From bf9d136112d7a61fe2e8dd73c639448709ab81d5 Mon Sep 17 00:00:00 2001 From: Miyamizu-MitsuhaSang <2510681107@qq.com> Date: Fri, 7 Nov 2025 17:35:30 +0800 Subject: [PATCH] =?UTF-8?q?=E5=89=8D=E7=AB=AF=E9=80=82=E9=85=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/search_dict/service.py | 94 ++++++++++++++++++++++------------ 1 file changed, 60 insertions(+), 34 deletions(-) diff --git a/app/api/search_dict/service.py b/app/api/search_dict/service.py index 994b3bc..9c21e80 100644 --- a/app/api/search_dict/service.py +++ b/app/api/search_dict/service.py @@ -140,19 +140,18 @@ async def suggest_autocomplete( text_field: str = "text", hira_field: str = "hiragana", freq_field: str = "freq", - english_field: str = "eng_explanation", limit: int = 10, ) -> List[Dict[str, str]]: """ - 通用自动补全建议接口（增强版）： - - 法语: 按 search_text / text 搜索，同时反查 DefinitionFr 的英文释义 - - 日语: 先按原文 text 匹配，再按假名 search_text 匹配 + 通用自动补全建议接口： + - 法语: 按 search_text / text 搜索 + 反查 DefinitionFr 英/中释义 + - 日语: 先按原文 text 匹配，再按假名匹配 + 反查 DefinitionJp 中文释义 统一返回结构： [ { "word": "étudier", "hiragana": None, - "meanings": [], + "meanings": ["学习", "研究"], "english": ["to study", "to learn"] } ] @@ -161,19 +160,25 @@ async def suggest_autocomplete( if not keyword: return [] - # ========== 法语 ========== + # ========== 法语分支 ========== if dict_lang == "fr": - start_condition = Q(**{f"{search_field}__istartswith": keyword}) | Q(**{f"{text_field}__istartswith": keyword}) - contain_condition = Q(**{f"{search_field}__icontains": keyword}) | Q(**{f"{text_field}__icontains": keyword}) + start_condition = ( + Q(**{f"{search_field}__istartswith": keyword}) + | Q(**{f"{text_field}__istartswith": keyword}) + ) + contain_condition = ( + Q(**{f"{search_field}__icontains": keyword}) + | Q(**{f"{text_field}__icontains": keyword}) + ) value_fields = ["id", text_field, freq_field, search_field] - # ========== 日语 ========== + # ========== 日语分支 ========== elif dict_lang == "jp": kana_word = all_in_kana(keyword) - start_condition = Q(**{f"{text_field}__istartswith": keyword}) + start_condition = Q(**{f"{text_field}__startswith": keyword}) contain_condition = Q(**{f"{text_field}__icontains": keyword}) - kana_start = Q(**{f"{hira_field}__istartswith": kana_word}) + kana_start = Q(**{f"{hira_field}__startswith": kana_word}) kana_contain = Q(**{f"{hira_field}__icontains": kana_word}) start_condition |= kana_start @@ -183,7 +188,7 @@ async def suggest_autocomplete( else: return [] - # ✅ 1. 开头匹配 + # ✅ 获取匹配单词 start_matches = await ( model.filter(start_condition) .order_by(f"-{freq_field}", "id") @@ -191,7 +196,6 @@ async def suggest_autocomplete( .values(*value_fields) ) - # ✅ 2. 包含匹配 contain_matches = await ( model.filter(contain_condition & ~start_condition) .order_by(f"-{freq_field}", "id") @@ -199,33 +203,55 @@ async def suggest_autocomplete( .values(*value_fields) ) - # ✅ 3. 合并去重 results = [] seen_ids = set() for row in start_matches + contain_matches: - if row["id"] in seen_ids: - continue - seen_ids.add(row["id"]) + if row["id"] not in seen_ids: + seen_ids.add(row["id"]) + results.append({ + "id": row["id"], + "word": row[text_field], + "hiragana": row.get(hira_field) if dict_lang == "jp" else None, + "meanings": [], + "english": [], + }) - result = { - "word": row[text_field], - "hiragana": row.get(hira_field) if dict_lang == "jp" else None, - "meanings": [], - "english": [], - } + # ✅ 批量反查 Definition 表，防止 N+1 查询 + if dict_lang == "fr": + from app.models import DefinitionFr # 避免循环导入 + word_ids = [r["id"] for r in results] + defs = await DefinitionFr.filter(word_id__in=word_ids).values("word_id", "meaning", "eng_explanation") - # ✅ 若为法语，则反查 DefinitionFr 的英文释义 - if dict_lang == "fr": - # 获取关联的 definitions - word_obj = await model.get(id=row["id"]).prefetch_related("definitions") - english_list = [ - d.eng_explanation.strip() - for d in word_obj.definitions - if d.eng_explanation and d.eng_explanation.strip() - ] - result["english"] = list(set(english_list)) + meaning_map: Dict[int, Dict[str, List[str]]] = {} + for d in defs: + meaning_map.setdefault(d["word_id"], {"meanings": [], "english": []}) + if d["meaning"]: + meaning_map[d["word_id"]]["meanings"].append(d["meaning"].strip()) + if d["eng_explanation"]: + meaning_map[d["word_id"]]["english"].append(d["eng_explanation"].strip()) - results.append(result) + for r in results: + if r["id"] in meaning_map: + r["meanings"] = list(set(meaning_map[r["id"]]["meanings"])) + r["english"] = list(set(meaning_map[r["id"]]["english"])) + + elif dict_lang == "jp": + from app.models import DefinitionJp + word_ids = [r["id"] for r in results] + defs = await DefinitionJp.filter(word_id__in=word_ids).values("word_id", "meaning") + + meaning_map: Dict[int, List[str]] = {} + for d in defs: + if d["meaning"]: + meaning_map.setdefault(d["word_id"], []).append(d["meaning"].strip()) + + for r in results: + if r["id"] in meaning_map: + r["meanings"] = list(set(meaning_map[r["id"]])) + + # ✅ 删除 id，只保留用户需要字段 + for r in results: + r.pop("id", None) return results[:limit]