feat：支持回复和at正确解析

src/heart_flow/chat_state_info.py

@@ -10,7 +10,7 @@ class ChatState(enum.Enum):
 
 class ChatStateInfo:
     def __init__(self):
-        self.chat_status: ChatState = ChatState.ABSENT
+        self.chat_status: ChatState = ChatState.CHAT
         self.current_state_time = 120
 
         self.mood_manager = MoodManager()

src/heart_flow/sub_heartflow.py

@@ -295,9 +295,6 @@ class SubHeartflow:
     def get_all_observations(self) -> list[Observation]:
         return self.observations
 
-    def clear_observations(self):
-        self.observations.clear()
-
     def _get_primary_observation(self) -> Optional[ChattingObservation]:
         if self.observations and isinstance(self.observations[0], ChattingObservation):
             return self.observations[0]

src/plugins/heartFC_chat/expressors/exprssion_learner.py

@@ -1,4 +1,5 @@
 import time
+import random
 from typing import List, Dict, Optional, Any, Tuple, Coroutine
 from src.common.logger_manager import get_logger
 from src.plugins.models.utils_model import LLMRequest
@@ -8,6 +9,9 @@ from src.plugins.heartFC_chat.heartflow_prompt_builder import Prompt, global_pro
 import os
 import json
 
+
+MAX_EXPRESSION_COUNT = 300
+
 logger = get_logger("expressor")
 
 
@@ -52,6 +56,18 @@ class ExpressionLearner:
             expressions: List[dict] = json.load(f)
         return expressions
 
+    def is_similar(self, s1: str, s2: str) -> bool:
+        """
+        判断两个字符串是否相似（只考虑长度大于5且有80%以上重合，不考虑子串）
+        """
+        if not s1 or not s2:
+            return False
+        min_len = min(len(s1), len(s2))
+        if min_len < 5:
+            return False
+        same = sum(1 for a, b in zip(s1, s2) if a == b)
+        return same / min_len > 0.8
+
     async def learn_and_store_expression(self) -> List[Tuple[str, str, str]]:
         """选择从当前到最近1小时内的随机10条消息，然后学习这些消息的表达方式"""
         logger.info("开始学习表达方式...")
@@ -74,15 +90,40 @@ class ExpressionLearner:
             file_path = os.path.join(dir_path, "expressions.json")
             # 若已存在，先读出合并
             if os.path.exists(file_path):
-                old_data: List[Dict[str, str]] = []
+                old_data: List[Dict[str, str, str]] = []
                 try:
                     with open(file_path, "r", encoding="utf-8") as f:
                         old_data = json.load(f)
                 except Exception:
                     old_data = []
-                expr_list = old_data + expr_list
+            else:
+                old_data = []
+            # 超过最大数量时，20%概率移除count=1的项
+            if len(old_data) >= MAX_EXPRESSION_COUNT:
+                delete = True
+                new_old_data = []
+                for item in old_data:
+                    if item.get("count", 1) == 1 and random.random() < 0.2:
+                        continue  # 20%概率移除
+                    new_old_data.append(item)
+                old_data = new_old_data
+            # 合并逻辑
+            for new_expr in expr_list:
+                found = False
+                for old_expr in old_data:
+                    if self.is_similar(new_expr["situation"], old_expr.get("situation", "")) and self.is_similar(new_expr["style"], old_expr.get("style", "")):
+                        found = True
+                        # 50%概率替换
+                        if random.random() < 0.5:
+                            old_expr["situation"] = new_expr["situation"]
+                            old_expr["style"] = new_expr["style"]
+                        old_expr["count"] = old_expr.get("count", 1) + 1
+                        break
+                if not found:
+                    new_expr["count"] = 1
+                    old_data.append(new_expr)
             with open(file_path, "w", encoding="utf-8") as f:
-                json.dump(expr_list, f, ensure_ascii=False, indent=2)
+                json.dump(old_data, f, ensure_ascii=False, indent=2)
         return expressions
 
     async def learn_expression(self) -> Optional[List[Tuple[str, str, str]]]:

src/plugins/heartFC_chat/heartflow_prompt_builder.py

@@ -86,7 +86,7 @@ def init_prompt():
 {{
     "action": "reply",
     "text": "你想表达的内容",
-    "emojis": "表情关键词",
+    "emojis": "描述当前使用表情包的场景",
     "target": "你想要回复的原始文本内容（非必须，仅文本，不包含发送者)",
     "reasoning": "你的决策理由",
 }}

src/plugins/utils/chat_message_builder.py

@@ -8,6 +8,7 @@ from src.config.config import global_config
 from typing import List, Dict, Any, Tuple  # 确保类型提示被导入
 import time  # 导入 time 模块以获取当前时间
 import random
+import re
 
 # 导入新的 repository 函数
 from src.common.message_repository import find_messages, count_messages
@@ -215,10 +216,43 @@ async def _build_readable_messages_internal(
             else:
                 person_name = "某人"
 
+        # 检查是否有 回复<aaa:bbb> 字段
+        reply_pattern = r"回复<([^:<>]+):([^:<>]+)>"
+        match = re.search(reply_pattern, content)
+        if match:
+            aaa = match.group(1)
+            bbb = match.group(2)
+            reply_person_id = person_info_manager.get_person_id(platform, bbb)
+            reply_person_name = await person_info_manager.get_value(reply_person_id, "person_name")
+            if not reply_person_name:
+                reply_person_name = aaa
+            # 在内容前加上回复信息
+            content = re.sub(reply_pattern, f"回复 {reply_person_name}", content, count=1)
+
+        # 检查是否有 @<aaa:bbb> 字段 @<{member_info.get('nickname')}:{member_info.get('user_id')}>
+        at_pattern = r"@<([^:<>]+):([^:<>]+)>"
+        at_matches = list(re.finditer(at_pattern, content))
+        if at_matches:
+            new_content = ""
+            last_end = 0
+            for m in at_matches:
+                new_content += content[last_end:m.start()]
+                aaa = m.group(1)
+                bbb = m.group(2)
+                at_person_id = person_info_manager.get_person_id(platform, bbb)
+                at_person_name = await person_info_manager.get_value(at_person_id, "person_name")
+                if not at_person_name:
+                    at_person_name = aaa
+                new_content += f"@{at_person_name}"
+                last_end = m.end()
+            new_content += content[last_end:]
+            content = new_content
+
         message_details_raw.append((timestamp, person_name, content))
 
     if not message_details_raw:
-        return "", []
+        return "", []            
+        
 
     message_details_raw.sort(key=lambda x: x[0])  # 按时间戳(第一个元素)升序排序，越早的消息排在前面
 

template/bot_config_template.toml

@@ -66,11 +66,11 @@ time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运
 nonebot-qq="http://127.0.0.1:18002/api/message"
 
 [chat] #麦麦的聊天通用设置
-allow_focus_mode = true # 是否允许专注聊天状态
+allow_focus_mode = false # 是否允许专注聊天状态
 # 是否启用heart_flowC(HFC)模式
 # 启用后麦麦会自主选择进入heart_flowC模式（持续一段时间），进行主动的观察和回复，并给出回复，比较消耗token
-base_normal_chat_num = 8 # 最多允许多少个群进行普通聊天
+base_normal_chat_num = 999 # 最多允许多少个群进行普通聊天
-base_focused_chat_num = 5 # 最多允许多少个群进行专注聊天
+base_focused_chat_num = 4 # 最多允许多少个群进行专注聊天
 
 observation_context_size = 15 # 观察到的最长上下文大小,建议15，太短太长都会导致脑袋尖尖
 message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