756 lines
32 KiB
Python
756 lines
32 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, 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.chat.utils.utils import process_llm_response
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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.config.config import global_config
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from src.core.tooling import 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 .builtin_tool import build_builtin_tool_handlers as build_split_builtin_tool_handlers
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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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LLMContextMessage,
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SessionBackedMessage,
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ToolResultMessage,
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)
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from .message_adapter import (
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build_visible_text_from_sequence,
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clone_message_sequence,
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format_speaker_content,
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)
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from .planner_message_utils import build_planner_user_prefix_from_session_message
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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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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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@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_loop(self) -> None:
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"""独立消费消息批次，并执行对应的内部思考轮次。"""
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try:
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while self._runtime._running:
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cached_messages = await self._runtime._internal_turn_queue.get()
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timeout_triggered = cached_messages is None
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if not timeout_triggered and not cached_messages:
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self._runtime._internal_turn_queue.task_done()
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continue
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self._runtime._agent_state = self._runtime._STATE_RUNNING
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if cached_messages:
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self._clear_pending_wait_tool_call_id()
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await self._ingest_messages(cached_messages)
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anchor_message = cached_messages[-1]
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else:
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anchor_message = self._get_timeout_anchor_message()
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if anchor_message is None:
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logger.warning(
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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"
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)
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self._runtime._internal_turn_queue.task_done()
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continue
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logger.info(f"{self._runtime.log_prefix} 等待超时后开始新一轮思考")
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self._clear_pending_wait_tool_call_id()
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self._trim_chat_history()
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try:
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for round_index in range(self._runtime._max_internal_rounds):
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cycle_detail = self._start_cycle()
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self._runtime._log_cycle_started(cycle_detail, round_index)
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planner_started_at = time.time()
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try:
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logger.info(
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f"{self._runtime.log_prefix} 规划器开始执行: "
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f"回合={round_index + 1} "
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f"历史消息数={len(self._runtime._chat_history)} "
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f"开始时间={planner_started_at:.3f}"
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)
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interrupt_flag = asyncio.Event()
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self._runtime._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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response = await self._runtime._chat_loop_service.chat_loop_step(self._runtime._chat_history)
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finally:
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if self._runtime._planner_interrupt_flag is interrupt_flag:
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self._runtime._planner_interrupt_flag = None
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self._runtime._chat_loop_service.set_interrupt_flag(None)
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cycle_detail.time_records["planner"] = time.time() - planner_started_at
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logger.info(
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f"{self._runtime.log_prefix} 规划器执行完成: "
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f"回合={round_index + 1} "
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f"耗时={cycle_detail.time_records['planner']:.3f} 秒"
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)
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reasoning_content = response.content or ""
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if self._should_replace_reasoning(reasoning_content):
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response.content = "让我根据新情况重新思考："
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response.raw_message.content = "让我根据新情况重新思考："
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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")
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self._last_reasoning_content = reasoning_content
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self._runtime._render_context_usage_panel(
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selected_history_count=response.selected_history_count,
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prompt_tokens=response.prompt_tokens,
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)
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self._runtime._chat_history.append(response.raw_message)
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if response.tool_calls:
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tool_started_at = time.time()
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should_pause = await self._handle_tool_calls(
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response.tool_calls,
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response.content or "",
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anchor_message,
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)
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cycle_detail.time_records["tool_calls"] = time.time() - tool_started_at
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if should_pause:
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break
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continue
