docs: reorganize MVP interview documentation
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# AIOps Alert Acceptance Case
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# AIOps 告警验收用例
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## Goal
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## 1. 目标
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Validate that the legacy AIOps endpoint can act as a traceable alert-triggered diagnosis entry.
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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。
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## Input
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## 2. 输入
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- Session id: `mvp-demo-aiops-payment-cpu-001`
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- Endpoint: `POST /api/ai_ops`
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- Profile: `mvp-demo`
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- Alert:
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- Session id:`mvp-demo-aiops-payment-cpu-001`
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- Endpoint:`POST /api/ai_ops`
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- Profile:`mvp-demo`
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- 告警 payload:
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```json
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{
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@@ -23,16 +23,20 @@ Validate that the legacy AIOps endpoint can act as a traceable alert-triggered d
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}
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```
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## Acceptance Criteria
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## 3. 验收标准
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1. The SSE stream emits a `session` message containing the requested session id.
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2. The AIOps run creates or updates `diagnosis_session` with `agent_flow = AI_OPS`.
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3. The persisted session query contains the alert name, service, severity, time range, and description.
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4. If a final report is generated, `diagnosis_session.answer` contains that report.
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5. `GET /api/diagnosis/{sessionId}/trace` returns the AIOps session, ordered agent steps, and ordered tool invocations.
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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.
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1. SSE 流输出 `session` 消息,且包含请求中的 session id。
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2. AIOps 执行创建或更新 `diagnosis_session`,并写入 `agent_flow = AI_OPS`。
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3. 持久化的 session query 包含告警名、服务名、等级、时间范围和描述。
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4. 如果生成最终报告,`diagnosis_session.answer` 包含该报告。
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5. `GET /api/diagnosis/{sessionId}/trace` 返回 AIOps session、按顺序排列的 agent steps 和 tool invocations。
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6. payload 模式下,报告主线聚焦 `HighCPUUsage/payment-service`。
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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。
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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。
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## Known Limits
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## 4. 已知边界
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- 当前 AIOps 使用轻量规则评估器,不是完整 LLM Verifier。
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- 完整运行仍依赖有效的 DB、Redis、Milvus/Zilliz、模型和 embedding 配置。
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- `mvp-demo` profile 使用 mock Prometheus 和 mock CLS,主要用于稳定演示。
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- This slice does not add a Verifier Agent to AIOps.
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- Full runtime verification still depends on valid DB, Redis, Milvus/Zilliz, model, and embedding configuration.
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