mirror of https://github.com/Mai-with-u/MaiBot.git
848 lines
31 KiB
Python
848 lines
31 KiB
Python
import random
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import re
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import time
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import jieba
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import json
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import ast
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import numpy as np
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from collections import Counter
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from typing import Optional, Tuple, List, TYPE_CHECKING
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from src.common.logger import get_logger
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.message_repository import find_messages, count_messages
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from src.config.config import global_config, model_config
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from src.chat.message_receive.message import MessageRecv
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from src.chat.message_receive.chat_stream import get_chat_manager
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from src.llm_models.utils_model import LLMRequest
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from src.person_info.person_info import Person
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from .typo_generator import ChineseTypoGenerator
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if TYPE_CHECKING:
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from src.common.data_models.info_data_model import TargetPersonInfo
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logger = get_logger("chat_utils")
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def is_english_letter(char: str) -> bool:
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"""检查字符是否为英文字母（忽略大小写）"""
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return "a" <= char.lower() <= "z"
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def parse_platform_accounts(platforms: list[str]) -> dict[str, str]:
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"""解析 platforms 列表，返回平台到账号的映射
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Args:
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platforms: 格式为 ["platform:account"] 的列表，如 ["tg:123456789", "wx:wxid123"]
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Returns:
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字典，键为平台名，值为账号
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"""
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result = {}
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for platform_entry in platforms:
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if ":" in platform_entry:
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platform_name, account = platform_entry.split(":", 1)
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result[platform_name.strip()] = account.strip()
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return result
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def get_current_platform_account(platform: str, platform_accounts: dict[str, str], qq_account: str) -> str:
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"""根据当前平台获取对应的账号
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Args:
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platform: 当前消息的平台
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platform_accounts: 从 platforms 列表解析的平台账号映射
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qq_account: QQ 账号（兼容旧配置）
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Returns:
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当前平台对应的账号
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"""
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if platform == "qq":
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return qq_account
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elif platform == "telegram":
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# 优先使用 tg，其次使用 telegram
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return platform_accounts.get("tg", "") or platform_accounts.get("telegram", "")
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else:
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# 其他平台直接使用平台名作为键
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return platform_accounts.get(platform, "")
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def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, bool, float]:
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"""检查消息是否提到了机器人（统一多平台实现）"""
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text = message.processed_plain_text or ""
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platform = getattr(message.message_info, "platform", "") or ""
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# 获取各平台账号
