From af9bc64acf17f96215ad439125e5b04d65e12123 Mon Sep 17 00:00:00 2001 From: Bakadax Date: Fri, 16 May 2025 15:38:11 +0800 Subject: [PATCH] ruff x2 --- .../focus_chat/heartflow_prompt_builder.py | 3 +- src/chat/utils/utils.py | 33 +++++++++---------- 2 files changed, 17 insertions(+), 19 deletions(-) diff --git a/src/chat/focus_chat/heartflow_prompt_builder.py b/src/chat/focus_chat/heartflow_prompt_builder.py index f1db498d..c356aabb 100644 --- a/src/chat/focus_chat/heartflow_prompt_builder.py +++ b/src/chat/focus_chat/heartflow_prompt_builder.py @@ -6,14 +6,13 @@ from src.chat.utils.chat_message_builder import build_readable_messages, get_raw from src.chat.person_info.relationship_manager import relationship_manager from src.chat.utils.utils import get_embedding import time -from typing import Union, Optional, Dict, Any +from typing import Union, Optional from src.common.database import db from src.chat.utils.utils import get_recent_group_speaker from src.manager.mood_manager import mood_manager from src.chat.memory_system.Hippocampus import HippocampusManager from src.chat.knowledge.knowledge_lib import qa_manager from src.chat.focus_chat.expressors.exprssion_learner import expression_learner -import traceback import random from src.plugins.group_nickname.nickname_manager import nickname_manager diff --git a/src/chat/utils/utils.py b/src/chat/utils/utils.py index 425606e1..d30d96ba 100644 --- a/src/chat/utils/utils.py +++ b/src/chat/utils/utils.py @@ -194,7 +194,7 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: ) # 判断是否被@ - if re.search(f"@[\\s\\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text): + if re.search(f"@[\s\S]*?（id:{global_config.BOT_QQ}）", message.processed_plain_text): is_at = True is_mentioned = True @@ -205,7 +205,7 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]: if not is_mentioned: # 判断是否被回复 if re.match( - f"\\[回复 [\\s\\S]*?\\({str(global_config.BOT_QQ)}\\)：[\\s\\S]*?\\]，说：", + f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?\]，说：", message.processed_plain_text, ): is_mentioned = True @@ -430,11 +430,11 @@ def split_into_sentences_w_remove_punctuation(original_text: str) -> list[str]: return [] if len_text < 12: - split_strength = 0.5 + split_strength = 0.2 elif len_text < 32: - split_strength = 0.7 + split_strength = 0.5 else: - split_strength = 0.9 + split_strength = 0.7 merge_probability = 1.0 - split_strength if merge_probability == 1.0 and len(preliminary_final_sentences) > 1: @@ -497,13 +497,13 @@ def random_remove_punctuation(text: str) -> str: if char == "。" and i == text_len - 1: # 结尾的句号 if random.random() > 0.1: # 90%概率删除结尾句号 continue - # elif char == "，": - # rand = random.random() - # if rand < 0.25: # 25%概率删除逗号 - # continue - # elif rand < 0.2: # 20%概率把逗号变成空格 - # result += " " - # continue + elif char == "，": + rand = random.random() + if rand < 0.25: # 25%概率删除逗号 + continue + elif rand < 0.2: # 20%概率把逗号变成空格 + result += " " + continue result += char return result @@ -516,8 +516,8 @@ def process_llm_response(text: str) -> list[str]: else: protected_text = text kaomoji_mapping = {} - # 提取被 [] 包裹且包含中文的内容 - pattern = re.compile(r"[\[](?=.*[一-鿿]).*?[\]）]") + # 提取被 () 或 [] 包裹且包含中文的内容 + pattern = re.compile(r"[(\[（](?=.*[一-鿿]).*?[)\]）]") # _extracted_contents = pattern.findall(text) _extracted_contents = pattern.findall(protected_text) # 在保护后的文本上查找 # 去除 () 和 [] 及其包裹的内容 @@ -527,7 +527,6 @@ def process_llm_response(text: str) -> list[str]: return ["呃呃"] logger.debug(f"{text}去除括号处理后的文本: {cleaned_text}") - cleaned_text = protected_text # 对清理后的文本进行进一步处理 max_length = global_config.response_max_length * 2 max_sentence_num = global_config.response_max_sentence_num @@ -559,7 +558,7 @@ def process_llm_response(text: str) -> list[str]: else: sentences.append(sentence) - if len(sentences) > (max_sentence_num * 2): + 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 [f"{global_config.BOT_NICKNAME}不知道哦"] @@ -944,4 +943,4 @@ def recover_book_titles(sentences, book_title_mapping): for placeholder, original_content in book_title_mapping.items(): sentence = sentence.replace(placeholder, original_content) recovered_sentences.append(sentence) - return recovered_sentences \ No newline at end of file + return recovered_sentences