782 lines
29 KiB
Python
782 lines
29 KiB
Python
# -*- coding: utf-8 -*-
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"""情绪分析服务模块
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本模块提供情绪数据的分析和统计功能，包括情绪标签、词云、健康指数计算等。
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Classes:
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EmotionAnalyticsService: 情绪分析服务，提供各种情绪分析功能
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"""
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import json
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import statistics
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from typing import Any, Dict, List, Optional
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from app.core.logging_config import get_business_logger
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from app.repositories.neo4j.emotion_repository import EmotionRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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from app.utils.config_utils import resolve_config_id
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logger = get_business_logger()
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class EmotionSuggestion(BaseModel):
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"""情绪建议模型"""
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type: str = Field(...,
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description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management")
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title: str = Field(..., description="建议标题")
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content: str = Field(..., description="建议内容")
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priority: str = Field(..., description="优先级：high/medium/low")
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actionable_steps: List[str] = Field(..., description="可执行步骤列表（3个）")
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class EmotionSuggestionsResponse(BaseModel):
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"""情绪建议响应模型"""
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health_summary: str = Field(..., description="健康状态摘要（不超过50字）")
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suggestions: List[EmotionSuggestion] = Field(..., description="建议列表（3-5条）")
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class EmotionAnalyticsService:
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"""情绪分析服务
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提供情绪数据的分析和统计功能，包括：
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- 情绪标签统计
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- 情绪词云数据
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- 情绪健康指数计算
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- 个性化情绪建议生成
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Attributes:
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emotion_repo: 情绪数据仓储实例
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"""
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def __init__(self):
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"""初始化情绪分析服务"""
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connector = Neo4jConnector()
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self.emotion_repo = EmotionRepository(connector)
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logger.info("情绪分析服务初始化完成")
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async def get_emotion_tags(
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self,
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end_user_id: str,
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emotion_type: Optional[str] = None,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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limit: int = 10
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) -> Dict[str, Any]:
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"""获取情绪标签统计
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查询指定用户的情绪类型分布，包括计数、百分比和平均强度。
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确保返回所有6个情绪维度（joy、sadness、anger、fear、surprise、neutral），
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即使某些维度没有数据也会返回count=0的记录。
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"""
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try:
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logger.info(f"获取情绪标签统计: user={end_user_id}, type={emotion_type}, "
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f"start={start_date}, end={end_date}, limit={limit}")
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# 调用仓储层查询
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tags = await self.emotion_repo.get_emotion_tags(
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end_user_id=end_user_id,
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emotion_type=emotion_type,
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start_date=start_date,
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end_date=end_date,
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limit=limit
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)
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# 定义所有6个情绪维度
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all_emotion_types = ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']
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# 将查询结果转换为字典，方便查找
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tags_dict = {tag["emotion_type"]: tag for tag in tags}
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# 补全缺失的情绪维度
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complete_tags = []
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for emotion in all_emotion_types:
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if emotion in tags_dict:
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complete_tags.append(tags_dict[emotion])
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else:
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# 如果该情绪类型不存在，添加默认值
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complete_tags.append({
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"emotion_type": emotion,
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"count": 0,
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"percentage": 0.0,
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"avg_intensity": 0.0
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})
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# 计算总数
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total_count = sum(tag["count"] for tag in complete_tags)
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# 如果有数据，重新计算百分比（因为补全了0值项）
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if total_count > 0:
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for tag in complete_tags:
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if tag["count"] > 0:
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tag["percentage"] = round((tag["count"] / total_count) * 100, 2)
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# 构建时间范围信息
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time_range = {}
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if start_date:
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time_range["start_date"] = start_date
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if end_date:
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time_range["end_date"] = end_date
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# 格式化响应
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response = {
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"tags": complete_tags,
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"total_count": total_count,
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"time_range": time_range if time_range else None
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}
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logger.info(f"情绪标签统计完成: total_count={total_count}, tags_count={len(complete_tags)}")
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return response
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except Exception as e:
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logger.error(f"获取情绪标签统计失败: {str(e)}", exc_info=True)
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raise
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async def get_emotion_wordcloud(
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self,
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end_user_id: str,
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emotion_type: Optional[str] = None,
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limit: int = 50
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) -> Dict[str, Any]:
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"""获取情绪词云数据
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查询情绪关键词及其频率，用于生成词云可视化。
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Args:
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end_user_id: 宿主ID（用户组ID）
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emotion_type: 可选的情绪类型过滤
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limit: 返回关键词的最大数量
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Returns:
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Dict: 包含情绪词云数据的响应：
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- keywords: 关键词列表
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- total_keywords: 总关键词数量
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"""
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try:
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logger.info(f"获取情绪词云数据: user={end_user_id}, type={emotion_type}, limit={limit}")
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# 调用仓储层查询
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keywords = await self.emotion_repo.get_emotion_wordcloud(
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end_user_id=end_user_id,
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emotion_type=emotion_type,
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limit=limit
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)
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# 计算总关键词数量
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total_keywords = len(keywords)
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# 格式化响应
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response = {
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"keywords": keywords,
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"total_keywords": total_keywords
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}
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logger.info(f"情绪词云数据获取完成: total_keywords={total_keywords}")
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return response
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except Exception as e:
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logger.error(f"获取情绪词云数据失败: {str(e)}", exc_info=True)
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raise
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def _calculate_positivity_rate(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""计算积极率
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根据情绪类型分类正面、负面和中性情绪，计算积极率。
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公式：(正面数 / (正面数 + 负面数)) * 100
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Args:
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emotions: 情绪数据列表，每个包含 emotion_type 字段
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Returns:
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Dict: 包含积极率计算结果：
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- score: 积极率分数（0-100）
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- positive_count: 正面情绪数量
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- negative_count: 负面情绪数量
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- neutral_count: 中性情绪数量
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"""
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# 定义情绪分类
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positive_emotions = {'joy', 'surprise'}
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negative_emotions = {'sadness', 'anger', 'fear'}
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# 统计各类情绪数量
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positive_count = sum(1 for e in emotions if e.get('emotion_type') in positive_emotions)
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negative_count = sum(1 for e in emotions if e.get('emotion_type') in negative_emotions)
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neutral_count = sum(1 for e in emotions if e.get('emotion_type') == 'neutral')
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# 计算积极率
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total_non_neutral = positive_count + negative_count
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if total_non_neutral > 0:
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score = (positive_count / total_non_neutral) * 100
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else:
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score = 50.0 # 如果没有非中性情绪，默认为50
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logger.debug(f"积极率计算: positive={positive_count}, negative={negative_count}, "
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f"neutral={neutral_count}, score={score:.2f}")
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return {
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"score": round(score, 2),
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"positive_count": positive_count,
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"negative_count": negative_count,
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"neutral_count": neutral_count
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}
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def _calculate_stability(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""计算稳定性
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基于情绪强度的标准差计算情绪稳定性。
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公式：(1 - min(std_deviation, 1.0)) * 100
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Args:
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emotions: 情绪数据列表，每个包含 emotion_intensity 字段
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Returns:
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Dict: 包含稳定性计算结果：
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- score: 稳定性分数（0-100）
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- std_deviation: 标准差
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"""
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# 提取所有情绪强度
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intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None]
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# 计算标准差
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if len(intensities) >= 2:
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std_deviation = statistics.stdev(intensities)
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elif len(intensities) == 1:
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std_deviation = 0.0 # 只有一个数据点，标准差为0
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else:
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std_deviation = 0.0 # 没有数据，标准差为0
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# 计算稳定性分数
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# 标准差越小，稳定性越高
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score = (1 - min(std_deviation, 1.0)) * 100
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logger.debug(f"稳定性计算: intensities_count={len(intensities)}, "
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f"std_deviation={std_deviation:.3f}, score={score:.2f}")
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return {
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"score": round(score, 2),
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"std_deviation": round(std_deviation, 3)
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}
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def _calculate_resilience(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""计算恢复力
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分析情绪转换模式，统计从负面情绪恢复到正面情绪的能力。
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公式：(负面到正面转换次数 / 总负面情绪数) * 100
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Args:
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emotions: 情绪数据列表，每个包含 emotion_type 和 created_at 字段
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应该按时间顺序排列
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Returns:
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Dict: 包含恢复力计算结果：
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- score: 恢复力分数（0-100）
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- recovery_rate: 恢复率（转换次数/负面情绪数）
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"""
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# 定义情绪分类
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positive_emotions = {'joy', 'surprise'}
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negative_emotions = {'sadness', 'anger', 'fear'}
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# 统计负面到正面的转换次数
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recovery_count = 0
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negative_count = 0
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for i in range(len(emotions)):
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current_emotion = emotions[i].get('emotion_type')
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# 统计负面情绪总数
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if current_emotion in negative_emotions:
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negative_count += 1
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# 检查下一个情绪是否为正面
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if i + 1 < len(emotions):
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next_emotion = emotions[i + 1].get('emotion_type')
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if next_emotion in positive_emotions:
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recovery_count += 1
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# 计算恢复力分数
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if negative_count > 0:
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recovery_rate = recovery_count / negative_count
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score = recovery_rate * 100
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else:
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# 如果没有负面情绪，恢复力设为100（最佳状态）
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recovery_rate = 1.0
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score = 100.0
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logger.debug(f"恢复力计算: negative_count={negative_count}, "
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f"recovery_count={recovery_count}, score={score:.2f}")
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return {
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"score": round(score, 2),
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"recovery_rate": round(recovery_rate, 3)
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}
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async def calculate_emotion_health_index(
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self,
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end_user_id: str,
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time_range: str = "30d"
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) -> Dict[str, Any]:
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"""计算情绪健康指数
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综合积极率、稳定性和恢复力计算情绪健康指数。
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Args:
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end_user_id: 宿主ID（用户组ID）
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time_range: 时间范围（7d/30d/90d）
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Returns:
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Dict: 包含情绪健康指数的完整响应：
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- health_score: 综合健康分数（0-100）
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- level: 健康等级（优秀/良好/一般/较差）
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- dimensions: 各维度详细数据
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- positivity_rate: 积极率
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- stability: 稳定性
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- resilience: 恢复力
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- emotion_distribution: 情绪分布统计
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- time_range: 时间范围
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"""
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try:
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logger.info(f"计算情绪健康指数: user={end_user_id}, time_range={time_range}")
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# 获取时间范围内的情绪数据
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emotions = await self.emotion_repo.get_emotions_in_range(
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end_user_id=end_user_id,
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time_range=time_range
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)
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# 如果没有数据，返回默认值
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if not emotions:
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logger.warning(f"用户 {end_user_id} 在时间范围 {time_range} 内没有情绪数据")
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return {
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"health_score": 0.0,
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"level": "无数据",
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"dimensions": {
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"positivity_rate": {"score": 0.0, "positive_count": 0, "negative_count": 0, "neutral_count": 0},
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"stability": {"score": 0.0, "std_deviation": 0.0},
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"resilience": {"score": 0.0, "recovery_rate": 0.0}
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},
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"emotion_distribution": {},
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"time_range": time_range
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}
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# 计算各维度指标
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positivity_rate = self._calculate_positivity_rate(emotions)
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stability = self._calculate_stability(emotions)
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resilience = self._calculate_resilience(emotions)
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# 计算综合健康分数
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# 公式：positivity_rate * 0.4 + stability * 0.3 + resilience * 0.3
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health_score = (
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positivity_rate["score"] * 0.4 +
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stability["score"] * 0.3 +
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resilience["score"] * 0.3
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)
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# 确定健康等级
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if health_score >= 80:
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level = "优秀"
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elif health_score >= 60:
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level = "良好"
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elif health_score >= 40:
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level = "一般"
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else:
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level = "较差"
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# 统计情绪分布
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emotion_distribution = {}
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for emotion_type in ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']:
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count = sum(1 for e in emotions if e.get('emotion_type') == emotion_type)
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emotion_distribution[emotion_type] = count
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# 格式化响应
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response = {
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"health_score": round(health_score, 2),
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"level": level,
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"dimensions": {
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"positivity_rate": positivity_rate,
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"stability": stability,
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"resilience": resilience
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},
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"emotion_distribution": emotion_distribution,
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"time_range": time_range
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}
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logger.info(f"情绪健康指数计算完成: score={health_score:.2f}, level={level}")
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return response
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except Exception as e:
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logger.error(f"计算情绪健康指数失败: {str(e)}", exc_info=True)
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raise
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def _analyze_emotion_patterns(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""分析情绪模式
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识别主要负面情绪、情绪触发因素和波动时段。
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Args:
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emotions: 情绪数据列表，每个包含 emotion_type、emotion_intensity、created_at 字段
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Returns:
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Dict: 包含情绪模式分析结果：
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- dominant_negative_emotion: 主要负面情绪类型
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- high_intensity_emotions: 高强度情绪列表
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- emotion_volatility: 情绪波动性（高/中/低）
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"""
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negative_emotions = {'sadness', 'anger', 'fear'}
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# 统计负面情绪分布
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negative_emotion_counts = {}
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for emotion in emotions:
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emotion_type = emotion.get('emotion_type')
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if emotion_type in negative_emotions:
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negative_emotion_counts[emotion_type] = negative_emotion_counts.get(emotion_type, 0) + 1
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# 识别主要负面情绪
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dominant_negative_emotion = None
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if negative_emotion_counts:
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dominant_negative_emotion = max(negative_emotion_counts, key=negative_emotion_counts.get)
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# 识别高强度情绪（强度 >= 0.7）
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high_intensity_emotions = [
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{
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"type": e.get('emotion_type'),
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"intensity": e.get('emotion_intensity'),
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"created_at": e.get('created_at')
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}
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for e in emotions
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if e.get('emotion_intensity', 0) >= 0.7
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]
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# 评估情绪波动性
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intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None]
|
||
if len(intensities) >= 2:
|
||
std_dev = statistics.stdev(intensities)
|
||
if std_dev > 0.3:
|
||
volatility = "高"
|
||
elif std_dev > 0.15:
|
||
volatility = "中"
|
||
else:
|
||
volatility = "低"
|
||
else:
|
||
volatility = "未知"
|
||
|
||
logger.debug(f"情绪模式分析: dominant_negative={dominant_negative_emotion}, "
|
||
f"high_intensity_count={len(high_intensity_emotions)}, volatility={volatility}")
|
||
|
||
return {
|
||
"dominant_negative_emotion": dominant_negative_emotion,
|
||
"high_intensity_emotions": high_intensity_emotions[:5], # 最多返回5个
|
||
"emotion_volatility": volatility
|
||
}
|
||
|
||
async def generate_emotion_suggestions(
|
||
self,
|
||
end_user_id: str,
|
||
db: Session,
|
||
) -> Dict[str, Any]:
|
||
"""生成个性化情绪建议
|
||
|
||
基于情绪健康数据和用户画像生成个性化建议。
|
||
|
||
Args:
|
||
end_user_id: 宿主ID（用户组ID）
|
||
db: 数据库会话
|
||
|
||
Returns:
|
||
Dict: 包含个性化建议的响应：
|
||
- health_summary: 健康状态摘要
|
||
- suggestions: 建议列表（3-5条）
|
||
"""
|
||
try:
|
||
logger.info(f"生成个性化情绪建议: user={end_user_id}")
|
||
|
||
# 1. 从 end_user_id 获取关联的 memory_config_id
|
||
llm_client = None
|
||
try:
|
||
from app.services.memory_agent_service import (
|
||
get_end_user_connected_config,
|
||
)
|
||
|
||
connected_config = get_end_user_connected_config(end_user_id, db)
|
||
config_id = connected_config.get("memory_config_id")
|
||
config_id = resolve_config_id(config_id, db)
|
||
if config_id is not None:
|
||
from app.services.memory_config_service import (
|
||
MemoryConfigService,
|
||
)
|
||
config_service = MemoryConfigService(db)
|
||
memory_config = config_service.load_memory_config(
|
||
config_id=(config_id),
|
||
service_name="EmotionAnalyticsService.generate_emotion_suggestions"
|
||
)
|
||
from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
|
||
factory = MemoryClientFactory(db)
|
||
llm_client = factory.get_llm_client(str(memory_config.llm_model_id))
|
||
except Exception as e:
|
||
logger.warning(f"无法获取 end_user {end_user_id} 的配置，将使用默认配置: {e}")
|
||
|
||
# 2. 获取情绪健康数据
|
||
health_data = await self.calculate_emotion_health_index(end_user_id, time_range="30d")
|
||
|
||
# 3. 获取情绪数据用于模式分析
|
||
emotions = await self.emotion_repo.get_emotions_in_range(
|
||
end_user_id=end_user_id,
|
||
time_range="30d"
|
||
)
|
||
|
||
# 4. 分析情绪模式
|
||
patterns = self._analyze_emotion_patterns(emotions)
|
||
|
||
# 5. 获取用户画像数据（简化版，直接从Neo4j获取）
|
||
user_profile = await self._get_simple_user_profile(end_user_id)
|
||
|
||
# 6. 构建LLM prompt
|
||
prompt = await self._build_suggestion_prompt(health_data, patterns, user_profile)
|
||
|
||
# 7. 调用LLM生成建议（使用配置中的LLM）
|
||
if llm_client is None:
|
||
# 无法获取配置时，抛出错误而不是使用默认配置
|
||
raise ValueError("无法获取LLM配置，请确保end_user关联了有效的memory_config")
|
||
|
||
# 将 prompt 转换为 messages 格式
|
||
messages = [
|
||
{"role": "user", "content": prompt}
|
||
]
|
||
|
||
# 8. 使用结构化输出直接获取 Pydantic 模型
|
||
try:
|
||
suggestions_response = await llm_client.response_structured(
|
||
messages=messages,
|
||
response_model=EmotionSuggestionsResponse
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"LLM 结构化输出失败: {str(e)}")
|
||
# 返回默认建议
|
||
suggestions_response = self._get_default_suggestions(health_data)
|
||
|
||
# 8. 验证建议数量（3-5条）
|
||
if len(suggestions_response.suggestions) < 3:
|
||
logger.warning(f"建议数量不足: {len(suggestions_response.suggestions)}")
|
||
suggestions_response = self._get_default_suggestions(health_data)
|
||
elif len(suggestions_response.suggestions) > 5:
|
||
logger.warning(f"建议数量过多: {len(suggestions_response.suggestions)}")
|
||
suggestions_response.suggestions = suggestions_response.suggestions[:5]
|
||
|
||
# 9. 格式化响应
|
||
response = {
|
||
"health_summary": suggestions_response.health_summary,
|
||
"suggestions": [
|
||
{
|
||
"type": s.type,
|
||
"title": s.title,
|
||
"content": s.content,
|
||
"priority": s.priority,
|
||
"actionable_steps": s.actionable_steps
|
||
}
|
||
for s in suggestions_response.suggestions
|
||
]
|
||
}
|
||
|
||
logger.info(f"个性化建议生成完成: suggestions_count={len(response['suggestions'])}")
|
||
return response
|
||
|
||
except Exception as e:
|
||
logger.error(f"生成个性化建议失败: {str(e)}", exc_info=True)
|
||
raise
|
||
|
||
async def _get_simple_user_profile(self, end_user_id: str) -> Dict[str, Any]:
|
||
"""获取简化的用户画像数据
|
||
|
||
Args:
|
||
end_user_id: 用户ID
|
||
|
||
Returns:
|
||
Dict: 用户画像数据
|
||
"""
|
||
try:
|
||
connector = Neo4jConnector()
|
||
|
||
# 查询用户的实体和标签
|
||
query = """
|
||
MATCH (e:Entity)
|
||
WHERE e.end_user_id = $end_user_id
|
||
RETURN e.name as name, e.type as type
|
||
ORDER BY e.created_at DESC
|
||
LIMIT 20
|
||
"""
|
||
|
||
entities = await connector.execute_query(query, end_user_id=end_user_id)
|
||
|
||
# 提取兴趣标签
|
||
interests = [e["name"] for e in entities if e.get("type") in ["INTEREST", "HOBBY"]][:5]
|
||
# 后期会引入用户的习惯。。
|
||
return {
|
||
"interests": interests if interests else ["未知"]
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取用户画像失败: {str(e)}")
|
||
return {"interests": ["未知"]}
|
||
|
||
async def _build_suggestion_prompt(
|
||
self,
|
||
health_data: Dict[str, Any],
|
||
patterns: Dict[str, Any],
|
||
user_profile: Dict[str, Any]
|
||
) -> str:
|
||
"""构建情绪建议生成的prompt
|
||
|
||
Args:
|
||
health_data: 情绪健康数据
|
||
patterns: 情绪模式分析结果
|
||
user_profile: 用户画像数据
|
||
|
||
Returns:
|
||
str: LLM prompt
|
||
"""
|
||
from app.core.memory.utils.prompt.prompt_utils import (
|
||
render_emotion_suggestions_prompt,
|
||
)
|
||
|
||
prompt = await render_emotion_suggestions_prompt(
|
||
health_data=health_data,
|
||
patterns=patterns,
|
||
user_profile=user_profile
|
||
)
|
||
|
||
return prompt
|
||
|
||
def _get_default_suggestions(self, health_data: Dict[str, Any]) -> EmotionSuggestionsResponse:
|
||
"""获取默认建议（当LLM调用失败时使用）
|
||
|
||
Args:
|
||
health_data: 情绪健康数据
|
||
|
||
Returns:
|
||
EmotionSuggestionsResponse: 默认建议
|
||
"""
|
||
health_score = health_data.get('health_score', 0)
|
||
|
||
if health_score >= 80:
|
||
summary = "您的情绪健康状况优秀，请继续保持积极的生活态度。"
|
||
elif health_score >= 60:
|
||
summary = "您的情绪健康状况良好，可以通过一些调整进一步提升。"
|
||
elif health_score >= 40:
|
||
summary = "您的情绪健康需要关注，建议采取一些改善措施。"
|
||
else:
|
||
summary = "您的情绪健康需要重点关注，建议寻求专业帮助。"
|
||
|
||
suggestions = [
|
||
EmotionSuggestion(
|
||
type="emotion_balance",
|
||
title="保持情绪平衡",
|
||
content="通过正念冥想和深呼吸练习，帮助您更好地管理情绪波动，提升情绪稳定性。",
|
||
priority="high",
|
||
actionable_steps=[
|
||
"每天早晨进行5-10分钟的正念冥想",
|
||
"感到情绪波动时，进行3次深呼吸",
|
||
"记录每天的情绪变化，识别触发因素"
|
||
]
|
||
),
|
||
EmotionSuggestion(
|
||
type="activity_recommendation",
|
||
title="增加户外活动",
|
||
content="适度的户外运动可以有效改善情绪，增强身心健康。建议每周进行3-4次户外活动。",
|
||
priority="medium",
|
||
actionable_steps=[
|
||
"每周安排2-3次30分钟的散步",
|
||
"周末尝试户外运动如骑行或爬山",
|
||
"在户外活动时关注周围环境，放松心情"
|
||
]
|
||
),
|
||
EmotionSuggestion(
|
||
type="social_connection",
|
||
title="加强社交联系",
|
||
content="与朋友和家人保持良好的社交联系，可以提供情感支持，改善情绪健康。",
|
||
priority="medium",
|
||
actionable_steps=[
|
||
"每周至少与一位朋友或家人深入交流",
|
||
"参加感兴趣的社交活动或兴趣小组",
|
||
"主动分享自己的感受和想法"
|
||
]
|
||
)
|
||
]
|
||
|
||
return EmotionSuggestionsResponse(
|
||
health_summary=summary,
|
||
suggestions=suggestions
|
||
)
|
||
|
||
async def get_cached_suggestions(
|
||
self,
|
||
end_user_id: str,
|
||
db: Session,
|
||
) -> Optional[Dict[str, Any]]:
|
||
"""从 Redis 缓存获取个性化情绪建议
|
||
|
||
Args:
|
||
end_user_id: 宿主ID（用户组ID）
|
||
db: 数据库会话（保留参数以保持接口兼容性）
|
||
|
||
Returns:
|
||
Dict: 缓存的建议数据，如果不存在或已过期返回 None
|
||
"""
|
||
try:
|
||
from app.cache.memory.emotion_memory import EmotionMemoryCache
|
||
|
||
logger.info(f"尝试从 Redis 缓存获取情绪建议: user={end_user_id}")
|
||
|
||
# 从 Redis 获取缓存
|
||
cached_data = await EmotionMemoryCache.get_emotion_suggestions(end_user_id)
|
||
|
||
if cached_data is None:
|
||
logger.info(f"用户 {end_user_id} 的建议缓存不存在或已过期")
|
||
return None
|
||
|
||
logger.info(f"成功从 Redis 缓存获取建议: user={end_user_id}")
|
||
return cached_data
|
||
|
||
except Exception as e:
|
||
logger.error(f"从 Redis 缓存获取建议失败: {str(e)}", exc_info=True)
|
||
return None
|
||
|
||
async def save_suggestions_cache(
|
||
self,
|
||
end_user_id: str,
|
||
suggestions_data: Dict[str, Any],
|
||
db: Session,
|
||
expires_hours: int = 24
|
||
) -> None:
|
||
"""保存建议到 Redis 缓存
|
||
|
||
Args:
|
||
end_user_id: 宿主ID（用户组ID）
|
||
suggestions_data: 建议数据
|
||
db: 数据库会话（保留参数以保持接口兼容性）
|
||
expires_hours: 过期时间（小时），默认24小时
|
||
"""
|
||
try:
|
||
from app.cache.memory.emotion_memory import EmotionMemoryCache
|
||
|
||
logger.info(f"保存建议到 Redis 缓存: user={end_user_id}, expires={expires_hours}小时")
|
||
|
||
# 计算过期时间（秒）
|
||
expire_seconds = expires_hours * 3600
|
||
|
||
# 保存到 Redis
|
||
success = await EmotionMemoryCache.set_emotion_suggestions(
|
||
user_id=end_user_id,
|
||
suggestions_data=suggestions_data,
|
||
expire=expire_seconds
|
||
)
|
||
|
||
if success:
|
||
logger.info(f"建议缓存保存成功: user={end_user_id}")
|
||
else:
|
||
logger.warning(f"建议缓存保存失败: user={end_user_id}")
|
||
|
||
except Exception as e:
|
||
logger.error(f"保存建议缓存失败: {str(e)}", exc_info=True)
|
||
# 不抛出异常，缓存失败不应影响主流程 |