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platforms_list = getattr(global_config.bot, "platforms", []) or []
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platform_accounts = parse_platform_accounts(platforms_list)
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qq_account = str(getattr(global_config.bot, "qq_account", "") or "")
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# 获取当前平台对应的账号
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current_account = get_current_platform_account(platform, platform_accounts, qq_account)
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nickname = str(global_config.bot.nickname or "")
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alias_names = list(getattr(global_config.bot, "alias_names", []) or [])
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keywords = [nickname] + alias_names
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reply_probability = 0.0
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is_at = False
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is_mentioned = False
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# 1) 直接的 additional_config 标记
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add_cfg = getattr(message.message_info, "additional_config", None) or {}
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if isinstance(add_cfg, dict):
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if add_cfg.get("at_bot") or add_cfg.get("is_mentioned"):
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is_mentioned = True
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# 当提供数值型 is_mentioned 时，当作概率提升
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try:
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if add_cfg.get("is_mentioned") not in (None, ""):
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reply_probability = float(add_cfg.get("is_mentioned")) # type: ignore
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except Exception:
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pass
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# 2) 已经在上游设置过的 message.is_mentioned
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if getattr(message, "is_mentioned", False):
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is_mentioned = True
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# 3) 扫描分段：是否包含 mention_bot（适配器插入）
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def _has_mention_bot(seg) -> bool:
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try:
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if seg is None:
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return False
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if getattr(seg, "type", None) == "mention_bot":
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return True
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if getattr(seg, "type", None) == "seglist":
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for s in getattr(seg, "data", []) or []:
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if _has_mention_bot(s):
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return True
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return False
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except Exception:
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return False
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if _has_mention_bot(getattr(message, "message_segment", None)):
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is_at = True
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is_mentioned = True
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# 4) 统一的 @ 检测逻辑
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if current_account and not is_at and not is_mentioned:
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if platform == "qq":
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# QQ 格式: @<name:qq_id>
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if re.search(rf"@<(.+?):{re.escape(current_account)}>", text):
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is_at = True
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is_mentioned = True
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else:
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# 其他平台格式: @username 或 @account
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if re.search(rf"@{re.escape(current_account)}(\b|$)", text, flags=re.IGNORECASE):
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is_at = True
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is_mentioned = True
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# 5) 统一的回复检测逻辑
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if not is_mentioned:
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# 通用回复格式：包含 "(你)" 或 "（你）"
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if re.search(r"\[回复 .*?\(你\)：", text) or re.search(r"\[回复 .*?（你）：", text):
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is_mentioned = True
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# ID 形式的回复检测
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elif current_account:
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if re.search(rf"\[回复 (.+?)\({re.escape(current_account)}\)：(.+?)\]，说：", text):
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is_mentioned = True
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elif re.search(rf"\[回复<(.+?)(?=:{re.escape(current_account)}>)\:{re.escape(current_account)}>：(.+?)\]，说：", text):
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is_mentioned = True
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# 6) 名称/别名 提及（去除 @/回复标记后再匹配）
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if not is_mentioned and keywords:
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msg_content = text
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# 去除各种 @ 与 回复标记，避免误判
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msg_content = re.sub(r"@(.+?)（(\d+)）", "", msg_content)
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msg_content = re.sub(r"@<(.+?)(?=:(\d+))\:(\d+)>", "", msg_content)
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msg_content = re.sub(r"\[回复 (.+?)\(((\d+)|未知id|你)\)：(.+?)\]，说：", "", msg_content)
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msg_content = re.sub(r"\[回复<(.+?)(?=:(\d+))\:(\d+)>：(.+?)\]，说：", "", msg_content)
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for kw in keywords:
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if kw and kw in msg_content:
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is_mentioned = True
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break
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# 7) 概率设置
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if is_at and getattr(global_config.chat, "at_bot_inevitable_reply", 1):
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reply_probability = 1.0
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logger.debug("被@，回复概率设置为100%")
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elif is_mentioned and getattr(global_config.chat, "mentioned_bot_reply", 1):
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reply_probability = max(reply_probability, 1.0)
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logger.debug("被提及，回复概率设置为100%")
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return is_mentioned, is_at, reply_probability
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async def get_embedding(text, request_type="embedding") -> Optional[List[float]]:
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"""获取文本的embedding向量"""
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# 每次都创建新的LLMRequest实例以避免事件循环冲突
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llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type=request_type)
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try:
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embedding, _ = await llm.get_embedding(text)
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except Exception as e:
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logger.error(f"获取embedding失败: {str(e)}")
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embedding = None
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return embedding
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def split_into_sentences_w_remove_punctuation(text: str) -> list[str]:
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"""将文本分割成句子，并根据概率合并
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1. 识别分割点（, ， 。 ; 空格），但如果分割点左右都是英文字母则不分割。
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2. 将文本分割成 (内容, 分隔符) 的元组。
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3. 根据原始文本长度计算合并概率，概率性地合并相邻段落。
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注意：此函数假定颜文字已在上层被保护。
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Args:
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text: 要分割的文本字符串 (假定颜文字已被保护)
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Returns:
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List[str]: 分割和合并后的句子列表
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"""
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# 预处理：处理多余的换行符
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# 1. 将连续的换行符替换为单个换行符
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text = re.sub(r"\n\s*\n+", "\n", text)
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# 2. 处理换行符和其他分隔符的组合
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text = re.sub(r"\n\s*([，,。;\s])", r"\1", text)
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text = re.sub(r"([，,。;\s])\s*\n", r"\1", text)
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# 处理两个汉字中间的换行符
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text = re.sub(r"([\u4e00-\u9fff])\n([\u4e00-\u9fff])", r"\1。\2", text)
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len_text = len(text)
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if len_text < 3:
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return list(text) if random.random() < 0.01 else [text]
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# 定义分隔符
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separators = {"，", ",", " ", "。", ";"}
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segments = []
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current_segment = ""
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# 1. 分割成 (内容, 分隔符) 元组
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i = 0
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while i < len(text):
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char = text[i]
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if char in separators:
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# 检查分割条件：如果空格左右都是英文字母，则不分割（仅对空格应用此规则）
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can_split = True
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if 0 < i < len(text) - 1:
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prev_char = text[i - 1]
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next_char = text[i + 1]
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# 只对空格应用"不分割两个英文之间的空格"规则
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if char == ' ' and is_english_letter(prev_char) and is_english_letter(next_char):
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can_split = False
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if can_split:
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# 只有当当前段不为空时才添加
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if current_segment:
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segments.append((current_segment, char))
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# 如果当前段为空，但分隔符是空格，则也添加一个空段（保留空格）
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elif char == " ":
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segments.append(("", char))
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current_segment = ""
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else:
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# 不分割，将分隔符加入当前段
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current_segment += char
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else:
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current_segment += char
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i += 1
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# 添加最后一个段（没有后续分隔符）
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if current_segment:
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segments.append((current_segment, ""))
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# 过滤掉完全空的段（内容和分隔符都为空）
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segments = [(content, sep) for content, sep in segments if content or sep]
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# 如果分割后为空（例如，输入全是分隔符且不满足保留条件），恢复颜文字并返回
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if not segments:
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return [text] if text else [] # 如果原始文本非空，则返回原始文本（可能只包含未被分割的字符或颜文字占位符）
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# 2. 概率合并
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if len_text < 12:
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split_strength = 0.2
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elif len_text < 32:
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split_strength = 0.6
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else:
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split_strength = 0.7
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# 合并概率与分割强度相反
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merge_probability = 1.0 - split_strength
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merged_segments = []
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idx = 0
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while idx < len(segments):
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current_content, current_sep = segments[idx]
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# 检查是否可以与下一段合并
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# 条件：不是最后一段，且随机数小于合并概率，且当前段有内容（避免合并空段）
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if idx + 1 < len(segments) and random.random() < merge_probability and current_content:
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next_content, next_sep = segments[idx + 1]
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# 合并: (内容1 + 分隔符1 + 内容2, 分隔符2)
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# 只有当下一段也有内容时才合并文本，否则只传递分隔符
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if next_content:
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merged_content = current_content + current_sep + next_content
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merged_segments.append((merged_content, next_sep))
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else: # 下一段内容为空，只保留当前内容和下一段的分隔符
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merged_segments.append((current_content, next_sep))
