917 lines
38 KiB
Python
917 lines
38 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, cast
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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 sqlmodel import col, select
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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.replyer.replyer_manager import replyer_manager
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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 MessageSequence, TextComponent
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from src.common.database.database import get_db_session
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from src.common.database.database_model import PersonInfo
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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.know_u.knowledge_store import get_knowledge_store
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from src.learners.jargon_explainer import search_jargon
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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, send_service
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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 .tool_handlers import (
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handle_mcp_tool,
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handle_unknown_tool,
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)
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if TYPE_CHECKING:
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from .runtime import MaisakaHeartFlowChatting
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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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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._append_wait_interrupted_message_if_needed()
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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._runtime._chat_history.append(self._build_wait_timeout_message())
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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._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 _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 = self._build_planner_user_prefix(message)
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appended_component = False
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if global_config.maisaka.direct_image_input:
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source_sequence = getattr(message, "maisaka_original_raw_message", message.raw_message)
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else:
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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 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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||
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||
@staticmethod
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def _build_planner_user_prefix(message: SessionMessage) -> str:
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user_info = message.message_info.user_info
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timestamp_text = message.timestamp.strftime("%H:%M:%S")
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user_name = user_info.user_nickname or user_info.user_id
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group_card = user_info.user_cardname or ""
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message_id = message.message_id or ""
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||
return (
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f"[时间]{timestamp_text}\n"
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f"[用户]{user_name}\n"
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f"[用户群昵称]{group_card}\n"
