392 lines
12 KiB
Markdown
392 lines
12 KiB
Markdown
# 当前 MVP 架构
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**更新日期**：2026-07-05
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**状态**：当前可运行架构
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**适用范围**：Demo、面试讲解、后续迭代规划
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## 1. 系统定位
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SuperBizAgent MVP 不是通用 Chatbot，而是面向故障诊断的 Agent 工程项目。
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核心目标：
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- 支持用户主动发起的 Chat 诊断。
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- 支持 AIOps 告警触发的自动诊断。
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- 保留 Agent 的规划、执行、验证过程。
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- 工具调用必须显式、可追踪、可回放。
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- RAG 检索必须通过 `lookup_knowledge` 暴露证据链。
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- 每次诊断都沉淀 session、step、tool invocation 和 self evaluation。
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## 2. 总体分层
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```mermaid
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flowchart TB
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subgraph API["API Layer"]
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ChatController["ChatController"]
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TraceController["DiagnosisTraceController"]
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SearchController["SearchController"]
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DocumentController["DocumentController"]
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end
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subgraph App["Application Service"]
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ChatService["ChatService"]
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AiOpsService["AiOpsService"]
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TraceService["DiagnosisTraceService"]
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end
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subgraph Agent["Agent Orchestration"]
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Supervisor["Supervisor"]
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Planner["Planner"]
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Executor["Executor"]
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Verifier["Verifier"]
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end
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subgraph Tools["Evidence Tools"]
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KnowledgeTool["lookup_knowledge"]
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LogsTool["query_logs"]
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MetricsTool["query_metrics"]
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AlertsTool["queryPrometheusAlerts"]
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end
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subgraph RAG["RAG Retrieval"]
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L0["KnowledgeIndexService"]
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VectorSearch["VectorSearchService"]
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VectorStore["Spring AI VectorStore"]
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SdkFallback["Milvus SDK fallback"]
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end
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subgraph Store["Persistence and Trace"]
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Session["diagnosis_session"]
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Step["agent_step"]
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Invocation["tool_invocation"]
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ApiDoc["api_document"]
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Milvus["Milvus/Zilliz"]
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end
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API --> App
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ChatService --> Agent
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AiOpsService --> Agent
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Agent --> Tools
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KnowledgeTool --> RAG
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RAG --> Store
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Tools --> Invocation
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Agent --> Step
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App --> Session
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TraceService --> Session
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TraceService --> Step
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TraceService --> Invocation
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```
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```text
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API Layer
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-> ChatController
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-> DiagnosisTraceController
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-> SearchController
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-> DocumentController
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Application Service
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-> ChatService
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-> AiOpsService
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-> DiagnosisTraceService
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Agent Orchestration
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-> Supervisor
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-> Planner
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-> Executor
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-> Verifier
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Evidence Tools
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-> lookup_knowledge
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-> query_logs
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-> query_metrics
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-> queryPrometheusAlerts
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RAG Retrieval
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-> KnowledgeIndexService
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-> VectorSearchService
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-> Spring AI VectorStore
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-> Milvus SDK fallback
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Persistence
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-> diagnosis_session
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-> agent_step
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-> tool_invocation
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-> api_document
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-> Milvus/Zilliz collection
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Quality Gates
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-> chat verifier
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-> AIOps rule evaluation
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-> diagnosis eval baseline
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-> RAG retrieval baseline
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```
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## 3. Chat 诊断链路
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```mermaid
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sequenceDiagram
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autonumber
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actor User as 用户
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participant API as POST /api/chat
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participant Chat as ChatService
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participant Planner as Planner Agent
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participant Executor as Executor Agent
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participant Tool as Evidence Tools
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participant Verifier as Verifier Agent
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participant DB as Trace Tables
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participant Trace as Trace API
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User->>API: 提交诊断问题
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API->>Chat: execute chat strategy
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Chat->>Planner: 复杂问题进入规划
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Planner->>DB: 写入 agent_step
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Planner->>Executor: 下发排查方向
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Executor->>Tool: lookup_knowledge / logs / metrics
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Tool->>DB: 写入 tool_invocation
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Tool-->>Executor: 返回证据
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Executor->>Verifier: 生成候选诊断并校验
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Verifier->>DB: 合并 self_evaluation.verifier_evaluation
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Chat->>DB: 保存 diagnosis_session.answer
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User->>Trace: GET /api/diagnosis/{sessionId}/trace
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Trace->>DB: 聚合 session / step / tool
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Trace-->>User: 返回可回放诊断链路
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```
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```text
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POST /api/chat
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-> ChatService
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-> 简单问题：轻量回答
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-> 复杂诊断：Agent 编排
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-> Planner 制定排查方向
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-> Executor 调用证据工具
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-> lookup_knowledge
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-> query_logs
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-> query_metrics
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-> Verifier 校验最终诊断
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-> 保存 diagnosis_session
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-> 保存 agent_step
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-> 保存 tool_invocation
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-> 合并 self_evaluation.verifier_evaluation
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```
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Chat 链路的质量门禁是 LLM Verifier。Verifier 输出合并到 `diagnosis_session.self_evaluation.verifier_evaluation`，Trace API 会展示该验证结果。
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Agent 编排细节见 [agent-orchestration.md](agent-orchestration.md)。
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关键代码：
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- `src/main/java/com/superbiz/agent/controller/ChatController.java`
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- `src/main/java/com/superbiz/agent/service/ChatService.java`
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- `src/main/java/com/superbiz/agent/service/SelfEvaluationMergeService.java`
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- `src/main/java/com/superbiz/agent/service/DiagnosisTraceService.java`
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## 4. AIOps 诊断链路
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```mermaid
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flowchart TD
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Request["POST /api/ai_ops"] --> Payload{"包含告警 payload?"}
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Payload -->|是| Targeted["PAYLOAD_TARGETED"]
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Payload -->|否| Discovery["AUTO_DISCOVERY"]
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Targeted --> BuildPrompt["构造聚焦 payload 的诊断 prompt"]
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Targeted --> QueryAug["生成 recommended lookup_knowledge query"]
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Discovery --> DiscoverAlert["通过 queryPrometheusAlerts 发现活跃告警"]
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BuildPrompt --> Plan["Planner 规划排查"]
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QueryAug --> Plan
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DiscoverAlert --> Plan
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Plan --> Execute["Executor 收集证据"]
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Execute --> Knowledge["lookup_knowledge"]
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Execute --> Metrics["query_metrics / Prometheus"]
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Execute --> Logs["query_logs"]
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Knowledge --> Report["告警分析报告"]
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Metrics --> Report
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Logs --> Report
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Report --> RuleEval["AiOpsRuleEvaluationService"]
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RuleEval --> SelfEval["self_evaluation.aiops_rule_evaluation"]
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Report --> Trace["DiagnosisTraceService"]
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SelfEval --> Trace
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```
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```text
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POST /api/ai_ops
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-> AiOpsService
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-> 判断是否有告警 payload
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-> PAYLOAD_TARGETED
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-> AUTO_DISCOVERY
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-> 构造 AIOps 诊断 prompt
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-> payload 模式补充 recommended lookup_knowledge query
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-> Agent 编排
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-> Planner / Executor
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-> Prometheus / logs / knowledge tools
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-> 生成告警分析报告
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-> AiOpsRuleEvaluationService
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-> 合并 self_evaluation.aiops_rule_evaluation
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-> Trace API 可查看全链路
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```
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AIOps 保留两种模式：
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| 模式 | 触发条件 | 行为 |
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| `PAYLOAD_TARGETED` | 请求包含 alertName、service、severity、description、timeRange 等字段 | 以 payload 为唯一主诊断对象，并生成推荐知识库 query |
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| `AUTO_DISCOVERY` | 请求没有明确告警 payload | 先查询当前活跃告警，再选择目标排查 |
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AIOps 当前使用轻量规则验证器，重点检查：
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- 最终报告是否存在。
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- payload 模式是否聚焦输入告警。
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- 是否使用关键证据工具，例如 `lookup_knowledge`、日志、指标。
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关键代码：
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- `src/main/java/com/superbiz/agent/service/AiOpsService.java`
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- `src/main/java/com/superbiz/agent/service/AiOpsRuleEvaluationService.java`
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## 5. RAG 位置
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RAG 不是隐藏在 Chat Advisor 里的隐式能力，而是 Executor 可以显式调用的工具：
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```mermaid
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flowchart LR
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Executor["Executor Agent"] --> Tool["lookup_knowledge Tool"]
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Tool --> L0["L0 domain/entity hint"]
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Tool --> Search["VectorSearchService"]
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L0 --> Search
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Search --> VectorStore["Spring AI VectorStore"]
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Search --> Fallback["Milvus SDK fallback"]
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VectorStore --> Normalize["score/rawScore/scoreLabel"]
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Fallback --> Normalize
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Normalize --> Evidence["evidence output"]
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Evidence --> Invocation["tool_invocation"]
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Evidence --> Executor
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```
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```text
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Executor
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-> lookup_knowledge(query)
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-> L0 domain/entity hint
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-> VectorSearchService
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-> Spring AI VectorStore
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-> Milvus SDK fallback
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-> evidence shaping
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-> tool_invocation
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```
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保留显式工具的原因：
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- Agent 何时检索、检索什么、证据是什么，必须能在 trace 中解释。
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- AIOps payload 到 query 的业务映射需要项目内控制。
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- `tool_invocation` 是后续评测、回放和面试讲解的核心材料。
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RAG 总体设计见 [rag-architecture.md](rag-architecture.md)，检索运行细节见 [retrieval-observability.md](retrieval-observability.md)。
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## 6. 持久化模型
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当前诊断持久化以三张表为核心：
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```text
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diagnosis_session
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-> 一次诊断会话的主记录
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-> query / status / agent_flow / answer
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-> self_evaluation
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-> step_count / tool_call_count / duration
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agent_step
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-> Agent 模型调用步骤
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-> step_index / agent_name
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-> model_input / model_output / thought
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-> duration / token_count
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tool_invocation
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-> 工具调用事实
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-> tool_name / input_params / output_preview
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-> retrieval_layer / retrieval_details
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-> relevance_level / dedup_reason
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-> duration / success
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```
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说明：
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- 旧的 `diagnosis_record` 已不是当前主模型，迁移脚本中已经由 `diagnosis_session + agent_step + tool_invocation` 取代。
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- `api_document` 仍用于文档元数据管理。
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- 文档向量内容存放在 Milvus/Zilliz collection 中。
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会话和 Trace 生命周期见 [session-trace-lifecycle.md](session-trace-lifecycle.md)，完整数据关系见 [data-model.md](data-model.md)。
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## 7. Trace API
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```text
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GET /api/diagnosis/{sessionId}/trace
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```
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Trace API 聚合：
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- 会话状态和最终报告。
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- Agent step 序列。
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- 工具调用和检索细节。
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- Chat verifier 结果。
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- AIOps rule evaluation 结果。
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Trace 是本项目区别于普通问答系统的关键：答案不是孤立文本，而是可以追溯到 Agent 决策、工具调用和证据来源。
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Prompt、Hook、Verifier 和评测门禁的完整说明见 [harness-quality-gates.md](harness-quality-gates.md)，用户反馈与 `self_evaluation` 闭环见 [feedback-architecture.md](feedback-architecture.md)。
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## 8. 质量门禁
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当前质量门禁分层如下：
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| 门禁 | 位置 | 作用 |
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| Chat Verifier | `ChatService` | 校验普通诊断回答质量 |
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| AIOps Rule Evaluation | `AiOpsRuleEvaluationService` | 校验告警诊断是否聚焦 payload 并使用证据 |
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| Diagnosis Eval Baseline | `mvp/eval/` | 固化诊断 trace 和报告行为 |
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| RAG Retrieval Baseline | `eval/rag-retrieval/` | 固化检索召回行为，避免 RAG 重构回退 |
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| Live RAG Acceptance | `scripts/eval_rag_live_acceptance.py` | 在运行环境中验证重建索引后的真实检索 |
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## 9. 当前完成状态
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已经完成：
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- Chat 和 AIOps 两条入口链路。
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- 显式 `lookup_knowledge` Agent Tool。
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- L0 从最终决策降级为 domain/entity hint。
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- `VectorSearchService` 作为稳定检索门面。
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- Spring AI VectorStore 读取路径。
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- Milvus SDK fallback。
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- `score` / `rawScore` / `scoreLabel` 分数语义拆分。
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- `title`、`breadcrumb`、`content` 参与 embedding 文本。
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- `tool_invocation` 记录检索层、relevance level、dedup reason。
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- Chat verifier 和 AIOps rule evaluation 合并进 `self_evaluation`。
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- RAG offline baseline 和 live acceptance 脚本。
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暂不作为当前已完成能力声明：
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- 完整 QueryTransformer / MultiQuery。
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- BM25、RRF、cross-encoder rerank。
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- 完整邻居 chunk / section context expansion。
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- VectorStore 写入路径全面迁移。
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- 完整 LLM-based AIOps verifier。
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后续 Agent 拆分、Skill/Playbook、MCP 工具协议化和进程隔离等方向见 [evolution-roadmap.md](evolution-roadmap.md)。
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## 10. 关键代码索引
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| 能力 | 代码 |
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| Chat 入口与编排 | `ChatController`, `ChatService` |
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| AIOps 入口与编排 | `ChatController.aiOps`, `AiOpsService` |
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| AIOps 规则验证 | `AiOpsRuleEvaluationService` |
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| 知识库工具 | `LookupKnowledgeTool` |
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| L0 hint | `KnowledgeIndexService` |
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| 向量检索门面 | `VectorSearchService` |
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| 文档切片 | `DocumentChunkService` |
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| 向量写入 | `VectorIndexService` |
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| Spring AI VectorStore 配置辅助 | `SpringAiVectorStoreSidecarService` |
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| Trace 聚合 | `DiagnosisTraceService` |
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| 工具调用记录 | `ToolInvocationRecorder` |
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| self_evaluation 合并 | `SelfEvaluationMergeService` |
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