wip: phase_z2 evidence 파이프라인 + matching 실험(phase2~26) + 프론트 trace 패널 진행분 스냅샷

- src: phase_z2 composition/mapper/pipeline/placement_planner/retry, ai_fallback(prompts/schema/validate), mdx_text_atoms 신규 - Front: PipelineTracePanel 신규, FramePanel/SlideCanvas/Home/designAgentApi 등 갱신 + 테스트 4종 추가 - templates/phase_z2: catalog(component_expansion_registry, node_slot_mapping 신규), frames, families, slide_base 갱신 - tests/matching: phase2~26 매칭 실험 스크립트·리포트·온톨로지 전체 (미커밋 진행분) - tests: b4_v4 evidence, task5~28.5 시리즈, regression(imp95 baseline) 등 신규 테스트 대량 추가 - docs/reference: MDX 구조 인벤토리, MDX→Frame 구조 계약 문서 - scripts: mdx 계약/parity/coverage/viewport 체크, gitea comment, run sync 유틸 - .gitignore: tmp*.json, chromedriver, .orchestrator, *.pkl, Front_test* 등 임시/스냅샷 제외 미완성 작업의 보존용 스냅샷 커밋 (2026-07-02) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-02 17:03:42 +09:00
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+"""Phase 21b용 구조 ontology 기반 매칭
+- detect_mdx_structure_v3(): MDX 본문에서 같은 schema 속성 emit
+- structural_match_v3(): 속성 교집합 기반 score (0~1)
+- 가중치는 tie-breaker 수준 (0.05~0.10)
+- confidence high 일 때만 점수 부여
+"""
+import re
+import yaml
+from pathlib import Path
+
+HERE = Path(__file__).parent
+
+
+def load_ontology():
+    p = HERE / "structure_ontology.yaml"
+    with open(p, encoding="utf-8") as f:
+        return yaml.safe_load(f)["frames"]
+
+
+# ═══ MDX 구조 감지 v3 ═══
+def detect_mdx_structure_v3(text, mdx_title=""):
+    """schema emit: {family, surface, columns, items, has_table, has_cards, semantic_role, confidence}"""
+    lines = text.split("\n")
+    result = {
+        "family": None, "surface": None, "semantic_role": None,
+        "columns": None, "items": None,
+        "has_table": False, "has_cards": False, "has_diagram": False,
+        "confidence": "low",
+    }
+
+    # 1. ### X.Y 서브섹션 감지
+    subs = [ln for ln in lines if re.match(r"^###\s+\d+\.\d+", ln)]
+    subsection_texts = [ln for ln in subs]
+    n_subs = len(subs)
+
+    # 2. 표 감지
+    table_header = None
+    for ln in lines:
+        stripped = ln.strip()
+        if re.match(r"^\|.*\|.*\|", stripped) and not re.match(r"^\|[\s\-:]+\|", stripped):
+            table_header = stripped
+            break
+
+    # 3. 최상위 볼드 블릿 수
+    top_bullets = [ln for ln in lines if re.match(r"^[-*]\s+\*\*", ln)]
+    n_bullets = len(top_bullets)
+
+    full_text = (text + " " + mdx_title).lower()
+
+    # ═══ semantic_role 감지 (figma_audit과 동일 — best-match + priority) ═══
+    from figma_audit import _SEMANTIC_HINT_PATTERNS, _ROLE_PRIORITY
+    candidates = []
+    for role_name, req_kws, min_req in _SEMANTIC_HINT_PATTERNS:
+        hits = sum(1 for k in req_kws if k in full_text)
+        if hits >= min_req:
+            ratio = hits / len(req_kws)
+            candidates.append((hits, ratio, role_name))
+    if candidates:
+        # 1) hits 많음 2) ratio 높음 3) priority 높음
+        candidates.sort(key=lambda x: (-x[0], -x[1], -_ROLE_PRIORITY.get(x[2], 0)))
+        result["semantic_role"] = candidates[0][2]
+    else:
+        result["semantic_role"] = None
+
+    # ═══ family / surface / columns 분기 ═══
+    # 우선순위: ### 서브섹션 > 표 > 블릿
+    if n_subs >= 2:
+        # 과정/결과 비교 → compare family
+        sub_lower = " ".join(subsection_texts).lower()
+        if any(kw in sub_lower for kw in ["과정", "결과", "process", "product"]):
+            result["family"] = "compare"
+            result["surface"] = "banner-plus-2col"
+            result["columns"] = 2
+            result["items"] = 2
+            result["confidence"] = "high"
+        elif n_subs == 2:
+            result["family"] = "compare"
+            result["surface"] = "2col-paired"
+            result["columns"] = 2
+            result["items"] = 2
+            result["confidence"] = "high"
+        else:
+            result["family"] = "list"
+            result["surface"] = "cards-3-horizontal"
+            result["columns"] = n_subs
+            result["items"] = n_subs
+            result["confidence"] = "medium"
+    elif table_header:
+        result["has_table"] = True
+        cols = [c.strip().replace("*", "").lower() for c in table_header.strip("|").split("|") if c.strip()]
+        n_cols = len(cols) - 1  # 첫 열 = label
