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code merge error (#8183)

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Co-authored-by: crazywoola <427733928@qq.com>
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Jyong 2024-09-10 12:52:50 +08:00 committed by GitHub
parent c8df92d0eb
commit 85ff82a694
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2 changed files with 5 additions and 5 deletions
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4
api/controllers/console/datasets/datasets_document.py
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@ -302,6 +302,8 @@ class DatasetInitApi(Resource):
"doc_language", type=str, default="English", required=False, nullable=False, location="json" "doc_language", type=str, default="English", required=False, nullable=False, location="json"
) )
parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json") parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
parser.add_argument("embedding_model", type=str, required=False, nullable=True, location="json")
parser.add_argument("embedding_model_provider", type=str, required=False, nullable=True, location="json")
args = parser.parse_args() args = parser.parse_args()
# The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator # The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
@ -309,6 +311,8 @@ class DatasetInitApi(Resource):
raise Forbidden() raise Forbidden()
if args["indexing_technique"] == "high_quality": if args["indexing_technique"] == "high_quality":
if args["embedding_model"] is None or args["embedding_model_provider"] is None:
raise ValueError("embedding model and embedding model provider are required for high quality indexing.")
try: try:
model_manager = ModelManager() model_manager = ModelManager()
model_manager.get_default_model_instance( model_manager.get_default_model_instance(

6
api/services/dataset_service.py
View File

@ -1057,12 +1057,8 @@ class DocumentService:
dataset_collection_binding_id = None dataset_collection_binding_id = None
retrieval_model = None retrieval_model = None
if document_data["indexing_technique"] == "high_quality": if document_data["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_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding( dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
embedding_model.provider, embedding_model.model document_data["embedding_model_provider"], document_data["embedding_model"]
) )
dataset_collection_binding_id = dataset_collection_binding.id dataset_collection_binding_id = dataset_collection_binding.id
if document_data.get("retrieval_model"): if document_data.get("retrieval_model"):