remove：emoji插件，修复tool被截断的问题
This commit is contained in:
SengokuCola
202
src/chat/emoji_system/maisaka_tool.py
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202
src/chat/emoji_system/maisaka_tool.py
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@@ -0,0 +1,202 @@
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"""Maisaka 表情工具内置能力。"""
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from dataclasses import dataclass, field
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from typing import Sequence
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import random
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from src.common.data_models.image_data_model import MaiEmoji
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from src.common.data_models.llm_service_data_models import LLMGenerationOptions
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from src.common.logger import get_logger
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from src.common.utils.utils_image import ImageUtils
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from src.services import send_service
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from .emoji_manager import emoji_manager, emoji_manager_emotion_judge_llm
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logger = get_logger("emoji_maisaka_tool")
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@dataclass(slots=True)
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class MaisakaEmojiSendResult:
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"""Maisaka 表情发送结果。"""
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success: bool
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message: str
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emoji_base64: str = ""
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description: str = ""
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emotions: list[str] = field(default_factory=list)
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requested_emotion: str = ""
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matched_emotion: str = ""
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def _normalize_emotions(emoji: MaiEmoji) -> list[str]:
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"""提取并清洗单个表情的情绪标签。"""
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return [str(item).strip() for item in emoji.emotion if str(item).strip()]
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def _build_recent_context_text(context_texts: Sequence[str], max_items: int = 5) -> str:
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"""构建供情绪判断使用的最近上下文文本。"""
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normalized_items = [str(item).strip() for item in context_texts if str(item).strip()]
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if not normalized_items:
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return ""
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return "\n".join(normalized_items[-max_items:])
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async def _select_emoji_with_llm(
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*,
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sampled_emojis: Sequence[MaiEmoji],
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reasoning: str,
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context_text: str,
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) -> tuple[MaiEmoji, str]:
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"""让模型在采样表情中选择更合适的情绪标签。"""
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emotion_map: dict[str, list[MaiEmoji]] = {}
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for emoji in sampled_emojis:
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for emotion in _normalize_emotions(emoji):
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emotion_map.setdefault(emotion, []).append(emoji)
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available_emotions = list(emotion_map.keys())
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if not available_emotions:
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return random.choice(list(sampled_emojis)), ""
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prompt = (
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"你正在为聊天场景选择一个最合适的表情包情绪标签。\n"
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f"发送原因：{reasoning or '辅助表达当前语气和情绪'}\n"
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f"最近聊天记录：\n{context_text or '（暂无额外上下文）'}\n\n"
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"可选情绪标签如下：\n"
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f"{chr(10).join(available_emotions)}\n\n"
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"请只返回一个最匹配的情绪标签，不要解释。"
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)
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try:
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llm_result = await emoji_manager_emotion_judge_llm.generate_response(
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prompt,
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options=LLMGenerationOptions(temperature=0.3, max_tokens=60),
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)
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chosen_emotion = (llm_result.response or "").strip().strip("\"'")
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except Exception as exc:
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logger.warning(f"使用 LLM 选择表情情绪失败，将回退为随机选择: {exc}")
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chosen_emotion = ""
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if chosen_emotion and chosen_emotion in emotion_map:
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return random.choice(emotion_map[chosen_emotion]), chosen_emotion
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return random.choice(list(sampled_emojis)), ""
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async def select_emoji_for_maisaka(
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*,
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requested_emotion: str = "",
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reasoning: str = "",
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context_texts: Sequence[str] | None = None,
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sample_size: int = 30,
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) -> tuple[MaiEmoji | None, str]:
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"""为 Maisaka 选择一个合适的表情。"""
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available_emojis = list(emoji_manager.emojis)
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if not available_emojis:
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return None, ""
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normalized_requested_emotion = requested_emotion.strip()
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if normalized_requested_emotion:
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matched_emojis = [
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emoji
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for emoji in available_emojis
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if normalized_requested_emotion.lower() in (emotion.lower() for emotion in _normalize_emotions(emoji))
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]
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if matched_emojis:
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return random.choice(matched_emojis), normalized_requested_emotion
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sampled_emojis = random.sample(
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available_emojis,
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min(max(sample_size, 1), len(available_emojis)),
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)
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context_text = _build_recent_context_text(context_texts or [])
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return await _select_emoji_with_llm(
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sampled_emojis=sampled_emojis,
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reasoning=reasoning,
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context_text=context_text,
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)
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async def send_emoji_for_maisaka(
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*,
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stream_id: str,
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requested_emotion: str = "",
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reasoning: str = "",
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context_texts: Sequence[str] | None = None,
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) -> MaisakaEmojiSendResult:
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"""为 Maisaka 选择并发送一个表情。"""
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selected_emoji, matched_emotion = await select_emoji_for_maisaka(
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requested_emotion=requested_emotion,
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reasoning=reasoning,
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context_texts=context_texts,
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)
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if selected_emoji is None:
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return MaisakaEmojiSendResult(
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success=False,
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message="当前表情包库中没有可用表情。",
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requested_emotion=requested_emotion.strip(),
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)
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try:
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emoji_base64 = ImageUtils.image_path_to_base64(str(selected_emoji.full_path))
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if not emoji_base64:
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raise ValueError("表情图片转换为 base64 失败")
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except Exception as exc:
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return MaisakaEmojiSendResult(
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success=False,
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message=f"发送表情包失败：{exc}",
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description=selected_emoji.description.strip(),
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emotions=_normalize_emotions(selected_emoji),
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requested_emotion=requested_emotion.strip(),
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matched_emotion=matched_emotion,
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)
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try:
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sent = await send_service.emoji_to_stream(
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emoji_base64=emoji_base64,
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stream_id=stream_id,
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storage_message=True,
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set_reply=False,
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reply_message=None,
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)
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except Exception as exc:
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return MaisakaEmojiSendResult(
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success=False,
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message=f"发送表情包时发生异常：{exc}",
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description=selected_emoji.description.strip(),
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emotions=_normalize_emotions(selected_emoji),
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requested_emotion=requested_emotion.strip(),
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matched_emotion=matched_emotion,
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)
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description = selected_emoji.description.strip()
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emotions = _normalize_emotions(selected_emoji)
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if not sent:
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return MaisakaEmojiSendResult(
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success=False,
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message="发送表情包失败。",
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description=description,
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emotions=emotions,
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requested_emotion=requested_emotion.strip(),
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matched_emotion=matched_emotion,
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)
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emoji_manager.update_emoji_usage(selected_emoji)
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success_message = (
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f"已发送表情包：{description}（情绪：{', '.join(emotions)}）"
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if emotions
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else f"已发送表情包：{description}"
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)
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return MaisakaEmojiSendResult(
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success=True,
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message=success_message,
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emoji_base64=emoji_base64,
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description=description,
