From f2aedf7681a9b700c588640740eb85794307fc8d Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Wed, 1 Apr 2026 17:06:44 +0800 Subject: [PATCH] =?UTF-8?q?remove=EF=BC=9Aemoji=E6=8F=92=E4=BB=B6=EF=BC=8C?= =?UTF-8?q?=E4=BF=AE=E5=A4=8Dtool=E8=A2=AB=E6=88=AA=E6=96=AD=E7=9A=84?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/chat/emoji_system/maisaka_tool.py | 202 ++++++++++++++++++ src/maisaka/chat_loop_service.py | 33 ++- src/maisaka/reasoning_engine.py | 107 +++++++++- .../built_in/emoji_plugin/_manifest.json | 41 ---- src/plugins/built_in/emoji_plugin/plugin.py | 129 ----------- 5 files changed, 339 insertions(+), 173 deletions(-) create mode 100644 src/chat/emoji_system/maisaka_tool.py delete mode 100644 src/plugins/built_in/emoji_plugin/_manifest.json delete mode 100644 src/plugins/built_in/emoji_plugin/plugin.py diff --git a/src/chat/emoji_system/maisaka_tool.py b/src/chat/emoji_system/maisaka_tool.py new file mode 100644 index 00000000..1a9885bc --- /dev/null +++ b/src/chat/emoji_system/maisaka_tool.py @@ -0,0 +1,202 @@ +"""Maisaka 表情工具内置能力。""" + +from dataclasses import dataclass, field +from typing import Sequence + +import random + +from src.common.data_models.image_data_model import MaiEmoji +from src.common.data_models.llm_service_data_models import LLMGenerationOptions +from src.common.logger import get_logger +from src.common.utils.utils_image import ImageUtils +from src.services import send_service + +from .emoji_manager import emoji_manager, emoji_manager_emotion_judge_llm + +logger = get_logger("emoji_maisaka_tool") + + +@dataclass(slots=True) +class MaisakaEmojiSendResult: + """Maisaka 表情发送结果。""" + + success: bool + message: str + emoji_base64: str = "" + description: str = "" + emotions: list[str] = field(default_factory=list) + requested_emotion: str = "" + matched_emotion: str = "" + + +def _normalize_emotions(emoji: MaiEmoji) -> list[str]: + """提取并清洗单个表情的情绪标签。""" + + return [str(item).strip() for item in emoji.emotion if str(item).strip()] + + +def _build_recent_context_text(context_texts: Sequence[str], max_items: int = 5) -> str: + """构建供情绪判断使用的最近上下文文本。""" + + normalized_items = [str(item).strip() for item in context_texts if str(item).strip()] + if not normalized_items: + return "" + return "\n".join(normalized_items[-max_items:]) + + +async def _select_emoji_with_llm( + *, + sampled_emojis: Sequence[MaiEmoji], + reasoning: str, + context_text: str, +) -> tuple[MaiEmoji, str]: + """让模型在采样表情中选择更合适的情绪标签。""" + + emotion_map: dict[str, list[MaiEmoji]] = {} + for emoji in sampled_emojis: + for emotion in _normalize_emotions(emoji): + emotion_map.setdefault(emotion, []).append(emoji) + + available_emotions = list(emotion_map.keys()) + if not available_emotions: + return random.choice(list(sampled_emojis)), "" + + prompt = ( + "你正在为聊天场景选择一个最合适的表情包情绪标签。\n" + f"发送原因：{reasoning or '辅助表达当前语气和情绪'}\n" + f"最近聊天记录：\n{context_text or '（暂无额外上下文）'}\n\n" + "可选情绪标签如下：\n" + f"{chr(10).join(available_emotions)}\n\n" + "请只返回一个最匹配的情绪标签，不要解释。" + ) + + try: + llm_result = await emoji_manager_emotion_judge_llm.generate_response( + prompt, + options=LLMGenerationOptions(temperature=0.3, max_tokens=60), + ) + chosen_emotion = (llm_result.response or "").strip().strip("\"'") + except Exception as exc: + logger.warning(f"使用 LLM 选择表情情绪失败，将回退为随机选择: {exc}") + chosen_emotion = "" + + if chosen_emotion and chosen_emotion in emotion_map: + return random.choice(emotion_map[chosen_emotion]), chosen_emotion + return random.choice(list(sampled_emojis)), "" + + +async def select_emoji_for_maisaka( + *, + requested_emotion: str = "", + reasoning: str = "", + context_texts: Sequence[str] | None = None, + sample_size: int = 30, +) -> tuple[MaiEmoji | None, str]: + """为 Maisaka 选择一个合适的表情。""" + + available_emojis = list(emoji_manager.emojis) + if not available_emojis: + return None, "" + + normalized_requested_emotion = requested_emotion.strip() + if normalized_requested_emotion: + matched_emojis = [ + emoji + for emoji in available_emojis + if normalized_requested_emotion.lower() in (emotion.lower() for emotion in _normalize_emotions(emoji)) + ] + if matched_emojis: + return random.choice(matched_emojis), normalized_requested_emotion + + sampled_emojis = random.sample( + available_emojis, + min(max(sample_size, 1), len(available_emojis)), + ) + context_text = _build_recent_context_text(context_texts or []) + return await _select_emoji_with_llm( + sampled_emojis=sampled_emojis, + reasoning=reasoning, + context_text=context_text, + ) + + +async def send_emoji_for_maisaka( + *, + stream_id: str, + requested_emotion: str = "", + reasoning: str = "", + context_texts: Sequence[str] | None = None, +) -> MaisakaEmojiSendResult: + """为 Maisaka 选择并发送一个表情。""" + + selected_emoji, matched_emotion = await select_emoji_for_maisaka( + requested_emotion=requested_emotion, + reasoning=reasoning, + context_texts=context_texts, + ) + if selected_emoji is None: + return MaisakaEmojiSendResult( + success=False, + message="当前表情包库中没有可用表情。", + requested_emotion=requested_emotion.strip(), + ) + + try: + emoji_base64 = ImageUtils.image_path_to_base64(str(selected_emoji.full_path)) + if not emoji_base64: + raise ValueError("表情图片转换为 base64 失败") + except Exception as exc: + return MaisakaEmojiSendResult( + success=False, + message=f"发送表情包失败：{exc}", + description=selected_emoji.description.strip(), + emotions=_normalize_emotions(selected_emoji), + requested_emotion=requested_emotion.strip(), + matched_emotion=matched_emotion, + ) + + try: + sent = await send_service.emoji_to_stream( + emoji_base64=emoji_base64, + stream_id=stream_id, + storage_message=True, + set_reply=False, + reply_message=None, + ) + except Exception as exc: + return MaisakaEmojiSendResult( + success=False, + message=f"发送表情包时发生异常：{exc}", + description=selected_emoji.description.strip(), + emotions=_normalize_emotions(selected_emoji), + requested_emotion=requested_emotion.strip(), + matched_emotion=matched_emotion, + ) + + description = selected_emoji.description.strip() + emotions = _normalize_emotions(selected_emoji) + if not sent: + return MaisakaEmojiSendResult( + success=False, + message="发送表情包失败。", + description=description, + emotions=emotions, + requested_emotion=requested_emotion.strip(), + matched_emotion=matched_emotion, + ) + + emoji_manager.update_emoji_usage(selected_emoji) + success_message = ( + f"已发送表情包：{description}（情绪：{', '.join(emotions)}）" + if emotions + else f"已发送表情包：{description}" + ) + return MaisakaEmojiSendResult( + success=True, + message=success_message, + emoji_base64=emoji_base64, + description=description, + emotions=emotions, + requested_emotion=requested_emotion.strip(), + matched_emotion=matched_emotion, + ) diff --git a/src/maisaka/chat_loop_service.py b/src/maisaka/chat_loop_service.py index ccdf78cd..038fc0f0 100644 --- a/src/maisaka/chat_loop_service.py +++ b/src/maisaka/chat_loop_service.py @@ -28,7 +28,7 @@ from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionI from src.services.llm_service import LLMServiceClient from .builtin_tools import get_builtin_tools -from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage +from .context_messages import AssistantMessage, LLMContextMessage, SessionBackedMessage, ToolResultMessage from .message_adapter import format_speaker_content from .prompt_cli_renderer import PromptCLIVisualizer @@ -609,6 +609,7 @@ class MaisakaChatLoopService: selected_indices.reverse() selected_history = [chat_history[index] for index in selected_indices] + selected_history = MaisakaChatLoopService._drop_leading_orphan_tool_results(selected_history) return ( selected_history, ( @@ -617,6 +618,36 @@ class MaisakaChatLoopService: ), ) + @staticmethod + def _drop_leading_orphan_tool_results( + selected_history: List[LLMContextMessage], + ) -> List[LLMContextMessage]: + """移除窗口前缀中缺少对应 tool_call 的工具结果消息。""" + + if not selected_history: + return selected_history + + available_tool_call_ids = { + tool_call.call_id + for message in selected_history + if isinstance(message, AssistantMessage) + for tool_call in message.tool_calls + if tool_call.call_id + } + + first_valid_index = 0 + while first_valid_index < len(selected_history): + message = selected_history[first_valid_index] + if not isinstance(message, ToolResultMessage): + break + if message.tool_call_id in available_tool_call_ids: + break + first_valid_index += 1 + + if first_valid_index == 0: + return selected_history + return selected_history[first_valid_index:] + @staticmethod def build_chat_context(user_text: str) -> List[LLMContextMessage]: """根据用户输入构造最小对话上下文。 diff --git a/src/maisaka/reasoning_engine.py b/src/maisaka/reasoning_engine.py index 01ca66bb..e2f3d5bb 100644 --- a/src/maisaka/reasoning_engine.py +++ b/src/maisaka/reasoning_engine.py @@ -328,15 +328,48 @@ class MaisakaReasoningEngine: trimmed_history = list(self._runtime._chat_history) removed_count = 0 - while conversation_message_count >= self._runtime._max_context_size and trimmed_history: + while conversation_message_count > self._runtime._max_context_size and trimmed_history: removed_message = trimmed_history.pop(0) removed_count += 1 if removed_message.count_in_context: conversation_message_count -= 1 + trimmed_history, pruned_orphan_count = self._drop_leading_orphan_tool_results(trimmed_history) + removed_count += pruned_orphan_count + self._runtime._chat_history = trimmed_history self._runtime._log_history_trimmed(removed_count, conversation_message_count) + @staticmethod + def _drop_leading_orphan_tool_results( + chat_history: list[LLMContextMessage], + ) -> tuple[list[LLMContextMessage], int]: + """清理历史前缀中缺少对应 assistant tool_call 的工具结果消息。""" + + if not chat_history: + return chat_history, 0 + + available_tool_call_ids = { + tool_call.call_id + for message in chat_history + if isinstance(message, AssistantMessage) + for tool_call in message.tool_calls + if tool_call.call_id + } + + first_valid_index = 0 + while first_valid_index < len(chat_history): + message = chat_history[first_valid_index] + if not isinstance(message, ToolResultMessage): + break + if message.tool_call_id in available_tool_call_ids: + break + first_valid_index += 1 + + if first_valid_index == 0: + return chat_history, 0 + return chat_history[first_valid_index:], first_valid_index + @staticmethod def _calculate_similarity(text1: str, text2: str) -> float: """计算两个文本之间的相似度。 @@ -819,7 +852,77 @@ class MaisakaReasoningEngine: """执行 send_emoji 内置工具。""" del context - return await self._handle_send_emoji(self._build_tool_call_from_invocation(invocation)) + return await self._invoke_builtin_send_emoji(self._build_tool_call_from_invocation(invocation)) + + async def _invoke_builtin_send_emoji(self, tool_call: ToolCall) -> ToolExecutionResult: + """执行内置表情工具。""" + from src.chat.emoji_system.maisaka_tool import send_emoji_for_maisaka + + tool_args = tool_call.args or {} + emotion = str(tool_args.get("emotion") or "").strip() + context_texts = [ + message.get_history_text() + for message in self._runtime._chat_history[-5:] + if message.get_history_text().strip() + ] + structured_result: dict[str, Any] = { + "success": False, + "message": "", + "description": "", + "emotion": [], + "requested_emotion": emotion, + "matched_emotion": "", + } + + logger.info(f"{self._runtime.log_prefix} 触发表情包发送工具，请求情绪={emotion!r}") + + try: + send_result = await send_emoji_for_maisaka( + stream_id=self._runtime.session_id, + requested_emotion=emotion, + reasoning=self._last_reasoning_content, + context_texts=context_texts, + ) + except Exception as exc: + logger.exception(f"{self._runtime.log_prefix} 发送表情包时发生异常: {exc}") + structured_result["message"] = f"发送表情包时发生异常：{exc}" + return self._build_tool_failure_result( + tool_call.func_name, + structured_result["message"], + structured_content=structured_result, + ) + + structured_result["description"] = send_result.description + structured_result["emotion"] = list(send_result.emotions) + structured_result["matched_emotion"] = send_result.matched_emotion + structured_result["message"] = send_result.message + + if send_result.success: + logger.info( + f"{self._runtime.log_prefix} 表情包发送成功: " + f"描述={send_result.description!r} 情绪标签={send_result.emotions} " + f"请求情绪={emotion!r} 命中情绪={send_result.matched_emotion!r}" + ) + self._append_sent_emoji_to_chat_history( + emoji_base64=send_result.emoji_base64, + success_message=send_result.message, + ) + structured_result["success"] = True + return self._build_tool_success_result( + tool_call.func_name, + send_result.message, + structured_content=structured_result, + ) + + logger.warning( + f"{self._runtime.log_prefix} 表情包发送失败: " + f"请求情绪={emotion!r} 错误信息={send_result.message}" + ) + return self._build_tool_failure_result( + tool_call.func_name, + structured_result["message"], + structured_content=structured_result, + ) async def _handle_tool_calls( self, diff --git a/src/plugins/built_in/emoji_plugin/_manifest.json b/src/plugins/built_in/emoji_plugin/_manifest.json deleted file mode 100644 index 5b53abad..00000000 --- a/src/plugins/built_in/emoji_plugin/_manifest.json +++ /dev/null @@ -1,41 +0,0 @@ -{ - "manifest_version": 2, - "version": "2.0.0", - "name": "Emoji插件 (Emoji Actions)", - "description": "可以发送和管理 Emoji", - "author": { - "name": "SengokuCola", - "url": "https://github.com/MaiM-with-u" - }, - "license": "GPL-v3.0-or-later", - "urls": { - "repository": "https://github.com/MaiM-with-u/maibot", - "homepage": "https://github.com/MaiM-with-u/maibot", - "documentation": "https://github.com/MaiM-with-u/maibot", - "issues": "https://github.com/MaiM-with-u/maibot/issues" - }, - "host_application": { - "min_version": "1.0.0", - "max_version": "1.0.0" - }, - "sdk": { - "min_version": "2.0.0", - "max_version": "2.99.99" - }, - "dependencies": [], - "capabilities": [ - "emoji.get_random", - "message.get_recent", - "message.build_readable", - "llm.generate", - "send.emoji", - "config.get" - ], - "i18n": { - "default_locale": "zh-CN", - "supported_locales": [ - "zh-CN" - ] - }, - "id": "builtin.emoji-plugin" -} diff --git a/src/plugins/built_in/emoji_plugin/plugin.py b/src/plugins/built_in/emoji_plugin/plugin.py deleted file mode 100644 index cc6b87c5..00000000 --- a/src/plugins/built_in/emoji_plugin/plugin.py +++ /dev/null @@ -1,129 +0,0 @@ -"""Emoji 插件 — 新 SDK 版本 - -根据聊天上下文的情感，使用 LLM 选择并发送合适的表情包。 -""" - -from maibot_sdk import Action, MaiBotPlugin -from maibot_sdk.types import ActivationType - -import random - - -class EmojiPlugin(MaiBotPlugin): - """表情包插件""" - - @Action( - "emoji", - 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()