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>
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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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"""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,
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"rel": "/r/RelatedTo",
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"limit": 100
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}
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)
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response2.raise_for_status()
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data2 = response2.json()
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occupations.extend(self._parse_conceptnet_response(data2, lang_code))
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# 去重
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seen = set()
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unique_occupations = []
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for occ in occupations:
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if occ["name"] not in seen:
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seen.add(occ["name"])
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unique_occupations.append(occ)
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if unique_occupations:
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expert_cache.set(cache_key, unique_occupations)
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logger.info(f"ConceptNet fetched: {len(unique_occupations)} concepts")
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return self._random_select(unique_occupations, count)
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except Exception as e:
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logger.error(f"ConceptNet query failed: {e}")
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raise
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def _parse_conceptnet_response(self, data: dict, lang_code: str) -> List[dict]:
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"""解析 ConceptNet 回應"""
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results = []
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edges = data.get("edges", [])
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for edge in edges:
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# 取得 start 或 end 節點（取決於查詢方向）
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start = edge.get("start", {})
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end = edge.get("end", {})
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# 選擇非起始節點的概念
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node = end if start.get("@id", "").endswith("職業") or start.get("@id", "").endswith("occupation") else start
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label = node.get("label", "")
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term = node.get("term", "")
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# 過濾：確保是目標語言且有意義
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node_id = node.get("@id", "")
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if f"/c/{lang_code}/" in node_id and label and len(label) >= 2:
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results.append({
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"name": label,
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"domain": self._infer_domain_from_edge(edge)
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})
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return results
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def _infer_domain_from_edge(self, edge: dict) -> str:
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"""從 edge 資訊推斷領域"""
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# ConceptNet 的 edge 包含 surfaceText 可能有額外資訊
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surface = edge.get("surfaceText", "")
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rel = edge.get("rel", {}).get("label", "")
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if "專業" in surface:
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return "專業領域"
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elif "技術" in surface:
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return "技術領域"
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else:
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return "知識領域"
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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 ExpertSourceService:
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"""統一的專家來源服務"""
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def __init__(self):
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self.wikidata = WikidataProvider()
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self.conceptnet = ConceptNetProvider()
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async def get_experts(
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self,
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source: str,
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count: int,
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language: str = "zh",
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fallback_to_llm: bool = True
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) -> Tuple[List[dict], str]:
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"""
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從指定來源獲取專家資料
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Args:
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source: 來源類型 ("wikidata" | "conceptnet")
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count: 需要的專家數量
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language: 語言代碼
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fallback_to_llm: 失敗時是否允許 fallback（由呼叫者處理）
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Returns:
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(專家資料列表, 實際使用的來源)
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Raises:
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Exception: 當獲取失敗且不 fallback 時
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"""
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provider = self._get_provider(source)
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try:
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experts = await provider.fetch_occupations(count, language)
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if not experts:
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raise ValueError(f"No occupations found from {source}")
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return experts, source
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except Exception as e:
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logger.warning(f"Failed to fetch from {source}: {e}")
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raise
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def _get_provider(self, source: str) -> ExpertSourceProvider:
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"""根據來源類型取得對應的 provider"""
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if source == "wikidata":
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return self.wikidata
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elif source == "conceptnet":
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return self.conceptnet
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else:
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raise ValueError(f"Unknown source: {source}")
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async def close(self):
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"""關閉所有 HTTP clients"""
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await self.wikidata.close()
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await self.conceptnet.close()
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# 全域服務實例
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expert_source_service = ExpertSourceService()
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