feat:新增麦麦好奇功能,优化记忆构建

This commit is contained in:
SengokuCola
2025-09-29 23:46:49 +08:00
parent d519406e4a
commit e2310de6b5
20 changed files with 554 additions and 46 deletions

View File

@@ -0,0 +1,156 @@
# -*- coding: utf-8 -*-
"""
记忆系统工具函数
包含模糊查找、相似度计算等工具函数
"""
import re
from difflib import SequenceMatcher
from typing import List, Tuple, Optional
from src.common.database.database_model import MemoryChest as MemoryChestModel
from src.common.logger import get_logger
logger = get_logger("memory_utils")
def calculate_similarity(text1: str, text2: str) -> float:
"""
计算两个文本的相似度
Args:
text1: 第一个文本
text2: 第二个文本
Returns:
float: 相似度分数 (0-1)
"""
try:
# 预处理文本
text1 = preprocess_text(text1)
text2 = preprocess_text(text2)
# 使用SequenceMatcher计算相似度
similarity = SequenceMatcher(None, text1, text2).ratio()
# 如果其中一个文本包含另一个,提高相似度
if text1 in text2 or text2 in text1:
similarity = max(similarity, 0.8)
return similarity
except Exception as e:
logger.error(f"计算相似度时出错: {e}")
return 0.0
def preprocess_text(text: str) -> str:
"""
预处理文本,提高匹配准确性
Args:
text: 原始文本
Returns:
str: 预处理后的文本
"""
try:
# 转换为小写
text = text.lower()
# 移除标点符号和特殊字符
text = re.sub(r'[^\w\s]', '', text)
# 移除多余空格
text = re.sub(r'\s+', ' ', text).strip()
return text
except Exception as e:
logger.error(f"预处理文本时出错: {e}")
return text
def fuzzy_find_memory_by_title(target_title: str, similarity_threshold: float = 0.9) -> List[Tuple[str, str, float]]:
"""
根据标题模糊查找记忆
Args:
target_title: 目标标题
similarity_threshold: 相似度阈值默认0.9
Returns:
List[Tuple[str, str, float]]: 匹配的记忆列表,每个元素为(title, content, similarity_score)
"""
try:
# 获取所有记忆
all_memories = MemoryChestModel.select()
matches = []
for memory in all_memories:
similarity = calculate_similarity(target_title, memory.title)
if similarity >= similarity_threshold:
matches.append((memory.title, memory.content, similarity))
# 按相似度降序排序
matches.sort(key=lambda x: x[2], reverse=True)
logger.info(f"模糊查找标题 '{target_title}' 找到 {len(matches)} 个匹配项")
return matches
except Exception as e:
logger.error(f"模糊查找记忆时出错: {e}")
return []
def find_best_matching_memory(target_title: str, similarity_threshold: float = 0.9) -> Optional[Tuple[str, str, float]]:
"""
查找最佳匹配的记忆
Args:
target_title: 目标标题
similarity_threshold: 相似度阈值
Returns:
Optional[Tuple[str, str, float]]: 最佳匹配的记忆(title, content, similarity)或None
"""
try:
matches = fuzzy_find_memory_by_title(target_title, similarity_threshold)
if matches:
best_match = matches[0] # 已经按相似度排序,第一个是最佳匹配
logger.info(f"找到最佳匹配: '{best_match[0]}' (相似度: {best_match[2]:.3f})")
return best_match
else:
logger.info(f"未找到相似度 >= {similarity_threshold} 的记忆")
return None
except Exception as e:
logger.error(f"查找最佳匹配记忆时出错: {e}")
return None
def check_title_exists_fuzzy(target_title: str, similarity_threshold: float = 0.9) -> bool:
"""
检查标题是否已存在(模糊匹配)
Args:
target_title: 目标标题
similarity_threshold: 相似度阈值默认0.9(较高阈值避免误判)
Returns:
bool: 是否存在相似标题
"""
try:
matches = fuzzy_find_memory_by_title(target_title, similarity_threshold)
exists = len(matches) > 0
if exists:
logger.info(f"发现相似标题: '{matches[0][0]}' (相似度: {matches[0][2]:.3f})")
else:
logger.debug("未发现相似标题")
return exists
except Exception as e:
logger.error(f"检查标题是否存在时出错: {e}")
return False