#!/usr/bin/env python3 """deconstruct-book Skill：终态样张导出（零 LLM，纯库读渲染 markdown）。 跑序末环「export 样张呈报」的固化实现（批9 收尾落地）： - patterns：五书范式卡样张——每书每型抽实例数最多的代表卡（实例多=跨章生长充分， 在 review 全量审核未跑时作为质量代理指标），combat 型加倍抽样给创始人拍板 「题材绑定：打标签 vs 改写泛化」提供判断材料。 - upgrade：单书升格实体卡样张——按出场章数取头部实体（主角/核心配角自然浮上）， 重点渲染追加类字段的 [窗N] 生长轨迹与别名归并，这是升格管线的核心卖点。 用法： export-patterns [--top 1] [--combat-top 2] [--out 路径] export-upgrade --work-id N [--top 8] [--out 路径] """ import json import sys import click import psycopg sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[2] / "call-content-model" / "scripts")) # DSN 的真实来源是同目录的 parse_llm（升格执行器拆分后不再经 upgrade 转导）。 from parse_llm import DSN # noqa: E402 def render_val(v, limit=6): """字段值渲染：list 逐条列出（追加类字段的窗号轨迹），超长截断标注省略数。""" if isinstance(v, list): shown = v[:limit] tail = f"\n - …（另 {len(v) - limit} 条略）" if len(v) > limit else "" return "".join(f"\n - {x}" for x in shown) + tail return str(v) @click.group() def cli(): pass @cli.command("export-patterns") @click.option("--top", type=int, default=1, show_default=True, help="每书每型抽几张代表卡") @click.option("--combat-top", type=int, default=2, show_default=True, help="combat 型抽样数（拍板材料加倍）") @click.option("--out", default="", help="输出文件（默认打印到 stdout）") def export_patterns(top, combat_top, out): """五书范式卡终态样张：统计面貌 + 每型实例数 top 代表卡。""" lines = [] with psycopg.connect(DSN) as conn: works = conn.execute( """SELECT DISTINCT draft_payload->'出处'->>'书名' FROM muse_knowledge_draft WHERE source_type='parse_book' AND deleted=FALSE ORDER BY 1""").fetchall() # 全局统计头：五书×五型分布，给创始人 30 秒看清全库面貌 lines.append("# 范式卡终态样张（批9 五书收官）\n") lines.append("> 抽样规则：每书每型取**实例数最多**的代表卡（实例多=跨章复现充分）；" "combat 型加倍抽样，供「题材绑定：打标签 vs 改写泛化」拍板。\n>") lines.append("> 口径说明：卡按「出处.书名」归书统计；跨书判重归并后实例可能来自多本书" "（带书名标注的实例即是）——这是设计特性：同一范式跨书复现，实例累积是迁移性证据。\n") lines.append("## 全库面貌\n") lines.append("| 书 | 总卡数 | " + " | ".join(["craft", "trope", "scene_pattern", "emotion", "combat"]) + " | 多实例卡 |") lines.append("|---|---|---|---|---|---|---|---|") for (title,) in works: st = dict(conn.execute( """SELECT draft_payload->>'型', count(*) FROM muse_knowledge_draft WHERE source_type='parse_book' AND deleted=FALSE AND draft_payload->'出处'->>'书名'=%s GROUP BY 1""", (title,)).fetchall()) multi = conn.execute( """SELECT count(*) FROM muse_knowledge_draft WHERE source_type='parse_book' AND deleted=FALSE AND draft_payload->'出处'->>'书名'=%s AND jsonb_array_length(draft_payload->'实例') > 1""", (title,)).fetchone()[0] total = sum(st.values()) lines.append(f"| {title} | {total} | " + " | ".join( str(st.get(t, 0)) for t in ["craft", "trope", "scene_pattern", "emotion", "combat"]) + f" | {multi} |") # 逐书逐型代表卡 for (title,) in works: lines.append(f"\n## 《{title}》\n") for ttype in ["craft", "trope", "scene_pattern", "emotion", "combat"]: k = combat_top if ttype == "combat" else top rows = conn.execute( """SELECT draft_payload FROM muse_knowledge_draft WHERE source_type='parse_book' AND deleted=FALSE AND draft_payload->'出处'->>'书名'=%s AND draft_payload->>'型'=%s ORDER BY jsonb_array_length(draft_payload->'实例') DESC