382 lines
16 KiB
Java
382 lines
16 KiB
Java
package com.superbiz.agent.service;
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import org.springframework.ai.chat.model.ChatModel;
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import com.alibaba.cloud.ai.graph.OverAllState;
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import com.alibaba.cloud.ai.graph.agent.ReactAgent;
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import com.alibaba.cloud.ai.graph.agent.flow.agent.SupervisorAgent;
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import com.alibaba.cloud.ai.graph.agent.hook.Hook;
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import com.alibaba.cloud.ai.graph.agent.hook.skills.SkillsAgentHook;
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import com.alibaba.cloud.ai.graph.exception.GraphRunnerException;
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import com.alibaba.cloud.ai.graph.skills.registry.SkillRegistry;
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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.DiagnosisSession;
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import com.superbiz.agent.dto.AIOpsRequest;
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import com.superbiz.agent.hook.AgentLoggingHook;
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import com.superbiz.agent.hook.PlannerSkillMetadataHook;
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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.repository.ToolInvocationRepository;
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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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import org.springframework.ai.tool.ToolCallback;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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import com.superbiz.agent.config.AiOpsPromptProperties;
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import com.superbiz.agent.tool.LookupKnowledgeTool;
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import java.util.List;
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import java.util.Map;
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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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* 负责构建 Planner、Executor、Supervisor 多 Agent 编排流程，并持久化诊断会话与执行指标。
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*/
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@Service
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public class AiOpsService {
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private static final Logger logger = LoggerFactory.getLogger(AiOpsService.class);
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@Autowired
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private DateTimeTools dateTimeTools;
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@Autowired
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private InternalDocsTools internalDocsTools;
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@Autowired
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private QueryMetricsTools queryMetricsTools;
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@Autowired(required = false) // Mock 模式下才注册本地日志查询工具。
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private QueryLogsTools queryLogsTools;
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@Autowired
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private LookupKnowledgeTool lookupKnowledgeTool;
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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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@Autowired
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private ToolInvocationRepository toolInvocationRepository;
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@Autowired
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private AiOpsRuleEvaluationService aiOpsRuleEvaluationService;
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@Autowired
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private SelfEvaluationMergeService selfEvaluationMergeService;
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@Autowired(required = false)
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private SkillRegistry skillRegistry;
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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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* @param chatModel 大模型实例
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* @param toolCallbacks Spring AI 工具回调
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* @return 多 Agent 编排后的最终状态
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* @throws GraphRunnerException Agent 图执行失败时抛出
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*/
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public Optional<OverAllState> executeAiOpsAnalysis(ChatModel chatModel, ToolCallback[] toolCallbacks) throws GraphRunnerException {
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return executeAiOpsAnalysis(chatModel, toolCallbacks, null, resolveSessionId(null));
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}
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public Optional<OverAllState> executeAiOpsAnalysis(ChatModel chatModel, ToolCallback[] toolCallbacks,
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AIOpsRequest request, String sessionId) throws GraphRunnerException {
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logger.info("Starting AI Ops multi-agent analysis");
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String resolvedSessionId = isBlank(sessionId) ? resolveSessionId(request) : sessionId.trim();
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long startTime = System.currentTimeMillis();
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DiagnosisSession session = startDiagnosisSession(resolvedSessionId, request);
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diagnosisSessionRepository.save(session);
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// 让工具调用、Hook 和知识库检索能够拿到当前诊断会话 ID。
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SessionContextHolder.setSessionId(resolvedSessionId);
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try {
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ReactAgent plannerAgent = buildPlannerAgent(chatModel, toolCallbacks);
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ReactAgent executorAgent = buildExecutorAgent(chatModel, toolCallbacks);
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SupervisorAgent supervisorAgent = SupervisorAgent.builder()
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.name("ai_ops_supervisor")
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.description("Coordinates Planner and Executor agents")
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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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String taskPrompt = buildTaskPrompt(request);
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logger.info("Invoking AI Ops supervisor agent");
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Optional<OverAllState> stateOptional = supervisorAgent.invoke(taskPrompt);
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long duration = System.currentTimeMillis() - startTime;
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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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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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}
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/**
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* 从多 Agent 执行状态中提取最终报告文本。
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*
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* @param state Agent 图执行状态
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* @return Planner 输出中的最终报告
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*/
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public Optional<String> extractFinalReport(OverAllState state) {
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logger.info("Extracting final AI Ops report");
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Optional<AssistantMessage> plannerFinalOutput = state.value("planner_plan")
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.filter(AssistantMessage.class::isInstance)
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.map(AssistantMessage.class::cast);
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if (plannerFinalOutput.isPresent()) {
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String reportText = plannerFinalOutput.get().getText();
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logger.info("Extracted Planner final report, length: {}", reportText.length());
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return Optional.of(reportText);
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} else {
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logger.warn("Unable to extract Planner final report");
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return Optional.empty();
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}
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}
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public String resolveSessionId(AIOpsRequest request) {
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if (request != null && !isBlank(request.getSessionId())) {
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return request.getSessionId().trim();
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}
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return UUID.randomUUID().toString();
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}
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public void persistFinalReport(String sessionId, String finalReport) {
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persistFinalReport(sessionId, finalReport, null);
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}
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public void persistFinalReport(String sessionId, String finalReport, AIOpsRequest request) {
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if (isBlank(sessionId) || isBlank(finalReport)) {
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return;
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}
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diagnosisSessionRepository.findBySessionId(sessionId.trim()).ifPresent(session -> {
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session.setAnswer(finalReport);
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List<com.superbiz.agent.domain.entity.ToolInvocation> invocations =
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toolInvocationRepository.findBySessionIdOrderByIdAsc(session.getSessionId());
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Map<String, Object> evaluation = aiOpsRuleEvaluationService.evaluate(request, finalReport, invocations);
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session.setSelfEvaluation(selfEvaluationMergeService.mergeAiOpsRuleEvaluation(
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session.getSelfEvaluation(), evaluation));
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diagnosisSessionRepository.save(session);
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});
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}
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String buildQuerySummary(AIOpsRequest request) {
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if (request == null) {
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return "AI Ops alert analysis";
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}
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StringBuilder summary = new StringBuilder("AI Ops alert analysis");
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appendField(summary, "alert", request.getAlertName());
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appendField(summary, "service", request.getService());
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appendField(summary, "severity", request.getSeverity());
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appendField(summary, "timeRange", request.getTimeRange());
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appendField(summary, "description", request.getDescription());
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appendField(summary, "request", request.getUserRequest());
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return summary.toString();
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}
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boolean hasAlertPayload(AIOpsRequest request) {
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if (request == null) {
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return false;
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}
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return !isBlank(request.getAlertName())
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|| !isBlank(request.getService())
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|| !isBlank(request.getSeverity())
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|| !isBlank(request.getDescription())
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|| !isBlank(request.getTimeRange());
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}
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String buildKnowledgeRetrievalQuery(AIOpsRequest request) {
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if (request == null || !hasAlertPayload(request)) {
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return "";
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}
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StringBuilder query = new StringBuilder();
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appendQueryTerm(query, request.getAlertName());
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appendQueryTerm(query, request.getService());
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appendQueryTerm(query, request.getSeverity());
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appendQueryTerm(query, request.getDescription());
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appendQueryTerm(query, request.getTimeRange());
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appendQueryTerm(query, request.getUserRequest());
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return query.toString();
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}
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String buildTaskPrompt(AIOpsRequest request) {
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StringBuilder prompt = new StringBuilder();
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prompt.append("You are an enterprise SRE handling an automated alert diagnosis task. Combine tool evidence, run a plan-execute-replan loop, and output the final alert analysis report. Do not fabricate data; if repeated queries fail, clearly state why the task cannot be completed.");
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prompt.append("\n\nAlert input:\n");
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prompt.append(buildQuerySummary(request));
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if (hasAlertPayload(request)) {
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String knowledgeQuery = buildKnowledgeRetrievalQuery(request);
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prompt.append("\n\nAIOps scope mode: PAYLOAD_TARGETED\n");
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prompt.append("- The request includes an alert payload. Treat the supplied alert payload as the primary and only main diagnosis target.\n");
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prompt.append("- The final report must focus on the supplied alert fields such as alertName, service, severity, description, and timeRange.\n");
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prompt.append("- Recommended lookup_knowledge query: ").append(knowledgeQuery).append("\n");
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prompt.append("- If knowledge-base evidence is needed, call lookup_knowledge with the recommended query or a narrower query that preserves alertName and service.\n");
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prompt.append("- You may call queryPrometheusAlerts only to verify whether the supplied alert is still active or to identify related risk/context.\n");
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prompt.append("- If queryPrometheusAlerts returns unrelated active alerts, do not create full root-cause or remediation sections for them.\n");
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prompt.append("- Mention unrelated active alerts only briefly in a Related Risk section when they help explain the supplied alert.\n");
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} else {
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prompt.append("\n\nAIOps scope mode: AUTO_DISCOVERY\n");
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prompt.append("- The request does not include alert payload fields. First call queryPrometheusAlerts to discover current active/firing alerts.\n");
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prompt.append("- Prefer P0/P1 alerts or the longest-running firing alerts, then diagnose one or more alerts based on severity and evidence.\n");
