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feat: Add curated expert occupations with local data sources

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- Add curated occupations seed files (210 entries in zh/en) with specific domains
- Add DBpedia occupations data (2164 entries) for external source option
- Refactor expert_source_service to read from local JSON files
- Improve keyword generation prompts to leverage expert domain context
- Add architecture analysis documentation (ARCHITECTURE_ANALYSIS.md)
- Fix expert source selection bug (proper handling of empty custom_experts)
- Update frontend to support curated/dbpedia/wikidata expert sources

Key changes:
- backend/app/data/: Local occupation data files
- backend/app/services/expert_source_service.py: Simplified local file reading
- backend/app/prompts/expert_transformation_prompt.py: Better domain-aware prompts
- Removed expert_cache.py (no longer needed with local files)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
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gbanyan
2025-12-04 16:34:35 +08:00
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# novelty-seeking 系統流程與耦合度分析
> 生成日期: 2025-12-04
## 一、系統整體架構概覽
novelty-seeking 是一個創新思維引導系統，由三個核心 Agent 組成：
- **Attribute Agent**：從查詢到屬性節點的映射
- **Transformation Agent**：屬性到新關鍵字的轉換
- **Expert Transformation Agent**：多視角專家角度的屬性轉換
---
## 二、完整資料流程
```
┌─────────────────────────────────────────────────────────────────────┐
│ Attribute Agent │
├─────────────────────────────────────────────────────────────────────┤
│ 用戶輸入 Query (如「腳踏車」) │
│ ↓ │
│ Step 0: 類別分析 (category_mode 決定) │
│ → 產出: CategoryDefinition[] (如 材料/功能/用途/使用族群) │
│ ↓ │
│ Step 1: 屬性列表生成 │
│ → 產出: {材料: [鋼,木,碳纖維], 功能: [搬運,儲存], ...} │
│ ↓ │
│ Step 2: 關係生成 (DAG 邊) │
│ → 產出: AttributeDAG (nodes + edges) │
└─────────────────────────────────────────────────────────────────────┘
↓ (高耦合)
┌─────────────────────────────────────────────────────────────────────┐
│ Expert Transformation Agent │
├─────────────────────────────────────────────────────────────────────┤
│ 輸入: Query + Category + Attributes (來自 Attribute Agent) │
│ ↓ │
│ Step 0: 專家團隊生成 │
│ → expert_source 決定: llm / curated / dbpedia / wikidata │
│ → 產出: ExpertProfile[] (如 會計師/心理師/生態學家) │
│ ↓ │
│ Step 1: 專家視角關鍵字生成 (對每個 attribute) │
│ → 產出: ExpertKeyword[] (關鍵字 + 來源專家 + 來源屬性) │
│ ↓ │
│ Step 2: 描述生成 (對每個 keyword) │
│ → 產出: ExpertTransformationDescription[] │
└─────────────────────────────────────────────────────────────────────┘
```
---
## 三、Attribute Agent 詳細流程
### 3.1 流程架構
```
用戶查詢 (Query)
↓
┌─────────────────────────────────────────────┐
│ Step 0: 類別分析 (Category Mode 決定) │
├─────────────────────────────────────────────┤
│ 輸入: query, suggested_category_count │
│ 處理: │
│ - FIXED_ONLY: 使用 4 個固定類別 │
│ - FIXED_PLUS_CUSTOM: 固定 + 用戶自訂 │
│ - FIXED_PLUS_DYNAMIC: 固定 + LLM 推薦 │
│ - CUSTOM_ONLY: 僅 LLM 推薦 │
│ - DYNAMIC_AUTO: 純 LLM 推薦 (預設) │
│ 輸出: Step0Result (recommended categories) │
└─────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────┐
│ Step 1: 屬性列表生成 (Attributes) │
├─────────────────────────────────────────────┤
│ 輸入: query, final_categories │
│ LLM 處理: │
│ - 分析 query 在各類別下的屬性 │
│ - 每個類別生成 3-5 個屬性 │
│ 輸出: DynamicStep1Result │
└─────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────┐
│ Step 2: 關係映射 (Relationships → DAG) │
├─────────────────────────────────────────────┤
│ 輸入: query, categories, attributes │
│ LLM 處理: │
│ - 分析相鄰類別之間的因果關係 │
│ - 生成 (source, target) 關係對 │
│ 輸出: AttributeDAG │
└─────────────────────────────────────────────┘
```
### 3.2 關鍵輸入變數
| 變數 | 來源 | 影響範圍 | 作用 |
|------|------|--------|------|
| `query` | 用戶輸入 | Step 0-2 全部 | 決定分析的物件 |
| `category_mode` | 用戶選擇 | Step 0 | 決定使用哪些類別 |
| `suggested_category_count` | 用戶設定 | Step 0 | LLM 推薦類別的數量 |
| `temperature` | 用戶設定 | Step 0-2 | 控制 LLM 輸出的多樣性 |
| `model` | 用戶選擇 | Step 0-2 | 選擇不同的 LLM 模型 |
---
## 四、Expert Transformation Agent 詳細流程
### 4.1 流程架構
```
屬性列表 (attributes from Attribute Agent)
↓
┌─────────────────────────────────────────────┐
│ Step 0: 專家團隊生成 (Expert Generation) │
├─────────────────────────────────────────────┤
│ 決定因素: │
│ - expert_source = 'llm' → LLM 生成 │
│ - expert_source ∈ ['curated', 'dbpedia', │
│ 'wikidata'] → 本地檔案隨機選取 │
│ - 有 custom_experts → 結合 LLM │
│ │
│ 輸出: ExpertProfile[] │
│ [{id, name, domain, perspective}] │
└─────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────┐
│ Step 1: 專家視角關鍵字生成 (Keywords) │
├─────────────────────────────────────────────┤
│ 迴圈: for each attribute in attributes: │
│ LLM 為每個專家生成 keywords_per_expert │
│ 個關鍵字 │
│ │
│ 輸出: ExpertKeyword[] │
│ [{keyword, expert_id, expert_name, │
│ source_attribute}] │
└─────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────┐
│ Step 2: 描述生成 (Descriptions) │
├─────────────────────────────────────────────┤
│ 迴圈: for each expert_keyword: │
│ LLM 生成 15-30 字的創新應用描述 │
│ │
│ 輸出: ExpertTransformationDescription[] │
└─────────────────────────────────────────────┘
```
### 4.2 關鍵輸入變數
| 變數 | 來源 | 影響範圍 | 作用 |
|------|------|--------|------|
| `expert_source` | 用戶選擇 | Step 0 | 決定專家來源 (llm/curated/dbpedia/wikidata) |
| `expert_count` | 用戶設定 | Step 0 | 專家數量 (2-8) |
| `keywords_per_expert` | 用戶設定 | Step 1 | 每專家每屬性關鍵字數 (1-3) |
| `custom_experts` | 用戶輸入 | Step 0 | 用戶指定的專家名稱 |
| `temperature` | 用戶設定 | Step 0-2 | 控制多樣性 |
### 4.3 關鍵字生成公式
```
總關鍵字數 = len(attributes) × expert_count × keywords_per_expert
範例計算:
├─ 3 個屬性 (搬運, 儲存, 展示)
├─ 3 位專家 (會計師, 心理師, 生態學家)
├─ 1 個關鍵字/專家
└─ = 3 × 3 × 1 = 9 個關鍵字
```
---
## 五、關鍵字生成影響因素
| 階段 | 影響變數 | 影響程度 | 說明 |
|------|---------|---------|------|
| **屬性生成** | `query` | 極高 | 決定 LLM 分析的語義基礎 |
| | `category_mode` | 高 | 決定類別維度 |
| | `temperature` | 中 | 越高越多樣 |
| | `model` | 中 | 不同模型知識基礎不同 |
| **專家生成** | `expert_source` | 高 | 決定專家來源與品質 |
| | `expert_count` | 中 | 2-8 位專家 |
| | `custom_experts` | 中 | 與 LLM 結合 |
| **關鍵字生成** | `experts[].domain` | 極高 | 直接決定關鍵字視角 |
| | `keywords_per_expert` | 低 | 控制數量 |
| | `source_attribute` | 高 | 決定思考起點 |
---
## 六、耦合度分析
### 6.1 高耦合連接 ⚠️
| 連接 | 耦合度 | 原因 | 風險 |
|------|--------|------|------|
| Attribute → Expert Transform | 高 | Expert 依賴 Attribute 輸出 | 結構變更需同步修改 |
| Expert 生成 → Keyword 生成 | 高 | domain 直接影響關鍵字 | domain 品質差→關鍵字無關 |
| Prompt → LLM 輸出結構 | 高 | prompt 定義 JSON 格式 | 改 prompt 需改 schema |
### 6.2 低耦合連接 ✓
| 連接 | 耦合度 | 原因 | 優點 |
|------|--------|------|------|
| curated/dbpedia/wikidata | 低 | 獨立本地檔案 | 可單獨更新 |
| SSE 通信格式 | 低 | 標準化解耦 | 向後相容 |
| useAttribute/useExpertTransformation | 低 | 獨立 hook | 可單獨複用 |
### 6.3 耦合度矩陣
| | Attribute | Transformation | Expert Transform |
|----|-----------|---------------|----|
| **Attribute** | - | 低 | 高 |
| **Transformation** | 低 | - | 低 |
| **Expert Transform** | 高 | 低 | - |
---
## 七、專家來源比較
| 來源 | 檔案 | 筆數 | Domain 品質 | 特點 |
|------|------|------|------------|------|
| `llm` | - | 動態 | 高 | LLM 根據 query 生成相關專家 |
| `curated` | curated_occupations_zh/en.json | 210 | 高 | 精選職業，含具體領域 |
| `dbpedia` | dbpedia_occupations_en.json | 2164 | 低 | 全是 "Professional Field" |
| `wikidata` | - | - | - | 未實作本地化 |
---
## 八、決策變化追蹤範例
```
Query: "腳踏車"
↓
Category Mode: DYNAMIC_AUTO
→ LLM 建議 [材料, 功能, 用途, 使用族群]
↓
Expert Source: "curated"
→ 隨機選取 [外科醫師(醫療與健康), 軟體工程師(資訊科技), 主廚(餐飲與服務)]
↓
Attribute "搬運" + Expert "外科醫師"
→ LLM 思考: 醫療視角看搬運
→ Keyword: "器官運輸", "急救物流"
↓
Description 生成:
→ "從急救醫療角度，腳踏車可改良為緊急醫療運輸工具..."
```
---
## 九、改進建議
| 問題 | 現狀 | 建議改進 |
|------|------|---------|
| domain 品質 | DBpedia 全是通用值 | ✅ 已建立精選職業 |
| 重複計算 Expert | 每類別重新生成 | 考慮 Expert 全局化 |
| Temperature 統一 | 整流程同一值 | 可按 Step 分開設定 |
| 缺乏快取 | 每次重新分析 | 加入 Attribute 快取層 |
| 語言支援 | 主要中文 | ✅ 已建立英文版 |
---
## 十、關鍵檔案清單
### Backend
- `app/routers/analyze.py` - Attribute Agent 路由
- `app/routers/expert_transformation.py` - Expert Transformation 路由
- `app/prompts/step_prompts.py` - Attribute Agent 提示詞
- `app/prompts/expert_transformation_prompt.py` - Expert Transformation 提示詞
- `app/services/expert_source_service.py` - 專家來源服務
- `app/services/llm_service.py` - LLM 調用服務
- `app/data/curated_occupations_zh.json` - 精選職業（中文）
- `app/data/curated_occupations_en.json` - 精選職業（英文）
- `app/data/dbpedia_occupations_en.json` - DBpedia 職業
### Frontend
- `src/App.tsx` - 主狀態管理
- `src/hooks/useAttribute.ts` - Attribute Agent Hook
- `src/hooks/useExpertTransformation.ts` - Expert Transformation Hook
- `src/components/TransformationInputPanel.tsx` - 轉換控制面板
- `src/types/index.ts` - 類型定義

