Merge branch 'main' into fix/chore-fix

This commit is contained in:
Yeuoly
2025-01-08 20:36:22 +08:00
203 changed files with 3469 additions and 2327 deletions

View File

@ -86,7 +86,7 @@ class DatasetService:
else:
return [], 0
else:
if user.current_role not in (TenantAccountRole.OWNER, TenantAccountRole.ADMIN):
if user.current_role != TenantAccountRole.OWNER:
# show all datasets that the user has permission to access
if permitted_dataset_ids:
query = query.filter(
@ -382,7 +382,7 @@ class DatasetService:
if dataset.tenant_id != user.current_tenant_id:
logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
raise NoPermissionError("You do not have permission to access this dataset.")
if user.current_role not in (TenantAccountRole.OWNER, TenantAccountRole.ADMIN):
if user.current_role != TenantAccountRole.OWNER:
if dataset.permission == DatasetPermissionEnum.ONLY_ME and dataset.created_by != user.id:
logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
raise NoPermissionError("You do not have permission to access this dataset.")
@ -404,7 +404,7 @@ class DatasetService:
if not user:
raise ValueError("User not found")
if user.current_role not in (TenantAccountRole.OWNER, TenantAccountRole.ADMIN):
if user.current_role != TenantAccountRole.OWNER:
if dataset.permission == DatasetPermissionEnum.ONLY_ME:
if dataset.created_by != user.id:
raise NoPermissionError("You do not have permission to access this dataset.")
@ -434,6 +434,12 @@ class DatasetService:
@staticmethod
def get_dataset_auto_disable_logs(dataset_id: str) -> dict:
features = FeatureService.get_features(current_user.current_tenant_id)
if not features.billing.enabled or features.billing.subscription.plan == "sandbox":
return {
"document_ids": [],
"count": 0,
}
# get recent 30 days auto disable logs
start_date = datetime.datetime.now() - datetime.timedelta(days=30)
dataset_auto_disable_logs = DatasetAutoDisableLog.query.filter(
@ -786,13 +792,19 @@ class DocumentService:
dataset.indexing_technique = knowledge_config.indexing_technique
if knowledge_config.indexing_technique == "high_quality":
model_manager = ModelManager()
embedding_model = model_manager.get_default_model_instance(
tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
)
dataset.embedding_model = embedding_model.model
dataset.embedding_model_provider = embedding_model.provider
if knowledge_config.embedding_model and knowledge_config.embedding_model_provider:
dataset_embedding_model = knowledge_config.embedding_model
dataset_embedding_model_provider = knowledge_config.embedding_model_provider
else:
embedding_model = model_manager.get_default_model_instance(
tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
)
dataset_embedding_model = embedding_model.model
dataset_embedding_model_provider = embedding_model.provider
dataset.embedding_model = dataset_embedding_model
dataset.embedding_model_provider = dataset_embedding_model_provider
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
embedding_model.provider, embedding_model.model
dataset_embedding_model_provider, dataset_embedding_model
)
dataset.collection_binding_id = dataset_collection_binding.id
if not dataset.retrieval_model:
@ -804,7 +816,11 @@ class DocumentService:
"score_threshold_enabled": False,
}
dataset.retrieval_model = knowledge_config.retrieval_model.model_dump() or default_retrieval_model # type: ignore
dataset.retrieval_model = (
knowledge_config.retrieval_model.model_dump()
if knowledge_config.retrieval_model
else default_retrieval_model
) # type: ignore
documents = []
if knowledge_config.original_document_id: