7989f3a159
后端新增风险图谱算法模块、风险观察与反馈服务、规则 DSL 校验器和可解释性引擎，完善系统仪表盘和财务仪表盘统计， 优化 agent 运行和编排执行链路，清理旧开发文档，前端新增 系统趋势、负载热力图等多种仪表盘图表组件，完善操作反馈 对话框和工作台日期选择器，优化报销创建和审批详情交互， 补充单元测试覆盖。
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from __future__ import annotations
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from typing import Any
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COMPOSITE_RULE_TEMPLATE_KEY = "composite_rule_v1"
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COMPOSITE_RULE_OPERATORS = {
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"exists_any",
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"exists_all",
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"all_present",
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"in_scope",
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"not_in_scope",
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"not_in_set",
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"overlap",
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"not_overlap",
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"date_outside_range",
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"numeric_compare",
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"duplicate_value",
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"contains_any",
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"not_contains_any",
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}
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def build_dsl_from_semantic_plan(semantic_plan: dict[str, Any]) -> dict[str, Any]:
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"""把模型语义计划转换成可交给 validator 继续规范化的 DSL 草稿。"""
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if not isinstance(semantic_plan, dict):
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return {}
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text_parts = _semantic_text_parts(semantic_plan)
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field_keys = _semantic_field_keys(semantic_plan)
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if not text_parts and not field_keys:
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return {}
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return {
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"template_key": COMPOSITE_RULE_TEMPLATE_KEY,
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"field_keys": field_keys,
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"description": str(semantic_plan.get("rule_intent") or "").strip(),
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"condition_summary": "；".join(text_parts)[:800],
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"keywords": [],
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"rule_ir": {
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"facts": field_keys,
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"conditions": text_parts,
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"hit_logic": "由 DSL validator 根据字段本体和语义步骤生成受控条件",
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},
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}
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def _semantic_text_parts(semantic_plan: dict[str, Any]) -> list[str]:
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parts: list[str] = []
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for key in ("rule_intent", "scope", "judgment_steps", "exception_conditions", "risk_action"):
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parts.extend(_flatten_semantic_text(semantic_plan.get(key)))
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return [item for index, item in enumerate(parts) if item and item not in parts[:index]]
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def _semantic_field_keys(semantic_plan: dict[str, Any]) -> list[str]:
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keys: list[str] = []
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for value in (semantic_plan.get("required_fields"), semantic_plan.get("fields")):
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for item in value if isinstance(value, list) else []:
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key = item if isinstance(item, str) else next(
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(item.get(name) for name in ("field", "key", "field_key") if isinstance(item, dict) and item.get(name)),
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"",
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)
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text = str(key or "").strip()
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if "." in text and text not in keys:
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keys.append(text)
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return keys
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def _flatten_semantic_text(value: Any) -> list[str]:
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if isinstance(value, str):
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return [value.strip()] if value.strip() else []
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if isinstance(value, list):
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return [item for value_item in value for item in _flatten_semantic_text(value_item)]
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if isinstance(value, dict):
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return [item for value_item in value.values() for item in _flatten_semantic_text(value_item)]
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return []
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