feat:尝试建立hfc逻辑

This commit is contained in:
SengokuCola
2026-03-23 17:56:18 +08:00
parent e7ca3142e9
commit bfc9781c4f
3 changed files with 1442 additions and 163 deletions

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import asyncio
import time
import traceback
import random
from typing import List, Optional, Dict, Any, Tuple, TYPE_CHECKING
from rich.traceback import install
from src.config.config import global_config
from src.common.logger import get_logger
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.data_models.message_data_model import ReplyContentType
from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
from src.chat.utils.prompt_builder import global_prompt_manager
from src.chat.utils.timer_calculator import Timer
from src.chat.planner_actions.planner import ActionPlanner
from src.chat.planner_actions.action_modifier import ActionModifier
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.heart_flow.hfc_utils import CycleDetail
from src.bw_learner.expression_learner import expression_learner_manager
from src.chat.heart_flow.frequency_control import frequency_control_manager
from src.bw_learner.message_recorder import extract_and_distribute_messages
from src.person_info.person_info import Person
from src.plugin_system.base.component_types import EventType, ActionInfo
from src.plugin_system.core import events_manager
from src.plugin_system.apis import generator_api, send_api, message_api, database_api
from src.chat.utils.chat_message_builder import (
build_readable_messages_with_id,
get_raw_msg_before_timestamp_with_chat,
)
from src.chat.utils.utils import record_replyer_action_temp
from src.memory_system.chat_history_summarizer import ChatHistorySummarizer
if TYPE_CHECKING:
from src.common.data_models.database_data_model import DatabaseMessages
from src.common.data_models.message_data_model import ReplySetModel
ERROR_LOOP_INFO = {
"loop_plan_info": {
"action_result": {
"action_type": "error",
"action_data": {},
"reasoning": "循环处理失败",
},
},
"loop_action_info": {
"action_taken": False,
"reply_text": "",
"command": "",
"taken_time": time.time(),
},
}
install(extra_lines=3)
# 注释:原来的动作修改超时常量已移除,因为改为顺序执行
logger = get_logger("hfc") # Logger Name Changed
class HeartFChatting:
"""
管理一个连续的Focus Chat循环
用于在特定聊天流中生成回复。
其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。
"""
def __init__(self, chat_id: str):
"""
HeartFChatting 初始化函数
参数:
chat_id: 聊天流唯一标识符(如stream_id)
on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
performance_version: 性能记录版本号,用于区分不同启动版本
"""
# 基础属性
self.stream_id: str = chat_id # 聊天流ID
self.chat_stream: ChatStream = get_chat_manager().get_stream(self.stream_id) # type: ignore
if not self.chat_stream:
raise ValueError(f"无法找到聊天流: {self.stream_id}")
self.log_prefix = f"[{get_chat_manager().get_stream_name(self.stream_id) or self.stream_id}]"
self.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id)
self.action_manager = ActionManager()
self.action_planner = ActionPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
# 循环控制内部状态
self.running: bool = False
self._loop_task: Optional[asyncio.Task] = None # 主循环任务
# 添加循环信息管理相关的属性
self.history_loop: List[CycleDetail] = []
self._cycle_counter = 0
self._current_cycle_detail: CycleDetail = None # type: ignore
self.last_read_time = time.time() - 2
self.is_mute = False
self.last_active_time = time.time() # 记录上一次非noreply时间
self.question_probability_multiplier = 1
self.questioned = False
# 跟踪连续 no_reply 次数,用于动态调整阈值
self.consecutive_no_reply_count = 0
# 聊天内容概括器
self.chat_history_summarizer = ChatHistorySummarizer(chat_id=self.stream_id)
async def start(self):
"""检查是否需要启动主循环,如果未激活则启动。"""
# 如果循环已经激活,直接返回
if self.running:
logger.debug(f"{self.log_prefix} HeartFChatting 已激活,无需重复启动")
return
try:
# 标记为活动状态,防止重复启动
self.running = True
self._loop_task = asyncio.create_task(self._main_chat_loop())
self._loop_task.add_done_callback(self._handle_loop_completion)
# 启动聊天内容概括器的后台定期检查循环
await self.chat_history_summarizer.start()
logger.info(f"{self.log_prefix} HeartFChatting 启动完成")
except Exception as e:
# 启动失败时重置状态
self.running = False
self._loop_task = None
logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {e}")
raise
def _handle_loop_completion(self, task: asyncio.Task):
"""当 _hfc_loop 任务完成时执行的回调。"""
try:
if exception := task.exception():
logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
logger.error(traceback.format_exc()) # Log full traceback for exceptions
else:
logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)")
except asyncio.CancelledError:
logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天")
def start_cycle(self) -> Tuple[Dict[str, float], str]:
self._cycle_counter += 1
self._current_cycle_detail = CycleDetail(self._cycle_counter)
self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
cycle_timers = {}
return cycle_timers, self._current_cycle_detail.thinking_id
def end_cycle(self, loop_info, cycle_timers):
self._current_cycle_detail.set_loop_info(loop_info)
self.history_loop.append(self._current_cycle_detail)
self._current_cycle_detail.timers = cycle_timers
self._current_cycle_detail.end_time = time.time()
def print_cycle_info(self, cycle_timers):
# 记录循环信息和计时器结果
timer_strings = []
for name, elapsed in cycle_timers.items():
if elapsed < 0.1:
# 不显示小于0.1秒的计时器
continue
formatted_time = f"{elapsed:.2f}"
timer_strings.append(f"{name}: {formatted_time}")
logger.info(
f"{self.log_prefix}{self._current_cycle_detail.cycle_id}次思考,"
f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒;" # type: ignore
+ (f"详情: {'; '.join(timer_strings)}" if timer_strings else "")
)
async def _loopbody(self):
recent_messages_list = message_api.get_messages_by_time_in_chat(
chat_id=self.stream_id,
start_time=self.last_read_time,
end_time=time.time(),
limit=20,
limit_mode="latest",
filter_mai=True,
filter_command=False,
filter_intercept_message_level=0,
)
# 根据连续 no_reply 次数动态调整阈值
# 3次 no_reply 时,阈值调高到 1.550%概率为150%概率为2
# 5次 no_reply 时,提高到 2大于等于两条消息的阈值
if self.consecutive_no_reply_count >= 5:
threshold = 2
elif self.consecutive_no_reply_count >= 3:
# 1.5 的含义50%概率为150%概率为2
threshold = 2 if random.random() < 0.5 else 1
else:
threshold = 1
if len(recent_messages_list) >= threshold:
# for message in recent_messages_list:
# print(message.processed_plain_text)
self.last_read_time = time.time()
# !此处使at或者提及必定回复
mentioned_message = None
for message in recent_messages_list:
if (message.is_mentioned or message.is_at) and global_config.chat.mentioned_bot_reply:
mentioned_message = message
# logger.info(f"{self.log_prefix} 当前talk_value: {global_config.chat.get_talk_value(self.stream_id)}")
# *控制频率用
if mentioned_message:
await self._observe(recent_messages_list=recent_messages_list, force_reply_message=mentioned_message)
elif (
random.random()
< global_config.chat.get_talk_value(self.stream_id)
* frequency_control_manager.get_or_create_frequency_control(self.stream_id).get_talk_frequency_adjust()
):
await self._observe(recent_messages_list=recent_messages_list)
else:
# 没有提到继续保持沉默等待5秒防止频繁触发
await asyncio.sleep(10)
return True
else:
await asyncio.sleep(0.2)
return True
return True
async def _send_and_store_reply(
self,
response_set: "ReplySetModel",
action_message: "DatabaseMessages",
cycle_timers: Dict[str, float],
thinking_id,
actions,
selected_expressions: Optional[List[int]] = None,
quote_message: Optional[bool] = None,
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
with Timer("回复发送", cycle_timers):
reply_text = await self._send_response(
reply_set=response_set,
message_data=action_message,
selected_expressions=selected_expressions,
quote_message=quote_message,
)
# 获取 platform如果不存在则从 chat_stream 获取,如果还是 None 则使用默认值
platform = action_message.chat_info.platform
if platform is None:
platform = getattr(self.chat_stream, "platform", "unknown")
person = Person(platform=platform, user_id=action_message.user_info.user_id)
person_name = person.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.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},
action_name="reply",
)
# 构建循环信息
loop_info: Dict[str, Any] = {
"loop_plan_info": {
"action_result": actions,
},
"loop_action_info": {
"action_taken": True,
"reply_text": reply_text,
"command": "",
"taken_time": time.time(),
},
}
return loop_info, reply_text, cycle_timers
async def _observe(
self, # interest_value: float = 0.0,
recent_messages_list: Optional[List["DatabaseMessages"]] = None,
force_reply_message: Optional["DatabaseMessages"] = None,
) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
if recent_messages_list is None:
recent_messages_list = []
_reply_text = "" # 初始化reply_text变量避免UnboundLocalError
start_time = time.time()
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
# 通过 MessageRecorder 统一提取消息并分发给 expression_learner 和 jargon_miner
# 在 replyer 执行时触发,统一管理时间窗口,避免重复获取消息
asyncio.create_task(extract_and_distribute_messages(self.stream_id))
# 添加curious检测任务 - 检测聊天记录中的矛盾、冲突或需要提问的内容
# asyncio.create_task(check_and_make_question(self.stream_id))
# 添加聊天内容概括任务 - 累积、打包和压缩聊天记录
# 注意后台循环已在start()中启动,这里作为额外触发点,在有思考时立即处理
# asyncio.create_task(self.chat_history_summarizer.process())
cycle_timers, thinking_id = self.start_cycle()
logger.info(
f"{self.log_prefix} 开始第{self._cycle_counter}次思考(频率: {global_config.chat.get_talk_value(self.stream_id)})"
)
# 第一步:动作检查
available_actions: Dict[str, ActionInfo] = {}
try:
await self.action_modifier.modify_actions()
available_actions = self.action_manager.get_using_actions()
except Exception as e:
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
# 执行planner
is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=self.stream_id,
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.6),
filter_intercept_message_level=1,
)
chat_content_block, message_id_list = build_readable_messages_with_id(
messages=message_list_before_now,
timestamp_mode="normal_no_YMD",
read_mark=self.action_planner.last_obs_time_mark,
truncate=True,
show_actions=True,
)
prompt_info = await self.action_planner.build_planner_prompt(
is_group_chat=is_group_chat,
chat_target_info=chat_target_info,
current_available_actions=available_actions,
chat_content_block=chat_content_block,
message_id_list=message_id_list,
)
continue_flag, modified_message = await events_manager.handle_mai_events(
EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
)
if not continue_flag:
return False
if modified_message and modified_message._modify_flags.modify_llm_prompt:
prompt_info = (modified_message.llm_prompt, prompt_info[1])
with Timer("规划器", cycle_timers):
action_to_use_info = await self.action_planner.plan(
loop_start_time=self.last_read_time,
available_actions=available_actions,
force_reply_message=force_reply_message,
)
logger.info(
f"{self.log_prefix} 决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}"
)
# 3. 并行执行所有动作
action_tasks = [
asyncio.create_task(
self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
)
for action in action_to_use_info
]
# 并行执行所有任务
results = await asyncio.gather(*action_tasks, return_exceptions=True)
# 处理执行结果
reply_loop_info = None
reply_text_from_reply = ""
action_success = False
action_reply_text = ""
excute_result_str = ""
for result in results:
excute_result_str += f"{result['action_type']} 执行结果:{result['result']}\n"
if isinstance(result, BaseException):
logger.error(f"{self.log_prefix} 动作执行异常: {result}")
continue
if result["action_type"] != "reply":
action_success = result["success"]
action_reply_text = result["result"]
elif result["action_type"] == "reply":
if result["success"]:
reply_loop_info = result["loop_info"]
reply_text_from_reply = result["result"]
else:
logger.warning(f"{self.log_prefix} 回复动作执行失败")
self.action_planner.add_plan_excute_log(result=excute_result_str)
# 构建最终的循环信息
if reply_loop_info:
# 如果有回复信息使用回复的loop_info作为基础
loop_info = reply_loop_info
# 更新动作执行信息
loop_info["loop_action_info"].update(
{
"action_taken": action_success,
"taken_time": time.time(),
}
)
_reply_text = reply_text_from_reply
else:
# 没有回复信息构建纯动作的loop_info
loop_info = {
"loop_plan_info": {
"action_result": action_to_use_info,
},
"loop_action_info": {
"action_taken": action_success,
"reply_text": action_reply_text,
"taken_time": time.time(),
},
}
_reply_text = action_reply_text
self.end_cycle(loop_info, cycle_timers)
self.print_cycle_info(cycle_timers)
end_time = time.time()
if end_time - start_time < global_config.chat.planner_smooth:
wait_time = global_config.chat.planner_smooth - (end_time - start_time)
await asyncio.sleep(wait_time)
else:
await asyncio.sleep(0.1)
return True
async def _main_chat_loop(self):
"""主循环,持续进行计划并可能回复消息,直到被外部取消。"""
try:
while self.running:
# 主循环
success = await self._loopbody()
await asyncio.sleep(0.1)
