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better：优化了人格和其他配置文件，更加精简易懂

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This commit is contained in:
SengokuCola
2025-07-13 18:32:49 +08:00
parent e987b6331f
commit dfc73255a7
17 changed files with 235 additions and 1550 deletions
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10
src/chat/focus_chat/heartFC_chat.py
View File

@@ -91,9 +91,7 @@ class HeartFChatting:
# 新增：消息计数器和疲惫阈值
self._message_count = 0 # 发送的消息计数
# 基于exit_focus_threshold动态计算疲惫阈值
# 基础值30条，通过exit_focus_threshold调节：threshold越小，越容易疲惫
self._message_threshold = max(10, int(30 * global_config.chat.exit_focus_threshold))
self._message_threshold = max(10, int(30 * global_config.chat.focus_value))
self._fatigue_triggered = False # 是否已触发疲惫退出
self.action_manager = ActionManager()
@@ -127,7 +125,7 @@ class HeartFChatting:
self.priority_manager = None
logger.info(
f"{self.log_prefix} HeartFChatting 初始化完成，消息疲惫阈值: {self._message_threshold}条（基于exit_focus_threshold={global_config.chat.exit_focus_threshold}计算，仅在auto模式下生效）"
f"{self.log_prefix} HeartFChatting 初始化完成"
)
self.energy_value = 100
@@ -195,7 +193,7 @@ class HeartFChatting:
async def _loopbody(self):
if self.loop_mode == "focus":
self.energy_value -= 5 * (1 / global_config.chat.exit_focus_threshold)
self.energy_value -= 5 * global_config.chat.focus_value
if self.energy_value <= 0:
self.loop_mode = "normal"
return True
@@ -211,7 +209,7 @@ class HeartFChatting:
filter_bot=True,
)
if len(new_messages_data) > 4 * global_config.chat.auto_focus_threshold:
if len(new_messages_data) > 4 * global_config.chat.focus_value:
self.loop_mode = "focus"
self.energy_value = 100
return True

87
src/chat/heart_flow/sub_heartflow.py
View File

@@ -39,90 +39,3 @@ class SubHeartflow:
async def initialize(self):
"""异步初始化方法，创建兴趣流并确定聊天类型"""
await self.heart_fc_instance.start()
# async def _stop_heart_fc_chat(self):
# """停止并清理 HeartFChatting 实例"""
# if self.heart_fc_instance.running:
# logger.info(f"{self.log_prefix} 结束专注聊天...")
# try:
# await self.heart_fc_instance.shutdown()
# except Exception as e:
# logger.error(f"{self.log_prefix} 关闭 HeartFChatting 实例时出错: {e}")
# logger.error(traceback.format_exc())
# else:
# logger.info(f"{self.log_prefix} 没有专注聊天实例，无需停止专注聊天")
# async def _start_heart_fc_chat(self) -> bool:
# """启动 HeartFChatting 实例，确保 NormalChat 已停止"""
# try:
# # 如果任务已完成或不存在，则尝试重新启动
# if self.heart_fc_instance._loop_task is None or self.heart_fc_instance._loop_task.done():
# logger.info(f"{self.log_prefix} HeartFChatting 实例存在但循环未运行，尝试启动...")
# try:
# # 添加超时保护
# await asyncio.wait_for(self.heart_fc_instance.start(), timeout=15.0)
# logger.info(f"{self.log_prefix} HeartFChatting 循环已启动。")
# return True
# except Exception as e:
# logger.error(f"{self.log_prefix} 尝试启动现有 HeartFChatting 循环时出错: {e}")
# logger.error(traceback.format_exc())
# # 出错时清理实例，准备重新创建
# self.heart_fc_instance = None # type: ignore
# return False
# else:
# # 任务正在运行
# logger.debug(f"{self.log_prefix} HeartFChatting 已在运行中。")
# return True # 已经在运行
# except Exception as e:
# logger.error(f"{self.log_prefix} _start_heart_fc_chat 执行时出错: {e}")
# logger.error(traceback.format_exc())
# return False
# def is_in_focus_cooldown(self) -> bool:
# """检查是否在focus模式的冷却期内
# Returns:
# bool: 如果在冷却期内返回True，否则返回False
# """
# if self.last_focus_exit_time == 0:
# return False
# # 基础冷却时间10分钟，受auto_focus_threshold调控
# base_cooldown = 10 * 60 # 10分钟转换为秒
# cooldown_duration = base_cooldown / global_config.chat.auto_focus_threshold
# current_time = time.time()
# elapsed_since_exit = current_time - self.last_focus_exit_time
# is_cooling = elapsed_since_exit < cooldown_duration
# if is_cooling:
# remaining_time = cooldown_duration - elapsed_since_exit
# remaining_minutes = remaining_time / 60
# logger.debug(
# f"[{self.log_prefix}] focus冷却中，剩余时间: {remaining_minutes:.1f}分钟 (阈值: {global_config.chat.auto_focus_threshold})"
# )
# return is_cooling
# def get_cooldown_progress(self) -> float:
# """获取冷却进度，返回0-1之间的值
# Returns:
# float: 0表示刚开始冷却，1表示冷却完成
# """
# if self.last_focus_exit_time == 0:
# return 1.0 # 没有冷却，返回1表示完全恢复
# # 基础冷却时间10分钟，受auto_focus_threshold调控
# base_cooldown = 10 * 60 # 10分钟转换为秒
# cooldown_duration = base_cooldown / global_config.chat.auto_focus_threshold
# current_time = time.time()
# elapsed_since_exit = current_time - self.last_focus_exit_time
# if elapsed_since_exit >= cooldown_duration:
# return 1.0 # 冷却完成
# return elapsed_since_exit / cooldown_duration

