:FactorEvovlingItem, former_trace):
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tasks = []
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for i in to_be_finished_task_index:
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# find corresponding former trace for each task
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target_factor_task_information = evo.target_factor_tasks[i].get_factor_information()
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if target_factor_task_information in former_trace:
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tasks.append((i, evo.target_factor_tasks[i], former_trace[target_factor_task_information]))
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system_prompt = Template(
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scheduler_prompts["select_implementable_factor_system"],
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).render(
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data_info=get_data_folder_intro(),
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)
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session = APIBackend(use_chat_cache=False).build_chat_session(
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session_system_prompt=system_prompt,
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)
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while True:
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user_prompt = (
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Template(
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scheduler_prompts["select_implementable_factor_user"],
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)
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.render(
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factor_num = implementation_factors_per_round,
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target_factor_tasks=tasks,
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)
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)
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if (
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session.build_chat_completion_message_and_calculate_token(
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user_prompt,
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)
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< FactorImplementSettings().chat_token_limit
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):
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break
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response = session.build_chat_completion(
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user_prompt=user_prompt,
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json_mode=True,
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)
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try:
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selection = json.loads(response)["selected_factor"]
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if not isinstance(selection, list):
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return to_be_finished_task_index
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selection_index = [x for x in selection if isinstance(x, int)]
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except:
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return to_be_finished_task_index
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return selection_index
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