1495 lines
69 KiB
Python
1495 lines
69 KiB
Python
"""Maisaka 推理引擎。"""
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from datetime import datetime
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from typing import TYPE_CHECKING, Any, Literal, Optional
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import asyncio
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import difflib
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import json
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import time
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import traceback
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from src.chat.heart_flow.heartFC_utils import CycleDetail
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from src.chat.message_receive.message import SessionMessage
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from src.common.data_models.message_component_data_model import EmojiComponent, ImageComponent, MessageSequence, TextComponent
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from src.common.logger import get_logger
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from src.common.prompt_i18n import load_prompt
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from src.core.tooling import ToolAvailabilityContext, ToolExecutionContext, ToolExecutionResult, ToolInvocation, ToolSpec
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from src.llm_models.exceptions import ReqAbortException
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from src.llm_models.payload_content.tool_option import ToolCall
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from src.services import database_service as database_api
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from src.services.memory_service import memory_service
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from .builtin_tool import build_builtin_tool_handlers as build_split_builtin_tool_handlers
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from .builtin_tool import get_builtin_tool_visibility, is_builtin_tool_in_action_stage
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from .builtin_tool import get_timing_tools
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from .chat_loop_service import ChatResponse
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from .chat_history_visual_refresher import refresh_chat_history_visual_placeholders
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from .builtin_tool.context import BuiltinToolRuntimeContext
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from .context_messages import (
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AssistantMessage,
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ComplexSessionMessage,
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LLMContextMessage,
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SessionBackedMessage,
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TIMING_GATE_INVALID_TOOL_HINT_SOURCE,
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ToolResultMessage,
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contains_complex_message,
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)
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from .history_post_processor import process_chat_history_after_cycle
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from .history_utils import build_prefixed_message_sequence, build_session_message_visible_text
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from .monitor_events import (
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emit_cycle_start,
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emit_message_ingested,
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emit_planner_finalized,
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emit_timing_gate_result,
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)
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from .planner_message_utils import build_planner_user_prefix_from_session_message
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from .visual_mode_utils import resolve_enable_visual_planner
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if TYPE_CHECKING:
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from .runtime import MaisakaHeartFlowChatting
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from .tool_provider import BuiltinToolHandler
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logger = get_logger("maisaka_reasoning_engine")
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TIMING_GATE_CONTEXT_DROP_HEAD_RATIO = 0.7
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TIMING_GATE_MAX_ATTEMPTS = 3
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TIMING_GATE_TOOL_NAMES = {"continue", "no_reply", "wait"}
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HISTORY_SILENT_TOOL_NAMES = {"finish"}
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class MaisakaReasoningEngine:
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"""负责内部思考、推理与工具执行。"""
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def __init__(self, runtime: "MaisakaHeartFlowChatting") -> None:
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self._runtime = runtime
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self._last_reasoning_content: str = ""
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@staticmethod
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def _get_runtime_manager() -> Any:
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"""获取插件运行时管理器。
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Returns:
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Any: 插件运行时管理器单例。
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"""
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from src.plugin_runtime.integration import get_plugin_runtime_manager
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return get_plugin_runtime_manager()
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@property
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def last_reasoning_content(self) -> str:
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"""返回最近一轮思考文本。"""
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return self._last_reasoning_content
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def build_builtin_tool_handlers(self) -> dict[str, "BuiltinToolHandler"]:
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"""构造 Maisaka 内置工具处理器映射。
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Returns:
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dict[str, BuiltinToolHandler]: 工具名到处理器的映射。
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"""
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return build_split_builtin_tool_handlers(BuiltinToolRuntimeContext(self, self._runtime))
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async def _run_interruptible_planner(
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self,
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*,
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injected_user_messages: Optional[list[str]] = None,
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tool_definitions: Optional[list[dict[str, Any]]] = None,
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) -> Any:
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"""运行一轮可被新消息打断的主 planner 请求。"""
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interrupt_flag = asyncio.Event()
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interrupted = False
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self._runtime._bind_planner_interrupt_flag(interrupt_flag)
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self._runtime._chat_loop_service.set_interrupt_flag(interrupt_flag)
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try:
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return await self._runtime._chat_loop_service.chat_loop_step(
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self._runtime._chat_history,
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injected_user_messages=injected_user_messages,
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tool_definitions=tool_definitions,
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)
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except ReqAbortException:
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interrupted = True
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raise
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finally:
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self._runtime._unbind_planner_interrupt_flag(
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interrupt_flag,
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interrupted=interrupted,
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)
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self._runtime._chat_loop_service.set_interrupt_flag(None)
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async def _run_timing_gate_sub_agent(
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self,
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*,
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system_prompt: str,
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tool_definitions: list[dict[str, Any]],
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) -> Any:
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"""运行一轮 Timing Gate 子代理请求。
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Timing Gate 阶段不再响应新的 planner 打断，只有主 planner 阶段允许被打断。
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"""
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return await self._runtime.run_sub_agent(
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context_message_limit=self._runtime._max_context_size,
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drop_head_context_count=int(
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self._runtime._max_context_size * TIMING_GATE_CONTEXT_DROP_HEAD_RATIO,
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),
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system_prompt=system_prompt,
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request_kind="timing_gate",
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interrupt_flag=None,
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tool_definitions=tool_definitions,
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)
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@staticmethod
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def _build_timing_gate_fallback_prompt() -> str:
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"""构造 Timing Gate 子代理的兜底提示词。"""
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return (
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"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"
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"你必须且只能调用一个工具，不要输出普通文本答案。\n"
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"可用工具只有三个：\n"
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"1. wait: 适合暂时等待一段时间，再重新判断是否继续。\n"
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"2. no_reply: 适合当前不继续本轮，直接等待新的外部消息。\n"
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"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"
