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feat：修复表达方式的学习和使用，用subagent使用表达

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SengokuCola
2026-04-04 23:18:21 +08:00
parent 2fb911a8d5
commit 7b924774be
10 changed files with 497 additions and 569 deletions
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78
src/maisaka/runtime.py
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@@ -84,7 +84,6 @@ class MaisakaHeartFlowChatting:
self._enable_expression_use = expr_use
self._enable_expression_learning = expr_learn
self._enable_jargon_learning = jargon_learn
self._min_messages_for_extraction = 10
self._min_extraction_interval = 30
self._last_expression_extraction_time = 0.0
self._last_knowledge_extraction_time = 0.0
@@ -351,88 +350,84 @@ class MaisakaHeartFlowChatting:
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:
"""基于新收集的一批消息触发表达学习。"""
self._expression_learner.add_messages(messages)
"""?????????????????"""
if not self._enable_expression_learning:
logger.debug(f"{self.log_prefix} 表达学习未启用，跳过当前批次")
logger.debug(f"{self.log_prefix} ??????????????")
return
elapsed = time.time() - self._last_expression_extraction_time
if elapsed < self._min_extraction_interval:
logger.debug(
f"{self.log_prefix} 表达学习尚未达到触发间隔: "
f"已过={elapsed:.2f} 秒 阈值={self._min_extraction_interval} 秒"
f"{self.log_prefix} ????????????: "
f"??={elapsed:.2f} ? ??={self._min_extraction_interval} ?"
)
return
cache_size = self._expression_learner.get_cache_size()
if cache_size < self._min_messages_for_extraction:
pending_count = self._expression_learner.get_pending_count(self.message_cache)
if pending_count < self._expression_learner.min_messages_for_extraction:
logger.debug(
f"{self.log_prefix} 表达学习因缓存数量不足而跳过: "
f"学习器缓存={cache_size} 阈值={self._min_messages_for_extraction} "
f"消息总缓存={len(self.message_cache)}"
f"{self.log_prefix} ??????????????: "
f"??????={pending_count} ??={self._expression_learner.min_messages_for_extraction} "
f"?????={len(self.message_cache)}"
)
return
self._last_expression_extraction_time = time.time()
logger.info(
f"{self.log_prefix} 开始表达学习: "
f"新批次消息数={len(messages)} 学习器缓存={cache_size} "
f"消息总缓存={len(self.message_cache)} "
f"启用黑话学习={self._enable_jargon_learning}"
f"{self.log_prefix} ??????: "
f"??????={len(messages)} ??????={pending_count} "
f"?????={len(self.message_cache)} "
f"??????={self._enable_jargon_learning}"
)
try:
jargon_miner = self._jargon_miner if self._enable_jargon_learning else None
learnt_style = await self._expression_learner.learn(jargon_miner)
learnt_style = await self._expression_learner.learn(self.message_cache, jargon_miner)
if learnt_style:
logger.info(f"{self.log_prefix} 表达学习已完成")
logger.info(f"{self.log_prefix} ???????")
else:
logger.debug(f"{self.log_prefix} 表达学习已完成，但没有可用结果")
logger.debug(f"{self.log_prefix} ???????????????")
except Exception:
logger.exception(f"{self.log_prefix} 表达学习失败")
logger.exception(f"{self.log_prefix} ??????")
async def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
"""基于新收集的一批消息触发知识学习。"""
self._knowledge_learner.add_messages(messages)
"""?????????????????"""
if not global_config.maisaka.enable_knowledge_module:
logger.debug(f"{self.log_prefix} 知识学习未启用，跳过当前批次")
logger.debug(f"{self.log_prefix} ??????????????")
return
elapsed = time.time() - self._last_knowledge_extraction_time
if elapsed < self._min_extraction_interval:
logger.debug(
f"{self.log_prefix} 知识学习尚未达到触发间隔: "
f"已过={elapsed:.2f} 秒 阈值={self._min_extraction_interval} 秒"
f"{self.log_prefix} ????????????: "
f"??={elapsed:.2f} ? ??={self._min_extraction_interval} ?"
)
return
cache_size = self._knowledge_learner.get_cache_size()
if cache_size < self._min_messages_for_extraction:
pending_count = self._knowledge_learner.get_pending_count(self.message_cache)
if pending_count < self._knowledge_learner.min_messages_for_extraction:
logger.debug(
f"{self.log_prefix} 知识学习因缓存数量不足而跳过: "
f"学习器缓存={cache_size} 阈值={self._min_messages_for_extraction} "
f"消息总缓存={len(self.message_cache)}"
f"{self.log_prefix} ??????????????: "
f"??????={pending_count} ??={self._knowledge_learner.min_messages_for_extraction} "
f"?????={len(self.message_cache)}"
)
return
self._last_knowledge_extraction_time = time.time()
logger.info(
f"{self.log_prefix} 开始知识学习: "
f"新批次消息数={len(messages)} 学习器缓存={cache_size} "
f"消息总缓存={len(self.message_cache)}"
f"{self.log_prefix} ??????: "
f"??????={len(messages)} ??????={pending_count} "
f"?????={len(self.message_cache)}"
)
try:
added_count = await self._knowledge_learner.learn()
added_count = await self._knowledge_learner.learn(self.message_cache)
if added_count > 0:
logger.info(f"{self.log_prefix} 知识学习已完成: 新增条目数={added_count}")
logger.info(f"{self.log_prefix} ???????: ?????={added_count}")
else:
logger.debug(f"{self.log_prefix} 知识学习已完成，但没有可用结果")
logger.debug(f"{self.log_prefix} ???????????????")
except Exception:
logger.exception(f"{self.log_prefix} 知识学习失败")
logger.exception(f"{self.log_prefix} ??????")
async def _init_mcp(self) -> None:
"""初始化 MCP 工具并注册到统一工具层。"""
@@ -502,9 +497,8 @@ class MaisakaHeartFlowChatting:
session_name = chat_manager.get_session_name(self.session_id) or self.session_id
body_lines = [
f"聊天流: {session_name}",
f"Chat ID: {self.session_id}",
f"上下文占用: {selected_history_count}条 / {self._format_token_count(prompt_tokens)}",
f"上下文占用：{selected_history_count}/{self._max_context_size} 条",
f"本次请求token消耗：{self._format_token_count(prompt_tokens)}",
]
renderables: list[RenderableType] = [Text("\n".join(body_lines))]
@@ -584,7 +578,7 @@ class MaisakaHeartFlowChatting:
def _log_history_trimmed(self, removed_count: int, user_message_count: int) -> None:
logger.info(
f"{self.log_prefix} 已裁剪 {removed_count} 条历史消息; "
f"剩余计入上下文的消息数={user_message_count}"
# f"剩余计入上下文的消息数={user_message_count}"
)
def _log_internal_loop_cancelled(self) -> None: