diff --git a/api/app/repositories/memory_config_repository.py b/api/app/repositories/memory_config_repository.py index fbc04f2e..c20c79c1 100644 --- a/api/app/repositories/memory_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -32,6 +32,8 @@ db_logger = get_db_logger() config_logger = get_config_logger() TABLE_NAME = "memory_config" + + class MemoryConfigRepository: """记忆配置Repository @@ -189,7 +191,6 @@ class MemoryConfigRepository: raise RuntimeError("reflection config not found") return memory_config - @staticmethod def build_select_all(workspace_id: uuid.UUID) -> Tuple[str, Dict]: """构建查询所有配置的语句（SQLAlchemy text() 命名参数） @@ -289,7 +290,6 @@ class MemoryConfigRepository: db_logger.error(f"更新记忆配置失败: config_id={update.config_id} - {str(e)}") raise - @staticmethod def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[MemoryConfig]: """更新记忆萃取引擎配置 @@ -412,7 +412,7 @@ class MemoryConfigRepository: raise @staticmethod - def get_extracted_config(db: Session, config_id: UUID |int) -> Optional[Dict]: + def get_extracted_config(db: Session, config_id: UUID | int) -> Optional[Dict]: """获取萃取配置，通过主键查询某条配置 Args: @@ -422,7 +422,7 @@ class MemoryConfigRepository: Returns: Optional[Dict]: 萃取配置字典，不存在则返回None """ - config_id=resolve_config_id(config_id,db) + config_id = resolve_config_id(config_id, db) db_logger.debug(f"查询萃取配置: config_id={config_id}") try: db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() @@ -516,26 +516,27 @@ class MemoryConfigRepository: except Exception as e: db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}") raise + @staticmethod - def get_config_with_workspace(db: Session, config_id: uuid.UUID) -> Optional[tuple]: + def get_config_with_workspace(db: Session, config_id: uuid.UUID | int | str) -> Optional[tuple]: """Get memory config and its associated workspace information - + Args: db: Database session config_id: Configuration ID - + Returns: Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found - Raises: ValueError: Raised when config exists but workspace doesn't """ import time from app.models.workspace_model import Workspace - + start_time = time.time() - + config_id = resolve_config_id(config_id, db) + # Log configuration loading start config_logger.info( "Loading configuration with workspace", @@ -544,17 +545,16 @@ class MemoryConfigRepository: "config_id": config_id } ) - + db_logger.debug(f"Querying memory config and workspace: config_id={config_id}") - + try: # Use join query to get both config and workspace result = db.query(MemoryConfig, Workspace).join( Workspace, MemoryConfig.workspace_id == Workspace.id ).filter(MemoryConfig.config_id == config_id).first() - elapsed_ms = (time.time() - start_time) * 1000 - + if not result: # Check if config exists but workspace is missing config_only = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() @@ -583,9 +583,11 @@ class MemoryConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}") - raise ValueError(f"Workspace {config_only.workspace_id} not found for configuration {config_id}") - + db_logger.error( + f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}") + raise ValueError( + f"Workspace {config_only.workspace_id} not found for configuration {config_id}") + config_logger.debug( "Configuration not found", extra={ @@ -597,9 +599,9 @@ class MemoryConfigRepository: ) db_logger.debug(f"Memory config not found: config_id={config_id}") return None - + config, workspace = result - + # Log successful configuration loading config_logger.info( "Configuration with workspace loaded successfully", @@ -614,16 +616,17 @@ class