184 lines
4.7 KiB
Markdown
184 lines
4.7 KiB
Markdown
# MVP 演示手册
|
||
|
||
本目录用于演示 MVP 从用户问题到诊断 Trace 的完整闭环。
|
||
|
||
面试时建议先读：
|
||
|
||
- `ten-minute-interview-demo.md`：10 分钟现场演示脚本。
|
||
- `interview-walkthrough.md`：面试讲解话术。
|
||
- `evidence-pipeline-scenarios.md`：PASS / LOW_CONFID / REJECT / no-evidence 场景矩阵。
|
||
- `trace-inspection-checklist.md`：Trace 字段检查清单。
|
||
- `scripts/run-payment-timeout-demo.ps1`：本地可执行 Demo 脚本。
|
||
- `requests/payment-timeout-chat.json`：固定 Chat 请求 payload。
|
||
- `requests/narrow-highcpu-chat.json`：窄范围正向观察请求。
|
||
- `requests/hikari-no-evidence-chat.json`：no-evidence 负向观察请求。
|
||
- `requests/safety-unsupported-claim-chat.json`：安全降级讨论请求。
|
||
|
||
## 1. 前置条件
|
||
|
||
- MySQL、Redis、Milvus/Zilliz、LLM 和 embedding 配置可用。
|
||
- 安全和密钥清理不属于当前 MVP 演示范围。
|
||
- `mvp-demo` profile 会启用 mock Prometheus 和 mock CLS，让日志和指标工具返回可复现证据。
|
||
|
||
## 2. 启动服务
|
||
|
||
```powershell
|
||
mvn spring-boot:run "-Dspring-boot.run.profiles=mvp-demo"
|
||
```
|
||
|
||
服务地址：
|
||
|
||
```text
|
||
http://localhost:9900
|
||
```
|
||
|
||
## 3. Chat 诊断 Demo
|
||
|
||
最快方式：
|
||
|
||
```powershell
|
||
powershell -ExecutionPolicy Bypass -File mvp/demo/scripts/run-payment-timeout-demo.ps1
|
||
```
|
||
|
||
脚本会生成：
|
||
|
||
```text
|
||
mvp/demo/output/chat-response.json
|
||
mvp/demo/output/trace-response.json
|
||
mvp/demo/output/feedback-response.json
|
||
```
|
||
|
||
手动请求：
|
||
|
||
```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 $body
|
||
```
|
||
|
||
期望结果：
|
||
|
||
- `data.success = true`
|
||
- `data.sessionId = mvp-demo-payment-timeout-001`
|
||
- `data.answer` 包含诊断答复
|
||
|
||
## 4. 查询 Trace
|
||
|
||
```powershell
|
||
Invoke-RestMethod `
|
||
-Method Get `
|
||
-Uri "http://localhost:9900/api/diagnosis/$sessionId/trace"
|
||
```
|
||
|
||
期望结果：
|
||
|
||
- `code = 200`
|
||
- `data.session.sessionId` 等于 Chat session id
|
||
- `data.steps` 包含 planner / executor / verifier 等步骤
|
||
- `data.toolInvocations` 包含 `lookup_knowledge`、`query_logs`、`query_metrics` 等证据工具
|
||
- `data.session.selfEvaluation` 包含 verifier 或 rule evaluation
|
||
|
||
## 5. 提交反馈
|
||
|
||
```powershell
|
||
$feedback = @{
|
||
sessionId = $sessionId
|
||
feedback = "useful"
|
||
} | ConvertTo-Json
|
||
|
||
Invoke-RestMethod `
|
||
-Method Post `
|
||
-Uri "http://localhost:9900/api/feedback" `
|
||
-ContentType "application/json" `
|
||
-Body $feedback
|
||
```
|
||
|
||
期望结果：
|
||
|
||
- `success = true`
|
||
- 后续 Trace 中 `data.session.feedback = useful`
|
||
- useful 反馈会尝试沉淀 `case_library`
|
||
|
||
## 6. AIOps 告警诊断 Demo
|
||
|
||
```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 $aiopsBody
|
||
```
|
||
|
||
期望结果：
|
||
|
||
- SSE 首条包含 `session` 消息，sessionId 为 `mvp-demo-aiops-payment-cpu-001`
|
||
- 后续流式输出包含 AIOps 告警分析报告
|
||
- 报告聚焦输入的 `HighCPUUsage/payment-service`
|
||
- 同一 session 的 Trace 中 `data.session.agentFlow = AI_OPS`
|
||
- `data.session.answer` 包含最终告警报告
|
||
- `data.toolInvocations` 包含证据工具调用
|
||
|
||
查询 AIOps Trace：
|
||
|
||
```powershell
|
||
Invoke-RestMethod `
|
||
-Method Get `
|
||
-Uri "http://localhost:9900/api/diagnosis/$aiopsSessionId/trace"
|
||
```
|
||
|
||
## 7. Demo 主线
|
||
|
||
Chat 主线：
|
||
|
||
```text
|
||
一个 session id
|
||
-> 用户问题
|
||
-> 多 Agent 执行
|
||
-> 证据工具
|
||
-> Verifier / self_evaluation
|
||
-> 最终答案
|
||
-> 用户反馈
|
||
-> Trace API 回放
|
||
```
|
||
|
||
AIOps 主线：
|
||
|
||
```text
|
||
一个 session id
|
||
-> 告警 payload
|
||
-> AIOps Planner / Executor
|
||
-> 证据工具
|
||
-> 告警分析报告
|
||
-> AIOps rule evaluation
|
||
-> Trace API 回放
|
||
```
|
||
|
||
## 8. Evidence Pipeline 场景矩阵
|
||
|
||
面试时不要把所有安全场景都压到 live LLM 现场表现上。建议使用：
|
||
|
||
- `scripts/run-payment-timeout-demo.ps1` 跑主路径。
|
||
- `evidence-pipeline-scenarios.md` 讲解 PASS / LOW_CONFID / REJECT / no-evidence 矩阵。
|
||
- `mvp/eval/reports/baseline-report.md` 证明固定 fixture 10/10 通过。
|
||
|
||
这样可以同时展示真实链路和确定性回归能力。
|