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7ff7119 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | """drop_repeats_node — 70% word-overlap dedup between LLM and basic risks.
Drops the "same thing in different words" duplicates.
Input:
{"llm_risks_raw": list[Risk], "basic_risks": list[Risk], ...}
Output:
{"risks": list[Risk]} # final, filtered LLM risk list — merged into the
parent state's ``risks`` reducer
"""
from __future__ import annotations
from graph.states.pipeline_state import Risk
from nodes.risk.filter_llm_risks_node import _dict_to_risk, _risk_to_dict
from validation.llm_risk_filters import drop_repeats_of_basic
async def drop_repeats_node(state: dict) -> dict:
"""Drop LLM risks that overlap >=70% in content words with a basic risk.
After this node, ``llm_risks_raw`` is published into ``risks``, where the
``merge_risks`` reducer dedups it back into the parent state — closing
the LLM risk-analysis chain.
"""
raw = state.get("llm_risks_raw") or []
basic = state.get("basic_risks") or []
if not raw:
return {}
raw_dicts = [_risk_to_dict(r) for r in raw]
basic_dicts = [
_risk_to_dict(b) if isinstance(b, Risk)
else {"description": b.get("description", "") if isinstance(b, dict) else ""}
for b in basic
]
filtered_dicts = drop_repeats_of_basic(raw_dicts, basic_dicts)
filtered = [_dict_to_risk(d) for d in filtered_dicts]
# Close the chain: write the result under ``risks``, where merge_risks
# dedups it into the parent state.
return {"risks": filtered}
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