feat(ssa): Complete Phase I-IV intelligent dialogue and tool system development
Phase I - Session Blackboard + READ Layer: - SessionBlackboardService with Postgres-Only cache - DataProfileService for data overview generation - PicoInferenceService for LLM-driven PICO extraction - Frontend DataContextCard and VariableDictionaryPanel - E2E tests: 31/31 passed Phase II - Conversation Layer LLM + Intent Router: - ConversationService with SSE streaming - IntentRouterService (rule-first + LLM fallback, 6 intents) - SystemPromptService with 6-segment dynamic assembly - TokenTruncationService for context management - ChatHandlerService as unified chat entry - Frontend SSAChatPane and useSSAChat hook - E2E tests: 38/38 passed Phase III - Method Consultation + AskUser Standardization: - ToolRegistryService with Repository Pattern - MethodConsultService with DecisionTable + LLM enhancement - AskUserService with global interrupt handling - Frontend AskUserCard component - E2E tests: 13/13 passed Phase IV - Dialogue-Driven Analysis + QPER Integration: - ToolOrchestratorService (plan/execute/report) - analysis_plan SSE event for WorkflowPlan transmission - Dual-channel confirmation (ask_user card + workspace button) - PICO as optional hint for LLM parsing - E2E tests: 25/25 passed R Statistics Service: - 5 new R tools: anova_one, baseline_table, fisher, linear_reg, wilcoxon - Enhanced guardrails and block helpers - Comprehensive test suite (run_all_tools_test.js) Documentation: - Updated system status document (v5.9) - Updated SSA module status and development plan (v1.8) Total E2E: 107/107 passed (Phase I: 31, Phase II: 38, Phase III: 13, Phase IV: 25) Co-authored-by: Cursor <cursoragent@cursor.com>
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backend/scripts/seed-ssa-pico-prompt.ts
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backend/scripts/seed-ssa-pico-prompt.ts
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/**
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* SSA PICO Inference Prompt Seed 脚本 (Phase I)
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*
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* 将 SSA_PICO_INFERENCE prompt 写入 capability_schema.prompt_templates
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* 运行: npx tsx scripts/seed-ssa-pico-prompt.ts
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*/
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import { PrismaClient } from '@prisma/client';
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const prisma = new PrismaClient();
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const SSA_PICO_INFERENCE_PROMPT = `你是一个临床研究设计分析引擎。你的任务是根据数据概览信息，推断该数据集可能的 PICO 结构。
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## 输入信息
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### 数据概览
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{{dataOverviewSummary}}
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### 变量列表
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{{variableList}}
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## 你的任务
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请根据数据概览推断出 PICO 结构，以 JSON 格式输出（不要输出任何其他内容，只输出 JSON）：
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\`\`\`json
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{
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"population": "研究人群描述（必须基于数据特征推断，如 '311 例行口腔智齿拔除术的患者'）",
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"intervention": "干预措施（如果是观察性研究，输出 null）",
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"comparison": "对照组（如果无对照或无干预，输出 null）",
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"outcome": "结局指标描述（可列出多个结局变量）",
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"confidence": "high | medium | low",
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"reasoning": "你的推理过程，1-2 句话"
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}
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\`\`\`
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## 关键规则
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1. **并非所有数据都是随机对照试验（RCT）。** 如果数据是观察性研究（横断面调查、回顾性队列、病例对照等），在 intervention 和 comparison 字段输出 null，绝不要强行捏造干预措施。
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2. **变量名必须精确引用**数据概览中列出的真实变量名。
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3. population 应包含样本量（如"311 例"）和研究对象特征。
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4. outcome 应列出最可能作为结局变量的变量名。
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5. 如果数据中存在明显的分组变量（如 treatment/control、手术方式等），则推断为 intervention。
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## Confidence 评分准则
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- **high**: 数据中有明确的分组变量和结局变量，PICO 结构清晰。
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- **medium**: 能推断出大致的研究目的，但部分要素不确定。
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- **low**: 数据缺乏明显的研究设计特征，PICO 结构高度不确定。
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## Few-Shot 示例
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### 示例 1：有明确干预的 RCT
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数据概览: 200 行, 变量: patient_id, group (2 水平: Drug/Placebo), age, gender, SBP_before, SBP_after, adverse_event
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输出:
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\`\`\`json
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{"population":"200 例参与药物临床试验的患者","intervention":"药物治疗 (group=Drug)","comparison":"安慰剂 (group=Placebo)","outcome":"治疗后收缩压 (SBP_after)、不良事件 (adverse_event)","confidence":"high","reasoning":"group 变量有 Drug/Placebo 两水平，典型 RCT 设计；SBP_after 和 adverse_event 是可能的结局变量"}
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\`\`\`
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### 示例 2：观察性研究（无干预）
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数据概览: 500 行, 变量: id, age, sex, bmi, smoking, exercise, blood_pressure, cholesterol, diabetes
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输出:
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\`\`\`json
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{"population":"500 例社区居民健康调查样本","intervention":null,"comparison":null,"outcome":"血压 (blood_pressure)、胆固醇 (cholesterol)、糖尿病 (diabetes)","confidence":"medium","reasoning":"数据无明显分组/干预变量，为典型的横断面调查问卷，多个健康指标可作为结局变量"}
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\`\`\`
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### 示例 3：手术方式比较
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数据概览: 311 行, 变量: sex, smoke, age, bmi, mouth_open, toot_morph, operation (3 水平), time, Yqol
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输出:
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\`\`\`json
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{"population":"311 例行口腔智齿拔除术的患者","intervention":"不同手术方式 (operation)","comparison":null,"outcome":"手术时间 (time)、术后生活质量 (Yqol)","confidence":"medium","reasoning":"operation 变量有 3 个水平，可能代表不同手术方式；time 和 Yqol 是可能的结局变量。但 operation 是否为干预需用户确认"}
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\`\`\`
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请只输出 JSON，不要输出其他内容。`;
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async function main() {
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console.log('🚀 开始写入 SSA PICO Inference Prompt...\n');
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const existing = await prisma.prompt_templates.findUnique({
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where: { code: 'SSA_PICO_INFERENCE' }
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});
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if (existing) {
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console.log('⚠️ SSA_PICO_INFERENCE 已存在 (id=%d)，创建新版本...', existing.id);
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const latestVersion = await prisma.prompt_versions.findFirst({
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where: { template_id: existing.id },
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orderBy: { version: 'desc' }
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});
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const newVersion = (latestVersion?.version ?? 0) + 1;
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await prisma.prompt_versions.updateMany({
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where: { template_id: existing.id, status: 'ACTIVE' },
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data: { status: 'ARCHIVED' }
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});
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await prisma.prompt_versions.create({
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data: {
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template_id: existing.id,
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version: newVersion,
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content: SSA_PICO_INFERENCE_PROMPT,
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model_config: { model: 'deepseek-v3', temperature: 0.3, maxTokens: 1024 },
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status: 'ACTIVE',
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changelog: `Phase I v${newVersion}: PICO 推断 Prompt（含观察性研究 null 处理 — H3）`,
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created_by: 'system-seed',
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}
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});
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console.log(' ✅ 新版本 v%d 已创建并设为 ACTIVE', newVersion);
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} else {
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console.log('📝 创建 SSA_PICO_INFERENCE 模板...');
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const template = await prisma.prompt_templates.create({
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data: {
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code: 'SSA_PICO_INFERENCE',
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name: 'SSA PICO 推断 Prompt',
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module: 'SSA',
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description: 'Phase I — 根据数据概览推断 PICO 结构（支持观察性研究 null 输出）',
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variables: ['dataOverviewSummary', 'variableList'],
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}
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});
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await prisma.prompt_versions.create({
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data: {
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template_id: template.id,
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version: 1,
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content: SSA_PICO_INFERENCE_PROMPT,
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model_config: { model: 'deepseek-v3', temperature: 0.3, maxTokens: 1024 },
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status: 'ACTIVE',
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changelog: 'Phase I v1.0: PICO 推断初始版本（3 组 Few-Shot，含 H3 观察性研究处理）',
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created_by: 'system-seed',
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}
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});
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console.log(' ✅ 模板 id=%d + 版本 v1 已创建', template.id);
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}
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console.log('\n✅ SSA PICO Inference Prompt 写入完成！');
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}
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main()
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.catch(e => {
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console.error('❌ 写入失败:', e);
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process.exit(1);
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})
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.finally(() => prisma.$disconnect());
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