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This commit is contained in:
DrSmoothl
2026-02-21 16:24:24 +08:00
parent 2cb512120b
commit eaef7f0e98
82 changed files with 1881 additions and 1900 deletions
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11
src/chat/brain_chat/PFC/action_planner.py
View File

@@ -1,5 +1,5 @@
import time
from typing import Tuple, Optional, Dict, Any # 增加了 Optional
from typing import Tuple, Optional # 增加了 Optional
from src.common.logger import get_logger
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
@@ -120,7 +120,7 @@ class ActionPlanner:
def _get_personality_prompt(self) -> str:
"""获取个性提示信息"""
prompt_personality = global_config.personality.personality
# 检查是否需要随机替换为状态
if (
global_config.personality.states
@@ -128,7 +128,7 @@ class ActionPlanner:
and random.random() < global_config.personality.state_probability
):
prompt_personality = random.choice(global_config.personality.states)
bot_name = global_config.bot.nickname
return f"你的名字是{bot_name},你{prompt_personality};"
@@ -170,13 +170,10 @@ class ActionPlanner:
)
break
else:
logger.debug(
f"[私聊][{self.private_name}]聊天历史为空或尚未加载，跳过 Bot 发言时间检查。"
)
logger.debug(f"[私聊][{self.private_name}]聊天历史为空或尚未加载，跳过 Bot 发言时间检查。")
except Exception as e:
logger.debug(f"[私聊][{self.private_name}]获取 Bot 上次发言时间时出错: {e}")
# --- 获取超时提示信息 ---
# (这部分逻辑不变)
timeout_context = ""

4
src/chat/brain_chat/PFC/conversation.py
View File

@@ -112,10 +112,10 @@ class Conversation:
"user_nickname": msg.user_info.user_nickname if msg.user_info else "",
"user_cardname": msg.user_info.user_cardname if msg.user_info else None,
"platform": msg.user_info.platform if msg.user_info else "",
}
},
}
initial_messages_dict.append(msg_dict)
# 将加载的消息填充到 ObservationInfo 的 chat_history
self.observation_info.chat_history = initial_messages_dict
self.observation_info.chat_history_str = chat_talking_prompt + "\n"

4
src/chat/brain_chat/PFC/message_sender.py
View File

@@ -66,9 +66,9 @@ class DirectMessageSender:
# 发送消息（直接调用底层 API）
from src.chat.message_receive.uni_message_sender import _send_message
sent = await _send_message(message, show_log=True)
if sent:
# 存储消息
await self.storage.store_message(message, chat_stream)

8
src/chat/brain_chat/PFC/observation_info.py
View File

@@ -5,7 +5,7 @@ from src.common.logger import get_logger
from .chat_observer import ChatObserver
from .chat_states import NotificationHandler, NotificationType, Notification
from src.chat.utils.chat_message_builder import build_readable_messages
from src.common.data_models.database_data_model import DatabaseMessages, DatabaseUserInfo
from src.common.data_models.database_data_model import DatabaseMessages
import traceback # 导入 traceback 用于调试
logger = get_logger("observation_info")
@@ -13,15 +13,15 @@ logger = get_logger("observation_info")
def dict_to_database_message(msg_dict: Dict[str, Any]) -> DatabaseMessages:
"""Convert PFC dict format to DatabaseMessages object
Args:
msg_dict: Message in PFC dict format with nested user_info
Returns:
DatabaseMessages object compatible with build_readable_messages()
"""
user_info_dict: Dict[str, Any] = msg_dict.get("user_info", {})
return DatabaseMessages(
message_id=msg_dict.get("message_id", ""),
time=msg_dict.get("time", 0.0),

8
src/chat/brain_chat/PFC/pfc.py
View File

@@ -42,9 +42,7 @@ class GoalAnalyzer:
"""对话目标分析器"""
def __init__(self, stream_id: str, private_name: str):
self.llm = LLMRequest(
model_set=model_config.model_task_config.planner, request_type="conversation_goal"
)
self.llm = LLMRequest(model_set=model_config.model_task_config.planner, request_type="conversation_goal")
self.personality_info = self._get_personality_prompt()
self.name = global_config.bot.nickname
@@ -60,7 +58,7 @@ class GoalAnalyzer:
def _get_personality_prompt(self) -> str:
"""获取个性提示信息"""
prompt_personality = global_config.personality.personality
# 检查是否需要随机替换为状态
if (
global_config.personality.states
@@ -68,7 +66,7 @@ class GoalAnalyzer:
and random.random() < global_config.personality.state_probability
):
prompt_personality = random.choice(global_config.personality.states)
bot_name = global_config.bot.nickname
return f"你的名字是{bot_name},你{prompt_personality};"

10
src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py
View File

@@ -1,10 +1,10 @@
from typing import List, Tuple, Dict, Any
from src.common.logger import get_logger
# NOTE: HippocampusManager doesn't exist in v0.12.2 - memory system was redesigned
# from src.plugins.memory_system.Hippocampus import HippocampusManager
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
from src.chat.message_receive.message import Message
from src.config.config import model_config
from src.chat.knowledge import qa_manager
from src.chat.utils.chat_message_builder import build_readable_messages
from src.chat.brain_chat.PFC.observation_info import dict_to_database_message
@@ -16,9 +16,7 @@ class KnowledgeFetcher:
"""知识调取器"""
def __init__(self, private_name: str):
self.llm = LLMRequest(
model_set=model_config.model_task_config.utils
)
self.llm = LLMRequest(model_set=model_config.model_task_config.utils)
self.private_name = private_name
def _lpmm_get_knowledge(self, query: str) -> str:
@@ -64,7 +62,7 @@ class KnowledgeFetcher:
# TODO: Integrate with new memory system if needed
knowledge_text = ""
sources_text = "无记忆匹配" # 默认值
# # 从记忆中获取相关知识 (DISABLED - old Hippocampus API)
# related_memory = await HippocampusManager.get_instance().get_memory_from_text(
# text=f"{query}\n{chat_history_text}",

9
src/chat/brain_chat/PFC/reply_checker.py
View File

@@ -14,10 +14,7 @@ class ReplyChecker:
"""回复检查器"""
def __init__(self, stream_id: str, private_name: str):
self.llm = LLMRequest(
model_set=model_config.model_task_config.utils,
request_type="reply_check"
)
self.llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="reply_check")
self.personality_info = self._get_personality_prompt()
self.name = global_config.bot.nickname
self.private_name = private_name
@@ -27,7 +24,7 @@ class ReplyChecker:
def _get_personality_prompt(self) -> str:
"""获取个性提示信息"""
prompt_personality = global_config.personality.personality
# 检查是否需要随机替换为状态
if (
global_config.personality.states
@@ -35,7 +32,7 @@ class ReplyChecker:
and random.random() < global_config.personality.state_probability
):
prompt_personality = random.choice(global_config.personality.states)
bot_name = global_config.bot.nickname
return f"你的名字是{bot_name},你{prompt_personality};"

4
src/chat/brain_chat/PFC/reply_generator.py
View File

@@ -99,7 +99,7 @@ class ReplyGenerator:
def _get_personality_prompt(self) -> str:
"""获取个性提示信息"""
prompt_personality = global_config.personality.personality
# 检查是否需要随机替换为状态
if (
global_config.personality.states
@@ -107,7 +107,7 @@ class ReplyGenerator:
and random.random() < global_config.personality.state_probability
):
prompt_personality = random.choice(global_config.personality.states)
bot_name = global_config.bot.nickname
return f"你的名字是{bot_name},你{prompt_personality};"

14
src/chat/brain_chat/brain_chat.py
View File

@@ -704,10 +704,7 @@ class BrainChatting:
# 等待指定时间，但可被新消息打断
try:
await asyncio.wait_for(
self._new_message_event.wait(),
timeout=wait_seconds
)
await asyncio.wait_for(self._new_message_event.wait(), timeout=wait_seconds)
# 如果事件被触发，说明有新消息到达
logger.info(f"{self.log_prefix} wait 动作被新消息打断，提前结束等待")
except asyncio.TimeoutError:
@@ -731,7 +728,9 @@ class BrainChatting:
# 使用默认等待时间
wait_seconds = 3
logger.info(f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒（可被新消息打断）")
logger.info(
f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒（可被新消息打断）"
)
# 清除事件状态，准备等待新消息
self._new_message_event.clear()
@@ -749,10 +748,7 @@ class BrainChatting:
# 等待指定时间，但可被新消息打断
try:
await asyncio.wait_for(
self._new_message_event.wait(),
timeout=wait_seconds
)
await asyncio.wait_for(self._new_message_event.wait(), timeout=wait_seconds)
# 如果事件被触发，说明有新消息到达
logger.info(f"{self.log_prefix} listening 动作被新消息打断，提前结束等待")
except asyncio.TimeoutError:

24
src/chat/brain_chat/brain_planner.py
View File

@@ -431,15 +431,21 @@ class BrainPlanner:
except Exception as req_e:
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
extracted_reasoning = f"LLM 请求失败，模型出现问题: {req_e}"
return extracted_reasoning, [
ActionPlannerInfo(
action_type="complete_talk",
reasoning=extracted_reasoning,
action_data={},
action_message=None,
available_actions=available_actions,
)
], llm_content, llm_reasoning, llm_duration_ms
return (
extracted_reasoning,
[
ActionPlannerInfo(
action_type="complete_talk",
reasoning=extracted_reasoning,
action_data={},
action_message=None,
available_actions=available_actions,
)
],
llm_content,
llm_reasoning,
llm_duration_ms,
)
# 解析LLM响应
if llm_content: