feat: Add Expert Transformation Agent with multi-expert perspective system
- 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>
This commit is contained in:
@@ -3,7 +3,7 @@ from contextlib import asynccontextmanager
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from .routers import attributes
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from .routers import attributes, transformation, expert_transformation
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from .services.llm_service import ollama_provider
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@@ -29,6 +29,8 @@ app.add_middleware(
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)
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app.include_router(attributes.router)
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app.include_router(transformation.router)
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app.include_router(expert_transformation.router)
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@app.get("/")
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@@ -131,3 +131,92 @@ class DAGRelationship(BaseModel):
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source: str # source attribute name
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target_category: str
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target: str # target attribute name
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# ===== Transformation Agent schemas =====
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class TransformationRequest(BaseModel):
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"""Transformation Agent 請求"""
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query: str # 原始查詢 (e.g., "腳踏車")
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category: str # 類別名稱 (e.g., "功能")
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attributes: List[str] # 該類別的屬性列表
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model: Optional[str] = None
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temperature: Optional[float] = 0.7
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keyword_count: int = 3 # 要生成的新關鍵字數量
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class TransformationDescription(BaseModel):
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"""單一轉換描述"""
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keyword: str # 新關鍵字
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description: str # 與 query 結合的描述
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class TransformationCategoryResult(BaseModel):
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"""單一類別的轉換結果"""
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category: str
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original_attributes: List[str] # 原始屬性
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new_keywords: List[str] # 新生成的關鍵字
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descriptions: List[TransformationDescription]
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class TransformationDAGResult(BaseModel):
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"""完整 Transformation 結果"""
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query: str
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results: List[TransformationCategoryResult]
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# ===== Expert Transformation Agent schemas =====
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class ExpertProfile(BaseModel):
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"""專家檔案"""
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id: str # e.g., "expert-0"
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name: str # e.g., "藥師"
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domain: str # e.g., "醫療與健康"
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perspective: Optional[str] = None # e.g., "從藥物與健康管理角度思考"
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class ExpertKeyword(BaseModel):
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"""專家視角生成的關鍵字"""
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keyword: str # 關鍵字本身
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expert_id: str # 哪個專家生成的
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expert_name: str # 專家名稱（冗餘，方便前端）
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source_attribute: str # 來自哪個原始屬性
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class ExpertTransformationDescription(BaseModel):
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"""專家關鍵字的描述"""
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keyword: str
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expert_id: str
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expert_name: str
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description: str
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class ExpertTransformationCategoryResult(BaseModel):
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"""單一類別的轉換結果（專家版）"""
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category: str
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original_attributes: List[str]
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expert_keywords: List[ExpertKeyword] # 所有專家生成的關鍵字
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descriptions: List[ExpertTransformationDescription]
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class ExpertTransformationDAGResult(BaseModel):
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"""完整轉換結果（專家版）"""
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query: str
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experts: List[ExpertProfile] # 使用的專家列表
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results: List[ExpertTransformationCategoryResult]
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class ExpertTransformationRequest(BaseModel):
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"""Expert Transformation Agent 請求"""
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query: str
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category: str
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attributes: List[str]
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# Expert parameters
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expert_count: int = 3 # 專家數量 (2-8)
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keywords_per_expert: int = 1 # 每個專家為每個屬性生成幾個關鍵字 (1-3)
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custom_experts: Optional[List[str]] = None # 用戶指定專家 ["藥師", "工程師"]
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# LLM parameters
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model: Optional[str] = None
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temperature: Optional[float] = 0.7
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78
backend/app/prompts/expert_transformation_prompt.py
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78
backend/app/prompts/expert_transformation_prompt.py
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@@ -0,0 +1,78 @@
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"""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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97
backend/app/prompts/transformation_prompt.py
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97
backend/app/prompts/transformation_prompt.py
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"""Transformation Agent 提示詞模組"""
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from typing import List
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def get_keyword_generation_prompt(
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category: str,
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attributes: List[str],
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keyword_count: int = 3
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) -> str:
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"""
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Step 1: 生成新關鍵字
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給定類別和現有屬性，生成全新的、有創意的關鍵字。
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不考慮原始查詢，只專注於類別本身可能的延伸。
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"""
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attrs_text = "、".join(attributes)
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return f"""/no_think
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你是一個創意發想專家。給定一個類別和該類別下的現有屬性，請生成全新的、有創意的關鍵字或描述片段。
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【類別】{category}
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【現有屬性】{attrs_text}
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【重要規則】
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1. 生成 {keyword_count} 個全新的關鍵字
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2. 關鍵字必須符合「{category}」這個類別的範疇
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3. 關鍵字要有創意，不能與現有屬性重複或太相似
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4. 不要考慮任何特定物件，只專注於這個類別本身可能的延伸
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5. 每個關鍵字應該是 2-6 個字的詞彙或短語
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只回傳 JSON：
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{{
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"keywords": ["關鍵字1", "關鍵字2", "關鍵字3"]
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}}"""
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def get_description_generation_prompt(
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query: str,
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category: str,
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keyword: str
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) -> str:
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"""
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Step 2: 結合原始查詢生成描述
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用新關鍵字創造一個與原始查詢相關的創新應用描述。
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"""
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return f"""/no_think
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你是一個創新應用專家。請將一個新的關鍵字概念應用到特定物件上，創造出創新的應用描述。
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【物件】{query}
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【類別】{category}
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【新關鍵字】{keyword}
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【任務】
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請用「{keyword}」這個概念，為「{query}」創造一個創新的應用描述。
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描述應該是一個完整的句子或短語，說明如何將這個新概念應用到物件上。
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【範例格式】
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- 如果物件是「腳踏車」，關鍵字是「監視」，可以生成「腳踏車監視騎乘者的身體健康狀況」
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- 如果物件是「雨傘」，關鍵字是「發電」，可以生成「雨傘利用雨滴撞擊發電」
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只回傳 JSON：
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{{
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"description": "創新應用描述"
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}}"""
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def get_batch_description_prompt(
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query: str,
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category: str,
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keywords: List[str]
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) -> str:
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"""
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批次生成描述（可選的優化版本，一次處理多個關鍵字）
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"""
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keywords_text = "、".join(keywords)
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keywords_json = ", ".join([f'"{k}"' for k in keywords])
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return f"""/no_think
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你是一個創新應用專家。請將多個新的關鍵字概念應用到特定物件上，為每個關鍵字創造創新的應用描述。
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【物件】{query}
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【類別】{category}
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【新關鍵字】{keywords_text}
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【任務】
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為每個關鍵字創造一個與「{query}」相關的創新應用描述。
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每個描述應該是一個完整的句子或短語。
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只回傳 JSON：
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{{
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"descriptions": [
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{{"keyword": "關鍵字1", "description": "描述1"}},
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{{"keyword": "關鍵字2", "description": "描述2"}}
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]
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}}"""
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185
backend/app/routers/expert_transformation.py
Normal file
185
backend/app/routers/expert_transformation.py
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"""Expert Transformation Agent 路由模組"""
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import json
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import logging
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from typing import AsyncGenerator, List
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from fastapi import APIRouter
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from fastapi.responses import StreamingResponse
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from ..models.schemas import (
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ExpertTransformationRequest,
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ExpertProfile,
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ExpertKeyword,
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ExpertTransformationCategoryResult,
