feat: Add external expert sources (Wikidata SPARQL + ConceptNet API)
- Add expert_cache.py: TTL-based in-memory cache (1 hour default) - Add expert_source_service.py: WikidataProvider and ConceptNetProvider - Wikidata SPARQL queries for occupations with Chinese labels - ConceptNet API queries for occupation-related concepts - Random selection from cached pool - Update schemas.py: Add ExpertSource enum (llm/wikidata/conceptnet) - Update ExpertTransformationRequest with expert_source and expert_language - Update router: Conditionally use external sources with LLM fallback - New SSE events: expert_source, expert_fallback - Update frontend types with ExpertSource 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
@@ -206,6 +206,13 @@ class ExpertTransformationDAGResult(BaseModel):
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results: List[ExpertTransformationCategoryResult]
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class ExpertSource(str, Enum):
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"""專家來源類型"""
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LLM = "llm"
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WIKIDATA = "wikidata"
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CONCEPTNET = "conceptnet"
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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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@@ -217,6 +224,10 @@ class ExpertTransformationRequest(BaseModel):
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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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# Expert source parameters
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expert_source: ExpertSource = ExpertSource.LLM # 專家來源
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expert_language: str = "zh" # 外部來源的語言
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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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@@ -13,6 +13,7 @@ from ..models.schemas import (
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ExpertKeyword,
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ExpertTransformationCategoryResult,
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ExpertTransformationDescription,
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ExpertSource,
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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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@@ -20,6 +21,7 @@ from ..prompts.expert_transformation_prompt import (
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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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from ..services.expert_source_service import expert_source_service
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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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@@ -35,36 +37,98 @@ async def generate_expert_transformation_events(
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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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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"
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experts: List[ExpertProfile] = []
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actual_source = request.expert_source.value
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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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# 決定使用哪種來源生成專家
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use_llm = (
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request.expert_source == ExpertSource.LLM or
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request.custom_experts # 有自訂專家時，使用 LLM 補充
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)
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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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if use_llm:
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# LLM 生成專家
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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_data = extract_json_from_response(expert_response)
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experts_raw = expert_data.get("experts", [])
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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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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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expert_data = extract_json_from_response(expert_response)
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experts_raw = expert_data.get("experts", [])
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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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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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actual_source = "llm"
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except Exception as e:
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logger.error(f"Failed to generate experts via LLM: {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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else:
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# 外部來源生成專家
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try:
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experts_data, actual_source = await expert_source_service.get_experts(
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source=request.expert_source.value,
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count=request.expert_count,
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language=request.expert_language
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)
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for i, exp_data in enumerate(experts_data):
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experts.append(ExpertProfile(
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id=f"expert-{i}",
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name=exp_data["name"],
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domain=exp_data["domain"],
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perspective=f"從{exp_data['domain']}角度思考"
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))
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logger.info(f"Generated {len(experts)} experts from {actual_source}")
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except Exception as e:
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# 外部來源失敗，fallback 到 LLM
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logger.warning(f"External source failed: {e}, falling back to LLM")
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yield f"event: expert_fallback\ndata: {json.dumps({'original': request.expert_source.value, 'fallback': 'llm', 'reason': str(e)}, ensure_ascii=False)}\n\n"
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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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expert_response = await ollama_provider.generate(
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expert_prompt, model=model, temperature=temperature
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)
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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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actual_source = "llm"
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except Exception as llm_error:
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logger.error(f"LLM fallback also failed: {llm_error}")
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yield f"event: error\ndata: {json.dumps({'error': f'專家團隊生成失敗: {str(llm_error)}'}, ensure_ascii=False)}\n\n"
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return
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# 回報來源資訊
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yield f"event: expert_source\ndata: {json.dumps({'source': actual_source}, ensure_ascii=False)}\n\n"
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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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92
backend/app/services/expert_cache.py
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92
backend/app/services/expert_cache.py
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@@ -0,0 +1,92 @@
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"""Expert 資料快取模組
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提供 TTL-based 快取機制，減少外部 API 呼叫。
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"""
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import time
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from dataclasses import dataclass
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from typing import Dict, List, Optional
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@dataclass
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class CacheEntry:
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"""快取項目"""
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data: List[dict]
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timestamp: float
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class ExpertCache:
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"""TTL 快取，用於儲存外部來源的職業資料"""
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def __init__(self, ttl_seconds: int = 3600):
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"""
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初始化快取
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Args:
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ttl_seconds: 快取存活時間（預設 1 小時）
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"""
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self._cache: Dict[str, CacheEntry] = {}
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self._ttl = ttl_seconds
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def get(self, key: str) -> Optional[List[dict]]:
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"""
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取得快取資料
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Args:
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key: 快取鍵（如 "wikidata:zh:occupations"）
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Returns:
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快取的資料列表，若不存在或已過期則回傳 None
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"""
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entry = self._cache.get(key)
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if entry is None:
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return None
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# 檢查是否過期
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if time.time() - entry.timestamp > self._ttl:
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del self._cache[key]
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return None
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return entry.data
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def set(self, key: str, data: List[dict]) -> None:
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"""
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設定快取資料
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Args:
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key: 快取鍵
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data: 要快取的資料列表
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"""
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self._cache[key] = CacheEntry(
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data=data,
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timestamp=time.time()
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)
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def invalidate(self, key: Optional[str] = None) -> None:
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"""
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清除快取
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Args:
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key: 要清除的鍵，若為 None 則清除全部
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"""
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if key is None:
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self._cache.clear()
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elif key in self._cache:
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del self._cache[key]
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def get_stats(self) -> dict:
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"""取得快取統計資訊"""
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now = time.time()
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valid_count = sum(
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1 for entry in self._cache.values()
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if now - entry.timestamp <= self._ttl
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)
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return {
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"total_entries": len(self._cache),
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"valid_entries": valid_count,
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"ttl_seconds": self._ttl
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}
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# 全域快取實例
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expert_cache = ExpertCache()
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331
backend/app/services/expert_source_service.py
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331
backend/app/services/expert_source_service.py
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@@ -0,0 +1,331 @@
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"""Expert 外部資料來源服務
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提供從 Wikidata SPARQL 和 ConceptNet API 獲取職業/領域資料的功能。
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"""
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import logging
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import random
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from abc import ABC, abstractmethod
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from typing import List, Optional, Tuple
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import httpx
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from .expert_cache import expert_cache
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logger = logging.getLogger(__name__)
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class ExpertSourceProvider(ABC):
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"""外部來源提供者抽象類"""
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@abstractmethod
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async def fetch_occupations(
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self, count: int, language: str = "zh"
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) -> List[dict]:
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"""
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獲取職業列表
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Args:
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count: 需要的職業數量
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language: 語言代碼 (zh/en)
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Returns:
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職業資料列表 [{"name": "...", "domain": "..."}, ...]
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"""
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pass
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class WikidataProvider(ExpertSourceProvider):
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"""Wikidata SPARQL 查詢提供者"""
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ENDPOINT = "https://query.wikidata.org/sparql"
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def __init__(self):
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self.client = httpx.AsyncClient(timeout=30.0)
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async def fetch_occupations(
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self, count: int, language: str = "zh"
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) -> List[dict]:
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"""從 Wikidata 獲取職業列表"""
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cache_key = f"wikidata:{language}:occupations"
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# 檢查快取
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cached = expert_cache.get(cache_key)
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if cached:
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logger.info(f"Wikidata cache hit: {len(cached)} occupations")
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return self._random_select(cached, count)
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# SPARQL 查詢
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query = self._build_sparql_query(language)
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try:
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response = await self.client.get(
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self.ENDPOINT,
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params={"query": query, "format": "json"},
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headers={"Accept": "application/sparql-results+json"}
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)
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response.raise_for_status()
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data = response.json()
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occupations = self._parse_sparql_response(data, language)
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if occupations:
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expert_cache.set(cache_key, occupations)
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logger.info(f"Wikidata fetched: {len(occupations)} occupations")
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return self._random_select(occupations, count)
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except Exception as e:
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logger.error(f"Wikidata query failed: {e}")
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raise
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def _build_sparql_query(self, language: str) -> str:
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"""建構 SPARQL 查詢"""
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lang_filter = f'FILTER(LANG(?occupationLabel) = "{language}")'
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return f"""
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SELECT DISTINCT ?occupation ?occupationLabel ?fieldLabel WHERE {{
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?occupation wdt:P31 wd:Q28640.
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?occupation rdfs:label ?occupationLabel.
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{lang_filter}
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OPTIONAL {{
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?occupation wdt:P425 ?field.
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?field rdfs:label ?fieldLabel.
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FILTER(LANG(?fieldLabel) = "{language}")
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}}
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}}
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LIMIT 500
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"""
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def _parse_sparql_response(self, data: dict, language: str) -> List[dict]:
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"""解析 SPARQL 回應"""
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results = []
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bindings = data.get("results", {}).get("bindings", [])
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for item in bindings:
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name = item.get("occupationLabel", {}).get("value", "")
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field = item.get("fieldLabel", {}).get("value", "")
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if name and len(name) >= 2:
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results.append({
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"name": name,
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"domain": field if field else self._infer_domain(name)
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})
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return results
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def _infer_domain(self, occupation_name: str) -> str:
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"""根據職業名稱推斷領域"""
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# 簡單的領域推斷規則
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domain_keywords = {
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"醫": "醫療健康",
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"師": "專業服務",
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"工程": "工程技術",
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"設計": "設計創意",
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"藝術": "藝術文化",
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"運動": "體育運動",
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"農": "農業",
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"漁": "漁業",
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"商": "商業貿易",
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"法": "法律",
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"教": "教育",
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"研究": "學術研究",
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}
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for keyword, domain in domain_keywords.items():
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if keyword in occupation_name:
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return domain
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return "專業領域"
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def _random_select(self, items: List[dict], count: int) -> List[dict]:
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"""隨機選取指定數量"""
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if len(items) <= count:
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return items
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return random.sample(items, count)
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async def close(self):
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await self.client.aclose()
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class ConceptNetProvider(ExpertSourceProvider):
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"""ConceptNet API 查詢提供者"""
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ENDPOINT = "https://api.conceptnet.io"
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def __init__(self):
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self.client = httpx.AsyncClient(timeout=30.0)
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async def fetch_occupations(
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self, count: int, language: str = "zh"
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) -> List[dict]:
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"""從 ConceptNet 獲取職業相關概念"""
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cache_key = f"conceptnet:{language}:occupations"
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# 檢查快取
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cached = expert_cache.get(cache_key)
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if cached:
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logger.info(f"ConceptNet cache hit: {len(cached)} concepts")
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return self._random_select(cached, count)
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# 查詢職業相關概念
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lang_code = "zh" if language == "zh" else "en"
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start_concept = f"/c/{lang_code}/職業" if lang_code == "zh" else f"/c/{lang_code}/occupation"
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try:
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occupations = []
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# 查詢 IsA 關係
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response = await self.client.get(
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f"{self.ENDPOINT}/query",
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params={
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"start": start_concept,
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"rel": "/r/IsA",
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"limit": 100
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}
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)
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response.raise_for_status()
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data = response.json()
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occupations.extend(self._parse_conceptnet_response(data, lang_code))
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# 也查詢 RelatedTo 關係以獲取更多結果
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response2 = await self.client.get(
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f"{self.ENDPOINT}/query",
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params={
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"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()
|
||||
|
||||
|
||||
class ExpertSourceService:
|
||||
"""統一的專家來源服務"""
|
||||
|
||||
def __init__(self):
|
||||
self.wikidata = WikidataProvider()
|
||||
self.conceptnet = ConceptNetProvider()
|
||||
|
||||
async def get_experts(
|
||||
self,
|
||||
source: str,
|
||||
count: int,
|
||||
language: str = "zh",
|
||||
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")
|
||||
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）
|
||||
|
||||
Returns:
|
||||
(專家資料列表, 實際使用的來源)
|
||||
|
||||
Raises:
|
||||
Exception: 當獲取失敗且不 fallback 時
|
||||
"""
|
||||
provider = self._get_provider(source)
|
||||
|
||||
try:
|
||||
experts = await provider.fetch_occupations(count, language)
|
||||
|
||||
if not experts:
|
||||
raise ValueError(f"No occupations found from {source}")
|
||||
|
||||
return experts, source
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to fetch from {source}: {e}")
|
||||
raise
|
||||
|
||||
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}")
|
||||
|
||||
async def close(self):
|
||||
"""關閉所有 HTTP clients"""
|
||||
await self.wikidata.close()
|
||||
await self.conceptnet.close()
|
||||
|
||||
|
||||
# 全域服務實例
|
||||
expert_source_service = ExpertSourceService()
|
||||
@@ -230,6 +230,8 @@ export interface ExpertTransformationDAGResult {
|
||||
results: ExpertTransformationCategoryResult[];
|
||||
}
|
||||
|
||||
export type ExpertSource = 'llm' | 'wikidata' | 'conceptnet';
|
||||
|
||||
export interface ExpertTransformationRequest {
|
||||
query: string;
|
||||
category: string;
|
||||
@@ -237,6 +239,8 @@ export interface ExpertTransformationRequest {
|
||||
expert_count: number; // 2-8
|
||||
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')
|
||||
model?: string;
|
||||
temperature?: number;
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user