feat: 集成MCP统一外部接口管理系统
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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m38s
- 添加MCP Manager统一入口管理 - 实现Contact/Dictionary/News三个适配器 - 三层降级策略：MCP -> Database -> Mock - 保持原有api_client向后兼容 - 添加完整文档和测试
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@@ -1,192 +1,82 @@
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"""
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词典API调用工具
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Dictionary API Client
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支持 async 和真实数据库缓存
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词典API调用工具（使用MCP统一接口）
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"""
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from typing import Dict, Any, Optional
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from dataclasses import dataclass
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from ...mcp.mcp_manager import mcp_manager
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from ...mcp.adapters import DictionaryAdapter
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@dataclass
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class DictionaryAPIClient:
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"""
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词典API客户端 - 可扩展支持多种API和数据库缓存
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词典API客户端 - 使用MCP统一接口
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保持向后兼容，内部使用MCP适配器
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"""
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# 可以配置多个API
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# 保留配置字段用于向后兼容
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youdao_api_key: Optional[str] = None
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youdao_api_secret: Optional[str] = None
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# 数据库 Repository（可选，用于缓存单词查询）
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word_repository: Optional[Any] = None
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def __post_init__(self):
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"""初始化后，如果有 repository 则支持 async"""
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pass
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async def query_word_db(self, user_id: str, word: str) -> Optional[Dict[str, Any]]:
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"""从数据库缓存查询单词"""
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if not self.word_repository:
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return None
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"""初始化后设置MCP"""
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import asyncio
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try:
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entity = await self.word_repository.search_by_word(user_id, word)
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if entity:
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return {
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"phonetic": entity.phonetic,
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"part_of_speech": entity.part_of_speech,
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"definitions": [entity.definition] if entity.definition else [],
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"examples": [entity.examples] if entity.examples else []
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}
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except Exception as e:
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print(f"从数据库查询单词失败：{e}")
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return None
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asyncio.create_task(self._init_mcp())
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except RuntimeError:
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pass
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async def cache_word_db(self, user_id: str, word: str, data: Dict[str, Any]):
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"""把单词查询结果缓存到数据库"""
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if not self.word_repository:
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return
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try:
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from ...db.models import WordEntity
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entity = WordEntity(
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user_id=user_id,
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word=word,
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phonetic=data.get("phonetic", ""),
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part_of_speech=data.get("part_of_speech", ""),
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definition=data.get("definitions", [""])[0] if data.get("definitions") else "",
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examples=data.get("examples", [""])[0] if data.get("examples") else ""
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async def _init_mcp(self):
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"""初始化MCP系统"""
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if not mcp_manager.get_adapter("dictionary"):
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mcp_manager.register_adapter(
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DictionaryAdapter(word_repo=self.word_repository)
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)
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await self.word_repository.insert(entity)
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except Exception as e:
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print(f"缓存单词到数据库失败：{e}")
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await mcp_manager.initialize()
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async def query_word_youdao(self, word: str) -> Optional[Dict[str, Any]]:
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"""
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调用有道词典API查询单词（async 版本）
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注意：需要配置有道API密钥才能使用
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文档：https://ai.youdao.com/doc.s#guide
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"""
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if not self.youdao_api_key or not self.youdao_api_secret:
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return None
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try:
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# TODO: 实现真实的有道API调用（用 httpx 或 aiohttp）
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# 这里是示例结构
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return None
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except Exception as e:
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print(f"有道API调用失败：{e}")
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return None
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async def translate_baidu(self, text: str, from_lang: str = "auto", to_lang: str = "zh") -> Optional[Dict[str, Any]]:
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"""
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调用百度翻译API（async 版本）
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注意：需要配置百度API密钥才能使用
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文档：https://fanyi-api.baidu.com/doc/21
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"""
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# TODO: 实现真实的百度翻译API调用（用 httpx 或 aiohttp）
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return None
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async def query_word(
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self,
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user_id: str = "default",
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word: str = "",
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use_cache: bool = True
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) -> Dict[str, Any]:
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"""查询单词（统一入口）"""
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await self._init_mcp()
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result = await mcp_manager.execute(
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"dictionary", "query_word",
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user_id=user_id, word=word, use_cache=use_cache
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)
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if result.success:
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return result.data
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return self.query_word_mock(word)
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def query_word_mock(self, word: str) -> Dict[str, Any]:
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"""
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模拟词典API - 目前用于演示
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"""
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mock_db = {
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"serendipity": {
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"phonetic": "/ˌserənˈdipədē/",
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"part_of_speech": "n.",
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"definitions": ["意外发现珍奇事物的能力", "机缘凑巧"],
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"examples": ["Finding that old photo was pure serendipity."]