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{
"metadata": {
"source": "conceptnet",
"language": "en",
"fetched_at": "2025-12-04T07:26:30.695936+00:00",
"total_count": 0
},
"occupations": []
}

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{
"metadata": {
"source": "conceptnet",
"language": "zh",
"fetched_at": "2025-12-04T07:26:26.994914+00:00",
"total_count": 0
},
"occupations": []
}

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{
"metadata": {
"source": "curated",
"language": "en",
"created_at": "2025-12-04",
"total_count": 210,
"description": "Curated common professional occupations with specific domains"
},
"occupations": [
{"name": "Surgeon", "domain": "Healthcare"},
{"name": "Internist", "domain": "Healthcare"},
{"name": "Dentist", "domain": "Healthcare"},
{"name": "Ophthalmologist", "domain": "Healthcare"},
{"name": "Psychiatrist", "domain": "Healthcare"},
{"name": "Pediatrician", "domain": "Healthcare"},
{"name": "Nurse", "domain": "Healthcare"},
{"name": "Pharmacist", "domain": "Healthcare"},
{"name": "Clinical Psychologist", "domain": "Healthcare"},
{"name": "Physical Therapist", "domain": "Healthcare"},
{"name": "Occupational Therapist", "domain": "Healthcare"},
{"name": "Nutritionist", "domain": "Healthcare"},
{"name": "Traditional Chinese Medicine Doctor", "domain": "Healthcare"},
{"name": "Veterinarian", "domain": "Healthcare"},
{"name": "Software Engineer", "domain": "Information Technology"},
{"name": "Frontend Developer", "domain": "Information Technology"},
{"name": "Backend Developer", "domain": "Information Technology"},
{"name": "Data Scientist", "domain": "Information Technology"},
{"name": "Data Engineer", "domain": "Information Technology"},
{"name": "Machine Learning Engineer", "domain": "Information Technology"},
{"name": "Cybersecurity Engineer", "domain": "Information Technology"},
{"name": "DevOps Engineer", "domain": "Information Technology"},
{"name": "UI Designer", "domain": "Information Technology"},
{"name": "UX Designer", "domain": "Information Technology"},
{"name": "Product Manager", "domain": "Information Technology"},
{"name": "Systems Analyst", "domain": "Information Technology"},
{"name": "Network Engineer", "domain": "Information Technology"},
{"name": "Cloud Architect", "domain": "Information Technology"},
{"name": "Accountant", "domain": "Finance & Business"},
{"name": "Financial Analyst", "domain": "Finance & Business"},
{"name": "Investment Manager", "domain": "Finance & Business"},
{"name": "Risk Manager", "domain": "Finance & Business"},
{"name": "Actuary", "domain": "Finance & Business"},
{"name": "Bank Manager", "domain": "Finance & Business"},
{"name": "Securities Analyst", "domain": "Finance & Business"},
{"name": "Tax Consultant", "domain": "Finance & Business"},
{"name": "Business Consultant", "domain": "Finance & Business"},
{"name": "HR Manager", "domain": "Finance & Business"},
{"name": "Marketing Manager", "domain": "Finance & Business"},
{"name": "Sales Manager", "domain": "Finance & Business"},
{"name": "Procurement Manager", "domain": "Finance & Business"},
{"name": "Entrepreneur", "domain": "Finance & Business"},
{"name": "Lawyer", "domain": "Law & Policy"},
{"name": "Judge", "domain": "Law & Policy"},
{"name": "Prosecutor", "domain": "Law & Policy"},
{"name": "Notary", "domain": "Law & Policy"},
{"name": "Legal Counsel", "domain": "Law & Policy"},
{"name": "IP Attorney", "domain": "Law & Policy"},
{"name": "Policy Analyst", "domain": "Law & Policy"},
{"name": "Diplomat", "domain": "Law & Policy"},
{"name": "Civil Servant", "domain": "Law & Policy"},
{"name": "Legislator", "domain": "Law & Policy"},
{"name": "Mediator", "domain": "Law & Policy"},
{"name": "Legal Scholar", "domain": "Law & Policy"},
{"name": "University Professor", "domain": "Education & Academia"},
{"name": "High School Teacher", "domain": "Education & Academia"},
{"name": "Middle School Teacher", "domain": "Education & Academia"},
{"name": "Elementary School Teacher", "domain": "Education & Academia"},
{"name": "Preschool Teacher", "domain": "Education & Academia"},
{"name": "Special Education Teacher", "domain": "Education & Academia"},
{"name": "Tutor", "domain": "Education & Academia"},
{"name": "Researcher", "domain": "Education & Academia"},
{"name": "Librarian", "domain": "Education & Academia"},
{"name": "Education Administrator", "domain": "Education & Academia"},
{"name": "Academic Editor", "domain": "Education & Academia"},
{"name": "Education Consultant", "domain": "Education & Academia"},
{"name": "Speech Therapist", "domain": "Education & Academia"},
{"name": "Painter", "domain": "Arts & Creativity"},
{"name": "Sculptor", "domain": "Arts & Creativity"},
{"name": "Musician", "domain": "Arts & Creativity"},
{"name": "Composer", "domain": "Arts & Creativity"},
{"name": "Conductor", "domain": "Arts & Creativity"},
{"name": "Dancer", "domain": "Arts & Creativity"},
{"name": "Actor", "domain": "Arts & Creativity"},
{"name": "Film Director", "domain": "Arts & Creativity"},
{"name": "Screenwriter", "domain": "Arts & Creativity"},
{"name": "Photographer", "domain": "Arts & Creativity"},
{"name": "Illustrator", "domain": "Arts & Creativity"},
{"name": "Animator", "domain": "Arts & Creativity"},
{"name": "Graphic Designer", "domain": "Arts & Creativity"},
{"name": "Fashion Designer", "domain": "Arts & Creativity"},
{"name": "Jewelry Designer", "domain": "Arts & Creativity"},
{"name": "Mechanical Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Electrical Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Electronics Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Chemical Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Materials Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Industrial Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Automation Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Quality Control Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Process Engineer", "domain": "Engineering & Manufacturing"},
{"name": "R&D Engineer", "domain": "Engineering & Manufacturing"},
{"name": "Production Manager", "domain": "Engineering & Manufacturing"},
{"name": "Factory Manager", "domain": "Engineering & Manufacturing"},
{"name": "Technician", "domain": "Engineering & Manufacturing"},
{"name": "Architect", "domain": "Architecture & Space"},
{"name": "Interior Designer", "domain": "Architecture & Space"},
{"name": "Landscape Designer", "domain": "Architecture & Space"},
{"name": "Urban Planner", "domain": "Architecture & Space"},
{"name": "Structural Engineer", "domain": "Architecture & Space"},
{"name": "Civil Engineer", "domain": "Architecture & Space"},
{"name": "Construction Engineer", "domain": "Architecture & Space"},
{"name": "Site Supervisor", "domain": "Architecture & Space"},
{"name": "Surveyor", "domain": "Architecture & Space"},
{"name": "Architectural Drafter", "domain": "Architecture & Space"},
{"name": "Exhibition Designer", "domain": "Architecture & Space"},
{"name": "Lighting Designer", "domain": "Architecture & Space"},
{"name": "Journalist", "domain": "Media & Communications"},
{"name": "News Anchor", "domain": "Media & Communications"},
{"name": "Editor", "domain": "Media & Communications"},
{"name": "Copy Editor", "domain": "Media & Communications"},
{"name": "Video Editor", "domain": "Media & Communications"},
{"name": "PR Specialist", "domain": "Media & Communications"},
{"name": "Advertising Planner", "domain": "Media & Communications"},
{"name": "Social Media Manager", "domain": "Media & Communications"},
{"name": "Content Creator", "domain": "Media & Communications"},
{"name": "Podcast Host", "domain": "Media & Communications"},
{"name": "Publisher", "domain": "Media & Communications"},
{"name": "Translator", "domain": "Media & Communications"},
{"name": "Interpreter", "domain": "Media & Communications"},
{"name": "Agronomist", "domain": "Agriculture & Environment"},
{"name": "Horticulturist", "domain": "Agriculture & Environment"},
{"name": "Livestock Specialist", "domain": "Agriculture & Environment"},
{"name": "Aquaculture Specialist", "domain": "Agriculture & Environment"},
{"name": "Environmental Engineer", "domain": "Agriculture & Environment"},
{"name": "Ecologist", "domain": "Agriculture & Environment"},
{"name": "Forest Ranger", "domain": "Agriculture & Environment"},
{"name": "Meteorologist", "domain": "Agriculture & Environment"},
{"name": "Geologist", "domain": "Agriculture & Environment"},
{"name": "Environmental Inspector", "domain": "Agriculture & Environment"},
{"name": "Sustainability Consultant", "domain": "Agriculture & Environment"},
{"name": "Organic Farmer", "domain": "Agriculture & Environment"},
{"name": "Executive Chef", "domain": "Hospitality & Service"},
{"name": "Pastry Chef", "domain": "Hospitality & Service"},
{"name": "Bartender", "domain": "Hospitality & Service"},
{"name": "Sommelier", "domain": "Hospitality & Service"},
{"name": "Restaurant Manager", "domain": "Hospitality & Service"},
{"name": "Hotel Manager", "domain": "Hospitality & Service"},
{"name": "Travel Planner", "domain": "Hospitality & Service"},
{"name": "Tour Guide", "domain": "Hospitality & Service"},
{"name": "Barista", "domain": "Hospitality & Service"},
{"name": "Food Critic", "domain": "Hospitality & Service"},
{"name": "Wedding Planner", "domain": "Hospitality & Service"},
{"name": "Event Planner", "domain": "Hospitality & Service"},
{"name": "Sports Coach", "domain": "Sports & Fitness"},
{"name": "Personal Trainer", "domain": "Sports & Fitness"},
{"name": "Yoga Instructor", "domain": "Sports & Fitness"},
{"name": "Athletic Trainer", "domain": "Sports & Fitness"},
{"name": "Physical Education Teacher", "domain": "Sports & Fitness"},
{"name": "Sports Psychologist", "domain": "Sports & Fitness"},
{"name": "Sports Nutritionist", "domain": "Sports & Fitness"},
{"name": "Professional Athlete", "domain": "Sports & Fitness"},
{"name": "Referee", "domain": "Sports & Fitness"},
{"name": "Strength Coach", "domain": "Sports & Fitness"},
{"name": "Sports Agent", "domain": "Sports & Fitness"},
{"name": "Social Worker", "domain": "Social Services"},
{"name": "Counselor", "domain": "Social Services"},
{"name": "Guidance Counselor", "domain": "Social Services"},
{"name": "Volunteer Coordinator", "domain": "Social Services"},
{"name": "Nonprofit Manager", "domain": "Social Services"},
{"name": "Community Organizer", "domain": "Social Services"},
{"name": "Elderly Care Worker", "domain": "Social Services"},
{"name": "Youth Counselor", "domain": "Social Services"},
{"name": "Family Therapist", "domain": "Social Services"},
{"name": "Career Counselor", "domain": "Social Services"},
{"name": "Addiction Counselor", "domain": "Social Services"},
{"name": "Pilot", "domain": "Transportation & Logistics"},
{"name": "Ship Captain", "domain": "Transportation & Logistics"},
{"name": "Train Operator", "domain": "Transportation & Logistics"},
{"name": "Air Traffic Controller", "domain": "Transportation & Logistics"},
{"name": "Logistics Manager", "domain": "Transportation & Logistics"},
{"name": "Supply Chain Manager", "domain": "Transportation & Logistics"},
{"name": "Warehouse Manager", "domain": "Transportation & Logistics"},
{"name": "Customs Broker", "domain": "Transportation & Logistics"},
{"name": "Traffic Engineer", "domain": "Transportation & Logistics"},
{"name": "Port Authority Officer", "domain": "Transportation & Logistics"},
{"name": "Physicist", "domain": "Scientific Research"},
{"name": "Chemist", "domain": "Scientific Research"},
{"name": "Biologist", "domain": "Scientific Research"},
{"name": "Astronomer", "domain": "Scientific Research"},
{"name": "Mathematician", "domain": "Scientific Research"},
{"name": "Statistician", "domain": "Scientific Research"},
{"name": "Geneticist", "domain": "Scientific Research"},
{"name": "Neuroscientist", "domain": "Scientific Research"},
{"name": "Oceanographer", "domain": "Scientific Research"},
{"name": "Archaeologist", "domain": "Scientific Research"},
{"name": "Anthropologist", "domain": "Scientific Research"},
{"name": "Sociologist", "domain": "Scientific Research"},
{"name": "Economist", "domain": "Scientific Research"},
{"name": "Historian", "domain": "Scientific Research"},
{"name": "Philosopher", "domain": "Scientific Research"}
]
}

216
backend/app/data/curated_occupations_zh.json Normal file
View File

@@ -0,0 +1,216 @@
{
"metadata": {
"source": "curated",
"language": "zh",
"created_at": "2025-12-04",
"total_count": 210,
"description": "精選常見專家職業，含具體專業領域"
},
"occupations": [
{"name": "外科醫師", "domain": "醫療與健康"},
{"name": "內科醫師", "domain": "醫療與健康"},
{"name": "牙醫師", "domain": "醫療與健康"},
{"name": "眼科醫師", "domain": "醫療與健康"},
{"name": "精神科醫師", "domain": "醫療與健康"},
{"name": "小兒科醫師", "domain": "醫療與健康"},
{"name": "護理師", "domain": "醫療與健康"},
{"name": "藥師", "domain": "醫療與健康"},
{"name": "臨床心理師", "domain": "醫療與健康"},
{"name": "物理治療師", "domain": "醫療與健康"},
{"name": "職能治療師", "domain": "醫療與健康"},
{"name": "營養師", "domain": "醫療與健康"},
{"name": "中醫師", "domain": "醫療與健康"},
{"name": "獸醫師", "domain": "醫療與健康"},
{"name": "軟體工程師", "domain": "資訊科技"},
{"name": "前端工程師", "domain": "資訊科技"},
{"name": "後端工程師", "domain": "資訊科技"},
{"name": "資料科學家", "domain": "資訊科技"},
{"name": "資料工程師", "domain": "資訊科技"},
{"name": "機器學習工程師", "domain": "資訊科技"},
{"name": "資安工程師", "domain": "資訊科技"},
{"name": "DevOps工程師", "domain": "資訊科技"},
{"name": "UI設計師", "domain": "資訊科技"},
{"name": "UX設計師", "domain": "資訊科技"},
{"name": "產品經理", "domain": "資訊科技"},
{"name": "系統分析師", "domain": "資訊科技"},
{"name": "網路工程師", "domain": "資訊科技"},
{"name": "雲端架構師", "domain": "資訊科技"},
{"name": "會計師", "domain": "金融與商業"},
{"name": "財務分析師", "domain": "金融與商業"},
{"name": "投資經理", "domain": "金融與商業"},
{"name": "風險管理師", "domain": "金融與商業"},
{"name": "精算師", "domain": "金融與商業"},
{"name": "銀行經理", "domain": "金融與商業"},
{"name": "證券分析師", "domain": "金融與商業"},
{"name": "稅務顧問", "domain": "金融與商業"},
{"name": "企業顧問", "domain": "金融與商業"},
{"name": "人資經理", "domain": "金融與商業"},
{"name": "行銷經理", "domain": "金融與商業"},
{"name": "業務經理", "domain": "金融與商業"},
{"name": "採購經理", "domain": "金融與商業"},
{"name": "創業家", "domain": "金融與商業"},
{"name": "律師", "domain": "法律與政策"},
{"name": "法官", "domain": "法律與政策"},
{"name": "檢察官", "domain": "法律與政策"},
{"name": "公證人", "domain": "法律與政策"},
{"name": "法務專員", "domain": "法律與政策"},
{"name": "智財律師", "domain": "法律與政策"},
{"name": "政策分析師", "domain": "法律與政策"},
{"name": "外交官", "domain": "法律與政策"},
{"name": "公務員", "domain": "法律與政策"},
{"name": "立法委員", "domain": "法律與政策"},
{"name": "調解員", "domain": "法律與政策"},
{"name": "法律學者", "domain": "法律與政策"},
{"name": "大學教授", "domain": "教育與學術"},
{"name": "高中教師", "domain": "教育與學術"},
{"name": "國中教師", "domain": "教育與學術"},
{"name": "小學教師", "domain": "教育與學術"},
{"name": "幼教老師", "domain": "教育與學術"},
{"name": "特教老師", "domain": "教育與學術"},
{"name": "補習班老師", "domain": "教育與學術"},
{"name": "研究員", "domain": "教育與學術"},
{"name": "圖書館員", "domain": "教育與學術"},
{"name": "教育行政人員", "domain": "教育與學術"},
{"name": "學術編輯", "domain": "教育與學術"},
{"name": "教育顧問", "domain": "教育與學術"},
{"name": "語言治療師", "domain": "教育與學術"},
{"name": "畫家", "domain": "藝術與創意"},
{"name": "雕塑家", "domain": "藝術與創意"},
{"name": "音樂家", "domain": "藝術與創意"},
{"name": "作曲家", "domain": "藝術與創意"},
{"name": "指揮家", "domain": "藝術與創意"},
{"name": "舞蹈家", "domain": "藝術與創意"},
{"name": "演員", "domain": "藝術與創意"},
{"name": "導演", "domain": "藝術與創意"},
{"name": "編劇", "domain": "藝術與創意"},
{"name": "攝影師", "domain": "藝術與創意"},
{"name": "插畫家", "domain": "藝術與創意"},
{"name": "動畫師", "domain": "藝術與創意"},
{"name": "平面設計師", "domain": "藝術與創意"},
{"name": "時尚設計師", "domain": "藝術與創意"},
{"name": "珠寶設計師", "domain": "藝術與創意"},
{"name": "機械工程師", "domain": "工程與製造"},
{"name": "電機工程師", "domain": "工程與製造"},
{"name": "電子工程師", "domain": "工程與製造"},
{"name": "化學工程師", "domain": "工程與製造"},
{"name": "材料工程師", "domain": "工程與製造"},
{"name": "工業工程師", "domain": "工程與製造"},
{"name": "自動化工程師", "domain": "工程與製造"},
{"name": "品管工程師", "domain": "工程與製造"},
{"name": "製程工程師", "domain": "工程與製造"},
{"name": "研發工程師", "domain": "工程與製造"},
{"name": "生產經理", "domain": "工程與製造"},
{"name": "工廠廠長", "domain": "工程與製造"},
{"name": "技師", "domain": "工程與製造"},
{"name": "建築師", "domain": "建築與空間"},
{"name": "室內設計師", "domain": "建築與空間"},
{"name": "景觀設計師", "domain": "建築與空間"},
{"name": "都市規劃師", "domain": "建築與空間"},
{"name": "結構工程師", "domain": "建築與空間"},
{"name": "土木工程師", "domain": "建築與空間"},
{"name": "營造工程師", "domain": "建築與空間"},
{"name": "工地主任", "domain": "建築與空間"},
{"name": "測量師", "domain": "建築與空間"},
{"name": "建築繪圖員", "domain": "建築與空間"},
{"name": "展場設計師", "domain": "建築與空間"},
{"name": "燈光設計師", "domain": "建築與空間"},
{"name": "記者", "domain": "媒體與傳播"},
{"name": "主播", "domain": "媒體與傳播"},
{"name": "編輯", "domain": "媒體與傳播"},
{"name": "文字編輯", "domain": "媒體與傳播"},
{"name": "影片剪輯師", "domain": "媒體與傳播"},
{"name": "公關專員", "domain": "媒體與傳播"},
{"name": "廣告企劃", "domain": "媒體與傳播"},
{"name": "社群經理", "domain": "媒體與傳播"},
{"name": "內容創作者", "domain": "媒體與傳播"},
{"name": "播客主持人", "domain": "媒體與傳播"},
{"name": "出版人", "domain": "媒體與傳播"},
{"name": "翻譯師", "domain": "媒體與傳播"},
{"name": "口譯員", "domain": "媒體與傳播"},
{"name": "農藝師", "domain": "農業與環境"},
{"name": "園藝師", "domain": "農業與環境"},
{"name": "畜牧專家", "domain": "農業與環境"},
{"name": "水產養殖師", "domain": "農業與環境"},
{"name": "環境工程師", "domain": "農業與環境"},
{"name": "生態學家", "domain": "農業與環境"},
{"name": "森林保育員", "domain": "農業與環境"},
{"name": "氣象學家", "domain": "農業與環境"},
{"name": "地質學家", "domain": "農業與環境"},
{"name": "環保稽查員", "domain": "農業與環境"},
{"name": "永續發展顧問", "domain": "農業與環境"},
{"name": "有機農場主", "domain": "農業與環境"},
{"name": "主廚", "domain": "餐飲與服務"},
{"name": "西點師傅", "domain": "餐飲與服務"},
{"name": "調酒師", "domain": "餐飲與服務"},
{"name": "侍酒師", "domain": "餐飲與服務"},
{"name": "餐廳經理", "domain": "餐飲與服務"},
{"name": "飯店經理", "domain": "餐飲與服務"},
{"name": "旅遊規劃師", "domain": "餐飲與服務"},
{"name": "導遊", "domain": "餐飲與服務"},
{"name": "咖啡師", "domain": "餐飲與服務"},
{"name": "美食評論家", "domain": "餐飲與服務"},
{"name": "婚禮策劃師", "domain": "餐飲與服務"},
{"name": "活動企劃", "domain": "餐飲與服務"},
{"name": "運動教練", "domain": "運動與健身"},
{"name": "健身教練", "domain": "運動與健身"},
{"name": "瑜珈老師", "domain": "運動與健身"},
{"name": "運動防護員", "domain": "運動與健身"},
{"name": "體育老師", "domain": "運動與健身"},
{"name": "運動心理師", "domain": "運動與健身"},
{"name": "運動營養師", "domain": "運動與健身"},
{"name": "職業運動員", "domain": "運動與健身"},
{"name": "裁判", "domain": "運動與健身"},
{"name": "體能訓練師", "domain": "運動與健身"},
{"name": "運動經紀人", "domain": "運動與健身"},
{"name": "社工師", "domain": "社會服務"},
{"name": "心理諮商師", "domain": "社會服務"},
{"name": "輔導員", "domain": "社會服務"},
{"name": "志工協調員", "domain": "社會服務"},
{"name": "非營利組織經理", "domain": "社會服務"},
{"name": "社區營造員", "domain": "社會服務"},
{"name": "長照服務員", "domain": "社會服務"},
{"name": "青少年輔導員", "domain": "社會服務"},
{"name": "家庭治療師", "domain": "社會服務"},
{"name": "職涯諮詢師", "domain": "社會服務"},
{"name": "戒癮輔導員", "domain": "社會服務"},
{"name": "飛行員", "domain": "交通與物流"},
{"name": "船長", "domain": "交通與物流"},
{"name": "火車駕駛", "domain": "交通與物流"},
{"name": "航空管制員", "domain": "交通與物流"},
{"name": "物流經理", "domain": "交通與物流"},
{"name": "供應鏈經理", "domain": "交通與物流"},
{"name": "倉儲經理", "domain": "交通與物流"},
{"name": "報關員", "domain": "交通與物流"},
{"name": "交通工程師", "domain": "交通與物流"},
{"name": "港務人員", "domain": "交通與物流"},
{"name": "物理學家", "domain": "科學研究"},
{"name": "化學家", "domain": "科學研究"},
{"name": "生物學家", "domain": "科學研究"},
{"name": "天文學家", "domain": "科學研究"},
{"name": "數學家", "domain": "科學研究"},
{"name": "統計學家", "domain": "科學研究"},
{"name": "基因學家", "domain": "科學研究"},
{"name": "神經科學家", "domain": "科學研究"},
{"name": "海洋學家", "domain": "科學研究"},
{"name": "考古學家", "domain": "科學研究"},
{"name": "人類學家", "domain": "科學研究"},
{"name": "社會學家", "domain": "科學研究"},
{"name": "經濟學家", "domain": "科學研究"},
{"name": "歷史學家", "domain": "科學研究"},
{"name": "哲學家", "domain": "科學研究"}
]
}

8666
backend/app/data/dbpedia_occupations_en.json Normal file
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File diff suppressed because it is too large Load Diff

5
backend/app/models/schemas.py
View File

@@ -209,8 +209,9 @@ class ExpertTransformationDAGResult(BaseModel):
class ExpertSource(str, Enum):
"""專家來源類型"""
LLM = "llm"
CURATED = "curated" # 精選職業（210筆，含具體領域）
DBPEDIA = "dbpedia"
WIKIDATA = "wikidata"
CONCEPTNET = "conceptnet"
class ExpertTransformationRequest(BaseModel):
@@ -226,7 +227,7 @@ class ExpertTransformationRequest(BaseModel):
# Expert source parameters
expert_source: ExpertSource = ExpertSource.LLM # 專家來源
expert_language: str = "zh" # 外部來源的語言
expert_language: str = "en" # 外部來源的語言 (目前只有英文資料)
# LLM parameters
model: Optional[str] = None

34
backend/app/prompts/expert_transformation_prompt.py
View File

@@ -53,19 +53,32 @@ def get_expert_keyword_generation_prompt(
keywords_per_expert: int = 1
) -> str:
"""Step 1: 專家視角關鍵字生成"""
experts_info = ", ".join([f"{exp['id']}:{exp['name']}({exp['domain']})" for exp in experts])
# 建立專家列表，格式更清晰
experts_list = "\n".join([f"- {exp['id']}: {exp['name']}" for exp in experts])
return f"""/no_think
專家團隊：{experts_info}
屬性：「{attribute}」（{category}）
你需要扮演以下專家，為屬性生成創新關鍵字：
每位專家從自己的專業視角為此屬性生成 {keywords_per_expert} 個創新關鍵字（2-6字）。
關鍵字要反映該專家領域的獨特思考方式。
【專家名單】
{experts_list}
【任務】
屬性：「{attribute}」（類別：{category}）
請為每位專家：
1. 先理解該職業的專業背景、知識領域、工作內容
2. 從該職業的獨特視角思考「{attribute}」
3. 生成 {keywords_per_expert} 個與該專業相關的創新關鍵字（2-6字）
關鍵字必須反映該專家的專業思維方式，例如：
- 會計師 看「移動」→「資金流動」「成本效益」
- 建築師 看「移動」→「動線設計」「空間流動」
- 心理師 看「移動」→「行為動機」「情緒轉變」
回傳 JSON：
{{"keywords": [{{"keyword": "詞彙", "expert_id": "expert-X", "expert_name": "名稱"}}, ...]}}
共需 {len(experts) * keywords_per_expert} 個關鍵字。"""
共需 {len(experts) * keywords_per_expert} 個關鍵字，每個關鍵字必須明顯與對應專家的專業領域相關。"""
def get_single_description_prompt(
@@ -76,12 +89,17 @@ def get_single_description_prompt(
expert_domain: str
) -> str:
"""Step 2: 為單一關鍵字生成描述"""
# 如果 domain 是通用的，就只用職業名稱
domain_text = f"（{expert_domain}）" if expert_domain and expert_domain != "Professional Field" else ""
return f"""/no_think
物件：「{query}」
專家：{expert_name}（{expert_domain}）
專家：{expert_name}{domain_text}
關鍵字：{keyword}
從這位專家的視角，生成一段創新應用描述（15-30字），說明如何將「{keyword}」的概念應用到「{query}」上。
你是一位{expert_name}。從你的專業視角，生成一段創新應用描述（15-30字），說明如何將「{keyword}」的概念應用到「{query}」上。
描述要體現{expert_name}的專業思維和獨特觀點。
回傳 JSON：
{{"description": "應用描述"}}"""

23
backend/app/routers/expert_transformation.py
View File

@@ -37,16 +37,29 @@ async def generate_expert_transformation_events(
model = request.model
# ========== Step 0: Generate expert team ==========
logger.info(f"[DEBUG] expert_source from request: {request.expert_source}")
logger.info(f"[DEBUG] expert_source value: {request.expert_source.value}")
logger.info(f"[DEBUG] custom_experts: {request.custom_experts}")
yield f"event: expert_start\ndata: {json.dumps({'message': '正在組建專家團隊...', 'source': request.expert_source.value}, ensure_ascii=False)}\n\n"
experts: List[ExpertProfile] = []
actual_source = request.expert_source.value
# 過濾出實際有內容的自訂專家（排除空字串）
actual_custom_experts = [
e.strip() for e in (request.custom_experts or [])
if e and e.strip()
]
logger.info(f"[DEBUG] actual_custom_experts (filtered): {actual_custom_experts}")
# 決定使用哪種來源生成專家
# 只有在明確選擇 LLM 或有實際自訂專家時才使用 LLM
use_llm = (
request.expert_source == ExpertSource.LLM or
request.custom_experts # 有自訂專家時，使用 LLM 補充
len(actual_custom_experts) > 0 # 有實際自訂專家時，使用 LLM 補充
)
logger.info(f"[DEBUG] use_llm decision: {use_llm}")
if use_llm:
# LLM 生成專家
@@ -55,7 +68,7 @@ async def generate_expert_transformation_events(
query=request.query,
categories=all_categories,
expert_count=request.expert_count,
custom_experts=request.custom_experts
custom_experts=actual_custom_experts if actual_custom_experts else None
)
logger.info(f"Expert prompt: {expert_prompt[:200]}")
@@ -78,9 +91,9 @@ async def generate_expert_transformation_events(
yield f"event: error\ndata: {json.dumps({'error': f'專家團隊生成失敗: {str(e)}'}, ensure_ascii=False)}\n\n"
return
else:
# 外部來源生成專家
# 外部來源生成專家 (本地檔案，同步)
try:
experts_data, actual_source = await expert_source_service.get_experts(
experts_data, actual_source = expert_source_service.get_experts(
source=request.expert_source.value,
count=request.expert_count,
language=request.expert_language
@@ -106,7 +119,7 @@ async def generate_expert_transformation_events(
query=request.query,
categories=all_categories,
expert_count=request.expert_count,
custom_experts=request.custom_experts
custom_experts=actual_custom_experts if actual_custom_experts else None
)
expert_response = await ollama_provider.generate(

