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# chains/distractors/runner_without.py
import asyncio
from config.chain_configs import chain_configs
from app.helpers.exercise_standardizer import standardize_exercise
from config.llm_config import llms
async def run_distractors(
user_query: str,
model_choice_distractors_1: str,
model_choice_distractors_2: str,
model_choice_distractors_3: str,
exercise_format_distractors: str,
sampling_count_distractors: str,
intermediate_distractors_specification: str,
final_distractors_specification: str,
) -> tuple:
"""
Generate distractors by running the DistractorsChain multiple times in parallel.
1. Standardizes the exercise text once using a fixed LLM.
2. Constructs a DistractorsChain, where the user can pick two LLMs
(e.g. one low-temp, one mid-temp) for parallel brainstorming steps.
3. Invokes the chain ``num_samples`` times in parallel (based on ``sampling_count_distractors``),
each time producing one consolidated distractors output.
4. Pads the results to fill 10 output fields.
"""
# 0) Parse how many concurrent runs (samples) we want
num_samples = int("".join(filter(str.isdigit, sampling_count_distractors)))
# Fetch the DistractorsChain configuration.
config = chain_configs["distractors"]
# 1) Standardize the user query once for all tracks
standardized_exercise = await standardize_exercise(
user_query,
exercise_format_distractors,
config["template_standardize"],
config["llm_standardize"]
)
# 2) Build the DistractorsChain instance
chain_instance = config["class"](
template_distractors_brainstorm_1=config["template_distractors_brainstorm_1"],
template_distractors_brainstorm_2=config["template_distractors_brainstorm_2"],
llm_brainstorm_1=llms.get(model_choice_distractors_1, config["llm_brainstorm_1"]), # User-selected LLM 1
llm_brainstorm_2=llms.get(model_choice_distractors_2, config["llm_brainstorm_2"]), # User-selected LLM 2
template_consolidate=config["template_consolidate"],
llm_consolidate=llms.get(model_choice_distractors_3, config["llm_consolidate"]), # User-selected LLM 3
)
# 3) Create N tasks in parallel (one full distractors generation pipeline per sample)
tasks = [
chain_instance.run(standardized_exercise, intermediate_distractors_specification, final_distractors_specification) for _ in range(num_samples)
]
results = await asyncio.gather(*tasks)
# 4) Pad up to 10 outputs to correspond to 10 response fields
all_responses = list(results) + [""] * (10 - len(results))
return tuple(all_responses) + (standardized_exercise,)
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