docs(iit): Add IIT Manager Agent V2.9 development plan with multi-agent architecture
Features: - Add V2.9 enhancements: Cron Skill, User Profiling, Feedback Loop, Multi-Intent Handling - Create modular development plan documents (database, engines, services, memory, tasks) - Add V2.5/V2.6/V2.8/V2.9 design documents for architecture evolution - Add system design white papers and implementation guides Architecture: - Dual-Brain Architecture (SOP + ReAct engines) - Three-layer memory system (Flow Log, Hot Memory, History Book) - ProfilerService for personalized responses - SchedulerService with Cron Skill support Also includes: - Frontend nginx config updates - Backend test scripts for WeChat signature - Database backup files Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
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docs/03-业务模块/IIT Manager Agent/04-开发计划/04-记忆系统实现指南.md
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docs/03-业务模块/IIT Manager Agent/04-开发计划/04-记忆系统实现指南.md
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# IIT Manager Agent 记忆系统实现指南 (V2.8)
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> **版本：** V2.8
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> **更新日期：** 2026-02-05
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> **关联文档：** [IIT Manager Agent V2.6 综合开发计划](./IIT%20Manager%20Agent%20V2.6%20综合开发计划.md)
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---
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## 1. 记忆架构概览
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### 1.1 设计原则
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临床研究项目周期长达 1-3 年，需要"项目级长期记忆"来跨会话保持上下文。V2.8 架构采用**三层记忆体系**：
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| 层级 | 名称 | 存储位置 | 生命周期 | 检索方式 |
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|------|------|----------|----------|----------|
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| L1 | 流水账 | conversation_history | 30天 | 按需向量检索 |
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| L2 | 热记忆 | project_memory | 持久 | 每次注入 |
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| L3 | 历史书 | weekly_reports | 持久 | 按意图检索 |
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> **注意**：用户偏好和患者关键信息统一存储在 `project_memory` 的 Markdown 中，无需单独表。
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### 1.2 架构图
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```
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┌─────────────────────────────────────────────────────────────────────┐
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│ MemoryService │
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│ │
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│ ┌────────────────┐ ┌────────────────┐ ┌────────────────────────┐ │
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│ │ 流水账(L1) │ │ 热记忆(L2) │ │ 历史书(L3) │ │
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│ │ conversation_ │ │ project_memory │ │ weekly_reports │ │
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│ │ history │ │ │ │ │ │
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│ │ │ │ - 项目元信息 │ │ - 周报卷叠 │ │
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│ │ 30天自动过期 │ │ - 当前状态 │ │ - 关键决策归档 │ │
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│ │ 向量化存储 │ │ - 关键决策 │ │ │ │
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│ └────────────────┘ └────────────────┘ └────────────────────────┘ │
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│ │ │ │ │
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│ └───────────────────┴───────────────────────┘ │
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│ │ │
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│ ┌───────▼───────┐ │
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│ │ getContext │ │
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│ │ ForPrompt() │ │
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│ └───────────────┘ │
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└─────────────────────────────────────────────────────────────────────┘
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```
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---
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## 2. 数据模型
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### 2.1 流水账 (conversation_history)
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```prisma
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model iit_conversation_history {
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id String @id @default(cuid())
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project_id String
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user_id String
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role String // 'user' | 'assistant'
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content String
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intent String? // 意图标记
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embedding Unsupported("vector(1536)")?
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created_at DateTime @default(now())
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expires_at DateTime // 30天后过期
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@@index([project_id, user_id, created_at])
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@@index([project_id, expires_at])
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}
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```
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### 2.2 热记忆 (project_memory)
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```prisma
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model iit_project_memory {
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id String @id @default(cuid())
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project_id String
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type String // 'meta' | 'status' | 'decision' | 'preference'
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key String
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value Json
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priority Int @default(0) // 优先级，高的先注入
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created_at DateTime @default(now())
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updated_at DateTime @updatedAt
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@@unique([project_id, type, key])
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@@index([project_id, type])
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}
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```
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### 2.3 历史书 - 周报 (weekly_reports)
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```prisma
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model iit_weekly_reports {
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id String @id @default(cuid())
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project_id String
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week_start DateTime
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week_end DateTime
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summary String // 周报摘要（Markdown）
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key_events Json // 关键事件 JSON
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metrics Json // 统计指标
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embedding Unsupported("vector(1536)")?
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created_at DateTime @default(now())
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@@unique([project_id, week_start])
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@@index([project_id, week_start])
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}
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```
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---
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## 3. MemoryService 完整实现
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> **文件路径**: `backend/src/modules/iit-manager/services/MemoryService.ts`
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```typescript
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import { prisma } from '../../common/prisma';
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import { OpenAIEmbeddings } from '../../common/llm/embeddings';
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import { LLMFactory } from '../../common/llm/adapters/LLMFactory';
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export class MemoryService {
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private embeddings: OpenAIEmbeddings;
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private llm = LLMFactory.create('qwen');
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constructor() {
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this.embeddings = new OpenAIEmbeddings();
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}
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// ===== 1. 流水账操作 =====
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async saveConversation(data: {
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projectId: string;
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userId: string;
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role: 'user' | 'assistant';
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content: string;
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intent?: string;
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}): Promise<void> {
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const embedding = await this.embeddings.embed(data.content);
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const expiresAt = new Date();
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expiresAt.setDate(expiresAt.getDate() + 30);
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await prisma.iit_conversation_history.create({
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data: {
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project_id: data.projectId,
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user_id: data.userId,
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role: data.role,
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content: data.content,
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intent: data.intent,
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embedding: embedding,
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expires_at: expiresAt
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}
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});
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}
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async searchConversations(
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projectId: string,
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query: string,
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limit: number = 5
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): Promise<ConversationResult[]> {
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const queryEmbedding = await this.embeddings.embed(query);
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const results = await prisma.$queryRaw<ConversationResult[]>`
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SELECT id, content, role, intent, created_at,
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1 - (embedding <=> ${queryEmbedding}::vector) as similarity
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FROM iit_conversation_history
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WHERE project_id = ${projectId}
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AND expires_at > NOW()
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ORDER BY embedding <=> ${queryEmbedding}::vector
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LIMIT ${limit}
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`;
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return results;
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}
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// ===== 2. 热记忆操作 =====
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async getHotMemory(projectId: string): Promise<HotMemory> {
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const memories = await prisma.iit_project_memory.findMany({
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where: { project_id: projectId },
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orderBy: { priority: 'desc' }
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});
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return {
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meta: memories.filter(m => m.type === 'meta'),
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status: memories.filter(m => m.type === 'status'),
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decisions: memories.filter(m => m.type === 'decision'),
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preferences: memories.filter(m => m.type === 'preference')
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};
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}
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async updateHotMemory(
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projectId: string,
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type: string,
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key: string,
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value: any,
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priority: number = 0
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): Promise<void> {
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await prisma.iit_project_memory.upsert({
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where: {
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project_id_type_key: { project_id: projectId, type, key }
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},
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update: { value, priority },
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create: {
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project_id: projectId,
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type,
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key,
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value,
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priority
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}
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});
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}
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async refreshHotMemory(projectId: string): Promise<void> {
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// 从最近的对话中提取关键信息更新热记忆
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const recentConversations = await prisma.iit_conversation_history.findMany({
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where: { project_id: projectId },
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orderBy: { created_at: 'desc' },
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take: 50
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});
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if (recentConversations.length === 0) return;
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// 使用 LLM 提取关键信息
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const prompt = `分析以下对话，提取需要长期记住的关键信息：
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${recentConversations.map(c => `${c.role}: ${c.content}`).join('\n')}
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请提取以下类别的信息（JSON格式）：
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1. status: 项目当前状态变化
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2. decisions: 重要决策
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3. preferences: 用户偏好
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返回格式:
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{
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"status": [{"key": "...", "value": "...", "priority": 0-10}],
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"decisions": [...],
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"preferences": [...]
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}`;
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const response = await this.llm.chat([{ role: 'user', content: prompt }]);
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try {
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const extracted = JSON.parse(response.content);
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for (const [type, items] of Object.entries(extracted)) {
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for (const item of items as any[]) {
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await this.updateHotMemory(projectId, type, item.key, item.value, item.priority);
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}
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}
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} catch (e) {
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console.error('[MemoryService] 热记忆提取失败:', e);
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}
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}
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// ===== 3. 历史书操作 =====
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async saveWeeklyReport(projectId: string, report: string): Promise<void> {
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const weekStart = this.getWeekStart();
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const weekEnd = new Date(weekStart);
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weekEnd.setDate(weekStart.getDate() + 6);
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const embedding = await this.embeddings.embed(report);
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await prisma.iit_weekly_reports.upsert({
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where: {
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project_id_week_start: { project_id: projectId, week_start: weekStart }
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},
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update: {
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summary: report,
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embedding
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},
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create: {
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project_id: projectId,
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week_start: weekStart,
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week_end: weekEnd,
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summary: report,
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key_events: {},
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metrics: {},
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embedding
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}
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});
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}
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async searchWeeklyReports(
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projectId: string,
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query: string,
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limit: number = 3
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): Promise<WeeklyReport[]> {
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const queryEmbedding = await this.embeddings.embed(query);
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return prisma.$queryRaw<WeeklyReport[]>`
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SELECT id, week_start, week_end, summary,
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1 - (embedding <=> ${queryEmbedding}::vector) as similarity
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FROM iit_weekly_reports
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WHERE project_id = ${projectId}
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ORDER BY embedding <=> ${queryEmbedding}::vector
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LIMIT ${limit}
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`;
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}
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async rollupWeeklyMemory(projectId: string): Promise<void> {
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const weekStart = this.getWeekStart();
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const weekEnd = new Date(weekStart);
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weekEnd.setDate(weekStart.getDate() + 6);
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// 获取本周对话
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const conversations = await prisma.iit_conversation_history.findMany({
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where: {
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project_id: projectId,
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created_at: { gte: weekStart, lte: weekEnd }
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},
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orderBy: { created_at: 'asc' }
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});
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if (conversations.length === 0) return;
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// 使用 LLM 生成周报摘要
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const prompt = `你是一个临床研究项目的记录员。请根据本周的对话记录，生成一份周报摘要。
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对话记录：
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${conversations.map(c => `[${c.created_at.toISOString()}] ${c.role}: ${c.content}`).join('\n')}
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要求：
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1. 提取关键事件和决策
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2. 统计主要指标
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3. 记录重要问题和解决方案
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4. 简洁明了，不超过500字`;
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const response = await this.llm.chat([{ role: 'user', content: prompt }]);
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await this.saveWeeklyReport(projectId, response.content);
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}
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// ===== 4. 意图驱动的上下文组装 =====
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async getContextForPrompt(
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projectId: string,
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intent: IntentResult
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): Promise<string> {
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const parts: string[] = [];
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// ⚠️ L2 热记忆：始终注入
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const hotMemory = await this.getHotMemory(projectId);
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parts.push(this.formatHotMemory(hotMemory));
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// ⚠️ L3 历史书：按意图检索
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if (intent.type === 'QA_QUERY') {
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// 模糊查询需要历史上下文
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const relatedReports = await this.searchWeeklyReports(projectId, intent.entities?.query || '', 2);
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if (relatedReports.length > 0) {
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parts.push('## 相关历史记录\n' + relatedReports.map(r => r.summary).join('\n---\n'));
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}
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}
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// ⚠️ L1 流水账：仅按需检索（当历史书不足时）
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if (parts.length < 3 && intent.type === 'QA_QUERY') {
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const relatedConversations = await this.searchConversations(projectId, intent.entities?.query || '', 3);
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if (relatedConversations.length > 0) {
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parts.push('## 相关对话记录\n' + relatedConversations.map(c => `${c.role}: ${c.content}`).join('\n'));
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}
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}
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return parts.join('\n\n');
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}
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private formatHotMemory(hotMemory: HotMemory): string {
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const sections: string[] = [];
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if (hotMemory.meta.length > 0) {
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sections.push('## 项目信息\n' + hotMemory.meta.map(m => `- ${m.key}: ${JSON.stringify(m.value)}`).join('\n'));
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}
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if (hotMemory.status.length > 0) {
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sections.push('## 当前状态\n' + hotMemory.status.map(s => `- ${s.key}: ${JSON.stringify(s.value)}`).join('\n'));
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}
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if (hotMemory.decisions.length > 0) {
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sections.push('## 关键决策\n' + hotMemory.decisions.map(d => `- ${d.key}: ${JSON.stringify(d.value)}`).join('\n'));
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}
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return sections.join('\n\n');
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}
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private getWeekStart(): Date {
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const now = new Date();
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const weekStart = new Date(now);
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weekStart.setDate(now.getDate() - now.getDay() + 1);
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weekStart.setHours(0, 0, 0, 0);
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return weekStart;
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}
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// ===== 5. 清理过期数据 =====
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async cleanupExpiredData(): Promise<void> {
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await prisma.iit_conversation_history.deleteMany({
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where: { expires_at: { lt: new Date() } }
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||||
});
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||||
}
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}
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||||
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||||
// ===== 类型定义 =====
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||||
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||||
interface HotMemory {
|
||||
meta: Array<{ key: string; value: any }>;
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||||
status: Array<{ key: string; value: any }>;
|
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decisions: Array<{ key: string; value: any }>;
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preferences: Array<{ key: string; value: any }>;
|
||||
}
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||||
|
||||
interface ConversationResult {
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id: string;
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content: string;
|
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role: string;
|
||||
intent: string | null;
|
||||
created_at: Date;
|
||||
similarity: number;
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}
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||||
|
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interface WeeklyReport {
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id: string;
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week_start: Date;
|
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week_end: Date;
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||||
summary: string;
|
||||
similarity: number;
|
||||
}
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||||
|
||||
interface IntentResult {
|
||||
type: string;
|
||||
entities?: {
|
||||
record_id?: string;
|
||||
query?: string;
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. 记忆信息映射
|
||||
|
||||
### 4.1 信息类型与存储位置
|
||||
|
||||
| 信息类型 | 存储位置 | 更新机制 |
|
||||
|----------|----------|----------|
|
||||
| 项目名称、PI、入组目标 | project_memory (meta) | 初始化时写入 |
|
||||
| 当前入组人数、进度百分比 | project_memory (status) | 每日定时更新 |
|
||||
| 用户做出的关键决策 | project_memory (decision) | 对话中实时提取 |
|
||||
| 用户偏好设置 | project_memory (preference) | 用户明确表达时 |
|
||||
| 每次对话原文 | conversation_history | 对话实时写入 |
|
||||
| 每周总结 | weekly_reports | 周一凌晨卷叠 |
|
||||
| 患者特殊情况 | project_memory (当前状态) | 在热记忆中维护 |
|
||||
|
||||
### 4.2 检索策略
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ 意图识别结果 │
|
||||
└───────────────────────────┬─────────────────────────────────┘
|
||||
│
|
||||
┌───────────────────┼───────────────────┐
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
QC_TASK QA_QUERY PROTOCOL_QA
|
||||
│ │ │
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ 热记忆 + SOP │ │热记忆 + 历史书│ │ 热记忆 + RAG │
|
||||
│ 状态同步 │ │ + 流水账检索 │ │ 知识库检索 │
|
||||
└──────────────┘ └──────────────┘ └──────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. 性能优化建议
|
||||
|
||||
### 5.1 索引设计
|
||||
|
||||
```sql
|
||||
-- 向量索引（用于相似度搜索）
|
||||
CREATE INDEX idx_conversation_embedding
|
||||
ON iit_conversation_history
|
||||
USING ivfflat (embedding vector_cosine_ops)
|
||||
WITH (lists = 100);
|
||||
|
||||
CREATE INDEX idx_weekly_reports_embedding
|
||||
ON iit_weekly_reports
|
||||
USING ivfflat (embedding vector_cosine_ops)
|
||||
WITH (lists = 50);
|
||||
|
||||
-- 复合索引（用于过滤）
|
||||
CREATE INDEX idx_conversation_project_user_time
|
||||
ON iit_conversation_history (project_id, user_id, created_at DESC);
|
||||
```
|
||||
|
||||
### 5.2 缓存策略
|
||||
|
||||
```typescript
|
||||
// 热记忆缓存（使用 Redis）
|
||||
class MemoryCache {
|
||||
private redis: Redis;
|
||||
|
||||
async getHotMemory(projectId: string): Promise<HotMemory | null> {
|
||||
const cached = await this.redis.get(`hot_memory:${projectId}`);
|
||||
return cached ? JSON.parse(cached) : null;
|
||||
}
|
||||
|
||||
async setHotMemory(projectId: string, memory: HotMemory): Promise<void> {
|
||||
await this.redis.set(
|
||||
`hot_memory:${projectId}`,
|
||||
JSON.stringify(memory),
|
||||
'EX', 300 // 5分钟缓存
|
||||
);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 5.3 Token 预算控制
|
||||
|
||||
```typescript
|
||||
// 限制记忆上下文的 token 数量
|
||||
async getContextForPrompt(projectId: string, intent: IntentResult): Promise<string> {
|
||||
const MAX_TOKENS = 2000;
|
||||
let context = '';
|
||||
let tokenCount = 0;
|
||||
|
||||
// 优先注入热记忆
|
||||
const hotMemory = await this.getHotMemory(projectId);
|
||||
const hotMemoryStr = this.formatHotMemory(hotMemory);
|
||||
tokenCount += this.estimateTokens(hotMemoryStr);
|
||||
context += hotMemoryStr;
|
||||
|
||||
// 按优先级继续添加
|
||||
if (tokenCount < MAX_TOKENS) {
|
||||
// 添加历史书内容...
|
||||
}
|
||||
|
||||
return context;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. 验收标准
|
||||
|
||||
| 功能 | 验收标准 |
|
||||
|------|----------|
|
||||
| 流水账存储 | 对话消息 100ms 内写入成功 |
|
||||
| 向量检索 | 相似度搜索 500ms 内返回 |
|
||||
| 热记忆注入 | 每次请求正确注入项目上下文 |
|
||||
| 周报卷叠 | 周一凌晨自动生成周报 |
|
||||
| 过期清理 | 30天前的对话自动删除 |
|
||||
|
||||
---
|
||||
|
||||
**文档维护人**：AI Agent
|
||||
**最后更新**：2026-02-05
|
||||
Reference in New Issue
Block a user