64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
from __future__ import annotations
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from src.rag.qa_v1_1_73.answer_composer import AnswerComposer
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from src.rag.qa_v1_1_73.schemas import EvidenceItem, RetrievalHypothesis
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class _LLM:
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def __init__(self):
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self.calls = []
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def complete(self, system, user, *, temperature, max_tokens):
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self.calls.append((system, user, temperature, max_tokens))
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return "answer"
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def _evidence(evidence_id, relation, target):
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return EvidenceItem(
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evidence_id=evidence_id,
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kb="test",
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source="HSPs",
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relation=relation,
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target=target,
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evidence=f"HSPs {relation} {target}.",
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paper_id="paper-hsp",
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doi="10.1000/hsp",
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)
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def test_composer_receives_only_grouped_accepted_evidence_and_mechanism_shape():
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llm = _LLM()
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composer = AnswerComposer(llm)
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accepted = _evidence("Q1.A1.E1", "prevent", "protein aggregation")
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audit_only = _evidence("Q1.A1.E2", "upregulated_in", "heat stress")
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answer = composer.compose(
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"HSP基因家族如何帮助植物应对高温？",
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RetrievalHypothesis(question_type="mechanism"),
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[accepted],
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{},
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[],
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subquestion_evidence_groups=[
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{
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"subquestion_id": "Q1",
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"question": "HSP如何保护蛋白质？",
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"role": "required",
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"answer_slot": "molecular action",
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"status": "SATISFIED",
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"evidence": [accepted],
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"chains": [],
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}
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],
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)
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assert answer == "answer"
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prompt = llm.calls[0][1]
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assert "Agent-approved subquestion evidence groups" in prompt
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assert "Q1" in prompt
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assert "molecular action" in prompt
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assert "prevent" in prompt
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assert "upregulated_in" not in prompt
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assert "trigger/context → molecular action → protected target or process → biological outcome" in prompt
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assert "Do not add an intermediate step" in prompt
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assert audit_only.evidence_id not in prompt
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