"""Maisaka 推理引擎。""" from datetime import datetime from typing import TYPE_CHECKING, Any, Literal, Optional import asyncio import difflib import json import time import traceback from src.chat.heart_flow.heartFC_utils import CycleDetail from src.chat.message_receive.message import SessionMessage from src.common.data_models.message_component_data_model import EmojiComponent, ImageComponent, MessageSequence from src.common.logger import get_logger from src.common.prompt_i18n import load_prompt from src.config.config import global_config from src.core.tooling import ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec from src.llm_models.exceptions import ReqAbortException from src.llm_models.payload_content.tool_option import ToolCall from src.services import database_service as database_api from .builtin_tool import get_action_tool_specs from .builtin_tool import build_builtin_tool_handlers as build_split_builtin_tool_handlers from .builtin_tool import get_timing_tools from .chat_history_visual_refresher import refresh_chat_history_visual_placeholders from .builtin_tool.context import BuiltinToolRuntimeContext from .context_messages import ( ComplexSessionMessage, LLMContextMessage, SessionBackedMessage, ToolResultMessage, contains_complex_message, ) from .history_utils import build_prefixed_message_sequence, build_session_message_visible_text, drop_leading_orphan_tool_results from .monitor_events import ( emit_cycle_end, emit_cycle_start, emit_message_ingested, emit_planner_response, emit_timing_gate_result, emit_tool_execution, ) from .planner_message_utils import build_planner_user_prefix_from_session_message if TYPE_CHECKING: from .runtime import MaisakaHeartFlowChatting from .tool_provider import BuiltinToolHandler logger = get_logger("maisaka_reasoning_engine") TIMING_GATE_CONTEXT_LIMIT = 24 TIMING_GATE_MAX_TOKENS = 384 TIMING_GATE_TOOL_NAMES = {"continue", "no_reply", "wait"} ACTION_HIDDEN_TOOL_NAMES = {"continue", "no_reply", "wait"} ACTION_BUILTIN_TOOL_NAMES = {tool_spec.name for tool_spec in get_action_tool_specs()} class MaisakaReasoningEngine: """负责内部思考、推理与工具执行。""" def __init__(self, runtime: "MaisakaHeartFlowChatting") -> None: self._runtime = runtime self._last_reasoning_content: str = "" @staticmethod def _get_runtime_manager() -> Any: """获取插件运行时管理器。 Returns: Any: 插件运行时管理器单例。 """ from src.plugin_runtime.integration import get_plugin_runtime_manager return get_plugin_runtime_manager() @property def last_reasoning_content(self) -> str: """返回最近一轮思考文本。""" return self._last_reasoning_content 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 build_split_builtin_tool_handlers(BuiltinToolRuntimeContext(self, self._runtime)) async def _run_interruptible_planner( self, *, tool_definitions: Optional[list[dict[str, Any]]] = None, ) -> Any: """运行一轮可被新消息打断的主 planner 请求。""" interrupt_flag = asyncio.Event() interrupted = False self._runtime._bind_planner_interrupt_flag(interrupt_flag) self._runtime._chat_loop_service.set_interrupt_flag(interrupt_flag) try: return await self._runtime._chat_loop_service.chat_loop_step( self._runtime._chat_history, tool_definitions=tool_definitions, ) except ReqAbortException: interrupted = True raise finally: self._runtime._unbind_planner_interrupt_flag( interrupt_flag, interrupted=interrupted, ) self._runtime._chat_loop_service.set_interrupt_flag(None) async def _run_interruptible_sub_agent( self, *, context_message_limit: int, system_prompt: str, tool_definitions: list[dict[str, Any]], ) -> Any: """运行一轮可被新消息打断的临时子代理请求。""" interrupt_flag = asyncio.Event() interrupted = False self._runtime._bind_planner_interrupt_flag(interrupt_flag) try: return await self._runtime.run_sub_agent( context_message_limit=context_message_limit, system_prompt=system_prompt, request_kind="timing_gate", interrupt_flag=interrupt_flag, max_tokens=TIMING_GATE_MAX_TOKENS, temperature=0.1, tool_definitions=tool_definitions, ) except ReqAbortException: interrupted = True raise finally: self._runtime._unbind_planner_interrupt_flag( interrupt_flag, interrupted=interrupted, ) @staticmethod def _build_timing_gate_fallback_prompt() -> str: """构造 Timing Gate 子代理的兜底提示词。""" return ( "你是 Maisaka 的 timing gate 子代理，只负责决定当前会话下一步的节奏控制。