feat:添加提及必回，部分尺寸过大自动重试，移除无用配置项，正确解析at消息
This commit is contained in:
SengokuCola
@@ -55,7 +55,7 @@ CONFIG_DIR: Path = PROJECT_ROOT / "config"
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BOT_CONFIG_PATH: Path = (CONFIG_DIR / "bot_config.toml").resolve().absolute()
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MODEL_CONFIG_PATH: Path = (CONFIG_DIR / "model_config.toml").resolve().absolute()
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MMC_VERSION: str = "1.0.0"
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CONFIG_VERSION: str = "8.3.2"
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CONFIG_VERSION: str = "8.3.4"
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MODEL_CONFIG_VERSION: str = "1.13.1"
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logger = get_logger("config")
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@@ -346,6 +346,10 @@ def try_migrate_legacy_bot_config_dict(data: dict[str, Any]) -> MigrationResult:
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if chat is None:
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chat = {}
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data["chat"] = chat
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elif "private_plan_style" in chat:
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chat.pop("private_plan_style", None)
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migrated_any = True
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reasons.append("chat.private_plan_style_removed")
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mem = _as_dict(data.get("memory"))
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if mem is not None:
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@@ -366,7 +370,12 @@ def try_migrate_legacy_bot_config_dict(data: dict[str, Any]) -> MigrationResult:
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migrated_any = True
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reasons.append("experimental.chat_prompts")
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for key in ("private_plan_style", "group_chat_prompt", "private_chat_prompts", "chat_prompts"):
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if "private_plan_style" in exp:
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exp.pop("private_plan_style", None)
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migrated_any = True
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reasons.append("experimental.private_plan_style_removed")
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for key in ("group_chat_prompt", "private_chat_prompts", "chat_prompts"):
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if key in exp and key not in chat:
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chat[key] = exp[key]
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migrated_any = True
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@@ -113,21 +113,6 @@ class PersonalityConfig(ConfigBase):
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)
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"""每次构建回复时，从 multiple_reply_style 中随机替换 reply_style 的概率（0.0-1.0）"""
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plan_style: str = Field(
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default=(
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"1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用"
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"2.如果相同的action已经被执行，请不要重复执行该action"
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"3.如果有人对你感到厌烦，请减少回复"
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"4.如果有人在追问你，或者话题没有说完，请你继续回复"
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"5.请分析哪些对话是和你说的，哪些是其他人之间的互动，不要误认为其他人之间的互动是和你说的"
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),
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json_schema_extra={
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"x-widget": "textarea",
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"x-icon": "book-open",
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},
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)
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"""_wrap_麦麦的说话规则和行为规则"""
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visual_style: str = Field(
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default="请用中文描述这张图片的内容。如果有文字，请把文字描述概括出来，请留意其主题，直观感受，输出为一段平文本，最多30字，请注意不要分点，就输出一段文本",
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json_schema_extra={
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@@ -242,20 +227,6 @@ class ChatConfig(ConfigBase):
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},
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)
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"""每个聊天流最大保存的Plan/Reply日志数量，超过此数量时会自动删除最老的日志"""
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private_plan_style: str = Field(
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default=(
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"1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用\n"
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"2.如果相同的内容已经被执行，请不要重复执行\n"
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"3.某句话如果已经被回复过，不要重复回复"
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),
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json_schema_extra={
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"x-widget": "textarea",
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"x-icon": "user",
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},
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)
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"""_wrap_私聊说话规则，行为风格"""
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group_chat_prompt: str = Field(
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default="你需要控制自己发言的频率，如果是一对一聊天，可以以较均匀的频率发言；如果用户较多，不要每句都回复，控制回复频率，不要回复的太频繁！控制回复的频率，不要每个人的消息都回复。",
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json_schema_extra={
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@@ -2,14 +2,14 @@ from dataclasses import dataclass
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from enum import Enum
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from typing import Any, Callable, Dict, List, Optional, Set, Tuple
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from rich.traceback import install
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import asyncio
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import random
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import re
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import time
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import traceback
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from rich.traceback import install
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from src.common.logger import get_logger
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from src.common.data_models.llm_service_data_models import (
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LLMAudioTranscriptionResult,
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@@ -51,6 +51,13 @@ install(extra_lines=3)
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logger = get_logger("model_utils")
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DATA_URI_LIMIT_PATTERN = re.compile(
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r"Exceeded limit on max bytes per data-uri item\s*:\s*(?P<limit>\d+)",
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re.IGNORECASE,
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)
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DATA_URI_RETRY_MARGIN_BYTES = 128 * 1024
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MIN_COMPRESSED_IMAGE_TARGET_SIZE_BYTES = 512 * 1024
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class RequestType(Enum):
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"""请求类型枚举"""
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@@ -132,6 +139,49 @@ class LLMOrchestrator:
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f" 如果你认为该警告出现得过于频繁，请调整model_config.toml中对应任务的slow_threshold至符合你实际情况的合理值"
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)
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@staticmethod
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def _can_retry_with_compressed_images(
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active_request: ClientRequest,
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original_response_request: ResponseRequest | None,
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) -> bool:
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"""判断当前请求是否还可以通过压缩图片进行一次兜底重试。"""
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return (
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isinstance(active_request, ResponseRequest)
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and bool(active_request.message_list)
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and original_response_request is not None
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and active_request.message_list == original_response_request.message_list
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)
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@staticmethod
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def _extract_data_uri_limit_bytes(error: RespNotOkException) -> int | None:
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"""从兼容 OpenAI 的错误文本中提取 data URI 单项大小限制。"""
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candidate_messages = [error.message, str(error)]
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if error.__cause__ is not None:
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candidate_messages.append(str(error.__cause__))
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for candidate_message in candidate_messages:
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if not candidate_message:
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continue
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match = DATA_URI_LIMIT_PATTERN.search(candidate_message)
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if match is None:
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continue
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try:
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return int(match.group("limit"))
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except (TypeError, ValueError):
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return None
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return None
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@staticmethod
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def _build_data_uri_retry_target_size(limit_bytes: int) -> int:
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"""根据上游返回的 data URI 上限，计算压缩重试的安全目标值。"""
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return max(
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MIN_COMPRESSED_IMAGE_TARGET_SIZE_BYTES,
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limit_bytes - DATA_URI_RETRY_MARGIN_BYTES,
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)
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@staticmethod
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def _build_generation_result(
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content: str,
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@@ -735,6 +785,11 @@ class LLMOrchestrator:
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task_display = self.request_type or "未知任务"
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# 可重试的HTTP错误
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can_retry_with_compression = self._can_retry_with_compressed_images(
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active_request,
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original_response_request,
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)
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if e.status_code == 429 or e.status_code >= 500:
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retry_remain -= 1
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if retry_remain <= 0:
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@@ -750,12 +805,23 @@ class LLMOrchestrator:
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continue
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# 特殊处理413，尝试压缩
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data_uri_limit_bytes = self._extract_data_uri_limit_bytes(e)
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if data_uri_limit_bytes is not None and can_retry_with_compression:
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target_size = self._build_data_uri_retry_target_size(data_uri_limit_bytes)
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logger.warning(
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f"任务 '{task_display}' 的模型 '{model_info.name}' 返回 data URI 图片过大错误，"
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f"检测到单项上限 {data_uri_limit_bytes} 字节，尝试压缩图片后重试..."
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)
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compressed_messages = compress_messages(
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active_request.message_list,
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img_target_size=target_size,
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)
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active_request = active_request.copy_with(message_list=compressed_messages)
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continue
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if (
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e.status_code == 413
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and isinstance(active_request, ResponseRequest)
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and active_request.message_list
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and original_response_request is not None
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and active_request.message_list == original_response_request.message_list
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and can_retry_with_compression
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):
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logger.warning(
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f"任务 '{task_display}' 的模型 '{model_info.name}' 返回413请求体过大，尝试压缩后重试..."
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@@ -191,6 +191,7 @@ async def handle_tool(
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else:
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for sent_message in sent_messages:
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tool_ctx.append_sent_message_to_chat_history(sent_message)
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tool_ctx.runtime._clear_force_continue_until_reply()
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return tool_ctx.build_success_result(
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invocation.tool_name,
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"回复已生成并发送。",
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@@ -78,6 +78,24 @@ def _append_reply_component(builder: MessageBuilder, component: ReplyComponent)
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return True
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def _render_at_component_text(component: AtComponent) -> str:
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"""灏?AtComponent 娓叉煋涓烘枃鏈舰寮忋€?"""
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target_name = component.target_user_cardname or component.target_user_nickname or component.target_user_id
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return f"@{target_name}".strip()
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def _append_at_component(builder: MessageBuilder, component: AtComponent) -> bool:
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"""灏?@ 缁勪欢杞崲涓烘枃鏈苟鍐欏叆 LLM 娑堟伅銆?"""
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rendered_text = _render_at_component_text(component)
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if not rendered_text:
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return False
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builder.add_text_content(rendered_text)
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return True
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def contains_complex_message(message_sequence: MessageSequence) -> bool:
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"""判断消息序列中是否包含复杂消息组件。"""
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@@ -119,8 +137,7 @@ def _render_component_for_prompt(component: StandardMessageComponents) -> str:
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return component.content.strip() if component.content else "[语音消息]"
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if isinstance(component, AtComponent):
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target_name = component.target_user_cardname or component.target_user_nickname or component.target_user_id
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return f"@{target_name}".strip()
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return _render_at_component_text(component)
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if isinstance(component, ReplyComponent):
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sender_name = (
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@@ -224,6 +241,10 @@ def _build_message_from_sequence(
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has_content = _append_image_component(builder, component) or has_content
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continue
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if isinstance(component, AtComponent):
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has_content = _append_at_component(builder, component) or has_content
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continue
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if isinstance(component, ReplyComponent):
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has_content = _append_reply_component(builder, component) or has_content
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continue
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@@ -3,9 +3,11 @@
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from copy import deepcopy
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from datetime import datetime
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from typing import Optional
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import re
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from src.common.data_models.message_component_data_model import (
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AtComponent,
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EmojiComponent,
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ImageComponent,
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MessageSequence,
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@@ -26,13 +28,15 @@ def format_speaker_content(
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message_id: Optional[str] = None,
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) -> str:
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"""将可见文本格式化为带说话人前缀的样式。"""
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time_prefix = timestamp.strftime("%H:%M:%S") if timestamp is not None else ""
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message_id_prefix = f"[msg_id:{message_id}]" if message_id else ""
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return f"{time_prefix}{message_id_prefix}[{speaker_name}]{content}"
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def parse_speaker_content(content: str) -> tuple[Optional[str], str]:
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"""解析形如 [speaker]message 的可见文本。"""
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"""解析形如 `[speaker]message` 的可见文本。"""
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match = SPEAKER_PREFIX_PATTERN.match(content or "")
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if not match:
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return None, content or ""
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@@ -41,11 +45,20 @@ def parse_speaker_content(content: str) -> tuple[Optional[str], str]:
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def clone_message_sequence(message_sequence: MessageSequence) -> MessageSequence:
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"""复制消息片段序列。"""
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return MessageSequence([deepcopy(component) for component in message_sequence.components])
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def _render_at_component_text(component: AtComponent) -> str:
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"""将 AtComponent 渲染为文本。"""
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target_name = component.target_user_cardname or component.target_user_nickname or component.target_user_id
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return f"@{target_name}".strip()
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def build_visible_text_from_sequence(message_sequence: MessageSequence) -> str:
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"""从消息片段序列提取可见文本。"""
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parts: list[str] = []
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for component in message_sequence.components:
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if isinstance(component, TextComponent):
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@@ -73,6 +86,10 @@ def build_visible_text_from_sequence(message_sequence: MessageSequence) -> str:
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parts.append(component.content or "[图片]")
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continue
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if isinstance(component, AtComponent):
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parts.append(_render_at_component_text(component))
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continue
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if isinstance(component, ReplyComponent):
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target_message_id = component.target_message_id.strip()
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if target_message_id:
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@@ -23,9 +23,11 @@ from src.services import database_service as database_api
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from .builtin_tool import get_action_tool_specs
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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_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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@@ -229,6 +231,9 @@ class MaisakaReasoningEngine:
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) -> tuple[Literal["continue", "no_reply", "wait"], Any, list[str]]:
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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_continue_until_reply:
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return self._build_forced_continue_timing_result()
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response = await self._run_interruptible_sub_agent(
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context_message_limit=TIMING_GATE_CONTEXT_LIMIT,
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system_prompt=self._build_timing_gate_system_prompt(),
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@@ -264,191 +269,210 @@ class MaisakaReasoningEngine:
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return "continue", response, tool_result_summaries
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return timing_action, response, tool_result_summaries
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def _build_forced_continue_timing_result(self) -> tuple[Literal["continue"], ChatResponse, list[str]]:
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"""构造跳过 Timing Gate 时使用的伪 continue 结果。"""
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reason = self._runtime._build_force_continue_timing_reason()
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logger.info(f"{self._runtime.log_prefix} {reason}")
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return (
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"continue",
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ChatResponse(
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content=reason,
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tool_calls=[],
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raw_message=AssistantMessage(
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content="",
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timestamp=datetime.now(),
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source_kind="perception",
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),
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selected_history_count=min(
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sum(1 for message in self._runtime._chat_history if message.count_in_context),
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self._runtime._max_context_size,
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),
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prompt_tokens=0,
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built_message_count=0,
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completion_tokens=0,
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total_tokens=0,
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prompt_section=None,
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),
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[f"- continue [强制跳过]: {reason}"],
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)
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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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queue_item_done_count = 0
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try:
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queued_trigger = await self._runtime._internal_turn_queue.get()
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(
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message_triggered,
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timeout_triggered,
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queue_item_done_count,
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) = self._drain_ready_turn_triggers(queued_trigger)
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queued_trigger = await self._runtime._internal_turn_queue.get()
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message_triggered, timeout_triggered = self._drain_ready_turn_triggers(queued_trigger)
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if message_triggered:
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await self._runtime._wait_for_message_quiet_period()
