gbanyan
534fdbbcc4
- Backend: Add expert transformation router with 3-step SSE pipeline - Step 0: Generate diverse expert team (random domains) - Step 1: Each expert generates keywords for attributes - Step 2: Batch generate descriptions for expert keywords - Backend: Add simplified prompts for reliable JSON output - Frontend: Add TransformationPanel with React Flow visualization - Frontend: Add TransformationInputPanel for expert configuration - Expert count (2-8), keywords per expert (1-3) - Custom expert domains support - Frontend: Add expert keyword nodes with expert badges - Frontend: Improve description card layout (wider cards, more spacing) - Frontend: Add fallback for missing descriptions with visual indicators 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
79 lines
2.6 KiB
Python
79 lines
2.6 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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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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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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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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- 各專家的 domain 必須來自截然不同的領域，越多元越好"""
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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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experts_info = ", ".join([f"{exp['id']}:{exp['name']}({exp['domain']})" for exp in experts])
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return f"""/no_think
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專家團隊：{experts_info}
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屬性：「{attribute}」（{category}）
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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）。
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關鍵字要反映該專家領域的獨特思考方式。
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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。"""
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def get_expert_batch_description_prompt(
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query: str,
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category: str,
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expert_keywords: List[dict] # List[ExpertKeyword]
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) -> str:
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"""Step 2: 批次生成專家關鍵字的描述"""
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keywords_info = ", ".join([
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f"{kw['expert_name']}:{kw['keyword']}"
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for kw in expert_keywords
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])
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# 建立 keyword -> (expert_id, expert_name) 的對照
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keyword_expert_map = ", ".join([
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f"{kw['keyword']}→{kw['expert_id']}/{kw['expert_name']}"
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for kw in expert_keywords
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])
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return f"""/no_think
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物件：「{query}」
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關鍵字（專家:詞彙）：{keywords_info}
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對照：{keyword_expert_map}
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為每個關鍵字生成創新描述（15-30字），說明如何將該概念應用到「{query}」上。
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回傳 JSON：
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{{"descriptions": [{{"keyword": "詞彙", "expert_id": "expert-X", "expert_name": "名稱", "description": "應用描述"}}, ...]}}
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共需 {len(expert_keywords)} 個描述。"""
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