if not success:
break
except asyncio.CancelledError:
# 设置了关闭标志位后被取消是正常流程
logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
except Exception:
logger.error(f"{self.log_prefix} 麦麦聊天意外错误将于3s后尝试重新启动")
print(traceback.format_exc())
await asyncio.sleep(3)
self._loop_task = asyncio.create_task(self._main_chat_loop())
logger.error(f"{self.log_prefix} 结束了当前聊天循环")
async def _handle_action(
self,
action: str,
action_reasoning: str,
action_data: dict,
cycle_timers: Dict[str, float],
thinking_id: str,
action_message: Optional["DatabaseMessages"] = None,
) -> tuple[bool, str, str]:
"""
处理规划动作,使用动作工厂创建相应的动作处理器
参数:
action: 动作类型
action_reasoning: 决策理由
action_data: 动作数据,包含不同动作需要的参数
cycle_timers: 计时器字典
thinking_id: 思考ID
action_message: 消息数据
返回:
tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
"""
try:
# 使用工厂创建动作处理器实例
try:
action_handler = self.action_manager.create_action(
action_name=action,
action_data=action_data,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
chat_stream=self.chat_stream,
log_prefix=self.log_prefix,
action_reasoning=action_reasoning,
action_message=action_message,
)
except Exception as e:
logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}")
traceback.print_exc()
return False, ""
# 处理动作并获取结果(固定记录一次动作信息)
result = await action_handler.execute()
success, action_text = result
return success, action_text
except Exception as e:
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
traceback.print_exc()
return False, ""
async def _send_response(
self,
reply_set: "ReplySetModel",
message_data: "DatabaseMessages",
selected_expressions: Optional[List[int]] = None,
quote_message: Optional[bool] = None,
) -> str:
# 根据 llm_quote 配置决定是否使用 quote_message 参数
if global_config.chat.llm_quote:
# 如果配置为 true使用 llm_quote 参数决定是否引用回复
if quote_message is None:
logger.warning(f"{self.log_prefix} quote_message 参数为空,不引用")
need_reply = False
else:
need_reply = quote_message
if need_reply:
logger.info(f"{self.log_prefix} LLM 决定使用引用回复")
else:
# 如果配置为 false使用原来的模式
new_message_count = message_api.count_new_messages(
chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time()
)
need_reply = new_message_count >= random.randint(2, 3) or time.time() - self.last_read_time > 90
if need_reply:
logger.info(f"{self.log_prefix} 从思考到回复,共有{new_message_count}条新消息使用引用回复或者上次回复时间超过90秒")
reply_text = ""
first_replied = False
for reply_content in reply_set.reply_data:
if reply_content.content_type != ReplyContentType.TEXT:
continue
data: str = reply_content.content # type: ignore
if not first_replied:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.stream_id,
reply_message=message_data,
set_reply=need_reply,
typing=False,
selected_expressions=selected_expressions,
)
first_replied = True
else:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.stream_id,
reply_message=message_data,
set_reply=False,
typing=True,
selected_expressions=selected_expressions,
)
reply_text += data
return reply_text
async def _execute_action(
self,
action_planner_info: ActionPlannerInfo,
chosen_action_plan_infos: List[ActionPlannerInfo],
thinking_id: str,
available_actions: Dict[str, ActionInfo],
cycle_timers: Dict[str, float],
):
"""执行单个动作的通用函数"""
try:
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
# 直接当场执行no_reply逻辑
if action_planner_info.action_type == "no_reply":
# 直接处理no_reply逻辑不再通过动作系统
reason = action_planner_info.reasoning or "选择不回复"
# logger.info(f"{self.log_prefix} 选择不回复,原因: {reason}")
# 增加连续 no_reply 计数
self.consecutive_no_reply_count += 1
await database_api.store_action_info(
chat_stream=self.chat_stream,
action_build_into_prompt=False,
action_prompt_display=reason,
action_done=True,
thinking_id=thinking_id,
action_data={},
action_name="no_reply",
action_reasoning=reason,
)
return {"action_type": "no_reply", "success": True, "result": "选择不回复", "command": ""}
elif action_planner_info.action_type == "reply":
# 直接当场执行reply逻辑
self.questioned = False
# 刷新主动发言状态
# 重置连续 no_reply 计数
self.consecutive_no_reply_count = 0
reason = action_planner_info.reasoning or ""
# 根据 think_mode 配置决定 think_level 的值
think_mode = global_config.chat.think_mode
if think_mode == "default":
think_level = 0
elif think_mode == "deep":
think_level = 1
elif think_mode == "dynamic":
# dynamic 模式:从 planner 返回的 action_data 中获取
think_level = action_planner_info.action_data.get("think_level", 1)
else:
# 默认使用 default 模式
think_level = 0
# 使用 action_reasoningplanner 的整体思考理由)作为 reply_reason
planner_reasoning = action_planner_info.action_reasoning or reason
record_replyer_action_temp(
chat_id=self.stream_id,
reason=reason,
think_level=think_level,
)
await database_api.store_action_info(
chat_stream=self.chat_stream,
action_build_into_prompt=False,
action_prompt_display=reason,
action_done=True,
thinking_id=thinking_id,
action_data={},
action_name="reply",
action_reasoning=reason,
)
# 从 Planner 的 action_data 中提取未知词语列表(仅在 reply 时使用)
unknown_words = None
quote_message = None
if isinstance(action_planner_info.action_data, dict):
uw = action_planner_info.action_data.get("unknown_words")
if isinstance(uw, list):
cleaned_uw: List[str] = []
for item in uw:
if isinstance(item, str):
s = item.strip()
if s:
cleaned_uw.append(s)
if cleaned_uw:
unknown_words = cleaned_uw
# 从 Planner 的 action_data 中提取 quote_message 参数
qm = action_planner_info.action_data.get("quote")
if qm is not None:
# 支持多种格式true/false, "true"/"false", 1/0
if isinstance(qm, bool):
quote_message = qm
elif isinstance(qm, str):
quote_message = qm.lower() in ("true", "1", "yes")
elif isinstance(qm, (int, float)):
quote_message = bool(qm)
logger.info(f"{self.log_prefix} {qm}引用回复设置: {quote_message}")
success, llm_response = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_message=action_planner_info.action_message,
available_actions=available_actions,
chosen_actions=chosen_action_plan_infos,
reply_reason=planner_reasoning,
unknown_words=unknown_words,
enable_tool=global_config.tool.enable_tool,
request_type="replyer",
from_plugin=False,
reply_time_point=action_planner_info.action_data.get("loop_start_time", time.time()),
think_level=think_level,
)
if not success or not llm_response or not llm_response.reply_set:
if action_planner_info.action_message:
logger.info(f"{action_planner_info.action_message.processed_plain_text} 的回复生成失败")
else:
logger.info("回复生成失败")
return {"action_type": "reply", "success": False, "result": "回复生成失败", "loop_info": None}
response_set = llm_response.reply_set
selected_expressions = llm_response.selected_expressions
loop_info, reply_text, _ = await self._send_and_store_reply(
response_set=response_set,
action_message=action_planner_info.action_message, # type: ignore
cycle_timers=cycle_timers,
thinking_id=thinking_id,
actions=chosen_action_plan_infos,
selected_expressions=selected_expressions,
quote_message=quote_message,
)
self.last_active_time = time.time()
return {
"action_type": "reply",
"success": True,
"result": f"你使用reply动作' {action_planner_info.action_message.processed_plain_text} '这句话进行了回复,回复内容为: '{reply_text}'",
"loop_info": loop_info,
}
else:
# 执行普通动作
with Timer("动作执行", cycle_timers):
success, result = await self._handle_action(
action=action_planner_info.action_type,
action_reasoning=action_planner_info.action_reasoning or "",
action_data=action_planner_info.action_data or {},
cycle_timers=cycle_timers,
thinking_id=thinking_id,
action_message=action_planner_info.action_message,
)
self.last_active_time = time.time()
return {
"action_type": action_planner_info.action_type,
"success": success,
"result": result,
}
except Exception as e:
logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
return {
"action_type": action_planner_info.action_type,
"success": False,
"result": "",
"loop_info": None,
"error": str(e),
}

View File

@@ -1,231 +1,377 @@
from rich.traceback import install
from typing import Optional, List, TYPE_CHECKING
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import asyncio
import random
import time
import traceback
import random
from src.common.logger import get_logger
from src.common.utils.utils_config import ExpressionConfigUtils, ChatConfigUtils
from src.config.config import global_config
from src.config.file_watcher import FileChange
from src.chat.message_receive.chat_manager import chat_manager
from rich.traceback import install
from src.bw_learner.expression_learner import ExpressionLearner
from src.bw_learner.jargon_miner import JargonMiner
from src.chat.event_helpers import build_event_message
from src.chat.logger.plan_reply_logger import PlanReplyLogger
from src.chat.message_receive.chat_manager import BotChatSession
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.planner_actions.action_modifier import ActionModifier
from src.chat.planner_actions.planner import ActionPlanner
from src.chat.utils.prompt_builder import global_prompt_manager
from src.chat.utils.timer_calculator import Timer
from src.chat.utils.utils import record_replyer_action_temp
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
from src.common.logger import get_logger
from src.common.utils.utils_config import ChatConfigUtils, ExpressionConfigUtils
from src.config.config import global_config
from src.config.file_watcher import FileChange
from src.core.event_bus import event_bus
from src.core.types import ActionInfo, EventType
from src.person_info.person_info import Person
from src.services import (
database_service as database_api,
generator_service as generator_api,
message_service as message_api,
send_service as send_api,
)
from src.services.message_service import build_readable_messages_with_id, get_messages_before_time_in_chat
from .heartFC_utils import CycleDetail
if TYPE_CHECKING:
from src.chat.message_receive.message import SessionMessage
install(extra_lines=5)
logger = get_logger("heartFC_chat")
class HeartFChatting:
"""
管理一个连续的Focus Chat聊天会话
用于在特定的聊天会话里面生成回复
"""
"""管理一个持续运行的 Focus Chat 会话。"""
def __init__(self, session_id: str):
"""
初始化 HeartFChatting 实例
Args:
session_id: 聊天会话ID
"""
# 基础属性
self.session_id = session_id
session_name = chat_manager.get_session_name(session_id) or session_id
self.chat_stream: BotChatSession = _chat_manager.get_session_by_session_id(self.session_id) # type: ignore[assignment]
if not self.chat_stream:
raise ValueError(f"无法找到聊天会话 {self.session_id}")
session_name = _chat_manager.get_session_name(session_id) or session_id
self.log_prefix = f"[{session_name}]"
self.session_name = session_name
# 系统运行状态
self.action_manager = ActionManager()
self.action_planner = ActionPlanner(chat_id=self.session_id, action_manager=self.action_manager)
self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.session_id)
self._running: bool = False
self._loop_task: Optional[asyncio.Task] = None
self._cycle_counter: int = 0
self._hfc_lock: asyncio.Lock = asyncio.Lock() # 用于保护 _hfc_func 的并发访问
# 聊天频率相关
self._consecutive_no_reply_count = 0 # 跟踪连续 no_reply 次数,用于动态调整阈值
self._talk_frequency_adjust: float = 1.0 # 发言频率修正值默认为1.0,可以根据需要调整
# HFC内消息缓存
self.message_cache: List[SessionMessage] = []
# Asyncio Event 用于控制循环的开始和结束
self._cycle_event = asyncio.Event()
self._hfc_lock = asyncio.Lock()
self._cycle_counter = 0
self._current_cycle_detail: Optional[CycleDetail] = None
self.history_loop: List[CycleDetail] = []
self.last_read_time = time.time() - 2
self.last_active_time = time.time()
self._talk_frequency_adjust = 1.0
self._consecutive_no_reply_count = 0
self.message_cache: List["SessionMessage"] = []
self._min_messages_for_extraction = 30
self._min_extraction_interval = 60
self._last_extraction_time = 0.0
# 表达方式相关内容
self._min_messages_for_extraction = 30 # 最少提取消息数
self._min_extraction_interval = 60 # 最小提取时间间隔,单位为秒
self._last_extraction_time: float = 0.0 # 上次提取的时间戳
expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(session_id)
self._enable_expression_use = expr_use # 允许使用表达方式,但不一定启用学习
self._enable_expression_learning = expr_learn # 允许学习表达方式
self._enable_jargon_learning = jargon_learn # 允许学习黑话
# 表达学习器
self._expression_learner: ExpressionLearner = ExpressionLearner(session_id)
# 黑话挖掘器
self._jargon_miner: JargonMiner = JargonMiner(session_id, session_name=session_name)
# TODO: ChatSummarizer 聊天总结器重构
# ====== 公开方法 ======
self._enable_expression_use = expr_use
self._enable_expression_learning = expr_learn
self._enable_jargon_learning = jargon_learn
self._expression_learner = ExpressionLearner(session_id)
self._jargon_miner = JargonMiner(session_id, session_name=session_name)
async def start(self):
"""启动 HeartFChatting 的主循环"""
# 先检查是否已经启动运行
if self._running:
logger.debug(f"{self.log_prefix} 已经在运行中,无需重复启动")
logger.debug(f"{self.log_prefix} HeartFChatting 已在运行中")
return
try:
self._running = True
