feat(aia): Protocol Agent streaming + editable state panel + protocol generation plan
Day 2 Development (2026-01-24): Backend Enhancements: - Implement SSE streaming in ProtocolAgentController using createStreamingService - Add data condensation via LLM in ProtocolOrchestrator.handleProtocolSync - Support stage editing without resetting progress - Add explicit JSON output format for each stage in system prompt - Create independent seed script for Protocol Agent (seed-protocol-agent.ts) Frontend Improvements: - Integrate useAIStream hook for typewriter effect in ChatArea - Add MarkdownContent component for basic Markdown rendering - Implement StageEditModal for editing stage data (scientific question, PICO, etc.) - Add edit button to StageCard (visible on hover) - Fix routing paths from /aia to /ai-qa - Enhance CSS with full-screen layout and Markdown styles New Documentation: - One-click protocol generation development plan (v1.1) - Editor selection evaluation (Novel vs BlockNote vs Tiptap) - Novel fork strategy for AI-native editing Technical Decisions: - Choose Novel (Fork) as protocol editor for AI-first design - Two-stage progressive generation: summary in chat, full protocol in editor - 10-day development plan for protocol generation feature Code Stats: - Backend: 3 files modified, 1 new file - Frontend: 9 files modified, 2 new files - Docs: 3 new files Status: Streaming and editable features working, protocol generation pending
This commit is contained in:
390
backend/prisma/seed-protocol-agent.ts
Normal file
390
backend/prisma/seed-protocol-agent.ts
Normal file
@@ -0,0 +1,390 @@
|
||||
/**
|
||||
* Protocol Agent 独立 Seed 脚本
|
||||
*
|
||||
* 只初始化 Protocol Agent 相关的配置数据，不影响其他模块
|
||||
*
|
||||
* 运行方式：
|
||||
* npx ts-node prisma/seed-protocol-agent.ts
|
||||
* 或者：
|
||||
* npx tsx prisma/seed-protocol-agent.ts
|
||||
*/
|
||||
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
async function seedProtocolAgent() {
|
||||
console.log('🌱 开始初始化 Protocol Agent 配置...\n');
|
||||
|
||||
try {
|
||||
// ============================================
|
||||
// 1. 创建 Agent 定义
|
||||
// ============================================
|
||||
console.log('📌 创建 Agent 定义...');
|
||||
|
||||
const protocolAgent = await prisma.agentDefinition.upsert({
|
||||
where: { code: 'protocol_agent' },
|
||||
update: {
|
||||
name: '全流程研究方案制定',
|
||||
description: '引导用户完成5个核心阶段（科学问题、PICO、研究设计、样本量、观察指标），最终一键生成研究方案',
|
||||
version: '1.0.0',
|
||||
config: {
|
||||
defaultModel: 'deepseek-v3',
|
||||
maxTurns: 100,
|
||||
timeout: 120000,
|
||||
enableTrace: true,
|
||||
enableReflexion: true,
|
||||
},
|
||||
isActive: true,
|
||||
},
|
||||
create: {
|
||||
code: 'protocol_agent',
|
||||
name: '全流程研究方案制定',
|
||||
description: '引导用户完成5个核心阶段（科学问题、PICO、研究设计、样本量、观察指标），最终一键生成研究方案',
|
||||
version: '1.0.0',
|
||||
config: {
|
||||
defaultModel: 'deepseek-v3',
|
||||
maxTurns: 100,
|
||||
timeout: 120000,
|
||||
enableTrace: true,
|
||||
enableReflexion: true,
|
||||
},
|
||||
isActive: true,
|
||||
},
|
||||
});
|
||||
console.log(` ✅ Agent 定义创建成功: ${protocolAgent.name} (ID: ${protocolAgent.id})`);
|
||||
|
||||
// ============================================
|
||||
// 2. 创建 5 个阶段
|
||||
// ============================================
|
||||
console.log('📌 创建 5 个阶段配置...');
|
||||
|
||||
const stages = [
|
||||
{
|
||||
stageCode: 'scientific_question',
|
||||
stageName: '科学问题梳理',
|
||||
sortOrder: 1,
|
||||
isInitial: true,
|
||||
isFinal: false,
|
||||
nextStages: ['pico']
|
||||
},
|
||||
{
|
||||
stageCode: 'pico',
|
||||
stageName: 'PICO要素',
|
||||
sortOrder: 2,
|
||||
isInitial: false,
|
||||
isFinal: false,
|
||||
nextStages: ['study_design']
|
||||
},
|
||||
{
|
||||
stageCode: 'study_design',
|
||||
stageName: '研究设计',
|
||||
sortOrder: 3,
|
||||
isInitial: false,
|
||||
isFinal: false,
|
||||
nextStages: ['sample_size']
|
||||
},
|
||||
{
|
||||
stageCode: 'sample_size',
|
||||
stageName: '样本量计算',
|
||||
sortOrder: 4,
|
||||
isInitial: false,
|
||||
isFinal: false,
|
||||
nextStages: ['endpoints']
|
||||
},
|
||||
{
|
||||
stageCode: 'endpoints',
|
||||
stageName: '观察指标',
|
||||
sortOrder: 5,
|
||||
isInitial: false,
|
||||
isFinal: true,
|
||||
nextStages: []
|
||||
},
|
||||
];
|
||||
|
||||
const stageMap = new Map<string, string>();
|
||||
|
||||
for (const stage of stages) {
|
||||
const created = await prisma.agentStage.upsert({
|
||||
where: {
|
||||
agentId_stageCode: {
|
||||
agentId: protocolAgent.id,
|
||||
stageCode: stage.stageCode
|
||||
}
|
||||
},
|
||||
update: {
|
||||
stageName: stage.stageName,
|
||||
sortOrder: stage.sortOrder,
|
||||
isInitial: stage.isInitial,
|
||||
isFinal: stage.isFinal,
|
||||
nextStages: stage.nextStages,
|
||||
},
|
||||
create: {
|
||||
agentId: protocolAgent.id,
|
||||
stageCode: stage.stageCode,
|
||||
stageName: stage.stageName,
|
||||
sortOrder: stage.sortOrder,
|
||||
isInitial: stage.isInitial,
|
||||
isFinal: stage.isFinal,
|
||||
nextStages: stage.nextStages,
|
||||
},
|
||||
});
|
||||
stageMap.set(stage.stageCode, created.id);
|
||||
console.log(` ✅ 阶段 ${stage.sortOrder}: ${stage.stageName}`);
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 3. 创建系统 Prompt
|
||||
// ============================================
|
||||
console.log('📌 创建系统 Prompt...');
|
||||
|
||||
const systemPromptContent = `你是一位资深的临床研究方法学专家，正在帮助医生设计临床研究方案。
|
||||
|
||||
## 你的角色
|
||||
- 你是一位友好、专业的研究方案设计顾问
|
||||
- 你会引导用户一步步完成研究方案的核心要素
|
||||
- 你善于提问，帮助用户理清思路
|
||||
|
||||
## 当前工作流程
|
||||
你将引导用户完成5个核心阶段：
|
||||
1. 科学问题梳理 - 明确研究要解决的核心问题
|
||||
2. PICO要素 - 确定研究人群(P)、干预(I)、对照(C)和结局(O)
|
||||
3. 研究设计 - 选择合适的研究类型和方法
|
||||
4. 样本量计算 - 估算所需的样本量
|
||||
5. 观察指标 - 定义基线、暴露、结局指标和混杂因素
|
||||
|
||||
## 输出要求
|
||||
1. 回复要简洁、专业、有针对性
|
||||
2. 每次只关注当前阶段的问题
|
||||
3. 当用户提供了足够信息后，整理成结构化内容
|
||||
4. 整理完成后，在回复末尾输出提取的数据（用于同步到方案）
|
||||
|
||||
## 数据提取格式
|
||||
当你认为当前阶段的信息已经收集完整，请在回复末尾添加：
|
||||
<extracted_data>
|
||||
{
|
||||
"字段1": "值1",
|
||||
"字段2": "值2"
|
||||
}
|
||||
</extracted_data>
|
||||
|
||||
注意：只有在信息收集完整时才输出 extracted_data 标签。`;
|
||||
|
||||
await prisma.agentPrompt.upsert({
|
||||
where: {
|
||||
agentId_promptCode_version: {
|
||||
agentId: protocolAgent.id,
|
||||
promptCode: 'system',
|
||||
version: 1
|
||||
}
|
||||
},
|
||||
update: {
|
||||
content: systemPromptContent,
|
||||
isActive: true,
|
||||
},
|
||||
create: {
|
||||
agentId: protocolAgent.id,
|
||||
promptType: 'system',
|
||||
promptCode: 'system',
|
||||
content: systemPromptContent,
|
||||
variables: ['context', 'intent'],
|
||||
version: 1,
|
||||
isActive: true,
|
||||
},
|
||||
});
|
||||
console.log(` ✅ 系统 Prompt 创建成功`);
|
||||
|
||||
// ============================================
|
||||
// 4. 创建各阶段 Prompt
|
||||
// ============================================
|
||||
console.log('📌 创建各阶段 Prompt...');
|
||||
|
||||
const stagePrompts = [
|
||||
{
|
||||
stageCode: 'scientific_question',
|
||||
content: `## 当前阶段：科学问题梳理
|
||||
|
||||
### 目标
|
||||
帮助用户明确研究要解决的核心科学问题。
|
||||
|
||||
### 引导方向
|
||||
1. 了解用户的研究背景和动机
|
||||
2. 明确研究的核心问题是什么
|
||||
3. 确认问题的科学价值和临床意义
|
||||
|
||||
### 提问示例
|
||||
- "您想研究什么问题？"
|
||||
- "这个研究的临床背景是什么？"
|
||||
- "您希望通过这个研究解决什么问题？"
|
||||
|
||||
### 数据提取
|
||||
当信息完整时，提取以下字段：
|
||||
- content: 科学问题的完整描述
|
||||
- background: 研究背景
|
||||
- significance: 研究意义`,
|
||||
},
|
||||
{
|
||||
stageCode: 'pico',
|
||||
content: `## 当前阶段：PICO要素
|
||||
|
||||
### 目标
|
||||
帮助用户确定PICO四要素。
|
||||
|
||||
### PICO定义
|
||||
- P (Population): 研究人群 - 谁是研究对象？
|
||||
- I (Intervention): 干预措施 - 研究什么干预/暴露？
|
||||
- C (Comparison): 对照 - 与什么比较？
|
||||
- O (Outcome): 结局 - 关注什么结果？
|
||||
|
||||
### 引导方向
|
||||
1. 逐一确认每个要素
|
||||
2. 确保定义清晰、可操作
|
||||
|
||||
### 数据提取
|
||||
当信息完整时，提取以下字段：
|
||||
- population: 研究人群描述
|
||||
- intervention: 干预措施描述
|
||||
- comparison: 对照描述
|
||||
- outcome: 结局指标描述`,
|
||||
},
|
||||
{
|
||||
stageCode: 'study_design',
|
||||
content: `## 当前阶段：研究设计
|
||||
|
||||
### 目标
|
||||
帮助用户选择合适的研究设计类型。
|
||||
|
||||
### 常见研究类型
|
||||
- 随机对照试验 (RCT)
|
||||
- 队列研究 (Cohort)
|
||||
- 病例对照研究 (Case-Control)
|
||||
- 横断面研究 (Cross-sectional)
|
||||
- 前后对照研究 (Before-After)
|
||||
|
||||
### 引导方向
|
||||
1. 根据研究问题推荐合适的设计类型
|
||||
2. 讨论设计的优缺点
|
||||
3. 确定关键设计要素（如盲法、随机化方法等）
|
||||
|
||||
### 数据提取
|
||||
当信息完整时，提取以下字段：
|
||||
- studyType: 研究类型
|
||||
- design: 设计要素列表
|
||||
- features: 特殊设计特征`,
|
||||
},
|
||||
{
|
||||
stageCode: 'sample_size',
|
||||
content: `## 当前阶段：样本量计算
|
||||
|
||||
### 目标
|
||||
帮助用户估算所需的样本量。
|
||||
|
||||
### 关键参数
|
||||
- α (显著性水平): 通常0.05
|
||||
- β (检验效能): 通常0.80或0.90
|
||||
- 效应量: 预期的效果大小
|
||||
- 脱落率: 预计的失访比例
|
||||
|
||||
### 引导方向
|
||||
1. 了解主要结局指标的类型
|
||||
2. 讨论预期的效应量
|
||||
3. 考虑脱落率
|
||||
|
||||
### 数据提取
|
||||
当信息完整时，提取以下字段：
|
||||
- sampleSize: 计算的样本量
|
||||
- alpha: 显著性水平
|
||||
- power: 检验效能
|
||||
- effectSize: 效应量
|
||||
- dropoutRate: 脱落率`,
|
||||
},
|
||||
{
|
||||
stageCode: 'endpoints',
|
||||
content: `## 当前阶段：观察指标
|
||||
|
||||
### 目标
|
||||
帮助用户定义完整的观察指标体系。
|
||||
|
||||
### 指标分类
|
||||
1. 基线指标 - 人口学特征、病史、实验室检查
|
||||
2. 暴露/干预指标 - 干预措施的具体内容
|
||||
3. 结局指标 - 主要结局、次要结局、安全性指标
|
||||
4. 混杂因素 - 需要控制的混杂变量
|
||||
|
||||
### 引导方向
|
||||
1. 逐类确认各项指标
|
||||
2. 确保指标定义清晰、可测量
|
||||
3. 确定测量时点
|
||||
|
||||
### 数据提取
|
||||
当信息完整时，提取以下字段：
|
||||
- baseline: 基线指标列表
|
||||
- exposure: 暴露/干预指标
|
||||
- outcomes: 结局指标（primary, secondary, safety）
|
||||
- confounders: 混杂因素列表`,
|
||||
},
|
||||
];
|
||||
|
||||
for (const prompt of stagePrompts) {
|
||||
const stageId = stageMap.get(prompt.stageCode);
|
||||
await prisma.agentPrompt.upsert({
|
||||
where: {
|
||||
agentId_promptCode_version: {
|
||||
agentId: protocolAgent.id,
|
||||
promptCode: `stage_${prompt.stageCode}`,
|
||||
version: 1
|
||||
}
|
||||
},
|
||||
update: {
|
||||
stageId: stageId,
|
||||
content: prompt.content,
|
||||
isActive: true,
|
||||
},
|
||||
create: {
|
||||
agentId: protocolAgent.id,
|
||||
stageId: stageId,
|
||||
promptType: 'stage',
|
||||
promptCode: `stage_${prompt.stageCode}`,
|
||||
content: prompt.content,
|
||||
variables: ['context'],
|
||||
version: 1,
|
||||
isActive: true,
|
||||
},
|
||||
});
|
||||
console.log(` ✅ 阶段 Prompt: ${prompt.stageCode}`);
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 完成
|
||||
// ============================================
|
||||
console.log('\n🎉 Protocol Agent 配置初始化完成！\n');
|
||||
console.log('╔════════════════════════════════════════════════════════════╗');
|
||||
console.log('║ Protocol Agent 配置摘要 ║');
|
||||
console.log('╠════════════════════════════════════════════════════════════╣');
|
||||
console.log(`║ Agent ID: ${protocolAgent.id} ║`);
|
||||
console.log('║ Agent Code: protocol_agent ║');
|
||||
console.log('║ 阶段数量: 5 ║');
|
||||
console.log('║ Prompt 数量: 6 (1 系统 + 5 阶段) ║');
|
||||
console.log('╠════════════════════════════════════════════════════════════╣');
|
||||
console.log('║ 阶段流程: ║');
|
||||
console.log('║ 1. 科学问题梳理 → 2. PICO要素 → 3. 研究设计 ║');
|
||||
console.log('║ → 4. 样本量计算 → 5. 观察指标 → 一键生成 ║');
|
||||
console.log('╚════════════════════════════════════════════════════════════╝');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ Protocol Agent 初始化失败:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
// 执行
|
||||
seedProtocolAgent()
|
||||
.then(async () => {
|
||||
await prisma.$disconnect();
|
||||
})
|
||||
.catch(async (e) => {
|
||||
console.error(e);
|
||||
await prisma.$disconnect();
|
||||
process.exit(1);
|
||||
});
|
||||
|
||||
@@ -395,6 +395,7 @@ async function main() {
|
||||
// 11. 跳过Prompt模板（表尚未创建）
|
||||
// ============================================
|
||||
console.log('📌 跳过Prompt模板创建（capability_schema.prompt_templates 尚未创建）');
|
||||
console.log('💡 提示：Protocol Agent 配置请运行独立脚本: npx tsx prisma/seed-protocol-agent.ts');
|
||||
|
||||
// ============================================
|
||||
// 12. 创建租户模块订阅
|
||||
|
||||
@@ -10,6 +10,9 @@ import { PrismaClient } from '@prisma/client';
|
||||
import { ProtocolOrchestrator } from '../services/ProtocolOrchestrator.js';
|
||||
import { LLMServiceInterface } from '../../services/BaseAgentOrchestrator.js';
|
||||
import { ProtocolStageCode } from '../../types/index.js';
|
||||
import { createStreamingService } from '../../../../common/streaming/index.js';
|
||||
import type { OpenAIMessage } from '../../../../common/streaming/index.js';
|
||||
import { logger } from '../../../../common/logging/index.js';
|
||||
|
||||
// 请求类型定义
|
||||
interface SendMessageBody {
|
||||
@@ -38,46 +41,95 @@ interface GetContextParams {
|
||||
|
||||
export class ProtocolAgentController {
|
||||
private orchestrator: ProtocolOrchestrator;
|
||||
private prisma: PrismaClient;
|
||||
|
||||
constructor(prisma: PrismaClient, llmService: LLMServiceInterface) {
|
||||
this.prisma = prisma;
|
||||
this.orchestrator = new ProtocolOrchestrator({ prisma, llmService });
|
||||
}
|
||||
|
||||
/**
|
||||
* 发送消息
|
||||
* 发送消息（流式输出）
|
||||
* POST /api/aia/protocol-agent/message
|
||||
*
|
||||
* 使用通用 StreamingService 实现打字机效果
|
||||
*/
|
||||
async sendMessage(
|
||||
request: FastifyRequest<{ Body: SendMessageBody }>,
|
||||
reply: FastifyReply
|
||||
): Promise<void> {
|
||||
const { conversationId, content } = request.body;
|
||||
const userId = (request as any).user?.userId;
|
||||
|
||||
if (!userId) {
|
||||
reply.code(401).send({ error: 'Unauthorized' });
|
||||
return;
|
||||
}
|
||||
|
||||
if (!conversationId || !content) {
|
||||
reply.code(400).send({ error: 'Missing required fields: conversationId, content' });
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const { conversationId, content, messageId } = request.body;
|
||||
const userId = (request as any).user?.userId;
|
||||
// 1. 确保上下文存在
|
||||
const contextService = this.orchestrator.getContextService();
|
||||
const context = await contextService.getOrCreateContext(conversationId, userId);
|
||||
|
||||
// 2. 构建包含上下文的消息
|
||||
const messages = await this.buildMessagesWithContext(conversationId, content, context);
|
||||
|
||||
// 3. 保存用户消息到数据库
|
||||
await this.prisma.message.create({
|
||||
data: {
|
||||
conversationId,
|
||||
role: 'user',
|
||||
content,
|
||||
},
|
||||
});
|
||||
|
||||
if (!userId) {
|
||||
reply.code(401).send({ error: 'Unauthorized' });
|
||||
return;
|
||||
}
|
||||
|
||||
if (!conversationId || !content) {
|
||||
reply.code(400).send({ error: 'Missing required fields: conversationId, content' });
|
||||
return;
|
||||
}
|
||||
|
||||
const response = await this.orchestrator.handleMessage({
|
||||
conversationId,
|
||||
// 4. 使用通用 StreamingService 流式输出
|
||||
const streamingService = createStreamingService(reply, {
|
||||
model: 'deepseek-v3',
|
||||
temperature: 0.7,
|
||||
maxTokens: 4096,
|
||||
enableDeepThinking: false,
|
||||
userId,
|
||||
content,
|
||||
messageId,
|
||||
conversationId,
|
||||
});
|
||||
|
||||
reply.send({
|
||||
success: true,
|
||||
data: response,
|
||||
await streamingService.streamGenerate(messages, {
|
||||
onComplete: async (fullContent, thinkingContent) => {
|
||||
// 5. 保存 AI 回复到数据库
|
||||
await this.prisma.message.create({
|
||||
data: {
|
||||
conversationId,
|
||||
role: 'assistant',
|
||||
content: fullContent,
|
||||
thinkingContent: thinkingContent || null,
|
||||
model: 'deepseek-v3',
|
||||
},
|
||||
});
|
||||
|
||||
// 6. 更新对话时间
|
||||
await this.prisma.conversation.update({
|
||||
where: { id: conversationId },
|
||||
data: { updatedAt: new Date() },
|
||||
});
|
||||
|
||||
logger.info('[ProtocolAgent] 消息发送完成', {
|
||||
conversationId,
|
||||
stage: context.currentStage,
|
||||
hasThinking: !!thinkingContent,
|
||||
});
|
||||
},
|
||||
onError: (error) => {
|
||||
logger.error('[ProtocolAgent] 流式生成失败', { error, conversationId });
|
||||
},
|
||||
});
|
||||
|
||||
} catch (error) {
|
||||
console.error('[ProtocolAgentController] sendMessage error:', error);
|
||||
logger.error('[ProtocolAgentController] sendMessage error:', error);
|
||||
reply.code(500).send({
|
||||
success: false,
|
||||
error: error instanceof Error ? error.message : 'Internal server error',
|
||||
@@ -85,6 +137,165 @@ export class ProtocolAgentController {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 构建包含上下文的消息列表
|
||||
*/
|
||||
private async buildMessagesWithContext(
|
||||
conversationId: string,
|
||||
userContent: string,
|
||||
context: any
|
||||
): Promise<OpenAIMessage[]> {
|
||||
// 获取历史消息
|
||||
const historyMessages = await this.prisma.message.findMany({
|
||||
where: { conversationId },
|
||||
orderBy: { createdAt: 'asc' },
|
||||
take: 20,
|
||||
});
|
||||
|
||||
// 构建系统 Prompt（包含当前阶段和已完成的数据）
|
||||
const systemPrompt = this.buildSystemPrompt(context);
|
||||
|
||||
const messages: OpenAIMessage[] = [
|
||||
{ role: 'system', content: systemPrompt },
|
||||
];
|
||||
|
||||
// 添加历史消息
|
||||
for (const msg of historyMessages) {
|
||||
messages.push({
|
||||
role: msg.role as 'user' | 'assistant',
|
||||
content: msg.content,
|
||||
});
|
||||
}
|
||||
|
||||
// 添加当前用户消息
|
||||
messages.push({ role: 'user', content: userContent });
|
||||
|
||||
return messages;
|
||||
}
|
||||
|
||||
/**
|
||||
* 构建系统 Prompt（包含上下文数据）
|
||||
*/
|
||||
private buildSystemPrompt(context: any): string {
|
||||
const stageNames: Record<string, string> = {
|
||||
scientific_question: '科学问题梳理',
|
||||
pico: 'PICO要素',
|
||||
study_design: '研究设计',
|
||||
sample_size: '样本量计算',
|
||||
endpoints: '观察指标',
|
||||
};
|
||||
|
||||
const currentStageName = stageNames[context.currentStage] || context.currentStage;
|
||||
|
||||
// 构建已完成阶段的数据摘要（从 ProtocolContextData 各字段读取）
|
||||
let completedDataSummary = '';
|
||||
const completedStages = context.completedStages || [];
|
||||
|
||||
if (completedStages.includes('scientific_question') && context.scientificQuestion) {
|
||||
completedDataSummary += `\n\n### 已确认的科学问题\n${JSON.stringify(context.scientificQuestion, null, 2)}`;
|
||||
}
|
||||
if (completedStages.includes('pico') && context.pico) {
|
||||
completedDataSummary += `\n\n### 已确认的PICO要素\n${JSON.stringify(context.pico, null, 2)}`;
|
||||
}
|
||||
if (completedStages.includes('study_design') && context.studyDesign) {
|
||||
completedDataSummary += `\n\n### 已确认的研究设计\n${JSON.stringify(context.studyDesign, null, 2)}`;
|
||||
}
|
||||
if (completedStages.includes('sample_size') && context.sampleSize) {
|
||||
completedDataSummary += `\n\n### 已确认的样本量\n${JSON.stringify(context.sampleSize, null, 2)}`;
|
||||
}
|
||||
if (completedStages.includes('endpoints') && context.endpoints) {
|
||||
completedDataSummary += `\n\n### 已确认的观察指标\n${JSON.stringify(context.endpoints, null, 2)}`;
|
||||
}
|
||||
|
||||
// 计算进度
|
||||
const progress = Math.round((completedStages.length / 5) * 100);
|
||||
|
||||
// 获取当前阶段需要输出的字段格式
|
||||
const stageOutputFormat = this.getStageOutputFormat(context.currentStage);
|
||||
|
||||
return `你是一位资深的临床研究方法学专家，正在帮助医生设计临床研究方案。
|
||||
|
||||
## 当前状态
|
||||
- **当前阶段**: ${currentStageName}
|
||||
- **已完成阶段**: ${completedStages.map((s: string) => stageNames[s]).join(', ') || '无'}
|
||||
- **进度**: ${progress}%
|
||||
|
||||
## 已收集的数据${completedDataSummary || '\n暂无已确认的数据'}
|
||||
|
||||
## 你的任务
|
||||
1. **只围绕「${currentStageName}」阶段与用户对话**，不要跨阶段讨论
|
||||
2. 引导用户提供当前阶段所需的完整信息
|
||||
3. 当信息收集完整时，先用文字总结，然后**必须**在回复末尾输出结构化数据
|
||||
|
||||
## 当前阶段「${currentStageName}」的输出格式
|
||||
当信息完整时，**必须**在回复末尾添加以下格式的数据提取标签：
|
||||
|
||||
${stageOutputFormat}
|
||||
|
||||
## 重要提示
|
||||
- 只有当用户提供了足够的信息后才输出 <extracted_data> 标签
|
||||
- 输出的 JSON 必须是有效格式
|
||||
- 每次对话只关注当前阶段「${currentStageName}」
|
||||
- 回复使用 Markdown 格式，简洁专业`;
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取不同阶段的输出格式说明
|
||||
*/
|
||||
private getStageOutputFormat(stageCode: string): string {
|
||||
const formats: Record<string, string> = {
|
||||
scientific_question: `<extracted_data>
|
||||
{
|
||||
"content": "一句话科学问题（不超过50字）"
|
||||
}
|
||||
</extracted_data>`,
|
||||
|
||||
pico: `<extracted_data>
|
||||
{
|
||||
"population": "研究人群（不超过20字）",
|
||||
"intervention": "干预措施（不超过20字）",
|
||||
"comparison": "对照组（不超过20字）",
|
||||
"outcome": "结局指标（不超过20字）"
|
||||
}
|
||||
</extracted_data>`,
|
||||
|
||||
study_design: `<extracted_data>
|
||||
{
|
||||
"studyType": "研究类型，如：回顾性队列研究、前瞻性队列研究、随机对照试验等",
|
||||
"design": ["设计特征1", "设计特征2"]
|
||||
}
|
||||
</extracted_data>`,
|
||||
|
||||
sample_size: `<extracted_data>
|
||||
{
|
||||
"sampleSize": 样本量数字,
|
||||
"calculation": {
|
||||
"alpha": 显著性水平（如0.05）,
|
||||
"power": 检验效能（如0.8）,
|
||||
"effectSize": "效应量描述"
|
||||
}
|
||||
}
|
||||
</extracted_data>`,
|
||||
|
||||
endpoints: `<extracted_data>
|
||||
{
|
||||
"outcomes": {
|
||||
"primary": ["主要结局指标1", "主要结局指标2"],
|
||||
"secondary": ["次要结局指标1"],
|
||||
"safety": ["安全性指标"]
|
||||
},
|
||||
"confounders": ["混杂因素1", "混杂因素2"]
|
||||
}
|
||||
</extracted_data>`,
|
||||
};
|
||||
|
||||
return formats[stageCode] || `<extracted_data>
|
||||
{
|
||||
"key": "value"
|
||||
}
|
||||
</extracted_data>`;
|
||||
}
|
||||
|
||||
/**
|
||||
* 同步阶段数据
|
||||
* POST /api/aia/protocol-agent/sync
|
||||
|
||||
@@ -127,39 +127,63 @@ export class ProtocolOrchestrator extends BaseAgentOrchestrator {
|
||||
|
||||
/**
|
||||
* 处理Protocol同步请求
|
||||
* 支持两种场景：1. 首次同步（从对话中提取）2. 编辑更新（用户手动修改）
|
||||
*/
|
||||
async handleProtocolSync(
|
||||
conversationId: string,
|
||||
userId: string,
|
||||
stageCode: string,
|
||||
data: Record<string, unknown>
|
||||
data: Record<string, unknown>,
|
||||
isEdit: boolean = false // 是否是编辑更新
|
||||
): Promise<{
|
||||
success: boolean;
|
||||
context: ProtocolContextData;
|
||||
nextStage?: ProtocolStageCode;
|
||||
message?: string;
|
||||
condensedData?: Record<string, unknown>;
|
||||
}> {
|
||||
const stage = stageCode as ProtocolStageCode;
|
||||
|
||||
// 获取当前上下文
|
||||
const existingContext = await this.contextService.getContext(conversationId);
|
||||
const isAlreadyCompleted = existingContext?.completedStages.includes(stage);
|
||||
|
||||
let finalData: Record<string, unknown>;
|
||||
|
||||
if (isEdit || isAlreadyCompleted) {
|
||||
// 编辑模式：直接使用用户提供的数据，不再凝练
|
||||
finalData = data;
|
||||
} else {
|
||||
// 首次同步：使用 LLM 凝练数据
|
||||
finalData = await this.condenseStageData(stage, data);
|
||||
}
|
||||
|
||||
// 保存阶段数据
|
||||
await this.contextService.updateStageData(conversationId, stage, {
|
||||
...data,
|
||||
...finalData,
|
||||
confirmed: true,
|
||||
confirmedAt: new Date(),
|
||||
});
|
||||
|
||||
// 获取下一阶段
|
||||
const currentIndex = STAGE_ORDER.indexOf(stage);
|
||||
const nextStage = currentIndex < STAGE_ORDER.length - 1
|
||||
? STAGE_ORDER[currentIndex + 1]
|
||||
: undefined;
|
||||
let context: ProtocolContextData;
|
||||
let nextStage: ProtocolStageCode | undefined;
|
||||
|
||||
// 标记当前阶段完成，更新到下一阶段
|
||||
const context = await this.contextService.completeStage(
|
||||
conversationId,
|
||||
stage,
|
||||
nextStage
|
||||
);
|
||||
if (isAlreadyCompleted) {
|
||||
// 已完成的阶段：只更新数据，不改变当前阶段
|
||||
context = await this.contextService.getContext(conversationId) as ProtocolContextData;
|
||||
} else {
|
||||
// 首次完成：标记完成并进入下一阶段
|
||||
const currentIndex = STAGE_ORDER.indexOf(stage);
|
||||
nextStage = currentIndex < STAGE_ORDER.length - 1
|
||||
? STAGE_ORDER[currentIndex + 1]
|
||||
: undefined;
|
||||
|
||||
context = await this.contextService.completeStage(
|
||||
conversationId,
|
||||
stage,
|
||||
nextStage
|
||||
);
|
||||
}
|
||||
|
||||
// 检查是否所有阶段都已完成
|
||||
const allCompleted = this.contextService.isAllStagesCompleted(context);
|
||||
@@ -168,14 +192,104 @@ export class ProtocolOrchestrator extends BaseAgentOrchestrator {
|
||||
success: true,
|
||||
context,
|
||||
nextStage,
|
||||
message: allCompleted
|
||||
? '🎉 所有核心要素已完成！您可以点击「一键生成研究方案」生成完整方案。'
|
||||
: nextStage
|
||||
? `已同步${STAGE_NAMES[stage]}，进入${STAGE_NAMES[nextStage]}阶段`
|
||||
: `已同步${STAGE_NAMES[stage]}`,
|
||||
condensedData: finalData,
|
||||
message: isAlreadyCompleted
|
||||
? `✅ 已更新「${STAGE_NAMES[stage]}」`
|
||||
: allCompleted
|
||||
? '🎉 所有核心要素已完成！您可以点击「一键生成研究方案」生成完整方案。'
|
||||
: nextStage
|
||||
? `已同步${STAGE_NAMES[stage]}，进入${STAGE_NAMES[nextStage]}阶段`
|
||||
: `已同步${STAGE_NAMES[stage]}`,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用 LLM 凝练阶段数据
|
||||
*/
|
||||
private async condenseStageData(
|
||||
stageCode: ProtocolStageCode,
|
||||
data: Record<string, unknown>
|
||||
): Promise<Record<string, unknown>> {
|
||||
// 构建凝练 Prompt
|
||||
const condensePrompts: Record<ProtocolStageCode, string> = {
|
||||
scientific_question: `请将以下科学问题内容凝练成一句话（不超过50字），保留核心要点：
|
||||
|
||||
原始内容：
|
||||
${JSON.stringify(data, null, 2)}
|
||||
|
||||
要求：
|
||||
- 输出格式：{ "content": "一句话科学问题" }
|
||||
- 只输出 JSON，不要其他内容`,
|
||||
|
||||
pico: `请将以下 PICO 要素凝练成简短描述：
|
||||
|
||||
原始内容：
|
||||
${JSON.stringify(data, null, 2)}
|
||||
|
||||
要求：
|
||||
- 每个要素不超过20字
|
||||
- 输出格式：{ "population": "...", "intervention": "...", "comparison": "...", "outcome": "..." }
|
||||
- 只输出 JSON，不要其他内容`,
|
||||
|
||||
study_design: `请将以下研究设计凝练成关键标签：
|
||||
|
||||
原始内容：
|
||||
${JSON.stringify(data, null, 2)}
|
||||
|
||||
要求：
|
||||
- 输出格式：{ "studyType": "研究类型", "design": ["特征1", "特征2"] }
|
||||
- 只输出 JSON，不要其他内容`,
|
||||
|
||||
sample_size: `请提取样本量关键数据：
|
||||
|
||||
原始内容：
|
||||
${JSON.stringify(data, null, 2)}
|
||||
|
||||
要求：
|
||||
- 输出格式：{ "sampleSize": 数字, "calculation": { "alpha": 数字, "power": 数字 } }
|
||||
- 只输出 JSON，不要其他内容`,
|
||||
|
||||
endpoints: `请将以下观察指标凝练成简短列表：
|
||||
|
||||
原始内容：
|
||||
${JSON.stringify(data, null, 2)}
|
||||
|
||||
要求：
|
||||
- 每个指标不超过10字
|
||||
- 输出格式：{ "baseline": {...}, "exposure": {...}, "outcomes": {...}, "confounders": [...] }
|
||||
- 只输出 JSON，不要其他内容`,
|
||||
};
|
||||
|
||||
try {
|
||||
const prompt = condensePrompts[stageCode];
|
||||
if (!prompt) {
|
||||
return data;
|
||||
}
|
||||
|
||||
const response = await this.llmService.chat({
|
||||
messages: [
|
||||
{ role: 'system', content: '你是一位专业的临床研究方法学专家，擅长提炼和凝练研究要素。请严格按照要求的 JSON 格式输出。' },
|
||||
{ role: 'user', content: prompt },
|
||||
],
|
||||
temperature: 0.3,
|
||||
maxTokens: 500,
|
||||
});
|
||||
|
||||
// 解析 LLM 返回的 JSON
|
||||
const jsonMatch = response.content.match(/\{[\s\S]*\}/);
|
||||
if (jsonMatch) {
|
||||
const condensed = JSON.parse(jsonMatch[0]);
|
||||
return condensed;
|
||||
}
|
||||
|
||||
return data;
|
||||
} catch (error) {
|
||||
console.error('[ProtocolOrchestrator] condenseStageData error:', error);
|
||||
// 凝练失败时返回原始数据
|
||||
return data;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取Protocol上下文服务
|
||||
*/
|
||||
|
||||
@@ -24,6 +24,7 @@ import type { Agent, AgentStage } from '../types/index.js';
|
||||
* 用于从 PromptService 获取对应的提示词
|
||||
*/
|
||||
const AGENT_TO_PROMPT_CODE: Record<string, string> = {
|
||||
'PROTOCOL_AGENT': 'PROTOCOL_SYSTEM', // Protocol Agent使用自己的Prompt系统
|
||||
'TOPIC_01': 'AIA_SCIENTIFIC_QUESTION',
|
||||
'TOPIC_02': 'AIA_PICO_ANALYSIS',
|
||||
'TOPIC_03': 'AIA_TOPIC_EVALUATION',
|
||||
@@ -39,9 +40,22 @@ const AGENT_TO_PROMPT_CODE: Record<string, string> = {
|
||||
// ==================== 智能体配置 ====================
|
||||
|
||||
/**
|
||||
* 12个智能体配置（与前端保持一致）
|
||||
* 13个智能体配置（与前端保持一致）
|
||||
* 包含：12个传统智能体 + 1个Protocol Agent
|
||||
*/
|
||||
const AGENTS: Agent[] = [
|
||||
// Protocol Agent: 全流程研究方案制定
|
||||
{
|
||||
id: 'PROTOCOL_AGENT',
|
||||
name: '全流程研究方案制定',
|
||||
description: '一站式完成研究方案核心要素：科学问题→PICO→研究设计→样本量→观察指标，支持一键生成完整方案。',
|
||||
icon: '🚀',
|
||||
stage: 'protocol',
|
||||
color: '#6366F1',
|
||||
systemPrompt: `你是研究方案制定助手，将引导用户系统地完成临床研究方案的核心要素设计。`,
|
||||
welcomeMessage: '您好！我是研究方案制定助手。让我们开始制定您的研究方案吧！',
|
||||
},
|
||||
|
||||
// Phase 1: 选题优化智能体
|
||||
{
|
||||
id: 'TOPIC_01',
|
||||
|
||||
Reference in New Issue
Block a user