feat: 新增票据夹模块并优化 OCR 与员工画像服务
后端新增票据夹端点、数据模型和服务模块，优化 OCR 端点 Schema 和附件操作逻辑，完善员工行为画像服务和辅助函数， 前端新增票据夹视图和服务层，优化文档中心样式和侧边栏导 航，完善员工画像详情弹窗和权限控制，补充单元测试。
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caoxiaozhu
176
server/src/app/services/account_behavior_profile.py
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176
server/src/app/services/account_behavior_profile.py
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from __future__ import annotations
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from datetime import UTC, datetime, timedelta
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from decimal import Decimal
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.orm import Session, selectinload
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from app.algorithem.employee_behavior_profile import (
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LEVEL_LABELS,
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PROFILE_LABELS,
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ProfileComponent,
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evaluate_weighted_profile,
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score_by_bands,
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)
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from app.algorithem.employee_behavior_profile_tags import build_profile_radar, build_profile_tags
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from app.models.agent_run import AgentRun
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from app.schemas.employee_profile import EmployeeProfileLatestRead, EmployeeProfileRead
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from app.services.employee_behavior_profile_helpers import EmployeeBehaviorProfileMetricHelpers
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class AccountBehaviorProfileService(EmployeeBehaviorProfileMetricHelpers):
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def __init__(self, db: Session) -> None:
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self.db = db
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def get_latest_account_profile(
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self,
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*,
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account_id: str,
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account_name: str,
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identifiers: set[str],
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scene: str,
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window_days: int,
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expense_type_scope: str,
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) -> EmployeeProfileLatestRead:
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if scene != "operations":
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return EmployeeProfileLatestRead(
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employee_id=account_id,
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employee_name=account_name,
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scene=scene,
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window_days=window_days,
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expense_type_scope=expense_type_scope,
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empty_reason="当前账号未匹配员工目录，无法形成审批场景员工画像。",
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)
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runs = self._fetch_account_runs(identifiers, datetime.now(UTC) - timedelta(days=window_days))
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if not runs:
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return EmployeeProfileLatestRead(
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employee_id=account_id,
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employee_name=account_name,
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scene=scene,
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window_days=window_days,
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expense_type_scope=expense_type_scope,
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empty_reason="当前账号暂无可统计的智能体运行记录。",
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)
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result = self._calculate_account_ai_usage_profile(
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runs=runs,
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window_days=window_days,
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expense_type_scope=expense_type_scope,
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)
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payload = {
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"profile_type": result.profile_type,
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"profile_label": result.profile_label,
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"score": result.profile_score,
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"level": result.profile_level,
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"metrics": result.metrics,
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"top_contributors": result.top_contributors(),
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}
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tags = build_profile_tags([payload], scene=scene)
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radar = build_profile_radar([payload], tags, scene=scene)
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return EmployeeProfileLatestRead(
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employee_id=account_id,
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employee_name=account_name,
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scene=scene,
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window_days=window_days,
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expense_type_scope=expense_type_scope,
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calculated_at=datetime.now(UTC),
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review_priority_score=0,
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review_priority_level="normal",
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review_priority_label=LEVEL_LABELS["normal"],
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profiles=[
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EmployeeProfileRead(
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profile_type=payload["profile_type"],
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profile_label=PROFILE_LABELS.get(payload["profile_type"], payload["profile_type"]),
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score=payload["score"],
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level=payload["level"],
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level_label=LEVEL_LABELS.get(payload["level"], payload["level"]),
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metrics=payload["metrics"],
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top_contributors=payload["top_contributors"],
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)
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],
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profile_tags=tags,
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radar=radar,
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)
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def _calculate_account_ai_usage_profile(
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self,
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*,
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runs: list[AgentRun],
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window_days: int,
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expense_type_scope: str,
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):
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tool_calls = [tool for run in runs for tool in run.tool_calls]
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failed_calls = [
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tool for tool in tool_calls if str(tool.status or "").lower() not in {"success", "ok"}
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]
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estimated_tokens = self._estimate_tokens(runs)
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duration_ms = self._sum_agent_run_duration_ms(runs)
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token_mode = "estimated_token_count" if estimated_tokens else "unavailable"
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return evaluate_weighted_profile(
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"ai_usage",
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[
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ProfileComponent(
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"ai_call_count_score",
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"AI 调用次数",
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score_by_bands(len(runs), [(0, 0), (3, 25), (10, 65), (20, 100)]),
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len(runs),
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"次",
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Decimal("0.25"),
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),
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ProfileComponent(
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"token_cost_score",
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"Token 使用强度",
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score_by_bands(
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estimated_tokens, [(0, 0), (2000, 25), (8000, 65), (20000, 100)]
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),
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estimated_tokens,
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"tokens",
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Decimal("0.25"),
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),
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ProfileComponent(
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"ai_generated_claim_ratio_score",
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"AI 生成申请比例",
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score_by_bands(len(runs), [(0, 0), (2, 20), (8, 60), (16, 90)]),
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len(runs),
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"次",
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Decimal("0.20"),
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),
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ProfileComponent(
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"failed_ai_call_score",
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"AI 调用失败",
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score_by_bands(len(failed_calls), [(0, 0), (1, 35), (3, 80)]),
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len(failed_calls),
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"次",
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Decimal("0.10"),
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),
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],
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metrics={
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"window_days": window_days,
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"expense_type_scope": expense_type_scope,
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"peer_sample_size": 0,
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"ai_run_count": len(runs),
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"tool_call_count": len(tool_calls),
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"failed_tool_call_count": len(failed_calls),
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"token_count_mode": token_mode,
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"estimated_token_count": estimated_tokens,
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"exact_token_count": None,
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"ai_run_duration_ms": duration_ms,
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"ai_run_duration_mode": "elapsed_or_tool_call_fallback",
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},
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)
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def _fetch_account_runs(self, identifiers: set[str], cutoff: datetime) -> list[AgentRun]:
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normalized = {item for item in identifiers if str(item or "").strip()}
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if not normalized:
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return []
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stmt = (
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select(AgentRun)
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.options(selectinload(AgentRun.tool_calls))
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.where(AgentRun.started_at >= cutoff, AgentRun.user_id.in_(normalized))
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)
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return list(self.db.scalars(stmt).all())
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