152 lines
6.1 KiB
Python
152 lines
6.1 KiB
Python
import random
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from typing import Tuple
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# 导入新插件系统
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from src.plugin_system import BaseAction, ActionActivationType
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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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# 导入API模块 - 标准Python包方式
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from src.plugin_system.apis import emoji_api, llm_api, message_api
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# NoReplyAction已集成到heartFC_chat.py中，不再需要导入
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from src.config.config import global_config
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logger = get_logger("emoji")
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class EmojiAction(BaseAction):
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"""表情动作 - 发送表情包"""
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activation_type = ActionActivationType.RANDOM
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random_activation_probability = global_config.emoji.emoji_chance
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parallel_action = True
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# 动作基本信息
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action_name = "emoji"
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action_description = "发送表情包辅助表达情绪"
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# 动作参数定义
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action_parameters = {}
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# 动作使用场景
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action_require = [
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"发送表情包辅助表达情绪",
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"表达情绪时可以选择使用",
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"不要连续发送，如果你已经发过[表情包]，就不要选择此动作",
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]
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# 关联类型
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associated_types = ["emoji"]
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async def execute(self) -> Tuple[bool, str]:
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# sourcery skip: assign-if-exp, introduce-default-else, swap-if-else-branches, use-named-expression
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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_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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logger.warning(f"{self.log_prefix} 无法获取随机表情包")
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return False, "无法获取随机表情包"
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# 3. 准备情感数据
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emotion_map = {}
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for b64, desc, emo in sampled_emojis:
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if emo not in emotion_map:
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emotion_map[emo] = []
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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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emoji_base64, emoji_description, _ = random.choice(sampled_emojis)
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else:
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# 获取最近的5条消息内容用于判断
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recent_messages = message_api.get_recent_messages(chat_id=self.chat_id, limit=5)
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messages_text = ""
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if recent_messages:
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# 使用message_api构建可读的消息字符串
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messages_text = message_api.build_readable_messages(
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messages=recent_messages,
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timestamp_mode="normal_no_YMD",
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truncate=False,
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show_actions=False,
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)
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# 4. 构建prompt让LLM选择情感
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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：
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{available_emotions_str}
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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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"""
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if global_config.debug.show_prompt:
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logger.info(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
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else:
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logger.debug(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
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# 5. 调用LLM
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models = llm_api.get_available_models()
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chat_model_config = models.get("utils") # 使用字典访问方式
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if not chat_model_config:
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logger.error(f"{self.log_prefix} 未找到'utils'模型配置，无法调用LLM")
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return False, "未找到'utils'模型配置"
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success, chosen_emotion, _, _ = await llm_api.generate_with_model(
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prompt, model_config=chat_model_config, request_type="emoji.select"
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)
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if not success:
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logger.error(f"{self.log_prefix} LLM调用失败: {chosen_emotion}")
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return False, f"LLM调用失败: {chosen_emotion}"
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chosen_emotion = chosen_emotion.strip().replace('"', "").replace("'", "")
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logger.info(f"{self.log_prefix} LLM选择的情感: {chosen_emotion}")
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# 6. 根据选择的情感匹配表情包
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if chosen_emotion in emotion_map:
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emoji_base64, emoji_description = random.choice(emotion_map[chosen_emotion])
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logger.info(f"{self.log_prefix} 发送表情包[{chosen_emotion}]，原因: {reason}")
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else:
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logger.warning(
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f"{self.log_prefix} LLM选择的情感 '{chosen_emotion}' 不在可用列表中, 将随机选择一个表情包"
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)
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emoji_base64, emoji_description, _ = random.choice(sampled_emojis)
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# 7. 发送表情包
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success = await self.send_emoji(emoji_base64)
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if success:
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# 存储动作信息
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=f"你发送了表情包，原因：{reason}",
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action_done=True,
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)
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return True, f"成功发送表情包:[表情包：{chosen_emotion}]"
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else:
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error_msg = "发送表情包失败"
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logger.error(f"{self.log_prefix} {error_msg}")
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await self.send_text("执行表情包动作失败")
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return False, error_msg
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except Exception as e:
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logger.error(f"{self.log_prefix} 表情动作执行失败: {e}", exc_info=True)
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return False, f"表情发送失败: {str(e)}"
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