gbanyan
5571076406
- 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>
106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
"""Expert Transformation Agent 提示詞模組"""
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from typing import List, Optional
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def get_expert_generation_prompt(
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query: str,
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categories: List[str],
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expert_count: int,
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custom_experts: Optional[List[str]] = None
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) -> str:
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"""Step 0: 生成專家團隊（不依賴主題，純隨機多元）"""
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import time
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import random
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custom_text = ""
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if custom_experts and len(custom_experts) > 0:
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custom_text = f"（已指定：{', '.join(custom_experts[:expert_count])}）"
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# 加入時間戳和隨機數來增加多樣性
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seed = int(time.time() * 1000) % 10000
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diversity_hints = [
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"冷門、非主流、跨領域",
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"罕見職業、新興領域、邊緣學科",
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"非傳統、創新、小眾專業",
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"未來趨向、實驗性、非常規",
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"跨文化、混合領域、獨特視角"
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]
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hint = random.choice(diversity_hints)
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return f"""/no_think
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隨機組建 {expert_count} 個來自完全不同領域的專家團隊{custom_text}。
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【創新要求】（隨機種子:{seed}）
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- 優先選擇{hint}的專家
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- 避免常見職業（如醫生、工程師、教師、律師等）
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- 每個專家必須來自完全不相關的領域
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- 越罕見、越創新越好
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回傳 JSON：
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{{"experts": [{{"id": "expert-0", "name": "職業", "domain": "領域", "perspective": "角度"}}, ...]}}
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規則：
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- id 為 expert-0 到 expert-{expert_count - 1}
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- name 填寫職業名稱（非人名），2-5字
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- domain 要具體且獨特，不可重複類型"""
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def get_expert_keyword_generation_prompt(
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category: str,
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attribute: str,
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experts: List[dict], # List[ExpertProfile]
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keywords_per_expert: int = 1
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) -> str:
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"""Step 1: 專家視角關鍵字生成"""
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# 建立專家列表，格式更清晰
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experts_list = "\n".join([f"- {exp['id']}: {exp['name']}" for exp in experts])
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return f"""/no_think
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你需要扮演以下專家，為屬性生成創新關鍵字：
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【專家名單】
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{experts_list}
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【任務】
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屬性：「{attribute}」（類別：{category}）
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請為每位專家：
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1. 先理解該職業的專業背景、知識領域、工作內容
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2. 從該職業的獨特視角思考「{attribute}」
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3. 生成 {keywords_per_expert} 個與該專業相關的創新關鍵字（2-6字）
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關鍵字必須反映該專家的專業思維方式，例如：
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- 會計師 看「移動」→「資金流動」「成本效益」
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- 建築師 看「移動」→「動線設計」「空間流動」
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- 心理師 看「移動」→「行為動機」「情緒轉變」
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回傳 JSON：
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{{"keywords": [{{"keyword": "詞彙", "expert_id": "expert-X", "expert_name": "名稱"}}, ...]}}
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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。"""
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def get_single_description_prompt(
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query: str,
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keyword: str,
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expert_id: str,
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expert_name: str,
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expert_domain: str
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) -> str:
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"""Step 2: 為單一關鍵字生成描述"""
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# 如果 domain 是通用的，就只用職業名稱
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domain_text = f"（{expert_domain}）" if expert_domain and expert_domain != "Professional Field" else ""
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return f"""/no_think
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物件：「{query}」
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專家：{expert_name}{domain_text}
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關鍵字：{keyword}
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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。
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描述要體現{expert_name}的專業思維和獨特觀點。
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回傳 JSON：
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{{"description": "應用描述"}}"""
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