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 18~22 공통 모듈
+- analysis.md 로드
+- 동의어 사전 로드 + normalize
+- 키워드 추출 / df 분류
+- 점수 계산 (옵션별 축 포함/제외)
+"""
+import re
+import math
+import collections
+import json
+from pathlib import Path
+import yaml
+
+from methods import _get_kiwi, _extract_content_tokens, _detect_mdx_layout, _get_cross_encoder
+
+ROOT = Path(r"d:\ad-hoc\kei\design_agent")
+BLOCKS_DIR = ROOT / "figma_to_html_agent" / "blocks"
+PREVIEW_DIR = ROOT / "data" / "figma_previews"
+
+
+# ═══ 4개 테스트 유닛 ═══
+TARGET_UNITS = [
+    ("MDX01-2-details", "1. (MDX 1) 팝업 — DX와 BIM의 구분",         "18", "01.mdx", "2. 용어간 상호관계", None),
+    ("MDX02-2.2-table", "2. (MDX 2) 2.2 DX 시행 주체별 기대효과",       "14", "02.mdx", "2. DX 기반 Process 혁신에 따른 주체별 기대효과", "2.2 DX 시행 주체별 기대효과"),
+    ("MDX03-1",          "3. (MDX 03) 1. DX 시행을 위한 필수요건",       "13", "03.mdx", "1. DX 시행을 위한 필수 요건", None),
+    ("MDX03-2",          "4. (MDX 03) 2. Process 혁신과 Product 변화",   "29", "03.mdx", "2. Process의 혁신과 Product의 변화", None),
+]
+
+
+def load_synonyms():
+    p = Path(__file__).parent / "synonyms.yaml"
+    with open(p, encoding="utf-8") as f:
+        return yaml.safe_load(f)["synonyms"]
+
+
+def normalize_with_synonyms(text, synonyms):
+    """variants를 canonical 표기로 치환. 긴 variant 먼저."""
+    replacements = []
+    for canonical, variants in synonyms.items():
+        for v in variants:
+            replacements.append((len(v), v, canonical))
+    replacements.sort(reverse=True)  # 긴 것부터
+    for _, variant, canonical in replacements:
+        text = text.replace(variant, canonical)
+    return text
+
+
+def load_keyword_base():
+    """keyword_base.yaml 로드. normalize=false 는 MDX 치환 대상에서 제외.
+    반환: {canonical: [variants]} - synonyms.yaml 구조와 호환."""
+    p = Path(__file__).parent / "keyword_base.yaml"
+    if not p.exists():
+        return {}
+    with open(p, encoding="utf-8") as f:
+        data = yaml.safe_load(f)
+    out = {}
+    for canonical, entry in data.get('keywords', {}).items():
+        if entry.get('normalize', True) is False:
+            continue   # anchor-only, MDX 치환 비활성
+        variants = entry.get('variants', [])
+        if variants:
+            out[canonical] = variants
+        # variants 없으면 치환할 게 없으니 dict 에 추가 안 함
+    return out
+
+
+def normalize_with_keyword_base(text, kb):
+    """keyword_base 로 MDX 정규화. synonyms 와 동일 로직."""
+    return normalize_with_synonyms(text, kb)
+
+
+def parse_analysis_md(path):
+    """analysis.md 파싱. Legacy ('구조', '내용') + Milestone 2 mirror ('구조 매칭 정보', '내용 설명') 둘 다 지원."""
+    text = path.read_text(encoding="utf-8")
+    result = {"keywords": [], "layout": "", "detail": "", "content": ""}
+    sections = re.split(r"\n## ", text)
+    for sec in sections[1:]:
+        lines = sec.split("\n", 1)
+        heading = lines[0].strip()
+        body = lines[1].strip() if len(lines) > 1 else ""
+        if heading in ("구조", "구조 매칭 정보"):
+            for ln in body.split("\n"):
+                m = re.match(r"- \*\*layout\*\*:\s*(.+)", ln)
+                if m: result["layout"] = m.group(1).strip()
+                m = re.match(r"- \*\*detail\*\*:\s*(.+)", ln)
+                if m: result["detail"] = m.group(1).strip()
+        elif heading in ("내용", "내용 설명"):
+            result["content"] = body.split("\n\n")[0].strip()
+        elif heading == "후보 키워드":
+            raw = body.split("\n\n")[0]
+            result["keywords"] = [k.strip() for k in raw.split(",") if k.strip()]
+    return result
+
+
+def load_32_frames():
+    frames = {}
+    for d in sorted(BLOCKS_DIR.iterdir()):
+        if not d.is_dir(): continue
+        fid = d.name
+        if not fid.startswith("1171"): continue
+        a = d / "analysis.md"
+        if a.exists():
+            frames[fid] = parse_analysis_md(a)
+    return frames
+
+
+def compute_df_idf_tier(frames):
+    df = collections.Counter()
+    N = len(frames)
+    for v in frames.values():
+        for kw in set(v["keywords"]):
+            df[kw] += 1
+    tier = {}
+    for kw, cnt in df.items():
+        ratio = cnt / N
+        if ratio <= 0.10: tier[kw] = "core"
+        elif ratio >= 0.30: tier[kw] = "general"
+        else: tier[kw] = "mid"
+    idf = {kw: math.log(N / cnt) if cnt > 0 else 0 for kw, cnt in df.items()}
+    return df, idf, tier, N
+
+
+def extract_mdx_keywords(mdx_text, vocabulary, synonyms=None, keyword_base=None):
+    """MDX 텍스트 → 정규화 → direct canonical hit + Kiwi token hit → union.
+
+    보강 이유: compound canonical (설계Data, 공사비절감, 3D모델 등) 은
+    Kiwi 가 분해하여 원형이 tokens 에 없음. vocabulary 에서 직접 substring 매칭
+    으로 별도 검사 → Kiwi hit 와 union.
+
+    Args:
+        mdx_text: 원본 MDX 텍스트
+        vocabulary: Figma frame 의 canonical keyword set (비교 대상)
+        synonyms: legacy synonyms.yaml 사전 (하위호환)
+        keyword_base: keyword_base.yaml 로드 결과 (우선 적용)
+    """
+    if keyword_base is not None:
+        mdx_text = normalize_with_keyword_base(mdx_text, keyword_base)
+    elif synonyms is not None:
+        mdx_text = normalize_with_synonyms(mdx_text, synonyms)
+
+    # ① direct canonical hit (compound 보호)
+    direct = {t for t in vocabulary if len(t) >= 2 and t in mdx_text}
+
+    # ② Kiwi token hit
+    kiwi = _get_kiwi()
+    tokens = _extract_content_tokens(mdx_text, kiwi)
+    kiwi_hit = set(tokens) & vocabulary
+
+    return direct | kiwi_hit
+
+
+def keyword_score(mdx_kws, fig_kws, idf, tier):
+    """IDF × tier 가중 Jaccard"""
+    tier_weight = {"core": 1.0, "mid": 0.5, "general": 0.1}
+    inter = mdx_kws & fig_kws
+    union = mdx_kws | fig_kws
+    num = sum(idf.get(c, 0.5) * tier_weight.get(tier.get(c, "mid"), 0.5) for c in inter)
+    den = sum(idf.get(c, 0.5) * tier_weight.get(tier.get(c, "mid"), 0.5) for c in union)
+    return num / den if den > 0 else 0, inter
+
+
+def content_scores_batch(mdx_content, frames):
+    """Cross-encoder 일괄 채점 → {fid: [0,1]}"""
+    model = _get_cross_encoder()
+    fids = list(frames.keys())
+    pairs = [[mdx_content, frames[fid]["content"]] for fid in fids]
+    raw = model.predict(pairs, show_progress_bar=False)
+    return {fids[i]: 1 / (1 + math.exp(-float(raw[i]))) for i in range(len(fids))}
+
+
+# ═══ 구조 매칭 v3 — 우선순위 수정 + 호환도 점수(graded) ═══
+def detect_mdx_layout_v2(text):
+    """MDX 본문 정밀 구조 감지.
