fix: update embedding_mode_id to embedding_model_id across multiple files

Author: liuruibin

apps/application/chat_pipeline/step/search_dataset_step/impl/base_search_dataset_step.py

@@ -37,12 +37,12 @@ def get_model_by_id(_id, user_id):
 
 def get_embedding_id(knowledge_id_list):
     knowledge_list = QuerySet(Knowledge).filter(id__in=knowledge_id_list)
-    if len(set([knowledge.embedding_mode_id for knowledge in knowledge_list])) > 1:
+    if len(set([knowledge.embedding_model_id for knowledge in knowledge_list])) > 1:
         raise Exception(
             _("The vector model of the associated knowledge base is inconsistent and the segmentation cannot be recalled."))
     if len(knowledge_list) == 0:
         raise Exception(_("The knowledge base setting is wrong, please reset the knowledge base"))
-    return knowledge_list[0].embedding_mode_id
+    return knowledge_list[0].embedding_model_id
 
 
 class BaseSearchDatasetStep(ISearchDatasetStep):

apps/knowledge/serializers/document.py

@@ -298,7 +298,7 @@ def migrate(self, with_valid=True):
             else:
                 document_list.update(knowledge_id=target_knowledge_id)
             model_id = None
-            if knowledge.embedding_mode_id != target_knowledge.embedding_mode_id:
+            if knowledge.embedding_model_id != target_knowledge.embedding_model_id:
                 model_id = get_embedding_model_id_by_knowledge_id(target_knowledge_id)
 
             pid_list = [paragraph.id for paragraph in paragraph_list]

apps/knowledge/serializers/paragraph.py

@@ -501,8 +501,8 @@ def migrate(self, with_valid=True):
                 target_knowledge = QuerySet(Knowledge).filter(id=target_knowledge_id).first()
                 knowledge = QuerySet(Knowledge).filter(id=knowledge_id).first()
                 embedding_model_id = None
-                if target_knowledge.embedding_mode_id != knowledge.embedding_mode_id:
-                    embedding_model_id = str(target_knowledge.embedding_mode_id)
+                if target_knowledge.embedding_model_id != knowledge.embedding_model_id:
+                    embedding_model_id = str(target_knowledge.embedding_model_id)
                 pid_list = [paragraph.id for paragraph in paragraph_list]
                 # 修改段落信息
                 paragraph_list.update(knowledge_id=target_knowledge_id, document_id=target_document_id)