HaHafeng
52989cd03f
SSA Agent channel improvements (12 code files, +931/-203 lines): - Solution B: left/right separation of concerns (gaze guiding + state mutex + time-travel) - JWT token refresh mechanism (ensureFreshToken) to fix HTTP 401 during pipeline - Code truncation fix: LLM maxTokens 4000->8000 + CSS max-height 60vh - Retry streaming code generation with generateCodeStream() - R Docker structured errors: 20+ pattern matching + format_agent_error + line extraction - Prompt iron rules: strict output format in CoderAgent System Prompt - parseCode robustness: XML/Markdown/inference 3-tier matching + length validation - consoleOutput type defense: handle both array and scalar from R Docker unboxedJSON - Agent progress bar sync: derive phase from agentExecution.status - Export report / view code buttons restored for Agent mode - ExecutingProgress component: real-time timer + dynamic tips + step pulse animation Architecture design (3 review reports): - Plan-and-Execute step-by-step execution architecture approved - Code accumulation strategy (R Docker stays stateless) - 5 engineering guardrails: XML tags, AST pre-check, defensive prompts, high-fidelity schema, error classification circuit breaker Docs: update SSA module status v4.1, system status v6.7, deployment changelist Made-with: Cursor
924 lines
36 KiB
TypeScript
924 lines
36 KiB
TypeScript
/**
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* Phase II — 意图处理器（Intent Handlers）
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*
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* 按意图类型分发处理逻辑：
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* - chat: 直接 LLM 对话
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* - explore: READ 工具 + LLM 解读
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* - analyze: 转入 QPER 流水线 + LLM 摘要
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* - consult: LLM 方法推荐（Phase III 增强）
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* - discuss: LLM 结果解读（Phase V 增强）
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* - feedback: LLM 改进建议（Phase V 增强）
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*/
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import { logger } from '../../../common/logging/index.js';
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import { conversationService, type StreamWriter } from './ConversationService.js';
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import { sessionBlackboardService } from './SessionBlackboardService.js';
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import { tokenTruncationService } from './TokenTruncationService.js';
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import { methodConsultService } from './MethodConsultService.js';
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import { askUserService, type AskUserResponse } from './AskUserService.js';
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import { toolOrchestratorService } from './ToolOrchestratorService.js';
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import { agentPlannerService } from './AgentPlannerService.js';
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import { agentCoderService } from './AgentCoderService.js';
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import { codeRunnerService } from './CodeRunnerService.js';
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import { prisma } from '../../../config/database.js';
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import type { IntentType } from './SystemPromptService.js';
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import type { IntentResult } from './IntentRouterService.js';
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export interface HandleResult {
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messageId: string;
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intent: IntentType;
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success: boolean;
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error?: string;
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}
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export class ChatHandlerService {
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/**
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* 统一处理入口：按意图分发
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*/
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async handle(
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sessionId: string,
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conversationId: string,
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userContent: string,
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intentResult: IntentResult,
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writer: StreamWriter,
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placeholderMessageId: string,
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): Promise<HandleResult> {
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const intent = intentResult.intent;
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try {
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// 如果上下文守卫被触发且有提示消息，直接作为 LLM 上下文的一部分
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let toolOutputs: string | undefined;
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if (intentResult.guardTriggered && intentResult.guardMessage) {
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toolOutputs = `[系统提示] ${intentResult.guardMessage}`;
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}
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switch (intent) {
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case 'chat':
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return await this.handleChat(sessionId, conversationId, writer, placeholderMessageId, intent, toolOutputs);
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case 'explore':
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return await this.handleExplore(sessionId, conversationId, writer, placeholderMessageId, toolOutputs);
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case 'consult':
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return await this.handleConsult(sessionId, conversationId, writer, placeholderMessageId, toolOutputs);
