mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' into dev
2829798842
GitHub
commit
1ac0b2d7ea
|
|
@ -2,7 +2,8 @@
|
|||
|
||||
## [0.7.0] -2025-6-1
|
||||
- 重构数据库，弃用MongoDB，采用轻量sqlite,无需额外安装
|
||||
- 重构HFC，可扩展的聊天模式
|
||||
- 重构HFC，可扩展的聊天模式，支持独立的表达模式
|
||||
- HFC，丰富HFC的决策信息，更好的把握聊天内容
|
||||
- HFC初步支持插件v0.1（测试版）
|
||||
- 重构表情包模块
|
||||
- 移除日程系统
|
||||
|
|
@ -26,30 +27,39 @@
|
|||
- 插件：禁言动作
|
||||
- 表达器：装饰语言风格
|
||||
- 可通过插件添加和自定义HFC部件（目前只支持action定义）
|
||||
- 为专注模式添加关系线索
|
||||
- 在专注模式下，麦麦可以决定自行发送语音消息（需要搭配tts适配器）
|
||||
- 优化reply，减少复读
|
||||
|
||||
**新增表达方式学习**
|
||||
- 在专注模式下，麦麦可以有独特的表达方式
|
||||
- 自主学习群聊中的表达方式，更贴近群友
|
||||
- 可自定义的学习频率和开关
|
||||
- 根据人设生成额外的表达方式
|
||||
|
||||
**聊天管理**
|
||||
- 移除不在线状态
|
||||
- 优化自动模式下normal与focus聊天的切换机制
|
||||
- 大幅精简聊天状态切换规则，减少复杂度
|
||||
- 移除聊天限额数量
|
||||
|
||||
**插件系统**
|
||||
- 添加示例插件
|
||||
- 示例插件：禁言插件
|
||||
- 示例插件：豆包绘图插件
|
||||
|
||||
**新增表达方式学习**
|
||||
- 自主学习群聊中的表达方式，更贴近群友
|
||||
- 可自定义的学习频率和开关
|
||||
- 根据人设生成额外的表达方式
|
||||
|
||||
**聊天管理**
|
||||
- 移除不在线状态
|
||||
- 大幅精简聊天状态切换规则，减少复杂度
|
||||
- 移除聊天限额数量
|
||||
**人格**
|
||||
- 简化了人格身份的配置
|
||||
|
||||
**数据库重构**
|
||||
- 移除了默认使用MongoDB，采用轻量sqlite
|
||||
- 无需额外安装数据库
|
||||
- 提供迁移脚本
|
||||
- 移除了默认使用MongoDB，采用轻量sqlite
|
||||
- 无需额外安装数据库
|
||||
- 提供迁移脚本
|
||||
|
||||
**优化**
|
||||
- 移除日程系统，减少幻觉（将会在未来版本回归）
|
||||
- 移除主心流思考和LLM进入聊天判定
|
||||
- 移除日程系统，减少幻觉（将会在未来版本回归）
|
||||
- 移除主心流思考和LLM进入聊天判定
|
||||
- 支持qwen3模型，支持自定义是否思考和思考长度
|
||||
|
||||
|
||||
## [0.6.3-fix-4] - 2025-5-18
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from src.chat.heart_flow.heartflow import heartflow
|
||||
from src.chat.heart_flow.sub_heartflow import ChatState
|
||||
from src.common.logger_manager import get_logger
|
||||
import time
|
||||
|
||||
logger = get_logger("api")
|
||||
|
||||
|
|
@ -30,6 +31,29 @@ async def get_subheartflow_cycle_info(subheartflow_id: str, history_len: int) ->
|
|||
return None
|
||||
|
||||
|
||||
async def get_normal_chat_replies(subheartflow_id: str, limit: int = 10) -> list:
|
||||
"""获取子心流的NormalChat回复记录
|
||||
|
||||
Args:
|
||||
subheartflow_id: 子心流ID
|
||||
limit: 最大返回数量，默认10条
|
||||
|
||||
Returns:
|
||||
list: 回复记录列表，如果未找到则返回空列表
|
||||
"""
|
||||
replies = await heartflow.api_get_normal_chat_replies(subheartflow_id, limit)
|
||||
logger.debug(f"子心流 {subheartflow_id} NormalChat回复记录: 获取到 {len(replies) if replies else 0} 条")
|
||||
if replies:
|
||||
# 格式化时间戳为可读时间
|
||||
for reply in replies:
|
||||
if "time" in reply:
|
||||
reply["formatted_time"] = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(reply["time"]))
|
||||
return replies
|
||||
else:
|
||||
logger.warning(f"子心流 {subheartflow_id} NormalChat回复记录未找到")
|
||||
return []
|
||||
|
||||
|
||||
async def get_all_states():
|
||||
"""获取所有状态"""
|
||||
all_states = await heartflow.api_get_all_states()
|
||||
|
|
|
|||
|
|
@ -62,7 +62,6 @@ class APIBotConfig:
|
|||
# focus_chat
|
||||
reply_trigger_threshold: float # 回复触发阈值
|
||||
default_decay_rate_per_second: float # 默认每秒衰减率
|
||||
consecutive_no_reply_threshold: int # 连续不回复阈值
|
||||
|
||||
# compressed
|
||||
compressed_length: int # 压缩长度
|
||||
|
|
|
|||
|
|
@ -149,7 +149,7 @@ class MaiEmoji:
|
|||
emotion_str = ",".join(self.emotion) if self.emotion else ""
|
||||
|
||||
Emoji.create(
|
||||
hash=self.hash,
|
||||
emoji_hash=self.hash,
|
||||
full_path=self.full_path,
|
||||
format=self.format,
|
||||
description=self.description,
|
||||
|
|
@ -367,12 +367,14 @@ class EmojiManager:
|
|||
return cls._instance
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._initialized = None
|
||||
if self._initialized:
|
||||
return # 如果已经初始化过，直接返回
|
||||
|
||||
self._scan_task = None
|
||||
|
||||
self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.3, max_tokens=1000, request_type="emoji")
|
||||
self.llm_emotion_judge = LLMRequest(
|
||||
model=global_config.model.normal, max_tokens=600, request_type="emoji"
|
||||
model=global_config.model.utils, max_tokens=600, request_type="emoji"
|
||||
) # 更高的温度，更少的token（后续可以根据情绪来调整温度）
|
||||
|
||||
self.emoji_num = 0
|
||||
|
|
@ -389,6 +391,7 @@ class EmojiManager:
|
|||
raise RuntimeError("数据库连接失败")
|
||||
_ensure_emoji_dir()
|
||||
Emoji.create_table(safe=True) # Ensures table exists
|
||||
self._initialized = True
|
||||
|
||||
def _ensure_db(self) -> None:
|
||||
"""确保数据库已初始化"""
|
||||
|
|
@ -467,7 +470,7 @@ class EmojiManager:
|
|||
selected_emoji, similarity, matched_emotion = random.choice(top_emojis)
|
||||
|
||||
# 更新使用次数
|
||||
self.record_usage(selected_emoji.emoji_hash)
|
||||
self.record_usage(selected_emoji.hash)
|
||||
|
||||
_time_end = time.time()
|
||||
|
||||
|
|
@ -796,7 +799,7 @@ class EmojiManager:
|
|||
|
||||
# 删除选定的表情包
|
||||
logger.info(f"[决策] 删除表情包: {emoji_to_delete.description}")
|
||||
delete_success = await self.delete_emoji(emoji_to_delete.emoji_hash)
|
||||
delete_success = await self.delete_emoji(emoji_to_delete.hash)
|
||||
|
||||
if delete_success:
|
||||
# 修复：等待异步注册完成
|
||||
|
|
|
|||
|
|
@ -13,7 +13,6 @@ from src.chat.emoji_system.emoji_manager import emoji_manager
|
|||
from src.chat.focus_chat.heartFC_sender import HeartFCSender
|
||||
from src.chat.utils.utils import process_llm_response
|
||||
from src.chat.utils.info_catcher import info_catcher_manager
|
||||
from src.manager.mood_manager import mood_manager
|
||||
from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp
|
||||
|
|
@ -78,10 +77,10 @@ class DefaultExpressor:
|
|||
self.log_prefix = "expressor"
|
||||
# TODO: API-Adapter修改标记
|
||||
self.express_model = LLMRequest(
|
||||
model=global_config.model.normal,
|
||||
temperature=global_config.model.normal["temp"],
|
||||
model=global_config.model.focus_expressor,
|
||||
# temperature=global_config.model.focus_expressor["temp"],
|
||||
max_tokens=256,
|
||||
request_type="response_heartflow",
|
||||
request_type="focus_expressor",
|
||||
)
|
||||
self.heart_fc_sender = HeartFCSender()
|
||||
|
||||
|
|
@ -150,22 +149,22 @@ class DefaultExpressor:
|
|||
action_data=action_data,
|
||||
)
|
||||
|
||||
with Timer("选择表情", cycle_timers):
|
||||
emoji_keyword = action_data.get("emojis", [])
|
||||
emoji_base64 = await self._choose_emoji(emoji_keyword)
|
||||
if emoji_base64:
|
||||
reply.append(("emoji", emoji_base64))
|
||||
with Timer("选择表情", cycle_timers):
|
||||
emoji_keyword = action_data.get("emojis", [])
|
||||
emoji_base64 = await self._choose_emoji(emoji_keyword)
|
||||
if emoji_base64:
|
||||
reply.append(("emoji", emoji_base64))
|
||||
|
||||
if reply:
|
||||
with Timer("发送消息", cycle_timers):
|
||||
sent_msg_list = await self.send_response_messages(
|
||||
anchor_message=anchor_message,
|
||||
thinking_id=thinking_id,
|
||||
response_set=reply,
|
||||
)
|
||||
has_sent_something = True
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 文本回复生成失败")
|
||||
if reply:
|
||||
with Timer("发送消息", cycle_timers):
|
||||
sent_msg_list = await self.send_response_messages(
|
||||
anchor_message=anchor_message,
|
||||
thinking_id=thinking_id,
|
||||
response_set=reply,
|
||||
)
|
||||
has_sent_something = True
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 文本回复生成失败")
|
||||
|
||||
if not has_sent_something:
|
||||
logger.warning(f"{self.log_prefix} 回复动作未包含任何有效内容")
|
||||
|
|
@ -174,6 +173,7 @@ class DefaultExpressor:
|
|||
|
||||
except Exception as e:
|
||||
logger.error(f"回复失败: {e}")
|
||||
traceback.print_exc()
|
||||
return False, None
|
||||
|
||||
# --- 回复器 (Replier) 的定义 --- #
|
||||
|
|
@ -192,9 +192,9 @@ class DefaultExpressor:
|
|||
"""
|
||||
try:
|
||||
# 1. 获取情绪影响因子并调整模型温度
|
||||
arousal_multiplier = mood_manager.get_arousal_multiplier()
|
||||
current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
|
||||
self.express_model.params["temperature"] = current_temp # 动态调整温度
|
||||
# arousal_multiplier = mood_manager.get_arousal_multiplier()
|
||||
# current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
|
||||
# self.express_model.params["temperature"] = current_temp # 动态调整温度
|
||||
|
||||
# 2. 获取信息捕捉器
|
||||
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
|
||||
|
|
@ -439,7 +439,13 @@ class DefaultExpressor:
|
|||
if type == "emoji":
|
||||
typing = False
|
||||
|
||||
sent_msg = await self.heart_fc_sender.send_message(bot_message, has_thinking=True, typing=typing)
|
||||
if anchor_message.raw_message:
|
||||
set_reply = True
|
||||
else:
|
||||
set_reply = False
|
||||
sent_msg = await self.heart_fc_sender.send_message(
|
||||
bot_message, has_thinking=True, typing=typing, set_reply=set_reply
|
||||
)
|
||||
|
||||
reply_message_ids.append(part_message_id) # 记录我们生成的ID
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from src.common.logger_manager import get_logger
|
|||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_anonymous_messages
|
||||
from src.chat.focus_chat.heartflow_prompt_builder import Prompt, global_prompt_manager
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
import os
|
||||
import json
|
||||
|
||||
|
|
@ -61,10 +61,10 @@ class ExpressionLearner:
|
|||
def __init__(self) -> None:
|
||||
# TODO: API-Adapter修改标记
|
||||
self.express_learn_model: LLMRequest = LLMRequest(
|
||||
model=global_config.model.normal,
|
||||
model=global_config.model.focus_expressor,
|
||||
temperature=0.1,
|
||||
max_tokens=256,
|
||||
request_type="response_heartflow",
|
||||
request_type="learn_expression",
|
||||
)
|
||||
|
||||
async def get_expression_by_chat_id(self, chat_id: str) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]:
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import contextlib
|
|||
import time
|
||||
import traceback
|
||||
from collections import deque
|
||||
from typing import List, Optional, Dict, Any, Deque
|
||||
from typing import List, Optional, Dict, Any, Deque, Callable, Awaitable
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.chat.message_receive.chat_stream import chat_manager
|
||||
from rich.traceback import install
|
||||
|
|
@ -19,6 +19,7 @@ from src.chat.focus_chat.info_processors.working_memory_processor import Working
|
|||
from src.chat.focus_chat.info_processors.action_processor import ActionProcessor
|
||||
from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
|
||||
from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation
|
||||
from src.chat.heart_flow.observation.structure_observation import StructureObservation
|
||||
from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
|
||||
from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
|
||||
from src.chat.focus_chat.memory_activator import MemoryActivator
|
||||
|
|
@ -83,6 +84,7 @@ class HeartFChatting:
|
|||
self,
|
||||
chat_id: str,
|
||||
observations: list[Observation],
|
||||
on_stop_focus_chat: Optional[Callable[[], Awaitable[None]]] = None,
|
||||
):
|
||||
"""
|
||||
HeartFChatting 初始化函数
|
||||
|
|
@ -90,6 +92,7 @@ class HeartFChatting:
|
|||
参数:
|
||||
chat_id: 聊天流唯一标识符(如stream_id)
|
||||
observations: 关联的观察列表
|
||||
on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
|
||||
"""
|
||||
# 基础属性
|
||||
self.stream_id: str = chat_id # 聊天流ID
|
||||
|
|
@ -97,6 +100,7 @@ class HeartFChatting:
|
|||
self.log_prefix: str = str(chat_id) # Initial default, will be updated
|
||||
self.hfcloop_observation = HFCloopObservation(observe_id=self.stream_id)
|
||||
self.chatting_observation = observations[0]
|
||||
self.structure_observation = StructureObservation(observe_id=self.stream_id)
|
||||
|
||||
self.memory_activator = MemoryActivator()
|
||||
self.working_memory = WorkingMemory(chat_id=self.stream_id)
|
||||
|
|
@ -141,6 +145,9 @@ class HeartFChatting:
|
|||
self._current_cycle: Optional[CycleDetail] = None
|
||||
self._shutting_down: bool = False # 关闭标志位
|
||||
|
||||
# 存储回调函数
|
||||
self.on_stop_focus_chat = on_stop_focus_chat
|
||||
|
||||
async def _initialize(self) -> bool:
|
||||
"""
|
||||
执行懒初始化操作
|
||||
|
|
@ -286,6 +293,19 @@ class HeartFChatting:
|
|||
logger.debug(f"模板 {self.chat_stream.context.get_template_name()}")
|
||||
loop_info = await self._observe_process_plan_action_loop(cycle_timers, thinking_id)
|
||||
|
||||
print(loop_info["loop_action_info"]["command"])
|
||||
if loop_info["loop_action_info"]["command"] == "stop_focus_chat":
|
||||
logger.info(f"{self.log_prefix} 麦麦决定停止专注聊天")
|
||||
# 如果设置了回调函数，则调用它
|
||||
if self.on_stop_focus_chat:
|
||||
try:
|
||||
await self.on_stop_focus_chat()
|
||||
logger.info(f"{self.log_prefix} 成功调用回调函数处理停止专注聊天")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 调用停止专注聊天回调函数时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
break
|
||||
|
||||
self._current_cycle.set_loop_info(loop_info)
|
||||
|
||||
self.hfcloop_observation.add_loop_info(self._current_cycle)
|
||||
|
|
@ -408,17 +428,19 @@ class HeartFChatting:
|
|||
|
||||
return all_plan_info
|
||||
|
||||
async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> tuple[bool, str]:
|
||||
async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict:
|
||||
try:
|
||||
with Timer("观察", cycle_timers):
|
||||
# await self.observations[0].observe()
|
||||
await self.chatting_observation.observe()
|
||||
await self.working_observation.observe()
|
||||
await self.hfcloop_observation.observe()
|
||||
await self.structure_observation.observe()
|
||||
observations: List[Observation] = []
|
||||
observations.append(self.chatting_observation)
|
||||
observations.append(self.working_observation)
|
||||
observations.append(self.hfcloop_observation)
|
||||
observations.append(self.structure_observation)
|
||||
|
||||
loop_observation_info = {
|
||||
"observations": observations,
|
||||
|
|
@ -462,13 +484,14 @@ class HeartFChatting:
|
|||
|
||||
logger.info(f"{self.log_prefix} 麦麦决定'{action_str}', 原因'{reasoning}'")
|
||||
|
||||
success, reply_text = await self._handle_action(
|
||||
success, reply_text, command = await self._handle_action(
|
||||
action_type, reasoning, action_data, cycle_timers, thinking_id
|
||||
)
|
||||
|
||||
loop_action_info = {
|
||||
"action_taken": success,
|
||||
"reply_text": reply_text,
|
||||
"command": command,
|
||||
}
|
||||
|
||||
loop_info = {
|
||||
|
|
@ -483,7 +506,12 @@ class HeartFChatting:
|
|||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} FOCUS聊天处理失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return {}
|
||||
return {
|
||||
"loop_observation_info": {},
|
||||
"loop_processor_info": {},
|
||||
"loop_plan_info": {},
|
||||
"loop_action_info": {"action_taken": False, "reply_text": "", "command": ""},
|
||||
}
|
||||
|
||||
async def _handle_action(
|
||||
self,
|
||||
|
|
@ -492,7 +520,7 @@ class HeartFChatting:
|
|||
action_data: dict,
|
||||
cycle_timers: dict,
|
||||
thinking_id: str,
|
||||
) -> tuple[bool, str]:
|
||||
) -> 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
|
||||
|
||||
|
|
@ -504,36 +532,48 @@ class HeartFChatting:
|
|||
thinking_id: 思考ID
|
||||
|
||||
返回:
|
||||
tuple[bool, str]: (是否执行了动作, 思考消息ID)
|
||||
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
|
||||
action_handler = self.action_manager.create_action(
|
||||
action_name=action,