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emotions=emotions,
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requested_emotion=requested_emotion.strip(),
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matched_emotion=matched_emotion,
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)
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@@ -28,7 +28,7 @@ from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionI
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from src.services.llm_service import LLMServiceClient
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from .builtin_tools import get_builtin_tools
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from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage
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from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage, ToolResultMessage
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from .message_adapter import format_speaker_content
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from .prompt_cli_renderer import PromptCLIVisualizer
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@@ -609,6 +609,7 @@ class MaisakaChatLoopService:
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selected_indices.reverse()
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selected_history = [chat_history[index] for index in selected_indices]
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selected_history = MaisakaChatLoopService._drop_leading_orphan_tool_results(selected_history)
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return (
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selected_history,
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(
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@@ -617,6 +618,36 @@ class MaisakaChatLoopService:
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),
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)
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@staticmethod
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def _drop_leading_orphan_tool_results(
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selected_history: List[LLMContextMessage],
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) -> List[LLMContextMessage]:
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"""移除窗口前缀中缺少对应 tool_call 的工具结果消息。"""
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if not selected_history:
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return selected_history
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available_tool_call_ids = {
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tool_call.call_id
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for message in selected_history
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if isinstance(message, AssistantMessage)
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for tool_call in message.tool_calls
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if tool_call.call_id
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}
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first_valid_index = 0
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while first_valid_index < len(selected_history):
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message = selected_history[first_valid_index]
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if not isinstance(message, ToolResultMessage):
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break
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if message.tool_call_id in available_tool_call_ids:
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break
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first_valid_index += 1
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if first_valid_index == 0:
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return selected_history
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return selected_history[first_valid_index:]
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@staticmethod
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def build_chat_context(user_text: str) -> List[LLMContextMessage]:
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"""根据用户输入构造最小对话上下文。
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@@ -328,15 +328,48 @@ class MaisakaReasoningEngine:
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trimmed_history = list(self._runtime._chat_history)
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removed_count = 0
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while conversation_message_count >= self._runtime._max_context_size and trimmed_history:
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while conversation_message_count > self._runtime._max_context_size and trimmed_history:
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removed_message = trimmed_history.pop(0)
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removed_count += 1
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if removed_message.count_in_context:
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conversation_message_count -= 1
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trimmed_history, pruned_orphan_count = self._drop_leading_orphan_tool_results(trimmed_history)
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removed_count += pruned_orphan_count
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self._runtime._chat_history = trimmed_history
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self._runtime._log_history_trimmed(removed_count, conversation_message_count)
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@staticmethod
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def _drop_leading_orphan_tool_results(
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chat_history: list[LLMContextMessage],
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) -> tuple[list[LLMContextMessage], int]:
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"""清理历史前缀中缺少对应 assistant tool_call 的工具结果消息。"""
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if not chat_history:
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return chat_history, 0
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available_tool_call_ids = {
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tool_call.call_id
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for message in chat_history
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if isinstance(message, AssistantMessage)
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for tool_call in message.tool_calls
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if tool_call.call_id
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}
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first_valid_index = 0
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while first_valid_index < len(chat_history):
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message = chat_history[first_valid_index]
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if not isinstance(message, ToolResultMessage):
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break
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if message.tool_call_id in available_tool_call_ids:
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break
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first_valid_index += 1
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if first_valid_index == 0:
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return chat_history, 0
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return chat_history[first_valid_index:], first_valid_index
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@staticmethod
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def _calculate_similarity(text1: str, text2: str) -> float:
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"""计算两个文本之间的相似度。
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@@ -819,7 +852,77 @@ class MaisakaReasoningEngine:
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"""执行 send_emoji 内置工具。"""
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del context
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return await self._handle_send_emoji(self._build_tool_call_from_invocation(invocation))
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return await self._invoke_builtin_send_emoji(self._build_tool_call_from_invocation(invocation))
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async def _invoke_builtin_send_emoji(self, tool_call: ToolCall) -> ToolExecutionResult:
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"""执行内置表情工具。"""
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from src.chat.emoji_system.maisaka_tool import send_emoji_for_maisaka
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tool_args = tool_call.args or {}
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emotion = str(tool_args.get("emotion") or "").strip()
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context_texts = [
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message.get_history_text()
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for message in self._runtime._chat_history[-5:]
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if message.get_history_text().strip()
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]
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structured_result: dict[str, Any] = {
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"success": False,
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"message": "",
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"description": "",
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"emotion": [],
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"requested_emotion": emotion,
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"matched_emotion": "",
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}
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logger.info(f"{self._runtime.log_prefix} 触发表情包发送工具，请求情绪={emotion!r}")
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try:
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send_result = await send_emoji_for_maisaka(
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stream_id=self._runtime.session_id,
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requested_emotion=emotion,
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reasoning=self._last_reasoning_content,
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context_texts=context_texts,
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)
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except Exception as exc:
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logger.exception(f"{self._runtime.log_prefix} 发送表情包时发生异常: {exc}")
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structured_result["message"] = f"发送表情包时发生异常：{exc}"
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return self._build_tool_failure_result(
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tool_call.func_name,
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structured_result["message"],
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structured_content=structured_result,
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)
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structured_result["description"] = send_result.description
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structured_result["emotion"] = list(send_result.emotions)
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structured_result["matched_emotion"] = send_result.matched_emotion
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structured_result["message"] = send_result.message
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if send_result.success:
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logger.info(
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f"{self._runtime.log_prefix} 表情包发送成功: "
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f"描述={send_result.description!r} 情绪标签={send_result.emotions} "
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f"请求情绪={emotion!r} 命中情绪={send_result.matched_emotion!r}"
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)
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self._append_sent_emoji_to_chat_history(
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emoji_base64=send_result.emoji_base64,
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success_message=send_result.message,
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)
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structured_result["success"] = True
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return self._build_tool_success_result(
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tool_call.func_name,
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send_result.message,
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structured_content=structured_result,
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)
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logger.warning(
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f"{self._runtime.log_prefix} 表情包发送失败: "
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f"请求情绪={emotion!r} 错误信息={send_result.message}"
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)
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return self._build_tool_failure_result(
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tool_call.func_name,
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structured_result["message"],
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structured_content=structured_result,
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)
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async def _handle_tool_calls(
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self,
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@@ -1,41 +0,0 @@
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{
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"manifest_version": 2,
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"version": "2.0.0",
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"name": "Emoji插件 (Emoji Actions)",
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"description": "可以发送和管理 Emoji",
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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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"urls": {
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"repository": "https://github.com/MaiM-with-u/maibot",
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"homepage": "https://github.com/MaiM-with-u/maibot",
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"documentation": "https://github.com/MaiM-with-u/maibot",
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"issues": "https://github.com/MaiM-with-u/maibot/issues"
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},
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"host_application": {
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"min_version": "1.0.0",
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"max_version": "1.0.0"
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},
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"sdk": {
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"min_version": "2.0.0",
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"max_version": "2.99.99"
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},
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"dependencies": [],
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"capabilities": [
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"emoji.get_random",
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"message.get_recent",
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"message.build_readable",
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"llm.generate",
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"send.emoji",
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"config.get"
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],
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"i18n": {
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"default_locale": "zh-CN",
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"supported_locales": [
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"zh-CN"
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]
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},
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"id": "builtin.emoji-plugin"
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}
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@@ -1,129 +0,0 @@
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"""Emoji 插件 — 新 SDK 版本
|
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|
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根据聊天上下文的情感，使用 LLM 选择并发送合适的表情包。
|
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"""
|
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|
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from maibot_sdk import Action, MaiBotPlugin
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from maibot_sdk.types import ActivationType
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import random
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class EmojiPlugin(MaiBotPlugin):
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"""表情包插件"""
|
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|
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@Action(
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"emoji",
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||||
description="发送表情包辅助表达情绪",
|
||||
activation_type=ActivationType.RANDOM,
|
||||
activation_probability=0.3,
|
||||
parallel_action=True,
|
||||
action_require=[
|
||||
"发送表情包辅助表达情绪",
|
||||
"表达情绪时可以选择使用",
|
||||
"不要连续发送，如果你已经发过[表情包]，就不要选择此动作",
|
||||
],
|
||||
associated_types=["emoji"],
|
||||
)
|
||||
async def handle_emoji(self, stream_id: str = "", reasoning: str = "", chat_id: str = "", **kwargs):
|
||||
"""执行表情动作"""
|
||||
reason = reasoning or "表达当前情绪"
|
||||
|
||||
# 1. 随机获取30个表情包
|
||||
sampled_emojis = await self.ctx.emoji.get_random(30)
|
||||
if not sampled_emojis:
|
||||
return False, "无法获取随机表情包"
|
||||
|
||||
# 2. 按情感分组
|
||||
emotion_map: dict[str, list] = {}
|
||||
for emoji in sampled_emojis:
|
||||
emo = emoji.get("emotion", "")
|
||||
if emo not in emotion_map:
|
||||
emotion_map[emo] = []
|
||||
emotion_map[emo].append(emoji)
|
||||
|
||||
available_emotions = list(emotion_map.keys())
|
||||
|
||||
if not available_emotions:
|
||||
# 无情感标签，随机发送
|
||||
chosen = random.choice(sampled_emojis)
|
||||
await self.ctx.send.emoji(chosen["base64"], stream_id)
|
||||
return True, "随机发送了表情包"
|
||||
|
||||
# 3. 获取最近消息作为上下文
|
||||
messages_text = ""
|
||||
if chat_id:
|
||||
recent_messages = await self.ctx.message.get_recent(chat_id=chat_id, limit=5)
|
||||
if recent_messages:
|
||||
messages_text = await self.ctx.message.build_readable(
|
||||
recent_messages,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
truncate=False,
|
||||
)
|
||||
|
||||
# 4. 构建 prompt 让 LLM 选择情感
|
||||
available_emotions_str = "\n".join(available_emotions)
|
||||
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。
|
||||
请你根据以下原因和聊天记录进行选择
|
||||
原因：{reason}
|
||||
聊天记录：
|
||||
{messages_text}
|
||||
|
||||
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。
|
||||
"""
|
||||
|
||||
# 5. 调用 LLM
|
||||
llm_result = await self.ctx.llm.generate(prompt=prompt, model_name="utils")
|
||||
if not llm_result or not llm_result.get("success"):
|
||||
chosen = random.choice(sampled_emojis)
|
||||
await self.ctx.send.emoji(chosen["base64"], stream_id)
|
||||
return True, "LLM调用失败，随机发送了表情包"
|
||||
|
||||
chosen_emotion = llm_result.get("response", "").strip().replace('"', "").replace("'", "")
|
||||
|
||||
# 6. 根据选择的情感匹配表情包
|
||||
if chosen_emotion in emotion_map:
|
||||
chosen = random.choice(emotion_map[chosen_emotion])
|
||||
else:
|
||||
chosen = random.choice(sampled_emojis)
|
||||
|
||||
# 7. 发送
|
||||
send_ok = await self.ctx.send.emoji(chosen["base64"], stream_id)
|
||||
if send_ok:
|
||||
return True, f"成功发送表情包:[表情包：{chosen_emotion}]"
|
||||
return False, "发送表情包失败"
|
||||
|
||||
async def on_load(self) -> None:
|
||||
"""处理插件加载。"""
|
||||
|
||||
# 从插件配置读取 emoji_chance 来覆盖默认概率
|
||||
await self.ctx.config.get("emoji.emoji_chance")
|
||||
|
||||
async def on_unload(self) -> None:
|
||||
"""处理插件卸载。"""
|
||||
|
||||
async def on_config_update(self, scope: str, config_data: dict[str, object], version: str) -> None:
|
||||
"""处理配置热重载事件。
|
||||
|
||||
Args:
|
||||
scope: 配置变更范围。
|
||||
config_data: 最新配置数据。
|
||||
version: 配置版本号。
|
||||
"""
|
||||
|
||||
del config_data
|
||||
del version
|
||||
if scope == "self":
|
||||
await self.ctx.config.get("emoji.emoji_chance")
|
||||
|
||||
|
||||
def create_plugin() -> EmojiPlugin:
|
||||
"""创建 Emoji 插件实例。
|
||||
|
||||
Returns:
|
||||
EmojiPlugin: 新的 Emoji 插件实例。
|
||||
"""
|
||||
|
||||
return EmojiPlugin()
|
||||
Reference in New Issue
Block a user