self._cycle_event.clear() # 确保事件初始状态为未设置
self._cycle_event.clear()
self._loop_task = asyncio.create_task(self.main_loop())
self._loop_task.add_done_callback(self._handle_loop_completion)
logger.info(f"{self.log_prefix} HeartFChatting 启动完成")
except Exception as e:
logger.error(f"{self.log_prefix} 启动 HeartFChatting 失败: {e}", exc_info=True)
self._running = False # 确保状态正确
self._cycle_event.set() # 确保事件被设置,避免死锁
self._loop_task = None # 确保任务引用被清理
except Exception as exc:
logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {exc}", exc_info=True)
self._running = False
self._cycle_event.set()
self._loop_task = None
raise
async def stop(self):
"""停止 HeartFChatting 的主循环"""
if not self._running:
logger.debug(f"{self.log_prefix} HeartFChatting 已经停止,无需重复停止")
logger.debug(f"{self.log_prefix} HeartFChatting 已停止")
return
self._running = False
self._cycle_event.set() # 触发事件,通知循环结束
self._cycle_event.set()
if self._loop_task:
self._loop_task.cancel() # 取消主循环任务
self._loop_task.cancel()
try:
await self._loop_task # 等待任务完成
await self._loop_task
except asyncio.CancelledError:
logger.info(f"{self.log_prefix} HeartFChatting 主循环已成功取消")
except Exception as e:
logger.error(f"{self.log_prefix} 停止 HeartFChatting 时发生错误: {e}", exc_info=True)
logger.info(f"{self.log_prefix} HeartFChatting 主循环已取消")
except Exception as exc:
logger.error(f"{self.log_prefix} 停止 HeartFChatting 时发生错误: {exc}", exc_info=True)
finally:
self._loop_task = None # 确保任务引用被清理
self._loop_task = None
logger.info(f"{self.log_prefix} HeartFChatting 已停止")
def adjust_talk_frequency(self, new_value: float):
"""调整发言频率的调整值
Args:
new_value: 新的修正值,必须为非负数。值越大,修正发言频率越高;值越小,修正发言频率越低。
"""
self._talk_frequency_adjust = max(0.0, new_value)
async def register_message(self, message: "SessionMessage"):
"""注册一条消息到 HeartFChatting 的缓存中,并检测其是否产生提及,决定是否唤醒聊天
Args:
message: 待注册的消息对象
"""
self.message_cache.append(message)
# 先检查at必回复
if global_config.chat.inevitable_at_reply and message.is_at:
async with self._hfc_lock: # 确保与主循环逻辑的互斥访问
await self._judge_and_response(message)
return # 直接返回,避免同一条消息被主循环再次处理
# 再检查提及必回复
self.last_read_time = time.time()
async with self._hfc_lock:
await self._judge_and_response(mentioned_message=message, recent_messages_list=[message])
return
if global_config.chat.mentioned_bot_reply and message.is_mentioned:
# 直接获取锁,确保一定一定触发回复逻辑,不受当前是否正在执行主循环的影响
async with self._hfc_lock: # 确保与主循环逻辑的互斥访问
await self._judge_and_response(message)
self.last_read_time = time.time()
async with self._hfc_lock:
await self._judge_and_response(mentioned_message=message, recent_messages_list=[message])
return
async def main_loop(self):
try:
while self._running and not self._cycle_event.is_set():
if not self._hfc_lock.locked():
async with self._hfc_lock: # 确保主循环逻辑的互斥访问
async with self._hfc_lock:
await self._hfc_func()
await asyncio.sleep(5)
await asyncio.sleep(0.1)
except asyncio.CancelledError:
logger.info(f"{self.log_prefix} HeartFChatting: 主循环被取消,正在关闭")
except Exception as e:
logger.error(f"{self.log_prefix} 麦麦聊天意外错误: {e}将于3s后尝试重新启动")
await self.stop() # 确保状态正确
logger.info(f"{self.log_prefix} HeartFChatting: 主循环被取消")
except Exception as exc:
logger.error(f"{self.log_prefix} HeartFChatting: 主循环异常: {exc}", exc_info=True)
await self.stop()
await asyncio.sleep(3)
await self.start() # 尝试重新启动
await self.start()
async def _config_callback(self, file_change: Optional[FileChange] = None):
"""配置文件变更回调函数"""
# TODO: 根据配置文件变动重新计算相关参数:
"""
需要计算的参数:
self._enable_expression_use = expr_use # 允许使用表达方式,但不一定启用学习
self._enable_expression_learning = expr_learn # 允许学习表达方式
self._enable_jargon_learning = jargon_learn # 允许学习黑话
"""
del file_change
expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(self.session_id)
self._enable_expression_use = expr_use
self._enable_expression_learning = expr_learn
self._enable_jargon_learning = jargon_learn
# ====== 心流聊天核心逻辑 ======
async def _hfc_func(self, mentioned_message: Optional["SessionMessage"] = None):
"""心流聊天的主循环逻辑"""
if self._consecutive_no_reply_count >= 5:
threshold = 2
elif self._consecutive_no_reply_count >= 3:
threshold = 2 if random.random() < 0.5 else 1
else:
threshold = 1
async def _hfc_func(self):
recent_messages_list = message_api.get_messages_by_time_in_chat(
chat_id=self.session_id,
start_time=self.last_read_time,
end_time=time.time(),
limit=20,
limit_mode="latest",
filter_mai=True,
filter_command=False,
filter_intercept_message_level=1,
)
if len(self.message_cache) < threshold:
if len(recent_messages_list) < 1:
await asyncio.sleep(0.2)
return True
talk_value_threshold = (
random.random() * ChatConfigUtils.get_talk_value(self.session_id) * self._talk_frequency_adjust
)
if mentioned_message and global_config.chat.mentioned_bot_reply:
await self._judge_and_response(mentioned_message)
elif random.random() < talk_value_threshold:
await self._judge_and_response()
self.last_read_time = time.time()
mentioned_message: Optional["SessionMessage"] = None
for message in recent_messages_list:
if global_config.chat.inevitable_at_reply and message.is_at:
mentioned_message = message
elif global_config.chat.mentioned_bot_reply and message.is_mentioned:
mentioned_message = message
talk_value = ChatConfigUtils.get_talk_value(self.session_id) * self._talk_frequency_adjust
if mentioned_message:
await self._judge_and_response(mentioned_message=mentioned_message, recent_messages_list=recent_messages_list)
elif random.random() < talk_value:
await self._judge_and_response(recent_messages_list=recent_messages_list)
return True
async def _judge_and_response(self, mentioned_message: Optional["SessionMessage"] = None):
"""判定和生成回复"""
asyncio.create_task(self._trigger_expression_learning(self.message_cache))
# TODO: 完成反思器之后的逻辑
start_time = time.time()
current_cycle_detail = self._start_cycle()
async def _judge_and_response(
self,
mentioned_message: Optional["SessionMessage"] = None,
recent_messages_list: Optional[List["SessionMessage"]] = None,
):
recent_messages = list(recent_messages_list or self.message_cache[-20:])
if recent_messages:
asyncio.create_task(self._trigger_expression_learning(recent_messages))
cycle_timers, thinking_id = self._start_cycle()
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
# TODO: 动作检查逻辑
# TODO: Planner逻辑
# TODO: 动作执行逻辑
try:
async with global_prompt_manager.async_message_scope(self._get_template_name()):
available_actions: Dict[str, ActionInfo] = {}
