feat(ai): 종합 피드백 점수 산정을 하이브리드로 (앵커 루브릭 + per-answer 집계 기준값)

ai/src/ai_server/chain/feedback_generation_chain.py

@@ -34,6 +34,7 @@ async def generate(
         transcript: str,
         rag_context: str,
         voice_analysis_summary: str,
+        score_basis: str = "(없음)",
     ) -> FeedbackResult: ...
 
 
@@ -51,6 +52,7 @@ async def generate(
         transcript: str,
         rag_context: str,
         voice_analysis_summary: str = "",
+        score_basis: str = "(없음)",
     ) -> FeedbackResult:
         result = await self._chain.ainvoke(
             {
@@ -59,6 +61,7 @@ async def generate(
                 "total_question_count": total_question_count or 0,
                 "end_reason": end_reason or "USER_REQUEST",
                 "transcript": transcript,
+                "score_basis": score_basis or "(없음)",
                 "rag_context": rag_context or "(none)",
                 "voice_analysis_summary": voice_analysis_summary
                 or "No voice analysis summary was provided.",

ai/src/ai_server/chain/prompts/feedback_generation.py

@@ -32,13 +32,30 @@
     "  - If voice analysis is absent or sparse, do not invent voice-related findings.\n"
 )
 
+# 점수 앵커(캘리브레이션) + per-answer 집계 기준값 제약(하이브리드).
+SYSTEM_PROMPT += (
+    "\n- 점수 앵커 (0~100, 모든 차원 공통 기준):\n"
+    "  - 90~100: 정확하고 구체적이며 trade-off·근거까지 깊이 있음. 빈틈 거의 없음.\n"
+    "  - 70~89: 대체로 정확·구체적이나 일부 깊이/근거가 부족.\n"
+    "  - 50~69: 방향은 맞으나 추상적이거나 근거·구조가 미흡.\n"
+    "  - 30~49: 부분적으로만 타당하고 핵심 누락이 많음.\n"
+    "  - 0~29: 부정확하거나 거의 무응답.\n"
+    "- '점수 기준값' 섹션 (per-answer 평가를 집계한 차원별 기준값) 이 주어지면:\n"
+    "  - 각 차원 최종 점수는 그 기준값에서 **±15점 이내**로 산정한다.\n"
+    "  - ±15점을 넘겨야 한다면 그 사유를 strengths/weaknesses 에 반드시 명시한다.\n"
+    "  - 기준값이 '근거 없음'(예: 참고문서 미선택으로 correctness 미산정)인 차원은 "
+    "전사 내용으로 판단하되, 그 한계를 weaknesses 또는 점수 보수성(과대평가 금지)에 반영한다.\n"
+)
+
 HUMAN_PROMPT = (
     "직군: {job_category}\n"
     "면접 모드: {mode}\n"
     "총 질문 수: {total_question_count}\n"
     "종료 사유: {end_reason}\n\n"
     "=== 메시지 시퀀스 ===\n"
     "{transcript}\n\n"
+    "=== 점수 기준값 (per-answer 평가 집계 — 이 값을 기준으로 산정) ===\n"
+    "{score_basis}\n\n"
     "=== RAG 컨텍스트 청크 (참고용, 직접 인용 금지) ===\n"
     "{rag_context}\n\n"
     "=== Voice Analysis Summary ===\n"

ai/src/ai_server/messaging/consumers/feedback_consumer.py

@@ -87,6 +87,7 @@ async def handle(self, message: AbstractIncomingMessage) -> None:
             )
 
             transcript = _build_transcript(req.messages)
+            score_basis = _build_score_basis(req.messages)
             rag_context = await self._build_rag_context(req)
             voice_analysis_summary = _build_voice_analysis_summary(
                 req.voice_analysis_summary
@@ -98,6 +99,7 @@ async def handle(self, message: AbstractIncomingMessage) -> None:
                 total_question_count=req.total_question_count,
                 end_reason=req.end_reason,
                 transcript=transcript,
+                score_basis=score_basis,
                 rag_context=rag_context,
                 voice_analysis_summary=voice_analysis_summary,
             )
@@ -191,6 +193,67 @@ def _format_evaluation(e) -> str:
     return ", ".join(parts) if parts else "(없음)"
 
 
+_STRUCTURE_SCORE = {"FULL_STAR": 5.0, "PARTIAL_STAR": 2.5, "NONE": 0.0}
+
+
+def _mean(values: list[float]) -> float | None:
+    vals = [v for v in values if v is not None]
+    return sum(vals) / len(vals) if vals else None
+
+
+def _to_100(x: float | None) -> int | None:
+    return None if x is None else round(x * 20)
+
+
+def _build_score_basis(messages: list[FeedbackMessageItem]) -> str:
+    """per-answer 평가(0~5)를 차원별 0~100 기준값으로 결정론적 집계(하이브리드).
+
+    이 값을 LLM 에 '기준값'으로 제시하고 ±15점 이내 산정하도록 제약 → 재현성·캘리브레이션.
+    correctness(RAG 기반)가 전부 null 이면 technical_accuracy 기준값은 '근거 없음'.
+    """
+    evals = [
+        m.evaluation
+        for m in messages
+        if m.role == "INTERVIEWEE" and m.evaluation is not None
+    ]
+    if not evals:
+        return "(per-answer 평가 없음 — 전사 내용으로만 산정. 과대평가 금지.)"
+
+    spec = _mean([e.specificity for e in evals])
+    logic = _mean([e.logic for e in evals])
+    corr = _mean([e.correctness for e in evals])  # null 은 자동 제외
+    struct = _mean([_STRUCTURE_SCORE.get(e.structure) for e in evals])
+
+    tech_100 = _to_100(corr)
+    logic_100 = _to_100(logic)
+    comm_src = _mean([v for v in (spec, struct) if v is not None])
+    comm_100 = _to_100(comm_src)
+    overall_src = [v for v in (tech_100, logic_100, comm_100) if v is not None]
+    overall_100 = round(sum(overall_src) / len(overall_src)) if overall_src else None
+
+    def fmt5(x: float | None) -> str:
+        return f"{x:.1f}/5" if x is not None else "없음"
+
+    corr_count = sum(1 for e in evals if e.correctness is not None)
+    lines = [
+        f"- 채점된 답변 수: {len(evals)} (correctness 산정 {corr_count}건)",
+        f"- specificity 평균: {fmt5(spec)}, logic 평균: {fmt5(logic)}, "
+        f"structure 평균: {fmt5(struct)}, correctness 평균: {fmt5(corr)}",
+        "[차원별 기준값(0~100)]",
+        (
+            f"- technical_accuracy ≈ {tech_100} (correctness=RAG 사실일치 기반)"
+            if tech_100 is not None
+            else "- technical_accuracy: 근거 없음(참고문서 미선택→correctness 미산정). "
+            "전사로 보수적으로 판단."
+        ),
+        f"- logic_score ≈ {logic_100 if logic_100 is not None else '근거 없음'}",
+        f"- communication_score ≈ {comm_100 if comm_100 is not None else '근거 없음'} "
+        "(specificity 명료성 + structure 구조화)",
+        f"- overall_score ≈ {overall_100 if overall_100 is not None else '근거 없음'}",
+    ]
+    return "\n".join(lines)
+
+
 def _build_voice_analysis_summary(summary: VoiceAnalysisSummary | None) -> str:
     if summary is None:
         return "No voice analysis summary was provided."

ai/tests/test_feedback_consumer.py

@@ -9,11 +9,18 @@
     FeedbackResult,
     LlmFeedbackGenerator,
 )
-from ai_server.chain.prompts.feedback_generation import HUMAN_PROMPT
+from ai_server.chain.prompts.feedback_generation import HUMAN_PROMPT, SYSTEM_PROMPT
 from ai_server.core.client import EmbeddingSearchHit
-from ai_server.messaging.consumers.feedback_consumer import FeedbackConsumer
+from ai_server.messaging.consumers.feedback_consumer import (
+    FeedbackConsumer,
+    _build_score_basis,
+)
 from ai_server.messaging.idempotency import LruIdempotencyStore
-from ai_server.model.messages.feedback import FeedbackCallbackPayload
+from ai_server.model.messages.feedback import (
+    FeedbackCallbackPayload,
+    FeedbackMessageItem,
+    MessageEvaluation,
+)
 
 VOICE_SUMMARY = {
     "analyzedMessageCount": 2,
@@ -188,6 +195,48 @@ async def test_consumer_accepts_voice_summary_and_passes_it_to_generator():
     assert not hasattr(payload, "voice_analysis_summary")
 
 
+def _answer(seq: int, *, spec=None, logic=None, structure=None, correctness=None):
+    return FeedbackMessageItem(
+        id=seq,
+        sequence_number=seq,
+        role="INTERVIEWEE",
+        content="답변",
+        evaluation=MessageEvaluation(
+            specificity=spec, logic=logic, structure=structure, correctness=correctness
+        ),
+    )
+
+
+def test_build_score_basis_aggregates_to_0_100():
+    msgs = [
+        _answer(2, spec=4.0, logic=3.0, structure="FULL_STAR", correctness=3.0),
+        _answer(4, spec=2.0, logic=4.0, structure="PARTIAL_STAR", correctness=2.0),
+    ]
+    basis = _build_score_basis(msgs)
+    # correctness 평균 2.5 → technical_accuracy ≈ 50
+    assert "technical_accuracy ≈ 50" in basis
+    # logic 평균 3.5 → 70
+    assert "logic_score ≈ 70" in basis
+    assert "채점된 답변 수: 2" in basis
+
+
+def test_build_score_basis_marks_correctness_absent_without_rag():
+    # correctness 가 전부 null(참고문서 미선택) → technical_accuracy 근거 없음.
+    msgs = [_answer(2, spec=3.0, logic=3.0, structure="NONE", correctness=None)]
+    basis = _build_score_basis(msgs)
+    assert "technical_accuracy: 근거 없음" in basis
+
+
+def test_build_score_basis_empty_when_no_evaluations():
+    assert "per-answer 평가 없음" in _build_score_basis([])
+
+
+def test_feedback_prompt_has_score_anchor_and_slot():
+    assert "점수 앵커" in SYSTEM_PROMPT
+    assert "±15" in SYSTEM_PROMPT
+    assert "{score_basis}" in HUMAN_PROMPT
+
+
 @pytest.mark.asyncio
 async def test_llm_feedback_generator_includes_voice_summary_in_chain_input():
     class _FakeChain: