# MVP Demo Runbook

This demo proves the MVP flow from user question to persisted diagnosis trace.

## Prerequisites

- MySQL, Redis, Milvus/Zilliz, and LLM/embedding configuration are available through the current project configuration.
- Security and secret cleanup are intentionally out of scope for this MVP slice.
- The `mvp-demo` profile enables mock Prometheus and CLS providers so log and metric tools can return repeatable evidence.

## Start

```powershell
mvn spring-boot:run "-Dspring-boot.run.profiles=mvp-demo"
```

The service listens on:

```text
http://localhost:9900
```

## 1. Run Chat Diagnosis

```powershell
$sessionId = "mvp-demo-payment-timeout-001"
$body = @{
  Id = $sessionId
  Question = "支付接口最近出现超时，请结合知识库、日志和指标判断可能原因，并给出修复建议。"
} | ConvertTo-Json

Invoke-RestMethod `
  -Method Post `
  -Uri "http://localhost:9900/api/chat" `
  -ContentType "application/json" `
  -Body $body
```

Expected result:

- `data.success` is `true`.
- `data.sessionId` equals `mvp-demo-payment-timeout-001`.
- `data.answer` contains a diagnosis answer.

## 2. Query Trace

```powershell
Invoke-RestMethod `
  -Method Get `
  -Uri "http://localhost:9900/api/diagnosis/$sessionId/trace"
```

Expected result:

- `code` is `200`.
- `data.session.sessionId` equals the chat session id.
- `data.steps` contains planner/executor/verifier records for complex questions.
- `data.toolInvocations` contains evidence tool calls such as `lookup_knowledge`, `query_logs`, or `query_metrics`.
- `data.session.selfEvaluation` contains verifier or rule evaluation when available.

## 3. Submit Feedback

```powershell
$feedback = @{
  sessionId = $sessionId
  feedback = "useful"
} | ConvertTo-Json

Invoke-RestMethod `
  -Method Post `
  -Uri "http://localhost:9900/api/feedback" `
  -ContentType "application/json" `
  -Body $feedback
```

Expected result:

- `success` is `true`.
- A later trace query shows `data.session.feedback` as `useful`.

## 4. Run AIOps Alert Diagnosis

```powershell
$aiopsSessionId = "mvp-demo-aiops-payment-cpu-001"
$aiopsBody = @{
  sessionId = $aiopsSessionId
  alertName = "HighCPUUsage"
  service = "payment-service"
  severity = "P1"
  description = "服务 payment-service 的 CPU 使用率持续超过 80%，当前值为 92%。实例: pod-payment-service-7d8f9c6b5-x2k4m。"
  timeRange = "last_15m"
  userRequest = "请结合 Prometheus 活动告警、system-metrics 日志和知识库生成告警分析报告。"
} | ConvertTo-Json

Invoke-WebRequest `
  -Method Post `
  -Uri "http://localhost:9900/api/ai_ops" `
  -ContentType "application/json" `
  -Body $aiopsBody
```

Expected result:

- The SSE stream starts with a `session` message containing `mvp-demo-aiops-payment-cpu-001`.
- The stream later contains an AIOps alert analysis report focused on the supplied `HighCPUUsage/payment-service` payload.
- A trace query for the same session id returns `data.session.agentFlow` as `AI_OPS`.
- `data.session.answer` contains the final alert analysis report when a report is generated.
- `data.toolInvocations` contains evidence tools such as `lookup_knowledge`, `query_logs`, or `query_metrics` when the runtime uses them.

Query the AIOps trace:

```powershell
Invoke-RestMethod `
  -Method Get `
  -Uri "http://localhost:9900/api/diagnosis/$aiopsSessionId/trace"
```

## Demo Story

The important interview story is:

```text
one session id
-> user question
-> multi-agent execution
-> evidence tools
-> verifier/self-evaluation
-> final answer
-> feedback
-> trace API for replay and audit
```

The AIOps story uses the same audit spine:

```text
one session id
-> alert payload
-> AIOps planner/executor execution
-> evidence tools
-> alert analysis report
-> trace API for replay and audit
```