148 lines
3.5 KiB
Markdown
148 lines
3.5 KiB
Markdown
# Architecture
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## 系统分层
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```text
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API Layer
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-> ChatController / DiagnosisTraceController
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Agent Orchestration
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-> ChatService / AiOpsService
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Tools
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-> lookupKnowledgeTool / queryLogs / queryMetrics / queryPrometheusAlerts
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Persistence
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-> diagnosis_session / agent_step / tool_invocation
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Trace
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-> GET /api/diagnosis/{sessionId}/trace
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```
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## Chat 链路
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```mermaid
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flowchart TD
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User[User Question] --> ChatAPI[POST /api/chat]
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ChatAPI --> Strategy[ChatService.executeChatWithStrategy]
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Strategy --> Complexity{QuestionComplexity}
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Complexity -->|simple| Single[ReactAgent]
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Complexity -->|complex| Planner[Planner Agent]
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Planner --> Executor[Executor Agent]
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Executor --> Tools[Evidence Tools]
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Tools --> Executor
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Executor --> Verifier[Verifier Agent]
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Verifier --> Answer[Final Answer]
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Answer --> Session[diagnosis_session]
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Planner --> Steps[agent_step]
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Executor --> Steps
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Verifier --> Steps
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Tools --> Invocations[tool_invocation]
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Session --> Trace[GET /api/diagnosis/{sessionId}/trace]
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Steps --> Trace
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Invocations --> Trace
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```
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关键代码：
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- `ChatController.chat(...)`
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- `ChatService.executeChatWithStrategy(...)`
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- `ChatService.executeChatComplex(...)`
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- `AgentLoggingHook`
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- `ToolInvocationRecorder`
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- `DiagnosisTraceService.getTrace(...)`
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## AIOps 链路
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```mermaid
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flowchart TD
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Alert[Alert Payload or Empty Request] --> AiOpsAPI[POST /api/ai_ops]
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AiOpsAPI --> SessionEvent[SSE session event]
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AiOpsAPI --> AiOpsService[AiOpsService.executeAiOpsAnalysis]
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AiOpsService --> PromptMode{Payload?}
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PromptMode -->|yes| Targeted[PAYLOAD_TARGETED]
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PromptMode -->|no| Discovery[AUTO_DISCOVERY]
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Targeted --> Supervisor[ai_ops_supervisor]
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Discovery --> Supervisor
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Supervisor --> Planner[planner_agent]
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Supervisor --> Executor[executor_agent]
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Planner --> Tools[Prometheus / Logs / Knowledge]
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Executor --> Tools
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Tools --> Report[Alert Report]
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Report --> Persist[diagnosis_session.answer]
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Planner --> Steps[agent_step]
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Executor --> Steps
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Tools --> Invocations[tool_invocation]
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Persist --> Trace[GET /api/diagnosis/{sessionId}/trace]
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Steps --> Trace
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Invocations --> Trace
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```
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关键代码：
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- `ChatController.aiOps(...)`
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- `AIOpsRequest`
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- `AiOpsService.resolveSessionId(...)`
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- `AiOpsService.buildTaskPrompt(...)`
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- `AiOpsService.hasAlertPayload(...)`
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- `AiOpsService.persistFinalReport(...)`
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## Trace 数据模型
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### `diagnosis_session`
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记录一次诊断会话的主信息：
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- `session_id`
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- `query`
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- `status`
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- `agent_flow`
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- `total_duration_ms`
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- `total_token_count`
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- `step_count`
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- `tool_call_count`
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- `answer`
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- `self_evaluation`
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- `feedback`
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### `agent_step`
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记录 Agent 模型调用过程：
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- `session_id`
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- `step_index`
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- `agent_name`
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- `model_input`
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- `model_output`
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- `thought`
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- `has_tool_call`
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- `duration_ms`
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- `token_count`
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### `tool_invocation`
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记录真实工具调用：
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- `session_id`
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- `tool_name`
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- `input_params`
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- `output_preview`
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- `output_length`
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- `retrieval_layer`
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- `relevance_level`
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- `duration_ms`
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- `success`
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- `error_message`
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## 为什么 trace 是核心
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Agent 系统的风险不只是“答案错”，还包括“答案看起来对但无法解释”。这个项目把执行链路拆成 session、step、tool 三层，让面试官可以看到：
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- 模型为什么这么答
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- 调了哪些工具
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- 工具返回了什么证据
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- Verifier 如何判断答案可信度
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- 用户反馈如何回写到同一个 session
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这就是项目区别于普通 Chatbot 的地方。
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