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ExpertTransformationDescription,
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)
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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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)
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from ..services.llm_service import ollama_provider, extract_json_from_response
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/expert-transformation", tags=["expert-transformation"])
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async def generate_expert_transformation_events(
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request: ExpertTransformationRequest,
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all_categories: List[str] # For expert generation context
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) -> AsyncGenerator[str, None]:
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"""Generate SSE events for expert transformation process"""
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try:
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temperature = request.temperature if request.temperature is not None else 0.7
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model = request.model
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# ========== Step 0: Generate expert team ==========
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yield f"event: expert_start\ndata: {json.dumps({'message': '正在組建專家團隊...'}, ensure_ascii=False)}\n\n"
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experts: List[ExpertProfile] = []
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try:
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expert_prompt = get_expert_generation_prompt(
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query=request.query,
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categories=all_categories,
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expert_count=request.expert_count,
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custom_experts=request.custom_experts
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)
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logger.info(f"Expert prompt: {expert_prompt[:200]}")
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expert_response = await ollama_provider.generate(
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expert_prompt, model=model, temperature=temperature
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)
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logger.info(f"Expert response: {expert_response[:500]}")
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expert_data = extract_json_from_response(expert_response)
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experts_raw = expert_data.get("experts", [])
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for exp in experts_raw:
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if isinstance(exp, dict) and all(k in exp for k in ["id", "name", "domain"]):
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experts.append(ExpertProfile(**exp))
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except Exception as e:
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logger.error(f"Failed to generate experts: {e}")
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yield f"event: error\ndata: {json.dumps({'error': f'專家團隊生成失敗: {str(e)}'}, ensure_ascii=False)}\n\n"
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return
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yield f"event: expert_complete\ndata: {json.dumps({'experts': [e.model_dump() for e in experts]}, ensure_ascii=False)}\n\n"
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if not experts:
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yield f"event: error\ndata: {json.dumps({'error': '無法生成專家團隊'}, ensure_ascii=False)}\n\n"
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return
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# ========== Step 1: Generate keywords from expert perspectives ==========
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yield f"event: keyword_start\ndata: {json.dumps({'message': f'專家團隊為「{request.category}」的屬性生成關鍵字...'}, ensure_ascii=False)}\n\n"
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all_expert_keywords: List[ExpertKeyword] = []
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# For each attribute, ask all experts to generate keywords
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for attr_index, attribute in enumerate(request.attributes):
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try:
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kw_prompt = get_expert_keyword_generation_prompt(
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category=request.category,
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attribute=attribute,
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experts=[e.model_dump() for e in experts],
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keywords_per_expert=request.keywords_per_expert
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)
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logger.info(f"Keyword prompt for '{attribute}': {kw_prompt[:300]}")
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kw_response = await ollama_provider.generate(
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kw_prompt, model=model, temperature=temperature
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)
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logger.info(f"Keyword response for '{attribute}': {kw_response[:500]}")
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kw_data = extract_json_from_response(kw_response)
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keywords_raw = kw_data.get("keywords", [])
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# Add source_attribute to each keyword
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for kw in keywords_raw:
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if isinstance(kw, dict) and all(k in kw for k in ["keyword", "expert_id", "expert_name"]):
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all_expert_keywords.append(ExpertKeyword(
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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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source_attribute=attribute
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))
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# Emit progress
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yield f"event: keyword_progress\ndata: {json.dumps({'attribute': attribute, 'count': len(keywords_raw)}, ensure_ascii=False)}\n\n"
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except Exception as e:
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logger.warning(f"Failed to generate keywords for '{attribute}': {e}")
|
||||
