mirror of https://github.com/Mai-with-u/MaiBot.git
fix：表情包管理插件可工作
SengokuCola
parent
174cbbf2a4
commit
0964676bfb
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@ -18,18 +18,84 @@ from src.plugin_system import (
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)
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from maim_message import Seg
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from src.config.config import global_config
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from src.common.logger import get_logger
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logger = get_logger("emoji_manage_plugin")
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class AddEmojiCommand(BaseCommand):
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command_name = "add_emoji"
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command_description = "添加表情包"
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command_pattern = r".*/emoji add.*"
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async def execute(self) -> Tuple[bool, str, bool]:
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# 查找消息中的表情包
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logger.info(f"查找消息中的表情包: {self.message.message_segment}")
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emoji_base64_list = self.find_and_return_emoji_in_message(self.message.message_segment)
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return True, f"找到了{len(emoji_base64_list)}个表情包", True
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def find_and_return_emoji_in_message(self, message_segments: List[Seg]) -> List[str]:
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if not emoji_base64_list:
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return False, "未在消息中找到表情包或图片", False
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# 注册找到的表情包
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success_count = 0
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fail_count = 0
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results = []
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for i, emoji_base64 in enumerate(emoji_base64_list):
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try:
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# 使用emoji_api注册表情包
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result = await emoji_api.register_emoji(emoji_base64, filename=f"emoji_{i+1}")
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if result["success"]:
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success_count += 1
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description = result.get("description", "未知描述")
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emotions = result.get("emotions", [])
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replaced = result.get("replaced", False)
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result_msg = f"表情包 {i+1} 注册成功{'(替换旧表情包)' if replaced else '(新增表情包)'}"
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if description:
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result_msg += f"\n描述: {description}"
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if emotions:
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result_msg += f"\n情感标签: {', '.join(emotions)}"
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results.append(result_msg)
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else:
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fail_count += 1
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error_msg = result.get("message", "注册失败")
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results.append(f"表情包 {i+1} 注册失败: {error_msg}")
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except Exception as e:
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fail_count += 1
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results.append(f"表情包 {i+1} 注册时发生错误: {str(e)}")
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# 构建返回消息
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total_count = success_count + fail_count
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summary_msg = f"表情包注册完成: 成功 {success_count} 个，失败 {fail_count} 个，共处理 {total_count} 个"
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# 如果有结果详情，添加到返回消息中
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if results:
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details_msg = "\n" + "\n".join(results)
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final_msg = summary_msg + details_msg
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else:
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final_msg = summary_msg
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return success_count > 0, final_msg, success_count > 0
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def find_and_return_emoji_in_message(self, message_segments) -> List[str]:
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emoji_base64_list = []
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# 处理单个Seg对象的情况
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if isinstance(message_segments, Seg):
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if message_segments.type == "emoji":
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emoji_base64_list.append(message_segments.data)
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elif message_segments.type == "image":
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# 假设图片数据是base64编码的
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emoji_base64_list.append(message_segments.data)
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elif message_segments.type == "seglist":
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# 递归处理嵌套的Seg列表
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emoji_base64_list.extend(self.find_and_return_emoji_in_message(message_segments.data))
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return emoji_base64_list
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# 处理Seg列表的情况
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for seg in message_segments:
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if seg.type == "emoji":
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emoji_base64_list.append(seg.data)
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@ -66,67 +132,6 @@ class ListEmojiCommand(BaseCommand):
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return True, f"显示了当前时间: {time_str}", True
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class PrintMessage(BaseEventHandler):
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"""打印消息事件处理器 - 处理打印消息事件"""
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event_type = EventType.ON_MESSAGE
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handler_name = "print_message_handler"
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handler_description = "打印接收到的消息"
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async def execute(self, message: MaiMessages | None) -> Tuple[bool, bool, str | None, None, None]:
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"""执行打印消息事件处理"""
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# 打印接收到的消息
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if self.get_config("print_message.enabled", False):
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print(f"接收到消息: {message.raw_message if message else '无效消息'}")
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return True, True, "消息已打印", None, None
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class ForwardMessages(BaseEventHandler):
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"""
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把接收到的消息转发到指定聊天ID
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此组件是HYBRID消息和FORWARD消息的使用示例。
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每收到10条消息，就会以1%的概率使用HYBRID消息转发，否则使用FORWARD消息转发。
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"""
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event_type = EventType.ON_MESSAGE
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handler_name = "forward_messages_handler"
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handler_description = "把接收到的消息转发到指定聊天ID"
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.counter = 0 # 用于计数转发的消息数量
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self.messages: List[str] = []
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async def execute(self, message: MaiMessages | None) -> Tuple[bool, bool, None, None, None]:
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if not message:
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return True, True, None, None, None
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stream_id = message.stream_id or ""
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if message.plain_text:
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self.messages.append(message.plain_text)
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self.counter += 1
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if self.counter % 10 == 0:
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if random.random() < 0.01:
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success = await self.send_hybrid(stream_id, [(ReplyContentType.TEXT, msg) for msg in self.messages])
