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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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25
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()
@@ -516,8 +516,9 @@ 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:
@@ -526,7 +527,6 @@ class MemoryConfigRepository:
Returns:
Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found
Raises:
ValueError: Raised when config exists but workspace doesn't
"""
@@ -535,6 +535,7 @@ class MemoryConfigRepository:
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(
@@ -552,7 +553,6 @@ class MemoryConfigRepository:
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:
@@ -583,8 +583,10 @@ 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",
@@ -615,7 +617,8 @@ class MemoryConfigRepository:
}
)
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:
@@ -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

79
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")
@@ -55,12 +58,12 @@ class EmotionAnalyticsService:
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
@@ -71,7 +74,7 @@ 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(
@@ -133,10 +136,10 @@ class EmotionAnalyticsService:
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
@@ -211,7 +214,7 @@ class EmotionAnalyticsService:
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),
@@ -250,7 +253,7 @@ class EmotionAnalyticsService:
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),
@@ -303,7 +306,7 @@ class EmotionAnalyticsService:
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),
@@ -311,9 +314,9 @@ class EmotionAnalyticsService:
}
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
@@ -366,9 +369,9 @@ class EmotionAnalyticsService:
# 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
@@ -460,7 +463,7 @@ class EmotionAnalyticsService:
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,
@@ -469,9 +472,9 @@ class EmotionAnalyticsService:
}
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
@@ -498,7 +501,7 @@ class EmotionAnalyticsService:
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,
@@ -618,10 +621,10 @@ class EmotionAnalyticsService:
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
@@ -707,9 +710,9 @@ class EmotionAnalyticsService:
)
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
@@ -740,11 +743,11 @@ class EmotionAnalyticsService:
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

11
api/app/utils/config_utils.py
View File

@@ -7,7 +7,7 @@ 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
@@ -21,16 +21,17 @@ def resolve_config_id(config_id: UUID | int, db: Session) -> UUID:
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