206 lines
8.8 KiB
Python
206 lines
8.8 KiB
Python
"""预警研判、回访话术和工单摘要服务。"""
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from __future__ import annotations
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import json
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import logging
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from datetime import UTC, datetime
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.contracts import RequestContext
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from app.core.errors import ForbiddenAgentError, GenericResourceNotFoundError
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from app.infrastructure.db import SessionFactory
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from app.model.audit import InteractionAudit
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from app.model.fund import FundRiskAlert
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from app.repository.risk_repository import RiskRepository
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from app.service.authorization_service import AuthorizationService
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from app.service.risk_query_service import scope_from_context
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OUTPUT_TYPES = ("预警研判", "回访话术", "工单摘要")
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FORBIDDEN_ACTION_CLAIMS = (
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"我已确认接收", "我已关闭", "我已升级", "我已冻结", "我已放行",
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"已为您确认接收", "已为您关闭", "已为您升级",
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)
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SYSTEM_PROMPT = "你是奶龙风控智能助手，只能基于已给证据做研判辅助，不得自动处置交易或预警。"
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logger = logging.getLogger(__name__)
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class RiskAnalysisService:
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def __init__(
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self,
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session: AsyncSession,
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*,
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repository: RiskRepository | None = None,
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model_service: Any | None = None,
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endpoint_resolver: Any | None = None,
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) -> None:
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self.session = session
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self.repository = repository
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self.model_service = model_service
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self.endpoint_resolver = endpoint_resolver
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@classmethod
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async def generate_for_context(
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cls,
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context: RequestContext,
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alert_no: str,
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output_type: str,
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) -> dict[str, Any]:
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async with SessionFactory() as session:
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return await cls(session).generate(context, alert_no, output_type)
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async def generate(
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self,
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context: RequestContext,
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alert_no: str,
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output_type: str,
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) -> dict[str, Any]:
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if output_type not in OUTPUT_TYPES:
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raise ValueError("不支持的预警分析类型")
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await AuthorizationService.require(context, "risk:alert:read")
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repository = self.repository or RiskRepository(
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self.session,
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scope=scope_from_context(context),
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)
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detail = await repository.get_alert_detail(alert_no)
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if detail is None:
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raise GenericResourceNotFoundError("预警不存在")
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content, source = await self._generate_content(output_type, detail.to_dict())
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await self._save_result(context, alert_no, output_type, content, source)
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return {"type": output_type, "content": content, "source": source}
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async def _generate_content(
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self,
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output_type: str,
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detail: dict[str, Any],
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) -> tuple[str, str]:
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model_service = self.model_service
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resolver = self.endpoint_resolver
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if model_service is None or resolver is None:
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from app.service.agent.bootstrap import get_model_service
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from app.service.model_gateway import DatabaseModelEndpointResolver
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model_service = model_service or get_model_service()
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resolver = resolver or DatabaseModelEndpointResolver()
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try:
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endpoints = await resolver.resolve(
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agent_type="risk",
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task_type={
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"预警研判": "risk_analysis",
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"回访话术": "risk_script",
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"工单摘要": "risk_summary",
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}[output_type],
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)
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prompt = (
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f"{SYSTEM_PROMPT}\n{_task_instruction(output_type)}\n"
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f"证据如下：{json.dumps(detail, ensure_ascii=False, default=str)}"
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)
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execution = await model_service.generate(endpoints, prompt, max_attempts=2)
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content = execution.text.strip()
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if (
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content
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and len(content) <= 6000
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and not any(claim in content for claim in FORBIDDEN_ACTION_CLAIMS)
