199 lines
10 KiB
Python
199 lines
10 KiB
Python
r"""合规层种子数据：7 个零容忍负面词（+4 变体）与 6 类固定话术。
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背景：`docs/02` §10.2/§10.3 要求这些数据，但迁移只建表、没有 seed。而
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`app/service/agent/governance.py` L62-65 的取数条件是
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`status='active' AND reviewer_id IS NOT NULL AND reviewed_at IS NOT NULL`，
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两张表 0 行就意味着一**条规则都查不到**：客服 Agent 说"这只基金稳赚"不会被拦，
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免责声明也不会出现在任何回复里。合规过滤是静默失效的——不报错，只是不生效。
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取值来源（不臆造，均以真实 DDL 为准）：
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- `agent_negative_word.match_type` CHECK：`exact` / `contains` / `regex`，
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这里只用 `contains`；`regex` 被 governance.py L75-77 显式拒绝
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（Python `re` 无执行超时，底座不跑未经约束的管理员正则）。
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- `agent_negative_word.severity` CHECK：`warn` / `regenerate` / `block`，
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零容忍词一律 `block`。
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- `agent_negative_word.category` **无 CHECK 约束**，按语义取值（《理财销售办法》
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的口径：误导性 / 绝对化 / 承诺 / 收益）。
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- `agent_reply_template.scene` CHECK：`disclaimer` / `low_confidence` /
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`compliance_block` / `transfer` / `model_failure` / `system_busy` / `clarification`。
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- `agent_reply_template.active_key` 是 **生成列**（`status='active'` 时等于
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`template_code:locale`，否则 NULL）：不能写、也不该写，由库自己算。
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它上面有唯一键 `uk_reply_template_active_one`，所以同一 template_code 只能有一条 active。
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幂等：按 `rule_code`（唯一键 `uk_negative_rule_code`）与
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`template_code`+`version`（唯一键 `uk_reply_template_version`）upsert，可重复执行；
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重复执行不会新增行，只会把已存在的行重新置为 active 并刷新审核信息。
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执行：`.\.venv\Scripts\python.exe tools\seed_compliance_baseline.py`（可重复执行）
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"""
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from __future__ import annotations
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import asyncio
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import sys
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from datetime import UTC, datetime
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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if str(ROOT) not in sys.path:
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sys.path.insert(0, str(ROOT))
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from sqlalchemy import text # noqa: E402
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from app.infrastructure.db import SessionFactory # noqa: E402
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#: 审核人与创建人：`sys_user.id=9003`（T-ADMIN，由 tools/seed_test_rbac.py 种入）。
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#: `created_by`/`reviewer_id` 都有指向 `sys_user` 的外键，写不存在的用户会直接失败。
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ADMIN_ID = 9003
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#: 只对客服 Agent 生效，避免影响其他 Agent 的表达自由度。
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APPLICABLE_AGENTS = '["customer_service"]'
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#: 命中后统一的合规拒答话术（就是本脚本要种的 6 条之一，所以话术先插）。
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SAFE_REPLY_TEMPLATE_CODE = "TPL_COMPLIANCE_REFUSAL"
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#: `docs/02` §10.2 逐字要求的 7 个零容忍词。前 7 条规则必须恰好覆盖它们。
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REQUIRED_ZERO_TOLERANCE_WORDS = (
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"保本", "稳赚", "无风险", "保证收益", "预期收益率", "年化收益率", "安全",
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)
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#: (rule_code, word_pattern, category, reason)
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#: 前 7 条是 `docs/02` §10.2 逐字要求的零容忍词，一个都不能少；
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#: 后 4 条是专项设计 §6.5 评审建议的变体（换个说法绕过关键词的常见写法）。
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NEGATIVE_RULES: tuple[tuple[str, str, str, str], ...] = (
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("NEG-001", "保本", "guarantee", "资管新规后非保本"),
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("NEG-002", "稳赚", "misleading", "误导性表述"),
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("NEG-003", "无风险", "absolute", "绝对化表述"),
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("NEG-004", "保证收益", "guarantee", "禁止刚兑承诺"),
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("NEG-005", "预期收益率", "yield_claim", "《理财销售办法》明文禁止"),
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("NEG-006", "年化收益率", "yield_claim", "监管处罚点名措辞"),
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("NEG-007", "安全", "absolute", "绝对化表述"),
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("NEG-008", "零风险", "absolute", "绝对化表述变体"),
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("NEG-009", "稳赚不赔", "misleading", "误导性表述变体"),
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("NEG-010", "躺着赚", "misleading", "误导性表述变体"),
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("NEG-011", "坐享收益", "misleading", "误导性表述变体"),
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)
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#: (template_code, scene, title, content_text)
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#: scene 取自 `chk_template_scene` 的 7 个合法值里的 6 个，覆盖 6 类固定话术。
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#: TPL_AI_NOTICE 用 `clarification`（AI 生成标识属主动澄清披露）；
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#: TPL_COMPLIANCE_REFUSAL 用 `compliance_block`（合规拦截后替换的安全话术）。
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#:
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#: ⚠️ **话术文本本身不得命中 NEGATIVE_RULES 里的任何词**（`verify()` 会强制校验）。
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#: 治理层（governance.py L127-128）对 `contains` 是**朴素子串匹配**，没有否定式豁免：
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#: 旧文案里的「非保本」会命中「保本」规则，形成"合规话术被自己的规则拦截"的自绊，
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#: Task 2 接入话术后就会变成"替换文本又被过滤"的循环。因此合规语义必须用
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#: **不含禁用字面**的说法表达（"不承诺本金不受损失" 而不是 "非保本"）。
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REPLY_TEMPLATES: tuple[tuple[str, str, str, str], ...] = (
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("TPL_COMPLIANCE_REFUSAL", "compliance_block", "合规拒答",
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"根据监管要求，我不能对收益做出任何承诺，也不承诺本金不受损失。"
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"本产品收益可能为负，请以产品说明书为准。"),
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("TPL_DISCLAIMER", "disclaimer", "固定免责声明",
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"本内容仅为投资分析参考，不构成任何直接投资建议，不构成对任何产品的收益承诺，"
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"据此操作风险自负，请谨慎对待。"),
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("TPL_AI_NOTICE", "clarification", "AI 生成标识",
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"本回答由 AI 生成，仅供参考。"),
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("TPL_LOW_CONFIDENCE", "low_confidence", "低置信兜底",
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"抱歉，我暂时无法准确回答您的问题，建议您转接人工客服获取更准确的帮助。"),
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("TPL_TRANSFER_HUMAN", "transfer", "转人工提示",
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"已为您转接人工客服，工作时间为工作日 09:00-18:00，客服电话 15936583816。"),
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("TPL_SYSTEM_BUSY", "system_busy", "系统繁忙/模型故障",
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"系统繁忙，暂时无法回答，请稍后重试或联系人工客服 15936583816。"),
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)
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#: template_code -> content_text，供 `verify()` 做自绊检查（须定义在 REPLY_TEMPLATES 之后）。
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CONTENT_BY_TEMPLATE_CODE = {code: content for code, _scene, _title, content in REPLY_TEMPLATES}
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INSERT_NEGATIVE_RULE = text(
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"INSERT INTO agent_negative_word"
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" (rule_code, word_pattern, match_type, category, severity, applicable_agents,"
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" safe_reply_template_code, status, version, created_by, reviewer_id, reviewed_at,"
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" created_at, updated_at)"
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" VALUES (:rule_code, :word, 'contains', :category, 'block', :agents,"
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" :safe_code, 'active', 1, :admin, :admin, :now, :now, :now)"
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" ON DUPLICATE KEY UPDATE word_pattern=:word, match_type='contains', category=:category,"
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" severity='block', applicable_agents=:agents, safe_reply_template_code=:safe_code,"
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" status='active', reviewer_id=:admin, reviewed_at=:now, updated_at=:now"
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)
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INSERT_REPLY_TEMPLATE = text(
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"INSERT INTO agent_reply_template"
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" (template_code, scene, title, content_text, variables, locale, version, status,"
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" created_by, reviewer_id, reviewed_at, created_at, updated_at)"
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" VALUES (:code, :scene, :title, :content, '{}', 'zh-CN', 1, 'active',"
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" :admin, :admin, :now, :now, :now)"
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" ON DUPLICATE KEY UPDATE scene=:scene, title=:title, content_text=:content,"
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" status='active', reviewer_id=:admin, reviewed_at=:now, updated_at=:now"
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)
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async def seed() -> None:
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"""幂等落库。话术先于负面词插入：负面词会引用话术 code。"""
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now = datetime.now(UTC).replace(tzinfo=None)
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async with SessionFactory() as session, session.begin():
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for code, scene, title, content in REPLY_TEMPLATES:
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await session.execute(INSERT_REPLY_TEMPLATE, {
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"code": code, "scene": scene, "title": title, "content": content,
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"admin": ADMIN_ID, "now": now,
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})
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for rule_code, word, category, _reason in NEGATIVE_RULES:
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await session.execute(INSERT_NEGATIVE_RULE, {
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"rule_code": rule_code, "word": word, "category": category,
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"agents": APPLICABLE_AGENTS, "safe_code": SAFE_REPLY_TEMPLATE_CODE,
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"admin": ADMIN_ID, "now": now,
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})
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async def verify() -> None:
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"""按 governance.py 的**同一口径**复查，确认真的能被查到。"""
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async with SessionFactory() as session:
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rules = (await session.execute(text(
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"SELECT rule_code, word_pattern, category, severity, match_type"
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" FROM agent_negative_word"
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" WHERE status='active' AND reviewer_id IS NOT NULL AND reviewed_at IS NOT NULL"
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" ORDER BY rule_code"
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))).all()
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templates = (await session.execute(text(
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"SELECT template_code, scene, status FROM agent_reply_template"
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" WHERE status='active' AND reviewer_id IS NOT NULL AND reviewed_at IS NOT NULL"
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" ORDER BY template_code"
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))).all()
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for code, word, category, severity, match_type in rules:
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print(f" rule {code} {word!r} category={category} severity={severity}"
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f" match_type={match_type}")
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for code, scene, status in templates:
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print(f" tpl {code} scene={scene} status={status}")
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# 种子层的自绊检查：话术不得命中自己这套规则。治理层是朴素子串匹配，
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# 没有否定式豁免——「非保本」这类写法会命中「保本」规则。
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# 在种子阶段就拦住，比等 Task 2 接入话术后再发现在替换结果里循环排查便宜得多。
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self_trips = [
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(code, word)
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for code, content in CONTENT_BY_TEMPLATE_CODE.items()
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for word in (word for _, word, *_ in rules)
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if word in content
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]
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if self_trips:
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raise SystemExit(f"话术命中了自己的禁用词（自绊）：{self_trips}")
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patterns = {word for _, word, *_ in rules}
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missing = {
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word for word in REQUIRED_ZERO_TOLERANCE_WORDS
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if not any(word in pattern for pattern in patterns)
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}
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if missing:
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raise SystemExit(f"零容忍词未生效：{sorted(missing)}")
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scene_count = len({scene for _, scene, _ in templates})
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if scene_count < 6:
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raise SystemExit(f"生效话术场景不足 6 类：{scene_count}")
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print(f"verified: {len(rules)} active rules (含 7 个零容忍词), "
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f"{len(templates)} active templates covering {scene_count} scenes")
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async def main() -> None:
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await seed()
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print(f"seeded {len(NEGATIVE_RULES)} negative rules, {len(REPLY_TEMPLATES)} reply templates")
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await verify()
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if __name__ == "__main__":
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asyncio.run(main())
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