mirror of https://github.com/Mai-with-u/MaiBot.git
remove：无用代码
SengokuCola
parent
71a2a4282b
commit
837ecf702b
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{
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"manifest_version": 1,
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"name": "Deep Think插件 (Deep Think Actions)",
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"version": "1.0.0",
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"description": "可以深度思考",
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"author": {
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"name": "SengokuCola",
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"url": "https://github.com/MaiM-with-u"
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},
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"license": "GPL-v3.0-or-later",
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"host_application": {
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"min_version": "0.11.0"
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},
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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"keywords": ["deep", "think", "action", "built-in"],
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"categories": ["Deep Think"],
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"default_locale": "zh-CN",
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"locales_path": "_locales",
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"plugin_info": {
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"is_built_in": true,
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"plugin_type": "action_provider",
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"components": [
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{
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"type": "action",
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"name": "deep_think",
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"description": "发送深度思考"
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}
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]
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}
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}
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from typing import List, Tuple, Type, Any
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# 导入新插件系统
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from src.plugin_system import BasePlugin, register_plugin, ComponentInfo
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from src.plugin_system.base.config_types import ConfigField
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from src.person_info.person_info import Person
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from src.plugin_system.base.base_tool import BaseTool, ToolParamType
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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# from src.plugins.built_in.relation.relation import BuildRelationAction
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from src.plugin_system.apis import llm_api
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logger = get_logger("relation_actions")
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class DeepThinkTool(BaseTool):
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"""获取用户信息"""
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name = "deep_think"
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description = "深度思考，对某个知识，概念或逻辑问题进行全面且深入的思考，当面临复杂环境或重要问题时，使用此获得更好的解决方案。"
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parameters = [
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("question", ToolParamType.STRING, "需要思考的问题，越具体越好（从上下文中总结）", True, None),
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行比较两个数的大小
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Args:
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function_args: 工具参数
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Returns:
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dict: 工具执行结果
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"""
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question: str = function_args.get("question") # type: ignore
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print(f"question: {question}")
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prompt = f"""
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请你思考以下问题，以简洁的一段话回答：
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{question}
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"""
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models = llm_api.get_available_models()
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chat_model_config = models.get("replyer") # 使用字典访问方式
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success, thinking_result, _, _ = await llm_api.generate_with_model(
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prompt, model_config=chat_model_config, request_type="deep_think"
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)
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logger.info(f"{question}: {thinking_result}")
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thinking_result =f"思考结果：{thinking_result}\n**注意** 因为你进行了深度思考，最后的回复内容可以回复的长一些，更加详细一些，不用太简洁。\n"
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return {"content": thinking_result}
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@register_plugin
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class DeepThinkPlugin(BasePlugin):
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"""关系动作插件
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系统内置插件，提供基础的聊天交互功能：
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- Reply: 回复动作
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- NoReply: 不回复动作
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- Emoji: 表情动作
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注意：插件基本信息优先从_manifest.json文件中读取
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"""
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# 插件基本信息
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plugin_name: str = "deep_think" # 内部标识符
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enable_plugin: bool = True
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dependencies: list[str] = [] # 插件依赖列表
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python_dependencies: list[str] = [] # Python包依赖列表
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config_file_name: str = "config.toml"
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# 配置节描述
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config_section_descriptions = {
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"plugin": "插件启用配置",
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"components": "核心组件启用配置",
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}
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# 配置Schema定义
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config_schema: dict = {
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"plugin": {
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"enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
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"config_version": ConfigField(type=str, default="2.0.0", description="配置文件版本"),
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}
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}
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def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
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"""返回插件包含的组件列表"""
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# --- 根据配置注册组件 ---
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components = []
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components.append((DeepThinkTool.get_tool_info(), DeepThinkTool))
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return components
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{
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"manifest_version": 1,
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"name": "Jargon插件",
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"version": "1.0.0",
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"description": "记录和管理jargon（黑话/俚语）的解释",
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"author": {
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"name": "Mai",
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"url": "https://github.com/MaiM-with-u"
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},
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"license": "GPL-v3.0-or-later",
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"host_application": {
