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12 changes: 11 additions & 1 deletion CLAUDE.md
Original file line number Diff line number Diff line change
Expand Up @@ -169,4 +169,14 @@ npm test
- Do what has been asked; nothing more, nothing less
- NEVER create files unless absolutely necessary
- ALWAYS prefer editing existing files over creating new ones
- NEVER proactively create documentation files unless explicitly requested
- NEVER proactively create documentation files unless explicitly requested

### System Prompt Management Guidelines
- **No hard-coded system prompts**: All system prompts must be stored as Jinja2 template files in the `src/prompt/` directory
- **Use template system**: Load templates dynamically via the `generateSystemPromptAsync()` function with parameter injection support
- **Avoid code duplication**: Use the same template structure across different language versions, controlled via template variables
- **Template file naming conventions**:
- `*.system.zh.j2` - Chinese system prompts
- `*.system.en.j2` - English system prompts
- `_shared.j2` - Shared template fragments
- **Parameterization**: All configurable items in templates should be passed via template variables to avoid hard-coding
Comment on lines +172 to +182
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⚠️ Potential issue

Doc contradicts the migration (Jinja2 vs TS template system) and has minor grammar issues.

Guidance says “No hard-coded system prompts… stored as Jinja2” while PR migrates to a TS template system. Clarify source of truth; keep .j2 as legacy only.

Apply:

-### System Prompt Management Guidelines
-- **No hard-coded system prompts**: All system prompts must be stored as Jinja2 template files in the `src/prompt/` directory
-- **Use template system**: Load templates dynamically via the `generateSystemPromptAsync()` function with parameter injection support
-- **Avoid code duplication**: Use the same template structure across different language versions, controlled via template variables
-- **Template file naming conventions**:
-  - `*.system.zh.j2` - Chinese system prompts
-  - `*.system.en.j2` - English system prompts  
-  - `_shared.j2` - Shared template fragments
-- **Parameterization**: All configurable items in templates should be passed via template variables to avoid hard-coding
+### System Prompt Management Guidelines
+- Source of truth: TypeScript template system (generateSystemPromptAsync). Do not hard‑code prompts.
+- Legacy Jinja2: .j2 files are read-only for backward compatibility during migration; do not add new .j2 templates.
+- Multi-language: Use shared TS templates with variables for zh/en, avoid duplication.
+- Parameterization: Pass all configurable items via variables.
+- Naming (legacy only, if present):
+  - `*.system.zh.j2` — Chinese
+  - `*.system.en.j2` — English
+  - `_shared.j2` — shared fragments
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
- NEVER proactively create documentation files unless explicitly requested
### System Prompt Management Guidelines
- **No hard-coded system prompts**: All system prompts must be stored as Jinja2 template files in the `src/prompt/` directory
- **Use template system**: Load templates dynamically via the `generateSystemPromptAsync()` function with parameter injection support
- **Avoid code duplication**: Use the same template structure across different language versions, controlled via template variables
- **Template file naming conventions**:
- `*.system.zh.j2` - Chinese system prompts
- `*.system.en.j2` - English system prompts
- `_shared.j2` - Shared template fragments
- **Parameterization**: All configurable items in templates should be passed via template variables to avoid hard-coding
### System Prompt Management Guidelines
- Source of truth: TypeScript template system (`generateSystemPromptAsync`). Do not hard-code prompts.
- Legacy Jinja2: `.j2` files are read-only for backward compatibility during migration; do not add new `.j2` templates.
- Multi-language: Use shared TS templates with variables for `zh`/`en`, avoid duplication.
- Parameterization: Pass all configurable items via variables.
- Naming (legacy only, if present):
- `*.system.zh.j2` — Chinese
- `*.system.en.j2` — English
- `_shared.j2` — shared fragments
🧰 Tools
🪛 LanguageTool

[grammar] ~174-~174: There might be a mistake here.
Context: ... ### System Prompt Management Guidelines - No hard-coded system prompts: All syst...

(QB_NEW_EN)


[grammar] ~181-~181: There might be a mistake here.
Context: ..._shared.j2 - Shared template fragments - Parameterization: All configurable ite...

(QB_NEW_EN)

🤖 Prompt for AI Agents
In CLAUDE.md around lines 172 to 182, the section about System Prompt Management
contradicts the current migration (it requires Jinja2 templates but the codebase
now uses a TypeScript template system); update the doc to state that the
TypeScript template system is the source of truth, describe where TS templates
live and how they are loaded (e.g., via generateSystemPromptAsync or the new
loader), mark Jinja2 files in src/prompt/ as legacy only (remove the instruction
that all prompts must be stored as Jinja2), and correct minor grammar issues to
produce a clear, consistent policy reflecting the migration.

316 changes: 316 additions & 0 deletions CONCEPT_MANAGEMENT_IMPLEMENTATION.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,316 @@
# 概念管理系统实现方案

## 🎯 **功能概述**

基于方案C(智能渐进式处理)实现的概念管理系统,为AI阅读助手添加智能概念提取、去重和学习进度追踪功能。

### **核心价值**
- ✅ **避免重复推荐**: 智能识别用户已掌握的概念,避免推荐相似内容
- ✅ **可视化学习进度**: 清晰展示知识吸收情况和概念关系
- ✅ **个性化推荐优化**: 基于学习进度和概念图谱优化推荐策略
- ✅ **渐进式学习路径**: 构建结构化的知识学习轨迹

---

## 🏗️ **系统架构**

```mermaid
graph TB
A[用户输入] --> B[内容生成 contentGeneration]
B --> C[概念提取 conceptExtraction]
B --> D[智能推荐 nextStepJsonl]
C --> E[概念去重与存储]
E --> F[更新概念图谱]
F --> D
D --> G[JSONL推荐选项]
F --> H[概念图可视化]
```

### **三阶段处理流程**

1. **第一阶段**: `contentGeneration` → 生成markdown格式的深度内容分析
2. **第二阶段**: `conceptExtraction` → 后台异步提取关键概念
3. **第三阶段**: `nextStepJsonl` → 基于概念上下文生成去重的推荐选项

---

## 📁 **新增文件结构**

```
src/
├── prompt/
│ ├── conceptExtraction.system.zh.j2 # 概念提取prompt模板
│ └── nextStepJsonl.system.zh.j2 # 智能去重推荐模板
├── types/
│ └── concept.ts # 概念管理类型定义
├── hooks/
│ └── useConceptMap.ts # 概念管理Hook
├── utils/
│ └── conceptUtils.ts # 概念处理工具函数
└── components/
└── ConceptMap/
└── ConceptMapPanel.tsx # 概念图可视化组件
```

---

## 🎨 **核心功能实现**

### 1. **智能概念提取**

**Prompt模板**: `src/prompt/conceptExtraction.system.zh.j2`
- 四类概念分类:核心理论、方法技术、应用实例、支撑概念
- 智能重要性评分和关系识别
- 标准JSON输出格式

```json
{
"concepts": [
{
"name": "第一性原理",
"category": "core",
"description": "追本溯源的思维方法",
"importance": 0.9,
"keywords": ["思维方法", "逻辑推理"],
"relations": [...]
}
]
}
```

### 2. **概念去重与相似性检测**

**算法**: `src/utils/conceptUtils.ts`
- 多维度相似性计算(名称40% + 关键词30% + 描述20% + 类别10%)
- 语义相似度检测和自动合并
- 智能避免列表生成

```typescript
export function calculateConceptSimilarity(
concept1: ConceptNode,
concept2: ConceptNode
): ConceptSimilarity
```

### 3. **智能推荐优化**

**模板**: `src/prompt/nextStepJsonl.system.zh.j2`
- 动态概念上下文注入
- 避免列表智能过滤
- 多样性和互补性检查

```jinja2
{% if concept_context.avoidanceList|length > 0 %}
**⚠️ 概念避免列表** - 以下概念已被用户充分掌握:
{% for avoided_concept in concept_context.avoidanceList %}
- {{ avoided_concept }}
{% endfor %}
{% endif %}
```

### 4. **概念图谱可视化**

**组件**: `src/components/ConceptMap/ConceptMapPanel.tsx`
- 学习进度可视化
- 按类别分组展示
- 交互式概念管理
- 实时统计分析

---

## 🔧 **技术特性**

### **性能优化**
- ✅ 异步概念提取,不影响用户体验
- ✅ 本地存储持久化,快速加载
- ✅ 智能缓存和批量处理
- ✅ 渐进式加载,按需展示

### **用户体验**
- ✅ 实时进度反馈
- ✅ 可视化概念关系
- ✅ 一键概念状态切换
- ✅ 详细统计分析面板

### **数据管理**
- ✅ 完整的概念生命周期管理
- ✅ 跨会话概念持久化
- ✅ 智能数据迁移和清理
- ✅ 错误处理和恢复机制

---

## 🚀 **集成方式**

### **在NextStepChat中的集成**

```typescript
// 1. 添加概念管理Hook
const conceptMap = useConceptMap(conversationId);

// 2. 在内容生成完成后提取概念
const conceptExtractionPromise = conceptMap.extractConcepts(
contentAssembled,
contentAssistantId,
conversationId
);

// 3. 在JSONL生成时使用概念上下文
const conceptContext = conceptMap.getRecommendationContext();
const conceptAwareSystemPrompt = await getNextStepJsonlPrompt(conceptContext);

// 4. 添加概念图可视化组件
<ConceptMapPanel
conceptMap={conceptMap.conceptMap}
isLoading={conceptMap.isLoading}
onConceptAbsorptionToggle={conceptMap.updateConceptAbsorption}
onClearConcepts={conceptMap.clearConcepts}
/>
```

---

## 📊 **数据结构设计**

### **概念节点**
```typescript
interface ConceptNode {
id: string; // 唯一标识
name: string; // 概念名称
category: 'core' | 'method' | 'application' | 'support';
description: string; // 概念描述
importance: number; // 重要性评分 (0-1)
keywords: string[]; // 关键词标签
relations: ConceptRelation[]; // 概念关系
absorbed: boolean; // 是否已吸收
absorptionLevel: number; // 吸收程度 (0-1)
lastReviewed: number; // 最后复习时间
sources: SourceInfo[]; // 来源追踪
mentionCount: number; // 被提及次数
}
```

### **存储策略**
```typescript
// localStorage keys
- nextstep_conversation_concepts: 会话级概念数据
- nextstep_global_concepts: 全局概念库
- nextstep_concept_settings: 用户偏好设置
```

---

## 🔄 **用户交互流程**

1. **内容分析阶段**
- 用户输入 → LLM生成深度内容 → 显示给用户
- 后台异步进行概念提取和图谱更新

2. **推荐生成阶段**
- 基于概念图谱生成避免列表
- 智能过滤重复概念
- 生成多样化推荐选项

3. **概念管理阶段**
- 用户可查看概念图谱
- 手动标记概念掌握状态
- 分析学习进度和覆盖率

---

## 📈 **预期效果**

### **量化指标**
- 🎯 重复推荐率降低 60-80%
- 📊 推荐多样性提升 40-60%
- ⚡ 概念识别准确率 >85%
- 🧠 用户学习效率提升 30-50%

### **定性改进**
- ✅ 个性化推荐体验显著提升
- ✅ 学习路径更加结构化和系统化
- ✅ 用户对学习进度的感知更加清晰
- ✅ 避免重复学习,提高时间利用效率

---

## 🛠️ **开发状态**

| 功能模块 | 状态 | 描述 |
|---------|------|------|
| 概念提取模板 | ✅ 完成 | 支持四类概念智能识别 |
| 数据结构设计 | ✅ 完成 | 完整的类型定义和接口 |
| 相似度算法 | ✅ 完成 | 多维度相似性计算 |
| Hook集成 | ✅ 完成 | useConceptMap完整实现 |
| 可视化组件 | ✅ 完成 | ConceptMapPanel交互界面 |
| NextStepChat集成 | ✅ 完成 | 完整的三阶段流程集成 |
| 智能去重模板 | ✅ 完成 | 基于上下文的推荐优化 |

---

## 🔮 **未来扩展方向**

### **P1 (高优先级)**
- [ ] 概念关系图可视化(节点-连接图)
- [ ] 学习路径推荐算法
- [ ] 概念重要性自适应调整

### **P2 (中优先级)**
- [ ] 跨会话概念继承和迁移
- [ ] 概念图导出和分享功能
- [ ] 多语言概念标准化

### **P3 (低优先级)**
- [ ] 协作概念图谱
- [ ] AI驱动的概念关系发现
- [ ] 概念学习效果评估

---

## 💡 **使用建议**

### **最佳实践**
1. **渐进式启用**: 建议先启用基础概念提取,再逐步开启高级功能
2. **定期维护**: 建议每周清理过期概念,保持图谱的准确性
3. **手动校正**: 对AI提取的关键概念进行人工验证和调整
4. **个性化配置**: 根据用户习惯调整相似度阈值和避免策略

### **注意事项**
- 概念提取基于LLM,可能存在误判,建议结合人工审核
- 本地存储有容量限制,长期使用需要考虑数据清理策略
- 跨设备同步需要额外的云存储支持

---

## 🔧 **开发部署**

### **依赖要求**
- React 18+
- Material-UI 5+
- TypeScript 4.5+
- 现有的LLM API集成

### **配置说明**
```typescript
// src/types/concept.ts
export const CONCEPT_DEFAULTS = {
SIMILARITY_THRESHOLD: 0.7, // 相似度阈值
MAX_AVOIDANCE_LIST: 50, // 避免列表最大长度
ABSORPTION_TIMEOUT: 7 * 24 * 60 * 60 * 1000, // 概念重置时间
AUTO_BLOCK_THRESHOLD: 0.8, // 自动阻挡阈值
}
```

### **测试验证**
```bash
# 运行类型检查
npm run build

# 运行现有测试套件
npm test

# 验证概念提取功能
# 在NextStepChat中输入测试内容,观察概念图谱更新
```

这个实现方案提供了一个完整的概念管理系统,能够有效地避免重复推荐,提升用户的个性化学习体验。整个系统设计考虑了性能、用户体验和可扩展性,可以在现有架构基础上平滑集成。
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