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prompt.append("- Use metrics, logs, and knowledge-base evidence before producing the final alert analysis report.\n");
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}
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return prompt.toString();
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}
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private DiagnosisSession startDiagnosisSession(String sessionId, AIOpsRequest request) {
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DiagnosisSession session = diagnosisSessionRepository.findBySessionId(sessionId)
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.orElseGet(() -> DiagnosisSession.builder()
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.sessionId(sessionId)
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.agentFlow("AI_OPS")
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.build());
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session.setQuery(buildQuerySummary(request));
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session.setStatus("RUNNING");
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session.setAgentFlow("AI_OPS");
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session.setAnswer(null);
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session.setTotalDurationMs(null);
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session.setTotalTokenCount(null);
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session.setStepCount(null);
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session.setToolCallCount(null);
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return session;
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}
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/**
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* 构建 Planner Agent。
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*/
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private ReactAgent buildPlannerAgent(ChatModel chatModel, ToolCallback[] toolCallbacks) {
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return ReactAgent.builder()
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.name("planner_agent")
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.description("Plans alert diagnosis steps")
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.model(chatModel)
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.systemPrompt(promptProperties.getPlanner())
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.hooks(buildHooks("planner"))
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.outputKey("planner_plan")
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.build();
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}
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/**
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* 构建 Executor Agent。
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*/
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private ReactAgent buildExecutorAgent(ChatModel chatModel, ToolCallback[] toolCallbacks) {
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return ReactAgent.builder()
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.name("executor_agent")
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.description("Executes the current Planner step and reports feedback")
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.model(chatModel)
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.systemPrompt(promptProperties.getExecutor())
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.methodTools(buildMethodToolsArray())
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.tools(toolCallbacks)
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.hooks(buildHooks("executor"))
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.outputKey("executor_feedback")
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.build();
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}
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/**
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* 根据运行模式构建方法工具数组。
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* Mock 模式注入本地 QueryLogsTools；真实模式下日志查询由外部 MCP 工具提供。
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*/
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private Object[] buildMethodToolsArray() {
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if (queryLogsTools != null) {
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return new Object[]{dateTimeTools, lookupKnowledgeTool, queryMetricsTools, queryLogsTools};
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}
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return new Object[]{dateTimeTools, lookupKnowledgeTool, queryMetricsTools};
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}
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private Hook[] buildHooks(String agentName) {
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AgentLoggingHook loggingHook = new AgentLoggingHook(agentStepRepository, agentName);
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if (skillRegistry == null || skillRegistry.size() == 0) {
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return new Hook[]{loggingHook};
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}
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if ("planner".equals(agentName)) {
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return new Hook[]{
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new PlannerSkillMetadataHook(skillRegistry),
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loggingHook
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};
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}
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return new Hook[]{
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loggingHook,
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SkillsAgentHook.builder()
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.skillRegistry(skillRegistry)
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.build()
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};
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}
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/**
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* 从 agent_step 和 tool_invocation 回填 diagnosis_session 的汇总指标。
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*/
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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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int totalTokens = 0;
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int stepCount = 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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}
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long toolCallCount = toolInvocationRepository.countBySessionId(session.getSessionId());
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session.setTotalTokenCount(totalTokens);
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session.setStepCount(stepCount);
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session.setToolCallCount(Math.toIntExact(toolCallCount));
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} catch (Exception e) {
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logger.warn("Failed to backfill AI Ops session metrics, sessionId={}", session.getSessionId(), e);
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}
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}
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private void appendField(StringBuilder builder, String label, String value) {
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if (!isBlank(value)) {
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builder.append("\n- ").append(label).append(": ").append(value.trim());
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}
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}
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private void appendQueryTerm(StringBuilder builder, String value) {
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if (!isBlank(value)) {
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if (!builder.isEmpty()) {
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builder.append(' ');
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}
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builder.append(value.trim());
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}
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}
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private boolean isBlank(String value) {
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return value == null || value.trim().isEmpty();
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}
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}
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