3bef6f4bab
通过以下改动修复嵌入生成过程中的事件循环相关问题： - 在 EmbeddingStore._get_embedding 中，改为同步创建-使用-销毁的新事件循环模式，彻底避免嵌套事件循环问题 - 调整批量嵌入 _get_embeddings_batch_threaded，确保每个线程使用独立、短生命周期的事件循环 - 新增 force_new 参数，LLM 请求嵌入任务时强制创建新的客户端实例，减少跨循环对象复用 - 在 OpenAI 客户端的 embedding 调用处补充详细日志，方便排查网络连接异常 - get_embedding() 每次都重建 LLMRequest，降低实例在多个事件循环中穿梭的概率 此次改动虽然以同步风格“硬掰”异步接口，但对现有接口零破坏，确保了向量数据库及相关知识检索功能的稳定性。(还有就是把的脚本文件夹移回来了)
394 lines
13 KiB
Python
394 lines
13 KiB
Python
import time
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import sys
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import os
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import re
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from typing import Dict, List, Tuple, Optional
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from datetime import datetime
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# Add project root to Python path
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, project_root)
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from src.common.database.database_model import Messages, ChatStreams #noqa
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def contains_emoji_or_image_tags(text: str) -> bool:
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"""Check if text contains [表情包xxxxx] or [图片xxxxx] tags"""
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if not text:
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return False
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# 检查是否包含 [表情包] 或 [图片] 标记
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emoji_pattern = r'\[表情包[^\]]*\]'
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image_pattern = r'\[图片[^\]]*\]'
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return bool(re.search(emoji_pattern, text) or re.search(image_pattern, text))
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def clean_reply_text(text: str) -> str:
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"""Remove reply references like [回复 xxxx...] from text"""
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if not text:
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return text
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# 匹配 [回复 xxxx...] 格式的内容
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# 使用非贪婪匹配，匹配到第一个 ] 就停止
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cleaned_text = re.sub(r'\[回复[^\]]*\]', '', text)
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# 去除多余的空白字符
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cleaned_text = cleaned_text.strip()
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return cleaned_text
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def get_chat_name(chat_id: str) -> str:
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"""Get chat name from chat_id by querying ChatStreams table directly"""
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try:
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chat_stream = ChatStreams.get_or_none(ChatStreams.stream_id == chat_id)
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if chat_stream is None:
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return f"未知聊天 ({chat_id})"
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if chat_stream.group_name:
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return f"{chat_stream.group_name} ({chat_id})"
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elif chat_stream.user_nickname:
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return f"{chat_stream.user_nickname}的私聊 ({chat_id})"
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else:
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return f"未知聊天 ({chat_id})"
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except Exception:
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return f"查询失败 ({chat_id})"
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def format_timestamp(timestamp: float) -> str:
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"""Format timestamp to readable date string"""
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try:
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return datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
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except (ValueError, OSError):
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return "未知时间"
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def calculate_text_length_distribution(messages) -> Dict[str, int]:
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"""Calculate distribution of processed_plain_text length"""
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distribution = {
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'0': 0, # 空文本
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'1-5': 0, # 极短文本
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'6-10': 0, # 很短文本
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'11-20': 0, # 短文本
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'21-30': 0, # 较短文本
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'31-50': 0, # 中短文本
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'51-70': 0, # 中等文本
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'71-100': 0, # 较长文本
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'101-150': 0, # 长文本
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'151-200': 0, # 很长文本
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'201-300': 0, # 超长文本
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'301-500': 0, # 极长文本
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'501-1000': 0, # 巨长文本
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'1000+': 0 # 超巨长文本
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}
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for msg in messages:
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if msg.processed_plain_text is None:
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continue
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# 排除包含表情包或图片标记的消息
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if contains_emoji_or_image_tags(msg.processed_plain_text):
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continue
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# 清理文本中的回复引用
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cleaned_text = clean_reply_text(msg.processed_plain_text)
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length = len(cleaned_text)
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if length == 0:
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distribution['0'] += 1
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elif length <= 5:
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distribution['1-5'] += 1
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elif length <= 10:
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distribution['6-10'] += 1
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elif length <= 20:
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distribution['11-20'] += 1
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elif length <= 30:
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distribution['21-30'] += 1
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elif length <= 50:
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distribution['31-50'] += 1
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elif length <= 70:
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distribution['51-70'] += 1
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elif length <= 100:
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distribution['71-100'] += 1
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elif length <= 150:
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distribution['101-150'] += 1
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elif length <= 200:
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distribution['151-200'] += 1
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elif length <= 300:
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distribution['201-300'] += 1
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elif length <= 500:
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distribution['301-500'] += 1
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elif length <= 1000:
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distribution['501-1000'] += 1
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else:
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distribution['1000+'] += 1
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return distribution
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def get_text_length_stats(messages) -> Dict[str, float]:
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"""Calculate basic statistics for processed_plain_text length"""
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lengths = []
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null_count = 0
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excluded_count = 0 # 被排除的消息数量
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for msg in messages:
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if msg.processed_plain_text is None:
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null_count += 1
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elif contains_emoji_or_image_tags(msg.processed_plain_text):
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# 排除包含表情包或图片标记的消息
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excluded_count += 1
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else:
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# 清理文本中的回复引用
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cleaned_text = clean_reply_text(msg.processed_plain_text)
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lengths.append(len(cleaned_text))
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if not lengths:
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return {
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'count': 0,
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'null_count': null_count,
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'excluded_count': excluded_count,
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'min': 0,
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'max': 0,
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'avg': 0,
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'median': 0
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}
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lengths.sort()
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count = len(lengths)
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return {
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'count': count,
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'null_count': null_count,
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'excluded_count': excluded_count,
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'min': min(lengths),
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'max': max(lengths),
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'avg': sum(lengths) / count,
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'median': lengths[count // 2] if count % 2 == 1 else (lengths[count // 2 - 1] + lengths[count // 2]) / 2
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}
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def get_available_chats() -> List[Tuple[str, str, int]]:
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"""Get all available chats with message counts"""
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try:
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# 获取所有有消息的chat_id，排除特殊类型消息
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chat_counts = {}
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for msg in Messages.select(Messages.chat_id).distinct():
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chat_id = msg.chat_id
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count = Messages.select().where(
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(Messages.chat_id == chat_id) &
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(Messages.is_emoji != 1) &
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(Messages.is_picid != 1) &
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(Messages.is_command != 1)
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).count()
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if count > 0:
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chat_counts[chat_id] = count
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# 获取聊天名称
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result = []
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for chat_id, count in chat_counts.items():
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chat_name = get_chat_name(chat_id)
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result.append((chat_id, chat_name, count))
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# 按消息数量排序
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result.sort(key=lambda x: x[2], reverse=True)
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return result
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except Exception as e:
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print(f"获取聊天列表失败: {e}")
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return []
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def get_time_range_input() -> Tuple[Optional[float], Optional[float]]:
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"""Get time range input from user"""
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print("\n时间范围选择:")
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print("1. 最近1天")
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print("2. 最近3天")
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print("3. 最近7天")
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print("4. 最近30天")
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print("5. 自定义时间范围")
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print("6. 不限制时间")
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choice = input("请选择时间范围 (1-6): ").strip()
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now = time.time()
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if choice == "1":
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return now - 24*3600, now
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elif choice == "2":
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return now - 3*24*3600, now
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elif choice == "3":
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return now - 7*24*3600, now
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elif choice == "4":
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return now - 30*24*3600, now
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elif choice == "5":
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print("请输入开始时间 (格式: YYYY-MM-DD HH:MM:SS):")
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start_str = input().strip()
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print("请输入结束时间 (格式: YYYY-MM-DD HH:MM:SS):")
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end_str = input().strip()
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try:
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start_time = datetime.strptime(start_str, "%Y-%m-%d %H:%M:%S").timestamp()
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end_time = datetime.strptime(end_str, "%Y-%m-%d %H:%M:%S").timestamp()
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return start_time, end_time
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except ValueError:
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print("时间格式错误，将不限制时间范围")
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return None, None
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else:
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return None, None
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def get_top_longest_messages(messages, top_n: int = 10) -> List[Tuple[str, int, str, str]]:
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"""Get top N longest messages"""
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message_lengths = []
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for msg in messages:
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if msg.processed_plain_text is not None:
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# 排除包含表情包或图片标记的消息
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if contains_emoji_or_image_tags(msg.processed_plain_text):
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continue
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# 清理文本中的回复引用
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cleaned_text = clean_reply_text(msg.processed_plain_text)
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length = len(cleaned_text)
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chat_name = get_chat_name(msg.chat_id)
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time_str = format_timestamp(msg.time)
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# 截取前100个字符作为预览
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preview = cleaned_text[:100] + "..." if len(cleaned_text) > 100 else cleaned_text
