da4736c323
主要更新内容: - 🚀 飞书多维表格集成，支持工单数据同步 - 🤖 AI建议与人工描述语义相似度计算 - 🎨 前端UI全面优化，现代化设计 - 📊 智能知识库入库策略（AI准确率<90%使用人工描述） - 🔧 代码重构，模块化架构优化 - 📚 完整文档整合和更新 - 🐛 修复配置导入和数据库字段问题 技术特性: - 使用sentence-transformers进行语义相似度计算 - 快速模式结合TF-IDF和语义方法 - 响应式设计，支持移动端 - 加载状态和动画效果 - 配置化AI准确率阈值
219 lines
7.0 KiB
Python
219 lines
7.0 KiB
Python
import logging
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import json
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import re
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from typing import Dict, List, Any, Optional
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from datetime import datetime, timedelta
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import hashlib
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def setup_logging(log_level: str = "INFO", log_file: str = "logs/tsp_assistant.log"):
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"""设置日志配置"""
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import os
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# 创建日志目录
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os.makedirs(os.path.dirname(log_file), exist_ok=True)
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logging.basicConfig(
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level=getattr(logging, log_level.upper()),
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(log_file, encoding='utf-8'),
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logging.StreamHandler()
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]
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)
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def validate_work_order_data(data: Dict[str, Any]) -> Dict[str, Any]:
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"""验证工单数据"""
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errors = []
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required_fields = ["title", "description", "category"]
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for field in required_fields:
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if not data.get(field):
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errors.append(f"缺少必填字段: {field}")
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# 验证优先级
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if "priority" in data and data["priority"] not in ["low", "medium", "high", "critical"]:
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errors.append("优先级必须是: low, medium, high, critical")
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# 验证类别
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valid_categories = [
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"技术问题", "账户问题", "支付问题", "产品问题",
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"服务问题", "投诉建议", "其他"
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]
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if "category" in data and data["category"] not in valid_categories:
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errors.append(f"类别必须是: {', '.join(valid_categories)}")
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return {
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"valid": len(errors) == 0,
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"errors": errors
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}
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def extract_keywords(text: str, max_keywords: int = 10) -> List[str]:
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"""提取文本关键词"""
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# 简单的关键词提取（可以后续改进为更复杂的算法）
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import jieba
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# 停用词
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stop_words = {
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"的", "了", "在", "是", "我", "有", "和", "就", "不", "人", "都", "一", "一个", "上", "也", "很", "到", "说", "要", "去", "你", "会", "着", "没有", "看", "好", "自己", "这"
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}
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# 分词
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words = jieba.cut(text)
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# 过滤停用词和短词
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keywords = []
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for word in words:
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if len(word) > 1 and word not in stop_words and not word.isdigit():
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keywords.append(word)
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# 统计词频
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word_count = {}
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for word in keywords:
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word_count[word] = word_count.get(word, 0) + 1
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# 返回频率最高的关键词
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sorted_words = sorted(word_count.items(), key=lambda x: x[1], reverse=True)
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return [word for word, count in sorted_words[:max_keywords]]
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def calculate_similarity(text1: str, text2: str) -> float:
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"""计算文本相似度（使用语义相似度）"""
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try:
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from src.utils.semantic_similarity import calculate_semantic_similarity
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return calculate_semantic_similarity(text1, text2)
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except Exception as e:
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logging.error(f"计算语义相似度失败: {e}")
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# 回退到传统方法
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try:
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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vectorizer = TfidfVectorizer()
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vectors = vectorizer.fit_transform([text1, text2])
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similarity = cosine_similarity(vectors[0:1], vectors[1:2])[0][0]
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return float(similarity)
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except Exception as e2:
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logging.error(f"计算TF-IDF相似度失败: {e2}")
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return 0.0
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def format_time_duration(seconds: float) -> str:
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"""格式化时间持续时间"""
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if seconds < 60:
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return f"{seconds:.0f}秒"
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elif seconds < 3600:
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minutes = seconds / 60
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return f"{minutes:.1f}分钟"
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elif seconds < 86400:
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hours = seconds / 3600
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return f"{hours:.1f}小时"
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else:
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days = seconds / 86400
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return f"{days:.1f}天"
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def generate_order_id() -> str:
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"""生成工单ID"""
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
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random_suffix = hashlib.md5(timestamp.encode()).hexdigest()[:6]
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return f"WO{timestamp}{random_suffix}"
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def parse_date_range(date_range: str) -> tuple:
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"""解析日期范围"""
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today = datetime.now().date()
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if date_range == "today":
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return today, today
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elif date_range == "yesterday":
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yesterday = today - timedelta(days=1)
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return yesterday, yesterday
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elif date_range == "week":
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start = today - timedelta(days=today.weekday())
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return start, today
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elif date_range == "month":
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start = today.replace(day=1)
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return start, today
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elif date_range == "last_7_days":
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start = today - timedelta(days=7)
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return start, today
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elif date_range == "last_30_days":
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start = today - timedelta(days=30)
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return start, today
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else:
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# 尝试解析自定义日期范围
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try:
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start_str, end_str = date_range.split(" to ")
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start = datetime.strptime(start_str, "%Y-%m-%d").date()
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end = datetime.strptime(end_str, "%Y-%m-%d").date()
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return start, end
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except:
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return today, today
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def sanitize_text(text: str) -> str:
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"""清理文本内容"""
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if not text:
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return ""
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# 移除多余的空白字符
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text = re.sub(r'\s+', ' ', text.strip())
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# 移除特殊字符（保留中文、英文、数字和基本标点）
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text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9\s.,!?;:()（）]', '', text)
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return text
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def chunk_text(text: str, max_length: int = 1000) -> List[str]:
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"""将长文本分割成小块"""
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if len(text) <= max_length:
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return [text]
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chunks = []
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current_chunk = ""
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# 按句子分割
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sentences = re.split(r'[。！？.!?]', text)
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for sentence in sentences:
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sentence = sentence.strip()
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if not sentence:
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continue
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if len(current_chunk) + len(sentence) <= max_length:
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current_chunk += sentence + "。"
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else:
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if current_chunk:
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chunks.append(current_chunk.strip())
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current_chunk = sentence + "。"
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if current_chunk:
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chunks.append(current_chunk.strip())
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return chunks
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def merge_json_safely(dict1: Dict[str, Any], dict2: Dict[str, Any]) -> Dict[str, Any]:
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"""安全合并两个字典"""
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result = dict1.copy()
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for key, value in dict2.items():
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if key in result:
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if isinstance(result[key], dict) and isinstance(value, dict):
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result[key] = merge_json_safely(result[key], value)
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elif isinstance(result[key], list) and isinstance(value, list):
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result[key].extend(value)
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else:
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result[key] = value
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else:
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result[key] = value
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return result
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def get_memory_usage() -> Dict[str, float]:
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"""获取内存使用情况"""
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import psutil
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process = psutil.Process()
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memory_info = process.memory_info()
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return {
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"rss_mb": memory_info.rss / 1024 / 1024, # 物理内存
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"vms_mb": memory_info.vms / 1024 / 1024, # 虚拟内存
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"percent": process.memory_percent()
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}
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