refactor(chat): 重构HeartFChatting为模块化架构

将原本超长的heartFC_chat.py拆分为6个功能内聚的子模块：
- hfc_context：上下文数据容器
- cycle_tracker：循环状态记录
- energy_manager：能量值独立管理
- proactive_thinker：主动思考逻辑抽离
- cycle_processor：单次循环处理器
- response_handler / normal_mode_handler：响应策略

删除冗余常量、错误样板及旧逻辑；大幅减少类体积；降低耦合度，提升可维护性。

src/chat/chat_loop/response_handler.py

@@ -0,0 +1,152 @@
+import time
+import random
+import traceback
+from typing import Optional, Dict, Any, List, Tuple
+
+from src.common.logger import get_logger
+from src.config.config import global_config
+from src.plugin_system.apis import generator_api, send_api, message_api, database_api
+from src.person_info.person_info import get_person_info_manager
+from .hfc_context import HfcContext
+
+logger = get_logger("hfc.response")
+
+class ResponseHandler:
+    def __init__(self, context: HfcContext):
+        self.context = context
+
+    async def generate_and_send_reply(
+        self,
+        response_set,
+        reply_to_str,
+        loop_start_time,
+        action_message,
+        cycle_timers: Dict[str, float],
+        thinking_id,
+        plan_result,
+    ) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
+        reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message)
+
+        person_info_manager = get_person_info_manager()
+        
+        platform = "default"
+        if self.context.chat_stream:
+            platform = (
+                action_message.get("chat_info_platform") or action_message.get("user_platform") or self.context.chat_stream.platform
+            )
+
+        user_id = action_message.get("user_id", "")
+        person_id = person_info_manager.get_person_id(platform, user_id)
+        person_name = await person_info_manager.get_value(person_id, "person_name")
+        action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
+
+        await database_api.store_action_info(
+            chat_stream=self.context.chat_stream,
+            action_build_into_prompt=False,
+            action_prompt_display=action_prompt_display,
+            action_done=True,
+            thinking_id=thinking_id,
+            action_data={"reply_text": reply_text, "reply_to": reply_to_str},
+            action_name="reply",
+        )
+
+        loop_info: Dict[str, Any] = {
+            "loop_plan_info": {
+                "action_result": plan_result.get("action_result", {}),
+            },
+            "loop_action_info": {
+                "action_taken": True,
+                "reply_text": reply_text,
+                "command": "",
+                "taken_time": time.time(),
+            },
+        }
+
+        return loop_info, reply_text, cycle_timers
+
+    async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data) -> str:
+        current_time = time.time()
+        new_message_count = message_api.count_new_messages(
+            chat_id=self.context.stream_id, start_time=thinking_start_time, end_time=current_time
+        )
+        platform = message_data.get("user_platform", "")
+        user_id = message_data.get("user_id", "")
+        reply_to_platform_id = f"{platform}:{user_id}"
+
+        need_reply = new_message_count >= random.randint(2, 4)
+
+        reply_text = ""
+        is_proactive_thinking = message_data.get("message_type") == "proactive_thinking"
+
+        first_replied = False
+        for reply_seg in reply_set:
+            # 调试日志：验证reply_seg的格式
+            logger.debug(f"Processing reply_seg type: {type(reply_seg)}, content: {reply_seg}")
+            
+            # 修正：正确处理元组格式 (格式为: (type, content))
+            if isinstance(reply_seg, tuple) and len(reply_seg) >= 2:
+                reply_type, data = reply_seg
+            else:
+                # 向下兼容：如果已经是字符串，则直接使用
+                data = str(reply_seg)
+                reply_type = "text"
+            
+            reply_text += data
+
+            if is_proactive_thinking and data.strip() == "沉默":
+                logger.info(f"{self.context.log_prefix} 主动思考决定保持沉默，不发送消息")
+                continue
+
+            if not first_replied:
+                if need_reply:
+                    await send_api.text_to_stream(
+                        text=data,
+                        stream_id=self.context.stream_id,
+                        reply_to=reply_to,
+                        reply_to_platform_id=reply_to_platform_id,
+                        typing=False,
+                    )
+                else:
+                    await send_api.text_to_stream(
+                        text=data,
+                        stream_id=self.context.stream_id,
+                        reply_to_platform_id=reply_to_platform_id,
+                        typing=False,
+                    )
+                first_replied = True
+            else:
+                await send_api.text_to_stream(
+                    text=data,
+                    stream_id=self.context.stream_id,
+                    reply_to_platform_id=reply_to_platform_id,
+                    typing=True,
+                )
+
+        return reply_text
+
+    async def generate_response(
+        self,
+        message_data: dict,
+        available_actions: Optional[Dict[str, Any]],
+        reply_to: str,
+        request_type: str = "chat.replyer.normal",
+    ) -> Optional[list]:
+        try:
+            success, reply_set, _ = await generator_api.generate_reply(
+                chat_stream=self.context.chat_stream,
+                reply_to=reply_to,
+                available_actions=available_actions,
+                enable_tool=global_config.tool.enable_tool,
+                request_type=request_type,
+                from_plugin=False,
+            )
+
+            if not success or not reply_set:
+                logger.info(f"对 {message_data.get('processed_plain_text')} 的回复生成失败")
+                return None
+
+            return reply_set
+
+        except Exception as e:
+            logger.error(f"{self.context.log_prefix}回复生成出现错误：{str(e)} {traceback.format_exc()}")
+            return None