143 lines
3.7 KiB
Markdown
143 lines
3.7 KiB
Markdown
# MVP Demo Runbook
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This demo proves the MVP flow from user question to persisted diagnosis trace.
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## Prerequisites
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- MySQL, Redis, Milvus/Zilliz, and LLM/embedding configuration are available through the current project configuration.
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- Security and secret cleanup are intentionally out of scope for this MVP slice.
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- The `mvp-demo` profile enables mock Prometheus and CLS providers so log and metric tools can return repeatable evidence.
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## Start
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```powershell
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mvn spring-boot:run "-Dspring-boot.run.profiles=mvp-demo"
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```
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The service listens on:
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```text
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http://localhost:9900
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```
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## 1. Run Chat Diagnosis
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```powershell
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$sessionId = "mvp-demo-payment-timeout-001"
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$body = @{
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Id = $sessionId
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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。"
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} | ConvertTo-Json
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Invoke-RestMethod `
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-Method Post `
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-Uri "http://localhost:9900/api/chat" `
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-ContentType "application/json" `
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-Body $body
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```
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Expected result:
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- `data.success` is `true`.
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- `data.sessionId` equals `mvp-demo-payment-timeout-001`.
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- `data.answer` contains a diagnosis answer.
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## 2. Query Trace
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```powershell
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Invoke-RestMethod `
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-Method Get `
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-Uri "http://localhost:9900/api/diagnosis/$sessionId/trace"
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```
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Expected result:
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- `code` is `200`.
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- `data.session.sessionId` equals the chat session id.
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- `data.steps` contains planner/executor/verifier records for complex questions.
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- `data.toolInvocations` contains evidence tool calls such as `lookup_knowledge`, `query_logs`, or `query_metrics`.
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- `data.session.selfEvaluation` contains verifier or rule evaluation when available.
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## 3. Submit Feedback
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```powershell
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$feedback = @{
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sessionId = $sessionId
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feedback = "useful"
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} | ConvertTo-Json
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Invoke-RestMethod `
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-Method Post `
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-Uri "http://localhost:9900/api/feedback" `
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-ContentType "application/json" `
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-Body $feedback
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```
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Expected result:
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- `success` is `true`.
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- A later trace query shows `data.session.feedback` as `useful`.
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## 4. Run AIOps Alert Diagnosis
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```powershell
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$aiopsSessionId = "mvp-demo-aiops-payment-cpu-001"
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$aiopsBody = @{
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sessionId = $aiopsSessionId
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alertName = "HighCPUUsage"
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service = "payment-service"
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severity = "P1"
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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。"
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timeRange = "last_15m"
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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。"
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} | ConvertTo-Json
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Invoke-WebRequest `
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-Method Post `
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-Uri "http://localhost:9900/api/ai_ops" `
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-ContentType "application/json" `
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-Body $aiopsBody
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```
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Expected result:
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- The SSE stream starts with a `session` message containing `mvp-demo-aiops-payment-cpu-001`.
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- The stream later contains an AIOps alert analysis report focused on the supplied `HighCPUUsage/payment-service` payload.
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- A trace query for the same session id returns `data.session.agentFlow` as `AI_OPS`.
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- `data.session.answer` contains the final alert analysis report when a report is generated.
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- `data.toolInvocations` contains evidence tools such as `lookup_knowledge`, `query_logs`, or `query_metrics` when the runtime uses them.
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Query the AIOps trace:
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```powershell
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Invoke-RestMethod `
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-Method Get `
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-Uri "http://localhost:9900/api/diagnosis/$aiopsSessionId/trace"
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```
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## Demo Story
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The important interview story is:
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```text
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one session id
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-> user question
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-> multi-agent execution
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-> evidence tools
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-> verifier/self-evaluation
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-> final answer
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-> feedback
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-> trace API for replay and audit
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```
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The AIOps story uses the same audit spine:
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```text
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one session id
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-> alert payload
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-> AIOps planner/executor execution
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-> evidence tools
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-> alert analysis report
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-> trace API for replay and audit
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```
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