148 lines
7.6 KiB
Python
148 lines
7.6 KiB
Python
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from __future__ import annotations
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import json
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from typing import Any
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def build_risk_rule_compiler_messages(
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*,
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domain: str,
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domain_label: str,
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expense_category: str | None,
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expense_category_label: str,
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natural_language: str,
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available_fields: list[dict[str, Any]],
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) -> list[dict[str, str]]:
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"""构造自然语言规则编译提示词。
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大模型只负责把业务语言拆成“语义计划”，后端会校验字段、操作符和模板。
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"""
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schema = {
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"name": "规则名称，短句",
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"description": "面向业务和审核人员的说明，不要写实现细节",
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"template_key": "field_required_v1 | field_compare_v1 | keyword_match_v1 | composite_rule_v1",
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"semantic_type": (
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"可选。可用稳定英文短语描述语义类型；"
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"已知差旅票据城市/路线一致性可使用 travel_route_city_consistency，其他规则按业务含义命名"
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),
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"field_keys": ["只能选择 available_fields.key"],
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"condition_summary": "用公式化语言描述判断依据，不要写'是否出现风险关键词'",
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"rule_ir": {
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"facts": "事实变量数组，例如 A=票据事实、B=业务申报事实、E=例外说明",
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"conditions": "条件数组，必须能被人解释",
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"hit_logic": "命中逻辑，例如 D AND ((A NOT_IN B) OR DATE_OUTSIDE(T,R)) AND NOT EXCEPTION(E)",
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},
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"conditions": [
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{
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"id": "稳定英文标识",
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"operator": (
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"exists_any | exists_all | in_scope | not_in_scope | overlap | "
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"not_overlap | date_outside_range | contains_any | not_contains_any"
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),
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"fields": ["exists/contains 类操作使用"],
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"left_fields": ["集合比较左侧字段"],
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"right_fields": ["集合比较右侧字段"],
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"date_fields": ["日期字段"],
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"range_start_fields": ["日期范围开始字段"],
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"range_end_fields": ["日期范围结束字段"],
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"keywords": ["例外或风险词"],
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}
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],
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"hit_logic": {"all": ["condition_id", {"any": ["condition_id"]}]},
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"formula": "可执行逻辑公式，字段使用事实变量表达",
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"message_template": "命中后的业务提示",
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"unsupported_fields": ["用户规则提到但 available_fields 中暂时没有的字段"],
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"keywords": "仅 keyword_match_v1 使用，且必须是真正风险词，不得把例外说明词当风险词",
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"exception_keywords": "例外说明词，例如绕行、跨城办事、临时改签",
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"risk_scoring_evidence": {
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"impact_level": "low | medium | high | critical",
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"violation_certainty": "low | medium | high | critical",
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"evidence_strength": "low | medium | high | critical",
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"exception_dependence": "low | medium | high | critical",
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"control_action": "remind | supplement | manual_review | return | block",
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"business_sensitivity": "low | medium | high | critical",
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"reason": "用一句话说明这些评分证据来自哪些业务语义",
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},
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"flow": {
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"start": "流程起点",
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"evidence": "读取哪些事实",
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"decision": "判断公式或分支条件",
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"pass": "未命中时说明",
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"fail": "命中时说明",
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},
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}
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guardrails = [
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"只能输出 JSON 对象，不能输出 Markdown 或解释。",
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"字段必须来自 available_fields，不能编造字段。",
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"多步骤规则要使用 composite_rule_v1：先抽取事实变量，再写 conditions 和 hit_logic，不要压扁成单个关键词判断。",
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"城市/地点/路线一致性必须用 field_compare_v1 或 semantic_type=travel_route_city_consistency。",
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"涉及多个字段、日期范围、金额范围、集合关系、例外说明的规则必须使用 composite_rule_v1。",
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"日期字段必须区分事实日期、票据日期和业务期间；如果只能拿到替代字段，要在 rule_ir 中说明这是 fallback evidence。",
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"composite_rule_v1 只能使用受控 operator：exists_any、exists_all、in_scope、not_in_scope、overlap、not_overlap、date_outside_range、contains_any、not_contains_any。",
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"差旅路线规则中，交通票行程城市和住宿发票城市属于附件城市集合。",
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"申报目的地和明细发生地点属于申报行程城市集合。",
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"员工常驻地/出发地如可用，属于合理起终点集合，不等同于申报目的地。",
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"绕行、跨城办事、临时改签是例外说明证据，不是风险命中关键词。",
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"如果票据路线出现申报目的地和常驻地之外的额外城市，应描述为中途周转/绕行异常。",
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"keyword_match_v1 只用于品名、摘要、票据全文中出现明确风险词的规则。",
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"不要直接指定 risk_level 或 risk_score；只输出 risk_scoring_evidence，后端会按固定评分模型计算 0-100 分和风险等级。",
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"评分证据必须围绕六个指标：业务影响、违规确定性、证据强度、例外/规避空间、处置强度、场景敏感度。",
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]
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examples = [
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{
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"user_rule": (
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"差旅报销时，交通票或住宿票据中的城市均无法与申报目的地、"
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"明细地点形成一致关系，且事由未说明绕行或改签原因，则高风险。"
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),
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"expected": {
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"template_key": "field_compare_v1",
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"semantic_type": "travel_route_city_consistency",
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"field_keys": [
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"attachment.route_cities",
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"attachment.hotel_city",
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"claim.location",
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"item.item_location",
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"employee.location",
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"claim.reason",
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"item.item_reason",
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],
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"condition_summary": (
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"A=交通票行程城市∪住宿发票城市，B=申报目的地∪明细发生地点，"
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"C=员工常驻地/合理起终点；A与B无交集且无合理说明，或A中出现B∪C之外城市时命中。"
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),
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"keywords": [],
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"exception_keywords": ["绕行", "跨城办事", "临时改签"],
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},
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}
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]
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return [
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{
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"role": "system",
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"content": "\n".join(
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[
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"你是 X-Financial 风险规则语义编译器。",
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"你的任务是把自然语言规则转换成可校验 JSON 语义计划。",
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"后端执行器只接受受控模板和受控字段，所以你必须严格遵守以下约束：",
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*[f"- {item}" for item in guardrails],
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]
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),
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},
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{
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"role": "user",
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"content": json.dumps(
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{
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"business_domain": domain,
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"business_domain_label": domain_label,
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"expense_category": expense_category,
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"expense_category_label": expense_category_label,
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"natural_language": natural_language,
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"available_fields": available_fields,
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"required_json_shape": schema,
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"examples": examples,
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},
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ensure_ascii=False,
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),
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},
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]
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