\n" "你必须且只能调用一个工具，不要输出普通文本答案。\n" "可用工具只有三个：\n" "1. wait: 适合暂时等待一段时间，再重新判断是否继续。\n" "2. no_reply: 适合当前不继续本轮，直接等待新的外部消息。\n" "3. continue: 适合现在立刻进入下一轮正常思考、回复、查询和其他工具执行。\n" "如果需要真正回复消息、查询信息或使用其他工具，应该调用 continue，让主分支继续执行，而不是在这里完成。\n" "不要连续调用多个工具，也不要输出工具之外的计划。" ) def _build_timing_gate_system_prompt(self) -> str: """构造 Timing Gate 子代理使用的系统提示词。""" try: return load_prompt( "maisaka_timing_gate", **self._runtime._chat_loop_service.build_prompt_template_context(), ) except Exception: return self._build_timing_gate_fallback_prompt() async def _build_action_tool_definitions(self) -> list[dict[str, Any]]: """构造 Action Loop 阶段可见的工具定义。""" if self._runtime._tool_registry is None: return [] tool_specs = await self._runtime._tool_registry.list_tools() return [ tool_spec.to_llm_definition() for tool_spec in tool_specs if tool_spec.name not in ACTION_HIDDEN_TOOL_NAMES and ( tool_spec.provider_name != "maisaka_builtin" or tool_spec.name in ACTION_BUILTIN_TOOL_NAMES ) ] async def _invoke_tool_call( self, tool_call: ToolCall, latest_thought: str, anchor_message: SessionMessage, *, append_history: bool = True, store_record: bool = True, ) -> tuple[ToolInvocation, ToolExecutionResult, Optional[ToolSpec]]: """执行单个工具调用，并按需写入记录与历史。""" invocation = self._build_tool_invocation(tool_call, latest_thought) if self._runtime._tool_registry is None: result = ToolExecutionResult( tool_name=tool_call.func_name, success=False, error_message="统一工具注册表尚未初始化。", ) if store_record: await self._store_tool_execution_record(invocation, result, None) if append_history: self._append_tool_execution_result(tool_call, result) return invocation, result, None execution_context = self._build_tool_execution_context(latest_thought, anchor_message) tool_spec = await self._runtime._tool_registry.get_tool_spec(invocation.tool_name) result = await self._runtime._tool_registry.invoke(invocation, execution_context) if store_record: await self._store_tool_execution_record(invocation, result, tool_spec) if append_history: self._append_tool_execution_result(tool_call, result) return invocation, result, tool_spec async def _run_timing_gate( self, anchor_message: SessionMessage, ) -> tuple[Literal["continue", "no_reply", "wait"], Any, list[str]]: """运行 Timing Gate 子代理并返回控制决策。""" response = await self._run_interruptible_sub_agent( context_message_limit=TIMING_GATE_CONTEXT_LIMIT, system_prompt=self._build_timing_gate_system_prompt(), tool_definitions=get_timing_tools(), ) tool_result_summaries: list[str] = [] selected_tool_call: Optional[ToolCall] = None for tool_call in response.tool_calls: if tool_call.func_name in TIMING_GATE_TOOL_NAMES: selected_tool_call = tool_call break if selected_tool_call is None: logger.warning(f"{self._runtime.log_prefix} Timing Gate 未返回有效控制工具，默认继续执行 Action Loop") return "continue", response, tool_result_summaries append_history = selected_tool_call.func_name != "continue" store_record = selected_tool_call.func_name != "continue" _, result, _ = await self._invoke_tool_call( selected_tool_call, response.content or "", anchor_message, append_history=append_history, store_record=store_record, ) tool_result_summaries.append(self._build_tool_result_summary(selected_tool_call, result)) timing_action = str(result.metadata.get("timing_action") or selected_tool_call.func_name).strip() if timing_action not in TIMING_GATE_TOOL_NAMES: logger.warning( f"{self._runtime.log_prefix} Timing Gate 返回未知动作 {timing_action!r}，将按 continue 处理" ) return "continue", response, tool_result_summaries return timing_action, response, tool_result_summaries 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。""" try: while self._runtime._running: queue_item_done_count = 0 try: queued_trigger = await self._runtime._internal_turn_queue.get() ( message_triggered, timeout_triggered, queue_item_done_count, ) = self._drain_ready_turn_triggers(queued_trigger) if message_triggered: await self._runtime._wait_for_message_quiet_period() self._runtime._message_turn_scheduled = False cached_messages = ( self._runtime._collect_pending_messages() if self._runtime._has_pending_messages() else [] ) if not timeout_triggered and not cached_messages and not message_triggered: continue self._runtime._agent_state = self._runtime._STATE_RUNNING if cached_messages: asyncio.create_task(self._runtime._trigger_batch_learning(cached_messages)) self._append_wait_interrupted_message_if_needed() await self._ingest_messages(cached_messages) anchor_message = cached_messages[-1] else: anchor_message = self._get_timeout_anchor_message() if anchor_message is None: logger.warning( f"{self._runtime.log_prefix} 等待超时后缺少可复用的锚点消息，跳过本轮继续思考" ) continue logger.info(f"{self._runtime.log_prefix} 等待超时后开始新一轮思考") if self._runtime._pending_wait_tool_call_id: self._runtime._chat_history.append(self._build_wait_timeout_message()) self._trim_chat_history() try: for round_index in range(self._runtime._max_internal_rounds): cycle_detail = self._start_cycle() self._runtime._log_cycle_started(cycle_detail, round_index) await emit_cycle_start( session_id=self._runtime.session_id, cycle_id=cycle_detail.cycle_id, round_index=round_index, max_rounds=self._runtime._max_internal_rounds, history_count=len(self._runtime._chat_history), ) planner_started_at = 0.0 try: visual_refresh_started_at = time.time() refreshed_message_count = await self._refresh_chat_history_visual_placeholders() cycle_detail.time_records["visual_refresh"] = time.time() - visual_refresh_started_at if refreshed_message_count > 0: logger.info( f"{self._runtime.log_prefix} 本轮思考前已刷新 {refreshed_message_count} 条视觉占位历史消息" ) timing_started_at = time.time() timing_action, timing_response, timing_tool_results = await self._run_timing_gate(anchor_message) timing_duration_ms = (time.time() - timing_started_at) * 1000 cycle_detail.time_records["timing_gate"] = timing_duration_ms / 1000 await emit_timing_gate_result( session_id=self._runtime.session_id, cycle_id=cycle_detail.cycle_id, action=timing_action, content=timing_response.content, tool_calls=timing_response.tool_calls, messages=[], prompt_tokens=timing_response.prompt_tokens, selected_history_count=timing_response.selected_history_count, duration_ms=timing_duration_ms, ) self._runtime._render_context_usage_panel( selected_history_count=timing_response.selected_history_count, prompt_tokens=timing_response.prompt_tokens, planner_response=timing_response.content or "", tool_calls=timing_response.tool_calls, tool_results=timing_tool_results, prompt_section=timing_response.prompt_section, ) if timing_action != "continue": logger.info( f"{self._runtime.log_prefix} Timing Gate 结束当前回合: " f"回合={round_index + 1} 动作={timing_action}" ) break planner_started_at = time.time() action_tool_definitions = await self._build_action_tool_definitions() logger.info( f"{self._runtime.log_prefix} 规划器开始执行: " f"回合={round_index + 1} " f"历史消息数={len(self._runtime._chat_history)} " f"开始时间={planner_started_at:.3f}" ) response = await self._run_interruptible_planner( tool_definitions=action_tool_definitions, ) planner_duration_ms = (time.time() - planner_started_at) * 1000 cycle_detail.time_records["planner"] = planner_duration_ms / 1000 logger.info( f"{self._runtime.log_prefix} 规划器执行完成: " f"回合={round_index + 1} " f"耗时={cycle_detail.time_records['planner']:.3f} 秒" ) await emit_planner_response( session_id=self._runtime.session_id, cycle_id=cycle_detail.cycle_id, content=response.content, tool_calls=response.tool_calls, prompt_tokens=response.prompt_tokens, completion_tokens=response.completion_tokens, total_tokens=response.total_tokens, duration_ms=planner_duration_ms, ) reasoning_content = response.content or "" if self._should_replace_reasoning(reasoning_content): response.content = "我应该根据我上面思考的内容进行反思，重新思考我下一步的行动，我需要分析当前场景，对话，以及我可以使用的工具，然后先输出想法再使用工具" response.raw_message.content = "我应该根据我上面思考的内容进行反思，重新思考我下一步的行动，我需要分析当前场景，对话，以及我可以使用的工具，然后先输出想法再使用工具" logger.info(f"{self._runtime.log_prefix} 当前思考与上一轮过于相似，已替换为重新思考提示") self._last_reasoning_content = reasoning_content self._runtime._chat_history.append(response.raw_message) tool_result_summaries: list[str] = [] if response.tool_calls: tool_started_at = time.time() should_pause, tool_result_summaries = await self._handle_tool_calls( response.tool_calls, response.content or "", anchor_message, ) cycle_detail.time_records["tool_calls"] = time.time() - tool_started_at self._runtime._render_context_usage_panel( selected_history_count=response.selected_history_count, prompt_tokens=response.prompt_tokens, planner_response=response.content or "", tool_calls=response.tool_calls, tool_results=tool_result_summaries, prompt_section=response.prompt_section, ) if should_pause: break continue self._runtime._render_context_usage_panel( selected_history_count=response.selected_history_count, prompt_tokens=response.prompt_tokens, planner_response=response.content or "", prompt_section=response.prompt_section, ) if not response.content: break except ReqAbortException: interrupted_at = time.time() logger.info( f"{self._runtime.log_prefix} 规划器打断成功: " f"回合={round_index + 1} " f"开始时间={planner_started_at:.3f} " f"打断时间={interrupted_at:.3f} " f"耗时={interrupted_at - planner_started_at:.3f} 秒" ) break finally: self._end_cycle(cycle_detail) await emit_cycle_end( session_id=self._runtime.session_id, cycle_id=cycle_detail.cycle_id, time_records=dict(cycle_detail.time_records), agent_state=self._runtime._agent_state, ) finally: if self._runtime._agent_state == self._runtime._STATE_RUNNING: self._runtime._agent_state = self._runtime._STATE_STOP finally: for _ in range(queue_item_done_count): self._runtime._internal_turn_queue.task_done() except asyncio.CancelledError: self._runtime._log_internal_loop_cancelled() raise except Exception: logger.exception(f"{self._runtime.log_prefix} Maisaka 内部循环发生异常") logger.error(traceback.format_exc()) raise def _drain_ready_turn_triggers( self, queued_trigger: Literal["message", "timeout"], ) -> tuple[bool, bool, int]: """合并当前已就绪的 turn 触发信号。""" queue_item_done_count = 1 message_triggered = queued_trigger == "message" timeout_triggered = queued_trigger == "timeout" while True: try: next_trigger = self._runtime._internal_turn_queue.get_nowait() except asyncio.QueueEmpty: break queue_item_done_count += 1 if next_trigger == "message": message_triggered = True continue if next_trigger == "timeout": timeout_triggered = True continue if message_triggered: # 这些消息触发将由当前 turn 接手，旧的事件位不应再污染后续 wait 判定。 self._runtime._new_message_event.clear() return message_triggered, timeout_triggered, queue_item_done_count def _get_timeout_anchor_message(self) -> Optional[SessionMessage]: """在 wait 超时后复用最近一条真实用户消息作为锚点。""" if self._runtime.message_cache: return self._runtime.message_cache[-1] return None def _build_wait_timeout_message(self) -> ToolResultMessage: """构造 wait 超时后的工具结果消息。""" tool_call_id = self._runtime._pending_wait_tool_call_id or "wait_timeout" self._runtime._pending_wait_tool_call_id = None return ToolResultMessage( content="等待已超时，期间没有收到新的用户输入。请基于现有上下文继续下一轮思考。", timestamp=datetime.now(), tool_call_id=tool_call_id, tool_name="wait", ) def _append_wait_interrupted_message_if_needed(self) -> None: """如果 wait 被新消息打断，则补一条对应的工具结果消息。""" tool_call_id = self._runtime._pending_wait_tool_call_id if not tool_call_id: return self._runtime._pending_wait_tool_call_id = None self._runtime._chat_history.append( ToolResultMessage( content="等待过程被新的用户输入打断，已继续处理最新消息。", timestamp=datetime.now(), tool_call_id=tool_call_id, tool_name="wait", ) ) async def _ingest_messages(self, messages: list[SessionMessage]) -> None: """处理传入消息列表，将其转换为历史消息并加入聊天历史缓存。""" for message in messages: history_message = await self._build_history_message(message) if history_message is None: continue self._insert_chat_history_message(history_message) self._trim_chat_history() # 向监控前端广播新消息注入事件 user_info = message.message_info.user_info speaker_name = user_info.user_cardname or user_info.user_nickname or user_info.user_id await emit_message_ingested( session_id=self._runtime.session_id, speaker_name=speaker_name, content=(message.processed_plain_text or "").strip(), message_id=message.message_id, timestamp=message.timestamp.timestamp(), ) async def _build_history_message( self, message: SessionMessage, *, source_kind: str = "user", ) -> Optional[LLMContextMessage]: """根据真实消息构造对应的上下文消息。""" source_sequence = message.raw_message visible_text = self._build_legacy_visible_text(message, source_sequence) planner_prefix = build_planner_user_prefix_from_session_message(message) if contains_complex_message(source_sequence): return ComplexSessionMessage.from_session_message( message, planner_prefix=planner_prefix, visible_text=visible_text, source_kind=source_kind, ) user_sequence = await self._build_message_sequence(message, planner_prefix=planner_prefix) if not user_sequence.components: return None return SessionBackedMessage.from_session_message( message, raw_message=user_sequence, visible_text=visible_text, source_kind=source_kind, ) async def _build_message_sequence( self, message: SessionMessage, *, planner_prefix: str, ) -> MessageSequence: message_sequence = build_prefixed_message_sequence(message.raw_message, planner_prefix) if global_config.chat.multimodal_planner: await self._hydrate_visual_components(message_sequence.components) return message_sequence async def _hydrate_visual_components(self, planner_components: list[object]) -> None: """在 Maisaka 真正需要图片或表情时，按需回填二进制数据。""" load_tasks: list[asyncio.Task[None]] = [] for component in planner_components: if isinstance(component, ImageComponent) and not component.binary_data: load_tasks.append(asyncio.create_task(component.load_image_binary())) continue if isinstance(component, EmojiComponent) and not component.binary_data: load_tasks.append(asyncio.create_task(component.load_emoji_binary())) if not load_tasks: return results = await asyncio.gather(*load_tasks, return_exceptions=True) for result in results: if isinstance(result, Exception): logger.warning(f"{self._runtime.log_prefix} 回填图片或表情二进制数据失败，Maisaka 将退化为文本占位: {result}") async def _refresh_chat_history_visual_placeholders(self) -> int: """在进入新一轮规划前，尝试用已完成的识图结果刷新历史占位。""" return await refresh_chat_history_visual_placeholders( chat_history=self._runtime._chat_history, build_history_message=lambda message, source_kind: self._build_history_message( message, source_kind=source_kind, ), build_visible_text=lambda message: self._build_legacy_visible_text(message, message.raw_message), ) def _build_legacy_visible_text(self, message: SessionMessage, source_sequence: MessageSequence) -> str: return build_session_message_visible_text(message, source_sequence) def _insert_chat_history_message(self, message: LLMContextMessage) -> int: """将消息按处理顺序追加到聊天历史末尾。""" self._runtime._chat_history.append(message) return len(self._runtime._chat_history) - 1 def _start_cycle(self) -> CycleDetail: """开始一轮 Maisaka 思考循环。""" self._runtime._cycle_counter += 1 self._runtime._current_cycle_detail = CycleDetail(cycle_id=self._runtime._cycle_counter) self._runtime._current_cycle_detail.thinking_id = f"maisaka_tid{round(time.time(), 2)}" return self._runtime._current_cycle_detail def _end_cycle(self, cycle_detail: CycleDetail, only_long_execution: bool = True) -> CycleDetail: """结束并记录一轮 Maisaka 思考循环。""" cycle_detail.end_time = time.time() self._runtime.history_loop.append(cycle_detail) timer_strings = [ f"{name}: {duration:.2f}s" for name, duration in cycle_detail.time_records.items() if not only_long_execution or duration >= 0.1 ] self._runtime._log_cycle_completed(cycle_detail, timer_strings) return cycle_detail def _trim_chat_history(self) -> None: """裁剪聊天历史，保证用户消息数量不超过配置限制。""" conversation_message_count = sum(1 for message in self._runtime._chat_history if message.count_in_context) if conversation_message_count <= self._runtime._max_context_size: return trimmed_history = list(self._runtime._chat_history) removed_count = 0 while conversation_message_count > self._runtime._max_context_size and trimmed_history: removed_message = trimmed_history.pop(0) removed_count += 1 if removed_message.count_in_context: conversation_message_count -= 1 trimmed_history, pruned_orphan_count = drop_leading_orphan_tool_results(trimmed_history) removed_count += pruned_orphan_count self._runtime._chat_history = trimmed_history self._runtime._log_history_trimmed(removed_count, conversation_message_count) @staticmethod def _drop_leading_orphan_tool_results( chat_history: list[LLMContextMessage], ) -> tuple[list[LLMContextMessage], int]: """清理历史前缀中缺少对应 assistant tool_call 的工具结果消息。""" return drop_leading_orphan_tool_results(chat_history) @staticmethod def _calculate_similarity(text1: str, text2: str) -> float: """计算两个文本之间的相似度。 Args: text1: 第一个文本 text2: 第二个文本 Returns: float: 相似度值，范围 0-1，1 表示完全相同 """ return difflib.SequenceMatcher(None, text1, text2).ratio() def _should_replace_reasoning(self, current_content: str) -> bool: """判断是否需要替换推理内容。 当当前推理内容与上一次相似度大于90%时，返回True。 Args: current_content: 当前的推理内容 Returns: bool: 是否需要替换 """ if not self._last_reasoning_content or not current_content: logger.info( f"{self._runtime.log_prefix} 跳过思考相似度判定: " f"上一轮为空={not bool(self._last_reasoning_content)} " f"当前为空={not bool(current_content)} 相似度=0.00" ) return False similarity = self._calculate_similarity(current_content, self._last_reasoning_content) logger.info(f"{self._runtime.log_prefix} 思考内容相似度: {similarity:.2f}") return similarity > 0.9 @staticmethod def _post_process_reply_text(reply_text: str) -> list[str]: """沿用旧回复链的文本后处理，执行分段与错别字注入。""" return BuiltinToolRuntimeContext.post_process_reply_text(reply_text) 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}, ) @staticmethod def _normalize_tool_record_value(value: Any) -> Any: """将工具记录中的任意值规范化为可序列化结构。 Args: value: 原始值。 Returns: Any: 适合写入 JSON 的规范化结果。 """ if value is None or isinstance(value, (str, int, float, bool)): return value if isinstance(value, datetime): return value.isoformat() if isinstance(value, dict): normalized_dict: dict[str, Any] = {} for key, item in value.items(): normalized_dict[str(key)] = MaisakaReasoningEngine._normalize_tool_record_value(item) return normalized_dict if isinstance(value, (list, tuple, set)): return [MaisakaReasoningEngine._normalize_tool_record_value(item) for item in value] if isinstance(value, bytes): return f"" if hasattr(value, "model_dump"): try: return MaisakaReasoningEngine._normalize_tool_record_value(value.model_dump()) except Exception: return str(value) if hasattr(value, "__dict__"): try: return MaisakaReasoningEngine._normalize_tool_record_value(dict(value.__dict__)) except Exception: return str(value) return str(value) @staticmethod def _truncate_tool_record_text(text: str, max_length: int = 180) -> str: """截断工具记录中的展示文本。 Args: text: 原始文本。 max_length: 最长保留字符数。 Returns: str: 截断后的文本。 """ normalized_text = text.strip() if len(normalized_text) <= max_length: return normalized_text return f"{normalized_text[: max_length - 1]}…" def _build_tool_record_payload( self, invocation: ToolInvocation, result: ToolExecutionResult, tool_spec: Optional[ToolSpec], ) -> dict[str, Any]: """构造统一工具落库数据。 Args: invocation: 工具调用对象。 result: 工具执行结果。 tool_spec: 对应的工具声明。 Returns: dict[str, Any]: 可直接写入数据库的工具记录数据。 """ payload: dict[str, Any] = { "call_id": invocation.call_id, "session_id": invocation.session_id, "stream_id": invocation.stream_id, "arguments": self._normalize_tool_record_value(invocation.arguments), "success": result.success, "content": result.content, "error_message": result.error_message, "history_content": result.get_history_content(), "structured_content": self._normalize_tool_record_value(result.structured_content), "metadata": self._normalize_tool_record_value(result.metadata), } if tool_spec is not None: payload["provider_name"] = tool_spec.provider_name payload["provider_type"] = tool_spec.provider_type payload["brief_description"] = tool_spec.brief_description payload["detailed_description"] = tool_spec.detailed_description payload["title"] = tool_spec.title return payload def _build_tool_display_prompt( self, invocation: ToolInvocation, result: ToolExecutionResult, tool_spec: Optional[ToolSpec], ) -> str: """构造展示给历史回放与 UI 的工具摘要。 Args: invocation: 工具调用对象。 result: 工具执行结果。 tool_spec: 对应的工具声明。 Returns: str: 用于展示的工具摘要文本。 """ custom_display_prompt = result.metadata.get("record_display_prompt") if isinstance(custom_display_prompt, str) and custom_display_prompt.strip(): return custom_display_prompt.strip() structured_content = ( result.structured_content if isinstance(result.structured_content, dict) else {} ) history_content = self._truncate_tool_record_text(result.get_history_content(), max_length=200) normalized_args = self._normalize_tool_record_value(invocation.arguments) if invocation.tool_name == "reply": target_user_name = str(structured_content.get("target_user_name") or "对方").strip() or "对方" reply_text = str(structured_content.get("reply_text") or "").strip() if result.success and reply_text: return f"你对{target_user_name}进行了回复：{reply_text}" target_message_id = str(invocation.arguments.get("msg_id") or "").strip() error_text = self._truncate_tool_record_text(result.error_message or history_content, max_length=120) return f"你尝试回复消息 {target_message_id or 'unknown'}，但失败了：{error_text}" if invocation.tool_name == "send_emoji": if result.success: return "你发送了表情包。" return f"你尝试发送表情包，但失败了：{self._truncate_tool_record_text(result.error_message or history_content, 120)}" if invocation.tool_name == "wait": wait_seconds = invocation.arguments.get("seconds", 30) return f"你让当前对话先等待 {wait_seconds} 秒。" if invocation.tool_name == "no_reply": return "你暂停了当前对话循环，等待新的外部消息。" if invocation.tool_name == "continue": return "你允许当前对话继续进入下一轮完整思考与工具执行。" if invocation.tool_name == "query_jargon": words = invocation.arguments.get("words", []) if isinstance(words, list): words_text = "、".join(str(item).strip() for item in words if str(item).strip()) else: words_text = "" if words_text: return f"你查询了这些黑话或词条：{words_text}" return "你查询了一次黑话或词条信息。" if invocation.tool_name == "query_person_info": person_name = str(invocation.arguments.get("person_name") or "").strip() if person_name: return f"你查询了人物信息：{person_name}" return "你查询了一次人物信息。" if invocation.tool_name == "query_memory": query_text = str(invocation.arguments.get("query") or "").strip() mode = str(invocation.arguments.get("mode") or "search").strip() or "search" hit_items = structured_content.get("hits") hit_count = len(hit_items) if isinstance(hit_items, list) else 0 if query_text: return f"你查询了长期记忆：{query_text}（模式：{mode}，命中 {hit_count} 条）" return f"你按时间范围查询了一次长期记忆（模式：{mode}，命中 {hit_count} 条）。" if invocation.tool_name == "view_complex_message": target_message_id = str(invocation.arguments.get("msg_id") or "").strip() if target_message_id: return f"你查看了复杂消息 {target_message_id} 的完整内容。" return "你查看了一条复杂消息的完整内容。" brief_description = "" if tool_spec is not None: brief_description = tool_spec.brief_description.strip() if normalized_args: arguments_text = self._truncate_tool_record_text( json.dumps(normalized_args, ensure_ascii=False), max_length=160, ) else: arguments_text = "{}" if result.success: if brief_description: return f"{brief_description} 参数={arguments_text}；结果：{history_content or '执行成功'}" return f"你调用了工具 {invocation.tool_name}，参数={arguments_text}；结果：{history_content or '执行成功'}" error_text = self._truncate_tool_record_text(result.error_message or history_content, max_length=160) return f"你调用了工具 {invocation.tool_name}，参数={arguments_text}；执行失败：{error_text}" async def _store_tool_execution_record( self, invocation: ToolInvocation, result: ToolExecutionResult, tool_spec: Optional[ToolSpec], ) -> None: """将工具执行结果落库到统一工具记录表。 Args: invocation: 工具调用对象。 result: 工具执行结果。 tool_spec: 对应的工具声明。 """ if self._runtime.chat_stream is None: logger.debug( f"{self._runtime.log_prefix} 当前没有 chat_stream，跳过工具记录存储: " f"工具={invocation.tool_name}" ) return builtin_prompt = "" if tool_spec is not None: builtin_prompt = tool_spec.build_llm_description() try: await database_api.store_tool_info( chat_stream=self._runtime.chat_stream, builtin_prompt=builtin_prompt, display_prompt=self._build_tool_display_prompt(invocation, result, tool_spec), tool_id=invocation.call_id, tool_data=self._build_tool_record_payload(invocation, result, tool_spec), tool_name=invocation.tool_name, tool_reasoning=invocation.reasoning, ) except Exception: logger.exception( f"{self._runtime.log_prefix} 写入工具记录失败: 工具={invocation.tool_name} 调用编号={invocation.call_id}" ) 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, success=result.success, ) ) def _build_tool_result_summary(self, tool_call: ToolCall, result: ToolExecutionResult) -> str: """构建用于终端展示的工具结果摘要。""" history_content = result.get_history_content().strip() if not history_content: history_content = result.error_message.strip() if not history_content: history_content = "执行成功" if result.success else "执行失败" summary_prefix = "[成功]" if result.success else "[失败]" normalized_content = self._truncate_tool_record_text(history_content, max_length=200) return f"- {tool_call.func_name} {summary_prefix}: {normalized_content}" async def _handle_tool_calls( self, tool_calls: list[ToolCall], latest_thought: str, anchor_message: SessionMessage, ) -> tuple[bool, list[str]]: """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: tuple[bool, list[str]]: 是否需要暂停当前思考循环，以及工具结果摘要列表。 """ tool_result_summaries: list[str] = [] if self._runtime._tool_registry is None: for tool_call in tool_calls: invocation = self._build_tool_invocation(tool_call, latest_thought) result = ToolExecutionResult( tool_name=tool_call.func_name, success=False, error_message="统一工具注册表尚未初始化。", ) await self._store_tool_execution_record(invocation, result, None) self._append_tool_execution_result(tool_call, result) tool_result_summaries.append(self._build_tool_result_summary(tool_call, result)) return False, tool_result_summaries execution_context = self._build_tool_execution_context(latest_thought, anchor_message) tool_spec_map = { tool_spec.name: tool_spec for tool_spec in await self._runtime._tool_registry.list_tools() } for tool_call in tool_calls: invocation = self._build_tool_invocation(tool_call, latest_thought) tool_started_at = time.time() result = await self._runtime._tool_registry.invoke(invocation, execution_context) tool_duration_ms = (time.time() - tool_started_at) * 1000 await self._store_tool_execution_record( invocation, result, tool_spec_map.get(invocation.tool_name), ) self._append_tool_execution_result(tool_call, result) tool_result_summaries.append(self._build_tool_result_summary(tool_call, result)) # 向监控前端广播工具执行结果 cycle_id = self._runtime._current_cycle_detail.cycle_id if self._runtime._current_cycle_detail else 0 await emit_tool_execution( session_id=self._runtime.session_id, cycle_id=cycle_id, tool_name=tool_call.func_name, tool_args=invocation.arguments if isinstance(invocation.arguments, dict) else {}, result_summary=result.content[:500] if result.content else (result.error_message or "")[:500], success=result.success, duration_ms=tool_duration_ms, ) if not result.success and tool_call.func_name == "reply": 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") if bool(result.metadata.get("pause_execution", False)): return True, tool_result_summaries return False, tool_result_summaries