feat: Introduce unified tooling system for plugins and MCP

- Added a new `tooling` module to define a unified model for tool declarations, invocations, and execution results, facilitating compatibility between plugins, legacy actions, and MCP tools.
- Implemented `ToolProvider` interface for various tool providers including built-in tools, MCP tools, and plugin runtime tools.
- Enhanced `MCPManager` and `MCPConnection` to support unified tool invocation and execution results.
- Updated `ComponentRegistry` and related classes to accommodate the new tool specifications and descriptions.
- Refactored existing components to utilize the new tooling system, ensuring backward compatibility with legacy actions.
- Improved error handling and logging for tool invocations across different providers.
This commit is contained in:
DrSmoothl
2026-03-30 23:11:56 +08:00
parent 898b693fe0
commit dc2bf02a42
35 changed files with 1663 additions and 6756 deletions

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@@ -1,501 +0,0 @@
import time
from typing import Tuple, Optional # 增加了 Optional
from src.common.logger import get_logger
from src.services.llm_service import LLMServiceClient
from src.config.config import global_config
import random
from .chat_observer import ChatObserver
from .pfc_utils import get_items_from_json
from src.services.message_service import build_readable_messages
from .observation_info import ObservationInfo, dict_to_session_message
from .conversation_info import ConversationInfo
logger = get_logger("pfc_action_planner")
# --- 定义 Prompt 模板 ---
# Prompt(1): 首次回复或非连续回复时的决策 Prompt
PROMPT_INITIAL_REPLY = """{persona_text}。现在你在参与一场QQ私聊请根据以下【所有信息】审慎且灵活的决策下一步行动可以回复可以倾听可以调取知识甚至可以屏蔽对方
【当前对话目标】
{goals_str}
{knowledge_info_str}
【最近行动历史概要】
{action_history_summary}
【上一次行动的详细情况和结果】
{last_action_context}
【时间和超时提示】
{time_since_last_bot_message_info}{timeout_context}
【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息)
{chat_history_text}
------
可选行动类型以及解释:
fetch_knowledge: 需要调取知识或记忆,当需要专业知识或特定信息时选择,对方若提到你不太认识的人名或实体也可以尝试选择
listening: 倾听对方发言,当你认为对方话才说到一半,发言明显未结束时选择
direct_reply: 直接回复对方
rethink_goal: 思考一个对话目标,当你觉得目前对话需要目标,或当前目标不再适用,或话题卡住时选择。注意私聊的环境是灵活的,有可能需要经常选择
end_conversation: 结束对话,对方长时间没回复或者当你觉得对话告一段落时可以选择
block_and_ignore: 更加极端的结束对话方式,直接结束对话并在一段时间内无视对方所有发言(屏蔽),当对话让你感到十分不适,或你遭到各类骚扰时选择
请以JSON格式输出你的决策
{{
"action": "选择的行动类型 (必须是上面列表中的一个)",
"reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的)"
}}
注意请严格按照JSON格式输出不要包含任何其他内容。"""
# Prompt(2): 上一次成功回复后,决定继续发言时的决策 Prompt
PROMPT_FOLLOW_UP = """{persona_text}。现在你在参与一场QQ私聊刚刚你已经回复了对方请根据以下【所有信息】审慎且灵活的决策下一步行动可以继续发送新消息可以等待可以倾听可以调取知识甚至可以屏蔽对方
【当前对话目标】
{goals_str}
{knowledge_info_str}
【最近行动历史概要】
{action_history_summary}
【上一次行动的详细情况和结果】
{last_action_context}
【时间和超时提示】
{time_since_last_bot_message_info}{timeout_context}
【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息)
{chat_history_text}
------
可选行动类型以及解释:
fetch_knowledge: 需要调取知识,当需要专业知识或特定信息时选择,对方若提到你不太认识的人名或实体也可以尝试选择
wait: 暂时不说话,留给对方交互空间,等待对方回复(尤其是在你刚发言后、或上次发言因重复、发言过多被拒时、或不确定做什么时,这是不错的选择)
listening: 倾听对方发言(虽然你刚发过言,但如果对方立刻回复且明显话没说完,可以选择这个)
send_new_message: 发送一条新消息继续对话,允许适当的追问、补充、深入话题,或开启相关新话题。**但是避免在因重复被拒后立即使用,也不要在对方没有回复的情况下过多的“消息轰炸”或重复发言**
rethink_goal: 思考一个对话目标,当你觉得目前对话需要目标,或当前目标不再适用,或话题卡住时选择。注意私聊的环境是灵活的,有可能需要经常选择
end_conversation: 结束对话,对方长时间没回复或者当你觉得对话告一段落时可以选择
block_and_ignore: 更加极端的结束对话方式,直接结束对话并在一段时间内无视对方所有发言(屏蔽),当对话让你感到十分不适,或你遭到各类骚扰时选择
请以JSON格式输出你的决策
{{
"action": "选择的行动类型 (必须是上面列表中的一个)",
"reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的。请说明你为什么选择继续发言而不是等待,以及打算发送什么类型的新消息连续发言,必须记录已经发言了几次)"
}}
注意请严格按照JSON格式输出不要包含任何其他内容。"""
# 新增Prompt(3): 决定是否在结束对话前发送告别语
PROMPT_END_DECISION = """{persona_text}。刚刚你决定结束一场 QQ 私聊。
【你们之前的聊天记录】
{chat_history_text}
你觉得你们的对话已经完整结束了吗?有时候,在对话自然结束后再说点什么可能会有点奇怪,但有时也可能需要一条简短的消息来圆满结束。
如果觉得确实有必要再发一条简短、自然、符合你人设的告别消息(比如 "好,下次再聊~""嗯,先这样吧"),就输出 "yes"
如果觉得当前状态下直接结束对话更好,没有必要再发消息,就输出 "no"
请以 JSON 格式输出你的选择:
{{
"say_bye": "yes/no",
"reason": "选择 yes 或 no 的原因和内心想法 (简要说明)"
}}
注意:请严格按照 JSON 格式输出,不要包含任何其他内容。"""
# ActionPlanner 类定义,顶格
class ActionPlanner:
"""行动规划器"""
def __init__(self, stream_id: str, private_name: str):
self.llm = LLMServiceClient(
task_name="planner",
request_type="action_planning",
)
self.personality_info = self._get_personality_prompt()
self.name = global_config.bot.nickname
self.private_name = private_name
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
# self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
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
and global_config.personality.state_probability > 0
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};"
# 修改 plan 方法签名,增加 last_successful_reply_action 参数
async def plan(
self,
observation_info: ObservationInfo,
conversation_info: ConversationInfo,
last_successful_reply_action: Optional[str],
) -> Tuple[str, str]:
"""规划下一步行动
Args:
observation_info: 决策信息
conversation_info: 对话信息
last_successful_reply_action: 上一次成功的回复动作类型 ('direct_reply''send_new_message' 或 None)
Returns:
Tuple[str, str]: (行动类型, 行动原因)
"""
# --- 获取 Bot 上次发言时间信息 ---
time_since_last_bot_message_info = ""
try:
bot_id = str(global_config.bot.qq_account)
chat_history = getattr(observation_info, "chat_history", None)
if chat_history and len(chat_history) > 0:
for i in range(len(chat_history) - 1, -1, -1):
msg = chat_history[i]
if not isinstance(msg, dict):
continue
sender_info = msg.get("user_info", {})
sender_id = str(sender_info.get("user_id")) if isinstance(sender_info, dict) else None
msg_time = msg.get("time")
if sender_id == bot_id and msg_time:
time_diff = time.time() - msg_time
if time_diff < 60.0:
time_since_last_bot_message_info = (
f"提示:你上一条成功发送的消息是在 {time_diff:.1f} 秒前。\n"
)
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。")
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 = ""
try:
if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
last_goal_dict = conversation_info.goal_list[-1]
if isinstance(last_goal_dict, dict) and "goal" in last_goal_dict:
last_goal_text = last_goal_dict["goal"]
if isinstance(last_goal_text, str) and "分钟,思考接下来要做什么" in last_goal_text:
try:
timeout_minutes_text = last_goal_text.split("")[0].replace("你等待了", "")
timeout_context = f"重要提示:对方已经长时间({timeout_minutes_text})没有回复你的消息了(这可能代表对方繁忙/不想回复/没注意到你的消息等情况,或在对方看来本次聊天已告一段落),请基于此情况规划下一步。\n"
except Exception:
timeout_context = "重要提示:对方已经长时间没有回复你的消息了(这可能代表对方繁忙/不想回复/没注意到你的消息等情况,或在对方看来本次聊天已告一段落),请基于此情况规划下一步。\n"
else:
logger.debug(
f"[私聊][{self.private_name}]Conversation info goal_list is empty or not available for timeout check."
)
except AttributeError:
logger.warning(
f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet for timeout check."
)
except Exception as e:
logger.warning(f"[私聊][{self.private_name}]检查超时目标时出错: {e}")
# --- 构建通用 Prompt 参数 ---
logger.debug(
f"[私聊][{self.private_name}]开始规划行动:当前目标: {getattr(conversation_info, 'goal_list', '不可用')}"
)
# 构建对话目标 (goals_str)
goals_str = ""
try:
if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
for goal_reason in conversation_info.goal_list:
if isinstance(goal_reason, dict):
goal = goal_reason.get("goal", "目标内容缺失")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
goal = str(goal_reason)
reasoning = "没有明确原因"
goal = str(goal) if goal is not None else "目标内容缺失"
reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
goals_str += f"- 目标:{goal}\n 原因:{reasoning}\n"
if not goals_str:
goals_str = "- 目前没有明确对话目标,请考虑设定一个。\n"
else:
goals_str = "- 目前没有明确对话目标,请考虑设定一个。\n"
except AttributeError:
logger.warning(
f"[私聊][{self.private_name}]ConversationInfo object might not have goal_list attribute yet."
)
goals_str = "- 获取对话目标时出错。\n"
except Exception as e:
logger.error(f"[私聊][{self.private_name}]构建对话目标字符串时出错: {e}")
goals_str = "- 构建对话目标时出错。\n"
# --- 知识信息字符串构建开始 ---
knowledge_info_str = "【已获取的相关知识和记忆】\n"
try:
# 检查 conversation_info 是否有 knowledge_list 并且不为空
if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
# 最多只显示最近的 5 条知识,防止 Prompt 过长
recent_knowledge = conversation_info.knowledge_list[-5:]
for i, knowledge_item in enumerate(recent_knowledge):
if isinstance(knowledge_item, dict):
query = knowledge_item.get("query", "未知查询")
knowledge = knowledge_item.get("knowledge", "无知识内容")
source = knowledge_item.get("source", "未知来源")
# 只取知识内容的前 2000 个字,避免太长
knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
knowledge_info_str += (
f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n {knowledge_snippet}\n"
)
else:
# 处理列表里不是字典的异常情况
knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
if not recent_knowledge: # 如果 knowledge_list 存在但为空
knowledge_info_str += "- 暂无相关知识和记忆。\n"
else:
# 如果 conversation_info 没有 knowledge_list 属性,或者列表为空
knowledge_info_str += "- 暂无相关知识记忆。\n"
except AttributeError:
logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
knowledge_info_str += "- 获取知识列表时出错。\n"
except Exception as e:
logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
knowledge_info_str += "- 处理知识列表时出错。\n"
# --- 知识信息字符串构建结束 ---
# 获取聊天历史记录 (chat_history_text)
try:
if hasattr(observation_info, "chat_history") and observation_info.chat_history:
chat_history_text = observation_info.chat_history_str or "还没有聊天记录。\n"
else:
chat_history_text = "还没有聊天记录。\n"
if hasattr(observation_info, "new_messages_count") and observation_info.new_messages_count > 0:
if hasattr(observation_info, "unprocessed_messages") and observation_info.unprocessed_messages:
new_messages_list = observation_info.unprocessed_messages
# Convert dict format to SessionMessage objects.
session_messages = [dict_to_session_message(m) for m in new_messages_list]
new_messages_str = build_readable_messages(
session_messages,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
)
chat_history_text += (
f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
)
else:
logger.warning(
f"[私聊][{self.private_name}]ObservationInfo has new_messages_count > 0 but unprocessed_messages is empty or missing."
)
except AttributeError:
logger.warning(
f"[私聊][{self.private_name}]ObservationInfo object might be missing expected attributes for chat history."
)
chat_history_text = "获取聊天记录时出错。\n"
except Exception as e:
logger.error(f"[私聊][{self.private_name}]处理聊天记录时发生未知错误: {e}")
chat_history_text = "处理聊天记录时出错。\n"
# 构建 Persona 文本 (persona_text)
persona_text = f"你的名字是{self.name}{self.personality_info}"
# 构建行动历史和上一次行动结果 (action_history_summary, last_action_context)
# (这部分逻辑不变)
action_history_summary = "你最近执行的行动历史:\n"
last_action_context = "关于你【上一次尝试】的行动:\n"
action_history_list = []
try:
if hasattr(conversation_info, "done_action") and conversation_info.done_action:
action_history_list = conversation_info.done_action[-5:]
else:
logger.debug(f"[私聊][{self.private_name}]Conversation info done_action is empty or not available.")
except AttributeError:
logger.warning(
f"[私聊][{self.private_name}]ConversationInfo object might not have done_action attribute yet."
)
except Exception as e:
logger.error(f"[私聊][{self.private_name}]访问行动历史时出错: {e}")
if not action_history_list:
action_history_summary += "- 还没有执行过行动。\n"
last_action_context += "- 这是你规划的第一个行动。\n"
else:
for i, action_data in enumerate(action_history_list):
action_type = "未知"
plan_reason = "未知"
status = "未知"
final_reason = ""
action_time = ""
if isinstance(action_data, dict):
action_type = action_data.get("action", "未知")
plan_reason = action_data.get("plan_reason", "未知规划原因")
status = action_data.get("status", "未知")
final_reason = action_data.get("final_reason", "")
action_time = action_data.get("time", "")
elif isinstance(action_data, tuple):
# 假设旧格式兼容
if len(action_data) > 0:
action_type = action_data[0]
if len(action_data) > 1:
plan_reason = action_data[1] # 可能是规划原因或最终原因
if len(action_data) > 2:
status = action_data[2]
if status == "recall" and len(action_data) > 3:
final_reason = action_data[3]
elif status == "done" and action_type in ["direct_reply", "send_new_message"]:
plan_reason = "成功发送" # 简化显示
reason_text = f", 失败/取消原因: {final_reason}" if final_reason else ""
summary_line = f"- 时间:{action_time}, 尝试行动:'{action_type}', 状态:{status}{reason_text}"
action_history_summary += summary_line + "\n"
if i == len(action_history_list) - 1:
last_action_context += f"- 上次【规划】的行动是: '{action_type}'\n"
last_action_context += f"- 当时规划的【原因】是: {plan_reason}\n"
if status == "done":
last_action_context += "- 该行动已【成功执行】。\n"
# 记录这次成功的行动类型,供下次决策
# self.last_successful_action_type = action_type # 不在这里记录,由 conversation 控制
elif status == "recall":
last_action_context += "- 但该行动最终【未能执行/被取消】。\n"
if final_reason:
last_action_context += f"- 【重要】失败/取消的具体原因是: “{final_reason}\n"
else:
last_action_context += "- 【重要】失败/取消原因未明确记录。\n"
# self.last_successful_action_type = None # 行动失败,清除记录
else:
last_action_context += f"- 该行动当前状态: {status}\n"
# self.last_successful_action_type = None # 非完成状态,清除记录
# --- 选择 Prompt ---
if last_successful_reply_action in ["direct_reply", "send_new_message"]:
prompt_template = PROMPT_FOLLOW_UP
logger.debug(f"[私聊][{self.private_name}]使用 PROMPT_FOLLOW_UP (追问决策)")
else:
prompt_template = PROMPT_INITIAL_REPLY
logger.debug(f"[私聊][{self.private_name}]使用 PROMPT_INITIAL_REPLY (首次/非连续回复决策)")
# --- 格式化最终的 Prompt ---
prompt = prompt_template.format(
persona_text=persona_text,
goals_str=goals_str if goals_str.strip() else "- 目前没有明确对话目标,请考虑设定一个。",
action_history_summary=action_history_summary,
last_action_context=last_action_context,
time_since_last_bot_message_info=time_since_last_bot_message_info,
timeout_context=timeout_context,
chat_history_text=chat_history_text if chat_history_text.strip() else "还没有聊天记录。",
knowledge_info_str=knowledge_info_str,
)
logger.debug(f"[私聊][{self.private_name}]发送到LLM的最终提示词:\n------\n{prompt}\n------")
try:
generation_result = await self.llm.generate_response(prompt)
content = generation_result.response
logger.debug(f"[私聊][{self.private_name}]LLM (行动规划) 原始返回内容: {content}")
# --- 初始行动规划解析 ---
success, initial_result = get_items_from_json(
content,
self.private_name,
"action",
"reason",
default_values={"action": "wait", "reason": "LLM返回格式错误或未提供原因默认等待"},
)
initial_action = initial_result.get("action", "wait")
initial_reason = initial_result.get("reason", "LLM未提供原因默认等待")
# 检查是否需要进行结束对话决策 ---
if initial_action == "end_conversation":
logger.info(f"[私聊][{self.private_name}]初步规划结束对话,进入告别决策...")
# 使用新的 PROMPT_END_DECISION
end_decision_prompt = PROMPT_END_DECISION.format(
persona_text=persona_text, # 复用之前的 persona_text
chat_history_text=chat_history_text, # 复用之前的 chat_history_text
)
logger.debug(
f"[私聊][{self.private_name}]发送到LLM的结束决策提示词:\n------\n{end_decision_prompt}\n------"
)
try:
end_generation_result = await self.llm.generate_response(end_decision_prompt)
end_content = end_generation_result.response # 再次调用LLM
logger.debug(f"[私聊][{self.private_name}]LLM (结束决策) 原始返回内容: {end_content}")
# 解析结束决策的JSON
end_success, end_result = get_items_from_json(
end_content,
self.private_name,
"say_bye",
"reason",
default_values={"say_bye": "no", "reason": "结束决策LLM返回格式错误默认不告别"},
required_types={"say_bye": str, "reason": str}, # 明确类型
)
say_bye_decision = end_result.get("say_bye", "no").lower() # 转小写方便比较
end_decision_reason = end_result.get("reason", "未提供原因")
if end_success and say_bye_decision == "yes":
# 决定要告别,返回新的 'say_goodbye' 动作
logger.info(
f"[私聊][{self.private_name}]结束决策: yes, 准备生成告别语. 原因: {end_decision_reason}"
)
# 注意:这里的 reason 可以考虑拼接初始原因和结束决策原因,或者只用结束决策原因
final_action = "say_goodbye"
final_reason = f"决定发送告别语。决策原因: {end_decision_reason} (原结束理由: {initial_reason})"
return final_action, final_reason
else:
# 决定不告别 (包括解析失败或明确说no)
logger.info(
f"[私聊][{self.private_name}]结束决策: no, 直接结束对话. 原因: {end_decision_reason}"
)
# 返回原始的 'end_conversation' 动作
final_action = "end_conversation"
final_reason = initial_reason # 保持原始的结束理由
return final_action, final_reason
except Exception as end_e:
logger.error(f"[私聊][{self.private_name}]调用结束决策LLM或处理结果时出错: {str(end_e)}")
# 出错时,默认执行原始的结束对话
logger.warning(f"[私聊][{self.private_name}]结束决策出错,将按原计划执行 end_conversation")
return "end_conversation", initial_reason # 返回原始动作和原因
else:
action = initial_action
reason = initial_reason
# 验证action类型 (保持不变)
valid_actions = [
"direct_reply",
"send_new_message",
"fetch_knowledge",
"wait",
"listening",
"rethink_goal",
"end_conversation", # 仍然需要验证,因为可能从上面决策后返回
"block_and_ignore",
"say_goodbye", # 也要验证这个新动作
]
if action not in valid_actions:
logger.warning(f"[私聊][{self.private_name}]LLM返回了未知的行动类型: '{action}',强制改为 wait")
reason = f"(原始行动'{action}'无效已强制改为wait) {reason}"
action = "wait"
logger.info(f"[私聊][{self.private_name}]规划的行动: {action}")
logger.info(f"[私聊][{self.private_name}]行动原因: {reason}")
return action, reason
except Exception as e:
# 外层异常处理保持不变
logger.error(f"[私聊][{self.private_name}]规划行动时调用 LLM 或处理结果出错: {str(e)}")
return "wait", f"行动规划处理中发生错误,暂时等待: {str(e)}"

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@@ -1,394 +0,0 @@
import time
import asyncio
import traceback
from datetime import datetime
from typing import Optional, Dict, Any, List
from src.common.logger import get_logger
from sqlmodel import select, col
from src.common.database.database import get_db_session
from src.common.database.database_model import Messages
from maim_message import UserInfo
from src.config.config import global_config
from .chat_states import NotificationManager, create_new_message_notification, create_cold_chat_notification
from rich.traceback import install
install(extra_lines=3)
logger = get_logger("chat_observer")
def _message_to_dict(message: Messages) -> Dict[str, Any]:
"""Convert Peewee Message model to dict for PFC compatibility
Args:
message: Peewee Messages model instance
Returns:
Dict[str, Any]: Message dictionary
"""
message_timestamp = message.timestamp.timestamp() if isinstance(message.timestamp, datetime) else message.timestamp
return {
"message_id": message.message_id,
"time": message_timestamp,
"chat_id": message.session_id,
"user_id": message.user_id,
"user_nickname": message.user_nickname,
"processed_plain_text": message.processed_plain_text,
"display_message": message.display_message,
"is_mentioned": message.is_mentioned,
"is_command": message.is_command,
# Add user_info dict for compatibility with existing code
"user_info": {
"user_id": message.user_id,
"user_nickname": message.user_nickname,
},
}
class ChatObserver:
"""聊天状态观察器"""
# 类级别的实例管理
_instances: Dict[str, "ChatObserver"] = {}
@classmethod
def get_instance(cls, stream_id: str, private_name: str) -> "ChatObserver":
"""获取或创建观察器实例
Args:
stream_id: 聊天流ID
private_name: 私聊名称
Returns:
ChatObserver: 观察器实例
"""
if stream_id not in cls._instances:
cls._instances[stream_id] = cls(stream_id, private_name)
return cls._instances[stream_id]
def __init__(self, stream_id: str, private_name: str):
"""初始化观察器
Args:
stream_id: 聊天流ID
"""
self.last_check_time = None
self.last_bot_speak_time = None
self.last_user_speak_time = None
if stream_id in self._instances:
raise RuntimeError(f"ChatObserver for {stream_id} already exists. Use get_instance() instead.")
self.stream_id = stream_id
self.private_name = private_name
self.last_message_read: Optional[Dict[str, Any]] = None
self.last_message_time: float = time.time()
self.waiting_start_time: float = time.time() # 等待开始时间,初始化为当前时间
# 运行状态
self._running: bool = False
self._task: Optional[asyncio.Task] = None
self._update_event = asyncio.Event() # 触发更新的事件
self._update_complete = asyncio.Event() # 更新完成的事件
# 通知管理器
self.notification_manager = NotificationManager()
# 冷场检查配置
self.cold_chat_threshold: float = 60.0 # 60秒无消息判定为冷场
self.last_cold_chat_check: float = time.time()
self.is_cold_chat_state: bool = False
self.update_event = asyncio.Event()
self.update_interval = 2 # 更新间隔(秒)
self.message_cache = []
self.update_running = False
async def check(self) -> bool:
"""检查距离上一次观察之后是否有了新消息
Returns:
bool: 是否有新消息
"""
logger.debug(f"[私聊][{self.private_name}]检查距离上一次观察之后是否有了新消息: {self.last_check_time}")
last_check_time = self.last_check_time or 0.0
last_check_dt = datetime.fromtimestamp(last_check_time)
with get_db_session() as session:
statement = select(Messages).where(
(col(Messages.session_id) == self.stream_id) & (col(Messages.timestamp) > last_check_dt)
)
new_message_exists = session.exec(statement).first() is not None
if new_message_exists:
logger.debug(f"[私聊][{self.private_name}]发现新消息")
self.last_check_time = time.time()
return new_message_exists
async def _add_message_to_history(self, message: Dict[str, Any]):
"""添加消息到历史记录并发送通知
Args:
message: 消息数据
"""
try:
# 发送新消息通知
notification = create_new_message_notification(
sender="chat_observer", target="observation_info", message=message
)
# print(self.notification_manager)
await self.notification_manager.send_notification(notification)
except Exception as e:
logger.error(f"[私聊][{self.private_name}]添加消息到历史记录时出错: {e}")
print(traceback.format_exc())
# 检查并更新冷场状态
await self._check_cold_chat()
async def _check_cold_chat(self):
"""检查是否处于冷场状态并发送通知"""
current_time = time.time()
# 每10秒检查一次冷场状态
if current_time - self.last_cold_chat_check < 10:
return
self.last_cold_chat_check = current_time
# 判断是否冷场
is_cold = (
True
if self.last_message_time is None
else (current_time - self.last_message_time) > self.cold_chat_threshold
)
# 如果冷场状态发生变化,发送通知
if is_cold != self.is_cold_chat_state:
self.is_cold_chat_state = is_cold
notification = create_cold_chat_notification(sender="chat_observer", target="pfc", is_cold=is_cold)
await self.notification_manager.send_notification(notification)
def new_message_after(self, time_point: float) -> bool:
"""判断是否在指定时间点后有新消息
Args:
time_point: 时间戳
Returns:
bool: 是否有新消息
"""
if self.last_message_time is None:
logger.debug(f"[私聊][{self.private_name}]没有最后消息时间,返回 False")
return False
has_new = self.last_message_time > time_point
logger.debug(
f"[私聊][{self.private_name}]判断是否在指定时间点后有新消息: {self.last_message_time} > {time_point} = {has_new}"
)
return has_new
async def _fetch_new_messages(self) -> List[Dict[str, Any]]:
"""获取新消息
Returns:
List[Dict[str, Any]]: 新消息列表
"""
last_message_time = self.last_message_time or 0.0
last_message_dt = datetime.fromtimestamp(last_message_time)
with get_db_session() as session:
statement = (
select(Messages)
.where((col(Messages.session_id) == self.stream_id) & (col(Messages.timestamp) > last_message_dt))
.order_by(col(Messages.timestamp))
)
new_messages = [_message_to_dict(msg) for msg in session.exec(statement).all()]
if new_messages:
self.last_message_read = new_messages[-1]
self.last_message_time = new_messages[-1]["time"]
# print(f"获取数据库中找到的新消息: {new_messages}")
return new_messages
async def _fetch_new_messages_before(self, time_point: float) -> List[Dict[str, Any]]:
"""获取指定时间点之前的消息
Args:
time_point: 时间戳
Returns:
List[Dict[str, Any]]: 最多5条消息
"""
time_point_dt = datetime.fromtimestamp(time_point)
with get_db_session() as session:
statement = (
select(Messages)
.where((col(Messages.session_id) == self.stream_id) & (col(Messages.timestamp) < time_point_dt))
.order_by(col(Messages.timestamp))
.limit(5)
)
messages = list(session.exec(statement).all())
messages.reverse()
new_messages = [_message_to_dict(msg) for msg in messages]
if new_messages:
self.last_message_read = new_messages[-1]["message_id"]
logger.debug(f"[私聊][{self.private_name}]获取指定时间点111之前的消息: {new_messages}")
return new_messages
"""主要观察循环"""
async def _update_loop(self):
"""更新循环"""
# try:
# start_time = time.time()
# messages = await self._fetch_new_messages_before(start_time)
# for message in messages:
# await self._add_message_to_history(message)
# logger.debug(f"[私聊][{self.private_name}]缓冲消息: {messages}")
# except Exception as e:
# logger.error(f"[私聊][{self.private_name}]缓冲消息出错: {e}")
while self._running:
try:
# 等待事件或超时1秒
try:
# print("等待事件")
await asyncio.wait_for(self._update_event.wait(), timeout=1)
except asyncio.TimeoutError:
# print("超时")
pass # 超时后也执行一次检查
self._update_event.clear() # 重置触发事件
self._update_complete.clear() # 重置完成事件
# 获取新消息
new_messages = await self._fetch_new_messages()
if new_messages:
# 处理新消息
for message in new_messages:
await self._add_message_to_history(message)
# 设置完成事件
self._update_complete.set()
except Exception as e:
logger.error(f"[私聊][{self.private_name}]更新循环出错: {e}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
self._update_complete.set() # 即使出错也要设置完成事件
def trigger_update(self):
"""触发一次立即更新"""
self._update_event.set()
async def wait_for_update(self, timeout: float = 5.0) -> bool:
"""等待更新完成
Args:
timeout: 超时时间(秒)
Returns:
bool: 是否成功完成更新False表示超时
"""
try:
await asyncio.wait_for(self._update_complete.wait(), timeout=timeout)
return True
except asyncio.TimeoutError:
logger.warning(f"[私聊][{self.private_name}]等待更新完成超时({timeout}秒)")
return False
def start(self):
"""启动观察器"""
if self._running:
return
self._running = True
self._task = asyncio.create_task(self._update_loop())
logger.debug(f"[私聊][{self.private_name}]ChatObserver for {self.stream_id} started")
def stop(self):
"""停止观察器"""
self._running = False
self._update_event.set() # 设置事件以解除等待
self._update_complete.set() # 设置完成事件以解除等待
if self._task:
self._task.cancel()
logger.debug(f"[私聊][{self.private_name}]ChatObserver for {self.stream_id} stopped")
async def process_chat_history(self, messages: list):
"""处理聊天历史
Args:
messages: 消息列表
"""
self.update_check_time()
for msg in messages:
try:
user_info = UserInfo.from_dict(msg.get("user_info", {}))
if user_info.user_id == global_config.bot.qq_account:
self.update_bot_speak_time(msg["time"])
else:
self.update_user_speak_time(msg["time"])
except Exception as e:
logger.warning(f"[私聊][{self.private_name}]处理消息时间时出错: {e}")
continue
def update_check_time(self):
"""更新查看时间"""
self.last_check_time = time.time()
def update_bot_speak_time(self, speak_time: Optional[float] = None):
"""更新机器人说话时间"""
self.last_bot_speak_time = speak_time or time.time()
def update_user_speak_time(self, speak_time: Optional[float] = None):
"""更新用户说话时间"""
self.last_user_speak_time = speak_time or time.time()
def get_time_info(self) -> str:
"""获取时间信息文本"""
current_time = time.time()
time_info = ""
if self.last_bot_speak_time:
bot_speak_ago = current_time - self.last_bot_speak_time
time_info += f"\n距离你上次发言已经过去了{int(bot_speak_ago)}"
if self.last_user_speak_time:
user_speak_ago = current_time - self.last_user_speak_time
time_info += f"\n距离对方上次发言已经过去了{int(user_speak_ago)}"
return time_info
def get_cached_messages(self, limit: int = 50) -> List[Dict[str, Any]]:
"""获取缓存的消息历史
Args:
limit: 获取的最大消息数量默认50
Returns:
List[Dict[str, Any]]: 缓存的消息历史列表
"""
return self.message_cache[-limit:]
def get_last_message(self) -> Optional[Dict[str, Any]]:
"""获取最后一条消息
Returns:
Optional[Dict[str, Any]]: 最后一条消息如果没有则返回None
"""
if not self.message_cache:
return None
return self.message_cache[-1]
def __str__(self):
return f"ChatObserver for {self.stream_id}"

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@@ -1,290 +0,0 @@
from enum import Enum, auto
from typing import Optional, Dict, Any, List, Set
from dataclasses import dataclass
from datetime import datetime
from abc import ABC, abstractmethod
class ChatState(Enum):
"""聊天状态枚举"""
NORMAL = auto() # 正常状态
NEW_MESSAGE = auto() # 有新消息
COLD_CHAT = auto() # 冷场状态
ACTIVE_CHAT = auto() # 活跃状态
BOT_SPEAKING = auto() # 机器人正在说话
USER_SPEAKING = auto() # 用户正在说话
SILENT = auto() # 沉默状态
ERROR = auto() # 错误状态
class NotificationType(Enum):
"""通知类型枚举"""
NEW_MESSAGE = auto() # 新消息通知
COLD_CHAT = auto() # 冷场通知
ACTIVE_CHAT = auto() # 活跃通知
BOT_SPEAKING = auto() # 机器人说话通知
USER_SPEAKING = auto() # 用户说话通知
MESSAGE_DELETED = auto() # 消息删除通知
USER_JOINED = auto() # 用户加入通知
USER_LEFT = auto() # 用户离开通知
ERROR = auto() # 错误通知
@dataclass
class ChatStateInfo:
"""聊天状态信息"""
state: ChatState
last_message_time: Optional[float] = None
last_message_content: Optional[str] = None
last_speaker: Optional[str] = None
message_count: int = 0
cold_duration: float = 0.0 # 冷场持续时间(秒)
active_duration: float = 0.0 # 活跃持续时间(秒)
@dataclass
class Notification:
"""通知基类"""
type: NotificationType
timestamp: float
sender: str # 发送者标识
target: str # 接收者标识
data: Dict[str, Any]
def to_dict(self) -> Dict[str, Any]:
"""转换为字典格式"""
return {"type": self.type.name, "timestamp": self.timestamp, "data": self.data}
@dataclass
class StateNotification(Notification):
"""持续状态通知"""
is_active: bool = True
def to_dict(self) -> Dict[str, Any]:
base_dict = super().to_dict()
base_dict["is_active"] = self.is_active
return base_dict
class NotificationHandler(ABC):
"""通知处理器接口"""
@abstractmethod
async def handle_notification(self, notification: Notification):
"""处理通知"""
pass
class NotificationManager:
"""通知管理器"""
def __init__(self):
# 按接收者和通知类型存储处理器
self._handlers: Dict[str, Dict[NotificationType, List[NotificationHandler]]] = {}
self._active_states: Set[NotificationType] = set()
self._notification_history: List[Notification] = []
def register_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler):
"""注册通知处理器
Args:
target: 接收者标识(例如:"pfc"
notification_type: 要处理的通知类型
handler: 处理器实例
"""
if target not in self._handlers:
self._handlers[target] = {}
if notification_type not in self._handlers[target]:
self._handlers[target][notification_type] = []
# print(self._handlers[target][notification_type])
self._handlers[target][notification_type].append(handler)
# print(self._handlers[target][notification_type])
def unregister_handler(self, target: str, notification_type: NotificationType, handler: NotificationHandler):
"""注销通知处理器
Args:
target: 接收者标识
notification_type: 通知类型
handler: 要注销的处理器实例
"""
if target in self._handlers and notification_type in self._handlers[target]:
handlers = self._handlers[target][notification_type]
if handler in handlers:
handlers.remove(handler)
# 如果该类型的处理器列表为空,删除该类型
if not handlers:
del self._handlers[target][notification_type]
# 如果该目标没有任何处理器,删除该目标
if not self._handlers[target]:
del self._handlers[target]
async def send_notification(self, notification: Notification):
"""发送通知"""
self._notification_history.append(notification)
# 如果是状态通知,更新活跃状态
if isinstance(notification, StateNotification):
if notification.is_active:
self._active_states.add(notification.type)
else:
self._active_states.discard(notification.type)
# 调用目标接收者的处理器
target = notification.target
if target in self._handlers:
handlers = self._handlers[target].get(notification.type, [])
# print(handlers)
for handler in handlers:
# print(f"调用处理器: {handler}")
await handler.handle_notification(notification)
def get_active_states(self) -> Set[NotificationType]:
"""获取当前活跃的状态"""
return self._active_states.copy()
def is_state_active(self, state_type: NotificationType) -> bool:
"""检查特定状态是否活跃"""
return state_type in self._active_states
def get_notification_history(
self, sender: Optional[str] = None, target: Optional[str] = None, limit: Optional[int] = None
) -> List[Notification]:
"""获取通知历史
Args:
sender: 过滤特定发送者的通知
target: 过滤特定接收者的通知
limit: 限制返回数量
"""
history = self._notification_history
if sender:
history = [n for n in history if n.sender == sender]
if target:
history = [n for n in history if n.target == target]
if limit is not None:
history = history[-limit:]
return history
def __str__(self):
str = ""
for target, handlers in self._handlers.items():
for notification_type, handler_list in handlers.items():
str += f"NotificationManager for {target} {notification_type} {handler_list}"
return str
# 一些常用的通知创建函数
def create_new_message_notification(sender: str, target: str, message: Dict[str, Any]) -> Notification:
"""创建新消息通知"""
return Notification(
type=NotificationType.NEW_MESSAGE,
timestamp=datetime.now().timestamp(),
sender=sender,
target=target,
data={
"message_id": message.get("message_id"),
"processed_plain_text": message.get("processed_plain_text"),
"detailed_plain_text": message.get("detailed_plain_text"),
"user_info": message.get("user_info"),
"time": message.get("time"),
},
)
def create_cold_chat_notification(sender: str, target: str, is_cold: bool) -> StateNotification:
"""创建冷场状态通知"""
return StateNotification(
type=NotificationType.COLD_CHAT,
timestamp=datetime.now().timestamp(),
sender=sender,
target=target,
data={"is_cold": is_cold},
is_active=is_cold,
)
def create_active_chat_notification(sender: str, target: str, is_active: bool) -> StateNotification:
"""创建活跃状态通知"""
return StateNotification(
type=NotificationType.ACTIVE_CHAT,
timestamp=datetime.now().timestamp(),
sender=sender,
target=target,
data={"is_active": is_active},
is_active=is_active,
)
class ChatStateManager:
"""聊天状态管理器"""
def __init__(self):
self.current_state = ChatState.NORMAL
self.state_info = ChatStateInfo(state=ChatState.NORMAL)
self.state_history: list[ChatStateInfo] = []
def update_state(self, new_state: ChatState, **kwargs):
"""更新聊天状态
Args:
new_state: 新的状态
**kwargs: 其他状态信息
"""
self.current_state = new_state
self.state_info.state = new_state
# 更新其他状态信息
for key, value in kwargs.items():
if hasattr(self.state_info, key):
setattr(self.state_info, key, value)
# 记录状态历史
self.state_history.append(self.state_info)
def get_current_state_info(self) -> ChatStateInfo:
"""获取当前状态信息"""
return self.state_info
def get_state_history(self) -> list[ChatStateInfo]:
"""获取状态历史"""
return self.state_history
def is_cold_chat(self, threshold: float = 60.0) -> bool:
"""判断是否处于冷场状态
Args:
threshold: 冷场阈值(秒)
Returns:
bool: 是否冷场
"""
if not self.state_info.last_message_time:
return True
current_time = datetime.now().timestamp()
return (current_time - self.state_info.last_message_time) > threshold
def is_active_chat(self, threshold: float = 5.0) -> bool:
"""判断是否处于活跃状态
Args:
threshold: 活跃阈值(秒)
Returns:
bool: 是否活跃
"""
if not self.state_info.last_message_time:
return False
current_time = datetime.now().timestamp()
return (current_time - self.state_info.last_message_time) <= threshold

View File

@@ -1,722 +0,0 @@
import asyncio
import datetime
import time
from typing import Dict, Any, Optional
from src.common.data_models.mai_message_data_model import MaiMessage
from src.services.message_service import build_readable_messages, get_messages_before_time_in_chat
# from .message_storage import MongoDBMessageStorage
# from src.config.config import global_config
from .pfc_types import ConversationState
from .pfc import ChatObserver, GoalAnalyzer
from .message_sender import DirectMessageSender
from src.common.logger import get_logger
from .action_planner import ActionPlanner
from .observation_info import ObservationInfo
from .conversation_info import ConversationInfo # 确保导入 ConversationInfo
from .reply_generator import ReplyGenerator
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
from maim_message import UserInfo
from .pfc_KnowledgeFetcher import KnowledgeFetcher
from .waiter import Waiter
import traceback
from rich.traceback import install
install(extra_lines=3)
logger = get_logger("pfc")
class Conversation:
"""对话类,负责管理单个对话的状态和行为"""
def __init__(self, stream_id: str, private_name: str):
"""初始化对话实例
Args:
stream_id: 聊天流ID
"""
self.stream_id = stream_id
self.private_name = private_name
self.state = ConversationState.INIT
self.should_continue = False
self.ignore_until_timestamp: Optional[float] = None
# 回复相关
self.generated_reply = ""
async def _initialize(self):
"""初始化实例,注册所有组件"""
try:
self.action_planner = ActionPlanner(self.stream_id, self.private_name)
self.goal_analyzer = GoalAnalyzer(self.stream_id, self.private_name)
self.reply_generator = ReplyGenerator(self.stream_id, self.private_name)
self.knowledge_fetcher = KnowledgeFetcher(self.private_name)
self.waiter = Waiter(self.stream_id, self.private_name)
self.direct_sender = DirectMessageSender(self.private_name)
# 获取聊天流信息
self.chat_stream = _chat_manager.get_session_by_session_id(self.stream_id)
self.stop_action_planner = False
except Exception as e:
logger.error(f"[私聊][{self.private_name}]初始化对话实例:注册运行组件失败: {e}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
raise
try:
# 决策所需要的信息,包括自身自信和观察信息两部分
# 注册观察器和观测信息
self.chat_observer = ChatObserver.get_instance(self.stream_id, self.private_name)
self.chat_observer.start()
self.observation_info = ObservationInfo(self.private_name)
self.observation_info.bind_to_chat_observer(self.chat_observer)
# print(self.chat_observer.get_cached_messages(limit=)
self.conversation_info = ConversationInfo()
except Exception as e:
logger.error(f"[私聊][{self.private_name}]初始化对话实例:注册信息组件失败: {e}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
raise
try:
logger.info(f"[私聊][{self.private_name}]为 {self.stream_id} 加载初始聊天记录...")
initial_messages = get_messages_before_time_in_chat(
chat_id=self.stream_id,
timestamp=time.time(),
limit=30, # 加载最近30条作为初始上下文可以调整
)
chat_talking_prompt = build_readable_messages(
initial_messages,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
)
if initial_messages:
# 将 SessionMessage 列表转换为 PFC 期望的 dict 格式(保持嵌套结构)
initial_messages_dict: list[dict] = []
for msg in initial_messages:
user_info = msg.message_info.user_info
msg_dict = {
"message_id": msg.message_id,
"time": msg.timestamp.timestamp(),
"chat_id": msg.session_id,
"processed_plain_text": msg.processed_plain_text,
"display_message": msg.display_message,
"is_mentioned": msg.is_mentioned,
"is_command": msg.is_command,
"user_info": {
"user_id": user_info.user_id,
"user_nickname": user_info.user_nickname,
"user_cardname": user_info.user_cardname,
"platform": msg.platform,
},
}
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"
self.observation_info.chat_history_count = len(initial_messages_dict)
# 更新 ObservationInfo 中的时间戳等信息
last_msg_dict: dict = initial_messages_dict[-1]
self.observation_info.last_message_time = last_msg_dict.get("time")
last_user_info = UserInfo.from_dict(last_msg_dict.get("user_info", {}))
self.observation_info.last_message_sender = last_user_info.user_id
self.observation_info.last_message_content = last_msg_dict.get("processed_plain_text", "")
logger.info(
f"[私聊][{self.private_name}]成功加载 {len(initial_messages_dict)} 条初始聊天记录。最后一条消息时间: {self.observation_info.last_message_time}"
)
# 让 ChatObserver 从加载的最后一条消息之后开始同步
if self.observation_info.last_message_time:
self.chat_observer.last_message_time = self.observation_info.last_message_time
self.chat_observer.last_message_read = last_msg_dict # 更新 observer 的最后读取记录
else:
logger.info(f"[私聊][{self.private_name}]没有找到初始聊天记录。")
except Exception as load_err:
logger.error(f"[私聊][{self.private_name}]加载初始聊天记录时出错: {load_err}")
# 出错也要继续,只是没有历史记录而已
# 组件准备完成,启动该论对话
self.should_continue = True
asyncio.create_task(self.start())
async def start(self):
"""开始对话流程"""
try:
logger.info(f"[私聊][{self.private_name}]对话系统启动中...")
asyncio.create_task(self._plan_and_action_loop())
except Exception as e:
logger.error(f"[私聊][{self.private_name}]启动对话系统失败: {e}")
raise
async def _plan_and_action_loop(self):
"""思考步PFC核心循环模块"""
while self.should_continue:
# 忽略逻辑
if self.ignore_until_timestamp and time.time() < self.ignore_until_timestamp:
await asyncio.sleep(30)
continue
elif self.ignore_until_timestamp and time.time() >= self.ignore_until_timestamp:
logger.info(f"[私聊][{self.private_name}]忽略时间已到 {self.stream_id},准备结束对话。")
self.ignore_until_timestamp = None
self.should_continue = False
continue
try:
# --- 在规划前记录当前新消息数量 ---
initial_new_message_count = 0
if hasattr(self.observation_info, "new_messages_count"):
initial_new_message_count = self.observation_info.new_messages_count + 1 # 算上麦麦自己发的那一条
else:
logger.warning(
f"[私聊][{self.private_name}]ObservationInfo missing 'new_messages_count' before planning."
)
# --- 调用 Action Planner ---
# 传递 self.conversation_info.last_successful_reply_action
action, reason = await self.action_planner.plan(
self.observation_info, self.conversation_info, self.conversation_info.last_successful_reply_action
)
# --- 规划后检查是否有 *更多* 新消息到达 ---
current_new_message_count = 0
if hasattr(self.observation_info, "new_messages_count"):
current_new_message_count = self.observation_info.new_messages_count
else:
logger.warning(
f"[私聊][{self.private_name}]ObservationInfo missing 'new_messages_count' after planning."
)
if current_new_message_count > initial_new_message_count + 2:
logger.info(
f"[私聊][{self.private_name}]规划期间发现新增消息 ({initial_new_message_count} -> {current_new_message_count}),跳过本次行动,重新规划"
)
# 如果规划期间有新消息,也应该重置上次回复状态,因为现在要响应新消息了
self.conversation_info.last_successful_reply_action = None
await asyncio.sleep(0.1)
continue
# 包含 send_new_message
if initial_new_message_count > 0 and action in ["direct_reply", "send_new_message"]:
if hasattr(self.observation_info, "clear_unprocessed_messages"):
logger.debug(
f"[私聊][{self.private_name}]准备执行 {action},清理 {initial_new_message_count} 条规划时已知的新消息。"
)
await self.observation_info.clear_unprocessed_messages()
if hasattr(self.observation_info, "new_messages_count"):
self.observation_info.new_messages_count = 0
else:
logger.error(
f"[私聊][{self.private_name}]无法清理未处理消息: ObservationInfo 缺少 clear_unprocessed_messages 方法!"
)
await self._handle_action(action, reason, self.observation_info, self.conversation_info)
# 检查是否需要结束对话 (逻辑不变)
goal_ended = False
if hasattr(self.conversation_info, "goal_list") and self.conversation_info.goal_list:
for goal_item in self.conversation_info.goal_list:
if isinstance(goal_item, dict):
current_goal = goal_item.get("goal")
if current_goal == "结束对话":
goal_ended = True
break
if goal_ended:
self.should_continue = False
logger.info(f"[私聊][{self.private_name}]检测到'结束对话'目标,停止循环。")
except Exception as loop_err:
logger.error(f"[私聊][{self.private_name}]PFC主循环出错: {loop_err}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
await asyncio.sleep(1)
if self.should_continue:
await asyncio.sleep(0.1)
logger.info(f"[私聊][{self.private_name}]PFC 循环结束 for stream_id: {self.stream_id}")
def _check_new_messages_after_planning(self):
"""检查在规划后是否有新消息"""
# 检查 ObservationInfo 是否已初始化并且有 new_messages_count 属性
if not hasattr(self, "observation_info") or not hasattr(self.observation_info, "new_messages_count"):
logger.warning(
f"[私聊][{self.private_name}]ObservationInfo 未初始化或缺少 'new_messages_count' 属性,无法检查新消息。"
)
return False # 或者根据需要抛出错误
if self.observation_info.new_messages_count > 2:
logger.info(
f"[私聊][{self.private_name}]生成/执行动作期间收到 {self.observation_info.new_messages_count} 条新消息,取消当前动作并重新规划"
)
# 如果有新消息,也应该重置上次回复状态
if hasattr(self, "conversation_info"): # 确保 conversation_info 已初始化
self.conversation_info.last_successful_reply_action = None
else:
logger.warning(
f"[私聊][{self.private_name}]ConversationInfo 未初始化,无法重置 last_successful_reply_action。"
)
return True
return False
def _convert_to_message(self, msg_dict: Dict[str, Any]) -> MaiMessage:
"""将消息字典转换为MaiMessage对象"""
from datetime import datetime as dt
from src.common.data_models.mai_message_data_model import UserInfo as MaiUserInfo, MessageInfo
from src.common.data_models.message_component_data_model import MessageSequence
try:
user_info_dict = msg_dict.get("user_info", {})
user_info = MaiUserInfo(
user_id=user_info_dict.get("user_id", ""),
user_nickname=user_info_dict.get("user_nickname", ""),
user_cardname=user_info_dict.get("user_cardname"),
)
msg = MaiMessage(
message_id=msg_dict.get("message_id", f"gen_{time.time()}"),
timestamp=dt.fromtimestamp(msg_dict.get("time", time.time())),
)
msg.message_info = MessageInfo(user_info=user_info)
msg.platform = user_info_dict.get("platform", "")
msg.session_id = self.stream_id
msg.processed_plain_text = msg_dict.get("processed_plain_text", "")
msg.raw_message = MessageSequence(components=[])
msg.initialized = True
return msg
except Exception as e:
logger.warning(f"[私聊][{self.private_name}]转换消息时出错: {e}")
raise ValueError(f"无法将字典转换为 MaiMessage 对象: {e}") from e
async def _handle_action(
self, action: str, reason: str, observation_info: ObservationInfo, conversation_info: ConversationInfo
):
"""处理规划的行动"""
logger.debug(f"[私聊][{self.private_name}]执行行动: {action}, 原因: {reason}")
# 记录action历史 (逻辑不变)
current_action_record = {
"action": action,
"plan_reason": reason,
"status": "start",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
"final_reason": None,
}
# 确保 done_action 列表存在
if not hasattr(conversation_info, "done_action"):
conversation_info.done_action = []
conversation_info.done_action.append(current_action_record)
action_index = len(conversation_info.done_action) - 1
action_successful = False # 用于标记动作是否成功完成
# --- 根据不同的 action 执行 ---
# send_new_message 失败后执行 wait
if action == "send_new_message":
max_reply_attempts = 3
reply_attempt_count = 0
is_suitable = False
need_replan = False
check_reason = "未进行尝试"
final_reply_to_send = ""
while reply_attempt_count < max_reply_attempts and not is_suitable:
reply_attempt_count += 1
logger.info(
f"[私聊][{self.private_name}]尝试生成追问回复 (第 {reply_attempt_count}/{max_reply_attempts} 次)..."
)
self.state = ConversationState.GENERATING
# 1. 生成回复 (调用 generate 时传入 action_type)
self.generated_reply = await self.reply_generator.generate(
observation_info, conversation_info, action_type="send_new_message"
)
logger.info(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次生成的追问回复: {self.generated_reply}"
)
# 2. 检查回复 (逻辑不变)
self.state = ConversationState.CHECKING
try:
current_goal_str = conversation_info.goal_list[0]["goal"] if conversation_info.goal_list else ""
is_suitable, check_reason, need_replan = await self.reply_generator.check_reply(
reply=self.generated_reply,
goal=current_goal_str,
chat_history=observation_info.chat_history,
chat_history_str=observation_info.chat_history_str,
retry_count=reply_attempt_count - 1,
)
logger.info(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次追问检查结果: 合适={is_suitable}, 原因='{check_reason}', 需重新规划={need_replan}"
)
if is_suitable:
final_reply_to_send = self.generated_reply
break
elif need_replan:
logger.warning(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次追问检查建议重新规划,停止尝试。原因: {check_reason}"
)
break
except Exception as check_err:
logger.error(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次调用 ReplyChecker (追问) 时出错: {check_err}"
)
check_reason = f"{reply_attempt_count} 次检查过程出错: {check_err}"
break
# 循环结束,处理最终结果
if is_suitable:
# 检查是否有新消息
if self._check_new_messages_after_planning():
logger.info(f"[私聊][{self.private_name}]生成追问回复期间收到新消息,取消发送,重新规划行动")
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"有新消息,取消发送追问: {final_reply_to_send}"}
)
return # 直接返回,重新规划
# 发送合适的回复
self.generated_reply = final_reply_to_send
# --- 在这里调用 _send_reply ---
await self._send_reply() # <--- 调用恢复后的函数
# 更新状态: 标记上次成功是 send_new_message
self.conversation_info.last_successful_reply_action = "send_new_message"
action_successful = True # 标记动作成功
elif need_replan:
# 打回动作决策
logger.warning(
f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试,追问回复决定打回动作决策。打回原因: {check_reason}"
)
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"追问尝试{reply_attempt_count}次后打回: {check_reason}"}
)
else:
# 追问失败
logger.warning(
f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试,未能生成合适的追问回复。最终原因: {check_reason}"
)
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"追问尝试{reply_attempt_count}次后失败: {check_reason}"}
)
# 重置状态: 追问失败,下次用初始 prompt
self.conversation_info.last_successful_reply_action = None
# 执行 Wait 操作
logger.info(f"[私聊][{self.private_name}]由于无法生成合适追问回复,执行 'wait' 操作...")
self.state = ConversationState.WAITING
await self.waiter.wait(self.conversation_info)
wait_action_record = {
"action": "wait",
"plan_reason": "因 send_new_message 多次尝试失败而执行的后备等待",
"status": "done",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
"final_reason": None,
}
conversation_info.done_action.append(wait_action_record)
elif action == "direct_reply":
max_reply_attempts = 3
reply_attempt_count = 0
is_suitable = False
need_replan = False
check_reason = "未进行尝试"
final_reply_to_send = ""
while reply_attempt_count < max_reply_attempts and not is_suitable:
reply_attempt_count += 1
logger.info(
f"[私聊][{self.private_name}]尝试生成首次回复 (第 {reply_attempt_count}/{max_reply_attempts} 次)..."
)
self.state = ConversationState.GENERATING
# 1. 生成回复
self.generated_reply = await self.reply_generator.generate(
observation_info, conversation_info, action_type="direct_reply"
)
logger.info(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次生成的首次回复: {self.generated_reply}"
)
# 2. 检查回复
self.state = ConversationState.CHECKING
try:
current_goal_str = conversation_info.goal_list[0]["goal"] if conversation_info.goal_list else ""
is_suitable, check_reason, need_replan = await self.reply_generator.check_reply(
reply=self.generated_reply,
goal=current_goal_str,
chat_history=observation_info.chat_history,
chat_history_str=observation_info.chat_history_str,
retry_count=reply_attempt_count - 1,
)
logger.info(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次首次回复检查结果: 合适={is_suitable}, 原因='{check_reason}', 需重新规划={need_replan}"
)
if is_suitable:
final_reply_to_send = self.generated_reply
break
elif need_replan:
logger.warning(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次首次回复检查建议重新规划,停止尝试。原因: {check_reason}"
)
break
except Exception as check_err:
logger.error(
f"[私聊][{self.private_name}]第 {reply_attempt_count} 次调用 ReplyChecker (首次回复) 时出错: {check_err}"
)
check_reason = f"{reply_attempt_count} 次检查过程出错: {check_err}"
break
# 循环结束,处理最终结果
if is_suitable:
# 检查是否有新消息
if self._check_new_messages_after_planning():
logger.info(f"[私聊][{self.private_name}]生成首次回复期间收到新消息,取消发送,重新规划行动")
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"有新消息,取消发送首次回复: {final_reply_to_send}"}
)
return # 直接返回,重新规划
# 发送合适的回复
self.generated_reply = final_reply_to_send
# --- 在这里调用 _send_reply ---
await self._send_reply() # <--- 调用恢复后的函数
# 更新状态: 标记上次成功是 direct_reply
self.conversation_info.last_successful_reply_action = "direct_reply"
action_successful = True # 标记动作成功
elif need_replan:
# 打回动作决策
logger.warning(
f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试,首次回复决定打回动作决策。打回原因: {check_reason}"
)
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"首次回复尝试{reply_attempt_count}次后打回: {check_reason}"}
)
else:
# 首次回复失败
logger.warning(
f"[私聊][{self.private_name}]经过 {reply_attempt_count} 次尝试,未能生成合适的首次回复。最终原因: {check_reason}"
)
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"首次回复尝试{reply_attempt_count}次后失败: {check_reason}"}
)
# 重置状态: 首次回复失败,下次还是用初始 prompt
self.conversation_info.last_successful_reply_action = None
# 执行 Wait 操作 (保持原有逻辑)
logger.info(f"[私聊][{self.private_name}]由于无法生成合适首次回复,执行 'wait' 操作...")
self.state = ConversationState.WAITING
await self.waiter.wait(self.conversation_info)
wait_action_record = {
"action": "wait",
"plan_reason": "因 direct_reply 多次尝试失败而执行的后备等待",
"status": "done",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
"final_reason": None,
}
conversation_info.done_action.append(wait_action_record)
elif action == "fetch_knowledge":
self.state = ConversationState.FETCHING
knowledge_query = reason
try:
# 检查 knowledge_fetcher 是否存在
if not hasattr(self, "knowledge_fetcher"):
logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化,无法获取知识。")
raise AttributeError("KnowledgeFetcher not initialized")
knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history)
logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}")
if knowledge:
# 确保 knowledge_list 存在
if not hasattr(conversation_info, "knowledge_list"):
conversation_info.knowledge_list = []
conversation_info.knowledge_list.append(
{"query": knowledge_query, "knowledge": knowledge, "source": source}
)
action_successful = True
except Exception as fetch_err:
logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}")
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"}
)
self.conversation_info.last_successful_reply_action = None # 重置状态
elif action == "rethink_goal":
self.state = ConversationState.RETHINKING
try:
# 检查 goal_analyzer 是否存在
if not hasattr(self, "goal_analyzer"):
logger.error(f"[私聊][{self.private_name}]GoalAnalyzer 未初始化,无法重新思考目标。")
raise AttributeError("GoalAnalyzer not initialized")
await self.goal_analyzer.analyze_goal(conversation_info, observation_info)
action_successful = True
except Exception as rethink_err:
logger.error(f"[私聊][{self.private_name}]重新思考目标时出错: {rethink_err}")
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"重新思考目标失败: {rethink_err}"}
)
self.conversation_info.last_successful_reply_action = None # 重置状态
elif action == "listening":
self.state = ConversationState.LISTENING
logger.info(f"[私聊][{self.private_name}]倾听对方发言...")
try:
# 检查 waiter 是否存在
if not hasattr(self, "waiter"):
logger.error(f"[私聊][{self.private_name}]Waiter 未初始化,无法倾听。")
raise AttributeError("Waiter not initialized")
await self.waiter.wait_listening(conversation_info)
action_successful = True # Listening 完成就算成功
except Exception as listen_err:
logger.error(f"[私聊][{self.private_name}]倾听时出错: {listen_err}")
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"倾听失败: {listen_err}"}
)
self.conversation_info.last_successful_reply_action = None # 重置状态
elif action == "say_goodbye":
self.state = ConversationState.GENERATING # 也可以定义一个新的状态,如 ENDING
logger.info(f"[私聊][{self.private_name}]执行行动: 生成并发送告别语...")
try:
# 1. 生成告别语 (使用 'say_goodbye' action_type)
self.generated_reply = await self.reply_generator.generate(
observation_info, conversation_info, action_type="say_goodbye"
)
logger.info(f"[私聊][{self.private_name}]生成的告别语: {self.generated_reply}")
# 2. 直接发送告别语 (不经过检查)
if self.generated_reply: # 确保生成了内容
await self._send_reply() # 调用发送方法
# 发送成功后,标记动作成功
action_successful = True
logger.info(f"[私聊][{self.private_name}]告别语已发送。")
else:
logger.warning(f"[私聊][{self.private_name}]未能生成告别语内容,无法发送。")
action_successful = False # 标记动作失败
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": "未能生成告别语内容"}
)
# 3. 无论是否发送成功,都准备结束对话
self.should_continue = False
logger.info(f"[私聊][{self.private_name}]发送告别语流程结束,即将停止对话实例。")
except Exception as goodbye_err:
logger.error(f"[私聊][{self.private_name}]生成或发送告别语时出错: {goodbye_err}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
# 即使出错,也结束对话
self.should_continue = False
action_successful = False # 标记动作失败
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"生成或发送告别语时出错: {goodbye_err}"}
)
elif action == "end_conversation":
# 这个分支现在只会在 action_planner 最终决定不告别时被调用
self.should_continue = False
logger.info(f"[私聊][{self.private_name}]收到最终结束指令,停止对话...")
action_successful = True # 标记这个指令本身是成功的
elif action == "block_and_ignore":
logger.info(f"[私聊][{self.private_name}]不想再理你了...")
ignore_duration_seconds = 10 * 60
self.ignore_until_timestamp = time.time() + ignore_duration_seconds
logger.info(
f"[私聊][{self.private_name}]将忽略此对话直到: {datetime.datetime.fromtimestamp(self.ignore_until_timestamp)}"
)
self.state = ConversationState.IGNORED
action_successful = True # 标记动作成功
else: # 对应 'wait' 动作
self.state = ConversationState.WAITING
logger.info(f"[私聊][{self.private_name}]等待更多信息...")
try:
# 检查 waiter 是否存在
if not hasattr(self, "waiter"):
logger.error(f"[私聊][{self.private_name}]Waiter 未初始化,无法等待。")
raise AttributeError("Waiter not initialized")
_timeout_occurred = await self.waiter.wait(self.conversation_info)
action_successful = True # Wait 完成就算成功
except Exception as wait_err:
logger.error(f"[私聊][{self.private_name}]等待时出错: {wait_err}")
conversation_info.done_action[action_index].update(
{"status": "recall", "final_reason": f"等待失败: {wait_err}"}
)
self.conversation_info.last_successful_reply_action = None # 重置状态
# --- 更新 Action History 状态 ---
# 只有当动作本身成功时,才更新状态为 done
if action_successful:
conversation_info.done_action[action_index].update(
{
"status": "done",
"time": datetime.datetime.now().strftime("%H:%M:%S"),
}
)
# 重置状态: 对于非回复类动作的成功,清除上次回复状态
if action not in ["direct_reply", "send_new_message"]:
self.conversation_info.last_successful_reply_action = None
logger.debug(f"[私聊][{self.private_name}]动作 {action} 成功完成,重置 last_successful_reply_action")
# 如果动作是 recall 状态,在各自的处理逻辑中已经更新了 done_action
async def _send_reply(self):
"""发送回复"""
if not self.generated_reply:
logger.warning(f"[私聊][{self.private_name}]没有生成回复内容,无法发送。")
return
try:
_current_time = time.time()
reply_content = self.generated_reply
# 发送消息 (确保 direct_sender 和 chat_stream 有效)
if not hasattr(self, "direct_sender") or not self.direct_sender:
logger.error(f"[私聊][{self.private_name}]DirectMessageSender 未初始化,无法发送回复。")
return
if not self.chat_stream:
logger.error(f"[私聊][{self.private_name}]会话未初始化,无法发送回复。")
return
await self.direct_sender.send_message(chat_stream=self.chat_stream, content=reply_content)
# 发送成功后,手动触发 observer 更新可能导致重复处理自己发送的消息
# 更好的做法是依赖 observer 的自动轮询或数据库触发器(如果支持)
# 暂时注释掉,观察是否影响 ObservationInfo 的更新
# self.chat_observer.trigger_update()
# if not await self.chat_observer.wait_for_update():
# logger.warning(f"[私聊][{self.private_name}]等待 ChatObserver 更新完成超时")
self.state = ConversationState.ANALYZING # 更新状态
except Exception as e:
logger.error(f"[私聊][{self.private_name}]发送消息或更新状态时失败: {str(e)}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}")
self.state = ConversationState.ANALYZING
async def _send_timeout_message(self):
"""发送超时结束消息"""
try:
messages = self.chat_observer.get_cached_messages(limit=1)
if not messages:
return
latest_message = self._convert_to_message(messages[0])
await self.direct_sender.send_message(
chat_stream=self.chat_stream, content="TODO:超时消息", reply_to_message=latest_message
)
except Exception as e:
logger.error(f"[私聊][{self.private_name}]发送超时消息失败: {str(e)}")

View File

@@ -1,10 +0,0 @@
from typing import Optional
class ConversationInfo:
def __init__(self):
self.done_action: list = []
self.goal_list: list = []
self.knowledge_list: list = []
self.memory_list: list = []
self.last_successful_reply_action: Optional[str] = None

View File

@@ -1,61 +0,0 @@
"""PFC 侧消息发送封装。"""
from typing import Optional
from rich.traceback import install
from src.chat.message_receive.chat_manager import BotChatSession
from src.common.data_models.mai_message_data_model import MaiMessage
from src.common.logger import get_logger
from src.services import send_service as send_api
install(extra_lines=3)
logger = get_logger("message_sender")
class DirectMessageSender:
"""直接消息发送器。"""
def __init__(self, private_name: str) -> None:
"""初始化直接消息发送器。
Args:
private_name: 当前私聊实例的名称。
"""
self.private_name = private_name
async def send_message(
self,
chat_stream: BotChatSession,
content: str,
reply_to_message: Optional[MaiMessage] = None,
) -> None:
"""发送文本消息到聊天流。
Args:
chat_stream: 目标聊天会话。
content: 待发送的文本内容。
reply_to_message: 可选的引用回复锚点消息。
Raises:
RuntimeError: 当消息发送失败时抛出。
"""
try:
sent = await send_api.text_to_stream(
text=content,
stream_id=chat_stream.session_id,
set_reply=reply_to_message is not None,
reply_message=reply_to_message,
storage_message=True,
)
if sent:
logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
return
logger.error(f"[私聊][{self.private_name}]PFC消息发送失败")
raise RuntimeError("消息发送失败")
except Exception as exc:
logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {exc}")
raise

View File

@@ -1,429 +0,0 @@
from datetime import datetime
from typing import Any, Dict, List, Optional, Set
from maim_message import UserInfo
import time
from src.chat.message_receive.message import SessionMessage
from src.common.logger import get_logger
from src.common.data_models.mai_message_data_model import MessageInfo, UserInfo as MaiUserInfo
from src.services.message_service import build_readable_messages
from .chat_observer import ChatObserver
from .chat_states import NotificationHandler, NotificationType, Notification
import traceback # 导入 traceback 用于调试
logger = get_logger("observation_info")
def dict_to_session_message(msg_dict: Dict[str, Any]) -> SessionMessage:
"""Convert PFC dict format to SessionMessage object.
Args:
msg_dict: Message in PFC dict format with nested user_info
Returns:
SessionMessage object compatible with build_readable_messages()
"""
user_info_dict: Dict[str, Any] = msg_dict.get("user_info", {})
timestamp = msg_dict.get("time", 0.0)
platform = user_info_dict.get("platform", "")
message = SessionMessage(
message_id=msg_dict.get("message_id", ""),
timestamp=datetime.fromtimestamp(timestamp),
platform=platform,
)
message.message_info = MessageInfo(
user_info=MaiUserInfo(
user_id=user_info_dict.get("user_id", ""),
user_nickname=user_info_dict.get("user_nickname", ""),
user_cardname=user_info_dict.get("user_cardname"),
)
)
message.session_id = msg_dict.get("chat_id", "")
message.processed_plain_text = msg_dict.get("processed_plain_text", "")
message.display_message = msg_dict.get("display_message", "")
message.is_mentioned = msg_dict.get("is_mentioned", False)
message.is_command = msg_dict.get("is_command", False)
message.initialized = True
return message
class ObservationInfoHandler(NotificationHandler):
"""ObservationInfo的通知处理器"""
def __init__(self, observation_info: "ObservationInfo", private_name: str):
"""初始化处理器
Args:
observation_info: 要更新的ObservationInfo实例
private_name: 私聊对象的名称,用于日志记录
"""
self.observation_info = observation_info
# 将 private_name 存储在 handler 实例中
self.private_name = private_name
async def handle_notification(self, notification: Notification): # 添加类型提示
# 获取通知类型和数据
notification_type = notification.type
data = notification.data
try: # 添加错误处理块
if notification_type == NotificationType.NEW_MESSAGE:
# 处理新消息通知
# logger.debug(f"[私聊][{self.private_name}]收到新消息通知data: {data}") # 可以在需要时取消注释
message_id = data.get("message_id")
processed_plain_text = data.get("processed_plain_text")
detailed_plain_text = data.get("detailed_plain_text")
user_info_dict = data.get("user_info") # 先获取字典
time_value = data.get("time")
# 确保 user_info 是字典类型再创建 UserInfo 对象
user_info = None
if isinstance(user_info_dict, dict):
try:
user_info = UserInfo.from_dict(user_info_dict)
except Exception as e:
logger.error(
f"[私聊][{self.private_name}]从字典创建 UserInfo 时出错: {e}, 字典内容: {user_info_dict}"
)
# 可以选择在这里返回或记录错误,避免后续代码出错
return
elif user_info_dict is not None:
logger.warning(
f"[私聊][{self.private_name}]收到的 user_info 不是预期的字典类型: {type(user_info_dict)}"
)
# 根据需要处理非字典情况,这里暂时返回
return
message = {
"message_id": message_id,
"processed_plain_text": processed_plain_text,
"detailed_plain_text": detailed_plain_text,
"user_info": user_info_dict, # 存储原始字典或 UserInfo 对象,取决于你的 update_from_message 如何处理
"time": time_value,
}
# 传递 UserInfo 对象(如果成功创建)或原始字典
await self.observation_info.update_from_message(message, user_info) # 修改:传递 user_info 对象
elif notification_type == NotificationType.COLD_CHAT:
# 处理冷场通知
is_cold = data.get("is_cold", False)
await self.observation_info.update_cold_chat_status(is_cold, time.time()) # 修改:改为 await 调用
elif notification_type == NotificationType.ACTIVE_CHAT:
# 处理活跃通知 (通常由 COLD_CHAT 的反向状态处理)
is_active = data.get("is_active", False)
self.observation_info.is_cold = not is_active
elif notification_type == NotificationType.BOT_SPEAKING:
# 处理机器人说话通知 (按需实现)
self.observation_info.is_typing = False
self.observation_info.last_bot_speak_time = time.time()
elif notification_type == NotificationType.USER_SPEAKING:
# 处理用户说话通知
self.observation_info.is_typing = False
self.observation_info.last_user_speak_time = time.time()
elif notification_type == NotificationType.MESSAGE_DELETED:
# 处理消息删除通知
message_id = data.get("message_id")
# 从 unprocessed_messages 中移除被删除的消息
original_count = len(self.observation_info.unprocessed_messages)
self.observation_info.unprocessed_messages = [
msg for msg in self.observation_info.unprocessed_messages if msg.get("message_id") != message_id
]
if len(self.observation_info.unprocessed_messages) < original_count:
logger.info(f"[私聊][{self.private_name}]移除了未处理的消息 (ID: {message_id})")
elif notification_type == NotificationType.USER_JOINED:
# 处理用户加入通知 (如果适用私聊场景)
user_id = data.get("user_id")
if user_id:
self.observation_info.active_users.add(str(user_id)) # 确保是字符串
elif notification_type == NotificationType.USER_LEFT:
# 处理用户离开通知 (如果适用私聊场景)
user_id = data.get("user_id")
if user_id:
self.observation_info.active_users.discard(str(user_id)) # 确保是字符串
elif notification_type == NotificationType.ERROR:
# 处理错误通知
error_msg = data.get("error", "未提供错误信息")
logger.error(f"[私聊][{self.private_name}]收到错误通知: {error_msg}")
except Exception as e:
logger.error(f"[私聊][{self.private_name}]处理通知时发生错误: {e}")
logger.error(traceback.format_exc()) # 打印详细堆栈信息
# @dataclass <-- 这个,不需要了(递黄瓜)
class ObservationInfo:
"""决策信息类用于收集和管理来自chat_observer的通知信息 (手动实现 __init__)"""
# 类型提示保留,可用于文档和静态分析
private_name: str
chat_history: List[Dict[str, Any]]
chat_history_str: str
unprocessed_messages: List[Dict[str, Any]]
active_users: Set[str]
last_bot_speak_time: Optional[float]
last_user_speak_time: Optional[float]
last_message_time: Optional[float]
last_message_id: Optional[str]
last_message_content: str
last_message_sender: Optional[str]
bot_id: Optional[str]
chat_history_count: int
new_messages_count: int
cold_chat_start_time: Optional[float]
cold_chat_duration: float
is_typing: bool
is_cold_chat: bool
changed: bool
chat_observer: Optional[ChatObserver]
handler: Optional[ObservationInfoHandler]
def __init__(self, private_name: str):
"""
手动初始化 ObservationInfo 的所有实例变量。
"""
# 接收的参数
self.private_name: str = private_name
# data_list
self.chat_history: List[Dict[str, Any]] = []
self.chat_history_str: str = ""
self.unprocessed_messages: List[Dict[str, Any]] = []
self.active_users: Set[str] = set()
# data
self.last_bot_speak_time: Optional[float] = None
self.last_user_speak_time: Optional[float] = None
self.last_message_time: Optional[float] = None
self.last_message_id: Optional[str] = None
self.last_message_content: str = ""
self.last_message_sender: Optional[str] = None
self.bot_id: Optional[str] = None
self.chat_history_count: int = 0
self.new_messages_count: int = 0
self.cold_chat_start_time: Optional[float] = None
self.cold_chat_duration: float = 0.0
# state
self.is_typing: bool = False
self.is_cold_chat: bool = False
self.changed: bool = False
# 关联对象
self.chat_observer: Optional[ChatObserver] = None
self.handler: ObservationInfoHandler = ObservationInfoHandler(self, self.private_name)
def bind_to_chat_observer(self, chat_observer: ChatObserver):
"""绑定到指定的chat_observer
Args:
chat_observer: 要绑定的 ChatObserver 实例
"""
if self.chat_observer:
logger.warning(f"[私聊][{self.private_name}]尝试重复绑定 ChatObserver")
return
self.chat_observer = chat_observer
try:
if not self.handler: # 确保 handler 已经被创建
logger.error(f"[私聊][{self.private_name}] 尝试绑定时 handler 未初始化!")
self.chat_observer = None # 重置,防止后续错误
return
# 注册关心的通知类型
self.chat_observer.notification_manager.register_handler(
target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler
)
self.chat_observer.notification_manager.register_handler(
target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler
)
# 可以根据需要注册更多通知类型
# self.chat_observer.notification_manager.register_handler(
# target="observation_info", notification_type=NotificationType.MESSAGE_DELETED, handler=self.handler
# )
logger.info(f"[私聊][{self.private_name}]成功绑定到 ChatObserver")
except Exception as e:
logger.error(f"[私聊][{self.private_name}]绑定到 ChatObserver 时出错: {e}")
self.chat_observer = None # 绑定失败,重置
def unbind_from_chat_observer(self):
"""解除与chat_observer的绑定"""
if (
self.chat_observer and hasattr(self.chat_observer, "notification_manager") and self.handler
): # 增加 handler 检查
try:
self.chat_observer.notification_manager.unregister_handler(
target="observation_info", notification_type=NotificationType.NEW_MESSAGE, handler=self.handler
)
self.chat_observer.notification_manager.unregister_handler(
target="observation_info", notification_type=NotificationType.COLD_CHAT, handler=self.handler
)
# 如果注册了其他类型,也要在这里注销
# self.chat_observer.notification_manager.unregister_handler(
# target="observation_info", notification_type=NotificationType.MESSAGE_DELETED, handler=self.handler
# )
logger.info(f"[私聊][{self.private_name}]成功从 ChatObserver 解绑")
except Exception as e:
logger.error(f"[私聊][{self.private_name}]从 ChatObserver 解绑时出错: {e}")
finally: # 确保 chat_observer 被重置
self.chat_observer = None
else:
logger.warning(f"[私聊][{self.private_name}]尝试解绑时 ChatObserver 不存在、无效或 handler 未设置")
# 修改update_from_message 接收 UserInfo 对象
async def update_from_message(self, message: Dict[str, Any], user_info: Optional[UserInfo]):
"""从消息更新信息
Args:
message: 消息数据字典
user_info: 解析后的 UserInfo 对象 (可能为 None)
"""
message_time = message.get("time")
message_id = message.get("message_id")
processed_text = message.get("processed_plain_text", "")
# 只有在新消息到达时才更新 last_message 相关信息
if message_time and message_time > (self.last_message_time or 0):
self.last_message_time = message_time
self.last_message_id = message_id
self.last_message_content = processed_text
# 重置冷场计时器
self.is_cold_chat = False
self.cold_chat_start_time = None
self.cold_chat_duration = 0.0
if user_info:
sender_id = str(user_info.user_id) # 确保是字符串
self.last_message_sender = sender_id
# 更新发言时间
if sender_id == self.bot_id:
self.last_bot_speak_time = message_time
else:
self.last_user_speak_time = message_time
self.active_users.add(sender_id) # 用户发言则认为其活跃
else:
logger.warning(
f"[私聊][{self.private_name}]处理消息更新时缺少有效的 UserInfo 对象, message_id: {message_id}"
)
self.last_message_sender = None # 发送者未知
# 将原始消息字典添加到未处理列表
self.unprocessed_messages.append(message)
self.new_messages_count = len(self.unprocessed_messages) # 直接用列表长度
# logger.debug(f"[私聊][{self.private_name}]消息更新: last_time={self.last_message_time}, new_count={self.new_messages_count}")
self.update_changed() # 标记状态已改变
else:
# 如果消息时间戳不是最新的,可能不需要处理,或者记录一个警告
pass
# logger.warning(f"[私聊][{self.private_name}]收到过时或无效时间戳的消息: ID={message_id}, time={message_time}")
def update_changed(self):
"""标记状态已改变,并重置标记"""
# logger.debug(f"[私聊][{self.private_name}]状态标记为已改变 (changed=True)")
self.changed = True
async def update_cold_chat_status(self, is_cold: bool, current_time: float):
"""更新冷场状态
Args:
is_cold: 是否处于冷场状态
current_time: 当前时间戳
"""
if is_cold != self.is_cold_chat: # 仅在状态变化时更新
self.is_cold_chat = is_cold
if is_cold:
# 进入冷场状态
self.cold_chat_start_time = (
self.last_message_time or current_time
) # 从最后消息时间开始算,或从当前时间开始
logger.info(f"[私聊][{self.private_name}]进入冷场状态,开始时间: {self.cold_chat_start_time}")
else:
# 结束冷场状态
if self.cold_chat_start_time:
self.cold_chat_duration = current_time - self.cold_chat_start_time
logger.info(f"[私聊][{self.private_name}]结束冷场状态,持续时间: {self.cold_chat_duration:.2f}")
self.cold_chat_start_time = None # 重置开始时间
self.update_changed() # 状态变化,标记改变
# 即使状态没变,如果是冷场状态,也更新持续时间
if self.is_cold_chat and self.cold_chat_start_time:
self.cold_chat_duration = current_time - self.cold_chat_start_time
def get_active_duration(self) -> float:
"""获取当前活跃时长 (距离最后一条消息的时间)
Returns:
float: 最后一条消息到现在的时长(秒)
"""
if not self.last_message_time:
return 0.0
return time.time() - self.last_message_time
def get_user_response_time(self) -> Optional[float]:
"""获取用户最后响应时间 (距离用户最后发言的时间)
Returns:
Optional[float]: 用户最后发言到现在的时长如果没有用户发言则返回None
"""
if not self.last_user_speak_time:
return None
return time.time() - self.last_user_speak_time
def get_bot_response_time(self) -> Optional[float]:
"""获取机器人最后响应时间 (距离机器人最后发言的时间)
Returns:
Optional[float]: 机器人最后发言到现在的时长如果没有机器人发言则返回None
"""
if not self.last_bot_speak_time:
return None
return time.time() - self.last_bot_speak_time
async def clear_unprocessed_messages(self):
"""将未处理消息移入历史记录,并更新相关状态"""
if not self.unprocessed_messages:
return # 没有未处理消息,直接返回
# logger.debug(f"[私聊][{self.private_name}]处理 {len(self.unprocessed_messages)} 条未处理消息...")
# 将未处理消息添加到历史记录中 (确保历史记录有长度限制,避免无限增长)
max_history_len = 100 # 示例最多保留100条历史记录
self.chat_history.extend(self.unprocessed_messages)
if len(self.chat_history) > max_history_len:
self.chat_history = self.chat_history[-max_history_len:]
# 更新历史记录字符串 (只使用最近一部分生成例如20条)
history_slice_for_str = self.chat_history[-20:]
try:
# Convert dict format to SessionMessage objects.
session_messages = [dict_to_session_message(m) for m in history_slice_for_str]
self.chat_history_str = build_readable_messages(
session_messages,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0, # read_mark 可能需要根据逻辑调整
)
except Exception as e:
logger.error(f"[私聊][{self.private_name}]构建聊天记录字符串时出错: {e}")
self.chat_history_str = "[构建聊天记录出错]" # 提供错误提示
# 清空未处理消息列表和计数
# cleared_count = len(self.unprocessed_messages)
self.unprocessed_messages.clear()
self.new_messages_count = 0
# self.has_unread_messages = False # 这个状态可以通过 new_messages_count 判断
self.chat_history_count = len(self.chat_history) # 更新历史记录总数
# logger.debug(f"[私聊][{self.private_name}]已处理 {cleared_count} 条消息,当前历史记录 {self.chat_history_count} 条。")
self.update_changed() # 状态改变

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@@ -1,365 +0,0 @@
from typing import List, Tuple, TYPE_CHECKING
from src.common.logger import get_logger
from src.services.llm_service import LLMServiceClient
from src.config.config import global_config
import random
from .chat_observer import ChatObserver
from .pfc_utils import get_items_from_json
from .conversation_info import ConversationInfo
from src.services.message_service import build_readable_messages
from .observation_info import ObservationInfo, dict_to_session_message
from rich.traceback import install
install(extra_lines=3)
if TYPE_CHECKING:
pass
logger = get_logger("pfc")
def _calculate_similarity(goal1: str, goal2: str) -> float:
"""简单计算两个目标之间的相似度
这里使用一个简单的实现,实际可以使用更复杂的文本相似度算法
Args:
goal1: 第一个目标
goal2: 第二个目标
Returns:
float: 相似度得分 (0-1)
"""
# 简单实现:检查重叠字数比例
words1 = set(goal1)
words2 = set(goal2)
overlap = len(words1.intersection(words2))
total = len(words1.union(words2))
return overlap / total if total > 0 else 0
class GoalAnalyzer:
"""对话目标分析器"""
def __init__(self, stream_id: str, private_name: str):
self.llm = LLMServiceClient(
task_name="planner", request_type="conversation_goal"
)
self.personality_info = self._get_personality_prompt()
self.name = global_config.bot.nickname
self.nick_name = global_config.bot.alias_names
self.private_name = private_name
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
# 多目标存储结构
self.goals = [] # 存储多个目标
self.max_goals = 3 # 同时保持的最大目标数量
self.current_goal_and_reason = None
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
and global_config.personality.state_probability > 0
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};"
async def analyze_goal(self, conversation_info: ConversationInfo, observation_info: ObservationInfo):
"""分析对话历史并设定目标
Args:
conversation_info: 对话信息
observation_info: 观察信息
Returns:
Tuple[str, str, str]: (目标, 方法, 原因)
"""
# 构建对话目标
goals_str = ""
if conversation_info.goal_list:
for goal_reason in conversation_info.goal_list:
if isinstance(goal_reason, dict):
goal = goal_reason.get("goal", "目标内容缺失")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
goal = str(goal_reason)
reasoning = "没有明确原因"
goal_str = f"目标:{goal},产生该对话目标的原因:{reasoning}\n"
goals_str += goal_str
else:
goal = "目前没有明确对话目标"
reasoning = "目前没有明确对话目标,最好思考一个对话目标"
goals_str = f"目标:{goal},产生该对话目标的原因:{reasoning}\n"
# 获取聊天历史记录
chat_history_text = observation_info.chat_history_str
if observation_info.new_messages_count > 0:
new_messages_list = observation_info.unprocessed_messages
session_messages = [dict_to_session_message(m) for m in new_messages_list]
new_messages_str = build_readable_messages(
session_messages,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
)
chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
# await observation_info.clear_unprocessed_messages()
persona_text = f"你的名字是{self.name}{self.personality_info}"
# 构建action历史文本
action_history_list = conversation_info.done_action
action_history_text = "你之前做的事情是:"
for action in action_history_list:
action_history_text += f"{action}\n"
prompt = f"""{persona_text}。现在你在参与一场QQ聊天请分析以下聊天记录并根据你的性格特征确定多个明确的对话目标。
这些目标应该反映出对话的不同方面和意图。
{action_history_text}
当前对话目标:
{goals_str}
聊天记录:
{chat_history_text}
请分析当前对话并确定最适合的对话目标。你可以:
1. 保持现有目标不变
2. 修改现有目标
3. 添加新目标
4. 删除不再相关的目标
5. 如果你想结束对话请设置一个目标目标goal为"结束对话"原因reasoning为你希望结束对话
请以JSON数组格式输出当前的所有对话目标每个目标包含以下字段
1. goal: 对话目标(简短的一句话)
2. reasoning: 对话原因,为什么设定这个目标(简要解释)
输出格式示例:
[
{{
"goal": "回答用户关于Python编程的具体问题",
"reasoning": "用户提出了关于Python的技术问题需要专业且准确的解答"
}},
{{
"goal": "回答用户关于python安装的具体问题",
"reasoning": "用户提出了关于Python的技术问题需要专业且准确的解答"
}}
]"""
logger.debug(f"[私聊][{self.private_name}]发送到LLM的提示词: {prompt}")
try:
generation_result = await self.llm.generate_response(prompt)
content = generation_result.response
logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}")
except Exception as e:
logger.error(f"[私聊][{self.private_name}]分析对话目标时出错: {str(e)}")
content = ""
# 使用改进后的get_items_from_json函数处理JSON数组
success, result = get_items_from_json(
content,
self.private_name,
"goal",
"reasoning",
required_types={"goal": str, "reasoning": str},
allow_array=True,
)
if success:
# 判断结果是单个字典还是字典列表
if isinstance(result, list):
# 清空现有目标列表并添加新目标
conversation_info.goal_list = []
for item in result:
conversation_info.goal_list.append(item)
# 返回第一个目标作为当前主要目标(如果有)
if result:
first_goal = result[0]
return first_goal.get("goal", ""), "", first_goal.get("reasoning", "")
else:
# 单个目标的情况
conversation_info.goal_list.append(result)
goal_value = result.get("goal", "")
reasoning_value = result.get("reasoning", "")
return goal_value, "", reasoning_value
# 如果解析失败,返回默认值
return "", "", ""
async def _update_goals(self, new_goal: str, method: str, reasoning: str):
"""更新目标列表
Args:
new_goal: 新的目标
method: 实现目标的方法
reasoning: 目标的原因
"""
# 检查新目标是否与现有目标相似
for i, (existing_goal, _, _) in enumerate(self.goals):
if _calculate_similarity(new_goal, existing_goal) > 0.7: # 相似度阈值
# 更新现有目标
self.goals[i] = (new_goal, method, reasoning)
# 将此目标移到列表前面(最主要的位置)
self.goals.insert(0, self.goals.pop(i))
return
# 添加新目标到列表前面
self.goals.insert(0, (new_goal, method, reasoning))
# 限制目标数量
if len(self.goals) > self.max_goals:
self.goals.pop() # 移除最老的目标
async def get_all_goals(self) -> List[Tuple[str, str, str]]:
"""获取所有当前目标
Returns:
List[Tuple[str, str, str]]: 目标列表,每项为(目标, 方法, 原因)
"""
return self.goals.copy()
async def get_alternative_goals(self) -> List[Tuple[str, str, str]]:
"""获取除了当前主要目标外的其他备选目标
Returns:
List[Tuple[str, str, str]]: 备选目标列表
"""
if len(self.goals) <= 1:
return []
return self.goals[1:].copy()
async def analyze_conversation(self, goal, reasoning):
messages = self.chat_observer.get_cached_messages()
session_messages = [dict_to_session_message(m) for m in messages]
chat_history_text = build_readable_messages(
session_messages,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
)
persona_text = f"你的名字是{self.name}{self.personality_info}"
# ===> Persona 文本构建结束 <===
# --- 修改 Prompt 字符串,使用 persona_text ---
prompt = f"""{persona_text}。现在你在参与一场QQ聊天
当前对话目标:{goal}
产生该对话目标的原因:{reasoning}
请分析以下聊天记录,并根据你的性格特征评估该目标是否已经达到,或者你是否希望停止该次对话。
聊天记录:
{chat_history_text}
请以JSON格式输出包含以下字段
1. goal_achieved: 对话目标是否已经达到true/false
2. stop_conversation: 是否希望停止该次对话true/false
3. reason: 为什么希望停止该次对话(简要解释)
输出格式示例:
{{
"goal_achieved": true,
"stop_conversation": false,
"reason": "虽然目标已达成,但对话仍然有继续的价值"
}}"""
try:
generation_result = await self.llm.generate_response(prompt)
content = generation_result.response
logger.debug(f"[私聊][{self.private_name}]LLM原始返回内容: {content}")
# 尝试解析JSON
success, result = get_items_from_json(
content,
self.private_name,
"goal_achieved",
"stop_conversation",
"reason",
required_types={"goal_achieved": bool, "stop_conversation": bool, "reason": str},
)
if not success:
logger.error(f"[私聊][{self.private_name}]无法解析对话分析结果JSON")
return False, False, "解析结果失败"
goal_achieved = result["goal_achieved"]
stop_conversation = result["stop_conversation"]
reason = result["reason"]
return goal_achieved, stop_conversation, reason
except Exception as e:
logger.error(f"[私聊][{self.private_name}]分析对话状态时出错: {str(e)}")
return False, False, f"分析出错: {str(e)}"
# 先注释掉,万一以后出问题了还能开回来(((
# class DirectMessageSender:
# """直接发送消息到平台的发送器"""
# def __init__(self, private_name: str):
# self.logger = get_module_logger("direct_sender")
# self.storage = MessageStorage()
# self.private_name = private_name
# async def send_via_ws(self, message: MessageSending) -> None:
# try:
# await global_api.send_message(message)
# except Exception as e:
# raise ValueError(f"未找到平台:{message.message_info.platform} 的url配置请检查配置文件") from e
# async def send_message(
# self,
# chat_stream: ChatStream,
# content: str,
# reply_to_message: Optional[Message] = None,
# ) -> None:
# """直接发送消息到平台
# Args:
# chat_stream: 聊天流
# content: 消息内容
# reply_to_message: 要回复的消息
# """
# # 构建消息对象
# message_segment = Seg(type="text", data=content)
# bot_user_info = UserInfo(
# user_id=global_config.BOT_QQ,
# user_nickname=global_config.BOT_NICKNAME,
# platform=chat_stream.platform,
# )
# message = MessageSending(
# message_id=f"dm{round(time.time(), 2)}",
# chat_stream=chat_stream,
# bot_user_info=bot_user_info,
# sender_info=reply_to_message.message_info.user_info if reply_to_message else None,
# message_segment=message_segment,
# reply=reply_to_message,
# is_head=True,
# is_emoji=False,
# thinking_start_time=time.time(),
# )
# # 处理消息
# await message.process()
# _message_json = message.to_dict()
# # 发送消息
# try:
# await self.send_via_ws(message)
# await self.storage.store_message(message, chat_stream)
# logger.success(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
# except Exception as e:
# logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")

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@@ -1,77 +0,0 @@
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.services.llm_service import LLMServiceClient
from src.chat.knowledge import qa_manager
logger = get_logger("knowledge_fetcher")
class KnowledgeFetcher:
"""知识调取器"""
def __init__(self, private_name: str):
self.llm = LLMServiceClient(task_name="utils")
self.private_name = private_name
def _lpmm_get_knowledge(self, query: str) -> str:
"""获取相关知识
Args:
query: 查询内容
Returns:
str: 构造好的,带相关度的知识
"""
logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识")
try:
knowledge_info = qa_manager.get_knowledge(query)
logger.debug(f"[私聊][{self.private_name}]LPMM知识库查询结果: {knowledge_info:150}")
return knowledge_info
except Exception as e:
logger.error(f"[私聊][{self.private_name}]LPMM知识库搜索工具执行失败: {str(e)}")
return "未找到匹配的知识"
async def fetch(self, query: str, chat_history: List[Dict[str, Any]]) -> Tuple[str, str]:
"""获取相关知识
Args:
query: 查询内容
chat_history: 聊天历史 (PFC dict format)
Returns:
Tuple[str, str]: (获取的知识, 知识来源)
"""
_ = chat_history
# NOTE: Hippocampus memory system was redesigned in v0.12.2
# The old get_memory_from_text API no longer exists
# For now, we'll skip the memory retrieval part and only use LPMM knowledge
# 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}",
# max_memory_num=3,
# max_memory_length=2,
# max_depth=3,
# fast_retrieval=False,
# )
# if related_memory:
# sources = []
# for memory in related_memory:
# knowledge_text += memory[1] + "\n"
# sources.append(f"记忆片段{memory[0]}")
# knowledge_text = knowledge_text.strip()
# sources_text = "".join(sources)
knowledge_text += "\n现在有以下**知识**可供参考:\n "
knowledge_text += self._lpmm_get_knowledge(query)
knowledge_text += "\n请记住这些**知识**,并根据**知识**回答问题。\n"
return knowledge_text or "未找到相关知识", sources_text or "无记忆匹配"

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@@ -1,115 +0,0 @@
import time
from typing import Dict, Optional
from src.common.logger import get_logger
from .conversation import Conversation
import traceback
logger = get_logger("pfc_manager")
class PFCManager:
"""PFC对话管理器负责管理所有对话实例"""
# 单例模式
_instance = None
# 会话实例管理
_instances: Dict[str, Conversation] = {}
_initializing: Dict[str, bool] = {}
@classmethod
def get_instance(cls) -> "PFCManager":
"""获取管理器单例
Returns:
PFCManager: 管理器实例
"""
if cls._instance is None:
cls._instance = PFCManager()
return cls._instance
async def get_or_create_conversation(self, stream_id: str, private_name: str) -> Optional[Conversation]:
"""获取或创建对话实例
Args:
stream_id: 聊天流ID
private_name: 私聊名称
Returns:
Optional[Conversation]: 对话实例创建失败则返回None
"""
# 检查是否已经有实例
if stream_id in self._initializing and self._initializing[stream_id]:
logger.debug(f"[私聊][{private_name}]会话实例正在初始化中: {stream_id}")
return None
if stream_id in self._instances and self._instances[stream_id].should_continue:
logger.debug(f"[私聊][{private_name}]使用现有会话实例: {stream_id}")
return self._instances[stream_id]
if stream_id in self._instances:
instance = self._instances[stream_id]
if (
hasattr(instance, "ignore_until_timestamp")
and instance.ignore_until_timestamp
and time.time() < instance.ignore_until_timestamp
):
logger.debug(f"[私聊][{private_name}]会话实例当前处于忽略状态: {stream_id}")
# 返回 None 阻止交互。或者可以返回实例但标记它被忽略了喵?
# 还是返回 None 吧喵。
return None
# 检查 should_continue 状态
if instance.should_continue:
logger.debug(f"[私聊][{private_name}]使用现有会话实例: {stream_id}")
return instance
# else: 实例存在但不应继续
try:
# 创建新实例
logger.info(f"[私聊][{private_name}]创建新的对话实例: {stream_id}")
self._initializing[stream_id] = True
# 创建实例
conversation_instance = Conversation(stream_id, private_name)
self._instances[stream_id] = conversation_instance
# 启动实例初始化
await self._initialize_conversation(conversation_instance)
except Exception as e:
logger.error(f"[私聊][{private_name}]创建会话实例失败: {stream_id}, 错误: {e}")
return None
return conversation_instance
async def _initialize_conversation(self, conversation: Conversation):
"""初始化会话实例
Args:
conversation: 要初始化的会话实例
"""
stream_id = conversation.stream_id
private_name = conversation.private_name
try:
logger.info(f"[私聊][{private_name}]开始初始化会话实例: {stream_id}")
# 启动初始化流程
await conversation._initialize()
# 标记初始化完成
self._initializing[stream_id] = False
logger.info(f"[私聊][{private_name}]会话实例 {stream_id} 初始化完成")
except Exception as e:
logger.error(f"[私聊][{private_name}]管理器初始化会话实例失败: {stream_id}, 错误: {e}")
logger.error(f"[私聊][{private_name}]{traceback.format_exc()}")
# 清理失败的初始化
async def get_conversation(self, stream_id: str) -> Optional[Conversation]:
"""获取已存在的会话实例
Args:
stream_id: 聊天流ID
Returns:
Optional[Conversation]: 会话实例不存在则返回None
"""
return self._instances.get(stream_id)

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@@ -1,23 +0,0 @@
from enum import Enum
from typing import Literal
class ConversationState(Enum):
"""对话状态"""
INIT = "初始化"
RETHINKING = "重新思考"
ANALYZING = "分析历史"
PLANNING = "规划目标"
GENERATING = "生成回复"
CHECKING = "检查回复"
SENDING = "发送消息"
FETCHING = "获取知识"
WAITING = "等待"
LISTENING = "倾听"
ENDED = "结束"
JUDGING = "判断"
IGNORED = "屏蔽"
ActionType = Literal["direct_reply", "fetch_knowledge", "wait"]

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@@ -1,127 +0,0 @@
import json
import re
from typing import Dict, Any, Optional, Tuple, List, Union
from src.common.logger import get_logger
logger = get_logger("pfc_utils")
def get_items_from_json(
content: str,
private_name: str,
*items: str,
default_values: Optional[Dict[str, Any]] = None,
required_types: Optional[Dict[str, type]] = None,
allow_array: bool = True,
) -> Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]:
"""从文本中提取JSON内容并获取指定字段
Args:
content: 包含JSON的文本
private_name: 私聊名称
*items: 要提取的字段名
default_values: 字段的默认值,格式为 {字段名: 默认值}
required_types: 字段的必需类型,格式为 {字段名: 类型}
allow_array: 是否允许解析JSON数组
Returns:
Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: (是否成功, 提取的字段字典或字典列表)
"""
content = content.strip()
result = {}
# 设置默认值
if default_values:
result.update(default_values)
# 首先尝试解析为JSON数组
if allow_array:
try:
# 尝试找到文本中的JSON数组
array_pattern = r"\[[\s\S]*\]"
array_match = re.search(array_pattern, content)
if array_match:
array_content = array_match.group()
json_array = json.loads(array_content)
# 确认是数组类型
if isinstance(json_array, list):
# 验证数组中的每个项目是否包含所有必需字段
valid_items = []
for item in json_array:
if not isinstance(item, dict):
continue
# 检查是否有所有必需字段
if all(field in item for field in items):
# 验证字段类型
if required_types:
type_valid = True
for field, expected_type in required_types.items():
if field in item and not isinstance(item[field], expected_type):
type_valid = False
break
if not type_valid:
continue
# 验证字符串字段不为空
string_valid = True
for field in items:
if isinstance(item[field], str) and not item[field].strip():
string_valid = False
break
if not string_valid:
continue
valid_items.append(item)
if valid_items:
return True, valid_items
except json.JSONDecodeError:
logger.debug(f"[私聊][{private_name}]JSON数组解析失败尝试解析单个JSON对象")
except Exception as e:
logger.debug(f"[私聊][{private_name}]尝试解析JSON数组时出错: {str(e)}")
# 尝试解析JSON对象
try:
json_data = json.loads(content)
except json.JSONDecodeError:
# 如果直接解析失败尝试查找和提取JSON部分
json_pattern = r"\{[^{}]*\}"
json_match = re.search(json_pattern, content)
if json_match:
try:
json_data = json.loads(json_match.group())
except json.JSONDecodeError:
logger.error(f"[私聊][{private_name}]提取的JSON内容解析失败")
return False, result
else:
logger.error(f"[私聊][{private_name}]无法在返回内容中找到有效的JSON")
return False, result
# 提取字段
for item in items:
if item in json_data:
result[item] = json_data[item]
# 验证必需字段
if not all(item in result for item in items):
logger.error(f"[私聊][{private_name}]JSON缺少必要字段实际内容: {json_data}")
return False, result
# 验证字段类型
if required_types:
for field, expected_type in required_types.items():
if field in result and not isinstance(result[field], expected_type):
logger.error(f"[私聊][{private_name}]{field} 必须是 {expected_type.__name__} 类型")
return False, result
# 验证字符串字段不为空
for field in items:
if isinstance(result[field], str) and not result[field].strip():
logger.error(f"[私聊][{private_name}]{field} 不能为空")
return False, result
return True, result

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@@ -1,199 +0,0 @@
import json
import random
from typing import Tuple, List, Dict, Any
from src.common.logger import get_logger
from src.services.llm_service import LLMServiceClient
from src.config.config import global_config
from .chat_observer import ChatObserver
from maim_message import UserInfo
logger = get_logger("reply_checker")
class ReplyChecker:
"""回复检查器"""
def __init__(self, stream_id: str, private_name: str):
self.llm = LLMServiceClient(task_name="utils", request_type="reply_check")
self.personality_info = self._get_personality_prompt()
self.name = global_config.bot.nickname
self.private_name = private_name
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
self.max_retries = 3 # 最大重试次数
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
and global_config.personality.state_probability > 0
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};"
async def check(
self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_text: str, retry_count: int = 0
) -> Tuple[bool, str, bool]:
"""检查生成的回复是否合适
Args:
reply: 生成的回复
goal: 对话目标
chat_history: 对话历史记录
chat_history_text: 对话历史记录文本
retry_count: 当前重试次数
Returns:
Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划)
"""
# 不再从 observer 获取,直接使用传入的 chat_history
# messages = self.chat_observer.get_cached_messages(limit=20)
try:
# 筛选出最近由 Bot 自己发送的消息
bot_messages = []
for msg in reversed(chat_history):
user_info = UserInfo.from_dict(msg.get("user_info", {}))
if str(user_info.user_id) == str(global_config.bot.qq_account):
bot_messages.append(msg.get("processed_plain_text", ""))
if len(bot_messages) >= 2: # 只和最近的两条比较
break
# 进行比较
if bot_messages:
# 可以用简单比较,或者更复杂的相似度库 (如 difflib)
# 简单比较:是否完全相同
if reply == bot_messages[0]: # 和最近一条完全一样
logger.warning(
f"[私聊][{self.private_name}]ReplyChecker 检测到回复与上一条 Bot 消息完全相同: '{reply}'"
)
return (
False,
"被逻辑检查拒绝:回复内容与你上一条发言完全相同,可以选择深入话题或寻找其它话题或等待",
True,
) # 不合适,需要返回至决策层
# 2. 相似度检查 (如果精确匹配未通过)
import difflib # 导入 difflib 库
# 计算编辑距离相似度ratio() 返回 0 到 1 之间的浮点数
similarity_ratio = difflib.SequenceMatcher(None, reply, bot_messages[0]).ratio()
logger.debug(f"[私聊][{self.private_name}]ReplyChecker - 相似度: {similarity_ratio:.2f}")
# 设置一个相似度阈值
similarity_threshold = 0.9
if similarity_ratio > similarity_threshold:
logger.warning(
f"[私聊][{self.private_name}]ReplyChecker 检测到回复与上一条 Bot 消息高度相似 (相似度 {similarity_ratio:.2f}): '{reply}'"
)
return (
False,
f"被逻辑检查拒绝:回复内容与你上一条发言高度相似 (相似度 {similarity_ratio:.2f}),可以选择深入话题或寻找其它话题或等待。",
True,
)
except Exception as e:
import traceback
logger.error(f"[私聊][{self.private_name}]检查回复时出错: 类型={type(e)}, 值={e}")
logger.error(f"[私聊][{self.private_name}]{traceback.format_exc()}") # 打印详细的回溯信息
prompt = f"""你是一个聊天逻辑检查器,请检查以下回复或消息是否合适:
当前对话目标:{goal}
最新的对话记录:
{chat_history_text}
待检查的消息:
{reply}
请结合聊天记录检查以下几点:
1. 这条消息是否依然符合当前对话目标和实现方式
2. 这条消息是否与最新的对话记录保持一致性
3. 是否存在重复发言,或重复表达同质内容(尤其是只是换一种方式表达了相同的含义)
4. 这条消息是否包含违规内容(例如血腥暴力,政治敏感等)
5. 这条消息是否以发送者的角度发言(不要让发送者自己回复自己的消息)
6. 这条消息是否通俗易懂
7. 这条消息是否有些多余例如在对方没有回复的情况下依然连续多次“消息轰炸”尤其是已经连续发送3条信息的情况这很可能不合理需要着重判断
8. 这条消息是否使用了完全没必要的修辞
9. 这条消息是否逻辑通顺
10. 这条消息是否太过冗长了通常私聊的每条消息长度在20字以内除非特殊情况
11. 在连续多次发送消息的情况下,这条消息是否衔接自然,会不会显得奇怪(例如连续两条消息中部分内容重叠)
请以JSON格式输出包含以下字段
1. suitable: 是否合适 (true/false)
2. reason: 原因说明
3. need_replan: 是否需要重新决策 (true/false)当你认为此时已经不适合发消息需要规划其它行动时设为true
输出格式示例:
{{
"suitable": true,
"reason": "回复符合要求,虽然有可能略微偏离目标,但是整体内容流畅得体",
"need_replan": false
}}
注意请严格按照JSON格式输出不要包含任何其他内容。"""
try:
generation_result = await self.llm.generate_response(prompt)
content = generation_result.response
logger.debug(f"[私聊][{self.private_name}]检查回复的原始返回: {content}")
# 清理内容尝试提取JSON部分
content = content.strip()
try:
# 尝试直接解析
result: dict = json.loads(content)
except json.JSONDecodeError:
# 如果直接解析失败尝试查找和提取JSON部分
import re
json_pattern = r"\{[^{}]*\}"
json_match = re.search(json_pattern, content)
if json_match:
try:
result: dict = json.loads(json_match.group())
except json.JSONDecodeError:
# 如果JSON解析失败尝试从文本中提取结果
is_suitable = "不合适" not in content.lower() and "违规" not in content.lower()
reason = content[:100] if content else "无法解析响应"
need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower()
return is_suitable, reason, need_replan
else:
# 如果找不到JSON从文本中判断
is_suitable = "不合适" not in content.lower() and "违规" not in content.lower()
reason = content[:100] if content else "无法解析响应"
need_replan = "重新规划" in content.lower() or "目标不适合" in content.lower()
return is_suitable, reason, need_replan
# 验证JSON字段
suitable = result.get("suitable", None)
reason = result.get("reason", "未提供原因")
need_replan = result.get("need_replan", False)
# 如果suitable字段是字符串转换为布尔值
if isinstance(suitable, str):
suitable = suitable.lower() == "true"
# 如果suitable字段不存在或不是布尔值从reason中判断
if suitable is None:
suitable = "不合适" not in reason.lower() and "违规" not in reason.lower()
# 如果不合适且未达到最大重试次数,返回需要重试
if not suitable and retry_count < self.max_retries:
return False, reason, False
# 如果不合适且已达到最大重试次数,返回需要重新规划
if not suitable and retry_count >= self.max_retries:
return False, f"多次重试后仍不合适: {reason}", True
return suitable, reason, need_replan
except Exception as e:
logger.error(f"[私聊][{self.private_name}]检查回复时出错: {e}")
# 如果出错且已达到最大重试次数,建议重新规划
if retry_count >= self.max_retries:
return False, "多次检查失败,建议重新规划", True
return False, f"检查过程出错,建议重试: {str(e)}", False

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@@ -1,243 +0,0 @@
from typing import Tuple, List, Dict, Any
from src.common.logger import get_logger
from src.services.llm_service import LLMServiceClient
from src.config.config import global_config
import random
from .chat_observer import ChatObserver
from .reply_checker import ReplyChecker
from src.services.message_service import build_readable_messages
from .observation_info import ObservationInfo, dict_to_session_message
from .conversation_info import ConversationInfo
logger = get_logger("reply_generator")
# --- 定义 Prompt 模板 ---
# Prompt for direct_reply (首次回复)
PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊请根据以下信息生成一条回复
当前对话目标:{goals_str}
{knowledge_info_str}
最近的聊天记录:
{chat_history_text}
请根据上述信息,结合聊天记录,回复对方。该回复应该:
1. 符合对话目标,以""的角度发言(不要自己与自己对话!)
2. 符合你的性格特征和身份细节
3. 通俗易懂自然流畅像正常聊天一样简短通常20字以内除非特殊情况
4. 可以适当利用相关知识,但不要生硬引用
5. 自然、得体,结合聊天记录逻辑合理,且没有重复表达同质内容
请注意把握聊天内容,不要回复的太有条理,可以有个性。请分清""和对方说的话,不要把""说的话当做对方说的话,这是你自己说的话。
可以回复得自然随意自然一些,就像真人一样,注意把握聊天内容,整体风格可以平和、简短,不要刻意突出自身学科背景,不要说你说过的话,可以简短,多简短都可以,但是避免冗长。
请你注意不要输出多余内容(包括前后缀,冒号和引号,括号,表情等),只输出回复内容。
不要输出多余内容(包括前后缀冒号和引号括号表情包at或 @等 )。
请直接输出回复内容,不需要任何额外格式。"""
# Prompt for send_new_message (追问/补充)
PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊**刚刚你已经发送了一条或多条消息**,现在请根据以下信息再发一条新消息:
当前对话目标:{goals_str}
{knowledge_info_str}
最近的聊天记录:
{chat_history_text}
请根据上述信息,结合聊天记录,继续发一条新消息(例如对之前消息的补充,深入话题,或追问等等)。该消息应该:
1. 符合对话目标,以""的角度发言(不要自己与自己对话!)
2. 符合你的性格特征和身份细节
3. 通俗易懂自然流畅像正常聊天一样简短通常20字以内除非特殊情况
4. 可以适当利用相关知识,但不要生硬引用
5. 跟之前你发的消息自然的衔接,逻辑合理,且没有重复表达同质内容或部分重叠内容
请注意把握聊天内容,不用太有条理,可以有个性。请分清""和对方说的话,不要把""说的话当做对方说的话,这是你自己说的话。
这条消息可以自然随意自然一些,就像真人一样,注意把握聊天内容,整体风格可以平和、简短,不要刻意突出自身学科背景,不要说你说过的话,可以简短,多简短都可以,但是避免冗长。
请你注意不要输出多余内容(包括前后缀,冒号和引号,括号,表情等),只输出消息内容。
不要输出多余内容(包括前后缀冒号和引号括号表情包at或 @等 )。
请直接输出回复内容,不需要任何额外格式。"""
# Prompt for say_goodbye (告别语生成)
PROMPT_FAREWELL = """{persona_text}。你在参与一场 QQ 私聊,现在对话似乎已经结束,你决定再发一条最后的消息来圆满结束。
最近的聊天记录:
{chat_history_text}
请根据上述信息,结合聊天记录,构思一条**简短、自然、符合你人设**的最后的消息。
这条消息应该:
1. 从你自己的角度发言。
2. 符合你的性格特征和身份细节。
3. 通俗易懂,自然流畅,通常很简短。
4. 自然地为这场对话画上句号,避免开启新话题或显得冗长、刻意。
请像真人一样随意自然,**简洁是关键**。
不要输出多余内容包括前后缀、冒号、引号、括号、表情包、at或@等)。
请直接输出最终的告别消息内容,不需要任何额外格式。"""
class ReplyGenerator:
"""回复生成器"""
def __init__(self, stream_id: str, private_name: str):
self.llm = LLMServiceClient(
task_name="replyer",
request_type="reply_generation",
)
self.personality_info = self._get_personality_prompt()
self.name = global_config.bot.nickname
self.private_name = private_name
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
self.reply_checker = ReplyChecker(stream_id, private_name)
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
and global_config.personality.state_probability > 0
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};"
# 修改 generate 方法签名,增加 action_type 参数
async def generate(
self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str
) -> str:
"""生成回复
Args:
observation_info: 观察信息
conversation_info: 对话信息
action_type: 当前执行的动作类型 ('direct_reply''send_new_message')
Returns:
str: 生成的回复
"""
# 构建提示词
logger.debug(
f"[私聊][{self.private_name}]开始生成回复 (动作类型: {action_type}):当前目标: {conversation_info.goal_list}"
)
# --- 构建通用 Prompt 参数 ---
# (这部分逻辑基本不变)
# 构建对话目标 (goals_str)
goals_str = ""
if conversation_info.goal_list:
for goal_reason in conversation_info.goal_list:
if isinstance(goal_reason, dict):
goal = goal_reason.get("goal", "目标内容缺失")
reasoning = goal_reason.get("reasoning", "没有明确原因")
else:
goal = str(goal_reason)
reasoning = "没有明确原因"
goal = str(goal) if goal is not None else "目标内容缺失"
reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
goals_str += f"- 目标:{goal}\n 原因:{reasoning}\n"
else:
goals_str = "- 目前没有明确对话目标\n" # 简化无目标情况
# --- 新增:构建知识信息字符串 ---
knowledge_info_str = "【供参考的相关知识和记忆】\n" # 稍微改下标题,表明是供参考
try:
# 检查 conversation_info 是否有 knowledge_list 并且不为空
if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
# 最多只显示最近的 5 条知识
recent_knowledge = conversation_info.knowledge_list[-5:]
for i, knowledge_item in enumerate(recent_knowledge):
if isinstance(knowledge_item, dict):
query = knowledge_item.get("query", "未知查询")
knowledge = knowledge_item.get("knowledge", "无知识内容")
source = knowledge_item.get("source", "未知来源")
# 只取知识内容的前 2000 个字
knowledge_snippet = f"{knowledge[:2000]}..." if len(knowledge) > 2000 else knowledge
knowledge_info_str += (
f"{i + 1}. 关于 '{query}' (来源: {source}): {knowledge_snippet}\n" # 格式微调,更简洁
)
else:
knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
if not recent_knowledge:
knowledge_info_str += "- 暂无。\n" # 更简洁的提示
else:
knowledge_info_str += "- 暂无。\n"
except AttributeError:
logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
knowledge_info_str += "- 获取知识列表时出错。\n"
except Exception as e:
logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
knowledge_info_str += "- 处理知识列表时出错。\n"
# 获取聊天历史记录 (chat_history_text)
chat_history_text = observation_info.chat_history_str
if observation_info.new_messages_count > 0 and observation_info.unprocessed_messages:
new_messages_list = observation_info.unprocessed_messages
session_messages = [dict_to_session_message(m) for m in new_messages_list]
new_messages_str = build_readable_messages(
session_messages,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
)
chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
elif not chat_history_text:
chat_history_text = "还没有聊天记录。"
# 构建 Persona 文本 (persona_text)
persona_text = f"你的名字是{self.name}{self.personality_info}"
# --- 选择 Prompt ---
if action_type == "send_new_message":
prompt_template = PROMPT_SEND_NEW_MESSAGE
logger.info(f"[私聊][{self.private_name}]使用 PROMPT_SEND_NEW_MESSAGE (追问生成)")
elif action_type == "say_goodbye": # 处理告别动作
prompt_template = PROMPT_FAREWELL
logger.info(f"[私聊][{self.private_name}]使用 PROMPT_FAREWELL (告别语生成)")
else: # 默认使用 direct_reply 的 prompt (包括 'direct_reply' 或其他未明确处理的类型)
prompt_template = PROMPT_DIRECT_REPLY
logger.info(f"[私聊][{self.private_name}]使用 PROMPT_DIRECT_REPLY (首次/非连续回复生成)")
# --- 格式化最终的 Prompt ---
prompt = prompt_template.format(
persona_text=persona_text,
goals_str=goals_str,
chat_history_text=chat_history_text,
knowledge_info_str=knowledge_info_str,
)
# --- 调用 LLM 生成 ---
logger.debug(f"[私聊][{self.private_name}]发送到LLM的生成提示词:\n------\n{prompt}\n------")
try:
generation_result = await self.llm.generate_response(prompt)
content = generation_result.response
logger.debug(f"[私聊][{self.private_name}]生成的回复: {content}")
# 移除旧的检查新消息逻辑,这应该由 conversation 控制流处理
return content
except Exception as e:
logger.error(f"[私聊][{self.private_name}]生成回复时出错: {e}")
return "抱歉,我现在有点混乱,让我重新思考一下..."
# check_reply 方法保持不变
async def check_reply(
self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_str: str, retry_count: int = 0
) -> Tuple[bool, str, bool]:
"""检查回复是否合适
(此方法逻辑保持不变)
"""
return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count)

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@@ -1,79 +0,0 @@
from src.common.logger import get_logger
from .chat_observer import ChatObserver
from .conversation_info import ConversationInfo
# from src.individuality.individuality import Individuality # 不再需要
from src.config.config import global_config
import time
import asyncio
logger = get_logger("waiter")
# --- 在这里设定你想要的超时时间(秒) ---
# 例如: 120 秒 = 2 分钟
DESIRED_TIMEOUT_SECONDS = 300
class Waiter:
"""等待处理类"""
def __init__(self, stream_id: str, private_name: str):
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
self.name = global_config.bot.nickname
self.private_name = private_name
# self.wait_accumulated_time = 0 # 不再需要累加计时
async def wait(self, conversation_info: ConversationInfo) -> bool:
"""等待用户新消息或超时"""
wait_start_time = time.time()
logger.info(f"[私聊][{self.private_name}]进入常规等待状态 (超时: {DESIRED_TIMEOUT_SECONDS} 秒)...")
while True:
# 检查是否有新消息
if self.chat_observer.new_message_after(wait_start_time):
logger.info(f"[私聊][{self.private_name}]等待结束,收到新消息")
return False # 返回 False 表示不是超时
# 检查是否超时
elapsed_time = time.time() - wait_start_time
if elapsed_time > DESIRED_TIMEOUT_SECONDS:
logger.info(f"[私聊][{self.private_name}]等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。")
wait_goal = {
"goal": f"你等待了{elapsed_time / 60:.1f}分钟,注意可能在对方看来聊天已经结束,思考接下来要做什么",
"reasoning": "对方很久没有回复你的消息了",
}
conversation_info.goal_list.append(wait_goal)
logger.info(f"[私聊][{self.private_name}]添加目标: {wait_goal}")
return True # 返回 True 表示超时
await asyncio.sleep(5) # 每 5 秒检查一次
logger.debug(
f"[私聊][{self.private_name}]等待中..."
) # 可以考虑把这个频繁日志注释掉,只在超时或收到消息时输出
async def wait_listening(self, conversation_info: ConversationInfo) -> bool:
"""倾听用户发言或超时"""
wait_start_time = time.time()
logger.info(f"[私聊][{self.private_name}]进入倾听等待状态 (超时: {DESIRED_TIMEOUT_SECONDS} 秒)...")
while True:
# 检查是否有新消息
if self.chat_observer.new_message_after(wait_start_time):
logger.info(f"[私聊][{self.private_name}]倾听等待结束,收到新消息")
return False # 返回 False 表示不是超时
# 检查是否超时
elapsed_time = time.time() - wait_start_time
if elapsed_time > DESIRED_TIMEOUT_SECONDS:
logger.info(f"[私聊][{self.private_name}]倾听等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。")
wait_goal = {
# 保持 goal 文本一致
"goal": f"你等待了{elapsed_time / 60:.1f}分钟,对方似乎话说一半突然消失了,可能忙去了?也可能忘记了回复?要问问吗?还是结束对话?或继续等待?思考接下来要做什么",
"reasoning": "对方话说一半消失了,很久没有回复",
}
conversation_info.goal_list.append(wait_goal)
logger.info(f"[私聊][{self.private_name}]添加目标: {wait_goal}")
return True # 返回 True 表示超时
await asyncio.sleep(5) # 每 5 秒检查一次
logger.debug(f"[私聊][{self.private_name}]倾听等待中...") # 同上,可以考虑注释掉

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@@ -1,800 +0,0 @@
import asyncio
import random
import time
import traceback
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
from rich.traceback import install
from src.config.config import global_config
from src.common.logger import get_logger
from src.common.utils.utils_config import ExpressionConfigUtils
from src.learners.expression_learner import ExpressionLearner
from src.learners.jargon_miner import JargonMiner
from src.chat.message_receive.chat_manager import BotChatSession
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
from src.chat.message_receive.message import SessionMessage
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
from src.chat.utils.prompt_builder import global_prompt_manager
from src.chat.utils.timer_calculator import Timer
from src.chat.brain_chat.brain_planner import BrainPlanner
from src.chat.planner_actions.action_modifier import ActionModifier
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.heart_flow.heartFC_utils import CycleDetail
from src.person_info.person_info import Person
from src.core.types import ActionInfo, EventType
from src.core.event_bus import event_bus
from src.chat.event_helpers import build_event_message
from src.services import (
generator_service as generator_api,
send_service as send_api,
message_service as message_api,
database_service as database_api,
)
from src.services.message_service import build_readable_messages_with_id, get_messages_before_time_in_chat
if TYPE_CHECKING:
from src.chat.message_receive.message import SessionMessage
ERROR_LOOP_INFO = {
"loop_plan_info": {
"action_result": {
"action_type": "error",
"action_data": {},
"reasoning": "循环处理失败",
},
},
"loop_action_info": {
"action_taken": False,
"reply_text": "",
"command": "",
"taken_time": time.time(),
},
}
install(extra_lines=3)
# 注释:原来的动作修改超时常量已移除,因为改为顺序执行
logger = get_logger("bc") # Logger Name Changed
class BrainChatting:
"""
管理一个连续的私聊Brain Chat循环
用于在特定聊天流中生成回复。
"""
def __init__(self, session_id: str):
"""
BrainChatting 初始化函数
参数:
chat_id: 聊天流唯一标识符(如stream_id)
on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
performance_version: 性能记录版本号,用于区分不同启动版本
"""
# 基础属性
self.stream_id: str = session_id # 聊天流ID
self.chat_stream: BotChatSession = _chat_manager.get_session_by_session_id(self.stream_id) # type: ignore[assignment]
if not self.chat_stream:
raise ValueError(f"无法找到聊天流: {self.stream_id}")
self.log_prefix = f"[{_chat_manager.get_session_name(self.stream_id) or self.stream_id}]"
expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(self.stream_id)
self._enable_expression_use = expr_use
self._enable_expression_learning = expr_learn
self._enable_jargon_learning = jargon_learn
self._expression_learner = ExpressionLearner(self.stream_id)
self._jargon_miner = JargonMiner(self.stream_id, session_name=self.log_prefix.strip("[]"))
self._min_messages_for_extraction = 30
self._min_extraction_interval = 60
self._last_extraction_time = 0.0
self.action_manager = ActionManager()
self.action_planner = BrainPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
# 循环控制内部状态
self.running: bool = False
self._loop_task: Optional[asyncio.Task] = None # 主循环任务
self._new_message_event = asyncio.Event() # 新消息事件,用于打断 wait
# 添加循环信息管理相关的属性
self.history_loop: List[CycleDetail] = []
self._cycle_counter = 0
self._current_cycle_detail: CycleDetail = None # type: ignore
self.last_read_time = time.time() - 2
self.more_plan = False
# 最近一次是否成功进行了 reply用于选择 BrainPlanner 的 Prompt
self._last_successful_reply: bool = False
async def start(self):
"""检查是否需要启动主循环,如果未激活则启动。"""
# 如果循环已经激活,直接返回
if self.running:
logger.debug(f"{self.log_prefix} BrainChatting 已激活,无需重复启动")
return
try:
# 标记为活动状态,防止重复启动
self.running = True
self._loop_task = asyncio.create_task(self._main_chat_loop())
self._loop_task.add_done_callback(self._handle_loop_completion)
logger.info(f"{self.log_prefix} BrainChatting 启动完成")
except Exception as e:
# 启动失败时重置状态
self.running = False
self._loop_task = None
logger.error(f"{self.log_prefix} BrainChatting 启动失败: {e}")
raise
def _handle_loop_completion(self, task: asyncio.Task):
"""当 _hfc_loop 任务完成时执行的回调。"""
try:
if exception := task.exception():
logger.error(f"{self.log_prefix} BrainChatting: 脱离了聊天(异常): {exception}")
logger.error(traceback.format_exc()) # Log full traceback for exceptions
else:
logger.info(f"{self.log_prefix} BrainChatting: 脱离了聊天 (外部停止)")
except asyncio.CancelledError:
logger.info(f"{self.log_prefix} BrainChatting: 结束了聊天")
def start_cycle(self) -> Tuple[Dict[str, float], str]:
self._cycle_counter += 1
self._current_cycle_detail = CycleDetail(self._cycle_counter)
self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
cycle_timers = {}
return cycle_timers, self._current_cycle_detail.thinking_id
def end_cycle(self, loop_info, cycle_timers):
self._current_cycle_detail.set_loop_info(loop_info)
self.history_loop.append(self._current_cycle_detail)
self._current_cycle_detail.timers = cycle_timers
self._current_cycle_detail.end_time = time.time()
def print_cycle_info(self, cycle_timers):
# 记录循环信息和计时器结果
timer_strings = []
for name, elapsed in cycle_timers.items():
formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}"
timer_strings.append(f"{name}: {formatted_time}")
logger.info(
f"{self.log_prefix}{self._current_cycle_detail.cycle_id}次思考,"
f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}" # type: ignore
+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
)
async def _trigger_expression_learning(self, messages: List[SessionMessage]) -> None:
if not messages:
return
self._expression_learner.add_messages(messages)
if time.time() - self._last_extraction_time < self._min_extraction_interval:
return
if self._expression_learner.get_cache_size() < self._min_messages_for_extraction:
return
if not self._enable_expression_learning:
return
self._last_extraction_time = time.time()
try:
jargon_miner = self._jargon_miner if self._enable_jargon_learning else None
await self._expression_learner.learn(jargon_miner)
except Exception as exc:
logger.error(f"{self.log_prefix} 表达学习失败: {exc}", exc_info=True)
async def _loopbody(self): # sourcery skip: hoist-if-from-if
# 获取最新消息(用于上下文,但不影响是否调用 observe
recent_messages_list = message_api.get_messages_by_time_in_chat(
chat_id=self.stream_id,
start_time=self.last_read_time,
end_time=time.time(),
limit=20,
limit_mode="latest",
filter_mai=True,
filter_command=False,
filter_intercept_message_level=1,
)
# 如果有新消息,更新 last_read_time 并触发事件以打断正在进行的 wait
if len(recent_messages_list) >= 1:
self.last_read_time = time.time()
self._new_message_event.set() # 触发新消息事件,打断 wait
# 总是执行一次思考迭代(不管有没有新消息)
# wait 动作会在其内部等待,不需要在这里处理
should_continue = await self._observe(recent_messages_list=recent_messages_list)
if not should_continue:
# 选择了 complete_talk返回 False 表示需要等待新消息
return False
# 继续下一次迭代(除非选择了 complete_talk
# 短暂等待后再继续,避免过于频繁的循环
await asyncio.sleep(0.1)
return True
async def _send_and_store_reply(
self,
response_set: MessageSequence,
action_message: SessionMessage,
cycle_timers: Dict[str, float],
thinking_id,
actions,
selected_expressions: Optional[List[int]] = None,
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
with Timer("回复发送", cycle_timers):
reply_text = await self._send_response(
reply_set=response_set,
message_data=action_message,
selected_expressions=selected_expressions,
)
# 获取 platform如果不存在则从 chat_stream 获取,如果还是 None 则使用默认值
platform = action_message.platform
if platform is None:
platform = getattr(self.chat_stream, "platform", "unknown")
person = Person(platform=platform, user_id=action_message.message_info.user_info.user_id)
person_name = person.person_name
action_prompt_display = f"你对{person_name}进行了回复:{reply_text}"
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=action_prompt_display,
thinking_id=thinking_id,
action_data={"reply_text": reply_text},
action_name="reply",
)
# 构建循环信息
loop_info: Dict[str, Any] = {
"loop_plan_info": {
"action_result": actions,
},
"loop_action_info": {
"action_taken": True,
"reply_text": reply_text,
"command": "",
"taken_time": time.time(),
},
}
return loop_info, reply_text, cycle_timers
async def _observe(
self, # interest_value: float = 0.0,
recent_messages_list: Optional[List[SessionMessage]] = None,
) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
if recent_messages_list is None:
recent_messages_list = []
_reply_text = "" # 初始化reply_text变量避免UnboundLocalError
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
if recent_messages_list:
asyncio.create_task(self._trigger_expression_learning(recent_messages_list))
cycle_timers, thinking_id = self.start_cycle()
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
# 第一步:动作检查
available_actions: Dict[str, ActionInfo] = {}
try:
await self.action_modifier.modify_actions()
available_actions = self.action_manager.get_using_actions()
except Exception as e:
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
# 获取必要信息
is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
# 一次思考迭代Think - Act - Observe
# 获取聊天上下文
message_list_before_now = get_messages_before_time_in_chat(
chat_id=self.stream_id,
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.6),
filter_intercept_message_level=1,
)
chat_content_block, message_id_list = build_readable_messages_with_id(
messages=message_list_before_now,
timestamp_mode="normal_no_YMD",
read_mark=self.action_planner.last_obs_time_mark,
truncate=True,
show_actions=True,
)
prompt_info = await self.action_planner.build_planner_prompt(
chat_target_info=chat_target_info,
current_available_actions=available_actions,
chat_content_block=chat_content_block,
message_id_list=message_id_list,
prompt_key="brain_planner",
)
_event_msg = build_event_message(
EventType.ON_PLAN, llm_prompt=prompt_info[0], stream_id=self.chat_stream.session_id
)
continue_flag, modified_message = await event_bus.emit(EventType.ON_PLAN, _event_msg)
if not continue_flag:
return False
if modified_message and modified_message._modify_flags.modify_llm_prompt:
prompt_info = (modified_message.llm_prompt, prompt_info[1])
with Timer("规划器", cycle_timers):
action_to_use_info = await self.action_planner.plan(
loop_start_time=self.last_read_time,
available_actions=available_actions,
)
# 检查是否有 complete_talk 动作(会停止后续迭代)
has_complete_talk = any(action.action_type == "complete_talk" for action in action_to_use_info)
# 并行执行所有动作
action_tasks = [
asyncio.create_task(
self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
)
for action in action_to_use_info
]
# 并行执行所有任务
results = await asyncio.gather(*action_tasks, return_exceptions=True)
# 处理执行结果
reply_loop_info = None
reply_text_from_reply = ""
action_success = False
action_reply_text = ""
for result in results:
if isinstance(result, BaseException):
logger.error(f"{self.log_prefix} 动作执行异常: {result}")
continue
if result["action_type"] != "reply":
action_success = result["success"]
action_reply_text = result["reply_text"]
elif result["action_type"] == "reply":
if result["success"]:
reply_loop_info = result["loop_info"]
reply_text_from_reply = result["reply_text"]
else:
logger.warning(f"{self.log_prefix} 回复动作执行失败")
# 更新观察时间标记
self.action_planner.last_obs_time_mark = time.time()
# 如果选择了 complete_talk标记为完成不再继续迭代
if has_complete_talk:
logger.info(f"{self.log_prefix} 检测到 complete_talk 动作,本次思考完成")
# 构建循环信息
if reply_loop_info:
# 如果有回复信息使用回复的loop_info作为基础
loop_info = reply_loop_info
# 更新动作执行信息
loop_info["loop_action_info"].update(
{
"action_taken": action_success,
"taken_time": time.time(),
}
)
_reply_text = reply_text_from_reply
else:
# 没有回复信息构建纯动作的loop_info
loop_info = {
"loop_plan_info": {
"action_result": action_to_use_info,
},
"loop_action_info": {
"action_taken": action_success,
"reply_text": action_reply_text,
"taken_time": time.time(),
},
}
_reply_text = action_reply_text
# 如果选择了 complete_talk返回 False 以停止 _loopbody 的循环
# 否则返回 True让 _loopbody 继续下一次迭代
should_continue = not has_complete_talk
self.end_cycle(loop_info, cycle_timers)
self.print_cycle_info(cycle_timers)
# 如果选择了 complete_talk返回 False 停止循环
# 否则返回 True继续下一次思考迭代
return should_continue
async def _main_chat_loop(self):
"""主循环,持续进行计划并可能回复消息,直到被外部取消。"""
try:
while self.running:
# 主循环
success = await self._loopbody()
if not success:
# 选择了 complete等待新消息
logger.info(f"{self.log_prefix} 选择了 complete等待新消息...")
await self._wait_for_new_message()
# 有新消息后继续循环
continue
await asyncio.sleep(0.1)
except asyncio.CancelledError:
# 设置了关闭标志位后被取消是正常流程
logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
except Exception:
logger.error(f"{self.log_prefix} 麦麦聊天意外错误将于3s后尝试重新启动")
print(traceback.format_exc())
await asyncio.sleep(3)
self._loop_task = asyncio.create_task(self._main_chat_loop())
logger.error(f"{self.log_prefix} 结束了当前聊天循环")
async def _wait_for_new_message(self):
"""等待新消息到达"""
last_check_time = self.last_read_time
check_interval = 1.0 # 每秒检查一次
# 清除事件状态,准备等待新消息
self._new_message_event.clear()
while self.running:
# 检查是否有新消息
recent_messages_list = message_api.get_messages_by_time_in_chat(
chat_id=self.stream_id,
start_time=last_check_time,
end_time=time.time(),
limit=20,
limit_mode="latest",
filter_mai=True,
filter_command=False,
filter_intercept_message_level=1,
)
# 如果有新消息,更新 last_read_time 并返回
if len(recent_messages_list) >= 1:
self.last_read_time = time.time()
logger.info(f"{self.log_prefix} 检测到新消息,恢复循环")
return
# 等待新消息事件或超时后再次检查
try:
await asyncio.wait_for(self._new_message_event.wait(), timeout=check_interval)
# 事件被触发,说明有新消息
logger.info(f"{self.log_prefix} 检测到新消息事件,恢复循环")
return
except asyncio.TimeoutError:
# 超时后继续检查
continue
async def _handle_action(
self,
action: str,
reasoning: str,
action_data: dict,
cycle_timers: Dict[str, float],
thinking_id: str,
action_message: Optional[SessionMessage] = None,
) -> tuple[bool, str, str]:
"""
处理规划动作,使用动作工厂创建相应的动作处理器
参数:
action: 动作类型
reasoning: 决策理由
action_data: 动作数据,包含不同动作需要的参数
cycle_timers: 计时器字典
thinking_id: 思考ID
返回:
tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
"""
try:
# 使用工厂创建动作处理器实例
try:
action_handler = self.action_manager.create_action(
action_name=action,
action_data=action_data,
action_reasoning=reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
chat_stream=self.chat_stream,
log_prefix=self.log_prefix,
action_message=action_message,
)
except Exception as e:
logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}")
traceback.print_exc()
return False, "", ""
if not action_handler:
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}")
return False, "", ""
# 处理动作并获取结果(固定记录一次动作信息)
# BaseAction 定义了异步方法 execute() 作为统一执行入口
# 这里调用 execute() 以兼容所有 Action 实现
result = await action_handler.execute()
success, action_text = result
command = ""
return success, action_text, command
except Exception as e:
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
traceback.print_exc()
return False, "", ""
async def _send_response(
self,
reply_set: MessageSequence,
message_data: SessionMessage,
selected_expressions: Optional[List[int]] = None,
) -> str:
new_message_count = message_api.count_new_messages(
chat_id=self.chat_stream.session_id, start_time=self.last_read_time, end_time=time.time()
)
need_reply = new_message_count >= random.randint(2, 4)
if need_reply:
logger.info(f"{self.log_prefix} 从思考到回复,共有{new_message_count}条新消息,使用引用回复")
reply_text = ""
first_replied = False
for component in reply_set.components:
if not isinstance(component, TextComponent):
continue
data = component.text
if not first_replied:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.session_id,
reply_message=message_data,
set_reply=need_reply,
typing=False,
selected_expressions=selected_expressions,
)
first_replied = True
else:
await send_api.text_to_stream(
text=data,
stream_id=self.chat_stream.session_id,
reply_message=message_data,
set_reply=False,
typing=True,
selected_expressions=selected_expressions,
)
reply_text += data
return reply_text
async def _execute_action(
self,
action_planner_info: ActionPlannerInfo,
chosen_action_plan_infos: List[ActionPlannerInfo],
thinking_id: str,
available_actions: Dict[str, ActionInfo],
cycle_timers: Dict[str, float],
):
"""执行单个动作的通用函数"""
try:
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
if action_planner_info.action_type == "complete_talk":
# 直接处理complete_talk逻辑不再通过动作系统
reason = action_planner_info.reasoning or "选择完成对话"
logger.info(f"{self.log_prefix} 选择完成对话,原因: {reason}")
# 存储complete_talk信息到数据库
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=reason,
thinking_id=thinking_id,
action_data={"reason": reason},
action_name="complete_talk",
)
return {"action_type": "complete_talk", "success": True, "reply_text": "", "command": ""}
elif action_planner_info.action_type == "reply":
try:
# 从 Planner 的 action_data 中提取未知词语列表(仅在 reply 时使用)
unknown_words = None
if isinstance(action_planner_info.action_data, dict):
uw = action_planner_info.action_data.get("unknown_words")
if isinstance(uw, list):
cleaned_uw: List[str] = []
for item in uw:
if isinstance(item, str):
if stripped_item := item.strip():
cleaned_uw.append(stripped_item)
if cleaned_uw:
unknown_words = cleaned_uw
success, llm_response = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_message=action_planner_info.action_message,
available_actions=available_actions,
chosen_actions=chosen_action_plan_infos,
reply_reason=action_planner_info.reasoning or "",
unknown_words=unknown_words,
enable_tool=global_config.tool.enable_tool,
request_type="replyer",
from_plugin=False,
)
if not success or not llm_response or not llm_response.reply_set:
if action_planner_info.action_message:
logger.info(
f"{action_planner_info.action_message.processed_plain_text} 的回复生成失败"
)
else:
logger.info("回复生成失败")
return {
"action_type": "reply",
"success": False,
"reply_text": "",
"loop_info": None,
}
except asyncio.CancelledError:
logger.debug(f"{self.log_prefix} 并行执行:回复生成任务已被取消")
return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
response_set = llm_response.reply_set
selected_expressions = llm_response.selected_expressions
loop_info, reply_text, _ = await self._send_and_store_reply(
response_set=response_set,
action_message=action_planner_info.action_message, # type: ignore
cycle_timers=cycle_timers,
thinking_id=thinking_id,
actions=chosen_action_plan_infos,
selected_expressions=selected_expressions,
)
# 标记这次循环已经成功进行了回复
self._last_successful_reply = True
return {
"action_type": "reply",
"success": True,
"reply_text": reply_text,
"loop_info": loop_info,
}
# 其他动作
else:
# 内建 wait / listening不通过插件系统直接在这里处理
if action_planner_info.action_type in ["wait", "listening"]:
reason = action_planner_info.reasoning or ""
action_data = action_planner_info.action_data or {}
if action_planner_info.action_type == "wait":
# 获取等待时间(必填)
wait_seconds = action_data.get("wait_seconds")
if wait_seconds is None:
logger.warning(f"{self.log_prefix} wait 动作缺少 wait_seconds 参数,使用默认值 5 秒")
wait_seconds = 5
else:
try:
wait_seconds = float(wait_seconds)
if wait_seconds < 0:
logger.warning(f"{self.log_prefix} wait_seconds 不能为负数,使用默认值 5 秒")
wait_seconds = 5
except (ValueError, TypeError):
logger.warning(f"{self.log_prefix} wait_seconds 参数格式错误,使用默认值 5 秒")
wait_seconds = 5
logger.info(f"{self.log_prefix} 执行 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()
# 记录动作信息
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=reason or f"等待 {wait_seconds}",
thinking_id=thinking_id,
action_data={"reason": reason, "wait_seconds": wait_seconds},
action_name="wait",
)
# 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)
# 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:
# 超时正常完成
pass
logger.info(f"{self.log_prefix} wait 动作完成,继续下一次思考")
# 这些动作本身不产生文本回复
self._last_successful_reply = False
return {
"action_type": "wait",
"success": True,
"reply_text": "",
"command": "",
}
# listening 已合并到 wait如果遇到则转换为 wait向后兼容
elif action_planner_info.action_type == "listening":
logger.debug(f"{self.log_prefix} 检测到 listening 动作,已合并到 wait自动转换")
# 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)"
)
# 清除事件状态,准备等待新消息
self._new_message_event.clear()
# 记录动作信息
await database_api.store_action_info(
chat_stream=self.chat_stream,
display_prompt=reason or f"倾听并等待 {wait_seconds}",
thinking_id=thinking_id,
action_data={"reason": reason, "wait_seconds": wait_seconds},
action_name="listening",
)
# 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)
# 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:
# 超时正常完成
pass
logger.info(f"{self.log_prefix} listening 动作完成,继续下一次思考")
# 这些动作本身不产生文本回复
self._last_successful_reply = False
return {
"action_type": "listening",
"success": True,
"reply_text": "",
"command": "",
}
# 其余动作:走原有插件 Action 体系
with Timer("动作执行", cycle_timers):
success, reply_text, command = await self._handle_action(
action_planner_info.action_type,
action_planner_info.reasoning or "",
action_planner_info.action_data or {},
cycle_timers,
thinking_id,
action_planner_info.action_message,
)
# 非 reply 类动作执行成功时,清空最近成功回复标记,让下一轮回到 initial Prompt
if success and action_planner_info.action_type != "reply":
self._last_successful_reply = False
return {
"action_type": action_planner_info.action_type,
"success": success,
"reply_text": reply_text,
"command": command,
}
except Exception as e:
logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
return {
"action_type": action_planner_info.action_type,
"success": False,
"reply_text": "",
"loop_info": None,
"error": str(e),
}

View File

@@ -1,622 +0,0 @@
from datetime import datetime
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
import json
import random
import re
import time
import traceback
from json_repair import repair_json
from rich.traceback import install
from src.chat.logger.plan_reply_logger import PlanReplyLogger
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.utils.utils import get_chat_type_and_target_info
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.logger import get_logger
from src.common.utils.utils_action import ActionUtils
from src.config.config import global_config
from src.core.types import ActionActivationType, ActionInfo, ComponentType
from src.services.llm_service import LLMServiceClient
from src.plugin_runtime.component_query import component_query_service
from src.prompt.prompt_manager import prompt_manager
from src.services.message_service import (
build_readable_messages_with_id,
get_actions_by_timestamp_with_chat,
get_messages_before_time_in_chat,
)
if TYPE_CHECKING:
from src.common.data_models.info_data_model import TargetPersonInfo
from src.chat.message_receive.message import SessionMessage
logger = get_logger("planner")
install(extra_lines=3)
class BrainPlanner:
def __init__(self, chat_id: str, action_manager: ActionManager):
self.chat_id = chat_id
self.log_prefix = f"[{_chat_manager.get_session_name(chat_id) or chat_id}]"
self.action_manager = action_manager
# LLM规划器配置
self.planner_llm = LLMServiceClient(
task_name="planner", request_type="planner"
) # 用于动作规划
self.last_obs_time_mark = 0.0
# 计划日志记录
self.plan_log: List[Tuple[str, float, List[ActionPlannerInfo]]] = []
def find_message_by_id(
self, message_id: str, message_id_list: List[Tuple[str, "SessionMessage"]]
) -> Optional["SessionMessage"]:
# sourcery skip: use-next
"""
根据message_id从message_id_list中查找对应的原始消息
Args:
message_id: 要查找的消息ID
message_id_list: 消息ID列表格式为[{'id': str, 'message': dict}, ...]
Returns:
找到的原始消息字典如果未找到则返回None
"""
for item in message_id_list:
if item[0] == message_id:
return item[1]
return None
def _parse_single_action(
self,
action_json: dict,
message_id_list: List[Tuple[str, "SessionMessage"]],
current_available_actions: List[Tuple[str, ActionInfo]],
) -> List[ActionPlannerInfo]:
"""解析单个action JSON并返回ActionPlannerInfo列表"""
action_planner_infos = []
try:
action = action_json.get("action", "complete_talk")
logger.debug(f"{self.log_prefix}解析动作JSON: action={action}, json={action_json}")
reasoning = action_json.get("reason", "未提供原因")
action_data = {key: value for key, value in action_json.items() if key not in ["action", "reason"]}
# 非complete_talk动作需要target_message_id
target_message = None
if target_message_id := action_json.get("target_message_id"):
# 根据target_message_id查找原始消息
target_message = self.find_message_by_id(target_message_id, message_id_list)
if target_message is None:
logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
# 选择最新消息作为target_message
target_message = message_id_list[-1][1]
else:
target_message = message_id_list[-1][1]
logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id使用最新消息作为target_message")
# 验证action是否可用
available_action_names = [action_name for action_name, _ in current_available_actions]
# 内部保留动作(不依赖插件系统)
# 注意listening 已合并到 wait 中,如果遇到 listening 则转换为 wait
internal_action_names = ["complete_talk", "reply", "wait_time", "wait", "listening"]
logger.debug(
f"{self.log_prefix}动作验证: action={action}, internal={internal_action_names}, available={available_action_names}"
)
# 将 listening 转换为 wait向后兼容
if action == "listening":
logger.debug(f"{self.log_prefix}检测到 listening 动作,已合并到 wait自动转换")
action = "wait"
if action not in internal_action_names and action not in available_action_names:
logger.warning(
f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (内部动作: {internal_action_names}, 可用插件动作: {available_action_names}),将强制使用 'complete_talk'"
)
reasoning = (
f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
)
action = "complete_talk"
logger.warning(f"{self.log_prefix}动作已转换为 complete_talk")
# 创建ActionPlannerInfo对象
# 将列表转换为字典格式
available_actions_dict = dict(current_available_actions)
action_planner_infos.append(
ActionPlannerInfo(
action_type=action,
reasoning=reasoning,
action_data=action_data,
action_message=target_message,
available_actions=available_actions_dict,
)
)
except Exception as e:
logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
# 将列表转换为字典格式
available_actions_dict = dict(current_available_actions)
action_planner_infos.append(
ActionPlannerInfo(
action_type="complete_talk",
reasoning=f"解析单个action时出错: {e}",
action_data={},
action_message=None,
available_actions=available_actions_dict,
)
)
return action_planner_infos
async def plan(
self,
available_actions: Dict[str, ActionInfo],
loop_start_time: float = 0.0,
) -> List[ActionPlannerInfo]:
# sourcery skip: use-named-expression
"""
规划器 (Planner): 使用LLM根据上下文决定做出什么动作ReAct模式
"""
plan_start = time.perf_counter()
# 获取聊天上下文
message_list_before_now = get_messages_before_time_in_chat(
chat_id=self.chat_id,
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.6),
filter_intercept_message_level=1,
)
message_id_list: list[Tuple[str, "SessionMessage"]] = []
chat_content_block, message_id_list = build_readable_messages_with_id(
messages=message_list_before_now,
timestamp_mode="normal_no_YMD",
read_mark=self.last_obs_time_mark,
truncate=True,
show_actions=True,
)
message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
messages=message_list_before_now_short,
timestamp_mode="normal_no_YMD",
truncate=False,
show_actions=False,
)
self.last_obs_time_mark = time.time()
# 获取必要信息
is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
# 提及/被@ 的处理由心流或统一判定模块驱动Planner 不再做硬编码强制回复
# 应用激活类型过滤
filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
prompt_build_start = time.perf_counter()
# 构建包含所有动作的提示词:使用统一的 ReAct Prompt
prompt_key = "brain_planner"
# 这里不记录日志,避免重复打印,由调用方按需控制 log_prompt
prompt, message_id_list = await self.build_planner_prompt(
chat_target_info=chat_target_info,
current_available_actions=filtered_actions,
chat_content_block=chat_content_block,
message_id_list=message_id_list,
prompt_key=prompt_key,
)
prompt_build_ms = (time.perf_counter() - prompt_build_start) * 1000
# 调用LLM获取决策
reasoning, actions, llm_raw_output, llm_reasoning, llm_duration_ms = await self._execute_main_planner(
prompt=prompt,
message_id_list=message_id_list,
filtered_actions=filtered_actions,
available_actions=available_actions,
loop_start_time=loop_start_time,
)
# 记录和展示计划日志
logger.info(
f"{self.log_prefix}Planner: {reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
)
self.add_plan_log(reasoning, actions)
try:
PlanReplyLogger.log_plan(
chat_id=self.chat_id,
prompt=prompt,
reasoning=reasoning,
raw_output=llm_raw_output,
raw_reasoning=llm_reasoning,
actions=actions,
timing={
"prompt_build_ms": round(prompt_build_ms, 2),
"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
"total_plan_ms": round((time.perf_counter() - plan_start) * 1000, 2),
"loop_start_time": loop_start_time,
},
extra=None,
)
except Exception:
logger.exception(f"{self.log_prefix}记录plan日志失败")
return actions
async def build_planner_prompt(
self,
chat_target_info: Optional["TargetPersonInfo"],
current_available_actions: Dict[str, ActionInfo],
message_id_list: List[Tuple[str, "SessionMessage"]],
chat_content_block: str = "",
interest: str = "",
prompt_key: str = "brain_planner",
) -> tuple[str, List[Tuple[str, "SessionMessage"]]]:
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
try:
# 获取最近执行过的动作
actions_before_now = get_actions_by_timestamp_with_chat(
chat_id=self.chat_id,
timestamp_start=time.time() - 600,
timestamp_end=time.time(),
limit=6,
)
actions_before_now_block = ActionUtils.build_readable_action_records(actions_before_now)
if actions_before_now_block:
actions_before_now_block = f"你刚刚选择并执行过的action是\n{actions_before_now_block}"
else:
actions_before_now_block = ""
chat_context_description: str = ""
if chat_target_info:
# 构建聊天上下文描述
chat_context_description = (
f"你正在和 {chat_target_info.person_name or chat_target_info.user_nickname or '对方'} 聊天中"
)
# 构建动作选项块
action_options_block = await self._build_action_options_block(current_available_actions)
# 其他信息
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。"
time_block = f"当前时间:{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
bot_name = global_config.bot.nickname
bot_nickname = (
f",也可以叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
)
name_block = f"你的名字是{bot_name}{bot_nickname},请注意哪些是你自己的发言。"
# 获取主规划器模板并填充
planner_prompt_template = prompt_manager.get_prompt(prompt_key)
planner_prompt_template.add_context("time_block", time_block)
planner_prompt_template.add_context("chat_context_description", chat_context_description)
planner_prompt_template.add_context("chat_content_block", chat_content_block)
planner_prompt_template.add_context("actions_before_now_block", actions_before_now_block)
planner_prompt_template.add_context("action_options_text", action_options_block)
planner_prompt_template.add_context("moderation_prompt", moderation_prompt_block)
planner_prompt_template.add_context("name_block", name_block)
planner_prompt_template.add_context("interest", interest)
planner_prompt_template.add_context("plan_style", global_config.experimental.private_plan_style)
prompt = await prompt_manager.render_prompt(planner_prompt_template)
return prompt, message_id_list
except Exception as e:
logger.error(f"构建 Planner 提示词时出错: {e}")
logger.error(traceback.format_exc())
return "构建 Planner Prompt 时出错", []
def get_necessary_info(self) -> Tuple[bool, Optional["TargetPersonInfo"], Dict[str, ActionInfo]]:
"""
获取 Planner 需要的必要信息
"""
is_group_chat = True
is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}")
current_available_actions_dict = self.action_manager.get_using_actions()
# 获取完整的动作信息
all_registered_actions: Dict[str, ActionInfo] = component_query_service.get_components_by_type( # type: ignore
ComponentType.ACTION
)
current_available_actions = {}
for action_name in current_available_actions_dict:
if action_name in all_registered_actions:
current_available_actions[action_name] = all_registered_actions[action_name]
else:
logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
return is_group_chat, chat_target_info, current_available_actions
def _filter_actions_by_activation_type(
self, available_actions: Dict[str, ActionInfo], chat_content_block: str
) -> Dict[str, ActionInfo]:
"""根据激活类型过滤动作"""
filtered_actions = {}
for action_name, action_info in available_actions.items():
if action_info.activation_type == ActionActivationType.NEVER:
logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型,跳过")
continue
elif action_info.activation_type == ActionActivationType.ALWAYS:
filtered_actions[action_name] = action_info
elif action_info.activation_type == ActionActivationType.RANDOM:
if random.random() < action_info.random_activation_probability:
filtered_actions[action_name] = action_info
elif action_info.activation_type == ActionActivationType.KEYWORD:
if action_info.activation_keywords:
for keyword in action_info.activation_keywords:
if keyword in chat_content_block:
filtered_actions[action_name] = action_info
break
else:
logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type},跳过处理")
return filtered_actions
async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
# sourcery skip: use-join
"""构建动作选项块"""
if not current_available_actions:
return ""
action_options_block = ""
for action_name, action_info in current_available_actions.items():
# 构建参数文本
param_text = ""
if action_info.action_parameters:
param_text = "\n"
for param_name, param_description in action_info.action_parameters.items():
param_text += f' "{param_name}":"{param_description}"\n'
param_text = param_text.rstrip("\n")
# 构建要求文本
require_text = ""
for require_item in action_info.action_require:
require_text += f"- {require_item}\n"
require_text = require_text.rstrip("\n")
# 获取动作提示模板并填充
using_action_prompt_template = prompt_manager.get_prompt("brain_action")
using_action_prompt_template.add_context("action_name", action_name)
using_action_prompt_template.add_context("action_description", action_info.description)
using_action_prompt_template.add_context("action_parameters", param_text)
using_action_prompt_template.add_context("action_require", require_text)
using_action_prompt = await prompt_manager.render_prompt(using_action_prompt_template)
action_options_block += using_action_prompt
return action_options_block
async def _execute_main_planner(
self,
prompt: str,
message_id_list: List[Tuple[str, "SessionMessage"]],
filtered_actions: Dict[str, ActionInfo],
available_actions: Dict[str, ActionInfo],
loop_start_time: float,
) -> Tuple[str, List[ActionPlannerInfo], Optional[str], Optional[str], Optional[float]]:
"""执行主规划器"""
llm_content = None
actions: List[ActionPlannerInfo] = []
extracted_reasoning = ""
llm_reasoning = None
llm_duration_ms = None
try:
# 调用LLM
llm_start = time.perf_counter()
generation_result = await self.planner_llm.generate_response(prompt=prompt)
llm_content = generation_result.response
reasoning_content = generation_result.reasoning
llm_duration_ms = (time.perf_counter() - llm_start) * 1000
llm_reasoning = reasoning_content
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
if global_config.debug.show_planner_prompt:
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
if reasoning_content:
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
else:
logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
if reasoning_content:
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
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,
)
# 解析LLM响应
if llm_content:
try:
json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
if json_objects:
logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
for i, json_obj in enumerate(json_objects):
logger.info(f"{self.log_prefix}解析第{i + 1}个JSON对象: {json_obj}")
filtered_actions_list = list(filtered_actions.items())
for json_obj in json_objects:
parsed_actions = self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
logger.info(f"{self.log_prefix}解析后的动作: {[a.action_type for a in parsed_actions]}")
actions.extend(parsed_actions)
else:
# 尝试解析为直接的JSON
logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
extracted_reasoning = extracted_reasoning or "LLM没有返回可用动作"
actions = self._create_complete_talk(extracted_reasoning, available_actions)
except Exception as json_e:
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
extracted_reasoning = f"解析LLM响应JSON失败: {json_e}"
actions = self._create_complete_talk(extracted_reasoning, available_actions)
traceback.print_exc()
else:
extracted_reasoning = "规划器没有获得LLM响应"
actions = self._create_complete_talk(extracted_reasoning, available_actions)
# 添加循环开始时间到所有动作
for action in actions:
action.action_data = action.action_data or {}
action.action_data["loop_start_time"] = loop_start_time
logger.debug(
f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
)
return extracted_reasoning, actions, llm_content, llm_reasoning, llm_duration_ms
def _create_complete_talk(
self, reasoning: str, available_actions: Dict[str, ActionInfo]
) -> List[ActionPlannerInfo]:
"""创建complete_talk"""
return [
ActionPlannerInfo(
action_type="complete_talk",
reasoning=reasoning,
action_data={},
action_message=None,
available_actions=available_actions,
)
]
def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
"""添加计划日志"""
self.plan_log.append((reasoning, time.time(), actions))
if len(self.plan_log) > 20:
self.plan_log.pop(0)
def _extract_json_from_markdown(self, content: str) -> Tuple[List[dict], str]:
# sourcery skip: for-append-to-extend
"""从Markdown格式的内容中提取JSON对象和推理内容"""
json_objects = []
reasoning_content = ""
# 使用正则表达式查找```json包裹的JSON内容
json_pattern = r"```json\s*(.*?)\s*```"
markdown_matches = re.findall(json_pattern, content, re.DOTALL)
# 提取JSON之前的内容作为推理文本
first_json_pos = len(content)
if markdown_matches:
# 找到第一个```json的位置
first_json_pos = content.find("```json")
if first_json_pos > 0:
reasoning_content = content[:first_json_pos].strip()
# 清理推理内容中的注释标记
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
reasoning_content = reasoning_content.strip()
# 处理```json包裹的JSON
for match in markdown_matches:
try:
# 清理可能的注释和格式问题
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
if json_str := json_str.strip():
# 先尝试将整个块作为一个JSON对象或数组适用于多行JSON
try:
json_obj = json.loads(repair_json(json_str))
if isinstance(json_obj, dict):
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict):
json_objects.append(item)
except json.JSONDecodeError:
# 如果整个块解析失败尝试按行分割适用于多个单行JSON对象
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
for line in lines:
try:
# 尝试解析每一行作为独立的JSON对象
json_obj = json.loads(repair_json(line))
if isinstance(json_obj, dict):
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict):
json_objects.append(item)
except json.JSONDecodeError:
# 单行解析失败,继续下一行
continue
except Exception as e:
logger.warning(f"{self.log_prefix}解析JSON块失败: {e}, 块内容: {match[:100]}...")
continue
# 如果没有找到完整的```json```块,尝试查找不完整的代码块(缺少结尾```
if not json_objects:
json_start_pos = content.find("```json")
if json_start_pos != -1:
# 找到```json之后的内容
json_content_start = json_start_pos + 7 # ```json的长度
# 提取从```json之后到内容结尾的所有内容
incomplete_json_str = content[json_content_start:].strip()
# 提取JSON之前的内容作为推理文本
if json_start_pos > 0:
reasoning_content = content[:json_start_pos].strip()
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
reasoning_content = reasoning_content.strip()
if incomplete_json_str:
try:
# 清理可能的注释和格式问题
json_str = re.sub(r"//.*?\n", "\n", incomplete_json_str)
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
json_str = json_str.strip()
if json_str:
# 尝试按行分割每行可能是一个JSON对象
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
for line in lines:
try:
json_obj = json.loads(repair_json(line))
if isinstance(json_obj, dict):
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict):
json_objects.append(item)
except json.JSONDecodeError:
pass
# 如果按行解析没有成功尝试将整个块作为一个JSON对象或数组
if not json_objects:
try:
json_obj = json.loads(repair_json(json_str))
if isinstance(json_obj, dict):
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict):
json_objects.append(item)
except Exception as e:
logger.debug(f"尝试解析不完整的JSON代码块失败: {e}")
except Exception as e:
logger.debug(f"处理不完整的JSON代码块时出错: {e}")
return json_objects, reasoning_content

View File

@@ -14,7 +14,6 @@ from src.common.utils.utils_session import SessionUtils
from src.config.config import global_config from src.config.config import global_config
from src.platform_io.route_key_factory import RouteKeyFactory from src.platform_io.route_key_factory import RouteKeyFactory
# from src.chat.brain_chat.PFC.pfc_manager import PFCManager
from src.core.announcement_manager import global_announcement_manager from src.core.announcement_manager import global_announcement_manager
from src.plugin_runtime.component_query import component_query_service from src.plugin_runtime.component_query import component_query_service
@@ -31,36 +30,20 @@ logger = get_logger("chat")
class ChatBot: class ChatBot:
def __init__(self): def __init__(self) -> None:
"""初始化聊天机器人入口。"""
self.bot = None # bot 实例引用 self.bot = None # bot 实例引用
self._started = False self._started = False
self.heartflow_message_receiver = HeartFCMessageReceiver() # 新增 self.heartflow_message_receiver = HeartFCMessageReceiver()
# self.pfc_manager = PFCManager.get_instance() # PFC管理器 # TODO: PFC恢复
async def _ensure_started(self): async def _ensure_started(self) -> None:
"""确保所有任务已启动""" """确保所有后台任务已启动"""
if not self._started: if not self._started:
logger.debug("确保ChatBot所有任务已启动") logger.debug("确保ChatBot所有任务已启动")
self._started = True self._started = True
async def _create_pfc_chat(self, message: SessionMessage):
"""创建或获取PFC对话实例
Args:
message: 消息对象
"""
try:
chat_id = message.session_id
private_name = str(message.message_info.user_info.user_nickname)
logger.debug(f"[私聊][{private_name}]创建或获取PFC对话: {chat_id}")
await self.pfc_manager.get_or_create_conversation(chat_id, private_name)
except Exception as e:
logger.error(f"创建PFC聊天失败: {e}")
logger.error(traceback.format_exc())
async def _process_commands(self, message: SessionMessage) -> tuple[bool, Optional[str], bool]: async def _process_commands(self, message: SessionMessage) -> tuple[bool, Optional[str], bool]:
"""使用统一组件注册表处理命令。 """使用统一组件注册表处理命令。
@@ -177,11 +160,12 @@ class ChatBot:
recalled: Dict[str, Any] = {} recalled: Dict[str, Any] = {}
recalled_id = None recalled_id = None
if getattr(seg, "type", None) == "notify" and isinstance(getattr(seg, "data", None), dict): seg_data = getattr(seg, "data", None)
sub_type = seg.data.get("sub_type") if getattr(seg, "type", None) == "notify" and isinstance(seg_data, dict):
scene = seg.data.get("scene") sub_type = seg_data.get("sub_type")
msg_id = seg.data.get("message_id") scene = seg_data.get("scene")
recalled = seg.data.get("recalled_user_info") or {} msg_id = seg_data.get("message_id")
recalled = seg_data.get("recalled_user_info") or {}
if isinstance(recalled, dict): if isinstance(recalled, dict):
recalled_id = recalled.get("user_id") recalled_id = recalled.get("user_id")
@@ -369,23 +353,6 @@ class ChatBot:
# else: # else:
# template_group_name = None # template_group_name = None
# async def preprocess():
# # 根据聊天类型路由消息
# if group_info is None:
# # 私聊消息 -> PFC系统
# logger.debug("[私聊]检测到私聊消息路由到PFC系统")
# await MessageStorage.store_message(message, chat)
# await self._create_pfc_chat(message)
# else:
# # 群聊消息 -> HeartFlow系统
# logger.debug("[群聊]检测到群聊消息路由到HeartFlow系统")
# await self.heartflow_message_receiver.process_message(message)
# if template_group_name:
# async with global_prompt_manager.async_message_scope(template_group_name):
# await preprocess()
# else:
# await preprocess()
async def preprocess(): async def preprocess():
if group_info is None: if group_info is None:
logger.debug("[私聊]检测到私聊消息,路由到 Maisaka") logger.debug("[私聊]检测到私聊消息,路由到 Maisaka")

View File

@@ -1,137 +0,0 @@
from typing import Dict, Optional, Tuple
from src.chat.message_receive.chat_manager import BotChatSession
from src.chat.message_receive.message import SessionMessage
from src.common.logger import get_logger
from src.core.types import ActionInfo
from src.plugin_runtime.component_query import ActionExecutor, component_query_service
logger = get_logger("action_manager")
class ActionHandle:
"""Action 执行句柄
不依赖任何插件基类,内部持有 executor (async callable) 和绑定参数。
brain_chat 调用 ``await handle.execute()`` 即可。
"""
def __init__(self, executor: ActionExecutor, **kwargs):
self._executor = executor
self._kwargs = kwargs
async def execute(self) -> Tuple[bool, str]:
return await self._executor(**self._kwargs)
class ActionManager:
"""
动作管理器,用于管理各种类型的动作
使用插件运行时统一查询服务的 executor-based 模式。
"""
def __init__(self):
"""初始化动作管理器"""
# 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] = {}
# 初始化时将默认动作加载到使用中的动作
self._using_actions = component_query_service.get_default_actions()
# === 执行Action方法 ===
def create_action(
self,
action_name: str,
action_data: dict,
action_reasoning: str,
cycle_timers: dict,
thinking_id: str,
chat_stream: BotChatSession,
log_prefix: str,
shutting_down: bool = False,
action_message: Optional[SessionMessage] = None,
) -> Optional[ActionHandle]:
"""
创建动作执行句柄
Args:
action_name: 动作名称
action_data: 动作数据
action_reasoning: 执行理由
cycle_timers: 计时器字典
thinking_id: 思考ID
chat_stream: 聊天流
log_prefix: 日志前缀
shutting_down: 是否正在关闭
action_message: 动作消息记录
Returns:
Optional[ActionHandle]: 执行句柄,如果动作未注册则返回 None
"""
try:
executor = component_query_service.get_action_executor(action_name)
if not executor:
logger.warning(f"{log_prefix} 未找到Action组件: {action_name}")
return None
info = component_query_service.get_action_info(action_name)
if not info:
logger.warning(f"{log_prefix} 未找到Action组件信息: {action_name}")
return None
plugin_config = component_query_service.get_plugin_config(info.plugin_name) or {}
handle = ActionHandle(
executor,
action_data=action_data,
action_reasoning=action_reasoning,
cycle_timers=cycle_timers,
thinking_id=thinking_id,
chat_stream=chat_stream,
log_prefix=log_prefix,
shutting_down=shutting_down,
plugin_config=plugin_config,
action_message=action_message,
)
logger.debug(f"创建Action执行句柄成功: {action_name}")
return handle
except Exception as e:
logger.error(f"创建Action执行句柄失败 {action_name}: {e}")
import traceback
logger.error(traceback.format_exc())
return None
def get_using_actions(self) -> Dict[str, ActionInfo]:
"""获取当前正在使用的动作集合"""
return self._using_actions.copy()
# === Modify相关方法 ===
def remove_action_from_using(self, action_name: str) -> bool:
"""
从当前使用的动作集中移除指定动作
Args:
action_name: 动作名称
Returns:
bool: 移除是否成功
"""
if action_name not in self._using_actions:
logger.warning(f"移除失败: 动作 {action_name} 不在当前使用的动作集中")
return False
del self._using_actions[action_name]
logger.debug(f"已从使用集中移除动作 {action_name}")
return True
def restore_actions(self) -> None:
"""恢复到默认动作集"""
actions_to_restore = list(self._using_actions.keys())
self._using_actions = component_query_service.get_default_actions()
logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")

View File

@@ -1,233 +0,0 @@
import random
import time
from typing import List, Dict, Tuple
from src.common.logger import get_logger
from src.config.config import global_config
from src.chat.message_receive.chat_manager import BotChatSession, chat_manager as _chat_manager
from src.chat.planner_actions.action_manager import ActionManager
from src.services.message_service import build_readable_messages, get_messages_before_time_in_chat
from src.core.types import ActionActivationType, ActionInfo
from src.core.announcement_manager import global_announcement_manager
logger = get_logger("action_manager")
class ActionModifier:
"""动作处理器
用于处理Observation对象和根据激活类型处理actions。
集成了原有的modify_actions功能和新的激活类型处理功能。
支持并行判定和智能缓存优化。
"""
def __init__(self, action_manager: ActionManager, chat_id: str):
"""初始化动作处理器"""
self.chat_id = chat_id
self.chat_stream: BotChatSession = _chat_manager.get_session_by_session_id(self.chat_id) # type: ignore
self.log_prefix = f"[{_chat_manager.get_session_name(self.chat_id) or self.chat_id}]"
self.action_manager = action_manager
async def modify_actions(
self,
message_content: str = "",
): # sourcery skip: use-named-expression
"""
动作修改流程,整合传统观察处理和新的激活类型判定
这个方法处理完整的动作管理流程:
1. 基于观察的传统动作修改(循环历史分析、类型匹配等)
2. 基于激活类型的智能动作判定,最终确定可用动作集
处理后ActionManager 将包含最终的可用动作集,供规划器直接使用
"""
logger.debug(f"{self.log_prefix}开始完整动作修改流程")
removals_s1: List[Tuple[str, str]] = []
removals_s2: List[Tuple[str, str]] = []
# removals_s3: List[Tuple[str, str]] = []
self.action_manager.restore_actions()
all_actions = self.action_manager.get_using_actions()
message_list_before_now_half = get_messages_before_time_in_chat(
chat_id=self.chat_stream.session_id,
timestamp=time.time(),
limit=min(int(global_config.chat.max_context_size * 0.33), 10),
filter_intercept_message_level=1,
)
chat_content = build_readable_messages(
message_list_before_now_half,
replace_bot_name=True,
timestamp_mode="relative",
read_mark=0.0,
show_actions=True,
)
if message_content:
chat_content = chat_content + "\n" + f"现在,最新的消息是:{message_content}"
# === 第一阶段:去除用户自行禁用的 ===
disabled_actions = global_announcement_manager.get_disabled_chat_actions(self.chat_id)
if disabled_actions:
for disabled_action_name in disabled_actions:
if disabled_action_name in all_actions:
removals_s1.append((disabled_action_name, "用户自行禁用"))
self.action_manager.remove_action_from_using(disabled_action_name)
logger.debug(f"{self.log_prefix}阶段一移除动作: {disabled_action_name},原因: 用户自行禁用")
# === 第二阶段:检查动作的关联类型 ===
chat_context = self.chat_stream.context
type_mismatched_actions = self._check_action_associated_types(all_actions, chat_context)
if type_mismatched_actions:
removals_s2.extend(type_mismatched_actions)
# 应用第二阶段的移除
for action_name, reason in removals_s2:
self.action_manager.remove_action_from_using(action_name)
logger.debug(f"{self.log_prefix}阶段二移除动作: {action_name},原因: {reason}")
# === 第三阶段:激活类型判定 ===
# if chat_content is not None:
# logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
# 获取当前使用的动作集(经过第一阶段处理)
# current_using_actions = self.action_manager.get_using_actions()
# 获取因激活类型判定而需要移除的动作
# removals_s3 = await self._get_deactivated_actions_by_type(
# current_using_actions,
# chat_content,
# )
# 应用第三阶段的移除
# for action_name, reason in removals_s3:
# self.action_manager.remove_action_from_using(action_name)
# logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name},原因: {reason}")
# === 统一日志记录 ===
all_removals = removals_s1 + removals_s2
removals_summary: str = ""
if all_removals:
removals_summary = " | ".join([f"{name}({reason})" for name, reason in all_removals])
available_actions = list(self.action_manager.get_using_actions().keys())
available_actions_text = "".join(available_actions) if available_actions else ""
logger.debug(f"{self.log_prefix} 当前可用动作: {available_actions_text}||移除: {removals_summary}")
def _check_action_associated_types(self, all_actions: Dict[str, ActionInfo], chat_context: BotChatSession):
type_mismatched_actions: List[Tuple[str, str]] = []
for action_name, action_info in all_actions.items():
if action_info.associated_types and not chat_context.check_types(action_info.associated_types):
associated_types_str = ", ".join(action_info.associated_types)
reason = f"适配器不支持(需要: {associated_types_str}"
type_mismatched_actions.append((action_name, reason))
logger.debug(f"{self.log_prefix}决定移除动作: {action_name},原因: {reason}")
return type_mismatched_actions
async def _get_deactivated_actions_by_type(
self,
actions_with_info: Dict[str, ActionInfo],
chat_content: str = "",
) -> List[tuple[str, str]]:
"""
根据激活类型过滤,返回需要停用的动作列表及原因
Args:
actions_with_info: 带完整信息的动作字典
chat_content: 聊天内容
Returns:
List[Tuple[str, str]]: 需要停用的 (action_name, reason) 元组列表
"""
deactivated_actions = []
actions_to_check = list(actions_with_info.items())
random.shuffle(actions_to_check)
for action_name, action_info in actions_to_check:
activation_type = action_info.activation_type
if activation_type == ActionActivationType.ALWAYS:
continue # 总是激活,无需处理
elif activation_type == ActionActivationType.RANDOM:
probability = action_info.random_activation_probability
if random.random() >= probability:
reason = f"RANDOM类型未触发概率{probability}"
deactivated_actions.append((action_name, reason))
logger.debug(f"{self.log_prefix}未激活动作: {action_name},原因: {reason}")
elif activation_type == ActionActivationType.KEYWORD:
if not self._check_keyword_activation(action_name, action_info, chat_content):
keywords = action_info.activation_keywords
reason = f"关键词未匹配(关键词: {keywords}"
deactivated_actions.append((action_name, reason))
logger.debug(f"{self.log_prefix}未激活动作: {action_name},原因: {reason}")
elif activation_type == ActionActivationType.NEVER:
reason = "激活类型为never"
deactivated_actions.append((action_name, reason))
logger.debug(f"{self.log_prefix}未激活动作: {action_name},原因: 激活类型为never")
else:
logger.warning(f"{self.log_prefix}未知的激活类型: {activation_type},跳过处理")
return deactivated_actions
def _check_keyword_activation(
self,
action_name: str,
action_info: ActionInfo,
chat_content: str = "",
) -> bool:
"""
检查是否匹配关键词触发条件
Args:
action_name: 动作名称
action_info: 动作信息
observed_messages_str: 观察到的聊天消息
chat_context: 聊天上下文
extra_context: 额外上下文
Returns:
bool: 是否应该激活此action
"""
activation_keywords = action_info.activation_keywords
case_sensitive = action_info.keyword_case_sensitive
if not activation_keywords:
logger.warning(f"{self.log_prefix}动作 {action_name} 设置为关键词触发但未配置关键词")
return False
# 构建检索文本
search_text = ""
if chat_content:
search_text += chat_content
# if chat_context:
# search_text += f" {chat_context}"
# if extra_context:
# search_text += f" {extra_context}"
# 如果不区分大小写,转换为小写
if not case_sensitive:
search_text = search_text.lower()
# 检查每个关键词
matched_keywords = []
for keyword in activation_keywords:
check_keyword = keyword if case_sensitive else keyword.lower()
if check_keyword in search_text:
matched_keywords.append(keyword)
if matched_keywords:
logger.debug(f"{self.log_prefix}动作 {action_name} 匹配到关键词: {matched_keywords}")
return True
else:
logger.debug(f"{self.log_prefix}动作 {action_name} 未匹配到任何关键词: {activation_keywords}")
return False

