feat(digest): Phase 4 Global News Digest (cluster-level batch summarization)

7일 rolling window 뉴스를 country × topic 2-level로 묶어 매일 04:00 KST 배치 생성.
search 파이프라인 미사용. documents → clustering → cluster-level LLM summarization → digest.

핵심 결정:
- adaptive threshold (0.75/0.78/0.80) + EMA centroid (α=0.7) + time-decay (λ=ln(2)/3)
- min_articles=3, max_topics=10/country, top-5 MMR diversity, ai_summary[:300] truncate
- cluster-level LLM only, drop금지 fallback (topic_label="주요 뉴스 묶음" + top member ai_summary[:200])
- importance_score country별 0~1 normalize + raw_weight_sum 별도 보존, max(score, 0.01) floor
- per-call timeout 25s + pipeline hard cap 600s
- DELETE+INSERT idempotent (UNIQUE digest_date), AIClient._call_chat 직접 호출 (client.py 수정 없음)

신규:
- migrations/101_global_digests.sql (2테이블 정규화)
- app/models/digest.py (GlobalDigest + DigestTopic ORM)
- app/services/digest/{loader,clustering,selection,summarizer,pipeline}.py
- app/workers/digest_worker.py (PIPELINE_HARD_CAP + CLI 진입점)
- app/api/digest.py (/latest, ?date|country, /regenerate, inline Pydantic)
- app/prompts/digest_topic.txt (JSON-only + 절대 금지 블록)

main.py 4줄: import 2 + scheduler add_job 1 + include_router 1.
plan: ~/.claude/plans/quiet-herding-tome.md

app/services/digest/selection.py

@@ -0,0 +1,62 @@
+"""Cluster 내 LLM 입력 선정 — top-k + MMR diversity + ai_summary truncate.
+
+순수 top-relevance 는 동일 사건 중복 요약문에 편향되므로 MMR 로 다양성 확보.
+ai_summary 길이는 LLM 토큰 보호를 위해 SUMMARY_TRUNCATE 로 제한.
+"""
+
+import numpy as np
+
+K_PER_CLUSTER = 5
+LAMBDA_MMR = 0.7         # relevance 70% / diversity 30%
+SUMMARY_TRUNCATE = 300   # long tail ai_summary 방어
+
+
+def _normalize(v: np.ndarray) -> np.ndarray:
+    norm = float(np.linalg.norm(v))
+    if norm == 0.0:
+        return v
+    return v / norm
+
+
+def select_for_llm(cluster: dict, k: int = K_PER_CLUSTER) -> list[dict]:
+    """cluster 내 LLM 호출용 대표 article 들 선정.
+
+    Args:
+        cluster: clustering.cluster_country 결과 단일 cluster
+        k: 선정 개수 (기본 5)
+
+    Returns:
+        선정된 doc dict 리스트. 각 항목에 ai_summary_truncated 필드가 추가됨.
+    """
+    members = cluster["members"]
+    if len(members) <= k:
+        selected = list(members)
+    else:
+        centroid = cluster["centroid"]
+        # relevance = centroid 유사도 × decay weight
+        for m in members:
+            v = _normalize(m["embedding"])
+            m["_rel"] = float(np.dot(centroid, v)) * m["weight"]
+
+        first = max(members, key=lambda x: x["_rel"])
+        selected = [first]
+        candidates = [m for m in members if m is not first]
+
+        while len(selected) < k and candidates:
+            def mmr_score(c: dict) -> float:
+                v = _normalize(c["embedding"])
+                max_sim = max(
+                    float(np.dot(v, _normalize(s["embedding"])))
+                    for s in selected
+                )
+                return LAMBDA_MMR * c["_rel"] - (1.0 - LAMBDA_MMR) * max_sim
+
+            pick = max(candidates, key=mmr_score)
+            selected.append(pick)
+            candidates.remove(pick)
+
+    # LLM 입력 토큰 보호
+    for m in selected:
+        m["ai_summary_truncated"] = (m.get("ai_summary") or "")[:SUMMARY_TRUNCATE]
+
+    return selected