SuperBizAgent-java/mvp/demo

MVP Demo Runbook

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

For interview use, start with:

• interview-walkthrough.md for the talk track
• trace-inspection-checklist.md for fields to inspect
• scripts/run-payment-timeout-demo.ps1 for the runnable local demo
• requests/payment-timeout-chat.json for the fixed request payload

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

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

The service listens on:

http://localhost:9900

1. Run Chat Diagnosis

Fast path:

powershell -ExecutionPolicy Bypass -File mvp/demo/scripts/run-payment-timeout-demo.ps1

This writes:

mvp/demo/output/chat-response.json
mvp/demo/output/trace-response.json
mvp/demo/output/feedback-response.json

Manual path:

$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

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

$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

$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:

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

Demo Story

The important interview story is:

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:

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