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f"[msg_id]{message_id}\n"
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"[发言内容]"
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)
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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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legacy_sequence.text(format_speaker_content(speaker_name, "", message.timestamp, message.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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||
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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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return format_speaker_content(speaker_name, content, message.timestamp, message.message_id).strip()
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||
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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:
|
||
"""结束并记录一轮 Maisaka 思考循环。"""
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||
cycle_detail.end_time = time.time()
|
||
self._runtime.history_loop.append(cycle_detail)
|
||
|
||
timer_strings = [
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||
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)
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||
return cycle_detail
|
||
|
||
def _trim_chat_history(self) -> None:
|
||
"""裁剪聊天历史，保证用户消息数量不超过配置限制。"""
|
||
conversation_message_count = sum(1 for message in self._runtime._chat_history if message.count_in_context)
|
||
if conversation_message_count <= self._runtime._max_context_size:
|
||
return
|
||
|
||
trimmed_history = list(self._runtime._chat_history)
|
||
removed_count = 0
|
||
|
||
while conversation_message_count >= self._runtime._max_context_size and trimmed_history:
|
||
removed_message = trimmed_history.pop(0)
|
||
removed_count += 1
|
||
if removed_message.count_in_context:
|
||
conversation_message_count -= 1
|
||
|
||
self._runtime._chat_history = trimmed_history
|
||
self._runtime._log_history_trimmed(removed_count, conversation_message_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.info(f"{self._runtime.log_prefix} 思考内容相似度: {similarity:.2f}")
|
||
return similarity > 0.9
|
||
|
||
@staticmethod
|
||
def _post_process_reply_text(reply_text: str) -> list[str]:
|
||
"""沿用旧回复链的文本后处理，执行分段与错别字注入。"""
|
||
processed_segments: list[str] = []
|
||
for segment in process_llm_response(reply_text):
|
||
normalized_segment = segment.strip()
|
||
if normalized_segment:
|
||
processed_segments.append(normalized_segment)
|
||
|
||
if processed_segments:
|
||
return processed_segments
|
||
return [reply_text.strip()]
|
||
|
||
async def _handle_tool_calls(
|
||
self,
|
||
tool_calls: list[ToolCall],
|
||
latest_thought: str,
|
||
anchor_message: SessionMessage,
|
||
) -> bool:
|
||
for tool_call in tool_calls:
|
||
if tool_call.func_name == "reply":
|
||
reply_sent = await self._handle_reply(tool_call, latest_thought, anchor_message)
|
||
if not reply_sent:
|
||
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"
|
||
)
|
||
continue
|
||
|
||
if tool_call.func_name == "no_reply":
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(
|
||
tool_call,
|
||
"本轮未发送可见回复。",
|
||
)
|
||
)
|
||
continue
|
||
|
||
if tool_call.func_name == "query_jargon":
|
||
await self._handle_query_jargon(tool_call)
|
||
continue
|
||
|
||
if tool_call.func_name == "query_person_info":
|
||
await self._handle_query_person_info(tool_call)
|
||
continue
|
||
|
||
if tool_call.func_name == "wait":
|
||
seconds = (tool_call.args or {}).get("seconds", 30)
|
||
try:
|
||
wait_seconds = int(seconds)
|
||
except (TypeError, ValueError):
|
||
wait_seconds = 30
|
||
wait_seconds = max(0, wait_seconds)
|
||
self._runtime._enter_wait_state(seconds=wait_seconds, tool_call_id=tool_call.call_id)
|
||
return True
|
||
|
||
if tool_call.func_name == "stop":
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(
|
||
tool_call,
|
||
"当前对话循环已暂停，等待新消息到来。",
|
||
)
|
||
)
|
||
self._runtime._enter_stop_state()
|
||
return True
|
||
|
||
if tool_call.func_name == "send_emoji":
|
||
await self._handle_send_emoji(tool_call, anchor_message)
|
||
continue
|
||
|
||
if self._runtime._mcp_manager and self._runtime._mcp_manager.is_mcp_tool(tool_call.func_name):
|
||
await handle_mcp_tool(tool_call, self._runtime._chat_history, self._runtime._mcp_manager)
|
||
continue
|
||
|
||
await handle_unknown_tool(tool_call, self._runtime._chat_history)
|
||
|
||
return False
|
||
|
||
async def _handle_query_jargon(self, tool_call: ToolCall) -> None:
|
||
tool_args = tool_call.args or {}
|
||
raw_words = tool_args.get("words")
|
||
|
||
if not isinstance(raw_words, list):
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "查询黑话工具需要提供 `words` 数组参数。")
|
||
)
|
||
return
|
||
|
||
words: list[str] = []
|
||
seen_words: set[str] = set()
|
||
for item in raw_words:
|
||
if not isinstance(item, str):
|
||
continue
|
||
word = item.strip()
|
||
if not word or word in seen_words:
|
||
continue
|
||
seen_words.add(word)
|
||
words.append(word)
|
||
|
||
if not words:
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "查询黑话工具至少需要一个非空词条。")
|
||
)
|
||
return
|
||
|
||
logger.info(f"{self._runtime.log_prefix} 已触发黑话查询: 词条={words!r}")
|
||
|
||
results: list[dict[str, object]] = []
|
||
for word in words:
|
||
exact_matches = search_jargon(
|
||
keyword=word,
|
||
chat_id=self._runtime.session_id,
|
||
limit=5,
|
||
case_sensitive=False,
|
||
fuzzy=False,
|
||
)
|
||
matched_entries = exact_matches or search_jargon(
|
||
keyword=word,
|
||
chat_id=self._runtime.session_id,
|
||
limit=5,
|
||
case_sensitive=False,
|
||
fuzzy=True,
|
||
)
|
||
|
||
results.append(
|
||
{
|
||
"word": word,
|
||
"found": bool(matched_entries),
|
||
"matches": matched_entries,
|
||
}
|
||
)
|
||
|
||
logger.info(f"{self._runtime.log_prefix} 黑话查询完成: 结果={results!r}")
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(
|
||
tool_call,
|
||
json.dumps({"results": results}, ensure_ascii=False),
|
||
)
|
||
)
|
||
|
||
async def _handle_query_person_info(self, tool_call: ToolCall) -> None:
|
||
"""查询指定人物的档案和相关知识。"""
|
||
tool_args = tool_call.args or {}
|
||
raw_person_name = tool_args.get("person_name")
|
||
raw_limit = tool_args.get("limit", 3)
|
||
|
||
if not isinstance(raw_person_name, str):
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "查询人物信息工具需要提供字符串类型的 `person_name` 参数。")
|
||
)
|
||
return
|
||
|
||
person_name = raw_person_name.strip()
|
||
if not person_name:
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "查询人物信息工具需要提供非空的 `person_name` 参数。")
|
||
)
|
||
return
|
||
|
||
try:
|
||
limit = max(1, min(int(raw_limit), 10))
|
||
except (TypeError, ValueError):
|
||
limit = 3
|
||
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 已触发人物信息查询: "
|
||
f"人物名={person_name!r} 限制条数={limit}"
|
||
)
|
||
|
||
persons = self._query_person_records(person_name, limit)
|
||
result = {
|
||
"query": person_name,
|
||
"persons": persons,
|
||
"related_knowledge": self._query_related_knowledge(person_name, persons, limit),
|
||
}
|
||
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 人物信息查询完成: "
|
||
f"人物记录数={len(result['persons'])} 相关知识数={len(result['related_knowledge'])}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(
|
||
tool_call,
|
||
json.dumps(result, ensure_ascii=False),
|
||
)
|
||
)
|
||
|
||
def _query_person_records(self, person_name: str, limit: int) -> list[dict[str, Any]]:
|
||
"""按名称、昵称或用户 ID 查询人物档案。"""
|
||
with get_db_session() as session:
|
||
records = session.exec(
|
||
select(PersonInfo)
|
||
.where(
|
||
col(PersonInfo.person_name).contains(person_name)
|
||
| col(PersonInfo.user_nickname).contains(person_name)
|
||
| col(PersonInfo.user_id).contains(person_name)
|
||
)
|
||
.order_by(col(PersonInfo.last_known_time).desc(), col(PersonInfo.id).desc())
|
||
.limit(limit)
|
||
).all()
|
||
|
||
persons: list[dict[str, Any]] = []
|
||
for record in records:
|
||
memory_points: list[str] = []
|
||
if record.memory_points:
|
||
try:
|
||
parsed_points = json.loads(record.memory_points)
|
||
if isinstance(parsed_points, list):
|
||
memory_points = [str(point).strip() for point in parsed_points if str(point).strip()]
|
||
except (json.JSONDecodeError, TypeError, ValueError):
|
||
memory_points = []
|
||
|
||
persons.append(
|
||
{
|
||
"person_id": record.person_id,
|
||
"person_name": record.person_name or "",
|
||
"user_nickname": record.user_nickname,
|
||
"user_id": record.user_id,
|
||
"platform": record.platform,
|
||
"name_reason": record.name_reason or "",
|
||
"is_known": record.is_known,
|
||
"know_counts": record.know_counts,
|
||
"memory_points": memory_points[:20],
|
||
"last_known_time": (
|
||
record.last_known_time.isoformat() if record.last_known_time is not None else None
|
||
),
|
||
}
|
||
)
|
||
|
||
return persons
|
||
|
||
def _query_related_knowledge(
|
||
self,
|
||
person_name: str,
|
||
persons: list[dict[str, Any]],
|
||
limit: int,
|
||
) -> list[dict[str, Any]]:
|
||
"""从 Maisaka knowledge 中补充检索与该人物相关的条目。"""
|
||
store = get_knowledge_store()
|
||
knowledge_items: list[dict[str, Any]] = []
|
||
seen_ids: set[str] = set()
|
||
|
||
for person in persons:
|
||
matched_items = store.get_knowledge_by_user(
|
||
platform=str(person.get("platform", "")).strip(),
|
||
user_id=str(person.get("user_id", "")).strip(),
|
||
user_nickname=str(person.get("user_nickname", "")).strip(),
|
||
person_name=str(person.get("person_name", "")).strip(),
|
||
limit=max(limit, 5),
|
||
)
|
||
for item in matched_items:
|
||
item_id = str(item.get("id", "")).strip()
|
||
if item_id and item_id in seen_ids:
|
||
continue
|
||
if item_id:
|
||
seen_ids.add(item_id)
|
||
knowledge_items.append(item)
|
||
|
||
if not knowledge_items:
|
||
fallback_items = store.search_knowledge(person_name, limit=max(limit, 5))
|
||
for item in fallback_items:
|
||
item_id = str(item.get("id", "")).strip()
|
||
if item_id and item_id in seen_ids:
|
||
continue
|
||
if item_id:
|
||
seen_ids.add(item_id)
|
||
knowledge_items.append(item)
|
||
|
||
results: list[dict[str, Any]] = []
|
||
for item in knowledge_items:
|
||
results.append(
|
||
{
|
||
"id": str(item.get("id", "")).strip(),
|
||
"category_id": str(item.get("category_id", "")).strip(),
|
||
"category_name": str(item.get("category_name", "")).strip(),
|
||
"content": str(item.get("content", "")).strip(),
|
||
"metadata": item.get("metadata", {}),
|
||
"created_at": item.get("created_at"),
|
||
}
|
||
)
|
||
return results
|
||
|
||
async def _handle_reply(
|
||
self,
|
||
tool_call: ToolCall,
|
||
latest_thought: str,
|
||
anchor_message: SessionMessage,
|
||
) -> bool:
|
||
tool_args = tool_call.args or {}
|
||
target_message_id = str(tool_args.get("msg_id") or "").strip()
|
||
quote_reply = bool(tool_args.get("quote", True))
|
||
raw_unknown_words = tool_args.get("unknown_words")
|
||
unknown_words = raw_unknown_words if isinstance(raw_unknown_words, list) else None
|
||
if not target_message_id:
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "回复工具需要提供有效的 `msg_id` 参数。")
|
||
)
|
||
return False
|
||
|
||
target_message = self._runtime._source_messages_by_id.get(target_message_id)
|
||
if target_message is None:
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, f"未找到要回复的目标消息，msg_id={target_message_id}")
|
||
)
|
||
return False
|
||
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 已触发回复工具: "
|
||
f"目标消息编号={target_message_id} 引用回复={quote_reply} 最新思考={latest_thought!r}"
|
||
)
|
||
logger.info(f"{self._runtime.log_prefix} 正在获取 Maisaka 回复生成器")
|
||
try:
|
||
replyer = replyer_manager.get_replyer(
|
||
chat_stream=self._runtime.chat_stream,
|
||
request_type="maisaka_replyer",
|
||
replyer_type="maisaka",
|
||
)
|
||
except Exception:
|
||
logger.exception(
|
||
f"{self._runtime.log_prefix} 获取回复生成器时发生异常: "
|
||
f"目标消息编号={target_message_id}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "获取 Maisaka 回复生成器时发生异常。")
|
||
)
|
||
return False
|
||
|
||
if replyer is None:
|
||
logger.error(f"{self._runtime.log_prefix} 获取 Maisaka 回复生成器失败")
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "Maisaka 回复生成器当前不可用。")
|
||
)
|
||
return False
|
||
|
||
from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
|
||
|
||
replyer = cast(MaisakaReplyGenerator, replyer)
|
||
logger.info(f"{self._runtime.log_prefix} 已成功获取 Maisaka 回复生成器")
|
||
|
||
logger.info(f"{self._runtime.log_prefix} 正在调用回复生成接口: 目标消息编号={target_message_id}")
|
||
try:
|
||
success, reply_result = await replyer.generate_reply_with_context(
|
||
reply_reason=latest_thought,
|
||
stream_id=self._runtime.session_id,
|
||
reply_message=target_message,
|
||
chat_history=self._runtime._chat_history,
|
||
unknown_words=unknown_words,
|
||
log_reply=False,
|
||
)
|
||
except Exception as exc:
|
||
import traceback
|
||
logger.error(
|
||
f"{self._runtime.log_prefix} 回复生成器执行异常: 目标消息编号={target_message_id} "
|
||
f"异常类型={type(exc).__name__} 异常信息={str(exc)}\n{traceback.format_exc()}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "生成可见回复时发生异常。")
|
||
)
|
||
return False
|
||
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 回复生成完成: "
|
||
f"成功={success} 回复文本={reply_result.completion.response_text!r} "
|
||
f"错误信息={reply_result.error_message!r}"
|
||
)
|
||
reply_text = reply_result.completion.response_text.strip() if success else ""
|
||
if not reply_text:
|
||
logger.warning(
|
||
f"{self._runtime.log_prefix} 回复生成器返回空文本: "
|
||
f"目标消息编号={target_message_id} 错误信息={reply_result.error_message!r}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "生成可见回复失败。")
|
||
)
|
||
return False
|
||
|
||
reply_segments = self._post_process_reply_text(reply_text)
|
||
combined_reply_text = "".join(reply_segments)
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 回复后处理完成: "
|
||
f"目标消息编号={target_message_id} 分段数={len(reply_segments)} "
|
||
f"分段内容={reply_segments!r}"
|
||
)
|
||
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 正在发送引导回复: "
|
||
f"目标消息编号={target_message_id} 引用回复={quote_reply} 回复分段={reply_segments!r}"
|
||
)
|
||
try:
|
||
sent = False
|
||
for index, segment in enumerate(reply_segments):
|
||
sent = await send_service.text_to_stream(
|
||
text=segment,
|
||
stream_id=self._runtime.session_id,
|
||
set_reply=quote_reply if index == 0 else False,
|
||
reply_message=target_message if quote_reply and index == 0 else None,
|
||
selected_expressions=reply_result.selected_expression_ids or None,
|
||
typing=index > 0,
|
||
)
|
||
if not sent:
|
||
break
|
||
except Exception:
|
||
logger.exception(
|
||
f"{self._runtime.log_prefix} 发送文字消息时发生异常，目标消息编号={target_message_id}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "发送可见回复时发生异常。")
|
||
)
|
||
return False
|
||
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 引导回复发送结果: "
|
||
f"目标消息编号={target_message_id} 发送成功={sent}"
|
||
)
|
||
tool_result = "可见回复已生成并发送。" if sent else "可见回复生成成功，但发送失败。"
|
||
self._runtime._chat_history.append(self._build_tool_message(tool_call, tool_result))
|
||
if not sent:
|
||
return False
|
||
|
||
target_user_info = target_message.message_info.user_info
|
||
target_user_name = (
|
||
target_user_info.user_cardname
|
||
or target_user_info.user_nickname
|
||
or target_user_info.user_id
|
||
)
|
||
if self._runtime.chat_stream is not None:
|
||
await database_api.store_tool_info(
|
||
chat_stream=self._runtime.chat_stream,
|
||
display_prompt=f"你对{target_user_name}进行了回复：{combined_reply_text}",
|
||
tool_data={
|
||
"msg_id": target_message_id,
|
||
"quote": quote_reply,
|
||
"reply_text": combined_reply_text,
|
||