+        col_text = " ".join(cols)
+        if any(kw in col_text for kw in ["발주자", "시공자", "설계자"]):
+            result["family"] = "cards"
+            result["surface"] = "table-persona-3col"
+            result["semantic_role"] = result.get("semantic_role") or "persona-benefits"
+            result["columns"] = 3
+            result["items"] = 3
+            result["has_cards"] = True
+            result["confidence"] = "high"
+        elif any(kw in col_text for kw in ["제조업", "건축", "토목"]) and "토목" in col_text:
+            result["family"] = "table"
+            result["surface"] = "table-3col"
+            result["columns"] = 3
+            result["items"] = 3
+            result["confidence"] = "high"
+        elif ("bim" in col_text) and ("dx" in col_text):
+            result["family"] = "compare"
+            result["surface"] = "table-multi-row"
+            result["columns"] = 2
+            result["confidence"] = "high"
+        else:
+            result["family"] = "table"
+            result["surface"] = f"table-{n_cols}col"
+            result["columns"] = n_cols
+            result["confidence"] = "medium"
+    elif n_bullets >= 1:
+        result["has_cards"] = True
+        if n_bullets == 3:
+            result["family"] = "list"
+            result["surface"] = "bullets-3"
+            result["columns"] = 3
+            result["items"] = 3
+            result["confidence"] = "high"
+        elif n_bullets == 2:
+            result["family"] = "compare"
+            result["surface"] = "bullets-2col"
+            result["columns"] = 2
+            result["items"] = 2
+            result["confidence"] = "medium"
+        elif n_bullets == 4:
+            result["family"] = "cards"
+            result["surface"] = "cards-4-grid"
+            result["columns"] = 4
+            result["items"] = 4
+            result["confidence"] = "medium"
+        else:
+            result["family"] = "list"
+            result["surface"] = "mixed-bullets"
+            result["items"] = n_bullets
+            result["confidence"] = "low"
+    else:
+        result["family"] = None
+        result["surface"] = "single-column"
+        result["confidence"] = "low"
+
+    return result
+
+
+# ═══ 구조 매칭 v3 ═══
+def structural_match_v3(mdx_schema, fig_schema):
+    """속성 교집합 기반 score (0~1). confidence high + 주요 속성 일치시에만 점수 부여.
+    Tie-breaker 용도라 가중치는 호출 측에서 낮게 적용 (0.05~0.10 권장)."""
+    if not mdx_schema or not fig_schema:
+        return 0.0, []
+    # confidence 체크: low면 신뢰 안 함
+    if mdx_schema.get("confidence") == "low":
+        return 0.0, ["mdx confidence low → 0"]
+    # 속성 5개 비교
+    aligns = []
+    scores = []
+    # family (가장 중요)
+    m_fam = mdx_schema.get("family")
+    f_fam = fig_schema.get("family")
+    if m_fam and f_fam and m_fam == f_fam:
+        scores.append(0.40)
+        aligns.append(f"family:{m_fam}✓")
+    elif m_fam and f_fam:
+        aligns.append(f"family:{m_fam}≠{f_fam}")
+    # columns / items
+    m_cols = mdx_schema.get("columns") or mdx_schema.get("items")
+    f_cols = fig_schema.get("columns") or fig_schema.get("items")
+    if m_cols and f_cols and m_cols == f_cols:
+        scores.append(0.25)
+        aligns.append(f"columns:{m_cols}✓")
+    elif m_cols and f_cols:
+        aligns.append(f"columns:{m_cols}≠{f_cols}")
+    # has_table
+    if mdx_schema.get("has_table") is not None and fig_schema.get("has_table") is not None:
+        if mdx_schema["has_table"] == fig_schema["has_table"]:
+            scores.append(0.10)
+            aligns.append(f"has_table:{mdx_schema['has_table']}✓")
+    # has_cards
+    if mdx_schema.get("has_cards") is not None and fig_schema.get("has_cards") is not None:
+        if mdx_schema["has_cards"] == fig_schema["has_cards"]:
+            scores.append(0.10)
+            aligns.append(f"has_cards:{mdx_schema['has_cards']}✓")
+    # semantic_role (bonus)
+    m_role = mdx_schema.get("semantic_role")
+    f_role = fig_schema.get("semantic_role")
+    if m_role and f_role and m_role == f_role and m_role != "(검수 필요)":
+        scores.append(0.15)
+        aligns.append(f"role:{m_role}✓")
+    total = sum(scores)
+    return total, aligns