@@ -181,7 +181,8 @@ def get_aggregation_document_status_by_dataset_id(dataset_id):
181181 def aggregation_document_status ():
182182 sql = get_file_content (
183183 os .path .join (PROJECT_DIR , "apps" , "dataset" , 'sql' , 'update_document_status_meta.sql' ))
184- native_update ({'document_custom_sql' : QuerySet (Document ).filter (dataset_id = dataset_id )}, sql )
184+ native_update ({'document_custom_sql' : QuerySet (Document ).filter (dataset_id = dataset_id )}, sql ,
185+ with_table_name = True )
185186
186187 return aggregation_document_status
187188
@@ -190,7 +191,7 @@ def get_aggregation_document_status_by_query_set(queryset):
190191 def aggregation_document_status ():
191192 sql = get_file_content (
192193 os .path .join (PROJECT_DIR , "apps" , "dataset" , 'sql' , 'update_document_status_meta.sql' ))
193- native_update ({'document_custom_sql' : queryset }, sql )
194+ native_update ({'document_custom_sql' : queryset }, sql , with_table_name = True )
194195
195196 return aggregation_document_status
196197
@@ -249,19 +250,23 @@ def embedding_by_document(document_id, embedding_model: Embeddings):
249250 """
250251 if not try_lock ('embedding' + str (document_id )):
251252 return
252- max_kb .info (f"开始--->向量化文档:{ document_id } " )
253- # 批量修改状态为PADDING
254- ListenerManagement .update_status (QuerySet (Document ).filter (id = document_id ), TaskType .EMBEDDING , State .STARTED )
255253 try :
256- # 删除文档向量数据
257- VectorStore .get_embedding_vector ().delete_by_document_id (document_id )
258-
259254 def is_the_task_interrupted ():
260255 document = QuerySet (Document ).filter (id = document_id ).first ()
261256 if document is None or Status (document .status )[TaskType .EMBEDDING ] == State .REVOKE :
262257 return True
263258 return False
264259
260+ if is_the_task_interrupted ():
261+ return
262+ max_kb .info (f"开始--->向量化文档:{ document_id } " )
263+ # 批量修改状态为PADDING
264+ ListenerManagement .update_status (QuerySet (Document ).filter (id = document_id ), TaskType .EMBEDDING ,
265+ State .STARTED )
266+
267+ # 删除文档向量数据
268+ VectorStore .get_embedding_vector ().delete_by_document_id (document_id )
269+
265270 # 根据段落进行向量化处理
266271 page (QuerySet (Paragraph ).filter (document_id = document_id ).values ('id' ), 5 ,
267272 ListenerManagement .get_embedding_paragraph_apply (embedding_model , is_the_task_interrupted ,
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