zhuyongxin
bb44140901
- RetrievedDocTracker: sessionId → Set<filePath> 会话级去重，LookupKnowledgeTool Step 5 过滤已检索文档 - KnowledgeDomainService: 域级聚合，LLM 生成 when_to_retrieve，构建 knowledge map YAML - DocumentFieldEnricher: 上传时 LLM 补全 covers + whenToRetrieve（含同域文档排除上下文） - KnowledgeDomain entity + V009 迁移: 域级元数据持久化，避免重启重复 LLM 调用 - ChatService: 注入 knowledge map 到 Planner prompt，会话结束时清理去重状态 - KnowledgeIndexService: 手写 JSON 解析替换为 Jackson ObjectMapper，启动时补建缺失域记录 - chat-planner-prompt: 新增知识库检索规则（按域 when_to_retrieve 判断，每域最多一次检索） - doc-field-enricher-prompt / domain-summary-prompt: 外部化 LLM 提示词
473 lines
19 KiB
Java
473 lines
19 KiB
Java
package com.superbiz.agent.service;
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import com.alibaba.cloud.ai.graph.OverAllState;
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import com.alibaba.cloud.ai.graph.RunnableConfig;
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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.exception.GraphRunnerException;
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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.DiagnosisSession;
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import com.superbiz.agent.hook.AgentLoggingHook;
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import com.superbiz.agent.hook.TokenTrackingChatModel;
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import com.superbiz.agent.hook.TokenUsageHolder;
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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.tool.LookupKnowledgeTool;
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import com.superbiz.agent.tool.RetrievedDocTracker;
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import com.superbiz.agent.util.QuestionComplexity;
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import com.superbiz.agent.util.SessionContextHolder;
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import com.superbiz.agent.service.KnowledgeDomainService;
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import jakarta.annotation.PostConstruct;
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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.chat.model.ChatModel;
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import org.springframework.ai.tool.ToolCallback;
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import org.springframework.ai.tool.ToolCallbackProvider;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.core.io.ClassPathResource;
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import org.springframework.stereotype.Service;
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import java.io.IOException;
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import java.nio.charset.StandardCharsets;
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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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* 聊天服务
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* 封装 ReactAgent 对话的公共逻辑，包括模型创建、系统提示词构建、Agent 配置等
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*/
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@Service
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public class ChatService {
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private static final Logger logger = LoggerFactory.getLogger(ChatService.class);
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/** 封装 answer + 后端生成的 sessionId，用于 feedback 关联 */
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public record ChatResult(String answer, String sessionId) {}
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@Autowired
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private InternalDocsTools internalDocsTools;
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@Autowired
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private DateTimeTools dateTimeTools;
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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，所以设置为 optional,真实环境通过mcp配置注入
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private QueryLogsTools queryLogsTools;
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@Autowired(required = false)
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private ToolCallbackProvider tools;
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@Autowired
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private ChatModel chatModel;
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@Autowired
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private LookupKnowledgeTool lookupKnowledgeTool;
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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 EvaluationService evaluationService;
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@Autowired
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private RetrievedDocTracker retrievedDocTracker;
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@Autowired
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private KnowledgeDomainService knowledgeDomainService;
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/** 多 Agent Chat 的 Prompt */
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private String chatPlannerPrompt;
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private String chatExecutorPrompt;
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@PostConstruct
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public void init() {
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// 加载 Prompt
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try {
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chatPlannerPrompt = new String(
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new ClassPathResource("prompts/chat-planner-prompt.md").getInputStream().readAllBytes(),
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StandardCharsets.UTF_8);
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chatExecutorPrompt = new String(
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new ClassPathResource("prompts/chat-executor-prompt.md").getInputStream().readAllBytes(),
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StandardCharsets.UTF_8);
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logger.info("Chat 多 Agent Prompts 加载成功");
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} catch (IOException e) {
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logger.error("加载 Chat Prompt 文件失败", e);
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throw new RuntimeException("Failed to load chat prompts", e);
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}
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// 包装 ChatModel 以捕获 token 用量
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chatModel = new TokenTrackingChatModel(chatModel);
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logger.info("ChatModel 已包装 TokenTrackingChatModel");
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}
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/**
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* 获取注入的 ChatModel
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*/
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public ChatModel getChatModel() {
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return chatModel;
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}
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/**
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* 构建系统提示词（包含历史消息）
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* @param history 历史消息列表
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* @return 完整的系统提示词
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*/
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public String buildSystemPrompt(List<Map<String, String>> history) {
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StringBuilder systemPromptBuilder = new StringBuilder();
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// 基础系统提示
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systemPromptBuilder.append("你是一个专业的智能助手，可以获取当前时间、查询天气信息、搜索内部文档知识库，以及查询 Prometheus 告警信息。\n");
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systemPromptBuilder.append("当用户询问时间相关问题时，**必须每次都调用 getCurrentDateTime 工具**，因为时间会不断变化。即使历史消息中有时间信息，也不要直接复用，必须重新查询最新时间。\n");
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systemPromptBuilder.append("当用户需要查询公司内部文档、流程、最佳实践或技术指南时，使用 lookupKnowledgeTool 工具。\n");
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systemPromptBuilder.append("当用户需要查询 Prometheus 告警、监控指标或系统告警状态时，使用 queryPrometheusAlerts 工具。\n");
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systemPromptBuilder.append("当用户需要查询腾讯云日志时，请调用腾讯云mcp服务查询,默认查询地域ap-guangzhou,查询时间范围为近一个月。\n\n");
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// 添加历史消息（过滤时间查询相关内容）
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if (!history.isEmpty()) {
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systemPromptBuilder.append("--- 对话历史 ---\n");
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for (Map<String, String> msg : history) {
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String role = msg.get("role");
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String content = msg.get("content");
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// 🔧 过滤时间查询相关的历史消息，避免 LLM 复用旧的时间信息
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if ("user".equals(role) && isTimeQuery(content)) {
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continue; // 跳过时间查询问题
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}
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if ("assistant".equals(role) && containsTimeInfo(content)) {
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continue; // 跳过包含时间信息的回答
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}
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if ("user".equals(role)) {
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systemPromptBuilder.append("用户: ").append(content).append("\n");
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} else if ("assistant".equals(role)) {
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systemPromptBuilder.append("助手: ").append(content).append("\n");
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}
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}
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systemPromptBuilder.append("--- 对话历史结束 ---\n\n");
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}
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systemPromptBuilder.append("请基于以上对话历史，回答用户的新问题。");
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return systemPromptBuilder.toString();
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}
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/**
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* 判断是否为时间查询问题
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*/
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private boolean isTimeQuery(String content) {
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if (content == null) {
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return false;
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}
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// 匹配常见的时间查询模式
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return content.matches(".*(现在|当前|此时).*(几点|时间).*") ||
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content.matches(".*(几点|时间).*(了|呢|[？?]).*") ||
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content.toLowerCase().matches(".*(what.*time|current.*time).*");
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}
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/**
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* 判断是否包含时间信息
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*/
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private boolean containsTimeInfo(String content) {
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if (content == null) {
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return false;
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}
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// 匹配日期时间格式：2026年5月31日、15:57、下午3点 等
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return content.matches(".*(\\d{4}年\\d{1,2}月\\d{1,2}日|\\d{1,2}:\\d{2}|[上下午]+\\d{1,2}[点时]).*");
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}
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/**
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* 动态构建方法工具数组
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* 根据 cls.mock-enabled 决定是否包含 QueryLogsTools
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*/
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public Object[] buildMethodToolsArray() {
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if (queryLogsTools != null) {
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// Mock 模式：包含 QueryLogsTools
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return new Object[]{dateTimeTools, lookupKnowledgeTool};
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} else {
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// 真实模式：不包含 QueryLogsTools（由 MCP 提供日志查询功能）
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return new Object[]{dateTimeTools, lookupKnowledgeTool, queryMetricsTools};
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}
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}
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/**
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* 获取工具回调列表，mcp服务提供的工具
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*/
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public ToolCallback[] getToolCallbacks() {
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if (tools == null) {
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return new ToolCallback[0];
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}
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return tools.getToolCallbacks();
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}
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/**
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* 记录可用工具列表：mcp服务提供的工具
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*/
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public void logAvailableTools() {
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if (tools == null) {
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logger.info("MCP 未启用，无远程工具");
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return;
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}
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ToolCallback[] toolCallbacks = tools.getToolCallbacks();
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logger.info("可用工具列表:");
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for (ToolCallback toolCallback : toolCallbacks) {
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logger.info(">>> {}", toolCallback.getToolDefinition().name());
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}
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}
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/**
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* 创建 ReactAgent
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* @param chatModel 聊天模型
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* @param systemPrompt 系统提示词
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* @return 配置好的 ReactAgent
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*/
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public ReactAgent createReactAgent(ChatModel chatModel, String systemPrompt) {
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return ReactAgent.builder()
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.name("intelligent_assistant")
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.model(chatModel)
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.systemPrompt(systemPrompt)
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.methodTools(buildMethodToolsArray())
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.tools(getToolCallbacks())
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.hooks(new AgentLoggingHook(agentStepRepository, "intelligent_assistant"))
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.build();
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}
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/**
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* 执行 ReactAgent 对话（非流式）
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* @param agent ReactAgent 实例
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* @param question 用户问题
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* @return ChatResult（answer + sessionId）
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*/
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public ChatResult executeChat(ReactAgent agent, String question) throws GraphRunnerException {
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logger.info("========================================");
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logger.info("📝 用户问题: {}", question);
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String sessionId = UUID.randomUUID().toString().substring(0, 8);
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long startTime = System.currentTimeMillis();
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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(question)
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.status("RUNNING")
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.agentFlow("CHAT")
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.build();
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diagnosisSessionRepository.save(session);
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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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try {
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// 通过 RunnableConfig 将 sessionId 传入 Hook（线程安全，异步也兼容）
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var config = RunnableConfig.builder()
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.addMetadata("sessionId", sessionId)
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.build();
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var response = agent.call(question, config);
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long duration = System.currentTimeMillis() - startTime;
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String answer = response.getText();
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// 更新诊断会话
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session.setStatus("SUCCESS");
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session.setAnswer(answer);
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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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evaluationService.evaluate(sessionId, answer);
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logger.info("⏱️ 总耗时: {} ms", duration);
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logger.info("📏 输出长度: {} 字符", answer.length());
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logger.info("========================================");
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return new ChatResult(answer, sessionId);
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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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retrievedDocTracker.clearSession(sessionId);
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SessionContextHolder.clear();
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}
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}
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/**
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* 根据问题复杂度自动选择执行策略
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* @param chatModel 聊天模型
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* @param toolCallbacks 工具回调
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* @param question 用户问题
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* @param history 历史消息
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* @return ChatResult（answer + sessionId）
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*/
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public ChatResult executeChatWithStrategy(ChatModel chatModel, ToolCallback[] toolCallbacks,
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String question, List<Map<String, String>> history) throws GraphRunnerException {
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if (QuestionComplexity.isComplex(question)) {
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logger.info("📊 问题判定为复杂，使用多 Agent（Planner + Executor）执行");
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return executeChatComplex(chatModel, toolCallbacks, question, history);
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} else {
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logger.info("📊 问题判定为简单，使用单 Agent 执行");
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String systemPrompt = buildSystemPrompt(history);
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ReactAgent agent = createReactAgent(chatModel, systemPrompt);
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return executeChat(agent, question);
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}
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}
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/**
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* 多 Agent 复杂对话执行（Planner + Executor + Supervisor）
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*/
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public ChatResult executeChatComplex(ChatModel chatModel, ToolCallback[] toolCallbacks,
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String question, List<Map<String, String>> history) throws GraphRunnerException {
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String sessionId = UUID.randomUUID().toString().substring(0, 8);
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long startTime = System.currentTimeMillis();
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DiagnosisSession session = DiagnosisSession.builder()
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.sessionId(sessionId)
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.query(question)
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.status("RUNNING")
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.agentFlow("CHAT")
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.build();
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diagnosisSessionRepository.save(session);
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SessionContextHolder.setSessionId(sessionId);
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try {
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ReactAgent planner = buildChatPlannerAgent(chatModel, toolCallbacks, history);
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ReactAgent executor = buildChatExecutorAgent(chatModel, toolCallbacks, history);
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SupervisorAgent supervisor = SupervisorAgent.builder()
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.name("chat_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("你是一个智能任务调度器。分析用户问题，调用 Planner 拆解步骤，调用 Executor 执行各步骤。")
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.subAgents(List.of(planner, executor))
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.build();
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Optional<OverAllState> stateOptional = supervisor.invoke(question);
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long duration = System.currentTimeMillis() - startTime;
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String answer = null;
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if (stateOptional.isPresent()) {
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// 从 state 中提取 Executor 的最终输出
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OverAllState state = stateOptional.get();
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Optional<AssistantMessage> executorOutput = state.value("executor_feedback")
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.filter(AssistantMessage.class::isInstance)
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.map(AssistantMessage.class::cast);
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if (executorOutput.isPresent()) {
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answer = executorOutput.get().getText();
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}
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}
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if (answer == null || answer.isBlank()) {
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answer = "抱歉，多 Agent 分析未能生成有效结论。";
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}
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session.setStatus("SUCCESS");
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session.setAnswer(answer);
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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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evaluationService.evaluate(sessionId, answer);
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logger.info("⏱️ 多 Agent 总耗时: {} ms", duration);
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logger.info("📏 输出长度: {} 字符", answer.length());
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return new ChatResult(answer, sessionId);
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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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logger.error("多 Agent 执行失败", e);
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return new ChatResult("执行失败: " + e.getMessage(), sessionId);
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} finally {
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retrievedDocTracker.clearSession(sessionId);
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SessionContextHolder.clear();
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}
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}
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private ReactAgent buildChatPlannerAgent(ChatModel chatModel, ToolCallback[] toolCallbacks,
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List<Map<String, String>> history) {
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StringBuilder prompt = new StringBuilder(chatPlannerPrompt);
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// 注入 knowledge map
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String knowledgeMap = knowledgeDomainService.buildKnowledgeMap();
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if (!knowledgeMap.isBlank()) {
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prompt.append("\n\n## 可用知识库\n\n").append(knowledgeMap);
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}
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if (!history.isEmpty()) {
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prompt.append("\n\n--- 对话历史 ---\n");
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for (Map<String, String> msg : history) {
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prompt.append(msg.get("role")).append(": ").append(msg.get("content")).append("\n");
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}
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prompt.append("--- 对话历史结束 ---\n");
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}
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return ReactAgent.builder()
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.name("chat_planner")
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.description("负责拆解问题、规划步骤")
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.model(chatModel)
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.systemPrompt(prompt.toString())
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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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private ReactAgent buildChatExecutorAgent(ChatModel chatModel, ToolCallback[] toolCallbacks,
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List<Map<String, String>> history) {
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StringBuilder prompt = new StringBuilder(chatExecutorPrompt);
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if (!history.isEmpty()) {
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prompt.append("\n\n--- 对话历史 ---\n");
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for (Map<String, String> msg : history) {
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prompt.append(msg.get("role")).append(": ").append(msg.get("content")).append("\n");
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}
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prompt.append("--- 对话历史结束 ---\n");
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}
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return ReactAgent.builder()
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.name("chat_executor")
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.description("负责执行具体步骤并及时反馈")
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.model(chatModel)
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||
.systemPrompt(prompt.toString())
|
||
.methodTools(buildMethodToolsArray())
|
||
.tools(toolCallbacks)
|
||
.hooks(new AgentLoggingHook(agentStepRepository, "executor"))
|
||
.outputKey("executor_feedback")
|
||
.build();
|
||
}
|
||
|
||
/** 从 agent_step 汇总 token、步数等指标回填 diagnosis_session */
|
||
private void backfillSessionMetrics(DiagnosisSession session) {
|
||
try {
|
||
List<com.superbiz.agent.domain.entity.AgentStep> steps =
|
||
agentStepRepository.findBySessionIdOrderByStepIndex(session.getSessionId());
|
||
|
||
if (steps.isEmpty()) return;
|
||
|
||
int totalTokens = 0;
|
||
int stepCount = 0;
|
||
int toolCallCount = 0;
|
||
for (var s : steps) {
|
||
stepCount++;
|
||
if (s.getTokenCount() != null) totalTokens += s.getTokenCount();
|
||
if (Boolean.TRUE.equals(s.getHasToolCall())) toolCallCount++;
|
||
}
|
||
session.setTotalTokenCount(totalTokens);
|
||
session.setStepCount(stepCount);
|
||
session.setToolCallCount(toolCallCount);
|
||
} catch (Exception e) {
|
||
logger.warn("回填会话指标失败: sessionId={}", session.getSessionId(), e);
|
||
}
|
||
}
|
||
}
|