feat: Improve expert diversity and description reliability
- Add random seed and diversity hints to expert generation prompt - Explicitly avoid common professions (醫生、工程師、教師、律師等) - Change description generation from batch to one-by-one for reliability - Increase default temperature from 0.7 to 0.95 for more creative output - Add description_progress SSE event for real-time feedback 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -10,20 +10,40 @@ def get_expert_generation_prompt(
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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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- 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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- 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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@@ -48,31 +68,20 @@ def get_expert_keyword_generation_prompt(
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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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def get_single_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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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: 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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"""Step 2: 為單一關鍵字生成描述"""
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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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專家：{expert_name}（{expert_domain}）
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關鍵字：{keyword}
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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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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。
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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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{{"description": "應用描述"}}"""
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@@ -17,7 +17,7 @@ from ..models.schemas import (
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from ..prompts.expert_transformation_prompt import (
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get_expert_generation_prompt,
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get_expert_keyword_generation_prompt,
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get_expert_batch_description_prompt,
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get_single_description_prompt,
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)
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from ..services.llm_service import ollama_provider, extract_json_from_response
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@@ -119,34 +119,48 @@ async def generate_expert_transformation_events(
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yield f"event: error\ndata: {json.dumps({'error': '無法生成關鍵字'}, ensure_ascii=False)}\n\n"
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return
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# ========== Step 2: Generate descriptions for each expert keyword ==========
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yield f"event: description_start\ndata: {json.dumps({'message': '為專家關鍵字生成創新應用描述...'}, ensure_ascii=False)}\n\n"
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# ========== Step 2: Generate descriptions one by one ==========
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yield f"event: description_start\ndata: {json.dumps({'message': '為專家關鍵字生成創新應用描述...', 'total': len(all_expert_keywords)}, ensure_ascii=False)}\n\n"
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descriptions: List[ExpertTransformationDescription] = []
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# Build expert lookup for domain info
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expert_lookup = {exp.id: exp for exp in experts}
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for idx, kw in enumerate(all_expert_keywords):
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try:
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desc_prompt = get_expert_batch_description_prompt(
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expert = expert_lookup.get(kw.expert_id)
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expert_domain = expert.domain if expert else ""
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desc_prompt = get_single_description_prompt(
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query=request.query,
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category=request.category,
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expert_keywords=[kw.model_dump() for kw in all_expert_keywords]
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keyword=kw.keyword,
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expert_id=kw.expert_id,
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expert_name=kw.expert_name,
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expert_domain=expert_domain
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)
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logger.info(f"Description prompt: {desc_prompt[:300]}")
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desc_response = await ollama_provider.generate(
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desc_prompt, model=model, temperature=temperature
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)
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logger.info(f"Description response: {desc_response[:500]}")
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desc_data = extract_json_from_response(desc_response)
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descriptions_raw = desc_data.get("descriptions", [])
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desc_text = desc_data.get("description", "")
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for desc in descriptions_raw:
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if isinstance(desc, dict) and all(k in desc for k in ["keyword", "expert_id", "expert_name", "description"]):
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descriptions.append(ExpertTransformationDescription(**desc))
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if desc_text:
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descriptions.append(ExpertTransformationDescription(
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keyword=kw.keyword,
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expert_id=kw.expert_id,
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expert_name=kw.expert_name,
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description=desc_text
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))
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# Send progress update
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yield f"event: description_progress\ndata: {json.dumps({'current': idx + 1, 'total': len(all_expert_keywords), 'keyword': kw.keyword}, ensure_ascii=False)}\n\n"
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except Exception as e:
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logger.warning(f"Failed to generate descriptions: {e}")
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# Continue without descriptions - at least we have keywords
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logger.warning(f"Failed to generate description for '{kw.keyword}': {e}")
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# Continue with next keyword
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yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions)}, ensure_ascii=False)}\n\n"
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@@ -33,7 +33,7 @@ function App() {
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// Transformation Agent settings
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const [transformModel, setTransformModel] = useState<string>('');
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const [transformTemperature, setTransformTemperature] = useState<number>(0.7);
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const [transformTemperature, setTransformTemperature] = useState<number>(0.95);
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const [expertConfig, setExpertConfig] = useState<{
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expert_count: number;
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keywords_per_expert: number;
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