112
src/chat/planner_actions/action_modifier.py
View File

@@ -436,72 +436,72 @@ class ActionModifier:
logger.debug(f"{self.log_prefix}动作 {action_name} 未匹配到任何关键词: {activation_keywords}")
return False
async def analyze_loop_actions(self, history_loop: List[CycleDetail]) -> List[tuple[str, str]]:
"""分析最近的循环内容并决定动作的移除
# async def analyze_loop_actions(self, history_loop: List[CycleDetail]) -> List[tuple[str, str]]:
# """分析最近的循环内容并决定动作的移除
Returns:
List[Tuple[str, str]]: 包含要删除的动作及原因的元组列表
[("action3", "some reason")]
"""
removals = []
# Returns:
# List[Tuple[str, str]]: 包含要删除的动作及原因的元组列表
# [("action3", "some reason")]
# """
# removals = []
# 获取最近10次循环
recent_cycles = history_loop[-10:] if len(history_loop) > 10 else history_loop
if not recent_cycles:
return removals
# # 获取最近10次循环
# recent_cycles = history_loop[-10:] if len(history_loop) > 10 else history_loop
# if not recent_cycles:
# return removals
reply_sequence = [] # 记录最近的动作序列
# reply_sequence = [] # 记录最近的动作序列
for cycle in recent_cycles:
action_result = cycle.loop_plan_info.get("action_result", {})
action_type = action_result.get("action_type", "unknown")
reply_sequence.append(action_type == "reply")
# for cycle in recent_cycles:
# action_result = cycle.loop_plan_info.get("action_result", {})
# action_type = action_result.get("action_type", "unknown")
# reply_sequence.append(action_type == "reply")
# 计算连续回复的相关阈值
# # 计算连续回复的相关阈值
max_reply_num = int(global_config.focus_chat.consecutive_replies * 3.2)
sec_thres_reply_num = int(global_config.focus_chat.consecutive_replies * 2)
one_thres_reply_num = int(global_config.focus_chat.consecutive_replies * 1.5)
# max_reply_num = int(global_config.focus_chat.consecutive_replies * 3.2)
# sec_thres_reply_num = int(global_config.focus_chat.consecutive_replies * 2)
# one_thres_reply_num = int(global_config.focus_chat.consecutive_replies * 1.5)
# 获取最近max_reply_num次的reply状态
if len(reply_sequence) >= max_reply_num:
last_max_reply_num = reply_sequence[-max_reply_num:]
else:
last_max_reply_num = reply_sequence[:]
# # 获取最近max_reply_num次的reply状态
# if len(reply_sequence) >= max_reply_num:
# last_max_reply_num = reply_sequence[-max_reply_num:]
# else:
# last_max_reply_num = reply_sequence[:]
# 详细打印阈值和序列信息，便于调试
logger.info(
f"连续回复阈值: max={max_reply_num}, sec={sec_thres_reply_num}, one={one_thres_reply_num}，"
f"最近reply序列: {last_max_reply_num}"
)
# print(f"consecutive_replies: {consecutive_replies}")
# # 详细打印阈值和序列信息，便于调试
# logger.info(
# f"连续回复阈值: max={max_reply_num}, sec={sec_thres_reply_num}, one={one_thres_reply_num}，"
# f"最近reply序列: {last_max_reply_num}"
# )
# # print(f"consecutive_replies: {consecutive_replies}")
# 根据最近的reply情况决定是否移除reply动作
if len(last_max_reply_num) >= max_reply_num and all(last_max_reply_num):
# 如果最近max_reply_num次都是reply，直接移除
reason = f"连续回复过多（最近{len(last_max_reply_num)}次全是reply，超过阈值{max_reply_num}）"
removals.append(("reply", reason))
# reply_count = len(last_max_reply_num) - no_reply_count
elif len(last_max_reply_num) >= sec_thres_reply_num and all(last_max_reply_num[-sec_thres_reply_num:]):
# 如果最近sec_thres_reply_num次都是reply，40%概率移除
removal_probability = 0.4 / global_config.focus_chat.consecutive_replies
if random.random() < removal_probability:
reason = (
f"连续回复较多（最近{sec_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
)
removals.append(("reply", reason))
elif len(last_max_reply_num) >= one_thres_reply_num and all(last_max_reply_num[-one_thres_reply_num:]):
# 如果最近one_thres_reply_num次都是reply，20%概率移除
removal_probability = 0.2 / global_config.focus_chat.consecutive_replies
if random.random() < removal_probability:
reason = (
f"连续回复检测（最近{one_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
)
removals.append(("reply", reason))
else:
logger.debug(f"{self.log_prefix}连续回复检测：无需移除reply动作，最近回复模式正常")
# # 根据最近的reply情况决定是否移除reply动作
# if len(last_max_reply_num) >= max_reply_num and all(last_max_reply_num):
# # 如果最近max_reply_num次都是reply，直接移除
# reason = f"连续回复过多（最近{len(last_max_reply_num)}次全是reply，超过阈值{max_reply_num}）"
# removals.append(("reply", reason))
# # reply_count = len(last_max_reply_num) - no_reply_count
# elif len(last_max_reply_num) >= sec_thres_reply_num and all(last_max_reply_num[-sec_thres_reply_num:]):
# # 如果最近sec_thres_reply_num次都是reply，40%概率移除
# removal_probability = 0.4 / global_config.focus_chat.consecutive_replies
# if random.random() < removal_probability:
# reason = (
# f"连续回复较多（最近{sec_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
# )
# removals.append(("reply", reason))
# elif len(last_max_reply_num) >= one_thres_reply_num and all(last_max_reply_num[-one_thres_reply_num:]):
# # 如果最近one_thres_reply_num次都是reply，20%概率移除
# removal_probability = 0.2 / global_config.focus_chat.consecutive_replies
# if random.random() < removal_probability:
# reason = (
# f"连续回复检测（最近{one_thres_reply_num}次全是reply，{removal_probability:.2f}概率移除，触发移除）"
# )
# removals.append(("reply", reason))
# else:
# logger.debug(f"{self.log_prefix}连续回复检测：无需移除reply动作，最近回复模式正常")
return removals
# return removals
# def get_available_actions_count(self, mode: str = "focus") -> int:
# """获取当前可用动作数量（排除默认的no_action）"""

86
src/chat/replyer/default_generator.py
View File

@@ -10,6 +10,7 @@ from datetime import datetime
from src.common.logger import get_logger
from src.config.config import global_config
from src.individuality.individuality import get_individuality
from src.llm_models.utils_model import LLMRequest
from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
from src.chat.message_receive.chat_stream import ChatStream
@@ -561,7 +562,6 @@ class DefaultReplyer:
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
person_info_manager = get_person_info_manager()
bot_person_id = person_info_manager.get_person_id("system", "bot_id")
is_group_chat = bool(chat_stream.group_info)
reply_to = reply_data.get("reply_to", "none")
extra_info_block = reply_data.get("extra_info", "") or reply_data.get("extra_info_block", "")
@@ -661,31 +661,7 @@ class DefaultReplyer:
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
# logger.debug("开始构建 focus prompt")
bot_name = global_config.bot.nickname
if global_config.bot.alias_names:
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
else:
bot_nickname = ""
short_impression = await person_info_manager.get_value(bot_person_id, "short_impression")
# 解析字符串形式的Python列表
try:
if isinstance(short_impression, str) and short_impression.strip():
short_impression = ast.literal_eval(short_impression)
elif not short_impression:
logger.warning("short_impression为空，使用默认值")
short_impression = ["友好活泼", "人类"]
except (ValueError, SyntaxError) as e:
logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}")
short_impression = ["友好活泼", "人类"]
# 确保short_impression是列表格式且有足够的元素
if not isinstance(short_impression, list) or len(short_impression) < 2:
logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值")
short_impression = ["友好活泼", "人类"]
personality = short_impression[0]
identity = short_impression[1]
prompt_personality = f"{personality}，{identity}"
identity_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
identity_block = get_individuality().get_personality_block()
moderation_prompt_block = (
"请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。"
@@ -732,24 +708,24 @@ class DefaultReplyer:
"chat_target_private2", sender_name=chat_target_name
)
target_user_id = ""
if sender:
# 根据sender通过person_info_manager反向查找person_id，再获取user_id
person_id = person_info_manager.get_person_id_by_person_name(sender)
# 根据配置选择使用哪种 prompt 构建模式
if global_config.chat.use_s4u_prompt_mode:
if global_config.chat.use_s4u_prompt_mode and person_id:
# 使用 s4u 对话构建模式：分离当前对话对象和其他对话
# 获取目标用户ID用于消息过滤
target_user_id = ""
if sender:
# 根据sender通过person_info_manager反向查找person_id，再获取user_id
person_id = person_info_manager.get_person_id_by_person_name(sender)
if person_id:
# 通过person_info_manager获取person_id对应的user_id字段
try:
user_id_value = await person_info_manager.get_value(person_id, "user_id")
if user_id_value:
target_user_id = str(user_id_value)
except Exception as e:
logger.warning(f"无法从person_id {person_id} 获取user_id: {e}")
target_user_id = ""
try:
user_id_value = await person_info_manager.get_value(person_id, "user_id")
if user_id_value:
target_user_id = str(user_id_value)
except Exception as e:
logger.warning(f"无法从person_id {person_id} 获取user_id: {e}")
target_user_id = ""
# 构建分离的对话 prompt
core_dialogue_prompt, background_dialogue_prompt = self.build_s4u_chat_history_prompts(
@@ -811,8 +787,6 @@ class DefaultReplyer:
) -> str:
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
person_info_manager = get_person_info_manager()
bot_person_id = person_info_manager.get_person_id("system", "bot_id")
is_group_chat = bool(chat_stream.group_info)
reply_to = reply_data.get("reply_to", "none")
@@ -844,29 +818,7 @@ class DefaultReplyer:
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
bot_name = global_config.bot.nickname
if global_config.bot.alias_names:
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
else:
bot_nickname = ""
short_impression = await person_info_manager.get_value(bot_person_id, "short_impression")
try:
if isinstance(short_impression, str) and short_impression.strip():
short_impression = ast.literal_eval(short_impression)
elif not short_impression:
logger.warning("short_impression为空，使用默认值")
short_impression = ["友好活泼", "人类"]
except (ValueError, SyntaxError) as e:
logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}")
short_impression = ["友好活泼", "人类"]
# 确保short_impression是列表格式且有足够的元素
if not isinstance(short_impression, list) or len(short_impression) < 2:
logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值")
short_impression = ["友好活泼", "人类"]
personality = short_impression[0]
identity = short_impression[1]
prompt_personality = f"{personality}，{identity}"
identity_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}："
identity_block = get_individuality().get_personality_block()
moderation_prompt_block = (
"请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。"

84
src/chat/working_memory/memory_item.py
View File

@@ -1,84 +0,0 @@
from typing import Tuple
import time
import random
import string
class MemoryItem:
"""记忆项类，用于存储单个记忆的所有相关信息"""
def __init__(self, summary: str, from_source: str = "", brief: str = ""):
"""
初始化记忆项
Args:
summary: 记忆内容概括
from_source: 数据来源
brief: 记忆内容主题
"""
# 生成可读ID：时间戳_随机字符串
timestamp = int(time.time())
random_str = "".join(random.choices(string.ascii_lowercase + string.digits, k=2))
self.id = f"{timestamp}_{random_str}"
self.from_source = from_source
self.brief = brief
self.timestamp = time.time()
# 记忆内容概括
self.summary = summary
# 记忆精简次数
self.compress_count = 0
# 记忆提取次数
self.retrieval_count = 0
# 记忆强度 (初始为10)
self.memory_strength = 10.0
# 记忆操作历史记录
# 格式: [(操作类型, 时间戳, 当时精简次数, 当时强度), ...]
self.history = [("create", self.timestamp, self.compress_count, self.memory_strength)]
def matches_source(self, source: str) -> bool:
"""检查来源是否匹配"""
return self.from_source == source
def increase_strength(self, amount: float) -> None:
"""增加记忆强度"""
self.memory_strength = min(10.0, self.memory_strength + amount)
# 记录操作历史
self.record_operation("strengthen")
def decrease_strength(self, amount: float) -> None:
"""减少记忆强度"""
self.memory_strength = max(0.1, self.memory_strength - amount)
# 记录操作历史
self.record_operation("weaken")
def increase_compress_count(self) -> None:
"""增加精简次数并减弱记忆强度"""
self.compress_count += 1
# 记录操作历史
self.record_operation("compress")
def record_retrieval(self) -> None:
"""记录记忆被提取的情况"""
self.retrieval_count += 1
# 提取后强度翻倍
self.memory_strength = min(10.0, self.memory_strength * 2)
# 记录操作历史
self.record_operation("retrieval")
def record_operation(self, operation_type: str) -> None:
"""记录操作历史"""
current_time = time.time()
self.history.append((operation_type, current_time, self.compress_count, self.memory_strength))
def to_tuple(self) -> Tuple[str, str, float, str]:
"""转换为元组格式（为了兼容性）"""
return (self.summary, self.from_source, self.timestamp, self.id)
def is_memory_valid(self) -> bool:
"""检查记忆是否有效（强度是否大于等于1）"""
return self.memory_strength >= 1.0

413
src/chat/working_memory/memory_manager.py
View File

@@ -1,413 +0,0 @@
from typing import Dict, TypeVar, List, Optional
import traceback
from json_repair import repair_json
from rich.traceback import install
from src.common.logger import get_logger
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
from src.chat.focus_chat.working_memory.memory_item import MemoryItem
import json # 添加json模块导入
install(extra_lines=3)
logger = get_logger("working_memory")
T = TypeVar("T")
class MemoryManager:
def __init__(self, chat_id: str):
"""
初始化工作记忆
Args:
chat_id: 关联的聊天ID，用于标识该工作记忆属于哪个聊天
"""
# 关联的聊天ID
self._chat_id = chat_id
# 记忆项列表
self._memories: List[MemoryItem] = []
# ID到记忆项的映射
self._id_map: Dict[str, MemoryItem] = {}
self.llm_summarizer = LLMRequest(
model=global_config.model.memory,
temperature=0.3,
request_type="working_memory",
)
@property
def chat_id(self) -> str:
"""获取关联的聊天ID"""
return self._chat_id
@chat_id.setter
def chat_id(self, value: str):
"""设置关联的聊天ID"""
self._chat_id = value
def push_item(self, memory_item: MemoryItem) -> str:
"""
推送一个已创建的记忆项到工作记忆中
Args:
memory_item: 要存储的记忆项
Returns:
记忆项的ID
"""
# 添加到内存和ID映射
self._memories.append(memory_item)
self._id_map[memory_item.id] = memory_item
return memory_item.id
def get_by_id(self, memory_id: str) -> Optional[MemoryItem]:
"""
通过ID获取记忆项
Args:
memory_id: 记忆项ID
Returns:
找到的记忆项，如果不存在则返回None
"""
memory_item = self._id_map.get(memory_id)
if memory_item:
# 检查记忆强度，如果小于1则删除
if not memory_item.is_memory_valid():
print(f"记忆 {memory_id} 强度过低 ({memory_item.memory_strength})，已自动移除")
self.delete(memory_id)
return None
return memory_item
def get_all_items(self) -> List[MemoryItem]:
"""获取所有记忆项"""
return list(self._id_map.values())
def find_items(
self,
source: Optional[str] = None,
start_time: Optional[float] = None,
end_time: Optional[float] = None,
memory_id: Optional[str] = None,
limit: Optional[int] = None,
newest_first: bool = False,
min_strength: float = 0.0,
) -> List[MemoryItem]:
"""
按条件查找记忆项
Args:
source: 数据来源
start_time: 开始时间戳
end_time: 结束时间戳
memory_id: 特定记忆项ID
limit: 返回结果的最大数量
newest_first: 是否按最新优先排序
min_strength: 最小记忆强度
Returns:
符合条件的记忆项列表
"""
# 如果提供了特定ID，直接查找
if memory_id:
item = self.get_by_id(memory_id)
return [item] if item else []
results = []
# 获取所有项目
items = self._memories
# 如果需要最新优先，则反转遍历顺序
if newest_first:
items_to_check = list(reversed(items))
else:
items_to_check = items
# 遍历项目
for item in items_to_check:
# 检查来源是否匹配
if source is not None and not item.matches_source(source):
continue
# 检查时间范围
if start_time is not None and item.timestamp < start_time:
continue
if end_time is not None and item.timestamp > end_time:
continue
# 检查记忆强度
if min_strength > 0 and item.memory_strength < min_strength:
continue
# 所有条件都满足，添加到结果中
results.append(item)
# 如果达到限制数量，提前返回
if limit is not None and len(results) >= limit:
return results
return results
async def summarize_memory_item(self, content: str) -> Dict[str, str]:
"""
使用LLM总结记忆项
Args:
content: 需要总结的内容
Returns:
包含brief和summary的字典
"""
prompt = f"""请对以下内容进行总结，总结成记忆，输出两部分：
1. 记忆内容主题（精简，20字以内）：让用户可以一眼看出记忆内容是什么
2. 记忆内容概括：对内容进行概括，保留重要信息，200字以内
内容：
{content}
请按以下JSON格式输出：
{{
"brief": "记忆内容主题",
"summary": "记忆内容概括"
}}
请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
"""
default_summary = {
"brief": "主题未知的记忆",
"summary": "无法概括的记忆内容",
}
try:
# 调用LLM生成总结
response, _ = await self.llm_summarizer.generate_response_async(prompt)
# 使用repair_json解析响应
try:
# 使用repair_json修复JSON格式
fixed_json_string = repair_json(response)
# 如果repair_json返回的是字符串，需要解析为Python对象
if isinstance(fixed_json_string, str):
try:
json_result = json.loads(fixed_json_string)
except json.JSONDecodeError as decode_error:
logger.error(f"JSON解析错误: {str(decode_error)}")
return default_summary
else:
# 如果repair_json直接返回了字典对象，直接使用
json_result = fixed_json_string
# 进行额外的类型检查
if not isinstance(json_result, dict):
logger.error(f"修复后的JSON不是字典类型: {type(json_result)}")
return default_summary
# 确保所有必要字段都存在且类型正确
if "brief" not in json_result or not isinstance(json_result["brief"], str):
json_result["brief"] = "主题未知的记忆"
if "summary" not in json_result or not isinstance(json_result["summary"], str):
json_result["summary"] = "无法概括的记忆内容"
return json_result
except Exception as json_error:
logger.error(f"JSON处理失败: {str(json_error)}，将使用默认摘要")
return default_summary
except Exception as e:
logger.error(f"生成总结时出错: {str(e)}")
return default_summary
def decay_memory(self, memory_id: str, decay_factor: float = 0.8) -> bool:
"""
使单个记忆衰减
Args:
memory_id: 记忆ID
decay_factor: 衰减因子(0-1之间)
Returns:
是否成功衰减
"""
memory_item = self.get_by_id(memory_id)
if not memory_item:
return False
# 计算衰减量（当前强度 * (1-衰减因子)）
old_strength = memory_item.memory_strength
decay_amount = old_strength * (1 - decay_factor)
# 更新强度
memory_item.memory_strength = decay_amount
return True
def delete(self, memory_id: str) -> bool:
"""
删除指定ID的记忆项
Args:
memory_id: 要删除的记忆项ID
Returns:
是否成功删除
"""
if memory_id not in self._id_map:
return False
# 获取要删除的项
self._id_map[memory_id]
# 从内存中删除
self._memories = [i for i in self._memories if i.id != memory_id]
# 从ID映射中删除
del self._id_map[memory_id]
return True
def clear(self) -> None:
"""清除所有记忆"""
self._memories.clear()
self._id_map.clear()
async def merge_memories(
self, memory_id1: str, memory_id2: str, reason: str, delete_originals: bool = True
) -> MemoryItem:
"""
合并两个记忆项
Args:
memory_id1: 第一个记忆项ID
memory_id2: 第二个记忆项ID
reason: 合并原因
delete_originals: 是否删除原始记忆，默认为True
Returns:
合并后的记忆项
"""
# 获取两个记忆项
memory_item1 = self.get_by_id(memory_id1)
memory_item2 = self.get_by_id(memory_id2)
if not memory_item1 or not memory_item2:
raise ValueError("无法找到指定的记忆项")
# 构建合并提示
prompt = f"""
请根据以下原因，将两段记忆内容有机合并成一段新的记忆内容。
合并时保留两段记忆的重要信息，避免重复，确保生成的内容连贯、自然。
合并原因：{reason}
记忆1主题：{memory_item1.brief}
记忆1内容：{memory_item1.summary}
记忆2主题：{memory_item2.brief}
记忆2内容：{memory_item2.summary}
请按以下JSON格式输出合并结果：
{{
"brief": "合并后的主题（20字以内）",
"summary": "合并后的内容概括（200字以内）"
}}
请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
"""
# 默认合并结果
default_merged = {
"brief": f"合并：{memory_item1.brief} + {memory_item2.brief}",
"summary": f"合并的记忆：{memory_item1.summary}\n{memory_item2.summary}",
}
try:
# 调用LLM合并记忆
response, _ = await self.llm_summarizer.generate_response_async(prompt)
# 处理LLM返回的合并结果
try:
# 修复JSON格式
fixed_json_string = repair_json(response)
# 将修复后的字符串解析为Python对象
if isinstance(fixed_json_string, str):
try:
merged_data = json.loads(fixed_json_string)
except json.JSONDecodeError as decode_error:
logger.error(f"JSON解析错误: {str(decode_error)}")
merged_data = default_merged
else:
# 如果repair_json直接返回了字典对象，直接使用
merged_data = fixed_json_string
# 确保是字典类型
if not isinstance(merged_data, dict):
logger.error(f"修复后的JSON不是字典类型: {type(merged_data)}")
merged_data = default_merged
if "brief" not in merged_data or not isinstance(merged_data["brief"], str):
merged_data["brief"] = default_merged["brief"]
if "summary" not in merged_data or not isinstance(merged_data["summary"], str):
merged_data["summary"] = default_merged["summary"]
except Exception as e:
logger.error(f"合并记忆时处理JSON出错: {str(e)}")
traceback.print_exc()
merged_data = default_merged
except Exception as e:
logger.error(f"合并记忆调用LLM出错: {str(e)}")
traceback.print_exc()
merged_data = default_merged
# 创建新的记忆项
# 取两个记忆项中更强的来源
merged_source = (
memory_item1.from_source
if memory_item1.memory_strength >= memory_item2.memory_strength
else memory_item2.from_source
)
# 创建新的记忆项
merged_memory = MemoryItem(
summary=merged_data["summary"], from_source=merged_source, brief=merged_data["brief"]
)
# 记忆强度取两者最大值
merged_memory.memory_strength = max(memory_item1.memory_strength, memory_item2.memory_strength)
# 添加到存储中
self.push_item(merged_memory)
# 如果需要，删除原始记忆
if delete_originals:
self.delete(memory_id1)
self.delete(memory_id2)
return merged_memory
def delete_earliest_memory(self) -> bool:
"""
删除最早的记忆项
Returns:
是否成功删除
"""
# 获取所有记忆项
all_memories = self.get_all_items()
if not all_memories:
return False
# 按时间戳排序，找到最早的记忆项
earliest_memory = min(all_memories, key=lambda item: item.timestamp)
# 删除最早的记忆项
return self.delete(earliest_memory.id)

