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feat：拆分maisak一些混杂结构

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This commit is contained in:
SengokuCola
2026-04-05 17:44:28 +08:00
parent 499abe89aa
commit 80be746be0
8 changed files with 248 additions and 221 deletions
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100
src/maisaka/runtime.py
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@@ -30,6 +30,7 @@ from src.plugin_runtime.tool_provider import PluginToolProvider
from .chat_loop_service import ChatResponse, MaisakaChatLoopService
from .context_messages import LLMContextMessage
from .display_utils import build_tool_call_summary_lines, format_token_count
from .reasoning_engine import MaisakaReasoningEngine
from .tool_provider import MaisakaBuiltinToolProvider
@@ -410,27 +411,49 @@ class MaisakaHeartFlowChatting:
if isinstance(knowledge_result, Exception):
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:
"""?????????????????"""
if not self._enable_expression_learning:
logger.debug(f"{self.log_prefix} ??????????????")
return
def _should_trigger_learning(
self,
*,
enabled: bool,
feature_name: str,
last_extraction_time: float,
pending_count: int,
min_messages_for_extraction: int,
) -> bool:
"""判断周期性学习任务是否满足执行条件。"""
elapsed = time.time() - self._last_expression_extraction_time
if not enabled:
logger.debug(f"{self.log_prefix} {feature_name}未启用，跳过本轮学习")
return False
elapsed = time.time() - last_extraction_time
if elapsed < self._min_extraction_interval:
logger.debug(
f"{self.log_prefix} ????????????: "
f"??={elapsed:.2f} ? ??={self._min_extraction_interval} ?"
f"{self.log_prefix} {feature_name}触发间隔不足: "
f"已过={elapsed:.2f} 秒 阈值={self._min_extraction_interval} 秒"
)
return
return False
pending_count = self._expression_learner.get_pending_count(self.message_cache)
if pending_count < self._expression_learner.min_messages_for_extraction:
if pending_count < min_messages_for_extraction:
logger.debug(
f"{self.log_prefix} ??????????????: "
f"??????={pending_count} ??={self._expression_learner.min_messages_for_extraction} "
f"?????={len(self.message_cache)}"
f"{self.log_prefix} {feature_name}待处理消息不足: "
f"待处理={pending_count} 阈值={min_messages_for_extraction} "
f"缓存总量={len(self.message_cache)}"
)
return False
return True
async def _trigger_expression_learning(self, messages: list[SessionMessage]) -> None:
"""?????????????????"""
pending_count = self._expression_learner.get_pending_count(self.message_cache)
if not self._should_trigger_learning(
enabled=self._enable_expression_learning,
feature_name="表达学习",
last_extraction_time=self._last_expression_extraction_time,
pending_count=pending_count,
min_messages_for_extraction=self._expression_learner.min_messages_for_extraction,
):
return
self._last_expression_extraction_time = time.time()
@@ -453,25 +476,14 @@ class MaisakaHeartFlowChatting:
async def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
"""?????????????????"""
if not global_config.maisaka.enable_knowledge_module:
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} ????????????: "
f"??={elapsed:.2f} ? ??={self._min_extraction_interval} ?"
)
return
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} ??????????????: "
f"??????={pending_count} ??={self._knowledge_learner.min_messages_for_extraction} "
f"?????={len(self.message_cache)}"
)
if not self._should_trigger_learning(
enabled=global_config.maisaka.enable_knowledge_module,
feature_name="知识学习",
last_extraction_time=self._last_knowledge_extraction_time,
pending_count=pending_count,
min_messages_for_extraction=self._knowledge_learner.min_messages_for_extraction,
):
return
self._last_knowledge_extraction_time = time.time()
@@ -535,13 +547,6 @@ class MaisakaHeartFlowChatting:
return GroupInfo(group_id=group_info.group_id, group_name=group_info.group_name)
@staticmethod
def _format_token_count(token_count: int) -> str:
"""格式化 token 数量展示文本。"""
if token_count >= 10_000:
return f"{token_count / 1000:.1f}k"
return str(token_count)
def _render_context_usage_panel(
self,
*,
@@ -558,7 +563,7 @@ class MaisakaHeartFlowChatting:
body_lines = [
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)}",
f"本次请求token消耗：{format_token_count(prompt_tokens)}",
]
renderables: list[RenderableType] = [Text("\n".join(body_lines))]
@@ -576,7 +581,7 @@ class MaisakaHeartFlowChatting:
)
)
normalized_tool_calls = self._build_tool_call_summary_lines(tool_calls or [])
normalized_tool_calls = build_tool_call_summary_lines(tool_calls or [])
if normalized_tool_calls:
renderables.append(
Panel(
@@ -607,19 +612,6 @@ class MaisakaHeartFlowChatting:
)
)
@staticmethod
def _build_tool_call_summary_lines(tool_calls: list[Any]) -> list[str]:
"""构建工具调用摘要文本。"""
summary_lines: list[str] = []
for tool_call in tool_calls:
tool_name = str(getattr(tool_call, "func_name", getattr(tool_call, "name", "")) or "").strip() or "unknown"
tool_args = getattr(tool_call, "args", getattr(tool_call, "arguments", None))
if isinstance(tool_args, dict) and tool_args:
summary_lines.append(f"- {tool_name}: {tool_args}")
else:
summary_lines.append(f"- {tool_name}")
return summary_lines
def _log_cycle_started(self, cycle_detail: CycleDetail, round_index: int) -> None:
logger.info(
f"{self.log_prefix} MaiSaka 轮次开始: 循环编号={cycle_detail.cycle_id} "