feat(session): 会话存储体系实现 & Chat多Agent路由
- 新增诊断会话(diagnosis_session/agent_step/tool_invocation)三表 - AgentLoggingHook 持久化 agent_step，记录决策链和耗时 - LookupKnowledgeTool 写入 tool_invocation，记录L0/L1检索质量 - TokenTrackingChatModel 捕获真实token用量 - Chat接口支持意图路由：简单问题单Agent，复杂问题多Agent(Planner+Executor) - Prompt外置到 src/main/resources/prompts/ - 删除旧 diagnosis_record 表及相关文件 - 新增SessionContextHolder(ThreadLocal传递sessionId) - QuestionComplexity 复杂度判断工具 - 测试覆盖三张新表的Repository
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zhuyongxin
@@ -9,6 +9,13 @@ import com.superbiz.agent.agent.tool.DateTimeTools;
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import com.superbiz.agent.agent.tool.InternalDocsTools;
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import com.superbiz.agent.agent.tool.QueryLogsTools;
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import com.superbiz.agent.agent.tool.QueryMetricsTools;
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import com.superbiz.agent.domain.entity.AgentStep;
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import com.superbiz.agent.domain.entity.AgentStep;
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import com.superbiz.agent.domain.entity.DiagnosisSession;
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import com.superbiz.agent.hook.AgentLoggingHook;
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import com.superbiz.agent.repository.AgentStepRepository;
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import com.superbiz.agent.repository.DiagnosisSessionRepository;
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import com.superbiz.agent.util.SessionContextHolder;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.ai.chat.messages.AssistantMessage;
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@@ -20,6 +27,7 @@ import com.superbiz.agent.tool.LookupKnowledgeTool;
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import java.util.List;
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import java.util.Optional;
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import java.util.UUID;
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/**
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* AI Ops 智能运维服务
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@@ -48,6 +56,12 @@ public class AiOpsService {
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@Autowired
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private AiOpsPromptProperties promptProperties;
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@Autowired
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private DiagnosisSessionRepository diagnosisSessionRepository;
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@Autowired
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private AgentStepRepository agentStepRepository;
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/**
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* 执行 AI Ops 告警分析流程
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*
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@@ -59,34 +73,65 @@ public class AiOpsService {
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public Optional<OverAllState> executeAiOpsAnalysis(ChatModel chatModel, ToolCallback[] toolCallbacks) throws GraphRunnerException {
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logger.info("开始执行 AI Ops 多 Agent 协作流程");
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// 构建 Planner 和 Executor Agent
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ReactAgent plannerAgent = buildPlannerAgent(chatModel, toolCallbacks);
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ReactAgent executorAgent = buildExecutorAgent(chatModel, toolCallbacks);
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String sessionId = UUID.randomUUID().toString().substring(0, 8);
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long startTime = System.currentTimeMillis();
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// 构建 Supervisor Agent
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SupervisorAgent supervisorAgent = SupervisorAgent.builder()
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.name("ai_ops_supervisor")
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.description("负责调度 Planner 与 Executor 的多 Agent 控制器")
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.model(chatModel)
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.systemPrompt(promptProperties.getSupervisor())
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.subAgents(List.of(plannerAgent, executorAgent))
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// 创建诊断会话
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DiagnosisSession session = DiagnosisSession.builder()
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.sessionId(sessionId)
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.query("AI Ops 告警分析")
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.status("RUNNING")
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.agentFlow("AI_OPS")
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.build();
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diagnosisSessionRepository.save(session);
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String taskPrompt = "你是企业级 SRE，接到了自动化告警排查任务。请结合工具调用，执行**规划→执行→再规划**的闭环，并最终按照固定模板输出《告警分析报告》。禁止编造虚假数据，如连续多次查询失败需诚实反馈无法完成的原因。";
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// 设置 ThreadLocal 上下文（LookupKnowledgeTool 通过此获取 sessionId）
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SessionContextHolder.setSessionId(sessionId);
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logger.info("调用 Supervisor Agent 开始编排...");
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try {
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// 构建 Planner 和 Executor Agent（每个 Agent 各自带 Hook）
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ReactAgent plannerAgent = buildPlannerAgent(chatModel, toolCallbacks);
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ReactAgent executorAgent = buildExecutorAgent(chatModel, toolCallbacks);
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Optional<OverAllState> stateOptional = supervisorAgent.invoke(taskPrompt);
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// 构建 Supervisor Agent（不加 Hook）
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SupervisorAgent supervisorAgent = SupervisorAgent.builder()
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.name("ai_ops_supervisor")
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.description("负责调度 Planner 与 Executor 的多 Agent 控制器")
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.model(chatModel)
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.systemPrompt(promptProperties.getSupervisor())
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.subAgents(List.of(plannerAgent, executorAgent))
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.build();
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// 添加调试代码
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if (stateOptional.isPresent()) {
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OverAllState state = stateOptional.get();
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logger.debug("Final State Keys: {}", state.data().keySet()); // 打印所有 key
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logger.debug("Planner Plan: {}", state.value("planner_plan"));
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logger.debug("Executor Feedback: {}", state.value("executor_feedback"));
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String taskPrompt = "你是企业级 SRE，接到了自动化告警排查任务。请结合工具调用，执行**规划→执行→再规划**的闭环，并最终按照固定模板输出《告警分析报告》。禁止编造虚假数据，如连续多次查询失败需诚实反馈无法完成的原因。";
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logger.info("调用 Supervisor Agent 开始编排...");
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Optional<OverAllState> stateOptional = supervisorAgent.invoke(taskPrompt);
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long duration = System.currentTimeMillis() - startTime;
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// 更新诊断会话
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session.setStatus(stateOptional.isPresent() ? "SUCCESS" : "FAILED");
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session.setTotalDurationMs((int) duration);
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backfillSessionMetrics(session);
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diagnosisSessionRepository.save(session);
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// 添加调试代码
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if (stateOptional.isPresent()) {
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OverAllState state = stateOptional.get();
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logger.debug("Final State Keys: {}", state.data().keySet());
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logger.debug("Planner Plan: {}", state.value("planner_plan"));
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logger.debug("Executor Feedback: {}", state.value("executor_feedback"));
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}
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return stateOptional;
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} catch (Exception e) {
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session.setStatus("FAILED");
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diagnosisSessionRepository.save(session);
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throw e;
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} finally {
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SessionContextHolder.clear();
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}
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return stateOptional;
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}
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/**
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@@ -124,6 +169,7 @@ public class AiOpsService {
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.systemPrompt(promptProperties.getPlanner())
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.methodTools(buildMethodToolsArray())
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.tools(toolCallbacks)
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.hooks(new AgentLoggingHook(agentStepRepository, "planner"))
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.outputKey("planner_plan")
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.build();
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}
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@@ -139,6 +185,7 @@ public class AiOpsService {
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.systemPrompt(promptProperties.getExecutor())
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.methodTools(buildMethodToolsArray())
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.tools(toolCallbacks)
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.hooks(new AgentLoggingHook(agentStepRepository, "executor"))
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.outputKey("executor_feedback")
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.build();
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}
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@@ -157,4 +204,26 @@ public class AiOpsService {
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return new Object[]{dateTimeTools, lookupKnowledgeTool, queryMetricsTools};
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}
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}
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/** 从 agent_step 汇总指标回填 diagnosis_session */
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private void backfillSessionMetrics(DiagnosisSession session) {
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try {
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List<AgentStep> steps = agentStepRepository.findBySessionIdOrderByStepIndex(session.getSessionId());
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if (steps.isEmpty()) return;
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int totalTokens = 0;
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int stepCount = 0;
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int toolCallCount = 0;
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for (AgentStep s : steps) {
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stepCount++;
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if (s.getTokenCount() != null) totalTokens += s.getTokenCount();
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if (Boolean.TRUE.equals(s.getHasToolCall())) toolCallCount++;
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}
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session.setTotalTokenCount(totalTokens);
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session.setStepCount(stepCount);
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session.setToolCallCount(toolCallCount);
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} catch (Exception e) {
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logger.warn("回填会话指标失败: sessionId={}", session.getSessionId(), e);
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}
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}
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}
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