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idx += 2 # 跳过下一段，因为它已被合并
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else:
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# 不合并，直接添加当前段
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merged_segments.append((current_content, current_sep))
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idx += 1
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# 提取最终的句子内容
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final_sentences = [content for content, sep in merged_segments if content] # 只保留有内容的段
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# 清理可能引入的空字符串和仅包含空白的字符串
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final_sentences = [
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s for s in final_sentences if s.strip()
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] # 过滤掉空字符串以及仅包含空白（如换行符、空格）的字符串
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logger.debug(f"分割并合并后的句子: {final_sentences}")
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return final_sentences
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def random_remove_punctuation(text: str) -> str:
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"""随机处理标点符号，模拟人类打字习惯
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Args:
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text: 要处理的文本
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Returns:
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str: 处理后的文本
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"""
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result = ""
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text_len = len(text)
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for i, char in enumerate(text):
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if char == "。" and i == text_len - 1: # 结尾的句号
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if random.random() > 0.1: # 90%概率删除结尾句号
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continue
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elif char == "，":
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rand = random.random()
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if rand < 0.05: # 5%概率删除逗号
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continue
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elif rand < 0.25: # 20%概率把逗号变成空格
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result += " "
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continue
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result += char
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return result
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def _get_random_default_reply() -> str:
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"""获取随机默认回复"""
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default_replies = [
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f"{global_config.bot.nickname}不知道哦",
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f"{global_config.bot.nickname}不知道",
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"不知道哦",
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"不知道",
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"不晓得",
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"懒得说",
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"()"
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]
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return random.choice(default_replies)
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def process_llm_response(text: str, enable_splitter: bool = True, enable_chinese_typo: bool = True) -> list[str]:
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if not global_config.response_post_process.enable_response_post_process:
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return [text]
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# 先保护颜文字
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if global_config.response_splitter.enable_kaomoji_protection:
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protected_text, kaomoji_mapping = protect_kaomoji(text)
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logger.debug(f"保护颜文字后的文本: {protected_text}")
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else:
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protected_text = text
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kaomoji_mapping = {}
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# 提取被 () 或 [] 或 （）包裹且包含中文的内容
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pattern = re.compile(r"[(\[（](?=.*[一-鿿]).*?[)\]）]")
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_extracted_contents = pattern.findall(protected_text) # 在保护后的文本上查找
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# 去除 () 和 [] 及其包裹的内容
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cleaned_text = pattern.sub("", protected_text)
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|
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if cleaned_text == "":
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return ["呃呃"]
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logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}")
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|
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# 对清理后的文本进行进一步处理
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max_length = global_config.response_splitter.max_length * 2
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max_sentence_num = global_config.response_splitter.max_sentence_num
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# 如果基本上是中文，则进行长度过滤
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if get_western_ratio(cleaned_text) < 0.1 and len(cleaned_text) > max_length:
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logger.warning(f"回复过长 ({len(cleaned_text)} 字符)，返回默认回复")
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return [_get_random_default_reply()]
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|
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typo_generator = ChineseTypoGenerator(
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error_rate=global_config.chinese_typo.error_rate,
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min_freq=global_config.chinese_typo.min_freq,
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||
tone_error_rate=global_config.chinese_typo.tone_error_rate,
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word_replace_rate=global_config.chinese_typo.word_replace_rate,
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)
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||
|
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if global_config.response_splitter.enable and enable_splitter:
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split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text)
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else:
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split_sentences = [cleaned_text]
|
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|
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sentences: List[str] = []
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for sentence in split_sentences:
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if global_config.chinese_typo.enable and enable_chinese_typo:
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typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence)
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||
if typo_corrections:
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# 50%概率新增正确字/词，50%概率用正确分句替换错别字分句
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||
if random.random() < 0.5:
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sentences.append(typoed_text)
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sentences.append(typo_corrections)
|
||
else:
|
||
# 用正确的分句替换错别字分句
|
||
sentences.append(sentence)
|
||
else:
|
||
sentences.append(typoed_text)
|
||
else:
|
||
sentences.append(sentence)
|
||
|
||
if len(sentences) > max_sentence_num:
|
||
logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
|
||
return [_get_random_default_reply()]
|
||
|
||
# if extracted_contents:
|
||
# for content in extracted_contents:
|
||
# sentences.append(content)
|
||
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# 在所有句子处理完毕后，对包含占位符的列表进行恢复
|
||
if global_config.response_splitter.enable_kaomoji_protection:
|
||
sentences = recover_kaomoji(sentences, kaomoji_mapping)
|
||
|
||
return sentences
|
||
|
||
|
||
def calculate_typing_time(
|
||
input_string: str,
|
||
thinking_start_time: float,
|
||
chinese_time: float = 0.3,
|
||
english_time: float = 0.15,
|
||
is_emoji: bool = False,
|
||
) -> float:
|
||
"""
|
||
计算输入字符串所需的时间，中文和英文字符有不同的输入时间
|
||
input_string (str): 输入的字符串
|
||
chinese_time (float): 中文字符的输入时间，默认为0.2秒
|
||
english_time (float): 英文字符的输入时间，默认为0.1秒
|
||
is_emoji (bool): 是否为emoji，默认为False
|
||
|
||
特殊情况：
|
||
- 如果只有一个中文字符，将使用3倍的中文输入时间
|
||
- 在所有输入结束后，额外加上回车时间0.3秒
|
||
- 如果is_emoji为True，将使用固定1秒的输入时间
|
||
"""
|
||
# chinese_time *= 1 / typing_speed_multiplier
|
||
# english_time *= 1 / typing_speed_multiplier
|
||
# 计算中文字符数
|
||
chinese_chars = sum("\u4e00" <= char <= "\u9fff" for char in input_string)
|
||
|
||
# 如果只有一个中文字符，使用3倍时间
|
||
if chinese_chars == 1 and len(input_string.strip()) == 1:
|
||
return chinese_time * 3 + 0.3 # 加上回车时间
|
||
|
||
# 正常计算所有字符的输入时间
|
||
total_time = 0.0
|
||
for char in input_string:
|
||
total_time += chinese_time if "\u4e00" <= char <= "\u9fff" else english_time
|
||
if is_emoji:
|
||
total_time = 1
|
||
|
||
if time.time() - thinking_start_time > 10:
|
||
total_time = 1
|
||
|
||
# print(f"thinking_start_time:{thinking_start_time}")
|
||
# print(f"nowtime:{time.time()}")
|
||
# print(f"nowtime - thinking_start_time:{time.time() - thinking_start_time}")
|
||
# print(f"{total_time}")
|
||
|
||
return total_time # 加上回车时间
|
||
|
||
|
||
|
||
def truncate_message(message: str, max_length=20) -> str:
|
||
"""截断消息，使其不超过指定长度"""
|
||
return f"{message[:max_length]}..." if len(message) > max_length else message
|
||
|
||
|
||
def protect_kaomoji(sentence):
|
||
""" "
|
||
识别并保护句子中的颜文字（含括号与无括号），将其替换为占位符，
|
||
并返回替换后的句子和占位符到颜文字的映射表。
|
||
Args:
|
||
sentence (str): 输入的原始句子
|
||
Returns:
|
||
tuple: (处理后的句子, {占位符: 颜文字})
|
||
"""
|
||
kaomoji_pattern = re.compile(
|
||
r"("
|
||
r"[(\[（【]" # 左括号
|
||
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
|
||
r"[^一-龥a-zA-Z0-9\s]" # 非中文、非英文、非数字、非空格字符（必须包含至少一个）
|
||
r"[^()\[\]（）【】]*?" # 非括号字符（惰性匹配）
|
||
r"[)\]）】" # 右括号
|
||
r"]"
|
||
r")"
|
||
r"|"
|
||
r"([▼▽・ᴥω･﹏^><≧≦￣｀´∀ヮДд︿﹀へ｡ﾟ╥╯╰︶︹•⁄]{2,15})"
|
||
)
|
||
|
||
kaomoji_matches = kaomoji_pattern.findall(sentence)
|
||
placeholder_to_kaomoji = {}
|
||
|
||
for idx, match in enumerate(kaomoji_matches):
|
||
kaomoji = match[0] or match[1]
|
||
placeholder = f"__KAOMOJI_{idx}__"
|
||
sentence = sentence.replace(kaomoji, placeholder, 1)
|
||
placeholder_to_kaomoji[placeholder] = kaomoji
|
||
|
||
return sentence, placeholder_to_kaomoji
|
||
|
||
|
||
def recover_kaomoji(sentences, placeholder_to_kaomoji):
|
||
"""
|
||
根据映射表恢复句子中的颜文字。
|
||
Args:
|
||
sentences (list): 含有占位符的句子列表
|
||
placeholder_to_kaomoji (dict): 占位符到颜文字的映射表
|
||
Returns:
|
||
list: 恢复颜文字后的句子列表
|
||
"""
|
||
recovered_sentences = []
|
||
for sentence in sentences:
|
||
for placeholder, kaomoji in placeholder_to_kaomoji.items():
|
||
sentence = sentence.replace(placeholder, kaomoji)
|
||
recovered_sentences.append(sentence)
|
||
return recovered_sentences
|
||
|
||
|
||
def get_western_ratio(paragraph):
|
||
"""计算段落中字母数字字符的西文比例
|
||
原理：检查段落中字母数字字符的西文比例
|
||
通过is_english_letter函数判断每个字符是否为西文
|
||
只检查字母数字字符，忽略标点符号和空格等非字母数字字符
|
||
|
||
Args:
|
||
paragraph: 要检查的文本段落
|
||
|
||
Returns:
|
||
float: 西文字符比例(0.0-1.0)，如果没有字母数字字符则返回0.0
|
||
"""
|
||
alnum_chars = [char for char in paragraph if char.isalnum()]
|
||
if not alnum_chars:
|
||
return 0.0
|
||
|
||
western_count = sum(bool(is_english_letter(char)) for char in alnum_chars)
|
||
return western_count / len(alnum_chars)
|
||
|
||
|
||
|
||
def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> str:
|
||
# sourcery skip: merge-comparisons, merge-duplicate-blocks, switch
|
||
"""将时间戳转换为人类可读的时间格式
|
||
|
||
Args:
|
||
timestamp: 时间戳
|
||
mode: 转换模式，"normal"为标准格式，"relative"为相对时间格式
|
||
|
||
Returns:
|
||
str: 格式化后的时间字符串
|
||
"""
|
||
if mode == "normal":
|
||
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp))
|
||
elif mode == "normal_no_YMD":
|
||
return time.strftime("%H:%M:%S", time.localtime(timestamp))
|
||
elif mode == "relative":
|
||
now = time.time()
|
||
diff = now - timestamp
|
||
|
||
if diff < 20:
|
||
return "刚刚"
|
||
elif diff < 60:
|
||
return f"{int(diff)}秒前"
|
||
elif diff < 3600:
|
||
return f"{int(diff / 60)}分钟前"
|
||
elif diff < 86400:
|
||
return f"{int(diff / 3600)}小时前"
|
||
elif diff < 86400 * 2:
|
||
return f"{int(diff / 86400)}天前"
|
||
else:
|
||
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) + ":"
|
||
else: # mode = "lite" or unknown
|
||
# 只返回时分秒格式
|
||
return time.strftime("%H:%M:%S", time.localtime(timestamp))
|
||
|
||
|
||
def get_chat_type_and_target_info(chat_id: str) -> Tuple[bool, Optional["TargetPersonInfo"]]:
|
||
"""
|
||
获取聊天类型（是否群聊）和私聊对象信息。
|
||
|
||
Args:
|
||
chat_id: 聊天流ID
|
||
|
||
Returns:
|
||
Tuple[bool, Optional[Dict]]:
|
||
- bool: 是否为群聊 (True 是群聊, False 是私聊或未知)
|
||
- Optional[Dict]: 如果是私聊，包含对方信息的字典；否则为 None。
|
||
字典包含: platform, user_id, user_nickname, person_id, person_name
|
||
"""
|
||
is_group_chat = False # Default to private/unknown
|
||
chat_target_info = None
|
||
|
||
try:
|
||
if chat_stream := get_chat_manager().get_stream(chat_id):
|
||
if chat_stream.group_info:
|
||
is_group_chat = True
|
||
chat_target_info = None # Explicitly None for group chat
|
||
elif chat_stream.user_info: # It's a private chat
|
||
is_group_chat = False
|
||
user_info = chat_stream.user_info
|
||
platform: str = chat_stream.platform
|
||
user_id: str = user_info.user_id # type: ignore
|
||
|
||
from src.common.data_models.info_data_model import TargetPersonInfo # 解决循环导入问题
|
||
|
||
# Initialize target_info with basic info
|
||
target_info = TargetPersonInfo(
|
||
platform=platform,
|
||
user_id=user_id,
|
||
user_nickname=user_info.user_nickname, # type: ignore
|
||
person_id=None,
|
||
person_name=None,
|
||
)
|
||
|
||
# Try to fetch person info
|
||
try:
|
||
person = Person(platform=platform, user_id=user_id)
|
||
if not person.is_known:
|
||
logger.warning(f"用户 {user_info.user_nickname} 尚未认识")
|
||
# 如果用户尚未认识，则返回False和None
|
||
return False, None
|
||
if person.person_id:
|
||
target_info.person_id = person.person_id
|
||
target_info.person_name = person.person_name
|
||
except Exception as person_e:
|
||
logger.warning(
|
||
f"获取 person_id 或 person_name 时出错 for {platform}:{user_id} in utils: {person_e}"
|
||
)
|
||
|
||
chat_target_info = target_info
|
||
else:
|
||
logger.warning(f"无法获取 chat_stream for {chat_id} in utils")
|
||
except Exception as e:
|
||
logger.error(f"获取聊天类型和目标信息时出错 for {chat_id}: {e}", exc_info=True)
|
||
|
||
return is_group_chat, chat_target_info
|
||
|
||
|
||
def assign_message_ids(messages: List[DatabaseMessages]) -> List[Tuple[str, DatabaseMessages]]:
|
||
"""
|
||
为消息列表中的每个消息分配唯一的简短随机ID
|
||
|
||
Args:
|
||
messages: 消息列表
|
||
|
||
Returns:
|
||
List[DatabaseMessages]: 分配了唯一ID的消息列表(写入message_id属性)
|
||
"""
|
||
result: List[Tuple[str, DatabaseMessages]] = [] # 复制原始消息列表
|
||
used_ids = set()
|
||
len_i = len(messages)
|
||
if len_i > 100:
|
||
a = 10
|
||
b = 99
|
||
else:
|
||
a = 1
|
||
b = 9
|
||
|
||
for i, message in enumerate(messages):
|
||
# 生成唯一的简短ID
|
||
while True:
|
||
# 使用索引+随机数生成简短ID
|
||
random_suffix = random.randint(a, b)
|
||
message_id = f"m{i + 1}{random_suffix}"
|
||
|
||
if message_id not in used_ids:
|
||
used_ids.add(message_id)
|
||
break
|
||
result.append((message_id, message))
|
||
|
||
return result
|
||
|
||
|
||
def parse_keywords_string(keywords_input) -> list[str]:
|
||
# sourcery skip: use-contextlib-suppress
|
||
"""
|
||
统一的关键词解析函数，支持多种格式的关键词字符串解析
|
||
|
||
支持的格式：
|
||
1. 字符串列表格式：'["utils.py", "修改", "代码", "动作"]'
|
||
2. 斜杠分隔格式：'utils.py/修改/代码/动作'
|
||
3. 逗号分隔格式：'utils.py,修改,代码,动作'
|
||
4. 空格分隔格式：'utils.py 修改 代码 动作'
|
||
5. 已经是列表的情况：["utils.py", "修改", "代码", "动作"]
|
||
6. JSON格式字符串：'{"keywords": ["utils.py", "修改", "代码", "动作"]}'
|
||
|
||
Args:
|
||
keywords_input: 关键词输入，可以是字符串或列表
|
||
|
||
Returns:
|
||
list[str]: 解析后的关键词列表，去除空白项
|
||
"""
|
||
if not keywords_input:
|
||
return []
|
||
|
||
# 如果已经是列表，直接处理
|
||
if isinstance(keywords_input, list):
|
||
return [str(k).strip() for k in keywords_input if str(k).strip()]
|
||
|
||
# 转换为字符串处理
|
||
keywords_str = str(keywords_input).strip()
|
||
if not keywords_str:
|
||
return []
|
||
|
||
try:
|
||
# 尝试作为JSON对象解析（支持 {"keywords": [...]} 格式）
|
||
json_data = json.loads(keywords_str)
|
||
if isinstance(json_data, dict) and "keywords" in json_data:
|
||
keywords_list = json_data["keywords"]
|
||
if isinstance(keywords_list, list):
|
||
return [str(k).strip() for k in keywords_list if str(k).strip()]
|
||
elif isinstance(json_data, list):
|
||
# 直接是JSON数组格式
|
||
return [str(k).strip() for k in json_data if str(k).strip()]
|
||
except (json.JSONDecodeError, ValueError):
|
||
pass
|
||
|
||
try:
|
||
# 尝试使用 ast.literal_eval 解析（支持Python字面量格式）
|
||
parsed = ast.literal_eval(keywords_str)
|
||
if isinstance(parsed, list):
|
||
return [str(k).strip() for k in parsed if str(k).strip()]
|
||
except (ValueError, SyntaxError):
|
||
pass
|
||
|
||
# 尝试不同的分隔符
|
||
separators = ["/", ",", " ", "|", ";"]
|
||
|
||
for separator in separators:
|
||
if separator in keywords_str:
|
||
keywords_list = [k.strip() for k in keywords_str.split(separator) if k.strip()]
|
||
if len(keywords_list) > 1: # 确保分割有效
|
||
return keywords_list
|
||
|
||
# 如果没有分隔符，返回单个关键词
|
||
return [keywords_str] if keywords_str else []
|
||
|
||
|
||
def cut_key_words(concept_name: str) -> list[str]:
|
||
"""对概念名称进行jieba分词，并过滤掉关键词列表中的关键词"""
|
||
concept_name_tokens = list(jieba.cut(concept_name))
|
||
|
||
# 定义常见连词、停用词与标点
|
||
conjunctions = {"和", "与", "及", "跟", "以及", "并且", "而且", "或", "或者", "并"}
|
||
stop_words = {
|
||
"的",
|
||
"了",
|
||
"呢",
|
||
"吗",
|
||
"吧",
|
||
"啊",
|
||
"哦",
|
||
"恩",
|
||
"嗯",
|
||
"呀",
|
||
"嘛",
|
||
"哇",
|
||
"在",
|
||
"是",
|
||
"很",
|
||
"也",
|
||
"又",
|
||
"就",
|
||
"都",
|
||
"还",
|
||
"更",
|
||
"最",
|
||
"被",
|
||
"把",
|
||
"给",
|
||
"对",
|
||
"和",
|
||
"与",
|
||
"及",
|
||
"跟",
|
||
"并",
|
||
"而且",
|
||
"或者",
|
||
"或",
|
||
"以及",
|
||
}
|
||
chinese_punctuations = set("，。！？、；：（）【】《》“”‘’—…·-——,.!?;:()[]<>'\"/\\")
|
||
|
||
# 清理空白并初步过滤纯标点
|
||
cleaned_tokens = []
|
||
for tok in concept_name_tokens:
|
||
t = tok.strip()
|
||
if not t:
|
||
continue
|
||
# 去除纯标点
|
||
if all(ch in chinese_punctuations for ch in t):
|
||
continue
|
||
cleaned_tokens.append(t)
|
||
|
||
# 合并连词两侧的词（仅当两侧都存在且不是标点/停用词时）
|
||
merged_tokens = []
|
||
i = 0
|
||
n = len(cleaned_tokens)
|
||
while i < n:
|
||
tok = cleaned_tokens[i]
|
||
if tok in conjunctions and merged_tokens and i + 1 < n:
|
||
left = merged_tokens[-1]
|
||
right = cleaned_tokens[i + 1]
|
||
# 左右都需要是有效词
|
||
if (
|
||
left
|
||
and right
|
||
and left not in conjunctions
|
||
and right not in conjunctions
|
||
and left not in stop_words
|
||
and right not in stop_words
|
||
and not all(ch in chinese_punctuations for ch in left)
|
||
and not all(ch in chinese_punctuations for ch in right)
|
||
):
|
||
# 合并为一个新词，并替换掉左侧与跳过右侧
|
||
combined = f"{left}{tok}{right}"
|
||
merged_tokens[-1] = combined
|
||
i += 2
|
||
continue
|
||
# 常规推进
|
||
merged_tokens.append(tok)
|
||
i += 1
|
||
|
||
# 二次过滤：去除停用词、单字符纯标点与无意义项
|
||
result_tokens = []
|
||
seen = set()
|
||
# ban_words = set(getattr(global_config.memory, "memory_ban_words", []) or [])
|
||
for tok in merged_tokens:
|
||
if tok in conjunctions:
|
||
# 独立连词丢弃
|
||
continue
|
||
if tok in stop_words:
|
||
continue
|
||
# if tok in ban_words:
|
||
# continue
|
||
if all(ch in chinese_punctuations for ch in tok):
|
||
continue
|
||
if tok.strip() == "":
|
||
continue
|
||
if tok not in seen:
|
||
seen.add(tok)
|
||
result_tokens.append(tok)
|
||
|
||
filtered_concept_name_tokens = result_tokens
|
||
return filtered_concept_name_tokens
|