feat：复用jargon和expression的部分代码，代码层面合并，合并配置项
缓解bot重复学习自身表达的问题 缓解单字黑话推断时消耗过高的问题 修复count过高时推断过长的问题 移除表达方式学习强度配置
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SengokuCola
252
src/bw_learner/expression_reflector.py
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252
src/bw_learner/expression_reflector.py
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import random
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import time
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from typing import Optional, Dict
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from src.common.logger import get_logger
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from src.common.database.database_model import Expression
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from src.config.config import global_config
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from src.chat.message_receive.chat_stream import get_chat_manager
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from src.plugin_system.apis import send_api
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logger = get_logger("expression_reflector")
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class ExpressionReflector:
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"""表达反思器，管理单个聊天流的表达反思提问"""
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def __init__(self, chat_id: str):
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self.chat_id = chat_id
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self.last_ask_time: float = 0.0
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async def check_and_ask(self) -> bool:
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"""
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检查是否需要提问表达反思，如果需要则提问
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Returns:
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bool: 是否执行了提问
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"""
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try:
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logger.debug(f"[Expression Reflection] 开始检查是否需要提问 (stream_id: {self.chat_id})")
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if not global_config.expression.reflect:
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logger.debug("[Expression Reflection] 表达反思功能未启用，跳过")
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return False
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operator_config = global_config.expression.reflect_operator_id
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if not operator_config:
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logger.debug("[Expression Reflection] Operator ID 未配置，跳过")
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return False
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# 检查是否在允许列表中
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allow_reflect = global_config.expression.allow_reflect
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if allow_reflect:
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# 将 allow_reflect 中的 platform:id:type 格式转换为 chat_id 列表
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allow_reflect_chat_ids = []
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for stream_config in allow_reflect:
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parsed_chat_id = global_config.expression._parse_stream_config_to_chat_id(stream_config)
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if parsed_chat_id:
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allow_reflect_chat_ids.append(parsed_chat_id)
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else:
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logger.warning(f"[Expression Reflection] 无法解析 allow_reflect 配置项: {stream_config}")
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if self.chat_id not in allow_reflect_chat_ids:
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logger.info(f"[Expression Reflection] 当前聊天流 {self.chat_id} 不在允许列表中，跳过")
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return False
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# 检查上一次提问时间
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current_time = time.time()
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time_since_last_ask = current_time - self.last_ask_time
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# 5-10分钟间隔，随机选择
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min_interval = 10 * 60 # 5分钟
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max_interval = 15 * 60 # 10分钟
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interval = random.uniform(min_interval, max_interval)
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logger.info(
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f"[Expression Reflection] 上次提问时间: {self.last_ask_time:.2f}, 当前时间: {current_time:.2f}, 已过时间: {time_since_last_ask:.2f}秒 ({time_since_last_ask / 60:.2f}分钟), 需要间隔: {interval:.2f}秒 ({interval / 60:.2f}分钟)"
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)
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if time_since_last_ask < interval:
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remaining_time = interval - time_since_last_ask
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logger.info(
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f"[Expression Reflection] 距离上次提问时间不足，还需等待 {remaining_time:.2f}秒 ({remaining_time / 60:.2f}分钟)，跳过"
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)
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return False
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# 检查是否已经有针对该 Operator 的 Tracker 在运行
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logger.info(f"[Expression Reflection] 检查 Operator {operator_config} 是否已有活跃的 Tracker")
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if await _check_tracker_exists(operator_config):
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logger.info(f"[Expression Reflection] Operator {operator_config} 已有活跃的 Tracker，跳过本次提问")
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return False
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# 获取未检查的表达
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try:
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logger.info("[Expression Reflection] 查询未检查且未拒绝的表达")
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expressions = (
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Expression.select().where((~Expression.checked) & (~Expression.rejected)).limit(50)
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)
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expr_list = list(expressions)
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logger.info(f"[Expression Reflection] 找到 {len(expr_list)} 个候选表达")
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if not expr_list:
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logger.info("[Expression Reflection] 没有可用的表达，跳过")
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return False
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target_expr: Expression = random.choice(expr_list)
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logger.info(
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f"[Expression Reflection] 随机选择了表达 ID: {target_expr.id}, Situation: {target_expr.situation}, Style: {target_expr.style}"
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)
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# 生成询问文本
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ask_text = _generate_ask_text(target_expr)
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if not ask_text:
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logger.warning("[Expression Reflection] 生成询问文本失败，跳过")
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return False
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logger.info(f"[Expression Reflection] 准备向 Operator {operator_config} 发送提问")
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# 发送给 Operator
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await _send_to_operator(operator_config, ask_text, target_expr)
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# 更新上一次提问时间
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self.last_ask_time = current_time
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logger.info(f"[Expression Reflection] 提问成功，已更新上次提问时间为 {current_time:.2f}")
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return True
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except Exception as e:
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logger.error(f"[Expression Reflection] 检查或提问过程中出错: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return False
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except Exception as e:
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logger.error(f"[Expression Reflection] 检查或提问过程中出错: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return False
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class ExpressionReflectorManager:
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"""表达反思管理器，管理多个聊天流的表达反思实例"""
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def __init__(self):
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self.reflectors: Dict[str, ExpressionReflector] = {}
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def get_or_create_reflector(self, chat_id: str) -> ExpressionReflector:
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"""获取或创建指定聊天流的表达反思实例"""
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if chat_id not in self.reflectors:
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self.reflectors[chat_id] = ExpressionReflector(chat_id)
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return self.reflectors[chat_id]
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# 创建全局实例
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expression_reflector_manager = ExpressionReflectorManager()
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async def _check_tracker_exists(operator_config: str) -> bool:
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"""检查指定 Operator 是否已有活跃的 Tracker"""
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from src.express.reflect_tracker import reflect_tracker_manager
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chat_manager = get_chat_manager()
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chat_stream = None
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# 尝试解析配置字符串 "platform:id:type"
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parts = operator_config.split(":")
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if len(parts) == 3:
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platform = parts[0]
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id_str = parts[1]
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stream_type = parts[2]
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user_info = None
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group_info = None
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from maim_message import UserInfo, GroupInfo
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if stream_type == "group":
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group_info = GroupInfo(group_id=id_str, platform=platform)
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user_info = UserInfo(user_id="system", user_nickname="System", platform=platform)
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elif stream_type == "private":
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user_info = UserInfo(user_id=id_str, platform=platform, user_nickname="Operator")
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else:
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return False
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if user_info:
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try:
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chat_stream = await chat_manager.get_or_create_stream(platform, user_info, group_info)
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except Exception as e:
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logger.error(f"Failed to get or create chat stream for checking tracker: {e}")
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return False
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else:
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chat_stream = chat_manager.get_stream(operator_config)
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if not chat_stream:
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return False
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return reflect_tracker_manager.get_tracker(chat_stream.stream_id) is not None
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def _generate_ask_text(expr: Expression) -> Optional[str]:
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try:
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ask_text = (
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f"我正在学习新的表达方式，请帮我看看这个是否合适？\n\n"
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f"**学习到的表达信息**\n"
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f"- 情景 (Situation): {expr.situation}\n"
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f"- 风格 (Style): {expr.style}\n"
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)
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return ask_text
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except Exception as e:
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logger.error(f"Failed to generate ask text: {e}")
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return None
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async def _send_to_operator(operator_config: str, text: str, expr: Expression):
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chat_manager = get_chat_manager()
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chat_stream = None
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# 尝试解析配置字符串 "platform:id:type"
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parts = operator_config.split(":")
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if len(parts) == 3:
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platform = parts[0]
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id_str = parts[1]
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stream_type = parts[2]
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user_info = None
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group_info = None
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from maim_message import UserInfo, GroupInfo
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if stream_type == "group":
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group_info = GroupInfo(group_id=id_str, platform=platform)
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user_info = UserInfo(user_id="system", user_nickname="System", platform=platform)
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elif stream_type == "private":
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user_info = UserInfo(user_id=id_str, platform=platform, user_nickname="Operator")
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else:
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logger.warning(f"Unknown stream type in operator config: {stream_type}")
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return
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if user_info:
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try:
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chat_stream = await chat_manager.get_or_create_stream(platform, user_info, group_info)
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except Exception as e:
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logger.error(f"Failed to get or create chat stream for operator {operator_config}: {e}")
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return
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else:
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chat_stream = chat_manager.get_stream(operator_config)
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if not chat_stream:
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logger.warning(f"Could not find or create chat stream for operator: {operator_config}")
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return
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stream_id = chat_stream.stream_id
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# 注册 Tracker
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from src.express.reflect_tracker import ReflectTracker, reflect_tracker_manager
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tracker = ReflectTracker(chat_stream=chat_stream, expression=expr, created_time=time.time())
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reflect_tracker_manager.add_tracker(stream_id, tracker)
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# 发送消息
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await send_api.text_to_stream(text=text, stream_id=stream_id, typing=True)
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logger.info(f"Sent expression reflect query to operator {operator_config} for expr {expr.id}")
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