245 lines
8.6 KiB
Python
245 lines
8.6 KiB
Python
import time
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import asyncio
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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.database.database_model import MemoryConflict
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from src.chat.utils.chat_message_builder import (
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get_raw_msg_by_timestamp_with_chat,
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build_readable_messages,
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)
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config, global_config
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logger = get_logger("conflict_tracker")
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logger = get_logger("conflict_tracker")
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install(extra_lines=3)
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class QuestionTracker:
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"""
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用于跟踪一个问题在后续聊天中的解答情况
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"""
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def __init__(self, question: str, chat_id: str) -> None:
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self.question = question
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self.chat_id = chat_id
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now = time.time()
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self.start_time = now
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self.last_read_time = now
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self.active = True
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# 将 LLM 实例作为类属性，使用 utils 模型
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self.llm_request = LLMRequest(model_set=model_config.model_task_config.utils, request_type="conflict.judge")
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def stop(self) -> None:
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self.active = False
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async def judge_answer(self, conversation_text: str) -> tuple[bool, str]:
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"""
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使用小模型判定问题是否已得到解答。
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返回 (已解答, 答案)
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"""
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prompt = (
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"你是一个严谨的判定器。下面给出聊天记录以及一个问题。\n"
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"任务：判断在这段聊天中，该问题是否已经得到明确解答。或从聊天内容中可以整理出答案\n"
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"如果已解答，请只输出：YES: <简短答案>\n"
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"如果没有，请只输出：NO\n\n"
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f"问题：{self.question}\n"
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"聊天记录如下：\n"
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f"{conversation_text}"
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)
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if global_config.debug.show_prompt:
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logger.info(f"判定提示词: {prompt}")
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else:
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logger.debug("已发送判定提示词")
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result_text, _ = await self.llm_request.generate_response_async(prompt, temperature=0.2)
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if not result_text:
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return False, ""
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logger.info(f"判定提示词: {prompt},问题: {self.question},result: {result_text}")
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text = result_text.strip()
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if text.upper().startswith("YES:"):
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answer = text[4:].strip()
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return True, answer
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if text.upper().startswith("YES"):
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# 兼容仅输出 YES 或 YES <answer>
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answer = text[3:].strip().lstrip(":").strip()
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return True, answer
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return False, ""
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class ConflictTracker:
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"""
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记忆整合冲突追踪器
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用于记录和存储记忆整合过程中的冲突内容
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"""
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async def record_conflict(self, conflict_content: str, start_following: bool = False,chat_id: str = "") -> bool:
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"""
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记录冲突内容
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Args:k
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conflict_content: 冲突内容
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Returns:
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bool: 是否成功记录
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"""
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try:
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if not conflict_content or conflict_content.strip() == "":
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return False
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# 若需要跟随后续消息以判断是否得到解答，则进入跟踪流程
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if start_following and chat_id:
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tracker = QuestionTracker(conflict_content.strip(), chat_id)
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# 后台启动跟踪任务，避免阻塞
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asyncio.create_task(self._follow_and_record(tracker, conflict_content.strip()))
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return True
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# 默认：直接记录，不进行跟踪
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MemoryConflict.create(
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conflict_content=conflict_content,
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create_time=time.time(),
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update_time=time.time(),
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answer="",
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)
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logger.info(f"记录冲突内容: {len(conflict_content)} 字符")
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return True
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except Exception as e:
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logger.error(f"记录冲突内容时出错: {e}")
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return False
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async def _follow_and_record(self, tracker: QuestionTracker, original_question: str) -> None:
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"""
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后台任务：跟踪问题是否被解答，并写入数据库。
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"""
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try:
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max_duration = 30 * 60 # 30 分钟
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max_messages = 100 # 最多 100 条消息
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poll_interval = 2.0 # 秒
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while tracker.active:
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now_ts = time.time()
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# 终止条件：时长达到上限
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if now_ts - tracker.start_time >= max_duration:
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logger.info("问题跟踪达到30分钟上限，判定为未解答")
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break
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# 统计最近一段是否有新消息（不过滤机器人，过滤命令）
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recent_msgs = get_raw_msg_by_timestamp_with_chat(
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chat_id=tracker.chat_id,
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timestamp_start=tracker.last_read_time,
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timestamp_end=now_ts,
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limit=0,
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limit_mode="latest",
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filter_bot=False,
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filter_command=True,
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)
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if len(recent_msgs) > 0:
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tracker.last_read_time = now_ts
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# 统计从开始到现在的总消息数（用于触发100条上限）
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all_msgs = get_raw_msg_by_timestamp_with_chat(
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chat_id=tracker.chat_id,
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timestamp_start=tracker.start_time,
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timestamp_end=now_ts,
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limit=0,
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limit_mode="latest",
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filter_bot=False,
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filter_command=True,
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)
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# 构建可读聊天文本
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chat_text = build_readable_messages(
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all_msgs,
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replace_bot_name=True,
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timestamp_mode="relative",
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read_mark=0.0,
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truncate=False,
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show_actions=False,
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show_pic=False,
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remove_emoji_stickers=True,
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)
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# 让小模型判断是否有答案
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answered, answer_text = await tracker.judge_answer(chat_text)
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if answered:
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logger.info("问题已得到解答，结束跟踪并写入答案")
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tracker.stop()
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MemoryConflict.create(
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conflict_content=tracker.question,
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create_time=tracker.start_time,
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update_time=time.time(),
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answer=answer_text or "",
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)
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return
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if len(all_msgs) >= max_messages:
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logger.info("问题跟踪达到100条消息上限，判定为未解答")
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break
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# 无新消息时稍作等待
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await asyncio.sleep(poll_interval)
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# 未获取到答案，仅存储问题
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MemoryConflict.create(
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conflict_content=original_question,
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create_time=time.time(),
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update_time=time.time(),
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answer="",
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)
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logger.info(f"记录冲突内容(未解答): {len(original_question)} 字符")
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except Exception as e:
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logger.error(f"后台问题跟踪任务异常: {e}")
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async def record_memory_merge_conflict(self, part2_content: str) -> bool:
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"""
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记录记忆整合过程中的冲突内容（part2）
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Args:
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part2_content: 冲突内容（part2）
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Returns:
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bool: 是否成功记录
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"""
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if not part2_content or part2_content.strip() == "":
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return False
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return await self.record_conflict(part2_content)
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async def get_all_conflicts(self) -> list:
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"""
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获取所有冲突记录
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Returns:
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list: 冲突记录列表
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"""
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try:
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conflicts = list(MemoryConflict.select())
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return conflicts
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except Exception as e:
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logger.error(f"获取冲突记录时出错: {e}")
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return []
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async def get_conflict_count(self) -> int:
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"""
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获取冲突记录数量
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Returns:
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int: 记录数量
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"""
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try:
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return MemoryConflict.select().count()
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except Exception as e:
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logger.error(f"获取冲突记录数量时出错: {e}")
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return 0
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# 全局冲突追踪器实例
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global_conflict_tracker = ConflictTracker() |