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"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"
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"不要连续调用多个工具，也不要输出工具之外的计划。"
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)
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def _build_timing_gate_system_prompt(self) -> str:
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"""构造 Timing Gate 子代理使用的系统提示词。"""
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try:
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return load_prompt(
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"maisaka_timing_gate",
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**self._runtime._chat_loop_service.build_prompt_template_context(),
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)
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except Exception:
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return self._build_timing_gate_fallback_prompt()
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async def _build_action_tool_definitions(self) -> tuple[list[dict[str, Any]], str]:
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"""构造 Action Loop 阶段可见的工具定义与 deferred tools 提示。"""
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if self._runtime._tool_registry is None:
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self._runtime.update_deferred_tool_specs([])
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self._runtime.set_current_action_tool_names([])
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return [], ""
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availability_context = self._build_tool_availability_context()
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tool_specs = await self._runtime._tool_registry.list_tools(availability_context)
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visible_builtin_tool_specs: list[ToolSpec] = []
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deferred_tool_specs: list[ToolSpec] = []
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for tool_spec in tool_specs:
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if tool_spec.provider_name == "maisaka_builtin":
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if not is_builtin_tool_in_action_stage(tool_spec):
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continue
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visibility = get_builtin_tool_visibility(tool_spec)
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if visibility == "visible":
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visible_builtin_tool_specs.append(tool_spec)
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elif visibility == "deferred":
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deferred_tool_specs.append(tool_spec)
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continue
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deferred_tool_specs.append(tool_spec)
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self._runtime.update_deferred_tool_specs(deferred_tool_specs)
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selected_history, _ = self._runtime._chat_loop_service.select_llm_context_messages(
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self._runtime._chat_history,
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request_kind="planner",
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)
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self._runtime.sync_discovered_deferred_tools_with_context(selected_history)
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discovered_deferred_tool_specs = self._runtime.get_discovered_deferred_tool_specs()
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visible_tool_specs = [*visible_builtin_tool_specs, *discovered_deferred_tool_specs]
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self._runtime.set_current_action_tool_names([tool_spec.name for tool_spec in visible_tool_specs])
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return (
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[tool_spec.to_llm_definition() for tool_spec in visible_tool_specs],
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self._runtime.build_deferred_tools_reminder(),
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)
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async def _invoke_tool_call(
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self,
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tool_call: ToolCall,
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latest_thought: str,
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anchor_message: SessionMessage,
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*,
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append_history: bool = True,
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store_record: bool = True,
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) -> tuple[ToolInvocation, ToolExecutionResult, Optional[ToolSpec]]:
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"""执行单个工具调用，并按需写入记录与历史。"""
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invocation = self._build_tool_invocation(tool_call, latest_thought)
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if self._runtime._tool_registry is None:
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result = ToolExecutionResult(
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tool_name=tool_call.func_name,
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success=False,
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error_message="统一工具注册表尚未初始化。",
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)
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if store_record:
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await self._store_tool_execution_record(invocation, result, None)
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if append_history:
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self._append_tool_execution_result(tool_call, result)
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return invocation, result, None
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execution_context = self._build_tool_execution_context(latest_thought, anchor_message)
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tool_spec = await self._runtime._tool_registry.get_tool_spec(invocation.tool_name)
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result = await self._runtime._tool_registry.invoke(invocation, execution_context)
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if store_record:
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await self._store_tool_execution_record(invocation, result, tool_spec)
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if append_history:
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self._append_tool_execution_result(tool_call, result)
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return invocation, result, tool_spec
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async def _run_timing_gate(
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self,
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anchor_message: SessionMessage,
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) -> tuple[Literal["continue", "no_reply", "wait"], Any, list[str], list[dict[str, Any]]]:
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"""运行 Timing Gate 子代理并返回控制决策。"""
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if self._runtime._force_next_timing_continue:
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return self._build_forced_continue_timing_result()
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tool_result_summaries: list[str] = []
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tool_monitor_results: list[dict[str, Any]] = []
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response: Any = None
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selected_tool_call: Optional[ToolCall] = None
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invalid_tool_text = ""
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for attempt_index in range(TIMING_GATE_MAX_ATTEMPTS):
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response = await self._run_timing_gate_sub_agent(
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system_prompt=self._build_timing_gate_system_prompt(),
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tool_definitions=get_timing_tools(),
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)
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selected_tool_call = None
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for tool_call in response.tool_calls:
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if tool_call.func_name in TIMING_GATE_TOOL_NAMES:
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selected_tool_call = tool_call
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break
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|
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if selected_tool_call is not None:
|
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break
|
||
|
||
invalid_tool_names = [
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str(tool_call.func_name).strip()
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for tool_call in response.tool_calls
|
||
if str(tool_call.func_name).strip()
|
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]
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invalid_tool_text = "、".join(invalid_tool_names) if invalid_tool_names else "无工具"
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||
self._append_timing_gate_invalid_tool_hint(invalid_tool_text)
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remaining_attempts = TIMING_GATE_MAX_ATTEMPTS - attempt_index - 1
|
||
if remaining_attempts > 0:
|
||
logger.warning(
|
||
f"{self._runtime.log_prefix} Timing Gate 未返回有效控制工具：{invalid_tool_text}，"
|
||
f"将重试 ({attempt_index + 1}/{TIMING_GATE_MAX_ATTEMPTS})"
|
||
)
|
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tool_result_summaries.append(
|
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f"- retry [非法 Timing 工具]: 返回了 {invalid_tool_text}，将重试 "
|
||
f"({attempt_index + 1}/{TIMING_GATE_MAX_ATTEMPTS})"
|
||
)
|
||
continue
|
||
|
||
logger.warning(
|
||
f"{self._runtime.log_prefix} Timing Gate 连续 {TIMING_GATE_MAX_ATTEMPTS} 次未返回有效控制工具："
|
||
f"{invalid_tool_text}，将按 no_reply 处理"
|
||
)
|
||
self._runtime._enter_stop_state()
|
||
tool_result_summaries.append(
|
||
f"- no_reply [非法 Timing 工具]: 返回了 {invalid_tool_text}，已停止本轮并等待新消息"
|
||
)
|
||
return "no_reply", response, tool_result_summaries, tool_monitor_results
|
||
|
||
if selected_tool_call is None:
|
||
self._runtime._enter_stop_state()
|
||
tool_result_summaries.append(
|
||
"- no_reply [非法 Timing 工具]: 已停止本轮并等待新消息"
|
||
)
|
||
return "no_reply", response, tool_result_summaries, tool_monitor_results
|
||
|
||
if invalid_tool_text:
|
||
self._runtime._chat_history = [
|
||
message
|
||
for message in self._runtime._chat_history
|
||
if message.source != TIMING_GATE_INVALID_TOOL_HINT_SOURCE
|
||
]
|
||
|
||
append_history = False
|
||