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):
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return content, "模型"
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except Exception:
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logger.exception("%s模型生成失败，已使用模板降级输出", output_type)
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return self._fallback(output_type, detail), "模板降级输出"
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async def _save_result(
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self,
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context: RequestContext,
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alert_no: str,
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output_type: str,
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content: str,
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source: str,
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) -> None:
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statement = (
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select(FundRiskAlert)
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.where(FundRiskAlert.alert_no == alert_no)
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.with_for_update()
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)
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if context.data_scope != "all":
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if not context.customer_ids:
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raise ForbiddenAgentError("无权访问当前预警")
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statement = statement.where(
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FundRiskAlert.customer_id.in_(
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tuple(int(customer_id) for customer_id in context.customer_ids)
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)
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)
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alert = await self.session.scalar(statement)
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if alert is None:
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raise GenericResourceNotFoundError("预警不存在")
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now = datetime.now(UTC).replace(tzinfo=None)
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analysis = dict(alert.ai_analysis or {})
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analysis[output_type] = {
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"type": output_type,
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"content": content,
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"source": source,
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"generated_at": now.isoformat(),
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}
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alert.ai_analysis = analysis
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alert.updated_at = now
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self.session.add(InteractionAudit(
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actor_type="agent",
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actor_id=int(context.user_id),
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target_customer_id=alert.customer_id,
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portal="api",
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action_type="risk_ai_analysis_generated",
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detail={"alert_no": alert_no, "output_type": output_type, "source": source},
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created_at=now,
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))
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await self.session.commit()
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@staticmethod
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def _fallback(output_type: str, detail: dict[str, Any]) -> str:
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alert = detail.get("alert") or {}
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customer = detail.get("customer") or {}
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transaction = detail.get("transaction") or {}
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customer_name = customer.get("name") or "当前客户"
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customer_no = customer.get("customer_no") or "-"
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amount = transaction.get("amount") or "-"
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rules = "，".join(alert.get("rule_codes") or []) or "-"
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evidence = alert.get("evidence_summary") or "暂无证据摘要"
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if output_type == "回访话术":
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return (
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f"回访对象：{customer_name}（{customer_no}）\n"
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"开场说明：您好，我们在例行风控复核中关注到您近期账户交易存在需要核实的情况，"
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"本次沟通仅用于确认交易意愿并完善留痕。\n"
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f"核实问题：请确认近期交易金额 {amount} 元是否由您本人发起；"
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"请说明资金入金和赎回用途；请确认是否本人常用设备操作。\n"
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f"风险提示：本次预警命中 {rules}，核心证据为：{evidence}。\n"
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"留痕要求：请记录客户确认结果、异常解释、回访问答时间，并提交风控专员复核。"
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)
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if output_type == "工单摘要":
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return (
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f"工单标题：{alert.get('alert_type') or '风险预警'}人工复核工单\n"
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f"客户信息：{customer_name}（{customer_no}）\n"
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f"命中规则：{rules}\n"
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f"关键证据：{evidence}\n"
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f"风险等级：{alert.get('risk_level') or '-'}\n"
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"建议动作：分派风控专员核验证据链，补充客户回访记录，"
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"确认后选择继续调查、升级或关闭误报。\n"
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f"处理时限：{alert.get('due_time') or '按风险等级时限处理'}"
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)
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return (
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f"风险结论：{alert.get('alert_type') or '风险预警'}命中 {rules}，建议进入人工复核。\n"
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f"核心证据：{evidence}。\n"
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f"客户特征：{customer_name}（{customer_no}），"
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f"风险等级 {customer.get('risk_level') or '-'}。\n"
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"复核重点：核实资金来源和赎回用途，检查是否本人操作及设备是否异常，"
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"确认交易是否符合客户历史行为。\n"
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"处置建议：由风控专员人工复核并留痕，必要时发起客户回访或升级处理。"
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)
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def _task_instruction(output_type: str) -> str:
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return {
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"预警研判": "请生成预警研判，输出风险结论、核心证据、复核重点和处置建议。",
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"回访话术": "请生成客户回访话术，输出开场说明、核实问题、风险提示和留痕提醒。",
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"工单摘要": "请生成工单摘要，输出工单标题、命中规则、关键证据、建议动作和处理时限。",
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}[output_type]
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