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chore: make sure token size below limit (#1225)

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* chore: make sure token size below limit

* refactor: refactor and add import

---------

Co-authored-by: amstrongzyf <amstrongzyf@126.com>
This commit is contained in:
Tim
2025-09-04 15:07:42 +08:00
committed by GitHub
parent be3433f26b
commit d68b0f4223
1 changed files with 18 additions and 4 deletions
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rdagent/scenarios/data_science/proposal/exp_gen/select/submit.py
+18 -4
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@@ -8,6 +8,7 @@ import pandas as pd
from rdagent.app.data_science.conf import DS_RD_SETTING
from rdagent.core.proposal import ExperimentFeedback, SOTAexpSelector, Trace
from rdagent.log import rdagent_logger as logger
from rdagent.oai.llm_conf import LLM_SETTINGS
from rdagent.oai.llm_utils import APIBackend, md5_hash
from rdagent.scenarios.data_science.experiment.experiment import DSExperiment
from rdagent.scenarios.data_science.proposal.exp_gen.base import DSHypothesis, DSTrace
@@ -64,7 +65,7 @@ class AutoSOTAexpSelector(SOTAexpSelector):
f"Auto SOTA selector: Multiple SOTA in trace, calling LLM to select the best one in {DS_RD_SETTING.max_sota_retrieved_num} SOTA experiments"
)
SOAT_exp_with_desc_and_scores = "Historical SOTA experiments:\n\n"
SOTA_exp_with_desc_and_scores = "Historical SOTA experiments:\n\n"
leaves: list[int] = trace.get_leaves()
@@ -111,20 +112,33 @@ class AutoSOTAexpSelector(SOTAexpSelector):
reverse=not trace.scen.metric_direction,
)[-DS_RD_SETTING.max_sota_retrieved_num :]
system_prompt = T(".prompts:auto_sota_selector.system").r(scenario=trace.scen.get_scenario_all_desc())
for i, (exp, ef) in enumerate(sota_exp_fb_list):
if exp:
current_final_score = pd.DataFrame(exp.result).loc["ensemble"].iloc[0]
desc = T("scenarios.data_science.share:describe.exp").r(
exp=exp, heading="SOTA of previous exploration of the scenario"
)
SOAT_exp_with_desc_and_scores += f"""SOTA experiment No. {i+1}:
new_experiment_content = f"""SOTA experiment No. {i+1}:
Description: {desc}
Final score: {current_final_score}\n\n"""
system_prompt = T(".prompts:auto_sota_selector.system").r(scenario=trace.scen.get_scenario_all_desc())
temp_user_prompt = T(".prompts:auto_sota_selector.user").r(
historical_sota_exp_with_desc_and_scores=SOTA_exp_with_desc_and_scores + new_experiment_content,
)
token_size = APIBackend().build_messages_and_calculate_token(
user_prompt=temp_user_prompt,
system_prompt=system_prompt,
)
if token_size >= LLM_SETTINGS.chat_token_limit:
logger.warning(f"Token limit reached at experiment {i+1}. Stopping.")
break
SOTA_exp_with_desc_and_scores += new_experiment_content
user_prompt = T(".prompts:auto_sota_selector.user").r(
historical_sota_exp_with_desc_and_scores=SOAT_exp_with_desc_and_scores,
historical_sota_exp_with_desc_and_scores=SOTA_exp_with_desc_and_scores
)
response = APIBackend().build_messages_and_create_chat_completion(