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@@ -1,935 +0,0 @@
from collections import OrderedDict
from datetime import datetime
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import contextlib
import json
import random
import re
import time
import traceback
from json_repair import repair_json
from rich.traceback import install
from src.chat.logger.plan_reply_logger import PlanReplyLogger
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
from src.chat.message_receive.message import SessionMessage
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.utils.utils import get_chat_type_and_target_info, is_bot_self
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.logger import get_logger
from src.config.config import global_config
from src.core.types import ActionActivationType, ActionInfo, ComponentType
from src.services.llm_service import LLMServiceClient
from src.person_info.person_info import Person
from src.plugin_runtime.component_query import component_query_service
from src.prompt.prompt_manager import prompt_manager
from src.services.message_service import (
build_readable_messages_with_id,
get_messages_before_time_in_chat,
replace_user_references,
translate_pid_to_description,
)
if TYPE_CHECKING:
from src.common.data_models.info_data_model import TargetPersonInfo
logger = get_logger("planner")
install(extra_lines=3)
class ActionPlanner:
def __init__(self, chat_id: str, action_manager: ActionManager):
self.chat_id = chat_id
self.log_prefix = f"[{_chat_manager.get_session_name(chat_id) or chat_id}]"
self.action_manager = action_manager
# LLM规划器配置
self.planner_llm = LLMServiceClient(
task_name="planner", request_type="planner"
) # 用于动作规划
self.last_obs_time_mark = 0.0
self.plan_log: List[Tuple[str, float, Union[List[ActionPlannerInfo], str]]] = []
# 黑话缓存:使用 OrderedDict 实现 LRU最多缓存10个
self.unknown_words_cache: OrderedDict[str, None] = OrderedDict()
self.unknown_words_cache_limit = 10
def find_message_by_id(
self, message_id: str, message_id_list: List[Tuple[str, "SessionMessage"]]
) -> Optional["SessionMessage"]:
# sourcery skip: use-next
"""
根据message_id从message_id_list中查找对应的原始消息
Args:
message_id: 要查找的消息ID
message_id_list: 消息ID列表格式为[{'id': str, 'message': dict}, ...]
Returns:
找到的原始消息字典如果未找到则返回None
"""
for item in message_id_list:
if item[0] == message_id:
return item[1]
return None
def _replace_message_ids_with_text(
self, text: Optional[str], message_id_list: List[Tuple[str, "SessionMessage"]]
) -> Optional[str]:
"""将文本中的 m+数字 消息ID替换为原消息内容并添加双引号"""
if not text:
return text
id_to_message = dict(message_id_list)
# 匹配m后带2-4位数字前后不是字母数字下划线
pattern = r"(?<![A-Za-z0-9_])m\d{2,4}(?![A-Za-z0-9_])"
matches = re.findall(pattern, text)
if matches:
available_ids = set(id_to_message.keys())
found_ids = set(matches)
missing_ids = found_ids - available_ids
if missing_ids:
logger.info(
f"{self.log_prefix}planner理由中引用的消息ID不在当前上下文中: {missing_ids}, 可用ID: {list(available_ids)[:10]}..."
)
logger.info(
f"{self.log_prefix}planner理由替换: 找到{len(matches)}个消息ID引用其中{len(found_ids & available_ids)}个在上下文中"
)
def _replace(match: re.Match[str]) -> str:
msg_id = match.group(0)
message = id_to_message.get(msg_id)
if not message:
logger.warning(f"{self.log_prefix}planner理由引用 {msg_id} 未找到对应消息,保持原样")
return msg_id
msg_text = (message.processed_plain_text or "").strip()
if not msg_text:
logger.warning(f"{self.log_prefix}planner理由引用 {msg_id} 的消息内容为空,保持原样")
return msg_id
# 替换 [picid:xxx] 为 [图片:描述]
pic_pattern = r"\[picid:([^\]]+)\]"
def replace_pic_id(pic_match: re.Match) -> str:
pic_id = pic_match.group(1)
description = translate_pid_to_description(pic_id)
return f"[图片:{description}]"
msg_text = re.sub(pic_pattern, replace_pic_id, msg_text)
# 替换用户引用格式:回复<aaa:bbb> 和 @<aaa:bbb>
platform = message.platform or ""
if not platform:
logger.warning(
f"{self.log_prefix}planner: message {message.message_id} has no platform set, bot-self detection will be skipped"
)
msg_text = replace_user_references(msg_text, platform, replace_bot_name=True)
# 替换单独的 <用户名:用户ID> 格式replace_user_references 已处理回复<和@<格式)
# 匹配所有 <aaa:bbb> 格式,由于 replace_user_references 已经替换了回复<和@<格式,
# 这里匹配到的应该都是单独的格式
user_ref_pattern = r"<([^:<>]+):([^:<>]+)>"
def replace_user_ref(user_match: re.Match) -> str:
user_name = user_match.group(1)
user_id = user_match.group(2)
try:
# 检查是否是机器人自己
if is_bot_self(platform, str(user_id)):
return f"{global_config.bot.nickname}(你)"
person = Person(platform=platform, user_id=user_id)
return person.person_name or user_name
except Exception:
# 如果解析失败,使用原始昵称
return user_name
msg_text = re.sub(user_ref_pattern, replace_user_ref, msg_text)
preview = msg_text if len(msg_text) <= 100 else f"{msg_text[:97]}..."
logger.info(f"{self.log_prefix}planner理由引用 {msg_id} -> 消息({preview}")
return f"消息({msg_text}"
return re.sub(pattern, _replace, text)
def _parse_single_action(
self,
action_json: dict,
message_id_list: List[Tuple[str, "SessionMessage"]],
current_available_actions: List[Tuple[str, ActionInfo]],
extracted_reasoning: str = "",
) -> List[ActionPlannerInfo]:
"""解析单个action JSON并返回ActionPlannerInfo列表"""
action_planner_infos = []
try:
action = action_json.get("action", "no_reply")
# 使用 extracted_reasoning整体推理文本作为 reasoning
if extracted_reasoning:
reasoning = self._replace_message_ids_with_text(extracted_reasoning, message_id_list)
if reasoning is None:
reasoning = extracted_reasoning
else:
reasoning = "未提供原因"
action_data = {key: value for key, value in action_json.items() if key not in ["action"]}
# 非no_reply动作需要target_message_id
target_message = None
target_message_id = action_json.get("target_message_id")
if target_message_id:
# 根据target_message_id查找原始消息
target_message = self.find_message_by_id(target_message_id, message_id_list)
if target_message is None:
logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
# 选择最新消息作为target_message
target_message = message_id_list[-1][1]
else:
target_message = message_id_list[-1][1]
logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id使用最新消息作为target_message")
if action != "no_reply" and target_message is not None and self._is_message_from_self(target_message):
logger.info(
f"{self.log_prefix}Planner选择了自己的消息 {target_message_id or target_message.message_id} 作为目标,强制使用 no_reply"
)
reasoning = f"目标消息 {target_message_id or target_message.message_id} 来自机器人自身,违反不回复自身消息规则。原始理由: {reasoning}"
action = "no_reply"
target_message = None
# 验证action是否可用
available_action_names = [action_name for action_name, _ in current_available_actions]
internal_action_names = ["no_reply", "reply", "wait_time"]
if action not in internal_action_names and action not in available_action_names:
logger.warning(
f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names}),将强制使用 'no_reply'"
)
reasoning = (
f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
)
action = "no_reply"
# 创建ActionPlannerInfo对象
# 将列表转换为字典格式
available_actions_dict = dict(current_available_actions)
action_planner_infos.append(
ActionPlannerInfo(
action_type=action,
reasoning=reasoning,
action_data=action_data,
action_message=target_message,
available_actions=available_actions_dict,
action_reasoning=extracted_reasoning or None,
)
)
except Exception as e:
logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
# 将列表转换为字典格式
available_actions_dict = dict(current_available_actions)
action_planner_infos.append(
ActionPlannerInfo(
action_type="no_reply",
reasoning=f"解析单个action时出错: {e}",
action_data={},
action_message=None,
available_actions=available_actions_dict,
action_reasoning=extracted_reasoning or None,
)
)
return action_planner_infos
def _is_message_from_self(self, message: "SessionMessage") -> bool:
"""判断消息是否由机器人自身发送(支持多平台,包括 WebUI"""
try:
return is_bot_self(message.platform or "", str(message.message_info.user_info.user_id))
except AttributeError:
logger.warning(f"{self.log_prefix}检测消息发送者失败,缺少必要字段")
return False
def _update_unknown_words_cache(self, new_words: List[str]) -> None:
"""
更新黑话缓存,将新的黑话加入缓存
Args:
new_words: 新提取的黑话列表
"""
for word in new_words:
if not isinstance(word, str):
continue
word = word.strip()
if not word:
continue
# 如果已存在移到末尾LRU
if word in self.unknown_words_cache:
self.unknown_words_cache.move_to_end(word)
else:
# 添加新词
self.unknown_words_cache[word] = None
# 如果超过限制,移除最老的
if len(self.unknown_words_cache) > self.unknown_words_cache_limit:
self.unknown_words_cache.popitem(last=False)
logger.debug(f"{self.log_prefix}黑话缓存已满,移除最老的黑话")
def _merge_unknown_words_with_cache(self, new_words: Optional[List[str]]) -> List[str]:
"""
合并新提取的黑话和缓存中的黑话
Args:
new_words: 新提取的黑话列表可能为None
Returns:
合并后的黑话列表(去重)
"""
# 清理新提取的黑话
cleaned_new_words: List[str] = []
if new_words:
for word in new_words:
if isinstance(word, str):
if word := word.strip():
cleaned_new_words.append(word)
# 获取缓存中的黑话列表
cached_words = list(self.unknown_words_cache.keys())
# 合并并去重(保留顺序:新提取的在前,缓存的在后)
merged_words: List[str] = []
seen = set()
# 先添加新提取的
for word in cleaned_new_words:
if word not in seen:
merged_words.append(word)
seen.add(word)
# 再添加缓存的(如果不在新提取的列表中)
for word in cached_words:
if word not in seen:
merged_words.append(word)
seen.add(word)
return merged_words
def _process_unknown_words_cache(self, actions: List[ActionPlannerInfo]) -> None:
"""
处理黑话缓存逻辑:
1. 检查是否有 reply action 提取了 unknown_words
2. 如果没有提取移除最老的1个
3. 如果缓存数量大于5移除最老的2个
4. 对于每个 reply action合并缓存和新提取的黑话
5. 更新缓存
Args:
actions: 解析后的动作列表
"""
# 先检查缓存数量如果大于5移除最老的2个
if len(self.unknown_words_cache) > 5:
# 移除最老的2个
removed_count = 0
for _ in range(2):
if len(self.unknown_words_cache) > 0:
self.unknown_words_cache.popitem(last=False)
removed_count += 1
if removed_count > 0:
logger.debug(f"{self.log_prefix}缓存数量大于5移除最老的{removed_count}个缓存")
# 检查是否有 reply action 提取了 unknown_words
has_extracted_unknown_words = False
for action in actions:
if action.action_type == "reply":
action_data = action.action_data or {}
unknown_words = action_data.get("unknown_words")
if unknown_words and isinstance(unknown_words, list) and len(unknown_words) > 0:
has_extracted_unknown_words = True
break
# 如果当前 plan 的 reply 没有提取移除最老的1个
if not has_extracted_unknown_words and len(self.unknown_words_cache) > 0:
self.unknown_words_cache.popitem(last=False)
logger.debug(f"{self.log_prefix}当前 plan 的 reply 没有提取黑话移除最老的1个缓存")
# 对于每个 reply action合并缓存和新提取的黑话
for action in actions:
if action.action_type == "reply":
action_data = action.action_data or {}
new_words = action_data.get("unknown_words")
# 合并新提取的和缓存的黑话列表
if merged_words := self._merge_unknown_words_with_cache(new_words):
action_data["unknown_words"] = merged_words
logger.debug(
f"{self.log_prefix}合并黑话:新提取 {len(new_words) if new_words else 0} 个,"
f"缓存 {len(self.unknown_words_cache)} 个,合并后 {len(merged_words)}"
)
else:
# 如果没有合并后的黑话,移除 unknown_words 字段
action_data.pop("unknown_words", None)
# 更新缓存(将新提取的黑话加入缓存)
if new_words:
self._update_unknown_words_cache(new_words)
async def plan(
self,
available_actions: Dict[str, ActionInfo],
loop_start_time: float = 0.0,
force_reply_message: Optional["SessionMessage"] = None,
) -> List[ActionPlannerInfo]:
# sourcery skip: use-named-expression
"""
规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
"""
plan_start = time.perf_counter()
# 获取聊天上下文
message_list_before_now = get_messages_before_time_in_chat(
chat_id=self.chat_id,
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.6),
filter_intercept_message_level=1,
)
message_id_list: list[Tuple[str, "SessionMessage"]] = []
chat_content_block, message_id_list = build_readable_messages_with_id(
messages=message_list_before_now,
timestamp_mode="normal_no_YMD",
read_mark=self.last_obs_time_mark,
truncate=True,
show_actions=True,
)
message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
messages=message_list_before_now_short,
timestamp_mode="normal_no_YMD",
truncate=False,
show_actions=False,
)
self.last_obs_time_mark = time.time()
# 获取必要信息
is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
# 应用激活类型过滤
filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
prompt_build_start = time.perf_counter()
# 构建包含所有动作的提示词
prompt, message_id_list = await self.build_planner_prompt(
is_group_chat=is_group_chat,
chat_target_info=chat_target_info,
current_available_actions=filtered_actions,
chat_content_block=chat_content_block,
message_id_list=message_id_list,
)
prompt_build_ms = (time.perf_counter() - prompt_build_start) * 1000
# 调用LLM获取决策
reasoning, actions, llm_raw_output, llm_reasoning, llm_duration_ms = await self._execute_main_planner(
prompt=prompt,
message_id_list=message_id_list,
filtered_actions=filtered_actions,
available_actions=available_actions,
loop_start_time=loop_start_time,
)
# 如果有强制回复消息,确保回复该消息
if force_reply_message:
# 检查是否已经有回复该消息的 action
has_reply_to_force_message = any(
action.action_type == "reply"
and action.action_message
and action.action_message.message_id == force_reply_message.message_id
for action in actions
)
# 如果没有回复该消息,强制添加回复 action
if not has_reply_to_force_message:
# 移除所有 no_reply action如果有
actions = [a for a in actions if a.action_type != "no_reply"]
# 创建强制回复 action
available_actions_dict = dict(current_available_actions)
force_reply_action = ActionPlannerInfo(
action_type="reply",
reasoning="用户提及了我,必须回复该消息",
action_data={"loop_start_time": loop_start_time},
action_message=force_reply_message,
available_actions=available_actions_dict,
action_reasoning=None,
)
# 将强制回复 action 放在最前面
actions.insert(0, force_reply_action)
logger.info(f"{self.log_prefix} 检测到强制回复消息,已添加回复动作")
logger.info(
f"{self.log_prefix}Planner:{reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
)
self.add_plan_log(reasoning, actions)
try:
PlanReplyLogger.log_plan(
chat_id=self.chat_id,
prompt=prompt,
reasoning=reasoning,
raw_output=llm_raw_output,
raw_reasoning=llm_reasoning,
actions=actions,
timing={
"prompt_build_ms": round(prompt_build_ms, 2),
"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
"total_plan_ms": round((time.perf_counter() - plan_start) * 1000, 2),
"loop_start_time": loop_start_time,
},
extra=None,
)
except Exception:
logger.exception(f"{self.log_prefix}记录plan日志失败")
return actions
def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
self.plan_log.append((reasoning, time.time(), actions))
if len(self.plan_log) > 20:
self.plan_log.pop(0)
def add_plan_excute_log(self, result: str):
self.plan_log.append(("", time.time(), result))
if len(self.plan_log) > 20:
self.plan_log.pop(0)
def get_plan_log_str(self, max_action_records: int = 2, max_execution_records: int = 5) -> str:
"""
获取计划日志字符串
Args:
max_action_records: 显示多少条最新的action记录默认2
max_execution_records: 显示多少条最新执行结果记录默认8
Returns:
格式化的日志字符串
"""
action_records = []
execution_records = []
# 从后往前遍历,收集最新的记录
for reasoning, timestamp, content in reversed(self.plan_log):
if isinstance(content, list) and all(isinstance(action, ActionPlannerInfo) for action in content):
if len(action_records) < max_action_records:
action_records.append((reasoning, timestamp, content, "action"))
elif len(execution_records) < max_execution_records:
execution_records.append((reasoning, timestamp, content, "execution"))
# 合并所有记录并按时间戳排序
all_records = action_records + execution_records
all_records.sort(key=lambda x: x[1]) # 按时间戳排序
plan_log_str = ""
# 按时间顺序添加所有记录
for reasoning, timestamp, content, record_type in all_records:
time_str = datetime.fromtimestamp(timestamp).strftime("%H:%M:%S")
if record_type == "action":
# plan_log_str += f"{time_str}:{reasoning}|你使用了{','.join([action.action_type for action in content])}\n"
plan_log_str += f"{time_str}:{reasoning}\n"
else:
plan_log_str += f"{time_str}:你执行了action:{content}\n"
return plan_log_str
async def build_planner_prompt(
self,
is_group_chat: bool,
chat_target_info: Optional["TargetPersonInfo"],
current_available_actions: Dict[str, ActionInfo],
message_id_list: List[Tuple[str, "SessionMessage"]],
chat_content_block: str = "",
interest: str = "",
) -> tuple[str, List[Tuple[str, "SessionMessage"]]]:
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
try:
actions_before_now_block = self.get_plan_log_str()
# 构建聊天上下文描述
chat_context_description = "你现在正在一个群聊中"
# 构建动作选项块
action_options_block = await self._build_action_options_block(current_available_actions)
# 其他信息
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。"
time_block = f"当前时间:{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
bot_name = global_config.bot.nickname
bot_nickname = (
f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
)
name_block = f"你的名字是{bot_name}{bot_nickname},请注意哪些是你自己的发言。"
# 根据 think_mode 配置决定 reply action 的示例 JSON
# 在 JSON 中直接作为 action 参数携带 unknown_words
if global_config.chat.think_mode == "classic":
reply_action_example = ""
if global_config.chat.llm_quote:
reply_action_example += (
"5.如果要明确回复消息使用quote如果消息不多不需要明确回复设置quote为false\n"
)
reply_action_example += (
'{{"action":"reply", "target_message_id":"消息id(m+数字)", "unknown_words":["词语1","词语2"]'
)
if global_config.chat.llm_quote:
reply_action_example += ', "quote":"如果需要引用该message设置为true"'
reply_action_example += "}"
else:
reply_action_example = (
"5.think_level表示思考深度0表示该回复不需要思考和回忆1表示该回复需要进行回忆和思考\n"
)
if global_config.chat.llm_quote:
reply_action_example += (
"6.如果要明确回复消息使用quote如果消息不多不需要明确回复设置quote为false\n"
)
reply_action_example += (
'{{"action":"reply", "think_level":数值等级(0或1), '
'"target_message_id":"消息id(m+数字)", '
'"unknown_words":["词语1","词语2"]'
)
if global_config.chat.llm_quote:
reply_action_example += ', "quote":"如果需要引用该message设置为true"'
reply_action_example += "}"
planner_prompt_template = prompt_manager.get_prompt("planner")
planner_prompt_template.add_context("time_block", time_block)
planner_prompt_template.add_context("chat_context_description", chat_context_description)
planner_prompt_template.add_context("chat_content_block", chat_content_block)
planner_prompt_template.add_context("actions_before_now_block", actions_before_now_block)
planner_prompt_template.add_context("action_options_text", action_options_block)
planner_prompt_template.add_context("moderation_prompt", moderation_prompt_block)
planner_prompt_template.add_context("name_block", name_block)
planner_prompt_template.add_context("interest", interest)
planner_prompt_template.add_context("plan_style", global_config.personality.plan_style)
planner_prompt_template.add_context("reply_action_example", reply_action_example)
prompt = await prompt_manager.render_prompt(planner_prompt_template)
return prompt, message_id_list
except Exception as e:
logger.error(f"构建 Planner 提示词时出错: {e}")
logger.error(traceback.format_exc())
return "构建 Planner Prompt 时出错", []
def get_necessary_info(self) -> Tuple[bool, Optional["TargetPersonInfo"], Dict[str, ActionInfo]]:
"""
获取 Planner 需要的必要信息
"""
is_group_chat = True
is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}")
current_available_actions_dict = self.action_manager.get_using_actions()
# 获取完整的动作信息
all_registered_actions: Dict[str, ActionInfo] = component_query_service.get_components_by_type( # type: ignore
ComponentType.ACTION
)
current_available_actions = {}
for action_name in current_available_actions_dict:
if action_name in all_registered_actions:
current_available_actions[action_name] = all_registered_actions[action_name]
else:
logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
return is_group_chat, chat_target_info, current_available_actions
def _filter_actions_by_activation_type(
self, available_actions: Dict[str, ActionInfo], chat_content_block: str
) -> Dict[str, ActionInfo]:
"""根据激活类型过滤动作"""
filtered_actions = {}
for action_name, action_info in available_actions.items():
if action_info.activation_type == ActionActivationType.NEVER:
logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型,跳过")
continue
elif action_info.activation_type == ActionActivationType.ALWAYS:
filtered_actions[action_name] = action_info
elif action_info.activation_type == ActionActivationType.RANDOM:
if random.random() < action_info.random_activation_probability:
filtered_actions[action_name] = action_info
elif action_info.activation_type == ActionActivationType.KEYWORD:
if action_info.activation_keywords:
for keyword in action_info.activation_keywords:
if keyword in chat_content_block:
filtered_actions[action_name] = action_info
break
else:
logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type},跳过处理")
return filtered_actions
async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
"""构建动作选项块"""
if not current_available_actions:
return ""
action_options_block = ""
for action_name, action_info in current_available_actions.items():
# 构建参数文本
param_text = ""
if action_info.action_parameters:
param_text = "\n"
for param_name, param_description in action_info.action_parameters.items():
param_text += f' "{param_name}":"{param_description}"\n'
param_text = param_text.rstrip("\n")
# 构建要求文本
require_text = "\n".join(f"- {require_item}" for require_item in action_info.action_require)
parallel_text = "" if action_info.parallel_action else "(当选择这个动作时,请不要选择其他动作)"
# 获取动作提示模板并填充
using_action_prompt = prompt_manager.get_prompt("action")
using_action_prompt.add_context("action_name", action_name)
using_action_prompt.add_context("action_description", action_info.description)
using_action_prompt.add_context("action_parameters", param_text)
using_action_prompt.add_context("action_require", require_text)
using_action_prompt.add_context("parallel_text", parallel_text)
using_action_rendered_prompt = await prompt_manager.render_prompt(using_action_prompt)
action_options_block += using_action_rendered_prompt
return action_options_block
async def _execute_main_planner(
self,
prompt: str,
message_id_list: List[Tuple[str, "SessionMessage"]],
filtered_actions: Dict[str, ActionInfo],
available_actions: Dict[str, ActionInfo],
loop_start_time: float,
) -> Tuple[str, List[ActionPlannerInfo], Optional[str], Optional[str], Optional[float]]:
"""执行主规划器"""
llm_content = None
actions: List[ActionPlannerInfo] = []
llm_reasoning = None
llm_duration_ms = None
try:
# 调用LLM
llm_start = time.perf_counter()
generation_result = await self.planner_llm.generate_response(prompt=prompt)
llm_content = generation_result.response
reasoning_content = generation_result.reasoning
llm_duration_ms = (time.perf_counter() - llm_start) * 1000
llm_reasoning = reasoning_content
if global_config.debug.show_planner_prompt:
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
if reasoning_content:
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
else:
logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
if reasoning_content:
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
except Exception as req_e:
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
return (
f"LLM 请求失败,模型出现问题: {req_e}",
[
ActionPlannerInfo(
action_type="no_reply",
reasoning=f"LLM 请求失败,模型出现问题: {req_e}",
action_data={},
action_message=None,
available_actions=available_actions,
)
],
llm_content,
llm_reasoning,
llm_duration_ms,
)
# 解析LLM响应
extracted_reasoning = ""
if llm_content:
try:
json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
extracted_reasoning = self._replace_message_ids_with_text(extracted_reasoning, message_id_list) or ""
if json_objects:
logger.debug(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
filtered_actions_list = list(filtered_actions.items())
for json_obj in json_objects:
actions.extend(
self._parse_single_action(
json_obj, message_id_list, filtered_actions_list, extracted_reasoning
)
)
else:
# 尝试解析为直接的JSON
logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
extracted_reasoning = "LLM没有返回可用动作"
actions = self._create_no_reply("LLM没有返回可用动作", available_actions)
except Exception as json_e:
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
extracted_reasoning = f"解析LLM响应JSON失败: {json_e}"
actions = self._create_no_reply(f"解析LLM响应JSON失败: {json_e}", available_actions)
traceback.print_exc()
else:
extracted_reasoning = "规划器没有获得LLM响应"
actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)
# 添加循环开始时间到所有非no_reply动作
for action in actions:
action.action_data = action.action_data or {}
action.action_data["loop_start_time"] = loop_start_time
# 去重:如果同一个动作被选择了多次,随机选择其中一个
if actions:
shuffled = actions.copy()
random.shuffle(shuffled)
actions = list({a.action_type: a for a in shuffled}.values())
# 处理黑话缓存逻辑
self._process_unknown_words_cache(actions)
logger.debug(f"{self.log_prefix}规划器选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}")
return extracted_reasoning, actions, llm_content, llm_reasoning, llm_duration_ms
def _create_no_reply(self, reasoning: str, available_actions: Dict[str, ActionInfo]) -> List[ActionPlannerInfo]:
"""创建no_reply"""
return [
ActionPlannerInfo(
action_type="no_reply",
reasoning=reasoning,
action_data={},
action_message=None,
available_actions=available_actions,
)
]
def _extract_json_from_markdown(self, content: str) -> Tuple[List[dict], str]:
# sourcery skip: for-append-to-extend
"""从Markdown格式的内容中提取JSON对象和推理内容"""
json_objects = []
reasoning_content = ""
# 使用正则表达式查找```json包裹的JSON内容
json_pattern = r"```json\s*(.*?)\s*```"
markdown_matches = re.findall(json_pattern, content, re.DOTALL)
# 提取JSON之前的内容作为推理文本
first_json_pos = len(content)
if markdown_matches:
# 找到第一个```json的位置
first_json_pos = content.find("```json")
if first_json_pos > 0:
reasoning_content = content[:first_json_pos].strip()
# 清理推理内容中的注释标记
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
reasoning_content = reasoning_content.strip()
# 处理```json包裹的JSON
for match in markdown_matches:
try:
# 清理可能的注释和格式问题
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
if json_str := json_str.strip():
# 尝试按行分割每行可能是一个JSON对象
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
for line in lines:
with contextlib.suppress(json.JSONDecodeError):
json_obj = json.loads(repair_json(line))
if isinstance(json_obj, dict):
if json_obj:
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict) and item:
json_objects.append(item)
# 如果按行解析没有成功或只得到空字典尝试将整个块作为一个JSON对象或数组
if not json_objects:
json_obj = json.loads(repair_json(json_str))
if isinstance(json_obj, dict):
# 过滤掉空字典
if json_obj:
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict) and item:
json_objects.append(item)
except Exception as e:
logger.warning(f"解析JSON块失败: {e}, 块内容: {match[:100]}...")
continue
# 如果没有找到完整的```json```块,尝试查找不完整的代码块(缺少结尾```
if not json_objects:
json_start_pos = content.find("```json")
if json_start_pos != -1:
# 找到```json之后的内容
json_content_start = json_start_pos + 7 # ```json的长度
# 提取从```json之后到内容结尾的所有内容
incomplete_json_str = content[json_content_start:].strip()
# 提取JSON之前的内容作为推理文本
if json_start_pos > 0:
reasoning_content = content[:json_start_pos].strip()
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
reasoning_content = reasoning_content.strip()
if incomplete_json_str:
try:
# 清理可能的注释和格式问题
json_str = re.sub(r"//.*?\n", "\n", incomplete_json_str)
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
json_str = json_str.strip()
if json_str:
# 尝试按行分割每行可能是一个JSON对象
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
for line in lines:
try:
json_obj = json.loads(repair_json(line))
if isinstance(json_obj, dict):
# 过滤掉空字典,避免单个 { 字符被错误修复为 {} 的情况
if json_obj:
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict) and item:
json_objects.append(item)
except json.JSONDecodeError:
pass
# 如果按行解析没有成功或只得到空字典尝试将整个块作为一个JSON对象或数组
if not json_objects:
try:
json_obj = json.loads(repair_json(json_str))
if isinstance(json_obj, dict):
# 过滤掉空字典
if json_obj:
json_objects.append(json_obj)
elif isinstance(json_obj, list):
for item in json_obj:
if isinstance(item, dict) and item:
json_objects.append(item)
except Exception as e:
logger.debug(f"尝试解析不完整的JSON代码块失败: {e}")
except Exception as e:
logger.debug(f"处理不完整的JSON代码块时出错: {e}")
return json_objects, reasoning_content

335
src/core/tooling.py Normal file
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@@ -0,0 +1,335 @@
"""统一工具抽象。
该模块定义主程序内部统一使用的工具声明、调用与执行结果模型,
用于收敛插件 Tool、兼容旧 Action、MaiSaka 内置 Tool 与 MCP Tool。
"""
from __future__ import annotations
from copy import deepcopy
from dataclasses import dataclass, field
from typing import Any, Dict, Optional, Protocol, runtime_checkable
import json
from src.common.logger import get_logger
from src.llm_models.payload_content.tool_option import ToolDefinitionInput
logger = get_logger("core.tooling")
def _normalize_schema_type(raw_type: Any) -> str:
"""将原始 Schema 类型值规范化为可读字符串。
Args:
raw_type: 原始类型值。
Returns:
str: 规范化后的类型名称。
"""
normalized_type = str(raw_type or "").strip().lower()
if not normalized_type:
return "string"
if normalized_type == "number":
return "number"
if normalized_type == "integer":
return "integer"
if normalized_type == "boolean":
return "boolean"
if normalized_type == "array":
return "array"
if normalized_type == "object":
return "object"
return normalized_type
def build_tool_detailed_description(
parameters_schema: Optional[Dict[str, Any]],
fallback_description: str = "",
) -> str:
"""根据参数 Schema 构建工具详细描述。
Args:
parameters_schema: 工具参数对象级 Schema。
fallback_description: 无法从 Schema 解析时使用的兜底说明。
Returns:
str: 生成后的详细描述文本。
"""
if not parameters_schema:
return fallback_description.strip()
properties = parameters_schema.get("properties")
if not isinstance(properties, dict) or not properties:
return fallback_description.strip()
required_names = {
str(name).strip()
for name in parameters_schema.get("required", [])
if str(name).strip()
}
lines = ["参数说明:"]
for parameter_name, parameter_schema in properties.items():
if not isinstance(parameter_schema, dict):
continue
normalized_name = str(parameter_name).strip()
parameter_type = _normalize_schema_type(parameter_schema.get("type"))
required_text = "必填" if normalized_name in required_names else "可选"
parameter_description = str(parameter_schema.get("description", "") or "").strip() or "无额外说明"
line = f"- {normalized_name}{parameter_type}{required_text}{parameter_description}"
if isinstance(parameter_schema.get("enum"), list) and parameter_schema["enum"]:
enum_values = "".join(str(item) for item in parameter_schema["enum"])
line += f" 可选值:{enum_values}"
if "default" in parameter_schema:
line += f" 默认值:{parameter_schema['default']}"
lines.append(line)
if len(lines) == 1:
return fallback_description.strip()
if fallback_description.strip():
lines.append("")
lines.append(fallback_description.strip())
return "\n".join(lines).strip()
@dataclass(slots=True)
class ToolSpec:
"""统一工具声明。"""
name: str
brief_description: str
detailed_description: str = ""
parameters_schema: Dict[str, Any] | None = None
provider_name: str = ""
provider_type: str = ""
enabled: bool = True
metadata: Dict[str, Any] = field(default_factory=dict)
def build_llm_description(self) -> str:
"""构建供 LLM 使用的描述文本。
Returns:
str: 合并后的单段工具描述。
"""
parts = [self.brief_description.strip()]
if self.detailed_description.strip():
parts.append(self.detailed_description.strip())
return "\n\n".join(part for part in parts if part).strip()
def to_llm_definition(self) -> ToolDefinitionInput:
"""转换为统一的 LLM 工具定义。
Returns:
ToolDefinitionInput: 可直接交给模型层的工具定义。
"""
definition: Dict[str, Any] = {
"name": self.name,
"description": self.build_llm_description(),
}
if self.parameters_schema is not None:
definition["parameters_schema"] = deepcopy(self.parameters_schema)
return definition
@dataclass(slots=True)
class ToolInvocation:
"""统一工具调用请求。"""
tool_name: str
arguments: Dict[str, Any] = field(default_factory=dict)
call_id: str = ""
session_id: str = ""
stream_id: str = ""
reasoning: str = ""
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass(slots=True)
class ToolExecutionContext:
"""统一工具执行上下文。"""
session_id: str = ""
stream_id: str = ""
reasoning: str = ""
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass(slots=True)
class ToolExecutionResult:
"""统一工具执行结果。"""
tool_name: str
success: bool
content: str = ""
error_message: str = ""
structured_content: Any = None
metadata: Dict[str, Any] = field(default_factory=dict)
def get_history_content(self) -> str:
"""获取适合写入对话历史的结果文本。
Returns:
str: 优先使用文本内容,其次使用错误信息。
"""
if self.content.strip():
return self.content.strip()
if self.structured_content is not None:
if isinstance(self.structured_content, str):
return self.structured_content.strip()
try:
return json.dumps(self.structured_content, ensure_ascii=False)
except (TypeError, ValueError):
return str(self.structured_content).strip()
return self.error_message.strip()
@runtime_checkable
class ToolProvider(Protocol):
"""统一工具提供者协议。"""
provider_name: str
provider_type: str
async def list_tools(self) -> list[ToolSpec]:
"""列出当前 Provider 暴露的全部工具。"""
...
async def invoke(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行指定工具调用。"""
...
async def close(self) -> None:
"""释放 Provider 资源。"""
...
class ToolRegistry:
"""统一工具注册表。"""
def __init__(self) -> None:
self._providers: list[ToolProvider] = []
def register_provider(self, provider: ToolProvider) -> None:
"""注册一个工具提供者。
Args:
provider: 待注册的工具提供者。
"""
self._providers = [item for item in self._providers if item.provider_name != provider.provider_name]
self._providers.append(provider)
def unregister_provider(self, provider_name: str) -> None:
"""注销指定名称的工具提供者。
Args:
provider_name: 待移除的 Provider 名称。
"""
self._providers = [item for item in self._providers if item.provider_name != provider_name]
async def list_tools(self) -> list[ToolSpec]:
"""按 Provider 顺序列出全部去重后的工具。
Returns:
list[ToolSpec]: 去重后的工具列表。
"""
collected_specs: list[ToolSpec] = []
seen_names: set[str] = set()
for provider in self._providers:
provider_specs = await provider.list_tools()
for spec in provider_specs:
if not spec.enabled:
continue
if spec.name in seen_names:
logger.warning(
f"检测到重复工具名 {spec.name},保留先注册的工具,跳过 provider={provider.provider_name}"
)
continue
seen_names.add(spec.name)
collected_specs.append(spec)
return collected_specs
async def get_tool_spec(self, tool_name: str) -> Optional[ToolSpec]:
"""查询指定工具声明。
Args:
tool_name: 工具名称。
Returns:
Optional[ToolSpec]: 匹配到的工具声明。
"""
for spec in await self.list_tools():
if spec.name == tool_name:
return spec
return None
async def has_tool(self, tool_name: str) -> bool:
"""判断指定工具是否存在。
Args:
tool_name: 工具名称。
Returns:
bool: 是否存在。
"""
return await self.get_tool_spec(tool_name) is not None
async def get_llm_definitions(self) -> list[ToolDefinitionInput]:
"""获取供 LLM 使用的工具定义列表。
Returns:
list[ToolDefinitionInput]: 统一工具定义列表。
"""
return [spec.to_llm_definition() for spec in await self.list_tools()]
async def invoke(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行一次工具调用。
Args:
invocation: 工具调用请求。
context: 执行上下文。
Returns:
ToolExecutionResult: 工具执行结果。
"""
for provider in self._providers:
provider_specs = await provider.list_tools()
if any(spec.name == invocation.tool_name and spec.enabled for spec in provider_specs):
return await provider.invoke(invocation, context)
return ToolExecutionResult(
tool_name=invocation.tool_name,
success=False,
error_message=f"未找到工具:{invocation.tool_name}",
)
async def close(self) -> None:
"""关闭全部 Provider。"""
for provider in self._providers:
await provider.close()

View File

@@ -1,124 +1,163 @@
""" """Maisaka 内置工具声明。"""
MaiSaka built-in tool definitions.
"""
from typing import List from copy import deepcopy
from typing import Any, Dict, List
from src.llm_models.payload_content.tool_option import ToolOption, ToolParamType from src.core.tooling import ToolSpec, build_tool_detailed_description
from src.llm_models.payload_content.tool_option import ToolDefinitionInput
def create_builtin_tools() -> List[ToolOption]: def _build_tool_spec(
"""Create built-in tools exposed to the main chat-loop model.""" name: str,
from src.llm_models.payload_content.tool_option import ToolOptionBuilder brief_description: str,
parameters_schema: Dict[str, Any] | None = None,
detailed_description: str = "",
) -> ToolSpec:
"""构建单个内置工具声明。
tools: List[ToolOption] = [] Args:
name: 工具名称。
brief_description: 简要描述。
parameters_schema: 参数 Schema。
detailed_description: 详细描述;为空时自动根据参数生成。
wait_builder = ToolOptionBuilder() Returns:
wait_builder.set_name("wait") ToolSpec: 构建完成的工具声明。
wait_builder.set_description("Pause speaking and wait for the user to provide more input.") """
wait_builder.add_param(
name="seconds", normalized_schema = deepcopy(parameters_schema) if parameters_schema is not None else None
param_type=ToolParamType.INTEGER, return ToolSpec(
description="How many seconds to wait before timing out.", name=name,
required=True, brief_description=brief_description,
enum_values=None, detailed_description=(
detailed_description.strip()
or build_tool_detailed_description(normalized_schema)
),
parameters_schema=normalized_schema,
provider_name="maisaka_builtin",
provider_type="builtin",
) )
tools.append(wait_builder.build())
reply_builder = ToolOptionBuilder()
reply_builder.set_name("reply")
reply_builder.set_description(
"Generate and emit a visible reply based on the current thought. "
"You must specify the target user msg_id to reply to."
)
reply_builder.add_param(
name="msg_id",
param_type=ToolParamType.STRING,
description="The msg_id of the specific user message that this reply should target.",
required=True,
enum_values=None,
)
reply_builder.add_param(
name="quote",
param_type=ToolParamType.BOOLEAN,
description="Whether the visible reply should be sent as a quoted reply to the target msg_id.",
required=False,
enum_values=None,
)
reply_builder.add_param(
name="unknown_words",
param_type=ToolParamType.ARRAY,
description="Optional list of words or phrases that may need jargon lookup before replying.",
required=False,
enum_values=None,
items_schema={"type": "string"},
)
tools.append(reply_builder.build())
query_jargon_builder = ToolOptionBuilder()
query_jargon_builder.set_name("query_jargon")
query_jargon_builder.set_description(
"Query the meanings of one or more jargon words in the current chat context."
)
query_jargon_builder.add_param(
name="words",
param_type=ToolParamType.ARRAY,
description="A list of words or phrases to query from the jargon store.",
required=True,
enum_values=None,
items_schema={"type": "string"},
)
tools.append(query_jargon_builder.build())
query_person_info_builder = ToolOptionBuilder()
query_person_info_builder.set_name("query_person_info")
query_person_info_builder.set_description(
"Query profile and memory information about a specific person by person name, nickname, or user ID."
)
query_person_info_builder.add_param(
name="person_name",
param_type=ToolParamType.STRING,
description="The person's name, nickname, or user ID to search for.",
required=True,
enum_values=None,
)
query_person_info_builder.add_param(
name="limit",
param_type=ToolParamType.INTEGER,
description="Maximum number of matched person records to return. Defaults to 3.",
required=False,
enum_values=None,
)
tools.append(query_person_info_builder.build())
no_reply_builder = ToolOptionBuilder()
no_reply_builder.set_name("no_reply")
no_reply_builder.set_description("Do not emit a visible reply this round and continue thinking.")
tools.append(no_reply_builder.build())
stop_builder = ToolOptionBuilder()
stop_builder.set_name("stop")
stop_builder.set_description("Stop the current inner loop and return control to the outer chat flow.")
tools.append(stop_builder.build())
send_emoji_builder = ToolOptionBuilder()
send_emoji_builder.set_name("send_emoji")
send_emoji_builder.set_description(
"Send an emoji sticker to help express emotions. "
"You should specify the emotion type to select an appropriate emoji."
)
send_emoji_builder.add_param(
name="emotion",
param_type=ToolParamType.STRING,
description="The emotion type for selecting an appropriate emoji (e.g., 'happy', 'sad', 'angry', 'surprised', etc.).",
required=False,
enum_values=None,
)
tools.append(send_emoji_builder.build())
return tools
def get_builtin_tools() -> List[ToolOption]: def create_builtin_tool_specs() -> List[ToolSpec]:
"""Return built-in tools.""" """创建 Maisaka 内置工具声明列表。
return create_builtin_tools()
Returns:
List[ToolSpec]: 内置工具声明列表。
"""
return [
_build_tool_spec(
name="wait",
brief_description="暂停当前对话并等待用户新的输入。",
parameters_schema={
"type": "object",
"properties": {
"seconds": {
"type": "integer",
"description": "等待的秒数。",
},
},
"required": ["seconds"],
},
),
_build_tool_spec(
name="reply",
brief_description="根据当前思考生成并发送一条可见回复。",
parameters_schema={
"type": "object",
"properties": {
"msg_id": {
"type": "string",
"description": "要回复的目标用户消息编号。",
},
"quote": {
"type": "boolean",
"description": "是否以引用回复的方式发送。",
"default": True,
},
"unknown_words": {
"type": "array",
"description": "回复前可能需要查询的黑话或词条列表。",
"items": {"type": "string"},
},
},
"required": ["msg_id"],
},
),
_build_tool_spec(
name="query_jargon",
brief_description="查询当前聊天上下文中的黑话或词条含义。",
parameters_schema={
"type": "object",
"properties": {
"words": {
"type": "array",
"description": "要查询的词条列表。",
"items": {"type": "string"},
},
},
"required": ["words"],
},
),
_build_tool_spec(
name="query_person_info",
brief_description="查询某个人的档案和相关记忆信息。",
parameters_schema={
"type": "object",
"properties": {
"person_name": {
"type": "string",
"description": "人物名称、昵称或用户 ID。",
},
"limit": {
"type": "integer",
"description": "最多返回多少条匹配记录。",
"default": 3,
},
},
"required": ["person_name"],
},
),
_build_tool_spec(
name="no_reply",
brief_description="本轮不发送可见回复,继续下一步思考。",
),
_build_tool_spec(
name="stop",
brief_description="暂停当前内部循环,等待新的外部消息。",
),
_build_tool_spec(
name="send_emoji",
brief_description="发送一个合适的表情包来辅助表达情绪。",
parameters_schema={
"type": "object",
"properties": {
"emotion": {
"type": "string",
"description": "希望表达的情绪,例如 happy、sad、angry 等。",
},
},
},
),
]
def get_builtin_tool_specs() -> List[ToolSpec]:
"""获取 Maisaka 内置工具声明。
Returns:
List[ToolSpec]: 内置工具声明列表。
"""
return create_builtin_tool_specs()
def get_builtin_tools() -> List[ToolDefinitionInput]:
"""获取兼容旧模型层的内置工具定义。
Returns:
List[ToolDefinitionInput]: 可直接传给模型层的工具定义。
"""
return [tool_spec.to_llm_definition() for tool_spec in create_builtin_tool_specs()]

View File

@@ -1,5 +1,7 @@
from dataclasses import dataclass """Maisaka 对话循环服务。"""
from base64 import b64decode from base64 import b64decode
from dataclasses import dataclass
from datetime import datetime from datetime import datetime
from io import BytesIO from io import BytesIO
from time import perf_counter from time import perf_counter
@@ -20,6 +22,7 @@ from src.common.data_models.message_component_data_model import MessageSequence,
from src.common.logger import get_logger from src.common.logger import get_logger
from src.common.prompt_i18n import load_prompt from src.common.prompt_i18n import load_prompt
from src.config.config import global_config from src.config.config import global_config
from src.core.tooling import ToolRegistry
from src.know_u.knowledge import extract_category_ids_from_result from src.know_u.knowledge import extract_category_ids_from_result
from src.llm_models.model_client.base_client import BaseClient from src.llm_models.model_client.base_client import BaseClient
from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
@@ -52,10 +55,19 @@ class MaisakaChatLoopService:
temperature: float = 0.5, temperature: float = 0.5,
max_tokens: int = 2048, max_tokens: int = 2048,
) -> None: ) -> None:
"""初始化 Maisaka 对话循环服务。
Args:
chat_system_prompt: 可选的系统提示词。
temperature: 规划器温度参数。
max_tokens: 规划器最大输出长度。
"""
self._temperature = temperature self._temperature = temperature
self._max_tokens = max_tokens self._max_tokens = max_tokens
self._extra_tools: List[ToolOption] = [] self._extra_tools: List[ToolOption] = []
self._interrupt_flag: asyncio.Event | None = None self._interrupt_flag: asyncio.Event | None = None
self._tool_registry: ToolRegistry | None = None
self._prompts_loaded = False self._prompts_loaded = False
self._prompt_load_lock = asyncio.Lock() self._prompt_load_lock = asyncio.Lock()
self._personality_prompt = self._build_personality_prompt() self._personality_prompt = self._build_personality_prompt()
@@ -67,9 +79,13 @@ class MaisakaChatLoopService:
@property @property
def personality_prompt(self) -> str: def personality_prompt(self) -> str:
"""返回当前人格提示词。"""
return self._personality_prompt return self._personality_prompt
def _build_personality_prompt(self) -> str: def _build_personality_prompt(self) -> str:
"""构造人格提示词。"""
try: try:
bot_name = global_config.bot.nickname bot_name = global_config.bot.nickname
if global_config.bot.alias_names: if global_config.bot.alias_names:
@@ -92,6 +108,12 @@ class MaisakaChatLoopService:
return "Your name is MaiMai; persona: lively and cute AI assistant." return "Your name is MaiMai; persona: lively and cute AI assistant."
async def ensure_chat_prompt_loaded(self, tools_section: str = "") -> None: async def ensure_chat_prompt_loaded(self, tools_section: str = "") -> None:
"""确保主聊天提示词已经加载完成。
Args:
tools_section: 额外注入到提示词中的工具说明片段。
"""
if self._prompts_loaded: if self._prompts_loaded:
return return
@@ -112,8 +134,23 @@ class MaisakaChatLoopService:
self._prompts_loaded = True self._prompts_loaded = True
def set_extra_tools(self, tools: List[ToolDefinitionInput]) -> None: def set_extra_tools(self, tools: List[ToolDefinitionInput]) -> None:
"""设置额外工具定义。
Args:
tools: 兼容旧接口的额外工具定义列表。
"""
self._extra_tools = normalize_tool_options(tools) or [] self._extra_tools = normalize_tool_options(tools) or []
def set_tool_registry(self, tool_registry: ToolRegistry | None) -> None:
"""设置统一工具注册表。
Args:
tool_registry: 统一工具注册表;传入 ``None`` 时退回旧工具列表模式。
"""
self._tool_registry = tool_registry
def set_interrupt_flag(self, interrupt_flag: asyncio.Event | None) -> None: def set_interrupt_flag(self, interrupt_flag: asyncio.Event | None) -> None:
"""设置当前 planner 请求使用的中断标记。""" """设置当前 planner 请求使用的中断标记。"""
self._interrupt_flag = interrupt_flag self._interrupt_flag = interrupt_flag
@@ -329,6 +366,15 @@ class MaisakaChatLoopService:
) )
async def chat_loop_step(self, chat_history: List[LLMContextMessage]) -> ChatResponse: async def chat_loop_step(self, chat_history: List[LLMContextMessage]) -> ChatResponse:
"""执行一轮 Maisaka 规划器请求。
Args:
chat_history: 当前对话历史。
Returns:
ChatResponse: 本轮规划器返回结果。
"""
await self.ensure_chat_prompt_loaded() await self.ensure_chat_prompt_loaded()
selected_history, selection_reason = self._select_llm_context_messages(chat_history) selected_history, selection_reason = self._select_llm_context_messages(chat_history)
@@ -336,7 +382,11 @@ class MaisakaChatLoopService:
del _client del _client
return self._build_request_messages(selected_history) return self._build_request_messages(selected_history)
all_tools: List[ToolDefinitionInput] = [*get_builtin_tools(), *self._extra_tools] all_tools: List[ToolDefinitionInput]
if self._tool_registry is not None:
all_tools = await self._tool_registry.get_llm_definitions()
else:
all_tools = [*get_builtin_tools(), *self._extra_tools]
built_messages = self._build_request_messages(selected_history) built_messages = self._build_request_messages(selected_history)
ordered_panels: List[Panel] = [] ordered_panels: List[Panel] = []

View File

@@ -20,6 +20,7 @@ from src.common.database.database import get_db_session
from src.common.database.database_model import PersonInfo from src.common.database.database_model import PersonInfo
from src.common.logger import get_logger from src.common.logger import get_logger
from src.config.config import global_config from src.config.config import global_config
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation
from src.know_u.knowledge_store import get_knowledge_store from src.know_u.knowledge_store import get_knowledge_store
from src.learners.jargon_explainer import search_jargon from src.learners.jargon_explainer import search_jargon
from src.llm_models.exceptions import ReqAbortException from src.llm_models.exceptions import ReqAbortException
@@ -37,13 +38,10 @@ from .message_adapter import (
clone_message_sequence, clone_message_sequence,
format_speaker_content, format_speaker_content,
) )
from .tool_handlers import (
handle_mcp_tool,
handle_unknown_tool,
)
if TYPE_CHECKING: if TYPE_CHECKING:
from .runtime import MaisakaHeartFlowChatting from .runtime import MaisakaHeartFlowChatting
from .tool_provider import BuiltinToolHandler
logger = get_logger("maisaka_reasoning_engine") logger = get_logger("maisaka_reasoning_engine")
@@ -55,6 +53,23 @@ class MaisakaReasoningEngine:
self._runtime = runtime self._runtime = runtime
self._last_reasoning_content: str = "" self._last_reasoning_content: str = ""
def build_builtin_tool_handlers(self) -> dict[str, "BuiltinToolHandler"]:
"""构造 Maisaka 内置工具处理器映射。
Returns:
dict[str, BuiltinToolHandler]: 工具名到处理器的映射。
"""
return {
"reply": self._invoke_reply_tool,
"no_reply": self._invoke_no_reply_tool,
"query_jargon": self._invoke_query_jargon_tool,
"query_person_info": self._invoke_query_person_info_tool,
"wait": self._invoke_wait_tool,
"stop": self._invoke_stop_tool,
"send_emoji": self._invoke_send_emoji_tool,
}
async def run_loop(self) -> None: async def run_loop(self) -> None:
"""独立消费消息批次,并执行对应的内部思考轮次。""" """独立消费消息批次,并执行对应的内部思考轮次。"""
try: try:
@@ -360,79 +375,287 @@ class MaisakaReasoningEngine:
return processed_segments return processed_segments
return [reply_text.strip()] return [reply_text.strip()]
def _build_tool_invocation(self, tool_call: ToolCall, latest_thought: str) -> ToolInvocation:
"""将模型输出的工具调用转换为统一调用对象。
Args:
tool_call: 模型返回的工具调用。
latest_thought: 当前轮的最新思考文本。
Returns:
ToolInvocation: 统一工具调用对象。
"""
return ToolInvocation(
tool_name=tool_call.func_name,
arguments=dict(tool_call.args or {}),
call_id=tool_call.call_id,
session_id=self._runtime.session_id,
stream_id=self._runtime.session_id,
reasoning=latest_thought,
)
def _build_tool_execution_context(
self,
latest_thought: str,
anchor_message: SessionMessage,
) -> ToolExecutionContext:
"""构造统一工具执行上下文。
Args:
latest_thought: 当前轮的最新思考文本。
anchor_message: 当前轮的锚点消息。
Returns:
ToolExecutionContext: 统一工具执行上下文。
"""
return ToolExecutionContext(
session_id=self._runtime.session_id,
stream_id=self._runtime.session_id,
reasoning=latest_thought,
metadata={"anchor_message": anchor_message},
)
def _append_tool_execution_result(self, tool_call: ToolCall, result: ToolExecutionResult) -> None:
"""将统一工具执行结果写回 Maisaka 历史。
Args:
tool_call: 原始工具调用对象。
result: 统一工具执行结果。
"""
history_content = result.get_history_content()
if not history_content:
history_content = "工具执行成功。" if result.success else f"工具 {tool_call.func_name} 执行失败。"
self._runtime._chat_history.append(
ToolResultMessage(
content=history_content,
timestamp=datetime.now(),
tool_call_id=tool_call.call_id,
tool_name=tool_call.func_name,
)
)
@staticmethod
def _build_tool_call_from_invocation(invocation: ToolInvocation) -> ToolCall:
"""将统一工具调用对象恢复为 `ToolCall` 兼容对象。
Args:
invocation: 统一工具调用对象。
Returns:
ToolCall: 兼容旧内部逻辑的工具调用对象。
"""
return ToolCall(
call_id=invocation.call_id or f"{invocation.tool_name}_call",
func_name=invocation.tool_name,
args=dict(invocation.arguments),
)
@staticmethod
def _build_tool_success_result(
tool_name: str,
content: str = "",
structured_content: Any = None,
metadata: Optional[dict[str, Any]] = None,
) -> ToolExecutionResult:
"""构造统一工具成功结果。
Args:
tool_name: 工具名称。
content: 结果文本。
structured_content: 结构化结果。
metadata: 附加元数据。
Returns:
ToolExecutionResult: 统一工具成功结果。
"""
return ToolExecutionResult(
tool_name=tool_name,
success=True,
content=content,
structured_content=structured_content,
metadata=dict(metadata or {}),
)
@staticmethod
def _build_tool_failure_result(
tool_name: str,
error_message: str,
structured_content: Any = None,
metadata: Optional[dict[str, Any]] = None,
) -> ToolExecutionResult:
"""构造统一工具失败结果。
Args:
tool_name: 工具名称。
error_message: 错误信息。
structured_content: 结构化结果。
metadata: 附加元数据。
Returns:
ToolExecutionResult: 统一工具失败结果。
"""
return ToolExecutionResult(
tool_name=tool_name,
success=False,
error_message=error_message,
structured_content=structured_content,
metadata=dict(metadata or {}),
)
async def _invoke_reply_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 reply 内置工具。"""
latest_thought = context.reasoning if context is not None else invocation.reasoning
return await self._handle_reply(self._build_tool_call_from_invocation(invocation), latest_thought)
async def _invoke_no_reply_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 no_reply 内置工具。"""
del context
return self._build_tool_success_result(invocation.tool_name, "本轮未发送可见回复。")
async def _invoke_query_jargon_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 query_jargon 内置工具。"""
del context
return await self._handle_query_jargon(self._build_tool_call_from_invocation(invocation))
async def _invoke_query_person_info_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 query_person_info 内置工具。"""
del context
return await self._handle_query_person_info(self._build_tool_call_from_invocation(invocation))
async def _invoke_wait_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 wait 内置工具。"""
del context
seconds = invocation.arguments.get("seconds", 30)
try:
wait_seconds = int(seconds)
except (TypeError, ValueError):
wait_seconds = 30
wait_seconds = max(0, wait_seconds)
self._runtime._enter_wait_state(seconds=wait_seconds, tool_call_id=invocation.call_id)
return self._build_tool_success_result(
invocation.tool_name,
f"当前对话循环进入等待状态,最长等待 {wait_seconds} 秒。",
metadata={"pause_execution": True},
)
async def _invoke_stop_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 stop 内置工具。"""
del context
self._runtime._enter_stop_state()
return self._build_tool_success_result(
invocation.tool_name,
"当前对话循环已暂停,等待新消息到来。",
metadata={"pause_execution": True},
)
async def _invoke_send_emoji_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行 send_emoji 内置工具。"""
del context
return await self._handle_send_emoji(self._build_tool_call_from_invocation(invocation))
async def _handle_tool_calls( async def _handle_tool_calls(
self, self,
tool_calls: list[ToolCall], tool_calls: list[ToolCall],
latest_thought: str, latest_thought: str,
anchor_message: SessionMessage, anchor_message: SessionMessage,
) -> bool: ) -> bool:
"""执行一批统一工具调用。
Args:
tool_calls: 模型返回的工具调用列表。
latest_thought: 当前轮的最新思考文本。
anchor_message: 当前轮的锚点消息。
Returns:
bool: 是否需要暂停当前思考循环。
"""
if self._runtime._tool_registry is None:
for tool_call in tool_calls:
self._append_tool_execution_result(
tool_call,
ToolExecutionResult(
tool_name=tool_call.func_name,
success=False,
error_message="统一工具注册表尚未初始化。",
),
)
return False
execution_context = self._build_tool_execution_context(latest_thought, anchor_message)
for tool_call in tool_calls: for tool_call in tool_calls:
if tool_call.func_name == "reply": invocation = self._build_tool_invocation(tool_call, latest_thought)
reply_sent = await self._handle_reply(tool_call, latest_thought, anchor_message) result = await self._runtime._tool_registry.invoke(invocation, execution_context)
if not reply_sent: self._append_tool_execution_result(tool_call, result)
logger.warning(
f"{self._runtime.log_prefix} 回复工具未生成可见消息,将继续下一轮循环"
)
continue
if tool_call.func_name == "no_reply": if not result.success and tool_call.func_name == "reply":
self._runtime._chat_history.append( logger.warning(f"{self._runtime.log_prefix} 回复工具未生成可见消息,将继续下一轮循环")
self._build_tool_message(
tool_call,
"本轮未发送可见回复。",
)
)
continue
if tool_call.func_name == "query_jargon": if bool(result.metadata.get("pause_execution", False)):
await self._handle_query_jargon(tool_call)
continue
if tool_call.func_name == "query_person_info":
await self._handle_query_person_info(tool_call)
continue
if tool_call.func_name == "wait":
seconds = (tool_call.args or {}).get("seconds", 30)
try:
wait_seconds = int(seconds)
except (TypeError, ValueError):
wait_seconds = 30
wait_seconds = max(0, wait_seconds)
self._runtime._enter_wait_state(seconds=wait_seconds, tool_call_id=tool_call.call_id)
return True return True
if tool_call.func_name == "stop":
self._runtime._chat_history.append(
self._build_tool_message(
tool_call,
"当前对话循环已暂停,等待新消息到来。",
)
)
self._runtime._enter_stop_state()
return True
if tool_call.func_name == "send_emoji":
await self._handle_send_emoji(tool_call, anchor_message)
continue
if self._runtime._mcp_manager and self._runtime._mcp_manager.is_mcp_tool(tool_call.func_name):
await handle_mcp_tool(tool_call, self._runtime._chat_history, self._runtime._mcp_manager)
continue
await handle_unknown_tool(tool_call, self._runtime._chat_history)
return False return False
async def _handle_query_jargon(self, tool_call: ToolCall) -> None: async def _handle_query_jargon(self, tool_call: ToolCall) -> ToolExecutionResult:
"""查询黑话解释并返回统一工具结果。
Args:
tool_call: 当前工具调用。
Returns:
ToolExecutionResult: 统一工具执行结果。
"""
tool_args = tool_call.args or {} tool_args = tool_call.args or {}
raw_words = tool_args.get("words") raw_words = tool_args.get("words")
if not isinstance(raw_words, list): if not isinstance(raw_words, list):
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "查询黑话工具需要提供 `words` 数组参数。") tool_call.func_name,
"查询黑话工具需要提供 `words` 数组参数。",
) )
return
words: list[str] = [] words: list[str] = []
seen_words: set[str] = set() seen_words: set[str] = set()
@@ -446,10 +669,10 @@ class MaisakaReasoningEngine:
words.append(word) words.append(word)
if not words: if not words:
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "查询黑话工具至少需要一个非空词条。") tool_call.func_name,
"查询黑话工具至少需要一个非空词条。",
) )
return
logger.info(f"{self._runtime.log_prefix} 已触发黑话查询: 词条={words!r}") logger.info(f"{self._runtime.log_prefix} 已触发黑话查询: 词条={words!r}")
@@ -479,31 +702,38 @@ class MaisakaReasoningEngine:
) )
logger.info(f"{self._runtime.log_prefix} 黑话查询完成: 结果={results!r}") logger.info(f"{self._runtime.log_prefix} 黑话查询完成: 结果={results!r}")
self._runtime._chat_history.append( return self._build_tool_success_result(
self._build_tool_message( tool_call.func_name,
tool_call, json.dumps({"results": results}, ensure_ascii=False),
json.dumps({"results": results}, ensure_ascii=False), structured_content={"results": results},
)
) )
async def _handle_query_person_info(self, tool_call: ToolCall) -> None: async def _handle_query_person_info(self, tool_call: ToolCall) -> ToolExecutionResult:
"""查询指定人物的档案和相关知识。""" """查询指定人物的档案和相关知识。
Args:
tool_call: 当前工具调用。
Returns:
ToolExecutionResult: 统一工具执行结果。
"""
tool_args = tool_call.args or {} tool_args = tool_call.args or {}
raw_person_name = tool_args.get("person_name") raw_person_name = tool_args.get("person_name")
raw_limit = tool_args.get("limit", 3) raw_limit = tool_args.get("limit", 3)
if not isinstance(raw_person_name, str): if not isinstance(raw_person_name, str):
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "查询人物信息工具需要提供字符串类型的 `person_name` 参数。") tool_call.func_name,
"查询人物信息工具需要提供字符串类型的 `person_name` 参数。",
) )
return
person_name = raw_person_name.strip() person_name = raw_person_name.strip()
if not person_name: if not person_name:
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "查询人物信息工具需要提供非空的 `person_name` 参数。") tool_call.func_name,
"查询人物信息工具需要提供非空的 `person_name` 参数。",
) )
return
try: try:
limit = max(1, min(int(raw_limit), 10)) limit = max(1, min(int(raw_limit), 10))
@@ -526,11 +756,10 @@ class MaisakaReasoningEngine:
f"{self._runtime.log_prefix} 人物信息查询完成: " f"{self._runtime.log_prefix} 人物信息查询完成: "
f"人物记录数={len(result['persons'])} 相关知识数={len(result['related_knowledge'])}" f"人物记录数={len(result['persons'])} 相关知识数={len(result['related_knowledge'])}"
) )
self._runtime._chat_history.append( return self._build_tool_success_result(
self._build_tool_message( tool_call.func_name,
tool_call, json.dumps(result, ensure_ascii=False),
json.dumps(result, ensure_ascii=False), structured_content=result,
)
) )
def _query_person_records(self, person_name: str, limit: int) -> list[dict[str, Any]]: def _query_person_records(self, person_name: str, limit: int) -> list[dict[str, Any]]:
@@ -632,25 +861,34 @@ class MaisakaReasoningEngine:
self, self,
tool_call: ToolCall, tool_call: ToolCall,
latest_thought: str, latest_thought: str,
anchor_message: SessionMessage, ) -> ToolExecutionResult:
) -> bool: """执行 reply 工具并生成可见回复。
Args:
tool_call: 当前工具调用。
latest_thought: 当前轮的最新思考文本。
Returns:
ToolExecutionResult: 统一工具执行结果。
"""
tool_args = tool_call.args or {} tool_args = tool_call.args or {}
target_message_id = str(tool_args.get("msg_id") or "").strip() target_message_id = str(tool_args.get("msg_id") or "").strip()
quote_reply = bool(tool_args.get("quote", True)) quote_reply = bool(tool_args.get("quote", True))
raw_unknown_words = tool_args.get("unknown_words") raw_unknown_words = tool_args.get("unknown_words")
unknown_words = raw_unknown_words if isinstance(raw_unknown_words, list) else None unknown_words = raw_unknown_words if isinstance(raw_unknown_words, list) else None
if not target_message_id: if not target_message_id:
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "回复工具需要提供有效的 `msg_id` 参数。") tool_call.func_name,
"回复工具需要提供有效的 `msg_id` 参数。",
) )
return False
target_message = self._runtime._source_messages_by_id.get(target_message_id) target_message = self._runtime._source_messages_by_id.get(target_message_id)
if target_message is None: if target_message is None:
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, f"未找到要回复的目标消息msg_id={target_message_id}") tool_call.func_name,
f"未找到要回复的目标消息msg_id={target_message_id}",
) )
return False
logger.info( logger.info(
f"{self._runtime.log_prefix} 已触发回复工具: " f"{self._runtime.log_prefix} 已触发回复工具: "
@@ -668,17 +906,17 @@ class MaisakaReasoningEngine:
f"{self._runtime.log_prefix} 获取回复生成器时发生异常: " f"{self._runtime.log_prefix} 获取回复生成器时发生异常: "
f"目标消息编号={target_message_id}" f"目标消息编号={target_message_id}"
) )
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "获取 Maisaka 回复生成器时发生异常。") tool_call.func_name,
"获取 Maisaka 回复生成器时发生异常。",
) )
return False
if replyer is None: if replyer is None:
logger.error(f"{self._runtime.log_prefix} 获取 Maisaka 回复生成器失败") logger.error(f"{self._runtime.log_prefix} 获取 Maisaka 回复生成器失败")
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "Maisaka 回复生成器当前不可用。") tool_call.func_name,
"Maisaka 回复生成器当前不可用。",
) )
return False
from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
@@ -701,10 +939,10 @@ class MaisakaReasoningEngine:
f"{self._runtime.log_prefix} 回复生成器执行异常: 目标消息编号={target_message_id} " f"{self._runtime.log_prefix} 回复生成器执行异常: 目标消息编号={target_message_id} "
f"异常类型={type(exc).__name__} 异常信息={str(exc)}\n{traceback.format_exc()}" f"异常类型={type(exc).__name__} 异常信息={str(exc)}\n{traceback.format_exc()}"
) )
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "生成可见回复时发生异常。") tool_call.func_name,
"生成可见回复时发生异常。",
) )
return False
logger.info( logger.info(
f"{self._runtime.log_prefix} 回复生成完成: " f"{self._runtime.log_prefix} 回复生成完成: "
@@ -717,10 +955,10 @@ class MaisakaReasoningEngine:
f"{self._runtime.log_prefix} 回复生成器返回空文本: " f"{self._runtime.log_prefix} 回复生成器返回空文本: "
f"目标消息编号={target_message_id} 错误信息={reply_result.error_message!r}" f"目标消息编号={target_message_id} 错误信息={reply_result.error_message!r}"
) )
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "生成可见回复失败。") tool_call.func_name,
"生成可见回复失败。",
) )
return False
reply_segments = self._post_process_reply_text(reply_text) reply_segments = self._post_process_reply_text(reply_text)
combined_reply_text = "".join(reply_segments) combined_reply_text = "".join(reply_segments)
@@ -751,19 +989,25 @@ class MaisakaReasoningEngine:
logger.exception( logger.exception(
f"{self._runtime.log_prefix} 发送文字消息时发生异常,目标消息编号={target_message_id}" f"{self._runtime.log_prefix} 发送文字消息时发生异常,目标消息编号={target_message_id}"
) )
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "发送可见回复时发生异常。") tool_call.func_name,
"发送可见回复时发生异常。",
) )
return False
logger.info( logger.info(
f"{self._runtime.log_prefix} 引导回复发送结果: " f"{self._runtime.log_prefix} 引导回复发送结果: "
f"目标消息编号={target_message_id} 发送成功={sent}" f"目标消息编号={target_message_id} 发送成功={sent}"
) )
tool_result = "可见回复已生成并发送。" if sent else "可见回复生成成功,但发送失败。"
self._runtime._chat_history.append(self._build_tool_message(tool_call, tool_result))
if not sent: if not sent:
return False return self._build_tool_failure_result(
tool_call.func_name,
"可见回复生成成功,但发送失败。",
structured_content={
"msg_id": target_message_id,
"quote": quote_reply,
"reply_segments": reply_segments,
},
)
target_user_info = target_message.message_info.user_info target_user_info = target_message.message_info.user_info
target_user_name = ( target_user_name = (
@@ -807,14 +1051,26 @@ class MaisakaReasoningEngine:
) )
history_message.visible_text = visible_reply_text history_message.visible_text = visible_reply_text
self._runtime._chat_history.append(history_message) self._runtime._chat_history.append(history_message)
return True return self._build_tool_success_result(
tool_call.func_name,
"可见回复已生成并发送。",
structured_content={
"msg_id": target_message_id,
"quote": quote_reply,
"reply_text": combined_reply_text,
"reply_segments": reply_segments,
"target_user_name": target_user_name,
},
)
async def _handle_send_emoji(self, tool_call: ToolCall, anchor_message: SessionMessage) -> None: async def _handle_send_emoji(self, tool_call: ToolCall) -> ToolExecutionResult:
"""处理发送表情包的工具调用。 """处理发送表情包的工具调用。
Args: Args:
tool_call: 工具调用对象 tool_call: 工具调用对象
anchor_message: 锚点消息
Returns:
ToolExecutionResult: 统一工具执行结果。
""" """
from src.chat.emoji_system.emoji_manager import emoji_manager from src.chat.emoji_system.emoji_manager import emoji_manager
from src.common.utils.utils_image import ImageUtils from src.common.utils.utils_image import ImageUtils
@@ -827,10 +1083,10 @@ class MaisakaReasoningEngine:
# 获取表情包列表 # 获取表情包列表
if not emoji_manager.emojis: if not emoji_manager.emojis:
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, "当前表情包库中没有可用表情。") tool_call.func_name,
"当前表情包库中没有可用表情。",
) )
return
# 根据情感选择表情包 # 根据情感选择表情包
selected_emoji = None selected_emoji = None
@@ -867,10 +1123,10 @@ class MaisakaReasoningEngine:
logger.error( logger.error(
f"{self._runtime.log_prefix} 表情图片转换为 base64 失败: {exc}" f"{self._runtime.log_prefix} 表情图片转换为 base64 失败: {exc}"
) )
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, f"发送表情包失败:{exc}") tool_call.func_name,
f"发送表情包失败:{exc}",
) )
return
# 发送表情包 # 发送表情包
try: try:
@@ -885,32 +1141,26 @@ class MaisakaReasoningEngine:
logger.exception( logger.exception(
f"{self._runtime.log_prefix} 发送表情包时发生异常: {exc}" f"{self._runtime.log_prefix} 发送表情包时发生异常: {exc}"
) )
self._runtime._chat_history.append( return self._build_tool_failure_result(
self._build_tool_message(tool_call, f"发送表情包时发生异常:{exc}") tool_call.func_name,
f"发送表情包时发生异常:{exc}",
) )
return
if sent: if sent:
logger.info( logger.info(
f"{self._runtime.log_prefix} 表情包发送成功: " f"{self._runtime.log_prefix} 表情包发送成功: "
f"描述={selected_emoji.description!r} 情绪标签={selected_emoji.emotion}" f"描述={selected_emoji.description!r} 情绪标签={selected_emoji.emotion}"
) )
self._runtime._chat_history.append( return self._build_tool_success_result(
self._build_tool_message( tool_call.func_name,
tool_call, f"已发送表情包:{selected_emoji.description}(情绪:{', '.join(selected_emoji.emotion)}",
f"已发送表情包:{selected_emoji.description}(情绪:{', '.join(selected_emoji.emotion)}" structured_content={
) "description": selected_emoji.description,
"emotion": list(selected_emoji.emotion),
},
) )
else: logger.warning(f"{self._runtime.log_prefix} 表情包发送失败")
logger.warning(f"{self._runtime.log_prefix} 表情包发送失败") return self._build_tool_failure_result(
self._runtime._chat_history.append( tool_call.func_name,
self._build_tool_message(tool_call, "发送表情包失败。") "发送表情包失败。",
)
def _build_tool_message(self, tool_call: ToolCall, content: str) -> ToolResultMessage:
return ToolResultMessage(
content=content,
timestamp=datetime.now(),
tool_call_id=tool_call.call_id,
tool_name=tool_call.func_name,
) )

View File

@@ -1,7 +1,7 @@
"""Maisaka runtime for non-CLI integrations.""" """Maisaka 非 CLI 运行时。"""
from pathlib import Path from pathlib import Path
from typing import Literal, Optional, cast from typing import Literal, Optional
import asyncio import asyncio
import time import time
@@ -13,21 +13,24 @@ from src.common.data_models.mai_message_data_model import GroupInfo, UserInfo
from src.common.logger import get_logger from src.common.logger import get_logger
from src.common.utils.utils_config import ExpressionConfigUtils from src.common.utils.utils_config import ExpressionConfigUtils
from src.config.config import global_config from src.config.config import global_config
from src.core.tooling import ToolRegistry
from src.know_u.knowledge import KnowledgeLearner from src.know_u.knowledge import KnowledgeLearner
from src.learners.expression_learner import ExpressionLearner from src.learners.expression_learner import ExpressionLearner
from src.learners.jargon_miner import JargonMiner from src.learners.jargon_miner import JargonMiner
from src.llm_models.payload_content.tool_option import ToolDefinitionInput
from src.mcp_module import MCPManager from src.mcp_module import MCPManager
from src.mcp_module.provider import MCPToolProvider
from src.plugin_runtime.tool_provider import PluginToolProvider
from .chat_loop_service import MaisakaChatLoopService from .chat_loop_service import MaisakaChatLoopService
from .context_messages import LLMContextMessage from .context_messages import LLMContextMessage
from .reasoning_engine import MaisakaReasoningEngine from .reasoning_engine import MaisakaReasoningEngine
from .tool_provider import MaisakaBuiltinToolProvider
logger = get_logger("maisaka_runtime") logger = get_logger("maisaka_runtime")
class MaisakaHeartFlowChatting: class MaisakaHeartFlowChatting:
"""Session-scoped Maisaka runtime.""" """会话级别的 Maisaka 运行时。"""
_STATE_RUNNING: Literal["running"] = "running" _STATE_RUNNING: Literal["running"] = "running"
_STATE_WAIT: Literal["wait"] = "wait" _STATE_WAIT: Literal["wait"] = "wait"
@@ -79,9 +82,11 @@ class MaisakaHeartFlowChatting:
self._knowledge_learner = KnowledgeLearner(session_id) self._knowledge_learner = KnowledgeLearner(session_id)
self._reasoning_engine = MaisakaReasoningEngine(self) self._reasoning_engine = MaisakaReasoningEngine(self)
self._tool_registry = ToolRegistry()
self._register_tool_providers()
async def start(self) -> None: async def start(self) -> None:
"""Start the runtime loop.""" """启动运行时主循环。"""
if self._running: if self._running:
self._ensure_background_tasks_running() self._ensure_background_tasks_running()
return return
@@ -94,7 +99,7 @@ class MaisakaHeartFlowChatting:
logger.info(f"{self.log_prefix} Maisaka 运行时已启动") logger.info(f"{self.log_prefix} Maisaka 运行时已启动")
async def stop(self) -> None: async def stop(self) -> None:
"""Stop the runtime loop.""" """停止运行时主循环。"""
if not self._running: if not self._running:
return return
@@ -121,18 +126,17 @@ class MaisakaHeartFlowChatting:
finally: finally:
self._internal_loop_task = None self._internal_loop_task = None
if self._mcp_manager is not None: await self._tool_registry.close()
await self._mcp_manager.close() self._mcp_manager = None
self._mcp_manager = None
logger.info(f"{self.log_prefix} Maisaka 运行时已停止") logger.info(f"{self.log_prefix} Maisaka 运行时已停止")
def adjust_talk_frequency(self, frequency: float) -> None: def adjust_talk_frequency(self, frequency: float) -> None:
"""Compatibility shim for the existing manager API.""" """兼容现有管理器接口的占位方法。"""
_ = frequency _ = frequency
async def register_message(self, message: SessionMessage) -> None: async def register_message(self, message: SessionMessage) -> None:
"""Cache a new message and wake the main loop.""" """缓存一条新消息并唤醒主循环。"""
if self._running: if self._running:
self._ensure_background_tasks_running() self._ensure_background_tasks_running()
self.message_cache.append(message) self.message_cache.append(message)
@@ -175,6 +179,15 @@ class MaisakaHeartFlowChatting:
self._loop_task = asyncio.create_task(self._main_loop()) self._loop_task = asyncio.create_task(self._main_loop())
logger.warning(f"{self.log_prefix} 已重新拉起 Maisaka 主循环任务") logger.warning(f"{self.log_prefix} 已重新拉起 Maisaka 主循环任务")
def _register_tool_providers(self) -> None:
"""注册 Maisaka 运行时默认启用的工具 Provider。"""
self._tool_registry.register_provider(
MaisakaBuiltinToolProvider(self._reasoning_engine.build_builtin_tool_handlers())
)
self._tool_registry.register_provider(PluginToolProvider())
self._chat_loop_service.set_tool_registry(self._tool_registry)
async def _main_loop(self) -> None: async def _main_loop(self) -> None:
try: try:
while self._running: while self._running:
@@ -215,7 +228,7 @@ class MaisakaHeartFlowChatting:
return self._last_processed_index < len(self.message_cache) return self._last_processed_index < len(self.message_cache)
def _collect_pending_messages(self) -> list[SessionMessage]: def _collect_pending_messages(self) -> list[SessionMessage]:
"""Collect one batch of unprocessed messages from message_cache.""" """从消息缓存中收集一批尚未处理的消息。"""
start_index = self._last_processed_index start_index = self._last_processed_index
pending_messages = self.message_cache[start_index:] pending_messages = self.message_cache[start_index:]
if not pending_messages: if not pending_messages:
@@ -264,13 +277,13 @@ class MaisakaHeartFlowChatting:
return "timeout" return "timeout"
def _enter_wait_state(self, seconds: Optional[float] = None, tool_call_id: Optional[str] = None) -> None: def _enter_wait_state(self, seconds: Optional[float] = None, tool_call_id: Optional[str] = None) -> None:
"""Enter wait state.""" """切换到等待状态。"""
self._agent_state = self._STATE_WAIT self._agent_state = self._STATE_WAIT
self._wait_until = None if seconds is None else time.time() + seconds self._wait_until = None if seconds is None else time.time() + seconds
self._pending_wait_tool_call_id = tool_call_id self._pending_wait_tool_call_id = tool_call_id
def _enter_stop_state(self) -> None: def _enter_stop_state(self) -> None:
"""Enter stop state.""" """切换到停止状态。"""
self._agent_state = self._STATE_STOP self._agent_state = self._STATE_STOP
self._wait_until = None self._wait_until = None
self._pending_wait_tool_call_id = None self._pending_wait_tool_call_id = None
@@ -288,7 +301,7 @@ class MaisakaHeartFlowChatting:
logger.error(f"{self.log_prefix} 知识学习任务异常退出: {knowledge_result}") logger.error(f"{self.log_prefix} 知识学习任务异常退出: {knowledge_result}")
async def _trigger_expression_learning(self, messages: list[SessionMessage]) -> None: async def _trigger_expression_learning(self, messages: list[SessionMessage]) -> None:
"""Trigger expression learning from the newly collected batch.""" """基于新收集的一批消息触发表达学习。"""
self._expression_learner.add_messages(messages) self._expression_learner.add_messages(messages)
if not self._enable_expression_learning: if not self._enable_expression_learning:
@@ -331,7 +344,7 @@ class MaisakaHeartFlowChatting:
logger.exception(f"{self.log_prefix} 表达学习失败") logger.exception(f"{self.log_prefix} 表达学习失败")
async def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None: async def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
"""Trigger knowledge learning from the newly collected batch.""" """基于新收集的一批消息触发知识学习。"""
self._knowledge_learner.add_messages(messages) self._knowledge_learner.add_messages(messages)
if not global_config.maisaka.enable_knowledge_module: if not global_config.maisaka.enable_knowledge_module:
@@ -372,22 +385,21 @@ class MaisakaHeartFlowChatting:
logger.exception(f"{self.log_prefix} 知识学习失败") logger.exception(f"{self.log_prefix} 知识学习失败")
async def _init_mcp(self) -> None: async def _init_mcp(self) -> None:
"""Initialize MCP tools and inject them into the planner.""" """初始化 MCP 工具并注册到统一工具层。"""
config_path = Path(__file__).resolve().parents[2] / "config" / "mcp_config.json" config_path = Path(__file__).resolve().parents[2] / "config" / "mcp_config.json"
self._mcp_manager = await MCPManager.from_config(str(config_path)) self._mcp_manager = await MCPManager.from_config(str(config_path))
if self._mcp_manager is None: if self._mcp_manager is None:
logger.info(f"{self.log_prefix} MCP 管理器不可用") logger.info(f"{self.log_prefix} MCP 管理器不可用")
return return
mcp_tools = self._mcp_manager.get_openai_tools() mcp_tool_specs = self._mcp_manager.get_tool_specs()
if not mcp_tools: if not mcp_tool_specs:
logger.info(f"{self.log_prefix} 没有可供 Maisaka 使用的 MCP 工具") logger.info(f"{self.log_prefix} 没有可供 Maisaka 使用的 MCP 工具")
return return
mcp_tool_definitions = [cast(ToolDefinitionInput, tool) for tool in mcp_tools] self._tool_registry.register_provider(MCPToolProvider(self._mcp_manager))
self._chat_loop_service.set_extra_tools(mcp_tool_definitions)
logger.info( logger.info(
f"{self.log_prefix} 已向 Maisaka 加载 {len(mcp_tools)} 个 MCP 工具:\n" f"{self.log_prefix} 已向 Maisaka 加载 {len(mcp_tool_specs)} 个 MCP 工具:\n"
f"{self._mcp_manager.get_tool_summary()}" f"{self._mcp_manager.get_tool_summary()}"
) )

