feat(ssa): Implement dual-channel architecture Phase 1-3 (QPER + LLM Agent pipeline)
Completed: - Phase 1: DB schema (execution_mode + ssa_agent_executions), ModeToggle component, Session PATCH API - Phase 2: AgentPlannerService + AgentCoderService (streaming) + CodeRunnerService + R Docker /execute-code endpoint - Phase 3: AgentCodePanel (3-step confirmation UI), SSE event handling (7 agent events), streaming code display - Three-step confirmation pipeline: plan -> user confirm -> stream code -> user confirm -> execute R code -> results - R Docker sandbox /execute-code endpoint with 120s timeout + block_helpers preloaded - E2E dual-channel test script (8 tests) - Updated R engine architecture doc (v1.5) and SSA module status doc (v4.0) Technical details: - AgentCoderService uses LLM streaming (chatStream) for real-time code generation feedback - ReviewerAgent temporarily disabled, prioritizing Plan -> Code -> Execute flow - CodeRunnerService wraps user code with auto data loading (df variable injection) - Frontend handles agent_planning, agent_plan_ready, code_generating, code_generated, code_executing, code_result events - ask_user mechanism used for plan and code confirmation steps Files: 24 files (4 new services, 2 new components, 1 migration, 1 E2E test, 16 modified) Made-with: Cursor
This commit is contained in:
@@ -17,6 +17,10 @@ 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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@@ -387,6 +391,377 @@ export class ChatHandlerService {
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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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): Promise<HandleResult> {
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try {
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// 1. 检查是否有等待确认的执行记录
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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') {
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if (action === 'confirm') {
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return await this.agentStreamCode(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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if (activeExec.status === 'code_pending') {
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if (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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}
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// 2. 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);
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} catch (error: any) {
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logger.error('[SSA:ChatHandler] Agent mode error', { sessionId, error: error.message });
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await conversationService.markAssistantError(placeholderMessageId, error.message);
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return { messageId: placeholderMessageId, intent: 'analyze', success: false, error: error.message };
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}
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}
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/** 解析用户回复中的确认/取消意图 */
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private parseAgentAction(content: string): 'confirm' | 'cancel' | 'other' {
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const lc = content.toLowerCase();
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if (lc.includes('agent_confirm') || lc.includes('确认') || lc.includes('执行代码') || lc.includes('开始生成')) return 'confirm';
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if (lc.includes('agent_cancel') || lc.includes('取消')) return 'cancel';
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return 'other';
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}
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// ── Agent Step 1: 生成分析计划 → 等用户确认 ──
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private async agentGeneratePlan(
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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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): Promise<HandleResult> {
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const sendEvent = (type: string, data: Record<string, any>) => {
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writer.write(`data: ${JSON.stringify({ type, ...data })}\n\n`);
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};
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const execution = await (prisma as any).ssaAgentExecution.create({
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data: { sessionId, query: userContent, status: 'planning' },
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});
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sendEvent('agent_planning', { executionId: execution.id, message: '正在制定分析计划...' });
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const conversationHistory = await conversationService.buildContext(sessionId, conversationId, 'analyze');
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const plan = await agentPlannerService.generatePlan(sessionId, userContent, conversationHistory);
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// planText 存原始文本, reviewResult(JSON) 存结构化计划以便恢复
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: {
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planText: plan.rawText,
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reviewResult: JSON.parse(JSON.stringify(plan)),
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status: 'plan_pending',
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},
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});
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sendEvent('agent_plan_ready', {
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executionId: execution.id,
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plan: { title: plan.title, designType: plan.designType, steps: plan.steps },
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planText: plan.rawText,
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});
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// 流式解释计划
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const hint = [
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`[系统指令] 你刚刚制定了分析计划「${plan.title}」，包含 ${plan.steps.length} 个步骤。`,
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'请用简洁的自然语言解释这个计划，然后告知用户可以确认后开始生成 R 代码。',
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'【禁止】不要编造数值或结果。',
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].join('\n');
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const msgs = await conversationService.buildContext(sessionId, conversationId, 'analyze', hint);
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const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.5, maxTokens: 800 });
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await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
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sendEvent('ask_user', {
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questionId: `agent_plan_${execution.id}`,
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text: '请确认分析计划',
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options: [
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{ id: 'agent_confirm_plan', label: '确认方案，生成代码' },
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{ id: 'agent_cancel', label: '取消' },
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],
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});
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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// ── Agent Step 2: 流式生成代码 → 等用户确认 ──
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private async agentStreamCode(
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execution: any,
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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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): Promise<HandleResult> {
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const sendEvent = (type: string, data: Record<string, any>) => {
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writer.write(`data: ${JSON.stringify({ type, ...data })}\n\n`);
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};
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: { status: 'coding' },
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});
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sendEvent('code_generating', { executionId: execution.id, partialCode: '', message: '正在生成 R 代码...' });
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const plan = execution.reviewResult as any;
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const generated = await agentCoderService.generateCodeStream(
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sessionId, plan,
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(accumulated: string) => {
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sendEvent('code_generating', { executionId: execution.id, partialCode: accumulated });
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},
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);
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: { generatedCode: generated.code, status: 'code_pending' },
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});
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sendEvent('code_generated', {
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executionId: execution.id,
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code: generated.code,
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explanation: generated.explanation,
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});
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// 流式说明代码
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const hint = [
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`[系统指令] R 代码已生成（${generated.code.split('\n').length} 行），使用 ${generated.requiredPackages.join(', ') || '基础包'}。`,
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'请简要说明代码逻辑，告知用户可以确认执行。',
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'【禁止】不要在对话中贴代码（工作区已展示），不要编造结果。',
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].join('\n');
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const msgs = await conversationService.buildContext(sessionId, conversationId, 'analyze', hint);
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const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.5, maxTokens: 600 });
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await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
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sendEvent('ask_user', {
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questionId: `agent_code_${execution.id}`,
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text: '代码已生成，请确认执行',
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options: [
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{ id: 'agent_confirm_code', label: '执行代码' },
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{ id: 'agent_cancel', label: '取消' },
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],
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});
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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// ── Agent Step 3: 执行代码 + 重试循环 ──
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private async agentExecuteCode(
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execution: any,
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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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): Promise<HandleResult> {
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const sendEvent = (type: string, data: Record<string, any>) => {
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writer.write(`data: ${JSON.stringify({ type, ...data })}\n\n`);
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};
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: { status: 'executing' },
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});
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const plan = execution.reviewResult as any;
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let currentCode = execution.generatedCode as string;
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let lastError: string | null = null;
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for (let attempt = 0; attempt <= codeRunnerService.maxRetries; attempt++) {
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sendEvent('code_executing', {
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executionId: execution.id,
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attempt: attempt + 1,
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message: attempt === 0 ? '正在执行 R 代码...' : `第 ${attempt + 1} 次重试执行...`,
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});
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const execResult = await codeRunnerService.executeCode(sessionId, currentCode);
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if (execResult.success) {
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const durationMs = execResult.durationMs || 0;
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: {
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executionResult: execResult as any,
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reportBlocks: execResult.reportBlocks as any,
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generatedCode: currentCode,
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status: 'completed',
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retryCount: attempt,
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durationMs,
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},
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});
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sendEvent('code_result', {
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executionId: execution.id,
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reportBlocks: execResult.reportBlocks,
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code: currentCode,
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durationMs,
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});
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// 流式总结结果
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const hint = [
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`[系统指令] R 代码执行完成（${durationMs}ms），生成 ${(execResult.reportBlocks || []).length} 个报告模块。`,
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'请简要解释结果的统计学意义。',
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'【禁止】不要编造数值（工作区已展示完整结果）。',
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].join('\n');
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const msgs = await conversationService.buildContext(sessionId, conversationId, 'analyze', hint);
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const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.5, maxTokens: 800 });
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await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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lastError = execResult.error || '执行失败';
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if (attempt < codeRunnerService.maxRetries) {
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sendEvent('code_error', { executionId: execution.id, message: lastError, willRetry: true });
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sendEvent('code_retry', { executionId: execution.id, retryCount: attempt + 1, message: `错误: ${lastError}，正在修复代码...` });
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const retry = await agentCoderService.generateCode(sessionId, plan, lastError);
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currentCode = retry.code;
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: { generatedCode: currentCode, retryCount: attempt + 1 },
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});
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sendEvent('code_generated', { executionId: execution.id, code: currentCode });
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}
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}
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: { status: 'error', errorMessage: lastError, retryCount: codeRunnerService.maxRetries },
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});
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sendEvent('code_error', {
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executionId: execution.id,
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message: `经过 ${codeRunnerService.maxRetries + 1} 次尝试仍然失败: ${lastError}`,
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willRetry: false,
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});
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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// ── Agent 取消 ──
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private async agentCancel(
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execution: any,
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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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): Promise<HandleResult> {
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await (prisma as any).ssaAgentExecution.update({
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where: { id: execution.id },
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data: { status: 'error', errorMessage: '用户取消' },
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});
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const msgs = await conversationService.buildContext(
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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。',
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);
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const sr = await conversationService.streamToSSE(msgs, writer, { temperature: 0.5, maxTokens: 200 });
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await conversationService.finalizeAssistantMessage(placeholderMessageId, sr.content, sr.thinking, sr.tokens);
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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// ── Agent 自由对话 ──
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private async handleAgentChat(
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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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blackboard: any,
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): Promise<HandleResult> {
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let toolOutputs = '';
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if (blackboard) {
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const truncated = tokenTruncationService.truncate(blackboard, { maxTokens: 2000, strategy: 'balanced' });
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const parts: string[] = [];
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if (truncated.overview) parts.push(`数据概览:\n${truncated.overview}`);
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if (truncated.variables) parts.push(`变量列表:\n${truncated.variables}`);
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if (truncated.pico) parts.push(`PICO 推断:\n${truncated.pico}`);
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if (parts.length > 0) toolOutputs = parts.join('\n\n');
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}
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const agentHint = [
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'[当前模式: Agent 代码生成通道]',
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'你是一位高级统计分析 Agent。你可以：',
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'1. 与临床研究者自由讨论统计分析需求',
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'2. 帮助理解数据特征、研究设计、统计方法选择',
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'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',
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'',
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'注意：禁止编造或模拟任何分析结果和数值。',
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blackboard?.dataOverview
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? '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。'
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: '用户尚未上传数据。请引导用户先上传研究数据文件。',
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].join('\n');
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const fullToolOutputs = toolOutputs ? `${toolOutputs}\n\n${agentHint}` : agentHint;
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const messages = await conversationService.buildContext(sessionId, conversationId, 'analyze', fullToolOutputs);
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const result = await conversationService.streamToSSE(messages, writer, { temperature: 0.7, maxTokens: 2000 });
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await conversationService.finalizeAssistantMessage(placeholderMessageId, result.content, result.thinking, result.tokens);
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return { messageId: placeholderMessageId, intent: 'analyze', success: true };
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}
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/** 简单启发式：判断用户消息是否像分析请求 */
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private looksLikeAnalysisRequest(content: string): boolean {
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const kw = [
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'分析', '比较', '检验', '回归', '相关', '差异', '统计',
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'生存', '卡方', 'logistic', 't检验', 'anova',
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'基线', '特征表', '描述性', '预测', '影响因素',
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'帮我做', '帮我跑', '开始分析', '执行分析',
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];
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const lc = content.toLowerCase();
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return kw.some(k => lc.includes(k));
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}
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// ────────────────────────────────────────────
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// ask_user 响应处理（Phase III）
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// ────────────────────────────────────────────
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Reference in New Issue
Block a user