gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat：保存关键词到message数据库

Browse Source
This commit is contained in:
SengokuCola
2025-08-10 21:12:49 +08:00
parent 22a625ce46
commit 69a855df8d
8 changed files with 150 additions and 109 deletions
Download Patch File Download Diff File Expand all files Collapse all files

186
src/chat/chat_loop/heartFC_chat.py
View File

@@ -4,6 +4,7 @@ import traceback
import random
from typing import List, Optional, Dict, Any, Tuple
from rich.traceback import install
from collections import deque
from src.config.config import global_config
from src.common.logger import get_logger
@@ -121,6 +122,8 @@ class HeartFChatting:
self.focus_energy = 1
self.no_reply_consecutive = 0
# 最近三次no_reply的新消息兴趣度记录
self.recent_interest_records: deque = deque(maxlen=3)
async def start(self):
"""检查是否需要启动主循环，如果未激活则启动。"""
@@ -210,13 +213,10 @@ class HeartFChatting:
self.focus_energy = 1
else:
# 计算最近三次记录的兴趣度总和
total_recent_interest = sum(NoReplyAction._recent_interest_records)
# 获取当前聊天频率和意愿系数
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
total_recent_interest = sum(self.recent_interest_records)
# 计算调整后的阈值
adjusted_threshold = 3 / talk_frequency
adjusted_threshold = 3 / global_config.chat.get_current_talk_frequency(self.stream_id)
logger.info(f"{self.log_prefix} 最近三次兴趣度总和: {total_recent_interest:.2f}, 调整后阈值: {adjusted_threshold:.2f}")
@@ -228,57 +228,74 @@ class HeartFChatting:
logger.info(f"{self.log_prefix} 兴趣度充足")
self.focus_energy = 1
async def _execute_no_reply(self, new_message:List[Dict[str, Any]]) -> Tuple[bool, str]:
"""执行breaking形式的no_reply（原有逻辑）"""
async def _should_process_messages(self, new_message: List[Dict[str, Any]], mode: ChatMode) -> bool:
"""
判断是否应该处理消息
Args:
new_message: 新消息列表
mode: 当前聊天模式
Returns:
bool: 是否应该处理消息
"""
new_message_count = len(new_message)
# 检查消息数量是否达到阈值
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
modified_exit_count_threshold = self.focus_energy / talk_frequency
if new_message_count >= modified_exit_count_threshold:
# 记录兴趣度到列表
total_interest = 0.0
for msg_dict in new_message:
interest_value = msg_dict.get("interest_value", 0.0)
if msg_dict.get("processed_plain_text", ""):
total_interest += interest_value
if mode == ChatMode.NORMAL:
# Normal模式：简单的消息数量判断
return new_message_count >= self.focus_energy
NoReplyAction._recent_interest_records.append(total_interest)
elif mode == ChatMode.FOCUS:
# Focus模式：原有的breaking形式no_reply逻辑
talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
modified_exit_count_threshold = self.focus_energy / talk_frequency
logger.info(
f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold})，结束等待"
)
return True
# 检查累计兴趣值
if new_message_count > 0:
accumulated_interest = 0.0
for msg_dict in new_message:
text = msg_dict.get("processed_plain_text", "")
interest_value = msg_dict.get("interest_value", 0.0)
if text:
accumulated_interest += interest_value
# 只在兴趣值变化时输出log
if not hasattr(self, "_last_accumulated_interest") or accumulated_interest != self._last_accumulated_interest:
logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {accumulated_interest:.2f}, 当前聊天频率: {talk_frequency:.2f}")
self._last_accumulated_interest = accumulated_interest
if accumulated_interest >= 3 / talk_frequency:
if new_message_count >= modified_exit_count_threshold:
# 记录兴趣度到列表
NoReplyAction._recent_interest_records.append(accumulated_interest)
total_interest = 0.0
for msg_dict in new_message:
interest_value = msg_dict.get("interest_value", 0.0)
if msg_dict.get("processed_plain_text", ""):
total_interest += interest_value
self.recent_interest_records.append(total_interest)
logger.info(
f"{self.log_prefix} 累计兴趣值达到{accumulated_interest:.2f}(>{5 / talk_frequency})，结束等待"
f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold})，结束等待"
)
return True
# 每10秒输出一次等待状态
if int(time.time() - self.last_read_time) > 0 and int(time.time() - self.last_read_time) % 10 == 0:
logger.info(
f"{self.log_prefix} 已等待{time.time() - self.last_read_time:.0f}秒，累计{new_message_count}条消息，继续等待..."
)
# 检查累计兴趣值
if new_message_count > 0:
accumulated_interest = 0.0
for msg_dict in new_message:
text = msg_dict.get("processed_plain_text", "")
interest_value = msg_dict.get("interest_value", 0.0)
if text:
accumulated_interest += interest_value
# 只在兴趣值变化时输出log
if not hasattr(self, "_last_accumulated_interest") or accumulated_interest != self._last_accumulated_interest:
logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {accumulated_interest:.2f}, 当前聊天频率: {talk_frequency:.2f}")
self._last_accumulated_interest = accumulated_interest
if accumulated_interest >= 3 / talk_frequency:
# 记录兴趣度到列表
self.recent_interest_records.append(accumulated_interest)
logger.info(
f"{self.log_prefix} 累计兴趣值达到{accumulated_interest:.2f}(>{5 / talk_frequency})，结束等待"
)
return True
# 每10秒输出一次等待状态
if int(time.time() - self.last_read_time) > 0 and int(time.time() - self.last_read_time) % 10 == 0:
logger.info(
f"{self.log_prefix} 已等待{time.time() - self.last_read_time:.0f}秒，累计{new_message_count}条消息，继续等待..."
)
await asyncio.sleep(0.5)
return False
async def _loopbody(self):
@@ -291,51 +308,50 @@ class HeartFChatting:
filter_mai=True,
filter_command=True,
)
new_message_count = len(recent_messages_dict)
# 先进行focus判定
if self.loop_mode == ChatMode.FOCUS:
if self.last_action == "no_reply":
if not await self._execute_no_reply(recent_messages_dict):
self.energy_value -= 0.3 / global_config.chat.focus_value
logger.info(f"{self.log_prefix} 能量值减少，当前能量值：{self.energy_value:.1f}")
await asyncio.sleep(0.5)
return True
self.last_read_time = time.time()
if await self._observe():
self.energy_value += 1 / global_config.chat.focus_value
logger.info(f"{self.log_prefix} 能量值增加，当前能量值：{self.energy_value:.1f}")
if self.energy_value <= 1:
logger.info(f"{self.log_prefix} 能量值过低，进入normal模式")
self.energy_value = 1
self.loop_mode = ChatMode.NORMAL
return True
return True
elif self.loop_mode == ChatMode.NORMAL:
if global_config.chat.focus_value != 0:
if new_message_count > 3 / pow(global_config.chat.focus_value, 0.5):
self.loop_mode = ChatMode.FOCUS
self.energy_value = (
10 + (new_message_count / (3 / pow(global_config.chat.focus_value, 0.5))) * 10
)
return True
if global_config.chat.focus_value != 0 and self.energy_value >= 30:
self.loop_mode = ChatMode.FOCUS
return True
if self.energy_value >= 30:
self.loop_mode = ChatMode.FOCUS
return True
# 统一的消息处理逻辑
should_process = await self._should_process_messages(recent_messages_dict, self.loop_mode)
if new_message_count >= self.focus_energy:
earliest_messages_data = recent_messages_dict[0]
self.last_read_time = earliest_messages_data.get("time")
if self.loop_mode == ChatMode.FOCUS:
# Focus模式处理
if self.last_action == "no_reply" and not should_process:
# 需要继续等待
self.energy_value -= 0.3 / global_config.chat.focus_value
logger.info(f"{self.log_prefix} 能量值减少，当前能量值：{self.energy_value:.1f}")
await asyncio.sleep(0.5)
return True
if_think = await self.normal_response(earliest_messages_data)
if should_process:
# Focus模式：设置last_read_time并执行observe
self.last_read_time = time.time()
if await self._observe():
self.energy_value += 1 / global_config.chat.focus_value
logger.info(f"{self.log_prefix} 能量值增加，当前能量值：{self.energy_value:.1f}")
return True
elif self.loop_mode == ChatMode.NORMAL:
# Normal模式处理
if should_process:
# Normal模式：设置last_read_time为最早消息的时间并调用normal_response
earliest_message_data = recent_messages_dict[0]
self.last_read_time = earliest_message_data.get("time")
if_think = await self.normal_response(earliest_message_data)
if if_think:
factor = max(global_config.chat.focus_value, 0.1)
self.energy_value *= 1.1 * factor
self.energy_value *= 1.1 * pow(factor, 0.5)
logger.info(f"{self.log_prefix} 进行了思考，能量值按倍数增加，当前能量值：{self.energy_value:.1f}")
else:
self.energy_value += 0.1 * global_config.chat.focus_value
@@ -343,10 +359,12 @@ class HeartFChatting:
logger.debug(f"{self.log_prefix} 当前能量值：{self.energy_value:.1f}")
return True
else:
# Normal模式：消息数量不足，等待
await asyncio.sleep(0.5)
return True
await asyncio.sleep(0.5)
return True
return True
async def build_reply_to_str(self, message_data: dict):
person_info_manager = get_person_info_manager()
@@ -705,13 +723,13 @@ class HeartFChatting:
# 管理no_reply计数器：当执行了非no_reply动作时，重置计数器
if action_type != "no_reply" and action_type != "no_action":
# 导入NoReplyAction并重置计数器
NoReplyAction.reset_consecutive_count()
self.recent_interest_records.clear()
self.no_reply_consecutive = 0
logger.info(f"{self.log_prefix} 执行了{action_type}动作，重置no_reply计数器")
return True
elif action_type == "no_action":
# 当执行回复动作时，也重置no_reply计数
NoReplyAction.reset_consecutive_count()
self.recent_interest_records.clear()
self.no_reply_consecutive = 0
logger.info(f"{self.log_prefix} 执行了回复动作，重置no_reply计数器")