try:
await self.action_modifier.modify_actions()
available_actions = self.action_manager.get_using_actions()
except Exception as exc:
logger.error(f"{self.log_prefix} 动作修改失败: {exc}", exc_info=True)
cycle_detail = self._end_cycle(current_cycle_detail)
if wait_time := global_config.chat.planner_smooth - (time.time() - start_time) > 0:
await asyncio.sleep(wait_time)
else:
await asyncio.sleep(0.1) # 最小等待时间,避免过快循环
return True
is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
message_list_before_now = get_messages_before_time_in_chat(
chat_id=self.session_id,
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.6),
filter_intercept_message_level=1,
)
chat_content_block, message_id_list = build_readable_messages_with_id(
messages=message_list_before_now,
timestamp_mode="normal_no_YMD",
read_mark=self.action_planner.last_obs_time_mark,
truncate=True,
show_actions=True,
)
prompt, filtered_actions = await self._build_planner_prompt_with_event(
available_actions=available_actions,
is_group_chat=is_group_chat,
chat_target_info=chat_target_info,
chat_content_block=chat_content_block,
message_id_list=message_id_list,
)
if prompt is None:
return False
with Timer("规划器", cycle_timers):
reasoning, action_to_use_info, llm_raw_output, llm_reasoning, llm_duration_ms = (
await self.action_planner._execute_main_planner(
prompt=prompt,
message_id_list=message_id_list,
filtered_actions=filtered_actions,
available_actions=available_actions,
loop_start_time=self.last_read_time,
)
)
action_to_use_info = self._ensure_force_reply_action(
actions=action_to_use_info,
force_reply_message=mentioned_message,
available_actions=available_actions,
)
self.action_planner.add_plan_log(reasoning, action_to_use_info)
self.action_planner.last_obs_time_mark = time.time()
self._log_plan(
prompt=prompt,
reasoning=reasoning,
llm_raw_output=llm_raw_output,
llm_reasoning=llm_reasoning,
llm_duration_ms=llm_duration_ms,
actions=action_to_use_info,
)
logger.info(
f"{self.log_prefix} 决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}"
)
action_tasks = [
asyncio.create_task(
self._execute_action(
action,
action_to_use_info,
thinking_id,
available_actions,
cycle_timers,
)
)
for action in action_to_use_info
]
results = await asyncio.gather(*action_tasks, return_exceptions=True)
reply_loop_info = None
reply_text_from_reply = ""
action_success = False
action_reply_text = ""
execute_result_str = ""
for result in results:
if isinstance(result, BaseException):
logger.error(f"{self.log_prefix} 动作执行异常: {result}", exc_info=True)
continue
execute_result_str += f"{result['action_type']} 执行结果:{result['result']}\n"
if result["action_type"] == "reply":
if result["success"]:
reply_loop_info = result["loop_info"]
reply_text_from_reply = result["result"]
else:
logger.warning(f"{self.log_prefix} reply 动作执行失败")
else:
action_success = result["success"]
action_reply_text = result["result"]
self.action_planner.add_plan_excute_log(result=execute_result_str)
if reply_loop_info:
loop_info = reply_loop_info
loop_info["loop_action_info"].update(
{
"action_taken": action_success,
"taken_time": time.time(),
}
)
else:
loop_info = {
"loop_plan_info": {
"action_result": action_to_use_info,
},
"loop_action_info": {
"action_taken": action_success,
"reply_text": action_reply_text,
"taken_time": time.time(),
},
}
reply_text_from_reply = action_reply_text
current_cycle_detail = self._end_cycle(self._current_cycle_detail, loop_info)
logger.debug(f"{self.log_prefix} 本轮最终输出: {reply_text_from_reply}")
return current_cycle_detail is not None
except Exception as exc:
logger.error(f"{self.log_prefix} 判定与回复流程失败: {exc}", exc_info=True)
if self._current_cycle_detail:
self._end_cycle(
self._current_cycle_detail,
{
"loop_plan_info": {"action_result": []},
"loop_action_info": {
"action_taken": False,
"reply_text": "",
"taken_time": time.time(),
"error": str(exc),
},
},
)
return False
def _handle_loop_completion(self, task: asyncio.Task):
"""当 _hfc_func 任务完成时执行的回调。"""
try:
if exception := task.exception():
logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
logger.error(traceback.format_exc()) # Log full traceback for exceptions
logger.error(f"{self.log_prefix} HeartFChatting: 主循环异常退出: {exception}")
logger.error(traceback.format_exc())
else:
logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)")
logger.info(f"{self.log_prefix} HeartFChatting: 主循环已退出")
except asyncio.CancelledError:
logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天")
logger.info(f"{self.log_prefix} HeartFChatting: 聊天已结束")
# ====== 学习器触发逻辑 ======
async def _trigger_expression_learning(self, messages: List["SessionMessage"]):
if not messages:
return
self._expression_learner.add_messages(messages)
if time.time() - self._last_extraction_time < self._min_extraction_interval:
return
@@ -233,12 +379,14 @@ class HeartFChatting:
return
if not self._enable_expression_learning:
return
extraction_end_time = time.time()
logger.info(
f"聊天流 {self.session_name} 提取到 {len(messages)} 条消息,"
f"时间窗口: {self._last_extraction_time:.2f} - {extraction_end_time:.2f}"
)
self._last_extraction_time = extraction_end_time
try:
jargon_miner = self._jargon_miner if self._enable_jargon_learning else None
learnt_style = await self._expression_learner.learn(jargon_miner)
@@ -246,43 +394,398 @@ class HeartFChatting:
logger.info(f"{self.log_prefix} 表达学习完成")
else:
logger.debug(f"{self.log_prefix} 表达学习未获得有效结果")
except Exception as e:
logger.error(f"{self.log_prefix} 表达学习失败: {e}", exc_info=True)
except Exception as exc:
logger.error(f"{self.log_prefix} 表达学习失败: {exc}", exc_info=True)
# ====== 记录循环执行信息相关逻辑 ======
def _start_cycle(self) -> CycleDetail:
def _start_cycle(self) -> Tuple[Dict[str, float], str]:
self._cycle_counter += 1
current_cycle_detail = CycleDetail(cycle_id=self._cycle_counter)
current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
return current_cycle_detail
self._current_cycle_detail = CycleDetail(cycle_id=self._cycle_counter)
self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
return self._current_cycle_detail.time_records, self._current_cycle_detail.thinking_id
def _end_cycle(self, cycle_detail: CycleDetail, only_long_execution: bool = True):
def _end_cycle(self, cycle_detail: Optional[CycleDetail], loop_info: Optional[Dict[str, Any]] = None):
if cycle_detail is None:
return None