|
||||
action_data=action_data,
|
||||
reasoning=reasoning,
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
observations=self.all_observations,
|
||||
expressor=self.expressor,
|
||||
chat_stream=self.chat_stream,
|
||||
log_prefix=self.log_prefix,
|
||||
shutting_down=self._shutting_down,
|
||||
)
|
||||
try:
|
||||
action_handler = self.action_manager.create_action(
|
||||
action_name=action,
|
||||
action_data=action_data,
|
||||
reasoning=reasoning,
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
observations=self.all_observations,
|
||||
expressor=self.expressor,
|
||||
chat_stream=self.chat_stream,
|
||||
log_prefix=self.log_prefix,
|
||||
shutting_down=self._shutting_down,
|
||||
)
|
||||
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, "", ""
|
||||
|
||||
if not action_handler:
|
||||
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}, 原因: {reasoning}")
|
||||
return False, ""
|
||||
return False, "", ""
|
||||
|
||||
# 处理动作并获取结果
|
||||
success, reply_text = await action_handler.handle_action()
|
||||
|
||||
return success, reply_text
|
||||
result = await action_handler.handle_action()
|
||||
if len(result) == 3:
|
||||
success, reply_text, command = result
|
||||
else:
|
||||
success, reply_text = result
|
||||
command = ""
|
||||
logger.info(
|
||||
f"{self.log_prefix} 麦麦决定'{action}', 原因'{reasoning}'，返回结果'{success}', '{reply_text}', '{command}'"
|
||||
)
|
||||
return success, reply_text, command
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
|
||||
traceback.print_exc()
|
||||
return False, ""
|
||||
return False, "", ""
|
||||
|
||||
async def shutdown(self):
|
||||
"""优雅关闭HeartFChatting实例，取消活动循环任务"""
|
||||
|
|
|
|||
|
|
@ -73,7 +73,7 @@ class HeartFCSender:
|
|||
thinking_message = self.thinking_messages.get(chat_id, {}).get(message_id)
|
||||
return thinking_message.thinking_start_time if thinking_message else None
|
||||
|
||||
async def send_message(self, message: MessageSending, has_thinking=False, typing=False):
|
||||
async def send_message(self, message: MessageSending, has_thinking=False, typing=False, set_reply=False):
|
||||
"""
|
||||
处理、发送并存储一条消息。
|
||||
|
||||
|
|
@ -97,7 +97,7 @@ class HeartFCSender:
|
|||
message_id = message.message_info.message_id
|
||||
|
||||
try:
|
||||
if has_thinking:
|
||||
if set_reply:
|
||||
_ = message.update_thinking_time()
|
||||
|
||||
# --- 条件应用 set_reply 逻辑 ---
|
||||
|
|
|
|||
|
|
@ -205,8 +205,8 @@ class HeartFCMessageReceiver:
|
|||
|
||||
# 6. 兴趣度计算与更新
|
||||
interested_rate, is_mentioned = await _calculate_interest(message)
|
||||
await subheartflow.interest_chatting.increase_interest(value=interested_rate)
|
||||
subheartflow.interest_chatting.add_interest_dict(message, interested_rate, is_mentioned)
|
||||
# await subheartflow.interest_chatting.increase_interest(value=interested_rate)
|
||||
subheartflow.add_interest_message(message, interested_rate, is_mentioned)
|
||||
|
||||
# 7. 日志记录
|
||||
mes_name = chat.group_info.group_name if chat.group_info else "私聊"
|
||||
|
|
@ -219,7 +219,8 @@ class HeartFCMessageReceiver:
|
|||
)
|
||||
|
||||
# 8. 关系处理
|
||||
await _process_relationship(message)
|
||||
if global_config.relationship.give_name:
|
||||
await _process_relationship(message)
|
||||
|
||||
except Exception as e:
|
||||
await _handle_error(e, "消息处理失败", message)
|
||||
|
|
@ -76,7 +76,10 @@ class StructuredInfo:
|
|||
"""
|
||||
|
||||
info_str = ""
|
||||
# print(f"self.data: {self.data}")
|
||||
|
||||
for key, value in self.data.items():
|
||||
# print(f"key: {key}, value: {value}")
|
||||
info_str += f"信息类型：{key}，信息内容：{value}\n"
|
||||
|
||||
return info_str
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservati
|
|||
from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
|
||||
from src.chat.message_receive.chat_stream import chat_manager
|
||||
from typing import Dict
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import random
|
||||
|
||||
|
|
@ -21,15 +20,11 @@ class ActionProcessor(BaseProcessor):
|
|||
用于处理Observation对象，将其转换为ObsInfo对象。
|
||||
"""
|
||||
|
||||
log_prefix = "聊天信息处理"
|
||||
log_prefix = "动作处理"
|
||||
|
||||
def __init__(self):
|
||||
"""初始化观察处理器"""
|
||||
super().__init__()
|
||||
# TODO: API-Adapter修改标记
|
||||
self.model_summary = LLMRequest(
|
||||
model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
|
||||
)
|
||||
|
||||
async def process_info(
|
||||
self,
|
||||
|
|
@ -56,45 +51,57 @@ class ActionProcessor(BaseProcessor):
|
|||
all_actions = None
|
||||
hfc_obs = None
|
||||
chat_obs = None
|
||||
|
||||
# 收集所有观察对象
|
||||
for obs in observations:
|
||||
if isinstance(obs, HFCloopObservation):
|
||||
hfc_obs = obs
|
||||
if isinstance(obs, ChattingObservation):
|
||||
chat_obs = obs
|
||||
|
||||
# 合并所有动作变更
|
||||
merged_action_changes = {"add": [], "remove": []}
|
||||
reasons = []
|
||||
|
||||
# 处理HFCloopObservation
|
||||
if hfc_obs:
|
||||
obs = hfc_obs
|
||||
# 创建动作信息
|
||||
all_actions = obs.all_actions
|
||||
action_changes = await self.analyze_loop_actions(obs)
|
||||
if action_changes["add"] or action_changes["remove"]:
|
||||
action_info.set_action_changes(action_changes)
|
||||
# 设置变更原因
|
||||
reasons = []
|
||||
# 合并动作变更
|
||||
merged_action_changes["add"].extend(action_changes["add"])
|
||||
merged_action_changes["remove"].extend(action_changes["remove"])
|
||||
|
||||
# 收集变更原因
|
||||
if action_changes["add"]:
|
||||
reasons.append(f"添加动作{action_changes['add']}因为检测到大量无回复")
|
||||
if action_changes["remove"]:
|
||||
reasons.append(f"移除动作{action_changes['remove']}因为检测到连续回复")
|
||||
action_info.set_reason(" | ".join(reasons))
|
||||
|
||||
# 处理ChattingObservation
|
||||
if chat_obs and all_actions is not None:
|
||||
obs = chat_obs
|
||||
action_changes = {"add": [], "remove": []}
|
||||
# 检查动作的关联类型
|
||||
chat_context = chat_manager.get_stream(obs.chat_id).context
|
||||
type_mismatched_actions = []
|
||||
|
||||
for action_name in all_actions.keys():
|
||||
data = all_actions[action_name]
|
||||
if data.get("associated_types"):
|
||||
if not chat_context.check_types(data["associated_types"]):
|
||||
action_changes["remove"].append(action_name)
|
||||
type_mismatched_actions.append(action_name)
|
||||
logger.debug(f"{self.log_prefix} 动作 {action_name} 关联类型不匹配，移除该动作")
|
||||
if len(action_changes["remove"]) > 0:
|
||||
action_info.set_action_changes(action_changes)
|
||||
# 设置变更原因
|
||||
reasons = []
|
||||
if action_info.get_reason():
|
||||
reasons.append(action_info.get_reason())
|
||||
if action_changes["remove"]:
|
||||
reasons.append(f"移除动作{action_changes['remove']}因为关联类型不匹配")
|
||||
action_info.set_reason(" | ".join(reasons))
|
||||
|
||||
if type_mismatched_actions:
|
||||
# 合并到移除列表中
|
||||
merged_action_changes["remove"].extend(type_mismatched_actions)
|
||||
reasons.append(f"移除动作{type_mismatched_actions}因为关联类型不匹配")
|
||||
|
||||
# 如果有任何动作变更，设置到action_info中
|
||||
if merged_action_changes["add"] or merged_action_changes["remove"]:
|
||||
action_info.set_action_changes(merged_action_changes)
|
||||
action_info.set_reason(" | ".join(reasons))
|
||||
|
||||
processed_infos.append(action_info)
|
||||
|
||||
|
|
@ -128,8 +135,15 @@ class ActionProcessor(BaseProcessor):
|
|||
reply_sequence.append(action_type == "reply")
|
||||
|
||||
# 检查no_reply比例
|
||||
if len(recent_cycles) >= 5 and (no_reply_count / len(recent_cycles)) >= 0.8:
|
||||
result["add"].append("exit_focus_chat")
|
||||
print(f"no_reply_count: {no_reply_count}, len(recent_cycles): {len(recent_cycles)}")
|
||||
# print(1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111)
|
||||
if len(recent_cycles) >= (4 * global_config.focus_chat.exit_focus_threshold) and (
|
||||
no_reply_count / len(recent_cycles)
|
||||
) >= (0.6 * global_config.focus_chat.exit_focus_threshold):
|
||||
if global_config.chat.chat_mode == "auto":
|
||||
result["add"].append("exit_focus_chat")
|
||||
result["remove"].append("no_reply")
|
||||
result["remove"].append("reply")
|
||||
|
||||
# 获取最近三次的reply状态
|
||||
last_three = reply_sequence[-3:] if len(reply_sequence) >= 3 else reply_sequence
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ class ChattingInfoProcessor(BaseProcessor):
|
|||
super().__init__()
|
||||
# TODO: API-Adapter修改标记
|
||||
self.model_summary = LLMRequest(
|
||||
model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
|
||||
model=global_config.model.utils_small, temperature=0.7, max_tokens=300, request_type="chat_observation"
|
||||
)
|
||||
|
||||
async def process_info(
|
||||
|
|
|
|||
|
|
@ -71,10 +71,10 @@ class MindProcessor(BaseProcessor):
|
|||
self.subheartflow_id = subheartflow_id
|
||||
|
||||
self.llm_model = LLMRequest(
|
||||
model=global_config.model.sub_heartflow,
|
||||
temperature=global_config.model.sub_heartflow["temp"],
|
||||
model=global_config.model.focus_chat_mind,
|
||||
temperature=global_config.model.focus_chat_mind["temp"],
|
||||
max_tokens=800,
|
||||
request_type="sub_heart_flow",
|
||||
request_type="focus_chat_mind",
|
||||
)
|
||||
|
||||
self.current_mind = ""
|
||||
|
|
|
|||
|
|
@ -61,10 +61,10 @@ class WorkingMemoryProcessor(BaseProcessor):
|
|||
self.subheartflow_id = subheartflow_id
|
||||
|
||||
self.llm_model = LLMRequest(
|
||||
model=global_config.model.sub_heartflow,
|
||||
temperature=global_config.model.sub_heartflow["temp"],
|
||||
model=global_config.model.focus_chat_mind,
|
||||
temperature=global_config.model.focus_chat_mind["temp"],
|
||||
max_tokens=800,
|
||||
request_type="working_memory",
|
||||
request_type="focus_working_memory",
|
||||
)
|
||||
|
||||
name = chat_manager.get_stream_name(self.subheartflow_id)
|
||||
|
|
|
|||
|
|
@ -4,24 +4,58 @@ from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservati
|
|||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.chat.utils.prompt_builder import Prompt
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
from datetime import datetime
|
||||
from src.chat.memory_system.Hippocampus import HippocampusManager
|
||||
from typing import List, Dict
|
||||
import difflib
|
||||
import json
|
||||
from json_repair import repair_json
|
||||
|
||||
|
||||
logger = get_logger("memory_activator")
|
||||
|
||||
|
||||
def get_keywords_from_json(json_str):
|
||||
"""
|
||||
从JSON字符串中提取关键词列表
|
||||
|
||||
Args:
|
||||
json_str: JSON格式的字符串
|
||||
|
||||
Returns:
|
||||
List[str]: 关键词列表
|
||||
"""
|
||||
try:
|
||||
# 使用repair_json修复JSON格式
|
||||
fixed_json = repair_json(json_str)
|
||||
|
||||
# 如果repair_json返回的是字符串，需要解析为Python对象
|
||||
if isinstance(fixed_json, str):
|
||||
result = json.loads(fixed_json)
|
||||
else:
|
||||
# 如果repair_json直接返回了字典对象，直接使用
|
||||
result = fixed_json
|
||||
|
||||
# 提取关键词
|
||||
keywords = result.get("keywords", [])
|
||||
return keywords
|
||||
except Exception as e:
|
||||
logger.error(f"解析关键词JSON失败: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def init_prompt():
|
||||
# --- Group Chat Prompt ---
|
||||
memory_activator_prompt = """
|
||||
你是一个记忆分析器，你需要根据以下信息来进行会议
|
||||
你是一个记忆分析器，你需要根据以下信息来进行回忆
|
||||
以下是一场聊天中的信息，请根据这些信息，总结出几个关键词作为记忆回忆的触发词
|
||||
|
||||
{obs_info_text}
|
||||
|
||||
历史关键词（请避免重复提取这些关键词）：
|
||||
{cached_keywords}
|
||||
|
||||
请输出一个json格式，包含以下字段：
|
||||
{{
|
||||
"keywords": ["关键词1", "关键词2", "关键词3",......]
|
||||
|
|
@ -36,9 +70,10 @@ class MemoryActivator:
|
|||
def __init__(self):
|
||||
# TODO: API-Adapter修改标记
|
||||
self.summary_model = LLMRequest(
|
||||
model=global_config.model.summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
|
||||
model=global_config.model.memory_summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
|
||||
)
|
||||
self.running_memory = []
|
||||
self.cached_keywords = set() # 用于缓存历史关键词
|
||||
|
||||
async def activate_memory(self, observations) -> List[Dict]:
|
||||
"""
|
||||
|
|
@ -61,31 +96,47 @@ class MemoryActivator:
|
|||
elif isinstance(observation, HFCloopObservation):
|
||||
obs_info_text += observation.get_observe_info()
|
||||
|
||||
logger.debug(f"回忆待检索内容：obs_info_text: {obs_info_text}")
|
||||
# logger.debug(f"回忆待检索内容：obs_info_text: {obs_info_text}")
|
||||
|
||||
# prompt = await global_prompt_manager.format_prompt(
|
||||
# "memory_activator_prompt",
|
||||
# obs_info_text=obs_info_text,
|
||||
# )
|
||||
# 将缓存的关键词转换为字符串，用于prompt
|
||||
cached_keywords_str = ", ".join(self.cached_keywords) if self.cached_keywords else "暂无历史关键词"
|
||||
|
||||
# logger.debug(f"prompt: {prompt}")
|
||||
|
||||
# response = await self.summary_model.generate_response(prompt)
|
||||
|
||||
# logger.debug(f"response: {response}")
|
||||
|
||||
# # 只取response的第一个元素（字符串）
|
||||
# response_str = response[0]
|
||||
# keywords = list(get_keywords_from_json(response_str))
|
||||
|
||||
# #调用记忆系统获取相关记忆
|
||||
# related_memory = await HippocampusManager.get_instance().get_memory_from_topic(
|
||||
# valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
|
||||
# )
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=True
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"memory_activator_prompt",
|
||||
obs_info_text=obs_info_text,
|
||||
cached_keywords=cached_keywords_str,
|
||||
)
|
||||
|
||||
logger.debug(f"prompt: {prompt}")
|
||||
|
||||
response = await self.summary_model.generate_response(prompt)
|
||||
|
||||
logger.debug(f"response: {response}")
|
||||
|
||||
# 只取response的第一个元素（字符串）
|
||||
response_str = response[0]
|
||||
keywords = list(get_keywords_from_json(response_str))
|
||||
|
||||
# 更新关键词缓存
|
||||
if keywords:
|
||||
# 限制缓存大小，最多保留10个关键词
|
||||
if len(self.cached_keywords) > 10:
|
||||
# 转换为列表，移除最早的关键词
|
||||
cached_list = list(self.cached_keywords)
|
||||
self.cached_keywords = set(cached_list[-8:])
|
||||
|
||||
# 添加新的关键词到缓存
|
||||
self.cached_keywords.update(keywords)
|
||||
logger.debug(f"更新关键词缓存: {self.cached_keywords}")
|
||||
|
||||
# 调用记忆系统获取相关记忆
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_topic(
|
||||
valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3
|
||||
)
|
||||
# related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
# text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||
# )
|
||||
|
||||
# logger.debug(f"获取到的记忆: {related_memory}")
|
||||
|
||||
# 激活时，所有已有记忆的duration+1，达到3则移除
|
||||
|
|
|
|||
|
|
@ -28,8 +28,6 @@ class ActionManager:
|
|||
self._registered_actions: Dict[str, ActionInfo] = {}
|
||||
# 当前正在使用的动作集合，默认加载默认动作
|
||||
self._using_actions: Dict[str, ActionInfo] = {}
|
||||
# 临时备份原始使用中的动作
|
||||
self._original_actions_backup: Optional[Dict[str, ActionInfo]] = None
|
||||
|
||||
# 默认动作集，仅作为快照，用于恢复默认
|
||||
self._default_actions: Dict[str, ActionInfo] = {}
|
||||
|
|
@ -160,9 +158,9 @@ class ActionManager:
|
|||
Optional[BaseAction]: 创建的动作处理器实例，如果动作名称未注册则返回None
|
||||
"""
|
||||
# 检查动作是否在当前使用的动作集中
|
||||
if action_name not in self._using_actions:
|
||||
logger.warning(f"当前不可用的动作类型: {action_name}")
|
||||
return None
|
||||
# if action_name not in self._using_actions:
|
||||
# logger.warning(f"当前不可用的动作类型: {action_name}")
|
||||
# return None
|
||||
|
||||
handler_class = _ACTION_REGISTRY.get(action_name)
|
||||
if not handler_class:
|
||||
|
|
@ -278,22 +276,20 @@ class ActionManager:
|
|||
return True
|
||||
|
||||
def temporarily_remove_actions(self, actions_to_remove: List[str]) -> None:
|
||||
"""临时移除使用集中的指定动作，备份原始使用集"""
|
||||
if self._original_actions_backup is None:
|
||||
self._original_actions_backup = self._using_actions.copy()
|
||||
"""临时移除使用集中的指定动作"""
|
||||
for name in actions_to_remove:
|
||||
self._using_actions.pop(name, None)
|
||||
|
||||
def restore_actions(self) -> None:
|
||||
"""恢复之前备份的原始使用集"""
|
||||
if self._original_actions_backup is not None:
|
||||
self._using_actions = self._original_actions_backup.copy()
|
||||
self._original_actions_backup = None
|
||||
"""恢复到默认动作集"""
|
||||
logger.debug(
|
||||
f"恢复动作集: 从 {list(self._using_actions.keys())} 恢复到默认动作集 {list(self._default_actions.keys())}"
|
||||
)
|
||||
self._using_actions = self._default_actions.copy()
|
||||
|
||||
def restore_default_actions(self) -> None:
|
||||
"""恢复默认动作集到使用集"""
|
||||
self._using_actions = self._default_actions.copy()
|
||||
self._original_actions_backup = None
|
||||
|
||||
def get_action(self, action_name: str) -> Optional[Type[BaseAction]]:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
# 导入所有动作模块以确保装饰器被执行
|
||||
from . import reply_action # noqa
|
||||
from . import no_reply_action # noqa
|
||||
from . import exit_focus_chat_action # noqa
|
||||
|
||||
# 在此处添加更多动作模块导入
|
||||
|
|
|
|||
|
|
@ -5,8 +5,6 @@ from src.chat.focus_chat.planners.actions.base_action import BaseAction, registe
|
|||
from typing import Tuple, List
|
||||
from src.chat.heart_flow.observation.observation import Observation
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.chat.heart_flow.heartflow import heartflow
|
||||
from src.chat.heart_flow.sub_heartflow import ChatState
|
||||
|
||||
logger = get_logger("action_taken")
|
||||
|
||||
|
|
@ -27,7 +25,7 @@ class ExitFocusChatAction(BaseAction):
|
|||
"当前内容不需要持续专注关注，你决定退出专注聊天",
|
||||
"聊天内容已经完成，你决定退出专注聊天",
|
||||
]
|
||||
default = True
|
||||
default = False
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -56,7 +54,6 @@ class ExitFocusChatAction(BaseAction):
|
|||
self.observations = observations
|
||||
self.log_prefix = log_prefix
|
||||
self._shutting_down = shutting_down
|
||||
self.chat_id = chat_stream.stream_id
|
||||
|
||||
async def handle_action(self) -> Tuple[bool, str]:
|
||||
"""
|
||||
|
|
@ -74,23 +71,8 @@ class ExitFocusChatAction(BaseAction):
|
|||
try:
|
||||
# 转换状态
|
||||
status_message = ""
|
||||
self.sub_heartflow = await heartflow.get_or_create_subheartflow(self.chat_id)
|
||||
if self.sub_heartflow:
|
||||
try:
|
||||
# 转换为normal_chat状态
|
||||
await self.sub_heartflow.change_chat_state(ChatState.CHAT)
|
||||
status_message = "已成功切换到普通聊天模式"
|
||||
logger.info(f"{self.log_prefix} {status_message}")
|
||||
except Exception as e:
|
||||
error_msg = f"切换到普通聊天模式失败: {str(e)}"
|
||||
logger.error(f"{self.log_prefix} {error_msg}")
|
||||
return False, error_msg
|
||||
else:
|
||||
warning_msg = "未找到有效的sub heartflow实例，无法切换状态"
|
||||
logger.warning(f"{self.log_prefix} {warning_msg}")
|
||||
return False, warning_msg
|
||||
|
||||
return True, status_message
|
||||
command = "stop_focus_chat"
|
||||
return True, status_message, command
|
||||
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} 处理 'exit_focus_chat' 时等待被中断 (CancelledError)")
|
||||
|
|
@ -99,4 +81,4 @@ class ExitFocusChatAction(BaseAction):
|
|||
error_msg = f"处理 'exit_focus_chat' 时发生错误: {str(e)}"
|
||||
logger.error(f"{self.log_prefix} {error_msg}")
|
||||
logger.error(traceback.format_exc())
|
||||
return False, error_msg
|
||||
return False, "", ""
|
||||
|
|
|
|||
|
|
@ -33,7 +33,10 @@ class MemoryManager:
|
|||
self._id_map: Dict[str, MemoryItem] = {}
|
||||
|
||||
self.llm_summarizer = LLMRequest(
|
||||
model=global_config.model.summary, temperature=0.3, max_tokens=512, request_type="memory_summarization"
|
||||
model=global_config.model.focus_working_memory,
|
||||
temperature=0.3,
|
||||
max_tokens=512,
|
||||
request_type="memory_summarization",
|
||||
)
|
||||
|
||||
@property
|
||||
|
|
|
|||
|
|
@ -88,34 +88,36 @@ class BackgroundTaskManager:
|
|||
f"聊天状态更新任务已启动 间隔:{STATE_UPDATE_INTERVAL_SECONDS}s",
|
||||
"_state_update_task",
|
||||
),
|
||||
(
|
||||
self._run_cleanup_cycle,
|
||||
"info",
|
||||
f"清理任务已启动 间隔:{CLEANUP_INTERVAL_SECONDS}s",
|
||||
"_cleanup_task",
|
||||
),
|
||||
# 新增私聊激活任务配置
|
||||
(
|
||||
# Use lambda to pass the interval to the runner function
|
||||
lambda: self._run_private_chat_activation_cycle(PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS),
|
||||
"debug",
|
||||
f"私聊激活检查任务已启动 间隔:{PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS}s",
|
||||
"_private_chat_activation_task",
|
||||
),
|
||||
]
|
||||
|
||||
# 根据 chat_mode 条件添加专注评估任务
|
||||
# 根据 chat_mode 条件添加其他任务
|
||||
if not (global_config.chat.chat_mode == "normal"):
|
||||
task_configs.append(
|
||||
(
|
||||
self._run_into_focus_cycle,
|
||||
"debug", # 设为debug，避免过多日志
|
||||
f"专注评估任务已启动 间隔:{INTEREST_EVAL_INTERVAL_SECONDS}s",
|
||||
"_into_focus_task",
|
||||
)
|
||||
task_configs.extend(
|
||||
[
|
||||
(
|
||||
self._run_cleanup_cycle,
|
||||
"info",
|
||||
f"清理任务已启动 间隔:{CLEANUP_INTERVAL_SECONDS}s",
|
||||
"_cleanup_task",
|
||||
),
|
||||
# 新增私聊激活任务配置
|
||||
(
|
||||
# Use lambda to pass the interval to the runner function
|
||||
lambda: self._run_private_chat_activation_cycle(PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS),
|
||||
"debug",
|
||||
f"私聊激活检查任务已启动 间隔:{PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS}s",
|
||||
"_private_chat_activation_task",
|
||||
),
|
||||
# (
|
||||
# self._run_into_focus_cycle,
|
||||
# "debug", # 设为debug，避免过多日志
|
||||
# f"专注评估任务已启动 间隔:{INTEREST_EVAL_INTERVAL_SECONDS}s",
|
||||
# "_into_focus_task",
|
||||
# )
|
||||
]
|
||||
)
|
||||
else:
|
||||
logger.info("聊天模式为 normal，跳过启动专注评估任务")
|
||||
logger.info("聊天模式为 normal，跳过启动清理任务、私聊激活任务和专注评估任务")
|
||||
|
||||
# 统一启动所有任务
|
||||
for task_func, log_level, log_msg, task_attr_name in task_configs:
|
||||
|
|
@ -215,10 +217,10 @@ class BackgroundTaskManager:
|
|||
logger.info(f"[清理任务] 清理完成, 共停止 {stopped_count}/{len(flows_to_stop)} 个子心流")
|
||||
|
||||
# --- 新增兴趣评估工作函数 ---
|
||||
async def _perform_into_focus_work(self):
|
||||
"""执行一轮子心流兴趣评估与提升检查。"""
|
||||
# 直接调用 subheartflow_manager 的方法，并传递当前状态信息
|
||||
await self.subheartflow_manager.sbhf_normal_into_focus()
|
||||
# async def _perform_into_focus_work(self):
|
||||
# """执行一轮子心流兴趣评估与提升检查。"""
|
||||
# # 直接调用 subheartflow_manager 的方法，并传递当前状态信息
|
||||
# await self.subheartflow_manager.sbhf_normal_into_focus()
|
||||
|
||||
async def _run_state_update_cycle(self, interval: int):
|
||||
await _run_periodic_loop(task_name="State Update", interval=interval, task_func=self._perform_state_update_work)
|
||||
|
|
@ -229,12 +231,12 @@ class BackgroundTaskManager:
|
|||
)
|
||||
|
||||
# --- 新增兴趣评估任务运行器 ---
|
||||
async def _run_into_focus_cycle(self):
|
||||
await _run_periodic_loop(
|
||||
task_name="Into Focus",
|
||||
interval=INTEREST_EVAL_INTERVAL_SECONDS,
|
||||
task_func=self._perform_into_focus_work,
|
||||
)
|
||||
# async def _run_into_focus_cycle(self):
|
||||
# await _run_periodic_loop(
|
||||
# task_name="Into Focus",
|
||||
# interval=INTEREST_EVAL_INTERVAL_SECONDS,
|
||||
# task_func=self._perform_into_focus_work,
|
||||
# )
|
||||
|
||||
# 新增私聊激活任务运行器
|
||||
async def _run_private_chat_activation_cycle(self, interval: int):
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from src.chat.heart_flow.sub_heartflow import SubHeartflow, ChatState
|
||||
from src.common.logger_manager import get_logger
|
||||
from typing import Any, Optional
|
||||
from typing import Any, Optional, List
|
||||
from src.chat.heart_flow.mai_state_manager import MaiStateInfo, MaiStateManager
|
||||
from src.chat.heart_flow.subheartflow_manager import SubHeartflowManager
|
||||
from src.chat.heart_flow.background_tasks import BackgroundTaskManager # Import BackgroundTaskManager
|
||||
|
|
@ -57,6 +57,23 @@ class Heartflow:
|
|||
|
||||
return heartfc_instance.get_cycle_history(last_n=history_len)
|
||||
|
||||
async def api_get_normal_chat_replies(self, subheartflow_id: str, limit: int = 10) -> Optional[List[dict]]:
|
||||
"""获取子心流的NormalChat回复记录
|
||||
|
||||
Args:
|
||||
subheartflow_id: 子心流ID
|
||||
limit: 最大返回数量，默认10条
|
||||
|
||||
Returns:
|
||||
Optional[List[dict]]: 回复记录列表，如果子心流不存在则返回None
|
||||
"""
|
||||
subheartflow = await self.subheartflow_manager.get_or_create_subheartflow(subheartflow_id)
|
||||
if not subheartflow:
|
||||
logger.warning(f"尝试获取不存在的子心流 {subheartflow_id} 的NormalChat回复记录")
|
||||
return None
|
||||
|
||||
return subheartflow.get_normal_chat_recent_replies(limit)
|
||||
|
||||
async def heartflow_start_working(self):
|
||||
"""启动后台任务"""
|
||||
await self.background_task_manager.start_tasks()
|
||||
|
|
|
|||
|
|
@ -1,200 +0,0 @@
|
|||
import asyncio
|
||||
from src.config.config import global_config
|
||||
from typing import Optional, Dict
|
||||
import traceback
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.chat.message_receive.message import MessageRecv
|
||||
import math
|
||||
|
||||
|
||||
# 定义常量 (从 interest.py 移动过来)
|
||||
MAX_INTEREST = 15.0
|
||||
|
||||
logger = get_logger("interest_chatting")
|
||||
|
||||
PROBABILITY_INCREASE_RATE_PER_SECOND = 0.1
|
||||
PROBABILITY_DECREASE_RATE_PER_SECOND = 0.1
|
||||
MAX_REPLY_PROBABILITY = 1
|
||||
|
||||
|
||||
class InterestChatting:
|
||||
def __init__(
|
||||
self,
|
||||
decay_rate=global_config.focus_chat.default_decay_rate_per_second,
|
||||
max_interest=MAX_INTEREST,
|
||||
trigger_threshold=global_config.focus_chat.reply_trigger_threshold,
|
||||
max_probability=MAX_REPLY_PROBABILITY,
|
||||
):
|
||||
# 基础属性初始化
|
||||
self.interest_level: float = 0.0
|
||||
self.decay_rate_per_second: float = decay_rate
|
||||
self.max_interest: float = max_interest
|
||||
|
||||
self.trigger_threshold: float = trigger_threshold
|
||||
self.max_reply_probability: float = max_probability
|
||||
self.is_above_threshold: bool = False
|
||||
|
||||
# 任务相关属性初始化
|
||||
self.update_task: Optional[asyncio.Task] = None
|
||||
self._stop_event = asyncio.Event()
|
||||
self._task_lock = asyncio.Lock()
|
||||
self._is_running = False
|
||||
|
||||
self.interest_dict: Dict[str, tuple[MessageRecv, float, bool]] = {}
|
||||
self.update_interval = 1.0
|
||||
|
||||
self.above_threshold = False
|
||||
self.start_hfc_probability = 0.0
|
||||
|
||||
async def initialize(self):
|
||||
async with self._task_lock:
|
||||
if self._is_running:
|
||||
logger.debug("后台兴趣更新任务已在运行中。")
|
||||
return
|
||||
|
||||
# 清理已完成或已取消的任务
|
||||
if self.update_task and (self.update_task.done() or self.update_task.cancelled()):
|
||||
self.update_task = None
|
||||
|
||||
if not self.update_task:
|
||||
self._stop_event.clear()
|
||||
self._is_running = True
|
||||
self.update_task = asyncio.create_task(self._run_update_loop(self.update_interval))
|
||||
logger.debug("后台兴趣更新任务已创建并启动。")
|
||||
|
||||
def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
|
||||
"""添加消息到兴趣字典
|
||||
|
||||
参数:
|
||||
message: 接收到的消息
|
||||
interest_value: 兴趣值
|
||||
is_mentioned: 是否被提及
|
||||
|
||||
功能:
|
||||
1. 将消息添加到兴趣字典
|
||||
2. 更新最后交互时间
|
||||
3. 如果字典长度超过10，删除最旧的消息
|
||||
"""
|
||||
# 添加新消息
|
||||
self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
|
||||
|
||||
# 如果字典长度超过10，删除最旧的消息
|
||||
if len(self.interest_dict) > 10:
|
||||
oldest_key = next(iter(self.interest_dict))
|
||||
self.interest_dict.pop(oldest_key)
|
||||
|
||||
async def _calculate_decay(self):
|
||||
"""计算兴趣值的衰减
|
||||
|
||||
参数:
|
||||
current_time: 当前时间戳
|
||||
|
||||
处理逻辑:
|
||||
1. 计算时间差
|
||||
2. 处理各种异常情况(负值/零值)
|
||||
3. 正常计算衰减
|
||||
4. 更新最后更新时间
|
||||
"""
|
||||
|
||||
# 处理极小兴趣值情况
|
||||
if self.interest_level < 1e-9:
|
||||
self.interest_level = 0.0
|
||||
return
|
||||
|
||||
# 异常情况处理
|
||||
if self.decay_rate_per_second <= 0:
|
||||
logger.warning(f"衰减率({self.decay_rate_per_second})无效，重置兴趣值为0")
|
||||
self.interest_level = 0.0
|
||||
return
|
||||
|
||||
# 正常衰减计算
|
||||
try:
|
||||
decay_factor = math.pow(self.decay_rate_per_second, self.update_interval)
|
||||
self.interest_level *= decay_factor
|
||||
except ValueError as e:
|
||||
logger.error(
|
||||
f"衰减计算错误: {e} 参数: 衰减率={self.decay_rate_per_second} 时间差={self.update_interval} 当前兴趣={self.interest_level}"
|
||||
)
|
||||
self.interest_level = 0.0
|
||||
|
||||
async def _update_reply_probability(self):
|
||||
self.above_threshold = self.interest_level >= self.trigger_threshold
|
||||
if self.above_threshold:
|
||||
self.start_hfc_probability += PROBABILITY_INCREASE_RATE_PER_SECOND
|
||||
else:
|
||||
if self.start_hfc_probability > 0:
|
||||
self.start_hfc_probability = max(0, self.start_hfc_probability - PROBABILITY_DECREASE_RATE_PER_SECOND)
|
||||
|
||||
async def increase_interest(self, value: float):
|
||||
self.interest_level += value
|
||||
self.interest_level = min(self.interest_level, self.max_interest)
|
||||
|
||||
async def decrease_interest(self, value: float):
|
||||
self.interest_level -= value
|
||||
self.interest_level = max(self.interest_level, 0.0)
|
||||
|
||||
async def get_interest(self) -> float:
|
||||
return self.interest_level
|
||||
|
||||
async def get_state(self) -> dict:
|
||||
interest = self.interest_level # 直接使用属性值
|
||||
return {
|
||||
"interest_level": round(interest, 2),
|
||||
"start_hfc_probability": round(self.start_hfc_probability, 4),
|
||||
"above_threshold": self.above_threshold,
|
||||
}
|
||||
|
||||
# --- 新增后台更新任务相关方法 ---
|
||||
async def _run_update_loop(self, update_interval: float = 1.0):
|
||||
"""后台循环，定期更新兴趣和回复概率。"""
|
||||
try:
|
||||
while not self._stop_event.is_set():
|
||||
try:
|
||||
if self.interest_level != 0:
|
||||
await self._calculate_decay()
|
||||
|
||||
await self._update_reply_probability()
|
||||
|
||||
# 等待下一个周期或停止事件
|
||||
await asyncio.wait_for(self._stop_event.wait(), timeout=update_interval)
|
||||
except asyncio.TimeoutError:
|
||||
# 正常超时，继续循环
|
||||
continue
|
||||
except Exception as e:
|
||||
logger.error(f"InterestChatting 更新循环出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
# 防止错误导致CPU飙升，稍作等待
|
||||
await asyncio.sleep(5)
|
||||
except asyncio.CancelledError:
|
||||
logger.info("InterestChatting 更新循环被取消。")
|
||||
finally:
|
||||
self._is_running = False
|
||||
logger.info("InterestChatting 更新循环已停止。")
|
||||
|
||||
async def stop_updates(self):
|
||||
"""停止后台更新任务，使用锁确保并发安全"""
|
||||
async with self._task_lock:
|
||||
if not self._is_running:
|
||||
logger.debug("后台兴趣更新任务未运行。")
|
||||
return
|
||||
|
||||
logger.info("正在停止 InterestChatting 后台更新任务...")
|
||||
self._stop_event.set()
|
||||
|
||||
if self.update_task and not self.update_task.done():
|
||||
try:
|
||||
# 等待任务结束，设置超时
|
||||
await asyncio.wait_for(self.update_task, timeout=5.0)
|
||||
logger.info("InterestChatting 后台更新任务已成功停止。")
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning("停止 InterestChatting 后台任务超时，尝试取消...")
|
||||
self.update_task.cancel()
|
||||
try:
|
||||
await self.update_task # 等待取消完成
|
||||
except asyncio.CancelledError:
|
||||
logger.info("InterestChatting 后台更新任务已被取消。")
|
||||
except Exception as e:
|
||||
logger.error(f"停止 InterestChatting 后台任务时发生异常: {e}")
|
||||
finally:
|
||||
self.update_task = None
|
||||
self._is_running = False
|
||||
|
|
@ -1,5 +1,4 @@
|
|||
from datetime import datetime
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import traceback
|
||||
from src.chat.utils.chat_message_builder import (
|
||||
|
|
@ -66,10 +65,6 @@ class ChattingObservation(Observation):
|
|||
self.oldest_messages = []
|
||||
self.oldest_messages_str = ""
|
||||
self.compressor_prompt = ""
|
||||
# TODO: API-Adapter修改标记
|
||||
self.model_summary = LLMRequest(
|
||||
model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
|
||||
)
|
||||
|
||||
async def initialize(self):
|
||||
self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id)
|
||||
|
|
|
|||
|
|
@ -84,10 +84,4 @@ class HFCloopObservation:
|
|||
else:
|
||||
cycle_info_block += "\n你还没看过消息\n"
|
||||
|
||||
using_actions = self.action_manager.get_using_actions()
|
||||
for action_name, action_info in using_actions.items():
|
||||
action_description = action_info["description"]
|
||||
cycle_info_block += f"\n你在聊天中可以使用{action_name}，这个动作的描述是{action_description}\n"
|
||||
cycle_info_block += "注意，除了上述动作选项之外，你在群聊里不能做其他任何事情，这是你能力的边界\n"
|
||||
|
||||
self.observe_info = cycle_info_block
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ class StructureObservation:
|
|||
for structured_info in self.structured_info:
|
||||
if structured_info.get("ttl") > 0:
|
||||
structured_info["ttl"] -= 1
|
||||
observed_structured_infos.append(structured_info)
|
||||
logger.debug(f"观察到结构化信息仍旧在: {structured_info}")
|
||||
observed_structured_infos.append(structured_info)
|
||||
logger.debug(f"观察到结构化信息仍旧在: {structured_info}")
|
||||
|
||||
self.structured_info = observed_structured_infos
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ from src.chat.normal_chat.normal_chat import NormalChat
|
|||
from src.chat.heart_flow.mai_state_manager import MaiStateInfo
|
||||
from src.chat.heart_flow.chat_state_info import ChatState, ChatStateInfo
|
||||
from .utils_chat import get_chat_type_and_target_info
|
||||
from .interest_chatting import InterestChatting
|
||||
from src.config.config import global_config
|
||||
|
||||
|
||||
|
|
@ -51,7 +50,7 @@ class SubHeartflow:
|
|||
# --- End Initialization ---
|
||||
|
||||
# 兴趣检测器
|
||||
self.interest_chatting: InterestChatting = InterestChatting()
|
||||
self.interest_dict: Dict[str, tuple[MessageRecv, float, bool]] = {}
|
||||
|
||||
# 活动状态管理
|
||||
self.should_stop = False # 停止标志
|
||||
|
|
@ -85,8 +84,8 @@ class SubHeartflow:
|
|||
# --- End using utility function ---
|
||||
|
||||
# Initialize interest system (existing logic)
|
||||
await self.interest_chatting.initialize()
|
||||
logger.debug(f"{self.log_prefix} InterestChatting 实例已初始化。")
|
||||
# await self.interest_chatting.initialize()
|
||||
# logger.debug(f"{self.log_prefix} InterestChatting 实例已初始化。")
|
||||
|
||||
# 根据配置决定初始状态
|
||||
if global_config.chat.chat_mode == "focus":
|
||||
|
|
@ -129,7 +128,12 @@ class SubHeartflow:
|
|||
return False
|
||||
# 在 rewind 为 True 或 NormalChat 实例尚未创建时，创建新实例
|
||||
if rewind or not self.normal_chat_instance:
|
||||
self.normal_chat_instance = NormalChat(chat_stream=chat_stream, interest_dict=self.get_interest_dict())
|
||||
# 提供回调函数，用于接收需要切换到focus模式的通知
|
||||
self.normal_chat_instance = NormalChat(
|
||||
chat_stream=chat_stream,
|
||||
interest_dict=self.interest_dict,
|
||||
on_switch_to_focus_callback=self._handle_switch_to_focus_request,
|
||||
)
|
||||
|
||||
# 进行异步初始化
|
||||
await self.normal_chat_instance.initialize()
|
||||
|
|
@ -144,6 +148,38 @@ class SubHeartflow:
|
|||
self.normal_chat_instance = None # 启动/初始化失败，清理实例
|
||||
return False
|
||||
|
||||
async def _handle_switch_to_focus_request(self) -> None:
|
||||
"""
|
||||
处理来自NormalChat的切换到focus模式的请求
|
||||
|
||||
Args:
|
||||
stream_id: 请求切换的stream_id
|
||||
"""
|
||||
logger.info(f"{self.log_prefix} 收到NormalChat请求切换到focus模式")
|
||||
|
||||
# 切换到focus模式
|
||||
current_state = self.chat_state.chat_status
|
||||
if current_state == ChatState.NORMAL:
|
||||
await self.change_chat_state(ChatState.FOCUSED)
|
||||
logger.info(f"{self.log_prefix} 已根据NormalChat请求从NORMAL切换到FOCUSED状态")
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 当前状态为{current_state.value}，无法切换到FOCUSED状态")
|
||||
|
||||
async def _handle_stop_focus_chat_request(self) -> None:
|
||||
"""
|
||||
处理来自HeartFChatting的停止focus模式的请求
|
||||
当收到stop_focus_chat命令时被调用
|
||||
"""
|
||||
logger.info(f"{self.log_prefix} 收到HeartFChatting请求停止focus模式")
|
||||
|
||||
# 切换到normal模式
|
||||
current_state = self.chat_state.chat_status
|
||||
if current_state == ChatState.FOCUSED:
|
||||
await self.change_chat_state(ChatState.NORMAL)
|
||||
logger.info(f"{self.log_prefix} 已根据HeartFChatting请求从FOCUSED切换到NORMAL状态")
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 当前状态为{current_state.value}，无法切换到NORMAL状态")
|
||||
|
||||
async def _stop_heart_fc_chat(self):
|
||||
"""停止并清理 HeartFChatting 实例"""
|
||||
if self.heart_fc_instance:
|
||||
|
|
@ -160,7 +196,7 @@ class SubHeartflow:
|
|||
async def _start_heart_fc_chat(self) -> bool:
|
||||
"""启动 HeartFChatting 实例，确保 NormalChat 已停止"""
|
||||
await self._stop_normal_chat() # 确保普通聊天监控已停止
|
||||
self.clear_interest_dict() # 清理兴趣字典，准备专注聊天
|
||||
self.interest_dict.clear()
|
||||
|
||||
log_prefix = self.log_prefix
|
||||
# 如果实例已存在，检查其循环任务状态
|
||||
|
|
@ -189,6 +225,7 @@ class SubHeartflow:
|
|||
self.heart_fc_instance = HeartFChatting(
|
||||
chat_id=self.subheartflow_id,
|
||||
observations=self.observations,
|
||||
on_stop_focus_chat=self._handle_stop_focus_chat_request,
|
||||
)
|
||||
|
||||
# 初始化并启动 HeartFChatting
|
||||
|
|
@ -237,7 +274,7 @@ class SubHeartflow:
|
|||
|
||||
elif new_state == ChatState.ABSENT:
|
||||
logger.info(f"{log_prefix} 进入 ABSENT 状态，停止所有聊天活动...")
|
||||
self.clear_interest_dict()
|
||||
self.interest_dict.clear()
|
||||
await self._stop_normal_chat()
|
||||
await self._stop_heart_fc_chat()
|
||||
state_changed = True
|
||||
|
|
@ -278,25 +315,35 @@ class SubHeartflow:
|
|||
logger.warning(f"SubHeartflow {self.subheartflow_id} 没有找到有效的 ChattingObservation")
|
||||
return None
|
||||
|
||||
async def get_interest_state(self) -> dict:
|
||||
return await self.interest_chatting.get_state()
|
||||
|
||||
def get_normal_chat_last_speak_time(self) -> float:
|
||||
if self.normal_chat_instance:
|
||||
return self.normal_chat_instance.last_speak_time
|
||||
return 0
|
||||
|
||||
def get_interest_dict(self) -> Dict[str, tuple[MessageRecv, float, bool]]:
|
||||
return self.interest_chatting.interest_dict
|
||||
def get_normal_chat_recent_replies(self, limit: int = 10) -> List[dict]:
|
||||
"""获取NormalChat实例的最近回复记录
|
||||
|
||||
def clear_interest_dict(self):
|
||||
self.interest_chatting.interest_dict.clear()
|
||||
Args:
|
||||
limit: 最大返回数量，默认10条
|
||||
|
||||
Returns:
|
||||
List[dict]: 最近的回复记录列表，如果没有NormalChat实例则返回空列表
|
||||
"""
|
||||
if self.normal_chat_instance:
|
||||
return self.normal_chat_instance.get_recent_replies(limit)
|
||||
return []
|
||||
|
||||
def add_interest_message(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
|
||||
self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
|
||||
# 如果字典长度超过10，删除最旧的消息
|
||||
if len(self.interest_dict) > 10:
|
||||
oldest_key = next(iter(self.interest_dict))
|
||||
self.interest_dict.pop(oldest_key)
|
||||
|
||||
async def get_full_state(self) -> dict:
|
||||
"""获取子心流的完整状态，包括兴趣、思维和聊天状态。"""
|
||||
interest_state = await self.get_interest_state()
|
||||
return {
|
||||
"interest_state": interest_state,
|
||||
"interest_state": "interest_state",
|
||||
"chat_state": self.chat_state.chat_status.value,
|
||||
"chat_state_changed_time": self.chat_state_changed_time,
|
||||
}
|
||||
|
|
@ -314,11 +361,6 @@ class SubHeartflow:
|
|||
await self._stop_normal_chat()
|
||||
await self._stop_heart_fc_chat()
|
||||
|
||||
# 停止兴趣更新任务
|
||||
if self.interest_chatting:
|
||||
logger.info(f"{self.log_prefix} 停止兴趣系统后台任务...")
|
||||
await self.interest_chatting.stop_updates()
|
||||
|
||||
# 取消可能存在的旧后台任务 (self.task)
|
||||
if self.task and not self.task.done():
|
||||
logger.debug(f"{self.log_prefix} 取消子心流主任务 (Shutdown)...")
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import asyncio
|
||||
import time
|
||||
import random
|
||||
from typing import Dict, Any, Optional, List
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.chat.message_receive.chat_stream import chat_manager
|
||||
|
|
@ -186,41 +185,41 @@ class SubHeartflowManager:
|
|||
f"{log_prefix} 完成，共处理 {processed_count} 个子心流，成功将 {changed_count} 个非 ABSENT 子心流的状态更改为 ABSENT。"
|
||||
)
|
||||
|
||||
async def sbhf_normal_into_focus(self):
|
||||
"""评估子心流兴趣度，满足条件则提升到FOCUSED状态（基于start_hfc_probability）"""
|
||||
try:
|
||||
for sub_hf in list(self.subheartflows.values()):
|
||||
flow_id = sub_hf.subheartflow_id
|
||||
stream_name = chat_manager.get_stream_name(flow_id) or flow_id
|
||||
# async def sbhf_normal_into_focus(self):
|
||||
# """评估子心流兴趣度，满足条件则提升到FOCUSED状态（基于start_hfc_probability）"""
|
||||
# try:
|
||||
# for sub_hf in list(self.subheartflows.values()):
|
||||
# flow_id = sub_hf.subheartflow_id
|
||||
# stream_name = chat_manager.get_stream_name(flow_id) or flow_id
|
||||
|
||||
# 跳过已经是FOCUSED状态的子心流
|
||||
if sub_hf.chat_state.chat_status == ChatState.FOCUSED:
|
||||
continue
|
||||
# # 跳过已经是FOCUSED状态的子心流
|
||||
# if sub_hf.chat_state.chat_status == ChatState.FOCUSED:
|
||||
# continue
|
||||
|
||||
if sub_hf.interest_chatting.start_hfc_probability == 0:
|
||||
continue
|
||||
else:
|
||||
logger.debug(
|
||||
f"{stream_name}，现在状态: {sub_hf.chat_state.chat_status.value}，进入专注概率: {sub_hf.interest_chatting.start_hfc_probability}"
|
||||
)
|
||||
# if sub_hf.interest_chatting.start_hfc_probability == 0:
|
||||
# continue
|
||||
# else:
|
||||
# logger.debug(
|
||||
# f"{stream_name}，现在状态: {sub_hf.chat_state.chat_status.value}，进入专注概率: {sub_hf.interest_chatting.start_hfc_probability}"
|
||||
# )
|
||||
|
||||
if random.random() >= sub_hf.interest_chatting.start_hfc_probability:
|
||||
continue
|
||||
# if random.random() >= sub_hf.interest_chatting.start_hfc_probability:
|
||||
# continue
|
||||
|
||||
# 获取最新状态并执行提升
|
||||
current_subflow = self.subheartflows.get(flow_id)
|
||||
if not current_subflow:
|
||||
continue
|
||||
# # 获取最新状态并执行提升
|
||||
# current_subflow = self.subheartflows.get(flow_id)
|
||||
# if not current_subflow:
|
||||
# continue
|
||||
|
||||
logger.info(
|
||||
f"{stream_name} 触发 认真水群 (概率={current_subflow.interest_chatting.start_hfc_probability:.2f})"
|
||||
)
|
||||
# logger.info(
|
||||
# f"{stream_name} 触发 认真水群 (概率={current_subflow.interest_chatting.start_hfc_probability:.2f})"
|
||||
# )
|
||||
|
||||
# 执行状态提升
|
||||
await current_subflow.change_chat_state(ChatState.FOCUSED)
|
||||
# # 执行状态提升
|
||||
# await current_subflow.change_chat_state(ChatState.FOCUSED)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"启动HFC 兴趣评估失败: {e}", exc_info=True)
|
||||
# except Exception as e:
|
||||
# logger.error(f"启动HFC 兴趣评估失败: {e}", exc_info=True)
|
||||
|
||||
async def sbhf_focus_into_normal(self, subflow_id: Any):
|
||||
"""
|
||||
|
|
@ -249,7 +248,7 @@ class SubHeartflowManager:
|
|||
)
|
||||
try:
|
||||
# 从HFC到CHAT时，清空兴趣字典
|
||||
subflow.clear_interest_dict()
|
||||
subflow.interest_dict.clear()
|
||||
await subflow.change_chat_state(target_state)
|
||||
final_state = subflow.chat_state.chat_status
|
||||
if final_state == target_state:
|
||||
|
|
|
|||
|
|
@ -193,7 +193,6 @@ class MemoryGraph:
|
|||
class Hippocampus:
|
||||
def __init__(self):
|
||||
self.memory_graph = MemoryGraph()
|
||||
self.llm_topic_judge = None
|
||||
self.model_summary = None
|
||||
self.entorhinal_cortex = None
|
||||
self.parahippocampal_gyrus = None
|
||||
|
|
@ -205,8 +204,7 @@ class Hippocampus:
|
|||
# 从数据库加载记忆图
|
||||
self.entorhinal_cortex.sync_memory_from_db()
|
||||
# TODO: API-Adapter修改标记
|
||||
self.llm_topic_judge = LLMRequest(global_config.model.topic_judge, request_type="memory")
|
||||
self.model_summary = LLMRequest(global_config.model.summary, request_type="memory")
|
||||
self.model_summary = LLMRequest(global_config.model.memory_summary, request_type="memory")
|
||||
|
||||
def get_all_node_names(self) -> list:
|
||||
"""获取记忆图中所有节点的名字列表"""
|
||||
|
|
@ -344,7 +342,7 @@ class Hippocampus:
|
|||
# 使用LLM提取关键词
|
||||
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
|
||||
# logger.info(f"提取关键词数量: {topic_num}")
|
||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||||
topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
|
||||
|
||||
# 提取关键词
|
||||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
|
|
@ -528,12 +526,12 @@ class Hippocampus:
|
|||
if not keywords:
|
||||
return []
|
||||
|
||||
# logger.info(f"提取的关键词: {', '.join(keywords)}")
|
||||
logger.info(f"提取的关键词: {', '.join(keywords)}")
|
||||
|
||||
# 过滤掉不存在于记忆图中的关键词
|
||||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||||
if not valid_keywords:
|
||||
# logger.info("没有找到有效的关键词节点")
|
||||
logger.info("没有找到有效的关键词节点")
|
||||
return []
|
||||
|
||||
logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
|
||||
|
|
@ -699,7 +697,7 @@ class Hippocampus:
|
|||
# 使用LLM提取关键词
|
||||
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
|
||||
# logger.info(f"提取关键词数量: {topic_num}")
|
||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||||
topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
|
||||
|
||||
# 提取关键词
|
||||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||||
|
|
@ -1126,7 +1124,7 @@ class ParahippocampalGyrus:
|
|||
|
||||
# 2. 使用LLM提取关键主题
|
||||
topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
|
||||
topics_response = await self.hippocampus.llm_topic_judge.generate_response(
|
||||
topics_response = await self.hippocampus.model_summary.generate_response(
|
||||
self.hippocampus.find_topic_llm(input_text, topic_num)
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,63 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import asyncio
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
|
||||
# 添加项目根目录到系统路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
|
||||
from src.chat.memory_system.Hippocampus import HippocampusManager
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
||||
async def test_memory_system():
|
||||
"""测试记忆系统的主要功能"""
|
||||
try:
|
||||
# 初始化记忆系统
|
||||
print("开始初始化记忆系统...")
|
||||
hippocampus_manager = HippocampusManager.get_instance()
|
||||
hippocampus_manager.initialize()
|
||||
print("记忆系统初始化完成")
|
||||
|
||||
# 测试记忆构建
|
||||
# print("开始测试记忆构建...")
|
||||
# await hippocampus_manager.build_memory()
|
||||
# print("记忆构建完成")
|
||||
|
||||
# 测试记忆检索
|
||||
test_text = "千石可乐在群里聊天"
|
||||
|
||||
# test_text = '''千石可乐：分不清AI的陪伴和人类的陪伴,是这样吗？'''
|
||||
print(f"开始测试记忆检索，测试文本: {test_text}\n")
|
||||
memories = await hippocampus_manager.get_memory_from_text(
|
||||
text=test_text, max_memory_num=3, max_memory_length=2, max_depth=3, fast_retrieval=False
|
||||
)
|
||||
|
||||
await asyncio.sleep(1)
|
||||
|
||||
print("检索到的记忆:")
|
||||
for topic, memory_items in memories:
|
||||
print(f"主题: {topic}")
|
||||
print(f"- {memory_items}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"测试过程中出现错误: {e}")
|
||||
raise
|
||||
|
||||
|
||||
async def main():
|
||||
"""主函数"""
|
||||
try:
|
||||
start_time = time.time()
|
||||
await test_memory_system()
|
||||
end_time = time.time()
|
||||
print(f"测试完成，总耗时: {end_time - start_time:.2f} 秒")
|
||||
except Exception as e:
|
||||
print(f"程序执行出错: {e}")
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,365 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
import datetime
|
||||
from rich.console import Console
|
||||
from Hippocampus import Hippocampus # 海马体和记忆图
|
||||
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
|
||||
"""
|
||||
我想 总有那么一个瞬间
|
||||
你会想和某天才变态少女助手一样
|
||||
往Bot的海马体里插上几个电极 不是吗
|
||||
|
||||
Let's do some dirty job.