4
src/chat/heart_flow/heartflow_message_processor.py
View File

@@ -57,9 +57,11 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool, list[s
with Timer("记忆激活"):
interested_rate, keywords = await hippocampus_manager.get_activate_from_text(
message.processed_plain_text,
max_depth= 5,
max_depth= 4,
fast_retrieval=False,
)
message.key_words = keywords
message.key_words_lite = keywords
logger.debug(f"记忆激活率: {interested_rate:.2f}, 关键词: {keywords}")
text_len = len(message.processed_plain_text)

6
src/chat/memory_system/Hippocampus.py
View File

@@ -322,14 +322,14 @@ class Hippocampus:
# 使用LLM提取关键词 - 根据详细文本长度分布优化topic_num计算
text_length = len(text)
topic_num: int | list[int] = 0
if text_length <= 5:
if text_length <= 6:
words = jieba.cut(text)
keywords = [word for word in words if len(word) > 1]
keywords = list(set(keywords))[:3] # 限制最多3个关键词
if keywords:
logger.debug(f"提取关键词: {keywords}")
return keywords
elif text_length <= 10:
elif text_length <= 12:
topic_num = [1, 3] # 6-10字符: 1个关键词 (27.18%的文本)
elif text_length <= 20:
topic_num = [2, 4] # 11-20字符: 2个关键词 (22.76%的文本)
@@ -776,7 +776,7 @@ class Hippocampus:
total_nodes = len(self.memory_graph.G.nodes())
# activated_nodes = len(activate_map)
activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
activation_ratio = activation_ratio * 60
activation_ratio = activation_ratio * 50
logger.debug(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
return activation_ratio, keywords

3
src/chat/message_receive/message.py
View File

@@ -117,6 +117,9 @@ class MessageRecv(Message):
self.priority_info = None
self.interest_value: float = None # type: ignore
self.key_words = []
self.key_words_lite = []
def update_chat_stream(self, chat_stream: "ChatStream"):
self.chat_stream = chat_stream

25
src/chat/message_receive/storage.py
View File

@@ -1,4 +1,5 @@
import re
import json
import traceback
from typing import Union
@@ -11,6 +12,23 @@ logger = get_logger("message_storage")
class MessageStorage:
@staticmethod
def _serialize_keywords(keywords) -> str:
"""将关键词列表序列化为JSON字符串"""
if isinstance(keywords, list):
return json.dumps(keywords, ensure_ascii=False)
return "[]"
@staticmethod
def _deserialize_keywords(keywords_str: str) -> list:
"""将JSON字符串反序列化为关键词列表"""
if not keywords_str:
return []
try:
return json.loads(keywords_str)
except (json.JSONDecodeError, TypeError):
return []
@staticmethod
async def store_message(message: Union[MessageSending, MessageRecv], chat_stream: ChatStream) -> None:
"""存储消息到数据库"""
@@ -45,6 +63,8 @@ class MessageStorage:
is_picid = False
is_notify = False
is_command = False
key_words = ""
key_words_lite = ""
else:
filtered_display_message = ""
interest_value = message.interest_value
@@ -56,6 +76,9 @@ class MessageStorage:
is_picid = message.is_picid
is_notify = message.is_notify
is_command = message.is_command
# 序列化关键词列表为JSON字符串
key_words = MessageStorage._serialize_keywords(message.key_words)
key_words_lite = MessageStorage._serialize_keywords(message.key_words_lite)
chat_info_dict = chat_stream.to_dict()
user_info_dict = message.message_info.user_info.to_dict() # type: ignore
@@ -102,6 +125,8 @@ class MessageStorage:
is_picid=is_picid,
is_notify=is_notify,
is_command=is_command,
key_words=key_words,
key_words_lite=key_words_lite,
)
except Exception:
logger.exception("存储消息失败")

9
src/chat/replyer/default_generator.py
View File

@@ -79,10 +79,13 @@ def init_prompt():
{identity}
{action_descriptions}
你现在的主要任务是和 {sender_name} 聊天。同时，也有其他用户会参与你们的聊天，你可以参考他们的回复内容，但是你主要还是关注你和{sender_name}的聊天内容。
你现在的主要任务是和 {sender_name} 聊天。同时，也有其他用户会参与聊天，你可以参考他们的回复内容，但是你主要还是关注你和{sender_name}的聊天内容。
{time_block}
这是所有聊天内容：
{background_dialogue_prompt}
--------------------------------
{time_block}
这是你和{sender_name}的对话，你们正在交流中：
@@ -585,8 +588,8 @@ class DefaultReplyer:
# 构建背景对话 prompt
background_dialogue_prompt = ""
if background_dialogue_list:
latest_25_msgs = background_dialogue_list[-int(global_config.chat.max_context_size * 0.5) :]
if message_list_before_now:
latest_25_msgs = message_list_before_now[-int(global_config.chat.max_context_size * 0.5) :]
background_dialogue_prompt_str = build_readable_messages(
latest_25_msgs,
replace_bot_name=True,

3
src/common/database/database_model.py
View File

@@ -130,6 +130,9 @@ class Messages(BaseModel):
reply_to = TextField(null=True)
interest_value = DoubleField(null=True)
key_words = TextField(null=True)
key_words_lite = TextField(null=True)
is_mentioned = BooleanField(null=True)
# 从 chat_info 扁平化而来的字段

15
src/plugins/built_in/core_actions/no_reply.py
View File

@@ -32,8 +32,7 @@ class NoReplyAction(BaseAction):
action_name = "no_reply"
action_description = "暂时不回复消息"
# 最近三次no_reply的新消息兴趣度记录
_recent_interest_records: deque = deque(maxlen=3)
# 兴趣值退出阈值
_interest_exit_threshold = 3.0
@@ -75,15 +74,3 @@ class NoReplyAction(BaseAction):
action_done=True,
)
return False, f"不回复动作执行失败: {e}"
@classmethod
def reset_consecutive_count(cls):
"""重置连续计数器和兴趣度记录"""
cls._recent_interest_records.clear()
logger.debug("NoReplyAction连续计数器和兴趣度记录已重置")
@classmethod
def get_recent_interest_records(cls) -> List[float]:
"""获取最近的兴趣度记录"""
return list(cls._recent_interest_records)