231 lines
7.5 KiB
Python
231 lines
7.5 KiB
Python
import re
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from typing import List, Tuple, Dict, Literal, Type
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from fastapi import HTTPException
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from tortoise import Tortoise, Model
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from tortoise.expressions import Q
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from app.api.search_dict.search_schemas import SearchRequest, ProverbSearchResponse, ProverbSearchRequest
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from app.models import WordlistFr, WordlistJp
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from app.models.fr import ProverbFr
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from app.utils.all_kana import all_in_kana
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from app.utils.textnorm import normalize_text
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from settings import TORTOISE_ORM
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def detect_language(text: str) -> Literal["fr", "zh", "jp", "other"]:
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"""
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自动检测输入语言:
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返回 'zh' / 'jp' / 'fr' / 'other'
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"""
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if re.search(r"[\u4e00-\u9fff]", text):
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return "zh"
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elif re.search(r"[\u3040-\u30ff\u31f0-\u31ff]", text): # 日文假名范围
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return "jp"
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elif re.search(r"[a-zA-ZÀ-ÿ]", text):
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return "fr"
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return "other"
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async def accurate_proverb(proverb_id: int) -> ProverbSearchResponse:
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"""对于查询法语谚语的精准查询，返回详细信息"""
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proverb = await ProverbFr.get_or_none(id=proverb_id)
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if not proverb:
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raise HTTPException(status_code=404, detail="Proverb not found")
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return ProverbSearchResponse(
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proverb_text=proverb.text,
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chi_exp=proverb.chi_exp,
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)
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async def suggest_proverb(
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query: str,
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lang: Literal["fr", "zh", "jp"],
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model: Type[Model],
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search_field: str = "search_text",
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target_field: str = "text",
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chi_exp_field: str = "chi_exp",
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limit: int = 10,
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) -> List[Dict[str, str]]:
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"""
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通用搜索建议函数，用于多语言谚语表。
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参数:
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query: 搜索关键词
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lang: 'fr' 或 'zh'
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model: Tortoise ORM 模型类，例如 ProverbFr
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proverb_field: 外语谚语字段名
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chi_exp_field: 中文释义字段名
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limit: 每类匹配的最大返回数量
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搜索逻辑:
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1. 根据语言选择搜索字段；
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2. 优先匹配以输入开头的结果；
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3. 其次匹配包含输入但非开头的结果；
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4. 合并去重后返回。
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"""
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keyword = query.strip()
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if not keyword:
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return []
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# ✅ 根据语言选择搜索字段
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if lang == "zh":
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startswith_field = f"{chi_exp_field}__istartswith"
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contains_field = f"{chi_exp_field}__icontains"
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else:
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startswith_field = f"{search_field}__istartswith"
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contains_field = f"{search_field}__icontains"
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# ✅ 1. 开头匹配
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start_matches = await (
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model.filter(**{startswith_field: keyword})
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.order_by("-freq")
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.limit(limit)
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.values("id", target_field, search_field, chi_exp_field)
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)
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# ✅ 2. 包含匹配（非开头）
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contain_matches = await (
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model.filter(
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Q(**{contains_field: keyword}) & ~Q(**{startswith_field: keyword})
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)
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.order_by("-freq")
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.limit(limit)
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.values("id", target_field, search_field, chi_exp_field)
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)
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# ✅ 3. 合并去重并保持顺序
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results: List[Dict[str, str]] = []
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seen_ids = set()
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for row in start_matches + contain_matches:
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if row["id"] not in seen_ids:
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seen_ids.add(row["id"])
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results.append({
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"id": row["id"],
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"proverb": row[target_field],
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"search_text": row[search_field],
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"chi_exp": row[chi_exp_field]
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})
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return results
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async def suggest_autocomplete(query: SearchRequest, limit: int = 10):
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"""
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:param query: 当前用户输入的内容
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:param limit: 返回列表限制长度
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:return: 联想的单词列表（非完整信息，单纯单词）
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"""
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if query.language == 'fr':
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query_word = normalize_text(query.query)
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exact = await (
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WordlistFr
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.get_or_none(search_text=query.query)
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.values("text", "freq")
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)
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if exact:
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exact_word = [(exact.get("text"), exact.get("freq"))]
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else:
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exact_word = []
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qs_prefix = (
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WordlistFr
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.filter(Q(search_text__startswith=query_word) | Q(text__startswith=query.query))
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.exclude(search_text=query.query)
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.only("text", "freq")
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)
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prefix_objs = await qs_prefix[:limit]
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prefix: List[Tuple[str, int]] = [(o.text, o.freq) for o in prefix_objs]
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need = max(0, limit - len(prefix))
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contains: List[Tuple[str, int]] = []
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if need > 0:
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qs_contain = (
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WordlistFr
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.filter(Q(search_text__icontains=query_word) | Q(text__icontains=query.query))
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.exclude(Q(search_text__startswith=query_word) | Q(text__startswith=query.query) | Q(text=query.query))
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.only("text", "freq")
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.only("text", "freq")
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)
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contains_objs = await qs_contain[: need * 2]
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contains = [(o.text, o.freq) for o in contains_objs]
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seen_text, out = set(), []
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for text, freq in list(exact_word) + list(prefix) + list(contains):
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key = text
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if key not in seen_text:
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seen_text.add(key)
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out.append((text, freq))
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if len(out) >= limit:
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break
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out = sorted(out, key=lambda w: (-w[2], len(w[0]), w[0]))
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return [text for text, _ in out]
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else:
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query_word = all_in_kana(query.query)
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exact = await (
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WordlistJp
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.get_or_none(
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text=query.query
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)
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.only("text", "hiragana", "freq")
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)
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if exact:
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exact_word = [(exact.text, exact.hiragana, exact.freq)]
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else:
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exact_word = []
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qs_prefix = (
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WordlistJp
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.filter(Q(hiragana__startswith=query_word) | Q(text__startswith=query.query))
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.exclude(text=query.query)
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.only("text", "hiragana", "freq")
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)
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prefix_objs = await qs_prefix[:limit]
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prefix: List[Tuple[str, str, int]] = [(o.text, o.hiragana, o.freq) for o in prefix_objs]
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need = max(0, limit - len(prefix))
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contains: List[Tuple[str, str, int]] = []
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if need > 0:
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qs_contain = await (
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WordlistJp
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.filter(Q(hiragana__icontains=query_word) | Q(text__icontains=query.query))
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.exclude(Q(hiragana__startswith=query_word) | Q(text__startswith=query.query) | Q(text=query.query))
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.only("text", "hiragana", "freq")
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)
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contains_objs = qs_contain[:need * 2]
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contains: List[Tuple[str, str, int]] = [(o.text, o.hiragana, o.freq) for o in contains_objs]
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seen_text, out = set(), []
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for text, hiragana, freq in list(exact_word) + list(prefix) + list(contains):
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key = (text, hiragana)
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if key not in seen_text:
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seen_text.add(key)
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out.append((text, hiragana, freq))
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if len(out) >= limit:
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break
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out = sorted(out, key=lambda w: (-w[2], len(w[0]), w[0]))
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return [(text, hiragana) for text, hiragana, _ in out]
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async def __test():
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query_word: str = '棋逢'
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return await (
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suggest_proverb(
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query=ProverbSearchRequest(query=query_word),
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lang='zh'
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)
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)
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async def __main():
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await Tortoise.init(config=TORTOISE_ORM)
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print(await __test())
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if __name__ == '__main__':
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# asyncio.run(__main())
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print(detect_language(text="ahsjdasd"))
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