|
||||
"""
|
||||
|
||||
# 获取当前文件的目录
|
||||
current_dir = Path(__file__).resolve().parent
|
||||
# 获取项目根目录（上三层目录）
|
||||
project_root = current_dir.parent.parent.parent
|
||||
# env.dev文件路径
|
||||
env_path = project_root / ".env.dev"
|
||||
|
||||
# from chat.config import global_config
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.common.logger import get_module_logger # noqa E402
|
||||
from common.database.database import db # noqa E402
|
||||
|
||||
logger = get_module_logger("mem_alter")
|
||||
console = Console()
|
||||
|
||||
# 加载环境变量
|
||||
if env_path.exists():
|
||||
logger.info(f"从 {env_path} 加载环境变量")
|
||||
load_dotenv(env_path)
|
||||
else:
|
||||
logger.warning(f"未找到环境变量文件: {env_path}")
|
||||
logger.info("将使用默认配置")
|
||||
|
||||
|
||||
# 查询节点信息
|
||||
def query_mem_info(hippocampus: Hippocampus):
|
||||
while True:
|
||||
query = input("\n请输入新的查询概念（输入'退出'以结束）：")
|
||||
if query.lower() == "退出":
|
||||
break
|
||||
|
||||
items_list = hippocampus.memory_graph.get_related_item(query)
|
||||
if items_list:
|
||||
have_memory = False
|
||||
first_layer, second_layer = items_list
|
||||
if first_layer:
|
||||
have_memory = True
|
||||
print("\n直接相关的记忆：")
|
||||
for item in first_layer:
|
||||
print(f"- {item}")
|
||||
if second_layer:
|
||||
have_memory = True
|
||||
print("\n间接相关的记忆：")
|
||||
for item in second_layer:
|
||||
print(f"- {item}")
|
||||
if not have_memory:
|
||||
print("\n未找到相关记忆。")
|
||||
else:
|
||||
print("未找到相关记忆。")
|
||||
|
||||
|
||||
# 增加概念节点
|
||||
def add_mem_node(hippocampus: Hippocampus):
|
||||
while True:
|
||||
concept = input("请输入节点概念名:\n")
|
||||
result = db.graph_data.nodes.count_documents({"concept": concept})
|
||||
|
||||
if result != 0:
|
||||
console.print("[yellow]已存在名为“{concept}”的节点，行为已取消[/yellow]")
|
||||
continue
|
||||
|
||||
memory_items = list()
|
||||
while True:
|
||||
context = input("请输入节点描述信息（输入'终止'以结束）")
|
||||
if context.lower() == "终止":
|
||||
break
|
||||
memory_items.append(context)
|
||||
|
||||
current_time = datetime.datetime.now().timestamp()
|
||||
hippocampus.memory_graph.G.add_node(
|
||||
concept, memory_items=memory_items, created_time=current_time, last_modified=current_time
|
||||
)
|
||||
|
||||
|
||||
# 删除概念节点（及连接到它的边）
|
||||
def remove_mem_node(hippocampus: Hippocampus):
|
||||
concept = input("请输入节点概念名:\n")
|
||||
result = db.graph_data.nodes.count_documents({"concept": concept})
|
||||
|
||||
if result == 0:
|
||||
console.print(f"[red]不存在名为“{concept}”的节点[/red]")
|
||||
|
||||
edges = db.graph_data.edges.find({"$or": [{"source": concept}, {"target": concept}]})
|
||||
|
||||
for edge in edges:
|
||||
console.print(f"[yellow]存在边“{edge['source']} -> {edge['target']}”, 请慎重考虑[/yellow]")
|
||||
|
||||
console.print(f"[yellow]确定要移除名为“{concept}”的节点以及其相关边吗[/yellow]")
|
||||
destory = console.input(f"[red]请输入“{concept}”以删除节点 其他输入将被视为取消操作[/red]\n")
|
||||
if destory == concept:
|
||||
hippocampus.memory_graph.G.remove_node(concept)
|
||||
else:
|
||||
logger.info("[green]删除操作已取消[/green]")
|
||||
|
||||
|
||||
# 增加节点间边
|
||||
def add_mem_edge(hippocampus: Hippocampus):
|
||||
while True:
|
||||
source = input("请输入 **第一个节点** 名称（输入'退出'以结束）：\n")
|
||||
if source.lower() == "退出":
|
||||
break
|
||||
if db.graph_data.nodes.count_documents({"concept": source}) == 0:
|
||||
console.print(f"[yellow]“{source}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
target = input("请输入 **第二个节点** 名称：\n")
|
||||
if db.graph_data.nodes.count_documents({"concept": target}) == 0:
|
||||
console.print(f"[yellow]“{target}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
if source == target:
|
||||
console.print(f"[yellow]试图创建“{source} <-> {target}”自环，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
hippocampus.memory_graph.connect_dot(source, target)
|
||||
edge = hippocampus.memory_graph.G.get_edge_data(source, target)
|
||||
if edge["strength"] == 1:
|
||||
console.print(f"[green]成功创建边“{source} <-> {target}”，默认权重1[/green]")
|
||||
else:
|
||||
console.print(
|
||||
f"[yellow]边“{source} <-> {target}”已存在，"
|
||||
f"更新权重: {edge['strength'] - 1} <-> {edge['strength']}[/yellow]"
|
||||
)
|
||||
|
||||
|
||||
# 删除节点间边
|
||||
def remove_mem_edge(hippocampus: Hippocampus):
|
||||
while True:
|
||||
source = input("请输入 **第一个节点** 名称（输入'退出'以结束）：\n")
|
||||
if source.lower() == "退出":
|
||||
break
|
||||
if db.graph_data.nodes.count_documents({"concept": source}) == 0:
|
||||
console.print("[yellow]“{source}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
target = input("请输入 **第二个节点** 名称：\n")
|
||||
if db.graph_data.nodes.count_documents({"concept": target}) == 0:
|
||||
console.print("[yellow]“{target}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
if source == target:
|
||||
console.print("[yellow]试图创建“{source} <-> {target}”自环，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
edge = hippocampus.memory_graph.G.get_edge_data(source, target)
|
||||
if edge is None:
|
||||
console.print("[yellow]边“{source} <-> {target}”不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
else:
|
||||
accept = console.input("[orange]请输入“确认”以确认删除操作（其他输入视为取消）[/orange]\n")
|
||||
if accept.lower() == "确认":
|
||||
hippocampus.memory_graph.G.remove_edge(source, target)
|
||||
console.print(f"[green]边“{source} <-> {target}”已删除。[green]")
|
||||
|
||||
|
||||
# 修改节点信息
|
||||
def alter_mem_node(hippocampus: Hippocampus):
|
||||
batch_environment = dict()
|
||||
while True:
|
||||
concept = input("请输入节点概念名（输入'终止'以结束）:\n")
|
||||
if concept.lower() == "终止":
|
||||
break
|
||||
_, node = hippocampus.memory_graph.get_dot(concept)
|
||||
if node is None:
|
||||
console.print(f"[yellow]“{concept}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
console.print("[yellow]注意，请确保你知道自己在做什么[/yellow]")
|
||||
console.print("[yellow]你将获得一个执行任意代码的环境[/yellow]")
|
||||
console.print("[red]你已经被警告过了。[/red]\n")
|
||||
|
||||
node_environment = {"concept": "<节点名>", "memory_items": "<记忆文本数组>"}
|
||||
console.print(
|
||||
"[green]环境变量中会有env与batchEnv两个dict, env在切换节点时会清空, batchEnv在操作终止时才会清空[/green]"
|
||||
)
|
||||
console.print(
|
||||
f"[green] env 会被初始化为[/green]\n{node_environment}\n[green]且会在用户代码执行完毕后被提交 [/green]"
|
||||
)
|
||||
console.print(
|
||||
"[yellow]为便于书写临时脚本，请手动在输入代码通过Ctrl+C等方式触发KeyboardInterrupt来结束代码执行[/yellow]"
|
||||
)
|
||||
|
||||
# 拷贝数据以防操作炸了
|
||||
node_environment = dict(node)
|
||||
node_environment["concept"] = concept
|
||||
|
||||
while True:
|
||||
|
||||
def user_exec(script, env, batch_env):
|
||||
return eval(script, env, batch_env)
|
||||
|
||||
try:
|
||||
command = console.input()
|
||||
except KeyboardInterrupt:
|
||||
# 稍微防一下小天才
|
||||
try:
|
||||
if isinstance(node_environment["memory_items"], list):
|
||||
node["memory_items"] = node_environment["memory_items"]
|
||||
else:
|
||||
raise Exception
|
||||
|
||||
except Exception as e:
|
||||
console.print(
|
||||
f"[red]我不知道你做了什么，但显然nodeEnviroment['memory_items']已经不是个数组了，"
|
||||
f"操作已取消: {str(e)}[/red]"
|
||||
)
|
||||
break
|
||||
|
||||
try:
|
||||
user_exec(command, node_environment, batch_environment)
|
||||
except Exception as e:
|
||||
console.print(e)
|
||||
console.print(
|
||||
"[red]自定义代码执行时发生异常，已捕获，请重试（可通过 console.print(locals()) 检查环境状态）[/red]"
|
||||
)
|
||||
|
||||
|
||||
# 修改边信息
|
||||
def alter_mem_edge(hippocampus: Hippocampus):
|
||||
batch_enviroment = dict()
|
||||
while True:
|
||||
source = input("请输入 **第一个节点** 名称（输入'终止'以结束）：\n")
|
||||
if source.lower() == "终止":
|
||||
break
|
||||
if hippocampus.memory_graph.get_dot(source) is None:
|
||||
console.print(f"[yellow]“{source}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
target = input("请输入 **第二个节点** 名称：\n")
|
||||
if hippocampus.memory_graph.get_dot(target) is None:
|
||||
console.print(f"[yellow]“{target}”节点不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
edge = hippocampus.memory_graph.G.get_edge_data(source, target)
|
||||
if edge is None:
|
||||
console.print(f"[yellow]边“{source} <-> {target}”不存在，操作已取消。[/yellow]")
|
||||
continue
|
||||
|
||||
console.print("[yellow]注意，请确保你知道自己在做什么[/yellow]")
|
||||
console.print("[yellow]你将获得一个执行任意代码的环境[/yellow]")
|
||||
console.print("[red]你已经被警告过了。[/red]\n")
|
||||
|
||||
edge_environment = {"source": "<节点名>", "target": "<节点名>", "strength": "<强度值,装在一个list里>"}
|
||||
console.print(
|
||||
"[green]环境变量中会有env与batchEnv两个dict, env在切换节点时会清空, batchEnv在操作终止时才会清空[/green]"
|
||||
)
|
||||
console.print(
|
||||
f"[green] env 会被初始化为[/green]\n{edge_environment}\n[green]且会在用户代码执行完毕后被提交 [/green]"
|
||||
)
|
||||
console.print(
|
||||
"[yellow]为便于书写临时脚本，请手动在输入代码通过Ctrl+C等方式触发KeyboardInterrupt来结束代码执行[/yellow]"
|
||||
)
|
||||
|
||||
# 拷贝数据以防操作炸了
|
||||
edge_environment["strength"] = [edge["strength"]]
|
||||
edge_environment["source"] = source
|
||||
edge_environment["target"] = target
|
||||
|
||||
while True:
|
||||
|
||||
def user_exec(script, env, batch_env):
|
||||
return eval(script, env, batch_env)
|
||||
|
||||
try:
|
||||
command = console.input()
|
||||
except KeyboardInterrupt:
|
||||
# 稍微防一下小天才
|
||||
try:
|
||||
if isinstance(edge_environment["strength"][0], int):
|
||||
edge["strength"] = edge_environment["strength"][0]
|
||||
else:
|
||||
raise Exception
|
||||
|
||||
except Exception as e:
|
||||
console.print(
|
||||
f"[red]我不知道你做了什么，但显然edgeEnviroment['strength']已经不是个int了，"
|
||||
f"操作已取消: {str(e)}[/red]"
|
||||
)
|
||||
break
|
||||
|
||||
try:
|
||||
user_exec(command, edge_environment, batch_enviroment)
|
||||
except Exception as e:
|
||||
console.print(e)
|
||||
console.print(
|
||||
"[red]自定义代码执行时发生异常，已捕获，请重试（可通过 console.print(locals()) 检查环境状态）[/red]"
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
start_time = time.time()
|
||||
|
||||
# 创建海马体
|
||||
hippocampus = Hippocampus()
|
||||
|
||||
# 从数据库同步数据
|
||||
hippocampus.entorhinal_cortex.sync_memory_from_db()
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
||||
|
||||
while True:
|
||||
try:
|
||||
query = int(
|
||||
input(
|
||||
"""请输入操作类型
|
||||
0 -> 查询节点; 1 -> 增加节点; 2 -> 移除节点; 3 -> 增加边; 4 -> 移除边;
|
||||
5 -> 修改节点; 6 -> 修改边; 其他任意输入 -> 退出
|
||||
"""
|
||||
)
|
||||
)
|
||||
except ValueError:
|
||||
query = -1
|
||||
|
||||
if query == 0:
|
||||
query_mem_info(hippocampus.memory_graph)
|
||||
elif query == 1:
|
||||
add_mem_node(hippocampus)
|
||||
elif query == 2:
|
||||
remove_mem_node(hippocampus)
|
||||
elif query == 3:
|
||||
add_mem_edge(hippocampus)
|
||||
elif query == 4:
|
||||
remove_mem_edge(hippocampus)
|
||||
elif query == 5:
|
||||
alter_mem_node(hippocampus)
|
||||
elif query == 6:
|
||||
alter_mem_edge(hippocampus)
|
||||
else:
|
||||
print("已结束操作")
|
||||
break
|
||||
|
||||
hippocampus.entorhinal_cortex.sync_memory_to_db()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,126 +0,0 @@
|
|||
import asyncio
|
||||
import os
|
||||
import time
|
||||
from typing import Tuple, Union
|
||||
|
||||
import aiohttp
|
||||
import requests
|
||||
from src.common.logger import get_module_logger
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
logger = get_module_logger("offline_llm")
|
||||
|
||||
|
||||
class LLMRequestOff:
|
||||
def __init__(self, model_name="deepseek-ai/DeepSeek-V3", **kwargs):
|
||||
self.model_name = model_name
|
||||
self.params = kwargs
|
||||
self.api_key = os.getenv("SILICONFLOW_KEY")
|
||||
self.base_url = os.getenv("SILICONFLOW_BASE_URL")
|
||||
|
||||
if not self.api_key or not self.base_url:
|
||||
raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
|
||||
|
||||
logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
|
||||
|
||||
def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
|
||||
"""根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.5,
|
||||
**self.params,
|
||||
}
|
||||
|
||||
# 发送请求到完整的 chat/completions 端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions"
|
||||
logger.info(f"Request URL: {api_url}") # 记录请求的 URL
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15 # 基础等待时间（秒）
|
||||
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
|
||||
if response.status_code == 429:
|
||||
wait_time = base_wait_time * (2**retry) # 指数退避
|
||||
logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
time.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except Exception as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2**retry)
|
||||
logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
logger.error(f"请求失败: {str(e)}")
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
logger.error("达到最大重试次数，请求仍然失败")
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
|
||||
async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]:
|
||||
"""异步方式根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.5,
|
||||
**self.params,
|
||||
}
|
||||
|
||||
# 发送请求到完整的 chat/completions 端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions"
|
||||
logger.info(f"Request URL: {api_url}") # 记录请求的 URL
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
async with session.post(api_url, headers=headers, json=data) as response:
|
||||
if response.status == 429:
|
||||
wait_time = base_wait_time * (2**retry) # 指数退避
|
||||
logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
await asyncio.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = await response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except Exception as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2**retry)
|
||||
logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
await asyncio.sleep(wait_time)
|
||||
else:
|
||||
logger.error(f"请求失败: {str(e)}")
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
logger.error("达到最大重试次数，请求仍然失败")
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
|
|
@ -7,7 +7,7 @@ from src.chat.message_receive.chat_stream import chat_manager
|
|||
from src.chat.message_receive.message import MessageRecv
|
||||
from src.experimental.only_message_process import MessageProcessor
|
||||
from src.experimental.PFC.pfc_manager import PFCManager
|
||||
from src.chat.focus_chat.heartflow_message_revceiver import HeartFCMessageReceiver
|
||||
from src.chat.focus_chat.heartflow_message_processor import HeartFCMessageReceiver
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
from src.config.config import global_config
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from ..person_info.person_info import person_info_manager
|
||||
from src.person_info.person_info import person_info_manager
|
||||
from src.common.logger_manager import get_logger
|
||||
import asyncio
|
||||
from dataclasses import dataclass, field
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ logger = get_logger("normal_chat")
|
|||
|
||||
|
||||
class NormalChat:
|
||||
def __init__(self, chat_stream: ChatStream, interest_dict: dict = None):
|
||||
def __init__(self, chat_stream: ChatStream, interest_dict: dict = None, on_switch_to_focus_callback=None):
|
||||
"""初始化 NormalChat 实例。只进行同步操作。"""
|
||||
|
||||
# Basic info from chat_stream (sync)
|
||||
|
|
@ -50,6 +50,17 @@ class NormalChat:
|
|||
self._chat_task: Optional[asyncio.Task] = None
|
||||
self._initialized = False # Track initialization status
|
||||
|
||||
# 记录最近的回复内容，每项包含: {time, user_message, response, is_mentioned, is_reference_reply}
|
||||
self.recent_replies = []
|
||||
self.max_replies_history = 20 # 最多保存最近20条回复记录
|
||||
|
||||
# 添加回调函数，用于在满足条件时通知切换到focus_chat模式
|
||||
self.on_switch_to_focus_callback = on_switch_to_focus_callback
|
||||
|
||||
# 最近回复检查相关
|
||||
self._last_check_time = time.time()
|
||||
self._check_interval = 10 # 每10秒检查一次是否需要切换到focus模式
|
||||
|
||||
async def initialize(self):
|
||||
"""异步初始化，获取聊天类型和目标信息。"""
|
||||
if self._initialized:
|
||||
|
|
@ -197,6 +208,12 @@ class NormalChat:
|
|||
logger.info(f"[{self.stream_name}] 兴趣监控任务被取消或置空，退出")
|
||||
break
|
||||
|
||||
# 定期检查是否需要切换到focus模式
|
||||
# current_time = time.time()
|
||||
# if current_time - self._last_check_time > self._check_interval:
|
||||
# await self._check_switch_to_focus()
|
||||
# self._last_check_time = current_time
|
||||
|
||||
items_to_process = list(self.interest_dict.items())
|
||||
if not items_to_process:
|
||||
continue
|
||||
|
|
@ -312,6 +329,28 @@ class NormalChat:
|
|||
# 检查 first_bot_msg 是否为 None (例如思考消息已被移除的情况)
|
||||
if first_bot_msg:
|
||||
info_catcher.catch_after_response(timing_results["消息发送"], response_set, first_bot_msg)
|
||||
|
||||
# 记录回复信息到最近回复列表中
|
||||
reply_info = {
|
||||
"time": time.time(),
|
||||
"user_message": message.processed_plain_text,
|
||||
"user_info": {
|
||||
"user_id": message.message_info.user_info.user_id,
|
||||
"user_nickname": message.message_info.user_info.user_nickname,
|
||||
},
|
||||
"response": response_set,
|
||||
"is_mentioned": is_mentioned,
|
||||
"is_reference_reply": message.reply is not None, # 判断是否为引用回复
|
||||
"timing": {k: round(v, 2) for k, v in timing_results.items()},
|
||||
}
|
||||
self.recent_replies.append(reply_info)
|
||||
# 保持最近回复历史在限定数量内
|
||||
if len(self.recent_replies) > self.max_replies_history:
|
||||
self.recent_replies = self.recent_replies[-self.max_replies_history :]
|
||||
|
||||
# 检查是否需要切换到focus模式
|
||||
await self._check_switch_to_focus()
|
||||
|
||||
else:
|
||||
logger.warning(f"[{self.stream_name}] 思考消息 {thinking_id} 在发送前丢失，无法记录 info_catcher")
|
||||
|
||||
|
|
@ -520,3 +559,49 @@ class NormalChat:
|
|||
except Exception as e:
|
||||
logger.error(f"[{self.stream_name}] 清理思考消息时出错: {e}")
|
||||
traceback.print_exc()
|
||||
|
||||
# 获取最近回复记录的方法
|
||||
def get_recent_replies(self, limit: int = 10) -> List[dict]:
|
||||
"""获取最近的回复记录
|
||||
|
||||
Args:
|
||||
limit: 最大返回数量，默认10条
|
||||
|
||||
Returns:
|
||||
List[dict]: 最近的回复记录列表，每项包含：
|
||||
time: 回复时间戳
|
||||
user_message: 用户消息内容
|
||||
user_info: 用户信息(user_id, user_nickname)
|
||||
response: 回复内容
|
||||
is_mentioned: 是否被提及(@)
|
||||
is_reference_reply: 是否为引用回复
|
||||
timing: 各阶段耗时
|
||||
"""
|
||||
# 返回最近的limit条记录，按时间倒序排列
|
||||
return sorted(self.recent_replies[-limit:], key=lambda x: x["time"], reverse=True)
|
||||
|
||||
async def _check_switch_to_focus(self) -> None:
|
||||
"""检查是否满足切换到focus模式的条件"""
|
||||
if not self.on_switch_to_focus_callback:
|
||||
return # 如果没有设置回调函数，直接返回
|
||||
current_time = time.time()
|
||||
|
||||
time_threshold = 120 / global_config.focus_chat.auto_focus_threshold
|
||||
reply_threshold = 6 * global_config.focus_chat.auto_focus_threshold
|
||||
|
||||
one_minute_ago = current_time - time_threshold
|
||||
|
||||
# 统计1分钟内的回复数量
|
||||
recent_reply_count = sum(1 for reply in self.recent_replies if reply["time"] > one_minute_ago)
|
||||
# print(111111111111111333333333333333333333333331111111111111111111111111111111111)
|
||||
# print(recent_reply_count)
|
||||
# 如果1分钟内回复数量大于8，触发切换到focus模式
|
||||
if recent_reply_count > reply_threshold:
|
||||
logger.info(
|
||||
f"[{self.stream_name}] 检测到1分钟内回复数量({recent_reply_count})大于{reply_threshold}，触发切换到focus模式"
|
||||
)
|
||||
try:
|
||||
# 调用回调函数通知上层切换到focus模式
|
||||
await self.on_switch_to_focus_callback()
|
||||
except Exception as e:
|
||||
logger.error(f"[{self.stream_name}] 触发切换到focus模式时出错: {e}\n{traceback.format_exc()}")
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import random
|
|||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.chat.message_receive.message import MessageThinking
|
||||
from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
|
||||
from src.chat.normal_chat.normal_prompt import prompt_builder
|
||||
from src.chat.utils.utils import process_llm_response
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.common.logger_manager import get_logger
|
||||
|
|
@ -17,20 +17,20 @@ class NormalChatGenerator:
|
|||
def __init__(self):
|
||||
# TODO: API-Adapter修改标记
|
||||
self.model_reasoning = LLMRequest(
|
||||
model=global_config.model.reasoning,
|
||||
temperature=0.7,
|
||||
model=global_config.model.normal_chat_1,