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message_lengths.append((chat_name, length, time_str, preview))
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# 按长度排序，取前N个
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message_lengths.sort(key=lambda x: x[1], reverse=True)
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return message_lengths[:top_n]
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def analyze_text_lengths(chat_id: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None) -> None:
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"""Analyze processed_plain_text lengths with optional filters"""
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# 构建查询条件，排除特殊类型的消息
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query = Messages.select().where(
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(Messages.is_emoji != 1) &
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(Messages.is_picid != 1) &
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(Messages.is_command != 1)
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)
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if chat_id:
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query = query.where(Messages.chat_id == chat_id)
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if start_time:
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query = query.where(Messages.time >= start_time)
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if end_time:
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query = query.where(Messages.time <= end_time)
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messages = list(query)
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if not messages:
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print("没有找到符合条件的消息")
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return
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# 计算统计信息
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distribution = calculate_text_length_distribution(messages)
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stats = get_text_length_stats(messages)
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top_longest = get_top_longest_messages(messages, 10)
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# 显示结果
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print("\n=== Processed Plain Text 长度分析结果 ===")
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print("(已排除表情、图片ID、命令类型消息，已排除[表情包]和[图片]标记消息，已清理回复引用)")
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if chat_id:
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print(f"聊天: {get_chat_name(chat_id)}")
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else:
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print("聊天: 全部聊天")
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if start_time and end_time:
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print(f"时间范围: {format_timestamp(start_time)} 到 {format_timestamp(end_time)}")
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elif start_time:
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print(f"时间范围: {format_timestamp(start_time)} 之后")
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elif end_time:
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print(f"时间范围: {format_timestamp(end_time)} 之前")
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else:
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print("时间范围: 不限制")
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print("\n基本统计:")
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print(f"总消息数量: {len(messages)}")
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print(f"有文本消息数量: {stats['count']}")
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print(f"空文本消息数量: {stats['null_count']}")
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print(f"被排除的消息数量: {stats['excluded_count']}")
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if stats['count'] > 0:
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print(f"最短长度: {stats['min']} 字符")
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print(f"最长长度: {stats['max']} 字符")
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print(f"平均长度: {stats['avg']:.2f} 字符")
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print(f"中位数长度: {stats['median']:.2f} 字符")
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print("\n文本长度分布:")
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total = stats['count']
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if total > 0:
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for range_name, count in distribution.items():
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if count > 0:
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percentage = count / total * 100
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print(f"{range_name} 字符: {count} ({percentage:.2f}%)")
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# 显示最长的消息
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if top_longest:
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print(f"\n最长的 {len(top_longest)} 条消息:")
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for i, (chat_name, length, time_str, preview) in enumerate(top_longest, 1):
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print(f"{i}. [{chat_name}] {time_str}")
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print(f" 长度: {length} 字符")
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print(f" 预览: {preview}")
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print()
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def interactive_menu() -> None:
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"""Interactive menu for text length analysis"""
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while True:
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print("\n" + "="*50)
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print("Processed Plain Text 长度分析工具")
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print("="*50)
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print("1. 分析全部聊天")
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print("2. 选择特定聊天分析")
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print("q. 退出")
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choice = input("\n请选择分析模式 (1-2, q): ").strip()
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if choice.lower() == 'q':
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print("再见！")
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break
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chat_id = None
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if choice == "2":
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# 显示可用的聊天列表
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chats = get_available_chats()
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if not chats:
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print("没有找到聊天数据")
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continue
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print(f"\n可用的聊天 (共{len(chats)}个):")
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for i, (_cid, name, count) in enumerate(chats, 1):
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print(f"{i}. {name} ({count}条消息)")
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try:
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chat_choice = int(input(f"\n请选择聊天 (1-{len(chats)}): ").strip())
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if 1 <= chat_choice <= len(chats):
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chat_id = chats[chat_choice - 1][0]
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else:
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print("无效选择")
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continue
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except ValueError:
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print("请输入有效数字")
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continue
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elif choice != "1":
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print("无效选择")
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continue
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# 获取时间范围
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start_time, end_time = get_time_range_input()
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# 执行分析
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analyze_text_lengths(chat_id, start_time, end_time)
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input("\n按回车键继续...")
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if __name__ == "__main__":
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interactive_menu() |