92
backend/app/services/expert_cache.py
View File

@@ -1,92 +0,0 @@
"""Expert 資料快取模組
提供 TTL-based 快取機制，減少外部 API 呼叫。
"""
import time
from dataclasses import dataclass
from typing import Dict, List, Optional
@dataclass
class CacheEntry:
"""快取項目"""
data: List[dict]
timestamp: float
class ExpertCache:
"""TTL 快取，用於儲存外部來源的職業資料"""
def __init__(self, ttl_seconds: int = 3600):
"""
初始化快取
Args:
ttl_seconds: 快取存活時間（預設 1 小時）
"""
self._cache: Dict[str, CacheEntry] = {}
self._ttl = ttl_seconds
def get(self, key: str) -> Optional[List[dict]]:
"""
取得快取資料
Args:
key: 快取鍵（如 "wikidata:zh:occupations"）
Returns:
快取的資料列表，若不存在或已過期則回傳 None
"""
entry = self._cache.get(key)
if entry is None:
return None
# 檢查是否過期
if time.time() - entry.timestamp > self._ttl:
del self._cache[key]
return None
return entry.data
def set(self, key: str, data: List[dict]) -> None:
"""
設定快取資料
Args:
key: 快取鍵
data: 要快取的資料列表
"""
self._cache[key] = CacheEntry(
data=data,
timestamp=time.time()
)
def invalidate(self, key: Optional[str] = None) -> None:
"""
清除快取
Args:
key: 要清除的鍵，若為 None 則清除全部
"""
if key is None:
self._cache.clear()
elif key in self._cache:
del self._cache[key]
def get_stats(self) -> dict:
"""取得快取統計資訊"""
now = time.time()
valid_count = sum(
1 for entry in self._cache.values()
if now - entry.timestamp <= self._ttl
)
return {
"total_entries": len(self._cache),
"valid_entries": valid_count,
"ttl_seconds": self._ttl
}
# 全域快取實例
expert_cache = ExpertCache()

394
backend/app/services/expert_source_service.py
View File

@@ -1,293 +1,111 @@
"""Expert 外部資料來源服務
"""Expert 本地資料來源服務
提供從 Wikidata SPARQL 和 ConceptNet API 獲取職業/領域資料的功能。
從本地 JSON 檔案讀取職業資料，提供隨機選取功能。
"""
import json
import logging
import random
from abc import ABC, abstractmethod
from typing import List, Optional, Tuple
import httpx
from .expert_cache import expert_cache
from pathlib import Path
from typing import List, Tuple
logger = logging.getLogger(__name__)
# 資料目錄
DATA_DIR = Path(__file__).parent.parent / "data"
class ExpertSourceProvider(ABC):
"""外部來源提供者抽象類"""
@abstractmethod
async def fetch_occupations(
self, count: int, language: str = "zh"
) -> List[dict]:
class LocalDataProvider:
"""從本地 JSON 檔案讀取職業資料"""
def __init__(self, source: str):
"""
獲取職業列表
Args:
source: 資料來源名稱 (dbpedia/wikidata)
"""
self.source = source
self._cache: dict = {} # 記憶體快取
def load_occupations(self, language: str = "en") -> List[dict]:
"""
載入職業資料
Args:
count: 需要的職業數量
language: 語言代碼 (zh/en)
language: 語言代碼 (en/zh)
Returns:
職業資料列表 [{"name": "...", "domain": "..."}, ...]
職業列表 [{"name": "...", "domain": "..."}, ...]
"""
pass
cache_key = f"{self.source}:{language}"
# 檢查記憶體快取
if cache_key in self._cache:
return self._cache[cache_key]
class WikidataProvider(ExpertSourceProvider):
"""Wikidata SPARQL 查詢提供者"""
# 讀取檔案
file_path = DATA_DIR / f"{self.source}_occupations_{language}.json"
ENDPOINT = "https://query.wikidata.org/sparql"
def __init__(self):
self.client = httpx.AsyncClient(timeout=30.0)
async def fetch_occupations(
self, count: int, language: str = "zh"
) -> List[dict]:
"""從 Wikidata 獲取職業列表"""
cache_key = f"wikidata:{language}:occupations"
# 檢查快取
cached = expert_cache.get(cache_key)
if cached:
logger.info(f"Wikidata cache hit: {len(cached)} occupations")
return self._random_select(cached, count)
# SPARQL 查詢
query = self._build_sparql_query(language)
if not file_path.exists():
logger.warning(f"資料檔案不存在: {file_path}")
return []
try:
response = await self.client.get(
self.ENDPOINT,
params={"query": query, "format": "json"},
headers={"Accept": "application/sparql-results+json"}
)
response.raise_for_status()
with open(file_path, "r", encoding="utf-8") as f:
data = json.load(f)
data = response.json()
occupations = self._parse_sparql_response(data, language)
occupations = data.get("occupations", [])
logger.info(f"載入 {len(occupations)} 筆 {self.source} {language} 職業")
if occupations:
expert_cache.set(cache_key, occupations)
logger.info(f"Wikidata fetched: {len(occupations)} occupations")
return self._random_select(occupations, count)
# 存入快取
self._cache[cache_key] = occupations
return occupations
except Exception as e:
logger.error(f"Wikidata query failed: {e}")
raise
logger.error(f"讀取職業資料失敗: {e}")
return []
def _build_sparql_query(self, language: str) -> str:
"""建構 SPARQL 查詢"""
lang_filter = f'FILTER(LANG(?occupationLabel) = "{language}")'
return f"""
SELECT DISTINCT ?occupation ?occupationLabel ?fieldLabel WHERE {{
?occupation wdt:P31 wd:Q28640.
?occupation rdfs:label ?occupationLabel.
{lang_filter}
OPTIONAL {{
?occupation wdt:P425 ?field.
?field rdfs:label ?fieldLabel.
FILTER(LANG(?fieldLabel) = "{language}")
}}
}}
LIMIT 500
def random_select(self, count: int, language: str = "en") -> List[dict]:
"""
隨機選取指定數量的職業
def _parse_sparql_response(self, data: dict, language: str) -> List[dict]:
"""解析 SPARQL 回應"""
results = []
bindings = data.get("results", {}).get("bindings", [])
Args:
count: 需要的數量
language: 語言代碼
for item in bindings:
name = item.get("occupationLabel", {}).get("value", "")
field = item.get("fieldLabel", {}).get("value", "")
Returns:
隨機選取的職業列表
"""
all_occupations = self.load_occupations(language)
if name and len(name) >= 2:
results.append({
"name": name,
"domain": field if field else self._infer_domain(name)
})
if not all_occupations:
return []
return results
if len(all_occupations) <= count:
return all_occupations
def _infer_domain(self, occupation_name: str) -> str:
"""根據職業名稱推斷領域"""
# 簡單的領域推斷規則
domain_keywords = {
"醫": "醫療健康",
"師": "專業服務",
"工程": "工程技術",
"設計": "設計創意",
"藝術": "藝術文化",
"運動": "體育運動",
"農": "農業",
"漁": "漁業",
"商": "商業貿易",
"法": "法律",
"教": "教育",
"研究": "學術研究",
}
for keyword, domain in domain_keywords.items():
if keyword in occupation_name:
return domain
return "專業領域"
def _random_select(self, items: List[dict], count: int) -> List[dict]:
"""隨機選取指定數量"""
if len(items) <= count:
return items
return random.sample(items, count)
async def close(self):
await self.client.aclose()
class ConceptNetProvider(ExpertSourceProvider):