store_record = selected_tool_call.func_name != "continue"
|
||
invocation, result, tool_spec = 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))
|
||
tool_monitor_results.append(
|
||
self._build_tool_monitor_result(
|
||
selected_tool_call,
|
||
invocation,
|
||
result,
|
||
duration_ms=0.0,
|
||
tool_spec=tool_spec,
|
||
)
|
||
)
|
||
self._append_timing_gate_execution_result(response, 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 no_reply 处理"
|
||
)
|
||
self._runtime._enter_stop_state()
|
||
tool_result_summaries.append(
|
||
f"- no_reply [未知 Timing 动作]: 返回了 {timing_action!r}，已停止本轮并等待新消息"
|
||
)
|
||
return "no_reply", response, tool_result_summaries, tool_monitor_results
|
||
return timing_action, response, tool_result_summaries, tool_monitor_results
|
||
|
||
def _build_forced_continue_timing_result(
|
||
self,
|
||
) -> tuple[Literal["continue"], ChatResponse, list[str], list[dict[str, Any]]]:
|
||
"""构造跳过 Timing Gate 时使用的伪 continue 结果。"""
|
||
|
||
reason = self._runtime._consume_force_next_timing_continue_reason() or "本轮直接跳过 Timing Gate 并视作 continue。"
|
||
logger.info(f"{self._runtime.log_prefix} {reason}")
|
||
return (
|
||
"continue",
|
||
ChatResponse(
|
||
content=reason,
|
||
tool_calls=[],
|
||
request_messages=[],
|
||
raw_message=AssistantMessage(
|
||
content="",
|
||
timestamp=datetime.now(),
|
||
source_kind="perception",
|
||
),
|
||
selected_history_count=min(
|
||
sum(1 for message in self._runtime._chat_history if message.count_in_context),
|
||
self._runtime._max_context_size,
|
||
),
|
||
tool_count=0,
|
||
prompt_tokens=0,
|
||
built_message_count=0,
|
||
completion_tokens=0,
|
||
total_tokens=0,
|
||
prompt_section=None,
|
||
),
|
||
[f"- continue [强制跳过]: {reason}"],
|
||
[],
|
||
)
|
||
|
||
def _append_timing_gate_invalid_tool_hint(self, invalid_tool_text: str) -> None:
|
||
"""写入一条仅 Timing Gate 可见的非法工具提示，并保证最多保留最新一条。"""
|
||
|
||
self._runtime._chat_history = [
|
||
message
|
||
for message in self._runtime._chat_history
|
||
if message.source != TIMING_GATE_INVALID_TOOL_HINT_SOURCE
|
||
]
|
||
normalized_tool_text = invalid_tool_text.strip() or "无工具"
|
||
hint_content = (
|
||
"Timing Gate 上一轮选择了非法工具："
|
||
f"{normalized_tool_text}。\n"
|
||
"Timing Gate 只能调用 continue、wait 或 no_reply 中的一个工具。"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
SessionBackedMessage(
|
||
raw_message=MessageSequence([TextComponent(hint_content)]),
|
||
visible_text=hint_content,
|
||
timestamp=datetime.now(),
|
||
source_kind=TIMING_GATE_INVALID_TOOL_HINT_SOURCE,
|
||
)
|
||
)
|
||
|
||
@staticmethod
|
||
def _mark_timing_gate_completed(timing_action: str) -> bool:
|
||
"""根据门控动作决定下一轮是否还需要重新执行 timing。"""
|
||
|
||
return timing_action != "continue"
|
||
|
||
@staticmethod
|
||
def _should_retry_planner_after_interrupt(
|
||
*,
|
||
round_index: int,
|
||
max_internal_rounds: int,
|
||
has_pending_messages: bool,
|
||
) -> bool:
|
||
return has_pending_messages and round_index + 1 < max_internal_rounds
|
||
|
||
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:
|
||
queued_trigger = await self._runtime._internal_turn_queue.get()
|
||
message_triggered, timeout_triggered = self._drain_ready_turn_triggers(queued_trigger)
|
||
|
||
if self._runtime._agent_state == self._runtime._STATE_WAIT and not timeout_triggered:
|
||
self._runtime._message_turn_scheduled = False
|
||
logger.debug(
|
||
f"{self._runtime.log_prefix} 当前仍处于 wait 状态，忽略消息触发并继续等待超时"
|
||
)
|
||
continue
|
||
|
||
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:
|
||
continue
|
||
|
||
self._runtime._agent_state = self._runtime._STATE_RUNNING
|
||
self._runtime._update_stage_status(
|
||
"消息整理",
|
||
f"待处理消息 {len(cached_messages)} 条" if cached_messages else "准备复用超时锚点",
|
||
)
|
||
if cached_messages:
|
||
asyncio.create_task(self._runtime._trigger_batch_learning(cached_messages))
|
||
if timeout_triggered:
|
||
self._runtime._chat_history.append(
|
||
self._build_wait_completed_message(has_new_messages=True)
|
||
)
|
||
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_completed_message(has_new_messages=False)
|
||
)
|
||
try:
|
||
timing_gate_required = True
|
||
for round_index in range(self._runtime._max_internal_rounds):
|
||
cycle_detail = self._start_cycle()
|
||
round_text = f"第 {round_index + 1}/{self._runtime._max_internal_rounds} 轮"
|
||
self._runtime._log_cycle_started(cycle_detail, round_index)
|
||
self._runtime._update_stage_status("启动循环", f"循环 {cycle_detail.cycle_id}", round_text=round_text)
|
||
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
|
||
planner_duration_ms = 0.0
|
||
timing_duration_ms = 0.0
|
||
current_stage_started_at = 0.0
|
||
timing_action: Optional[str] = None
|
||
timing_response: Optional[ChatResponse] = None
|
||
timing_tool_results: Optional[list[str]] = None
|
||
timing_tool_monitor_results: Optional[list[dict[str, Any]]] = None
|
||
response: Optional[ChatResponse] = None
|
||
action_tool_definitions: list[dict[str, Any]] = []
|
||
planner_extra_lines: list[str] = []
|
||
tool_result_summaries: list[str] = []
|
||
tool_monitor_results: list[dict[str, Any]] = []
|
||
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"
|
||
)
|
||
|
||
if timing_gate_required:
|
||
self._runtime._update_stage_status("Timing Gate", "等待门控决策", round_text=round_text)
|
||
current_stage_started_at = time.time()
|
||
timing_started_at = time.time()
|
||
(
|
||
timing_action,
|
||
timing_response,
|
||
timing_tool_results,
|
||
timing_tool_monitor_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,
|
||
)
|
||
timing_gate_required = self._mark_timing_gate_completed(timing_action)
|
||
if timing_action != "continue":
|
||
logger.debug(
|
||
f"{self._runtime.log_prefix} Timing Gate 结束当前回合: "
|
||
f"回合={round_index + 1} 动作={timing_action}"
|
||
)
|
||
break
|
||
else:
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 跳过 Timing Gate，继续执行 Planner: "
|
||
f"回合={round_index + 1}"
|
||
)
|
||
|
||
planner_started_at = time.time()
|
||
current_stage_started_at = planner_started_at
|
||
self._runtime._update_stage_status("Planner", "组织上下文并请求模型", round_text=round_text)
|
||
action_tool_definitions, deferred_tools_reminder = 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(
|
||
injected_user_messages=[deferred_tools_reminder] if deferred_tools_reminder else None,
|
||
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"
|
||
# )
|
||
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，然后直接输出我的想法"
|
||
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，然后直接输出我的想法"
|
||
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_monitor_results = []
|
||
|
||
if response.tool_calls:
|
||
tool_started_at = time.time()
|
||
should_pause, tool_result_summaries, tool_monitor_results = await self._handle_tool_calls(
|
||
response.tool_calls,
|
||
response.content or "",
|
||
anchor_message,
|
||
)
|
||
cycle_detail.time_records["tool_calls"] = time.time() - tool_started_at
|
||
if should_pause:
|
||
break
|
||
continue
|
||
|
||
if not response.content:
|
||
break
|
||
except ReqAbortException as exc:
|
||
self._runtime._update_stage_status(
|
||
"Planner 已打断",
|
||
str(exc) or "收到外部中断信号",
|
||
round_text=round_text,
|
||
)
|
||
interrupted_at = time.time()
|
||
interrupted_stage_label = "Planner"
|
||
interrupted_text = "Planner 收到新消息，开始重新决策"
|
||
interrupted_response = ChatResponse(
|
||
content=interrupted_text or None,
|
||
tool_calls=[],
|
||
request_messages=[],
|
||
raw_message=AssistantMessage(
|
||
content=interrupted_text,
|
||
timestamp=datetime.now(),
|
||
tool_calls=[],
|
||
source_kind="perception",
|
||
),
|
||
selected_history_count=len(self._runtime._chat_history),
|
||
tool_count=len(action_tool_definitions),
|
||
prompt_tokens=0,
|
||
built_message_count=0,
|
||
completion_tokens=0,
|
||
total_tokens=0,
|
||
prompt_section=None,
|
||
)
|
||
interrupted_extra_lines = [
|
||
"状态：已被新消息打断",
|
||
f"打断位置：{interrupted_stage_label} 请求流式响应阶段",
|
||
f"打断耗时：{interrupted_at - current_stage_started_at:.3f} 秒",
|
||
]
|
||
response = interrupted_response
|
||
planner_extra_lines = interrupted_extra_lines
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} {interrupted_stage_label} 打断成功: "
|
||
f"回合={round_index + 1} "
|
||
f"开始时间={current_stage_started_at:.3f} "
|
||
f"打断时间={interrupted_at:.3f} "
|
||
f"耗时={interrupted_at - current_stage_started_at:.3f} 秒"
|
||
)
|
||
if not self._should_retry_planner_after_interrupt(
|
||
round_index=round_index,
|
||
max_internal_rounds=self._runtime._max_internal_rounds,
|
||
has_pending_messages=self._runtime._has_pending_messages(),
|
||
):
|
||
break
|
||
|
||
await self._runtime._wait_for_message_quiet_period()
|
||
self._runtime._message_turn_scheduled = False
|
||
interrupted_messages = self._runtime._collect_pending_messages()
|
||
if not interrupted_messages:
|
||
break
|
||
|
||
asyncio.create_task(self._runtime._trigger_batch_learning(interrupted_messages))
|
||
await self._ingest_messages(interrupted_messages)
|
||
anchor_message = interrupted_messages[-1]
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 淇濇寔娲昏穬鐘舵€侊紝璺宠繃 Timing Gate 鐩存帴閲嶈瘯 Planner: "
|
||
f"鍥炲悎={round_index + 2}"
|
||
)
|
||
continue
|
||
finally:
|
||
completed_cycle = self._end_cycle(cycle_detail)
|
||
self._runtime._render_context_usage_panel(
|
||
cycle_id=cycle_detail.cycle_id,
|
||
time_records=dict(completed_cycle.time_records),
|
||
timing_selected_history_count=(
|
||
timing_response.selected_history_count if timing_response is not None else None
|
||
),
|
||
timing_prompt_tokens=(
|
||
timing_response.prompt_tokens if timing_response is not None else None
|
||
),
|
||
timing_action=timing_action or "",
|
||
timing_response=timing_response.content or "" if timing_response is not None else "",
|
||
timing_tool_calls=timing_response.tool_calls if timing_response is not None else None,
|
||
timing_tool_results=timing_tool_results,
|
||
timing_tool_detail_results=timing_tool_monitor_results,
|
||
timing_prompt_section=(
|
||
timing_response.prompt_section if timing_response is not None else None
|
||
),
|
||
planner_selected_history_count=(
|
||
response.selected_history_count if response is not None else None
|
||
),
|
||
planner_prompt_tokens=response.prompt_tokens if response is not None else None,
|
||
planner_response=response.content or "" if response is not None else "",
|
||
planner_tool_calls=response.tool_calls if response is not None else None,
|
||
planner_tool_results=tool_result_summaries,
|
||
planner_tool_detail_results=tool_monitor_results,
|
||
planner_prompt_section=response.prompt_section if response is not None else None,
|
||
planner_extra_lines=planner_extra_lines,
|
||
)
|
||
await emit_planner_finalized(
|
||
session_id=self._runtime.session_id,
|
||
cycle_id=cycle_detail.cycle_id,
|
||
timing_request_messages=(
|
||
timing_response.request_messages if timing_response is not None else None
|
||
),
|
||
timing_selected_history_count=(
|
||
timing_response.selected_history_count if timing_response is not None else None
|
||
),
|
||
timing_tool_count=timing_response.tool_count if timing_response is not None else None,
|
||
timing_action=timing_action,
|
||
timing_content=timing_response.content if timing_response is not None else None,
|
||
timing_tool_calls=timing_response.tool_calls if timing_response is not None else None,
|
||
timing_tool_results=timing_tool_results,
|
||
timing_prompt_tokens=timing_response.prompt_tokens if timing_response is not None else None,
|
||
timing_completion_tokens=(
|
||
timing_response.completion_tokens if timing_response is not None else None
|
||
),
|
||
timing_total_tokens=timing_response.total_tokens if timing_response is not None else None,
|
||
timing_duration_ms=timing_duration_ms if timing_response is not None else None,
|
||
planner_request_messages=response.request_messages if response is not None else None,
|
||
planner_selected_history_count=(
|
||
response.selected_history_count if response is not None else None
|
||
),
|
||
planner_tool_count=response.tool_count if response is not None else None,
|
||
planner_content=response.content if response is not None else None,
|
||
planner_tool_calls=response.tool_calls if response is not None else None,
|
||
planner_prompt_tokens=response.prompt_tokens if response is not None else None,
|
||
planner_completion_tokens=(
|
||
response.completion_tokens if response is not None else None
|
||
),
|
||
planner_total_tokens=response.total_tokens if response is not None else None,
|
||
planner_duration_ms=planner_duration_ms if response is not None else None,
|
||
planner_prompt_html_uri=response.prompt_html_uri if response is not None else None,
|
||
tools=tool_monitor_results,
|
||
time_records=dict(completed_cycle.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
|
||
if self._runtime._running:
|
||
self._runtime._update_stage_status("等待消息", "本轮处理结束")
|
||
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]:
|
||
"""合并当前已就绪的 turn 触发信号。"""
|
||
|
||
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
|
||
|
||
if next_trigger == "message":
|
||
message_triggered = True
|
||
continue
|
||
if next_trigger == "timeout":
|
||
timeout_triggered = True
|
||
continue
|
||
|
||
return message_triggered, timeout_triggered
|
||
|
||
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_completed_message(self, *, has_new_messages: bool) -> ToolResultMessage:
|
||
"""构造 wait 完成后的工具结果消息。"""
|
||
tool_call_id = self._runtime._pending_wait_tool_call_id or "wait_timeout"
|
||
self._runtime._pending_wait_tool_call_id = None
|
||
content = (
|
||
"等待已结束，期间收到了新的用户输入。请结合这些新消息继续下一轮思考。"
|
||
if has_new_messages
|
||
else "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。"
|
||
)
|
||
return ToolResultMessage(
|
||
content=content,
|
||
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)
|
||
|
||
# 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, source_kind=source_kind)
|
||
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 resolve_enable_visual_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, source_kind: self._build_legacy_visible_text(
|
||
message,
|
||
message.raw_message,
|
||
source_kind=source_kind,
|
||
),
|
||
)
|
||
|
||
def _build_legacy_visible_text(
|
||
self,
|
||
message: SessionMessage,
|
||
source_sequence: MessageSequence,
|
||
*,
|
||
source_kind: str = "user",
|
||
) -> str:
|
||
return build_session_message_visible_text(
|
||
message,
|
||
source_sequence,
|
||
include_reply_components=source_kind != "guided_reply",
|
||
)
|
||
|
||
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)
|
||
self._post_process_chat_history_after_cycle()
|
||
|
||
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 _post_process_chat_history_after_cycle(self) -> None:
|
||
"""裁剪聊天历史，保证用户消息数量不超过配置限制。"""
|
||
process_result = process_chat_history_after_cycle(
|
||
self._runtime._chat_history,
|
||
max_context_size=self._runtime._max_context_size,
|
||
)
|
||
if process_result.changed_count <= 0:
|
||
return
|
||
|
||
self._runtime._chat_history = process_result.history
|
||
if process_result.removed_count <= 0:
|
||
return
|
||
self._runtime._log_history_trimmed(
|
||
process_result.removed_count,
|
||
process_result.remaining_context_count,
|
||
)
|
||
|
||
@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.debug(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_availability_context(self) -> ToolAvailabilityContext:
|
||
"""构造当前聊天的工具暴露上下文。"""
|
||
|
||
chat_stream = self._runtime.chat_stream
|
||
return ToolAvailabilityContext(
|
||
session_id=self._runtime.session_id,
|
||
stream_id=self._runtime.session_id,
|
||
is_group_chat=chat_stream.is_group_session,
|
||
group_id=str(getattr(chat_stream, "group_id", "") or "").strip(),
|
||
user_id=str(getattr(chat_stream, "user_id", "") or "").strip(),
|
||
platform=str(getattr(chat_stream, "platform", "") or "").strip(),
|
||
)
|
||
|
||
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"<bytes:{len(value)}>"
|
||
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 == "finish":
|
||
return "你结束了本轮思考，等待新的外部消息后再继续。"
|
||
|
||
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:
|
||
tool_record_payload = self._build_tool_record_payload(invocation, result, tool_spec)
|
||
saved_record = 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=tool_record_payload,
|
||
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}"
|
||
)
|
||
return
|
||
|
||
if invocation.tool_name == "query_memory" and isinstance(saved_record, dict):
|
||
try:
|
||
enqueue_payload = await memory_service.enqueue_feedback_task(
|
||
query_tool_id=str(saved_record.get("tool_id") or invocation.call_id or "").strip(),
|
||
session_id=str(saved_record.get("session_id") or self._runtime.chat_stream.session_id or "").strip(),
|
||
query_timestamp=saved_record.get("timestamp"),
|
||
structured_content=tool_record_payload.get("structured_content")
|
||
if isinstance(tool_record_payload.get("structured_content"), dict)
|
||
else {},
|
||
)
|
||
except Exception:
|
||
logger.exception(
|
||
f"{self._runtime.log_prefix} 反馈纠错任务入队失败: tool_call_id={invocation.call_id}"
|
||
)
|
||
else:
|
||
if not bool(enqueue_payload.get("success")):
|
||
logger.debug(
|
||
f"{self._runtime.log_prefix} 反馈纠错任务未入队: "
|
||
f"tool_call_id={invocation.call_id} reason={enqueue_payload.get('reason', '')}"
|
||
)
|
||
|
||
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。
|
||
"""
|
||
|
||
if tool_call.func_name in HISTORY_SILENT_TOOL_NAMES:
|
||
self._remove_tool_call_from_history(tool_call)
|
||
return
|
||
|
||
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 _remove_tool_call_from_history(self, tool_call: ToolCall) -> None:
|
||
"""从历史里的 assistant 消息中移除控制类工具调用。"""
|
||
|
||
tool_call_id = str(tool_call.call_id or "").strip()
|
||
if not tool_call_id:
|
||
return
|
||
|
||
for index in range(len(self._runtime._chat_history) - 1, -1, -1):
|
||
message = self._runtime._chat_history[index]
|
||
if not isinstance(message, AssistantMessage) or not message.tool_calls:
|
||
continue
|
||
|
||
remaining_tool_calls = [
|
||
existing_tool_call
|
||
for existing_tool_call in message.tool_calls
|
||
if str(existing_tool_call.call_id or "").strip() != tool_call_id
|
||
]
|
||
if len(remaining_tool_calls) == len(message.tool_calls):
|
||
continue
|
||
|
||
if remaining_tool_calls:
|
||
message.tool_calls = remaining_tool_calls
|
||
elif message.content.strip():
|
||
message.tool_calls = []
|
||
else:
|
||
del self._runtime._chat_history[index]
|
||
return
|
||
|
||
def _append_timing_gate_execution_result(
|
||
self,
|
||
response: ChatResponse,
|
||
tool_call: ToolCall,
|
||
result: ToolExecutionResult,
|
||
) -> None:
|
||
"""将 Timing Gate 的决策链写入历史，供后续门控复用。"""
|
||
|
||
self._runtime._chat_history.append(
|
||
AssistantMessage(
|
||
content=response.content or "",
|
||
timestamp=response.raw_message.timestamp,
|
||
tool_calls=[tool_call],
|
||
source_kind="timing_gate",
|
||
)
|
||
)
|
||
if tool_call.func_name == "wait":
|
||
return
|
||
self._append_tool_execution_result(tool_call, result)
|
||
|
||
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}"
|
||
|
||
def _build_tool_monitor_result(
|
||
self,
|
||
tool_call: ToolCall,
|
||
invocation: ToolInvocation,
|
||
result: ToolExecutionResult,
|
||
duration_ms: float,
|
||
tool_spec: Optional[ToolSpec] = None,
|
||
) -> dict[str, Any]:
|
||
"""构建 planner.finalized 中单个工具的监控结果。"""
|
||
|
||
monitor_detail = result.metadata.get("monitor_detail")
|
||
normalized_detail = None
|
||
if monitor_detail is not None:
|
||
normalized_detail = self._normalize_tool_record_value(monitor_detail)
|
||
|
||
monitor_card = result.metadata.get("monitor_card")
|
||
normalized_card = None
|
||
if monitor_card is not None:
|
||
normalized_card = self._normalize_tool_record_value(monitor_card)
|
||
|
||
monitor_sub_cards = result.metadata.get("monitor_sub_cards")
|
||
normalized_sub_cards = None
|
||
if monitor_sub_cards is not None:
|
||
normalized_sub_cards = self._normalize_tool_record_value(monitor_sub_cards)
|
||
|
||
return {
|
||
"tool_call_id": tool_call.call_id,
|
||
"tool_name": tool_call.func_name,
|
||
"tool_title": tool_spec.title.strip() if tool_spec is not None and tool_spec.title.strip() else "",
|
||
"tool_args": self._normalize_tool_record_value(
|
||
invocation.arguments if isinstance(invocation.arguments, dict) else {}
|
||
),
|
||
"success": result.success,
|
||
"duration_ms": round(duration_ms, 2),
|
||
"summary": self._build_tool_result_summary(tool_call, result),
|
||
"detail": normalized_detail,
|
||
"card": normalized_card,
|
||
"sub_cards": normalized_sub_cards,
|
||
}
|
||
|
||
async def _handle_tool_calls(
|
||
self,
|
||
tool_calls: list[ToolCall],
|
||
latest_thought: str,
|
||
anchor_message: SessionMessage,
|
||
) -> tuple[bool, list[str], list[dict[str, Any]]]:
|
||
"""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], list[dict[str, Any]]]: 是否需要暂停当前思考循环、
|
||
工具结果摘要列表，以及最终监控事件使用的工具详情列表。
|
||
"""
|
||
|
||
tool_result_summaries: list[str] = []
|
||
tool_monitor_results: list[dict[str, Any]] = []
|
||
|
||
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))
|
||
tool_monitor_results.append(
|
||
self._build_tool_monitor_result(tool_call, invocation, result, duration_ms=0.0, tool_spec=None)
|
||
)
|
||
return False, tool_result_summaries, tool_monitor_results
|
||
|
||
execution_context = self._build_tool_execution_context(latest_thought, anchor_message)
|
||
availability_context = self._build_tool_availability_context()
|
||
tool_spec_map = {
|
||
tool_spec.name: tool_spec
|
||
for tool_spec in await self._runtime._tool_registry.list_tools(availability_context)
|
||
}
|
||
total_tool_count = len(tool_calls)
|
||
for tool_index, tool_call in enumerate(tool_calls, start=1):
|
||
invocation = self._build_tool_invocation(tool_call, latest_thought)
|
||
self._runtime._update_stage_status(
|
||
f"工具执行 · {invocation.tool_name}",
|
||
f"第 {tool_index}/{total_tool_count} 个工具",
|
||
)
|
||
tool_started_at = time.time()
|
||
if not self._runtime.is_action_tool_currently_available(invocation.tool_name):
|
||
result = ToolExecutionResult(
|
||
tool_name=invocation.tool_name,
|
||
success=False,
|
||
error_message=(
|
||
f"工具 {invocation.tool_name} 当前未直接暴露给 planner。"
|
||
"如果它在 deferred tools 提示中，请先调用 tool_search。"
|
||
),
|
||
)
|
||
else:
|
||
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))
|
||
tool_monitor_results.append(
|
||
self._build_tool_monitor_result(
|
||
tool_call,
|
||
invocation,
|
||
result,
|
||
tool_duration_ms,
|
||
tool_spec=tool_spec_map.get(invocation.tool_name),
|
||
)
|
||
)
|
||
|
||
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, tool_monitor_results
|
||
|
||
return False, tool_result_summaries, tool_monitor_results
|