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。
199 lines
7.9 KiB
Python
199 lines
7.9 KiB
Python
from __future__ import annotations
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from sqlalchemy import select
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from app.core.agent_enums import (
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AgentAssetContentType,
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AgentAssetDomain,
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AgentAssetStatus,
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AgentAssetType,
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AgentName,
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)
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from app.models.agent_asset import AgentAsset
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from app.services.agent_foundation_constants import (
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DIGITAL_EMPLOYEE_PROFILE_SCAN_TASK_CODE,
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DIGITAL_EMPLOYEE_RISK_GRAPH_SCAN_TASK_CODE,
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DIGITAL_EMPLOYEE_RULE_DISCOVERY_TASK_CODE,
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DIGITAL_EMPLOYEE_SKILL_CATEGORIES,
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)
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class AgentFoundationDigitalEmployeeTaskMixin:
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def _runtime_digital_employee_task_specs(self) -> tuple[dict[str, object], ...]:
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return (
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{
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"code": DIGITAL_EMPLOYEE_RISK_GRAPH_SCAN_TASK_CODE,
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"name": "财务风险图谱巡检",
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"description": "按计划扫描报销单、票据、审批链、员工画像和规则命中结果，生成风险观察与可复核证据链。",
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"scenario_json": ["schedule", "expense", "risk_graph", "risk_observation"],
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"owner": "风控与审计部",
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"reviewer": "顾承宇",
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"cron": "0 9 * * *",
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"skill_category": "评估",
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"markdown": self._financial_risk_graph_scan_skill_markdown,
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"change_note": "初始化财务风险图谱巡检能力。",
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"config": {
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"skill_name": "financial-risk-graph-scanner",
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"scan_scope": [
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"expense_claims",
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"invoices",
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"approval_chain",
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"employee_profiles",
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"risk_rules",
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],
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"output_format": "risk_observation_report",
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"writes_risk_observations": True,
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},
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},
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{
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"code": DIGITAL_EMPLOYEE_PROFILE_SCAN_TASK_CODE,
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"name": "员工行为画像巡检",
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"description": "按计划更新员工费用行为、材料完整性、审批效率和智能协作画像，为风险图谱提供画像基线。",
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"scenario_json": ["schedule", "employee_profile", "baseline", "risk_graph"],
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"owner": "风控与审计部",
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"reviewer": "顾承宇",
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"cron": "30 8 * * 1",
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"skill_category": "评估",
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"markdown": self._employee_behavior_profile_scan_skill_markdown,
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"change_note": "初始化员工行为画像巡检能力。",
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"config": {
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"skill_name": "employee-behavior-profile-scanner",
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"profile_dimensions": [
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"expense_intensity",
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"material_completeness",
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"approval_efficiency",
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"ai_collaboration",
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],
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"output_format": "employee_behavior_profile_snapshot",
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"writes_profile_snapshots": True,
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},
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},
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{
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"code": DIGITAL_EMPLOYEE_RULE_DISCOVERY_TASK_CODE,
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"name": "风险规则候选发现",
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"description": "按计划复盘风险观察和人工反馈，生成带证据、来源和置信度的候选规则，不直接上线。",
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"scenario_json": ["schedule", "risk_observation", "feedback", "rule_candidate"],
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"owner": "风控与审计部",
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"reviewer": "顾承宇",
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"cron": "0 10 * * 1",
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"skill_category": "升级",
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"markdown": self._risk_rule_discovery_skill_markdown,
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"change_note": "初始化风险规则候选发现能力。",
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"config": {
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"skill_name": "risk-rule-discovery",
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"input_sources": [
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"risk_observations",
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"risk_observation_feedback",
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"algorithm_replay_sets",
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],
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"output_format": "candidate_risk_rules",
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"auto_publish": False,
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},
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},
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)
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def _upsert_runtime_digital_employee_tasks(self, existing_codes: set[str]) -> None:
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for spec in self._runtime_digital_employee_task_specs():
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self._upsert_runtime_digital_employee_task(existing_codes, spec)
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def _upsert_runtime_digital_employee_task(
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self,
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existing_codes: set[str],
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spec: dict[str, object],
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) -> None:
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code = str(spec["code"])
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config = self._build_runtime_digital_employee_config(spec)
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if code not in existing_codes:
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asset = self._create_seed_asset(
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asset_type=AgentAssetType.TASK.value,
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code=code,
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name=str(spec["name"]),
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description=str(spec["description"]),
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domain=AgentAssetDomain.SYSTEM.value,
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scenario_json=list(spec["scenario_json"]),
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owner=str(spec["owner"]),
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reviewer=str(spec["reviewer"]),
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status=AgentAssetStatus.ACTIVE.value,
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current_version="v1.0.0",
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config_json=config,
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)
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else:
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asset = self.db.scalar(select(AgentAsset).where(AgentAsset.code == code))
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if asset is None:
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return
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self._refresh_runtime_digital_employee_asset(asset, spec)
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markdown_builder = spec["markdown"]
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if not callable(markdown_builder):
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return
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self._ensure_asset_version(
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asset,
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version="v1.0.0",
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content=markdown_builder(),
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content_type=AgentAssetContentType.MARKDOWN.value,
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change_note=str(spec["change_note"]),
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created_by="系统初始化",
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)
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def _build_runtime_digital_employee_config(
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self,
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spec: dict[str, object],
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*,
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existing_config: dict[str, object] | None = None,
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) -> dict[str, object]:
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code = str(spec["code"])
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cron = str(spec["cron"])
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base = {
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**self._digital_employee_task_config(code, cron),
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"schedule": cron,
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"cron_expression": cron,
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**dict(spec["config"]),
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}
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if not existing_config:
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return base
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existing_cron = (
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existing_config.get("cron")
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or existing_config.get("schedule")
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or existing_config.get("cron_expression")
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)
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schedule_config = (
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{"cron": existing_cron, "schedule": existing_cron, "cron_expression": existing_cron}
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if existing_cron
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else {}
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)
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return {
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**existing_config,
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"agent": AgentName.HERMES.value,
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"task_type": code.replace("task.hermes.", "").replace(".", "_"),
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"skill_category": str(spec["skill_category"]),
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"skill_category_options": list(DIGITAL_EMPLOYEE_SKILL_CATEGORIES),
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**dict(spec["config"]),
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**schedule_config,
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}
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def _refresh_runtime_digital_employee_asset(
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self,
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asset: AgentAsset,
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spec: dict[str, object],
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) -> None:
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asset.name = str(spec["name"])
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asset.description = str(spec["description"])
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asset.owner = str(spec["owner"])
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asset.reviewer = str(spec["reviewer"])
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asset.domain = AgentAssetDomain.SYSTEM.value
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asset.scenario_json = list(spec["scenario_json"])
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if not str(asset.status or "").strip():
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asset.status = AgentAssetStatus.ACTIVE.value
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if not str(asset.current_version or "").strip():
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asset.current_version = "v1.0.0"
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if not str(asset.working_version or "").strip():
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asset.working_version = asset.current_version
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asset.config_json = self._build_runtime_digital_employee_config(
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spec,
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existing_config=dict(asset.config_json or {}),
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)
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self.db.add(asset)
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