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self._runtime._message_turn_scheduled = False
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if message_triggered:
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await self._runtime._wait_for_message_quiet_period()
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self._runtime._message_turn_scheduled = False
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cached_messages = (
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self._runtime._collect_pending_messages()
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if self._runtime._has_pending_messages()
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else []
|
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)
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if not timeout_triggered and not cached_messages and not message_triggered:
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cached_messages = (
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self._runtime._collect_pending_messages()
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if self._runtime._has_pending_messages()
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else []
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)
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if not timeout_triggered and not cached_messages and not message_triggered:
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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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asyncio.create_task(self._runtime._trigger_batch_learning(cached_messages))
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self._append_wait_interrupted_message_if_needed()
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||||
await self._ingest_messages(cached_messages)
|
||||
anchor_message = cached_messages[-1]
|
||||
else:
|
||||
anchor_message = self._get_timeout_anchor_message()
|
||||
if anchor_message is None:
|
||||
logger.warning(
|
||||
f"{self._runtime.log_prefix} 等待超时后缺少可复用的锚点消息，跳过本轮继续思考"
|
||||
)
|
||||
continue
|
||||
logger.info(f"{self._runtime.log_prefix} 等待超时后开始新一轮思考")
|
||||
if self._runtime._pending_wait_tool_call_id:
|
||||
self._runtime._chat_history.append(self._build_wait_timeout_message())
|
||||
self._trim_chat_history()
|
||||
|
||||
self._runtime._agent_state = self._runtime._STATE_RUNNING
|
||||
if cached_messages:
|
||||
asyncio.create_task(self._runtime._trigger_batch_learning(cached_messages))
|
||||
self._append_wait_interrupted_message_if_needed()
|
||||
await self._ingest_messages(cached_messages)
|
||||
anchor_message = cached_messages[-1]
|
||||
else:
|
||||
anchor_message = self._get_timeout_anchor_message()
|
||||
if anchor_message is None:
|
||||
logger.warning(
|
||||
f"{self._runtime.log_prefix} 等待超时后缺少可复用的锚点消息，跳过本轮继续思考"
|
||||
)
|
||||
continue
|
||||
logger.info(f"{self._runtime.log_prefix} 等待超时后开始新一轮思考")
|
||||
if self._runtime._pending_wait_tool_call_id:
|
||||
self._runtime._chat_history.append(self._build_wait_timeout_message())
|
||||
self._trim_chat_history()
|
||||
try:
|
||||
for round_index in range(self._runtime._max_internal_rounds):
|
||||
cycle_detail = self._start_cycle()
|
||||
self._runtime._log_cycle_started(cycle_detail, round_index)
|
||||
await emit_cycle_start(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
round_index=round_index,
|
||||
max_rounds=self._runtime._max_internal_rounds,
|
||||
history_count=len(self._runtime._chat_history),
|
||||
)
|
||||
planner_started_at = 0.0
|
||||
try:
|
||||
visual_refresh_started_at = time.time()
|
||||
refreshed_message_count = await self._refresh_chat_history_visual_placeholders()
|
||||
cycle_detail.time_records["visual_refresh"] = time.time() - visual_refresh_started_at
|
||||
if refreshed_message_count > 0:
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 本轮思考前已刷新 {refreshed_message_count} 条视觉占位历史消息"
|
||||
)
|
||||
|
||||
try:
|
||||
for round_index in range(self._runtime._max_internal_rounds):
|
||||
cycle_detail = self._start_cycle()
|
||||
self._runtime._log_cycle_started(cycle_detail, round_index)
|
||||
await emit_cycle_start(
|
||||
timing_started_at = time.time()
|
||||
timing_action, timing_response, timing_tool_results = await self._run_timing_gate(anchor_message)
|
||||
timing_duration_ms = (time.time() - timing_started_at) * 1000
|
||||
cycle_detail.time_records["timing_gate"] = timing_duration_ms / 1000
|
||||
await emit_timing_gate_result(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
round_index=round_index,
|
||||
max_rounds=self._runtime._max_internal_rounds,
|
||||
history_count=len(self._runtime._chat_history),
|
||||
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,
|
||||
)
|
||||
planner_started_at = 0.0
|
||||
try:
|
||||
visual_refresh_started_at = time.time()
|
||||
refreshed_message_count = await self._refresh_chat_history_visual_placeholders()
|
||||
cycle_detail.time_records["visual_refresh"] = time.time() - visual_refresh_started_at
|
||||
if refreshed_message_count > 0:
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 本轮思考前已刷新 {refreshed_message_count} 条视觉占位历史消息"
|
||||
)
|
||||
|
||||
timing_started_at = time.time()
|
||||
timing_action, timing_response, timing_tool_results = await self._run_timing_gate(anchor_message)
|
||||
timing_duration_ms = (time.time() - timing_started_at) * 1000
|
||||
cycle_detail.time_records["timing_gate"] = timing_duration_ms / 1000
|
||||
await emit_timing_gate_result(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
action=timing_action,
|
||||
content=timing_response.content,
|
||||
tool_calls=timing_response.tool_calls,
|
||||
messages=[],
|
||||
prompt_tokens=timing_response.prompt_tokens,
|
||||
selected_history_count=timing_response.selected_history_count,
|
||||
duration_ms=timing_duration_ms,
|
||||
)
|
||||
self._runtime._render_context_usage_panel(
|
||||
selected_history_count=timing_response.selected_history_count,
|
||||
prompt_tokens=timing_response.prompt_tokens,
|
||||
planner_response=timing_response.content or "",
|
||||
tool_calls=timing_response.tool_calls,
|
||||
tool_results=timing_tool_results,
|
||||
prompt_section=timing_response.prompt_section,
|
||||
)
|
||||
if timing_action != "continue":
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} Timing Gate 结束当前回合: "
|
||||
f"回合={round_index + 1} 动作={timing_action}"
|
||||
)
|
||||
break
|
||||
|
||||
planner_started_at = time.time()
|
||||
action_tool_definitions = await self._build_action_tool_definitions()
|
||||
self._runtime._render_context_usage_panel(
|
||||
selected_history_count=timing_response.selected_history_count,
|
||||
prompt_tokens=timing_response.prompt_tokens,
|
||||
planner_response=timing_response.content or "",
|
||||
tool_calls=timing_response.tool_calls,
|
||||
tool_results=timing_tool_results,
|
||||
prompt_section=timing_response.prompt_section,
|
||||
)
|
||||
if timing_action != "continue":
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 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}"
|
||||
f"{self._runtime.log_prefix} Timing Gate 结束当前回合: "
|
||||
f"回合={round_index + 1} 动作={timing_action}"
|
||||
)
|
||||
response = await self._run_interruptible_planner(
|
||||
tool_definitions=action_tool_definitions,
|
||||
break
|
||||
|
||||
planner_started_at = time.time()
|
||||
action_tool_definitions = await self._build_action_tool_definitions()
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 规划器开始执行: "
|
||||
f"回合={round_index + 1} "
|
||||
f"历史消息数={len(self._runtime._chat_history)} "
|
||||
f"开始时间={planner_started_at:.3f}"
|
||||
)
|
||||
response = await self._run_interruptible_planner(
|
||||
tool_definitions=action_tool_definitions,
|
||||
)
|
||||
planner_duration_ms = (time.time() - planner_started_at) * 1000
|
||||
cycle_detail.time_records["planner"] = planner_duration_ms / 1000
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 规划器执行完成: "
|
||||
f"回合={round_index + 1} "
|
||||
f"耗时={cycle_detail.time_records['planner']:.3f} 秒"
|
||||
)
|
||||
await emit_planner_response(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
content=response.content,
|
||||
tool_calls=response.tool_calls,
|
||||
prompt_tokens=response.prompt_tokens,
|
||||
completion_tokens=response.completion_tokens,
|
||||
total_tokens=response.total_tokens,
|
||||
duration_ms=planner_duration_ms,
|
||||
)
|
||||
|
||||
reasoning_content = response.content or ""
|
||||
if self._should_replace_reasoning(reasoning_content):
|
||||
response.content = "我应该根据我上面思考的内容进行反思，重新思考我下一步的行动，我需要分析当前场景，对话，以及我可以使用的工具，然后先输出想法再使用工具"
|
||||
response.raw_message.content = "我应该根据我上面思考的内容进行反思，重新思考我下一步的行动，我需要分析当前场景，对话，以及我可以使用的工具，然后先输出想法再使用工具"
|
||||
logger.info(f"{self._runtime.log_prefix} 当前思考与上一轮过于相似，已替换为重新思考提示")
|
||||
|
||||
self._last_reasoning_content = reasoning_content
|
||||
self._runtime._chat_history.append(response.raw_message)
|
||||
tool_result_summaries: list[str] = []
|
||||
|
||||
if response.tool_calls:
|
||||
tool_started_at = time.time()
|
||||
should_pause, tool_result_summaries = await self._handle_tool_calls(
|
||||
response.tool_calls,
|
||||
response.content or "",
|
||||
anchor_message,
|
||||
)
|
||||
planner_duration_ms = (time.time() - planner_started_at) * 1000
|
||||
cycle_detail.time_records["planner"] = planner_duration_ms / 1000
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 规划器执行完成: "
|
||||
f"回合={round_index + 1} "
|
||||
f"耗时={cycle_detail.time_records['planner']:.3f} 秒"
|
||||
)
|
||||
await emit_planner_response(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
content=response.content,
|
||||
tool_calls=response.tool_calls,
|
||||
prompt_tokens=response.prompt_tokens,
|
||||
completion_tokens=response.completion_tokens,
|
||||
total_tokens=response.total_tokens,
|
||||
duration_ms=planner_duration_ms,
|
||||
)
|
||||
|
||||
reasoning_content = response.content or ""
|
||||
if self._should_replace_reasoning(reasoning_content):
|
||||
response.content = "我应该根据我上面思考的内容进行反思，重新思考我下一步的行动，我需要分析当前场景，对话，以及我可以使用的工具，然后先输出想法再使用工具"
|
||||
response.raw_message.content = "我应该根据我上面思考的内容进行反思，重新思考我下一步的行动，我需要分析当前场景，对话，以及我可以使用的工具，然后先输出想法再使用工具"
|
||||
logger.info(f"{self._runtime.log_prefix} 当前思考与上一轮过于相似，已替换为重新思考提示")
|
||||
|
||||
self._last_reasoning_content = reasoning_content
|
||||
self._runtime._chat_history.append(response.raw_message)
|
||||
tool_result_summaries: list[str] = []
|
||||
|
||||
if response.tool_calls:
|
||||
tool_started_at = time.time()
|
||||
should_pause, tool_result_summaries = await self._handle_tool_calls(
|
||||
response.tool_calls,
|
||||
response.content or "",
|
||||
anchor_message,
|
||||
)
|
||||
cycle_detail.time_records["tool_calls"] = time.time() - tool_started_at
|
||||
self._runtime._render_context_usage_panel(
|
||||
selected_history_count=response.selected_history_count,
|
||||
prompt_tokens=response.prompt_tokens,
|
||||
planner_response=response.content or "",
|
||||
tool_calls=response.tool_calls,
|
||||
tool_results=tool_result_summaries,
|
||||
prompt_section=response.prompt_section,
|
||||
)
|
||||
if should_pause:
|
||||
break
|
||||
continue
|
||||
|
||||
cycle_detail.time_records["tool_calls"] = time.time() - tool_started_at
|
||||
self._runtime._render_context_usage_panel(
|
||||
selected_history_count=response.selected_history_count,
|
||||
prompt_tokens=response.prompt_tokens,
|
||||
planner_response=response.content or "",
|
||||
tool_calls=response.tool_calls,
|
||||
tool_results=tool_result_summaries,
|
||||
prompt_section=response.prompt_section,
|
||||
)
|
||||
if not response.content:
|
||||
if should_pause:
|
||||
break
|
||||
except ReqAbortException:
|
||||
interrupted_at = time.time()
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 规划器打断成功: "
|
||||
f"回合={round_index + 1} "
|
||||
f"开始时间={planner_started_at:.3f} "
|
||||
f"打断时间={interrupted_at:.3f} "
|
||||
f"耗时={interrupted_at - planner_started_at:.3f} 秒"
|
||||
)
|
||||
continue
|
||||
|
||||
self._runtime._render_context_usage_panel(
|
||||
selected_history_count=response.selected_history_count,
|
||||
prompt_tokens=response.prompt_tokens,
|
||||
planner_response=response.content or "",
|
||||
prompt_section=response.prompt_section,
|
||||
)
|
||||
if not response.content:
|
||||
break
|
||||
finally:
|
||||
self._end_cycle(cycle_detail)
|
||||
await emit_cycle_end(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
time_records=dict(cycle_detail.time_records),
|
||||
agent_state=self._runtime._agent_state,
|
||||
)
|
||||
finally:
|
||||
if self._runtime._agent_state == self._runtime._STATE_RUNNING:
|
||||
self._runtime._agent_state = self._runtime._STATE_STOP
|
||||
except ReqAbortException:
|
||||
interrupted_at = time.time()
|
||||
logger.info(
|
||||
f"{self._runtime.log_prefix} 规划器打断成功: "
|
||||
f"回合={round_index + 1} "
|
||||
f"开始时间={planner_started_at:.3f} "
|
||||
f"打断时间={interrupted_at:.3f} "
|
||||
f"耗时={interrupted_at - planner_started_at:.3f} 秒"
|
||||
)
|
||||
break
|
||||
finally:
|
||||
self._end_cycle(cycle_detail)
|
||||
await emit_cycle_end(
|
||||
session_id=self._runtime.session_id,
|
||||
cycle_id=cycle_detail.cycle_id,
|
||||
time_records=dict(cycle_detail.time_records),
|
||||
agent_state=self._runtime._agent_state,
|
||||
)
|
||||
finally:
|
||||
for _ in range(queue_item_done_count):
|
||||
self._runtime._internal_turn_queue.task_done()
|
||||
if self._runtime._agent_state == self._runtime._STATE_RUNNING:
|
||||
self._runtime._agent_state = self._runtime._STATE_STOP
|
||||
except asyncio.CancelledError:
|
||||
self._runtime._log_internal_loop_cancelled()
|
||||
raise
|
||||
@@ -460,10 +484,9 @@ class MaisakaReasoningEngine:
|
||||
def _drain_ready_turn_triggers(
|
||||
self,
|
||||
queued_trigger: Literal["message", "timeout"],
|
||||
) -> tuple[bool, bool, int]:
|
||||
) -> tuple[bool, bool]:
|
||||
"""合并当前已就绪的 turn 触发信号。"""
|
||||
|
||||
queue_item_done_count = 1
|
||||
message_triggered = queued_trigger == "message"
|
||||
timeout_triggered = queued_trigger == "timeout"
|
||||
|
||||
@@ -473,7 +496,6 @@ class MaisakaReasoningEngine:
|
||||
except asyncio.QueueEmpty:
|
||||
break
|
||||
|
||||
queue_item_done_count += 1
|
||||
if next_trigger == "message":
|
||||
message_triggered = True
|
||||
continue
|
||||
@@ -481,11 +503,7 @@ class MaisakaReasoningEngine:
|
||||
timeout_triggered = True
|
||||
continue
|
||||
|
||||
if message_triggered:
|
||||
# 这些消息触发将由当前 turn 接手，旧的事件位不应再污染后续 wait 判定。
|
||||
self._runtime._new_message_event.clear()
|
||||
|
||||
return message_triggered, timeout_triggered, queue_item_done_count
|
||||
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。"""
|
||||
|
||||
@@ -13,6 +13,7 @@ from src.cli.console import console
|
||||
from src.chat.heart_flow.heartFC_utils import CycleDetail
|
||||
from src.chat.message_receive.chat_manager import BotChatSession, chat_manager
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.utils.utils import is_mentioned_bot_in_message
|
||||
from src.common.data_models.mai_message_data_model import GroupInfo, UserInfo
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_config import ExpressionConfigUtils
|
||||
@@ -72,8 +73,6 @@ class MaisakaHeartFlowChatting:
|
||||
self._running = False
|
||||
self._cycle_counter = 0
|
||||
self._internal_loop_task: Optional[asyncio.Task] = None
|
||||
self._loop_task: Optional[asyncio.Task] = None
|
||||
self._new_message_event = asyncio.Event()
|
||||
self._message_turn_scheduled = False
|
||||
self._message_debounce_seconds = 1.0
|
||||
self._message_debounce_required = False
|
||||
@@ -84,6 +83,9 @@ class MaisakaHeartFlowChatting:
|
||||
self._agent_state: Literal["running", "wait", "stop"] = self._STATE_STOP
|
||||
self._wait_until: Optional[float] = None
|
||||
self._pending_wait_tool_call_id: Optional[str] = None
|
||||
self._force_continue_until_reply = False
|
||||
self._force_continue_trigger_message_id = ""
|
||||
self._force_continue_trigger_reason = ""
|
||||
self._planner_interrupt_flag: Optional[asyncio.Event] = None
|
||||
self._planner_interrupt_requested = False
|
||||
self._planner_interrupt_consecutive_count = 0
|
||||
@@ -118,6 +120,7 @@ class MaisakaHeartFlowChatting:
|
||||
|
||||
self._running = True
|
||||
self._ensure_background_tasks_running()
|
||||
self._schedule_message_turn()
|
||||
logger.info(f"{self.log_prefix} Maisaka 运行时已启动")
|
||||
|
||||
async def stop(self) -> None:
|
||||
@@ -126,22 +129,12 @@ class MaisakaHeartFlowChatting:
|
||||
return
|
||||
|
||||
self._running = False
|
||||
self._new_message_event.set()
|
||||
self._message_turn_scheduled = False
|
||||
self._message_debounce_required = False
|
||||
self._cancel_wait_timeout_task()
|
||||
while not self._internal_turn_queue.empty():
|
||||
_ = self._internal_turn_queue.get_nowait()
|
||||
|
||||
if self._loop_task is not None:
|
||||
self._loop_task.cancel()
|
||||
try:
|
||||
await self._loop_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
finally:
|
||||
self._loop_task = None
|
||||
|
||||
if self._internal_loop_task is not None:
|
||||
self._internal_loop_task.cancel()
|
||||
try:
|
||||
@@ -166,6 +159,7 @@ class MaisakaHeartFlowChatting:
|
||||
if self._running:
|
||||
self._ensure_background_tasks_running()
|
||||
self._last_message_received_at = time.time()
|
||||
self._update_message_trigger_state(message)
|
||||
self.message_cache.append(message)
|
||||
self._source_messages_by_id[message.message_id] = message
|
||||
if self._agent_state == self._STATE_WAIT:
|
||||
@@ -199,7 +193,78 @@ class MaisakaHeartFlowChatting:
|
||||
f"{self._planner_interrupt_max_consecutive_count}"
|
||||
)
|
||||
self._planner_interrupt_flag.set()
|
||||
self._new_message_event.set()
|
||||
if self._running:
|
||||
self._schedule_message_turn()
|
||||
|
||||
def _update_message_trigger_state(self, message: SessionMessage) -> None:
|
||||
"""补齐消息中的 @/提及 标记，并在命中时启用强制 continue。"""
|
||||
|
||||
detected_mentioned, detected_at, _ = is_mentioned_bot_in_message(message)
|
||||
if detected_at:
|
||||
message.is_at = True
|
||||
if detected_mentioned:
|
||||
message.is_mentioned = True
|
||||
|
||||
if not message.is_at and not message.is_mentioned:
|
||||
return
|
||||
|
||||
self._arm_force_continue_until_reply(
|
||||
message,
|
||||
is_at=message.is_at,
|
||||
is_mentioned=message.is_mentioned,
|
||||
)
|
||||
|
||||
def _arm_force_continue_until_reply(
|
||||
self,
|
||||
message: SessionMessage,
|
||||
*,
|
||||
is_at: bool,
|
||||
is_mentioned: bool,
|
||||
) -> None:
|
||||
"""在检测到 @ 或提及时，要求后续轮次跳过 Timing Gate 直到成功 reply。"""
|
||||
|
||||
trigger_reason = "@消息" if is_at else "提及消息" if is_mentioned else "触发消息"
|
||||
was_armed = self._force_continue_until_reply
|
||||
self._force_continue_until_reply = True
|
||||
self._force_continue_trigger_message_id = message.message_id
|
||||
self._force_continue_trigger_reason = trigger_reason
|
||||
|
||||
if was_armed:
|
||||
logger.info(
|
||||
f"{self.log_prefix} 检测到新的{trigger_reason}，刷新强制 continue 状态；"
|
||||
f"消息编号={message.message_id}"
|
||||
)
|
||||
return
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 检测到{trigger_reason}，将跳过 Timing Gate 直到成功发送一条 reply；"
|
||||
f"消息编号={message.message_id}"
|
||||
)
|
||||
|
||||
def _clear_force_continue_until_reply(self) -> None:
|
||||
"""在成功发送 reply 后清理强制 continue 状态。"""
|
||||
|
||||
if not self._force_continue_until_reply:
|
||||
return
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 已成功发送 reply，恢复 Timing Gate；"
|
||||
f"触发原因={self._force_continue_trigger_reason or '未知'} "
|
||||
f"触发消息编号={self._force_continue_trigger_message_id or 'unknown'}"
|
||||
)
|
||||
self._force_continue_until_reply = False
|
||||
self._force_continue_trigger_message_id = ""
|
||||
self._force_continue_trigger_reason = ""
|
||||
|
||||
def _build_force_continue_timing_reason(self) -> str:
|
||||
"""返回当前强制跳过 Timing Gate 的原因描述。"""
|
||||
|
||||
trigger_reason = self._force_continue_trigger_reason or "@/提及消息"
|
||||
trigger_message_id = self._force_continue_trigger_message_id or "unknown"
|
||||
return (
|
||||
f"检测到新的{trigger_reason}（消息编号={trigger_message_id}），"
|
||||
"本轮直接跳过 Timing Gate 并视作 continue，直到成功发送一条 reply。"
|
||||
)
|
||||
|
||||
def _bind_planner_interrupt_flag(self, interrupt_flag: asyncio.Event) -> None:
|
||||
"""绑定当前可打断请求使用的中断标记。"""
|
||||
@@ -235,17 +300,6 @@ class MaisakaHeartFlowChatting:
|
||||
self._internal_loop_task = asyncio.create_task(self._reasoning_engine.run_loop())
|
||||
logger.warning(f"{self.log_prefix} 已重新拉起 Maisaka 内部循环任务")
|
||||
|
||||
if self._loop_task is None or self._loop_task.done():
|
||||
if self._loop_task is not None and not self._loop_task.cancelled():
|
||||
try:
|
||||
exc = self._loop_task.exception()
|
||||
except Exception:
|
||||
exc = None
|
||||
if exc is not None:
|
||||
logger.error(f"{self.log_prefix} 主循环任务异常退出: {exc}")
|
||||
self._loop_task = asyncio.create_task(self._main_loop())
|
||||
logger.warning(f"{self.log_prefix} 已重新拉起 Maisaka 主循环任务")
|
||||
|
||||
def _register_tool_providers(self) -> None:
|
||||
"""注册 Maisaka 运行时默认启用的工具 Provider。"""
|
||||
|
||||
@@ -293,22 +347,17 @@ class MaisakaHeartFlowChatting:
|
||||
tool_definitions=[] if tool_definitions is None else tool_definitions,
|
||||
)
|
||||
|
||||
async def _main_loop(self) -> None:
|
||||
try:
|
||||
while self._running:
|
||||
if self._has_pending_messages() and not self._message_turn_scheduled:
|
||||
self._message_turn_scheduled = True
|
||||
await self._internal_turn_queue.put("message")
|
||||
continue
|
||||
|
||||
self._new_message_event.clear()
|
||||
await self._new_message_event.wait()
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} Maisaka 运行时主循环已取消")
|
||||
|
||||
def _has_pending_messages(self) -> bool:
|
||||
return self._last_processed_index < len(self.message_cache)
|
||||
|
||||
def _schedule_message_turn(self) -> None:
|
||||
"""为当前待处理消息安排一次内部 turn。"""
|
||||
if not self._has_pending_messages() or self._message_turn_scheduled:
|
||||
return
|
||||
|
||||
self._message_turn_scheduled = True
|
||||
self._internal_turn_queue.put_nowait("message")
|
||||
|
||||
def _collect_pending_messages(self) -> list[SessionMessage]:
|
||||
"""从消息缓存中收集一批尚未处理的消息。"""
|
||||
start_index = self._last_processed_index
|
||||
@@ -360,8 +409,6 @@ class MaisakaHeartFlowChatting:
|
||||
self._wait_timeout_task = asyncio.create_task(
|
||||
self._schedule_wait_timeout(seconds=seconds, tool_call_id=tool_call_id)
|
||||
)
|
||||
# 清理旧的消息触发信号，避免 wait 被历史消息残留事件立即唤醒。
|
||||
self._new_message_event.clear()
|
||||
|
||||
def _enter_stop_state(self) -> None:
|
||||
"""切换到停止状态。"""
|
||||
|
||||
Reference in New Issue
Block a user