|
||||
# temperature=0.7,
|
||||
max_tokens=3000,
|
||||
request_type="response_reasoning",
|
||||
request_type="normal_chat_1",
|
||||
)
|
||||
self.model_normal = LLMRequest(
|
||||
model=global_config.model.normal,
|
||||
temperature=global_config.model.normal["temp"],
|
||||
model=global_config.model.normal_chat_2,
|
||||
# temperature=global_config.model.normal_chat_2["temp"],
|
||||
max_tokens=256,
|
||||
request_type="response_reasoning",
|
||||
request_type="normal_chat_2",
|
||||
)
|
||||
|
||||
self.model_sum = LLMRequest(
|
||||
model=global_config.model.summary, temperature=0.7, max_tokens=3000, request_type="relation"
|
||||
model=global_config.model.memory_summary, temperature=0.7, max_tokens=3000, request_type="relation"
|
||||
)
|
||||
self.current_model_type = "r1" # 默认使用 R1
|
||||
self.current_model_name = "unknown model"
|
||||
|
|
@ -38,7 +38,7 @@ class NormalChatGenerator:
|
|||
async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
|
||||
"""根据当前模型类型选择对应的生成函数"""
|
||||
# 从global_config中获取模型概率值并选择模型
|
||||
if random.random() < global_config.normal_chat.reasoning_model_probability:
|
||||
if random.random() < global_config.normal_chat.normal_chat_first_probability:
|
||||
self.current_model_type = "深深地"
|
||||
current_model = self.model_reasoning
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -17,14 +17,14 @@ logger = get_logger("prompt")
|
|||
|
||||
|
||||
def init_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
你有以下信息可供参考：
|
||||
{structured_info}
|
||||
以上的消息是你获取到的消息，或许可以帮助你更好地回复。
|
||||
""",
|
||||
"info_from_tools",
|
||||
)
|
||||
# Prompt(
|
||||
# """
|
||||
# 你有以下信息可供参考：
|
||||
# {structured_info}
|
||||
# 以上的消息是你获取到的消息，或许可以帮助你更好地回复。
|
||||
# """,
|
||||
# "info_from_tools",
|
||||
# )
|
||||
|
||||
Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
|
||||
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
|
||||
|
|
@ -94,9 +94,7 @@ class PromptBuilder:
|
|||
in_mind_reply=None,
|
||||
target_message=None,
|
||||
) -> Optional[str]:
|
||||
if build_mode == "normal":
|
||||
return await self._build_prompt_normal(chat_stream, message_txt or "", sender_name)
|
||||
return None
|
||||
return await self._build_prompt_normal(chat_stream, message_txt or "", sender_name)
|
||||
|
||||
async def _build_prompt_normal(self, chat_stream, message_txt: str, sender_name: str = "某人") -> str:
|
||||
prompt_personality = individuality.get_prompt(x_person=2, level=2)
|
||||
|
|
@ -107,7 +105,7 @@ class PromptBuilder:
|
|||
who_chat_in_group = get_recent_group_speaker(
|
||||
chat_stream.stream_id,
|
||||
(chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
|
||||
limit=global_config.focus_chat.observation_context_size,
|
||||
limit=global_config.normal_chat.max_context_size,
|
||||
)
|
||||
elif chat_stream.user_info:
|
||||
who_chat_in_group.append(
|
||||
|
|
@ -118,8 +116,7 @@ class PromptBuilder:
|
|||
for person in who_chat_in_group:
|
||||
if len(person) >= 3 and person[0] and person[1]:
|
||||
relation_prompt += await relationship_manager.build_relationship_info(person)
|
||||
else:
|
||||
logger.warning(f"Invalid person tuple encountered for relationship prompt: {person}")
|
||||
|
||||
mood_prompt = mood_manager.get_mood_prompt()
|
||||
reply_styles1 = [
|
||||
("然后给出日常且口语化的回复，平淡一些", 0.4),
|
||||
|
|
@ -194,6 +191,8 @@ class PromptBuilder:
|
|||
if random.random() < 0.04:
|
||||
prompt_ger += "你喜欢用流行梗"
|
||||
|
||||
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。"
|
||||
|
||||
# 知识构建
|
||||
start_time = time.time()
|
||||
prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
|
||||
|
|
@ -231,7 +230,7 @@ class PromptBuilder:
|
|||
keywords_reaction_prompt=keywords_reaction_prompt,
|
||||
prompt_ger=prompt_ger,
|
||||
# moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
|
||||
moderation_prompt="",
|
||||
moderation_prompt=moderation_prompt_block,
|
||||
)
|
||||
else:
|
||||
template_name = "reasoning_prompt_private_main"
|
||||
|
|
@ -254,7 +253,7 @@ class PromptBuilder:
|
|||
keywords_reaction_prompt=keywords_reaction_prompt,
|
||||
prompt_ger=prompt_ger,
|
||||
# moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
|
||||
moderation_prompt="",
|
||||
moderation_prompt=moderation_prompt_block,
|
||||
)
|
||||
# --- End choosing template ---
|
||||
|
||||
|
|
@ -63,7 +63,7 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
|
|||
)
|
||||
|
||||
# 判断是否被@
|
||||
if re.search(f"@[\s\S]*?（id:{global_config.bot.qq_account}）", message.processed_plain_text):
|
||||
if re.search(rf"@<(.+?):{global_config.bot.qq_account}>", message.processed_plain_text):
|
||||
is_at = True
|
||||
is_mentioned = True
|
||||
|
||||
|
|
@ -74,13 +74,18 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
|
|||
if not is_mentioned:
|
||||
# 判断是否被回复
|
||||
if re.match(
|
||||
f"\[回复 [\s\S]*?\({str(global_config.bot.qq_account)}\)：[\s\S]*?]，说：", message.processed_plain_text
|
||||
rf"\[回复 (.+?)\({str(global_config.bot.qq_account)}\)：(.+?)\]，说：", message.processed_plain_text
|
||||
) or re.match(
|
||||
rf"\[回复<(.+?)(?=:{str(global_config.bot.qq_account)}>)\:{str(global_config.bot.qq_account)}>：(.+?)\]，说：",
|
||||
message.processed_plain_text,
|
||||
):
|
||||
is_mentioned = True
|
||||
else:
|
||||
# 判断内容中是否被提及
|
||||
message_content = re.sub(r"@[\s\S]*?（(\d+)）", "", message.processed_plain_text)
|
||||
message_content = re.sub(r"\[回复 [\s\S]*?\(((\d+)|未知id)\)：[\s\S]*?]，说：", "", message_content)
|
||||
message_content = re.sub(r"@(.+?)（(\d+)）", "", message.processed_plain_text)
|
||||
message_content = re.sub(r"@<(.+?)(?=:(\d+))\:(\d+)>", "", message_content)
|
||||
message_content = re.sub(r"\[回复 (.+?)\(((\d+)|未知id)\)：(.+?)\]，说：", "", message_content)
|
||||
message_content = re.sub(r"\[回复<(.+?)(?=:(\d+))\:(\d+)>：(.+?)\]，说：", "", message_content)
|
||||
for keyword in keywords:
|
||||
if keyword in message_content:
|
||||
is_mentioned = True
|
||||
|
|
|
|||
|
|
@ -83,7 +83,7 @@ class ImageManager:
|
|||
current_timestamp = time.time()
|
||||
defaults = {"description": description, "timestamp": current_timestamp}
|
||||
desc_obj, created = ImageDescriptions.get_or_create(
|
||||
hash=image_hash, type=description_type, defaults=defaults
|
||||
image_description_hash=image_hash, type=description_type, defaults=defaults
|
||||
)
|
||||
if not created: # 如果记录已存在，则更新
|
||||
desc_obj.description = description
|
||||
|
|
@ -130,6 +130,7 @@ class ImageManager:
|
|||
# 根据配置决定是否保存图片
|
||||
if global_config.emoji.save_emoji:
|
||||
# 生成文件名和路径
|
||||
logger.debug(f"保存表情包: {image_hash}")
|
||||
current_timestamp = time.time()
|
||||
filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
|
||||
emoji_dir = os.path.join(self.IMAGE_DIR, "emoji")
|
||||
|
|
@ -150,13 +151,13 @@ class ImageManager:
|
|||
img_obj.save()
|
||||
except Images.DoesNotExist:
|
||||
Images.create(
|
||||
hash=image_hash,
|
||||
emoji_hash=image_hash,
|
||||
path=file_path,
|
||||
type="emoji",
|
||||
description=description,
|
||||
timestamp=current_timestamp,
|
||||
)
|
||||
logger.trace(f"保存表情包元数据: {file_path}")
|
||||
# logger.debug(f"保存表情包元数据: {file_path}")
|
||||
except Exception as e:
|
||||
logger.error(f"保存表情包文件或元数据失败: {str(e)}")
|
||||
|
||||
|
|
@ -223,7 +224,7 @@ class ImageManager:
|
|||
img_obj.save()
|
||||
except Images.DoesNotExist:
|
||||
Images.create(
|
||||
hash=image_hash,
|
||||
emoji_hash=image_hash,
|
||||
path=file_path,
|
||||
type="image",
|
||||
description=description,
|
||||
|
|
|
|||
|
|
@ -663,11 +663,11 @@ PROCESSOR_STYLE_CONFIG = {
|
|||
|
||||
PLANNER_STYLE_CONFIG = {
|
||||
"advanced": {
|
||||
"console_format": "<level>{time:HH:mm:ss}</level> | <fg #4DCDFF>规划器</fg #4DCDFF> | <fg #4DCDFF>{message}</fg #4DCDFF>",
|
||||
"console_format": "<level>{time:HH:mm:ss}</level> | <fg #069AFF>规划器</fg #069AFF> | <fg #069AFF>{message}</fg #069AFF>",
|
||||
"file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 规划器 | {message}",
|
||||
},
|
||||
"simple": {
|
||||
"console_format": "<level>{time:HH:mm:ss}</level> | <fg #4DCDFF>规划器</fg #4DCDFF> | <fg #4DCDFF>{message}</fg #4DCDFF>",
|
||||
"console_format": "<level>{time:HH:mm:ss}</level> | <fg #069AFF>规划器</fg #069AFF> | <fg #069AFF>{message}</fg #069AFF>",
|
||||
"file_format": "{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 规划器 | {message}",
|
||||
},
|
||||
}
|
||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ from src.config.official_configs import (
|
|||
FocusChatProcessorConfig,
|
||||
MessageReceiveConfig,
|
||||
MaimMessageConfig,
|
||||
RelationshipConfig,
|
||||
)
|
||||
|
||||
install(extra_lines=3)
|
||||
|
|
@ -45,7 +46,7 @@ TEMPLATE_DIR = "template"
|
|||
|
||||
# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
|
||||
# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
|
||||
MMC_VERSION = "0.7.0-snapshot.1"
|
||||
MMC_VERSION = "0.7.0-snapshot.2"
|
||||
|
||||
|
||||
def update_config():
|
||||
|
|
@ -143,6 +144,7 @@ class Config(ConfigBase):
|
|||
bot: BotConfig
|
||||
personality: PersonalityConfig
|
||||
identity: IdentityConfig
|
||||
relationship: RelationshipConfig
|
||||
chat: ChatConfig
|
||||
message_receive: MessageReceiveConfig
|
||||
normal_chat: NormalChatConfig
|
||||
|
|
|
|||
|
|
@ -41,25 +41,18 @@ class PersonalityConfig(ConfigBase):
|
|||
class IdentityConfig(ConfigBase):
|
||||
"""个体特征配置类"""
|
||||
|
||||
height: int = 170
|
||||
"""身高（单位：厘米）"""
|
||||
|
||||
weight: float = 50
|
||||
"""体重（单位：千克）"""
|
||||
|
||||
age: int = 18
|
||||
"""年龄（单位：岁）"""
|
||||
|
||||
gender: str = "女"
|
||||
"""性别（男/女）"""
|
||||
|
||||
appearance: str = "可爱"
|
||||
"""外貌描述"""
|
||||
|
||||
identity_detail: list[str] = field(default_factory=lambda: [])
|
||||
"""身份特征"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class RelationshipConfig(ConfigBase):
|
||||
"""关系配置类"""
|
||||
|
||||
give_name: bool = False
|
||||
"""是否给其他人取名"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class ChatConfig(ConfigBase):
|
||||
"""聊天配置类"""
|
||||
|
|
@ -83,7 +76,7 @@ class MessageReceiveConfig(ConfigBase):
|
|||
class NormalChatConfig(ConfigBase):
|
||||
"""普通聊天配置类"""
|
||||
|
||||
reasoning_model_probability: float = 0.3
|
||||
normal_chat_first_probability: float = 0.3
|
||||
"""
|
||||
发言时选择推理模型的概率（0-1之间）
|
||||
选择普通模型的概率为 1 - reasoning_normal_model_probability
|
||||
|
|
@ -130,18 +123,15 @@ class NormalChatConfig(ConfigBase):
|
|||
class FocusChatConfig(ConfigBase):
|
||||
"""专注聊天配置类"""
|
||||
|
||||
reply_trigger_threshold: float = 3.0
|
||||
"""心流聊天触发阈值，越低越容易触发"""
|
||||
auto_focus_threshold: float = 1.0
|
||||
"""自动切换到专注聊天的阈值，越低越容易进入专注聊天"""
|
||||
|
||||
default_decay_rate_per_second: float = 0.98
|
||||
"""默认衰减率，越大衰减越快"""
|
||||
exit_focus_threshold: float = 1.0
|
||||
"""自动退出专注聊天的阈值，越低越容易退出专注聊天"""
|
||||
|
||||
observation_context_size: int = 12
|
||||
"""可观察到的最长上下文大小，超过这个值的上下文会被压缩"""
|
||||
|
||||
consecutive_no_reply_threshold: int = 3
|
||||
"""连续不回复的次数阈值"""
|
||||
|
||||
compressed_length: int = 5
|
||||
"""心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5"""
|
||||
|
||||
|
|
@ -193,9 +183,12 @@ class EmojiConfig(ConfigBase):
|
|||
check_interval: int = 120
|
||||
"""表情包检查间隔（分钟）"""
|
||||
|
||||
save_pic: bool = False
|
||||
save_pic: bool = True
|
||||
"""是否保存图片"""
|
||||
|
||||
save_emoji: bool = True
|
||||
"""是否保存表情包"""
|
||||
|
||||
cache_emoji: bool = True
|
||||
"""是否缓存表情包"""
|
||||
|
||||
|
|
@ -390,32 +383,41 @@ class ModelConfig(ConfigBase):
|
|||
|
||||
model_max_output_length: int = 800 # 最大回复长度
|
||||
|
||||
reasoning: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""推理模型配置"""
|
||||
utils: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""组件模型配置"""
|
||||
|
||||
normal: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""普通模型配置"""
|
||||
utils_small: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""组件小模型配置"""
|
||||
|
||||
topic_judge: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""主题判断模型配置"""
|
||||
normal_chat_1: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""normal_chat首要回复模型模型配置"""
|
||||
|
||||
summary: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""摘要模型配置"""
|
||||
normal_chat_2: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""normal_chat次要回复模型配置"""
|
||||
|
||||
memory_summary: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""记忆的概括模型配置"""
|
||||
|
||||
vlm: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""视觉语言模型配置"""
|
||||
|
||||
heartflow: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""心流模型配置"""
|
||||
focus_working_memory: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注工作记忆模型配置"""
|
||||
|
||||
observation: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""观察模型配置"""
|
||||
focus_chat_mind: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注聊天规划模型配置"""
|
||||
|
||||
sub_heartflow: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""子心流模型配置"""
|
||||
focus_self_recognize: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注自我识别模型配置"""
|
||||
|
||||
plan: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""计划模型配置"""
|
||||
focus_tool_use: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注工具使用模型配置"""
|
||||
|
||||
focus_planner: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注规划模型配置"""
|
||||
|
||||
focus_expressor: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注表达器模型配置"""
|
||||
|
||||
embedding: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""嵌入模型配置"""
|
||||
|
|
@ -428,6 +430,3 @@ class ModelConfig(ConfigBase):
|
|||
|
||||
pfc_reply_checker: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""PFC回复检查模型配置"""
|
||||
|
||||
tool_use: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""工具使用模型配置"""
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ class GoalAnalyzer:
|
|||
def __init__(self, stream_id: str, private_name: str):
|
||||
# TODO: API-Adapter修改标记
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.model.normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
|
||||
model=global_config.model.utils, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
|
||||
)
|
||||
|
||||
self.personality_info = individuality.get_prompt(x_person=2, level=3)
|
||||
|
|
|
|||
|
|
@ -16,8 +16,8 @@ class KnowledgeFetcher:
|
|||
def __init__(self, private_name: str):
|
||||
# TODO: API-Adapter修改标记
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.model.normal,
|
||||
temperature=global_config.model.normal["temp"],
|
||||
model=global_config.model.utils,
|
||||
temperature=global_config.model.utils["temp"],
|
||||
max_tokens=1000,
|
||||
request_type="knowledge_fetch",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -33,10 +33,10 @@ def init_prompt() -> None:
|
|||
class PersonalityExpression:
|
||||
def __init__(self):
|
||||
self.express_learn_model: LLMRequest = LLMRequest(
|
||||
model=global_config.model.normal,
|
||||
model=global_config.model.focus_expressor,
|
||||
temperature=0.1,
|
||||
max_tokens=256,
|
||||
request_type="response_heartflow",
|
||||
request_type="learn_expression",
|
||||
)
|
||||
self.meta_file_path = os.path.join("data", "expression", "personality", "expression_style_meta.json")
|
||||
self.expressions_file_path = os.path.join("data", "expression", "personality", "expressions.json")
|
||||
|
|
@ -83,7 +83,7 @@ class PersonalityExpression:
|
|||
logger.error(f"删除旧的表达文件 {self.expressions_file_path} 失败: {e}")
|
||||
|
||||
if count >= self.max_calculations:
|
||||
logger.info(f"对于风格 '{current_style_text}' 已达到最大计算次数 ({self.max_calculations})。跳过提取。")
|
||||
logger.debug(f"对于风格 '{current_style_text}' 已达到最大计算次数 ({self.max_calculations})。跳过提取。")
|
||||
# 即使跳过，也更新元数据以反映当前风格已被识别且计数已满
|
||||
self._write_meta_data({"last_style_text": current_style_text, "count": count})
|
||||
return
|
||||
|
|
|
|||
|
|
@ -7,99 +7,24 @@ class Identity:
|
|||
"""身份特征类"""
|
||||
|
||||
identity_detail: List[str] # 身份细节描述
|
||||
height: int # 身高（厘米）
|
||||
weight: float # 体重（千克）
|
||||
age: int # 年龄
|
||||
gender: str # 性别
|
||||
appearance: str # 外貌特征
|
||||
|
||||
_instance = None
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
identity_detail: List[str] = None,
|
||||
height: int = 0,
|
||||
weight: float = 0,
|
||||
age: int = 0,
|
||||
gender: str = "",
|
||||
appearance: str = "",
|
||||
):
|
||||
def __init__(self, identity_detail: List[str] = None):
|
||||
"""初始化身份特征
|
||||
|
||||
Args:
|
||||
identity_detail: 身份细节描述列表
|
||||
height: 身高（厘米）
|
||||
weight: 体重（千克）
|
||||
age: 年龄
|
||||
gender: 性别
|
||||
appearance: 外貌特征
|
||||
"""
|
||||
if identity_detail is None:
|
||||
identity_detail = []
|
||||
self.identity_detail = identity_detail
|
||||
self.height = height
|
||||
self.weight = weight
|
||||
self.age = age
|
||||
self.gender = gender
|
||||
self.appearance = appearance
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls) -> "Identity":
|
||||
"""获取Identity单例实例
|
||||
|
||||
Returns:
|
||||
Identity: 单例实例
|
||||
"""
|
||||
if cls._instance is None:
|
||||
cls._instance = cls()
|
||||
return cls._instance
|
||||
|
||||
@classmethod
|
||||
def initialize(
|
||||
cls, identity_detail: List[str], height: int, weight: float, age: int, gender: str, appearance: str
|
||||
) -> "Identity":
|
||||
"""初始化身份特征
|
||||
|
||||
Args:
|
||||
identity_detail: 身份细节描述列表
|
||||
height: 身高（厘米）
|
||||
weight: 体重（千克）
|
||||
age: 年龄
|
||||
gender: 性别
|
||||
appearance: 外貌特征
|
||||
|
||||
Returns:
|
||||
Identity: 初始化后的身份特征实例
|
||||
"""
|
||||
instance = cls.get_instance()
|
||||
instance.identity_detail = identity_detail
|
||||
instance.height = height
|
||||
instance.weight = weight
|
||||
instance.age = age
|
||||
instance.gender = gender
|
||||
instance.appearance = appearance
|
||||
return instance
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""将身份特征转换为字典格式"""
|
||||
return {
|
||||
"identity_detail": self.identity_detail,
|
||||
"height": self.height,
|
||||
"weight": self.weight,
|
||||
"age": self.age,
|
||||
"gender": self.gender,
|
||||
"appearance": self.appearance,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict) -> "Identity":
|
||||
"""从字典创建身份特征实例"""
|
||||
instance = cls.get_instance()
|
||||
for key, value in data.items():
|
||||
setattr(instance, key, value)
|
||||
return instance
|
||||
return cls(identity_detail=data.get("identity_detail", []))
|
||||
|
|
|
|||
|
|
@ -1,6 +1,4 @@
|
|||
from typing import Optional
|
||||
|
||||
from numpy import double
|
||||
from .personality import Personality
|
||||
from .identity import Identity
|
||||
from .expression_style import PersonalityExpression
|
||||
|
|
@ -27,11 +25,6 @@ class Individuality:
|
|||
personality_core: str,
|
||||
personality_sides: list,
|
||||
identity_detail: list,
|
||||
height: int,
|
||||
weight: double,
|
||||
age: int,
|
||||
gender: str,
|
||||
appearance: str,
|
||||
) -> None:
|
||||
"""初始化个体特征
|
||||
|
||||
|
|
@ -40,11 +33,6 @@ class Individuality:
|
|||
personality_core: 人格核心特点
|
||||
personality_sides: 人格侧面描述
|
||||
identity_detail: 身份细节描述
|
||||
height: 身高（厘米）
|
||||
weight: 体重（千克）
|
||||
age: 年龄
|
||||
gender: 性别
|
||||
appearance: 外貌特征
|
||||
"""
|
||||
# 初始化人格
|
||||
self.personality = Personality.initialize(
|
||||
|
|
@ -52,9 +40,7 @@ class Individuality:
|
|||
)
|
||||
|
||||
# 初始化身份
|
||||
self.identity = Identity.initialize(
|
||||
identity_detail=identity_detail, height=height, weight=weight, age=age, gender=gender, appearance=appearance
|
||||
)
|
||||
self.identity = Identity(identity_detail=identity_detail)
|
||||
|
||||
await self.express_style.extract_and_store_personality_expressions()
|
||||
|
||||
|
|
@ -120,7 +106,7 @@ class Individuality:
|
|||
获取身份特征的prompt
|
||||
|
||||
Args:
|
||||
level (int): 详细程度 (1: 随机细节, 2: 所有细节+外貌年龄性别, 3: 同2)
|
||||
level (int): 详细程度 (1: 随机细节, 2: 所有细节, 3: 同2)
|
||||
x_person (int, optional): 人称代词 (0: 无人称, 1: 我, 2: 你). 默认为 2.