yield f"event: keyword_progress\ndata: {json.dumps({'attribute': attribute, 'count': 0, 'error': str(e)}, ensure_ascii=False)}\n\n"
|
||||
# Continue with next attribute instead of stopping
|
||||
|
||||
yield f"event: keyword_complete\ndata: {json.dumps({'total_keywords': len(all_expert_keywords)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
if not all_expert_keywords:
|
||||
yield f"event: error\ndata: {json.dumps({'error': '無法生成關鍵字'}, ensure_ascii=False)}\n\n"
|
||||
return
|
||||
|
||||
# ========== Step 2: Generate descriptions for each expert keyword ==========
|
||||
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"
|
||||
|
||||
descriptions: List[ExpertTransformationDescription] = []
|
||||
|
||||
try:
|
||||
desc_prompt = get_expert_batch_description_prompt(
|
||||
query=request.query,
|
||||
category=request.category,
|
||||
expert_keywords=[kw.model_dump() for kw in all_expert_keywords]
|
||||
)
|
||||
logger.info(f"Description prompt: {desc_prompt[:300]}")
|
||||
|
||||
desc_response = await ollama_provider.generate(
|
||||
desc_prompt, model=model, temperature=temperature
|
||||
)
|
||||
logger.info(f"Description response: {desc_response[:500]}")
|
||||
|
||||
desc_data = extract_json_from_response(desc_response)
|
||||
descriptions_raw = desc_data.get("descriptions", [])
|
||||
|
||||
for desc in descriptions_raw:
|
||||
if isinstance(desc, dict) and all(k in desc for k in ["keyword", "expert_id", "expert_name", "description"]):
|
||||
descriptions.append(ExpertTransformationDescription(**desc))
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to generate descriptions: {e}")
|
||||
# Continue without descriptions - at least we have keywords
|
||||
|
||||
yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
# ========== Build final result ==========
|
||||
result = ExpertTransformationCategoryResult(
|
||||
category=request.category,
|
||||
original_attributes=request.attributes,
|
||||
expert_keywords=all_expert_keywords,
|
||||
descriptions=descriptions
|
||||
)
|
||||
|
||||
final_data = {
|
||||
"result": result.model_dump(),
|
||||
"experts": [e.model_dump() for e in experts]
|
||||
}
|
||||
yield f"event: done\ndata: {json.dumps(final_data, ensure_ascii=False)}\n\n"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Expert transformation error: {e}", exc_info=True)
|
||||
yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
|
||||
@router.post("/category")
|
||||
async def expert_transform_category(request: ExpertTransformationRequest):
|
||||
"""處理單一類別的專家視角轉換"""
|
||||
# Extract all categories from request (should be passed separately in production)
|
||||
# For now, use just the single category
|
||||
return StreamingResponse(
|
||||
generate_expert_transformation_events(request, [request.category]),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
116
backend/app/routers/transformation.py
Normal file
116
backend/app/routers/transformation.py
Normal file
@@ -0,0 +1,116 @@
|
||||
"""Transformation Agent 路由模組"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import AsyncGenerator, List
|
||||
|
||||
from fastapi import APIRouter
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
||||
from ..models.schemas import (
|
||||
TransformationRequest,
|
||||
TransformationCategoryResult,
|
||||
TransformationDescription,
|
||||
)
|
||||
from ..prompts.transformation_prompt import (
|
||||
get_keyword_generation_prompt,
|
||||
get_batch_description_prompt,
|
||||
)
|
||||
from ..services.llm_service import ollama_provider, extract_json_from_response
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/transformation", tags=["transformation"])
|
||||
|
||||
|
||||
async def generate_transformation_events(
|
||||
request: TransformationRequest
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Generate SSE events for transformation process"""
|
||||
try:
|
||||
temperature = request.temperature if request.temperature is not None else 0.7
|
||||
model = request.model
|
||||
|
||||
# ========== Step 1: Generate new keywords ==========
|
||||
yield f"event: keyword_start\ndata: {json.dumps({'message': f'為「{request.category}」生成新關鍵字...'}, ensure_ascii=False)}\n\n"
|
||||
|
||||
keyword_prompt = get_keyword_generation_prompt(
|
||||
category=request.category,
|
||||
attributes=request.attributes,
|
||||
keyword_count=request.keyword_count
|
||||
)
|
||||
logger.info(f"Keyword prompt: {keyword_prompt[:200]}")
|
||||
|
||||
keyword_response = await ollama_provider.generate(
|
||||
keyword_prompt, model=model, temperature=temperature
|
||||
)
|
||||
logger.info(f"Keyword response: {keyword_response[:500]}")
|
||||
|
||||
keyword_data = extract_json_from_response(keyword_response)
|
||||
new_keywords = keyword_data.get("keywords", [])
|
||||
|
||||
yield f"event: keyword_complete\ndata: {json.dumps({'keywords': new_keywords}, ensure_ascii=False)}\n\n"
|
||||
|
||||
if not new_keywords:
|
||||
yield f"event: error\ndata: {json.dumps({'error': '無法生成新關鍵字'}, ensure_ascii=False)}\n\n"
|
||||
return
|
||||
|
||||
# ========== Step 2: Generate descriptions for each keyword ==========
|
||||
yield f"event: description_start\ndata: {json.dumps({'message': '生成創新應用描述...'}, ensure_ascii=False)}\n\n"
|
||||
|
||||
# Use batch description prompt for efficiency
|
||||
desc_prompt = get_batch_description_prompt(
|
||||
query=request.query,
|
||||
category=request.category,
|
||||
keywords=new_keywords
|
||||
)
|
||||
logger.info(f"Description prompt: {desc_prompt[:300]}")
|
||||
|
||||
desc_response = await ollama_provider.generate(
|
||||
desc_prompt, model=model, temperature=temperature
|
||||
)
|
||||
logger.info(f"Description response: {desc_response[:500]}")
|
||||
|
||||
desc_data = extract_json_from_response(desc_response)
|
||||
descriptions_raw = desc_data.get("descriptions", [])
|
||||
|
||||
# Convert to TransformationDescription objects
|
||||
descriptions: List[TransformationDescription] = []
|
||||
for desc in descriptions_raw:
|
||||
if isinstance(desc, dict) and "keyword" in desc and "description" in desc:
|
||||
descriptions.append(TransformationDescription(
|
||||
keyword=desc["keyword"],
|
||||
description=desc["description"]
|
||||
))
|
||||
|
||||
yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
# ========== Build final result ==========
|
||||
result = TransformationCategoryResult(
|
||||
category=request.category,
|
||||
original_attributes=request.attributes,
|
||||
new_keywords=new_keywords,
|
||||
descriptions=descriptions
|
||||
)
|
||||
|
||||
final_data = {
|
||||
"result": result.model_dump()
|
||||
}
|
||||
yield f"event: done\ndata: {json.dumps(final_data, ensure_ascii=False)}\n\n"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Transformation error: {e}")
|
||||
yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||||
|
||||
|
||||
@router.post("/category")
|
||||
async def transform_category(request: TransformationRequest):
|
||||
"""處理單一類別的轉換"""
|
||||
return StreamingResponse(
|
||||
generate_transformation_events(request),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
Reference in New Issue
Block a user