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Merge tag 'v0.2.5-hotfix-1' into develop

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v2.0.5-hotfix

# Conflicts:
#	api/app/cache/__init__.py
#	api/app/cache/memory/__init__.py
#	api/app/celery_app.py
#	api/app/core/config.py
#	web/src/api/memory.ts
#	web/src/views/Workflow/components/Chat/Chat.tsx
This commit is contained in:
Mark
2026-03-05 14:37:35 +08:00
parent 6301528301 130f15665c
commit 404f78af0f
21 changed files with 811 additions and 430 deletions
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118
api/app/controllers/emotion_controller.py
View File

@@ -208,14 +208,64 @@ async def get_emotion_health(
# @router.post("/check-data", response_model=ApiResponse)
# async def check_emotion_data_exists(
# request: EmotionSuggestionsRequest,
# db: Session = Depends(get_db),
# current_user: User = Depends(get_current_user),
# ):
# """检查用户情绪建议数据是否存在
# Args:
# request: 包含 end_user_id
# db: 数据库会话
# current_user: 当前用户
# Returns:
# 数据存在状态
# """
# try:
# api_logger.info(
# f"检查用户情绪建议数据是否存在: {request.end_user_id}",
# extra={"end_user_id": request.end_user_id}
# )
# # 从数据库获取建议
# data = await emotion_service.get_cached_suggestions(
# end_user_id=request.end_user_id,
# db=db
# )
# if data is None:
# api_logger.info(f"用户 {request.end_user_id} 的情绪建议数据不存在")
# return fail(
# BizCode.NOT_FOUND,
# "情绪建议数据不存在，请点击右上角刷新进行初始化",
# {"exists": False}
# )
# api_logger.info(f"用户 {request.end_user_id} 的情绪建议数据存在")
# return success(data={"exists": True}, msg="情绪建议数据已存在")
# except Exception as e:
# api_logger.error(
# f"检查情绪建议数据失败: {str(e)}",
# extra={"end_user_id": request.end_user_id},
# exc_info=True
# )
# raise HTTPException(
# status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
# detail=f"检查情绪建议数据失败: {str(e)}"
# )
@router.post("/suggestions", response_model=ApiResponse)
async def get_emotion_suggestions(
request: EmotionSuggestionsRequest,
language_type: str = Header(default=None, alias="X-Language-Type"),
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user),
):
"""获取个性化情绪建议（从缓存读取）
"""获取个性化情绪建议（从数据库读取）
Args:
request: 包含 end_user_id 和可选的 config_id
@@ -223,77 +273,42 @@ async def get_emotion_suggestions(
current_user: 当前用户
Returns:
缓存的个性化情绪建议响应
存储的个性化情绪建议响应
"""
try:
# 使用集中化的语言校验
language = get_language_from_header(language_type)
api_logger.info(
f"用户 {current_user.username} 请求获取个性化情绪建议（缓存）",
f"用户 {current_user.username} 请求获取个性化情绪建议",
extra={
"end_user_id": request.end_user_id,
"config_id": request.config_id
}
)
# 从缓存获取建议
# 从数据库获取建议
data = await emotion_service.get_cached_suggestions(
end_user_id=request.end_user_id,
db=db
)
if data is None:
# 缓存不存在或已过期，自动触发生成
api_logger.info(
f"用户 {request.end_user_id} 的建议缓存不存在或已过期，自动生成新建议",
f"用户 {request.end_user_id} 的建议数据不存在",
extra={"end_user_id": request.end_user_id}
)
try:
data = await emotion_service.generate_emotion_suggestions(
end_user_id=request.end_user_id,
db=db,
language=language
)
# 保存到缓存
await emotion_service.save_suggestions_cache(
end_user_id=request.end_user_id,
suggestions_data=data,
db=db,
expires_hours=24
)
except (ValueError, KeyError) as gen_e:
# 预期内的业务异常：配置缺失、数据格式问题等
api_logger.warning(
f"自动生成建议失败（业务异常）: {str(gen_e)}",
extra={"end_user_id": request.end_user_id}
)
return fail(
BizCode.NOT_FOUND,
f"自动生成建议失败: {str(gen_e)}",
""
)
except Exception as gen_e:
# 非预期异常：记录完整 traceback 便于排查
api_logger.error(
f"自动生成建议时发生未预期异常: {str(gen_e)}",
extra={"end_user_id": request.end_user_id},
exc_info=True
)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"生成建议时发生内部错误: {str(gen_e)}"
)
return success(
data={"exists": False},
msg="情绪建议数据不存在，请点击右上角刷新进行初始化"
)
api_logger.info(
"个性化建议获取成功（缓存）",
"个性化建议获取成功",
extra={
"end_user_id": request.end_user_id,
"suggestions_count": len(data.get("suggestions", []))
}
)
return success(data=data, msg="个性化建议获取成功（缓存）")
return success(data=data, msg="个性化建议获取成功")
except Exception as e:
api_logger.error(
@@ -314,7 +329,7 @@ async def generate_emotion_suggestions(
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user),
):
"""生成个性化情绪建议（调用LLM并缓存）
"""生成个性化情绪建议（调用LLM并保存到数据库）
Args:
request: 包含 end_user_id
@@ -342,12 +357,11 @@ async def generate_emotion_suggestions(
language=language
)
# 保存到缓存
# 保存到数据库
await emotion_service.save_suggestions_cache(
end_user_id=request.end_user_id,
suggestions_data=data,
db=db,
expires_hours=24
db=db
)
api_logger.info(
@@ -369,4 +383,4 @@ async def generate_emotion_suggestions(
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"生成个性化建议失败: {str(e)}"
)
)

74
api/app/controllers/implicit_memory_controller.py
View File

@@ -122,6 +122,48 @@ def validate_confidence_threshold(threshold: float) -> None:
raise ValueError("confidence_threshold must be between 0.0 and 1.0")
@router.get("/check-data/{end_user_id}", response_model=ApiResponse)
@cur_workspace_access_guard()
async def check_user_data_exists(
end_user_id: str,
db: Session = Depends(get_db),
current_user: User = Depends(get_current_user)
) -> ApiResponse:
"""
检查用户画像数据是否存在
Args:
end_user_id: 目标用户ID
Returns:
数据存在状态
"""
api_logger.info(f"检查用户画像数据是否存在: {end_user_id}")
try:
# Validate inputs
validate_user_id(end_user_id)
# Create service with user-specific config
service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
# Get cached profile
cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
if cached_profile is None:
api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
return success(
data={"exists": False},
msg="画像数据不存在，请点击右上角刷新进行初始化"
)
api_logger.info(f"用户 {end_user_id} 的画像数据存在")
return success(data={"exists": True}, msg="画像数据已存在")
except Exception as e:
return handle_implicit_memory_error(e, "检查画像数据", end_user_id)
@router.get("/preferences/{end_user_id}", response_model=ApiResponse)
@cur_workspace_access_guard()
async def get_preference_tags(
@@ -159,12 +201,8 @@ async def get_preference_tags(
cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
if cached_profile is None:
api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
return fail(
BizCode.NOT_FOUND,
"画像缓存不存在或已过期，请右上角刷新生成新画像",
""
)
api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
return fail(BizCode.NOT_FOUND, "", "")
# Extract preferences from cache
preferences = cached_profile.get("preferences", [])
@@ -230,12 +268,8 @@ async def get_dimension_portrait(
cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
if cached_profile is None:
api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
return fail(
BizCode.NOT_FOUND,
"画像缓存不存在或已过期，请右上角刷新生成新画像",
""
)
api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
return fail(BizCode.NOT_FOUND, "", "")
# Extract portrait from cache
portrait = cached_profile.get("portrait", {})
@@ -278,12 +312,8 @@ async def get_interest_area_distribution(
cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
if cached_profile is None:
api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
return fail(
BizCode.NOT_FOUND,
"画像缓存不存在或已过期，请右上角刷新生成新画像",
""
)
api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
return fail(BizCode.NOT_FOUND, "", "")
# Extract interest areas from cache
interest_areas = cached_profile.get("interest_areas", {})
@@ -330,12 +360,8 @@ async def get_behavior_habits(
cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
if cached_profile is None:
api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
return fail(
BizCode.NOT_FOUND,
"画像缓存不存在或已过期，请右上角刷新生成新画像",
""
)
api_logger.info(f"用户 {end_user_id} 的画像数据不存在")
return fail(BizCode.NOT_FOUND, "", "")
# Extract habits from cache
habits = cached_profile.get("habits", [])