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Merge branch 'release/v0.2.2'

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# Conflicts:
#	api/app/repositories/memory_config_repository.py
#	api/app/services/emotion_analytics_service.py
#	api/app/utils/config_utils.py
This commit is contained in:
Mark
2026-01-31 15:55:58 +08:00
parent 0159fdf149 1b853aa893
commit eab7225d83
3 changed files with 202 additions and 195 deletions
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51
api/app/repositories/memory_config_repository.py
View File

@@ -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

329
api/app/services/emotion_analytics_service.py
View File

@@ -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

17
api/app/utils/config_utils.py
View File

@@ -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