docs: reorganize MVP interview documentation

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aruo
2026-07-05 15:29:28 +08:00
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# AIOps Alert Acceptance Case
# AIOps 告警验收用例
## Goal
## 1. 目标
Validate that the legacy AIOps endpoint can act as a traceable alert-triggered diagnosis entry.
验证旧版 `/api/ai_ops` 入口可以作为可追踪的告警触发诊断入口,并且 payload 模式下报告聚焦输入告警。
## Input
## 2. 输入
- Session id: `mvp-demo-aiops-payment-cpu-001`
- Endpoint: `POST /api/ai_ops`
- Profile: `mvp-demo`
- Alert:
- Session id:`mvp-demo-aiops-payment-cpu-001`
- Endpoint:`POST /api/ai_ops`
- Profile:`mvp-demo`
- 告警 payload:
```json
{
@@ -23,16 +23,20 @@ Validate that the legacy AIOps endpoint can act as a traceable alert-triggered d
}
```
## Acceptance Criteria
## 3. 验收标准
1. The SSE stream emits a `session` message containing the requested session id.
2. The AIOps run creates or updates `diagnosis_session` with `agent_flow = AI_OPS`.
3. The persisted session query contains the alert name, service, severity, time range, and description.
4. If a final report is generated, `diagnosis_session.answer` contains that report.
5. `GET /api/diagnosis/{sessionId}/trace` returns the AIOps session, ordered agent steps, and ordered tool invocations.
6. In payload mode, the report focuses on `HighCPUUsage/payment-service`; unrelated active alerts may appear only as related risk or context, not as separate full root-cause sections.
1. SSE 流输出 `session` 消息,且包含请求中的 session id。
2. AIOps 执行创建或更新 `diagnosis_session`,并写入 `agent_flow = AI_OPS`。
3. 持久化的 session query 包含告警名、服务名、等级、时间范围和描述。
4. 如果生成最终报告,`diagnosis_session.answer` 包含该报告。
5. `GET /api/diagnosis/{sessionId}/trace` 返回 AIOps session、按顺序排列的 agent steps 和 tool invocations。
6. payload 模式下,报告主线聚焦 `HighCPUUsage/payment-service`。
7. 其他活跃告警最多作为相关风险或上下文出现,不应展开成完整独立根因章节。
8. `self_evaluation.aiops_rule_evaluation` 存在,并能反映报告完整性、payload 聚焦和证据工具覆盖情况。
## Known Limits
## 4. 已知边界
- 当前 AIOps 使用轻量规则评估器,不是完整 LLM Verifier。
- 完整运行仍依赖有效的 DB、Redis、Milvus/Zilliz、模型和 embedding 配置。
- `mvp-demo` profile 使用 mock Prometheus 和 mock CLS,主要用于稳定演示。
- This slice does not add a Verifier Agent to AIOps.
- Full runtime verification still depends on valid DB, Redis, Milvus/Zilliz, model, and embedding configuration.