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else:
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success = await self.send_forward(
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stream_id,
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[
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(
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str(global_config.bot.qq_account),
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str(global_config.bot.nickname),
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[(ReplyContentType.TEXT, msg)],
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)
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for msg in self.messages
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],
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)
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if not success:
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raise ValueError("转发消息失败")
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self.messages = []
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return True, True, None, None, None
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class RandomEmojis(BaseCommand):
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command_name = "random_emojis"
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command_description = "发送多张随机表情包"
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@ -169,36 +174,13 @@ class EmojiManagePlugin(BasePlugin):
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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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"version": ConfigField(type=str, default="1.0.0", description="插件版本"),
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"enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
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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="1.0.1", 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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return [
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(PrintMessage.get_handler_info(), PrintMessage),
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(ForwardMessages.get_handler_info(), ForwardMessages),
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(RandomEmojis.get_command_info(), RandomEmojis),
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]
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# @register_plugin
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# class HelloWorldEventPlugin(BaseEPlugin):
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# """Hello World事件插件 - 处理问候和告别事件"""
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# plugin_name = "hello_world_event_plugin"
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# enable_plugin = False
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# dependencies = []
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# python_dependencies = []
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# config_file_name = "event_config.toml"
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# config_schema = {
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# "plugin": {
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# "name": ConfigField(type=str, default="hello_world_event_plugin", description="插件名称"),
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# "version": ConfigField(type=str, default="1.0.0", description="插件版本"),
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# "enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
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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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# return [(PrintMessage.get_handler_info(), PrintMessage)]
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(AddEmojiCommand.get_command_info(), AddEmojiCommand),
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]
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@ -237,8 +237,7 @@ class HelloWorldPlugin(BasePlugin):
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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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"name": ConfigField(type=str, default="hello_world_plugin", description="插件名称"),
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"version": ConfigField(type=str, default="1.0.0", description="插件版本"),
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"config_version": ConfigField(type=str, default="1.0.0", description="配置文件版本"),
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"enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
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},
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"greeting": {
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@ -62,6 +62,7 @@ class ExpressionLearner:
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model_set=model_config.model_task_config.replyer, request_type="expression.learner"
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)
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self.chat_id = chat_id
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self.chat_stream = get_chat_manager().get_stream(chat_id)
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self.chat_name = get_chat_manager().get_stream_name(chat_id) or chat_id
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# 维护每个chat的上次学习时间
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@ -69,24 +70,8 @@ class ExpressionLearner:
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# 学习参数
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self.min_messages_for_learning = 25 # 触发学习所需的最少消息数
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self.min_learning_interval = 300 # 最短学习时间间隔（秒）
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def can_learn_for_chat(self) -> bool:
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"""
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检查指定聊天流是否允许学习表达
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Args:
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chat_id: 聊天流ID
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Returns:
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bool: 是否允许学习
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"""
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try:
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use_expression, enable_learning, _ = global_config.expression.get_expression_config_for_chat(self.chat_id)
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return enable_learning
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except Exception as e:
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logger.error(f"检查学习权限失败: {e}")
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return False
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_, self.enable_learning, self.learning_intensity = global_config.expression.get_expression_config_for_chat(self.chat_id)
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self.min_learning_interval = 300 / self.learning_intensity
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def should_trigger_learning(self) -> bool:
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"""
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@ -98,27 +83,13 @@ class ExpressionLearner:
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Returns:
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bool: 是否应该触发学习
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"""
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current_time = time.time()
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# 获取该聊天流的学习强度
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try:
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_, enable_learning, learning_intensity = global_config.expression.get_expression_config_for_chat(
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self.chat_id
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)
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except Exception as e:
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logger.error(f"获取聊天流 {self.chat_id} 的学习配置失败: {e}")
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return False
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# 检查是否允许学习
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if not enable_learning:
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if not self.enable_learning:
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return False
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# 根据学习强度计算最短学习时间间隔
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min_interval = self.min_learning_interval / learning_intensity
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# 检查时间间隔
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time_diff = current_time - self.last_learning_time
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if time_diff < min_interval:
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time_diff = time.time() - self.last_learning_time
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if time_diff < self.min_learning_interval:
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return False
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# 检查消息数量（只检查指定聊天流的消息）
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@ -228,32 +199,17 @@ class ExpressionLearner:
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"""
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学习并存储表达方式
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"""
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# 检查是否允许在此聊天流中学习（在函数最前面检查）
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if not self.can_learn_for_chat():
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logger.debug(f"聊天流 {self.chat_name} 不允许学习表达，跳过学习")
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return []
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res = await self.learn_expression(num)
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if res is None:
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logger.info("没有学习到表达风格")
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return []
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learnt_expressions, chat_id = res
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chat_stream = get_chat_manager().get_stream(chat_id)
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if chat_stream is None:
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group_name = f"聊天流 {chat_id}"
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elif chat_stream.group_info:
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group_name = chat_stream.group_info.group_name
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else:
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group_name = f"{chat_stream.user_info.user_nickname}的私聊"
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learnt_expressions_str = ""
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for _chat_id, situation, style in learnt_expressions:
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learnt_expressions_str += f"{situation}->{style}\n"
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logger.info(f"在 {group_name} 学习到表达风格:\n{learnt_expressions_str}")
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if not learnt_expressions:
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logger.info("没有学习到表达风格")
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return []
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logger.info(f"在 {self.chat_name} 学习到表达风格:\n{learnt_expressions_str}")
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# 按chat_id分组
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chat_dict: Dict[str, List[Dict[str, Any]]] = {}
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@ -316,7 +272,7 @@ class ExpressionLearner:
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current_time = time.time()
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# 获取上次学习时间
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# 获取上次学习之后的消息
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random_msg = get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_learning_time,
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@ -330,14 +286,15 @@ class ExpressionLearner:
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chat_id: str = random_msg[0].chat_id
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# random_msg_str: str = build_readable_messages(random_msg, timestamp_mode="normal")
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random_msg_str: str = await build_anonymous_messages(random_msg)
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# print(f"random_msg_str:{random_msg_str}")
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prompt: str = await global_prompt_manager.format_prompt(
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prompt,
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chat_str=random_msg_str,
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)
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logger.debug(f"学习{type_str}的prompt: {prompt}")
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print(f"random_msg_str:{random_msg_str}")
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logger.info(f"学习{type_str}的prompt: {prompt}")
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try:
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response, _ = await self.express_learn_model.generate_response_async(prompt, temperature=0.3)
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@ -344,6 +344,10 @@ class HeartFChatting:
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available_actions=available_actions,
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)
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)
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logger.info(
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f"{self.log_prefix}决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}"
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)
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# 3. 并行执行所有动作
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action_tasks = [
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@ -502,8 +502,8 @@ class ActionPlanner:
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action.action_data = action.action_data or {}
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action.action_data["loop_start_time"] = loop_start_time
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logger.info(
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f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
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logger.debug(
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f"{self.log_prefix}规划器选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
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)
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return actions
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@ -46,8 +46,9 @@ class EmojiAction(BaseAction):
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"""执行表情动作"""
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try:
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# 1. 获取发送表情的原因
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reason = self.action_data.get("reason", "表达当前情绪")
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# reason = self.action_data.get("reason", "表达当前情绪")
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reason = self.reasoning
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# 2. 随机获取20个表情包
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sampled_emojis = await emoji_api.get_random(30)
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if not sampled_emojis:
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@ -62,6 +63,9 @@ class EmojiAction(BaseAction):
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emotion_map[emo].append((b64, desc))
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available_emotions = list(emotion_map.keys())
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available_emotions_str = ""
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for emotion in available_emotions:
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available_emotions_str += f"{emotion}\n"
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if not available_emotions:
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logger.warning(f"{self.log_prefix} 获取到的表情包均无情感标签, 将随机发送")
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@ -80,14 +84,15 @@ class EmojiAction(BaseAction):
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)
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# 4. 构建prompt让LLM选择情感
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prompt = f"""
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你是一个正在进行聊天的网友，你需要根据一个理由和最近的聊天记录，从一个情感标签列表中选择最匹配的一个。
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这是最近的聊天记录：
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{messages_text}
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这是理由：“{reason}”
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这里是可用的情感标签：{available_emotions}
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请直接返回最匹配的那个情感标签，不要进行任何解释或添加其他多余的文字。
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prompt = f"""你正在进行QQ聊天，你需要根据聊天记录，选出一个合适的情感标签。
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请你根据以下原因和聊天记录进行选择
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原因：{reason}
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聊天记录：
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{messages_text}
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这里是可用的情感标签：
|
||||
{available_emotions_str}
|
||||
请直接返回最匹配的那个情感标签，不要进行任何解释或添加其他多余的文字。
|
||||
"""
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
|
|
@ -97,10 +102,10 @@ class EmojiAction(BaseAction):
|
|||
|
||||
# 5. 调用LLM
|
||||
models = llm_api.get_available_models()
|
||||
chat_model_config = models.get("utils_small") # 使用字典访问方式
|
||||
chat_model_config = models.get("replyer") # 使用字典访问方式
|
||||
if not chat_model_config:
|
||||
logger.error(f"{self.log_prefix} 未找到'utils_small'模型配置，无法调用LLM")
|
||||
return False, "未找到'utils_small'模型配置"
|
||||
logger.error(f"{self.log_prefix} 未找到'replyer'模型配置，无法调用LLM")
|
||||
return False, "未找到'replyer'模型配置"
|
||||
|
||||
success, chosen_emotion, _, _ = await llm_api.generate_with_model(
|
||||
prompt, model_config=chat_model_config, request_type="emoji"
|
||||
|
|
|
|||
Loading…
Reference in New Issue