+    우선순위: ### 서브섹션 > 표 > 블릿 (이전엔 표 먼저였음)"""
+    lines = text.split("\n")
+
+    # 1. ### 서브섹션 우선 (### X.Y 패턴)
+    subs = [ln for ln in lines if re.match(r"^###\s+\d+\.\d+", ln)]
+    if len(subs) == 2:
+        sub_text = " ".join(subs).lower()
+        if any(kw in sub_text for kw in ["과정", "결과", "process", "product", "as-is", "to-be"]):
+            return "compare-2banner"
+        return "compare-2col"
+    if len(subs) >= 3:
+        return "multi-section"
+
+    # 2. 표 감지 (서브섹션 없을 때)
+    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
+    if table_header:
+        cols = [c.strip().replace("*", "").lower() for c in table_header.strip("|").split("|") if c.strip()]
+        col_text = " ".join(cols)
+        if any(kw in col_text for kw in ["발주자", "시공자", "설계자"]):
+            return "persona-3col"
+        if any(kw in col_text for kw in ["제조업", "건축", "토목"]) and "토목" in col_text:
+            return "table-3col"
+        if any(kw in col_text for kw in ["bim", "dx"]) and ("bim" in col_text and "dx" in col_text):
+            return "compare-rows"
+        n_cols = len(cols) - 1
+        if n_cols == 2: return "table-2col"
+        if n_cols >= 3: return "table-3col"
+        return "compare-rows"
+
+    # 3. 최상위 볼드 블릿
+    top_bullets = [ln for ln in lines if re.match(r"^[-*]\s+\*\*", ln)]
+    n = len(top_bullets)
+    if n == 3: return "3col-parallel"
+    if n == 2: return "compare-2col"
+    if n == 4: return "cards-4"
+    if n >= 5: return "multi-parallel"
+    return "single-column"
+
+
+# ═══ 호환도 매트릭스 (compatibility) ═══
+# MDX 구조 × Figma layout = 0.0~1.0
+# "이 MDX 내용을 이 Figma 레이아웃으로 렌더링 시 얼마나 적합한가"
+_COMPAT = {
+    # 2-column 계열 (서로 호환성 높음)
+    "compare-2col": {
+        "compare-2col": 1.0, "compare-2banner-top-2col-bottom": 0.9,
+        "table-2col": 0.9, "2col-paired": 0.85, "2-boxes": 0.75,
+        "central-split": 0.7, "paired-rows": 0.6,
+        "compare-rows": 0.55, "table-3col": 0.3, "persona-3col": 0.3,
+        "3col-parallel": 0.3, "single-column": 0.5,
+    },
+    "compare-2banner": {
+        "compare-2banner-top-2col-bottom": 1.0, "compare-2col": 0.9,
+        "table-2col": 0.85, "2col-paired": 0.8, "2-boxes": 0.7,
+        "paired-rows": 0.7, "central-split": 0.7,
+        "compare-rows": 0.5, "table-3col": 0.3, "persona-3col": 0.3,
+        "3col-parallel": 0.3, "single-column": 0.5,
+    },
+    "table-2col": {
+        "table-2col": 1.0, "compare-2col": 0.9,
+        "compare-2banner-top-2col-bottom": 0.85,
+        "compare-rows": 0.75, "2col-paired": 0.75,
+        "table-3col": 0.5, "persona-3col": 0.4, "3col-parallel": 0.3,
+        "single-column": 0.5,
+    },
+
+    # 3-column 계열
+    "persona-3col": {
+        "persona-3col": 1.0, "3col-parallel": 0.75,
+        "3col-cards": 0.75, "table-3col": 0.7, "3col-compare": 0.65,
+        "cards-4plus5": 0.4, "compare-rows": 0.35,
+        "compare-2col": 0.3, "table-2col": 0.4, "single-column": 0.5,
+    },
+    "3col-parallel": {
+        "3col-parallel": 1.0, "3col-cards": 0.9, "3col-compare": 0.9,
+        "persona-3col": 0.75, "table-3col": 0.6,
+        "3-emphasis": 0.6, "3-category": 0.65, "3-section": 0.65,
+        "cards-4": 0.5, "cards-4plus5": 0.4,
+        "compare-2col": 0.3, "single-column": 0.5,
+    },
+    "table-3col": {
+        "table-3col": 1.0, "3col-parallel": 0.65,
+        "3col-cards": 0.65, "persona-3col": 0.7,
+        "compare-rows": 0.7, "3col-compare": 0.75,
+        "table-2col": 0.5, "single-column": 0.45,
+    },
+    "compare-rows": {
+        "compare-rows": 1.0, "table-2col": 0.75,
+        "table-3col": 0.7, "paired-rows": 0.75,
+        "compare-2col": 0.55, "compare-2banner-top-2col-bottom": 0.5,
+        "2col-paired": 0.5, "single-column": 0.45,
+    },
+
+    # 4+ / 다중
+    "cards-4": {
+        "cards-4": 1.0, "cards-4plus5": 0.9,
+        "policy-4card-plus-list": 0.85, "quadrant-issues": 0.85,
+        "3col-parallel": 0.5, "table-3col": 0.4, "single-column": 0.4,
+    },
+    "multi-parallel": {
+        "cards-4": 0.85, "cards-4plus5": 0.9,
+        "policy-4card-plus-list": 0.8, "quadrant-issues": 0.75,
+        "3col-parallel": 0.55, "persona-3col": 0.45,
+        "table-3col": 0.4, "single-column": 0.4,
+    },
+    "multi-section": {
+        "3-section": 1.0, "3-category": 0.9, "3-emphasis": 0.85,
+        "3col-parallel": 0.6, "cards-4": 0.5, "single-column": 0.5,
+    },
+
+    # 단일
+    "single-column": {
+        "bullet-cards": 0.85, "list-numbered": 0.85, "list-stacked": 0.85,
+        "side-card": 0.75, "split-panel-diagram": 0.65,
+        "split-panel-numbered": 0.65, "central-split": 0.55,
+        "diagram-labels": 0.55, "diagram-5": 0.55, "central-5-goals": 0.5,
+        "circular-nodes": 0.5, "cycle-3way": 0.5,
+        "intro": 0.6, "definition-list": 0.7,
+        "compare-2col": 0.5, "compare-rows": 0.5, "3col-parallel": 0.5,
+    },
+}
+
+
+def structural_match_v2(mdx_layout, fig_layout):
+    """구조 호환도 매칭 (0~1 graded). 정확일치=1.0, 유사=0.5~0.9, 무관=0.1~0.3."""
+    if not mdx_layout or not fig_layout: return 0.0
+    if mdx_layout == fig_layout: return 1.0
+    row = _COMPAT.get(mdx_layout, {})
+    return row.get(fig_layout, 0.15)  # 명시 안 됐으면 매우 약한 기본 호환도
+
+
+# ═══ 유닛 로드 ═══
+def load_target_units():
+    """4개 타겟 유닛의 MDX 본문 + 제목 hierarchy 반환"""
+    from extract_units import extract_units
+    from phase10 import extract_titles_only_mdx
+    units_full = extract_units()
+    units_title = {}
+    for uid, _, _, fname, mid, sub in TARGET_UNITS:
+        units_title[uid] = extract_titles_only_mdx(fname, mid, sub)
+    return units_full, units_title
+
+
+def load_frame_index():
+    with open(PREVIEW_DIR / "index.json", encoding="utf-8") as f:
+        idx_data = json.load(f)
+    frame_to_short = {info["frame_id"]: sid for sid, info in idx_data.items()}
+    return idx_data, frame_to_short