"reply_segments": reply_segments,
|
||
},
|
||
tool_name="reply",
|
||
tool_reasoning=latest_thought,
|
||
)
|
||
|
||
bot_name = global_config.bot.nickname.strip() or "MaiSaka"
|
||
reply_timestamp = datetime.now()
|
||
planner_prefix = (
|
||
f"[时间]{reply_timestamp.strftime('%H:%M:%S')}\n"
|
||
f"[用户]{bot_name}\n"
|
||
"[用户群昵称]\n"
|
||
"[msg_id]\n"
|
||
"[发言内容]"
|
||
)
|
||
history_message = SessionBackedMessage(
|
||
raw_message=MessageSequence([TextComponent(f"{planner_prefix}{combined_reply_text}")]),
|
||
visible_text="",
|
||
timestamp=reply_timestamp,
|
||
source_kind="guided_reply",
|
||
)
|
||
visible_reply_text = format_speaker_content(
|
||
bot_name,
|
||
combined_reply_text,
|
||
reply_timestamp,
|
||
)
|
||
history_message.visible_text = visible_reply_text
|
||
self._runtime._chat_history.append(history_message)
|
||
return True
|
||
|
||
async def _handle_send_emoji(self, tool_call: ToolCall, anchor_message: SessionMessage) -> None:
|
||
"""处理发送表情包的工具调用。
|
||
|
||
Args:
|
||
tool_call: 工具调用对象
|
||
anchor_message: 锚点消息
|
||
"""
|
||
from src.chat.emoji_system.emoji_manager import emoji_manager
|
||
from src.common.utils.utils_image import ImageUtils
|
||
import random
|
||
|
||
tool_args = tool_call.args or {}
|
||
emotion = str(tool_args.get("emotion") or "").strip()
|
||
|
||
logger.info(f"{self._runtime.log_prefix} 已触发表情包发送工具: 情绪={emotion!r}")
|
||
|
||
# 获取表情包列表
|
||
if not emoji_manager.emojis:
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "当前表情包库中没有可用表情。")
|
||
)
|
||
return
|
||
|
||
# 根据情感选择表情包
|
||
selected_emoji = None
|
||
if emotion:
|
||
# 尝试找到匹配情感的表情包
|
||
matching_emojis = [
|
||
emoji for emoji in emoji_manager.emojis
|
||
if emotion.lower() in (e.lower() for e in emoji.emotion)
|
||
]
|
||
if matching_emojis:
|
||
selected_emoji = random.choice(matching_emojis)
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 找到 {len(matching_emojis)} 个匹配情绪 {emotion!r} 的表情包，"
|
||
f"已选择：{selected_emoji.description}"
|
||
)
|
||
|
||
# 如果没有找到匹配的情感表情包，随机选择一个
|
||
if selected_emoji is None:
|
||
selected_emoji = random.choice(emoji_manager.emojis)
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 没有表情包匹配情绪 {emotion!r}，"
|
||
f"已随机选择：{selected_emoji.description}"
|
||
)
|
||
|
||
# 更新表情包使用次数
|
||
emoji_manager.update_emoji_usage(selected_emoji)
|
||
|
||
# 获取表情包的 base64 数据
|
||
try:
|
||
emoji_base64 = ImageUtils.image_path_to_base64(str(selected_emoji.full_path))
|
||
if not emoji_base64:
|
||
raise ValueError("表情图片转换为 base64 失败")
|
||
except Exception as exc:
|
||
logger.error(
|
||
f"{self._runtime.log_prefix} 表情图片转换为 base64 失败: {exc}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, f"发送表情包失败：{exc}")
|
||
)
|
||
return
|
||
|
||
# 发送表情包
|
||
try:
|
||
sent = await send_service.emoji_to_stream(
|
||
emoji_base64=emoji_base64,
|
||
stream_id=self._runtime.session_id,
|
||
storage_message=True,
|
||
set_reply=False,
|
||
reply_message=None,
|
||
)
|
||
except Exception as exc:
|
||
logger.exception(
|
||
f"{self._runtime.log_prefix} 发送表情包时发生异常: {exc}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, f"发送表情包时发生异常：{exc}")
|
||
)
|
||
return
|
||
|
||
if sent:
|
||
logger.info(
|
||
f"{self._runtime.log_prefix} 表情包发送成功: "
|
||
f"描述={selected_emoji.description!r} 情绪标签={selected_emoji.emotion}"
|
||
)
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(
|
||
tool_call,
|
||
f"已发送表情包：{selected_emoji.description}（情绪：{', '.join(selected_emoji.emotion)}）"
|
||
)
|
||
)
|
||
else:
|
||
logger.warning(f"{self._runtime.log_prefix} 表情包发送失败")
|
||
self._runtime._chat_history.append(
|
||
self._build_tool_message(tool_call, "发送表情包失败。")
|
||
)
|
||
|
||
def _build_tool_message(self, tool_call: ToolCall, content: str) -> ToolResultMessage:
|
||
return ToolResultMessage(
|
||
content=content,
|
||
timestamp=datetime.now(),
|
||
tool_call_id=tool_call.call_id,
|
||
tool_name=tool_call.func_name,
|
||
)
|