156
src/chat/working_memory/working_memory.py
View File

@@ -1,156 +0,0 @@
from typing import List, Any, Optional
import asyncio
from src.common.logger import get_logger
from src.chat.focus_chat.working_memory.memory_manager import MemoryManager, MemoryItem
from src.config.config import global_config
logger = get_logger(__name__)
# 问题是我不知道这个manager是不是需要和其他manager统一管理，因为这个manager是从属于每一个聊天流，都有自己的定时任务
class WorkingMemory:
"""
工作记忆，负责协调和运作记忆
从属于特定的流，用chat_id来标识
"""
def __init__(self, chat_id: str, max_memories_per_chat: int = 10, auto_decay_interval: int = 60):
"""
初始化工作记忆管理器
Args:
max_memories_per_chat: 每个聊天的最大记忆数量
auto_decay_interval: 自动衰减记忆的时间间隔(秒)
"""
self.memory_manager = MemoryManager(chat_id)
# 记忆容量上限
self.max_memories_per_chat = max_memories_per_chat
# 自动衰减间隔
self.auto_decay_interval = auto_decay_interval
# 衰减任务
self.decay_task = None
# 只有在工作记忆处理器启用时才启动自动衰减任务
if global_config.focus_chat_processor.working_memory_processor:
self._start_auto_decay()
else:
logger.debug(f"工作记忆处理器已禁用，跳过启动自动衰减任务 (chat_id: {chat_id})")
def _start_auto_decay(self):
"""启动自动衰减任务"""
if self.decay_task is None:
self.decay_task = asyncio.create_task(self._auto_decay_loop())
async def _auto_decay_loop(self):
"""自动衰减循环"""
while True:
await asyncio.sleep(self.auto_decay_interval)
try:
await self.decay_all_memories()
except Exception as e:
print(f"自动衰减记忆时出错: {str(e)}")
async def add_memory(self, summary: Any, from_source: str = "", brief: str = ""):
"""
添加一段记忆到指定聊天
Args:
summary: 记忆内容
from_source: 数据来源
Returns:
记忆项
"""
# 如果是字符串类型，生成总结
memory = MemoryItem(summary, from_source, brief)
# 添加到管理器
self.memory_manager.push_item(memory)
# 如果超过最大记忆数量，删除最早的记忆
if len(self.memory_manager.get_all_items()) > self.max_memories_per_chat:
self.remove_earliest_memory()
return memory
def remove_earliest_memory(self):
"""
删除最早的记忆
"""
return self.memory_manager.delete_earliest_memory()
async def retrieve_memory(self, memory_id: str) -> Optional[MemoryItem]:
"""
检索记忆
Args:
chat_id: 聊天ID
memory_id: 记忆ID
Returns:
检索到的记忆项，如果不存在则返回None
"""
memory_item = self.memory_manager.get_by_id(memory_id)
if memory_item:
memory_item.retrieval_count += 1
memory_item.increase_strength(5)
return memory_item
return None
async def decay_all_memories(self, decay_factor: float = 0.5):
"""
对所有聊天的所有记忆进行衰减
衰减：对记忆进行refine压缩，强度会变为原先的0.5
Args:
decay_factor: 衰减因子(0-1之间)
"""
logger.debug(f"开始对所有记忆进行衰减，衰减因子: {decay_factor}")
all_memories = self.memory_manager.get_all_items()
for memory_item in all_memories:
# 如果压缩完小于1会被删除
memory_id = memory_item.id
self.memory_manager.decay_memory(memory_id, decay_factor)
if memory_item.memory_strength < 1:
self.memory_manager.delete(memory_id)
continue
# 计算衰减量
# if memory_item.memory_strength < 5:
# await self.memory_manager.refine_memory(
# memory_id, f"由于时间过去了{self.auto_decay_interval}秒，记忆变的模糊，所以需要压缩"
# )
async def merge_memory(self, memory_id1: str, memory_id2: str) -> MemoryItem:
"""合并记忆
Args:
memory_str: 记忆内容
"""
return await self.memory_manager.merge_memories(
memory_id1=memory_id1, memory_id2=memory_id2, reason="两端记忆有重复的内容"
)
async def shutdown(self) -> None:
"""关闭管理器，停止所有任务"""
if self.decay_task and not self.decay_task.done():
self.decay_task.cancel()
try:
await self.decay_task
except asyncio.CancelledError:
pass
def get_all_memories(self) -> List[MemoryItem]:
"""
获取所有记忆项目
Returns:
List[MemoryItem]: 当前工作记忆中的所有记忆项目列表
"""
return self.memory_manager.get_all_items()

261
src/chat/working_memory/working_memory_processor.py
View File

@@ -1,261 +0,0 @@
from src.chat.focus_chat.observation.chatting_observation import ChattingObservation
from src.chat.focus_chat.observation.observation import Observation
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
import time
import traceback
from src.common.logger import get_logger
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.chat.message_receive.chat_stream import get_chat_manager
from typing import List
from src.chat.focus_chat.observation.working_observation import WorkingMemoryObservation
from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
from src.chat.focus_chat.info.info_base import InfoBase
from json_repair import repair_json
from src.chat.focus_chat.info.workingmemory_info import WorkingMemoryInfo
import asyncio
import json
logger = get_logger("processor")
def init_prompt():
memory_proces_prompt = """
你的名字是{bot_name}
现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容：
{chat_observe_info}
以下是你已经总结的记忆摘要，你可以调取这些记忆查看内容来帮助你聊天，不要一次调取太多记忆，最多调取3个左右记忆：
{memory_str}
观察聊天内容和已经总结的记忆，思考如果有相近的记忆，请合并记忆，输出merge_memory，
合并记忆的格式为[["id1", "id2"], ["id3", "id4"],...]，你可以进行多组合并，但是每组合并只能有两个记忆id，不要输出其他内容
请根据聊天内容选择你需要调取的记忆并考虑是否添加新记忆，以JSON格式输出，格式如下：
```json
{{
"selected_memory_ids": ["id1", "id2", ...]
"merge_memory": [["id1", "id2"], ["id3", "id4"],...]
}}
```
"""
Prompt(memory_proces_prompt, "prompt_memory_proces")
class WorkingMemoryProcessor:
log_prefix = "工作记忆"
def __init__(self, subheartflow_id: str):
self.subheartflow_id = subheartflow_id
self.llm_model = LLMRequest(
model=global_config.model.planner,
request_type="focus.processor.working_memory",
)
name = get_chat_manager().get_stream_name(self.subheartflow_id)
self.log_prefix = f"[{name}] "
async def process_info(self, observations: List[Observation] = None, *infos) -> List[InfoBase]:
"""处理信息对象
Args:
*infos: 可变数量的InfoBase类型的信息对象
Returns:
List[InfoBase]: 处理后的结构化信息列表
"""
working_memory = None
chat_info = ""
chat_obs = None
try:
for observation in observations:
if isinstance(observation, WorkingMemoryObservation):
working_memory = observation.get_observe_info()
if isinstance(observation, ChattingObservation):
chat_info = observation.get_observe_info()
chat_obs = observation
# 检查是否有待压缩内容
if chat_obs and chat_obs.compressor_prompt:
logger.debug(f"{self.log_prefix} 压缩聊天记忆")
await self.compress_chat_memory(working_memory, chat_obs)
# 检查working_memory是否为None
if working_memory is None:
logger.debug(f"{self.log_prefix} 没有找到工作记忆观察，跳过处理")
return []
all_memory = working_memory.get_all_memories()
if not all_memory:
logger.debug(f"{self.log_prefix} 目前没有工作记忆，跳过提取")
return []
memory_prompts = []
for memory in all_memory:
memory_id = memory.id
memory_brief = memory.brief
memory_single_prompt = f"记忆id:{memory_id},记忆摘要:{memory_brief}\n"
memory_prompts.append(memory_single_prompt)
memory_choose_str = "".join(memory_prompts)
# 使用提示模板进行处理
prompt = (await global_prompt_manager.get_prompt_async("prompt_memory_proces")).format(
bot_name=global_config.bot.nickname,
time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
chat_observe_info=chat_info,
memory_str=memory_choose_str,
)
# 调用LLM处理记忆
content = ""
try:
content, _ = await self.llm_model.generate_response_async(prompt=prompt)
# print(f"prompt: {prompt}---------------------------------")
# print(f"content: {content}---------------------------------")
if not content:
logger.warning(f"{self.log_prefix} LLM返回空结果，处理工作记忆失败。")
return []
except Exception as e:
logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
logger.error(traceback.format_exc())
return []
# 解析LLM返回的JSON
try:
result = repair_json(content)
if isinstance(result, str):
result = json.loads(result)
if not isinstance(result, dict):
logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败，结果不是字典类型: {type(result)}")
return []
selected_memory_ids = result.get("selected_memory_ids", [])
merge_memory = result.get("merge_memory", [])
except Exception as e:
logger.error(f"{self.log_prefix} 解析LLM返回的JSON失败: {e}")
logger.error(traceback.format_exc())
return []
logger.debug(
f"{self.log_prefix} 解析LLM返回的JSON,selected_memory_ids: {selected_memory_ids}, merge_memory: {merge_memory}"
)
# 根据selected_memory_ids，调取记忆
memory_str = ""
selected_ids = set(selected_memory_ids) # 转换为集合以便快速查找
# 遍历所有记忆
for memory in all_memory:
if memory.id in selected_ids:
# 选中的记忆显示详细内容
memory = await working_memory.retrieve_memory(memory.id)
if memory:
memory_str += f"{memory.summary}\n"
else:
# 未选中的记忆显示梗概
memory_str += f"{memory.brief}\n"
working_memory_info = WorkingMemoryInfo()
if memory_str:
working_memory_info.add_working_memory(memory_str)
logger.debug(f"{self.log_prefix} 取得工作记忆: {memory_str}")
else:
logger.debug(f"{self.log_prefix} 没有找到工作记忆")
if merge_memory:
for merge_pairs in merge_memory:
memory1 = await working_memory.retrieve_memory(merge_pairs[0])
memory2 = await working_memory.retrieve_memory(merge_pairs[1])
if memory1 and memory2:
asyncio.create_task(self.merge_memory_async(working_memory, merge_pairs[0], merge_pairs[1]))
return [working_memory_info]
except Exception as e:
logger.error(f"{self.log_prefix} 处理观察时出错: {e}")
logger.error(traceback.format_exc())
return []
async def compress_chat_memory(self, working_memory: WorkingMemory, obs: ChattingObservation):
"""压缩聊天记忆
Args:
working_memory: 工作记忆对象
obs: 聊天观察对象
"""
# 检查working_memory是否为None
if working_memory is None:
logger.warning(f"{self.log_prefix} 工作记忆对象为None，无法压缩聊天记忆")
return
try:
summary_result, _ = await self.llm_model.generate_response_async(obs.compressor_prompt)
if not summary_result:
logger.debug(f"{self.log_prefix} 压缩聊天记忆失败: 没有生成摘要")
return
print(f"compressor_prompt: {obs.compressor_prompt}")
print(f"summary_result: {summary_result}")
# 修复并解析JSON
try:
fixed_json = repair_json(summary_result)
summary_data = json.loads(fixed_json)
if not isinstance(summary_data, dict):
logger.error(f"{self.log_prefix} 解析压缩结果失败: 不是有效的JSON对象")
return
theme = summary_data.get("theme", "")
content = summary_data.get("content", "")
if not theme or not content:
logger.error(f"{self.log_prefix} 解析压缩结果失败: 缺少必要字段")
return
# 创建新记忆
await working_memory.add_memory(from_source="chat_compress", summary=content, brief=theme)
logger.debug(f"{self.log_prefix} 压缩聊天记忆成功: {theme} - {content}")
except Exception as e:
logger.error(f"{self.log_prefix} 解析压缩结果失败: {e}")
logger.error(traceback.format_exc())
return
# 清理压缩状态
obs.compressor_prompt = ""
obs.oldest_messages = []
obs.oldest_messages_str = ""
except Exception as e:
logger.error(f"{self.log_prefix} 压缩聊天记忆失败: {e}")
logger.error(traceback.format_exc())
async def merge_memory_async(self, working_memory: WorkingMemory, memory_id1: str, memory_id2: str):
"""异步合并记忆，不阻塞主流程
Args:
working_memory: 工作记忆对象
memory_id1: 第一个记忆ID
memory_id2: 第二个记忆ID
"""
# 检查working_memory是否为None
if working_memory is None:
logger.warning(f"{self.log_prefix} 工作记忆对象为None，无法合并记忆")
return
try:
merged_memory = await working_memory.merge_memory(memory_id1, memory_id2)
logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.brief}")
logger.debug(f"{self.log_prefix} 合并后的记忆内容: {merged_memory.summary}")
except Exception as e:
logger.error(f"{self.log_prefix} 异步合并记忆失败: {e}")
logger.error(traceback.format_exc())
init_prompt()