View File

@@ -0,0 +1,64 @@
"""Maisaka 内置工具 Provider。"""
from __future__ import annotations
from collections.abc import Awaitable, Callable
from typing import Dict, Optional
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolProvider, ToolSpec
from .builtin_tools import get_builtin_tool_specs
BuiltinToolHandler = Callable[[ToolInvocation, Optional[ToolExecutionContext]], Awaitable[ToolExecutionResult]]
class MaisakaBuiltinToolProvider(ToolProvider):
"""Maisaka 内置工具提供者。"""
provider_name = "maisaka_builtin"
provider_type = "builtin"
def __init__(self, handlers: Optional[Dict[str, BuiltinToolHandler]] = None) -> None:
"""初始化内置工具 Provider。
Args:
handlers: 工具名到异步处理器的映射。
"""
self._handlers = dict(handlers or {})
async def list_tools(self) -> list[ToolSpec]:
"""列出全部内置工具。"""
return list(get_builtin_tool_specs())
async def invoke(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行指定内置工具。
Args:
invocation: 工具调用请求。
context: 执行上下文。
Returns:
ToolExecutionResult: 工具执行结果。
"""
handler = self._handlers.get(invocation.tool_name)
if handler is None:
return ToolExecutionResult(
tool_name=invocation.tool_name,
success=False,
error_message=f"未找到内置工具处理器:{invocation.tool_name}",
)
return await handler(invocation, context)
async def close(self) -> None:
"""关闭 Provider。
内置 Provider 无需释放额外资源。
"""

View File

@@ -6,6 +6,8 @@ MaiSaka - 单个 MCP 服务器连接管理
from contextlib import AsyncExitStack from contextlib import AsyncExitStack
from typing import Any, Optional from typing import Any, Optional
from src.core.tooling import ToolExecutionResult
from src.cli.console import console from src.cli.console import console
from .config import MCPServerConfig from .config import MCPServerConfig
@@ -79,6 +81,8 @@ class MCPConnection:
return False return False
# 创建并初始化 MCP 会话 # 创建并初始化 MCP 会话
if ClientSession is None:
raise RuntimeError("当前环境未安装可用的 MCP ClientSession")
self.session = await self._exit_stack.enter_async_context(ClientSession(read_stream, write_stream)) self.session = await self._exit_stack.enter_async_context(ClientSession(read_stream, write_stream))
await self.session.initialize() await self.session.initialize()
@@ -95,6 +99,11 @@ class MCPConnection:
async def _connect_stdio(self): async def _connect_stdio(self):
"""建立 Stdio 传输连接。""" """建立 Stdio 传输连接。"""
if StdioServerParameters is None or stdio_client is None:
raise RuntimeError("当前环境未安装可用的 MCP stdio 客户端")
if not self.config.command:
raise ValueError(f"MCP 服务器 '{self.config.name}' 缺少 stdio command 配置")
params = StdioServerParameters( params = StdioServerParameters(
command=self.config.command, command=self.config.command,
args=self.config.args, args=self.config.args,
@@ -106,39 +115,63 @@ class MCPConnection:
"""建立 SSE 传输连接。""" """建立 SSE 传输连接。"""
if not SSE_AVAILABLE: if not SSE_AVAILABLE:
raise ImportError("SSE 传输需要额外依赖,请运行: pip install mcp[sse]") raise ImportError("SSE 传输需要额外依赖,请运行: pip install mcp[sse]")
if sse_client is None:
raise RuntimeError("当前环境未安装可用的 MCP SSE 客户端")
if not self.config.url:
raise ValueError(f"MCP 服务器 '{self.config.name}' 缺少 SSE url 配置")
return await self._exit_stack.enter_async_context(sse_client(url=self.config.url, headers=self.config.headers)) return await self._exit_stack.enter_async_context(sse_client(url=self.config.url, headers=self.config.headers))
async def call_tool(self, tool_name: str, arguments: dict) -> str: async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> ToolExecutionResult:
""" """调用 MCP 工具并返回统一执行结果。
调用 MCP 工具并返回结果文本。
Args: Args:
tool_name: 工具名称 tool_name: 工具名称
arguments: 工具参数字典 arguments: 工具参数字典
Returns: Returns:
工具执行结果的文本表示 ToolExecutionResult: 统一执行结果
""" """
if not self.session: if not self.session:
return f"MCP 服务器 '{self.config.name}' 未连接" return ToolExecutionResult(
tool_name=tool_name,
success=False,
error_message=f"MCP 服务器 '{self.config.name}' 未连接",
)
result = await self.session.call_tool(tool_name, arguments=arguments) try:
result = await self.session.call_tool(tool_name, arguments=arguments)
except Exception as exc:
return ToolExecutionResult(
tool_name=tool_name,
success=False,
error_message=f"MCP 工具 '{tool_name}' 执行失败: {exc}",
metadata={"server_name": self.config.name},
)
# 将结果内容转换为文本 text_parts: list[str] = []
parts: list[str] = [] binary_parts: list[dict[str, Any]] = []
for content in result.content: for content in result.content:
if hasattr(content, "text"): if hasattr(content, "text"):
parts.append(content.text) text_parts.append(str(content.text))
elif hasattr(content, "data"): elif hasattr(content, "data"):
# 二进制/图片内容,展示类型信息
content_type = getattr(content, "mimeType", "unknown") content_type = getattr(content, "mimeType", "unknown")
parts.append(f"[{content_type} 二进制内容]") binary_parts.append({"mime_type": content_type, "type": "binary"})
text_parts.append(f"[{content_type} 二进制内容]")
elif hasattr(content, "type"): elif hasattr(content, "type"):
parts.append(f"[{content.type} 内容]") text_parts.append(f"[{content.type} 内容]")
return "\n".join(parts) if parts else "工具执行成功(无输出)" return ToolExecutionResult(
tool_name=tool_name,
success=True,
content="\n".join(text_parts) if text_parts else "工具执行成功(无输出)",
metadata={
"server_name": self.config.name,
"binary_parts": binary_parts,
},
)
async def close(self): async def close(self) -> None:
"""关闭连接并释放资源。""" """关闭连接并释放资源。"""
try: try:
await self._exit_stack.aclose() await self._exit_stack.aclose()

View File

@@ -3,9 +3,15 @@ MaiSaka - MCP 管理器
管理所有 MCP 服务器连接,提供统一的工具发现与调用接口。 管理所有 MCP 服务器连接,提供统一的工具发现与调用接口。
""" """
from typing import Optional from typing import Any, Optional
from src.cli.console import console from src.cli.console import console
from src.core.tooling import (
ToolExecutionResult,
ToolInvocation,
ToolSpec,
build_tool_detailed_description,
)
from .config import DEFAULT_MCP_CONFIG_PATH, MCPServerConfig, load_mcp_config from .config import DEFAULT_MCP_CONFIG_PATH, MCPServerConfig, load_mcp_config
from .connection import MCPConnection, MCP_AVAILABLE from .connection import MCPConnection, MCP_AVAILABLE
@@ -74,7 +80,7 @@ class MCPManager:
# ──────── 连接管理 ──────── # ──────── 连接管理 ────────
async def _connect_all(self, configs: list[MCPServerConfig]): async def _connect_all(self, configs: list[MCPServerConfig]) -> None:
"""连接所有配置的 MCP 服务器,跳过失败的连接。""" """连接所有配置的 MCP 服务器,跳过失败的连接。"""
for cfg in configs: for cfg in configs:
conn = MCPConnection(cfg) conn = MCPConnection(cfg)
@@ -112,44 +118,75 @@ class MCPManager:
# ──────── 工具发现 ──────── # ──────── 工具发现 ────────
def get_openai_tools(self) -> list[dict]: def _build_tool_parameters_schema(self, tool: Any) -> dict[str, Any] | None:
""" """构造单个 MCP 工具的对象级参数 Schema。
将所有已注册的 MCP 工具转换为 OpenAI function calling 格式。
Args:
tool: MCP SDK 返回的原始工具对象。
Returns: Returns:
OpenAI tools 格式的工具定义列表 dict[str, Any] | None: 参数 Schema
""" """
tools: list[dict] = []
parameters_schema = (
dict(tool.inputSchema)
if hasattr(tool, "inputSchema") and tool.inputSchema
else {"type": "object", "properties": {}}
)
parameters_schema.pop("$schema", None)
return parameters_schema
def get_tool_specs(self) -> list[ToolSpec]:
"""获取全部已注册 MCP 工具的统一声明。
Returns:
list[ToolSpec]: MCP 工具声明列表。
"""
tool_specs: list[ToolSpec] = []
for server_name, conn in self._connections.items(): for server_name, conn in self._connections.items():
for tool in conn.tools: for tool in conn.tools:
# 只包含成功注册的工具
if tool.name not in self._tool_to_server: if tool.name not in self._tool_to_server:
continue continue
if self._tool_to_server[tool.name] != server_name: if self._tool_to_server[tool.name] != server_name:
continue continue
# MCP inputSchema → OpenAI parameters parameters_schema = self._build_tool_parameters_schema(tool)
parameters = ( brief_description = str(tool.description or f"来自 {server_name} 的 MCP 工具").strip()
dict(tool.inputSchema) tool_specs.append(
if hasattr(tool, "inputSchema") and tool.inputSchema ToolSpec(
else {"type": "object", "properties": {}} name=str(tool.name),
brief_description=brief_description,
detailed_description=build_tool_detailed_description(
parameters_schema,
fallback_description=f"工具来源MCP 服务 {server_name}",
),
parameters_schema=parameters_schema,
provider_name="mcp",
provider_type="mcp",
metadata={"server_name": server_name},
)
) )
# 移除 $schema 字段(部分 MCP 服务器会带上OpenAI 不接受) return tool_specs
parameters.pop("$schema", None)
tools.append( def get_openai_tools(self) -> list[dict[str, Any]]:
{ """获取兼容旧模型层的 MCP 工具定义。
"type": "function",
"function": {
"name": tool.name,
"description": (tool.description or f"MCP tool from {server_name}"),
"parameters": parameters,
},
}
)
return tools Returns:
list[dict[str, Any]]: OpenAI function tool 格式列表。
"""
return [
{
"type": "function",
"function": {
"name": tool_spec.name,
"description": tool_spec.build_llm_description(),
"parameters": tool_spec.parameters_schema or {"type": "object", "properties": {}},
},
}
for tool_spec in self.get_tool_specs()
]
# ──────── 工具调用 ──────── # ──────── 工具调用 ────────
@@ -157,28 +194,46 @@ class MCPManager:
"""判断工具名是否为已注册的 MCP 工具。""" """判断工具名是否为已注册的 MCP 工具。"""
return tool_name in self._tool_to_server return tool_name in self._tool_to_server
async def call_tool(self, tool_name: str, arguments: dict) -> str: async def call_tool_invocation(self, invocation: ToolInvocation) -> ToolExecutionResult:
""" """执行统一的 MCP 工具调用。
调用指定的 MCP 工具。
自动路由到正确的 MCP 服务器。
Args: Args:
tool_name: 工具名称 invocation: 统一工具调用请求。
arguments: 工具参数
Returns: Returns:
工具执行结果文本 ToolExecutionResult: 统一工具执行结果。
""" """
tool_name = invocation.tool_name
server_name = self._tool_to_server.get(tool_name) server_name = self._tool_to_server.get(tool_name)
if not server_name or server_name not in self._connections: if not server_name or server_name not in self._connections:
return f"MCP 工具 '{tool_name}' 未找到" return ToolExecutionResult(
tool_name=tool_name,
success=False,
error_message=f"MCP 工具 '{tool_name}' 未找到",
)
conn = self._connections[server_name] conn = self._connections[server_name]
try: return await conn.call_tool(tool_name, invocation.arguments)
return await conn.call_tool(tool_name, arguments)
except Exception as e: async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> str:
return f"MCP 工具 '{tool_name}' 执行失败: {e}" """兼容旧接口,返回 MCP 工具的文本结果。
Args:
tool_name: 工具名称。
arguments: 工具参数。
Returns:
str: 工具结果文本。
"""
result = await self.call_tool_invocation(
ToolInvocation(
tool_name=tool_name,
arguments=arguments,
)
)
return result.get_history_content()
# ──────── 信息展示 ──────── # ──────── 信息展示 ────────
@@ -207,7 +262,7 @@ class MCPManager:
# ──────── 生命周期 ──────── # ──────── 生命周期 ────────
async def close(self): async def close(self) -> None:
"""关闭所有 MCP 服务器连接。""" """关闭所有 MCP 服务器连接。"""
for conn in self._connections.values(): for conn in self._connections.values():
await conn.close() await conn.close()

View File

@@ -0,0 +1,54 @@
"""MCP 工具 Provider。"""
from __future__ import annotations
from typing import Optional
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolProvider, ToolSpec
from .manager import MCPManager
class MCPToolProvider(ToolProvider):
"""基于 MCPManager 的工具 Provider。"""
provider_name = "mcp"
provider_type = "mcp"
def __init__(self, manager: MCPManager) -> None:
"""初始化 MCP 工具 Provider。
Args:
manager: MCP 管理器实例。
"""
self._manager = manager
async def list_tools(self) -> list[ToolSpec]:
"""列出全部 MCP 工具。"""
return self._manager.get_tool_specs()
async def invoke(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行指定 MCP 工具。
Args:
invocation: 工具调用请求。
context: 执行上下文。
Returns:
ToolExecutionResult: 工具执行结果。
"""
del context
return await self._manager.call_tool_invocation(invocation)
async def close(self) -> None:
"""关闭 Provider 并释放 MCP 连接。"""
await self._manager.close()

View File

@@ -29,6 +29,19 @@ class _RuntimeComponentManagerProtocol(Protocol):
def _build_api_unavailable_error(self, entry: "APIEntry") -> str: ... def _build_api_unavailable_error(self, entry: "APIEntry") -> str: ...
def _collect_api_reference_matches(
self,
caller_plugin_id: str,
normalized_api_name: str,
normalized_version: str,
) -> tuple[List[tuple["PluginSupervisor", "APIEntry"]], List[tuple["PluginSupervisor", "APIEntry"]], bool]: ...
def _collect_api_toggle_reference_matches(
self,
normalized_name: str,
normalized_version: str,
) -> List[tuple["PluginSupervisor", "APIEntry"]]: ...
def _get_supervisor_for_plugin(self, plugin_id: str) -> Optional["PluginSupervisor"]: ... def _get_supervisor_for_plugin(self, plugin_id: str) -> Optional["PluginSupervisor"]: ...
def _resolve_api_target( def _resolve_api_target(
@@ -136,7 +149,10 @@ class RuntimeComponentCapabilityMixin:
str: 统一转为大写后的组件类型名。 str: 统一转为大写后的组件类型名。
""" """
return str(component_type or "").strip().upper() normalized_component_type = str(component_type or "").strip().upper()
if normalized_component_type == "ACTION":
return "TOOL"
return normalized_component_type
@classmethod @classmethod
def _is_api_component_type(cls, component_type: str) -> bool: def _is_api_component_type(cls, component_type: str) -> bool:

View File

@@ -1,14 +1,21 @@
"""插件运行时统一组件查询服务。 """插件运行时统一组件查询服务。
该模块统一从插件运行时的 Host ComponentRegistry 中聚合只读视图, 该模块统一从插件运行时的 Host ComponentRegistry 中聚合只读视图,
供 HFC/PFC、Planner、ToolExecutor 和运行时能力层查询与调用。 供 HFC、ToolExecutor 和运行时能力层查询与调用。
""" """
from __future__ import annotations from __future__ import annotations
from typing import TYPE_CHECKING, Any, Awaitable, Callable, Dict, Optional, Tuple from typing import TYPE_CHECKING, Any, Awaitable, Callable, Dict, Optional, Tuple, cast
from src.common.logger import get_logger from src.common.logger import get_logger
from src.core.tooling import (
ToolExecutionContext,
ToolExecutionResult,
ToolInvocation,
ToolSpec,
build_tool_detailed_description,
)
from src.core.types import ActionActivationType, ActionInfo, CommandInfo, ComponentInfo, ComponentType, ToolInfo from src.core.types import ActionActivationType, ActionInfo, CommandInfo, ComponentInfo, ComponentType, ToolInfo
from src.llm_models.payload_content.tool_option import normalize_tool_option from src.llm_models.payload_content.tool_option import normalize_tool_option
@@ -240,12 +247,38 @@ class ComponentQueryService:
return ToolInfo( return ToolInfo(
name=entry.name, name=entry.name,
description=entry.description, description=entry.brief_description or entry.description,
enabled=bool(entry.enabled), enabled=bool(entry.enabled),
plugin_name=entry.plugin_id, plugin_name=entry.plugin_id,
parameters_schema=ComponentQueryService._build_tool_parameters_schema(entry), parameters_schema=ComponentQueryService._build_tool_parameters_schema(entry),
) )
@staticmethod
def _build_tool_spec(entry: "ToolEntry") -> ToolSpec:
"""将运行时 Tool 条目转换为统一工具声明。
Args:
entry: 插件运行时中的 Tool 条目。
Returns:
ToolSpec: 统一工具声明。
"""
parameters_schema = ComponentQueryService._build_tool_parameters_schema(entry)
return ToolSpec(
name=entry.name,
brief_description=entry.brief_description or entry.description or f"工具 {entry.name}",
detailed_description=entry.detailed_description or build_tool_detailed_description(parameters_schema),
parameters_schema=parameters_schema,
provider_name=entry.plugin_id,
provider_type="plugin",
metadata={
"plugin_id": entry.plugin_id,
"invoke_method": entry.invoke_method,
"legacy_component_type": entry.legacy_component_type,
},
)
@staticmethod @staticmethod
def _log_duplicate_component(component_type: ComponentType, component_name: str) -> None: def _log_duplicate_component(component_type: ComponentType, component_name: str) -> None:
"""记录重复组件名称冲突。 """记录重复组件名称冲突。
@@ -475,7 +508,12 @@ class ComponentQueryService:
return _executor return _executor
@staticmethod @staticmethod
def _build_tool_executor(supervisor: "PluginSupervisor", plugin_id: str, component_name: str) -> ToolExecutor: def _build_tool_executor(
supervisor: "PluginSupervisor",
plugin_id: str,
component_name: str,
invoke_method: str = "plugin.invoke_tool",
) -> ToolExecutor:
"""构造工具执行 RPC 闭包。 """构造工具执行 RPC 闭包。
Args: Args:
@@ -499,7 +537,7 @@ class ComponentQueryService:
try: try:
response = await supervisor.invoke_plugin( response = await supervisor.invoke_plugin(
method="plugin.invoke_tool", method=invoke_method,
plugin_id=plugin_id, plugin_id=plugin_id,
component_name=component_name, component_name=component_name,
args=function_args, args=function_args,
@@ -615,7 +653,162 @@ class ComponentQueryService:
if matched_entry is None: if matched_entry is None:
return None return None
supervisor, entry = matched_entry supervisor, entry = matched_entry
return self._build_tool_executor(supervisor, entry.plugin_id, entry.name) tool_entry = cast("ToolEntry", entry)
return self._build_tool_executor(supervisor, tool_entry.plugin_id, tool_entry.name, tool_entry.invoke_method)
def get_llm_available_tool_specs(self) -> Dict[str, ToolSpec]:
"""获取当前可供 LLM 使用的统一工具声明集合。
Returns:
Dict[str, ToolSpec]: 工具名到工具声明的映射。
"""
collected_specs: Dict[str, ToolSpec] = {}
for _supervisor, entry in self._iter_component_entries(ComponentType.TOOL):
if entry.name in collected_specs:
self._log_duplicate_component(ComponentType.TOOL, entry.name)
continue
collected_specs[entry.name] = self._build_tool_spec(entry) # type: ignore[arg-type]
return collected_specs
@staticmethod
def _build_tool_invocation_payload(
entry: "ToolEntry",
invocation: ToolInvocation,
context: Optional[ToolExecutionContext],
) -> Dict[str, Any]:
"""构造插件工具执行时发送给 Runner 的参数。
Args:
entry: 目标工具条目。
invocation: 统一工具调用请求。
context: 统一工具执行上下文。
Returns:
Dict[str, Any]: 发往 Runner 的参数字典。
"""
payload = dict(invocation.arguments)
if entry.invoke_method == "plugin.invoke_action":
stream_id = context.stream_id if context is not None else invocation.stream_id
reasoning = context.reasoning if context is not None else invocation.reasoning
payload = {
**payload,
"stream_id": stream_id,
"chat_id": stream_id,
"reasoning": reasoning,
"action_data": dict(invocation.arguments),
}
return payload
@staticmethod
def _parse_tool_invoke_result(
entry: "ToolEntry",
result: Any,
) -> ToolExecutionResult:
"""将插件组件返回值转换为统一工具执行结果。
Args:
entry: 目标工具条目。
result: 插件组件原始返回值。
Returns:
ToolExecutionResult: 统一执行结果。
"""
if isinstance(result, dict):
success = bool(result.get("success", True))
content = str(result.get("content", result.get("message", "")) or "").strip()
error_message = ""
if not success:
error_message = str(result.get("error", result.get("message", "插件工具执行失败")) or "").strip()
return ToolExecutionResult(
tool_name=entry.name,
success=success,
content=content,
error_message=error_message,
structured_content=result,
metadata={"plugin_id": entry.plugin_id},
)
if isinstance(result, (list, tuple)) and result:
if isinstance(result[0], bool):
success = bool(result[0])
message = "" if len(result) < 2 or result[1] is None else str(result[1]).strip()
return ToolExecutionResult(
tool_name=entry.name,
success=success,
content=message if success else "",
error_message="" if success else message,
structured_content=list(result),
metadata={"plugin_id": entry.plugin_id},
)
normalized_content = "" if result is None else str(result).strip()
return ToolExecutionResult(
tool_name=entry.name,
success=True,
content=normalized_content,
structured_content=result,
metadata={"plugin_id": entry.plugin_id},
)
async def invoke_tool_as_tool(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""按统一工具语义执行插件工具。
Args:
invocation: 统一工具调用请求。
context: 执行上下文。
Returns:
ToolExecutionResult: 统一工具执行结果。
"""
matched_entry = self._get_unique_component_entry(ComponentType.TOOL, invocation.tool_name)
if matched_entry is None:
return ToolExecutionResult(
tool_name=invocation.tool_name,
success=False,
error_message=f"未找到插件工具:{invocation.tool_name}",
)
supervisor, entry = matched_entry
tool_entry = cast("ToolEntry", entry)
invoke_payload = self._build_tool_invocation_payload(tool_entry, invocation, context)
try:
response = await supervisor.invoke_plugin(
method=tool_entry.invoke_method,
plugin_id=tool_entry.plugin_id,
component_name=tool_entry.name,
args=invoke_payload,
timeout_ms=30000,
)
except Exception as exc:
logger.error(f"运行时工具 {tool_entry.plugin_id}.{tool_entry.name} 执行失败: {exc}", exc_info=True)
return ToolExecutionResult(
tool_name=tool_entry.name,
success=False,
error_message=str(exc),
metadata={"plugin_id": tool_entry.plugin_id},
)
payload = response.payload if isinstance(response.payload, dict) else {}
transport_success = bool(payload.get("success", False))
result = payload.get("result")
if not transport_success:
return ToolExecutionResult(
tool_name=tool_entry.name,
success=False,
error_message="" if result is None else str(result),
structured_content=result,
metadata={"plugin_id": tool_entry.plugin_id},
)
return self._parse_tool_invoke_result(tool_entry, result)
def get_llm_available_tools(self) -> Dict[str, ToolInfo]: def get_llm_available_tools(self) -> Dict[str, ToolInfo]:
"""获取当前可供 LLM 选择的工具集合。 """获取当前可供 LLM 选择的工具集合。

View File

@@ -1,4 +1,4 @@
"""Host-side ComponentRegistry """Host 侧组件注册表。
对齐旧系统 component_registry.py 的核心能力: 对齐旧系统 component_registry.py 的核心能力:
- 按类型注册组件action / command / tool / event_handler / hook_handler / message_gateway - 按类型注册组件action / command / tool / event_handler / hook_handler / message_gateway
@@ -16,6 +16,7 @@ import contextlib
import re import re
from src.common.logger import get_logger from src.common.logger import get_logger
from src.core.tooling import build_tool_detailed_description
logger = get_logger("plugin_runtime.host.component_registry") logger = get_logger("plugin_runtime.host.component_registry")
@@ -89,11 +90,81 @@ class ToolEntry(ComponentEntry):
"""Tool 组件条目""" """Tool 组件条目"""
def __init__(self, name: str, component_type: str, plugin_id: str, metadata: Dict[str, Any]) -> None: def __init__(self, name: str, component_type: str, plugin_id: str, metadata: Dict[str, Any]) -> None:
self.description: str = metadata.get("description", "") self.description: str = str(metadata.get("description", "") or "").strip()
self.brief_description: str = str(
metadata.get("brief_description", self.description) or self.description or f"工具 {name}"
).strip()
self.parameters: List[Dict[str, Any]] = metadata.get("parameters", []) self.parameters: List[Dict[str, Any]] = metadata.get("parameters", [])
self.parameters_raw: Dict[str, Any] | List[Dict[str, Any]] = metadata.get("parameters_raw", {}) self.parameters_raw: Dict[str, Any] | List[Dict[str, Any]] = metadata.get("parameters_raw", {})
detailed_description = str(metadata.get("detailed_description", "") or "").strip()
self.detailed_description: str = detailed_description
self.invoke_method: str = str(metadata.get("invoke_method", "plugin.invoke_tool") or "plugin.invoke_tool").strip()
self.legacy_component_type: str = str(metadata.get("legacy_component_type", "") or "").strip()
super().__init__(name, component_type, plugin_id, metadata) super().__init__(name, component_type, plugin_id, metadata)
if not self.detailed_description:
parameters_schema = self._get_parameters_schema()
self.detailed_description = build_tool_detailed_description(parameters_schema)
def _get_parameters_schema(self) -> Dict[str, Any] | None:
"""获取当前工具条目的对象级参数 Schema。
Returns:
Dict[str, Any] | None: 归一化后的参数 Schema。
"""
if isinstance(self.parameters_raw, dict) and self.parameters_raw:
if self.parameters_raw.get("type") == "object" or "properties" in self.parameters_raw:
return dict(self.parameters_raw)
required_names: List[str] = []
normalized_properties: Dict[str, Any] = {}
for property_name, property_schema in self.parameters_raw.items():
if not isinstance(property_schema, dict):
continue
property_schema_copy = dict(property_schema)
if bool(property_schema_copy.pop("required", False)):
required_names.append(str(property_name))
normalized_properties[str(property_name)] = property_schema_copy
schema: Dict[str, Any] = {
"type": "object",
"properties": normalized_properties,
}
if required_names:
schema["required"] = required_names
return schema
if isinstance(self.parameters, list) and self.parameters:
properties: Dict[str, Any] = {}
required_names: List[str] = []
for parameter in self.parameters:
if not isinstance(parameter, dict):
continue
parameter_name = str(parameter.get("name", "") or "").strip()
if not parameter_name:
continue
if bool(parameter.get("required", False)):
required_names.append(parameter_name)
properties[parameter_name] = {
key: value
for key, value in parameter.items()
if key not in {"name", "required", "param_type"}
}
properties[parameter_name]["type"] = str(
parameter.get("type", parameter.get("param_type", "string")) or "string"
)
schema = {
"type": "object",
"properties": properties,
}
if required_names:
schema["required"] = required_names
return schema
return None
class EventHandlerEntry(ComponentEntry): class EventHandlerEntry(ComponentEntry):
"""EventHandler 组件条目""" """EventHandler 组件条目"""
@@ -282,7 +353,7 @@ class MessageGatewayEntry(ComponentEntry):
class ComponentRegistry: class ComponentRegistry:
"""Host-side 组件注册表 """Host 组件注册表
由 Supervisor 在收到 plugin.register_components 时调用。 由 Supervisor 在收到 plugin.register_components 时调用。
供业务层查询可用组件、匹配命令、调度 action/event 等。 供业务层查询可用组件、匹配命令、调度 action/event 等。
@@ -300,6 +371,86 @@ class ComponentRegistry:
# 按插件索引 # 按插件索引
self._by_plugin: Dict[str, List[ComponentEntry]] = {} self._by_plugin: Dict[str, List[ComponentEntry]] = {}
@staticmethod
def _convert_action_metadata_to_tool_metadata(
name: str,
metadata: Dict[str, Any],
) -> Dict[str, Any]:
"""将旧 Action 元数据转换为统一 Tool 元数据。
Args:
name: 组件名称。
metadata: Action 原始元数据。
Returns:
Dict[str, Any]: 转换后的 Tool 元数据。
"""
action_parameters = metadata.get("action_parameters")
parameters_schema: Dict[str, Any] | None = None
if isinstance(action_parameters, dict) and action_parameters:
properties: Dict[str, Any] = {}
for parameter_name, parameter_description in action_parameters.items():
normalized_name = str(parameter_name or "").strip()
if not normalized_name:
continue
properties[normalized_name] = {
"type": "string",
"description": str(parameter_description or "").strip() or "兼容旧 Action 参数",
}
if properties:
parameters_schema = {
"type": "object",
"properties": properties,
}
detailed_parts: List[str] = []
if parameters_schema is not None:
parameter_description = build_tool_detailed_description(parameters_schema)
if parameter_description:
detailed_parts.append(parameter_description)
action_require = [
str(item).strip()
for item in (metadata.get("action_require") or [])
if str(item).strip()
]
if action_require:
detailed_parts.append("使用建议:\n" + "\n".join(f"- {item}" for item in action_require))
associated_types = [
str(item).strip()
for item in (metadata.get("associated_types") or [])
if str(item).strip()
]
if associated_types:
detailed_parts.append(f"适用消息类型:{''.join(associated_types)}")
activation_type = str(metadata.get("activation_type", "always") or "always").strip()
activation_keywords = [
str(item).strip()
for item in (metadata.get("activation_keywords") or [])
if str(item).strip()
]
activation_lines = [f"兼容旧 Action 激活方式:{activation_type}"]
if activation_keywords:
activation_lines.append(f"激活关键词:{''.join(activation_keywords)}")
if str(metadata.get("action_prompt", "") or "").strip():
activation_lines.append(f"原始 Action 提示语:{str(metadata['action_prompt']).strip()}")
detailed_parts.append("\n".join(activation_lines))
brief_description = str(metadata.get("brief_description", metadata.get("description", "") or f"工具 {name}")).strip()
return {
**metadata,
"description": brief_description,
"brief_description": brief_description,
"detailed_description": "\n\n".join(part for part in detailed_parts if part).strip(),
"parameters_raw": parameters_schema or {},
"invoke_method": "plugin.invoke_action",
"legacy_action": True,
"legacy_component_type": "ACTION",
}
@staticmethod @staticmethod
def _normalize_component_type(component_type: str) -> ComponentTypes: def _normalize_component_type(component_type: str) -> ComponentTypes:
"""规范化组件类型输入。 """规范化组件类型输入。
@@ -338,18 +489,20 @@ class ComponentRegistry:
""" """
try: try:
normalized_type = self._normalize_component_type(component_type) normalized_type = self._normalize_component_type(component_type)
normalized_metadata = dict(metadata)
if normalized_type == ComponentTypes.ACTION: if normalized_type == ComponentTypes.ACTION:
comp = ActionEntry(name, normalized_type.value, plugin_id, metadata) normalized_metadata = self._convert_action_metadata_to_tool_metadata(name, normalized_metadata)
comp = ToolEntry(name, ComponentTypes.TOOL.value, plugin_id, normalized_metadata)
elif normalized_type == ComponentTypes.COMMAND: elif normalized_type == ComponentTypes.COMMAND:
comp = CommandEntry(name, normalized_type.value, plugin_id, metadata) comp = CommandEntry(name, normalized_type.value, plugin_id, normalized_metadata)
elif normalized_type == ComponentTypes.TOOL: elif normalized_type == ComponentTypes.TOOL:
comp = ToolEntry(name, normalized_type.value, plugin_id, metadata) comp = ToolEntry(name, normalized_type.value, plugin_id, normalized_metadata)
elif normalized_type == ComponentTypes.EVENT_HANDLER: elif normalized_type == ComponentTypes.EVENT_HANDLER:
comp = EventHandlerEntry(name, normalized_type.value, plugin_id, metadata) comp = EventHandlerEntry(name, normalized_type.value, plugin_id, normalized_metadata)
elif normalized_type == ComponentTypes.HOOK_HANDLER: elif normalized_type == ComponentTypes.HOOK_HANDLER:
comp = HookHandlerEntry(name, normalized_type.value, plugin_id, metadata) comp = HookHandlerEntry(name, normalized_type.value, plugin_id, normalized_metadata)
elif normalized_type == ComponentTypes.MESSAGE_GATEWAY: elif normalized_type == ComponentTypes.MESSAGE_GATEWAY:
comp = MessageGatewayEntry(name, normalized_type.value, plugin_id, metadata) comp = MessageGatewayEntry(name, normalized_type.value, plugin_id, normalized_metadata)
else: else:
raise ValueError(f"组件类型 {component_type} 不存在") raise ValueError(f"组件类型 {component_type} 不存在")
except ValueError: except ValueError:

View File

@@ -0,0 +1,48 @@
"""插件运行时工具 Provider。"""
from __future__ import annotations
from typing import Optional
from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolProvider, ToolSpec
from .component_query import component_query_service
class PluginToolProvider(ToolProvider):
"""将插件 Tool 与兼容旧 Action 暴露为统一工具 Provider。"""
provider_name = "plugin_runtime"
provider_type = "plugin"
async def list_tools(self) -> list[ToolSpec]:
"""列出插件运行时当前可用的工具声明。"""
return list(component_query_service.get_llm_available_tool_specs().values())
async def invoke(
self,
invocation: ToolInvocation,
context: Optional[ToolExecutionContext] = None,
) -> ToolExecutionResult:
"""执行插件工具或兼容旧 Action 的工具调用。
Args:
invocation: 工具调用请求。
context: 执行上下文。
Returns:
ToolExecutionResult: 工具执行结果。
"""
return await component_query_service.invoke_tool_as_tool(
invocation=invocation,
context=context,
)
async def close(self) -> None:
"""关闭 Provider。
插件运行时工具 Provider 不持有独立资源。
"""

View File

@@ -6,7 +6,7 @@
from maibot_sdk import Command, MaiBotPlugin from maibot_sdk import Command, MaiBotPlugin
_VALID_COMPONENT_TYPES = ("action", "command", "event_handler") _VALID_COMPONENT_TYPES = ("tool", "command", "event_handler")
HELP_ALL = ( HELP_ALL = (
"管理命令帮助\n" "管理命令帮助\n"
@@ -37,7 +37,7 @@ HELP_COMPONENT = (
"/pm component enable local <component_name> <component_type> 本聊天启用组件\n" "/pm component enable local <component_name> <component_type> 本聊天启用组件\n"
"/pm component disable global <component_name> <component_type> 全局禁用组件\n" "/pm component disable global <component_name> <component_type> 全局禁用组件\n"
"/pm component disable local <component_name> <component_type> 本聊天禁用组件\n" "/pm component disable local <component_name> <component_type> 本聊天禁用组件\n"
" - <component_type> 可选项: action, command, event_handler\n" " - <component_type> 可选项: tool, command, event_handler\n"
) )