|
||||
|
||||
Returns:
|
||||
|
|
@ -145,23 +131,10 @@ class Individuality:
|
|||
identity_detail = list(self.identity.identity_detail)
|
||||
random.shuffle(identity_detail)
|
||||
if level == 1:
|
||||
identity_parts.append(f"身份是{identity_detail[0]}")
|
||||
identity_parts.append(f"{identity_detail[0]}")
|
||||
elif level >= 2:
|
||||
details_str = "、".join(identity_detail)
|
||||
identity_parts.append(f"身份是{details_str}")
|
||||
|
||||
# 根据level添加其他身份信息
|
||||
if level >= 3:
|
||||
if self.identity.appearance:
|
||||
identity_parts.append(f"{self.identity.appearance}")
|
||||
if self.identity.age > 0:
|
||||
identity_parts.append(f"年龄大约{self.identity.age}岁")
|
||||
if self.identity.gender:
|
||||
identity_parts.append(f"性别是{self.identity.gender}")
|
||||
if self.identity.height:
|
||||
identity_parts.append(f"身高大约{self.identity.height}厘米")
|
||||
if self.identity.weight:
|
||||
identity_parts.append(f"体重大约{self.identity.weight}千克")
|
||||
identity_parts.append(f"{details_str}")
|
||||
|
||||
if identity_parts:
|
||||
details_str = "，".join(identity_parts)
|
||||
|
|
|
|||
|
|
@ -117,6 +117,9 @@ class LLMRequest:
|
|||
self.model_name: str = model["name"]
|
||||
self.params = kwargs
|
||||
|
||||
self.enable_thinking = model.get("enable_thinking", False)
|
||||
self.temp = model.get("temp", 0.7)
|
||||
self.thinking_budget = model.get("thinking_budget", 4096)
|
||||
self.stream = model.get("stream", False)
|
||||
self.pri_in = model.get("pri_in", 0)
|
||||
self.pri_out = model.get("pri_out", 0)
|
||||
|
|
@ -435,7 +438,7 @@ class LLMRequest:
|
|||
logger.error(
|
||||
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
|
||||
)
|
||||
raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
|
||||
raise RuntimeError("服务器负载过高，模型回复失败QAQ")
|
||||
else:
|
||||
logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
|
||||
raise RuntimeError("请求限制(429)")
|
||||
|
|
@ -459,6 +462,8 @@ class LLMRequest:
|
|||
logger.error(
|
||||
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
|
||||
)
|
||||
print(request_content)
|
||||
print(response)
|
||||
# 尝试获取并记录服务器返回的详细错误信息
|
||||
try:
|
||||
error_json = await response.json()
|
||||
|
|
@ -495,11 +500,11 @@ class LLMRequest:
|
|||
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
|
||||
|
||||
# 对全局配置进行更新
|
||||
if global_config.model.normal.get("name") == old_model_name:
|
||||
global_config.model.normal["name"] = self.model_name
|
||||
if global_config.model.normal_chat_2.get("name") == old_model_name:
|
||||
global_config.model.normal_chat_2["name"] = self.model_name
|
||||
logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
|
||||
if global_config.model.reasoning.get("name") == old_model_name:
|
||||
global_config.model.reasoning["name"] = self.model_name
|
||||
if global_config.model.normal_chat_1.get("name") == old_model_name:
|
||||
global_config.model.normal_chat_1["name"] = self.model_name
|
||||
logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
|
||||
|
||||
if payload and "model" in payload:
|
||||
|
|
@ -599,8 +604,9 @@ class LLMRequest:
|
|||
new_params = dict(params)
|
||||
|
||||
if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION:
|
||||
# 删除 'temperature' 参数（如果存在）
|
||||
new_params.pop("temperature", None)
|
||||
# 删除 'temperature' 参数（如果存在），但避免删除我们在_build_payload中添加的自定义温度
|
||||
if "temperature" in new_params and new_params["temperature"] == 0.7:
|
||||
new_params.pop("temperature")
|
||||
# 如果存在 'max_tokens'，则重命名为 'max_completion_tokens'
|
||||
if "max_tokens" in new_params:
|
||||
new_params["max_completion_tokens"] = new_params.pop("max_tokens")
|
||||
|
|
@ -630,6 +636,18 @@ class LLMRequest:
|
|||
"messages": messages,
|
||||
**params_copy,
|
||||
}
|
||||
|
||||
# 添加temp参数（如果不是默认值0.7）
|
||||
if self.temp != 0.7:
|
||||
payload["temperature"] = self.temp
|
||||
|
||||
# 添加enable_thinking参数（如果不是默认值False）
|
||||
if not self.enable_thinking:
|
||||
payload["enable_thinking"] = False
|
||||
|
||||
if self.thinking_budget != 4096:
|
||||
payload["thinking_budget"] = self.thinking_budget
|
||||
|
||||
if "max_tokens" not in payload and "max_completion_tokens" not in payload:
|
||||
payload["max_tokens"] = global_config.model.model_max_output_length
|
||||
# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
|
||||
|
|
|
|||
|
|
@ -96,11 +96,6 @@ class MainSystem:
|
|||
personality_core=global_config.personality.personality_core,
|
||||
personality_sides=global_config.personality.personality_sides,
|
||||
identity_detail=global_config.identity.identity_detail,
|
||||
height=global_config.identity.height,
|
||||
weight=global_config.identity.weight,
|
||||
age=global_config.identity.age,
|
||||
gender=global_config.identity.gender,
|
||||
appearance=global_config.identity.appearance,
|
||||
)
|
||||
logger.success("个体特征初始化成功")
|
||||
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ class PersonInfoManager:
|
|||
self.person_name_list = {}
|
||||
# TODO: API-Adapter修改标记
|
||||
self.qv_name_llm = LLMRequest(
|
||||
model=global_config.model.normal,
|
||||
model=global_config.model.utils,
|
||||
max_tokens=256,
|
||||
request_type="qv_name",
|
||||
)
|
||||
|
|
@ -104,10 +104,14 @@ class PersonInfoManager:
|
|||
|
||||
def get_person_id_by_person_name(self, person_name: str):
|
||||
"""根据用户名获取用户ID"""
|
||||
document = db.person_info.find_one({"person_name": person_name})
|
||||
if document:
|
||||
return document["person_id"]
|
||||
else:
|
||||
try:
|
||||
record = PersonInfo.get_or_none(PersonInfo.person_name == person_name)
|
||||
if record:
|
||||
return record.person_id
|
||||
else:
|
||||
return ""
|
||||
except Exception as e:
|
||||
logger.error(f"根据用户名 {person_name} 获取用户ID时出错 (Peewee): {e}")
|
||||
return ""
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -257,21 +261,19 @@ class PersonInfoManager:
|
|||
current_name_set = set(self.person_name_list.values())
|
||||
|
||||
while current_try < max_retries:
|
||||
prompt_personality = individuality.get_prompt(x_person=2, level=1)
|
||||
# prompt_personality = individuality.get_prompt(x_person=2, level=1)
|
||||
bot_name = individuality.personality.bot_nickname
|
||||
|
||||
qv_name_prompt = f"你是{bot_name}，{prompt_personality}"
|
||||
qv_name_prompt += f"现在你想给一个用户取一个昵称，用户是的qq昵称是{user_nickname}，"
|
||||
qv_name_prompt = f"你是{bot_name}，一个伪装成人类的AI，你不能让别人发现这一点，"
|
||||
qv_name_prompt += f"现在你想给一个用户取一个昵称，用户的qq昵称是{user_nickname}，"
|
||||
qv_name_prompt += f"用户的qq群昵称名是{user_cardname}，"
|
||||
if user_avatar:
|
||||
qv_name_prompt += f"用户的qq头像是{user_avatar}，"
|
||||
if old_name:
|
||||
qv_name_prompt += f"你之前叫他{old_name}，是因为{old_reason}，"
|
||||
|
||||
qv_name_prompt += f"\n其他取名的要求是：{request}，不要太浮夸"
|
||||
qv_name_prompt += (
|
||||
"\n请根据以上用户信息，想想你叫他什么比较好，不要太浮夸，请最好使用用户的qq昵称，可以稍作修改"
|
||||
)
|
||||
qv_name_prompt += f"\n其他取名的要求是：{request}，不要太浮夸，简短，"
|
||||
qv_name_prompt += "\n请根据以上用户信息，想想你叫他什么比较好，不要太浮夸，请最好使用用户的qq昵称，可以稍作修改，优先使用原文。优先使用用户的qq昵称或者群昵称原文。"
|
||||
|
||||
if existing_names_str:
|
||||
qv_name_prompt += f"\n请注意，以下名称已被你尝试过或已知存在，请避免：{existing_names_str}。\n"
|
||||
|
|
|
|||
|
|
@ -297,6 +297,8 @@ class RelationshipManager:
|
|||
relationship_value = await person_info_manager.get_value(person_id, "relationship_value")
|
||||
level_num = self.calculate_level_num(relationship_value)
|
||||
|
||||
relation_value_prompt = ""
|
||||
|
||||
if level_num == 0 or level_num == 5:
|
||||
relationship_level = ["厌恶", "冷漠以对", "认识", "友好对待", "喜欢", "暧昧"]
|
||||
relation_prompt2_list = [
|
||||
|
|
@ -307,9 +309,11 @@ class RelationshipManager:
|
|||
"积极回复",
|
||||
"友善和包容的回复",
|
||||
]
|
||||
return f"你{relationship_level[level_num]}{person_name}，打算{relation_prompt2_list[level_num]}。\n"
|
||||
relation_value_prompt = (
|
||||
f"你{relationship_level[level_num]}{person_name}，打算{relation_prompt2_list[level_num]}。"
|
||||
)
|
||||
elif level_num == 2:
|
||||
return ""
|
||||
relation_value_prompt = ""
|
||||
else:
|
||||
if random.random() < 0.6:
|
||||
relationship_level = ["厌恶", "冷漠以对", "认识", "友好对待", "喜欢", "暧昧"]
|
||||
|
|
@ -321,9 +325,20 @@ class RelationshipManager:
|
|||
"积极回复",
|
||||
"友善和包容的回复",
|
||||
]
|
||||
return f"你{relationship_level[level_num]}{person_name}，打算{relation_prompt2_list[level_num]}。\n"
|
||||
relation_value_prompt = (
|
||||
f"你{relationship_level[level_num]}{person_name}，打算{relation_prompt2_list[level_num]}。"
|
||||
)
|
||||
else:
|
||||
return ""
|
||||
relation_value_prompt = ""
|
||||
|
||||
if relation_value_prompt:
|
||||
nickname_str = await person_info_manager.get_value(person_id, "nickname")
|
||||
platform = await person_info_manager.get_value(person_id, "platform")
|
||||
relation_prompt = f"{relation_value_prompt}，ta在{platform}上的昵称是{nickname_str}。\n"
|
||||
else:
|
||||
relation_prompt = ""
|
||||
|
||||
return relation_prompt
|
||||
|
||||
@staticmethod
|
||||
def calculate_level_num(relationship_value) -> int:
|
||||
|
|
|
|||
|
|
@ -3,5 +3,5 @@
|
|||
# 导入所有动作模块以确保装饰器被执行
|
||||
from . import test_action # noqa
|
||||
|
||||
from . import online_action # noqa
|
||||
# from . import online_action # noqa
|
||||
from . import mute_action # noqa
|
||||
|
|
|
|||
|
|
@ -0,0 +1,63 @@
|
|||
from src.common.logger_manager import get_logger
|
||||
from src.chat.focus_chat.planners.actions.plugin_action import PluginAction, register_action
|
||||
from typing import Tuple
|
||||
|
||||
logger = get_logger("group_whole_ban_action")
|
||||
|
||||
|
||||
@register_action
|
||||
class GroupWholeBanAction(PluginAction):
|
||||
"""群聊全体禁言动作处理类"""
|
||||
|
||||
action_name = "group_whole_ban_action"
|
||||
action_description = "开启或关闭群聊全体禁言，当群聊过于混乱或需要安静时使用"
|
||||
action_parameters = {
|
||||
"enable": "是否开启全体禁言，输入True开启，False关闭，必填",
|
||||
}
|
||||
action_require = [
|
||||
"当群聊过于混乱需要安静时使用",
|
||||
"当需要临时暂停群聊讨论时使用",
|
||||
"当有人要求开启全体禁言时使用",
|
||||
"当管理员需要发布重要公告时使用",
|
||||
]
|
||||
default = False
|
||||
associated_types = ["command", "text"]
|
||||
|
||||
async def process(self) -> Tuple[bool, str]:
|
||||
"""处理群聊全体禁言动作"""
|
||||
logger.info(f"{self.log_prefix} 执行全体禁言动作: {self.reasoning}")
|
||||
|
||||
# 获取参数
|
||||
enable = self.action_data.get("enable")
|
||||
|
||||
if enable is None:
|
||||
error_msg = "全体禁言参数不完整，需要enable参数"
|
||||
logger.error(f"{self.log_prefix} {error_msg}")
|
||||
return False, error_msg
|
||||
|
||||
# 确保enable是布尔类型
|
||||
if isinstance(enable, str):
|
||||
if enable.lower() in ["true", "1", "yes", "开启", "是"]:
|
||||
enable = True
|
||||
elif enable.lower() in ["false", "0", "no", "关闭", "否"]:
|
||||
enable = False
|
||||
else:
|
||||
error_msg = f"无效的enable参数: {enable}，应该是True或False"
|
||||
logger.error(f"{self.log_prefix} {error_msg}")
|
||||
return False, error_msg
|
||||
|
||||
# 发送表达情绪的消息
|
||||
action_text = "开启" if enable else "关闭"
|
||||
await self.send_message_by_expressor(f"我要{action_text}全体禁言")
|
||||
|
||||
try:
|
||||
# 发送群聊全体禁言命令，按照新格式
|
||||
await self.send_message(type="command", data={"name": "GROUP_WHOLE_BAN", "args": {"enable": enable}})
|
||||
|
||||
logger.info(f"{self.log_prefix} 成功{action_text}全体禁言")
|
||||
return True, f"成功{action_text}全体禁言"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行全体禁言动作时出错: {e}")
|
||||
await self.send_message_by_expressor(f"执行全体禁言动作时出错: {e}")
|
||||
return False, f"执行全体禁言动作时出错: {e}"
|
||||
|
|
@ -7,15 +7,14 @@ logger = get_logger("mute_action")
|
|||
|
||||
@register_action
|
||||
class MuteAction(PluginAction):
|
||||
"""测试动作处理类"""
|
||||
"""群聊禁言动作处理类"""
|
||||
|
||||
action_name = "mute_action"
|
||||
action_description = (
|
||||
"如果某人违反了公序良俗，或者别人戳你太多，，或者某人刷屏，一定要禁言某人，如果你很生气，可以禁言某人"
|
||||
)
|
||||
action_description = "如果某人违反了公序良俗，或者别人戳你太多，或者某人刷屏，一定要禁言某人，如果你很生气，可以禁言某人，可以自选禁言时长，视严重程度而定。"
|
||||
action_parameters = {
|
||||
"target": "禁言对象，输入你要禁言的对象的名字，必填，",
|
||||
"duration": "禁言时长，输入你要禁言的时长，单位为秒，必填",
|
||||
"target": "禁言对象，输入你要禁言的对象的名字，必填",
|
||||
"duration": "禁言时长，输入你要禁言的时长，单位为秒，必填，必须为数字",
|
||||
"reason": "禁言理由，可选",
|
||||
}
|
||||
action_require = [
|
||||
"当有人违反了公序良俗时使用",
|
||||
|
|
@ -25,32 +24,48 @@ class MuteAction(PluginAction):
|
|||
"当千石可乐或可乐酱要求你禁言时使用",
|
||||
"当你想回避某个话题时使用",
|
||||
]
|
||||
default = True # 不是默认动作，需要手动添加到使用集
|
||||
associated_types = ["command", "text"]
|
||||
default = True # 默认动作，是否手动添加到使用集
|
||||
# associated_types = ["command", "text"]
|
||||
associated_types = ["text"]
|
||||
|
||||
async def process(self) -> Tuple[bool, str]:
|
||||
"""处理测试动作"""
|
||||
logger.info(f"{self.log_prefix} 执行online动作: {self.reasoning}")
|
||||
"""处理群聊禁言动作"""
|
||||
logger.info(f"{self.log_prefix} 执行禁言动作: {self.reasoning}")
|
||||
|
||||
# 发送测试消息
|
||||
# 获取参数
|
||||
target = self.action_data.get("target")
|
||||
duration = self.action_data.get("duration")
|
||||
reason = self.action_data.get("reason")
|
||||
reason = self.action_data.get("reason", "违反群规")
|
||||
|
||||
if not target or not duration:
|
||||
error_msg = "禁言参数不完整，需要target和duration"
|
||||
logger.error(f"{self.log_prefix} {error_msg}")
|
||||
return False, error_msg
|
||||
|
||||
# 获取用户ID
|
||||
platform, user_id = await self.get_user_id_by_person_name(target)
|
||||
|
||||
await self.send_message_by_expressor(f"我要禁言{target}，{platform},时长{duration}秒，理由{reason}，表达情绪")
|
||||
if not user_id:
|
||||
error_msg = f"未找到用户 {target} 的ID"
|
||||
logger.error(f"{self.log_prefix} {error_msg}")
|
||||
return False, error_msg
|
||||
|
||||
# 发送表达情绪的消息
|
||||
await self.send_message_by_expressor(f"我要禁言{target}，时长{duration}秒，理由：{reason}")
|
||||
|
||||
try:
|
||||
# 确保duration是字符串类型
|
||||
duration_str = str(duration)
|
||||
|
||||
# 发送群聊禁言命令，按照新格式
|
||||
await self.send_message(
|
||||
type="command",
|
||||
data={"name": "GROUP_BAN", "args": {"qq_id": f"{user_id}", "duration": f"{duration}"}},
|
||||
# target = target
|
||||
type="command", data={"name": "GROUP_BAN", "args": {"qq_id": str(user_id), "duration": duration_str}}
|
||||
)
|
||||
|
||||
logger.info(f"{self.log_prefix} 成功禁言用户 {target}({user_id})，时长 {duration} 秒")
|
||||
return True, f"成功禁言 {target}，时长 {duration} 秒"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行mute动作时出错: {e}")
|
||||
await self.send_message_by_expressor(f"执行mute动作时出错: {e}")
|
||||
|
||||
return False, "执行mute动作时出错"
|
||||
|
||||
return True, "测试动作执行成功"
|
||||
logger.error(f"{self.log_prefix} 执行禁言动作时出错: {e}")
|
||||
await self.send_message_by_expressor(f"执行禁言动作时出错: {e}")
|
||||
return False, f"执行禁言动作时出错: {e}"
|
||||
|
|
|
|||
|
|
@ -1,44 +0,0 @@
|
|||
from src.common.logger_manager import get_logger
|
||||
from src.chat.focus_chat.planners.actions.plugin_action import PluginAction, register_action
|
||||
from typing import Tuple
|
||||
|
||||
logger = get_logger("check_online_action")
|
||||
|
||||
|
||||
@register_action
|
||||
class CheckOnlineAction(PluginAction):
|
||||
"""测试动作处理类"""
|
||||
|
||||
action_name = "check_online_action"
|
||||
action_description = "这是一个检查在线状态的动作，当有人要求你检查Maibot（麦麦 机器人）在线状态时使用"
|
||||
action_parameters = {"mode": "查看模式"}
|
||||
action_require = [
|
||||
"当有人要求你检查Maibot（麦麦 机器人）在线状态时使用",
|
||||
"mode参数为version时查看在线版本状态，默认用这种",
|
||||
"mode参数为type时查看在线系统类型分布",
|
||||
]
|
||||
default = False # 不是默认动作，需要手动添加到使用集
|
||||
associated_types = ["text"]
|
||||
|
||||
async def process(self) -> Tuple[bool, str]:
|
||||
"""处理测试动作"""
|
||||
logger.info(f"{self.log_prefix} 执行online动作: {self.reasoning}")
|
||||
|
||||
# 发送测试消息
|
||||
mode = self.action_data.get("mode", "type")
|
||||
|
||||
await self.send_message_by_expressor("我看看")
|
||||
|
||||
try:
|
||||
if mode == "type":
|
||||
await self.send_message("text", "#online detail")
|
||||
elif mode == "version":
|
||||
await self.send_message("text", "#online")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行online动作时出错: {e}")
|
||||
await self.send_message_by_expressor("执行online动作时出错: {e}")
|
||||
|
||||
return False, "执行online动作时出错"
|
||||
|
||||
return True, "测试动作执行成功"
|
||||
|
|
@ -153,7 +153,7 @@ class PicAction(PluginAction):
|
|||
|
||||
if encode_success:
|
||||
base64_image_string = encode_result
|
||||
send_success = await self.send_message(type="emoji", data=base64_image_string)
|
||||
send_success = await self.send_message(type="image", data=base64_image_string)
|
||||
if send_success:
|
||||
await self.send_message_by_expressor("图片表情已发送！")
|
||||
return True, "图片表情已发送"
|
||||
|
|
|
|||
|
|
@ -1,5 +1,3 @@
|
|||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import json
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.tools.tool_can_use import get_all_tool_definitions, get_tool_instance
|
||||
|
|
@ -8,11 +6,6 @@ logger = get_logger("tool_use")
|
|||
|
||||
|
||||
class ToolUser:
|
||||
def __init__(self):
|
||||
self.llm_model_tool = LLMRequest(
|
||||
model=global_config.model.tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _define_tools():
|
||||
"""获取所有已注册工具的定义
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "2.4.0"
|
||||
version = "2.5.0"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请在修改后将version的值进行变更
|
||||
|
|
@ -18,28 +18,26 @@ nickname = "麦麦"
|
|||
alias_names = ["麦叠", "牢麦"] #仅在 专注聊天 有效
|
||||
|
||||
[personality]
|
||||
personality_core = "用一句话或几句话描述人格的核心特点" # 建议20字以内，谁再写3000字小作文敲谁脑袋
|
||||
personality_core = "是一个积极向上的女大学生" # 建议20字以内，谁再写3000字小作文敲谁脑袋
|
||||
personality_sides = [
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
"用一句话或几句话描述人格的一些细节",
|
||||
]# 条数任意，不能为0, 该选项还在调试中，可能未完全生效
|
||||
|
||||
# 身份特点 部分选项仅在 专注聊天 有效
|
||||
[identity] #アイデンティティがない 生まれないらららら
|
||||
identity_detail = [
|
||||
"身份特点",
|
||||
"身份特点",
|
||||
]# 条数任意，不能为0
|
||||
|
||||
#外貌特征
|
||||
age = 18 # 年龄 单位岁
|
||||
gender = "女" # 性别
|
||||
height = "170" # 身高（单位cm）
|
||||
weight = "50" # 体重（单位kg）
|
||||
appearance = "用一句或几句话描述外貌特征" # 外貌特征
|
||||
# 身份特点
|
||||
[identity] #アイデンティティがない 生まれないらららら
|
||||
identity_detail = [
|
||||
"年龄为19岁",
|
||||
"是女孩子",
|
||||
"身高为160cm",
|
||||
"有橙色的短发",
|
||||
]
|
||||
# 可以描述外贸，性别，身高，职业，属性等等描述
|
||||
# 条数任意，不能为0
|
||||
|
||||
[relationship]
|
||||
give_name = true # 麦麦是否给其他人取名，关闭后无法使用禁言功能
|
||||
|
||||
[chat] #麦麦的聊天通用设置
|
||||
chat_mode = "normal" # 聊天模式 —— 普通模式：normal，专注模式：focus，在普通模式和专注模式之间自动切换
|
||||
|
|
@ -65,7 +63,7 @@ ban_msgs_regex = [
|
|||
|
||||
[normal_chat] #普通聊天
|
||||
#一般回复参数
|
||||
reasoning_model_probability = 0.3 # 麦麦回答时选择推理模型的概率（与之相对的，普通模型的概率为1 - reasoning_model_probability）
|
||||
normal_chat_first_probability = 0.3 # 麦麦回答时选择首要模型的概率（与之相对的，次要模型的概率为1 - normal_chat_first_probability）
|
||||
max_context_size = 15 #上下文长度
|
||||
emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发
|
||||
thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢）
|
||||
|
|
@ -81,11 +79,10 @@ at_bot_inevitable_reply = false # @bot 必然回复
|
|||
talk_frequency_down_groups = [] #降低回复频率的群号码
|
||||
|
||||
[focus_chat] #专注聊天
|
||||
reply_trigger_threshold = 3.0 # 专注聊天触发阈值，越低越容易进入专注聊天
|
||||
default_decay_rate_per_second = 0.98 # 默认衰减率，越大衰减越快，越高越难进入专注聊天
|
||||
consecutive_no_reply_threshold = 3 # 连续不回复的阈值，越低越容易结束专注聊天
|
||||
auto_focus_threshold = 1 # 自动切换到专注聊天的阈值，越低越容易进入专注聊天
|
||||
exit_focus_threshold = 1 # 自动退出专注聊天的阈值，越低越容易退出专注聊天
|
||||
|
||||
think_interval = 1 # 思考间隔 单位秒
|
||||
think_interval = 3 # 思考间隔 单位秒，可以有效减少消耗
|
||||
|
||||
observation_context_size = 15 # 观察到的最长上下文大小,建议15，太短太长都会导致脑袋尖尖
|
||||
compressed_length = 5 # 不能大于chat.observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
|
||||
|
|
@ -93,23 +90,21 @@ compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下
|
|||
|
||||
[focus_chat_processor] # 专注聊天处理器，打开可以实现更多功能，但是会增加token消耗
|
||||
self_identify_processor = true # 是否启用自我识别处理器
|
||||
tool_use_processor = true # 是否启用工具使用处理器
|
||||
working_memory_processor = true # 是否启用工作记忆处理器
|
||||
|
||||
|
||||
tool_use_processor = false # 是否启用工具使用处理器
|
||||
working_memory_processor = false # 是否启用工作记忆处理器
|
||||
|
||||
[expression]
|
||||
# 表达方式
|
||||
expression_style = "描述麦麦说话的表达风格，表达习惯"
|
||||
enable_expression_learning = true # 是否启用表达学习
|
||||
learning_interval = 300 # 学习间隔 单位秒
|
||||
learning_interval = 600 # 学习间隔 单位秒
|
||||
|
||||
|
||||
[emoji]
|
||||
max_reg_num = 40 # 表情包最大注册数量
|
||||
do_replace = true # 开启则在达到最大数量时删除（替换）表情包，关闭则达到最大数量时不会继续收集表情包
|
||||
check_interval = 120 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
|
||||
save_pic = false # 是否保存图片
|
||||
save_pic = true # 是否保存图片
|
||||
cache_emoji = true # 是否缓存表情包
|
||||
steal_emoji = true # 是否偷取表情包，让麦麦可以发送她保存的这些表情包
|
||||
content_filtration = false # 是否启用表情包过滤，只有符合该要求的表情包才会被保存
|
||||
|
|
@ -138,7 +133,7 @@ mood_update_interval = 1.0 # 情绪更新间隔 单位秒
|
|||
mood_decay_rate = 0.95 # 情绪衰减率
|
||||
mood_intensity_factor = 1.0 # 情绪强度因子
|
||||
|
||||
[keyword_reaction] # 针对某个关键词作出反应
|
||||
[keyword_reaction] # 针对某个关键词作出反应，仅在 普通聊天 有效
|
||||
enable = true # 关键词反应功能的总开关
|
||||
|
||||
[[keyword_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可
|
||||
|
|
@ -186,10 +181,9 @@ enable = true
|
|||
|
||||
[experimental] #实验性功能
|
||||
enable_friend_chat = false # 是否启用好友聊天
|
||||
pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与回复模式独立
|
||||
pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与回复模式独立，在0.7.0暂时无效
|
||||
|
||||
#下面的模型若使用硅基流动则不需要更改，使用ds官方则改成.env自定义的宏，使用自定义模型则选择定位相似的模型自己填写
|
||||
#推理模型
|
||||
|
||||
# 额外字段
|
||||
# 下面的模型有以下额外字段可以添加：
|
||||
|
|
@ -200,14 +194,9 @@ pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与
|
|||
[model]
|
||||
model_max_output_length = 800 # 模型单次返回的最大token数
|
||||
|
||||
#这个模型必须是推理模型
|
||||
[model.reasoning] # 一般聊天模式的推理回复模型
|
||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.0 #模型的输入价格（非必填，可以记录消耗）
|
||||
pri_out = 4.0 #模型的输出价格（非必填，可以记录消耗）
|
||||
#------------必填：组件模型------------
|
||||
|
||||
[model.normal] #V3 回复模型 专注和一般聊天模式共用的回复模型
|
||||
[model.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，消耗量不大
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 2 #模型的输入价格（非必填，可以记录消耗）
|
||||
|
|
@ -215,17 +204,20 @@ pri_out = 8 #模型的输出价格（非必填，可以记录消耗）
|
|||
#默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数
|
||||
temp = 0.2 #模型的温度，新V3建议0.1-0.3
|
||||
|
||||
[model.topic_judge] #主题判断模型：建议使用qwen2.5 7b
|
||||
name = "Pro/Qwen/Qwen2.5-7B-Instruct"
|
||||
[model.utils_small] # 在麦麦的一些组件中使用的小模型，消耗量较大
|
||||
# 强烈建议使用免费的小模型
|
||||
name = "Qwen/Qwen3-8B"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0.35
|
||||
pri_out = 0.35
|
||||
enable_thinking = false # 是否启用思考
|
||||
pri_in = 0
|
||||
pri_out = 0
|
||||
|
||||
[model.summary] #概括模型，建议使用qwen2.5 32b 及以上
|
||||
name = "Qwen/Qwen2.5-32B-Instruct"
|
||||
[model.memory_summary] # 记忆的概括模型
|
||||
name = "Qwen/Qwen3-30B-A3B"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.26
|
||||
pri_out = 1.26
|
||||
enable_thinking = false # 是否启用思考
|
||||
pri_in = 0.7
|
||||
pri_out = 2.8
|
||||
|
||||
[model.vlm] # 图像识别模型
|
||||
name = "Pro/Qwen/Qwen2.5-VL-7B-Instruct"
|
||||
|
|
@ -233,40 +225,85 @@ provider = "SILICONFLOW"
|
|||
pri_in = 0.35
|
||||
pri_out = 0.35
|
||||
|
||||
[model.heartflow] # 用于控制麦麦是否参与聊天的模型
|
||||
name = "Qwen/Qwen2.5-32B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.26
|
||||
pri_out = 1.26
|
||||
|
||||
[model.observation] #观察模型，压缩聊天内容，建议用免费的
|
||||
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
|
||||
name = "Qwen/Qwen2.5-7B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0
|
||||
pri_out = 0
|
||||
|
||||
[model.sub_heartflow] #心流：认真聊天时,生成麦麦的内心想法，必须使用具有工具调用能力的模型
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 2
|
||||
pri_out = 8
|
||||
temp = 0.3 #模型的温度，新V3建议0.1-0.3
|
||||
|
||||
[model.plan] #决策：认真聊天时,负责决定麦麦该做什么
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 2
|
||||
pri_out = 8
|
||||
|
||||
#嵌入模型
|
||||
|
||||
[model.embedding] #嵌入
|
||||
[model.embedding]
|
||||
name = "BAAI/bge-m3"
|
||||
provider = "SILICONFLOW"
|
||||
provider = "DEV"
|
||||
pri_in = 0
|
||||
pri_out = 0
|
||||
|
||||
#------------普通聊天必填模型------------
|
||||
|
||||
[model.normal_chat_1] # 一般聊天模式的首要回复模型，推荐使用 推理模型
|
||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 4.0 #模型的输入价格（非必填，可以记录消耗）
|
||||
pri_out = 16.0 #模型的输出价格（非必填，可以记录消耗）
|
||||
|
||||
[model.normal_chat_2] # 一般聊天模式的次要回复模型，推荐使用 非推理模型
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 2 #模型的输入价格（非必填，可以记录消耗）
|
||||
pri_out = 8 #模型的输出价格（非必填，可以记录消耗）
|
||||
#默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数
|
||||
temp = 0.2 #模型的温度，新V3建议0.1-0.3
|
||||
|
||||
#------------专注聊天必填模型------------
|
||||
|
||||
[model.focus_working_memory] #工作记忆模型
|
||||
name = "Qwen/Qwen3-30B-A3B"
|
||||
provider = "SILICONFLOW"
|
||||
enable_thinking = false # 是否启用思考
|
||||
pri_in = 0.7
|
||||
pri_out = 2.8
|
||||
|
||||
[model.focus_chat_mind] #聊天规划：认真聊天时,生成麦麦对聊天的规划想法
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
# name = "Qwen/Qwen3-30B-A3B"
|
||||
provider = "SILICONFLOW"
|
||||
# enable_thinking = false # 是否启用思考
|
||||
pri_in = 2
|
||||
pri_out = 8
|
||||
temp = 0.3
|
||||
|
||||
[model.focus_tool_use] #工具调用模型，需要使用支持工具调用的模型
|
||||
name = "Qwen/Qwen3-14B"
|
||||
provider = "SILICONFLOW"
|
||||
enable_thinking = false # 是否启用思考
|
||||
pri_in = 0.5
|
||||
pri_out = 2
|
||||
|
||||
[model.focus_planner] #决策：认真聊天时,负责决定麦麦该做什么
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
# name = "Qwen/Qwen3-30B-A3B"
|
||||
provider = "SILICONFLOW"
|
||||
# enable_thinking = false # 是否启用思考
|
||||
pri_in = 2
|
||||
pri_out = 8
|
||||
temp = 0.3
|
||||
|
||||
#表达器模型，用于表达麦麦的想法，生成最终回复，对语言风格影响极大
|
||||
#也用于表达方式学习
|
||||
[model.focus_expressor]
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
# name = "Qwen/Qwen3-30B-A3B"
|
||||
provider = "SILICONFLOW"
|
||||
# enable_thinking = false # 是否启用思考
|
||||
pri_in = 2
|
||||
pri_out = 8
|
||||
temp = 0.3
|
||||
|
||||
#自我识别模型，用于自我认知和身份识别
|
||||
[model.focus_self_recognize]
|
||||
# name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
name = "Qwen/Qwen3-30B-A3B"
|
||||
provider = "SILICONFLOW"
|
||||
enable_thinking = false # 是否启用思考
|
||||
pri_in = 0.7
|
||||
pri_out = 2.8
|
||||
temp = 0.7
|
||||
|
||||
|
||||
|
||||
#私聊PFC：需要开启PFC功能，默认三个模型均为硅基流动v3，如果需要支持多人同时私聊或频繁调用，建议把其中的一个或两个换成官方v3或其它模型，以免撞到429
|
||||
|
||||
|
|
@ -294,15 +331,6 @@ pri_in = 2
|
|||
pri_out = 8
|
||||
|
||||
|
||||
#以下模型暂时没有使用！！
|
||||
#以下模型暂时没有使用！！
|
||||
#以下模型暂时没有使用！！
|
||||
#以下模型暂时没有使用！！
|
||||
#以下模型暂时没有使用！！
|
||||
|
||||
[model.tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b
|
||||
name = "Qwen/Qwen2.5-32B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.26
|
||||
pri_out = 1.26
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue