mirror of https://github.com/Mai-with-u/MaiBot.git
SengokuCola
parent
d10e08f15d
commit
bc2b07302b
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@ -2,6 +2,9 @@
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## [0.10.1] - 2025-8-
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- planner现在改为主+副结构
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- 优化识图的表现
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- 为planner添加单独控制的提示词
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- 修复激活值计算异常的BUG
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- 修复lpmm日志错误
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@ -9,6 +12,8 @@
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- 修复emoji管理器的一个BUG
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- 优化对模型请求的处理
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- 重构内部代码
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- 暂时禁用记忆
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- 优化关系的表现的效率
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## [0.10.0] - 2025-8-18
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### 🌟 主要功能更改
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@ -16,6 +16,7 @@ from src.chat.utils.chat_message_builder import replace_user_references
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from src.common.logger import get_logger
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from src.mood.mood_manager import mood_manager
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from src.person_info.person_info import Person
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from src.common.database.database_model import Images
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if TYPE_CHECKING:
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from src.chat.heart_flow.heartFC_chat import HeartFChatting
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@ -31,6 +32,9 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
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Returns:
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Tuple[float, bool, list[str]]: (兴趣度, 是否被提及, 关键词)
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"""
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if message.is_picid:
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return 0.0, []
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is_mentioned, _ = is_mentioned_bot_in_message(message)
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interested_rate = 0.0
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@ -129,13 +133,26 @@ class HeartFCMessageReceiver:
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# 3. 日志记录
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mes_name = chat.group_info.group_name if chat.group_info else "私聊"
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# 如果消息中包含图片标识，则将 [picid:...] 替换为 [图片]
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# 用这个pattern截取出id部分，picid是一个list，并替换成对应的图片描述
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picid_pattern = r"\[picid:([^\]]+)\]"
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processed_plain_text = re.sub(picid_pattern, "[图片]", message.processed_plain_text)
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picid_list = re.findall(picid_pattern, message.processed_plain_text)
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# 创建替换后的文本
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processed_text = message.processed_plain_text
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if picid_list:
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for picid in picid_list:
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image = Images.get_or_none(Images.image_id == picid)
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if image and image.description:
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# 将[picid:xxxx]替换成图片描述
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processed_text = processed_text.replace(f"[picid:{picid}]", f"[图片：{image.description}]")
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else:
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# 如果没有找到图片描述，则移除[picid:xxxx]标记
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processed_text = processed_text.replace(f"[picid:{picid}]", "[图片：网络不好，图片无法加载]")
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# 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
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processed_plain_text = replace_user_references(
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processed_plain_text,
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processed_text,
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message.message_info.platform, # type: ignore
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replace_bot_name=True
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)
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@ -357,7 +357,7 @@ class ActionPlanner:
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# --- 调用 LLM (普通文本生成) ---
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llm_content = None
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action_planner_infos = [] # 存储多个ActionPlannerInfo对象
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action_planner_infos: List[ActionPlannerInfo] = [] # 存储多个ActionPlannerInfo对象
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try:
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llm_content, (reasoning_content, _, _) = await self.planner_small_llm.generate_response_async(prompt=prompt)
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@ -581,7 +581,7 @@ class ActionPlanner:
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sub_plan_results = await asyncio.gather(*sub_plan_tasks)
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# 收集所有结果
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all_sub_planner_results = []
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all_sub_planner_results: List[ActionPlannerInfo] = []
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for sub_result in sub_plan_results:
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all_sub_planner_results.extend(sub_result)
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@ -679,9 +679,12 @@ class ActionPlanner:
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reasoning = f"Planner 内部处理错误: {outer_e}"
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is_parallel = True
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if mode == ChatMode.NORMAL and action in current_available_actions:
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if is_parallel:
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is_parallel = current_available_actions[action].parallel_action
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for action_planner_info in all_sub_planner_results:
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if action_planner_info.action_type == "no_action":
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continue
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if not current_available_actions[action_planner_info.action_type].parallel_action:
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is_parallel = False
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break
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action_data["loop_start_time"] = loop_start_time
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@ -720,8 +723,11 @@ class ActionPlanner:
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)
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]
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action_str = ""
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for action in actions:
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action_str += f"{action.action_type} "
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logger.info(
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f"{self.log_prefix}并行模式：返回主规划器{len(main_actions)}个action + 副规划器{len(all_sub_planner_results)}个action，过滤后总计{len(actions)}个action"
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f"{self.log_prefix}大脑小脑决定执行{len(actions)}个动作: {action_str}"
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)
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else:
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# 如果为假，只返回副规划器的结果
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@ -739,7 +745,7 @@ class ActionPlanner:
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)
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]
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logger.info(f"{self.log_prefix}非并行模式：返回副规划器的{len(actions)}个action（已过滤no_action）")
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logger.info(f"{self.log_prefix}跳过大脑，执行小脑的{len(actions)}个动作")
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return actions, target_message
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@ -514,7 +514,7 @@ class ImageManager:
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)
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# 启动异步VLM处理
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asyncio.create_task(self._process_image_with_vlm(image_id, image_base64))
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await self._process_image_with_vlm(image_id, image_base64)
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return image_id, f"[picid:{image_id}]"
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@ -568,17 +568,16 @@ class ImageManager:
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prompt = global_config.custom_prompt.image_prompt
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# 获取VLM描述
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logger.info(f"[VLM异步调用] 为图片生成描述 (ID: {image_id}, Hash: {image_hash[:8]}...)")
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description, _ = await self.vlm.generate_response_for_image(
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prompt, image_base64, image_format, temperature=0.4, max_tokens=300
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)
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if description is None:
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logger.warning("VLM未能生成图片描述")
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description = "无法生成描述"
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description = ""
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if cached_description := self._get_description_from_db(image_hash, "image"):
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logger.warning(f"虽然生成了描述，但是找到缓存图片描述: {cached_description}")
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logger.info(f"虽然生成了描述，但是找到缓存图片描述: {cached_description}")
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description = cached_description
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# 更新数据库
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@ -589,8 +588,6 @@ class ImageManager:
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# 保存描述到ImageDescriptions表作为备用缓存
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self._save_description_to_db(image_hash, description, "image")
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logger.info(f"[VLM异步完成] 图片描述生成: {description[:50]}...")
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except Exception as e:
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logger.error(f"VLM处理图片失败: {str(e)}")
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