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if response.content:
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continue
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break
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except ReqAbortException:
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interrupted_at = time.time()
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logger.info(
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f"{self._runtime.log_prefix} 规划器打断成功: "
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f"回合={round_index + 1} "
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f"开始时间={planner_started_at:.3f} "
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f"打断时间={interrupted_at:.3f} "
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f"耗时={interrupted_at - planner_started_at:.3f} 秒"
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)
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break
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finally:
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self._end_cycle(cycle_detail)
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finally:
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if self._runtime._agent_state == self._runtime._STATE_RUNNING:
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self._runtime._agent_state = self._runtime._STATE_STOP
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self._runtime._internal_turn_queue.task_done()
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except asyncio.CancelledError:
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self._runtime._log_internal_loop_cancelled()
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raise
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except Exception:
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logger.exception(f"{self._runtime.log_prefix} Maisaka 内部循环发生异常")
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logger.error(traceback.format_exc())
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raise
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def _get_timeout_anchor_message(self) -> Optional[SessionMessage]:
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"""在 wait 超时后复用最近一条真实用户消息作为锚点。"""
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if self._runtime.message_cache:
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return self._runtime.message_cache[-1]
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return None
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def _clear_pending_wait_tool_call_id(self) -> None:
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"""清理等待状态残留的 wait 工具调用编号。"""
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self._runtime._pending_wait_tool_call_id = None
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def _build_wait_timeout_message(self) -> ToolResultMessage:
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"""构造 wait 超时后的工具结果消息。"""
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tool_call_id = self._runtime._pending_wait_tool_call_id or "wait_timeout"
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self._runtime._pending_wait_tool_call_id = None
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return ToolResultMessage(
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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。",
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timestamp=datetime.now(),
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tool_call_id=tool_call_id,
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tool_name="wait",
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)
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def _append_wait_interrupted_message_if_needed(self) -> None:
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"""如果 wait 被新消息打断，则补一条对应的工具结果消息。"""
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tool_call_id = self._runtime._pending_wait_tool_call_id
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if not tool_call_id:
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return
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self._runtime._pending_wait_tool_call_id = None
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self._runtime._chat_history.append(
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ToolResultMessage(
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content="等待过程被新的用户输入打断，已继续处理最新消息。",
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timestamp=datetime.now(),
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tool_call_id=tool_call_id,
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tool_name="wait",
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)
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)
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async def _ingest_messages(self, messages: list[SessionMessage]) -> None:
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"""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。"""
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for message in messages:
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# 构建用户消息序列
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user_sequence, visible_text = await self._build_message_sequence(message)
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if not user_sequence.components:
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continue
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history_message = SessionBackedMessage.from_session_message(
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message,
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raw_message=user_sequence,
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visible_text=visible_text,
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source_kind="user",
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)
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self._insert_chat_history_message(history_message)
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self._trim_chat_history()
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async def _build_message_sequence(self, message: SessionMessage) -> tuple[MessageSequence, str]:
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message_sequence = MessageSequence([])
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planner_prefix = build_planner_user_prefix_from_session_message(message)
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appended_component = False
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source_sequence = message.raw_message
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planner_components = clone_message_sequence(source_sequence).components
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if global_config.maisaka.direct_image_input:
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await self._hydrate_visual_components(planner_components)
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if planner_components and isinstance(planner_components[0], TextComponent):
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planner_components[0].text = planner_prefix + planner_components[0].text
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else:
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planner_components.insert(0, TextComponent(planner_prefix))
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for component in planner_components:
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message_sequence.components.append(component)
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appended_component = True
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legacy_visible_text = self._build_legacy_visible_text(message, source_sequence)
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if not appended_component:
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if not message.processed_plain_text:
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await message.process()
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content = (message.processed_plain_text or "").strip()
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if content:
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message_sequence.text(planner_prefix + content)
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legacy_visible_text = self._build_legacy_visible_text_from_text(message, content)
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return message_sequence, legacy_visible_text
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async def _hydrate_visual_components(self, planner_components: list[object]) -> None:
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"""在 Maisaka 真正需要图片或表情时，按需回填二进制数据。"""
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load_tasks: list[asyncio.Task[None]] = []
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for component in planner_components:
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if isinstance(component, ImageComponent) and not component.binary_data:
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load_tasks.append(asyncio.create_task(component.load_image_binary()))
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continue
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if isinstance(component, EmojiComponent) and not component.binary_data:
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load_tasks.append(asyncio.create_task(component.load_emoji_binary()))
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if not load_tasks:
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return
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results = await asyncio.gather(*load_tasks, return_exceptions=True)
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for result in results:
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if isinstance(result, Exception):
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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}")
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def _build_legacy_visible_text(self, message: SessionMessage, source_sequence: MessageSequence) -> str:
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user_info = message.message_info.user_info
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speaker_name = user_info.user_cardname or user_info.user_nickname or user_info.user_id
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legacy_sequence = MessageSequence([])
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visible_message_id = None if message.is_notify else message.message_id
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legacy_sequence.text(format_speaker_content(speaker_name, "", message.timestamp, visible_message_id))
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for component in clone_message_sequence(source_sequence).components:
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legacy_sequence.components.append(component)
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return build_visible_text_from_sequence(legacy_sequence).strip()
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def _build_legacy_visible_text_from_text(self, message: SessionMessage, content: str) -> str:
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user_info = message.message_info.user_info
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speaker_name = user_info.user_cardname or user_info.user_nickname or user_info.user_id
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visible_message_id = None if message.is_notify else message.message_id
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return format_speaker_content(speaker_name, content, message.timestamp, visible_message_id).strip()
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def _insert_chat_history_message(self, message: LLMContextMessage) -> int:
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"""将消息按处理顺序追加到聊天历史末尾。"""
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self._runtime._chat_history.append(message)
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return len(self._runtime._chat_history) - 1
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def _start_cycle(self) -> CycleDetail:
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"""开始一轮 Maisaka 思考循环。"""
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self._runtime._cycle_counter += 1
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self._runtime._current_cycle_detail = CycleDetail(cycle_id=self._runtime._cycle_counter)
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self._runtime._current_cycle_detail.thinking_id = f"maisaka_tid{round(time.time(), 2)}"
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return self._runtime._current_cycle_detail
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def _end_cycle(self, cycle_detail: CycleDetail, only_long_execution: bool = True) -> CycleDetail:
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"""结束并记录一轮 Maisaka 思考循环。"""
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cycle_detail.end_time = time.time()
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self._runtime.history_loop.append(cycle_detail)
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timer_strings = [
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f"{name}: {duration:.2f}s"
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for name, duration in cycle_detail.time_records.items()
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if not only_long_execution or duration >= 0.1
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]
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self._runtime._log_cycle_completed(cycle_detail, timer_strings)
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return cycle_detail
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def _trim_chat_history(self) -> None:
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"""裁剪聊天历史，保证用户消息数量不超过配置限制。"""
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conversation_message_count = sum(1 for message in self._runtime._chat_history if message.count_in_context)
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if conversation_message_count <= self._runtime._max_context_size:
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return
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trimmed_history = list(self._runtime._chat_history)
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removed_count = 0
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while conversation_message_count > self._runtime._max_context_size and trimmed_history:
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removed_message = trimmed_history.pop(0)
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removed_count += 1
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if removed_message.count_in_context:
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conversation_message_count -= 1
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trimmed_history, pruned_orphan_count = self._drop_leading_orphan_tool_results(trimmed_history)
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removed_count += pruned_orphan_count
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self._runtime._chat_history = trimmed_history
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self._runtime._log_history_trimmed(removed_count, conversation_message_count)
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@staticmethod
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def _drop_leading_orphan_tool_results(
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chat_history: list[LLMContextMessage],
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) -> tuple[list[LLMContextMessage], int]:
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"""清理历史前缀中缺少对应 assistant tool_call 的工具结果消息。"""
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if not chat_history:
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return chat_history, 0
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available_tool_call_ids = {
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tool_call.call_id
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for message in chat_history
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if isinstance(message, AssistantMessage)
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for tool_call in message.tool_calls
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if tool_call.call_id
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}
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first_valid_index = 0
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while first_valid_index < len(chat_history):
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message = chat_history[first_valid_index]
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if not isinstance(message, ToolResultMessage):
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break
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if message.tool_call_id in available_tool_call_ids:
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break
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first_valid_index += 1
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if first_valid_index == 0:
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return chat_history, 0
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return chat_history[first_valid_index:], first_valid_index
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@staticmethod
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def _calculate_similarity(text1: str, text2: str) -> float:
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"""计算两个文本之间的相似度。
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Args:
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text1: 第一个文本
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text2: 第二个文本
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Returns:
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float: 相似度值，范围 0-1，1 表示完全相同
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"""
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return difflib.SequenceMatcher(None, text1, text2).ratio()
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def _should_replace_reasoning(self, current_content: str) -> bool:
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"""判断是否需要替换推理内容。
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当当前推理内容与上一次相似度大于90%时，返回True。
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Args:
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current_content: 当前的推理内容
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Returns:
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bool: 是否需要替换
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"""
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if not self._last_reasoning_content or not current_content:
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logger.info(
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f"{self._runtime.log_prefix} 跳过思考相似度判定: "
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f"上一轮为空={not bool(self._last_reasoning_content)} "
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f"当前为空={not bool(current_content)} 相似度=0.00"
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)
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return False
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similarity = self._calculate_similarity(current_content, self._last_reasoning_content)
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logger.info(f"{self._runtime.log_prefix} 思考内容相似度: {similarity:.2f}")
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return similarity > 0.9
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@staticmethod
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def _post_process_reply_text(reply_text: str) -> list[str]:
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"""沿用旧回复链的文本后处理，执行分段与错别字注入。"""
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processed_segments: list[str] = []
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for segment in process_llm_response(reply_text):
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normalized_segment = segment.strip()
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if normalized_segment:
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processed_segments.append(normalized_segment)
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if processed_segments:
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return processed_segments
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return [reply_text.strip()]
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def _build_tool_invocation(self, tool_call: ToolCall, latest_thought: str) -> ToolInvocation:
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"""将模型输出的工具调用转换为统一调用对象。
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Args:
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tool_call: 模型返回的工具调用。
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latest_thought: 当前轮的最新思考文本。
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Returns:
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ToolInvocation: 统一工具调用对象。
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"""
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return ToolInvocation(
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tool_name=tool_call.func_name,
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arguments=dict(tool_call.args or {}),
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call_id=tool_call.call_id,
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session_id=self._runtime.session_id,
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stream_id=self._runtime.session_id,
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reasoning=latest_thought,
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)
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def _build_tool_execution_context(
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self,
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latest_thought: str,
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anchor_message: SessionMessage,
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) -> ToolExecutionContext:
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"""构造统一工具执行上下文。
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|
||
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":
|
||
description = str(structured_content.get("description") or "").strip()
|
||
emotion_list = structured_content.get("emotion")
|
||
if isinstance(emotion_list, list):
|
||
emotion_text = "、".join(str(item).strip() for item in emotion_list if str(item).strip())
|
||
else:
|
||
emotion_text = ""
|
||
if result.success and description:
|
||
if emotion_text:
|
||
return f"你发送了表情包：{description}（情绪：{emotion_text}）"
|
||
return f"你发送了表情包：{description}"
|
||
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 == "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。"
|
||
|
||
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,
|
||
)
|
||
)
|
||
|
||
async def _handle_tool_calls(
|
||
self,
|
||
tool_calls: list[ToolCall],
|
||
latest_thought: str,
|
||
anchor_message: SessionMessage,
|
||
) -> bool:
|
||
"""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:
|
||
bool: 是否需要暂停当前思考循环。
|
||
"""
|
||
|
||
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)
|
||
return False
|
||
|
||
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)
|
||
result = await self._runtime._tool_registry.invoke(invocation, execution_context)
|
||
await self._store_tool_execution_record(
|
||
invocation,
|
||
result,
|
||
tool_spec_map.get(invocation.tool_name),
|
||
)
|
||
self._append_tool_execution_result(tool_call, result)
|
||
|
||
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
|
||
|
||
return False
|
||
|