cycle_detail.loop_plan_info = (loop_info or {}).get("loop_plan_info")
cycle_detail.loop_action_info = (loop_info or {}).get("loop_action_info")
cycle_detail.end_time = time.time()
timer_strings: List[str] = [
self.history_loop.append(cycle_detail)
timer_strings = [
f"{name}: {duration:.2f}s"
for name, duration in cycle_detail.time_records.items()
if not only_long_execution or duration >= 0.1
if duration >= 0.1
]
logger.info(
f"{self.log_prefix} {cycle_detail.cycle_id} 个心流循环完成"
f"耗时: {cycle_detail.end_time - cycle_detail.start_time:.2f}\n"
f"{self.log_prefix}{cycle_detail.cycle_id} 个心流循环完成"
f"耗时: {cycle_detail.end_time - cycle_detail.start_time:.2f}s"
f"详细计时: {', '.join(timer_strings) if timer_strings else ''}"
)
return cycle_detail
# ====== Action相关逻辑 ======
async def _execute_action(self, *args, **kwargs):
"""原ExecuteAction"""
raise NotImplementedError("执行动作的逻辑尚未实现") # TODO: 实现动作执行的逻辑,替换掉*args, **kwargs*占位符
async def _execute_action(
self,
action_planner_info: ActionPlannerInfo,
chosen_action_plan_infos: List[ActionPlannerInfo],
thinking_id: str,
available_actions: Dict[str, ActionInfo],
cycle_timers: Dict[str, float],
):
try:
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
if action_planner_info.action_type == "no_reply":
reason = action_planner_info.reasoning or "选择不回复"
self._consecutive_no_reply_count += 1
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=reason,
thinking_id=thinking_id,
action_data={},
action_name="no_reply",
action_reasoning=reason,
)
return {
"action_type": "no_reply",
"success": True,
"result": "选择不回复",
"loop_info": None,
}
async def _execute_other_actions(self, *args, **kwargs):
"""原HandleAction"""
raise NotImplementedError(
"执行其他动作的逻辑尚未实现"
) # TODO: 实现其他动作执行的逻辑, 替换掉*args, **kwargs*占位符
if action_planner_info.action_type == "reply":
self._consecutive_no_reply_count = 0
reason = action_planner_info.reasoning or ""
think_level = self._get_think_level(action_planner_info)
planner_reasoning = action_planner_info.action_reasoning or reason
# ====== 响应发送相关方法 ======
async def _send_response(self, *args, **kwargs):
raise NotImplementedError("发送回复的逻辑尚未实现") # TODO: 实现发送回复的逻辑,替换掉*args, **kwargs*占位符
# 传入的消息至少应该是个MessageSequence实例最好是SessionMessage实例随后可直接转化为MessageSending实例
record_replyer_action_temp(
chat_id=self.session_id,
reason=reason,
think_level=think_level,
)
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=reason,
thinking_id=thinking_id,
action_data={},
action_name="reply",
action_reasoning=reason,
)
unknown_words, quote_message = self._extract_reply_metadata(action_planner_info)
success, llm_response = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_message=action_planner_info.action_message,
available_actions=available_actions,
chosen_actions=chosen_action_plan_infos,
reply_reason=planner_reasoning,
unknown_words=unknown_words,
enable_tool=global_config.tool.enable_tool,
request_type="replyer",
from_plugin=False,
reply_time_point=action_planner_info.action_data.get("loop_start_time", time.time())
if action_planner_info.action_data
else time.time(),
think_level=think_level,
)
if not success or not llm_response or not llm_response.reply_set:
if action_planner_info.action_message:
logger.info(
f"{action_planner_info.action_message.processed_plain_text} 的回复生成失败"
)
else:
logger.info(f"{self.log_prefix} 回复生成失败")
return {
"action_type": "reply",
"success": False,
"result": "回复生成失败",
"loop_info": None,
}
loop_info, reply_text, _ = await self._send_and_store_reply(
response_set=llm_response.reply_set,
action_message=action_planner_info.action_message, # type: ignore[arg-type]
cycle_timers=cycle_timers,
thinking_id=thinking_id,
actions=chosen_action_plan_infos,
selected_expressions=llm_response.selected_expressions,
quote_message=quote_message,
)
self.last_active_time = time.time()
return {
"action_type": "reply",
"success": True,
"result": reply_text,
"loop_info": loop_info,
}
with Timer("动作执行", cycle_timers):
success, result = await self._handle_action(
action=action_planner_info.action_type,
action_reasoning=action_planner_info.action_reasoning or "",
action_data=action_planner_info.action_data or {},
cycle_timers=cycle_timers,
thinking_id=thinking_id,
action_message=action_planner_info.action_message,
)
if success:
self.last_active_time = time.time()
return {
"action_type": action_planner_info.action_type,
"success": success,
"result": result,
"loop_info": None,
}
except Exception as exc:
logger.error(f"{self.log_prefix} 执行动作时出错: {exc}", exc_info=True)
return {
"action_type": action_planner_info.action_type,
"success": False,
"result": "",
"loop_info": None,
"error": str(exc),
}
async def _handle_action(
self,
action: str,
action_reasoning: str,
action_data: dict,
cycle_timers: Dict[str, float],
thinking_id: str,
action_message: Optional["SessionMessage"] = None,
) -> Tuple[bool, str]:
try:
action_handler = self.action_manager.create_action(
action_name=action,
action_data=action_data,
action_reasoning=action_reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
chat_stream=self.chat_stream,
log_prefix=self.log_prefix,
action_message=action_message,
)
if not action_handler:
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}")
return False, ""
success, action_text = await action_handler.execute()
return success, action_text
except Exception as exc:
logger.error(f"{self.log_prefix} 处理动作 {action} 时出错: {exc}", exc_info=True)
return False, ""
async def _send_and_store_reply(
self,
response_set: MessageSequence,
action_message: "SessionMessage",
cycle_timers: Dict[str, float],
thinking_id: str,
actions: List[ActionPlannerInfo],
selected_expressions: Optional[List[int]] = None,
quote_message: Optional[bool] = None,
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
with Timer("回复发送", cycle_timers):
reply_text = await self._send_response(
reply_set=response_set,
message_data=action_message,
selected_expressions=selected_expressions,
quote_message=quote_message,
)
platform = action_message.platform or getattr(self.chat_stream, "platform", "unknown")
person = Person(platform=platform, user_id=action_message.message_info.user_info.user_id)
action_prompt_display = f"你对{person.person_name}进行了回复:{reply_text}"
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=action_prompt_display,
thinking_id=thinking_id,
action_data={"reply_text": reply_text},
action_name="reply",
)
loop_info: Dict[str, Any] = {
"loop_plan_info": {
"action_result": actions,
},
"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: MessageSequence,
message_data: "SessionMessage",
selected_expressions: Optional[List[int]] = None,
quote_message: Optional[bool] = None,
) -> str:
if global_config.chat.llm_quote:
need_reply = bool(quote_message)
else:
new_message_count = message_api.count_new_messages(
chat_id=self.session_id,
start_time=self.last_read_time,
end_time=time.time(),
)
need_reply = new_message_count >= random.randint(2, 3) or time.time() - self.last_read_time > 90
reply_text = ""
first_replied = False
for component in reply_set.components:
if not isinstance(component, TextComponent):
continue
data = component.text
if not first_replied:
await send_api.text_to_stream(
text=data,
stream_id=self.session_id,
reply_message=message_data,
set_reply=need_reply,
typing=False,
selected_expressions=selected_expressions,
)
first_replied = True
else:
await send_api.text_to_stream(
text=data,
stream_id=self.session_id,
reply_message=message_data,
set_reply=False,
typing=True,
selected_expressions=selected_expressions,
)
reply_text += data
return reply_text
async def _build_planner_prompt_with_event(
self,
available_actions: Dict[str, ActionInfo],
is_group_chat: bool,
chat_target_info: Any,
chat_content_block: str,
message_id_list: List[Tuple[str, "SessionMessage"]],
) -> Tuple[Optional[str], Dict[str, ActionInfo]]:
filtered_actions = self.action_planner._filter_actions_by_activation_type(available_actions, chat_content_block)
prompt, _ = await self.action_planner.build_planner_prompt(
is_group_chat=is_group_chat,
chat_target_info=chat_target_info,
current_available_actions=filtered_actions,
chat_content_block=chat_content_block,
message_id_list=message_id_list,
)
event_message = build_event_message(EventType.ON_PLAN, llm_prompt=prompt, stream_id=self.session_id)
continue_flag, modified_message = await event_bus.emit(EventType.ON_PLAN, event_message)
if not continue_flag:
logger.info(f"{self.log_prefix} ON_PLAN 事件中止了本轮 HFC")
return None, filtered_actions
if modified_message and modified_message._modify_flags.modify_llm_prompt and modified_message.llm_prompt:
prompt = modified_message.llm_prompt
return prompt, filtered_actions
def _ensure_force_reply_action(
self,
actions: List[ActionPlannerInfo],
force_reply_message: Optional["SessionMessage"],
available_actions: Dict[str, ActionInfo],
) -> List[ActionPlannerInfo]:
if not force_reply_message:
return actions
has_reply_to_force_message = any(
action.action_type == "reply"
and action.action_message
and action.action_message.message_id == force_reply_message.message_id
for action in actions
)
if has_reply_to_force_message:
return actions
actions = [action for action in actions if action.action_type != "no_reply"]
actions.insert(
0,
ActionPlannerInfo(
action_type="reply",
reasoning="用户提及了我,必须回复该消息",
action_data={"loop_start_time": self.last_read_time},
action_message=force_reply_message,
available_actions=available_actions,
action_reasoning=None,
),
)
logger.info(f"{self.log_prefix} 检测到强制回复消息,已补充 reply 动作")
return actions
def _log_plan(
self,
prompt: str,
reasoning: str,
llm_raw_output: Optional[str],
llm_reasoning: Optional[str],
llm_duration_ms: Optional[float],
actions: List[ActionPlannerInfo],
) -> None:
try:
PlanReplyLogger.log_plan(
chat_id=self.session_id,
prompt=prompt,
reasoning=reasoning,
raw_output=llm_raw_output,
raw_reasoning=llm_reasoning,
actions=actions,
timing={
"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
"loop_start_time": self.last_read_time,
},
extra=None,
)
except Exception:
logger.exception(f"{self.log_prefix} 记录 plan 日志失败")
def _extract_reply_metadata(
self,
action_planner_info: ActionPlannerInfo,
) -> Tuple[Optional[List[str]], Optional[bool]]:
unknown_words: Optional[List[str]] = None
quote_message: Optional[bool] = None
action_data = action_planner_info.action_data or {}
raw_unknown_words = action_data.get("unknown_words")
if isinstance(raw_unknown_words, list):
cleaned_unknown_words = []
for item in raw_unknown_words:
if isinstance(item, str) and (cleaned_item := item.strip()):
cleaned_unknown_words.append(cleaned_item)
if cleaned_unknown_words:
unknown_words = cleaned_unknown_words
raw_quote = action_data.get("quote")
if isinstance(raw_quote, bool):
quote_message = raw_quote
elif isinstance(raw_quote, str):
quote_message = raw_quote.lower() in {"true", "1", "yes"}
elif isinstance(raw_quote, (int, float)):
quote_message = bool(raw_quote)
return unknown_words, quote_message
def _get_think_level(self, action_planner_info: ActionPlannerInfo) -> int:
think_mode = global_config.chat.think_mode
if think_mode == "default":
return 0
if think_mode == "deep":
return 1
if think_mode == "dynamic":
action_data = action_planner_info.action_data or {}
return int(action_data.get("think_level", 1))
return 0
def _get_template_name(self) -> Optional[str]:
if self.chat_stream.context:
return self.chat_stream.context.template_name
return None

View File

@@ -0,0 +1,42 @@
import traceback
from typing import Any, Optional, Dict
from src.chat.message_receive.chat_stream import get_chat_manager
from src.common.logger import get_logger
from src.chat.heart_flow.heartFC_chat import HeartFChatting
from src.chat.brain_chat.brain_chat import BrainChatting
from src.chat.message_receive.chat_stream import ChatStream
logger = get_logger("heartflow")
class Heartflow:
"""主心流协调器,负责初始化并协调聊天"""
def __init__(self):
self.heartflow_chat_list: Dict[Any, HeartFChatting | BrainChatting] = {}
async def get_or_create_heartflow_chat(self, chat_id: Any) -> Optional[HeartFChatting | BrainChatting]:
"""获取或创建一个新的HeartFChatting实例"""
try:
if chat_id in self.heartflow_chat_list:
if chat := self.heartflow_chat_list.get(chat_id):
return chat
else:
chat_stream: ChatStream | None = get_chat_manager().get_stream(chat_id)
if not chat_stream:
raise ValueError(f"未找到 chat_id={chat_id} 的聊天流")
if chat_stream.group_info:
new_chat = HeartFChatting(chat_id=chat_id)
else:
new_chat = BrainChatting(chat_id=chat_id)
await new_chat.start()
self.heartflow_chat_list[chat_id] = new_chat
return new_chat
except Exception as e:
logger.error(f"创建心流聊天 {chat_id} 失败: {e}", exc_info=True)
traceback.print_exc()
return None
heartflow = Heartflow()