"""ConceptNet API 查詢提供者"""
ENDPOINT = "https://api.conceptnet.io"
def __init__(self):
self.client = httpx.AsyncClient(timeout=30.0)
async def fetch_occupations(
self, count: int, language: str = "zh"
) -> List[dict]:
"""從 ConceptNet 獲取職業相關概念"""
cache_key = f"conceptnet:{language}:occupations"
# 檢查快取
cached = expert_cache.get(cache_key)
if cached:
logger.info(f"ConceptNet cache hit: {len(cached)} concepts")
return self._random_select(cached, count)
# 查詢職業相關概念
lang_code = "zh" if language == "zh" else "en"
start_concept = f"/c/{lang_code}/職業" if lang_code == "zh" else f"/c/{lang_code}/occupation"
try:
occupations = []
# 查詢 IsA 關係
response = await self.client.get(
f"{self.ENDPOINT}/query",
params={
"start": start_concept,
"rel": "/r/IsA",
"limit": 100
}
)
response.raise_for_status()
data = response.json()
occupations.extend(self._parse_conceptnet_response(data, lang_code))
# 也查詢 RelatedTo 關係以獲取更多結果
response2 = await self.client.get(
f"{self.ENDPOINT}/query",
params={
"node": start_concept,
"rel": "/r/RelatedTo",
"limit": 100
}
)
response2.raise_for_status()
data2 = response2.json()
occupations.extend(self._parse_conceptnet_response(data2, lang_code))
# 去重
seen = set()
unique_occupations = []
for occ in occupations:
if occ["name"] not in seen:
seen.add(occ["name"])
unique_occupations.append(occ)
if unique_occupations:
expert_cache.set(cache_key, unique_occupations)
logger.info(f"ConceptNet fetched: {len(unique_occupations)} concepts")
return self._random_select(unique_occupations, count)
except Exception as e:
logger.error(f"ConceptNet query failed: {e}")
raise
def _parse_conceptnet_response(self, data: dict, lang_code: str) -> List[dict]:
"""解析 ConceptNet 回應"""
results = []
edges = data.get("edges", [])
for edge in edges:
# 取得 start 或 end 節點（取決於查詢方向）
start = edge.get("start", {})
end = edge.get("end", {})
# 選擇非起始節點的概念
node = end if start.get("@id", "").endswith("職業") or start.get("@id", "").endswith("occupation") else start
label = node.get("label", "")
term = node.get("term", "")
# 過濾：確保是目標語言且有意義
node_id = node.get("@id", "")
if f"/c/{lang_code}/" in node_id and label and len(label) >= 2:
results.append({
"name": label,
"domain": self._infer_domain_from_edge(edge)
})
return results
def _infer_domain_from_edge(self, edge: dict) -> str:
"""從 edge 資訊推斷領域"""
# ConceptNet 的 edge 包含 surfaceText 可能有額外資訊
surface = edge.get("surfaceText", "")
rel = edge.get("rel", {}).get("label", "")
if "專業" in surface:
return "專業領域"
elif "技術" in surface:
return "技術領域"
else:
return "知識領域"
def _random_select(self, items: List[dict], count: int) -> List[dict]:
"""隨機選取指定數量"""
if len(items) <= count:
return items
return random.sample(items, count)
async def close(self):
await self.client.aclose()
return random.sample(all_occupations, count)
class ExpertSourceService:
"""統一的專家來源服務"""
def __init__(self):
self.wikidata = WikidataProvider()
self.conceptnet = ConceptNetProvider()
self.curated = LocalDataProvider("curated") # 精選職業
self.dbpedia = LocalDataProvider("dbpedia")
self.wikidata = LocalDataProvider("wikidata")
async def get_experts(
def get_experts(
self,
source: str,
count: int,
language: str = "zh",
language: str = "en",
fallback_to_llm: bool = True
) -> Tuple[List[dict], str]:
"""
從指定來源獲取專家資料
Args:
source: 來源類型 ("wikidata" | "conceptnet")
source: 來源類型 ("dbpedia" | "wikidata")
count: 需要的專家數量
language: 語言代碼
fallback_to_llm: 失敗時是否允許 fallback（由呼叫者處理）
@@ -296,35 +114,87 @@ class ExpertSourceService:
(專家資料列表, 實際使用的來源)
Raises:
Exception: 當獲取失敗且不 fallback 時
ValueError: 當獲取失敗且資料為空時
"""
provider = self._get_provider(source)
# 選擇 provider
if source == "curated":
provider = self.curated
# 精選職業支援 zh 和 en，預設使用 zh
if language not in ["zh", "en"]:
language = "zh"
elif source == "wikidata":
provider = self.wikidata
else:
# 預設使用 dbpedia
provider = self.dbpedia
source = "dbpedia"
try:
experts = await provider.fetch_occupations(count, language)
experts = provider.random_select(count, language)
if not experts:
raise ValueError(f"No occupations found from {source}")
raise ValueError(f"No occupations found from {source} ({language})")
logger.info(f"從 {source} 取得 {len(experts)} 位專家")
return experts, source
except Exception as e:
logger.warning(f"Failed to fetch from {source}: {e}")
raise
def get_available_sources(self) -> List[dict]:
"""
取得可用的資料來源資訊
def _get_provider(self, source: str) -> ExpertSourceProvider:
"""根據來源類型取得對應的 provider"""
if source == "wikidata":
return self.wikidata
elif source == "conceptnet":
return self.conceptnet
else:
raise ValueError(f"Unknown source: {source}")
Returns:
來源資訊列表
"""
sources = []
async def close(self):
"""關閉所有 HTTP clients"""
await self.wikidata.close()
await self.conceptnet.close()
# 檢查精選職業（中文）
curated_zh = DATA_DIR / "curated_occupations_zh.json"
if curated_zh.exists():
with open(curated_zh, "r", encoding="utf-8") as f:
data = json.load(f)
sources.append({
"source": "curated",
"language": "zh",
"count": data["metadata"]["total_count"],
"created_at": data["metadata"]["created_at"]
})
# 檢查精選職業（英文）
curated_en = DATA_DIR / "curated_occupations_en.json"
if curated_en.exists():
with open(curated_en, "r", encoding="utf-8") as f:
data = json.load(f)
sources.append({
"source": "curated",
"language": "en",
"count": data["metadata"]["total_count"],
"created_at": data["metadata"]["created_at"]
})
# 檢查 DBpedia
dbpedia_en = DATA_DIR / "dbpedia_occupations_en.json"
if dbpedia_en.exists():
with open(dbpedia_en, "r", encoding="utf-8") as f:
data = json.load(f)
sources.append({
"source": "dbpedia",
"language": "en",
"count": data["metadata"]["total_count"],
"fetched_at": data["metadata"]["fetched_at"]
})
# 檢查 Wikidata
wikidata_zh = DATA_DIR / "wikidata_occupations_zh.json"
if wikidata_zh.exists():
with open(wikidata_zh, "r", encoding="utf-8") as f:
data = json.load(f)
sources.append({
"source": "wikidata",
"language": "zh",
"count": data["metadata"]["total_count"],
"fetched_at": data["metadata"]["fetched_at"]
})
return sources
# 全域服務實例