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case 'analyze':
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return await this.handleAnalyze(sessionId, conversationId, userContent, writer, placeholderMessageId, toolOutputs);
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case 'discuss':
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return await this.handleDiscuss(sessionId, conversationId, writer, placeholderMessageId, toolOutputs);
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case 'feedback':
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return await this.handleChat(sessionId, conversationId, writer, placeholderMessageId, 'feedback', toolOutputs);
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default:
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return await this.handleChat(sessionId, conversationId, writer, placeholderMessageId, 'chat', toolOutputs);
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}
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} catch (error: any) {
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logger.error('[SSA:ChatHandler] Handler error', {
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sessionId, intent, error: error.message,
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});
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await conversationService.markAssistantError(placeholderMessageId, error.message);
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return {
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messageId: placeholderMessageId,
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intent,
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success: false,
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error: error.message,
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};
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}
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}
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// ────────────────────────────────────────────
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// chat / consult / feedback — 直接 LLM 对话
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// ────────────────────────────────────────────
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private async handleChat(
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sessionId: string,
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conversationId: string,
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writer: StreamWriter,
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placeholderMessageId: string,
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intent: IntentType,
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toolOutputs?: string,
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): Promise<HandleResult> {
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const messages = await conversationService.buildContext(
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sessionId, conversationId, intent, toolOutputs,
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);
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const result = await conversationService.streamToSSE(messages, writer, {
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temperature: 0.7,
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maxTokens: 2000,
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});
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await conversationService.finalizeAssistantMessage(
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placeholderMessageId, result.content, result.thinking, result.tokens,
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);
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return {
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messageId: placeholderMessageId,
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intent,
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success: true,
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};
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}
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// ────────────────────────────────────────────
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// explore — 数据探索（READ 工具 + LLM 解读）
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// ────────────────────────────────────────────
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private async handleExplore(
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sessionId: string,
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conversationId: string,
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writer: StreamWriter,
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placeholderMessageId: string,
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guardToolOutput?: string,
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): Promise<HandleResult> {
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// 从 SessionBlackboard 提取数据摘要作为 tool output
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const blackboard = await sessionBlackboardService.get(sessionId);
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let toolOutputs = guardToolOutput || '';
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if (blackboard) {
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const truncated = tokenTruncationService.truncate(blackboard, {
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maxTokens: 1500,
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strategy: 'balanced',
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});
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const exploreData: string[] = [];
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if (truncated.overview) {
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exploreData.push(`数据概览:\n${truncated.overview}`);
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}
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if (truncated.variables) {
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exploreData.push(`变量列表:\n${truncated.variables}`);
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}
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if (truncated.pico) {
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exploreData.push(`PICO 推断:\n${truncated.pico}`);
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}
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if (truncated.report) {
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exploreData.push(`数据诊断:\n${truncated.report}`);
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}
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if (exploreData.length > 0) {
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toolOutputs = (toolOutputs ? toolOutputs + '\n\n' : '') + exploreData.join('\n\n');
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}
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}
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const messages = await conversationService.buildContext(
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sessionId, conversationId, 'explore', toolOutputs || undefined,
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);
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const result = await conversationService.streamToSSE(messages, writer, {
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temperature: 0.7,
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maxTokens: 2000,
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});
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await conversationService.finalizeAssistantMessage(
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placeholderMessageId, result.content, result.thinking, result.tokens,
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);
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return {
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messageId: placeholderMessageId,
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intent: 'explore',
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success: true,
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};
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}
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// ────────────────────────────────────────────
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// analyze — 转入 QPER（混合模式：LLM 摘要 + WorkspacePane 详情）
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// ────────────────────────────────────────────
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/**
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* Phase IV: analyze 意图 — 对话驱动分析
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*
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* 流程: plan 生成 → SSE 推 analysis_plan → LLM 方案说明 → ask_user 确认
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* 执行由前端通过 /workflow/{id}/stream 触发（D1: 保留独立 workflow SSE）
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*/
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private async handleAnalyze(
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sessionId: string,
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conversationId: string,
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userMessage: string,
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writer: StreamWriter,
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placeholderMessageId: string,
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guardToolOutput?: string,
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): Promise<HandleResult> {
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// 1. 调用 ToolOrchestratorService 生成计划（D5: PICO hint 自动注入）
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const planResult = await toolOrchestratorService.plan(sessionId, userMessage);
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if (!planResult.success || !planResult.plan) {
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const fallbackHint = [
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guardToolOutput,
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`[系统提示] 分析计划生成失败: ${planResult.error || '未知错误'}。`,
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'请友好地告知用户需要更明确的分析需求描述，例如需要指明要分析哪些变量、比较什么。',
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].filter(Boolean).join('\n');
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const messages = await conversationService.buildContext(
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sessionId, conversationId, 'analyze', fallbackHint,
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);
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const result = await conversationService.streamToSSE(messages, writer, {
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temperature: 0.5, maxTokens: 800,
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});
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await conversationService.finalizeAssistantMessage(
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placeholderMessageId, result.content, result.thinking, result.tokens,
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);
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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const plan = planResult.plan;
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// 2. SSE 推送 analysis_plan 事件（D2: 前端自动创建 AnalysisRecord）
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const planEvent = `data: ${JSON.stringify({
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type: 'analysis_plan',
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plan,
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})}\n\n`;
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writer.write(planEvent);
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logger.info('[SSA:ChatHandler] analysis_plan pushed via SSE', {
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sessionId, workflowId: plan.workflow_id, totalSteps: plan.total_steps,
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});
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// 3. LLM 流式生成方案说明
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const planSummary = toolOrchestratorService.formatPlanForLLM(plan);
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const toolOutputs = [
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guardToolOutput,
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planSummary,
