feat：重构maisaka的消息类型，添加打断功能
This commit is contained in:
SengokuCola
@@ -23,7 +23,13 @@ from src.config.config import config_manager, global_config
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from src.mcp_module import MCPManager
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from src.maisaka.chat_loop_service import MaisakaChatLoopService
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from src.maisaka.message_adapter import build_message, format_speaker_content, remove_last_perception
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from src.maisaka.context_messages import (
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AssistantMessage,
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LLMContextMessage,
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SessionBackedMessage,
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ToolResultMessage,
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)
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from src.maisaka.message_adapter import format_speaker_content
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from src.maisaka.tool_handlers import (
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ToolHandlerContext,
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handle_mcp_tool,
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@@ -43,7 +49,7 @@ class BufferCLI:
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self._chat_loop_service: Optional[MaisakaChatLoopService] = None
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self._reply_generator = MaisakaReplyGenerator()
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self._reader = InputReader()
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self._chat_history: Optional[list[SessionMessage]] = None
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self._chat_history: Optional[list[LLMContextMessage]] = None
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self._knowledge_store = get_knowledge_store()
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self._knowledge_learner = KnowledgeLearner("maisaka_cli")
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self._knowledge_min_messages_for_extraction = 10
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@@ -118,22 +124,78 @@ class BufferCLI:
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self._chat_start_time = now
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self._last_assistant_response_time = None
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self._chat_history = self._chat_loop_service.build_chat_context(user_text)
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self._trigger_knowledge_learning([self._chat_history[-1]])
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self._trigger_knowledge_learning([self._build_cli_session_message(user_text, now)])
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else:
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self._chat_history.append(
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build_message(
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role="user",
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content=format_speaker_content(
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global_config.maisaka.user_name.strip() or "User",
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user_text,
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now,
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),
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self._build_cli_context_message(
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user_text=user_text,
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timestamp=now,
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source_kind="user",
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)
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)
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self._trigger_knowledge_learning([self._chat_history[-1]])
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self._trigger_knowledge_learning([self._build_cli_session_message(user_text, now)])
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await self._run_llm_loop(self._chat_history)
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@staticmethod
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def _build_cli_context_message(
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user_text: str,
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timestamp: datetime,
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source_kind: str = "user",
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speaker_name: Optional[str] = None,
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) -> SessionBackedMessage:
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"""为 CLI 构造新的上下文消息。"""
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resolved_speaker_name = speaker_name or global_config.maisaka.user_name.strip() or "User"
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visible_text = format_speaker_content(
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resolved_speaker_name,
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user_text,
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timestamp,
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)
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planner_prefix = (
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f"[时间]{timestamp.strftime('%H:%M:%S')}\n"
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f"[用户]{resolved_speaker_name}\n"
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"[用户群昵称]\n"
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"[msg_id]\n"
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"[发言内容]"
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)
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from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
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return SessionBackedMessage(
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raw_message=MessageSequence([TextComponent(f"{planner_prefix}{user_text}")]),
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visible_text=visible_text,
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timestamp=timestamp,
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source_kind=source_kind,
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)
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@staticmethod
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def _build_cli_session_message(user_text: str, timestamp: datetime) -> SessionMessage:
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"""为 CLI 的知识学习构造兼容 SessionMessage。"""
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from src.common.data_models.mai_message_data_model import MessageInfo, UserInfo
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from src.common.data_models.message_component_data_model import MessageSequence
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message = SessionMessage(message_id=f"maisaka_cli_{int(timestamp.timestamp() * 1000)}", timestamp=timestamp, platform="maisaka")
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message.message_info = MessageInfo(
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user_info=UserInfo(
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user_id="maisaka_user",
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user_nickname=global_config.maisaka.user_name.strip() or "User",
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user_cardname=None,
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),
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group_info=None,
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additional_config={},
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)
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message.session_id = "maisaka_cli"
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message.raw_message = MessageSequence([])
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visible_text = format_speaker_content(
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global_config.maisaka.user_name.strip() or "User",
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user_text,
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timestamp,
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)
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message.raw_message.text(visible_text)
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message.processed_plain_text = visible_text
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message.display_message = visible_text
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message.initialized = True
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return message
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def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
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"""在 CLI 会话中按批次触发 knowledge 学习。"""
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if not global_config.maisaka.enable_knowledge_module:
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@@ -161,7 +223,7 @@ class BufferCLI:
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except Exception as exc:
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console.print(f"[warning]Knowledge learning failed: {exc}[/warning]")
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async def _run_llm_loop(self, chat_history: list[SessionMessage]) -> None:
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async def _run_llm_loop(self, chat_history: list[LLMContextMessage]) -> None:
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"""
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Main inner loop for the Maisaka planner.
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@@ -210,7 +272,8 @@ class BufferCLI:
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)
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)
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remove_last_perception(chat_history)
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if chat_history and isinstance(chat_history[-1], AssistantMessage) and chat_history[-1].source == "perception":
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chat_history.pop()
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perception_parts = []
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if knowledge_analysis:
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@@ -218,11 +281,10 @@ class BufferCLI:
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if perception_parts:
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chat_history.append(
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build_message(
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role="assistant",
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AssistantMessage(
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content="\n\n".join(perception_parts),
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message_kind="perception",
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source="assistant",
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timestamp=datetime.now(),
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source_kind="perception",
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)
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)
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elif global_config.maisaka.show_thinking:
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@@ -273,22 +335,19 @@ class BufferCLI:
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elif tool_call.func_name == "reply":
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reply = await self._generate_visible_reply(chat_history, response.content)
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chat_history.append(
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build_message(
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role="tool",
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ToolResultMessage(
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content="Visible reply generated and recorded.",
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source="tool",
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timestamp=datetime.now(),
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tool_call_id=tool_call.call_id,
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tool_name=tool_call.func_name,
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)
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)
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chat_history.append(
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build_message(
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role="user",
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content=format_speaker_content(
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global_config.bot.nickname.strip() or "MaiSaka",
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reply,
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datetime.now(),
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),
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source="guided_reply",
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self._build_cli_context_message(
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user_text=reply,
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timestamp=datetime.now(),
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source_kind="guided_reply",
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speaker_name=global_config.bot.nickname.strip() or "MaiSaka",
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)
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)
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@@ -296,11 +355,11 @@ class BufferCLI:
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if global_config.maisaka.show_thinking:
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console.print("[muted]No visible reply this round.[/muted]")
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chat_history.append(
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build_message(
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role="tool",
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ToolResultMessage(
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content="No visible reply was sent for this round.",
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source="tool",
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timestamp=datetime.now(),
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tool_call_id=tool_call.call_id,
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tool_name=tool_call.func_name,
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)
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)
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@@ -342,7 +401,7 @@ class BufferCLI:
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)
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)
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async def _generate_visible_reply(self, chat_history: list[SessionMessage], latest_thought: str) -> str:
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async def _generate_visible_reply(self, chat_history: list[LLMContextMessage], latest_thought: str) -> str:
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"""根据最新思考生成并输出可见回复。"""
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if not latest_thought:
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return ""
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