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"min_version": "0.10.4"
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},
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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"keywords": ["jargon", "slang", "built-in"],
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"categories": ["Jargon"],
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"default_locale": "zh-CN",
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"locales_path": "_locales",
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"plugin_info": {
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"is_built_in": true,
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"plugin_type": "tool_provider",
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"components": [
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{
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"type": "record_jargon_explanation",
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"name": "record_jargon_explanation",
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"description": "记录聊天中明确解释的jargon词义"
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}
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]
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}
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}
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from typing import Any, Dict, List, Tuple
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from src.common.logger import get_logger
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from src.common.database.database_model import Jargon
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from src.plugin_system import BaseTool, ToolParamType
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logger = get_logger("jargon_explanation")
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class RecordJargonExplanationTool(BaseTool):
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"""记录jargon解释工具
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检测聊天记录中是否有对某个词义的明确解释，如果有则记录到jargon表中
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"""
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name: str = "record_explanation"
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description: str = (
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"当检测到有人明确解释了某个缩写，拼音缩写，中文缩写，英文缩写的含义时（例如：'xxx是yyy的意思'、'xxx指的是yyy'等)"
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"当某人明确纠正了对某个词汇的错误解释时（例如：'xxx不是yyy的意思'、'xxx不是指的是yyy'等)"
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)
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parameters: List[Tuple[str, ToolParamType, str, bool, None]] = [
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("content", ToolParamType.STRING, "被解释的目标词汇（黑话/俚语/缩写），例如：yyds、内卷、社死等", True, None),
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("translation", ToolParamType.STRING, "词汇的翻译或简称，例如：永远的神、社会性死亡等", True, None),
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("meaning", ToolParamType.STRING, "词汇的详细含义说明", True, None),
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]
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available_for_llm: bool = True
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async def execute(self, function_args: Dict[str, Any]) -> Dict[str, str]:
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"""执行jargon解释检测和记录
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Args:
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function_args: 工具参数，包含content、translation、meaning
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Returns:
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dict: 工具执行结果
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"""
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if not self.chat_id:
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return {"name": self.name, "content": "无法记录jargon解释：缺少chat_id"}
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try:
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# 从参数中获取信息
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content = str(function_args.get("content", "")).strip()
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translation = str(function_args.get("translation", "")).strip()
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meaning = str(function_args.get("meaning", "")).strip()
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if not content:
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return {"name": self.name, "content": "目标词汇不能为空"}
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if not translation and not meaning:
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return {"name": self.name, "content": "翻译和含义至少需要提供一个"}
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# 检查是否已存在相同的jargon
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query = Jargon.select().where(
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(Jargon.chat_id == self.chat_id) &
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(Jargon.content == content)
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)
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if query.exists():
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# 已存在，更新translation和meaning（追加，用/分隔）
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obj = query.get()
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existing_translation = obj.translation or ""
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existing_meaning = obj.meaning or ""
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# 追加新内容
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if translation:
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if existing_translation:
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obj.translation = f"{existing_translation}/{translation}"
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else:
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obj.translation = translation
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if meaning:
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if existing_meaning:
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obj.meaning = f"{existing_meaning}/{meaning}"
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else:
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obj.meaning = meaning
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# 确保is_jargon为True
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obj.is_jargon = True
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obj.save()
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logger.info(f"更新jargon解释: {content}, translation={obj.translation}, meaning={obj.meaning}")
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# 优先使用meaning，如果没有则使用translation
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explanation = obj.meaning or obj.translation or ""
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return {"name": self.name, "content": f"你了解到 {content}的含义应该是 {explanation}"}
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else:
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# 新建记录
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Jargon.create(
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content=content,
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chat_id=self.chat_id,
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translation=translation,
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meaning=meaning,
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is_jargon=True,
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is_global=False,
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count=0,
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)
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logger.info(f"记录新jargon解释: {content}, translation={translation}, meaning={meaning}")
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# 优先使用meaning，如果没有则使用translation
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explanation = meaning or translation or ""
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return {"name": self.name, "content": f"你了解到 {content}的含义应该是 {explanation}"}
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except Exception as exc:
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logger.error(f"记录jargon解释失败: {exc}", exc_info=True)
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return {"name": self.name, "content": f"记录jargon解释失败: {exc}"}
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class LookupJargonMeaningTool(BaseTool):
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"""查询jargon含义工具
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输入一个可能意义不明的词或缩写，查询数据库中是否已有匹配且带有含义或翻译的记录。
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命中则返回解释字符串（优先meaning，其次translation），未命中返回空字符串。
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"""
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name: str = "lookup_jargon_meaning"
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description: str = (
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"查询是否存在已知的jargon解释（含meaning或translation），若存在返回解释，否则返回空字符串"
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)
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parameters: List[Tuple[str, ToolParamType, str, bool, None]] = [
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("content", ToolParamType.STRING, "待查询的目标词汇（黑话/俚语/缩写）", True, None),
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]
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available_for_llm: bool = True
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async def execute(self, function_args: Dict[str, Any]) -> Dict[str, str]:
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if not self.chat_id:
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# 和其它工具保持一致的返回结构
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return {"name": self.name, "content": ""}
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try:
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content = str(function_args.get("content", "")).strip()
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if not content:
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return {"name": self.name, "content": ""}
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# 优先在当前会话或global中查找该content，且需要meaning或translation非空
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# Peewee 条件：
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# (content == 输入) AND ((chat_id == 当前chat) OR is_global) AND ((meaning非空) OR (translation非空))
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candidates = (
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Jargon.select()
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.where(
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(Jargon.content == content)
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& ((Jargon.chat_id == self.chat_id) | Jargon.is_global)
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& (
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((Jargon.meaning.is_null(False)) & (Jargon.meaning != ""))
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| ((Jargon.translation.is_null(False)) & (Jargon.translation != ""))
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)
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)
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.limit(1)
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)
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if candidates.exists():
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obj = candidates.get()
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translation = (obj.translation or "").strip()
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meaning = (obj.meaning or "").strip()
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formatted = f"“{content}可能为黑话或者网络简写，翻译为：{translation}，含义为：{meaning}”"
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return {"name": self.name, "content": formatted}
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# 未命中：允许退化为全库搜索（不限chat_id），以提升命中率
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fallback = (
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Jargon.select()
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.where(
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(Jargon.content == content)
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& (
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((Jargon.meaning.is_null(False)) & (Jargon.meaning != ""))
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| ((Jargon.translation.is_null(False)) & (Jargon.translation != ""))
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)
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)
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.limit(1)
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)
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if fallback.exists():
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obj = fallback.get()
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translation = (obj.translation or "").strip()
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meaning = (obj.meaning or "").strip()
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formatted = f"“{content}可能为黑话或者网络简写，翻译为：{translation}，含义为：{meaning}”"
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return {"name": self.name, "content": formatted}
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# 彻底未命中
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return {"name": self.name, "content": ""}
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except Exception as exc:
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logger.error(f"查询jargon解释失败: {exc}", exc_info=True)
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return {"name": self.name, "content": ""}
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@ -1,56 +0,0 @@
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from typing import List, Tuple, Type
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# 导入新插件系统
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from src.plugin_system import BasePlugin, ComponentInfo, register_plugin
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from src.plugin_system.base.config_types import ConfigField
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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from src.plugins.built_in.jargon.jargon_explanation import RecordJargonExplanationTool, LookupJargonMeaningTool
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logger = get_logger("jargon_plugin")
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@register_plugin
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class JargonPlugin(BasePlugin):
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"""Jargon插件
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系统内置插件，提供jargon相关的功能：
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- RecordJargonExplanation: 记录聊天中明确解释的jargon词义
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- LookupJargonMeaning: 查询未知词是否已有解释
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注意：插件基本信息优先从_manifest.json文件中读取
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"""
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# 插件基本信息
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plugin_name: str = "jargon" # 内部标识符
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enable_plugin: bool = True
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dependencies: list[str] = [] # 插件依赖列表
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python_dependencies: list[str] = [] # Python包依赖列表
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config_file_name: str = "config.toml"
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# 配置节描述
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config_section_descriptions = {
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"plugin": "插件启用配置",
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"components": "核心组件启用配置",
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}
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# 配置Schema定义
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config_schema: dict = {
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"plugin": {
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"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
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"config_version": ConfigField(type=str, default="1.0.0", description="配置文件版本"),
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},
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}
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def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
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"""返回插件包含的组件列表"""
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# --- 根据配置注册组件 ---
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components = []
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components.append((RecordJargonExplanationTool.get_tool_info(), RecordJargonExplanationTool))
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components.append((LookupJargonMeaningTool.get_tool_info(), LookupJargonMeaningTool))
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return components
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Reference in New Issue