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'[系统指令] 你刚刚为用户制定了上述分析方案。请用自然语言向用户解释这个方案：包括为什么选这些方法、分析步骤的逻辑。不要重复列步骤编号和工具代码，要用用户能理解的语言说明。最后提示用户确认方案后即可执行。',
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'【禁止事项】不要预测、模拟或编造任何分析结果、数值或表格。方案只是计划，R 引擎尚未执行，你不知道结果是什么。',
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].filter(Boolean).join('\n\n');
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const messages = await conversationService.buildContext(
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sessionId, conversationId, 'analyze', toolOutputs,
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);
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const result = await conversationService.streamToSSE(messages, writer, {
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temperature: 0.5, maxTokens: 1200,
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});
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await conversationService.finalizeAssistantMessage(
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placeholderMessageId, result.content, result.thinking, result.tokens,
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);
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// 4. 推送 ask_user 确认卡片（复用 Phase III AskUserService）
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const confirmQ = {
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inputType: 'confirm' as const,
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question: '请确认上述分析方案',
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context: `方案: ${plan.title}，共 ${plan.total_steps} 个步骤`,
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options: [
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{ id: 'confirm_plan', label: '确认执行', value: 'confirm_plan' },
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{ id: 'change_method', label: '修改方案', value: 'change_method' },
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],
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metadata: {
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workflowId: plan.workflow_id,
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planTitle: plan.title,
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},
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};
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const event = await askUserService.createQuestion(sessionId, confirmQ);
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writer.write(askUserService.formatSSE(event));
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return {
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messageId: placeholderMessageId,
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intent: 'analyze',
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success: true,
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};
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}
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// ────────────────────────────────────────────
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// discuss — 结果讨论（注入分析结果上下文）
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// ────────────────────────────────────────────
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private async handleDiscuss(
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sessionId: string,
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conversationId: string,
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writer: StreamWriter,
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placeholderMessageId: string,
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guardToolOutput?: string,
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): Promise<HandleResult> {
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const blackboard = await sessionBlackboardService.get(sessionId);
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let toolOutputs = guardToolOutput || '';
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// 注入 QPER trace 摘要
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if (blackboard?.qperTrace && blackboard.qperTrace.length > 0) {
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const traceItems = blackboard.qperTrace
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.filter(t => t.status === 'success')
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.slice(-5)
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.map(t => `- 步骤${t.stepIndex}: ${t.toolCode} → ${t.summary}`)
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.join('\n');
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if (traceItems) {
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toolOutputs = (toolOutputs ? toolOutputs + '\n\n' : '') +
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`最近分析结果:\n${traceItems}`;
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}
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}
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const messages = await conversationService.buildContext(
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sessionId, conversationId, 'discuss', toolOutputs || undefined,
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);
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const result = await conversationService.streamToSSE(messages, writer, {
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temperature: 0.7,
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maxTokens: 2000,
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});
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await conversationService.finalizeAssistantMessage(
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placeholderMessageId, result.content, result.thinking, result.tokens,
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);
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return {
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messageId: placeholderMessageId,
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intent: 'discuss',
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success: true,
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};
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}
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// ────────────────────────────────────────────
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// consult — 方法推荐（Phase III: method_consult + ask_user）
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// ────────────────────────────────────────────
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private async handleConsult(
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sessionId: string,
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conversationId: string,
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writer: StreamWriter,
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placeholderMessageId: string,
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guardToolOutput?: string,
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): Promise<HandleResult> {
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let toolOutputs = guardToolOutput || '';
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// 1. 调用 MethodConsultService 获取推荐
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const recommendation = await methodConsultService.recommend(sessionId);
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const recText = methodConsultService.formatForLLM(recommendation);
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toolOutputs = (toolOutputs ? toolOutputs + '\n\n' : '') + recText;
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logger.info('[SSA:ChatHandler] Method consult result', {
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sessionId,
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matched: recommendation.matched,
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primaryMethod: recommendation.primaryMethod?.code,
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matchScore: recommendation.matchScore,
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needsClarification: recommendation.needsClarification,
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});
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// 2. LLM 生成自然语言推荐（P1: 结论先行 + 结构化列表）
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const messages = await conversationService.buildContext(
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sessionId, conversationId, 'consult', toolOutputs,
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);
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const result = await conversationService.streamToSSE(messages, writer, {
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temperature: 0.5,
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maxTokens: 1500,
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});
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await conversationService.finalizeAssistantMessage(
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placeholderMessageId, result.content, result.thinking, result.tokens,
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);
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// 3. 如果有明确推荐，推送 ask_user 确认卡片
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if (recommendation.matched && recommendation.primaryMethod) {
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const confirmQ = askUserService.buildMethodConfirmQuestion(
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recommendation.primaryMethod.name,
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recommendation.primaryMethod.code,
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recommendation.fallbackMethod?.name,
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);
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const event = await askUserService.createQuestion(sessionId, confirmQ);
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writer.write(askUserService.formatSSE(event));
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}
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return {
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messageId: placeholderMessageId,
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intent: 'consult',
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success: true,
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};
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}
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// ────────────────────────────────────────────
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// Agent 通道入口（双通道架构 Phase 1 骨架）
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// ────────────────────────────────────────────
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/**
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* Agent 模式入口 — 三步确认式管线
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*
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* 状态机：
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* 新请求 → agentGeneratePlan → plan_pending（等用户确认）
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* 用户确认计划 → agentStreamCode → code_pending（等用户确认）
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* 用户确认代码 → agentExecuteCode → completed
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*/
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async handleAgentMode(
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sessionId: string,
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conversationId: string,
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userContent: string,
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writer: StreamWriter,
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placeholderMessageId: string,
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metadata?: Record<string, any>,
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): Promise<HandleResult> {
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try {
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const agentAction = metadata?.agentAction as string | undefined;
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// 1. 右侧工作区按钮操作（confirm_plan / confirm_code / cancel）
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if (agentAction) {
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const activeExec = await (prisma as any).ssaAgentExecution.findFirst({
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where: { sessionId, status: { in: ['plan_pending', 'code_pending'] } },
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orderBy: { createdAt: 'desc' },
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});
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if (!activeExec) {
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return await this.handleAgentChat(sessionId, conversationId, writer, placeholderMessageId, null);
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}
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if (agentAction === 'confirm_plan' && activeExec.status === 'plan_pending') {
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return await this.agentStreamCode(activeExec, sessionId, conversationId, writer, placeholderMessageId);
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}
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if (agentAction === 'confirm_code' && activeExec.status === 'code_pending') {
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return await this.agentExecuteCode(activeExec, sessionId, conversationId, writer, placeholderMessageId);
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}
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if (agentAction === 'cancel') {
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return await this.agentCancel(activeExec, sessionId, conversationId, writer, placeholderMessageId);
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}
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}
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// 2. 检查是否有等待确认的执行记录（用户在对话区直接打字确认）
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const activeExec = await (prisma as any).ssaAgentExecution.findFirst({
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where: { sessionId, status: { in: ['plan_pending', 'code_pending'] } },
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orderBy: { createdAt: 'desc' },
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});
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if (activeExec) {
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const action = this.parseAgentAction(userContent);
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if (activeExec.status === 'plan_pending' && action === 'confirm') {
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return await this.agentStreamCode(activeExec, sessionId, conversationId, writer, placeholderMessageId);
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}
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if (activeExec.status === 'code_pending' && action === 'confirm') {
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return await this.agentExecuteCode(activeExec, sessionId, conversationId, writer, placeholderMessageId);
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}
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if (action === 'cancel') {
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return await this.agentCancel(activeExec, sessionId, conversationId, writer, placeholderMessageId);
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}
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}
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// 3. 无挂起确认 — 检查是否是分析请求
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const blackboard = await sessionBlackboardService.get(sessionId);
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const hasData = !!blackboard?.dataOverview;
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if (hasData && this.looksLikeAnalysisRequest(userContent)) {
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return await this.agentGeneratePlan(sessionId, conversationId, userContent, writer, placeholderMessageId);
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}
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return await this.handleAgentChat(sessionId, conversationId, writer, placeholderMessageId, blackboard);
|
||
} catch (error: any) {
|
||
logger.error('[SSA:ChatHandler] Agent mode error', { sessionId, error: error.message });
|
||
await conversationService.markAssistantError(placeholderMessageId, error.message);
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: false, error: error.message };
|
||
}
|
||
}
|
||
|
||
private parseAgentAction(content: string): 'confirm' | 'cancel' | 'other' {
|
||
const lc = content.toLowerCase();
|
||
if (lc.includes('agent_confirm') || lc.includes('确认') || lc.includes('执行代码') || lc.includes('开始生成') || lc.includes('执行')) return 'confirm';
|
||
if (lc.includes('agent_cancel') || lc.includes('取消')) return 'cancel';
|
||
return 'other';
|
||
}
|
||
|
||
// ── Agent Step 1: 生成分析计划 → 等用户确认 ──
|
||
|
||
private async agentGeneratePlan(
|
||
sessionId: string,
|
||
conversationId: string,
|
||
userContent: string,
|
||
writer: StreamWriter,
|
||
placeholderMessageId: string,
|
||
): Promise<HandleResult> {
|
||
const sendEvent = (type: string, data: Record<string, any>) => {
|
||
writer.write(`data: ${JSON.stringify({ type, ...data })}\n\n`);
|
||
};
|
||
|
||
const execution = await (prisma as any).ssaAgentExecution.create({
|
||
data: { sessionId, query: userContent, status: 'planning' },
|
||
});
|
||
|
||
sendEvent('agent_planning', { executionId: execution.id, message: '正在制定分析计划...' });
|
||
|
||
const conversationHistory = await conversationService.buildContext(sessionId, conversationId, 'analyze');
|
||
const plan = await agentPlannerService.generatePlan(sessionId, userContent, conversationHistory);
|
||
|
||
// planText 存原始文本, reviewResult(JSON) 存结构化计划以便恢复
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: {
|
||
planText: plan.rawText,
|
||
reviewResult: JSON.parse(JSON.stringify(plan)),
|
||
status: 'plan_pending',
|
||
},
|
||
});
|
||
|
||
sendEvent('agent_plan_ready', {
|
||
executionId: execution.id,
|
||
plan: { title: plan.title, designType: plan.designType, steps: plan.steps },
|
||
planText: plan.rawText,
|
||
});
|
||
|
||
// 简短视线牵引语（方案 B：左侧只输出简短提示，详细内容在右侧工作区）
|
||
const hint = [
|
||
`[系统指令——严格遵守] 分析计划已生成（${plan.steps.length} 步），标题「${plan.title}」。`,
|
||
'你只需回复 1-2 句话，引导用户去右侧工作区查看。',
|
||
'示例回复：「我已为您拟定了详细的分析计划，请在右侧工作区核对并点击确认。」',
|
||
'【铁律】禁止在对话中重复计划内容、禁止编造任何数值。回复不超过 2 句话。',
|
||
].join('\n');
|
||
|
||
const msgs = await conversationService.buildContext(sessionId, conversationId, 'analyze', hint);
|
||
const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.3, maxTokens: 150 });
|
||
await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
}
|
||
|
||
// ── Agent Step 2: 流式生成代码 → 等用户确认 ──
|
||
|
||
private async agentStreamCode(
|
||
execution: any,
|
||
sessionId: string,
|
||
conversationId: string,
|
||
writer: StreamWriter,
|
||
placeholderMessageId: string,
|
||
): Promise<HandleResult> {
|
||
const sendEvent = (type: string, data: Record<string, any>) => {
|
||
writer.write(`data: ${JSON.stringify({ type, ...data })}\n\n`);
|
||
};
|
||
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: { status: 'coding' },
|
||
});
|
||
|
||
sendEvent('code_generating', { executionId: execution.id, partialCode: '', message: '正在生成 R 代码...' });
|
||
|
||
const plan = execution.reviewResult as any;
|
||
|
||
const generated = await agentCoderService.generateCodeStream(
|
||
sessionId, plan,
|
||
(accumulated: string) => {
|
||
sendEvent('code_generating', { executionId: execution.id, partialCode: accumulated });
|
||
},
|
||
);
|
||
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: { generatedCode: generated.code, status: 'code_pending' },
|
||
});
|
||
|
||
sendEvent('code_generated', {
|
||
executionId: execution.id,
|
||
code: generated.code,
|
||
explanation: generated.explanation,
|
||
});
|
||
|
||
// 简短视线牵引语
|
||
const hint = [
|
||
`[系统指令——严格遵守] R 代码已生成（${generated.code.split('\n').length} 行），使用 ${generated.requiredPackages.join(', ') || '基础包'}。`,
|
||
'你只需回复 1-2 句话，引导用户去右侧工作区核对代码并点击执行。',
|
||
'示例回复：「R 代码已生成，请在右侧工作区核对代码并点击 "确认并执行"。」',
|
||
'【铁律】禁止在对话中贴代码、禁止编造结果。回复不超过 2 句话。',
|
||
].join('\n');
|
||
|
||
const msgs = await conversationService.buildContext(sessionId, conversationId, 'analyze', hint);
|
||
const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.3, maxTokens: 150 });
|
||
await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
}
|
||
|
||
// ── Agent Step 3: 执行代码 + 重试循环 ──
|
||
|
||
private async agentExecuteCode(
|
||
execution: any,
|
||
sessionId: string,
|
||
conversationId: string,
|
||
writer: StreamWriter,
|
||
placeholderMessageId: string,
|
||
): Promise<HandleResult> {
|
||
const sendEvent = (type: string, data: Record<string, any>) => {
|
||
writer.write(`data: ${JSON.stringify({ type, ...data })}\n\n`);
|
||
};
|
||
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: { status: 'executing' },
|
||
});
|
||
|
||
const plan = execution.reviewResult as any;
|
||
let currentCode = execution.generatedCode as string;
|
||
let lastError: string | null = null;
|
||
|
||
for (let attempt = 0; attempt <= codeRunnerService.maxRetries; attempt++) {
|
||
sendEvent('code_executing', {
|
||
executionId: execution.id,
|
||
attempt: attempt + 1,
|
||
message: attempt === 0 ? '正在执行 R 代码...' : `第 ${attempt + 1} 次重试执行...`,
|
||
});
|
||
|
||
const execResult = await codeRunnerService.executeCode(sessionId, currentCode);
|
||
|
||
if (execResult.success) {
|
||
const durationMs = execResult.durationMs || 0;
|
||
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: {
|
||
executionResult: execResult as any,
|
||
reportBlocks: execResult.reportBlocks as any,
|
||
generatedCode: currentCode,
|
||
status: 'completed',
|
||
retryCount: attempt,
|
||
durationMs,
|
||
},
|
||
});
|
||
|
||
sendEvent('code_result', {
|
||
executionId: execution.id,
|
||
reportBlocks: execResult.reportBlocks,
|
||
code: currentCode,
|
||
durationMs,
|
||
});
|
||
|
||
// 流式总结结果
|
||
const hint = [
|
||
`[系统指令] R 代码执行完成（${durationMs}ms），生成 ${(execResult.reportBlocks || []).length} 个报告模块。`,
|
||
'请简要解释结果的统计学意义。',
|
||
'【禁止】不要编造数值（工作区已展示完整结果）。',
|
||
].join('\n');
|
||
|
||
const msgs = await conversationService.buildContext(sessionId, conversationId, 'analyze', hint);
|
||
const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.5, maxTokens: 800 });
|
||
await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
}
|
||
|
||
lastError = execResult.error || '执行失败';
|
||
const rawConsole = execResult.consoleOutput;
|
||
const consoleArr = Array.isArray(rawConsole) ? rawConsole : (rawConsole ? [String(rawConsole)] : []);
|
||
const consoleSnippet = consoleArr.slice(-20).join('\n');
|
||
|
||
if (attempt < codeRunnerService.maxRetries) {
|
||
const errorDetail = consoleSnippet
|
||
? `${lastError}\n\n--- R console output (last 20 lines) ---\n${consoleSnippet}`
|
||
: lastError;
|
||
|
||
sendEvent('code_error', {
|
||
executionId: execution.id,
|
||
message: lastError,
|
||
consoleOutput: consoleSnippet || undefined,
|
||
willRetry: true,
|
||
retryCount: attempt + 1,
|
||
});
|
||
|
||
sendEvent('code_retry', {
|
||
executionId: execution.id,
|
||
retryCount: attempt + 1,
|
||
message: `第 ${attempt + 1} 次执行失败，Agent 正在重新生成代码...`,
|
||
previousError: lastError,
|
||
});
|
||
|
||
const retry = await agentCoderService.generateCodeStream(
|
||
sessionId,
|
||
plan,
|
||
(accumulated) => {
|
||
sendEvent('code_generating', {
|
||
executionId: execution.id,
|
||
partialCode: accumulated,
|
||
});
|
||
},
|
||
errorDetail,
|
||
currentCode,
|
||
);
|
||
currentCode = retry.code;
|
||
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: { generatedCode: currentCode, retryCount: attempt + 1 },
|
||
});
|
||
|
||
sendEvent('code_generated', { executionId: execution.id, code: currentCode });
|
||
}
|
||
}
|
||
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: { status: 'error', errorMessage: lastError, retryCount: codeRunnerService.maxRetries },
|
||
});
|
||
|
||
sendEvent('code_error', {
|
||
executionId: execution.id,
|
||
message: `经过 ${codeRunnerService.maxRetries + 1} 次尝试仍然失败: ${lastError}`,
|
||
willRetry: false,
|
||
});
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
}
|
||
|
||
// ── Agent 取消 ──
|
||
|
||
private async agentCancel(
|
||
execution: any,
|
||
sessionId: string,
|
||
conversationId: string,
|
||
writer: StreamWriter,
|
||
placeholderMessageId: string,
|
||
): Promise<HandleResult> {
|
||
await (prisma as any).ssaAgentExecution.update({
|
||
where: { id: execution.id },
|
||
data: { status: 'error', errorMessage: '用户取消' },
|
||
});
|
||
|
||
const msgs = await conversationService.buildContext(
|
||
sessionId, conversationId, 'analyze', '[系统指令] 用户取消了当前分析流程。请简短回复确认取消。',
|
||
);
|
||
const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.5, maxTokens: 200 });
|
||
await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
}
|
||
|
||
// ── Agent 自由对话 ──
|
||
|
||
private async handleAgentChat(
|
||
sessionId: string,
|
||
conversationId: string,
|
||
writer: StreamWriter,
|
||
placeholderMessageId: string,
|
||
blackboard: any,
|
||
): Promise<HandleResult> {
|
||
let toolOutputs = '';
|
||
|
||
if (blackboard) {
|
||
const truncated = tokenTruncationService.truncate(blackboard, { maxTokens: 2000, strategy: 'balanced' });
|
||
const parts: string[] = [];
|
||
if (truncated.overview) parts.push(`数据概览:\n${truncated.overview}`);
|
||
if (truncated.variables) parts.push(`变量列表:\n${truncated.variables}`);
|
||
if (truncated.pico) parts.push(`PICO 推断:\n${truncated.pico}`);
|
||
if (parts.length > 0) toolOutputs = parts.join('\n\n');
|
||
}
|
||
|
||
const agentHint = [
|
||
'[当前模式: Agent 代码生成通道]',
|
||
'你是一位高级统计分析 Agent。你可以：',
|
||
'1. 与临床研究者自由讨论统计分析需求',
|
||
'2. 帮助理解数据特征、研究设计、统计方法选择',
|
||
'3. 当用户明确分析需求后，自动制定计划并生成 R 代码执行',
|
||
'',
|
||
'注意：禁止编造或模拟任何分析结果和数值。',
|
||
blackboard?.dataOverview
|
||
? '用户已上传数据，你可以结合数据概览回答问题。当用户提出分析需求时，会自动触发分析流程。'
|
||
: '用户尚未上传数据。请引导用户先上传研究数据文件。',
|
||
].join('\n');
|
||
|
||
const fullToolOutputs = toolOutputs ? `${toolOutputs}\n\n${agentHint}` : agentHint;
|
||
const messages = await conversationService.buildContext(sessionId, conversationId, 'analyze', fullToolOutputs);
|
||
const result = await conversationService.streamToSSE(messages, writer, { temperature: 0.7, maxTokens: 2000 });
|
||
await conversationService.finalizeAssistantMessage(placeholderMessageId, result.content, result.thinking, result.tokens);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
}
|
||
|
||
/** 简单启发式：判断用户消息是否像分析请求 */
|
||
private looksLikeAnalysisRequest(content: string): boolean {
|
||
const kw = [
|
||
'分析', '比较', '检验', '回归', '相关', '差异', '统计',
|
||
'生存', '卡方', 'logistic', 't检验', 'anova',
|
||
'基线', '特征表', '描述性', '预测', '影响因素',
|
||
'帮我做', '帮我跑', '开始分析', '执行分析',
|
||
];
|
||
const lc = content.toLowerCase();
|
||
return kw.some(k => lc.includes(k));
|
||
}
|
||
|
||
// ────────────────────────────────────────────
|
||
// ask_user 响应处理（Phase III）
|
||
// ────────────────────────────────────────────
|
||
|
||
async handleAskUserResponse(
|
||
sessionId: string,
|
||
conversationId: string,
|
||
response: AskUserResponse,
|
||
writer: StreamWriter,
|
||
placeholderMessageId: string,
|
||
): Promise<HandleResult> {
|
||
// 先读取 pending 元数据（含 workflowId），再清除
|
||
const pending = await askUserService.getPending(sessionId);
|
||
const pendingMeta = pending?.metadata || {};
|
||
await askUserService.clearPending(sessionId);
|
||
|
||
if (response.action === 'skip') {
|
||
// 用户跳过 — 生成友好回复
|
||
const messages = await conversationService.buildContext(
|
||
sessionId, conversationId, 'chat',
|
||
'[系统提示] 用户跳过了上一个确认问题。请友好地回应，表示随时可以继续。',
|
||
);
|
||
|
||
const result = await conversationService.streamToSSE(messages, writer, {
|
||
temperature: 0.7,
|
||
maxTokens: 500,
|
||
});
|
||
|
||
await conversationService.finalizeAssistantMessage(
|
||
placeholderMessageId, result.content, result.thinking, result.tokens,
|
||
);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'chat', success: true };
|
||
}
|
||
|
||
// 用户选择了具体选项
|
||
const selectedValue = response.selectedValues?.[0];
|
||
|
||
if (selectedValue === 'confirm_plan') {
|
||
// Phase IV: 确认分析方案 → 前端打开工作区，用户手动点击执行
|
||
const workflowId = pendingMeta.workflowId || response.metadata?.workflowId || '';
|
||
const messages = await conversationService.buildContext(
|
||
sessionId, conversationId, 'analyze',
|
||
[
|
||
`[系统指令——严格遵守] 用户已确认分析方案（workflow: ${workflowId}）。`,
|
||
'你只需回复一句简短的确认消息，例如："好的，方案已确认。请在右侧工作区点击「开始执行分析」启动 R 引擎。"',
|
||
'【铁律】禁止在此回复中生成任何分析结果、表格、P值、统计量、数值。',
|
||
'你不是计算引擎，所有数值结果将由 R 统计引擎独立计算后返回。',
|
||
'你的回复不得超过 2 句话。',
|
||
].join('\n'),
|
||
);
|
||
|
||
const result = await conversationService.streamToSSE(messages, writer, {
|
||
temperature: 0.1, maxTokens: 150,
|
||
});
|
||
|
||
await conversationService.finalizeAssistantMessage(
|
||
placeholderMessageId, result.content, result.thinking, result.tokens,
|
||
);
|
||
|
||
// 推送 plan_confirmed 事件，前端据此触发 executeWorkflow
|
||
const confirmEvent = `data: ${JSON.stringify({
|
||
type: 'plan_confirmed',
|
||
workflowId,
|
||
})}\n\n`;
|
||
writer.write(confirmEvent);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
|
||
} else if (selectedValue === 'confirm') {
|
||
// Phase III: 确认使用推荐方法 → 提示可以开始分析
|
||
const messages = await conversationService.buildContext(
|
||
sessionId, conversationId, 'analyze',
|
||
'[系统指令] 用户已确认使用推荐的统计方法。请简要确认方案，告知用户可以在对话中说"开始分析"或在右侧面板触发执行。禁止生成任何数值或假设的分析结果。',
|
||
);
|
||
|
||
const result = await conversationService.streamToSSE(messages, writer, {
|
||
temperature: 0.3,
|
||
maxTokens: 500,
|
||
});
|
||
|
||
await conversationService.finalizeAssistantMessage(
|
||
placeholderMessageId, result.content, result.thinking, result.tokens,
|
||
);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'analyze', success: true };
|
||
|
||
} else if (selectedValue === 'use_fallback') {
|
||
// 使用备选方案
|
||
const messages = await conversationService.buildContext(
|
||
sessionId, conversationId, 'consult',
|
||
'[系统提示] 用户选择使用备选方案。请确认切换，并简要说明备选方案的适用场景。',
|
||
);
|
||
|
||
const result = await conversationService.streamToSSE(messages, writer, {
|
||
temperature: 0.5,
|
||
maxTokens: 800,
|
||
});
|
||
|
||
await conversationService.finalizeAssistantMessage(
|
||
placeholderMessageId, result.content, result.thinking, result.tokens,
|
||
);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'consult', success: true };
|
||
|
||
} else if (selectedValue === 'change_method') {
|
||
// 用户想换方法 → 引导重新描述
|
||
const messages = await conversationService.buildContext(
|
||
sessionId, conversationId, 'consult',
|
||
'[系统提示] 用户不满意当前推荐，想换方法。请询问用户希望使用什么方法，或引导其更详细地描述分析需求。',
|
||
);
|
||
|
||
const result = await conversationService.streamToSSE(messages, writer, {
|
||
temperature: 0.7,
|
||
maxTokens: 800,
|
||
});
|
||
|
||
await conversationService.finalizeAssistantMessage(
|
||
placeholderMessageId, result.content, result.thinking, result.tokens,
|
||
);
|
||
|
||
return { messageId: placeholderMessageId, intent: 'consult', success: true };
|
||
}
|
||
|
||
// 其他情况：fallback
|
||
return await this.handleChat(sessionId, conversationId, writer, placeholderMessageId, 'chat');
|
||
}
|
||
}
|
||
|
||
export const chatHandlerService = new ChatHandlerService();
|