LIMIT %s""", (title, ttype, k)).fetchall() for (p,) in rows: inst = p.get("实例") or [] lines.append(f"### {p.get('名称')}（{ttype}·实例 {len(inst)} 条）\n") lines.append(f"**摘要**：{p.get('一句话摘要', '')}\n") for fk, fv in (p.get("字段") or {}).items(): lines.append(f"- **{fk}**：{render_val(fv, limit=4)}") for i in inst[:4]: # 实例渲染成可读行：dict 形态（章/定位/书）拼成「第N章·定位」，跨书实例标书名 if isinstance(i, dict): bk = f"《{i['书']}》" if i.get("书") else "" lines.append(f"- 实例：{bk}第{i.get('章', '?')}章·{i.get('定位', '')}") else: lines.append(f"- 实例：{i}") if len(inst) > 4: lines.append(f"- 实例：…（另 {len(inst) - 4} 条略）") lines.append("") _emit(lines, out) @cli.command("export-upgrade") @click.option("--work-id", type=int, required=True) @click.option("--top", type=int, default=8, show_default=True, help="按出场章数取头部实体数") @click.option("--out", default="", help="输出文件（默认打印到 stdout）") def export_upgrade(work_id, top, out): """单书升格实体卡终态样张：统计面貌 + 头部实体全字段生长轨迹 + 各型代表。""" lines = [] with psycopg.connect(DSN) as conn: title = conn.execute("SELECT title FROM muse_content_work WHERE id=%s", (work_id,)).fetchone()[0] cards = conn.execute( """SELECT d.id, d.draft_payload FROM muse_knowledge_draft d JOIN example_upgrade_card_state cs ON cs.draft_id=d.id WHERE d.work_id=%s AND d.deleted=FALSE""", (work_id,)).fetchall() st = {} for _, p in cards: st[p.get("type", "?")] = st.get(p.get("type", "?"), 0) + 1 n_alias = conn.execute( "SELECT count(*) FROM example_upgrade_alias WHERE work_id=%s AND deleted=FALSE", (work_id,)).fetchone()[0] lines.append(f"# 《{title}》升格实体卡终态样张（{len(cards)} 张）\n") lines.append(f"> 型分布：{st}｜别名归并 {n_alias} 条。") lines.append("> 字段三类演进：底色覆写（保终态+审计）/ 演进追加（带 [窗N] 时间线）/ 别名累积。\n") # 头部实体：出场章数排序（主角/核心配角自然浮上），展示完整生长轨迹 ranked = sorted(cards, key=lambda r: -len(r[1].get("出场章") or [])) picked = ranked[:top] # 各型代表补位：头部多为 character，其他型（faction/item/setting…）各补 1 张最厚的 seen_ids = {r[0] for r in picked} for ttype in sorted({p.get("type") for _, p in cards} - {"character", None}): cand = [r for r in ranked if r[1].get("type") == ttype and r[0] not in seen_ids] if cand: picked.append(cand[0]) seen_ids.add(cand[0][0]) for did, p in picked: app = p.get("出场章") or [] als = [a for (a,) in conn.execute( """SELECT alias FROM example_upgrade_alias WHERE work_id=%s AND canonical_name=%s AND deleted=FALSE""", (work_id, p.get("名称"))).fetchall()] span = f"{min(app)}–{max(app)} 章共 {len(app)} 次" if app else "无" lines.append(f"## {p.get('名称')}（{p.get('type')}·出场 {span}）\n") if als: lines.append(f"**别名**：{', '.join(als)}\n") lines.append(f"**摘要**：{p.get('一句话摘要', '')}\n") for fk, fv in (p.get("字段") or {}).items(): lines.append(f"- **{fk}**：{render_val(fv, limit=8)}") lines.append("") _emit(lines, out) def _emit(lines, out): """输出落盘或打印；落盘时报字数便于评估呈报体量。""" text = "\n".join(lines) if out: with open(out, "w", encoding="utf-8") as f: f.write(text) click.echo(f"已导出 {out}（{len(text)} 字）") else: click.echo(text) if __name__ == "__main__": try: cli() except psycopg.Error as e: click.echo(f"[错误] {type(e).__name__}: {e}", err=True) sys.exit(1)