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AIclinicalresearch/docs/02-通用能力层/快速引用卡片.md
HaHafeng 96290d2f76 feat(aia): Implement Protocol Agent MVP with reusable Agent framework
Sprint 1-3 Completed (Backend + Frontend):

Backend (Sprint 1-2):
- Implement 5-layer Agent framework (Query->Planner->Executor->Tools->Reflection)
- Create agent_schema with 6 tables (agent_definitions, stages, prompts, sessions, traces, reflexion_rules)
- Create protocol_schema with 2 tables (protocol_contexts, protocol_generations)
- Implement Protocol Agent core services (Orchestrator, ContextService, PromptBuilder)
- Integrate LLM service adapter (DeepSeek/Qwen/GPT-5/Claude)
- 6 API endpoints with full authentication
- 10/10 API tests passed

Frontend (Sprint 3):
- Add Protocol Agent entry in AgentHub (indigo theme card)
- Implement ProtocolAgentPage with 3-column layout
- Collapsible sidebar (Gemini style, 48px <-> 280px)
- StatePanel with 5 stage cards (scientific_question, pico, study_design, sample_size, endpoints)
- ChatArea with sync button and action cards integration
- 100% prototype design restoration (608 lines CSS)
- Detailed endpoints structure: baseline, exposure, outcomes, confounders

Features:
- 5-stage dialogue flow for research protocol design
- Conversation-driven interaction with sync-to-protocol button
- Real-time context state management
- One-click protocol generation button (UI ready, backend pending)

Database:
- agent_schema: 6 tables for reusable Agent framework
- protocol_schema: 2 tables for Protocol Agent
- Seed data: 1 agent + 5 stages + 9 prompts + 4 reflexion rules

Code Stats:
- Backend: 13 files, 4338 lines
- Frontend: 14 files, 2071 lines
- Total: 27 files, 6409 lines

Status: MVP core functionality completed, pending frontend-backend integration testing

Next: Sprint 4 - One-click protocol generation + Word export
2026-01-24 17:29:24 +08:00

4.9 KiB
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通用能力层 - 快速引用卡片

一页纸速查表,快速找到需要的通用能力


🎯 我需要...

💬 AI 对话功能

前端:

import { AIStreamChat } from '@/shared/components/Chat';
<AIStreamChat apiEndpoint="/api/v1/xxx/chat/stream" enableDeepThinking={true} />

后端:

import { createStreamingService } from '../../../common/streaming';
const service = createStreamingService(reply);
await service.streamGenerate(messages);

📚 详细文档


🤖 调用 LLM

import { LLMFactory } from '../../../common/llm/adapters/LLMFactory';

const llm = LLMFactory.getAdapter('deepseek-v3');
const response = await llm.chat(messages);

// 流式
for await (const chunk of llm.chatStream(messages)) {
  console.log(chunk.content);
}

📚 详细文档


📁 文件存储

import { storage } from '../../../common/storage';

// 上传
const url = await storage.upload('path/file.pdf', buffer);

// 下载
const buffer = await storage.download('path/file.pdf');

📚 详细文档


异步任务

import { JobFactory } from '../../../common/jobs';

const queue = JobFactory.getQueue();

// 创建任务
await queue.createJob('job-name', { taskId: 'xxx' });

// 注册Worker
queue.registerWorker('job-name', async (job) => {
  // 处理逻辑
});

📚 详细文档


📄 文档处理

import { ExtractionClient } from '../../../common/document/ExtractionClient';

const client = new ExtractionClient();
const text = await client.extractText(buffer, 'pdf');

📚 详细文档


🔍 知识库检索RAG

import { DifyClient } from '../../../common/rag/DifyClient';

const dify = new DifyClient(apiKey, baseURL);
const results = await dify.retrievalSearch(query, options);

📚 详细文档


💾 缓存服务

import { cache } from '../../../common/cache';

await cache.set('key', value, 3600);  // TTL: 3600秒
const value = await cache.get('key');
await cache.delete('key');

📚 详细文档


📝 日志记录

import { logger } from '../../../common/logging';

logger.info('[Module] 操作描述', { userId: 'xxx', detail: 'xxx' });
logger.error('[Module] 错误', { error, stack: error.stack });

📚 详细文档


🔐 认证授权

后端:

import { authenticate, getUserId } from '../../../common/auth';

// 路由
fastify.get('/api', { preHandler: [authenticate] }, handler);

// 控制器
const userId = getUserId(request);

前端:

import { getAccessToken } from '@/framework/auth/api';
const token = getAccessToken();

// 或使用 apiClient自动携带token
import apiClient from '@/common/api/axios';
await apiClient.get('/api/xxx');

📚 详细文档


📋 Prompt 管理

import { PromptService } from '../../../common/prompt/prompt.service';

const promptService = new PromptService();
const prompt = await promptService.getActivePrompt('template_code');
const rendered = promptService.renderPrompt(template, variables);

📚 详细文档


🏗️ 架构原则

正确做法

// 1. 使用通用能力,不要重复造轮子
import { createStreamingService } from '../../../common/streaming';

// 2. 遵循云原生规范
await storage.upload();  // 不要用 fs.writeFileSync()

// 3. 使用结构化日志
logger.info('[Module] 操作', { detail });  // 不要用 console.log()

// 4. 统一认证
fastify.get('/api', { preHandler: [authenticate] });

// 5. 标准化API格式
// OpenAI Compatible, not 自定义格式

错误做法

// 1. 自己实现已有的能力
reply.raw.write('data: ...');  // ❌ 应该用 StreamingService

// 2. 直接操作本地文件
fs.writeFileSync('/tmp/file');  // ❌ 应该用 storage

// 3. 使用 console.log
console.log('debug');  // ❌ 应该用 logger

// 4. 硬编码用户ID
const userId = 'test';  // ❌ 应该用 getUserId(request)

// 5. 自定义格式
{ type: 'delta', content: 'xxx' }  // ❌ 应该用 OpenAI Compatible

📚 完整文档


更新时间: 2026-01-14