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},
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"ephemeral": {
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"phonetic": "/əˈfem(ə)rəl/",
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"part_of_speech": "adj.",
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"definitions": ["短暂的，瞬息的"],
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"examples": ["Fame in the digital age is often ephemeral."]
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},
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"ubiquitous": {
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"phonetic": "/yo͞oˈbikwədəs/",
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"part_of_speech": "adj.",
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"definitions": ["无处不在的", "普遍存在的"],
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"examples": ["Smartphones have become ubiquitous in modern life."]
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},
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"eloquent": {
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"phonetic": "/ˈeləkwənt/",
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"part_of_speech": "adj.",
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"definitions": ["雄辩的，有说服力的"],
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"examples": ["She gave an eloquent speech at the conference."]
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},
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"resilient": {
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"phonetic": "/rəˈzilyənt/",
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"part_of_speech": "adj.",
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"definitions": ["有复原力的，能适应的"],
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"examples": ["The community has proven to be resilient in the face of challenges."]
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}
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"""模拟查询（保留用于向后兼容）"""
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return {
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"word": word,
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"phonetic": "",
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"part_of_speech": "n.",
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"definitions": [f"{word} 的释义1", f"{word} 的释义2"],
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"examples": [f"This is an example sentence with '{word}'."]
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}
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if word.lower() in mock_db:
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return mock_db[word.lower()]
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else:
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return {
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"phonetic": "",
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"part_of_speech": "n.",
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"definitions": [f"{word}的释义1", f"{word}的释义2"],
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"examples": [f"This is an example sentence with '{word}'."]
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}
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def translate_mock(self, text: str, from_lang: str = "auto", to_lang: str = "zh") -> Dict[str, Any]:
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"""
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模拟翻译API - 目前用于演示
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"""
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translations = {
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"你好": "Hello",
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"hello": "你好",
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"人工智能": "Artificial Intelligence",
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"artificial intelligence": "人工智能",
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"ai": "人工智能",
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"大模型": "Large Language Model",
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"自然语言处理": "Natural Language Processing"
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}
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"""模拟翻译（保留用于向后兼容）"""
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return {
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"translated_text": translations.get(text.lower(), f"【翻译结果】{text}"),
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"translated_text": f"【翻译】{text}",
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"confidence": 0.95
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}
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def extract_terms_mock(self, text: str) -> list:
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"""
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模拟术语提取API
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"""
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"""模拟术语提取（保留用于向后兼容）"""
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return [
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{"term": "AI", "type": "技术术语", "definition": "人工智能", "confidence": 0.95},
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{"term": "LLM", "type": "技术术语", "definition": "大语言模型", "confidence": 0.92},
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{"term": "NLP", "type": "技术术语", "definition": "自然语言处理", "confidence": 0.88}
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{"term": "大模型", "type": "技术术语", "definition": "大语言模型", "confidence": 0.92}
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]
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# ========== 统一入口（优先查缓存） ==========
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async def query_word(self, user_id: str = "default", word: str = "", use_cache: bool = True) -> Dict[str, Any]:
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"""
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查询单词（统一入口，优先查数据库缓存）
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"""
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# 1. 先查数据库缓存
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if use_cache:
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cached = await self.query_word_db(user_id, word)
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if cached:
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return cached
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# 2. 查第三方 API（暂未实现）
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api_result = await self.query_word_youdao(word)
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if api_result:
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if use_cache:
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await self.cache_word_db(user_id, word, api_result)
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return api_result
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# 3. 用模拟数据（兜底）
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mock_result = self.query_word_mock(word)
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if use_cache:
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await self.cache_word_db(user_id, word, mock_result)
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return mock_result
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# 单例实例（模拟模式，保持向后兼容）
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# 全局单例（保持向后兼容）
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dictionary_api = DictionaryAPIClient()
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