MemoryConfigRepository: "elapsed_ms": elapsed_ms } ) - - db_logger.debug(f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}") + + db_logger.debug( + f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}") return (config, workspace) - + except ValueError: # Re-raise known business exceptions raise except Exception as e: elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.error( "Failed to load configuration with workspace", extra={ @@ -636,9 +639,9 @@ class MemoryConfigRepository: }, exc_info=True ) - db_logger.error(f"Failed to query memory config and workspace: config_id={config_id} - {str(e)}") raise + @staticmethod def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[MemoryConfig]: """获取所有配置参数 diff --git a/api/app/services/emotion_analytics_service.py b/api/app/services/emotion_analytics_service.py index af98fb52..7bc776ed 100644 --- a/api/app/services/emotion_analytics_service.py +++ b/api/app/services/emotion_analytics_service.py @@ -17,12 +17,15 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector from pydantic import BaseModel, Field from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + logger = get_business_logger() class EmotionSuggestion(BaseModel): """情绪建议模型""" - type: str = Field(..., description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management") + type: str = Field(..., + description="建议类型：emotion_balance/activity_recommendation/social_connection/stress_management") title: str = Field(..., description="建议标题") content: str = Field(..., description="建议内容") priority: str = Field(..., description="优先级：high/medium/low") @@ -37,33 +40,33 @@ class EmotionSuggestionsResponse(BaseModel): class EmotionAnalyticsService: """情绪分析服务 - + 提供情绪数据的分析和统计功能，包括： - 情绪标签统计 - 情绪词云数据 - 情绪健康指数计算 - 个性化情绪建议生成 - + Attributes: emotion_repo: 情绪数据仓储实例 """ - + def __init__(self): """初始化情绪分析服务""" connector = Neo4jConnector() self.emotion_repo = EmotionRepository(connector) logger.info("情绪分析服务初始化完成") - + async def get_emotion_tags( - self, - end_user_id: str, - emotion_type: Optional[str] = None, - start_date: Optional[str] = None, - end_date: Optional[str] = None, - limit: int = 10 + self, + end_user_id: str, + emotion_type: Optional[str] = None, + start_date: Optional[str] = None, + end_date: Optional[str] = None, + limit: int = 10 ) -> Dict[str, Any]: """获取情绪标签统计 - + 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。 确保返回所有6个情绪维度（joy、sadness、anger、fear、surprise、neutral）， 即使某些维度没有数据也会返回count=0的记录。 @@ -71,8 +74,8 @@ class EmotionAnalyticsService: """ try: logger.info(f"获取情绪标签统计: user={end_user_id}, type={emotion_type}, " - f"start={start_date}, end={end_date}, limit={limit}") - + f"start={start_date}, end={end_date}, limit={limit}") + # 调用仓储层查询 tags = await self.emotion_repo.get_emotion_tags( end_user_id=end_user_id, @@ -81,13 +84,13 @@ class EmotionAnalyticsService: end_date=end_date, limit=limit ) - + # 定义所有6个情绪维度 all_emotion_types = ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral'] - + # 将查询结果转换为字典，方便查找 tags_dict = {tag["emotion_type"]: tag for tag in tags} - + # 补全缺失的情绪维度 complete_tags = [] for emotion in all_emotion_types: @@ -101,52 +104,52 @@ class EmotionAnalyticsService: "percentage": 0.0, "avg_intensity": 0.0 }) - + # 计算总数 total_count = sum(tag["count"] for tag in complete_tags) - + # 如果有数据，重新计算百分比（因为补全了0值项） if total_count > 0: for tag in complete_tags: if tag["count"] > 0: tag["percentage"] = round((tag["count"] / total_count) * 100, 2) - + # 构建时间范围信息 time_range = {} if start_date: time_range["start_date"] = start_date if end_date: time_range["end_date"] = end_date - + # 格式化响应 response = { "tags": complete_tags, "total_count": total_count, "time_range": time_range if time_range else None } - + logger.info(f"情绪标签统计完成: total_count={total_count}, tags_count={len(complete_tags)}") return response - + except Exception as e: logger.error(f"获取情绪标签统计失败: {str(e)}", exc_info=True) raise - + async def get_emotion_wordcloud( - self, - end_user_id: str, - emotion_type: Optional[str] = None, - limit: int = 50 + self, + end_user_id: str, + emotion_type: Optional[str] = None, + limit: int = 50 ) -> Dict[str, Any]: """获取情绪词云数据 - + 查询情绪关键词及其频率，用于生成词云可视化。 - + Args: end_user_id: 宿主ID（用户组ID） emotion_type: 可选的情绪类型过滤 limit: 返回关键词的最大数量 - + Returns: Dict: 包含情绪词云数据的响应： - keywords: 关键词列表 @@ -154,39 +157,39 @@ class EmotionAnalyticsService: """ try: logger.info(f"获取情绪词云数据: user={end_user_id}, type={emotion_type}, limit={limit}") - + # 调用仓储层查询 keywords = await self.emotion_repo.get_emotion_wordcloud( end_user_id=end_user_id, emotion_type=emotion_type, limit=limit ) - + # 计算总关键词数量 total_keywords = len(keywords) - + # 格式化响应 response = { "keywords": keywords, "total_keywords": total_keywords } - + logger.info(f"情绪词云数据获取完成: total_keywords={total_keywords}") return response - + except Exception as e: logger.error(f"获取情绪词云数据失败: {str(e)}", exc_info=True) raise - + def _calculate_positivity_rate(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """计算积极率 - + 根据情绪类型分类正面、负面和中性情绪，计算积极率。 公式：(正面数 / (正面数 + 负面数)) * 100 - + Args: emotions: 情绪数据列表，每个包含 emotion_type 字段 - + Returns: Dict: 包含积极率计算结果： - score: 积极率分数（0-100） @@ -197,38 +200,38 @@ class EmotionAnalyticsService: # 定义情绪分类 positive_emotions = {'joy', 'surprise'} negative_emotions = {'sadness', 'anger', 'fear'} - + # 统计各类情绪数量 positive_count = sum(1 for e in emotions if e.get('emotion_type') in positive_emotions) negative_count = sum(1 for e in emotions if e.get('emotion_type') in negative_emotions) neutral_count = sum(1 for e in emotions if e.get('emotion_type') == 'neutral') - + # 计算积极率 total_non_neutral = positive_count + negative_count if total_non_neutral > 0: score = (positive_count / total_non_neutral) * 100 else: score = 50.0 # 如果没有非中性情绪，默认为50 - + logger.debug(f"积极率计算: positive={positive_count}, negative={negative_count}, " - f"neutral={neutral_count}, score={score:.2f}") - + f"neutral={neutral_count}, score={score:.2f}") + return { "score": round(score, 2), "positive_count": positive_count, "negative_count": negative_count, "neutral_count": neutral_count } - + def _calculate_stability(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """计算稳定性 - + 基于情绪强度的标准差计算情绪稳定性。 公式：(1 - min(std_deviation, 1.0)) * 100 - + Args: emotions: 情绪数据列表，每个包含 emotion_intensity 字段 - + Returns: Dict: 包含稳定性计算结果： - score: 稳定性分数（0-100） @@ -236,7 +239,7 @@ class EmotionAnalyticsService: """ # 提取所有情绪强度 intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None] - + # 计算标准差 if len(intensities) >= 2: std_deviation = statistics.stdev(intensities) @@ -244,29 +247,29 @@ class EmotionAnalyticsService: std_deviation = 0.0 # 只有一个数据点，标准差为0 else: std_deviation = 0.0 # 没有数据，标准差为0 - + # 计算稳定性分数 # 标准差越小，稳定性越高 score = (1 - min(std_deviation, 1.0)) * 100 - + logger.debug(f"稳定性计算: intensities_count={len(intensities)}, " - f"std_deviation={std_deviation:.3f}, score={score:.2f}") - + f"std_deviation={std_deviation:.3f}, score={score:.2f}") + return { "score": round(score, 2), "std_deviation": round(std_deviation, 3) } - + def _calculate_resilience(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """计算恢复力 - + 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。 公式：(负面到正面转换次数 / 总负面情绪数) * 100 - + Args: emotions: 情绪数据列表，每个包含 emotion_type 和 created_at 字段 应该按时间顺序排列 - + Returns: Dict: 包含恢复力计算结果： - score: 恢复力分数（0-100） @@ -275,24 +278,24 @@ class EmotionAnalyticsService: # 定义情绪分类 positive_emotions = {'joy', 'surprise'} negative_emotions = {'sadness', 'anger', 'fear'} - + # 统计负面到正面的转换次数 recovery_count = 0 negative_count = 0 - + for i in range(len(emotions)): current_emotion = emotions[i].get('emotion_type') - + # 统计负面情绪总数 if current_emotion in negative_emotions: negative_count += 1 - + # 检查下一个情绪是否为正面 if i + 1 < len(emotions): next_emotion = emotions[i + 1].get('emotion_type') if next_emotion in positive_emotions: recovery_count += 1 - + # 计算恢复力分数 if negative_count > 0: recovery_rate = recovery_count / negative_count @@ -301,28 +304,28 @@ class EmotionAnalyticsService: # 如果没有负面情绪，恢复力设为100（最佳状态） recovery_rate = 1.0 score = 100.0 - + logger.debug(f"恢复力计算: negative_count={negative_count}, " - f"recovery_count={recovery_count}, score={score:.2f}") - + f"recovery_count={recovery_count}, score={score:.2f}") + return { "score": round(score, 2), "recovery_rate": round(recovery_rate, 3) } - + async def calculate_emotion_health_index( - self, - end_user_id: str, - time_range: str = "30d" + self, + end_user_id: str, + time_range: str = "30d" ) -> Dict[str, Any]: """计算情绪健康指数 - + 综合积极率、稳定性和恢复力计算情绪健康指数。 - + Args: end_user_id: 宿主ID（用户组ID） time_range: 时间范围（7d/30d/90d） - + Returns: Dict: 包含情绪健康指数的完整响应： - health_score: 综合健康分数（0-100） @@ -336,13 +339,13 @@ class EmotionAnalyticsService: """ try: logger.info(f"计算情绪健康指数: user={end_user_id}, time_range={time_range}") - + # 获取时间范围内的情绪数据 emotions = await self.emotion_repo.get_emotions_in_range( end_user_id=end_user_id, time_range=time_range ) - + # 如果没有数据，返回默认值 if not emotions: logger.warning(f"用户 {end_user_id} 在时间范围 {time_range} 内没有情绪数据") @@ -357,20 +360,20 @@ class EmotionAnalyticsService: "emotion_distribution": {}, "time_range": time_range } - + # 计算各维度指标 positivity_rate = self._calculate_positivity_rate(emotions) stability = self._calculate_stability(emotions) resilience = self._calculate_resilience(emotions) - + # 计算综合健康分数 # 公式：positivity_rate * 0.4 + stability * 0.3 + resilience * 0.3 health_score = ( - positivity_rate["score"] * 0.4 + - stability["score"] * 0.3 + - resilience["score"] * 0.3 + positivity_rate["score"] * 0.4 + + stability["score"] * 0.3 + + resilience["score"] * 0.3 ) - + # 确定健康等级 if health_score >= 80: level = "优秀" @@ -380,13 +383,13 @@ class EmotionAnalyticsService: level = "一般" else: level = "较差" - + # 统计情绪分布 emotion_distribution = {} for emotion_type in ['joy', 'sadness', 'anger', 'fear', 'surprise', 'neutral']: count = sum(1 for e in emotions if e.get('emotion_type') == emotion_type) emotion_distribution[emotion_type] = count - + # 格式化响应 response = { "health_score": round(health_score, 2), @@ -399,22 +402,22 @@ class EmotionAnalyticsService: "emotion_distribution": emotion_distribution, "time_range": time_range } - + logger.info(f"情绪健康指数计算完成: score={health_score:.2f}, level={level}") return response - + except Exception as e: logger.error(f"计算情绪健康指数失败: {str(e)}", exc_info=True) raise - + def _analyze_emotion_patterns(self, emotions: List[Dict[str, Any]]) -> Dict[str, Any]: """分析情绪模式 - + 识别主要负面情绪、情绪触发因素和波动时段。 - + Args: emotions: 情绪数据列表，每个包含 emotion_type、emotion_intensity、created_at 字段 - + Returns: Dict: 包含情绪模式分析结果： - dominant_negative_emotion: 主要负面情绪类型 @@ -422,19 +425,19 @@ class EmotionAnalyticsService: - emotion_volatility: 情绪波动性（高/中/低） """ negative_emotions = {'sadness', 'anger', 'fear'} - + # 统计负面情绪分布 negative_emotion_counts = {} for emotion in emotions: emotion_type = emotion.get('emotion_type') if emotion_type in negative_emotions: negative_emotion_counts[emotion_type] = negative_emotion_counts.get(emotion_type, 0) + 1 - + # 识别主要负面情绪 dominant_negative_emotion = None if negative_emotion_counts: dominant_negative_emotion = max(negative_emotion_counts, key=negative_emotion_counts.get) - + # 识别高强度情绪（强度 >= 0.7） high_intensity_emotions = [ { @@ -445,7 +448,7 @@ class EmotionAnalyticsService: for e in emotions if e.get('emotion_intensity', 0) >= 0.7 ] - + # 评估情绪波动性 intensities = [e.get('emotion_intensity', 0.0) for e in emotions if e.get('emotion_intensity') is not None] if len(intensities) >= 2: @@ -458,29 +461,29 @@ class EmotionAnalyticsService: 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}") - + 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, + 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 @@ -488,17 +491,17 @@ class EmotionAnalyticsService: """ 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, @@ -513,35 +516,35 @@ class EmotionAnalyticsService: 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( @@ -552,7 +555,7 @@ class EmotionAnalyticsService: 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)}") @@ -560,7 +563,7 @@ class EmotionAnalyticsService: 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, @@ -575,26 +578,26 @@ class EmotionAnalyticsService: 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) @@ -603,59 +606,59 @@ class EmotionAnalyticsService: 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] + 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: @@ -664,7 +667,7 @@ class EmotionAnalyticsService: 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", @@ -700,54 +703,54 @@ class EmotionAnalyticsService: ] ) ] - + return EmotionSuggestionsResponse( health_summary=summary, suggestions=suggestions ) - + async def get_cached_suggestions( - self, - end_user_id: str, - db: Session, + 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 + 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 @@ -756,24 +759,24 @@ class EmotionAnalyticsService: """ 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 \ No newline at end of file diff --git a/api/app/utils/config_utils.py b/api/app/utils/config_utils.py index 8863ea78..cc67afd2 100644 --- a/api/app/utils/config_utils.py +++ b/api/app/utils/config_utils.py @@ -7,30 +7,31 @@ from uuid import UUID from sqlalchemy.orm import Session -def resolve_config_id(config_id: UUID | int, db: Session) -> UUID: +def resolve_config_id(config_id: UUID | int|str, db: Session) -> UUID: """ 解析 config_id，如果是整数则通过 config_id_old 查找对应的 UUID - + Args: config_id: 配置ID（UUID 或整数） db: 数据库会话 - + Returns: UUID: 解析后的配置ID - + Raises: ValueError: 当找不到对应的配置时 """ + from app.models.memory_config_model import MemoryConfig if isinstance(config_id, UUID): return config_id if isinstance(config_id, str) and len(config_id)<=6: memory_config = db.query(MemoryConfig).filter( - MemoryConfig.config_id_old == config_id + MemoryConfig.config_id_old == int(config_id) ).first() - + print(memory_config) if not memory_config: - raise ValueError(f"未找到 config_id_old={config_id} 对应的配置") + raise ValueError(f"STR 未找到 config_id_old={config_id} 对应的配置") return memory_config.config_id if isinstance(config_id, int): memory_config = db.query(MemoryConfig).filter( @@ -38,7 +39,7 @@ def resolve_config_id(config_id: UUID | int, db: Session) -> UUID: ).first() if not memory_config: - raise ValueError(f"未找到 config_id_old={config_id} 对应的配置") + raise ValueError(f"INT 未找到 config_id_old={config_id} 对应的配置") return memory_config.config_id