386
backend/scripts/fetch_occupations.py Normal file
View File

@@ -0,0 +1,386 @@
#!/usr/bin/env python3
"""
職業資料抓取腳本
從 Wikidata SPARQL 和 ConceptNet API 抓取職業資料，
儲存為本地 JSON 檔案供應用程式使用。
使用方式:
cd backend
python scripts/fetch_occupations.py
"""
import json
import sys
from datetime import datetime, timezone
from pathlib import Path
from typing import List
import httpx
# 輸出目錄
DATA_DIR = Path(__file__).parent.parent / "app" / "data"
def fetch_wikidata_occupations(language: str) -> List[dict]:
"""
從 Wikidata SPARQL 端點抓取所有職業（使用分頁）
Args:
language: 語言代碼 (zh/en)
Returns:
職業列表 [{"name": "...", "domain": "..."}, ...]
"""
print(f"[Wikidata] 正在抓取 {language} 職業資料（分頁模式）...")
endpoint = "https://query.wikidata.org/sparql"
page_size = 500 # 每頁筆數
all_bindings = []
offset = 0
try:
with httpx.Client(timeout=120.0) as client:
while True:
# SPARQL 查詢 - 使用 SERVICE wikibase:label (更高效)
query = f"""
SELECT DISTINCT ?occupation ?occupationLabel ?fieldLabel WHERE {{
?occupation wdt:P31 wd:Q28640.
OPTIONAL {{ ?occupation wdt:P425 ?field. }}
SERVICE wikibase:label {{ bd:serviceParam wikibase:language "{language},en". }}
}}
LIMIT {page_size}
OFFSET {offset}
"""
print(f"[Wikidata] 抓取第 {offset // page_size + 1} 頁 (offset={offset})...")
response = client.get(
endpoint,
params={"query": query, "format": "json"},
headers={
"Accept": "application/sparql-results+json",
"User-Agent": "NoveltySeeking/1.0",
},
)
response.raise_for_status()
data = response.json()
bindings = data.get("results", {}).get("bindings", [])
print(f"[Wikidata] 取得 {len(bindings)} 筆")
if not bindings:
# 沒有更多資料了
break
all_bindings.extend(bindings)
offset += page_size
# 如果取得的筆數少於 page_size，表示已經是最後一頁
if len(bindings) < page_size:
break
print(f"[Wikidata] 總共取得 {len(all_bindings)} 筆原始資料")
# 解析回應
occupations = []
for item in all_bindings:
name = item.get("occupationLabel", {}).get("value", "")
field = item.get("fieldLabel", {}).get("value", "")
if name and len(name) >= 2:
occupations.append({
"name": name,
"domain": field if field else infer_domain(name, language),
})
# 去重
seen = set()
unique = []
for occ in occupations:
if occ["name"] not in seen:
seen.add(occ["name"])
unique.append(occ)
print(f"[Wikidata] 去重後: {len(unique)} 筆職業")
return unique
except Exception as e:
print(f"[Wikidata] 錯誤: {e}")
raise
def fetch_conceptnet_occupations(language: str) -> List[dict]:
"""
從 ConceptNet API 抓取職業相關概念（使用分頁）
Args:
language: 語言代碼 (zh/en)
Returns:
職業列表 [{"name": "...", "domain": "..."}, ...]
"""
print(f"[ConceptNet] 正在抓取 {language} 職業資料（分頁模式）...")
endpoint = "https://api.conceptnet.io"
lang_code = language
page_size = 100 # ConceptNet 建議的 limit
# 起始概念
start_concepts = {
"zh": ["/c/zh/職業", "/c/zh/專業", "/c/zh/工作", "/c/zh/職務"],
"en": ["/c/en/occupation", "/c/en/profession", "/c/en/job", "/c/en/career"],
}
# 要查詢的關係類型
relations = ["/r/IsA", "/r/RelatedTo", "/r/HasA", "/r/AtLocation"]
all_occupations = []
try:
with httpx.Client(timeout=60.0) as client:
for concept in start_concepts.get(lang_code, start_concepts["zh"]):
for rel in relations:
offset = 0
max_pages = 5 # 每個組合最多抓 5 頁
for page in range(max_pages):
try:
print(f"[ConceptNet] 查詢 {concept} {rel} (offset={offset})...")
# 查詢 start 參數
response = client.get(
f"{endpoint}/query",
params={
"start": concept,
"rel": rel,
"limit": page_size,
"offset": offset,
},
)
if response.status_code != 200:
print(f"[ConceptNet] HTTP {response.status_code}, 跳過")
break
data = response.json()
edges = data.get("edges", [])
if not edges:
break
parsed = parse_conceptnet_response(data, lang_code)
all_occupations.extend(parsed)
print(f"[ConceptNet] 取得 {len(parsed)} 筆")
if len(edges) < page_size:
break
offset += page_size
except Exception as e:
print(f"[ConceptNet] 錯誤: {e}")
break
# 去重
seen = set()
unique = []
for occ in all_occupations:
if occ["name"] not in seen:
seen.add(occ["name"])
unique.append(occ)
print(f"[ConceptNet] 去重後: {len(unique)} 筆概念")
return unique
except Exception as e:
print(f"[ConceptNet] 錯誤: {e}")
raise
def parse_conceptnet_response(data: dict, lang_code: str) -> List[dict]:
"""解析 ConceptNet API 回應"""
results = []
edges = data.get("edges", [])
for edge in edges:
start = edge.get("start", {})
end = edge.get("end", {})
# 嘗試從兩端取得有意義的概念
for node in [start, end]:
node_id = node.get("@id", "")
label = node.get("label", "")
# 過濾：確保是目標語言且有意義
if f"/c/{lang_code}/" in node_id and label and len(label) >= 2:
# 排除過於泛用的詞
if label not in ["職業", "工作", "專業", "occupation", "job", "profession"]:
results.append({
"name": label,
"domain": infer_domain(label, lang_code),
})
return results
def infer_domain(occupation_name: str, language: str) -> str:
"""根據職業名稱推斷領域"""
if language == "zh":
domain_keywords = {
"醫": "醫療健康",
"護": "醫療健康",
"藥": "醫療健康",
"師": "專業服務",
"工程": "工程技術",
"技術": "工程技術",
"設計": "設計創意",
"藝術": "藝術文化",
"音樂": "藝術文化",
"運動": "體育運動",
"農": "農業",
"漁": "漁業",
"商": "商業貿易",
"銷": "商業貿易",
"法": "法律",
"律": "法律",
"教": "教育",
"研究": "學術研究",
"科學": "學術研究",
"廚": "餐飲服務",
"烹": "餐飲服務",
"建築": "建築營造",
"軍": "軍事國防",
"警": "公共安全",
"消防": "公共安全",
"記者": "媒體傳播",
"編輯": "媒體傳播",
"作家": "文學創作",
"程式": "資訊科技",
"軟體": "資訊科技",
"電腦": "資訊科技",
}
else:
domain_keywords = {
"doctor": "Healthcare",
"nurse": "Healthcare",
"medical": "Healthcare",
"engineer": "Engineering",
"technical": "Engineering",
"design": "Design & Creative",
"artist": "Arts & Culture",
"music": "Arts & Culture",
"sport": "Sports",
"athletic": "Sports",
"farm": "Agriculture",
"fish": "Fishery",
"business": "Business",
"sales": "Business",
"law": "Legal",
"attorney": "Legal",
"teach": "Education",
"professor": "Education",
"research": "Academic Research",
"scien": "Academic Research",
"chef": "Culinary",
"cook": "Culinary",
"architect": "Architecture",
"military": "Military",
"police": "Public Safety",
"fire": "Public Safety",
"journal": "Media",
"editor": "Media",
"writer": "Literature",
"author": "Literature",
"program": "Information Technology",
"software": "Information Technology",
"computer": "Information Technology",
"develop": "Information Technology",
}
name_lower = occupation_name.lower()
for keyword, domain in domain_keywords.items():
if keyword in name_lower:
return domain
return "專業領域" if language == "zh" else "Professional Field"
def save_json(data: List[dict], source: str, language: str) -> None:
"""儲存資料到 JSON 檔案"""
filename = f"{source}_occupations_{language}.json"
filepath = DATA_DIR / filename
output = {
"metadata": {
"source": source,
"language": language,
"fetched_at": datetime.now(timezone.utc).isoformat(),
"total_count": len(data),
},
"occupations": data,
}
with open(filepath, "w", encoding="utf-8") as f:
json.dump(output, f, ensure_ascii=False, indent=2)
print(f"[儲存] {filepath} ({len(data)} 筆)")
def main():
"""主程式"""
print("=" * 60)
print("職業資料抓取腳本")
print(f"輸出目錄: {DATA_DIR}")
print("=" * 60)
print()
# 確保輸出目錄存在
DATA_DIR.mkdir(parents=True, exist_ok=True)
# 抓取 Wikidata
print("--- Wikidata ---")
try:
wikidata_zh = fetch_wikidata_occupations("zh")
save_json(wikidata_zh, "wikidata", "zh")
except Exception as e:
print(f"Wikidata 中文抓取失敗: {e}")
wikidata_zh = []
try:
wikidata_en = fetch_wikidata_occupations("en")
save_json(wikidata_en, "wikidata", "en")
except Exception as e:
print(f"Wikidata 英文抓取失敗: {e}")
wikidata_en = []
print()
# 抓取 ConceptNet
print("--- ConceptNet ---")
try:
conceptnet_zh = fetch_conceptnet_occupations("zh")
save_json(conceptnet_zh, "conceptnet", "zh")
except Exception as e:
print(f"ConceptNet 中文抓取失敗: {e}")
conceptnet_zh = []
try:
conceptnet_en = fetch_conceptnet_occupations("en")
save_json(conceptnet_en, "conceptnet", "en")
except Exception as e:
print(f"ConceptNet 英文抓取失敗: {e}")
conceptnet_en = []
print()
print("=" * 60)
print("抓取完成！")
print(f" Wikidata 中文: {len(wikidata_zh)} 筆")
print(f" Wikidata 英文: {len(wikidata_en)} 筆")
print(f" ConceptNet 中文: {len(conceptnet_zh)} 筆")
print(f" ConceptNet 英文: {len(conceptnet_en)} 筆")
print("=" * 60)
if __name__ == "__main__":
main()

5
frontend/src/components/TransformationInputPanel.tsx
View File

@@ -7,8 +7,9 @@ const { Title, Text } = Typography;
const EXPERT_SOURCE_OPTIONS = [
{ label: 'LLM 生成', value: 'llm' as ExpertSource, description: '使用 AI 模型生成專家' },
{ label: 'Wikidata', value: 'wikidata' as ExpertSource, description: '從維基數據查詢職業' },
{ label: 'ConceptNet', value: 'conceptnet' as ExpertSource, description: '從知識圖譜查詢概念' },
{ label: '精選職業', value: 'curated' as ExpertSource, description: '從 210 個常見職業隨機選取（含具體領域）' },
{ label: 'DBpedia', value: 'dbpedia' as ExpertSource, description: '從 DBpedia 隨機選取職業 (2164 筆)' },
{ label: 'Wikidata', value: 'wikidata' as ExpertSource, description: '從 Wikidata 查詢職業 (需等待 API)' },
];
interface TransformationInputPanelProps {

2
frontend/src/hooks/useExpertTransformation.ts
View File

@@ -155,7 +155,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
});
});
},
[options.model, options.temperature]
[options.model, options.temperature, options.expertSource]
);
const transformAll = useCallback(

4
frontend/src/types/index.ts
View File

@@ -230,7 +230,7 @@ export interface ExpertTransformationDAGResult {
results: ExpertTransformationCategoryResult[];
}
export type ExpertSource = 'llm' | 'wikidata' | 'conceptnet';
export type ExpertSource = 'llm' | 'curated' | 'dbpedia' | 'wikidata';
export interface ExpertTransformationRequest {
query: string;
@@ -240,7 +240,7 @@ export interface ExpertTransformationRequest {
keywords_per_expert: number; // 1-3
custom_experts?: string[]; // ["藥師", "工程師"]
expert_source?: ExpertSource; // 專家來源 (default: 'llm')
expert_language?: string; // 外部來源語言 (default: 'zh')
expert_language?: string; // 外部來源語言 (default: 'en')
model?: string;
temperature?: number;
}